keyword stringclasses 7 values | repo_name stringlengths 8 98 | file_path stringlengths 4 244 | file_extension stringclasses 29 values | file_size int64 0 84.1M | line_count int64 0 1.6M | content stringlengths 1 84.1M ⌀ | language stringclasses 14 values |
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3D | chenz53/MIM-Med3D | code/losses/contrastive.py | .py | 3,273 | 93 | import torch
from torch.nn import functional as F
from torch.nn.modules.loss import _Loss
from monai.utils import deprecated_arg
class ContrastiveLoss(_Loss):
"""
Compute the Contrastive loss defined in:
Chen, Ting, et al. "A simple framework for contrastive learning of visual representations." International
conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v119/chen20j.html)
Adapted from:
https://github.com/Sara-Ahmed/SiT/blob/1aacd6adcd39b71efc903d16b4e9095b97dda76f/losses.py#L5
"""
@deprecated_arg(
name="reduction", since="0.8", msg_suffix="`reduction` is no longer supported."
)
def __init__(
self, temperature: float = 0.5, batch_size: int = 1, reduction="sum"
) -> None:
"""
Args:
temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.
batch_size: The number of samples.
Raises:
ValueError: When an input of dimension length > 2 is passed
ValueError: When input and target are of different shapes
.. deprecated:: 0.8.0
`reduction` is no longer supported.
"""
super().__init__()
self.batch_size = batch_size
self.temperature = temperature
def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Args:
input: the shape should be B[F].
target: the shape should be B[F].
"""
if len(target.shape) > 2 or len(input.shape) > 2:
raise ValueError(
f"Either target or input has dimensions greater than 2 where target "
f"shape is ({target.shape}) and input shape is ({input.shape})"
)
if target.shape != input.shape:
raise ValueError(
f"ground truth has differing shape ({target.shape}) from input ({input.shape})"
)
self.batch_size = input.shape[0]
temperature_tensor = torch.tensor(self.temperature).to(input.device)
norm_i = F.normalize(input, dim=1)
norm_j = F.normalize(target, dim=1)
# negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)
# negatives_mask = torch.tensor(negatives_mask, dtype=torch.float)
# negatives_mask = torch.clone(torch.as_tensor(negatives_mask)).to(input.device)
negatives_mask = torch.ones(
self.batch_size * 2,
self.batch_size * 2,
dtype=torch.float,
device=input.device,
)
negatives_mask.fill_diagonal_(0.0)
repr = torch.cat([norm_i, norm_j], dim=0)
sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)
sim_ij = torch.diag(sim_matrix, self.batch_size)
sim_ji = torch.diag(sim_matrix, -self.batch_size)
positives = torch.cat([sim_ij, sim_ji], dim=0)
nominator = torch.exp(positives / temperature_tensor)
denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor)
loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))
return torch.sum(loss_partial) / (2 * self.batch_size)
| Python |
3D | chenz53/MIM-Med3D | code/losses/__init__.py | .py | 41 | 2 | from .contrastive import ContrastiveLoss
| Python |
3D | chenz53/MIM-Med3D | code/optimizers/lr_scheduler.py | .py | 6,791 | 209 | import math
import warnings
from typing import List
from torch.optim.lr_scheduler import LambdaLR, _LRScheduler
from torch import nn as nn
from torch.optim import Adam, Optimizer
from torch.optim.lr_scheduler import _LRScheduler
from pytorch_lightning.utilities.cli import LR_SCHEDULER_REGISTRY
__all__ = ["LinearLR", "ExponentialLR"]
# @LR_SCHEDULER_REGISTRY
# class _LRSchedulerMONAI(_LRScheduler):
# """Base class for increasing the learning rate between two boundaries over a number
# of iterations"""
# def __init__(
# self, optimizer: Optimizer, end_lr: float, num_iter: int, last_epoch: int = -1
# ) -> None:
# """
# Args:
# optimizer: wrapped optimizer.
# end_lr: the final learning rate.
# num_iter: the number of iterations over which the test occurs.
# last_epoch: the index of last epoch.
# Returns:
# None
# """
# self.end_lr = end_lr
# self.num_iter = num_iter
# super(_LRSchedulerMONAI, self).__init__(optimizer, last_epoch)
# @LR_SCHEDULER_REGISTRY
# class LinearLR(_LRSchedulerMONAI):
# """Linearly increases the learning rate between two boundaries over a number of
# iterations.
# """
# def get_lr(self):
# r = self.last_epoch / (self.num_iter - 1)
# return [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]
# @LR_SCHEDULER_REGISTRY
# class ExponentialLR(_LRSchedulerMONAI):
# """Exponentially increases the learning rate between two boundaries over a number of
# iterations.
# """
# def get_lr(self):
# r = self.last_epoch / (self.num_iter - 1)
# return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]
@LR_SCHEDULER_REGISTRY
class WarmupCosineSchedule(LambdaLR):
"""Linear warmup and then cosine decay.
Based on https://huggingface.co/ implementation.
"""
def __init__(
self,
optimizer: Optimizer,
warmup_steps: int,
t_total: int,
cycles: float = 0.5,
last_epoch: int = -1,
) -> None:
"""
Args:
optimizer: wrapped optimizer.
warmup_steps: number of warmup iterations.
t_total: total number of training iterations.
cycles: cosine cycles parameter.
last_epoch: the index of last epoch.
Returns:
None
"""
self.warmup_steps = warmup_steps
self.t_total = t_total
self.cycles = cycles
super(WarmupCosineSchedule, self).__init__(
optimizer, self.lr_lambda, last_epoch
)
def lr_lambda(self, step):
if step < self.warmup_steps:
return float(step) / float(max(1.0, self.warmup_steps))
progress = float(step - self.warmup_steps) / float(
max(1, self.t_total - self.warmup_steps)
)
return max(
0.0, 0.5 * (1.0 + math.cos(math.pi * float(self.cycles) * 2.0 * progress))
)
@LR_SCHEDULER_REGISTRY
class LinearWarmupCosineAnnealingLR(_LRScheduler):
def __init__(
self,
optimizer: Optimizer,
warmup_epochs: int,
max_epochs: int,
warmup_start_lr: float = 0.0,
eta_min: float = 0.0,
last_epoch: int = -1,
) -> None:
"""
Args:
optimizer (Optimizer): Wrapped optimizer.
warmup_epochs (int): Maximum number of iterations for linear warmup
max_epochs (int): Maximum number of iterations
warmup_start_lr (float): Learning rate to start the linear warmup. Default: 0.
eta_min (float): Minimum learning rate. Default: 0.
last_epoch (int): The index of last epoch. Default: -1.
"""
self.warmup_epochs = warmup_epochs
self.max_epochs = max_epochs
self.warmup_start_lr = warmup_start_lr
self.eta_min = eta_min
super(LinearWarmupCosineAnnealingLR, self).__init__(optimizer, last_epoch)
def get_lr(self) -> List[float]:
"""
Compute learning rate using chainable form of the scheduler
"""
if not self._get_lr_called_within_step:
warnings.warn(
"To get the last learning rate computed by the scheduler, "
"please use `get_last_lr()`.",
UserWarning,
)
if self.last_epoch == 0:
return [self.warmup_start_lr] * len(self.base_lrs)
elif self.last_epoch < self.warmup_epochs:
return [
group["lr"]
+ (base_lr - self.warmup_start_lr) / (self.warmup_epochs - 1)
for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
]
elif self.last_epoch == self.warmup_epochs:
return self.base_lrs
elif (self.last_epoch - 1 - self.max_epochs) % (
2 * (self.max_epochs - self.warmup_epochs)
) == 0:
return [
group["lr"]
+ (base_lr - self.eta_min)
* (1 - math.cos(math.pi / (self.max_epochs - self.warmup_epochs)))
/ 2
for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
]
return [
(
1
+ math.cos(
math.pi
* (self.last_epoch - self.warmup_epochs)
/ (self.max_epochs - self.warmup_epochs)
)
)
/ (
1
+ math.cos(
math.pi
* (self.last_epoch - self.warmup_epochs - 1)
/ (self.max_epochs - self.warmup_epochs)
)
)
* (group["lr"] - self.eta_min)
+ self.eta_min
for group in self.optimizer.param_groups
]
def _get_closed_form_lr(self) -> List[float]:
"""
Called when epoch is passed as a param to the `step` function of the scheduler.
"""
if self.last_epoch < self.warmup_epochs:
return [
self.warmup_start_lr
+ self.last_epoch
* (base_lr - self.warmup_start_lr)
/ (self.warmup_epochs - 1)
for base_lr in self.base_lrs
]
return [
self.eta_min
+ 0.5
* (base_lr - self.eta_min)
* (
1
+ math.cos(
math.pi
* (self.last_epoch - self.warmup_epochs)
/ (self.max_epochs - self.warmup_epochs)
)
)
for base_lr in self.base_lrs
]
| Python |
3D | chenz53/MIM-Med3D | code/optimizers/__init__.py | .py | 56 | 2 | from .lr_scheduler import LinearWarmupCosineAnnealingLR
| Python |
3D | chenz53/MIM-Med3D | code/data/btcv_dataset.py | .py | 11,225 | 316 | from typing import Optional, Sequence, Union
import torch
import torch.distributed as ptdist
import pytorch_lightning as pl
from monai.data import (
CacheDataset,
Dataset,
partition_dataset,
PersistentDataset,
load_decathlon_datalist,
list_data_collate,
decollate_batch,
)
from monai.transforms import (
AsDiscrete,
AddChanneld,
Compose,
CropForegroundd,
LoadImaged,
Orientationd,
RandFlipd,
RandCropByPosNegLabeld,
RandSpatialCropSamplesd,
RandShiftIntensityd,
CenterSpatialCropd,
ScaleIntensityRanged,
Spacingd,
RandRotate90d,
ToTensord,
)
from monai.data.utils import pad_list_data_collate
class BTCVDataset(pl.LightningDataModule):
def __init__(
self,
root_dir: str,
json_path: str,
cache_dir: str,
downsample_ratio: Sequence[float],
batch_size: int = 1,
val_batch_size: int = 1,
num_workers: int = 4,
cache_num: int = 0,
cache_rate: float = 0.0,
is_ssl: bool = False,
dist: bool = False,
):
super().__init__()
self.root_dir = root_dir
self.json_path = json_path
self.cache_dir = cache_dir
self.downsample_ratio = downsample_ratio
self.batch_size = batch_size
self.val_batch_size = val_batch_size
self.num_workers = num_workers
self.cache_num = cache_num
self.cache_rate = cache_rate
self.is_ssl = is_ssl
self.dist = dist
self.train_list = load_decathlon_datalist(
base_dir=self.root_dir,
data_list_file_path=self.json_path,
is_segmentation=True,
data_list_key="training",
)
self.val_list = load_decathlon_datalist(
base_dir=self.root_dir,
data_list_file_path=self.json_path,
is_segmentation=True,
data_list_key="validation",
)
# self.test_list = load_decathlon_datalist(
# base_dir=self.root_dir,
# data_list_file_path=self.json_path,
# is_segmentation=True,
# data_list_key="test",
# )
def val_transforms(self, is_ssl=False):
if not is_ssl:
transforms = Compose(
[
LoadImaged(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
Spacingd(
keys=["image", "label"],
pixdim=self.downsample_ratio,
mode=("bilinear", "nearest"),
),
ScaleIntensityRanged(
keys=["image"],
a_min=-175,
a_max=250,
b_min=0.0,
b_max=1.0,
clip=True,
),
CropForegroundd(keys=["image", "label"], source_key="image"),
ToTensord(keys=["image", "label"]),
]
)
else:
transforms = self.train_transforms(is_ssl)
return transforms
def train_transforms(self, is_ssl=False):
if not is_ssl:
transforms = Compose(
[
LoadImaged(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
Spacingd(
keys=["image", "label"],
pixdim=self.downsample_ratio,
mode=("bilinear", "nearest"),
),
ScaleIntensityRanged(
keys=["image"],
a_min=-175,
a_max=250,
b_min=0.0,
b_max=1.0,
clip=True,
),
CropForegroundd(keys=["image", "label"], source_key="image"),
RandCropByPosNegLabeld(
keys=["image", "label"],
label_key="label",
spatial_size=(96, 96, 96),
pos=1,
neg=1,
num_samples=4,
image_key="image",
image_threshold=0,
),
RandFlipd(keys=["image", "label"], spatial_axis=[0], prob=0.10,),
RandFlipd(keys=["image", "label"], spatial_axis=[1], prob=0.10,),
RandFlipd(keys=["image", "label"], spatial_axis=[2], prob=0.10,),
RandRotate90d(keys=["image", "label"], prob=0.10, max_k=3,),
RandShiftIntensityd(keys=["image"], offsets=0.10, prob=0.50,),
ToTensord(keys=["image", "label"]),
]
)
else:
transforms = Compose(
[
# load 4 Nifti images and stack them together
LoadImaged(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
Spacingd(
keys=["image", "label"],
pixdim=self.downsample_ratio,
mode=("bilinear", "nearest"),
),
ScaleIntensityRanged(
keys=["image"],
a_min=-175,
a_max=250,
b_min=0.0,
b_max=1.0,
clip=True,
),
CropForegroundd(keys=["image", "label"], source_key="image"),
RandSpatialCropSamplesd(
keys=["image", "label"],
roi_size=(96, 96, 96),
random_size=False,
num_samples=4,
),
# RandScaleIntensityd(keys="image", factors=0.1, prob=0.5),
# RandShiftIntensityd(keys="image", offsets=0.1, prob=0.5),
ToTensord(keys=["image", "label"]),
]
)
return transforms
def test_transforms(self):
transforms = Compose(
[
LoadImaged(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
Spacingd(
keys=["image", "label"],
pixdim=self.downsample_ratio,
mode=("bilinear", "nearest"),
),
ScaleIntensityRanged(
keys=["image"],
a_min=-175,
a_max=250,
b_min=0.0,
b_max=1.0,
clip=True,
),
CropForegroundd(keys=["image", "label"], source_key="image"),
CenterSpatialCropd(keys=["image", "label"], roi_size=(192, 192, 96)),
ToTensord(keys=["image", "label"]),
]
)
return transforms
def setup(self, stage: Optional[str] = None):
# Assign Train split(s) for use in Dataloaders
if stage in [None, "fit"]:
if self.dist:
train_partition = partition_dataset(
data=self.train_list,
num_partitions=ptdist.get_world_size(),
shuffle=True,
even_divisible=True,
drop_last=False,
)[ptdist.get_rank()]
valid_partition = partition_dataset(
data=self.val_list,
num_partitions=ptdist.get_world_size(),
shuffle=False,
even_divisible=True,
drop_last=False,
)[ptdist.get_rank()]
# self.cache_num //= ptdist.get_world_size()
else:
train_partition = self.train_list
valid_partition = self.val_list
if any([self.cache_num, self.cache_rate]) > 0:
self.train_ds = CacheDataset(
train_partition,
cache_num=self.cache_num,
cache_rate=self.cache_rate,
num_workers=self.num_workers,
transform=self.train_transforms(self.is_ssl),
)
self.valid_ds = CacheDataset(
valid_partition,
cache_num=self.cache_num // 4,
cache_rate=self.cache_rate,
num_workers=self.num_workers,
transform=self.val_transforms(self.is_ssl),
)
else:
self.train_ds = PersistentDataset(
train_partition,
transform=self.train_transforms(self.is_ssl),
cache_dir=self.cache_dir,
)
self.valid_ds = PersistentDataset(
valid_partition,
transform=self.val_transforms(self.is_ssl),
cache_dir=self.cache_dir,
)
if stage in [None, "test"]:
if any([self.cache_num, self.cache_rate]) > 0:
self.test_ds = CacheDataset(
self.val_list,
cache_num=self.cache_num // 4,
cache_rate=self.cache_rate,
num_workers=self.num_workers,
transform=self.val_transforms(self.is_ssl),
)
else:
self.test_ds = PersistentDataset(
self.val_list,
transform=self.val_transforms(self.is_ssl),
cache_dir=self.cache_dir,
)
def train_dataloader(self):
return torch.utils.data.DataLoader(
self.train_ds,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=True,
shuffle=True,
collate_fn=pad_list_data_collate,
# drop_last=False,
# prefetch_factor=4,
)
def val_dataloader(self):
return torch.utils.data.DataLoader(
self.valid_ds,
batch_size=self.val_batch_size,
num_workers=self.num_workers,
pin_memory=True,
shuffle=False,
# drop_last=False,
collate_fn=pad_list_data_collate,
# prefetch_factor=4,
)
def test_dataloader(self):
return torch.utils.data.DataLoader(
self.test_ds,
batch_size=self.val_batch_size,
num_workers=self.num_workers,
pin_memory=True,
shuffle=False,
# drop_last=False,
collate_fn=pad_list_data_collate,
# prefetch_factor=4,
)
| Python |
3D | chenz53/MIM-Med3D | code/data/__init__.py | .py | 242 | 11 | from .btcv_dataset import BTCVDataset
from .brats_dataset import BratsDataset
from .utils import (
list_splitter,
get_modalities,
StackStuff,
StackStuffM,
ConvertToMultiChannelBasedOnBratsClassesd,
DataAugmentation,
)
| Python |
3D | chenz53/MIM-Med3D | code/data/utils.py | .py | 21,957 | 532 | from typing import Any, Callable, List, Sequence, Tuple, Union
import glob
import numpy as np
import kornia.augmentation as K
from einops import rearrange, repeat
import torch
import torch.nn.functional as F
from monai.data.utils import (
compute_importance_map,
dense_patch_slices,
get_valid_patch_size,
)
from monai.utils import BlendMode, PytorchPadMode, fall_back_tuple, look_up_option
from monai.transforms import MapTransform
def list_splitter(list_to_split, ratio):
first_half = int(len(list_to_split) * ratio)
return list_to_split[:first_half], list_to_split[first_half:]
def get_modalities(path_to_files: list) -> dict:
all_files = glob.glob(path_to_files + "/*nii.gz")
if len(all_files) != 5:
raise ValueError(f"Number of files are not equal to 5 in {path_to_files}")
modality_finder = lambda x: x.split("/")[-1].split("_")[-1].split(".")[0]
return {modality_finder(x): x for x in all_files}
class StackStuff(MapTransform):
def __call__(self, data):
d = dict(data)
list_of_keys = ["flair", "t1ce", "t1", "t2"]
list_of_images = [
torch.from_numpy(data[x].astype(np.float32)) for x in list_of_keys
]
d["image"] = torch.stack(list_of_images)
for key in list_of_keys:
d.pop(key)
return d
class StackStuffM(MapTransform):
def __call__(self, data):
d = dict(data)
list_of_keys = ["flair", "t1ce", "t1", "t2", "Mask"]
list_of_images = [
torch.from_numpy(data[x].astype(np.float32)) for x in list_of_keys
]
mask = data["Mask"].astype(bool)
for index in range(len(list_of_images)):
list_of_images[index][~mask] = 0
d["image"] = torch.stack(list_of_images[:-1])
for key in list_of_keys[1:]:
d.pop(key)
return d
class ConvertToMultiChannelBasedOnBratsClassesd(MapTransform):
"""
Convert labels to multi channels based on brats classes:
label 2 is the peritumoral edema
label 4 is the GD-enhancing tumor
label 1 is the necrotic tumor core
The possible classes are TC (Tumor core), WT (Whole tumor)
and ET (Enhancing tumor).
"""
def __call__(self, data):
d = dict(data)
for key in self.keys:
result = []
# merge label 2 and label 3 to construct TC
result.append(np.logical_or(d[key] == 4, d[key] == 1))
# merge labels 1, 2 and 3 to construct WT
result.append(
np.logical_or(np.logical_or(d[key] == 1, d[key] == 2), d[key] == 4)
)
# label 2 is ET
result.append(d[key] == 4)
d["label"] = np.stack(result, axis=0).astype(np.float32)
return d
class DataAugmentation(torch.nn.Module):
def __init__(
self, flip_probability=0.5, affine_probability=0.5,
):
super(DataAugmentation, self).__init__()
self.flip_probability = flip_probability
self.affine_probability = affine_probability
self.flip_0 = K.RandomDepthicalFlip3D(p=self.flip_probability)
self.flip_1 = K.RandomHorizontalFlip3D(p=self.flip_probability)
self.flip_2 = K.RandomVerticalFlip3D(p=self.flip_probability)
affine_args = {
"degrees": 10,
"translate": None,
"scale": (1.15, 0.85),
"shears": 5,
"p": self.affine_probability,
}
self.image_affine = K.RandomAffine3D(resample="BILINEAR", **affine_args)
self.mask_affine = K.RandomAffine3D(resample="NEAREST", **affine_args)
@torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
@torch.no_grad()
def forward(self, img: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
for flip_index in range(3):
flip = getattr(self, f"flip_{flip_index}")
img = flip(img)
mask = flip(mask, flip._params)
img = self.image_affine(img)
mask = self.mask_affine(mask, self.image_affine._params)
return img, mask
def sliding_window_reconstruction(
inputs: torch.Tensor,
roi_size: Union[Sequence[int], int],
sw_batch_size: int,
predictor: Callable[..., torch.Tensor],
overlap: float = 0.25,
mode: Union[BlendMode, str] = BlendMode.CONSTANT,
sigma_scale: Union[Sequence[float], float] = 0.125,
padding_mode: Union[PytorchPadMode, str] = PytorchPadMode.CONSTANT,
cval: float = 0.0,
sw_device: Union[torch.device, str, None] = None,
device: Union[torch.device, str, None] = None,
reshape: bool = True,
patch_size: Sequence[int] = [16, 16, 16],
masked_value: int = 0,
*args: Any,
**kwargs: Any,
) -> torch.Tensor:
"""
Sliding window reconstruction on `inputs` with `predictor`.
When roi_size is larger than the inputs' spatial size, the input image are padded during inference.
To maintain the same spatial sizes, the output image will be cropped to the original input size.
Args:
inputs: input image to be processed (assuming NCHW[D])
roi_size: the spatial window size for inferences.
When its components have None or non-positives, the corresponding inputs dimension will be used.
if the components of the `roi_size` are non-positive values, the transform will use the
corresponding components of img size. For example, `roi_size=(32, -1)` will be adapted
to `(32, 64)` if the second spatial dimension size of img is `64`.
sw_batch_size: the batch size to run window slices.
predictor: given input tensor `patch_data` in shape NCHW[D], `predictor(patch_data)`
should return a prediction with the same spatial shape and batch_size, i.e. NMHW[D];
where HW[D] represents the patch spatial size, M is the number of output channels, N is `sw_batch_size`.
overlap: Amount of overlap between scans.
mode: {``"constant"``, ``"gaussian"``}
How to blend output of overlapping windows. Defaults to ``"constant"``.
- ``"constant``": gives equal weight to all predictions.
- ``"gaussian``": gives less weight to predictions on edges of windows.
sigma_scale: the standard deviation coefficient of the Gaussian window when `mode` is ``"gaussian"``.
Default: 0.125. Actual window sigma is ``sigma_scale`` * ``dim_size``.
When sigma_scale is a sequence of floats, the values denote sigma_scale at the corresponding
spatial dimensions.
padding_mode: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}
Padding mode for ``inputs``, when ``roi_size`` is larger than inputs. Defaults to ``"constant"``
See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
cval: fill value for 'constant' padding mode. Default: 0
sw_device: device for the window data.
By default the device (and accordingly the memory) of the `inputs` is used.
Normally `sw_device` should be consistent with the device where `predictor` is defined.
device: device for the stitched output prediction.
By default the device (and accordingly the memory) of the `inputs` is used. If for example
set to device=torch.device('cpu') the gpu memory consumption is less and independent of the
`inputs` and `roi_size`. Output is on the `device`.
args: optional args to be passed to ``predictor``.
kwargs: optional keyword args to be passed to ``predictor``.
Note:
- input must be channel-first and have a batch dim, supports N-D sliding window.
"""
num_spatial_dims = len(inputs.shape) - 2
if overlap < 0 or overlap >= 1:
raise AssertionError("overlap must be >= 0 and < 1.")
# determine image spatial size and batch size
# Note: all input images must have the same image size and batch size
image_size_ = list(inputs.shape[2:])
batch_size = inputs.shape[0]
if device is None:
device = inputs.device
if sw_device is None:
sw_device = inputs.device
roi_size = fall_back_tuple(roi_size, image_size_)
# in case that image size is smaller than roi size
image_size = tuple(
max(image_size_[i], roi_size[i]) for i in range(num_spatial_dims)
)
pad_size = []
for k in range(len(inputs.shape) - 1, 1, -1):
diff = max(roi_size[k - 2] - inputs.shape[k], 0)
half = diff // 2
pad_size.extend([half, diff - half])
inputs = F.pad(
inputs,
pad=pad_size,
mode=look_up_option(padding_mode, PytorchPadMode).value,
value=cval,
)
scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims, overlap)
# Store all slices in list
slices = dense_patch_slices(image_size, roi_size, scan_interval)
num_win = len(slices) # number of windows per image
total_slices = num_win * batch_size # total number of windows
# Create window-level importance map
importance_map = compute_importance_map(
get_valid_patch_size(image_size, roi_size),
mode=mode,
sigma_scale=sigma_scale,
device=device,
)
# Perform predictions
output_image, count_map = (
torch.tensor(0.0, device=device),
torch.tensor(0.0, device=device),
)
masked_image = torch.tensor(0.0, device=device)
_initialized = False
for slice_g in range(0, total_slices, sw_batch_size):
slice_range = range(slice_g, min(slice_g + sw_batch_size, total_slices))
unravel_slice = [
[slice(int(idx / num_win), int(idx / num_win) + 1), slice(None)]
+ list(slices[idx % num_win])
for idx in slice_range
]
window_data = torch.cat([inputs[win_slice] for win_slice in unravel_slice]).to(
sw_device
)
# if using MAE
if reshape:
pred_pixel_values, patches, batch_range, masked_indices = predictor(
window_data, *args, **kwargs
)
# batch, num_patches, dim = patches.shape
# masked_patches = patches.clone()
# masked_patches[batch_range, masked_indices] = masked_value
# mask_tokens = repeat(
# torch.tensor([1.0], device=device),
# "1 -> b n d",
# b=batch,
# n=num_patches,
# d=dim,
# )
# masked_bool_mask = (
# torch.zeros((batch, num_patches), device=device)
# .scatter_(-1, masked_indices, 1)
# .bool()
# )
# # mask tokens
# masked_patches = torch.where(
# masked_bool_mask[..., None], mask_tokens, patches
# )
patches[batch_range, masked_indices] = masked_value
mask_prob = rearrange(
patches,
"b (h w d) (p1 p2 p3 c) -> b c (h p1) (w p2) (d p3)",
h=roi_size[0] // patch_size[0],
w=roi_size[1] // patch_size[1],
d=roi_size[2] // patch_size[2],
c=inputs.shape[1],
p1=patch_size[0],
p2=patch_size[1],
p3=patch_size[2],
)
patches[batch_range, masked_indices] = pred_pixel_values
seg_prob = rearrange(
patches,
"b (h w d) (p1 p2 p3 c) -> b c (h p1) (w p2) (d p3)",
h=roi_size[0] // patch_size[0],
w=roi_size[1] // patch_size[1],
d=roi_size[2] // patch_size[2],
c=inputs.shape[1],
p1=patch_size[0],
p2=patch_size[1],
p3=patch_size[2],
)
else:
seg_prob = predictor(window_data, *args, **kwargs).to(
device
) # batched patch segmentation
if not _initialized: # init. buffer at the first iteration
output_classes = seg_prob.shape[1]
output_shape = [batch_size, output_classes] + list(image_size)
# allocate memory to store the full output and the count for overlapping parts
output_image = torch.zeros(output_shape, dtype=torch.float32, device=device)
# masked image reconstruction
masked_image = torch.zeros(output_shape, dtype=torch.float32, device=device)
count_map = torch.zeros(output_shape, dtype=torch.float32, device=device)
_initialized = True
# store the result in the proper location of the full output. Apply weights from importance map.
for idx, original_idx in zip(slice_range, unravel_slice):
output_image[original_idx] += importance_map * seg_prob[idx - slice_g]
masked_image[original_idx] += importance_map * mask_prob[idx - slice_g]
count_map[original_idx] += importance_map
# account for any overlapping sections
output_image = output_image / count_map
masked_image = masked_image / count_map
final_slicing: List[slice] = []
for sp in range(num_spatial_dims):
slice_dim = slice(
pad_size[sp * 2], image_size_[num_spatial_dims - sp - 1] + pad_size[sp * 2]
)
final_slicing.insert(0, slice_dim)
while len(final_slicing) < len(output_image.shape):
final_slicing.insert(0, slice(None))
return output_image[final_slicing], masked_image[final_slicing]
def sliding_window_embedding(
inputs: torch.Tensor,
roi_size: Union[Sequence[int], int],
sw_batch_size: int,
predictor: Callable[..., torch.Tensor],
overlap: float = 0.25,
mode: Union[BlendMode, str] = BlendMode.CONSTANT,
sigma_scale: Union[Sequence[float], float] = 0.125,
padding_mode: Union[PytorchPadMode, str] = PytorchPadMode.CONSTANT,
cval: float = 0.0,
sw_device: Union[torch.device, str, None] = None,
device: Union[torch.device, str, None] = None,
*args: Any,
**kwargs: Any,
) -> torch.Tensor:
"""
Sliding window inference on `inputs` with `predictor`.
When roi_size is larger than the inputs' spatial size, the input image are padded during inference.
To maintain the same spatial sizes, the output image will be cropped to the original input size.
Args:
inputs: input image to be processed (assuming NCHW[D])
roi_size: the spatial window size for inferences.
When its components have None or non-positives, the corresponding inputs dimension will be used.
if the components of the `roi_size` are non-positive values, the transform will use the
corresponding components of img size. For example, `roi_size=(32, -1)` will be adapted
to `(32, 64)` if the second spatial dimension size of img is `64`.
sw_batch_size: the batch size to run window slices.
predictor: given input tensor `patch_data` in shape NCHW[D], `predictor(patch_data)`
should return a prediction with the same spatial shape and batch_size, i.e. NMHW[D];
where HW[D] represents the patch spatial size, M is the number of output channels, N is `sw_batch_size`.
overlap: Amount of overlap between scans.
mode: {``"constant"``, ``"gaussian"``}
How to blend output of overlapping windows. Defaults to ``"constant"``.
- ``"constant``": gives equal weight to all predictions.
- ``"gaussian``": gives less weight to predictions on edges of windows.
sigma_scale: the standard deviation coefficient of the Gaussian window when `mode` is ``"gaussian"``.
Default: 0.125. Actual window sigma is ``sigma_scale`` * ``dim_size``.
When sigma_scale is a sequence of floats, the values denote sigma_scale at the corresponding
spatial dimensions.
padding_mode: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}
Padding mode for ``inputs``, when ``roi_size`` is larger than inputs. Defaults to ``"constant"``
See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
cval: fill value for 'constant' padding mode. Default: 0
sw_device: device for the window data.
By default the device (and accordingly the memory) of the `inputs` is used.
Normally `sw_device` should be consistent with the device where `predictor` is defined.
device: device for the stitched output prediction.
By default the device (and accordingly the memory) of the `inputs` is used. If for example
set to device=torch.device('cpu') the gpu memory consumption is less and independent of the
`inputs` and `roi_size`. Output is on the `device`.
args: optional args to be passed to ``predictor``.
kwargs: optional keyword args to be passed to ``predictor``.
Note:
- input must be channel-first and have a batch dim, supports N-D sliding window.
"""
num_spatial_dims = len(inputs.shape) - 2
if overlap < 0 or overlap >= 1:
raise AssertionError("overlap must be >= 0 and < 1.")
# determine image spatial size and batch size
# Note: all input images must have the same image size and batch size
image_size_ = list(inputs.shape[2:])
batch_size = inputs.shape[0]
if device is None:
device = inputs.device
if sw_device is None:
sw_device = inputs.device
roi_size = fall_back_tuple(roi_size, image_size_)
# in case that image size is smaller than roi size
image_size = tuple(
max(image_size_[i], roi_size[i]) for i in range(num_spatial_dims)
)
pad_size = []
for k in range(len(inputs.shape) - 1, 1, -1):
diff = max(roi_size[k - 2] - inputs.shape[k], 0)
half = diff // 2
pad_size.extend([half, diff - half])
inputs = F.pad(
inputs,
pad=pad_size,
mode=look_up_option(padding_mode, PytorchPadMode).value,
value=cval,
)
scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims, overlap)
# Store all slices in list
slices = dense_patch_slices(image_size, roi_size, scan_interval)
num_win = len(slices) # number of windows per image
total_slices = num_win * batch_size # total number of windows
# Create window-level importance map
importance_map = compute_importance_map(
get_valid_patch_size(image_size, roi_size),
mode=mode,
sigma_scale=sigma_scale,
device=device,
)
# Perform predictions
output_image, count_map = (
torch.tensor(0.0, device=device),
torch.tensor(0.0, device=device),
)
_initialized = False
res = []
for slice_g in range(0, total_slices, sw_batch_size):
slice_range = range(slice_g, min(slice_g + sw_batch_size, total_slices))
unravel_slice = [
[slice(int(idx / num_win), int(idx / num_win) + 1), slice(None)]
+ list(slices[idx % num_win])
for idx in slice_range
]
window_data = torch.cat([inputs[win_slice] for win_slice in unravel_slice]).to(
sw_device
)
seg_prob, _ = predictor(
window_data, *args, **kwargs
) # batched patch segmentation
seg_prob = seg_prob.to(sw_device)
res.append(seg_prob)
# if not _initialized: # init. buffer at the first iteration
# output_classes = seg_prob.shape[1]
# output_shape = [batch_size, output_classes] + list(image_size)
# # allocate memory to store the full output and the count for overlapping parts
# output_image = torch.zeros(output_shape, dtype=torch.float32, device=device)
# count_map = torch.zeros(output_shape, dtype=torch.float32, device=device)
# _initialized = True
# # store the result in the proper location of the full output. Apply weights from importance map.
# for idx, original_idx in zip(slice_range, unravel_slice):
# output_image[original_idx] += importance_map * seg_prob[idx - slice_g]
# count_map[original_idx] += importance_map
# # account for any overlapping sections
# output_image = output_image / count_map
# final_slicing: List[slice] = []
# for sp in range(num_spatial_dims):
# slice_dim = slice(
# pad_size[sp * 2], image_size_[num_spatial_dims - sp - 1] + pad_size[sp * 2]
# )
# final_slicing.insert(0, slice_dim)
# while len(final_slicing) < len(output_image.shape):
# final_slicing.insert(0, slice(None))
# return output_image[final_slicing]
return res
def _get_scan_interval(
image_size: Sequence[int],
roi_size: Sequence[int],
num_spatial_dims: int,
overlap: float,
) -> Tuple[int, ...]:
"""
Compute scan interval according to the image size, roi size and overlap.
Scan interval will be `int((1 - overlap) * roi_size)`, if interval is 0,
use 1 instead to make sure sliding window works.
"""
if len(image_size) != num_spatial_dims:
raise ValueError("image coord different from spatial dims.")
if len(roi_size) != num_spatial_dims:
raise ValueError("roi coord different from spatial dims.")
scan_interval = []
for i in range(num_spatial_dims):
if roi_size[i] == image_size[i]:
scan_interval.append(int(roi_size[i]))
else:
interval = int(roi_size[i] * (1 - overlap))
scan_interval.append(interval if interval > 0 else 1)
return tuple(scan_interval)
if __name__ == "__main__":
from monai.data.utils import dense_patch_slices
slices = dense_patch_slices(
image_size=(224, 224, 100), patch_size=(16, 16, 16), scan_interval=(16, 16, 16),
)
print(slices)
| Python |
3D | chenz53/MIM-Med3D | code/data/brats_dataset.py | .py | 7,990 | 239 | import logging
from typing import Union, Sequence
import pytorch_lightning as pl
from pytorch_lightning.utilities.cli import DATAMODULE_REGISTRY
import torch.distributed as dist
from monai.data import (
CacheDataset,
Dataset,
partition_dataset,
DataLoader,
PersistentDataset,
load_decathlon_datalist,
)
from monai.transforms import (
Compose,
AddChanneld,
EnsureTyped,
LoadImaged,
NormalizeIntensityd,
Orientationd,
RandScaleIntensityd,
Spacingd,
RandShiftIntensityd,
CropForegroundd,
SpatialPadd,
RandSpatialCropSamplesd,
RandCropByPosNegLabeld,
MapTransform,
)
from monai.data.utils import pad_list_data_collate
# from .utils import ConvertToMultiChannelBasedOnBratsClassesd, StackStuff, get_modalities
class changToImage(MapTransform):
def __call__(self, data):
d = dict(data)
d["image"] = d[self.keys[0]]
del d[self.keys[0]]
return d
@DATAMODULE_REGISTRY
class BratsDataset(pl.LightningDataModule):
def __init__(
self,
root_dir: str,
json_path: str,
cache_dir: str,
modality: str,
batch_size: int = 1,
val_batch_size: int = 1,
num_workers: int = 8,
cache_num: int = 0,
cache_rate: float = 0.0,
spatial_size: Sequence[int] = (96, 96, 96),
num_samples: int = 4,
dist: bool = False,
):
super().__init__()
self.root_dir = root_dir
self.json_path = json_path
self.cache_dir = cache_dir
self.modality = modality
self.batch_size = batch_size
self.val_batch_size = val_batch_size
self.num_workers = num_workers
self.cache_num = cache_num
self.cache_rate = cache_rate
self.spatial_size = spatial_size
self.num_samples = num_samples
self.dist = dist
self.train_list = load_decathlon_datalist(
base_dir=self.root_dir,
data_list_file_path=self.json_path,
is_segmentation=True,
data_list_key="training",
)
self.valid_list = load_decathlon_datalist(
base_dir=self.root_dir,
data_list_file_path=self.json_path,
is_segmentation=True,
data_list_key="validation",
)
# self.common_transform_list = [
# LoadImaged(keys=["t1", "t1ce", "t2", "flair", "seg"]),
# Orientationd(keys=["t1", "t1ce", "t2", "flair", "seg"], axcodes="RAS"),
# ConvertToMultiChannelBasedOnBratsClassesd(keys=["seg"]),
# NormalizeIntensityd(
# keys=["t1", "t1ce", "t2", "flair"], nonzero=True, channel_wise=True
# ),
# StackStuff(keys=""),
# Spacingd(
# keys=["image", "label"],
# pixdim=(1.0, 1.0, 1.0),
# mode=["bilinear", "nearest"],
# ),
# EnsureTyped(keys=["image", "label"]),
# ]
self.common_transform_list = [
LoadImaged(keys=[self.modality]),
AddChanneld(keys=[self.modality]),
Orientationd(keys=[self.modality], axcodes="RAS"),
NormalizeIntensityd(keys=[self.modality], nonzero=True, channel_wise=True),
Spacingd(keys=[self.modality], pixdim=(1.0, 1.0, 1.0), mode=["bilinear"],),
# changToImage(keys=""),
EnsureTyped(keys=[self.modality]),
]
def train_transforms(self):
transforms = Compose(
self.common_transform_list
+ [
# CropForegroundd(keys=["image"], source_key="image"),
SpatialPadd(keys=[self.modality], spatial_size=self.spatial_size),
# RandCropByPosNegLabeld(
# keys=["image", "label"],
# label_key="label",
# spatial_size=self.spatial_size,
# pos=1,
# neg=1,
# num_samples=self.num_samples,
# ),
RandSpatialCropSamplesd(
keys=[self.modality],
roi_size=self.spatial_size,
random_size=False,
num_samples=self.num_samples,
),
changToImage(keys=[self.modality])
# RandScaleIntensityd(keys="image", factors=0.1, prob=0.5),
# RandShiftIntensityd(keys="image", offsets=0.1, prob=0.5),
# EnsureTyped(keys=["image", "label"]),
]
)
return transforms
def val_transforms(self):
# return Compose(
# self.common_transform_list
# + [
# SpatialPadd(keys=["image"], spatial_size=(224, 224, 144)),
# CenterSpatialCropd(keys=["image"], roi_size=(224, 224, 144)),
# ]
# )
return self.train_transforms()
def test_transforms(self):
# transforms = Compose(
# self.common_transform_list + [EnsureTyped(keys=["image", "label"]),]
# )
# return transforms
return self.val_transforms()
def setup(self, stage=None):
if stage in [None, "fit"]:
if self.dist:
train_partition = partition_dataset(
data=self.train_list,
num_partitions=dist.get_world_size(),
shuffle=True,
even_divisible=True,
)[dist.get_rank()]
valid_partition = partition_dataset(
data=self.valid_list,
num_partitions=dist.get_world_size(),
shuffle=False,
even_divisible=True,
)[dist.get_rank()]
else:
train_partition = self.train_list
valid_partition = self.valid_list
if any([self.cache_num, self.cache_rate]) > 0:
self.train_ds = CacheDataset(
train_partition,
cache_num=self.cache_num,
cache_rate=self.cache_rate,
num_workers=self.num_workers,
transform=self.train_transforms(),
)
self.valid_ds = CacheDataset(
valid_partition,
cache_num=self.cache_num,
cache_rate=self.cache_rate,
num_workers=self.num_workers,
transform=self.val_transforms(),
)
else:
logging.info("Loading Persistent Dataset...")
self.train_ds = PersistentDataset(
train_partition,
transform=self.train_transforms(),
cache_dir=self.cache_dir,
)
self.valid_ds = PersistentDataset(
valid_partition,
transform=self.val_transforms(),
cache_dir=self.cache_dir,
)
# self.test_ds = Dataset(self.test_dict, transform=self.test_transforms())
def train_dataloader(self):
return DataLoader(
self.train_ds,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=True,
shuffle=True,
drop_last=True,
collate_fn=pad_list_data_collate,
# prefetch_factor=4,
)
def val_dataloader(self):
return DataLoader(
self.valid_ds,
batch_size=self.val_batch_size,
num_workers=self.num_workers,
pin_memory=True,
collate_fn=pad_list_data_collate,
shuffle=False,
drop_last=False,
# prefetch_factor=4,
)
# def test_dataloader(self):
# return DataLoader(
# self.test_ds, batch_size=1, num_workers=self.num_workers, pin_memory=True,
# )
| Python |
3D | flystar233/3d_dna_pipe | generate_site_positions.py | .py | 6,710 | 281 | #!/usr/bin/env python
# Generate site positions in genome from given restriction enzyme
# Juicer 1.5
from __future__ import print_function
import sys
import re
def usage():
print('Usage: {} <restriction enzyme> <genome> [location]'.format(sys.argv[0]), file=sys.stderr)
sys.exit(1)
# ------------------------------------------------------------------------------
def process_args(args):
# Genome to filename mappings
#
# You may hardcode filenames belonging to frequently used genomes by inserting
# elements into this dictionary.
filenames = {
'hg19': '/seq/references/Homo_sapiens_assembly19.fasta',
'mm9' : '/seq/references/Mus_musculus_assembly9.fasta',
'mm10': '/seq/references/Mus_musculus_assembly10.fasta',
'hg18': '/seq/references/Homo_sapiens_assembly18.fasta',
}
# Enzyme to search pattern mappings
#
# You may provide your own patterns by inserting elements into this dictionary.
# Multiple patterns are supported in the form of lists. If you enumerate more
# than one patterns for an enzyme, then a match will be reported when at least
# one of them can be found at a given position.
#
# Wildcards:
# N: A, C, G or T
# M: A or C
# R: A or G
# W: A or T
# Y: C or T
# S: C or G
# K: G or T
# H: A, C or T
# B: C, G or T
# V: A, C or G
# D: A, G or T
patterns = {
'HindIII' : 'AAGCTT',
'DpnII' : 'GATC',
'MboI' : 'GATC',
'Sau3AI' : 'GATC',
'Arima' : [ 'GATC', 'GANTC' ],
}
if len(args) != 3 and len(args) != 4:
usage()
enzyme = args[1]
genome = args[2]
inputfile = ''
outputfile = ''
pattern = ''
if len(args) == 4:
inputfile = args[3]
elif genome in filenames:
inputfile = filenames[genome]
else:
print('<genome> not found and [location] not defined', file=sys.stderr)
usage()
if enzyme in patterns:
pattern = patterns[enzyme]
# Convert a simple string to a list.
if not isinstance(pattern, list):
pattern = [ pattern ]
# Make patterns uppercase.
pattern = [ p.upper() for p in pattern ]
else:
print('<restriction enzyme> must be one of {}'.format(list(patterns.keys())), file=sys.stderr)
usage()
outputfile = genome + '_' + enzyme + '.txt'
return {
'enzyme' : enzyme,
'genome' : genome,
'pattern' : pattern,
'inputfile' : inputfile,
'outputfile' : outputfile,
}
# ------------------------------------------------------------------------------
def has_wildcard(pattern):
# Input pattern can be a list or a string.
wildcards = re.compile(r'[NMRWYSKHBVD]')
if (isinstance(pattern, list)):
for p in pattern:
if re.search(wildcards, p):
return True
else:
if re.search(wildcards, pattern):
return True
return False
# ------------------------------------------------------------------------------
def pattern2regexp(pattern):
# Input pattern can be a list or a string.
wildcards = {
'N': '[ACGT]',
'M': '[AC]',
'R': '[AG]',
'W': '[AT]',
'Y': '[CT]',
'S': '[CG]',
'K': '[GT]',
'H': '[ACT]',
'B': '[CGT]',
'V': '[ACG]',
'D': '[AGT]',
}
if isinstance(pattern, list):
return [ pattern2regexp(p) for p in pattern ]
pattern = pattern.upper()
for p, r in wildcards.items():
pattern = re.sub(p, r, pattern)
return re.compile(pattern.upper())
# ------------------------------------------------------------------------------
def get_match_func(pattern):
# Input pattern can be a list or a string.
if not isinstance(pattern, list):
pattern = [ pattern ]
if has_wildcard(pattern):
pattern = pattern2regexp(pattern)
if len(pattern) == 1: # There is only a single pattern.
pattern = pattern[0] # Use the only element from the list as a single regexp.
def match_single_regexp(segment):
if re.match(pattern, segment):
return True
return False
return match_single_regexp
else: # There are multiple patterns.
def match_multi_regexp(segment):
for p in pattern:
if re.match(p, segment):
return True
return False
return match_multi_regexp
else: # No wildcard in any of the patterns.
if len(pattern) == 1: # There is only a single pattern.
pattern = pattern[0] # Use the only element from the list as a single string.
def match_single_string(segment):
if segment.startswith(pattern):
return True
return False
return match_single_string
else: # There are multiple patterns.
def match_multi_string(segment):
for p in pattern:
if segment.startswith(p):
return True
return False
return match_multi_string
# ------------------------------------------------------------------------------
def process_input(params):
f = open(params['inputfile' ], 'r')
g = open(params['outputfile'], 'w')
minsize = min([ len(p) for p in params['pattern'] ])
maxsize = max([ len(p) for p in params['pattern'] ])
matches = get_match_func(params['pattern'])
segment = ''
counter = 0
endl = ''
for line in f:
line = line.strip()
if line.startswith('>'):
# This is the beginning of a new sequence, but before starting it we must
# finish processing of the remaining segment of the previous sequence.
while len(segment) > minsize:
segment = segment[1:]
if matches(segment):
g.write(' ' + str(counter - len(segment) + 1))
if counter > 0:
g.write(' ' + str(counter)) # Close the previous sequence here.
firststr=re.split('\s+',line[1:])
g.write(endl+firststr[0])
segment = ''
counter = 0
endl = '\n'
continue
# Process next line of the sequence.
line = line.upper()
for symbol in line:
counter += 1
segment += symbol
while len(segment) > maxsize:
segment = segment[1:]
# Do pattern matching only if segment size equals maxsize.
if len(segment) == maxsize:
if matches(segment):
g.write(' ' + str(counter - maxsize + 1)) # maxsize == len(segment)
# Finish the last sequence.
while len(segment) > minsize:
segment = segment[1:]
if matches(segment):
g.write(' ' + str(counter - len(segment) + 1))
if counter > 0:
g.write(' ' + str(counter))
g.write('\n') # End the output file with a newline.
# Close files.
g.close()
f.close()
# ------------------------------------------------------------------------------
params = process_args(sys.argv)
process_input(params)
| Python |
3D | flystar233/3d_dna_pipe | getread.sh | .sh | 710 | 18 | #!/bin/sh
(($# == 3)) || { echo -e "\nUsage: $0 rawdata_allValidPairs fq1.gz fq2.gz\n"; exit; }
file=$1
fq1=$2
fq2=$3
sign=`head -1 $file |grep "/"`
sign_fq=`zcat $fq1|head -1|grep "/"`
if [ ! -n "$sign" ]; then
cut -f 1 $file > fastq.name
else
cut -f 1 -d "/" $file > fastq.name
fi
if [ ! -n "$sign_fq" ]; then
/zfssz3/NASCT_BACKUP/MS_PMO2017/xutengfei1/software/miniconda3/bin/python /ldfssz1/MS_OP/USER/xutengfei1/script_py/hic_scr/getread.py --input fastq.name --r1 $fq1 --r2 $fq2 --header old
else
/zfssz3/NASCT_BACKUP/MS_PMO2017/xutengfei1/software/miniconda3/bin/python /ldfssz1/MS_OP/USER/xutengfei1/script_py/hic_scr/getread.py --input fastq.name --r1 $fq1 --r2 $fq2 --header new
fi
| Shell |
3D | flystar233/3d_dna_pipe | getread.py | .py | 1,820 | 47 | import pyfastx
from collections import defaultdict
import gzip
import argparse
import time
def fastq_grep():
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument( "--input", action="store", dest="seqname", required=True,help="File of allValidPairs")
parser.add_argument( "--r1", action="store", dest="r1seq", required=True,help="File of r1 seq(gzip)")
parser.add_argument( "--r2", action="store", dest="r2seq", required=True,help="File of r2 seq(gzip)")
parser.add_argument( "--header",type=str, dest="header", required=True,help="Type of fq header")
args = parser.parse_args()
seqname = args.seqname
r1seq = args.r1seq
r2seq = args.r2seq
header = args.header
myname = defaultdict(int)
with open(seqname) as IN:
for i in IN:
myname[i.strip()]=1
vaild_len = len(myname)
count = 0
print_len = vaild_len // 100 #per 1/100 print progress
with gzip.open(filename='valid_R1.fastq.gz',compresslevel=6,mode='wt') as OUT1,gzip.open(filename='valid_R2.fastq.gz',compresslevel=6,mode='wt') as OUT2:
for r1,r2 in zip(pyfastx.Fastq(r1seq,build_index=False,full_name=True),pyfastx.Fastq(r2seq,build_index=False,full_name=True)):
if header =='new':
tmp_r1_name = r1[0].rstrip('1').rstrip('/')
else:
tmp_r1_name = r1[0].split()[0]
if myname[tmp_r1_name]==1:
count += 1
if count % print_len == 0:
print(f'[{time.asctime(time.localtime(time.time()))}] {count/vaild_len*100}% done.\n')
OUT1.write("@"+r1[0]+"\n")
OUT1.write(r1[1]+"\n")
OUT1.write("+"+"\n")
OUT1.write(r1[2]+"\n")
OUT2.write("@"+r2[0]+"\n")
OUT2.write(r2[1]+"\n")
OUT2.write("+"+"\n")
OUT2.write(r2[2]+"\n")
else:
pass
if __name__=="__main__":
fastq_grep()
| Python |
3D | flystar233/3d_dna_pipe | juicer_3d_dna_pipe.py | .py | 4,499 | 89 | #coding=utf8
'''
Created by xutengfei <xutengfei1@genomics.cn> on 2020.12.29.
__author__ = "<xutengfei1@genomics.cn>"
__version__ = "v1.0"
'''
import re,os
import argparse
import textwrap
parser = argparse.ArgumentParser(description=textwrap.dedent('''
==============================================================
Juicer&3d-dna pipeline v1.0
xutengfei1@genomics.cn
--------------------------------------------------------------
'''), formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("--fq",dest="fq",help="Valid fastq(getreads.pl && cat),*_R*.fastq(.gz),[R]",required=True,nargs="+")
parser.add_argument("--ref",dest="reference",help="Path of the Reference sequence file,[R]",required=True)
parser.add_argument("--res_name",dest="res_name",help="Restriction site name,[default:\"MboI\"]",default="MboI",type=str,choices=["HindIII","MboI","DpnII","NcoI"])
parser.add_argument("--n_cpus",dest="n_cpus",help="The required cpu numbers,[int,default=1]",default=1,type=int)
args = parser.parse_args()
print(args)
########################check##########################
assert os.path.exists(args.reference),"Please check Reference file"
for fq in args.fq:
assert os.path.isfile(fq),"Please check fastq file"
assert re.match(r".*_R.\.fastq\.*",fq),"The fastq file name is error"
########################mkdir#########################
if not os.path.exists("01.Juicer"):
os.mkdir("01.Juicer")
if not os.path.exists("02.3d_dna"):
os.mkdir("02.3d_dna")
if not os.path.exists("01.Juicer/genome"):
os.mkdir("01.Juicer/genome")
if not os.path.exists("01.Juicer/fastq"):
os.mkdir("01.Juicer/fastq")
######################get parameter####################
reference = os.path.abspath(args.reference)
ref_base = os.path.basename(reference)
fastqs = args.fq
n_cpus = args.n_cpus
res_name = args.res_name
####################fastq&&genome########################
os.system("echo \"export PATH=/zfssz3/NASCT_BACKUP/MS_PMO2017/xutengfei1/software/bwa-0.7.17:\$PATH\" > 01.Juicer/shell.sh")
os.system(f"ln -s {reference} 01.Juicer/genome/")
for fq in fastqs:
fq_path = os.path.abspath(fq)
os.system(f"ln -s {fq_path} 01.Juicer/fastq/")
########################bwa index########################
bwa_final = reference+".sa"
if os.path.isfile(bwa_final):
os.system("ln -s %s{.amb,.ann,.bwt,.pac,.sa} 01.Juicer/genome/"%(reference))
else:
os.system(f"echo \"bwa index genome/{ref_base}\" >> 01.Juicer/shell.sh")
########################MboI.txt &length#################
os.system(f"echo \"python /zfssz3/NASCT_BACKUP/MS_PMO2017/xutengfei1/HIC_Juicer_3d/generate_site_positions.py {res_name} genome/{ref_base} genome/{ref_base} \" >> 01.Juicer/shell.sh")
chrom_size = "genome/"+ref_base+".size.txt"
os.system(f"echo \"python /ldfssz1/MS_OP/USER/xutengfei1/script_py/fa_length.py genome/{ref_base} genome/{ref_base}.size.txt\">>01.Juicer/shell.sh")
res_file = "genome/" + ref_base + "_" + res_name + ".txt"
########################Juicer############################
p_now = os.getcwd()
os.system("mkdir 01.Juicer/tmp")
juicer_tmpdir = os.path.abspath("01.Juicer/tmp")
with open("01.Juicer/shell.sh","a") as f:
f.write(r'export JAVA_TOOL_OPTIONS="-Djava.io.tmpdir=%s -XX:ParallelGCThreads=%d"'%(juicer_tmpdir,int(n_cpus)) + "\n" +
r"export TMPDIR=%s"%(juicer_tmpdir) + "\n" +
r"/zfssz3/NASCT_BACKUP/MS_PMO2017/xutengfei1/software/juicer-1.6/scripts/juicer.sh -z %s -p %s -y %s -s %s -d %s -D /zfssz3/NASCT_BACKUP/MS_PMO2017/xutengfei1/software/juicer-1.6 -t %s"%("genome/" + ref_base,chrom_size,res_file,res_name,p_now+"/01.Juicer",n_cpus))
f.write("\n")
os.chdir("01.Juicer/")
os.chdir("../")
########################3d-dna############################
os.system("mkdir 02.3d_dna/tmp")
dna_tmpdir = os.path.abspath("02.3d_dna/tmp")
merged_nodups = p_now+'/01.Juicer/aligned/merged_nodups.txt'
with open("02.3d_dna/shell.sh","w") as f:
f.write(r"export TMPDIR=%s"%(dna_tmpdir) + "\n" +
r"export PYTHONPATH=/hwfssz1/ST_OCEAN/USER/liuqun/lib/python_2.7:$PYTHONPATH;" + "\n" +
r"export PATH=/share/app/python-2.7.10/bin:/hwfssz1/ST_OCEAN/USER/liuqun/software/gawk-4.0.2/bin:/hwfssz1/ST_OCEAN/USER/liuqun/software/coreutils-8.11/bin:/hwfssz1/ST_OCEAN/USER/liuqun/software/parallel-20150322/bin:$PATH" + "\n" +
r"/zfssz3/NASCT_BACKUP/MS_PMO2017/xutengfei1/software/3d-dna_bin/run-asm-pipeline.sh -m haploid -r 3 %s %s"%(reference,merged_nodups))
f.write("\n")
os.chdir("02.3d_dna")
| Python |
3D | flystar233/3d_dna_pipe | fa_length.py | .py | 729 | 24 | import sys
def length_fa(faname,out):
with open(faname,'rt') as IN, open(out,'wt') as OUT:
Dict = {}
result=[]
for line in IN:
if line[0] == '>':
key = line[1:-1]
Dict[key] = []
else:
Dict[key].append(line.strip("\n"))
for key, value in Dict.items():
Dict[key] = ''.join(value)
for key, value in Dict.items():
tmp_result = key+"\t"+str(len(value))
result.append(tmp_result)
result = "\n".join(result)
OUT.write(result)
if (len(sys.argv)==3):
length_fa(sys.argv[1],sys.argv[2])
else:
print("Usage: python fa_length.py test.fa out.txt")
| Python |
3D | Hejrati/cDAL | metrics.py | .py | 7,972 | 183 | import math
import os
import random
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from PIL import Image
from sklearn.metrics import f1_score, jaccard_score
from torch.utils.data import DataLoader
from tqdm import tqdm
from preprocess_dataset.MoNu import MonuDataset
def calculate_metrics(x, gt):
predict = x.detach().cpu().numpy().astype('uint8')
target = gt.detach().cpu().numpy().astype('uint8')
return f1_score(target.flatten(), predict.flatten()), jaccard_score(target.flatten(), predict.flatten()),
def set_random_seed_for_iterations(seed):
"""Set random seed.
Args:
seed (int): Seed to be used.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
def sampling_major_vote_func(pos_coeff, sample_from_model, netG, output_folder, dataset, logger, step, args, device):
batch_size = 1
if isinstance(dataset, MonuDataset):
major_vote_number = 5
else:
major_vote_number = 30
loader = DataLoader(dataset, batch_size=batch_size)
loader_iter = iter(loader)
n_rounds = len(loader_iter)
f1_score_list = []
miou_list = []
with torch.no_grad():
for _ in tqdm(range(n_rounds), desc="Generating image samples for Dice and mIoU evaluation."):
gt_mask, condition_on, name = next(loader_iter)
set_random_seed_for_iterations(major_vote_number)
gt_mask = (gt_mask + 1.0) / 2.0
condition_on = condition_on["conditioned_image"]
former_frame_for_feature_extraction = condition_on.to(device)
gt_mask = gt_mask.to(device)
for i in range(gt_mask.shape[0]):
gt_img = Image.fromarray((gt_mask[i][0].detach().cpu().numpy() - 1).astype(np.uint8))
gt_img.save(os.path.join(output_folder, f"{name[i]}_gt.png"))
if isinstance(dataset, MonuDataset):
_, _, W, H = former_frame_for_feature_extraction.shape
kernel_size = dataset.image_size
stride = 256
patches = []
for y, x in np.ndindex((((W - kernel_size) // stride) + 1, ((H - kernel_size) // stride) + 1)):
y = y * stride
x = x * stride
patches.append(former_frame_for_feature_extraction[0,
:,
y: min(y + kernel_size, W),
x: min(x + kernel_size, H)])
patches = torch.stack(patches)
major_vote_list = []
for i in range(major_vote_number):
x_list = []
for index in range(math.ceil(patches.shape[0] / 4)):
model_kwargs = {"conditioned_image": patches[index * 4: min((index + 1) * 4, patches.shape[0])]}
x_t_1 = torch.randn_like(
torch.zeros(model_kwargs["conditioned_image"].shape[0], gt_mask.shape[1],
model_kwargs["conditioned_image"].shape[2],
model_kwargs["conditioned_image"].shape[3])).to(device)
y_cond = model_kwargs["conditioned_image"]
x = sample_from_model(pos_coeff, netG, args.num_timesteps, x_t_1, y_cond, args)
x_list.append(x)
out = torch.cat(x_list)
output = torch.zeros((former_frame_for_feature_extraction.shape[0], gt_mask.shape[1],
former_frame_for_feature_extraction.shape[2],
former_frame_for_feature_extraction.shape[3]))
idx_sum = torch.zeros((former_frame_for_feature_extraction.shape[0], gt_mask.shape[1],
former_frame_for_feature_extraction.shape[2],
former_frame_for_feature_extraction.shape[3]))
for index, val in enumerate(out):
y, x = np.unravel_index(index,
(((W - kernel_size) // stride) + 1, ((H - kernel_size) // stride) + 1))
y = y * stride
x = x * stride
idx_sum[0,
:,
y: min(y + kernel_size, W),
x: min(x + kernel_size, H)] += 1
output[0,
:,
y: min(y + kernel_size, W),
x: min(x + kernel_size, H)] += val[:, :min(y + kernel_size, W) - y,
:min(x + kernel_size, H) - x].cpu().data.numpy()
output = output / idx_sum
major_vote_list.append(output)
x = torch.cat(major_vote_list)
else:
model_kwargs = {
"conditioned_image": torch.cat([former_frame_for_feature_extraction] * major_vote_number)}
x_t_1 = torch.randn_like(
torch.zeros(major_vote_number, gt_mask.shape[1],
model_kwargs["conditioned_image"].shape[2],
model_kwargs["conditioned_image"].shape[3])).to(device)
y_cond = model_kwargs["conditioned_image"]
x = sample_from_model(pos_coeff, netG, args.num_timesteps, x_t_1, y_cond, args)
x = (x + 1.0) / 2.0
if x.shape[2] != gt_mask.shape[2] or x.shape[3] != gt_mask.shape[3]:
x = F.interpolate(x, gt_mask.shape[2:], mode='bilinear')
x = torch.clamp(x, 0.0, 1.0)
x = x.mean(dim=0, keepdim=True).round()
for i in range(x.shape[0]):
# save as outer training ids
out_img = Image.fromarray((x[i][0].detach().cpu().numpy() - 1).astype(np.uint8))
out_img.save(os.path.join(output_folder, f"{name[i]}_model_output.png"))
for index, (gt_im, out_im) in enumerate(zip(gt_mask, x)):
### We need to ensure that the background is 0 and the foreground (object) is 1
if isinstance(dataset, MonuDataset):
f1, miou = calculate_metrics(out_im[0], gt_im[0])
else:
f1, miou = calculate_metrics(-out_im[0] + 1, -gt_mask[0].squeeze() + 1)
f1_score_list.append(f1)
miou_list.append(miou)
logger.info(f"{name[index]} iou {miou_list[-1]}, f1_Score {f1_score_list[-1]}")
my_length = len(miou_list)
length_of_data = torch.tensor(len(miou_list), device=device)
gathered_length_of_data = [torch.tensor(1, device=device) for _ in range(dist.get_world_size())]
dist.all_gather(gathered_length_of_data, length_of_data)
max_len = torch.max(torch.stack(gathered_length_of_data))
iou_tensor = torch.tensor(miou_list + [torch.tensor(-1)] * (max_len - my_length), device=device)
f1_tensor = torch.tensor(f1_score_list + [torch.tensor(-1)] * (max_len - my_length), device=device)
gathered_miou = [torch.ones_like(iou_tensor) * -1 for _ in range(dist.get_world_size())]
gathered_f1 = [torch.ones_like(f1_tensor) * -1 for _ in range(dist.get_world_size())]
dist.all_gather(gathered_miou, iou_tensor)
dist.all_gather(gathered_f1, f1_tensor)
logger.info("measure total avg")
gathered_miou = torch.cat(gathered_miou)
gathered_miou = gathered_miou[gathered_miou != -1]
logger.info(f"mean iou {gathered_miou.mean()}")
gathered_f1 = torch.cat(gathered_f1)
gathered_f1 = gathered_f1[gathered_f1 != -1]
logger.info(f"mean f1 {gathered_f1.mean()}")
dist.barrier()
return gathered_miou.mean().item(), gathered_f1.mean().item()
| Python |
3D | Hejrati/cDAL | train_cDAL_hippo.py | .py | 21,122 | 620 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# for Denoising Diffusion GAN. To view a copy of this license, see the LICENSE file.
# ---------------------------------------------------------------
import argparse
import torch
import numpy as np
import os
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from matplotlib import pyplot as plt
from monai.data import decollate_batch
from monai.transforms import Compose, EnsureType, AsDiscrete
from tqdm import tqdm
from torch.multiprocessing import Process
import torch.distributed as dist
import shutil
from preprocess_dataset.dataset import create_dataset
from preprocess_dataset.hippo import HippocampusDecathlonDataModule
import logger
from metrics_hippo import sampling_major_vote_func
from utils import *
from score_sde.models.unet import UNetModel
def copy_source(file, output_dir):
shutil.copyfile(file, os.path.join(output_dir, os.path.basename(file)))
def broadcast_params(params):
for param in params:
dist.broadcast(param.data, src=0)
# %% Diffusion coefficients
def var_func_vp(t, beta_min, beta_max):
log_mean_coeff = -0.25 * t ** 2 * (beta_max - beta_min) - 0.5 * t * beta_min
var = 1. - torch.exp(2. * log_mean_coeff)
return var
def var_func_geometric(t, beta_min, beta_max):
return beta_min * ((beta_max / beta_min) ** t)
def extract(input, t, shape):
out = torch.gather(input, 0, t)
reshape = [shape[0]] + [1] * (len(shape) - 1)
out = out.reshape(*reshape)
return out
def get_time_schedule(args, device):
n_timestep = args.num_timesteps
eps_small = 1e-3
t = np.arange(0, n_timestep + 1, dtype=np.float64)
t = t / n_timestep
t = torch.from_numpy(t) * (1. - eps_small) + eps_small
return t.to(device)
def get_sigma_schedule(args, device):
n_timestep = args.num_timesteps
beta_min = args.beta_min
beta_max = args.beta_max
eps_small = 1e-3
t = np.arange(0, n_timestep + 1, dtype=np.float64)
t = t / n_timestep
t = torch.from_numpy(t) * (1. - eps_small) + eps_small
if args.use_geometric:
var = var_func_geometric(t, beta_min, beta_max)
else:
var = var_func_vp(t, beta_min, beta_max)
alpha_bars = 1.0 - var
betas = 1 - alpha_bars[1:] / alpha_bars[:-1]
first = torch.tensor(1e-8)
betas = torch.cat((first[None], betas)).to(device)
betas = betas.type(torch.float32)
sigmas = betas ** 0.5
a_s = torch.sqrt(1 - betas)
return sigmas, a_s, betas
class Diffusion_Coefficients():
def __init__(self, args, device):
self.sigmas, self.a_s, _ = get_sigma_schedule(args, device=device)
self.a_s_cum = np.cumprod(self.a_s.cpu())
self.sigmas_cum = np.sqrt(1 - self.a_s_cum ** 2)
self.a_s_prev = self.a_s.clone()
self.a_s_prev[-1] = 1
self.a_s_cum = self.a_s_cum.to(device)
self.sigmas_cum = self.sigmas_cum.to(device)
self.a_s_prev = self.a_s_prev.to(device)
def q_sample(coeff, x_start, t, *, noise=None):
"""
Diffuse the data (t == 0 means diffused for t step)
"""
if noise is None:
noise = torch.randn_like(x_start)
x_t = extract(coeff.a_s_cum, t, x_start.shape) * x_start + \
extract(coeff.sigmas_cum, t, x_start.shape) * noise
return x_t
def q_sample_pairs(coeff, x_start, t):
"""
Generate a pair of disturbed images for training
:param x_start: x_0
:param t: time step t
:return: x_t, x_{t+1}
"""
noise = torch.randn_like(x_start)
x_t = q_sample(coeff, x_start, t)
x_t_plus_one = extract(coeff.a_s, t + 1, x_start.shape) * x_t + \
extract(coeff.sigmas, t + 1, x_start.shape) * noise
return x_t, x_t_plus_one
# %% posterior sampling
class Posterior_Coefficients():
def __init__(self, args, device):
_, _, self.betas = get_sigma_schedule(args, device=device)
# we don't need the zeros
self.betas = self.betas.type(torch.float32)[1:]
self.alphas = 1 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, 0)
self.alphas_cumprod_prev = torch.cat(
(torch.tensor([1.], dtype=torch.float32, device=device), self.alphas_cumprod[:-1]), 0
)
self.posterior_variance = self.betas * (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod)
self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod)
self.sqrt_recip_alphas_cumprod = torch.rsqrt(self.alphas_cumprod)
self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1 / self.alphas_cumprod - 1)
self.posterior_mean_coef1 = (self.betas * torch.sqrt(self.alphas_cumprod_prev) / (1 - self.alphas_cumprod))
self.posterior_mean_coef2 = (
(1 - self.alphas_cumprod_prev) * torch.sqrt(self.alphas) / (1 - self.alphas_cumprod))
self.posterior_log_variance_clipped = torch.log(self.posterior_variance.clamp(min=1e-20))
def sample_posterior(coefficients, x_0, x_t, t):
def q_posterior(x_0, x_t, t):
mean = (
extract(coefficients.posterior_mean_coef1, t, x_t.shape) * x_0
+ extract(coefficients.posterior_mean_coef2, t, x_t.shape) * x_t
)
var = extract(coefficients.posterior_variance, t, x_t.shape)
log_var_clipped = extract(coefficients.posterior_log_variance_clipped, t, x_t.shape)
return mean, var, log_var_clipped
def p_sample(x_0, x_t, t):
mean, _, log_var = q_posterior(x_0, x_t, t)
noise = torch.randn_like(x_t)
nonzero_mask = (1 - (t == 0).type(torch.float32))
return mean + nonzero_mask[:, None, None, None] * torch.exp(0.5 * log_var) * noise
sample_x_pos = p_sample(x_0, x_t, t)
return sample_x_pos
def sample_from_model(coefficients, generator, n_time, x_init, y, opt):
x = x_init
with torch.no_grad():
for i in reversed(range(n_time)):
t = torch.full((x.size(0),), i, dtype=torch.int64).to(x.device)
t_time = t
latent_z = torch.randn(x.size(0), opt.nz, device=x.device)
x_0 = generator(x, t_time, y, latent_z)
# x_0 = generator(x, t_time, y)
x_new = sample_posterior(coefficients, x_0, x, t)
x = x_new.detach()
return x
# %%
def train(rank, gpu, args):
from score_sde.models.discriminator import Discriminator_small
from score_sde.models.ncsnpp_generator_adagn import NCSNpp
from EMA import EMA
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
device = torch.device('cuda:{}'.format(gpu))
exp = args.exp
parent_dir = "./saved_info/{}".format(args.dataset)
exp_path = os.path.join(parent_dir, exp)
if rank == 0:
if not os.path.exists(exp_path):
os.makedirs(exp_path)
copy_source(__file__, exp_path)
# shutil.copytree('score_sde/models', os.path.join(exp_path, 'score_sde/models'))
shutil.copytree('score_sde/', os.path.join(exp_path, 'score_sde/'))
logger.configure(dir=str(exp_path))
logger.info('device: {}'.format(device))
nz = args.nz # latent dimension
if args.dataset == 'hippo':
data_module = HippocampusDecathlonDataModule(root_dir=args.daset_root)
data_module.setup("fit")
data_loader = data_module.train_dataloader()
dataset_test = data_module.val_dataloader()
else:
raise ValueError('Unknown dataset: {}'.format(args.dataset))
netG = NCSNpp(args).to(device)
netD = Discriminator_small(args.num_channels_disc, ngf=args.ngf,
t_emb_dim=args.t_emb_dim,
act=nn.LeakyReLU(0.2)).to(device)
broadcast_params(netG.parameters())
broadcast_params(netD.parameters())
optimizerD = optim.Adam(netD.parameters(), lr=args.lr_d, betas=(args.beta1, args.beta2))
optimizerG = optim.Adam(netG.parameters(), lr=args.lr_g, betas=(args.beta1, args.beta2))
if args.use_ema:
optimizerG = EMA(optimizerG, ema_decay=args.ema_decay)
schedulerG = torch.optim.lr_scheduler.CosineAnnealingLR(optimizerG, args.num_epoch, eta_min=1e-5)
schedulerD = torch.optim.lr_scheduler.CosineAnnealingLR(optimizerD, args.num_epoch, eta_min=1e-5)
# ddp
# netG = nn.parallel.DistributedDataParallel(netG, device_ids=[gpu], find_unused_parameters=True)
netG = nn.parallel.DistributedDataParallel(netG, device_ids=[gpu])
netD = CustomDDPWrapper(netD, device_ids=[gpu])
coeff = Diffusion_Coefficients(args, device)
pos_coeff = Posterior_Coefficients(args, device)
T = get_time_schedule(args, device)
if args.resume:
checkpoint_file = os.path.join(exp_path, 'content.pth')
checkpoint = torch.load(checkpoint_file, map_location=device)
init_epoch = checkpoint['epoch']
epoch = init_epoch
netG.load_state_dict(checkpoint['netG_dict'])
# load G
optimizerG.load_state_dict(checkpoint['optimizerG'])
schedulerG.load_state_dict(checkpoint['schedulerG'])
# load D
netD.load_state_dict(checkpoint['netD_dict'])
optimizerD.load_state_dict(checkpoint['optimizerD'])
schedulerD.load_state_dict(checkpoint['schedulerD'])
global_step = checkpoint['global_step']
print("=> loaded checkpoint (epoch {})"
.format(checkpoint['epoch']))
else:
global_step, epoch, init_epoch = 0, 0, 0
# Beginning of training
terms = dict()
err_epoch = dict()
err_epoch["errD_epoch"] = 0
err_epoch["errG_epoch"] = 0
batch_size = args.batch_size
best_mIoU, errD_total, mse_total = 0, 0, 0
post_label = Compose([EnsureType(), AsDiscrete(to_onehot=3)])
num_classes = 3
for epoch in range(init_epoch, args.num_epoch + 1):
for _, pairs in enumerate(data_loader):
onehot_labels = [post_label(label) for label in decollate_batch(pairs['label'])]
labels = torch.stack(onehot_labels, dim=0).permute(0, 4, 1, 2, 3).reshape(-1, num_classes, 32, 32)
img = pairs['image'].permute(0, 4, 1, 2, 3).reshape(-1, 1, 32, 32)
x = 2 * labels - 1
cond_img = 2 * img - 1
for p in netD.parameters():
p.requires_grad = True
netD.zero_grad()
# sample from p(x_0)
real_data = x.to(device, non_blocking=True)
# sample t
t = torch.randint(0, args.num_timesteps, (real_data.size(0),), device=device)
x_t, x_tp1 = q_sample_pairs(coeff, real_data, t)
x_t.requires_grad = True
# I- train Discriminator with real
D_real = netD(x_t, t, x_tp1.detach()).view(-1)
errD_real = F.softplus(-D_real)
terms["errD_real"] = errD_real
errD_real = errD_real.mean()
errD_real.backward(retain_graph=True)
if args.lazy_reg is None:
grad_real = torch.autograd.grad(
outputs=D_real.sum(), inputs=x_t, create_graph=True
)[0]
grad_penalty = (
grad_real.view(grad_real.size(0), -1).norm(2, dim=1) ** 2
).mean()
grad_penalty = args.r1_gamma / 2 * grad_penalty
grad_penalty.backward()
else:
if global_step % args.lazy_reg == 0:
grad_real = torch.autograd.grad(
outputs=D_real.sum(), inputs=x_t, create_graph=True
)[0]
grad_penalty = (
grad_real.view(grad_real.size(0), -1).norm(2, dim=1) ** 2
).mean()
grad_penalty = args.r1_gamma / 2 * grad_penalty
grad_penalty.backward()
# II- train Discriminator with fake
latent_z = torch.randn(batch_size, nz, device=device)
# calculate attention
x_0_predict = netG(x_tp1.detach(), t, cond_img, latent_z)
x_pos_sample = sample_posterior(pos_coeff, x_0_predict, x_tp1, t)
output = netD(x_pos_sample, t, x_tp1.detach()).view(-1)
errD_fake = F.softplus(output)
terms["errD_fake"] = errD_fake
errD_fake = errD_fake.mean()
errD_fake.backward()
errD = errD_real + errD_fake
# Update D
optimizerD.step()
# III- train Generator
for p in netD.parameters():
p.requires_grad = False
netG.zero_grad()
t = torch.randint(0, args.num_timesteps, (real_data.size(0),), device=device)
x_t, x_tp1 = q_sample_pairs(coeff, real_data, t)
latent_z = torch.randn(batch_size, nz, device=device)
# get attention
hs = netD.get_features(x_t, t, x_tp1.detach())
# print(hs[args.attn_scale].size())
attn = F.interpolate(hs[args.attn_scale].mean(dim=1).unsqueeze(1), real_data.size()[-1], mode='bilinear')
att_real_data = attn * real_data
_, x_tp1_attn = q_sample_pairs(coeff, att_real_data, t)
x_0_predict = netG(x_tp1_attn.detach(), t, cond_img, latent_z)
mse = mean_flat((real_data - x_0_predict) ** 2)
terms["mse"] = mse
terms["mse"].mean().backward()
optimizerG.step()
global_step += 1
mse_total += mse
errD_total += errD.item()
if global_step % args.log_step == 0:
terms["total_errD"] = errD_total / global_step
terms["total_mse"] = mse_total / global_step
logger.log_loss_dict(terms)
logger.log_loss_dict(global_step)
logger.dumpkvs()
logger.log('epoch {}, step: {}'.format(epoch, global_step))
if not args.no_lr_decay:
schedulerG.step()
schedulerD.step()
if epoch % args.save_ckpt_every == 0 and epoch != 0:
sample_path = os.path.join(exp_path, 'major_sampling_epoch_{}'.format(epoch))
if not os.path.exists(sample_path):
os.makedirs(sample_path)
mIoU_post, mIoU_ant = sampling_major_vote_func(pos_coeff, sample_from_model, netG, sample_path, dataset_test,
logger, epoch, args, device)
mean_total = (mIoU_post + mIoU_ant) / 2
if mean_total > best_mIoU:
best_mIoU = mean_total
if args.use_ema:
optimizerG.swap_parameters_with_ema(store_params_in_ema=True)
torch.save(netG.state_dict(),
os.path.join(exp_path, f'netG_{epoch}_mean_{best_mIoU:.4f}_Post_{mIoU_post:.4f}_Ant_{mIoU_ant:.4f}.pth'))
if args.save_content:
if epoch % args.save_content_every == 0:
print('Saving content.')
content = {'epoch': epoch + 1, 'global_step': global_step, 'args': args,
'netG_dict': netG.state_dict(), 'optimizerG': optimizerG.state_dict(),
'schedulerG': schedulerG.state_dict(), 'netD_dict': netD.state_dict(),
'optimizerD': optimizerD.state_dict(), 'schedulerD': schedulerD.state_dict()}
torch.save(content, os.path.join(exp_path, 'content.pth'))
def init_processes(rank, size, fn, args):
""" Initialize the distributed environment. """
os.environ['MASTER_ADDR'] = args.master_address
os.environ['MASTER_PORT'] = '6021'
torch.cuda.set_device(args.local_rank)
gpu = args.local_rank
dist.init_process_group(backend='nccl', init_method='env://', rank=rank, world_size=size)
fn(rank, gpu, args)
dist.barrier()
dist.destroy_process_group()
# %%
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def add_dict_to_argparser(parser, default_dict):
for k, v in default_dict.items():
v_type = type(v)
if v is None:
v_type = str
elif isinstance(v, bool):
v_type = str2bool
parser.add_argument(f"--{k}", default=v, type=v_type)
def args_to_dict(args, keys):
return {k: getattr(args, k) for k in keys}
def str2bool(v):
"""
https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
"""
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected")
def log_loss_dict(args, ts, losses):
if isinstance(losses, dict):
for key, values in losses.items():
if isinstance(values, torch.Tensor):
logger.logkv_mean(key, values.mean().item())
else:
logger.logkv_mean(key, values)
else:
logger.logkv_mean("step", losses)
class CustomDDPWrapper(nn.parallel.DistributedDataParallel):
def __getattr__(self, name):
if name == "__call__":
return super().__getattr__(name)
try:
return super().__getattr__(name)
except AttributeError:
return getattr(self.module, name)
if __name__ == '__main__':
parser = argparse.ArgumentParser('ddgan parameters')
defaults = dict(
seed=10,
resume=False,
image_size=32,
num_channels=3,
num_channels_disc=6,
centered=True,
use_geometric=False,
beta_min=0.1,
beta_max=20.,
cond_enc_layers=3,
cond_enc_num_res_blocks=2,
num_channels_dae=32,
n_mlp=3,
ch_mult=(1, 2, 2, 2,),
num_res_blocks=1,
attn_resolutions=(16,),
dropout=0.,
resamp_with_conv=True, # False in ddpm, True in biggan
conditional=True,
fir=True,
fir_kernel=[1, 3, 3, 1],
skip_rescale=True,
resblock_type='biggan', # 'type of resnet block, choice in biggan and ddpm'
progressive='none', # choices=['none', 'output_skip', 'residual']
progressive_input='residual', # choices=['none', 'input_skip', 'residual']
progressive_combine='sum', # choices=['sum', 'cat']
embedding_type='positional',
fourier_scale=16.,
not_use_tanh=False,
# geenrator and training
exp='biggann_',
dataset='hippo',
daset_root = '/mnt/c/Users/Public/Documents/Datasets' ,
nz=50,
num_timesteps=4,
attn_scale=2,
z_emb_dim=128,
t_emb_dim=128,
batch_size=32 * 4, # d x num_samples
num_epoch=12000,
ngf=64,
lr_g=1.6e-4,
lr_d=1.25e-4,
beta1=0.5,
beta2=0.9,
no_lr_decay=True,
use_ema=True,
ema_decay=0.999,
r1_gamma=1.0,
lazy_reg=10,
save_content=False, # to save all models and data
save_content_every=5,
save_ckpt_every=5, # to save the model each checkpoint
log_step=20,
###ddp
num_proc_node=1,
num_process_per_node=1,
node_rank=0,
local_rank=0,
master_address='127.0.0.1',
)
defaults.update(exp=f"experiment_rerun_fold0_{defaults.get('dataset')}_attn_scale_{defaults.get('attn_scale')}"
f"_timesteps_{defaults.get('num_timesteps')}_nz_{defaults.get('nz')}_bs_{defaults.get('batch_size')}"
f"_lr_g_{defaults.get('lr_g')}_lr_d_{defaults.get('lr_d')}_r1_gamma_{defaults.get('r1_gamma')}"
f"_lazy_reg_{defaults.get('lazy_reg')}_ema_{defaults.get('use_ema')}_ema_decay_{defaults.get('ema_decay')}"
f"_ngf_{defaults.get('ngf')}_beta_min_{defaults.get('beta_min')}_beta_max_{defaults.get('beta_max')}")
add_dict_to_argparser(parser, defaults)
args = parser.parse_args()
args.world_size = args.num_proc_node * args.num_process_per_node
size = args.num_process_per_node
if size > 1:
processes = []
for rank in range(size):
args.local_rank = rank
global_rank = rank + args.node_rank * args.num_process_per_node
global_size = args.num_proc_node * args.num_process_per_node
args.global_rank = global_rank
print('Node rank %d, local proc %d, global proc %d' % (args.node_rank, rank, global_rank))
p = Process(target=init_processes, args=(global_rank, global_size, train, args))
p.start()
processes.append(p)
for p in processes:
p.join()
else:
print('starting in debug mode')
init_processes(0, size, train, args)
| Python |
3D | Hejrati/cDAL | sampling_monu_and_lung.py | .py | 3,120 | 92 | """
Generate a large batch of image samples from a model and save them as a large
numpy array. This can be used to produce samples for FID evaluation.
"""
import argparse
import json
import os
from pathlib import Path
import torch.distributed
from torch import nn
import torch.distributed as dist
from preprocess_dataset.dataset import create_dataset
import logger
from score_sde.models.ncsnpp_generator_adagn import NCSNpp
from train_cDal_monu_and_lung import Posterior_Coefficients, sample_from_model
from utils import set_random_seed_for_iterations, dev, broadcast_params
from metrics import sampling_major_vote_func
def main():
args = create_argparser().parse_args()
device = dev(args.local_rank)
if args.dataset == 'monu':
dataset_test = create_dataset(data_dir="/home/share/Data/Medical", mode='val', image_size=256,
dataset_name='monu')
elif args.dataset == 'lung':
dataset_test = create_dataset(data_dir="/home/share/Data/Lung", mode='test', image_size=256,
dataset_name='lung')
else:
raise ValueError('Unknown dataset: {}'.format(args.dataset))
""" Initialize the distributed environment. """
os.environ['MASTER_ADDR'] = args.master_address
os.environ['MASTER_PORT'] = args.master_port
torch.cuda.set_device(args.local_rank)
gpu = args.local_rank
dist.init_process_group(backend='nccl', init_method='env://', rank=0, world_size=args.num_process_per_node)
checkpoint = torch.load(args.model_path, map_location=device)
netG = NCSNpp(args).to(device)
netG = nn.parallel.DistributedDataParallel(netG, device_ids=[gpu])
broadcast_params(netG.parameters())
netG.load_state_dict(checkpoint, strict=True)
netG.eval()
pos_coeff = Posterior_Coefficients(args, device)
if args.__dict__.get("seed") is None:
seed = 1234
else:
seed = int(args.__dict__.get("seed"))
set_random_seed_for_iterations(seed)
logger.log("sampling major vote val")
sampling_major_vote_func(pos_coeff, sample_from_model, netG, args.output_folder, dataset_test,
logger, None, args, device)
logger.log("sampling complete")
def create_argparser():
defaults = dict(
model_path="./saved_info/dd_gan/lung/experiment_lung_cDAL_fold0/",
model_name="netG_0_mIoU_0.8486591200665663_F1_0.9160836870891729.pth",
output_folder="output",
)
parameters_path = f"parameters_{defaults['dataset']}.json"
if os.path.exists(parameters_path):
with open('parameters.json', 'r') as f:
defaults.update(json.load(f))
output_dir = os.path.join(defaults["model_path"], defaults["output_folder"])
defaults.update(model_path=os.path.join(defaults["model_path"], defaults["model_name"]))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
defaults.update(output_folder=output_dir)
parser = argparse.ArgumentParser()
logger.add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
main()
| Python |
3D | Hejrati/cDAL | train_cDal_monu_and_lung.py | .py | 17,554 | 506 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# for Denoising Diffusion GAN. To view a copy of this license, 01see the LICENSE file.
# ---------------------------------------------------------------
import argparse
import json
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import torch.distributed as dist
from metrics import sampling_major_vote_func
from utils import *
from score_sde.models.discriminator import Discriminator_large
from score_sde.models.ncsnpp_generator_adagn import NCSNpp
from EMA import EMA
from preprocess_dataset.dataset import create_dataset
import logger
# %% Diffusion coefficients
def var_func_vp(t, beta_min, beta_max):
log_mean_coeff = -0.25 * t ** 2 * (beta_max - beta_min) - 0.5 * t * beta_min
var = 1. - torch.exp(2. * log_mean_coeff)
return var
def var_func_geometric(t, beta_min, beta_max):
return beta_min * ((beta_max / beta_min) ** t)
def extract(input, t, shape):
out = torch.gather(input, 0, t)
reshape = [shape[0]] + [1] * (len(shape) - 1)
out = out.reshape(*reshape)
return out
def get_time_schedule(args, device):
n_timestep = args.num_timesteps
eps_small = 1e-3
t = np.arange(0, n_timestep + 1, dtype=np.float64)
t = t / n_timestep
t = torch.from_numpy(t) * (1. - eps_small) + eps_small
return t.to(device)
def get_sigma_schedule(args, device):
n_timestep = args.num_timesteps
beta_min = args.beta_min
beta_max = args.beta_max
eps_small = 1e-3
t = np.arange(0, n_timestep + 1, dtype=np.float64)
t = t / n_timestep
t = torch.from_numpy(t) * (1. - eps_small) + eps_small
if args.use_geometric:
var = var_func_geometric(t, beta_min, beta_max)
else:
var = var_func_vp(t, beta_min, beta_max)
alpha_bars = 1.0 - var
betas = 1 - alpha_bars[1:] / alpha_bars[:-1]
first = torch.tensor(1e-8)
betas = torch.cat((first[None], betas)).to(device)
betas = betas.type(torch.float32)
sigmas = betas ** 0.5
a_s = torch.sqrt(1 - betas)
return sigmas, a_s, betas
class Diffusion_Coefficients():
def __init__(self, args, device):
self.sigmas, self.a_s, _ = get_sigma_schedule(args, device=device)
self.a_s_cum = np.cumprod(self.a_s.cpu())
self.sigmas_cum = np.sqrt(1 - self.a_s_cum ** 2)
self.a_s_prev = self.a_s.clone()
self.a_s_prev[-1] = 1
self.a_s_cum = self.a_s_cum.to(device)
self.sigmas_cum = self.sigmas_cum.to(device)
self.a_s_prev = self.a_s_prev.to(device)
def q_sample(coeff, x_start, t, *, noise=None):
"""
Diffuse the data (t == 0 means diffused for t step)
"""
if noise is None:
noise = torch.randn_like(x_start)
x_t = extract(coeff.a_s_cum, t, x_start.shape) * x_start + \
extract(coeff.sigmas_cum, t, x_start.shape) * noise
return x_t
def q_sample_pairs(coeff, x_start, t):
"""
Generate a pair of disturbed images for training
:param x_start: x_0
:param t: time step t
:return: x_t, x_{t+1}
"""
noise = torch.randn_like(x_start)
x_t = q_sample(coeff, x_start, t)
x_t_plus_one = extract(coeff.a_s, t + 1, x_start.shape) * x_t + \
extract(coeff.sigmas, t + 1, x_start.shape) * noise
return x_t, x_t_plus_one
# %% posterior sampling
class Posterior_Coefficients():
def __init__(self, args, device):
_, _, self.betas = get_sigma_schedule(args, device=device)
# we don't need the zeros
self.betas = self.betas.type(torch.float32)[1:]
self.alphas = 1 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, 0)
self.alphas_cumprod_prev = torch.cat(
(torch.tensor([1.], dtype=torch.float32, device=device), self.alphas_cumprod[:-1]), 0
)
self.posterior_variance = self.betas * (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod)
self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod)
self.sqrt_recip_alphas_cumprod = torch.rsqrt(self.alphas_cumprod)
self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1 / self.alphas_cumprod - 1)
self.posterior_mean_coef1 = (self.betas * torch.sqrt(self.alphas_cumprod_prev) / (1 - self.alphas_cumprod))
self.posterior_mean_coef2 = (
(1 - self.alphas_cumprod_prev) * torch.sqrt(self.alphas) / (1 - self.alphas_cumprod))
self.posterior_log_variance_clipped = torch.log(self.posterior_variance.clamp(min=1e-20))
def sample_posterior(coefficients, x_0, x_t, t):
def q_posterior(x_0, x_t, t):
mean = (
extract(coefficients.posterior_mean_coef1, t, x_t.shape) * x_0
+ extract(coefficients.posterior_mean_coef2, t, x_t.shape) * x_t
)
var = extract(coefficients.posterior_variance, t, x_t.shape)
log_var_clipped = extract(coefficients.posterior_log_variance_clipped, t, x_t.shape)
return mean, var, log_var_clipped
def p_sample(x_0, x_t, t):
mean, _, log_var = q_posterior(x_0, x_t, t)
noise = torch.randn_like(x_t)
nonzero_mask = (1 - (t == 0).type(torch.float32))
return mean + nonzero_mask[:, None, None, None] * torch.exp(0.5 * log_var) * noise
sample_x_pos = p_sample(x_0, x_t, t)
return sample_x_pos
def sample_from_model(coefficients, generator, n_time, x_init, y, opt):
x = x_init
with torch.no_grad():
for i in reversed(range(n_time)):
t = torch.full((x.size(0),), i, dtype=torch.int64).to(x.device)
t_time = t
latent_z = torch.randn(x.size(0), opt.nz, device=x.device)
x_0 = generator(x, t_time, y, latent_z)
# x_0 = generator(x, t_time, y)
x_new = sample_posterior(coefficients, x_0, x, t)
x = x_new.detach()
return x
# %%
def train(rank, gpu, args):
set_random_seed_for_iterations(args.seed)
device = dev(gpu)
exp = args.exp
parent_dir = "./saved_info/dd_gan/{}".format(args.dataset)
exp_path = os.path.join(parent_dir, exp)
if rank == 0:
if not os.path.exists(exp_path):
os.makedirs(exp_path)
copy_source(__file__, exp_path)
shutil.copytree('score_sde/', os.path.join(exp_path, 'score_sde/'))
logger.configure(dir=str(exp_path))
logger.info('device: {}'.format(device))
batch_size = args.batch_size
nz = args.nz # latent dimension
if args.dataset == 'monu':
train_data = create_dataset(data_dir="/home/share/Data/Medical", mode='train', image_size=256, dataset_name='monu')
test_data = create_dataset(data_dir="/home/share/Data/Medical", mode='test', image_size=256, dataset_name='monu')
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=False, drop_last=True)
elif args.dataset == 'lung':
train_data = create_dataset(data_dir="/home/share/Data/Lung", mode='train', image_size=256, dataset_name='lung')
test_data = create_dataset(data_dir="/home/share/Data/Lung", mode='test', image_size=256, dataset_name='lung')
train_loader = DataLoader(train_data, batch_size=batch_size, num_workers=0, shuffle=False, drop_last=True)
else:
raise ValueError('Unknown dataset: {}'.format(args.dataset))
netG = NCSNpp(args).to(device)
netD = Discriminator_large(args.num_channels_disc, ngf=args.ngf,
t_emb_dim=args.t_emb_dim,
act=nn.LeakyReLU(0.2)).to(device)
broadcast_params(netG.parameters())
broadcast_params(netD.parameters())
optimizerD = optim.Adam(netD.parameters(), lr=args.lr_d, betas=(args.beta1, args.beta2))
optimizerG = optim.Adam(netG.parameters(), lr=args.lr_g, betas=(args.beta1, args.beta2))
if args.use_ema:
optimizerG = EMA(optimizerG, ema_decay=args.ema_decay)
schedulerG = torch.optim.lr_scheduler.CosineAnnealingLR(optimizerG, args.T_max, eta_min=1e-5)
schedulerD = torch.optim.lr_scheduler.CosineAnnealingLR(optimizerD, args.T_max, eta_min=1e-5)
# ddp
netG = nn.parallel.DistributedDataParallel(netG, device_ids=[gpu])
netD = CustomDDPWrapper(netD, device_ids=[gpu])
netD.apply(weights_init_normal)
coeff = Diffusion_Coefficients(args, device)
pos_coeff = Posterior_Coefficients(args, device)
T = get_time_schedule(args, device)
if args.resume:
checkpoint_file = os.path.join(exp_path, 'content.pth')
checkpoint = torch.load(checkpoint_file, map_location=device)
init_epoch = checkpoint['epoch']
netG.load_state_dict(checkpoint['netG_dict'])
# load G
optimizerG.load_state_dict(checkpoint['optimizerG'])
schedulerG.load_state_dict(checkpoint['schedulerG'])
# load D
netD.load_state_dict(checkpoint['netD_dict'])
optimizerD.load_state_dict(checkpoint['optimizerD'])
schedulerD.load_state_dict(checkpoint['schedulerD'])
global_step = checkpoint['global_step']
print("=> loaded checkpoint (epoch {})"
.format(checkpoint['epoch']))
else:
global_step, init_epoch = 0, 0
# Beginning of training
terms = dict()
best_mIoU, errD_total, mse_total = 0, 0, 0
for epoch in range(init_epoch, args.num_epoch + 1):
for _, pairs in enumerate(train_loader):
x = pairs[0] # This is 'x' (label)
cond_img = pairs[1]["conditioned_image"] # This is 'I' (image)
for p in netD.parameters():
p.requires_grad = True
netD.zero_grad()
# sample from p(x_0)
real_data = x.to(device, non_blocking=True)
# sample t
t = torch.randint(0, args.num_timesteps, (real_data.size(0),), device=device)
x_t, x_tp1 = q_sample_pairs(coeff, real_data, t)
x_t.requires_grad = True
# I- train Discriminator with real
D_real = netD(x_t, t, x_tp1.detach()).view(-1)
errD_real = F.softplus(-D_real)
terms["errD_real"] = errD_real
errD_real = errD_real.mean()
errD_real.backward(retain_graph=True)
if args.lazy_reg is None:
grad_real = torch.autograd.grad(
outputs=D_real.sum(), inputs=x_t, create_graph=True
)[0]
grad_penalty = (
grad_real.view(grad_real.size(0), -1).norm(2, dim=1) ** 2
).mean()
grad_penalty = args.r1_gamma / 2 * grad_penalty
grad_penalty.backward()
else:
if global_step % args.lazy_reg == 0:
grad_real = torch.autograd.grad(
outputs=D_real.sum(), inputs=x_t, create_graph=True
)[0]
grad_penalty = (
grad_real.view(grad_real.size(0), -1).norm(2, dim=1) ** 2
).mean()
grad_penalty = args.r1_gamma / 2 * grad_penalty
grad_penalty.backward()
# II- train Discriminator with fake
latent_z = torch.randn(batch_size, nz, device=device)
# calculate attention w.r.t real data
x_0_predict = netG(x_tp1.detach(), t, cond_img, latent_z)
x_pos_sample = sample_posterior(pos_coeff, x_0_predict, x_tp1, t)
output = netD(x_pos_sample, t, x_tp1.detach()).view(-1)
errD_fake = F.softplus(output)
terms["errD_fake"] = errD_fake
errD_fake = errD_fake.mean()
errD_fake.backward()
errD = errD_real + errD_fake
# Update D
optimizerD.step()
# III- train Generator
for p in netD.parameters():
p.requires_grad = False
netG.zero_grad()
t = torch.randint(0, args.num_timesteps, (real_data.size(0),), device=device)
x_t, x_tp1 = q_sample_pairs(coeff, real_data, t)
latent_z = torch.randn(batch_size, nz, device=device)
# get attention
hs = netD.get_features(x_t, t, x_tp1.detach())
# print(hs[args.attn_scale].size())
attn = F.interpolate(hs[args.attn_scale].mean(dim=1).unsqueeze(1), real_data.size()[-1], mode='bilinear')
att_real_data = attn * real_data
_, x_tp1_attn = q_sample_pairs(coeff, att_real_data, t)
x_0_predict = netG(x_tp1_attn.detach(), t, cond_img, latent_z)
mse = mean_flat((real_data - x_0_predict) ** 2)
terms["mse"] = mse
mse.mean().backward()
optimizerG.step()
global_step += 1
mse_total += mse
errD_total += errD.item()
if global_step % args.log_step == 0:
terms["total_errD"] = errD_total / global_step
terms["total_mse"] = mse_total / global_step
logger.log_loss_dict(terms)
logger.log_loss_dict(global_step)
logger.dumpkvs()
logger.log('epoch: {}, step: {}'.format(epoch, global_step))
if not args.no_lr_decay:
schedulerG.step()
schedulerD.step()
if rank == 0:
if epoch % args.save_ckpt_every == 0:
sample_path = os.path.join(exp_path, 'major_sampling_epoch_{}'.format(epoch))
if not os.path.exists(sample_path):
os.makedirs(sample_path)
mIoU, f1 = sampling_major_vote_func(pos_coeff, sample_from_model, netG, sample_path, test_data,
logger, epoch, args, device)
if mIoU > best_mIoU:
best_mIoU = mIoU
if args.use_ema:
optimizerG.swap_parameters_with_ema(store_params_in_ema=True)
torch.save(netG.state_dict(), os.path.join(exp_path, f'netG_{epoch}_mIoU_{mIoU}_F1_{f1}.pth'))
if args.save_content and epoch % args.save_content_every == 0:
print('Saving content...')
content = {'epoch': epoch + 1, 'global_step': global_step, 'args': args,
'netG_dict': netG.state_dict(), 'optimizerG': optimizerG.state_dict(),
'schedulerG': schedulerG.state_dict(), 'netD_dict': netD.state_dict(),
'optimizerD': optimizerD.state_dict(), 'schedulerD': schedulerD.state_dict()}
torch.save(content, os.path.join(exp_path, 'content.pth'))
if __name__ == '__main__':
parser = argparse.ArgumentParser('ddgan parameters')
defaults = dict(
seed=47,
resume=False,
image_size=256,
num_channels=1,
num_channels_disc=2,
centered=True,
use_geometric=False,
beta_min=0.1,
beta_max=20.,
cond_enc_layers=3,
cond_enc_num_res_blocks=2,
num_channels_dae=32,
n_mlp=3,
ch_mult=(1, 1, 2, 2, 4, 4),
num_res_blocks=1,
attn_resolutions=(16,),
dropout=0.,
resamp_with_conv=True, # False in ddpm, True in biggan
conditional=True,
fir=True,
fir_kernel=[1, 3, 3, 1],
skip_rescale=True,
resblock_type='biggan', # choice in biggan and ddpm
progressive='none',
progressive_input='residual',
progressive_combine='sum',
attn_scale=2,
embedding_type='positional',
fourier_scale=16.,
not_use_tanh=False,
# geenrator and training
exp='cDAL',
dataset='lung',
fold=0,
nz=100,
num_timesteps=2,
z_emb_dim=256,
t_emb_dim=256,
batch_size=3,
num_epoch=12000,
T_max=500,
ngf=64,
lr_g=2e-4,
lr_d=1e-5,
beta1=0.5,
beta2=0.9,
no_lr_decay=False,
use_ema=False,
ema_decay=0.9999,
###ddp
r1_gamma=1.,
lazy_reg=None,
save_content=False, # to save all models and data
save_content_every=2,
save_ckpt_every=2, # to save the model each checkpoint
log_step=10,
num_proc_node=1,
num_process_per_node=1,
node_rank=0,
local_rank=1,
master_address='127.0.0.1',
master_port="6021",
)
defaults.update(exp=f"experiment_attn{defaults.get('attn_scale')}_{defaults.get('dataset')}_{defaults.get('exp')}_fold{defaults.get('fold')}")
parameters_path = f"parameters_{defaults['dataset']}.json"
if os.path.exists(parameters_path):
with open(parameters_path, 'r') as f:
defaults = json.load(f)
else:
with open(parameters_path, 'w') as f:
json.dump(defaults, f, indent=4)
logger.add_dict_to_argparser(parser, defaults)
args = parser.parse_args()
""" Initialize the distributed environment. """
os.environ['MASTER_ADDR'] = args.master_address
os.environ['MASTER_PORT'] = args.master_port
torch.cuda.set_device(args.local_rank)
gpu = args.local_rank
dist.init_process_group(backend='nccl', init_method='env://', rank=0, world_size=args.num_process_per_node)
train(0, gpu, args)
dist.barrier()
dist.destroy_process_group()
| Python |
3D | Hejrati/cDAL | logger.py | .py | 15,139 | 536 | """
Logger copied from OpenAI baselines to avoid extra RL-based dependencies:
https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/logger.py
"""
import argparse
import os
import sys
import shutil
import os.path as osp
import json
import time
import datetime
import tempfile
import warnings
from collections import defaultdict
from contextlib import contextmanager
import torch
DEBUG = 10
INFO = 20
WARN = 30
ERROR = 40
DISABLED = 50
class KVWriter(object):
def writekvs(self, kvs):
raise NotImplementedError
class SeqWriter(object):
def writeseq(self, seq):
raise NotImplementedError
class HumanOutputFormat(KVWriter, SeqWriter):
def __init__(self, filename_or_file):
if isinstance(filename_or_file, str):
self.file = open(filename_or_file, "wt")
self.own_file = True
else:
assert hasattr(filename_or_file, "read"), (
"expected file or str, got %s" % filename_or_file
)
self.file = filename_or_file
self.own_file = False
def writekvs(self, kvs):
# Create strings for printing
key2str = {}
for (key, val) in sorted(kvs.items()):
if hasattr(val, "__float__"):
valstr = "%-8.3g" % val
else:
valstr = str(val)
key2str[self._truncate(key)] = self._truncate(valstr)
# Find max widths
if len(key2str) == 0:
print("WARNING: tried to write empty key-value dict")
return
else:
keywidth = max(map(len, key2str.keys()))
valwidth = max(map(len, key2str.values()))
# Write out the data
dashes = "-" * (keywidth + valwidth + 7)
lines = [dashes]
for (key, val) in sorted(key2str.items(), key=lambda kv: kv[0].lower()):
lines.append(
"| %s%s | %s%s |"
% (key, " " * (keywidth - len(key)), val, " " * (valwidth - len(val)))
)
lines.append(dashes)
self.file.write("\n".join(lines) + "\n")
# Flush the output to the file
self.file.flush()
def _truncate(self, s):
maxlen = 30
return s[: maxlen - 3] + "..." if len(s) > maxlen else s
def writeseq(self, seq):
seq = list(seq)
for (i, elem) in enumerate(seq):
self.file.write(f"{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S-%f')} {elem}")
if i < len(seq) - 1: # add space unless this is the last one
self.file.write(" ")
self.file.write("\n")
self.file.flush()
def close(self):
if self.own_file:
self.file.close()
class JSONOutputFormat(KVWriter):
def __init__(self, filename):
self.file = open(filename, "wt")
def writekvs(self, kvs):
for k, v in sorted(kvs.items()):
if hasattr(v, "dtype"):
kvs[k] = float(v)
self.file.write(json.dumps(kvs) + "\n")
self.file.flush()
def close(self):
self.file.close()
class CSVOutputFormat(KVWriter):
def __init__(self, filename):
self.file = open(filename, "w+t")
self.keys = []
self.sep = ","
def writekvs(self, kvs):
# Add our current row to the history
extra_keys = list(kvs.keys() - self.keys)
extra_keys.sort()
if extra_keys:
self.keys.extend(extra_keys)
self.file.seek(0)
lines = self.file.readlines()
self.file.seek(0)
for (i, k) in enumerate(self.keys):
if i > 0:
self.file.write(",")
self.file.write(k)
self.file.write("\n")
for line in lines[1:]:
self.file.write(line[:-1])
self.file.write(self.sep * len(extra_keys))
self.file.write("\n")
for (i, k) in enumerate(self.keys):
if i > 0:
self.file.write(",")
v = kvs.get(k)
if v is not None:
self.file.write(str(v))
self.file.write("\n")
self.file.flush()
def close(self):
self.file.close()
class TensorBoardOutputFormat(KVWriter):
"""
Dumps key/value pairs into TensorBoard's numeric format.
"""
def __init__(self, dir):
os.makedirs(dir, exist_ok=True)
self.dir = dir
self.step = 1
prefix = "events"
path = osp.join(osp.abspath(dir), prefix)
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
from tensorflow.core.util import event_pb2
from tensorflow.python.util import compat
self.tf = tf
self.event_pb2 = event_pb2
self.pywrap_tensorflow = pywrap_tensorflow
self.writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(path))
def writekvs(self, kvs):
def summary_val(k, v):
kwargs = {"tag": k, "simple_value": float(v)}
return self.tf.Summary.Value(**kwargs)
summary = self.tf.Summary(value=[summary_val(k, v) for k, v in kvs.items()])
event = self.event_pb2.Event(wall_time=time.time(), summary=summary)
event.step = (
self.step
) # is there any reason why you'd want to specify the step?
self.writer.WriteEvent(event)
self.writer.Flush()
self.step += 1
def close(self):
if self.writer:
self.writer.Close()
self.writer = None
def make_output_format(format, ev_dir, log_suffix=""):
os.makedirs(ev_dir, exist_ok=True)
if format == "stdout":
return HumanOutputFormat(sys.stdout)
elif format == "log":
return HumanOutputFormat(osp.join(ev_dir, "log%s.txt" % log_suffix))
elif format == "json":
return JSONOutputFormat(osp.join(ev_dir, "progress%s.json" % log_suffix))
elif format == "csv":
return CSVOutputFormat(osp.join(ev_dir, "progress%s.csv" % log_suffix))
elif format == "tensorboard":
return TensorBoardOutputFormat(osp.join(ev_dir, "tb%s" % log_suffix))
else:
raise ValueError("Unknown format specified: %s" % (format,))
# ================================================================
# API
# ================================================================
def logkv(key, val):
"""
Log a value of some diagnostic
Call this once for each diagnostic quantity, each iteration
If called many times, last value will be used.
"""
get_current().logkv(key, val)
def logkv_mean(key, val):
"""
The same as logkv(), but if called many times, values averaged.
"""
get_current().logkv_mean(key, val)
def logkvs(d):
"""
Log a dictionary of key-value pairs
"""
for (k, v) in d.items():
logkv(k, v)
def dumpkvs():
"""
Write all of the diagnostics from the current iteration
"""
return get_current().dumpkvs()
def getkvs():
return get_current().name2val
def log(*args, level=INFO):
"""
Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).
"""
get_current().log(*args, level=level)
def debug(*args):
log(*args, level=DEBUG)
def info(*args):
log(*args, level=INFO)
def warn(*args):
log(*args, level=WARN)
def error(*args):
log(*args, level=ERROR)
def set_level(level):
"""
Set logging threshold on current logger.
"""
get_current().set_level(level)
def set_comm(comm):
get_current().set_comm(comm)
def get_dir():
"""
Get directory that log files are being written to.
will be None if there is no output directory (i.e., if you didn't call start)
"""
return get_current().get_dir()
record_tabular = logkv
dump_tabular = dumpkvs
@contextmanager
def profile_kv(scopename):
logkey = "wait_" + scopename
tstart = time.time()
try:
yield
finally:
get_current().name2val[logkey] += time.time() - tstart
def profile(n):
"""
Usage:
@profile("my_func")
def my_func(): code
"""
def decorator_with_name(func):
def func_wrapper(*args, **kwargs):
with profile_kv(n):
return func(*args, **kwargs)
return func_wrapper
return decorator_with_name
# ================================================================
# Backend
# ================================================================
def get_current():
if Logger.CURRENT is None:
_configure_default_logger()
return Logger.CURRENT
class Logger(object):
DEFAULT = None # A logger with no output files. (See right below class definition)
# So that you can still log to the terminal without setting up any output files
CURRENT = None # Current logger being used by the free functions above
def __init__(self, dir, output_formats, comm=None):
self.name2val = defaultdict(float) # values this iteration
self.name2cnt = defaultdict(int)
self.level = INFO
self.dir = dir
self.output_formats = output_formats
self.comm = comm
# Logging API, forwarded
# ----------------------------------------
def logkv(self, key, val):
self.name2val[key] = val
def logkv_mean(self, key, val):
oldval, cnt = self.name2val[key], self.name2cnt[key]
self.name2val[key] = oldval * cnt / (cnt + 1) + val / (cnt + 1)
self.name2cnt[key] = cnt + 1
def dumpkvs(self):
if self.comm is None:
d = self.name2val
else:
d = mpi_weighted_mean(
self.comm,
{
name: (val, self.name2cnt.get(name, 1))
for (name, val) in self.name2val.items()
},
)
if self.comm.rank != 0:
d["dummy"] = 1 # so we don't get a warning about empty dict
out = d.copy() # Return the dict for unit testing purposes
for fmt in self.output_formats:
if isinstance(fmt, KVWriter):
fmt.writekvs(d)
self.name2val.clear()
self.name2cnt.clear()
return out
def log(self, *args, level=INFO):
if self.level <= level:
self._do_log(args)
# Configuration
# ----------------------------------------
def set_level(self, level):
self.level = level
def set_comm(self, comm):
self.comm = comm
def get_dir(self):
return self.dir
def close(self):
for fmt in self.output_formats:
fmt.close()
# Misc
# ----------------------------------------
def _do_log(self, args):
for fmt in self.output_formats:
if isinstance(fmt, SeqWriter):
fmt.writeseq(map(str, args))
def get_rank_without_mpi_import():
# check environment variables here instead of importing mpi4py
# to avoid calling MPI_Init() when this module is imported
for varname in ["PMI_RANK", "OMPI_COMM_WORLD_RANK"]:
if varname in os.environ:
return int(os.environ[varname])
return 0
def mpi_weighted_mean(comm, local_name2valcount):
"""
Copied from: https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/common/mpi_util.py#L110
Perform a weighted average over dicts that are each on a different node
Input: local_name2valcount: dict mapping key -> (value, count)
Returns: key -> mean
"""
all_name2valcount = comm.gather(local_name2valcount)
if comm.rank == 0:
name2sum = defaultdict(float)
name2count = defaultdict(float)
for n2vc in all_name2valcount:
for (name, (val, count)) in n2vc.items():
try:
val = float(val)
except ValueError:
if comm.rank == 0:
warnings.warn(
"WARNING: tried to compute mean on non-float {}={}".format(
name, val
)
)
else:
name2sum[name] += val * count
name2count[name] += count
return {name: name2sum[name] / name2count[name] for name in name2sum}
else:
return {}
def configure(dir=None, format_strs=None, comm=None, log_suffix=""):
"""
If comm is provided, average all numerical stats across that comm
"""
if dir is None:
dir = os.getenv("OPENAI_LOGDIR")
if dir is None:
dir = osp.join(
tempfile.gettempdir(),
datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"),
)
assert isinstance(dir, str)
dir = os.path.expanduser(dir)
os.makedirs(os.path.expanduser(dir), exist_ok=True)
rank = get_rank_without_mpi_import()
if rank > 0:
log_suffix = log_suffix + "-rank%03i" % rank
if format_strs is None:
if rank == 0:
format_strs = os.getenv("OPENAI_LOG_FORMAT", "stdout,log,csv").split(",")
else:
format_strs = os.getenv("OPENAI_LOG_FORMAT_MPI", "log").split(",")
format_strs = filter(None, format_strs)
output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs]
Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm)
if output_formats:
log("Logging to %s" % dir)
def _configure_default_logger():
configure()
Logger.DEFAULT = Logger.CURRENT
def reset():
if Logger.CURRENT is not Logger.DEFAULT:
Logger.CURRENT.close()
Logger.CURRENT = Logger.DEFAULT
log("Reset logger")
@contextmanager
def scoped_configure(dir=None, format_strs=None, comm=None):
prevlogger = Logger.CURRENT
configure(dir=dir, format_strs=format_strs, comm=comm)
try:
yield
finally:
Logger.CURRENT.close()
Logger.CURRENT = prevlogger
def add_dict_to_argparser(parser, default_dict):
for k, v in default_dict.items():
v_type = type(v)
if v is None:
v_type = str
elif isinstance(v, bool):
v_type = str2bool
parser.add_argument(f"--{k}", default=v, type=v_type)
def args_to_dict(args, keys):
return {k: getattr(args, k) for k in keys}
def str2bool(v):
"""
https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
"""
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected")
def log_loss_dict(losses):
if isinstance(losses, dict):
for key, values in losses.items():
if isinstance(values, torch.Tensor):
logkv_mean(key, values.mean().item())
else:
logkv_mean(key, values)
else:
logkv_mean("step", losses)
| Python |
3D | Hejrati/cDAL | metrics_hippo.py | .py | 7,997 | 213 | import math
import os
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from PIL import Image
import random
from monai.data import decollate_batch
from monai.inferers import sliding_window_inference
from monai.metrics import DiceMetric, ConfusionMatrixMetric
from monai.transforms import Compose, EnsureType, AsDiscrete
from monai.visualize import plot_2d_or_3d_image
from monai.visualize.img2tensorboard import SummaryWriter
from sklearn.metrics import f1_score, jaccard_score
from tqdm import tqdm
"""
The sliding window inference implementation has been adapted from VinAIResearch's 3D-UCaps repository
(see commit 4e3c29de3f7ba563dd3285209bc3aa2bd74ed6cb). or
https://github.com/VinAIResearch/3D-UCaps/blob/4e3c29de3f7ba563dd3285209bc3aa2bd74ed6cb/scripts/train_ucaps_hippocampus.sh
"""
def set_random_seed_for_iterations(seed):
"""Set random seed.
Args:
seed (int): Seed to be used.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
def dev(gpu):
"""
Get the device to use for torch.distributed.
"""
if torch.cuda.is_available():
return torch.device(gpu)
return torch.device("cpu")
def calculate_metrics(x, gt):
predict = x.detach().cpu().numpy().astype('uint8')
target = gt.detach().cpu().numpy().astype('uint8')
return f1_score(target.flatten(), predict.flatten()), jaccard_score(target.flatten(), predict.flatten()),
# WCov_metric(predict, target), FBound_metric(predict, target)
def print_metric(metric_name, scores, logger):
scores = np.mean(scores, axis=0)
agg_score = np.mean(scores)
logger.log("Validation {} score average: {:4f}".format(metric_name, agg_score))
for i, score in enumerate(scores):
print("Validation {} score class {}: {:4f}".format(metric_name, i + 1, score))
logger.log("Validation {} score class {}: {:4f}".format(metric_name, i + 1, score))
def sampling_major_vote_func(pos_coeff, sample_from_model, netG, output_folder, dataset, logger, step, args, device):
major_vote_number = 5
n_rounds = len(dataset)
ant_dice_list, post_dice_list = [], []
dice_metric = DiceMetric(include_background=False, reduction="none", get_not_nans=False)
precision_metric = ConfusionMatrixMetric(
include_background=False, metric_name="precision", compute_sample=True, reduction="none", get_not_nans=False
)
sensitivity_metric = ConfusionMatrixMetric(
include_background=False, metric_name="sensitivity", compute_sample=True, reduction="none", get_not_nans=False
)
post_label = Compose([EnsureType(), AsDiscrete(to_onehot=3)])
post_pred = Compose([EnsureType(), AsDiscrete(argmax=True, to_onehot=3)])
experiment = SummaryWriter(output_folder)
with torch.no_grad():
set_random_seed_for_iterations(step)
for i, data in enumerate(tqdm(dataset, total=n_rounds, desc="Major vote sampling")):
labels = data["label"].to(dev(device))
condition_image = data['image'].to(dev(device))
prediction = Predictor(pos_coeff, netG, args.num_timesteps, args, dev(device), major_vote_number)
# Use a more descriptive name for clarity: condition_image
val_outputs = sliding_window_inference(
condition_image,
roi_size=[32, 32, 32],
sw_batch_size=4,
predictor=prediction.forward,
overlap=0.75,
)
plot_2d_or_3d_image(condition_image, step=i, writer=experiment, max_channels=1,
tag=f"Input Image_{i}", )
plot_2d_or_3d_image(labels * 20, step=i, writer=experiment, tag=f"GT_Label_{i}")
plot_2d_or_3d_image(torch.argmax(val_outputs, dim=1, keepdim=True) * 20, step=i, writer=experiment,
tag=f"Prediction{i}", )
val_outputs = [post_pred(val_output) for val_output in decollate_batch(val_outputs)]
labels = [post_label(label) for label in decollate_batch(labels)]
dice = dice_metric(y_pred=val_outputs, y=labels)
# "labels": {
# "0": "background",
# "1": "Anterior",
# "2": "Posterior"
# },
ant_dice = dice[0][0]
post_dice = dice[0][1]
ant_dice_list.append(ant_dice)
post_dice_list.append(post_dice)
logger.info(f"{i} Post: {post_dice_list[-1]:.4f}, Ant: {ant_dice_list[-1]:.4f}")
precision_metric(y_pred=val_outputs, y=labels)
sensitivity_metric(y_pred=val_outputs, y=labels)
print_metric("dice", dice_metric.aggregate().cpu().numpy(), logger)
print_metric("precision", precision_metric.aggregate()[0].cpu().numpy(), logger)
print_metric("sensitivity", sensitivity_metric.aggregate()[0].cpu().numpy(), logger)
scores = np.mean(dice_metric.aggregate().cpu().numpy(), axis=0)
dist.barrier()
return scores[0], scores[1]
class Predictor:
def __init__(self, coefficients, generator, n_time, args, device, major_vote_number=5):
self.coefficients = coefficients
self.generator = generator
self.n_time = n_time
self.args = args
self.major_vote_number = major_vote_number
self.device = device
def forward(self, image):
bts, c, h, w, d = image.shape # image = [bts, 1, 32, 32, 32]
condition_image = image.permute(0, 4, 1, 2, 3).reshape(-1, c, h, w).to(self.device) # Axial view
stacked_condition_image = condition_image.repeat(self.major_vote_number, 1, 1, 1).reshape(-1, c, h, w)
x_t_1 = torch.randn(
stacked_condition_image.size(0),
3,
image.shape[2],
image.shape[3],
device=self.device
) # segmentation mask according to the number of major sampling
y_cond = 2 * stacked_condition_image - 1
x = x_t_1
# print(f'size of y_cond: {y_cond.size()}')
with torch.no_grad():
for i in reversed(range(self.n_time)):
t = torch.full((x.size(0),), i, dtype=torch.int64).to(x.device)
t_time = t
latent_z = torch.randn(x.size(0), self.args.nz, device=x.device)
x_0 = self.generator(x, t_time, y_cond, latent_z)
x_new = self.sample_posterior(self.coefficients, x_0, x, t)
x = x_new.detach()
x = x.view(self.major_vote_number, bts, d, 3, h, w) # [major_vote_number, batch, 32, 3, 32, 32]
x = (x + 1.0) / 2.0
x = torch.clamp(x, 0.0, 1.0)
x = x.mean(0).round() # mean over major votes
x = x.view(bts, d, 3, h, w).permute(0, 2, 3, 4, 1) # [batch, 3, 32, 32, 32]
return x
@staticmethod
def sample_posterior(coefficients, x_0, x_t, t):
def extract(input, t, shape):
out = torch.gather(input, 0, t)
reshape = [shape[0]] + [1] * (len(shape) - 1)
out = out.reshape(*reshape)
return out
def q_posterior(x_0, x_t, t):
mean = (
extract(coefficients.posterior_mean_coef1, t, x_t.shape) * x_0
+ extract(coefficients.posterior_mean_coef2, t, x_t.shape) * x_t
)
var = extract(coefficients.posterior_variance, t, x_t.shape)
log_var_clipped = extract(coefficients.posterior_log_variance_clipped, t, x_t.shape)
return mean, var, log_var_clipped
def p_sample(x_0, x_t, t):
mean, _, log_var = q_posterior(x_0, x_t, t)
noise = torch.randn_like(x_t)
nonzero_mask = (1 - (t == 0).type(torch.float32))
return mean + nonzero_mask[:, None, None, None] * torch.exp(0.5 * log_var) * noise
sample_x_pos = p_sample(x_0, x_t, t)
return sample_x_pos
| Python |
3D | Hejrati/cDAL | utils.py | .py | 1,453 | 60 | import random
from torch import nn
from torch.backends import cudnn
import torch
import shutil
import os
import numpy as np
import torch.distributed as dist
# %%
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def set_random_seed_for_iterations(seed):
"""Set random seed.
Args:
seed (int): Seed to be used.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def dev(gpu):
"""
Get the device to use for torch.distributed.
"""
if torch.cuda.is_available():
return torch.device('cuda:{}'.format(gpu))
return torch.device("cpu")
def copy_source(file, output_dir):
shutil.copyfile(file, os.path.join(output_dir, os.path.basename(file)))
def broadcast_params(params):
for param in params:
dist.broadcast(param.data, src=0)
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1 and classname.find("DownConv") == -1 and classname.find("UpConv") == -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
class CustomDDPWrapper(nn.parallel.DistributedDataParallel):
def __getattr__(self, name):
try:
return super().__getattr__(name)
except AttributeError:
return getattr(self.module, name) | Python |
3D | Hejrati/cDAL | EMA.py | .py | 3,321 | 91 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# for Denoising Diffusion GAN. To view a copy of this license, see the LICENSE file.
# ---------------------------------------------------------------
'''
Codes adapted from https://github.com/NVlabs/LSGM/blob/main/util/ema.py
'''
import warnings
import torch
from torch.optim import Optimizer
class EMA(Optimizer):
def __init__(self, opt, ema_decay):
self.ema_decay = ema_decay
self.apply_ema = self.ema_decay > 0.
self.optimizer = opt
self.state = opt.state
self.param_groups = opt.param_groups
def step(self, *args, **kwargs):
retval = self.optimizer.step(*args, **kwargs)
# stop here if we are not applying EMA
if not self.apply_ema:
return retval
ema, params = {}, {}
for group in self.optimizer.param_groups:
for i, p in enumerate(group['params']):
if p.grad is None:
continue
state = self.optimizer.state[p]
# State initialization
if 'ema' not in state:
state['ema'] = p.data.clone()
if p.shape not in params:
params[p.shape] = {'idx': 0, 'data': []}
ema[p.shape] = []
params[p.shape]['data'].append(p.data)
ema[p.shape].append(state['ema'])
for i in params:
params[i]['data'] = torch.stack(params[i]['data'], dim=0)
ema[i] = torch.stack(ema[i], dim=0)
ema[i].mul_(self.ema_decay).add_(params[i]['data'], alpha=1. - self.ema_decay)
for p in group['params']:
if p.grad is None:
continue
idx = params[p.shape]['idx']
self.optimizer.state[p]['ema'] = ema[p.shape][idx, :]
params[p.shape]['idx'] += 1
return retval
def load_state_dict(self, state_dict):
super(EMA, self).load_state_dict(state_dict)
# load_state_dict loads the data to self.state and self.param_groups. We need to pass this data to
# the underlying optimizer too.
self.optimizer.state = self.state
self.optimizer.param_groups = self.param_groups
def swap_parameters_with_ema(self, store_params_in_ema):
""" This function swaps parameters with their ema values. It records original parameters in the ema
parameters, if store_params_in_ema is true."""
# stop here if we are not applying EMA
if not self.apply_ema:
warnings.warn('swap_parameters_with_ema was called when there is no EMA weights.')
return
for group in self.optimizer.param_groups:
for i, p in enumerate(group['params']):
if not p.requires_grad:
continue
ema = self.optimizer.state[p]['ema']
if store_params_in_ema:
tmp = p.data.detach()
p.data = ema.detach()
self.optimizer.state[p]['ema'] = tmp
else:
p.data = ema.detach()
| Python |
3D | Hejrati/cDAL | sampling_hippo.py | .py | 4,538 | 158 | """
Generate a large batch of image samples from a model and save them as a large
numpy array. This can be used to produce samples for FID evaluation.
"""
import argparse
import json
import os
from pathlib import Path
from torch import nn
import torch
import torch.distributed as dist
import logger
from preprocess_dataset.hippo import HippocampusDecathlonDataModule
from score_sde.models.ncsnpp_generator_adagn import NCSNpp
from train_cDal_monu_and_lung import Posterior_Coefficients, sample_from_model
from utils import set_random_seed_for_iterations, dev, broadcast_params
from metrics_hippo import sampling_major_vote_func
def main():
args = create_argparser().parse_args()
device = dev(args.local_rank)
if args.dataset == 'hippo':
data_module = HippocampusDecathlonDataModule(root_dir=args.dataset_dir)
data_module.setup("fit")
dataset_test = data_module.val_dataloader()
else:
raise ValueError('Unknown dataset: {}'.format(args.dataset))
""" Initialize the distributed environment. """
os.environ['MASTER_ADDR'] = args.master_address
os.environ['MASTER_PORT'] = args.master_port
torch.cuda.set_device(args.local_rank)
gpu = args.local_rank
dist.init_process_group(backend='nccl', init_method='env://', rank=0, world_size=args.num_process_per_node)
checkpoint = torch.load(args.model_path, map_location=device)
netG = NCSNpp(args).to(device)
netG = nn.parallel.DistributedDataParallel(netG, device_ids=[gpu])
broadcast_params(netG.parameters())
netG.load_state_dict(checkpoint, strict=True)
netG.eval()
pos_coeff = Posterior_Coefficients(args, device)
if args.__dict__.get("seed") is None:
seed = 1234
else:
seed = int(args.__dict__.get("seed"))
set_random_seed_for_iterations(seed)
logger.log("sampling major vote val")
sampling_major_vote_func(pos_coeff, sample_from_model, netG, args.output_folder, dataset_test,
logger, 10, args, device)
logger.log("sampling complete")
def create_argparser():
defaults = dict(
dataset_dir="/mnt/c/Users/Public/Documents/Datasets",
model_path="./saved_models/",
model_name="Hippo_fold0.pth",
output_folder="output",
seed=10,
resume=False,
image_size=32,
num_channels=3,
num_channels_disc=6,
centered=True,
use_geometric=False,
beta_min=0.1,
beta_max=20.,
cond_enc_layers=3,
cond_enc_num_res_blocks=2,
num_channels_dae=32,
n_mlp=3,
ch_mult=(1, 2, 2, 2,),
num_res_blocks=2,
attn_resolutions=(16,),
dropout=0.,
resamp_with_conv=True, # False in ddpm, True in biggan
conditional=True,
fir=True,
fir_kernel=[1, 3, 3, 1],
skip_rescale=True,
resblock_type='biggan', # 'type of resnet block, choice in biggan and ddpm'
progressive='none', # choices=['none', 'output_skip', 'residual']
progressive_input='residual', # choices=['none', 'input_skip', 'residual']
progressive_combine='sum', # choices=['sum', 'cat']
embedding_type='positional',
fourier_scale=16.,
not_use_tanh=False,
# geenrator and training
exp='biggann_',
dataset='hippo',
nz=50,
num_timesteps=2,
attn_scale=2,
z_emb_dim=128,
t_emb_dim=128,
batch_size= 16,
num_epoch=12000,
ngf=32,
lr_g=1.6e-4,
lr_d=1.25e-4,
beta1=0.5,
beta2=0.9,
no_lr_decay=False,
use_ema=True,
ema_decay=0.999,
r1_gamma=1.0,
lazy_reg=10,
save_content=False, # to save all models and data
save_content_every=5,
save_ckpt_every=5, # to save the model each checkpoint
log_step=20,
###ddp
num_proc_node=1,
num_process_per_node=1,
node_rank=1,
local_rank=0,
master_address='127.0.0.1',
master_port='6020'
)
output_dir = os.path.join(defaults["model_path"], defaults["output_folder"])
defaults.update(model_path=os.path.join(defaults["model_path"], defaults["model_name"]))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
defaults.update(output_folder=output_dir)
parser = argparse.ArgumentParser()
logger.add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
main()
| Python |
3D | Hejrati/cDAL | preprocess_dataset/Lung.py | .py | 5,533 | 129 | import os
from pathlib import Path
import cv2
import torch
from torch.utils.data import DataLoader
from preprocess_dataset.transforms import \
Compose, ToPILImage, ToTensor, Resize, RandomHorizontalFlip, RandomVerticalFlip, RandomAffine, Normalize
def cv2_loader_lung(path, is_mask):
if is_mask:
img = cv2.imread(path)
img[img > 0] = 1
else:
img = cv2.imread(path)
return img
# similar Config used int the other baselines
def get_lung_transform(image_resize):
transform_train = Compose([
ToPILImage(),
Resize((image_resize, image_resize)),
# RandomHorizontalFlip(),
# RandomVerticalFlip(),
RandomAffine(int(22), scale=(float(0.75), float(1.25))),
ToTensor(),
Normalize(mean=[0.0, 0.0, 0.0], std=[1., 1., 1.]),
])
transform_test = Compose([
ToPILImage(),
Resize((image_resize, image_resize)),
ToTensor(),
Normalize(mean=[0.0, 0.0, 0.0], std=[1., 1., 1.]),
])
return transform_train, transform_test
class LungDataset(torch.utils.data.Dataset):
def __init__(self, root, transform=None, target_transform=None, train=False, loader=cv2_loader_lung,
image_size: int = 0, fold: int = 0):
self.root = root
self.imgs_root = os.path.join(f"{self.root}/CXR_png/")
self.masks_root = os.path.join(f"{self.root}/masks/")
self.fold = fold
# we have 704 masks but 800 images. Hence we are going to
# make a 1-1 correspondance from mask to images, not the usual other way.
mask = os.listdir(self.masks_root)
mask = [fName.split(".png")[0] for fName in mask]
check = [i for i in mask if "mask" in i]
print("Total mask that has modified name:", len(check))
self.testing_files = sorted(set(os.listdir(self.imgs_root)) & set(os.listdir(self.masks_root)))
self.training_files = sorted(check)
n = len(self.testing_files)
if self.fold == 1:
self.testing_files, self.training_files[:n] = self.training_files[:n], self.testing_files
elif self.fold == 2:
self.testing_files, self.training_files[-n:] = self.training_files[-n:], self.testing_files
elif self.fold != 0:
raise ValueError("Invalid fold value fo Lung Dataset. It should be 0, 1, or 2.")
self.paths = sorted(os.listdir(self.imgs_root))
self.image_size = image_size
self.transform = transform
self.loader = loader
self.train = train
self.target_transform = target_transform
# print('num of data:{}'.format(len(self.paths)))
print('num of train data:{}'.format(len(self.training_files)))
print('num of test data:{}'.format(len(self.testing_files)))
def __getitem__(self, index):
if self.train:
if self.fold == 0:
img = self.loader(os.path.join(self.imgs_root, self.training_files[index].split("_mask")[0] + ".png"),
is_mask=False)
mask = self.loader(os.path.join(self.masks_root, self.training_files[index] + ".png"), is_mask=True)
elif self.fold == 1:
if index < len(self.testing_files):
img = self.loader(os.path.join(self.imgs_root, list(self.training_files)[index]), is_mask=False)
mask = self.loader(os.path.join(self.masks_root, list(self.training_files)[index]), is_mask=True)
else:
img = self.loader(
os.path.join(self.imgs_root, self.training_files[index].split("_mask")[0] + ".png"),
is_mask=False)
mask = self.loader(os.path.join(self.masks_root, self.training_files[index] + ".png"), is_mask=True)
elif self.fold == 2:
if index > len(self.training_files) - len(self.testing_files) - 1:
img = self.loader(os.path.join(self.imgs_root, list(self.training_files)[index]), is_mask=False)
mask = self.loader(os.path.join(self.masks_root, list(self.training_files)[index]), is_mask=True)
else:
img = self.loader(
os.path.join(self.imgs_root, self.training_files[index].split("_mask")[0] + ".png"),
is_mask=False)
mask = self.loader(os.path.join(self.masks_root, self.training_files[index] + ".png"), is_mask=True)
else:
if self.fold == 0:
img = self.loader(os.path.join(self.imgs_root, list(self.testing_files)[index]), is_mask=False)
mask = self.loader(os.path.join(self.masks_root, list(self.testing_files)[index]), is_mask=True)
else:
img = self.loader(
os.path.join(self.imgs_root, self.testing_files[index].split("_mask")[0] + ".png"),
is_mask=False)
mask = self.loader(os.path.join(self.masks_root, self.testing_files[index] + ".png"), is_mask=True)
img, mask = self.transform(img, mask)
img = (img.mean(dim=0).unsqueeze(0) / 255.0) * 2 - 1
out_dict = {"conditioned_image": img}
mask = mask.mean(2)
mask = 2 * -mask + 1.0
return mask.unsqueeze(0), out_dict, f"{Path(self.paths[index]).stem}_{index}"
def __len__(self):
if self.train:
return len(self.training_files)
else:
return len(self.testing_files)
| Python |
3D | Hejrati/cDAL | preprocess_dataset/dataset.py | .py | 3,517 | 84 | import matplotlib.pyplot as plt
import numpy as np
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from preprocess_dataset.Lung import LungDataset, get_lung_transform
from preprocess_dataset.MoNu import MonuDataset, get_monu_transform
def create_dataset(data_dir: str, mode: str = "train", image_size: int = 256, dataset_name: str = "monu", fold: int = 0):
if dataset_name == "monu":
dataset_class = MonuDataset
transform_train, transform_test = get_monu_transform(image_size=image_size)
elif dataset_name == "lung":
dataset_class = LungDataset
image_size = image_size
transform_train, transform_test = get_lung_transform(image_resize=image_size)
else:
raise ValueError(
"Dataset name should be either \"monu\" , \"lung\", "
"Unknown dataset: {}".format(dataset_name))
if mode == "train":
return dataset_class(data_dir, train=True, transform=transform_train, image_size=image_size, fold=fold)
else:
return dataset_class(data_dir, train=False, transform=transform_test, image_size=image_size, fold=fold)
def load_data(*, data_dir: str, batch_size: int, image_size: int, deterministic=True, dataset_name: str = 'monu'):
"""
For a dataset, create a generator over (images, labels) pairs.
:param data_dir: a dataset directory.
:param batch_size: the batch size of each returned pair.
:param image_size: the size to which images are resized.
:param deterministic: if True, yield results in a deterministic order.
:param dataset_name: dataset name should be either "monu" or "davis"
"""
dataset_date = create_dataset(data_dir=data_dir, image_size=image_size, mode="train", dataset_name=dataset_name, fold=0)
if deterministic:
loader = DataLoader(dataset_date, batch_size=batch_size, shuffle=False, num_workers=0, drop_last=True)
else:
loader = DataLoader(dataset_date, batch_size=batch_size, shuffle=True, num_workers=0, drop_last=True)
while True:
yield from loader
if __name__ == "__main__":
# ['lung', 'monu']
for dataset in ['lung', 'monu']:
if dataset == 'monu':
# data_dir = "C:\\Users\\behzad\\Desktop\\Data\\Medical"
data_dir = "/home/share/Data/Medical"
elif dataset == 'lung':
# data_dir = "C:\\Users\\behzad\\Desktop\\Data\\Lung"
data_dir = "/home/share/Data/Lung"
else:
raise ValueError(
"Dataset name should be either \"monu\" or \"davis\", Unknown dataset: {}".format(dataset))
val_dataset = create_dataset(mode='test', image_size=256, data_dir=data_dir, dataset_name=dataset, fold=0)
ds = torch.utils.data.DataLoader(val_dataset, batch_size=1, num_workers=0, shuffle=False,
drop_last=True) # type:ignore
pbar = tqdm(ds)
for i, (query, out_dict, _) in enumerate(pbar):
# here print conditioned image
if dataset == 'monu':
plt.imshow(out_dict['conditioned_image'].squeeze().permute(1, 2, 0).numpy().astype(np.uint8))
plt.show()
elif dataset == 'lung':
plt.imshow(out_dict['conditioned_image'].squeeze().numpy())
plt.show()
# here print query image
plt.imshow(query.squeeze().numpy().astype(np.uint8), cmap='gray')
plt.show()
if i == 0:
break
| Python |
3D | Hejrati/cDAL | preprocess_dataset/make_mhd5.py | .py | 1,046 | 25 | import h5py
import os
from glob import glob
import numpy as np
from imageio import imread
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser(description='Convert Cityscapes to HDF5')
parser.add_argument('--images-path', type=str,
default="/home/share/Data/Cityscapes/leftimg8bit",
help='Path to <leftImg8bit> Cityscapes dataset')
parser.add_argument('--outdir', type=str,
default='/home/behzad/denoising-diffusion-gan-main/data/',
help='Save path (should be the same as the output path of generate_cityscapes_instances.py')
args = parser.parse_args()
images_path = glob(os.path.join(args.images_path, '**/**/*.png'))
short_path = [s[55:] for s in images_path]
file_name = 'all_images.hdf5'
with h5py.File(os.path.join(args.outdir, file_name), 'a') as f:
for i, path in enumerate(tqdm(images_path)):
image = imread(path)
data_seg = f.create_dataset(short_path[i], image.shape, data=image, compression="gzip", dtype=np.uint8) | Python |
3D | Hejrati/cDAL | preprocess_dataset/MoNu.py | .py | 2,811 | 80 | import os
from pathlib import Path
import imageio
import tifffile
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from preprocess_dataset.transforms import \
Compose, ToPILImage, ColorJitter, RandomHorizontalFlip, ToTensor, Normalize, RandomVerticalFlip, RandomAffine, \
Resize, RandomCrop
def cv2_loader(path, is_mask):
if is_mask:
img = imageio.imread(path)
img[img > 0] = 1
else:
img = tifffile.imread(path)
return img
# this code for image transformation of MoNuSeg dataset
# these parameters are set based on MoNuSeg requirements
def get_monu_transform(image_size: int = 256, image_resize: int = 512):
transform_train = Compose([
ToPILImage(),
Resize((image_resize, image_resize)),
RandomCrop((image_size, image_size)),
RandomHorizontalFlip(),
RandomVerticalFlip(),
RandomAffine(int(22), scale=(float(0.75), float(1.25))),
ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1), # type:ignore
ToTensor(),
Normalize(mean=[142.07, 98.48, 132.96], std=[65.78, 57.05, 57.78])
])
transform_test = Compose([
ToPILImage(),
Resize((512, 512)),
ToTensor(),
Normalize(mean=[142.07, 98.48, 132.96], std=[65.78, 57.05, 57.78])
])
return transform_train, transform_test
class MonuDataset(torch.utils.data.Dataset): # type:ignore
def __init__(self, root, transform=None, target_transform=None, train=False, loader=cv2_loader,
image_size: int = 0, fold: int = 0):
self.root = root
if train:
self.imgs_root = os.path.join(self.root, 'Training', 'img')
self.masks_root = os.path.join(self.root, 'Training', 'mask')
else:
self.imgs_root = os.path.join(self.root, 'Test', 'img')
self.masks_root = os.path.join(self.root, 'Test', 'mask')
self.paths = sorted(os.listdir(self.imgs_root))
self.image_size = image_size
self.transform = transform
self.loader = loader
self.target_transform = target_transform
print('num of data:{}'.format(len(self.paths)))
def __getitem__(self, index):
mask_path = self.paths[index].split('.')[0] + '.png'
img = self.loader(os.path.join(self.imgs_root, self.paths[index]), is_mask=False)
mask = self.loader(os.path.join(self.masks_root, mask_path), is_mask=True)
img, mask = self.transform(img, mask) # type:ignore
out_dict = {"conditioned_image": img}
mask = 2 * mask - 1.0 # range is between [-1, 1] , -1 is background and 1 is objects
return mask.unsqueeze(0), out_dict, f"{Path(self.paths[index]).stem}_{index}"
def __len__(self):
return len(self.paths)
| Python |
3D | Hejrati/cDAL | preprocess_dataset/hippo.py | .py | 8,253 | 192 | import os
import numpy as np
import torch
from matplotlib import pyplot as plt
from monai.data import CacheDataset, DataLoader, Dataset, PersistentDataset,\
load_decathlon_datalist, partition_dataset, decollate_batch
from monai.transforms import (
EnsureChannelFirstd,
Compose,
DeleteItemsd,
FgBgToIndicesd,
LoadImage,
LoadImaged,
Orientationd,
RandCropByPosNegLabeld,
ScaleIntensityd,
SpatialPadd,
ToTensord, EnsureType, AsDiscrete,
)
"""
Hippocampus Dataset Loader for the Decathlon Challenge
This script manages the loading and preprocessing of the Hippocampus dataset as part of the Decathlon challenge.
The configurations implemented in this module are derived from the repository:
https://github.com/VinAIResearch/3D-UCaps/blob/4e3c29de3f7ba563dd3285209bc3aa2bd74ed6cb/datamodule/hippocampus.py
"""
class HippocampusDecathlonDataModule:
class_weight = np.asarray([0.01361341, 0.47459406, 0.51179253])
def __init__(
self,
root_dir=".",
fold=0,
train_patch_size=(32, 32, 32),
num_samples=4,
batch_size=1,
cache_rate=0.,
cache_dir=None,
num_workers=4,
balance_sampling=True,
train_transforms=None,
val_transforms=None,
**kwargs
):
self.base_dir = root_dir + "/Task04_Hippocampus/"
self.fold = fold
self.batch_size = batch_size
self.cache_dir = cache_dir
self.cache_rate = cache_rate
self.num_workers = num_workers
print('================ Loading Hippocampus Dataset ================')
if balance_sampling:
pos = neg = 0.5
else:
pos = np.sum(self.class_weight[1:])
neg = self.class_weight[0]
if train_transforms is None:
self.train_transforms = Compose(
[
LoadImaged(keys=["image", "label"], reader="NibabelReader"),
EnsureChannelFirstd(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="LPI"),
ScaleIntensityd(keys=["image"], minv=0.0, maxv=1.0),
SpatialPadd(keys=["image", "label"], spatial_size=train_patch_size, mode="edge"),
FgBgToIndicesd(keys=["label"], image_key="image"),
RandCropByPosNegLabeld(
keys=["image", "label"],
label_key="label",
spatial_size=train_patch_size,
pos=pos,
neg=neg,
num_samples=num_samples,
fg_indices_key="label_fg_indices",
bg_indices_key="label_bg_indices",
),
DeleteItemsd(keys=["label_fg_indices", "label_bg_indices",
"image_meta_dict","label_meta_dict"]),
ToTensord(keys=["image", "label"]),
DeleteItemsd(keys=["image_transforms", "label_transforms"])
]
)
else:
self.train_transforms = train_transforms
if val_transforms is None:
self.val_transforms = Compose(
[
LoadImaged(keys=["image", "label"], reader="NibabelReader"),
EnsureChannelFirstd(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="LPI"),
ScaleIntensityd(keys=["image"], minv=0.0, maxv=1.0),
DeleteItemsd(keys=["image_meta_dict","label_meta_dict"]),
ToTensord(keys=["image", "label"]),
DeleteItemsd(keys=["image_transforms", "label_transforms"]),
]
)
else:
self.val_transforms = val_transforms
def _load_data_dicts(self, train=True):
if train:
data_dicts = load_decathlon_datalist(os.path.join(self.base_dir, "dataset.json"), data_list_key="training", base_dir=self.base_dir)
data_dicts_list = partition_dataset(data_dicts, num_partitions=4, shuffle=False, seed=0)
train_dicts, val_dicts = [], []
info = dict()
for i, data_dict in enumerate(data_dicts_list):
if i == self.fold:
val_dicts.extend(data_dict)
info["val"] = [os.path.basename(data_dict["image"]).split('.')[0] for data_dict in data_dicts_list[i]]
# info["val"] = sorted([os.path.basename(data_dict["image"]).split('.')[0] for data_dict in data_dicts_list[i]])
else:
train_dicts.extend(data_dict)
info["train"] = [os.path.basename(data_dict["image"]).split('.')[0] for data_dict in data_dicts_list[i]]
# info["train"] = sorted([os.path.basename(data_dict["image"]).split('.')[0] for data_dict in data_dicts_list[i]])
print("Train: ", info["train"][0:5])
print("Val: ", info["val"][0:5])
return train_dicts, val_dicts, info
else:
pass
def setup(self, stage=None):
if stage in (None, "fit"):
train_data_dicts, val_data_dicts, info = self._load_data_dicts()
if self.cache_rate is not None:
self.trainset = CacheDataset(data=train_data_dicts, transform=self.train_transforms, cache_rate=self.cache_rate, num_workers=self.num_workers,)
self.valset = CacheDataset(data=val_data_dicts, transform=self.val_transforms, cache_rate=self.cache_rate, num_workers=4,)
elif self.cache_dir is not None:
self.trainset = PersistentDataset(data=train_data_dicts, transform=self.train_transforms, cache_dir=self.cache_dir)
self.valset = PersistentDataset(data=val_data_dicts, transform=self.val_transforms, cache_dir=self.cache_dir)
else:
self.trainset = Dataset(data=train_data_dicts, transform=self.train_transforms)
self.valset = Dataset(data=val_data_dicts, transform=self.val_transforms)
elif stage == "validate":
_, val_data_dicts, info = self._load_data_dicts()
self.valset = CacheDataset(data=val_data_dicts, transform=self.val_transforms, cache_rate=1.0, num_workers=4)
return info
def train_dataloader(self):
return DataLoader(self.trainset, batch_size=self.batch_size, pin_memory=True, num_workers=self.num_workers,
drop_last=True, shuffle=False)
def val_dataloader(self):
return DataLoader(self.valset, batch_size=1, num_workers=4, shuffle=False)
def test_dataloader(self):
pass
def calculate_class_weight(self):
data_dicts = load_decathlon_datalist(
os.path.join(self.base_dir, "dataset.json"), data_list_key="training", base_dir=self.base_dir
)
class_weight = []
for data_dict in data_dicts:
label = LoadImage(reader="NibabelReader", image_only=True)(data_dict["label"])
_, counts = np.unique(label, return_counts=True)
counts = np.sum(counts) / counts
# Normalize
counts = counts / np.sum(counts)
class_weight.append(counts)
class_weight = np.asarray(class_weight)
class_weight = np.mean(class_weight, axis=0)
print("Class weight: ", class_weight)
def calculate_class_percentage(self):
data_dicts = load_decathlon_datalist(
os.path.join(self.base_dir, "dataset.json"), data_list_key="training", base_dir=self.base_dir
)
class_percentage = []
for data_dict in data_dicts:
label = LoadImage(reader="NibabelReader", image_only=True)(data_dict["label"])
_, counts = np.unique(label, return_counts=True)
# Normalize
counts = counts / np.sum(counts)
class_percentage.append(counts)
class_percentage = np.asarray(class_percentage)
class_percentage = np.mean(class_percentage, axis=0)
print("Class Percentage: ", class_percentage)
| Python |
3D | Hejrati/cDAL | preprocess_dataset/transforms.py | .py | 21,146 | 564 | from __future__ import division
import math
import random
import sys
import torch
from PIL import Image
try:
import accimage # type:ignore
except ImportError:
accimage = None
import numpy as np
import numbers
import collections
import warnings
from torchvision.transforms import functional as F
if sys.version_info < (3, 3):
Sequence = collections.Sequence
Iterable = collections.Iterable
else:
Sequence = collections.abc.Sequence # type:ignore
Iterable = collections.abc.Iterable # type:ignore
__all__ = ["Compose", "ToTensor", "ToPILImage", "Normalize", "Resize", "CenterCrop", "Pad",
"Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop", "RandomHorizontalFlip",
"RandomVerticalFlip", "RandomResizedCrop", "FiveCrop", "TenCrop",
"ColorJitter", "RandomRotation", "RandomAffine",
"RandomPerspective"]
_pil_interpolation_to_str = {
Image.NEAREST: 'PIL.Image.NEAREST',
Image.BILINEAR: 'PIL.Image.BILINEAR',
Image.BICUBIC: 'PIL.Image.BICUBIC',
Image.LANCZOS: 'PIL.Image.LANCZOS',
Image.HAMMING: 'PIL.Image.HAMMING',
Image.BOX: 'PIL.Image.BOX',
}
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img, mask):
for t in self.transforms:
img, mask = t(img, mask)
return img, mask
class ToTensor(object):
def __call__(self, img, mask):
# return F.to_tensor(img), F.to_tensor(mask)
img = torch.from_numpy(np.array(img)).permute(2, 0, 1).float()
mask = torch.from_numpy(np.array(mask)).float()
return img, mask
class ToPILImage(object):
def __init__(self, mode=None):
self.mode = mode
def __call__(self, img, mask):
return F.to_pil_image(img, self.mode), F.to_pil_image(mask, self.mode)
class Normalize(object):
def __init__(self, mean, std, inplace=False):
self.mean = mean
self.std = std
self.inplace = inplace
def __call__(self, img, mask):
return F.normalize(img, self.mean, self.std, self.inplace), mask
class Resize(object):
def __init__(self, size, interpolation=Image.BILINEAR, do_mask=True):
assert isinstance(size, int) or (isinstance(size, Iterable) and len(size) == 2)
self.size = size
self.interpolation = interpolation
self.do_mask = do_mask
def __call__(self, img, mask):
if self.do_mask:
return F.resize(img, self.size, self.interpolation), F.resize(mask, self.size, Image.NEAREST) # type:ignore
else:
return F.resize(img, self.size, self.interpolation), mask # type:ignore
class CenterCrop(object):
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size)) # type:ignore
else:
self.size = size
def __call__(self, img, mask):
return F.center_crop(img, self.size), F.center_crop(mask, self.size) # type:ignore
class Pad(object):
def __init__(self, padding, fill=0, padding_mode='constant'):
assert isinstance(padding, (numbers.Number, tuple))
assert isinstance(fill, (numbers.Number, str, tuple))
assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric']
if isinstance(padding, Sequence) and len(padding) not in [2, 4]: # type:ignore
raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " +
"{} element tuple".format(len(padding))) # type:ignore
self.padding = padding
self.fill = fill
self.padding_mode = padding_mode
def __call__(self, img, mask):
return F.pad(img, self.padding, self.fill, self.padding_mode), F.pad(mask, self.padding, self.fill, self.padding_mode) # type:ignore
class Lambda(object):
def __init__(self, lambd):
assert callable(lambd), repr(type(lambd).__name__) + " object is not callable"
self.lambd = lambd
def __call__(self, img, mask):
return self.lambd(img), self.lambd(mask)
class Lambda_image(object):
def __init__(self, lambd):
assert callable(lambd), repr(type(lambd).__name__) + " object is not callable"
self.lambd = lambd
def __call__(self, img, mask):
return self.lambd(img), mask
class RandomTransforms(object):
def __init__(self, transforms):
assert isinstance(transforms, (list, tuple))
self.transforms = transforms
def __call__(self, *args, **kwargs):
raise NotImplementedError()
class RandomApply(RandomTransforms):
def __init__(self, transforms, p=0.5):
super(RandomApply, self).__init__(transforms)
self.p = p
def __call__(self, img, mask):
if self.p < random.random():
return img, mask
for t in self.transforms:
img, mask = t(img, mask)
return img, mask
class RandomOrder(RandomTransforms):
def __call__(self, img, mask):
order = list(range(len(self.transforms)))
random.shuffle(order)
for i in order:
img, mask = self.transforms[i](img, mask)
return img, mask
class RandomChoice(RandomTransforms):
def __call__(self, img, mask):
t = random.choice(self.transforms)
return t(img, mask)
class RandomCrop(object):
def __init__(self, size, padding=None, pad_if_needed=False, fill=0, padding_mode='constant'):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size)) # type:ignore
else:
self.size = size
self.padding = padding
self.pad_if_needed = pad_if_needed
self.fill = fill
self.padding_mode = padding_mode
@staticmethod
def get_params(img, output_size):
w, h = img.size
th, tw = output_size
if w == tw and h == th:
return 0, 0, h, w
i = random.randint(0, h - th)
j = random.randint(0, w - tw)
return i, j, th, tw
def __call__(self, img, mask):
if self.padding is not None:
img = F.pad(img, self.padding, self.fill, self.padding_mode)
# pad the width if needed
if self.pad_if_needed and img.size[0] < self.size[1]: # type:ignore
img = F.pad(img, (self.size[1] - img.size[0], 0), self.fill, self.padding_mode) # type:ignore
# pad the height if needed
if self.pad_if_needed and img.size[1] < self.size[0]: # type:ignore
img = F.pad(img, (0, self.size[0] - img.size[1]), self.fill, self.padding_mode) # type:ignore
i, j, h, w = self.get_params(img, self.size)
return F.crop(img, i, j, h, w), F.crop(mask, i, j, h, w)
class RandomHorizontalFlip(object):
def __init__(self, p=0.5):
self.p = p
def __call__(self, img, mask):
if random.random() < self.p:
return F.hflip(img), F.hflip(mask)
return img, mask
class RandomVerticalFlip(object):
def __init__(self, p=0.5):
self.p = p
def __call__(self, img, mask):
if random.random() < self.p:
return F.vflip(img), F.vflip(mask)
return img, mask
class RandomPerspective(object):
def __init__(self, distortion_scale=0.5, p=0.5, interpolation=Image.BICUBIC):
self.p = p
self.interpolation = interpolation
self.distortion_scale = distortion_scale
def __call__(self, img, mask):
if not F._is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
if random.random() < self.p:
width, height = img.size
startpoints, endpoints = self.get_params(width, height, self.distortion_scale)
return F.perspective(img, startpoints, endpoints, self.interpolation), F.perspective(mask, startpoints, endpoints, Image.NEAREST) # type:ignore
return img, mask
@staticmethod
def get_params(width, height, distortion_scale):
half_height = int(height / 2)
half_width = int(width / 2)
topleft = (random.randint(0, int(distortion_scale * half_width)),
random.randint(0, int(distortion_scale * half_height)))
topright = (random.randint(width - int(distortion_scale * half_width) - 1, width - 1),
random.randint(0, int(distortion_scale * half_height)))
botright = (random.randint(width - int(distortion_scale * half_width) - 1, width - 1),
random.randint(height - int(distortion_scale * half_height) - 1, height - 1))
botleft = (random.randint(0, int(distortion_scale * half_width)),
random.randint(height - int(distortion_scale * half_height) - 1, height - 1))
startpoints = [(0, 0), (width - 1, 0), (width - 1, height - 1), (0, height - 1)]
endpoints = [topleft, topright, botright, botleft]
return startpoints, endpoints
class RandomResizedCrop(object):
def __init__(self, size, mask_size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Image.BILINEAR):
if isinstance(size, tuple):
self.size = size
self.mask_size = mask_size
else:
self.size = (size, size)
self.mask_size = (mask_size, mask_size)
if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
warnings.warn("range should be of kind (min, max)")
self.interpolation = interpolation
self.scale = scale
self.ratio = ratio
@staticmethod
def get_params(img, scale, ratio):
area = img.size[0] * img.size[1]
for attempt in range(10):
target_area = random.uniform(*scale) * area
log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
aspect_ratio = math.exp(random.uniform(*log_ratio))
w = int(round(math.sqrt(target_area * aspect_ratio)))
h = int(round(math.sqrt(target_area / aspect_ratio)))
if w <= img.size[0] and h <= img.size[1]:
i = random.randint(0, img.size[1] - h)
j = random.randint(0, img.size[0] - w)
return i, j, h, w
# Fallback to central crop
in_ratio = img.size[0] / img.size[1]
if (in_ratio < min(ratio)):
w = img.size[0]
h = w / min(ratio)
elif (in_ratio > max(ratio)):
h = img.size[1]
w = h * max(ratio)
else: # whole image
w = img.size[0]
h = img.size[1]
i = (img.size[1] - h) // 2
j = (img.size[0] - w) // 2
return i, j, h, w
def __call__(self, img, mask):
i, j, h, w = self.get_params(img, self.scale, self.ratio)
return F.resized_crop(img, i, j, h, w, self.size, self.interpolation), F.resized_crop(mask, i, j, h, w, self.mask_size, Image.NEAREST) # type:ignore
class FiveCrop(object):
def __init__(self, size):
self.size = size
if isinstance(size, numbers.Number):
self.size = (int(size), int(size)) # type:ignore
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
self.size = size
def __call__(self, img, mask):
return F.five_crop(img, self.size), F.five_crop(mask, self.size) # type:ignore
class TenCrop(object):
def __init__(self, size, vertical_flip=False):
self.size = size
if isinstance(size, numbers.Number):
self.size = (int(size), int(size)) # type:ignore
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
self.size = size
self.vertical_flip = vertical_flip
def __call__(self, img, mask):
return F.ten_crop(img, self.size, self.vertical_flip), F.ten_crop(mask, self.size, self.vertical_flip) # type:ignore
class ColorJitter(object):
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
self.brightness = self._check_input(brightness, 'brightness')
self.contrast = self._check_input(contrast, 'contrast')
self.saturation = self._check_input(saturation, 'saturation')
self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5),
clip_first_on_zero=False)
def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
if isinstance(value, numbers.Number):
if value < 0: # type:ignore
raise ValueError("If {} is a single number, it must be non negative.".format(name))
value = [center - value, center + value] # type:ignore
if clip_first_on_zero:
value[0] = max(value[0], 0)
elif isinstance(value, (tuple, list)) and len(value) == 2:
if not bound[0] <= value[0] <= value[1] <= bound[1]:
raise ValueError("{} values should be between {}".format(name, bound))
else:
raise TypeError("{} should be a single number or a list/tuple with lenght 2.".format(name))
# if value is 0 or (1., 1.) for brightness/contrast/saturation
# or (0., 0.) for hue, do nothing
if value[0] == value[1] == center:
value = None
return value
@staticmethod
def get_params(brightness, contrast, saturation, hue):
transforms = []
if brightness is not None:
brightness_factor = random.uniform(brightness[0], brightness[1])
transforms.append(Lambda_image(lambda img: F.adjust_brightness(img, brightness_factor)))
if contrast is not None:
contrast_factor = random.uniform(contrast[0], contrast[1])
transforms.append(Lambda_image(lambda img: F.adjust_contrast(img, contrast_factor)))
if saturation is not None:
saturation_factor = random.uniform(saturation[0], saturation[1])
transforms.append(Lambda_image(lambda img: F.adjust_saturation(img, saturation_factor)))
if hue is not None:
hue_factor = random.uniform(hue[0], hue[1])
transforms.append(Lambda_image(lambda img: F.adjust_hue(img, hue_factor)))
random.shuffle(transforms)
transform = Compose(transforms)
return transform
def __call__(self, img, mask):
transform = self.get_params(self.brightness, self.contrast,
self.saturation, self.hue)
return transform(img, mask)
class RandomRotation(object):
def __init__(self, degrees, resample=False, expand=False, center=None):
if isinstance(degrees, numbers.Number):
if degrees < 0: # type:ignore
raise ValueError("If degrees is a single number, it must be positive.")
self.degrees = (-degrees, degrees) # type:ignore
else:
if len(degrees) != 2:
raise ValueError("If degrees is a sequence, it must be of len 2.")
self.degrees = degrees
self.resample = resample
self.expand = expand
self.center = center
@staticmethod
def get_params(degrees):
angle = random.uniform(degrees[0], degrees[1])
return angle
def __call__(self, img, mask):
angle = self.get_params(self.degrees)
return F.rotate(img, angle, Image.BILINEAR, self.expand, self.center), F.rotate(mask, angle, Image.NEAREST, self.expand, self.center) # type:ignore
class RandomAffine(object):
def __init__(self, degrees, translate=None, scale=None, shear=None, resample=False, fillcolor=0):
if isinstance(degrees, numbers.Number):
if degrees < 0: # type:ignore
raise ValueError("If degrees is a single number, it must be positive.")
self.degrees = (-degrees, degrees) # type:ignore
else:
assert isinstance(degrees, (tuple, list)) and len(degrees) == 2, \
"degrees should be a list or tuple and it must be of length 2."
self.degrees = degrees
if translate is not None:
assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
"translate should be a list or tuple and it must be of length 2."
for t in translate:
if not (0.0 <= t <= 1.0):
raise ValueError("translation values should be between 0 and 1")
self.translate = translate
if scale is not None:
assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
"scale should be a list or tuple and it must be of length 2."
for s in scale:
if s <= 0:
raise ValueError("scale values should be positive")
self.scale = scale
if shear is not None:
if isinstance(shear, numbers.Number):
if shear < 0: # type:ignore
raise ValueError("If shear is a single number, it must be positive.")
self.shear = (-shear, shear) # type:ignore
else:
assert isinstance(shear, (tuple, list)) and len(shear) == 2, \
"shear should be a list or tuple and it must be of length 2."
self.shear = shear
else:
self.shear = shear
self.resample = resample
self.fillcolor = fillcolor
@staticmethod
def get_params(degrees, translate, scale_ranges, shears, img_size):
angle = random.uniform(degrees[0], degrees[1])
if translate is not None:
max_dx = translate[0] * img_size[0]
max_dy = translate[1] * img_size[1]
translations = (np.round(random.uniform(-max_dx, max_dx)),
np.round(random.uniform(-max_dy, max_dy)))
else:
translations = (0, 0)
if scale_ranges is not None:
scale = random.uniform(scale_ranges[0], scale_ranges[1])
else:
scale = 1.0
if shears is not None:
shear = random.uniform(shears[0], shears[1])
else:
shear = 0.0
return angle, translations, scale, shear
def __call__(self, img, mask):
ret = self.get_params(self.degrees, self.translate, self.scale, self.shear, img.size)
return F.affine(img, *ret, interpolation=Image.BILINEAR, fill=self.fillcolor), F.affine(mask, *ret, interpolation=Image.NEAREST, fill=self.fillcolor) # type:ignore
class RandomAffineFromSet(object):
def __init__(self, degrees, translate=None, scale=None, shear=None, resample=False, fillcolor=0):
assert isinstance(degrees, (tuple, list)), \
"degrees should be a list or tuple."
self.degrees = degrees
if translate is not None:
assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
"translate should be a list or tuple and it must be of length 2."
for t in translate:
if not (0.0 <= t <= 1.0):
raise ValueError("translation values should be between 0 and 1")
self.translate = translate
if scale is not None:
assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
"scale should be a list or tuple and it must be of length 2."
for s in scale:
if s <= 0:
raise ValueError("scale values should be positive")
self.scale = scale
if shear is not None:
if isinstance(shear, numbers.Number):
if shear < 0: # type:ignore
raise ValueError("If shear is a single number, it must be positive.")
self.shear = (-shear, shear) # type:ignore
else:
assert isinstance(shear, (tuple, list)) and len(shear) == 2, \
"shear should be a list or tuple and it must be of length 2."
self.shear = shear
else:
self.shear = shear
self.resample = resample
self.fillcolor = fillcolor
@staticmethod
def get_params(degrees, translate, scale_ranges, shears, img_size):
angle = random.choice(degrees)
if translate is not None:
max_dx = translate[0] * img_size[0]
max_dy = translate[1] * img_size[1]
translations = (np.round(random.uniform(-max_dx, max_dx)),
np.round(random.uniform(-max_dy, max_dy)))
else:
translations = (0, 0)
if scale_ranges is not None:
scale = random.uniform(scale_ranges[0], scale_ranges[1])
else:
scale = 1.0
if shears is not None:
shear = random.uniform(shears[0], shears[1])
else:
shear = 0.0
return angle, translations, scale, shear
def __call__(self, img, mask):
ret = self.get_params(self.degrees, self.translate, self.scale, self.shear, img.size)
return F.affine(img, *ret, resample=Image.BILINEAR, fillcolor=self.fillcolor), F.affine(mask, *ret, resample=Image.NEAREST, fillcolor=self.fillcolor) # type:ignore
| Python |
3D | Hejrati/cDAL | score_sde/distribution.py | .py | 5,512 | 143 | from torch.distributions import Normal, Independent
import torch
import torch.nn as nn
def truncated_normal_(tensor, mean=0, std=1):
size = tensor.shape
tmp = tensor.new_empty(size + (4,)).normal_()
valid = (tmp < 2) & (tmp > -2)
ind = valid.max(-1, keepdim=True)[1]
tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
tensor.data.mul_(std).add_(mean)
def init_weights(m):
if type(m) == nn.Conv2d or type(m) == nn.ConvTranspose2d:
nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
#nn.init.normal_(m.weight, std=0.001)
#nn.init.normal_(m.bias, std=0.001)
truncated_normal_(m.bias, mean=0, std=0.001)
def init_weights_orthogonal_normal(m):
if type(m) == nn.Conv2d or type(m) == nn.ConvTranspose2d:
nn.init.orthogonal_(m.weight)
truncated_normal_(m.bias, mean=0, std=0.001)
#nn.init.normal_(m.bias, std=0.001)
def l2_regularisation(m):
l2_reg = None
for W in m.parameters():
if l2_reg is None:
l2_reg = W.norm(2)
else:
l2_reg = l2_reg + W.norm(2)
return l2_reg
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class Encoder(nn.Module):
"""
A convolutional neural network, consisting of len(num_filters) times a block of no_convs_per_block convolutional layers,
after each block a pooling operation is performed. And after each convolutional layer a non-linear (ReLU) activation function is applied.
"""
def __init__(self, input_channels, num_filters, no_convs_per_block, padding=True, posterior=False):
super(Encoder, self).__init__()
self.contracting_path = nn.ModuleList()
self.input_channels = input_channels
self.num_filters = num_filters
if posterior:
# To accomodate for the mask that is concatenated at the channel axis, we increase the input_channels.
self.input_channels += 1
layers = []
for i in range(len(self.num_filters)):
"""
Determine input_dim and output_dim of conv layers in this block. The first layer is input x output,
All the subsequent layers are output x output.
"""
input_dim = self.input_channels if i == 0 else output_dim
output_dim = num_filters[i]
if i != 0:
layers.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=True))
layers.append(nn.Conv2d(input_dim, output_dim, kernel_size=3, padding=int(padding)))
layers.append(nn.ReLU(inplace=True))
for _ in range(no_convs_per_block - 1):
layers.append(nn.Conv2d(output_dim, output_dim, kernel_size=3, padding=int(padding)))
layers.append(nn.ReLU(inplace=True))
self.layers = nn.Sequential(*layers)
self.layers.apply(init_weights)
def forward(self, input):
output = self.layers(input)
return output
class AxisAlignedConvGaussian(nn.Module):
"""
A convolutional net that parametrizes a Gaussian distribution with axis aligned covariance matrix.
"""
def __init__(self, input_channels, num_filters, no_convs_per_block, latent_dim, initializers, posterior=False):
super(AxisAlignedConvGaussian, self).__init__()
self.input_channels = input_channels
self.channel_axis = 1
self.num_filters = num_filters
self.no_convs_per_block = no_convs_per_block
self.latent_dim = latent_dim
self.posterior = posterior
if self.posterior:
self.name = 'Posterior'
else:
self.name = 'Prior'
self.encoder = Encoder(self.input_channels, self.num_filters, self.no_convs_per_block, initializers,
posterior=self.posterior)
self.conv_layer = nn.Conv2d(num_filters[-1], 2 * self.latent_dim, (1, 1), stride=1)
self.show_img = 0
self.show_seg = 0
self.show_concat = 0
self.show_enc = 0
self.sum_input = 0
nn.init.kaiming_normal_(self.conv_layer.weight, mode='fan_in', nonlinearity='relu')
nn.init.normal_(self.conv_layer.bias)
def forward(self, input, segm=None):
# If segmentation is not none, concatenate the mask to the channel axis of the input
if segm is not None:
self.show_img = input
self.show_seg = segm
input = torch.cat((input, segm), dim=1)
self.show_concat = input
self.sum_input = torch.sum(input)
encoding = self.encoder(input)
self.show_enc = encoding
# We only want the mean of the resulting hxw image
encoding = torch.mean(encoding, dim=2, keepdim=True)
encoding = torch.mean(encoding, dim=3, keepdim=True)
# Convert encoding to 2 x latent dim and split up for mu and log_sigma
mu_log_sigma = self.conv_layer(encoding)
# We squeeze the second dimension twice, since otherwise it won't work when batch size is equal to 1
mu_log_sigma = torch.squeeze(mu_log_sigma, dim=2)
mu_log_sigma = torch.squeeze(mu_log_sigma, dim=2)
mu = mu_log_sigma[:, :self.latent_dim]
log_sigma = mu_log_sigma[:, self.latent_dim:]
# This is a multivariate normal with diagonal covariance matrix sigma
# https://github.com/pytorch/pytorch/pull/11178
dist = Independent(Normal(loc=mu, scale=torch.exp(log_sigma)), 1)
return dist | Python |
3D | Hejrati/cDAL | score_sde/__init__.py | .py | 0 | 0 | null | Python |
3D | Hejrati/cDAL | score_sde/models/nn.py | .py | 15,892 | 412 | """
Various utilities for neural networks.
"""
import abc
import math
import torch as th
import torch.nn as nn
import torch
from typing import Any, Optional, Type
class CrossAttentionEncoder(th.nn.Module, abc.ABC):
"""
An attention encoder that uses cross attention to encode the input image and the condition image
* extends: torch.nn.Module
* abstract class
* methods to implement:
* `forward`: Accept the input image as a `torch.Tensor` and the condition image as a `torch.Tensor` and return the encoded image as a `torch.Tensor`
"""
def __call__(self, x: th.Tensor, hs: Optional[tuple[th.Tensor, ...]] = None) -> th.Tensor:
return super().__call__(x, hs)
@abc.abstractmethod
def forward(self, x: th.Tensor, hs: Optional[tuple[th.Tensor, ...]] = None) -> th.Tensor:
return NotImplemented
class AttentionedFFParserConditionalEncoder(CrossAttentionEncoder):
"""
Encoder that uses attention and FFParser to encode the input image and the condition image
Code editted from `Generic_UNet` in https://github.com/WuJunde/MedSegDiff/blob/master/guided_diffusion/unet.py#L720
Removed redundant decoder part
* extends: CrossAttentionEncoder
"""
def __init__(self, input_channels: int, base_num_features: int, num_pool: int, num_conv_per_stage: int = 2, feat_map_mul_on_downscale: int = 2, convolutional_pooling=False):
super().__init__()
self.convolutional_pooling = convolutional_pooling
nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
dropout_op_kwargs = {'p': 0.5, 'inplace': True}
norm_op_kwargs = {'eps': 1e-5, 'affine': True, 'momentum': 0.1}
conv_kwargs = {'stride': 1, 'dilation': 1, 'bias': True}
self.conv_blocks_context = th.nn.ModuleList([])
self.conv_trans_blocks_a = th.nn.ModuleList([])
self.conv_trans_blocks_b = th.nn.ModuleList([])
self.conv_blocks_localization = th.nn.ModuleList([])
self.td = th.nn.ModuleList([])
self.ffparser = th.nn.ModuleList([])
output_features = base_num_features
input_features = input_channels
for d in range(num_pool):
# determine the first stride
if d != 0 and self.convolutional_pooling:
first_stride = 2
else:
first_stride = None
conv_kwargs['kernel_size'] = 3
conv_kwargs['padding'] = 1
# add convolutions
self.conv_blocks_context.append(StackedConvLayers(input_features, output_features, num_conv_per_stage, conv_kwargs=conv_kwargs, norm_op_kwargs=norm_op_kwargs, dropout_op_kwargs=dropout_op_kwargs, nonlin_kwargs=nonlin_kwargs, first_stride=first_stride))
if d < num_pool - 1:
self.conv_trans_blocks_a.append(th.nn.Conv2d(int(d/2 + 1) * 128, 2 **(d+5), 1))
self.conv_trans_blocks_b.append(th.nn.Conv2d(2 **(d+5), 1, 1))
if d != num_pool - 1:
self.ffparser.append(FFParser(output_features, 256 // (2 **(d+1)), 256 // (2 **(d+2))+1))
if not self.convolutional_pooling:
self.td.append(th.nn.MaxPool2d((2, 2)))
input_features = output_features
output_features = int(round(output_features * feat_map_mul_on_downscale))
# now the bottleneck.
# determine the first stride
if self.convolutional_pooling:
first_stride = 2
else:
first_stride = None
conv_kwargs['kernel_size'] = 3
conv_kwargs['padding'] = 1
self.conv_blocks_context.append(th.nn.Sequential(
StackedConvLayers(input_features, output_features, num_conv_per_stage - 1, conv_kwargs=conv_kwargs, norm_op_kwargs=norm_op_kwargs, dropout_op_kwargs=dropout_op_kwargs, nonlin_kwargs=nonlin_kwargs, first_stride=first_stride),
StackedConvLayers(output_features, output_features, 1, conv_kwargs=conv_kwargs, norm_op_kwargs=norm_op_kwargs, dropout_op_kwargs=dropout_op_kwargs, nonlin_kwargs=nonlin_kwargs)))
def forward(self, x: th.Tensor, hs: Optional[tuple[th.Tensor, ...]] = None) -> th.Tensor:
for d in range(len(self.conv_blocks_context) - 1):
x = self.conv_blocks_context[d](x)
if not self.convolutional_pooling:
x = self.td[d](x)
if hs:
h = hs[d]
h = self.conv_trans_blocks_a[d](h)
h = self.ffparser[d](h)
ha = self.conv_trans_blocks_b[d](h)
hb = th.mean(h, (2, 3))
hb = hb[:, :, None, None]
x = x * ha * hb
x = self.conv_blocks_context[-1](x)
emb: th.Tensor = th.nn.Conv2d(x.size(1), 512, 1).to(device=x.device)(x)
return emb
class ConvDropoutNormNonlin(torch.nn.Module):
"""
fixes a bug in ConvDropoutNormNonlin where lrelu was used regardless of nonlin. Bad.
"""
def __init__(self, input_channels: int, output_channels: int,
conv_op: Type[torch.nn.Conv2d] = torch.nn.Conv2d, conv_kwargs: Optional[dict[str, Any]] = None,
norm_op: Type[torch.nn.BatchNorm2d] = torch.nn.BatchNorm2d, norm_op_kwargs: Optional[dict[str, Any]] = None,
dropout_op: Type[torch.nn.Dropout2d] = torch.nn.Dropout2d, dropout_op_kwargs: Optional[dict[str, Any]] = None,
nonlin: Type[torch.nn.LeakyReLU] = torch.nn.LeakyReLU, nonlin_kwargs: Optional[dict[str, Any]] = None) -> None:
super(ConvDropoutNormNonlin, self).__init__()
if nonlin_kwargs is None:
nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
if dropout_op_kwargs is None:
dropout_op_kwargs = {'p': 0.5, 'inplace': True}
if norm_op_kwargs is None:
norm_op_kwargs = {'eps': 1e-5, 'affine': True, 'momentum': 0.1}
if conv_kwargs is None:
conv_kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1, 'dilation': 1, 'bias': True}
self.nonlin_kwargs = nonlin_kwargs
self.nonlin = nonlin
self.dropout_op = dropout_op
self.dropout_op_kwargs = dropout_op_kwargs
self.norm_op_kwargs = norm_op_kwargs
self.conv_kwargs = conv_kwargs
self.conv_op = conv_op
self.norm_op = norm_op
self.conv = self.conv_op(input_channels, output_channels, **self.conv_kwargs)
if self.dropout_op is not None and self.dropout_op_kwargs['p'] is not None and self.dropout_op_kwargs[
'p'] > 0:
self.dropout = self.dropout_op(**self.dropout_op_kwargs)
else:
self.dropout = None
self.instnorm = self.norm_op(output_channels, **self.norm_op_kwargs)
self.lrelu = self.nonlin(**self.nonlin_kwargs)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv(x)
if self.dropout is not None:
x = self.dropout(x)
return self.lrelu(self.instnorm(x))
class FFParser(torch.nn.Module):
def __init__(self, dim: int, h: int = 128, w: int = 65) -> None:
super().__init__()
self.complex_weight = torch.nn.Parameter(torch.randn(dim, h, w, 2, dtype=torch.float32) * 0.02)
self.w = w
self.h = h
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, C, H, W = x.shape
assert H == W, "height and width are not equal"
# x = x.view(B, a, b, C)
x = x.to(torch.float32)
x = torch.fft.rfft2(x, dim=(2, 3), norm='ortho')
weight = torch.view_as_complex(self.complex_weight)
x = x * weight
x = torch.fft.irfft2(x, s=(H, W), dim=(2, 3), norm='ortho')
x = x.reshape(B, C, H, W)
return x
class StackedConvLayers(torch.nn.Module):
def __init__(self, input_feature_channels: int, output_feature_channels: int, num_convs: int, conv_kwargs: Optional[dict[str, Any]] = None, norm_op_kwargs: Optional[dict[str, Any]] = None, dropout_op_kwargs: Optional[dict[str, Any]] = None, nonlin_kwargs: Optional[dict[str, Any]] = None, first_stride: Optional[int] = None):
'''
stacks ConvDropoutNormLReLU layers. initial_stride will only be applied to first layer in the stack. The other parameters affect all layers
:param input_feature_channels:
:param output_feature_channels:
:param num_convs:
:param dilation:
:param kernel_size:
:param padding:
:param dropout:
:param initial_stride:
:param conv_op:
:param norm_op:
:param dropout_op:
:param inplace:
:param neg_slope:
:param norm_affine:
:param conv_bias:
'''
self.input_channels = input_feature_channels
self.output_channels = output_feature_channels
if nonlin_kwargs is None:
nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
if dropout_op_kwargs is None:
dropout_op_kwargs = {'p': 0.5, 'inplace': True}
if norm_op_kwargs is None:
norm_op_kwargs = {'eps': 1e-5, 'affine': True, 'momentum': 0.1}
if conv_kwargs is None:
conv_kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1, 'dilation': 1, 'bias': True}
if first_stride is not None:
conv_kwargs_first_conv = {}
conv_kwargs_first_conv.update(conv_kwargs)
conv_kwargs_first_conv['stride'] = first_stride
else:
conv_kwargs_first_conv = conv_kwargs
super(StackedConvLayers, self).__init__()
self.blocks = torch.nn.Sequential(
*([ConvDropoutNormNonlin(input_feature_channels, output_feature_channels, conv_kwargs=conv_kwargs_first_conv, norm_op_kwargs=norm_op_kwargs, dropout_op_kwargs=dropout_op_kwargs,nonlin_kwargs=nonlin_kwargs)] +
[ConvDropoutNormNonlin(output_feature_channels, output_feature_channels, conv_kwargs=conv_kwargs_first_conv, norm_op_kwargs=norm_op_kwargs, dropout_op_kwargs=dropout_op_kwargs,nonlin_kwargs=nonlin_kwargs) for _ in range(num_convs - 1)]))
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.blocks(x)
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
def forward(self, x):
return x * th.sigmoid(x)
class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super().forward(x.float()).type(x.dtype)
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def conv_transpose_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D transposed convolution module.
"""
if dims == 1:
return nn.ConvTranspose1d(*args, **kwargs)
elif dims == 2:
return nn.ConvTranspose2d(*args, **kwargs)
elif dims == 3:
return nn.ConvTranspose3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def linear(*args, **kwargs):
"""
Create a linear module.
"""
return nn.Linear(*args, **kwargs)
def avg_pool_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D average pooling module.
"""
if dims == 1:
return nn.AvgPool1d(*args, **kwargs)
elif dims == 2:
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def update_ema(target_params, source_params, rate=0.99):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA rate (closer to 1 means slower).
"""
for targ, src in zip(target_params, source_params):
targ.detach().mul_(rate).add_(src, alpha=1 - rate)
def swap_ema(target_params, source_params):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
"""
for targ, src in zip(target_params, source_params):
temp = targ.data.clone()
targ.data.copy_(src.data)
src.data.copy_(temp)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def scale_module(module, scale):
"""
Scale the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().mul_(scale)
return module
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def normalization(channels):
"""
Make a standard normalization layer.
:param channels: number of input channels.
:return: an nn.Module for normalization.
"""
return GroupNorm32(32, channels)
def timestep_embedding(timesteps, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
half = dim // 2
freqs = th.exp(-math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half).to(device=timesteps.device)
args = timesteps[:, None].float() * freqs[None]
embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
if dim % 2:
embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def checkpoint(func, inputs, params, flag):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param params: a sequence of parameters `func` depends on but does not
explicitly take as arguments.
:param flag: if False, disable gradient checkpointing.
"""
if flag:
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
else:
return func(*inputs)
class CheckpointFunction(th.autograd.Function):
@staticmethod
def forward(ctx, run_function, length, *args):
ctx.run_function = run_function
ctx.input_tensors = list(args[:length])
ctx.input_params = list(args[length:])
with th.no_grad():
output_tensors = ctx.run_function(*ctx.input_tensors)
return output_tensors
@staticmethod
def backward(ctx, *output_grads):
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
with th.enable_grad():
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
# Tensors.
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
output_tensors = ctx.run_function(*shallow_copies)
input_grads = th.autograd.grad(
output_tensors,
ctx.input_tensors + ctx.input_params,
output_grads,
allow_unused=True,
)
del ctx.input_tensors
del ctx.input_params
del output_tensors
return (None, None) + input_grads
| Python |
3D | Hejrati/cDAL | score_sde/models/unet.py | .py | 21,768 | 621 | from abc import abstractmethod
import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from matplotlib import pyplot as plt
from .nn import (
SiLU,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
checkpoint
)
class ResBlockEncoder(nn.Module):
"""
A residual block specifically is designed for the encoder to have similar architecture as the Unet encoder
that can optionally change the number of channels. The difference is that it does not have time embedding.
:param channels: the number of input channels.
:param dropout: the rate of dropout.
:param out_channels: if specified, the number of out channels.
:param use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param use_checkpoint: if True, use gradient checkpointing on this module.
"""
def __init__(
self,
channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
use_checkpoint=False,
):
super().__init__()
self.channels = channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_checkpoint = use_checkpoint
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(normalization(channels), SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1), )
self.out_layers = nn.Sequential(normalization(self.out_channels), SiLU(), nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)), )
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
def forward(self, x):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:return: an [N x C x ...] Tensor of outputs.
"""
h = self.in_layers(x)
h = self.out_layers(h)
return self.skip_connection(x) + h
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class ModuleSequential(nn.Sequential):
"""
"""
def forward(self, x):
for layer in self:
x = layer(x)
return x
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
"""
A sequential module that passes timestep embeddings to the children that
support it as an extra input.
"""
def forward(self, x, emb):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb)
else:
x = layer(x)
return x
class Upsample(nn.Module):
"""
An upsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2):
super().__init__()
self.channels = channels
self.use_conv = use_conv
self.dims = dims
if use_conv:
self.conv = conv_nd(dims, channels, channels, 3, padding=1)
def forward(self, x):
assert x.shape[1] == self.channels
if self.dims == 3:
x = F.interpolate(
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
)
else:
x = F.interpolate(x, scale_factor=2, mode="nearest")
if self.use_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2):
super().__init__()
self.channels = channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(dims, channels, channels, 3, stride=stride, padding=1)
else:
self.op = avg_pool_nd(stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
class ResBlock(TimestepBlock):
"""
A residual block that can optionally change the number of channels.
:param channels: the number of input channels.
:param emb_channels: the number of timestep embedding channels.
:param dropout: the rate of dropout.
:param out_channels: if specified, the number of out channels.
:param use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param use_checkpoint: if True, use gradient checkpointing on this module.
"""
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
use_checkpoint=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_checkpoint = use_checkpoint
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(
normalization(channels),
SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1),
)
self.emb_layers = nn.Sequential(
SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(
dims, channels, self.out_channels, 3, padding=1
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
return checkpoint(
self._forward, (x, emb), self.parameters(), self.use_checkpoint
)
def _forward(self, x, emb):
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = th.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class AttentionBlock(nn.Module):
"""
An attention block that allows spatial positions to attend to each other.
Originally ported from here, but adapted to the N-d case.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
"""
def __init__(self, channels, num_heads=1, use_checkpoint=False):
super().__init__()
self.channels = channels
self.num_heads = num_heads
self.use_checkpoint = use_checkpoint
self.norm = normalization(channels)
self.qkv = conv_nd(1, channels, channels * 3, 1)
self.attention = QKVAttention()
self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
def forward(self, x):
return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint)
def _forward(self, x):
b, c, *spatial = x.shape
x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x))
qkv = qkv.reshape(b * self.num_heads, -1, qkv.shape[2])
h = self.attention(qkv)
h = h.reshape(b, -1, h.shape[-1])
h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial)
class QKVAttention(nn.Module):
"""
A module which performs QKV attention.
"""
def forward(self, qkv):
"""
Apply QKV attention.
:param qkv: an [N x (C * 3) x T] tensor of Qs, Ks, and Vs.
:return: an [N x C x T] tensor after attention.
"""
ch = qkv.shape[1] // 3
q, k, v = th.split(qkv, ch, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
return th.einsum("bts,bcs->bct", weight, v)
@staticmethod
def count_flops(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flops},
)
"""
b, c, *spatial = y[0].shape
num_spatial = int(np.prod(spatial))
# We perform two matmuls with the same number of ops.
# The first computes the weight matrix, the second computes
# the combination of the value vectors.
matmul_ops = 2 * b * (num_spatial ** 2) * c
model.total_ops += th.DoubleTensor([matmul_ops])
# This is the Lite SegDiff Model which uses a simple conditional encoder
class UNetModel(nn.Module):
"""
The full UNet model with attention and timestep embedding.
:param in_channels: channels in the input Tensor.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_classes: if specified (as an int), then this model will be
class-conditional with `num_classes` classes.
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
:param num_heads: the number of attention heads in each attention layer.
"""
def __init__(
self,
in_channels,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
num_classes=None,
use_checkpoint=False,
num_heads=1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
dataset='monu',
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.num_heads = num_heads
self.num_heads_upsample = num_heads_upsample
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
SiLU(),
linear(time_embed_dim, time_embed_dim),
)
if dataset == 'lung':
in_ch = 1
else:
in_ch = 3
self.condition_encoder = nn.ModuleList([(conv_nd(dims, in_ch, model_channels, 3, padding=1))])
ch = model_channels
self.num_res_blocks_condition_encoder = 1
for level, mult in enumerate(channel_mult):
if level < 5: # considering 5 levels
for _ in range(self.num_res_blocks_condition_encoder):
layers = [ResBlockEncoder(model_channels, dropout, dims=dims, use_checkpoint=use_checkpoint,
out_channels=model_channels,
use_scale_shift_norm=use_scale_shift_norm)]
self.condition_encoder.append(ModuleSequential(*layers))
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
)
]
)
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch, use_checkpoint=use_checkpoint, num_heads=num_heads
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
self.input_blocks.append(
TimestepEmbedSequential(Downsample(ch, conv_resample, dims=dims))
)
input_block_chans.append(ch)
ds *= 2
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
AttentionBlock(ch, use_checkpoint=use_checkpoint, num_heads=num_heads),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self.output_blocks = nn.ModuleList([])
for level, mult in list(enumerate(channel_mult))[::-1]:
for i in range(num_res_blocks + 1):
layers = [
ResBlock(
ch + input_block_chans.pop(),
time_embed_dim,
dropout,
out_channels=model_channels * mult,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = model_channels * mult
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads_upsample,
)
)
if level and i == num_res_blocks:
layers.append(Upsample(ch, conv_resample, dims=dims))
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self.out = nn.Sequential(
normalization(ch),
SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
)
@property
def inner_dtype(self):
"""
Get the dtype used by the torso of the model.
"""
return next(self.input_blocks.parameters()).dtype
def forward(self, x, timesteps, conditioned_image=None):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param conditioned_image: a conditional image.
:return: an [N x C x ...] Tensor of outputs.
"""
hs = []
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
h = conditioned_image.type(self.inner_dtype)
for i, module in enumerate(self.condition_encoder):
h = module(h)
# h = module(h, emb)
# print(h.shape)
# if attn is not None:
# h = torch.mul(attn, h)
former_frames_features = h
h = x.type(self.inner_dtype)
for i, module in enumerate(self.input_blocks):
h = module(h, emb)
if i == 0:
h = h + former_frames_features
hs.append(h)
h = self.middle_block(h, emb)
for module in self.output_blocks:
cat_in = th.cat([h, hs.pop()], dim=1)
h = module(cat_in, emb)
h = h.type(x.dtype)
return self.out(h)
def get_feature_vectors(self, x, timesteps, y=None):
"""
Apply the model and return all of the intermediate tensors.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param y: an [N] Tensor of labels, if class-conditional.
:return: a dict with the following keys:
- 'down': a list of hidden state tensors from downsampling.
- 'middle': the tensor of the output of the lowest-resolution
block in the model.
- 'up': a list of hidden state tensors from upsampling.
"""
hs = []
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
if self.num_classes is not None:
assert y.shape == (x.shape[0],)
emb = emb + self.label_emb(y)
result = dict(down=[], up=[])
h = x.type(self.inner_dtype)
for module in self.input_blocks:
h = module(h, emb)
hs.append(h)
result["down"].append(h.type(x.dtype))
h = self.middle_block(h, emb)
result["middle"] = h.type(x.dtype)
for module in self.output_blocks:
cat_in = th.cat([h, hs.pop()], dim=1)
h = module(cat_in, emb)
result["up"].append(h.type(x.dtype))
return result
class SuperResModel(UNetModel):
"""
A UNetModel that performs super-resolution.
Expects an extra kwarg `low_res` to condition on a low-resolution image.
"""
def __init__(self, in_channels, *args, **kwargs):
super().__init__(in_channels * 2, *args, **kwargs)
def forward(self, x, timesteps, low_res=None, **kwargs):
_, _, new_height, new_width = x.shape
upsampled = F.interpolate(low_res, (new_height, new_width), mode="nearest")
x = th.cat([x, upsampled], dim=1)
return super().forward(x, timesteps, **kwargs)
def get_feature_vectors(self, x, timesteps, low_res=None, **kwargs):
_, new_height, new_width, _ = x.shape
upsampled = F.interpolate(low_res, (new_height, new_width), mode="nearest")
x = th.cat([x, upsampled], dim=1)
return super().get_feature_vectors(x, timesteps, **kwargs)
| Python |
3D | Hejrati/cDAL | score_sde/models/up_or_down_sampling.py | .py | 9,107 | 263 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# ---------------------------------------------------------------
"""Layers used for up-sampling or down-sampling images.
Many functions are ported from https://github.com/NVlabs/stylegan2.
"""
import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
from score_sde.op import upfirdn2d
# Function ported from StyleGAN2
def get_weight(module,
shape,
weight_var='weight',
kernel_init=None):
"""Get/create weight tensor for a convolution or fully-connected layer."""
return module.param(weight_var, kernel_init, shape)
class Conv2d(nn.Module):
"""Conv2d layer with optimal upsampling and downsampling (StyleGAN2)."""
def __init__(self, in_ch, out_ch, kernel, up=False, down=False,
resample_kernel=(1, 3, 3, 1),
use_bias=True,
kernel_init=None):
super().__init__()
assert not (up and down)
assert kernel >= 1 and kernel % 2 == 1
self.weight = nn.Parameter(torch.zeros(out_ch, in_ch, kernel, kernel))
if kernel_init is not None:
self.weight.data = kernel_init(self.weight.data.shape)
if use_bias:
self.bias = nn.Parameter(torch.zeros(out_ch))
self.up = up
self.down = down
self.resample_kernel = resample_kernel
self.kernel = kernel
self.use_bias = use_bias
def forward(self, x):
if self.up:
x = upsample_conv_2d(x, self.weight, k=self.resample_kernel)
elif self.down:
x = conv_downsample_2d(x, self.weight, k=self.resample_kernel)
else:
x = F.conv2d(x, self.weight, stride=1, padding=self.kernel // 2)
if self.use_bias:
x = x + self.bias.reshape(1, -1, 1, 1)
return x
def naive_upsample_2d(x, factor=2):
_N, C, H, W = x.shape
x = torch.reshape(x, (-1, C, H, 1, W, 1))
x = x.repeat(1, 1, 1, factor, 1, factor)
return torch.reshape(x, (-1, C, H * factor, W * factor))
def naive_downsample_2d(x, factor=2):
_N, C, H, W = x.shape
x = torch.reshape(x, (-1, C, H // factor, factor, W // factor, factor))
return torch.mean(x, dim=(3, 5))
def upsample_conv_2d(x, w, k=None, factor=2, gain=1):
"""Fused `upsample_2d()` followed by `tf.nn.conv2d()`.
Padding is performed only once at the beginning, not between the
operations.
The fused op is considerably more efficient than performing the same
calculation
using standard TensorFlow ops. It supports gradients of arbitrary order.
Args:
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
w: Weight tensor of the shape `[filterH, filterW, inChannels,
outChannels]`. Grouped convolution can be performed by `inChannels =
x.shape[0] // numGroups`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to
nearest-neighbor upsampling.
factor: Integer upsampling factor (default: 2).
gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H * factor, W * factor]` or
`[N, H * factor, W * factor, C]`, and same datatype as `x`.
"""
assert isinstance(factor, int) and factor >= 1
# Check weight shape.
assert len(w.shape) == 4
convH = w.shape[2]
convW = w.shape[3]
inC = w.shape[1]
outC = w.shape[0]
assert convW == convH
# Setup filter kernel.
if k is None:
k = [1] * factor
k = _setup_kernel(k) * (gain * (factor ** 2))
p = (k.shape[0] - factor) - (convW - 1)
stride = (factor, factor)
# Determine data dimensions.
stride = [1, 1, factor, factor]
output_shape = ((_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW)
output_padding = (output_shape[0] - (_shape(x, 2) - 1) * stride[0] - convH,
output_shape[1] - (_shape(x, 3) - 1) * stride[1] - convW)
assert output_padding[0] >= 0 and output_padding[1] >= 0
num_groups = _shape(x, 1) // inC
# Transpose weights.
w = torch.reshape(w, (num_groups, -1, inC, convH, convW))
w = w[..., ::-1, ::-1].permute(0, 2, 1, 3, 4)
w = torch.reshape(w, (num_groups * inC, -1, convH, convW))
x = F.conv_transpose2d(x, w, stride=stride, output_padding=output_padding, padding=0)
## Original TF code.
# x = tf.nn.conv2d_transpose(
# x,
# w,
# output_shape=output_shape,
# strides=stride,
# padding='VALID',
# data_format=data_format)
## JAX equivalent
return upfirdn2d(x, torch.tensor(k, device=x.device),
pad=((p + 1) // 2 + factor - 1, p // 2 + 1))
def conv_downsample_2d(x, w, k=None, factor=2, gain=1):
"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`.
Padding is performed only once at the beginning, not between the operations.
The fused op is considerably more efficient than performing the same
calculation
using standard TensorFlow ops. It supports gradients of arbitrary order.
Args:
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
w: Weight tensor of the shape `[filterH, filterW, inChannels,
outChannels]`. Grouped convolution can be performed by `inChannels =
x.shape[0] // numGroups`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to
average pooling.
factor: Integer downsampling factor (default: 2).
gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H // factor, W // factor]` or
`[N, H // factor, W // factor, C]`, and same datatype as `x`.
"""
assert isinstance(factor, int) and factor >= 1
_outC, _inC, convH, convW = w.shape
assert convW == convH
if k is None:
k = [1] * factor
k = _setup_kernel(k) * gain
p = (k.shape[0] - factor) + (convW - 1)
s = [factor, factor]
x = upfirdn2d(x, torch.tensor(k, device=x.device),
pad=((p + 1) // 2, p // 2))
return F.conv2d(x, w, stride=s, padding=0)
def _setup_kernel(k):
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
assert k.ndim == 2
assert k.shape[0] == k.shape[1]
return k
def _shape(x, dim):
return x.shape[dim]
def upsample_2d(x, k=None, factor=2, gain=1):
r"""Upsample a batch of 2D images with the given filter.
Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
and upsamples each image with the given filter. The filter is normalized so
that
if the input pixels are constant, they will be scaled by the specified
`gain`.
Pixels outside the image are assumed to be zero, and the filter is padded
with
zeros so that its shape is a multiple of the upsampling factor.
Args:
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to
nearest-neighbor upsampling.
factor: Integer upsampling factor (default: 2).
gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H * factor, W * factor]`
"""
assert isinstance(factor, int) and factor >= 1
if k is None:
k = [1] * factor
k = _setup_kernel(k) * (gain * (factor ** 2))
p = k.shape[0] - factor
return upfirdn2d(x, torch.tensor(k, device=x.device),
up=factor, pad=((p + 1) // 2 + factor - 1, p // 2))
def downsample_2d(x, k=None, factor=2, gain=1):
r"""Downsample a batch of 2D images with the given filter.
Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
and downsamples each image with the given filter. The filter is normalized
so that
if the input pixels are constant, they will be scaled by the specified
`gain`.
Pixels outside the image are assumed to be zero, and the filter is padded
with
zeros so that its shape is a multiple of the downsampling factor.
Args:
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to
average pooling.
factor: Integer downsampling factor (default: 2).
gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H // factor, W // factor]`
"""
assert isinstance(factor, int) and factor >= 1
if k is None:
k = [1] * factor
k = _setup_kernel(k) * gain
p = k.shape[0] - factor
return upfirdn2d(x, torch.tensor(k, device=x.device),
down=factor, pad=((p + 1) // 2, p // 2))
| Python |
3D | Hejrati/cDAL | score_sde/models/ncsnpp_generator_adagn.py | .py | 22,578 | 567 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This file has been modified from a file in the Score SDE library
# which was released under the Apache License.
#
# Source:
# https://github.com/yang-song/score_sde_pytorch/blob/main/models/layerspp.py
#
# The license for the original version of this file can be
# found in this directory (LICENSE_Apache). The modifications
# to this file are subject to the same Apache License.
# ---------------------------------------------------------------
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: skip-file
''' Codes adapted from https://github.com/yang-song/score_sde_pytorch/blob/main/models/ncsnpp.py
'''
from . import utils, layers, layerspp, dense_layer
import torch.nn as nn
import functools
import torch
import numpy as np
from .nn import normalization, zero_module, conv_nd, SiLU, conv_transpose_nd
ResnetBlockDDPM = layerspp.ResnetBlockDDPMpp_Adagn
ResnetBlockBigGAN = layerspp.ResnetBlockBigGANpp_Adagn
ResnetBlockBigGAN_one = layerspp.ResnetBlockBigGANpp_Adagn_one
Combine = layerspp.Combine
conv3x3 = layerspp.conv3x3
conv1x1 = layerspp.conv1x1
get_act = layers.get_act
default_initializer = layers.default_init
dense = dense_layer.dense
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input / torch.sqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
class ModuleSequential(nn.Sequential):
"""
"""
def forward(self, x):
for layer in self:
x = layer(x)
return x
class ResBlockEncoder(nn.Module):
"""
A residual block specifically is designed for the encoder to have similar architecture as the Unet encoder
that can optionally change the number of channels. The difference is that it does not have time embedding.
:param channels: the number of input channels.
:param dropout: the rate of dropout.
:param out_channels: if specified, the number of out channels.
:param use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param use_checkpoint: if True, use gradient checkpointing on this module.
"""
def __init__(
self,
channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
use_checkpoint=False,
):
super().__init__()
self.channels = channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_checkpoint = use_checkpoint
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(normalization(channels), SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1), )
self.out_layers = nn.Sequential(normalization(self.out_channels), SiLU(), nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)), )
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
def forward(self, x):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:return: an [N x C x ...] Tensor of outputs.
"""
h = self.in_layers(x)
h = self.out_layers(h)
return self.skip_connection(x) + h
@utils.register_model(name='ncsnpp')
class NCSNpp(nn.Module):
"""NCSN++ model"""
def __init__(self, config):
super().__init__()
self.config = config
self.not_use_tanh = config.not_use_tanh
self.act = act = nn.SiLU()
self.z_emb_dim = z_emb_dim = config.z_emb_dim
self.nf = nf = config.num_channels_dae
ch_mult = config.ch_mult
self.num_res_blocks = num_res_blocks = config.num_res_blocks
self.attn_resolutions = attn_resolutions = config.attn_resolutions
dropout = config.dropout
resamp_with_conv = config.resamp_with_conv
self.num_resolutions = num_resolutions = len(ch_mult)
self.all_resolutions = all_resolutions = [config.image_size // (2 ** i) for i in range(num_resolutions)]
self.conditional = conditional = config.conditional # noise-conditional
fir = config.fir
fir_kernel = config.fir_kernel
self.skip_rescale = skip_rescale = config.skip_rescale
self.resblock_type = resblock_type = config.resblock_type.lower()
self.progressive = progressive = config.progressive.lower()
self.progressive_input = progressive_input = config.progressive_input.lower()
self.embedding_type = embedding_type = config.embedding_type.lower()
init_scale = 0.
assert progressive in ['none', 'output_skip', 'residual']
assert progressive_input in ['none', 'input_skip', 'residual']
assert embedding_type in ['fourier', 'positional']
combine_method = config.progressive_combine.lower()
combiner = functools.partial(Combine, method=combine_method)
modules = []
# timestep/noise_level embedding; only for continuous training
if embedding_type == 'fourier':
# Gaussian Fourier features embeddings.
# assert config.training.continuous, "Fourier features are only used for continuous training."
modules.append(layerspp.GaussianFourierProjection(
embedding_size=nf, scale=config.fourier_scale
))
embed_dim = 2 * nf
elif embedding_type == 'positional':
embed_dim = nf
else:
raise ValueError(f'embedding type {embedding_type} unknown.')
if conditional:
modules.append(nn.Linear(embed_dim, nf * 4))
modules[-1].weight.data = default_initializer()(modules[-1].weight.shape)
nn.init.zeros_(modules[-1].bias)
modules.append(nn.Linear(nf * 4, nf * 4))
modules[-1].weight.data = default_initializer()(modules[-1].weight.shape)
nn.init.zeros_(modules[-1].bias)
AttnBlock = functools.partial(layerspp.AttnBlockpp,
init_scale=init_scale,
skip_rescale=skip_rescale)
Upsample = functools.partial(layerspp.Upsample,
with_conv=resamp_with_conv, fir=fir, fir_kernel=fir_kernel)
if progressive == 'output_skip':
self.pyramid_upsample = layerspp.Upsample(fir=fir, fir_kernel=fir_kernel, with_conv=False)
elif progressive == 'residual':
pyramid_upsample = functools.partial(layerspp.Upsample,
fir=fir, fir_kernel=fir_kernel, with_conv=True)
Downsample = functools.partial(layerspp.Downsample,
with_conv=resamp_with_conv, fir=fir, fir_kernel=fir_kernel)
if progressive_input == 'input_skip':
self.pyramid_downsample = layerspp.Downsample(fir=fir, fir_kernel=fir_kernel, with_conv=False)
elif progressive_input == 'residual':
pyramid_downsample = functools.partial(layerspp.Downsample,
fir=fir, fir_kernel=fir_kernel, with_conv=True)
if resblock_type == 'ddpm':
ResnetBlock = functools.partial(ResnetBlockDDPM,
act=act,
dropout=dropout,
init_scale=init_scale,
skip_rescale=skip_rescale,
temb_dim=nf * 4,
zemb_dim=z_emb_dim)
elif resblock_type == 'biggan':
ResnetBlock = functools.partial(ResnetBlockBigGAN,
act=act,
dropout=dropout,
fir=fir,
fir_kernel=fir_kernel,
init_scale=init_scale,
skip_rescale=skip_rescale,
temb_dim=nf * 4,
zemb_dim=z_emb_dim)
elif resblock_type == 'biggan_oneadagn':
ResnetBlock = functools.partial(ResnetBlockBigGAN_one,
act=act,
dropout=dropout,
fir=fir,
fir_kernel=fir_kernel,
init_scale=init_scale,
skip_rescale=skip_rescale,
temb_dim=nf * 4,
zemb_dim=z_emb_dim)
else:
raise ValueError(f'resblock type {resblock_type} unrecognized.')
# upsample attention
# modules.append(nn.ConvTranspose2d(512, 1, kernel_size=4, stride=2, padding=1))
# modules.append(nn.ConvTranspose2d(1, 1, kernel_size=4, stride=2, padding=1))
# Conditional encoder
dims = 2
if self.config.dataset == 'monu':
in_ch = 3 # RGB image
else:
in_ch = 1 # grayscale image
modules.append(conv_nd(dims, in_ch, nf, 3, padding=1))
self.cond_enc_layer_sizes = 0
for _ in range(self.config.cond_enc_layers):
for _ in range(self.config.cond_enc_num_res_blocks):
layers = [ResBlockEncoder(nf, dropout, dims=dims, use_checkpoint=False,
out_channels=nf,
use_scale_shift_norm=False)]
modules.append(ModuleSequential(*layers))
self.cond_enc_layer_sizes += 1
# Down-sampling block
channels = config.num_channels
if progressive_input != 'none':
input_pyramid_ch = channels
modules.append(conv3x3(channels, nf))
hs_c = [nf]
in_ch = nf
for i_level in range(num_resolutions):
# Residual blocks for this resolution
for i_block in range(num_res_blocks):
out_ch = nf * ch_mult[i_level]
modules.append(ResnetBlock(in_ch=in_ch, out_ch=out_ch))
in_ch = out_ch
if all_resolutions[i_level] in attn_resolutions:
modules.append(AttnBlock(channels=in_ch))
hs_c.append(in_ch)
if i_level != num_resolutions - 1:
if resblock_type == 'ddpm':
modules.append(Downsample(in_ch=in_ch))
else:
modules.append(ResnetBlock(down=True, in_ch=in_ch))
if progressive_input == 'input_skip':
modules.append(combiner(dim1=input_pyramid_ch, dim2=in_ch))
if combine_method == 'cat':
in_ch *= 2
elif progressive_input == 'residual':
modules.append(pyramid_downsample(in_ch=input_pyramid_ch, out_ch=in_ch))
input_pyramid_ch = in_ch
hs_c.append(in_ch)
in_ch = hs_c[-1]
modules.append(ResnetBlock(in_ch=in_ch))
modules.append(AttnBlock(channels=in_ch))
modules.append(ResnetBlock(in_ch=in_ch))
pyramid_ch = 0
# Upsampling block
for i_level in reversed(range(num_resolutions)):
for i_block in range(num_res_blocks + 1):
out_ch = nf * ch_mult[i_level]
modules.append(ResnetBlock(in_ch=in_ch + hs_c.pop(),
out_ch=out_ch))
in_ch = out_ch
if all_resolutions[i_level] in attn_resolutions:
modules.append(AttnBlock(channels=in_ch))
if progressive != 'none':
if i_level == num_resolutions - 1:
if progressive == 'output_skip':
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32),
num_channels=in_ch, eps=1e-6))
modules.append(conv3x3(in_ch, channels, init_scale=init_scale))
pyramid_ch = channels
elif progressive == 'residual':
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32),
num_channels=in_ch, eps=1e-6))
modules.append(conv3x3(in_ch, in_ch, bias=True))
pyramid_ch = in_ch
else:
raise ValueError(f'{progressive} is not a valid name.')
else:
if progressive == 'output_skip':
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32),
num_channels=in_ch, eps=1e-6))
modules.append(conv3x3(in_ch, channels, bias=True, init_scale=init_scale))
pyramid_ch = channels
elif progressive == 'residual':
modules.append(pyramid_upsample(in_ch=pyramid_ch, out_ch=in_ch))
pyramid_ch = in_ch
else:
raise ValueError(f'{progressive} is not a valid name')
if i_level != 0:
if resblock_type == 'ddpm':
modules.append(Upsample(in_ch=in_ch))
else:
modules.append(ResnetBlock(in_ch=in_ch, up=True))
assert not hs_c
if progressive != 'output_skip':
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32),
num_channels=in_ch, eps=1e-6))
modules.append(conv3x3(in_ch, channels, init_scale=init_scale))
self.all_modules = nn.ModuleList(modules)
mapping_layers = [PixelNorm(),
dense(config.nz, z_emb_dim),
self.act, ]
for _ in range(config.n_mlp):
mapping_layers.append(dense(z_emb_dim, z_emb_dim))
mapping_layers.append(self.act)
self.z_transform = nn.Sequential(*mapping_layers)
def forward(self, x, time_cond, y, z):
# timestep/noise_level embedding; only for continuous training
zemb = self.z_transform(z)
modules = self.all_modules
m_idx = 0
# ### Time embedding
if self.embedding_type == 'fourier':
# Gaussian Fourier features embeddings.
used_sigmas = time_cond
temb = modules[m_idx](torch.log(used_sigmas))
m_idx += 1
elif self.embedding_type == 'positional':
# Sinusoidal positional embeddings.
timesteps = time_cond
temb = layers.get_timestep_embedding(timesteps, self.nf)
else:
raise ValueError(f'embedding type {self.embedding_type} unknown.')
if self.conditional:
temb = modules[m_idx](temb) # linear layer 1
m_idx += 1
temb = modules[m_idx](self.act(temb)) # linear layer 2
m_idx += 1
else:
temb = None
if not self.config.centered:
# If input data is in [0, 1], it will change it to [-1, 1].
x = 2 * x - 1.
# ### Condition encoder
# upsample_avg_feature = None
# if isinstance(y, list):
# disc_layer = y[1]
# # upsample_avg_feature = modules[m_idx](disc_layer)
# # m_idx += 1
# # upsample_avg_feature = modules[m_idx](upsample_avg_feature)
# # m_idx += 1
# # y = y[-1]
# # else:
# # m_idx += 2
# feature_map = torch.mean(abs(disc_layer), dim=[1], keepdim=True)
# upsample_avg_feature = torch.nn.functional.interpolate(feature_map, size=(x.shape[2], x.shape[3]), mode='nearest')
# y = y[-1]
h = y
for i in range(self.cond_enc_layer_sizes + 1):
h = modules[m_idx](h)
m_idx += 1
# if upsample_avg_feature is not None:
# h = torch.mul(h, upsample_avg_feature)
#
# # import matplotlib.pyplot as plt
# # plt.imshow(upsample_avg_feature[0].squeeze().detach().cpu())
# # plt.show()
# Downsampling block
input_pyramid = None
if self.progressive_input != 'none':
input_pyramid = x
hs = [modules[m_idx](x)]
m_idx += 1
for i_level in range(self.num_resolutions):
# Residual blocks for this resolution
# print(i_level)
for i_block in range(self.num_res_blocks):
if i_level == i_block == 0:
hs[-1] = h + hs[-1]
h = modules[m_idx](hs[-1], temb, zemb)
m_idx += 1
if h.shape[-1] in self.attn_resolutions:
h = modules[m_idx](h)
m_idx += 1
hs.append(h)
if i_level != self.num_resolutions - 1:
if self.resblock_type == 'ddpm':
h = modules[m_idx](hs[-1])
m_idx += 1
else:
h = modules[m_idx](hs[-1], temb, zemb)
m_idx += 1
if self.progressive_input == 'input_skip':
input_pyramid = self.pyramid_downsample(input_pyramid)
h = modules[m_idx](input_pyramid, h)
m_idx += 1
elif self.progressive_input == 'residual':
input_pyramid = modules[m_idx](input_pyramid)
m_idx += 1
if self.skip_rescale:
input_pyramid = (input_pyramid + h) / np.sqrt(2.)
else:
input_pyramid = input_pyramid + h
h = input_pyramid
hs.append(h)
h = hs[-1]
h = modules[m_idx](h, temb, zemb)
m_idx += 1
h = modules[m_idx](h)
m_idx += 1
h = modules[m_idx](h, temb, zemb)
m_idx += 1
pyramid = None
# Upsampling block
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks + 1):
h = modules[m_idx](torch.cat([h, hs.pop()], dim=1), temb, zemb)
m_idx += 1
if h.shape[-1] in self.attn_resolutions:
h = modules[m_idx](h)
m_idx += 1
if self.progressive != 'none':
if i_level == self.num_resolutions - 1:
if self.progressive == 'output_skip':
pyramid = self.act(modules[m_idx](h))
m_idx += 1
pyramid = modules[m_idx](pyramid)
m_idx += 1
elif self.progressive == 'residual':
pyramid = self.act(modules[m_idx](h))
m_idx += 1
pyramid = modules[m_idx](pyramid)
m_idx += 1
else:
raise ValueError(f'{self.progressive} is not a valid name.')
else:
if self.progressive == 'output_skip':
pyramid = self.pyramid_upsample(pyramid)
pyramid_h = self.act(modules[m_idx](h))
m_idx += 1
pyramid_h = modules[m_idx](pyramid_h)
m_idx += 1
pyramid = pyramid + pyramid_h
elif self.progressive == 'residual':
pyramid = modules[m_idx](pyramid)
m_idx += 1
if self.skip_rescale:
pyramid = (pyramid + h) / np.sqrt(2.)
else:
pyramid = pyramid + h
h = pyramid
else:
raise ValueError(f'{self.progressive} is not a valid name')
if i_level != 0:
if self.resblock_type == 'ddpm':
h = modules[m_idx](h)
m_idx += 1
else:
h = modules[m_idx](h, temb, zemb)
m_idx += 1
assert not hs
if self.progressive == 'output_skip':
h = pyramid
else:
h = self.act(modules[m_idx](h))
m_idx += 1
h = modules[m_idx](h)
m_idx += 1
assert m_idx == len(modules)
if not self.not_use_tanh:
return torch.tanh(h)
else:
return h
@property
def inner_dtype(self):
"""
Get the dtype used by the torso of the model.
"""
return next(self.input_blocks.parameters()).dtype
# import matplotlib.pyplot as plt
#
# plt.imshow(h.mean(1)[0].detach().cpu())
# plt.show() | Python |
3D | Hejrati/cDAL | score_sde/models/__init__.py | .py | 608 | 16 | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| Python |
3D | Hejrati/cDAL | score_sde/models/discriminator.py | .py | 8,192 | 264 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# for Denoising Diffusion GAN. To view a copy of this license, see the LICENSE file.
# ---------------------------------------------------------------
import torch
import torch.nn as nn
import numpy as np
from . import up_or_down_sampling
from . import dense_layer
from . import layers
dense = dense_layer.dense
conv2d = dense_layer.conv2d
get_sinusoidal_positional_embedding = layers.get_timestep_embedding
class TimestepEmbedding(nn.Module):
def __init__(self, embedding_dim, hidden_dim, output_dim, act=nn.LeakyReLU(0.2)):
super().__init__()
self.embedding_dim = embedding_dim
self.output_dim = output_dim
self.hidden_dim = hidden_dim
self.main = nn.Sequential(
dense(embedding_dim, hidden_dim),
act,
dense(hidden_dim, output_dim),
)
def forward(self, temp):
temb = get_sinusoidal_positional_embedding(temp, self.embedding_dim)
temb = self.main(temb)
return temb
# %%
class DownConvBlock(nn.Module):
def __init__(
self,
in_channel,
out_channel,
kernel_size=3,
padding=1,
t_emb_dim=128,
downsample=False,
act=nn.LeakyReLU(0.2),
fir_kernel=(1, 3, 3, 1)
):
super().__init__()
self.fir_kernel = fir_kernel
self.downsample = downsample
self.conv1 = nn.Sequential(
conv2d(in_channel, out_channel, kernel_size, padding=padding),
)
self.conv2 = nn.Sequential(
conv2d(out_channel, out_channel, kernel_size, padding=padding, init_scale=0.)
)
self.dense_t1 = dense(t_emb_dim, out_channel)
self.act = act
self.skip = nn.Sequential(
conv2d(in_channel, out_channel, 1, padding=0, bias=False),
)
def forward(self, input, t_emb):
out = self.act(input)
out = self.conv1(out)
out += self.dense_t1(t_emb)[..., None, None]
out = self.act(out)
if self.downsample:
out = up_or_down_sampling.downsample_2d(out, self.fir_kernel, factor=2)
input = up_or_down_sampling.downsample_2d(input, self.fir_kernel, factor=2)
out = self.conv2(out)
skip = self.skip(input)
out = (out + skip) / np.sqrt(2)
return out
class Discriminator_small(nn.Module):
"""A time-dependent discriminator for small images (CIFAR10, StackMNIST)."""
def __init__(self, nc=3, ngf=64, t_emb_dim=128, act=nn.LeakyReLU(0.2)):
super().__init__()
# Gaussian random feature embedding layer for time
self.act = act
self.t_embed = TimestepEmbedding(
embedding_dim=t_emb_dim,
hidden_dim=t_emb_dim,
output_dim=t_emb_dim,
act=act,
)
# Encoding layers where the resolution decreases
self.start_conv = conv2d(nc, ngf * 2, 1, padding=0)
self.conv1 = DownConvBlock(ngf * 2, ngf * 2, t_emb_dim=t_emb_dim, act=act)
self.conv2 = DownConvBlock(ngf * 2, ngf * 4, t_emb_dim=t_emb_dim, downsample=True, act=act)
self.conv3 = DownConvBlock(ngf * 4, ngf * 8, t_emb_dim=t_emb_dim, downsample=True, act=act)
self.conv4 = DownConvBlock(ngf * 8, ngf * 8, t_emb_dim=t_emb_dim, downsample=True, act=act)
self.final_conv = conv2d(ngf * 8 + 1, ngf * 8, 3, padding=1, init_scale=0.)
self.end_linear = dense(ngf * 8, 1)
self.stddev_group = 4
self.stddev_feat = 1
def forward(self, x, t, x_t):
t_embed = self.act(self.t_embed(t))
input_x = torch.cat((x, x_t), dim=1)
h0 = self.start_conv(input_x)
h1 = self.conv1(h0, t_embed)
h2 = self.conv2(h1, t_embed)
h3 = self.conv3(h2, t_embed)
out = self.conv4(h3, t_embed)
batch, channel, height, width = out.shape
group = min(batch, self.stddev_group)
stddev = out.view(
group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
)
stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
stddev = stddev.repeat(group, 1, height, width)
out = torch.cat([out, stddev], 1)
out = self.final_conv(out)
out = self.act(out)
out = out.view(out.shape[0], out.shape[1], -1).sum(2)
out = self.end_linear(out)
return out
def get_features(self, x, t, x_t):
with torch.no_grad():
t_embed = self.act(self.t_embed(t))
input_x = torch.cat((x, x_t), dim=1)
hs = []
h = self.start_conv(input_x)
hs.append(h)
h = self.conv1(h, t_embed)
hs.append(h)
h = self.conv2(h, t_embed)
hs.append(h)
h = self.conv3(h, t_embed)
hs.append(h)
h = self.conv4(h, t_embed)
hs.append(h)
return hs
class Discriminator_large(nn.Module):
"""A time-dependent discriminator for large images (CelebA, LSUN)."""
def __init__(self, nc=1, ngf=32, t_emb_dim=128, act=nn.LeakyReLU(0.2)):
super().__init__()
# Gaussian random feature embedding layer for time
self.act = act
self.t_embed = TimestepEmbedding(
embedding_dim=t_emb_dim,
hidden_dim=t_emb_dim,
output_dim=t_emb_dim,
act=act,
)
self.start_conv = conv2d(nc, ngf * 2, 1, padding=0)
self.conv1 = DownConvBlock(ngf * 2, ngf * 4, t_emb_dim=t_emb_dim, downsample=True, act=act)
self.conv2 = DownConvBlock(ngf * 4, ngf * 8, t_emb_dim=t_emb_dim, downsample=True, act=act)
self.conv3 = DownConvBlock(ngf * 8, ngf * 8, t_emb_dim=t_emb_dim, downsample=True, act=act)
self.conv4 = DownConvBlock(ngf * 8, ngf * 8, t_emb_dim=t_emb_dim, downsample=True, act=act)
self.conv5 = DownConvBlock(ngf * 8, ngf * 8, t_emb_dim=t_emb_dim, downsample=True, act=act)
self.conv6 = DownConvBlock(ngf * 8, ngf * 8, t_emb_dim=t_emb_dim, downsample=True, act=act)
self.final_conv = conv2d(ngf * 8 + 1, ngf * 8, 3, padding=1)
self.end_linear = dense(ngf * 8, 1)
self.stddev_group = 4
self.stddev_feat = 1
def forward(self, x, t, x_t):
t_embed = self.act(self.t_embed(t))
input_x = torch.cat((x, x_t), dim=1)
h = self.start_conv(input_x)
h = self.conv1(h, t_embed)
h = self.conv2(h, t_embed)
h = self.conv3(h, t_embed)
h = self.conv4(h, t_embed)
h = self.conv5(h, t_embed)
out = self.conv6(h, t_embed)
batch, channel, height, width = out.shape
group = min(batch, self.stddev_group)
stddev = out.view(
group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
)
stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
stddev = stddev.repeat(group, 1, height, width)
out = torch.cat([out, stddev], 1)
out = self.final_conv(out)
out = self.act(out)
out = out.view(out.shape[0], out.shape[1], -1).sum(2)
out = self.end_linear(out)
return out
def get_features(self, x, t, x_t):
with torch.no_grad():
t_embed = self.act(self.t_embed(t))
input_x = torch.cat((x, x_t), dim=1)
hs = []
h = self.start_conv(input_x)
hs.append(h)
h = self.conv1(h, t_embed)
hs.append(h)
h = self.conv2(h, t_embed)
hs.append(h)
h = self.conv3(h, t_embed)
hs.append(h)
h = self.conv4(h, t_embed)
hs.append(h)
h = self.conv5(h, t_embed)
hs.append(h)
out = self.conv6(h, t_embed)
hs.append(out)
return hs
| Python |
3D | Hejrati/cDAL | score_sde/models/dense_layer.py | .py | 3,234 | 83 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This file has been modified from a file released under the MIT License.
#
# Source:
# https://github.com/CW-Huang/sdeflow-light/blob/524650bc5ad69522b3e0905672deef0650374512/lib/models/unet.py
#
# The license for the original version of this file can be
# found in this directory (LICENSE_MIT). The modifications
# to this file are subject to the same MIT License.
# ---------------------------------------------------------------
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import _calculate_fan_in_and_fan_out
import numpy as np
def _calculate_correct_fan(tensor, mode):
"""
copied and modified from https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py#L337
"""
mode = mode.lower()
valid_modes = ['fan_in', 'fan_out', 'fan_avg']
if mode not in valid_modes:
raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
return fan_in if mode == 'fan_in' else fan_out
def kaiming_uniform_(tensor, gain=1., mode='fan_in'):
r"""Fills the input `Tensor` with values according to the method
described in `Delving deep into rectifiers: Surpassing human-level
performance on ImageNet classification` - He, K. et al. (2015), using a
uniform distribution. The resulting tensor will have values sampled from
:math:`\mathcal{U}(-\text{bound}, \text{bound})` where
.. math::
\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
Also known as He initialization.
Args:
tensor: an n-dimensional `torch.Tensor`
gain: multiplier to the dispersion
mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
preserves the magnitude of the variance of the weights in the
forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
backwards pass.
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.kaiming_uniform_(w, mode='fan_in')
"""
fan = _calculate_correct_fan(tensor, mode)
var = gain / max(1., fan)
bound = math.sqrt(3.0 * var) # Calculate uniform bounds from standard deviation
with torch.no_grad():
return tensor.uniform_(-bound, bound)
def variance_scaling_init_(tensor, scale):
return kaiming_uniform_(tensor, gain=1e-10 if scale == 0 else scale, mode='fan_avg')
def dense(in_channels, out_channels, init_scale=1.):
lin = nn.Linear(in_channels, out_channels)
variance_scaling_init_(lin.weight, scale=init_scale)
nn.init.zeros_(lin.bias)
return lin
def conv2d(in_planes, out_planes, kernel_size=(3, 3), stride=1, dilation=1, padding=1, bias=True, padding_mode='zeros',
init_scale=1.):
conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation,
bias=bias, padding_mode=padding_mode)
variance_scaling_init_(conv.weight, scale=init_scale)
if bias:
nn.init.zeros_(conv.bias)
return conv
| Python |
3D | Hejrati/cDAL | score_sde/models/layers.py | .py | 20,853 | 619 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This file has been modified from a file in the Score SDE library
# which was released under the Apache License.
#
# Source:
# https://github.com/yang-song/score_sde_pytorch/blob/main/models/layers.py
#
# The license for the original version of this file can be
# found in this directory (LICENSE_Apache). The modifications
# to this file are subject to the same Apache License.
# ---------------------------------------------------------------
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: skip-file
"""Common layers for defining score networks.
Adapted from https://github.com/yang-song/score_sde_pytorch/blob/main/models/layers.py
"""
import math
import string
from functools import partial
import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
def get_act(config):
"""Get activation functions from the config file."""
if config.model.nonlinearity.lower() == 'elu':
return nn.ELU()
elif config.model.nonlinearity.lower() == 'relu':
return nn.ReLU()
elif config.model.nonlinearity.lower() == 'lrelu':
return nn.LeakyReLU(negative_slope=0.2)
elif config.model.nonlinearity.lower() == 'swish':
return nn.SiLU()
else:
raise NotImplementedError('activation function does not exist!')
def ncsn_conv1x1(in_planes, out_planes, stride=1, bias=True, dilation=1, init_scale=1., padding=0):
"""1x1 convolution. Same as NCSNv1/v2."""
conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=bias, dilation=dilation,
padding=padding)
init_scale = 1e-10 if init_scale == 0 else init_scale
conv.weight.data *= init_scale
conv.bias.data *= init_scale
return conv
def variance_scaling(scale, mode, distribution,
in_axis=1, out_axis=0,
dtype=torch.float32,
device='cpu'):
"""Ported from JAX. """
def _compute_fans(shape, in_axis=1, out_axis=0):
receptive_field_size = np.prod(shape) / shape[in_axis] / shape[out_axis]
fan_in = shape[in_axis] * receptive_field_size
fan_out = shape[out_axis] * receptive_field_size
return fan_in, fan_out
def init(shape, dtype=dtype, device=device):
fan_in, fan_out = _compute_fans(shape, in_axis, out_axis)
if mode == "fan_in":
denominator = fan_in
elif mode == "fan_out":
denominator = fan_out
elif mode == "fan_avg":
denominator = (fan_in + fan_out) / 2
else:
raise ValueError(
"invalid mode for variance scaling initializer: {}".format(mode))
variance = scale / denominator
if distribution == "normal":
return torch.randn(*shape, dtype=dtype, device=device) * np.sqrt(variance)
elif distribution == "uniform":
return (torch.rand(*shape, dtype=dtype, device=device) * 2. - 1.) * np.sqrt(3 * variance)
else:
raise ValueError("invalid distribution for variance scaling initializer")
return init
def default_init(scale=1.):
"""The same initialization used in DDPM."""
scale = 1e-10 if scale == 0 else scale
return variance_scaling(scale, 'fan_avg', 'uniform')
class Dense(nn.Module):
"""Linear layer with `default_init`."""
def __init__(self):
super().__init__()
def ddpm_conv1x1(in_planes, out_planes, stride=1, bias=True, init_scale=1., padding=0):
"""1x1 convolution with DDPM initialization."""
conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=padding, bias=bias)
conv.weight.data = default_init(init_scale)(conv.weight.data.shape)
nn.init.zeros_(conv.bias)
return conv
def ncsn_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1, init_scale=1., padding=1):
"""3x3 convolution with PyTorch initialization. Same as NCSNv1/NCSNv2."""
init_scale = 1e-10 if init_scale == 0 else init_scale
conv = nn.Conv2d(in_planes, out_planes, stride=stride, bias=bias,
dilation=dilation, padding=padding, kernel_size=3)
conv.weight.data *= init_scale
conv.bias.data *= init_scale
return conv
def ddpm_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1, init_scale=1., padding=1):
"""3x3 convolution with DDPM initialization."""
conv = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=padding,
dilation=dilation, bias=bias)
conv.weight.data = default_init(init_scale)(conv.weight.data.shape)
nn.init.zeros_(conv.bias)
return conv
###########################################################################
# Functions below are ported over from the NCSNv1/NCSNv2 codebase:
# https://github.com/ermongroup/ncsn
# https://github.com/ermongroup/ncsnv2
###########################################################################
class CRPBlock(nn.Module):
def __init__(self, features, n_stages, act=nn.ReLU(), maxpool=True):
super().__init__()
self.convs = nn.ModuleList()
for i in range(n_stages):
self.convs.append(ncsn_conv3x3(features, features, stride=1, bias=False))
self.n_stages = n_stages
if maxpool:
self.pool = nn.MaxPool2d(kernel_size=5, stride=1, padding=2)
else:
self.pool = nn.AvgPool2d(kernel_size=5, stride=1, padding=2)
self.act = act
def forward(self, x):
x = self.act(x)
path = x
for i in range(self.n_stages):
path = self.pool(path)
path = self.convs[i](path)
x = path + x
return x
class CondCRPBlock(nn.Module):
def __init__(self, features, n_stages, num_classes, normalizer, act=nn.ReLU()):
super().__init__()
self.convs = nn.ModuleList()
self.norms = nn.ModuleList()
self.normalizer = normalizer
for i in range(n_stages):
self.norms.append(normalizer(features, num_classes, bias=True))
self.convs.append(ncsn_conv3x3(features, features, stride=1, bias=False))
self.n_stages = n_stages
self.pool = nn.AvgPool2d(kernel_size=5, stride=1, padding=2)
self.act = act
def forward(self, x, y):
x = self.act(x)
path = x
for i in range(self.n_stages):
path = self.norms[i](path, y)
path = self.pool(path)
path = self.convs[i](path)
x = path + x
return x
class RCUBlock(nn.Module):
def __init__(self, features, n_blocks, n_stages, act=nn.ReLU()):
super().__init__()
for i in range(n_blocks):
for j in range(n_stages):
setattr(self, '{}_{}_conv'.format(i + 1, j + 1), ncsn_conv3x3(features, features, stride=1, bias=False))
self.stride = 1
self.n_blocks = n_blocks
self.n_stages = n_stages
self.act = act
def forward(self, x):
for i in range(self.n_blocks):
residual = x
for j in range(self.n_stages):
x = self.act(x)
x = getattr(self, '{}_{}_conv'.format(i + 1, j + 1))(x)
x += residual
return x
class CondRCUBlock(nn.Module):
def __init__(self, features, n_blocks, n_stages, num_classes, normalizer, act=nn.ReLU()):
super().__init__()
for i in range(n_blocks):
for j in range(n_stages):
setattr(self, '{}_{}_norm'.format(i + 1, j + 1), normalizer(features, num_classes, bias=True))
setattr(self, '{}_{}_conv'.format(i + 1, j + 1), ncsn_conv3x3(features, features, stride=1, bias=False))
self.stride = 1
self.n_blocks = n_blocks
self.n_stages = n_stages
self.act = act
self.normalizer = normalizer
def forward(self, x, y):
for i in range(self.n_blocks):
residual = x
for j in range(self.n_stages):
x = getattr(self, '{}_{}_norm'.format(i + 1, j + 1))(x, y)
x = self.act(x)
x = getattr(self, '{}_{}_conv'.format(i + 1, j + 1))(x)
x += residual
return x
class MSFBlock(nn.Module):
def __init__(self, in_planes, features):
super().__init__()
assert isinstance(in_planes, list) or isinstance(in_planes, tuple)
self.convs = nn.ModuleList()
self.features = features
for i in range(len(in_planes)):
self.convs.append(ncsn_conv3x3(in_planes[i], features, stride=1, bias=True))
def forward(self, xs, shape):
sums = torch.zeros(xs[0].shape[0], self.features, *shape, device=xs[0].device)
for i in range(len(self.convs)):
h = self.convs[i](xs[i])
h = F.interpolate(h, size=shape, mode='bilinear', align_corners=True)
sums += h
return sums
class CondMSFBlock(nn.Module):
def __init__(self, in_planes, features, num_classes, normalizer):
super().__init__()
assert isinstance(in_planes, list) or isinstance(in_planes, tuple)
self.convs = nn.ModuleList()
self.norms = nn.ModuleList()
self.features = features
self.normalizer = normalizer
for i in range(len(in_planes)):
self.convs.append(ncsn_conv3x3(in_planes[i], features, stride=1, bias=True))
self.norms.append(normalizer(in_planes[i], num_classes, bias=True))
def forward(self, xs, y, shape):
sums = torch.zeros(xs[0].shape[0], self.features, *shape, device=xs[0].device)
for i in range(len(self.convs)):
h = self.norms[i](xs[i], y)
h = self.convs[i](h)
h = F.interpolate(h, size=shape, mode='bilinear', align_corners=True)
sums += h
return sums
class RefineBlock(nn.Module):
def __init__(self, in_planes, features, act=nn.ReLU(), start=False, end=False, maxpool=True):
super().__init__()
assert isinstance(in_planes, tuple) or isinstance(in_planes, list)
self.n_blocks = n_blocks = len(in_planes)
self.adapt_convs = nn.ModuleList()
for i in range(n_blocks):
self.adapt_convs.append(RCUBlock(in_planes[i], 2, 2, act))
self.output_convs = RCUBlock(features, 3 if end else 1, 2, act)
if not start:
self.msf = MSFBlock(in_planes, features)
self.crp = CRPBlock(features, 2, act, maxpool=maxpool)
def forward(self, xs, output_shape):
assert isinstance(xs, tuple) or isinstance(xs, list)
hs = []
for i in range(len(xs)):
h = self.adapt_convs[i](xs[i])
hs.append(h)
if self.n_blocks > 1:
h = self.msf(hs, output_shape)
else:
h = hs[0]
h = self.crp(h)
h = self.output_convs(h)
return h
class CondRefineBlock(nn.Module):
def __init__(self, in_planes, features, num_classes, normalizer, act=nn.ReLU(), start=False, end=False):
super().__init__()
assert isinstance(in_planes, tuple) or isinstance(in_planes, list)
self.n_blocks = n_blocks = len(in_planes)
self.adapt_convs = nn.ModuleList()
for i in range(n_blocks):
self.adapt_convs.append(
CondRCUBlock(in_planes[i], 2, 2, num_classes, normalizer, act)
)
self.output_convs = CondRCUBlock(features, 3 if end else 1, 2, num_classes, normalizer, act)
if not start:
self.msf = CondMSFBlock(in_planes, features, num_classes, normalizer)
self.crp = CondCRPBlock(features, 2, num_classes, normalizer, act)
def forward(self, xs, y, output_shape):
assert isinstance(xs, tuple) or isinstance(xs, list)
hs = []
for i in range(len(xs)):
h = self.adapt_convs[i](xs[i], y)
hs.append(h)
if self.n_blocks > 1:
h = self.msf(hs, y, output_shape)
else:
h = hs[0]
h = self.crp(h, y)
h = self.output_convs(h, y)
return h
class ConvMeanPool(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True, adjust_padding=False):
super().__init__()
if not adjust_padding:
conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1, padding=kernel_size // 2, bias=biases)
self.conv = conv
else:
conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1, padding=kernel_size // 2, bias=biases)
self.conv = nn.Sequential(
nn.ZeroPad2d((1, 0, 1, 0)),
conv
)
def forward(self, inputs):
output = self.conv(inputs)
output = sum([output[:, :, ::2, ::2], output[:, :, 1::2, ::2],
output[:, :, ::2, 1::2], output[:, :, 1::2, 1::2]]) / 4.
return output
class MeanPoolConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1, padding=kernel_size // 2, bias=biases)
def forward(self, inputs):
output = inputs
output = sum([output[:, :, ::2, ::2], output[:, :, 1::2, ::2],
output[:, :, ::2, 1::2], output[:, :, 1::2, 1::2]]) / 4.
return self.conv(output)
class UpsampleConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1, padding=kernel_size // 2, bias=biases)
self.pixelshuffle = nn.PixelShuffle(upscale_factor=2)
def forward(self, inputs):
output = inputs
output = torch.cat([output, output, output, output], dim=1)
output = self.pixelshuffle(output)
return self.conv(output)
class ResidualBlock(nn.Module):
def __init__(self, input_dim, output_dim, resample=None, act=nn.ELU(),
normalization=nn.InstanceNorm2d, adjust_padding=False, dilation=1):
super().__init__()
self.non_linearity = act
self.input_dim = input_dim
self.output_dim = output_dim
self.resample = resample
self.normalization = normalization
if resample == 'down':
if dilation > 1:
self.conv1 = ncsn_conv3x3(input_dim, input_dim, dilation=dilation)
self.normalize2 = normalization(input_dim)
self.conv2 = ncsn_conv3x3(input_dim, output_dim, dilation=dilation)
conv_shortcut = partial(ncsn_conv3x3, dilation=dilation)
else:
self.conv1 = ncsn_conv3x3(input_dim, input_dim)
self.normalize2 = normalization(input_dim)
self.conv2 = ConvMeanPool(input_dim, output_dim, 3, adjust_padding=adjust_padding)
conv_shortcut = partial(ConvMeanPool, kernel_size=1, adjust_padding=adjust_padding)
elif resample is None:
if dilation > 1:
conv_shortcut = partial(ncsn_conv3x3, dilation=dilation)
self.conv1 = ncsn_conv3x3(input_dim, output_dim, dilation=dilation)
self.normalize2 = normalization(output_dim)
self.conv2 = ncsn_conv3x3(output_dim, output_dim, dilation=dilation)
else:
# conv_shortcut = nn.Conv2d ### Something wierd here.
conv_shortcut = partial(ncsn_conv1x1)
self.conv1 = ncsn_conv3x3(input_dim, output_dim)
self.normalize2 = normalization(output_dim)
self.conv2 = ncsn_conv3x3(output_dim, output_dim)
else:
raise Exception('invalid resample value')
if output_dim != input_dim or resample is not None:
self.shortcut = conv_shortcut(input_dim, output_dim)
self.normalize1 = normalization(input_dim)
def forward(self, x):
output = self.normalize1(x)
output = self.non_linearity(output)
output = self.conv1(output)
output = self.normalize2(output)
output = self.non_linearity(output)
output = self.conv2(output)
if self.output_dim == self.input_dim and self.resample is None:
shortcut = x
else:
shortcut = self.shortcut(x)
return shortcut + output
###########################################################################
# Functions below are ported over from the DDPM codebase:
# https://github.com/hojonathanho/diffusion/blob/master/diffusion_tf/nn.py
###########################################################################
def get_timestep_embedding(timesteps, embedding_dim, max_positions=10000):
assert len(timesteps.shape) == 1 # and timesteps.dtype == tf.int32
half_dim = embedding_dim // 2
# magic number 10000 is from transformers
emb = math.log(max_positions) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) * -emb)
emb = timesteps.float()[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = F.pad(emb, (0, 1), mode='constant')
assert emb.shape == (timesteps.shape[0], embedding_dim)
return emb
def _einsum(a, b, c, x, y):
einsum_str = '{},{}->{}'.format(''.join(a), ''.join(b), ''.join(c))
return torch.einsum(einsum_str, x, y)
def contract_inner(x, y):
"""tensordot(x, y, 1)."""
x_chars = list(string.ascii_lowercase[:len(x.shape)])
y_chars = list(string.ascii_lowercase[len(x.shape):len(y.shape) + len(x.shape)])
y_chars[0] = x_chars[-1] # first axis of y and last of x get summed
out_chars = x_chars[:-1] + y_chars[1:]
return _einsum(x_chars, y_chars, out_chars, x, y)
class NIN(nn.Module):
def __init__(self, in_dim, num_units, init_scale=0.1):
super().__init__()
self.W = nn.Parameter(default_init(scale=init_scale)((in_dim, num_units)), requires_grad=True)
self.b = nn.Parameter(torch.zeros(num_units), requires_grad=True)
def forward(self, x):
x = x.permute(0, 2, 3, 1)
y = contract_inner(x, self.W) + self.b
return y.permute(0, 3, 1, 2)
class AttnBlock(nn.Module):
"""Channel-wise self-attention block."""
def __init__(self, channels):
super().__init__()
self.GroupNorm_0 = nn.GroupNorm(num_groups=32, num_channels=channels, eps=1e-6)
self.NIN_0 = NIN(channels, channels)
self.NIN_1 = NIN(channels, channels)
self.NIN_2 = NIN(channels, channels)
self.NIN_3 = NIN(channels, channels, init_scale=0.)
def forward(self, x):
B, C, H, W = x.shape
h = self.GroupNorm_0(x)
q = self.NIN_0(h)
k = self.NIN_1(h)
v = self.NIN_2(h)
w = torch.einsum('bchw,bcij->bhwij', q, k) * (int(C) ** (-0.5))
w = torch.reshape(w, (B, H, W, H * W))
w = F.softmax(w, dim=-1)
w = torch.reshape(w, (B, H, W, H, W))
h = torch.einsum('bhwij,bcij->bchw', w, v)
h = self.NIN_3(h)
return x + h
class Upsample(nn.Module):
def __init__(self, channels, with_conv=False):
super().__init__()
if with_conv:
self.Conv_0 = ddpm_conv3x3(channels, channels)
self.with_conv = with_conv
def forward(self, x):
B, C, H, W = x.shape
h = F.interpolate(x, (H * 2, W * 2), mode='nearest')
if self.with_conv:
h = self.Conv_0(h)
return h
class Downsample(nn.Module):
def __init__(self, channels, with_conv=False):
super().__init__()
if with_conv:
self.Conv_0 = ddpm_conv3x3(channels, channels, stride=2, padding=0)
self.with_conv = with_conv
def forward(self, x):
B, C, H, W = x.shape
# Emulate 'SAME' padding
if self.with_conv:
x = F.pad(x, (0, 1, 0, 1))
x = self.Conv_0(x)
else:
x = F.avg_pool2d(x, kernel_size=2, stride=2, padding=0)
assert x.shape == (B, C, H // 2, W // 2)
return x
class ResnetBlockDDPM(nn.Module):
"""The ResNet Blocks used in DDPM."""
def __init__(self, act, in_ch, out_ch=None, temb_dim=None, conv_shortcut=False, dropout=0.1):
super().__init__()
if out_ch is None:
out_ch = in_ch
self.GroupNorm_0 = nn.GroupNorm(num_groups=32, num_channels=in_ch, eps=1e-6)
self.act = act
self.Conv_0 = ddpm_conv3x3(in_ch, out_ch)
if temb_dim is not None:
self.Dense_0 = nn.Linear(temb_dim, out_ch)
self.Dense_0.weight.data = default_init()(self.Dense_0.weight.data.shape)
nn.init.zeros_(self.Dense_0.bias)
self.GroupNorm_1 = nn.GroupNorm(num_groups=32, num_channels=out_ch, eps=1e-6)
self.Dropout_0 = nn.Dropout(dropout)
self.Conv_1 = ddpm_conv3x3(out_ch, out_ch, init_scale=0.)
if in_ch != out_ch:
if conv_shortcut:
self.Conv_2 = ddpm_conv3x3(in_ch, out_ch)
else:
self.NIN_0 = NIN(in_ch, out_ch)
self.out_ch = out_ch
self.in_ch = in_ch
self.conv_shortcut = conv_shortcut
def forward(self, x, temb=None):
B, C, H, W = x.shape
assert C == self.in_ch
out_ch = self.out_ch if self.out_ch else self.in_ch
h = self.act(self.GroupNorm_0(x))
h = self.Conv_0(h)
# Add bias to each feature map conditioned on the time embedding
if temb is not None:
h += self.Dense_0(self.act(temb))[:, :, None, None]
h = self.act(self.GroupNorm_1(h))
h = self.Dropout_0(h)
h = self.Conv_1(h)
if C != out_ch:
if self.conv_shortcut:
x = self.Conv_2(x)
else:
x = self.NIN_0(x)
return x + h | Python |
3D | Hejrati/cDAL | score_sde/models/utils.py | .py | 4,342 | 148 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This file has been modified from a file in the Score SDE library
# which was released under the Apache License.
#
# Source:
# https://github.com/yang-song/score_sde_pytorch/blob/main/models/utils.py
#
# The license for the original version of this file can be
# found in this directory (LICENSE_Apache). The modifications
# to this file are subject to the same Apache License.
# ---------------------------------------------------------------
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import numpy as np
_MODELS = {}
def register_model(cls=None, *, name=None):
"""A decorator for registering model classes."""
def _register(cls):
if name is None:
local_name = cls.__name__
else:
local_name = name
if local_name in _MODELS:
raise ValueError(f'Already registered model with name: {local_name}')
_MODELS[local_name] = cls
return cls
if cls is None:
return _register
else:
return _register(cls)
def get_model(name):
return _MODELS[name]
def get_sigmas(config):
"""Get sigmas --- the set of noise levels for SMLD from config files.
Args:
config: A ConfigDict object parsed from the config file
Returns:
sigmas: a jax numpy arrary of noise levels
"""
sigmas = np.exp(
np.linspace(np.log(config.model.sigma_max), np.log(config.model.sigma_min), config.model.num_scales))
return sigmas
def get_ddpm_params(config):
"""Get betas and alphas --- parameters used in the original DDPM paper."""
num_diffusion_timesteps = 1000
# parameters need to be adapted if number of time steps differs from 1000
beta_start = config.model.beta_min / config.model.num_scales
beta_end = config.model.beta_max / config.model.num_scales
betas = np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
sqrt_alphas_cumprod = np.sqrt(alphas_cumprod)
sqrt_1m_alphas_cumprod = np.sqrt(1. - alphas_cumprod)
return {
'betas': betas,
'alphas': alphas,
'alphas_cumprod': alphas_cumprod,
'sqrt_alphas_cumprod': sqrt_alphas_cumprod,
'sqrt_1m_alphas_cumprod': sqrt_1m_alphas_cumprod,
'beta_min': beta_start * (num_diffusion_timesteps - 1),
'beta_max': beta_end * (num_diffusion_timesteps - 1),
'num_diffusion_timesteps': num_diffusion_timesteps
}
def create_model(config):
"""Create the score model."""
model_name = config.model.name
score_model = get_model(model_name)(config)
score_model = score_model.to(config.device)
score_model = torch.nn.DataParallel(score_model)
return score_model
def get_model_fn(model, train=False):
"""Create a function to give the output of the score-based model.
Args:
model: The score model.
train: `True` for training and `False` for evaluation.
Returns:
A model function.
"""
def model_fn(x, labels):
"""Compute the output of the score-based model.
Args:
x: A mini-batch of input data.
labels: A mini-batch of conditioning variables for time steps. Should be interpreted differently
for different models.
Returns:
A tuple of (model output, new mutable states)
"""
if not train:
model.eval()
return model(x, labels)
else:
model.train()
return model(x, labels)
return model_fn
def to_flattened_numpy(x):
"""Flatten a torch tensor `x` and convert it to numpy."""
return x.detach().cpu().numpy().reshape((-1,))
def from_flattened_numpy(x, shape):
"""Form a torch tensor with the given `shape` from a flattened numpy array `x`."""
return torch.from_numpy(x.reshape(shape)) | Python |
3D | Hejrati/cDAL | score_sde/models/layerspp.py | .py | 12,549 | 381 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This file has been modified from a file in the Score SDE library
# which was released under the Apache License.
#
# Source:
# https://github.com/yang-song/score_sde_pytorch/blob/main/models/layerspp.py
#
# The license for the original version of this file can be
# found in this directory (LICENSE_Apache). The modifications
# to this file are subject to the same Apache License.
# ---------------------------------------------------------------
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: skip-file
from . import layers
from . import up_or_down_sampling, dense_layer
import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
conv1x1 = layers.ddpm_conv1x1
conv3x3 = layers.ddpm_conv3x3
NIN = layers.NIN
default_init = layers.default_init
dense = dense_layer.dense
class AdaptiveGroupNorm(nn.Module):
def __init__(self, num_groups,in_channel, style_dim):
super().__init__()
self.norm = nn.GroupNorm(num_groups, in_channel, affine=False, eps=1e-6)
self.style = dense(style_dim, in_channel * 2)
self.style.bias.data[:in_channel] = 1
self.style.bias.data[in_channel:] = 0
def forward(self, input, style):
style = self.style(style).unsqueeze(2).unsqueeze(3)
gamma, beta = style.chunk(2, 1)
out = self.norm(input)
out = gamma * out + beta
return out
class GaussianFourierProjection(nn.Module):
"""Gaussian Fourier embeddings for noise levels."""
def __init__(self, embedding_size=256, scale=1.0):
super().__init__()
self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
def forward(self, x):
x_proj = x[:, None] * self.W[None, :] * 2 * np.pi
return torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
class Combine(nn.Module):
"""Combine information from skip connections."""
def __init__(self, dim1, dim2, method='cat'):
super().__init__()
self.Conv_0 = conv1x1(dim1, dim2)
self.method = method
def forward(self, x, y):
h = self.Conv_0(x)
if self.method == 'cat':
return torch.cat([h, y], dim=1)
elif self.method == 'sum':
return h + y
else:
raise ValueError(f'Method {self.method} not recognized.')
class AttnBlockpp(nn.Module):
"""Channel-wise self-attention block. Modified from DDPM."""
def __init__(self, channels, skip_rescale=False, init_scale=0.):
super().__init__()
self.GroupNorm_0 = nn.GroupNorm(num_groups=min(channels // 4, 32), num_channels=channels,
eps=1e-6)
self.NIN_0 = NIN(channels, channels)
self.NIN_1 = NIN(channels, channels)
self.NIN_2 = NIN(channels, channels)
self.NIN_3 = NIN(channels, channels, init_scale=init_scale)
self.skip_rescale = skip_rescale
def forward(self, x):
B, C, H, W = x.shape
h = self.GroupNorm_0(x)
q = self.NIN_0(h)
k = self.NIN_1(h)
v = self.NIN_2(h)
w = torch.einsum('bchw,bcij->bhwij', q, k) * (int(C) ** (-0.5))
w = torch.reshape(w, (B, H, W, H * W))
w = F.softmax(w, dim=-1)
w = torch.reshape(w, (B, H, W, H, W))
h = torch.einsum('bhwij,bcij->bchw', w, v)
h = self.NIN_3(h)
if not self.skip_rescale:
return x + h
else:
return (x + h) / np.sqrt(2.)
class Upsample(nn.Module):
def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir=False,
fir_kernel=(1, 3, 3, 1)):
super().__init__()
out_ch = out_ch if out_ch else in_ch
if not fir:
if with_conv:
self.Conv_0 = conv3x3(in_ch, out_ch)
else:
if with_conv:
self.Conv2d_0 = up_or_down_sampling.Conv2d(in_ch, out_ch,
kernel=3, up=True,
resample_kernel=fir_kernel,
use_bias=True,
kernel_init=default_init())
self.fir = fir
self.with_conv = with_conv
self.fir_kernel = fir_kernel
self.out_ch = out_ch
def forward(self, x):
B, C, H, W = x.shape
if not self.fir:
h = F.interpolate(x, (H * 2, W * 2), 'nearest')
if self.with_conv:
h = self.Conv_0(h)
else:
if not self.with_conv:
h = up_or_down_sampling.upsample_2d(x, self.fir_kernel, factor=2)
else:
h = self.Conv2d_0(x)
return h
class Downsample(nn.Module):
def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir=False,
fir_kernel=(1, 3, 3, 1)):
super().__init__()
out_ch = out_ch if out_ch else in_ch
if not fir:
if with_conv:
self.Conv_0 = conv3x3(in_ch, out_ch, stride=2, padding=0)
else:
if with_conv:
self.Conv2d_0 = up_or_down_sampling.Conv2d(in_ch, out_ch,
kernel=3, down=True,
resample_kernel=fir_kernel,
use_bias=True,
kernel_init=default_init())
self.fir = fir
self.fir_kernel = fir_kernel
self.with_conv = with_conv
self.out_ch = out_ch
def forward(self, x):
B, C, H, W = x.shape
if not self.fir:
if self.with_conv:
x = F.pad(x, (0, 1, 0, 1))
x = self.Conv_0(x)
else:
x = F.avg_pool2d(x, 2, stride=2)
else:
if not self.with_conv:
x = up_or_down_sampling.downsample_2d(x, self.fir_kernel, factor=2)
else:
x = self.Conv2d_0(x)
return x
class ResnetBlockDDPMpp_Adagn(nn.Module):
"""ResBlock adapted from DDPM."""
def __init__(self, act, in_ch, out_ch=None, temb_dim=None, zemb_dim=None, conv_shortcut=False,
dropout=0.1, skip_rescale=False, init_scale=0.):
super().__init__()
out_ch = out_ch if out_ch else in_ch
self.GroupNorm_0 = AdaptiveGroupNorm(min(in_ch // 4, 32), in_ch, zemb_dim)
self.Conv_0 = conv3x3(in_ch, out_ch)
if temb_dim is not None:
self.Dense_0 = nn.Linear(temb_dim, out_ch)
self.Dense_0.weight.data = default_init()(self.Dense_0.weight.data.shape)
nn.init.zeros_(self.Dense_0.bias)
self.GroupNorm_1 = AdaptiveGroupNorm(min(out_ch // 4, 32), out_ch, zemb_dim)
self.Dropout_0 = nn.Dropout(dropout)
self.Conv_1 = conv3x3(out_ch, out_ch, init_scale=init_scale)
if in_ch != out_ch:
if conv_shortcut:
self.Conv_2 = conv3x3(in_ch, out_ch)
else:
self.NIN_0 = NIN(in_ch, out_ch)
self.skip_rescale = skip_rescale
self.act = act
self.out_ch = out_ch
self.conv_shortcut = conv_shortcut
def forward(self, x, temb=None, zemb=None):
h = self.act(self.GroupNorm_0(x, zemb))
h = self.Conv_0(h)
if temb is not None:
h += self.Dense_0(self.act(temb))[:, :, None, None]
h = self.act(self.GroupNorm_1(h, zemb))
h = self.Dropout_0(h)
h = self.Conv_1(h)
if x.shape[1] != self.out_ch:
if self.conv_shortcut:
x = self.Conv_2(x)
else:
x = self.NIN_0(x)
if not self.skip_rescale:
return x + h
else:
return (x + h) / np.sqrt(2.)
class ResnetBlockBigGANpp_Adagn(nn.Module):
def __init__(self, act, in_ch, out_ch=None, temb_dim=None, zemb_dim=None, up=False, down=False,
dropout=0.1, fir=False, fir_kernel=(1, 3, 3, 1),
skip_rescale=True, init_scale=0.):
super().__init__()
out_ch = out_ch if out_ch else in_ch
self.GroupNorm_0 = AdaptiveGroupNorm(min(in_ch // 4, 32), in_ch, zemb_dim)
self.up = up
self.down = down
self.fir = fir
self.fir_kernel = fir_kernel
self.Conv_0 = conv3x3(in_ch, out_ch)
if temb_dim is not None:
self.Dense_0 = nn.Linear(temb_dim, out_ch)
self.Dense_0.weight.data = default_init()(self.Dense_0.weight.shape)
nn.init.zeros_(self.Dense_0.bias)
self.GroupNorm_1 = AdaptiveGroupNorm(min(out_ch // 4, 32), out_ch, zemb_dim)
self.Dropout_0 = nn.Dropout(dropout)
self.Conv_1 = conv3x3(out_ch, out_ch, init_scale=init_scale)
if in_ch != out_ch or up or down:
self.Conv_2 = conv1x1(in_ch, out_ch)
self.skip_rescale = skip_rescale
self.act = act
self.in_ch = in_ch
self.out_ch = out_ch
def forward(self, x, temb=None, zemb=None):
h = self.act(self.GroupNorm_0(x, zemb))
if self.up:
if self.fir:
h = up_or_down_sampling.upsample_2d(h, self.fir_kernel, factor=2)
x = up_or_down_sampling.upsample_2d(x, self.fir_kernel, factor=2)
else:
h = up_or_down_sampling.naive_upsample_2d(h, factor=2)
x = up_or_down_sampling.naive_upsample_2d(x, factor=2)
elif self.down:
if self.fir:
h = up_or_down_sampling.downsample_2d(h, self.fir_kernel, factor=2)
x = up_or_down_sampling.downsample_2d(x, self.fir_kernel, factor=2)
else:
h = up_or_down_sampling.naive_downsample_2d(h, factor=2)
x = up_or_down_sampling.naive_downsample_2d(x, factor=2)
h = self.Conv_0(h)
# Add bias to each feature map conditioned on the time embedding
if temb is not None:
h += self.Dense_0(self.act(temb))[:, :, None, None]
h = self.act(self.GroupNorm_1(h, zemb))
h = self.Dropout_0(h)
h = self.Conv_1(h)
if self.in_ch != self.out_ch or self.up or self.down:
x = self.Conv_2(x)
if not self.skip_rescale:
return x + h
else:
return (x + h) / np.sqrt(2.)
class ResnetBlockBigGANpp_Adagn_one(nn.Module):
def __init__(self, act, in_ch, out_ch=None, temb_dim=None, zemb_dim=None, up=False, down=False,
dropout=0.1, fir=False, fir_kernel=(1, 3, 3, 1),
skip_rescale=True, init_scale=0.):
super().__init__()
out_ch = out_ch if out_ch else in_ch
self.GroupNorm_0 = AdaptiveGroupNorm(min(in_ch // 4, 32), in_ch, zemb_dim)
self.up = up
self.down = down
self.fir = fir
self.fir_kernel = fir_kernel
self.Conv_0 = conv3x3(in_ch, out_ch)
if temb_dim is not None:
self.Dense_0 = nn.Linear(temb_dim, out_ch)
self.Dense_0.weight.data = default_init()(self.Dense_0.weight.shape)
nn.init.zeros_(self.Dense_0.bias)
self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
self.Dropout_0 = nn.Dropout(dropout)
self.Conv_1 = conv3x3(out_ch, out_ch, init_scale=init_scale)
if in_ch != out_ch or up or down:
self.Conv_2 = conv1x1(in_ch, out_ch)
self.skip_rescale = skip_rescale
self.act = act
self.in_ch = in_ch
self.out_ch = out_ch
def forward(self, x, temb=None, zemb=None):
h = self.act(self.GroupNorm_0(x, zemb))
if self.up:
if self.fir:
h = up_or_down_sampling.upsample_2d(h, self.fir_kernel, factor=2)
x = up_or_down_sampling.upsample_2d(x, self.fir_kernel, factor=2)
else:
h = up_or_down_sampling.naive_upsample_2d(h, factor=2)
x = up_or_down_sampling.naive_upsample_2d(x, factor=2)
elif self.down:
if self.fir:
h = up_or_down_sampling.downsample_2d(h, self.fir_kernel, factor=2)
x = up_or_down_sampling.downsample_2d(x, self.fir_kernel, factor=2)
else:
h = up_or_down_sampling.naive_downsample_2d(h, factor=2)
x = up_or_down_sampling.naive_downsample_2d(x, factor=2)
h = self.Conv_0(h)
# Add bias to each feature map conditioned on the time embedding
if temb is not None:
h += self.Dense_0(self.act(temb))[:, :, None, None]
h = self.act(self.GroupNorm_1(h))
h = self.Dropout_0(h)
h = self.Conv_1(h)
if self.in_ch != self.out_ch or self.up or self.down:
x = self.Conv_2(x)
if not self.skip_rescale:
return x + h
else:
return (x + h) / np.sqrt(2.)
| Python |
3D | Hejrati/cDAL | score_sde/op/fused_act.py | .py | 3,055 | 106 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# ---------------------------------------------------------------
""" Originated from https://github.com/rosinality/stylegan2-pytorch
The license for the original version of this file can be found in this directory (LICENSE_MIT).
"""
import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
fused = load(
"fused",
sources=[
os.path.join(module_path, "fused_bias_act.cpp"),
os.path.join(module_path, "fused_bias_act_kernel.cu"),
],
)
class FusedLeakyReLUFunctionBackward(Function):
@staticmethod
def forward(ctx, grad_output, out, negative_slope, scale):
ctx.save_for_backward(out)
ctx.negative_slope = negative_slope
ctx.scale = scale
empty = grad_output.new_empty(0)
grad_input = fused.fused_bias_act(
grad_output, empty, out, 3, 1, negative_slope, scale
)
dim = [0]
if grad_input.ndim > 2:
dim += list(range(2, grad_input.ndim))
grad_bias = grad_input.sum(dim).detach()
return grad_input, grad_bias
@staticmethod
def backward(ctx, gradgrad_input, gradgrad_bias):
out, = ctx.saved_tensors
gradgrad_out = fused.fused_bias_act(
gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
)
return gradgrad_out, None, None, None
class FusedLeakyReLUFunction(Function):
@staticmethod
def forward(ctx, input, bias, negative_slope, scale):
empty = input.new_empty(0)
out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
ctx.save_for_backward(out)
ctx.negative_slope = negative_slope
ctx.scale = scale
return out
@staticmethod
def backward(ctx, grad_output):
out, = ctx.saved_tensors
grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
grad_output, out, ctx.negative_slope, ctx.scale
)
return grad_input, grad_bias, None, None
class FusedLeakyReLU(nn.Module):
def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
super().__init__()
self.bias = nn.Parameter(torch.zeros(channel))
self.negative_slope = negative_slope
self.scale = scale
def forward(self, input):
return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
if input.device.type == "cpu":
rest_dim = [1] * (input.ndim - bias.ndim - 1)
return (
F.leaky_relu(
input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
)
* scale
)
else:
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
| Python |
3D | Hejrati/cDAL | score_sde/op/upfirdn2d.cpp | .cpp | 1,333 | 31 | // ---------------------------------------------------------------
// Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
// ---------------------------------------------------------------
// Originated from https://github.com/rosinality/stylegan2-pytorch
// The license for the original version of this file can be found in this directory (LICENSE_MIT).
#include <torch/extension.h>
torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
int up_x, int up_y, int down_x, int down_y,
int pad_x0, int pad_x1, int pad_y0, int pad_y1);
#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
int up_x, int up_y, int down_x, int down_y,
int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
CHECK_CUDA(input);
CHECK_CUDA(kernel);
return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
} | C++ |
3D | Hejrati/cDAL | score_sde/op/__init__.py | .py | 89 | 3 | from .fused_act import FusedLeakyReLU, fused_leaky_relu
from .upfirdn2d import upfirdn2d
| Python |
3D | Hejrati/cDAL | score_sde/op/fused_bias_act.cpp | .cpp | 1,192 | 28 | // ---------------------------------------------------------------
// Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
// ---------------------------------------------------------------
// Originated from https://github.com/rosinality/stylegan2-pytorch
// The license for the original version of this file can be found in this directory (LICENSE_MIT).
#include <torch/extension.h>
torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
int act, int grad, float alpha, float scale);
#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
int act, int grad, float alpha, float scale) {
CHECK_CUDA(input);
CHECK_CUDA(bias);
return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
} | C++ |
3D | Hejrati/cDAL | score_sde/op/upfirdn2d.py | .py | 6,516 | 226 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# ---------------------------------------------------------------
""" Originated from https://github.com/rosinality/stylegan2-pytorch
The license for the original version of this file can be found in this directory (LICENSE_MIT).
"""
import os
import torch
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
from collections import abc
module_path = os.path.dirname(__file__)
upfirdn2d_op = load(
"upfirdn2d",
sources=[
os.path.join(module_path, "upfirdn2d.cpp"),
os.path.join(module_path, "upfirdn2d_kernel.cu"),
],
)
class UpFirDn2dBackward(Function):
@staticmethod
def forward(
ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
):
up_x, up_y = up
down_x, down_y = down
g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
grad_input = upfirdn2d_op.upfirdn2d(
grad_output,
grad_kernel,
down_x,
down_y,
up_x,
up_y,
g_pad_x0,
g_pad_x1,
g_pad_y0,
g_pad_y1,
)
grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
ctx.save_for_backward(kernel)
pad_x0, pad_x1, pad_y0, pad_y1 = pad
ctx.up_x = up_x
ctx.up_y = up_y
ctx.down_x = down_x
ctx.down_y = down_y
ctx.pad_x0 = pad_x0
ctx.pad_x1 = pad_x1
ctx.pad_y0 = pad_y0
ctx.pad_y1 = pad_y1
ctx.in_size = in_size
ctx.out_size = out_size
return grad_input
@staticmethod
def backward(ctx, gradgrad_input):
kernel, = ctx.saved_tensors
gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
gradgrad_out = upfirdn2d_op.upfirdn2d(
gradgrad_input,
kernel,
ctx.up_x,
ctx.up_y,
ctx.down_x,
ctx.down_y,
ctx.pad_x0,
ctx.pad_x1,
ctx.pad_y0,
ctx.pad_y1,
)
# gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
gradgrad_out = gradgrad_out.view(
ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
)
return gradgrad_out, None, None, None, None, None, None, None, None
class UpFirDn2d(Function):
@staticmethod
def forward(ctx, input, kernel, up, down, pad):
up_x, up_y = up
down_x, down_y = down
pad_x0, pad_x1, pad_y0, pad_y1 = pad
kernel_h, kernel_w = kernel.shape
batch, channel, in_h, in_w = input.shape
ctx.in_size = input.shape
input = input.reshape(-1, in_h, in_w, 1)
ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
ctx.out_size = (out_h, out_w)
ctx.up = (up_x, up_y)
ctx.down = (down_x, down_y)
ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
g_pad_x0 = kernel_w - pad_x0 - 1
g_pad_y0 = kernel_h - pad_y0 - 1
g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
out = upfirdn2d_op.upfirdn2d(
input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
)
# out = out.view(major, out_h, out_w, minor)
out = out.view(-1, channel, out_h, out_w)
return out
@staticmethod
def backward(ctx, grad_output):
kernel, grad_kernel = ctx.saved_tensors
grad_input = UpFirDn2dBackward.apply(
grad_output,
kernel,
grad_kernel,
ctx.up,
ctx.down,
ctx.pad,
ctx.g_pad,
ctx.in_size,
ctx.out_size,
)
return grad_input, None, None, None, None
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
if input.device.type == "cpu":
out = upfirdn2d_native(
input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
)
else:
out = UpFirDn2d.apply(
input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
)
return out
def upfirdn2d_ada(input, kernel, up=1, down=1, pad=(0, 0)):
if not isinstance(up, abc.Iterable):
up = (up, up)
if not isinstance(down, abc.Iterable):
down = (down, down)
if len(pad) == 2:
pad = (pad[0], pad[1], pad[0], pad[1])
if input.device.type == "cpu":
out = upfirdn2d_native(input, kernel, *up, *down, *pad)
else:
out = UpFirDn2d.apply(input, kernel, up, down, pad)
return out
def upfirdn2d_native(
input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
):
_, channel, in_h, in_w = input.shape
input = input.reshape(-1, in_h, in_w, 1)
_, in_h, in_w, minor = input.shape
kernel_h, kernel_w = kernel.shape
out = input.view(-1, in_h, 1, in_w, 1, minor)
out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
out = out.view(-1, in_h * up_y, in_w * up_x, minor)
out = F.pad(
out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
)
out = out[
:,
max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
:,
]
out = out.permute(0, 3, 1, 2)
out = out.reshape(
[-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
)
w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
out = F.conv2d(out, w)
out = out.reshape(
-1,
minor,
in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
)
out = out.permute(0, 2, 3, 1)
out = out[:, ::down_y, ::down_x, :]
out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
return out.view(-1, channel, out_h, out_w)
| Python |
3D | viannegao/ChromaFold | ChromaFold - Visualize and Evaluate.ipynb | .ipynb | 4,888,953 | 953 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:47:54.097143Z",
"start_time": "2023-06-11T13:47:35.484137Z"
}
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scipy\n",
"import torch\n",
"from sklearn import metrics\n",
"\n",
"import pickle\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import warnings\n",
"import itertools\n",
"from tqdm import tqdm\n",
"\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:47:54.118901Z",
"start_time": "2023-06-11T13:47:54.108669Z"
}
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:47:54.157629Z",
"start_time": "2023-06-11T13:47:54.123346Z"
},
"code_folding": [
0,
10,
32,
41,
62,
69,
85,
90,
102
]
},
"outputs": [],
"source": [
"def get_starts(chrom_list, step = 5e4):\n",
" startl = []\n",
" chroml = []\n",
" for chrom in chrom_list:\n",
" chrom = 'chr{}'.format(chrom)\n",
" cur_starts = list(np.arange(start_dict[chrom],end_dict[chrom]-5000000, step).astype(int))\n",
" startl = startl + cur_starts\n",
" chroml = chroml + list(np.repeat(chrom, len(cur_starts)))\n",
" return startl, chroml\n",
"\n",
"def get_visualization(y_hat_list,y_true_list,avg_stripes = False):\n",
" y_hat_list_reshaped = np.concatenate([x.reshape(1,-1) for x in y_hat_list[start_ind_origami:start_ind_origami+plot_len]], axis = 0)\n",
" y_true_list_reshaped = np.concatenate([x.reshape(1,-1) for x in y_true_list[start_ind_origami:start_ind_origami+plot_len]], axis = 0)\n",
" mat = []\n",
" mat_ytrue = []\n",
" for i in range(plot_len):\n",
" mat.append(np.insert(np.zeros(plot_len), i, y_hat_list_reshaped[i]))\n",
" mat_ytrue.append(np.insert(np.zeros(plot_len), i, y_true_list_reshaped[i].clip(-16,16)))\n",
" if avg_stripes:\n",
" summed = (pd.DataFrame(np.array(mat)).reindex(np.arange(-1*pred_len,plot_len+1,1)).fillna(0).iloc[0:plot_len+pred_len+1,0:plot_len+pred_len+1].values+pd.DataFrame(np.array(mat)).reindex(\n",
" np.arange(-1*pred_len,plot_len+1,1)).fillna(0).T.iloc[0:plot_len+pred_len+1,0:plot_len+pred_len+1].values)/2\n",
" else:\n",
" summed = pd.DataFrame(np.array(mat)).reindex(np.arange(-1*pred_len,plot_len+1,1)).fillna(0).T.iloc[0:plot_len+pred_len+1,0:plot_len+pred_len+1].values\n",
" \n",
" comb = np.zeros((summed.shape[0],summed.shape[0]))\n",
"\n",
" comb[np.triu_indices(comb.shape[0])[0],np.triu_indices(comb.shape[0])[1]] = (summed[np.triu_indices(comb.shape[0])[0],\n",
" np.triu_indices(comb.shape[0])[1]])\n",
" comb[np.tril_indices(comb.shape[0],k = -1)[0],np.tril_indices(comb.shape[0],k = -1)[1]] = 0\n",
"\n",
" return comb\n",
"\n",
"def kth_diag_indices(a, k):\n",
" rows, cols = np.diag_indices_from(a)\n",
" if k < 0:\n",
" return rows[-k:], cols[:k]\n",
" elif k > 0:\n",
" return rows[:-k], cols[k:]\n",
" else:\n",
" return rows, cols\n",
" \n",
"def get_combined_yhat(y_hat_list, start_ind, end_ind, offset = 200, avg_stripe = False): \n",
" pred_len = 200\n",
" y_hat_list_reshaped = np.concatenate([x.reshape(1,-1) for x in y_hat_list[start_ind:end_ind]], axis = 0)\n",
" chrom_length = y_hat_list_reshaped.shape[0]\n",
" mat = []\n",
"\n",
" for i in tqdm(range(chrom_length)):\n",
" mat.append(np.insert(np.zeros(chrom_length+offset+1), i, np.insert(y_hat_list_reshaped[i],pred_len,0)))\n",
" summed = pd.DataFrame(\n",
" np.array(mat)).reindex(np.arange(-1*pred_len,chrom_length,1)\n",
" ).fillna(0).T.iloc[0:chrom_length+pred_len,0:chrom_length+pred_len].values\n",
" \n",
" if avg_stripe:\n",
" summed = (pd.DataFrame(np.array(mat)).reindex(np.arange(-1*pred_len,chrom_length,1)\n",
" ).fillna(0).iloc[0:chrom_length+pred_len,0:chrom_length+pred_len].values+pd.DataFrame(\n",
" np.array(mat)).reindex(np.arange(-1*pred_len,chrom_length,1)\n",
" ).fillna(0).T.iloc[0:chrom_length+pred_len,0:chrom_length+pred_len].values)/2\n",
" summed = summed[200:-200,200:-200] # remove padded region\n",
"\n",
" return summed\n",
"\n",
"def get_metacell_profile(tile_dict, nbrs):\n",
" metacell_tile_dict = {}\n",
" metacell = nbrs\n",
" for chrom in list(tile_dict.keys()):\n",
" metacell_tile_dict[chrom] = (scipy.sparse.csr_matrix(metacell) * tile_dict[chrom])\n",
" return metacell_tile_dict \n",
"\n",
"def cpu_jaccard_vstripe(x):\n",
" size = x.shape[1]\n",
" eps=1e-8\n",
" i = 2\n",
"\n",
" x = torch.where(x>0.0, torch.tensor([1.0]), torch.tensor([0.0]))\n",
" num = torch.mm(x, x.transpose(0,1))\n",
" \n",
" x = torch.where(x==0.0, torch.tensor([1.0]), torch.tensor([0.0]))\n",
" denom = torch.mm(x, x.transpose(0,1))\n",
" denom = size - denom\n",
"\n",
" num = torch.div(num, torch.max(denom, eps * torch.ones_like(denom)))\n",
" \n",
" return num\n",
"\n",
"def cpu_batch_corcoeff_vstripe(x):\n",
" c = cpu_jaccard_vstripe(x.permute(1,0))\n",
" c[c != c] = 0\n",
" return c\n",
"\n",
"def get_preds(chroms, path):\n",
"\n",
" y_z_hat_list = []\n",
" tmp = []\n",
" for chrom in chroms:\n",
" tmp.append(np.load(path + '{}.npz'.format(chrom))['arr_0'])\n",
" tmp = np.concatenate([y for y in tmp], axis = 0)\n",
" y_z_hat_list.append(tmp)\n",
" y_z_hat_list = np.concatenate([np.expand_dims(y,1) for y in y_z_hat_list], axis = 1)\n",
"\n",
" return y_z_hat_list\n",
"\n",
"def pcolormesh_45deg(C,vmax,vmin):\n",
" n = C.shape[0]\n",
" # create rotation/scaling matrix\n",
" t = np.array([[1,0.5],[-1,0.5]])\n",
" # create coordinate matrix and transform it\n",
" A = np.dot(np.array([(i[1],i[0]) for i in itertools.product(range(n,-1,-1),range(0,n+1,1))]),t)\n",
" # plot\n",
" img = plt.pcolormesh(A[:,1].reshape(n+1,n+1),A[:,0].reshape(n+1,n+1),np.flipud(C),\n",
" cmap = 'RdBu_r',\n",
" vmax = vmax, vmin = vmin\n",
" )\n",
" return img\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:47:54.188319Z",
"start_time": "2023-06-11T13:47:54.158987Z"
},
"code_folding": []
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:47:54.256296Z",
"start_time": "2023-06-11T13:47:54.190774Z"
}
},
"outputs": [],
"source": [
"ct = 'gcb'\n",
"genome = 'mm10'\n",
"mod = 'chromafold_CTCFmotif'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:47:55.193793Z",
"start_time": "2023-06-11T13:47:54.261116Z"
}
},
"outputs": [],
"source": [
"# Load Hi-C data for evaluation\n",
"hicdc = pickle.load(open(\"./data/processed_input/hic/{}_hic_zscore_dict.p\".format(ct), \"rb\"))\n",
"pval = pickle.load(open(\"./data/processed_input/hic/{}_hic_qvalue_dict.p\".format(ct), \"rb\"))\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:48:13.958167Z",
"start_time": "2023-06-11T13:47:55.195579Z"
}
},
"outputs": [],
"source": [
"# Load input data for visualization\n",
"ctcf_motif = pickle.load(open(\"./data/processed_input/dna/{}_ctcf_motif_score.p\".format(genome), 'rb'))\n",
"atac = pickle.load(open(\"./data/processed_input/atac/{}_tile_pbulk_50bp_dict.p\".format(ct), 'rb'))\n",
"scatac = pickle.load(open(\"./data/processed_input/atac/{}_tile_500bp_dict.p\".format(ct), 'rb'))\n",
"metacell_path = pd.read_csv('./data/processed_input/atac/{}_metacell_mask.csv'.format(ct), index_col= 0).values\n",
"scatac = get_metacell_profile(scatac, metacell_path)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quantitative Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:48:13.965587Z",
"start_time": "2023-06-11T13:48:13.962581Z"
}
},
"outputs": [],
"source": [
"chrom = '16'"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:48:14.276166Z",
"start_time": "2023-06-11T13:48:13.967949Z"
}
},
"outputs": [],
"source": [
"hicdc_mat = hicdc['chr{}'.format(chrom)].toarray()\n",
"hicdc_pval_mat = pval['chr{}'.format(chrom)].toarray()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:48:14.411701Z",
"start_time": "2023-06-11T13:48:14.278145Z"
}
},
"outputs": [],
"source": [
"# Get predictions\n",
"y_hat = get_preds([chrom], './predictions/prediction_{}_chr'.format(ct))\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:48:32.130194Z",
"start_time": "2023-06-11T13:48:24.839256Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 12%|█▏ | 1157/9520 [00:00<00:00, 11335.85it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Positive class proportion: 0.1000000550940207\n",
"Zval cutoff: 1.5275225565710697\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 9520/9520 [00:00<00:00, 15704.80it/s]\n"
]
}
],
"source": [
"# Evaluate\n",
"pearson_list = {}\n",
"spearman_list = {}\n",
"roc_list = {}\n",
"prc_list = {}\n",
"\n",
"# Get ground-truth Hi-C data\n",
"true_mat = hicdc_mat[:-501,:-501].clip(-16,16)\n",
"min_len = len(np.diag(true_mat,199)) # Compute shortest diagonal length\n",
"\n",
"# Significant interaction analysis\n",
"percentile_cutoff = 90 # Zscore percentile cutoff for significant interactions\n",
"bin_true = np.concatenate([np.diag(true_mat,i)[0:min_len] for i in range(1,200)]).reshape(-1, min_len)\n",
"bin_mask = (bin_true.sum(0) >= np.percentile(bin_true.sum(0),1)) #mask for low-mappability regions\n",
"all_zval = np.concatenate([np.diag(true_mat,i)[0:min_len] for i in range(1,200)])\n",
"zval_cutoff = np.percentile(all_zval, percentile_cutoff)\n",
"bin_zval = all_zval > zval_cutoff\n",
"print('Positive class proportion: {}'.format(bin_zval.sum()/bin_zval.shape[0]))\n",
"print('Zval cutoff: {}'.format(zval_cutoff))\n",
"\n",
"# Get processed predictions\n",
"pred_mat = get_combined_yhat(y_hat[:,0,:], start_ind = 0, end_ind = y_hat.shape[0], avg_stripe=True)\n",
"\n",
"pearson_list[mod] = []\n",
"spearman_list[mod] = []\n",
"roc_list[mod] = []\n",
"prc_list[mod] = []\n",
"\n",
"for i in range(1,200):\n",
" a = np.diag(true_mat,i)[0:min_len][bin_mask]\n",
" b = np.diag(pred_mat,i)[0:min_len][bin_mask]\n",
" p = np.diag(true_mat,i)[0:min_len][bin_mask] > zval_cutoff\n",
" \n",
" fpr, tpr, thresholds = metrics.roc_curve(p.astype(int), b)\n",
" precision, recall, thresholds = metrics.precision_recall_curve(p.astype(int), b)\n",
" pearson_list[mod].append(scipy.stats.pearsonr(a,b)[0]) \n",
" spearman_list[mod].append(scipy.stats.spearmanr(a,b)[0]) \n",
" roc_list[mod].append(metrics.auc(fpr, tpr))\n",
" prc_list[mod].append(metrics.auc(recall, precision))\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:48:32.442801Z",
"start_time": "2023-06-11T13:48:32.141700Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 1.0)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = 6,6\n",
"plt.plot(pearson_list[mod], label = mod)\n",
"# plt.legend()\n",
"plt.ylabel('Pearson Correlation', fontsize = 18)\n",
"plt.xlabel('Genomic Distance (10kb)', fontsize = 18)\n",
"plt.ylim(0,1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:48:32.564116Z",
"start_time": "2023-06-11T13:48:32.445356Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 1.0)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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eWK6xnNmjCa9OWMboueu4oG/zct23iBxewmqjoOg5JApUrn62RZiVuQWAZy/tTat6NaIaS68WKbSom8SDo+bw9ew1PH5BjzKv9hKRw1M4vZ5mAsPM7IBvSDOrCVztl6m0ZmVmk5yUQMu61aMdCmbG8Cv6cm7vZny/YD3vTFpZ8pNERA5COIniCaALMM3MbjOzgf7tdmAq0JlKNsprYTMzt9CjeTJmQSdW5adb02QeOfcIjmxdh89nFZ7zSUQkMkqdKJxzn+LNH9EUeBYY49+e8Zfd7pwbVQYxxoRde/JYtG4bPZonRzuUA5zdsymL1m1nwdqt0Q5FRCqhsC64c869gDd96cXAfcCfgIuA5s65FyMfXuyYt2YLefmOHs1Toh3KAYYc0YT4OOP9KZnk6iI8EYmwcBuzcc5lAx9EPpTYtX13LhOXbASgZwwmivo1qzKwUwNe+3kZ701Zyb8u7FluXXVFpPILTBRmFg88AiwvPPNcoXK34J1p3F/Z5qlYuHYbZz47nr15jsa1q9E4OTZHb/33Jb35fv46Xv95OXeOnEHdGonFTogkIhKOkqqergD+AEwpodxkvIvtLo1EULFkxC/LiI8zHjm3O68MK3JgxZhQs2oVhvZqxohrjqRlverc9vY0tu/OjXZYIlIJlJQoLgLGOOemBhXy139LJUgUzjny872Touyde/hk+irO6dWMy/u3onuz2GvILiyleiJPXNiTjTv28Or4ZdEOR0QqgZISRV+8nk2l8SPenNoV2rUjpnDnezMAeD8tg5y9+Qw7pnVUYwpXrxYpDO7WmJfHL2Xj9t3RDkdEKriSEkVdYH0pt7XBL19h5eU7Ji7dyBezVrMsawev/7yc/m3q0qVJ7WiHFra7T+tIzt48znhmAo99s4Ab3kzjl/SsaIclIhVQSYliG1C/lNuqBxwwDlRFsnTDdnL25pPv4Lo3prBmSw6/O6lDtMM6KO0b1uK9m46mQa2qvPjTEsYu2sC/Ri+MdlgiUgGVlCjmAqeWclun+OUrrLmrvQvW2tavwdINO+jTMoVj21fcnkN9W9Xhs9uPZf7fBnPv4M5MX5nNnFVboh2WiFQwJSWKj4FBZjY0qJA/V8UpwEeRCiwa5q7eQmKVOB48qytmcOegjjEzXMfBMjOSEuM5v29zkhLieWviimiHJCIVTEmJ4r9AOvC+mT1iZq1DV5pZazN7GHgfWOSXr7DmrNpKl8a1GNCpIVMfOIUTOjaIdkgRk5yUwDm9m/LpjFWs3Lgz2uGISAUSmCicc7uAM4BleEN2LDGzbDNbaWabgSV4w3gsA850zuWUdcBlxTnH3NVb6NrU6wJbt0b5zi9RHm4/qQOJVeL4v/dnaKgPESm1Esd6cs6lA72AO4AJQC7QGMgDxvvL+zjnlpRdmGUvc/Mutubk0q1pxevhVFrNUpJ4+JzuTF2xmWd/SI92OBLjFq/bxhcalVgo5VhP/pnCs/6tUipoyK4IF9UdiqG9mjF20Qae+WExfVvVqVTVaxJZD46ay6RlG+nTsg5NU5KiHU6xNmzbTb0aicTFVez2xFgW1uixlVmXJrW4//QudG5cK9qhlLlHzjmCjg1rcfs703jhp3QWr9vGjpDhPrbl7I1idBILlmftYOLSjeQ7GDklI9rh7Gf77lzu/2Q2GZt2kr1zD8c//gPvTvlt4q4tO/fyxazVql6NoLBHj62sWtWrwQ0ntI12GOUiKTGel69K5b5PZvH4Nwt5/JuFJMQbdw7qSPbOPbw8fhkX9m3Og2d1pVa1hGiHK2Vkb14+01Zspl+buuTmO14ev5S9uY5OjWsyPSOb+Dije9PavDdlJb8/qT1V4mPjd+UT3y7k7UkraZqSRLemtcnZm89PCzdwef9WAAwft4QXf1rCka3r8NxlfWhUOzYH8qxIlCgOUy3rVeft648iff125q3Zyjdz1vCvb70L8o5tX4+PpmWyfttu3ri2X5QjlbKQn++4+4OZjJqxmqcv7olz8Pg3+1+QOahLIy5Kbc6Nb03l+wXrOa1b4yhF+5sZGdm8MXE5ANNXbt43LtuU5ZvIz3fExRlTl2+mUe2qzFm1lb99Po/nL+8TxYgrByWKw1z7hjVp37AmZ/Vowpez11C1SjyndG3EQ5/N5d3JK8nNy4+ZX5ISOY9+s4BRM1ZTPTGe1yYsJz7OaNugBt/ccQJfzV7DmxOXc8uAdvRsnky9Gol8OWtNTCSKR76cR8NaVenVIoW05ZsBr10ie+deFq7bRrsGNZmZmc0VR7UiL9/xzuSVbM3ZS+0wzox37M6lRlV9NYbSN4AA3oV5Z/ZoyildGwFwRLNkdufmszRrR5Qjk0hbsXEHr05YxiVHtuC+07swe9UWZmRkc0X/ViRWieOc3s34+NZj6duqDlXi4zi5S0N+XLCePbnRrfOftnIzU5Zv5qYT2nF8hwZs3LGHCekb6NfaG2Ju0tKNzF29hd25+aS2qsM5vZuxJzefb2av3beNoKH335m0kv7/GEO3v3zLqBmryvz1VCRKFFKkbs28bsJzV4c35MeuPXnsVSNiTHvhxyXExxl3ndKR8/s0o1a1KiQleFfvF+XUro3ZtjuXX5duPKT9zlm1hU+mZxa7/uf0LMbMW0fm5p3879cVTFm+CfCuccrNy+eV8UupXa0KFx3Zgj4t6wCQszefwd0b0ywliV+XbmLqis2AN3xNz+bJtKlfg0+me1/670/JoPtfvuXq1yczb/X+88tPX7mZP4+aQ7OUJGpVrcIv6Yf2WisbnV9Jkdo1qElilTjmrtrKub1L/7zzXvyFPi1TeOTcI8ouODloE5ds5KNpmVxxVCsa+o28j5x7BDl780hOKrp65rgO9UlKiGf0vLWH1J36oc/mMj0jm5M6NyI5KYGcvXmMXbSBvq3qUD0xnmtHTGF3yFlLnMF1x7Vh9Lx1ZGzaSb6DWwa0o2bVKnRsVJPqifHs3JNHzxbJ9G9bl+/mrmPNll20qJu077Wd06sZ//5+EY9/s4DXfl5G58a1mJW5hWtHTOHHuweQlBjPrj153PneDBrXrsbr1/Tjd+9OZ2ZmNnvz8rlw+ESuP74NZ/ZoetCvuzIIK1GYWUvgJqAD3mixhTsuO+fcyRGKTaIoIT6Ozo1r7bu+pDRWbNzB/DVbydq+m4fP6V7hx8k6VM45pmdk07tFStSPxeRlm/jP94v4OX0jjWpX5eYT2+1bd3bP4C/BagnxnNCxPp9OX83aLTlccmRLBvlVlEEWrdtGh4Y1MTOWZe0gzf+1P27RBmonJXDHyOlk79zL0F5NGdK9Cbtz87lncGeqxBlHt6vHU98t4uXx3pf7LQPasS0nlxuO93omVomPo2fzFCYv30TXJsnccmI7fl2ykZmZWzi3d7N9MVx7XGtmr9rCCz8toW6NRN68th8rNu3kwuETeWncUu4Y1IH30zJYsXEnb1/fn+SkBHo2T+b5HzcwYXEWMzKy+SAtU4mitAXNbAjwCZCIN/z4prIKSmJDt6a1+XLWGpxzxX7R5ebl88Cnczi5SyPWbtkFeBdApa/fTodG+1+Tkr1zD0+MXsj2nFyObFN3X3fGtOWbuPO9GfRuWYd7BneieZ3qZfvCysiyrB3USIzf92t27KINXP36FIZf0YfB3ZuUSwxLNmzn5remcvtJ7RnaqxnOOR77ZiHDxy6hfs1EHjijC5f3b0VSYnxY273xhHbs2pvP/DXbuP7NNO46pSO/O6l9sZ+LtOWbuGD4RB4+pztXHNWKD6dmEGdQo2oVvp+/jrmrt5KSlEBqq7p8PWct23NySU5K4Ibj2+zrPPHSlX2ZmZlNrxZ1iC/iYrphx7SiV8sUkhLj6dCoFl/8/nj+PWYR5/X5rQqtVrUEXr6qL1/PWUuLOtVpWLsaDWtX4/QjGjN87BLO7NmEVycso1eLFI5p540U3aN5CvkOnv1hMQCTlm0kZ28e1RKCj5lzjr98NpfsnXv514U9qFrFK787N2/f34Xtzs1je04u9WpWLeEdiK5wzij+CWQB5zjn0sooHokh3Zom8+7kDDI376JF3aK/vP87bikjp2QwdtEGOjWuRXJSAlt27WVCehYdGtViT24+P6dn0aJuEvd8NJtZmdkkJyXw5WyvF82Mldnc8vZUGtSsyui5a/lpwXreu+loulaQoVRe/3kZnRvXpk+rFC4cPpFOjWvy9vVHAfDZTG/4i9Fz15VLoliyYTuXvzyJtVtzePGnJZzdsylPjPaSxKX9WvLgmV3DThAF+raqw5vX9iNnbx5/+ng2T323COe8L+sx89czedlGmiQnce1xbUhOStj32p/5fjFn9mjCx9NWcULHBtStnsiomavJy3c8dVFPujSpzZj56/h+wXrO691svx52VeLj6Nuq+LnQBndvst9xrVsjkb8N7X5AOTPj9CP2P/73DenCxCUbOfOZCezam8e9QzrvS3o9m3ujM0xbmU2tqlXYtjuX8YuzeHL0Qs7q2ZTbBrZny669VK0St1/yeP7HdN70R2fesTuXEzs1YNyiLH5cuJ6/D+3OJUe24JUJSxnUpRFtG9Tk3o9m8fH0VRgw8b6TY3p8uXASRWfgASWJw0fBuFfTVm7eL1HszcvnmtenAN6vrY6NarJo3XbWbMnhsv4t+Tk9i5/TszivT3NufmsqE/1GUDN47tI+dGpck0FPjWPk5JW8OzmDdg1qMvLGo9iWk8uFwydy7YgpfHLbMTRJjt1hI8C7gv3hL+fTNKUad57ckaztu9m4ZDdrt+RQp0YC381dB8APC9eXaTfj/HzHY34dfI2qVRh2dCvemLiC135ezvM/LuHi1BY8ck73iAxxUS0hnicv6klcnPH0mEU8/2M6e/Ly9/1AeGPict65/ii+nrOWtg28eV1OePxHtubk8si53dm5J4+Pp6+ice1qnNWzKQnxcfRskcLMjGxO7VZydVaktKhbnQ9uPporXplM4+Rq+3X9bVi7Gk2Sq7FmSw43ndiWZ75P596PZrFxxx5WbV7COb2bccGLv9C3lXdBH8D4xRt4YvQizunVlD6t6vDgqLl8v2A99WtWpVXd6jz+7QI2bNvN02MWkb5+Ozef2I6RUzLo1SKFGRnZzMzMZmCnhuX2+sMVTqLYAOwpq0Ak9vRonkLLutV545flDO31W73v9/PXMyE9i1b1qtOiTnXeueEohr02mbmrt3JCB29CxE+mrWLwv8exYdtu/np2N/LyHU2SqzHE/2V3ZOs6/HvMYnLzHW9c24+U6omkVE/k9WuO5LwXfuHp7xbx+AU9o/K6SyttxWby8h0Zm3bx4Kg51K+ZSNb2PXwxazWt69Vg2+5cLk5twXtpGUxdsZn+bSM3CdacVVt4adxS7jqlI6uyd/HfcUs5t3cz7hvSmaoJ8YycksHfv5hHs5QkHjq7W0THQTIzHj3vCGokxmNmnN+nOd2a1mb+2q0Me20yw16fzIZtu/nzmV35ctZqpizfzOvXHMnATg3ZmrOX5KQEbhvYjgQ/cd5yYlueGL2I4zuU77hj7RvW4ru7TmBvnjugaqtH82TWbMnhjB5N+WXJRn5ZspHeLVOYvjKb81/4hbVbc/hq9hoyNu0kuXoC93w4i3YNavDo+T2oluBdixQfZ9SvUZUFa7dxxrPjeXrMIsy8/58ODb1q2UfPP4Ih/xnP7Mwt1Eiswu3vTOOz24+jcXJsXU0eTqJ4CzgfeKaMYpEYEx9nXHdcG/7y2Vymrti0rxpg5JSVNKpdle/vOnHfr+Q/Du7MP7+az7Ht61M1IZ53Jq2kR91knr20N6mtD6w+uOTIlkxZvpnUVnX2JReALk1qc1q3Royet45/FPoVvmnHHl6bsIyfFq3npStTy22guhkZ2YyasYpL+7WkRZ3qXPLyr1zeryVLsraTEG90bOQ1+t85qCOfz1rNB2mZJFdPIDkpgftO78wn01cxZv66iCWKkZNX8udRc9ib58h33pdc7WpV+Od5R+yrChncvTGjZqzmz4dQ3RSkSnwcfy1UzdOtaTIPn9Odm/83japV4jipc0OGdG9MvnP76uhrV0sg7YFB+5KEF2uTcmvDKay4IWou6deShrWq0aZ+DYYc0YS5q7fy4uV9+eNHsxi3aANn92zKV7PX8OLYJWzavoe1W3P4+NZj9x3/0LPhrk1rM+zo1nw9Zw03HN+Wh7+cz0vjl9KxUU06N65N2/o1mJW5hXVbc1i/bTffzVvLZf1b8d28dTRJrkaXJrVJrPLb8cra7g2CWJ4dJMJJFCOAgWY2CvgP3hwUeYULOedWFl4mFdeFqc15eswiXhq3lP9eWZdV2bsYu2gDtw/cf+yfEzs24ES/6+TATg2Z9udTAutcz+jRhB8WrOemE9se8IEf3L0Jn85YzaRlmzi2vZdEvpu3jj9+OJPNO70BC7+fv44rj24d4Vd7oEe+nMfL45cBkLZ8M4O7N2ZmRjYbtuZQt2YiPZuncM+Qzjz69QIuTG2OGTz85XwS4+P40+mdSameyLHt6/Hh1EyuPa5NkdVp+fkOM0r1j79i4w4e/Gwu/dvUo3X96rw9aSUJcXFc0q/FfvXld5/aiaPb1uO0cqzOAe+9u+bY1iRWiaNmMVc3J1SAK/0Hdmq4ryroiv4tuSi1OVWrxHP/6V1ollKNB87oCngX6ZnBn4Z0oVeLlGK395ezunLf6Z3J2ZPPo1971VDn9fHO0ns0T+GXJVnMX+Mdlx8WrCexShz3fDQbgI6NavLODUcxaekmho9dwuxVW3just7l2hMrnESxAHB4XWLPDCgX+Z8vEjXVE6tw5VGteO7HdJZu2M6HU70Lpi5KbRH4vJIa5qolxBc7Bs+JHRuQlBDP13PW0KZ+DR79egGfzVxNt6a1effGo7jm9Sn8umxTmSeKX5Zk8fL4ZVyU2pw+Letw78ezmbN6C81SkliVvYvVW3K4fWB7jmxdl49uOQaAy/u3IiE+jlO7NdqXFO4/oytDn5vA7e9MZ+SNR+33RblrTx4DnviRWwe0Z9gxB76evHzH1BXemZeZdy1CQpzxxIU9iY8zPkjLZHdu/gHvR4u61bmkX8uyOzgB/nJWt6jst6yY2b4zok6Na/HP83oA8PuT27N55x5uHdCeo9sFny0WbKNqlXj6tanLL0s2cpKfiLo3S953UWC9Gon8smQjmZt30alRLa4/vg1/HjWHk58cy5Zde/ddP/Jz+saYTRR/w0sUcpi56ujW/HfsUv759QLGL97AGUc0KbYXVCQkJcYzsHMD3k/L5N3JGcTHGb8/uQO3DWxH1Srx9G9TlwnpGwO77R6qzM07+eOHs2hdrzp/Pbs71RLi+Gzman5dupFXr07l5remsnzjTo4qVJ2UlBh/wBd++4Y1+ef5Pfj9u9P5/bvT+c8lvfdVJYyZv451W3fz8fRVRSaK139exsNfzueBM7pQOymBHxdu4IEzuuyrw751QHtmr9pS6edRiUXtG9birev6h/28y/u3Yk9uPn1beVeX92j+23t392mduO/j2Sxev51HzzuCC1Nb0CwliT98OIsbjm/DzSe245oRU5iRkR2pl1EqpU4UzrmHyjAOiWENalXlvD7NGDklg6pV4rh3SOcy3+cVR7Vi6YYdDOzckMv6tdwvMfVvW49PZ6xmadYO2jWoGdH9jlu0gbQVm3n952XgYMS1/fbV8b94RV+WZe2gc+Pa3DGoA0+OXrTvn70kZ/dsyoZtu/n7F/NY8fzPHNu+Htcd13bfmEIzM7JZtzWH/45dyhHNa3Nu7+bszcvntQnLMPNHdjU4pl09rg5JKHcM6hDR1y9l74weTTijx29tMl2b1CbOoE39GpzXpxl//2IeCfFx+zqQHNO+Pj/fe9K+8r1apPD8j+ns3JNL9cTyGVxDQ3hIqVx/fBs+mJrJjSe0LZcL4o5pV59v7jyhyHX92hQMArfpgEQxb/VWFq3bxjkhV+eW1o8L13PN61Mwg36t6/LEhT33S1DJSQn76qHP7d2cc3sXPTZSca47rg11ayQw4uflvPHLCn5cuIEVG3dwYscGjF20gbs/mMn4xVmYwd5cb8js1VtyePyCHjzx7UIS4uN47rI+Gs23kqlRtQrn9GpGj+bJVK0Szz2DO1M7qUqxnRB6tfAuCJyduSWiPemChJ0ozCwe75qKOhQxqKBzblwE4pIY075hLX66ewDNYmBKzLb1a1C/ZlV+XpLFZf33r4d/6PO5TFm+idTWdcJOaO9OWkn9momM/cPAMhtmuiDB/JyexbDXJpOb7/jDaZ1YlrWD8Yuz6NKkNnWqJ/DHj2YB0LZBDS7o05wBnRpQJS4upi/KkoP31MW99v1dVBVkqIIfKzMysmMzUZjZPcC9QNBls2rMrqTKsl0iHN6Q6E0Y8ctyzuqxlsHdvYullmftYPIyb2SZdyev5A+nlb6KbP22HL5fsJ7rj29TLnMRHNu+Ps9e2pu0FZvp1rQ2p3ZtxCsTlvHnM7vQu0Udvp27lpWbdnJCxwbExRkNa8VWv3qJnno1q9KiblK5tlOEM9bT9XjDeIwFRgOPAE8De4HrgKXAC2UQo8gB7h3SmekZ2dz1/gzaNzyO9g1r8uHUTOIMerZI4b0pGdxxcsf9+p8H+XBqJnn5jotL6M0VSUOOaLLvAsTbBrbn2Pb1Oaad1x34YKrO5PDRu0UdfliwnncmreSi1OZlXh0ZztZvBn51zg0EXvKXfemcuxfoAbRGZxNSTqolxPPSlX2pEmf89fO57M7N46NpmZzQsQF3DupI1vY9+8YbKsnkZZt47od0jmlXj7YRbhwvrTo1EhnYOXaHcJDYcuegDnRsVJM/fTKbU58ex2czV7M794DL2iImnETRBfjA/7ugm2wVAOfcGrzkcUfkQhMJ1qh2Nf7vlI6MX5zFGc9MYM2WHIYd3Zrj29enR/Nk/vHVfDZs2x24jWVZO7jmdW+8n39f0qt8Ahc5RG0b1OSjW47hpSv7et3H351Ov0e+93rrlYFwEkUeUDAvZsF96NgMy/HmqRApN1cc1YoODWuyLGsHT17Yk4GdGxIXZzx5YU+2787l/k9mBz5/5JSV5OTm87/r+qsdQCoUM+PUbo355s4TeOPafgzs1KDMOjuE02q3EmgD4JzbbWYZwPHASH/9kWiOCilnCfFxvHldP7J37qVLk9/6WHRoVIvbBrTn6TGLWLlxJy3rHdgQn5/v+HzGak7oUL/cxo0SibT4ONtvCJ2yEM4ZxTjgjJDHHwA3mdlrZjYCuB74KoKxiZRKk+Sk/ZJEgYKZzkbPW0t+vmPFxh3k5f82uMCU5ZtYvSVHDcciJQjnjOI/wEwzS3LO7QL+AnQEhvnrR+N1nRWJCS3rVadz41qMnreOzTv38PyPS6ieGM/9/ixvn85YTZI/JLSIFK/UZxTOuYXOuf/6SQLn3A7n3Nl47RTJzrkhzrmwqp7MbLCZLTSzdDMrNsmY2ZFmlmdmF4SzfZFTuzYibfkmXhq3lBM7NqBJcjX+9+tK8vId385dy6CujcptGASRiuqQO98657Y457aH+zz/Cu/ngSFAV+BSM+taTLnHgG8PNVY5/JzarTH5zutO+8SFPTm/b3Pmr9nKN3PWsmnHnnIfhlukIjqYITyq410zUQ9vyPH9hDGERz8g3Tm31N/uSGAoMK9Qud8BH+E1louEpVvT2gzp3pjB3RvToFZVBnRsyOPfLOQfX80nId7KtAFQpLII58rsGnhXYl8FFDUtlOFdX1Hai+6aARkhjzOB/cbsNbNmwLnASQQkCjO7EbgRoGXL6IzBL7HJzHjxir77HndpUouGtaqyKnsXx3eoX+wMZyLym3DOKIYDlwOfAOOBzYe476ImEig838W/gXucc3lB8w44517Cv1o8NTVVc2ZIscy8s4gPpmaqEVuklMJJFEOBV51zN0Ro35lA6MA6zYHCYy6kAiP9JFEfON3Mcp1zn0YoBjkMDe3VjO/mr+PUro2jHYpIhRBOotgLTIngvqcAHcysDbAKuAS4LLSAc65Nwd/+tRpfKEnIoTquQ31mPHhqtMMQqTDC6fX0A4XaEA6Fcy4XuB2vN9N84H3n3Fwzu9nMbo7UfkRE5NCYc6Wr0jezlnhXZz8NvOCc21uWgR2s1NRUl5aWFu0wREQqFDOb6pxLLWpdOHNmrzSzPwFvAv8yszV4AwUWKubaHXyoIiISa8LpHns18CqwB1jIofd6EhGRCiCcxuz7gRnAac65rLIJR0REYk04jdnN8LrHKkmIiBxGwkkUC9l/oiIRETkMhJMo/gHcambNyyoYERGJPeG0UXTBuzBuvpl9Aiyj6F5Pf49UcCIiEn3hJIqHQv6+opgyDlCiEBGpRMJJFG1KLiIiIpVNqRKFP8T4MGCSc04TCImIHEZK1ZjtnNsB/In9R3sVEZHDQDi9npYAGpdZROQwE06ieAG4wczqlVUwIiISe8JpzN4GbAIWmtkbwGJgZ+FCzrk3IxSbiIjEgHASxYiQv/+vmDIOb3RZERGpJMJJFAPLLAoREYlZ4cxHMbYsAxERkdgUTmO2iIgchsKpegLAzFLx5s6uw4GJRmM9iYhUMuHMcJcEfAycChhew7X5q13IMiUKEZFKJJyqpwfxksQjeA3bhjesxxBgPDAF6BrpAEVEJLrCSRQXAB845x4E5vjLVvljPw0CEoGrIxueiIhEWziJogVQ0POpYB6KRADnXC7wLnBJ5EITEZFYEE6i2MZvbRrbgHygacj6LWgsKBGRSifcQQE7Ajjn8oC5eNVRmJkB5wEZkQ5QRESiK5xEMQY438zi/cf/BQab2RK8cZ8GAa9GOD4REYmycK6jeBR4C79LrHPuBTOrhjctah7wMvB4xCMUEZGoCmcIj+3AwkLLngKeinRQIiISOzSEh4iIBAorUZhZCzN7zcwyzWyPmZ3kL2/gLz+ybMIUEZFoKXWiMLM2QBpwPl6Pp4JGbZxzG4BU4PpIBygiItEVTmP2I3jXTnQHdgHrC63/CjgrQnGJiEiMCKfqaRDwgnMuA2/wv8JWAM0jEpWIiMSMcBJFbWBNwPpEDmLYchERiW3hJIoMoFvA+qOA9EMLR0REYk04ieJj4Foz6x6yzAGY2fnAhcD7EYxNRERiQDiJ4hEgE5gE/A8vSdxrZhPxEsRM4MmIRygiIlFV6kThnNsKHA28gtcV1oBTgE7AC8BA51xOWQQpIiLRE1bjs58s7gDuMLMGeMlig3OuqF5QIiJSCRx0LyX/IjsREankwk4UZnYRcC7Q1l+0FPjEOaeGbBGRSqjUicLMqgOjgJPwqpyy/fsjgYvM7CbgbOfcjjKIU0REoiScXk//AE4GngWaOufqOufq4E2H+iwwEK9nlIiIVCLhJIqLgQ+cc3c659YWLHTOrXXO3Ql85JcREZFKJNwhPH4MWP+DX0ZERCqRcBLFLKBDwPoOwOxDC0dERGJNOIniAeAGMztgKHEzG4o3F8WfIhWYiIjEhnC6x14OLAM+NbOFwHy8YTy64l2dPRu4wsyuCHmOc85dF6lgRUSk/IWTKK4O+buzfwvVw7+FcoAShYhIBVbqROGcC2t+bRERqRz05S8iIoEOeqwnM6sC9AOaAfOcc3MjFpWIiMSMwDMKMxtgZs+YWeNCy9sAU4HxwEhglpm9VnZhiohItJRU9XQ1MDT0SmzfG8ARwC/A08A8YJiZDYt4hCIiElUlJYojgc9DF5hZZ+A4YJxz7njn3N14VVCLgavKJEoREYmakhJFE2BRoWUD8Lq9vlKwwDm3C3iHA7vHiohIBVdSoqgK7Cq07Ej/fmyh5RlAcjg7N7PBZrbQzNLN7N4i1l9uZrP82y9m1jOc7YuIyKErKVGsBLoVWnYcsN45l1FoeXW8OSpKxczigeeBIXhXd19qZl0LFVsGnOic6wH8HXiptNsXEZHIKClRjAeuMrMjAMzsXLzB/74uouwRwKow9t0PSHfOLXXO7cHrPTU0tIBz7hfn3Gb/4a9A8zC2LyIiEVBSovgnXvXTDDNbD3wI7AGeDC3knx2cDUwIY9/N8KqrCmT6y4pzHUUnKBERKUOBicI5tww4EfgK2Ij3RT2giIvrBvrrR4Wxbytql0UWNBuIlyjuKWb9jWaWZmZpGzZsCCMEEREpSYlXZjvn0oADhhYvVGYMXtVTODKBFiGPmwOrCxcysx54PayGOOc2FrP/l/DbL1JTU4tMNiIicnCiOdbTFKCDmbUxs0TgEuCz0AJm1hL4GLjSOVe4m66IiJSDgx7r6VA553LN7HbgWyAeeM05N9fMbvbXDwceBOoBL5gZQK5zLjVaMYuIHI7MucpVU5OamurS0tKiHYaISIViZlOL+yGuYcZFRCSQEoWIiARSohARkUBKFCIiEkiJQkREAilRiIhIICUKEREJpEQhIiKBlChERCSQEoWIiARSohARkUBKFCIiEkiJQkREAilRiIhIICUKEREJpEQhIiKBlChERCSQEoWIiARSohARkUBKFCIiEkiJQkREAilRiIhIICUKEREJpEQhIiKBlChERCSQEoWIiARSohARkUBKFCIiEkiJQkREAilRiIhIICUKEREJpEQhIiKBlChERCSQEoWIiARSohARkUBKFCIiEkiJQkREAilRiIhIICUKEREJpEQhIiKBlChERCSQEoWIiARSohARkUBKFCIiEkiJQkREAilRiIhIICUKEREJpEQhIiKBlChERCSQEoWIiARSohARkUBKFCIiEkiJQkREAilRiIhIICUKEREJpEQhIiKBopoozGywmS00s3Qzu7eI9WZmz/jrZ5lZn2jEKSJyOItaojCzeOB5YAjQFbjUzLoWKjYE6ODfbgReLNcgRUQkqmcU/YB059xS59weYCQwtFCZocCbzvMrkGJmTco7UBGRw1k0E0UzICPkcaa/LNwyIiJShqpEcd9WxDJ3EGUwsxvxqqYAtpvZwoOMqT6QdZDPLWuxGlusxgWxG1usxgWxG5viCl+4sbUqbkU0E0Um0CLkcXNg9UGUwTn3EvDSoQZkZmnOudRD3U5ZiNXYYjUuiN3YYjUuiN3YFFf4IhlbNKuepgAdzKyNmSUClwCfFSrzGXCV3/vpKGCLc25NeQcqInI4i9oZhXMu18xuB74F4oHXnHNzzexmf/1w4CvgdCAd2AlcE614RUQOV9GsesI59xVeMghdNjzkbwfcVo4hHXL1VRmK1dhiNS6I3dhiNS6I3dgUV/giFpt538UiIiJF0xAeIiISSInCV9JwIuUYRwsz+9HM5pvZXDO7w1/+kJmtMrMZ/u30KMW33Mxm+zGk+cvqmtl3ZrbYv69TzjF1CjkuM8xsq5ndGa1jZmavmdl6M5sTsqzYY2Rm9/mfu4Vmdlo5x/UvM1vgD5HziZml+Mtbm9mukGM3vNgNl11sxb5/UT5m74XEtNzMZvjLy+2YBXxPlM3nzDl32N/wGtOXAG2BRGAm0DVKsTQB+vh/1wIW4Q1x8hBwdwwcq+VA/ULLHgfu9f++F3gsyu/lWrw+4VE5ZsAJQB9gTknHyH9vZwJVgTb+5zC+HOM6Faji//1YSFytQ8tF6ZgV+f5F+5gVWv8k8GB5H7OA74ky+ZzpjMJTmuFEyoVzbo1zbpr/9zZgPrF/NfpQ4A3/7zeAc6IXCicDS5xzK6IVgHNuHLCp0OLijtFQYKRzbrdzbhleD79+5RWXc260cy7Xf/gr3rVK5a6YY1acqB6zAmZmwEXAu2Wx7yAB3xNl8jlTovDE5FAhZtYa6A1M8hfd7lcRvFbe1TshHDDazKb6V8QDNHL+9S3+fcMoxQbe9Tih/7ixcMyg+GMUS5+9a4GvQx63MbPpZjbWzI6PUkxFvX+xcsyOB9Y55xaHLCv3Y1boe6JMPmdKFJ5SDRVSnsysJvARcKdzbiveyLntgF7AGrxT3mg41jnXB29k39vM7IQoxXEA8y7cPBv4wF8UK8csSEx89szsfiAXeNtftAZo6ZzrDdwFvGNmtcs5rOLev5g4ZsCl7P+jpNyPWRHfE8UWLWJZqY+ZEoWnVEOFlBczS8B78992zn0M4Jxb55zLc87lAy9TRqfaJXHOrfbv1wOf+HGsM39UX/9+fTRiw0te05xz6/wYY+KY+Yo7RlH/7JnZMOBM4HLnV2j7VRQb/b+n4tVpdyzPuALev1g4ZlWA84D3CpaV9zEr6nuCMvqcKVF4SjOcSLnw6z1fBeY7554KWR46vPq5wJzCzy2H2GqYWa2Cv/EaQufgHathfrFhwKjyjs233y+8WDhmIYo7Rp8Bl5hZVTNrgzf3yuTyCsrMBgP3AGc753aGLG9g3pwxmFlbP66l5RWXv9/i3r+oHjPfIGCBcy6zYEF5HrPivicoq89ZebTQV4Qb3lAhi/B+BdwfxTiOwzslnAXM8G+nA28Bs/3lnwFNohBbW7yeEzOBuQXHCagHfA8s9u/rRiG26sBGIDlkWVSOGV6yWgPsxfsld13QMQLu9z93C4Eh5RxXOl7ddcFnbbhf9nz/PZ4JTAPOisIxK/b9i+Yx85ePAG4uVLbcjlnA90SZfM50ZbaIiARS1ZOIiARSohARkUBKFCIiEkiJQkREAilRiIhIICUKqTDM7Gozc2Y2IAZi+cnMlkc7jlhlZtX8kVUfKaf9tfY/Gw+Vomyp3zsz62Vm+WZ24qHGWJEpUVQw/j/grWb2g5ltMLO9ZpZtZlPM7DEz6xztGCsSMxvhf8EU3HLMbJ2ZjTOzR/wLpyK5v17mDZ/dOpLbjUF3ASnAE6ELzewiM3vdzGb6n10XdCzMLNnMnjVvuPEcf0jtW/wLzsqcc24G8CnwZHntMxYpUVQg/pfWNOB5vPfuaeBG4M94F95cC8w1s6gPaFhG3gKSgHFlsO1bgCuBm4F/4Q1V/v+A+WZ2VxHlTwU6HcR+egF/wRuSulIysyTgD8DrzrnNhVbfijfywS68i7+CtpMIfIf3nrwH/A7vYrEX8I5hefk30BfvgrbDUlTnzJbS8//5vsQbJO0859wnRZSpBvwfUR7QsKw45/KAvDLa/IfOuazQBWbWEvgC79fkKudc6Lg+e8oojsrgMryziTeLWHcVsNo5l2tmzxGcbK8HjgR+75x71l/2spl9BPzJzF535TOc/Hi8eVhuxvsfPOzojKLiuB7oDPyrqCQB4JzLcc790/kD9xXwT98fM292q91+ldW7hatVQtoATjKzu81siV9+kT9w3AHM7Hozm2bezF5bzGy0mR1XRDnnV/OcZGYTzWynmWWa2T3++jpm9qp5s4ntNLMvzKxpMfENKLQ80cz+aN6sYjv9ONLM7PaSD2vxnHMrgQuAfGC/uvai6rnNrJuZfeBXk+w2s7XmzUJ2hr/+IeB1v/iPIdVdI/z1tczsYTObZGZZ/jbSzexRM6teaF8D/OdebWbX+FUyu81shZn9sajXY2a9/fjW+WUz/M9Bu0LlBvnvY7Zf3TPLzG4O49BdCKx1zk0vvMI5t9L9Nv9FSS4DduINCBjq30ACcHFJGzCz08xsm5mNt0LDzJtZWzMb5X9etpo3w98BVY3OG77iW2CweaO1HnZ0RlFxXODfvxLOk8wsGfgFaAm8hjcWTRO8KoBJZpZaxK+yf+BV8fwX2I1XLTPCzNKdcz+HbPsx4I94g4v9CW+mrRvxvgSHOue+KrTd3sBZwEt4vzYvAh41sxy8AcyW481q1h74vV9mUAmvLxHvn3gAMBr4H5ADHIE3uudzJRyiQM65RWY2HjjRzDo55xYWE0c94Af/4XBgBVAfSAX64/0S/Rjv2N+Id4zn++ULqmCa4f0g+Ah4B2/Y7xPxjnFvoKjpK28GGuENEJcNXAE8ZmaZzrl3QuI709/uDrzPUDrQ2N9m94IYzJtjZDjeJEaP+OVPAV40s3bOuT8EHS/zBsU7BvgxqFxJzCwOb2a5ac65nEKrJ+Ml7yNL2MYwvNf6OXBZoe3U8GOcDNyHN0jercBRZtbbObe20OYmAjfhjbH0zUG9qIqsLAf60i2ig4BtBLYUsTwe7wsp9JYUsv4/ePXBPQs9rxWwFRgRsuxqvGqr6UBiyPJmeAnj3ZBlnfD+WScUKtsU7wtrOSFTLfrbzQf6hyxLxBtwLR94plB8T/nP6VREfANClv3RX/aPIo5NXCmO6wj/+fUDyjzjlzkrZNlPwPKQx2f7ZS4qYX8HvIZCxyOhiOV/95/TL2TZAH/ZaiAlZHl1YAMwsYhl64FmxR0nvCSWA7xTRJn/4FX7tSvh9bXx43qqFMf+Ob9s6yLW1fPXvVfMc9cDv4Q8bu2Xf8h/fK//+IXCnwP/vXPAvwstP9dfPryI/RUMwvf/Svs/W5luqnqqOGrjfbEX1gXvSyD0dhvsG4r4crzG31VmVr/ghvdL8Ve8RtnCXnAhdfDOuVV4I+t2CCkzFG8ylMcLlV2N9+XbCu9XcKiJzrlJIWX34P2iM7wv41Dj/fsOBLsc2Az8rfAK581jEAkFxz1oEpot/v0QO8jJapxze5xze8Gb78CvjqsPjPGL9C/iaa8757JDtrET730NPW6n4f2AeNJ/Lwvvt+A4XYA3p/KroZ8VP4bP8aqqTy7hZTTw70s7rWlxCqradhezPiekTKg489o+/gn82Tl3a8Dn4NHQB86r0l1I0VP5bvTvozl7Y9So6qni2ErRX1TL8KoGAHqyf3fEBni/zE7FSyBFKeqfqKgx9DfiffkXaOPfzy2ibMG8AW2BtBK2W9ArZlkxy+sV8ZxQHYAZ7sDqiUgqOO7FziDmnBtrZm/inTFcbmZT8L7g33POzSvtjszsVrzqpG4c2IZY1FSuxb1XocetIGkc0GZQSBf/fkxAmUYlbKOgI8WhdiUtmBujajHrq4WUCXUnXhXo/c65fwRsP9sdWL0EXnXgOWZWwzm3I2R5weuplB1FSqJEUXHMAU4wszbOmxwdAP/DPAbAzAo3EhZ8uMcAj4Wxr+J6Flkxfx/qdnFej6aS9lns0w8ilnD08O+LbJ/YF4Rzw8zsX3jdKI/D6157v5nd6Zwrsa3EvG64T+K1tTyDV620B6/qbwRFdz4pTS+w0n7JFZS7Cq9KsCglTcRT8IOkbiniCrIZr8r0gK7eZlYVLxGOLeJ53wEnADeZ2UjnXHHxFncsivu8Fbye4n5wVWpKFBXHh3j/ANfjTUBSGhvw2gtqO+eCfiUejIIG2G4c2B++q39fHjOiLQK6mFlV51xx1RQHzcw6AscDi51zi0oq75ybg5fUHzezFLwJ7x81s+edV9kd9GV9JV7bzpDQ6hLzZqE7FAUJrjfeF2lxFvv3WYfwecnAO/MqqcowkHMu38ymAb2LeG/74SXNtCKeOht4EG/SnrFmdpJzbnER5eqYWeMizio6A+sLnU2A18ECojtLYtSojaLieAVYAPzBzM4tpsx+v4b8L5u3gX5mdkGRTzA72DrXz/C+9P5g3ty9BdtrAlyD1+unpKqOSHgbr0rmgcIr/Daag2bedRQf4P2fBCZnM6vr99TZx287WIZXl17NX7zdvy/qF3ce3jHdF7d5czPfexDhhxoNZAH/z/afXrRgHwX7ex+vTeCv5l23U7hcsv9rvlj+meF4im5PCde7eMfuxkLL78TrEfZ+MTHMxWvsj8dLFsWNVrDfcfX/rzrhXYld2FH+Pn8uYl2lpzOKCsI5t8vvj/8F8LGZ/YT3BbAWrw69M16/8jy8X3UF7geOBd43s/fxGjr34LU3nA5MxatXDzeehX41yx+BcWb2Hr91j60JXB5QnRRJ/8HrcvuAmR2Jd0xy8M50OlFC99oQF5jZdrz/iXp4v1rPxksSdzrnPijh+VcB/2dmn+B1Pd2L17X1NOB959wuv9wUvHah+/1+/TuAZX4j/4d4jbBfm9nHeO/rZf62DppzbqeZXedvf46ZFXSPbeDH9xQwyjmXaWa34P0omW9mb+El/AZ43Y3PwTtbXF7CLj8AzjCzfs65/eZlNrMT8M6Mwes6DHC7mWX7sT4cUvxlvB8dT5k3zMd8vM/sucDDoVWwRbzmBeaNz/QD8JOZnewnkAJZwHnmXavzE791j12H10U7NGYDhgDfOOe2cziKdrcr3cK74V3fcBteH/AsvC+RbLzT8McJ6U4a8pzqeMN8zMar992G90/3Mvt3V72a4rtu/kRId9CQ5TfgnTnk4FU5fAccX0Q5R0hX3JDlI/CvaSq0fID/nKtLig/v1/r9eA3rOf7xmALcWorjOcLfZsFtN17Xy/HAw0DbYp633/HAG5rjDbwv4B3+sZiJ105RtdBzhwHz8BL2vuOC9wv4Pn8bu/G+pB/Ha2Te1/WzuONTimPaD+/Xcpa//ZV412u0LVTuWOAT/zjswWsr+dF/LdVKcUyr4TWoP1vEuocKHe/9bkWUT8HrRrvaj3kecDt40ziHlGtd+Bj5y9viJbb1QI/Q985fN8p/r7b5f7cvIoYT/W2fEe3//2jdNGe2iEScmd2Ll/TaOOcOtatsVPlniS2BVHeYfmGqjUJEysK/8Xou3R3lOA6JmfXCu2borsM1SQA6oxARkWA6oxARkUBKFCIiEkiJQkREAilRiIhIICUKEREJpEQhIiKBlChERCSQEoWIiAT6/yj89M/Xugh8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = 6,6\n",
"plt.plot(spearman_list[mod], label = mod)\n",
"# plt.legend()\n",
"plt.ylabel('Spearman Correlation', fontsize = 18)\n",
"plt.xlabel('Genomic Distance (10kb)', fontsize = 18)\n",
"plt.ylim(0,1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:48:32.707610Z",
"start_time": "2023-06-11T13:48:32.565561Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 1.0)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = 6,6\n",
"plt.plot(roc_list[mod], label = mod)\n",
"plt.legend()\n",
"plt.ylabel('AUROC', fontsize = 18)\n",
"plt.xlabel('Genomic Distance (10kb)', fontsize = 18)\n",
"plt.ylim(0,1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:48:32.854153Z",
"start_time": "2023-06-11T13:48:32.708957Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 1.0)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = 6,6\n",
"plt.plot(prc_list[mod], label = mod)\n",
"plt.legend()\n",
"plt.ylabel('AUPRC', fontsize = 18)\n",
"plt.xlabel('Genomic Distance (10kb)', fontsize = 18)\n",
"plt.ylim(0,1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualize"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:48:37.641298Z",
"start_time": "2023-06-11T13:48:32.855613Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 9520/9520 [00:00<00:00, 16862.47it/s]\n"
]
}
],
"source": [
"pred_mat = get_combined_yhat(y_hat[:,0,:], start_ind = 0, end_ind = y_hat.shape[0], avg_stripe=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T13:54:54.971526Z",
"start_time": "2023-06-11T13:54:54.965976Z"
}
},
"outputs": [],
"source": [
"start = 2000\n"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T14:11:18.173276Z",
"start_time": "2023-06-11T14:11:17.618500Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Prediction\n",
"plt.rcParams['figure.figsize'] = 8, 3\n",
"img = pcolormesh_45deg(np.triu(pred_mat[start:start+700,start:start+700]), 4,-1)\n",
"plt.ylim(0,200)\n",
"plt.xlim(300,500)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T14:11:18.734864Z",
"start_time": "2023-06-11T14:11:18.175094Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Ground Truth\n",
"img = pcolormesh_45deg(np.triu(hicdc_mat[start:start+700,start:start+700]), 7,-1)\n",
"plt.ylim(0,200)\n",
"plt.xlim(300,500)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T14:11:22.114194Z",
"start_time": "2023-06-11T14:11:21.943097Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'CTCF motif score')"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x108 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = 8, 1.5\n",
"plt.plot(ctcf_motif['chr{}'.format(chrom)].toarray()[0][(start)*200 : (start+700)*200])\n",
"plt.xlim(60000, 100000)\n",
"plt.ylim(2.8,3.5)\n",
"plt.ylabel('CTCF motif score')\n"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T14:11:23.980871Z",
"start_time": "2023-06-11T14:11:23.815263Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'ATAC-seq signal')"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x108 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(atac['chr{}'.format(chrom)][(start)*200 : (start+700)*200])\n",
"plt.xlim(60000, 100000)\n",
"# plt.ylim(2.8,3.5)\n",
"plt.ylabel('ATAC-seq signal')"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T14:02:47.371485Z",
"start_time": "2023-06-11T14:02:45.570818Z"
}
},
"outputs": [],
"source": [
"tmp = cpu_batch_corcoeff_vstripe(torch.tensor(scatac['chr{}'.format(chrom)][:, (start)*20 : (start+700)*20].toarray()))\n",
"tmp = tmp.reshape(tmp.shape[0]//20, 20, -1).mean(axis=1).reshape(-1, tmp.shape[1]//20, 20).mean(axis=2)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T14:11:26.471401Z",
"start_time": "2023-06-11T14:11:25.907525Z"
},
"code_folding": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Jaccard similarity\n",
"plt.rcParams['figure.figsize'] = 8, 3\n",
"img = pcolormesh_45deg(tmp, vmax = 0.5, vmin = 0)\n",
"plt.ylim(0,200)\n",
"plt.xlim(300,500)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T14:03:04.275649Z",
"start_time": "2023-06-11T14:03:04.265701Z"
}
},
"outputs": [],
"source": [
"import coolbox\n",
"from coolbox.api import *\n",
"frame = XAxis() + GTF(\"/data/leslie/gaov/database/genome/gencode.vM10.basic.annotation.gtf\", \n",
" length_ratio_thresh = 0.005, fontsize = 32) + TrackHeight(4)\n"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-11T14:03:06.971379Z",
"start_time": "2023-06-11T14:03:06.400380Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1133.86x170.079 with 6 Axes>"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TEST_RANGE = \"chr{}:{}-{}\".format(chrom, (start+300)*200*50, (start+500)*200*50)\n",
"\n",
"frame.plot(TEST_RANGE)\n"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-31T04:31:06.537406Z",
"start_time": "2023-05-31T04:30:56.795051Z"
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 4724.41x826.772 with 6 Axes>"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame = XAxis() + Arcs(\"./peak_interactions/bedpe/{}_hicdc.bedpe\".format(ct), line_width = 0.15) + Inverted()+ TrackHeight(5)\n",
"frame.plot(\"chr16:{}-{}\".format(((start_ind)*20000+40000)*50 , ((start_ind)*20000+40000+40000)*50))\n"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"ExecuteTime": {
"end_time": "2023-05-31T04:31:19.824510Z",
"start_time": "2023-05-31T04:31:06.542598Z"
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 4724.41x826.772 with 6 Axes>"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frame = XAxis() + Arcs(\"./peak_interactions/bedpe/{}_chromafold.bedpe\".format(ct), line_width = 0.15) + Inverted()+ TrackHeight(5)\n",
"frame.plot(\"chr16:{}-{}\".format(((start_ind)*20000+40000)*50 , ((start_ind)*20000+40000+40000)*50))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:anaconda2-pytorch-p37]",
"language": "python",
"name": "conda-env-anaconda2-pytorch-p37-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
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"toc": {
"base_numbering": 1,
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"title_cell": "Table of Contents",
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| Unknown |
3D | viannegao/ChromaFold | process_input/Process Input - scATAC.ipynb | .ipynb | 11,898 | 434 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Process Input scATAC-seq\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T17:39:24.264881Z",
"start_time": "2023-06-13T17:39:19.010602Z"
}
},
"outputs": [],
"source": [
"import random\n",
"import sys\n",
"import numpy as np\n",
"import pysam\n",
"import pandas as pd\n",
"import os\n",
"import itertools\n",
"import scipy\n",
"from scipy.sparse import csr_matrix\n",
"from scipy import sparse\n",
"import pickle\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:35:41.813727Z",
"start_time": "2023-06-13T16:35:41.812033Z"
}
},
"outputs": [],
"source": [
"ct = 'imr90'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:02:54.343232Z",
"start_time": "2023-06-13T16:02:54.337576Z"
}
},
"outputs": [],
"source": [
"frag_path = './rawdata/scatac/fragments.tsv.gz'\n",
"valid_barcode = pd.read_csv('./rawdata/scatac/archr_filtered_barcode.csv', index_col = 0)\n",
"chrom_size = pd.read_csv('../genome/hg38_chrNameLength.txt', \n",
" sep = '\\t', header = None, index_col = 0)\n",
"\n",
"valid_chrom = ['chr1', 'chr2', 'chr3', 'chr4', 'chr5','chr6','chr7',\n",
" 'chr8', 'chr9','chr10','chr11','chr12','chr13','chr14',\n",
" 'chr15','chr16','chr17','chr18','chr19','chr20',\n",
" 'chr21','chr22','chrX']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Process scATAC at 500bp (for co-accessibility input)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:03:27.708274Z",
"start_time": "2023-06-13T16:03:03.926571Z"
}
},
"outputs": [],
"source": [
"frag = pd.read_csv(frag_path, sep = '\\t', header=None)\n",
"dist = frag[2]-frag[1]\n",
"\n",
"# Option to filter cell barcodes using cluster assignments or number of fragments\n",
"# valid_barcode = valid_barcode[valid_barcode['nFrags'] > 1000]\n",
"valid_barcode = valid_barcode.index\n",
"# Filter for valid barcodes only\n",
"frag = frag[frag[3].isin(valid_barcode)]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:16:34.837783Z",
"start_time": "2023-06-13T16:16:29.621449Z"
}
},
"outputs": [],
"source": [
"frag_stacked = pd.concat([frag[[0,1,3,4]],frag[[0,2,3,4]].rename(columns={2:1})], ignore_index=True)\n",
"frag_stacked[4] = 1 #binarize count\n",
"frag_stacked[1] = frag_stacked[1]//500 # aggregate counts per 500bp tile\n",
"\n",
"barcodes, barcode_id = np.unique(frag_stacked[3], return_inverse=True)\n",
"frag_stacked[5] = barcode_id\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:32:58.156263Z",
"start_time": "2023-06-13T16:31:27.117318Z"
},
"scrolled": true
},
"outputs": [],
"source": [
"tile_dict = {}\n",
"for chrom in list(set(frag_stacked[0])):\n",
" if chrom in valid_chrom:\n",
" frag_grouped = frag_stacked[frag_stacked[0]==chrom][[1,5,4]].groupby([5,1]).sum().reset_index()\n",
" frag_grouped = csr_matrix((frag_grouped[4].astype(np.float64), (frag_grouped[5].values, frag_grouped[1].values)), shape=(barcodes.shape[0], chrom_size.loc[chrom].values[0]//500+1))\n",
" tile_dict[chrom] = frag_grouped\n",
" print(chrom)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:36:07.211362Z",
"start_time": "2023-06-13T16:36:06.611045Z"
}
},
"outputs": [],
"source": [
"pickle.dump(tile_dict, open(\"./data/scatac/processed_input/{}_tile_500bp_dict.p\".format(ct), \"wb\"))\n",
"np.save(\"./data/scatac/processed_input/{}_tile_500bp_barcode.npy\".format(ct),barcodes.astype(str))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pseudo-bulk (for evaluation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:36:56.689223Z",
"start_time": "2023-06-13T16:36:52.711999Z"
}
},
"outputs": [],
"source": [
"frag_stacked = pd.concat([frag[[0,1,4]],frag[[0,2,4]].rename(columns={2:1})], ignore_index=True)\n",
"frag_stacked[4] = 1 #binarize counte\n",
"frag_stacked[1] = frag_stacked[1]//50 # aggregate counts per 50bp tile\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:38:26.317134Z",
"start_time": "2023-06-13T16:37:12.876855Z"
}
},
"outputs": [],
"source": [
"tile_dict = {}\n",
"for chrom in list(set(frag_stacked[0])):\n",
" if chrom in valid_chrom:\n",
" frag_grouped = frag_stacked[frag_stacked[0]==chrom][[1,4]].groupby([1]).sum().reset_index()\n",
" frag_grouped = pd.DataFrame(frag_grouped[4].values, index = frag_grouped[1].values).reindex(np.arange(0,chrom_size.loc[chrom].values[0]//50+1)).fillna(0).values\n",
" tile_dict[chrom] = frag_grouped\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:38:26.724725Z",
"start_time": "2023-06-13T16:38:26.318479Z"
}
},
"outputs": [],
"source": [
"import pickle\n",
"pickle.dump(tile_dict, open(\"./data/scatac/processed_input/{}_tile_pbulk_50bp_dict.p\".format(ct), \"wb\"))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Process scATAC at 50bp (for training)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:39:28.837978Z",
"start_time": "2023-06-13T16:39:27.481969Z"
}
},
"outputs": [],
"source": [
"frag_stacked = pd.concat([frag[[0,1,3,4]],frag[[0,2,3,4]].rename(columns={2:1})], ignore_index=True)\n",
"frag_stacked[4] = 1 #binarize counte\n",
"frag_stacked[1] = frag_stacked[1]//50 # aggregate counts per 50bp tile\n",
"\n",
"barcodes, barcode_id = np.unique(frag_stacked[3], return_inverse=True)\n",
"frag_stacked[5] = barcode_id\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T16:44:24.867469Z",
"start_time": "2023-06-13T16:42:47.341348Z"
},
"code_folding": []
},
"outputs": [],
"source": [
"tile_dict = {}\n",
"for chrom in valid_chrom:\n",
" frag_grouped = frag_stacked[frag_stacked[0]==chrom][[1,5,4]].groupby([5,1]).sum().reset_index()\n",
" frag_grouped = csr_matrix((frag_grouped[4].astype(np.float64), (frag_grouped[5], frag_grouped[1])), shape=(barcodes.shape[0], chrom_size.loc[chrom].values[0]//tile_size+1))\n",
" tile_dict[chrom] = frag_grouped\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-28T02:35:25.905455Z",
"start_time": "2022-12-28T02:35:24.455575Z"
}
},
"outputs": [],
"source": [
"import pickle\n",
"pickle.dump(tile_dict, open(\"./data/scatac/processed_input/{}_tile_50bp_dict.p\".format(ct), \"wb\")) # save it into a file named save.p\n",
"np.save(\"./data/scatac/processed_input/{}_tile_50bp_barcode.npy\".format(ct),barcodes.astype(str))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compute metacells"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T18:12:05.711252Z",
"start_time": "2023-06-13T18:12:05.709166Z"
}
},
"outputs": [],
"source": [
"import sys\n",
"from scipy.sparse import coo_matrix, csr_matrix, find\n",
"from sklearn.neighbors import NearestNeighbors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T18:12:06.361742Z",
"start_time": "2023-06-13T18:12:06.289608Z"
},
"code_folding": []
},
"outputs": [],
"source": [
"lsi = pd.read_csv('./data/scatac/archr_filtered_lsi.csv', index_col = 0)\n",
"lsi.index = [x.split('#')[1] for x in lsi.index]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T18:12:10.385853Z",
"start_time": "2023-06-13T18:12:10.381984Z"
},
"code_folding": [
5,
26,
38
]
},
"outputs": [],
"source": [
"def get_max_overlap(y,x):\n",
" return np.max(x.dot(y))\n",
"def generate_cicero_metacell(nbrs, max_overlap, sampled_id = [0]):\n",
" order = np.arange(nbrs.shape[0])\n",
" np.random.seed(10)\n",
" np.random.shuffle(order)\n",
"\n",
" selected_idx = [False]*nbrs.shape[0]\n",
" for i in sampled_id:\n",
" selected_idx[i] = True\n",
" \n",
" for idx in order:\n",
" selected_cells = nbrs[selected_idx]\n",
" candidate = nbrs[idx]\n",
" overlap = get_max_overlap(candidate,selected_cells)\n",
" if overlap < max_overlap:\n",
" selected_idx[idx] = True\n",
" \n",
" return selected_idx\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T18:16:48.034385Z",
"start_time": "2023-06-13T18:16:41.599804Z"
}
},
"outputs": [],
"source": [
"from sklearn.neighbors import NearestNeighbors\n",
"n_neighbors = 100\n",
"max_overlap = 33\n",
"\n",
"nbrs = NearestNeighbors(n_neighbors=n_neighbors, metric='euclidean').fit(lsi.values)\n",
"nbrs = nbrs.kneighbors_graph(lsi.values).toarray()\n",
"selected_idx = generate_cicero_metacell(nbrs, max_overlap = max_overlap)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T18:16:48.039948Z",
"start_time": "2023-06-13T18:16:48.036402Z"
}
},
"outputs": [],
"source": [
"metacell_assignment = nbrs[selected_idx,:]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:anaconda2-py36-sc] *",
"language": "python",
"name": "conda-env-anaconda2-py36-sc-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Unknown |
3D | viannegao/ChromaFold | process_input/Process Input - Hi-C.ipynb | .ipynb | 4,099 | 162 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Scripit to preprocess Hi-C/HiChIP data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T18:02:14.604155Z",
"start_time": "2023-06-13T18:02:14.602054Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import pickle\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.sparse import csr_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 1. Generate normalized .hic file\n",
"- 2. Extract hic matrices:\n",
"```\n",
"for i in {1..21}\n",
"do\n",
" java -jar juicer_tools_1.22.01.jar dump <normalization> NONE imr90.hic $i $i BP 10000 ./zvalue/imr90_chr\"$i\".txt\n",
"done\n",
"```\n",
"- 3. Run this notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Process from individual chrom text files"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-13T18:02:21.139208Z",
"start_time": "2023-06-13T18:02:21.135779Z"
}
},
"outputs": [],
"source": [
"\n",
"def load_hic(path,hic_resolution):\n",
" hic_dict = {}\n",
" for file in os.listdir(path):\n",
" if file.endswith('txt'):\n",
" print(file)\n",
" chrom = file.split('_')[0]\n",
" print(chrom)\n",
" hic = pd.read_table(path+file, header = None)\n",
" start = hic[[0,1]].values.min()\n",
" print(start)\n",
" end = hic[[0,1]].values.max()+hic_resolution\n",
" hic = hic.pivot(0,1,2).fillna(0)\n",
" hic = hic.reindex(columns=np.arange(0,end, hic_resolution).astype(int), index=np.arange(0,end, hic_resolution).astype(int),\n",
" fill_value = 0)\n",
" hic_dict[chrom] = csr_matrix(hic)\n",
"\n",
" return hic_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-11-22T03:36:00.588014Z",
"start_time": "2022-11-22T03:32:10.565929Z"
},
"scrolled": true
},
"outputs": [],
"source": [
"hic_resolution = 1e4\n",
"\n",
"path = './zvalue/'\n",
"hic_dict = load_hic(path,hic_resolution=hic_resolution)\n",
"pickle.dump(hic_dict, open(\"./zvalue/hic_zscore_dict.p\", \"wb\"), protocol=4) # save it into a file named save.p\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-11-22T03:39:36.255431Z",
"start_time": "2022-11-22T03:36:00.590357Z"
}
},
"outputs": [],
"source": [
"hic_resolution = 1e4\n",
"\n",
"path = './qvalue/'\n",
"hic_dict = load_hic(path,hic_resolution=hic_resolution)\n",
"pickle.dump(hic_dict, open(\"./qvalue/hic_qvalue_dict.p\", \"wb\"), protocol=4) # save it into a file named save.p\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:anaconda2-py36-sc] *",
"language": "python",
"name": "conda-env-anaconda2-py36-sc-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
},
"toc": {
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"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Unknown |
3D | viannegao/ChromaFold | process_input/ctcf_motif/get_ctcf_motif_mm10.R | .R | 3,108 | 69 | suppressMessages(library(AnnotationHub))
ah <- AnnotationHub()
#> snapshotDate(): 2022-10-31
query_data <- subset(ah, preparerclass == "CTCF")
# Explore the AnnotationHub object
subset(query_data,
genome == "mm10")
CTCF_mm10_all <- query_data[["AH104753"]]
suppressMessages(library(plyranges))
CTCF_mm10_all <- CTCF_mm10_all %>% keepStandardChromosomes() %>% sort()
#> [1] "Number of CTCF motifs at the 1e-6 threshold: 21671"
# Similarly, filter
CTCF_mm10_all_filtered <- CTCF_mm10_all %>% plyranges::filter(pvalue < 2)
print(paste("Number of CTCF motifs at the 1e-6 threshold:", length(CTCF_mm10_all)))
CTCF_mm10_all_filtered_forward <- CTCF_mm10_all_filtered[CTCF_mm10_all_filtered@strand == '+',]
CTCF_mm10_all_filtered_reverse <- CTCF_mm10_all_filtered[CTCF_mm10_all_filtered@strand == '-',]
# write R code to overlap a GRanges file with genome-wide 50bp bins.
# Load required packages
library(GenomicRanges)
library(GenomeInfoDb)
library(BSgenome.Mmusculus.UCSC.mm10)
mm10_seqinfo <- seqinfo(BSgenome.Mmusculus.UCSC.mm10)
# Extract the chromosome names and lengths from the hg38 genome assembly
chromosomes <- c(mm10_seqinfo@seqnames[0:21])
chrom_lengths <- c(mm10_seqinfo@seqlengths[0:21])
names(chrom_lengths) <- chromosomes
bin_size <- 50
for (chrom in chromosomes){
bins <- GRanges(seqnames = Rle(rep(chrom, floor(chrom_lengths[chrom] / bin_size)+1)),
ranges = IRanges(start = seq(0, floor(chrom_lengths[chrom] / bin_size)) * bin_size,
end = seq(0, floor(chrom_lengths[chrom] / bin_size)) * bin_size + bin_size))
# Find the overlapping bins for each element in the GRanges file
overlapping_bins <- findOverlaps(subject = bins, query = CTCF_mm10_all_filtered_forward, minoverlap = 10, ignore.strand=T)
scores <- CTCF_mm10_all_filtered_forward[queryHits(overlapping_bins)]$score
# Create a binary vector of whether there is an overlap
overlap_vector <- integer(length(bins))
overlap_vector[subjectHits(overlapping_bins)] <- scores
# Save the overlap vector to a binary file
write.csv(overlap_vector,file=paste0('~/data/dna/mm10_ctcf_motif_forward_', chrom,'.csv'),row.names=F)
}
for (chrom in chromosomes){
bins <- GRanges(seqnames = Rle(rep(chrom, floor(chrom_lengths[chrom] / bin_size)+1)),
ranges = IRanges(start = seq(0, floor(chrom_lengths[chrom] / bin_size)) * bin_size,
end = seq(0, floor(chrom_lengths[chrom] / bin_size)) * bin_size + bin_size))
# Find the overlapping bins for each element in the GRanges file
overlapping_bins <- findOverlaps(subject = bins, query = CTCF_mm10_all_filtered_reverse, minoverlap = 10, ignore.strand=T)
scores <- CTCF_mm10_all_filtered_reverse[queryHits(overlapping_bins)]$score
# Create a binary vector of whether there is an overlap
overlap_vector <- integer(length(bins))
overlap_vector[subjectHits(overlapping_bins)] <- scores
# Save the overlap vector to a binary file
write.csv(overlap_vector,file=paste0('~/data/dna/mm10_ctcf_motif_reverse_', chrom,'.csv'),row.names=F)
}
| R |
3D | viannegao/ChromaFold | process_input/ctcf_motif/get_ctcf_motif_hg38.R | .R | 3,513 | 79 | suppressMessages(library(AnnotationHub))
ah <- AnnotationHub()
#> snapshotDate(): 2022-10-31
query_data <- subset(ah, preparerclass == "CTCF")
# Explore the AnnotationHub object
subset(query_data, species == "Homo sapiens" &
genome == "hg38")
CTCF_hg38_all <- query_data[["AH104727"]]
CTCF_hg38 <- query_data[["AH104729"]]
suppressMessages(library(plyranges))
CTCF_hg38_all <- CTCF_hg38_all %>% keepStandardChromosomes() %>% sort()
CTCF_hg38 <- CTCF_hg38 %>% keepStandardChromosomes() %>% sort()
# Check length before filtering
print(paste("Number of CTCF motifs at the default 1e-4 threshold:", length(CTCF_hg38)))
#> [1] "Number of CTCF motifs at the default 1e-4 threshold: 887980"
# Filter and check length after filtering
CTCF_hg38_filtered <- CTCF_hg38 %>% plyranges::filter(pvalue < 2)
print(paste("Number of CTCF motifs at the 1e-6 threshold:", length(CTCF_hg38_filtered)))
#> [1] "Number of CTCF motifs at the 1e-6 threshold: 21671"
# Similarly, filter
CTCF_hg38_all_filtered <- CTCF_hg38_all %>% plyranges::filter(pvalue < 2)
CTCF_hg38_all_filtered_forward <- CTCF_hg38_all_filtered[CTCF_hg38_all_filtered@strand == '+',]
CTCF_hg38_all_filtered_reverse <- CTCF_hg38_all_filtered[CTCF_hg38_all_filtered@strand == '-',]
# write R code to overlap a GRanges file with genome-wide 50bp bins.
# Load required packages
library(GenomicRanges)
# Load the hg38 genome assembly
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
# Extract the chromosome names and lengths from the hg38 genome assembly
chromosomes <- unique(seqlevels(txdb))[0:25]
chrom_lengths <- seqlengths(txdb)[chromosomes][0:25]
bin_size <- 50
for (chrom in chromosomes){
bins <- GRanges(seqnames = Rle(rep(chrom, floor(chrom_lengths[chrom] / bin_size)+1)),
ranges = IRanges(start = seq(0, floor(chrom_lengths[chrom] / bin_size)) * bin_size,
end = seq(0, floor(chrom_lengths[chrom] / bin_size)) * bin_size + bin_size))
# Find the overlapping bins for each element in the GRanges file
overlapping_bins <- findOverlaps(subject = bins, query = CTCF_hg38_all_filtered_forward, minoverlap = 10, ignore.strand=T)
scores <- CTCF_hg38_all_filtered_forward[queryHits(overlapping_bins)]$score
# Create a binary vector of whether there is an overlap
overlap_vector <- integer(length(bins))
overlap_vector[subjectHits(overlapping_bins)] <- scores
# Save the overlap vector to a binary file
write.csv(overlap_vector,file=paste0('~/data/dna/hg38_ctcf_motif_forward_', chrom,'.csv'),row.names=F)
}
for (chrom in chromosomes){
bins <- GRanges(seqnames = Rle(rep(chrom, floor(chrom_lengths[chrom] / bin_size)+1)),
ranges = IRanges(start = seq(0, floor(chrom_lengths[chrom] / bin_size)) * bin_size,
end = seq(0, floor(chrom_lengths[chrom] / bin_size)) * bin_size + bin_size))
# Find the overlapping bins for each element in the GRanges file
overlapping_bins <- findOverlaps(subject = bins, query = CTCF_hg38_all_filtered_reverse, minoverlap = 10, ignore.strand=T)
scores <- CTCF_hg38_all_filtered_reverse[queryHits(overlapping_bins)]$score
# Create a binary vector of whether there is an overlap
overlap_vector <- integer(length(bins))
overlap_vector[subjectHits(overlapping_bins)] <- scores
# Save the overlap vector to a binary file
write.csv(overlap_vector,file=paste0('~/data/dna/hg38_ctcf_motif_reverse_', chrom,'.csv'),row.names=F)
}
| R |
3D | viannegao/ChromaFold | process_input/hic_normalization/without_installation/hicdcplus_pipeline.R | .R | 20,371 | 524 | #library(BSgenome.Hsapiens.UCSC.hg19)
library(BSgenome)
library(dplyr)
library(Rcpp)
library(base)
library(data.table)
library(dplyr)
library(tidyr)
library(GenomeInfoDb)
library(stats)
library(MASS)
options("scipen"=100, "digits"=4)
get_chr_sizes<-function(gen="Hsapiens",gen_ver="hg19",chrs=NULL){
genome <- paste("BSgenome.", gen, ".UCSC.", gen_ver, sep = "")
library(genome, character.only = TRUE)
if (is.null(chrs)) {
#get list of chromosomes
chrs<-get_chrs(gen,gen_ver)
chrs <- chrs[!chrs %in% c('chrY', 'chrM')]
}
sizes<-GenomeInfoDb::seqlengths(get(gen))[chrs]
return(sizes)
}
get_chrs<-function(gen="Hsapiens",gen_ver="hg19"){
genome <- paste("BSgenome.", gen, ".UCSC.", gen_ver, sep = "")
library(genome, character.only = TRUE)
chrs <- GenomeInfoDb::seqnames(get(gen))[
vapply(GenomeInfoDb::seqnames(get(gen)), nchar, FUN.VALUE = 1) <= 5]
chrs <- chrs[!chrs=='chrMT']
return(chrs)
}
#' hic2hicdc
#'
#' This function converts a .hic file into a HiC-DC readable matrix file for
#' each chromosome
#'@import BSgenome
#'@importFrom dplyr %>%
#'@importFrom rlang .data
#'@param hic_path path to the .hic file
#'@param bintolen_path path to the relevant feature file
#'@param gen name of the species: e.g., default Hsapiens
#'@param gen_ver genomic assembly version: e.g., default hg19
#'@param binsize binsize in bp, e.g., default 5000
#'@param Dthreshold maximum distance of interactions e.g., default 2000000
#'@param inter Interchromosomal interaction counts added as a covariate or not;
#'default FALSE
#'@param chrs select a subset of chromosomes' e.g., c("chrY","chrM"). Defaults
#'to all chromosomes in the genome.
#'@param output_path the path to the folder you want to place HiCDC-compatible
#'matrices into. Each file will have the .hic file preamble as its name prefix.
#'@param bin_type 'Bins-uniform' if uniformly binned by binsize in
#'bp, or 'Bins-RE-sites' if binned by number of
#'restriction enzyme fragment cutsites!
#'@return paths of a list of matrix files generated for each chromosome
#'readable by HiC-DC
#'@examples hic2hicdc(hic_path="~/HiCDCPlus_files/mm10/cb_ctrl1_map30_raw.hic",
#' bintolen_path=
#' "~/HiCDCPlus_files/mm10_features/features_combo_5kb_bintolen.rds",
#' straw_path="~/Downloads/straw-R.cpp",
#' gen="Mmusculus",gen_ver="mm10",binsize=5000,
#' inter=FALSE,Dthreshold=2000000,chrs=c("chr1"))
#'@export
hic2hicdc <-
function(hic_path,
bintolen_path,
straw_path,
gen = "Hsapiens",
gen_ver = "hg19",
binsize = 5000,
Dthreshold = 2000000,
bin_type = "Bins-uniform",
inter = FALSE,
chrs = NULL,
output_path) {
options(scipen = 9999)
#get straw
suppressWarnings({Rcpp::sourceCpp(path.expand(straw_path))})
#get list of chromosomes and their sizes
if (is.null(chrs)) {
chrom <- get_chrs(gen,gen_ver)
} else{
chrom <- chrs
}
genome_chromSizes <- data.frame(chr = chrom,
size = get_chr_sizes(gen,gen_ver,chrom))
#chrs <- gsub('chr', '', chrom) ######################### change chrom format
chrs <- chrom
#get bintolen
if (base::grepl('.txt',bintolen_path,ignore.case = TRUE)){
bintolen <- data.table::fread(bintolen_path,
sep = "\t",
header = TRUE,
stringsAsFactors = FALSE
)
}else if (base::grepl('.rds',bintolen_path,ignore.case = TRUE)){
bintolen<-readRDS(bintolen_path)
}
#prepare bintolen
bintolen <- bintolen %>% tidyr::separate("bins",
c("chr", "start", "end"),
sep = "-",
remove = FALSE,
convert = TRUE,
extra = "drop",
fill = "warn"
)
#modify output path (get string between last / and .)
hic_preamble <- rev(regmatches(hic_path,regexec('(.*?)/(.*?)\\.',
hic_path))[[1]])[1]
output_path <- paste0(output_path, hic_preamble)
filepaths = NULL
for (chr in chrs) {
print(paste0("Processing chromosome: ", chr))
#process bintolen for the chr
#filter bintolen to the chr
bintolen_chr <- bintolen[bintolen$chr == chr, ]
chrom_size <- genome_chromSizes[
genome_chromSizes$chr == chr, ]$size
#get all possible start values for the chr and complete bintolen
starts <- data.frame(start = seq(1,
genome_chromSizes[genome_chromSizes$chr == chr, ]$size,
binsize))
bintolen_chr <- dplyr::left_join(starts, bintolen_chr)
rm(starts)
bintolen_chr<-bintolen_chr%>%tidyr::replace_na(list(gc=0,map=0,len=0,
chr=chr))
bintolen_chr<-bintolen_chr%>%dplyr::mutate(
start=floor(.data$start/1000)*1000,
end=pmin(.data$start+ binsize, chrom_size),
mid=(.data$start + .data$end) / 2,
bin=paste0(.data$chr,"-",.data$start,"-",.data$end),
bins=NULL)
#get all bin combinations within distance threshold
print("Generating the features matrix")
numbins<-nrow(bintolen_chr)
maxbins<-ceiling(Dthreshold/binsize)
index1<-unlist(sapply(1:numbins,
function(x) rep(x,min(maxbins+1,numbins-x+1))))
index2<-unlist(sapply(1:numbins,
function(x) seq(x,min(x+maxbins,numbins),1)))
bintolen_chr<-dplyr::bind_cols(
(bintolen_chr%>%dplyr::rename_all(
function(x) paste0(x,"I")))[index1,],
(bintolen_chr%>%dplyr::rename_all(
function(x) paste0(x,"J")))[index2,]
)
rm(index1,index2)
bintolen_chr<-bintolen_chr%>%dplyr::mutate(D=abs(.data$midI-.data$midJ))
bintolen_chr<-bintolen_chr%>%dplyr::filter(D<=Dthreshold)
cols_bintolen_chr<-c("binI","binJ","chrI","chrJ","startI","startJ",
"endI","endJ","gcI","gcJ","mapI","mapJ","lenI","lenJ","D")
bintolen_chr<-bintolen_chr[cols_bintolen_chr]
gc()
print("Generated. Incorporating counts.")
count_matrix <- straw(
norm = 'NONE',
fname = path.expand(hic_path),
binsize = binsize,
chr1loc = chr,
chr2loc = chr,
unit = 'BP')
colnames(count_matrix) <- c("startI", "startJ", "counts")
count_matrix <-count_matrix %>% dplyr::mutate(
chrI = chr,
chrJ = chr,
startI = .data$startI,
startJ = .data$startJ
)
bintolen_chr <- dplyr::left_join(bintolen_chr,
count_matrix) %>%
tidyr::replace_na(list(counts = 0))
rm(count_matrix)
if (inter) {
print("Incorporated. Incorporating interchromosomal counts.")
count_matrix <- NULL
for (chr2 in chrs) {
if (chr2 != chr) {
count_matrix_add <- unique(
straw(
norm = 'NONE',
fname = path.expand(hic_path),
binsize = binsize,
chr1loc = chr,
chr2loc = chr2,
unit = 'BP'))
xmax <- max(count_matrix_add[, 1])
ymax <- max(count_matrix_add[, 2])
if (abs(chrom_size - xmax) <= abs(chrom_size - ymax)) {
count_matrix_add <- count_matrix_add[, c(1, 3)]
} else {
count_matrix_add <- count_matrix_add[, c(2, 3)]
}
colnames(count_matrix_add) <- c("start", "inter")
count_matrix <- dplyr::bind_rows(count_matrix, count_matrix_add)
}
}
rm(count_matrix_add)
count_matrix <- count_matrix %>% dplyr::group_by(.data$start) %>%
dplyr::summarize(inter = sum(.data$inter)) %>%
dplyr::mutate(start = .data$start)
bintolen_chr <-
dplyr::left_join(bintolen_chr,
count_matrix %>%
dplyr::rename("startI" = "start", "interI" = "inter")) %>%
tidyr::replace_na(list(interI = 0))
bintolen_chr <-
dplyr::left_join(bintolen_chr,
count_matrix %>%
dplyr::rename("startJ" = "start", "interJ" = "inter")) %>%
tidyr::replace_na(list(interJ = 0))}
print("Incorporated. Printing to file.")
filepath <- paste0(output_path, '_', binsize/1000, 'kb_chr', chr, '_matrix.txt')
data.table::fwrite(bintolen_chr,filepath,
quote = FALSE,
sep = "\t",
row.names = FALSE)
rm(bintolen_chr)
gc()
print(paste0("Chromosome: ", chr, " processed."))
filepaths <- append(filepaths, paste0(filepath))
}
return(filepaths)
}
hicdcplus_model=function(file="matrix.rds",
bin_type = 'Bins-uniform',
gen = "Hsapiens",
gen_ver = "hg19",
covariates = NULL,
distance_type = 'spline',
output_path = "K562_combined",
chrs = "chr1",
df = 6,
Dmin = 0,
Dmax = 2e6,
ssize = 0.01,
model_file = FALSE){
remove_outliers_nb <- function(dat, mod) {
mu <- stats::predict(mod, newdata = dat, type = 'response')
dat <- dat %>% dplyr::mutate(q = stats::qnbinom(0.975,size = mod$theta, mu = mu))
new.dat <- dat %>% dplyr::filter(.data$counts <= .data$q) %>% dplyr::select(-.data$q)
return(new.dat)
}
glm.nb.trycatch<- function(model.formula,data,theta.init=0.0001){
model<-tryCatch({
# try fitting distribution as requested by user
strip_glm(MASS::glm.nb(
model.formula,
data))
}, error = function(e) {
temp.model <- MASS::glm.nb(
model.formula,
data,
init.theta = theta.init)
return(temp.model)
}
)
return(model)
}
GLM_nb <- function(data,df,bdpts,covariates,distance_type) {
if (distance_type == 'spline') {
model.formula<-stats::as.formula(paste0("counts ~",paste(covariates, collapse = " + ")," + splines::bs(D,df=",df,",Boundary.knots = c(",paste(bdpts, collapse = ","),"))"))
} else {
model.formula<=stats::as.formula(paste0("counts ~", paste(covariates, collapse = " + "), " + logD"))
}
# Remove outliers
print(2)
tmp_data <<- data
mod<-glm.nb.trycatch(model.formula,data)
new.dat <- remove_outliers_nb(data, mod)
print(3)
# Refit the model
mod<-glm.nb.trycatch(model.formula,new.dat,theta.init = mod$theta)
print(4)
return(mod)
}
Normalize <- function(x) {
x.new <- ifelse(is.finite(x), (x -
base::mean(x[is.finite(x)])) / stats::sd(x[is.finite(x)]), NA)
return(x.new)
}
transform.vec <- function(x, y)
as.vector(ifelse(x==0 | y==0, 0, Normalize(log(x * y))))
strip_glm = function(m1) {
m1$data <- NULL
m1$y <- NULL
m1$linear.predictors <- NULL
m1$weights <- NULL
m1$fitted.values <- NULL
m1$model <- NULL
m1$prior.weights <- NULL
m1$residuals <- NULL
m1$effects <- NULL
m1$qr$qr <- NULL
m1$optim <- NULL
m1$start <- NULL
return(m1)
}
# Main body of algorithm
options("scipen" = 9999, "digits" = 4)
#set default covariates if need be
if (is.null(covariates)) {
covariates <- c("gc", "map", "len")
}
if (bin_type=='Bins-RE-sites' & is.null(covariates)) {
covariates <- c("gc", "map", "width")
}
#set default chrs if need be
if (is.null(chrs)) {
#get list of chromosomes
chrs<-get_chrs(gen,gen_ver)
chrs <- chrs[!chrs %in% c('chrY', 'chrM')]
}
new.x <- data.table::fread(paste0(file)) # reading in data
new.x <- new.x %>% dplyr::filter(.data$D >= Dmin & .data$D <= Dmax)
binsize<-min(new.x$D[new.x$D>Dmin&new.x$D%%1000==0])-Dmin
bdpts <- range(new.x$D)
ix <- rep(TRUE, nrow(new.x))
for (cov in covariates) {
result <- tryCatch({
x <- as.numeric(unlist(new.x[, paste0(cov, 'I'), with = FALSE]))
y <- as.numeric(unlist(new.x[, paste0(cov, 'J'), with = FALSE]))
list(x = x, y = y)}, error = function(e) {
x <- as.numeric(unlist(new.x[, paste0(cov, 'I')]))
y <- as.numeric(unlist(new.x[, paste0(cov, 'J')]))
return(list(x = x, y = y))})
new.x[, cov] <- transform.vec(result$x, result$y)
ix <- ix & is.finite(unlist(new.x[, cov, with=FALSE])) #& !is.na(unlist(new.x[, cov, with=FALSE]))
rm(result)
}
dat <- new.x[ix,]
dat <- dat[dat$gcI>0 & dat$gcJ>0 & dat$mapI>0 & dat$mapJ>0,]
rm(ix)
set.seed(1010)
#stratified sampling for training data
zeroPairedBins <- dat %>% dplyr::filter(.data$counts == 0)
countPairedBins <- dat %>% dplyr::filter(.data$counts > 0)
Intervals <- seq(min(dat$D), to = max(dat$D), by = binsize)
rm(dat)
cls.counts <- findInterval(countPairedBins$D, Intervals,
rightmost.closed = TRUE)
cls.zeros <- findInterval(zeroPairedBins$D, Intervals,
rightmost.closed = TRUE)
bins.counts <-split(seq_len(nrow(countPairedBins)), cls.counts)
bins.zeros <- split(seq_len(nrow(zeroPairedBins)), cls.zeros)
idx.counts <- unlist(lapply(bins.counts, function(x) {
sample(x, floor(length(x) * ssize), replace = FALSE)}))
idx.zeros <- unlist(lapply(bins.zeros, function(x) {
sample(x, floor(length(x) * ssize), replace = FALSE)}))
dat <- dplyr::bind_rows(countPairedBins[idx.counts, ],zeroPairedBins[idx.zeros, ])
rm(cls.counts,cls.zeros,bins.counts,bins.zeros,idx.counts,idx.zeros,Intervals)
print(0)
stime <- system.time({fit <- GLM_nb(dat, df, bdpts, covariates, distance_type)})[3] / 60
#normal model
print(1)
mu <- stats::predict(fit,newdata = new.x,dispersion = fit$theta ** (-1),type = "response")
sdev <- sqrt(mu + mu ** 2 / fit$theta)
ix <- !is.finite(mu)
new.x$pvalue <- 1
new.x$pvalue[!ix] <- stats::pnbinom(q = new.x$counts[!ix] - 1,size = fit$theta,mu = mu[!ix],lower.tail = FALSE)
new.x <- new.x %>% dplyr::mutate(mu = mu, sdev = sdev)
new.x$qvalue <- 1
new.x$qvalue[!ix] <- stats::p.adjust(new.x$pvalue[!ix],
method = 'fdr')
new.x <- new.x %>%
dplyr::mutate(
normcounts = .data$counts / .data$mu,
zvalue = (.data$counts - .data$mu) / .data$sdev ) %>%
tidyr::replace_na(list(mu = 0,pvalue = 1,qvalue = 1,sdev = 0,zvalue = 0,
normcounts = 0))
default_columns_prefix <- do.call(paste0,
expand.grid(c('bin', 'bin_id', 'chr', 'start', 'end'), c('I', 'J')))
columns_add <- sort(do.call(paste0,
expand.grid(covariates, c('I', 'J'))))
default_columns_suffix <- c('D','counts','pvalue','qvalue','normcounts',
'zvalue',
'mu',
'sdev')
column_set<-unique(c(
default_columns_prefix,
columns_add,
default_columns_suffix))
column_set<-column_set[column_set%in%colnames(new.x)]
new.x <- new.x[, column_set, with=FALSE]
if (model_file){
fitpath<-paste0(output_path, "_Model-Fit_on_", chrs, ".rds")
saveRDS(fit, file = fitpath)
}
saveRDS(new.x,paste0(output_path, "_Results_on_", chrs, ".rds"))
return(paste0("Saved results: ", output_path, "_Results_on_", chrs, ".rds"))
}
#' hicdc2hic
#'
#' This function converts HiC-DC Plus significant counts (uniformly binned)
#' for a set of chromosomes back into a .hic file
#'@import BSgenome splines
#'@importFrom dplyr %>%
#'@importFrom rlang .data
#'@param input_path the path to the folder and name prefix where
#'HiCDC-processed matrix text files for each chromosome reside.
#'Should have the same value as \code{output_path}
#'for \code{HiCDCPlus}.
#'@param jarfile path to the Juicebox .jar file. Juicebox command line tools
#'can be downloaded from:
#'\url{https://github.com/aidenlab/juicer/wiki/Download}
#'@param binsize binsize in bp, e.g., default 5000
#'@param gen name of the species: e.g., default \code{'Hsapiens'}
#'@param gen_ver genomic assembly version: e.g., default \code{'hg19'}
#'@param mode What to put to the .hic file
#'as score. Allowable options are: 'pvalue' for -log10 significance p-value,
#''qvalue' for -log10 FDR corrected p-value,
#''normcounts' for raw counts/expected counts, and
#''zvalue' for standardized counts and 'raw' to pass-through
#'raw counts. Defaults to 'zvalue'.
#'@param output_file the path to the .hic file
#'@param chrs select a subset of chromosomes' e.g.,
#'c("chr21","chr22"). Defaults to all chromosomes (except Y and M)
#'in the genome specified.
#'@return path of the.hic file
#'@examples hicdc2hic(input_path="~/Downloads/",
#'jarfile="~/Downloads/juicer_tools_1.13.02.jar",
#'binsize=5000,
#'output_file="~/Downloads/out.hic",chrs=c("chr22"))
#'@export
hicdc2hic<-function(input_path,jarfile,
binsize=5000,gen="Hsapiens",gen_ver="hg19",mode='zvalue',
output_file="~/Downloads/out.hic",chrs=sprintf("chr%s",c(1:22,"X"))){
options("scipen" = 9999, "warn" = -1)
#create path to the pre input
preinputpath<-path.expand(paste0(input_path,'_normcounts.txt'))
preoutputpath<-path.expand(output_file)
#get list of chromosomes if not specified
if (is.null(chrs)) {
#get list of chromosomes
chrs<-get_chrs(gen,gen_ver)
chrs <- chrs[!chrs %in% c('chrY', 'chrM')]
}
for (chr in chrs){
#read the HiC-DC result
input_path_chr<-paste0(input_path, "_Results_on_", chr, ".rds")
norm_data<-readRDS(path.expand(input_path_chr))
#convert to "short with score format" with scores as normalized counts
if (mode=='normcounts'){
norm_data<-norm_data%>%dplyr::mutate(score=.data$normcounts)
}
if (mode=='pvalue'){
norm_data<-norm_data%>%dplyr::mutate(score=pmax(-log10(.data$pvalue),0))
}
if (mode=='qvalue'){
norm_data<-norm_data%>%dplyr::mutate(score=pmax(-log10(.data$qvalue),0))
norm_data$score[!is.finite(norm_data$score)] <- 1
}
if (mode=='zvalue'){
norm_data<-norm_data%>%dplyr::mutate(score=.data$zvalue)
}
if(mode=='raw'){
norm_data<-norm_data%>%dplyr::mutate(score=.data$counts)
}
norm_data<-norm_data%>%dplyr::filter(.data$score>-2147400000)%>%
dplyr::mutate(strI=0,strJ=0,fragI=0,fragJ=1,
startI=.data$startI+binsize/2,
startJ=.data$startJ+binsize/2, chrI=gsub("chr","",chrI),
chrJ=gsub("chr","",chrJ))%>%
dplyr::select(.data$strI,.data$chrI,.data$startI,.data$fragI,.data$strJ,
.data$chrJ,.data$startJ,.data$fragJ,.data$score)
#dump output to file
data.table::fwrite(x=norm_data,file=preinputpath,append=TRUE,quote=FALSE,
sep="\t",row.names=FALSE,col.names=FALSE)
print(paste0("Output generated for chr: ", chr))
}
#run pre
if (mode=="zvalue"){
#make sure negative values get processed
system2("java", args = c('-Xmx8g', '-jar',
path.expand(jarfile), "pre", "-v", "-d", "-n",
"-r", binsize,
"-m", -2147400000,
path.expand(preinputpath), path.expand(preoutputpath),
gen_ver))
} else {
system2("java", args = c('-Xmx8g', '-jar',
path.expand(jarfile), "pre", "-v", "-d",
"-r", binsize, path.expand(preinputpath),
path.expand(preoutputpath),
gen_ver))
}
#system2('java',args=c('-Xmx8g', '-jar' ,path.expand(jarfile),
#'pre','-v','-d',path.expand(preinputpath),path.expand(preoutputpath),
#gen_ver))
#remove file
#system2('rm',args=path.expand(preinputpath))
}
| R |
3D | viannegao/ChromaFold | process_input/hic_normalization/without_installation/hicdcplus_run.R | .R | 3,567 | 95 | #!/usr/bin/env Rscript
# Run HiC-DC+ to generate z-score normalized Hi-C library.
#
# Usage
# screen
# bsub -n 2 -W 10:00 -R 'span[hosts=1] rusage[mem=64]' -Is /bin/bash
# source /miniconda3/etc/profile.d/conda.sh
# conda activate chromafold_env
# cd /chromafold/scripts
#
#
# Rscript /chromafold/process input/hic_normalization/hicdcplus_run.R \
# 10000 \
# "/scripts/hicdc_workflow/hg38_MboI_10kb_features.rds" \
# "/data/HiC/imr90/hicdc_out/" \
# "Hsapiens" \
# "hg38" \
# "/data/HiC/imr90/ENCFF281ILS.hic" \
# "scripts/hicdc_workflow/straw.cpp" \
# "/scripts/hicdc_workflow/juicer_tools.1.9.9_jcuda.0.8.jar" \
source("~/scripts/hic_normalization/without_installation/hicdcplus_pipeline.R")
setwd("~/scripts/hicdc_workflow/without_installation/")
args <- commandArgs(trailing = TRUE)
#####################################
# Step 0. Initial set-ups #
#####################################
#1. Initial set-up
binsize <- args[1] #resolution of Hi-C
bintolen_path <- args[2] #location of the genomic features (downloaded from google drive) #nolint
output_path <- args[3] #location for where to save the normalized files
gen <- args[4] #species
gen_ver <- args[5] #sequencing assembly
input_hic_file <- args[6] #location of the raw .hic file to be normalized
straw_path <- args[7] #location of the downloaded straw.cpp file
juicer_jar <- args[8]
##########################################
# Step 1. Prepare HiCDC object #
##########################################
hic2hicdc(hic_path = input_hic_file,
bintolen_path = bintolen_path,
straw_path = straw_path,
gen = gen, gen_ver = gen_ver, binsize = binsize,
bin_type = "Bins-uniform",
inter = FALSE, Dthreshold = 2e6,
output_path = output_path)
#########################################
# Step 2. HiCDC normalization #
#########################################
chrom <- c("chr1", "chr2", "chr3", "chr4", "chr5", "chr6",
"chr7", "chr8", "chr9", "chr10", "chr11", "chr12",
"chr13", "chr14", "chr15", "chr16", "chr17", "chr18",
"chr19", "chr20", "chr21", "chr22", "chrX")
# Calculate normalization for all chromosomes
for (chr in chrom){
print(chr)
hicdcplus_model(file=paste0(output_path,"_", binsize/10000, "kb_chr", chr, "_matrix.txt"), #nolint
bin_type = "Bins-uniform", gen = gen, gen_ver = gen_ver,
covariates = c("gc", "map", "len"), distance_type = "spline",
output_path = output_path, chrs = chr,
df = 6, Dmin = 0, Dmax = 2e6, ssize = 0.01, model_file = FALSE)
}
########################################################
# Step 3. Save normalized objects into files #
########################################################
#1. Save z-score normalization (z-score = (observed-expected)/std)
hicdc2hic(input_path = output_path, jarfile = juicer_jar,
binsize = binsize, gen = gen, gen_ver = gen_ver,
mode = "zvalue",
output_file = paste(output_path, "ENCFF281ILS_zvalue.hic", sep = ""),
chrs = sprintf("chr%s", c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, "X")))
#2. Save qvalues (adjusted p-value for significant interactions)
hicdc2hic(input_path = output_path, jarfile = juicer_jar,
binsize = binsize, gen = gen, gen_ver = gen_ver,
mode = "qvalue",
output_file = paste(output_path, "ENCFF281ILS_qvalue.hic", sep = ""),
chrs = sprintf("chr%s", c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, "X")))
| R |
3D | viannegao/ChromaFold | process_input/hic_normalization/hicdcplus/step2_python_save.py | .py | 2,408 | 70 | #!/usr/bin/env python
'''Script for processing scATAC fragment files.
Usage example:
screen
bsub -q gpuqueue -gpu - -W 4:00 -n 2 -R 'span[hosts=1] rusage[mem=128]' -Is /bin/bash
python ./hicdcplus/step2_python_save.py \
--assembly 'hg38'
'''
import pandas as pd
import argparse
import pickle
import numpy as np
from scipy.sparse import csr_matrix
parser = argparse.ArgumentParser(description="Set-up data preparations",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--assembly", help="Assembly of the Hi-C library.",
type=str, default="hg38")
args = parser.parse_args()
config = vars(args)
print(config)
ASSEMBLY = config['assembly']
def main():
args = parser.parse_args()
config = vars(args)
print(config)
ASSEMBLY = config['assembly']
PTH = './intermediate'
SAVE_PTH = './zvalue_intermediate'
hic_dict = {}
hic_resolution = 10000
if ASSEMBLY in ['hg19', 'hg38']:
chrom_length = 22
elif ASSEMBLY in ['mm9', 'mm10']:
chrom_length = 19
for i in list(range(1,int(chrom_length+1))) + ['X']:
chrom = f'chr{i}'
print(chrom)
a = pd.read_csv(f'{PTH}/normalized_{chrom}.txt', sep='\t')
a.loc[:,'zvalue'] = [(i-j)/k for i,j,k in zip(a.loc[:,'counts'].to_list(),
a.loc[:,'mu'].to_list(),
a.loc[:,'sdev'].to_list())]
a1 = a.loc[:,['start1','start2','zvalue']]
a1.to_csv(f'{SAVE_PTH}/normalized_{chrom}_simplified.txt', sep='\t',header=None,index=None)
del a1
# save into dictionary
hic = pd.read_table(f'{SAVE_PTH}/normalized_{chrom}_simplified.txt', header = None, sep='\t')
start = hic[[0,1]].values.min()
end = hic[[0,1]].values.max()+hic_resolution
hic = hic.pivot(0,1,2).fillna(0)
hic = hic.reindex(columns=np.arange(0,end, hic_resolution).astype(int),
index=np.arange(0,end, hic_resolution).astype(int),
fill_value = 0)
hic_dict[chrom] = csr_matrix(hic)
SAVE_PTH2 = './zvalue'
pickle.dump(hic_dict, open(f"{SAVE_PTH2}/normalized_hic_zscore_dict.p", "wb"), protocol=4)
if __name__ == '__main__':
main()
| Python |
3D | viannegao/ChromaFold | process_input/hic_normalization/hicdcplus/step1_hicdcplus_normalization_run.R | .R | 3,063 | 99 | #hicdc+ for normalization
#
# Usage
# screen
# bsub -n 2 -W 10:00 -R 'span[hosts=1] rusage[mem=128]' -Is /bin/bash
# source /miniconda3/etc/profile.d/conda.sh
# conda activate chromafold_env
# cd ./hic_normalization
#
# Rscript ./hicdcplus/step1_hicdcplus_normalization_run.R \
# imr90.hic \
# 10000 \
# 'hg38'
library(HiCDCPlus)
args <- commandArgs(trailing = TRUE)
#####################################
# Step 0. Initial set-ups #
#####################################
#1. Initial set-up
hicfile_path <- args[1] #location of the raw .hic file to be normalized
resolution <- as.integer(args[2]) #resolution of Hi-C
assembly <- args[3]
#2. Variable set-ups
if (assembly %in% c("mm9", "mm10")) {
species <- "Mmusculus"
chrom_length <- 19
} else if (assembly %in% c("hg19", "hg38")) {
species <- "Hsapiens"
chrom_length <- 22
} else {
species <- "Unknown"
print("Species not match")
}
outdir <- "./features/"
outpth <- "./intermediate/"
########################################
# Step 1. Construct features #
########################################
#Step 1. construct features (resolution, chrom specific)
chromosomes <- paste("chr", seq(1, chrom_length, 1), sep = "")
chromosomes[[chrom_length+1]] <- "chrX"
construct_features(output_path = paste0(outdir, assembly, "_", as.integer(resolution/1000),"kb_GATC_GANTC", sep = ""), # nolint
gen = species, gen_ver = assembly, sig = c("GATC", "GANTC"),
bin_type = "Bins-uniform", binsize = resolution, chrs = chromosomes)
#Step 2. generate gi_list instance
set.seed(1010)
#generate gi_list instance
gi_list <- generate_bintolen_gi_list(
bintolen_path = paste0(outdir, "/", assembly,
"_", as.integer(resolution / 1000), "kb_GATC_GANTC_bintolen.txt.gz"),
gen = species, gen_ver = assembly)
#add .hic counts
gi_list <- add_hic_counts(gi_list, hic_path = hicfile_path, chrs = names(gi_list)) #nolint
#############################################################
# Step 2. Modeling and generate normalized values #
#############################################################
#expand features for modeling
gi_list <- expand_1D_features(gi_list)
#run HiC-DC+ on 2 cores
gi_list <- HiCDCPlus_parallel(gi_list, ncore = 2)
head(gi_list$chr1)
##################################
# Step 3. Save results #
##################################
#write normalized counts (observed/expected) to a .hic file
hicdc2hic(gi_list,
hicfile = paste0(outpth, "normalized.hic"),
mode = "zvalue", gen_ver = assembly)
#write results to a text file
gi_list_write(gi_list,
fname = paste0(outpth, "normalized.txt.gz"))
for (chrom in names(gi_list)) {
print(chrom)
# Construct the file name
file_name <- paste0(outpth, "normalized_", chrom, ".txt")
# Extract the data for the current chromosome
data <- gi_list[[chrom]]
# Save the data to a text file
write.table(data, file = file_name, sep = "\t",
row.names = FALSE, col.names = TRUE, quote = FALSE)
}
| R |
3D | viannegao/ChromaFold | process_input/hic_normalization/hicdcplus/hicdcplus_normalization.sh | .sh | 1,283 | 49 | #!/bin/bash
# Usage
# bash ChromaFold/process_input/hicdc_normalization/hicdcplus_normalization.sh \
# hic_file='imr90.hic'
# resolution=10000
# assembly='hg38'
SCRIPT_DIR="/chromafold/process_input/hic_normalization/hicdcplus"
# Ensure required variables are set
if [ -z "$hic_file" ] || [ -z "$resolution" ] || [ -z "$assembly" ]; then
echo "Error: Missing required environment variables."
echo "Usage: hic_file='your_file.hic' resolution=10000 assembly='hg38' ./your_script.sh"
exit 1
fi
mkdir -p ./hicdc_normalization
mkdir -p ./hicdc_normalization/zvalue_intermediate
mkdir -p ./hicdc_normalization/zvalue
mkdir -p ./hicdc_normalization/features
mkdir -p ./hicdc_normalization/intermediate
cd ./hicdc_normalization
#1. Step 1. Use HiC-DC+ to calculate normalization
# Usage
# screen
source /miniconda3/etc/profile.d/conda.sh
conda activate chromafold_env
cd /chromafold/scripts
Rscript "${SCRIPT_DIR}"/step1_hicdcplus_normalization_run.R \
"$hic_file" \
"$resolution" \
"$assembly"
# exit from R env
conda deactivate
#2. Convert calculated z-values to input Hi-C format
# load python env
conda activate cuda111_torch
python "${SCRIPT_DIR}"/step2_python_save.py \
--assembly "$assembly"
#3. Normalized .p file should be saaved into ./hicdc_normalization/zvalue | Shell |
3D | viannegao/ChromaFold | preprocessing_pipeline/ArchR_preparation.R | .R | 3,266 | 116 | #!/usr/bin/env Rscript
# Run ArchR to generate metacell information for running ChromaFold
#
# Installation of ArchR
# install.packages("Rcpp", dependencies = TRUE) # nolint
# if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") # nolint
# install.packages("devtools") # nolint
# if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") # nolint
# devtools::install_github("GreenleafLab/ArchR", ref="master", repos = BiocManager::repositories()) # nolint
# library(ArchR) # nolint
# ArchR::installExtraPackages() # nolint
#
# Usage
# screen
# source /miniconda3/etc/profile.d/conda.sh
# conda activate chromafold_env
# cd /chromafold/scripts
#
# Rscript /chromafold/scripts/ArchR_preparation.R \
# "name_prefix" \
# "/chromafold/data/test_input/archr_data" \
# "/fragments.tsv.gz" \
# "mm10"
library(ArchR)
library(tidyr)
args <- commandArgs(trailing = TRUE)
#####################################
# Step 0. Initial set-ups #
#####################################
#1. Initial set-up
sample_name <- args[1]
archr_path <- args[2]
input_path <- args[3]
genome_assembly <- args[4]
setwd(archr_path)
#2. Hyper-parameters
# addArchRGenome("mm10") # nolint
addArchRGenome(genome_assembly)
addArchRThreads(threads = 1)
tile_mat_params <- list()
tile_mat_params$tileSize <- 500
#########################################
# Step 1. Load fragmens files #
#########################################
#1. Load fragments files
ArrowFiles <- createArrowFiles( # nolint
inputFiles = input_path,
sampleNames = sample_name,
TileMatParams = tile_mat_params,
filterTSS = 4, #Dont set this too high because you can always increase later
filterFrags = 1000,
addTileMat = TRUE,
addGeneScoreMat = FALSE
)
print("Finished creating ArrowFiles")
#2. Create archr project
proj <- ArchRProject(
ArrowFiles = c(paste0(archr_path, "/", sample_name, ".arrow")),
# outputDirectory = paste0(archr_path, "/archr_out/"), #nolint
outputDirectory = paste0(archr_path, "/"),
copyArrows = FALSE
)
print("Finished creating Archr project")
#3. Run lsi
proj <- addIterativeLSI(
ArchRProj = proj, useMatrix = "TileMatrix",
name = "IterativeLSI", iterations = 2,
clusterParams = list(
resolution = c(0.2), sampleCells = 10000, n.start = 10
),
varFeatures = 25000, dimsToUse = 1:30
)
print("Finished running lsi")
#3.1. Clustering
proj <- addClusters(
input = proj, reducedDims = "IterativeLSI",
method = "Seurat", name = "Clusters",
resolution = 2, force = TRUE
)
print("Finished clustering")
#3.2. tsne
# proj <- addTSNE(
# ArchRProj = proj, reducedDims = "IterativeLSI",
# name = "TSNE", perplexity = 30
# )
####################################
# Step 2. Save all files #
####################################
#1. Save
final_bc <- rownames(proj@cellColData)
write.csv(final_bc, file = paste0(archr_path, "/archr_filtered_barcode.csv"))
lsi <- getReducedDims(proj)
write.csv(lsi, file = paste0(archr_path, "/archr_filtered_lsi.csv"))
saveArchRProject(ArchRProj = proj, outputDirectory = archr_path, load = FALSE)
| R |
3D | viannegao/ChromaFold | preprocessing_pipeline/fragment_celltype_merge.py | .py | 3,663 | 94 | #!/usr/bin/env python
'''Script for processing scATAC fragment files.
Usage example:
screen
python /chromafold/scripts/fragment_celltype_merge.py \
--cell_type selected_cell_type \
--fragment_list /data/fragments_1.tsv.gz /data/fragments_2.tsv.gz \
--data_prefix_list "data_prefix" \
--save_name /data/merged_fragments.tsv.gz \
--cell_type_file /data/cell_type_file.csv \
--genome_assembly mm10
'''
import pandas as pd
import numpy as np
import argparse
parser = argparse.ArgumentParser(description="Set-up data preparations",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--cell_type", help="Which cell type to use in the fragment files.",
type=str, default="")
parser.add_argument("--fragment_list", help="Path and name of the input fragment files.",
nargs='+', type=str, default="")
parser.add_argument("--data_prefix_list", help="Prefix for the fragment files (length must match fragment_list).",
nargs='+', type=str, default="")
parser.add_argument("--save_name", help="Path and name of the filtered fragment file for specific cell type.",
type=str, default="")
parser.add_argument("--cell_type_file", help="General cell type list for the fragment files.",
type=str, default="")
parser.add_argument("--genome_assembly", help="Genome assembly of the scATAC files.",
type=str, default="")
args = parser.parse_args()
config = vars(args)
print(config)
CELL_TYPE = config['cell_type']
FRAG_LIST = config['fragment_list']
DATA_PREFIX_LIST = config['data_prefix_list']
SAVE_NAME = config['save_name']
CELL_TYPE_FILE = config['cell_type_file']
GENOME_ASSEMBLY = config['genome_assembly']
#########################
# Load functions #
#########################
def filter_celltype(FRAG_LIST, DATA_PREFIX_LIST, cell_type, CELL_TYPE_FILE, SAVE_NAME, nrows_skip = 52):
cell_type_path = CELL_TYPE_FILE
cell_types = pd.read_csv(cell_type_path)
cell_types.columns = ["barcode_id", "cell_type_id"]
cell_type_subdf = cell_types[cell_types["cell_type_id"] == cell_type]
cell_barcode_list = cell_type_subdf.loc[:,"barcode_id"].to_list()
i = 0
for INPUT_FRAG in FRAG_LIST:
DATA_PREFIX = DATA_PREFIX_LIST[i]
print(INPUT_FRAG, DATA_PREFIX)
frag = pd.read_csv(INPUT_FRAG, sep = '\t', header=None, skiprows = nrows_skip)
frag.columns = ["chrom","start","end","cell_id","counts"]
frag.loc[:,"cell_barcode"] = [f"{DATA_PREFIX}#{i}" for i in frag.loc[:,"cell_id"].to_list()]
print(frag.shape)
frag_sub = frag[frag["cell_barcode"].isin(cell_barcode_list)]
print(frag_sub.shape)
frag_sub.to_csv(SAVE_NAME, mode="a", sep="\t", index=None, header=False)
del frag, frag_sub
i += 1
return None
#########################
# Script running #
#########################
def main():
args = parser.parse_args()
config = vars(args)
print(config)
CELL_TYPE = config['cell_type']
FRAG_LIST = config['fragment_list']
DATA_PREFIX_LIST = config['data_prefix_list']
SAVE_NAME = config['save_name']
CELL_TYPE_FILE = config['cell_type_file']
GENOME_ASSEMBLY = config['genome_assembly']
_ = filter_celltype(FRAG_LIST, DATA_PREFIX_LIST,
cell_type=CELL_TYPE, CELL_TYPE_FILE = CELL_TYPE_FILE,
SAVE_NAME = SAVE_NAME) #f"{GENOME_ASSEMBLY}_{SAVE_NAME}"
if __name__ == '__main__':
main() | Python |
3D | viannegao/ChromaFold | preprocessing_pipeline/fragment_to_input.sh | .sh | 4,001 | 105 | #/bin/bash
# Copy from chromafold_data_preparation.sh
# Usage:
#
# screen
# bash /chromafold/scripts/pipeline/fragment_to_input.sh
#######################################################
# Step 0. Filter and merge fragment files #
#######################################################
'''
This section is for when you have multiple fragment files covering multiple cell types.
Input: fragments_1.tsv.gz, fragments_2.tsv.gz, cell_type_file.csv
Task:
- specify selected cell type name
- select from the corresponding fragments files
- merge all fragments for the specific cell type
'''
bsub -n 2 -W 10:00 -R 'span[hosts=1] rusage[mem=256]' -Is /bin/bash
python /chromafold/scripts/pipeline/fragment_celltype_merge.py \
--cell_type selected_cell_type \
--fragment_list /data/fragments_1.tsv.gz \
/data/fragments_2.tsv.gz \
--data_prefix_list "selected_cell_type" \
--save_name /data/selected_fragments.tsv.gz \
--cell_type_file /data/cell_type_file.csv \
--genome_assembly hg38
#######################################
# Step 1. Calculate ArchR #
#######################################
#--------------- Define arguments ---------------#
SAVE_LOC="/chromafold/data/giles_paper"
DATA_PREFIX="mm10_giles_TransCTLII_fragments"
FRAG_LOC="/chromafold/data/giles_paper/fragments_by_celltype"
FRAG_FILE_PREFIX="${DATA_PREFIX}"
GENOME_ASSEMBLY="mm10"
#----------------- Run scripts -----------------#
mkdir -p /chromafold/data/test_input/archr_data/"${DATA_PREFIX}"
ARCHR_LOC=/chromafold/data/test_input/archr_data/"${DATA_PREFIX}"
export PATH=/chromafold/packages/samtools/bin:$PATH
export PATH=/chromafold/packages/samtools/samtools/bin:$PATH
export PATH=/setd2/packages/bedtools2/bin:$PATH
gunzip -c "${FRAG_LOC}"/"${FRAG_FILE_PREFIX}.tsv.gz" | bgzip > "${FRAG_LOC}"/"${FRAG_FILE_PREFIX}_bgz.tsv.gz" # convert gzip to bgzip
sortBed -i "${FRAG_LOC}"/"${FRAG_FILE_PREFIX}_bgz.tsv.gz" > "${FRAG_LOC}"/"${FRAG_FILE_PREFIX}_bgz_sorted.tsv" # sort merged bgzip file
htsfile "${FRAG_LOC}"/"${FRAG_FILE_PREFIX}_bgz_sorted.tsv" # check whether file is in bed format
bgzip "${FRAG_LOC}"/"${FRAG_FILE_PREFIX}_bgz_sorted.tsv" # bgzip file for tabix
rm "${FRAG_LOC}"/"${FRAG_FILE_PREFIX}_bgz_sorted.tsv.gz.tbi" # remove previously calculated .tbi file
# rm "${FRAG_LOC}"/"${FRAG_FILE_PREFIX}.tsv.gz" # remove intermediate files
rm "${FRAG_LOC}"/"${FRAG_FILE_PREFIX}_bgz.tsv.gz" # remove intermediate files
cd "${SAVE_LOC}"
mkdir -p atac
mkdir -p dna
mkdir -p predictions
mkdir -p assembly
wget -P assembly https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes
wget -P assembly https://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/hg19.chrom.sizes
wget -P assembly https://hgdownload.soe.ucsc.edu/goldenPath/mm10/bigZips/mm10.chrom.sizes
wget -P assembly https://hgdownload.soe.ucsc.edu/goldenPath/mm9/bigZips/mm9.chrom.sizes
# Copy CTCF motif score to the designated folder
# CTCF motif can be downloaded from google drive: https://drive.google.com/drive/folders/1TlfXGix2U-K1UIrOYv8gWGIiSx10dP3M?usp=sharing
cp /dna/* "${SAVE_LOC}"/dna/
# Run R to create LSI file using ArchR
source /miniconda3/etc/profile.d/conda.sh # load conda
conda activate /environment/chromafold_env # activate conda environment for R
cd /chromafold/scripts
Rscript /chromafold/scripts/pipeline/ArchR_preparation.R \
"${DATA_PREFIX}" \
"${ARCHR_LOC}" \
"${FRAG_LOC}"/"${FRAG_FILE_PREFIX}_bgz_sorted.tsv.gz" \
"${GENOME_ASSEMBLY}"
# exit
############################################
# Step 2. Calculate tile files #
############################################
# Run Python to create tile files for scATAC data
python /chromafold/scripts/pipeline/scATAC_preparation.py \
--cell_type_prefix "${DATA_PREFIX}" \
--fragment_file "${FRAG_LOC}"/"${FRAG_FILE_PREFIX}_bgz_sorted.tsv.gz" \
--barcode_file "${ARCHR_LOC}"/archr_filtered_barcode.csv \
--lsi_file "${ARCHR_LOC}"/archr_filtered_lsi.csv \
--genome_assembly "${GENOME_ASSEMBLY}" \
--save_path "${SAVE_LOC}"
| Shell |
3D | viannegao/ChromaFold | preprocessing_pipeline/scATAC_preparation.py | .py | 9,050 | 203 | #!/usr/bin/env python
'''Script for processing scATAC fragment files.
Usage example:
screen
python chromafold/scripts/scATAC_preparation.py \
--cell_type_prefix cell_type_prefix \
--fragment_file /data/merged_fragments.tsv.gz \
--barcode_file /data/archr_data/archr_filtered_barcode.csv \
--lsi_file /data/archr_data/archr_filtered_lsi.csv \
--genome_assembly "mm10" \
--save_path /data/atac_data
'''
import random
import sys
import numpy as np
import pysam
import pandas as pd
import os, argparse
import itertools
import scipy
from scipy.sparse import csr_matrix
from scipy import sparse
import pickle
from scipy.sparse import coo_matrix, csr_matrix, find
from sklearn.neighbors import NearestNeighbors
parser = argparse.ArgumentParser(description="Set-up data preparations",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--cell_type_prefix", help="Cell type and its prefix for the file names.",
type=str, default="")
parser.add_argument("--fragment_file", help="Path and name of the input fragment file.",
type=str, default="")
parser.add_argument("--barcode_file", help="Path and name of the valid barcode file.",
type=str, default="")
parser.add_argument("--genome_assembly", help="Genome assembly of the scATAC files.",
type=str, default="")
parser.add_argument("--lsi_file", help="Path and name of the LSI file from ArchR running.",
type=str, default="")
parser.add_argument("--save_path", help="Path to save all the intermediate and final files.",
type=str, default="")
args = parser.parse_args()
config = vars(args)
print(config)
CELL_TYPE = config['cell_type_prefix']
FRAG_FILE = config['fragment_file']
BARCODE_FILE = config['barcode_file']
GENOME_ASSEMBLY = config['genome_assembly']
LSI_PATH = config['lsi_file']
SAVE_PATH = config['save_path']
chrom_size = pd.read_csv(f'/assembly/{GENOME_ASSEMBLY}.chrom.sizes', #downloaded from UCSC
sep = '\t', header = None, index_col = 0)
valid_chrom = ['chr1', 'chr2', 'chr3', 'chr4', 'chr5','chr6','chr7',
'chr8', 'chr9','chr10','chr11','chr12','chr13','chr14',
'chr15','chr16','chr17','chr18','chr19','chr20',
'chr21','chr22','chrX']
valid_chrom = [i for i in valid_chrom if i in chrom_size.index.to_list()]
#########################
# Load functions #
#########################
def atac_processing(frag_path, nrows_skip, valid_barcode_path,
save_path, cell_type_prefix, valid_chrom = valid_chrom):
#1. read fragment file and filter for valid cell barcode
frag = pd.read_csv(frag_path, sep = '\t', header=None, skiprows = nrows_skip)
dist = frag[2]-frag[1]
valid_barcode = pd.read_csv(valid_barcode_path, index_col = 0)
# valid_barcode = valid_barcode.index
valid_barcode = valid_barcode.iloc[:,0]
valid_barcode = [str.split(i,"#")[1] for i in valid_barcode.to_list()]
frag = frag[frag[3].isin(valid_barcode)]
print("Finished loading fragment file.")
########### Create aggregated tile file for 500bp tiles ###########
#2a. aggregate for each tile
frag_stacked = pd.concat([frag[[0,1,3,4]],frag[[0,2,3,4]].rename(columns={2:1})], ignore_index=True)
frag_stacked[4] = 1 #binarize count
frag_stacked[1] = frag_stacked[1]//500 # aggregate counts per 500bp tile
barcodes, barcode_id = np.unique(frag_stacked[3], return_inverse=True)
frag_stacked[5] = barcode_id
#2b. create tile files
tile_dict = {}
for chrom in list(set(frag_stacked[0])):
if chrom in valid_chrom:
frag_grouped = frag_stacked[frag_stacked[0]==chrom][[1,5,4]].groupby([5,1]).sum().reset_index()
frag_grouped = csr_matrix((frag_grouped[4].astype(np.float64),
(frag_grouped[5].values, frag_grouped[1].values)),
shape=(barcodes.shape[0], chrom_size.loc[chrom].values[0]//500+1))
tile_dict[chrom] = frag_grouped
print(chrom)
#2c. save to file
pickle.dump(tile_dict, open(f"{save_path}/atac/{cell_type_prefix}_tile_500bp_dict.p", "wb"))
np.save(f"{save_path}/atac/{cell_type_prefix}_tile_500bp_barcode.npy", barcodes.astype(str))
del frag_stacked, tile_dict
print("500bp tile data processed.")
########### Create pseudo-bulk data for evaluation ###########
#3a. aggregate for 50bp tiles
frag_stacked = pd.concat([frag[[0,1,4]],frag[[0,2,4]].rename(columns={2:1})], ignore_index=True)
frag_stacked[4] = 1 #binarize counte
frag_stacked[1] = frag_stacked[1]//50 # aggregate counts per 50bp tile
tile_dict = {}
for chrom in list(set(frag_stacked[0])):
if chrom in valid_chrom:
frag_grouped = frag_stacked[frag_stacked[0]==chrom][[1,4]].groupby([1]).sum().reset_index()
frag_grouped = pd.DataFrame(frag_grouped[4].values,
index = frag_grouped[1].values).reindex(np.arange(0,
chrom_size.loc[chrom].values[0]//50+1)).fillna(0).values
tile_dict[chrom] = frag_grouped
#3b. save
pickle.dump(tile_dict, open(f"{save_path}/atac/{cell_type_prefix}_tile_pbulk_50bp_dict.p", "wb"))
del frag_stacked, tile_dict
print("pseudo-bulk data processed.")
########### Create aggregated tile file for 50bp tiles ###########
#4a. aggregate for each tile
frag_stacked = pd.concat([frag[[0,1,3,4]],frag[[0,2,3,4]].rename(columns={2:1})], ignore_index=True)
frag_stacked[4] = 1 #binarize counte
frag_stacked[1] = frag_stacked[1]//50 # aggregate counts per 50bp tile
barcodes, barcode_id = np.unique(frag_stacked[3], return_inverse=True)
frag_stacked[5] = barcode_id
#4b. create tile files
tile_dict = {}
for chrom in valid_chrom:
frag_grouped = frag_stacked[frag_stacked[0]==chrom][[1,5,4]].groupby([5,1]).sum().reset_index()
frag_grouped = csr_matrix((frag_grouped[4].astype(np.float64),
(frag_grouped[5], frag_grouped[1])),
shape=(barcodes.shape[0], chrom_size.loc[chrom].values[0]//50+1))
tile_dict[chrom] = frag_grouped
#4c. save
pickle.dump(tile_dict, open(f"{save_path}/atac/{cell_type_prefix}_tile_50bp_dict.p", "wb")) # save it into a file named save.p
np.save(f"{save_path}/atac/{cell_type_prefix}_tile_50bp_barcode.npy",barcodes.astype(str))
del frag_stacked, tile_dict
print("50bp tile file processed.")
return None
def get_max_overlap(y,x):
return np.max(x.dot(y))
def generate_cicero_metacell(nbrs, max_overlap, sampled_id = [0]):
order = np.arange(nbrs.shape[0])
np.random.seed(10)
np.random.shuffle(order)
selected_idx = [False]*nbrs.shape[0]
for i in sampled_id:
selected_idx[i] = True
for idx in order:
selected_cells = nbrs[selected_idx]
candidate = nbrs[idx]
overlap = get_max_overlap(candidate,selected_cells)
if overlap < max_overlap:
selected_idx[idx] = True
return selected_idx
def metacell_computing(lsi_path, n_neighbors = 100, max_overlap = 33,
save_path = SAVE_PATH, cell_type_prefix = CELL_TYPE):
lsi = pd.read_csv(lsi_path, index_col = 0)
lsi.index = [x.split('#')[1] for x in lsi.index]
nbrs = NearestNeighbors(n_neighbors=n_neighbors, metric='euclidean').fit(lsi.values)
nbrs = nbrs.kneighbors_graph(lsi.values).toarray()
selected_idx = generate_cicero_metacell(nbrs, max_overlap = max_overlap)
metacell_assignment = nbrs[selected_idx,:]
pd.DataFrame(metacell_assignment).to_csv(f"{save_path}/atac/{cell_type_prefix}_metacell_mask.csv")
return None
#########################
# Script running #
#########################
def main():
args = parser.parse_args()
config = vars(args)
print(config)
CELL_TYPE = config['cell_type_prefix']
FRAG_FILE = config['fragment_file']
BARCODE_FILE = config['barcode_file']
GENOME_ASSEMBLY = config['genome_assembly']
LSI_PATH = config['lsi_file']
SAVE_PATH = config['save_path']
chrom_size = pd.read_csv(f'/assembly/{GENOME_ASSEMBLY}.chrom.sizes',
sep = '\t', header = None, index_col = 0)
_ = atac_processing(frag_path = FRAG_FILE, nrows_skip = 0,
valid_barcode_path = BARCODE_FILE,
save_path = SAVE_PATH, cell_type_prefix = CELL_TYPE)
_ = metacell_computing(lsi_path = LSI_PATH, save_path = SAVE_PATH, cell_type_prefix = CELL_TYPE)
if __name__ == '__main__':
main() | Python |
3D | viannegao/ChromaFold | chromafold/train.py | .py | 9,525 | 336 | import argparse
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import numpy as np
from tqdm import tqdm
import h5py
from utils import *
from dataloader import *
from model import *
from training_utils import *
# DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
parser = argparse.ArgumentParser(description="PyTorch chromaFold")
parser.add_argument(
"--data-path", metavar="DIR", default="./datasets", help="path to dataset"
)
parser.add_argument(
"--save-path", default="./checkpoints", help="path to save model chekpoints"
)
parser.add_argument(
"--bulk-checkpoint",
default="./checkpoints/deterministic/gm12878_hg38_umb_endo_imr90_CTCFmotifScore_seed1229.pth.tar",
help="pbulk chekpoint",
)
parser.add_argument("-ct", "--ct-list", nargs="+", default=[])
parser.add_argument("--genome", default="hg38", help="genome of training cell type")
parser.add_argument(
"--min-stripe-signal",
default=-170,
type=int,
help="Stripe signal cutoff; avoid training on low-signal stripes",
)
parser.add_argument(
"-j",
"--workers",
default=8,
type=int,
help="number of data loading workers (default: 8)",
)
parser.add_argument(
"--epochs", default=30, type=int, help="number of total epochs to run"
)
parser.add_argument(
"-b", "--batch-size", default=64, type=int, help="batch size (default: 64)"
)
parser.add_argument(
"--patience", default=10, type=int, help="training patience in epochs (default: 10)"
)
parser.add_argument(
"--lr",
"--learning-rate",
default=1e-6,
type=float,
metavar="LR",
help="initial learning rate",
dest="lr",
)
parser.add_argument(
"--wd",
"--weight-decay",
default=1e-3,
type=float,
metavar="W",
help="weight decay (default: 1e-3)",
dest="weight_decay",
)
parser.add_argument(
"--seed", default=1229, type=int, help="seed for initializing training. "
)
parser.add_argument("--disable-cuda", action="store_true", help="Disable CUDA")
parser.add_argument("--deterministic", action="store_true", help="cudnn deterministic")
parser.add_argument("--mod-name", default="", help="model name")
parser.add_argument("--use_chip", default="False", help="Whether to use Chip or motif score for CTCF, options = [True, False]")
def main():
args = parser.parse_args()
# check if gpu training is available
if not args.disable_cuda and torch.cuda.is_available():
args.device = torch.device("cuda")
if args.deterministic:
cudnn.deterministic = True
logging.warning(
"You have chosen to seed training. This will turn on the CUDNN deterministic setting, which can slow down "
"your training considerably! You may see unexpected behavior when restarting from checkpoints."
)
# cudnn.benchmark = True
else:
args.device = torch.device("cpu")
torch.manual_seed(args.seed)
data_path = args.data_path
save_path = args.save_path
BATCH_SIZE = args.batch_size
N_EPOCHS = args.epochs
initial_rate = args.lr
wd = args.weight_decay
ct_list = args.ct_list
genome = args.genome
use_chip = args.use_chip
# Verify validity of input and output paths.
assert os.path.isdir(
data_path
), "data_path does not exist. Please create directory first."
assert os.path.isdir(
save_path
), "save_path does not exist. Please create directory first."
if args.mod_name:
save_path = os.path.join(save_path, args.mod_name)
if not os.path.isdir(save_path):
os.mkdir(save_path)
print("Saving to {}".format(save_path))
# Training specifications.
train_chrom = [1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 19]
val_chrom = [3, 15]
test_chrom = [5, 18, 20, 21]
# The following should not be changed unless the user modifies the dimensions in the model accordingly.
input_size = 4010000
train_step = 5e04
val_step = 5e04
# start_dict = {}
# end_dict = {}
# for i in hic_dict[ct_list[1]].keys():
# idx = np.where(hic_dict[ct_list[0]][i].toarray().sum(1)>-600)[0]
# start_dict[i] = idx[0] * 10000
# end_dict[i] = idx[-1] * 10000
# for ct in ct_list[1:]:
# for i in hic_dict[ct_list[1]].keys():
# idx = np.where(hic_dict[ct][i].toarray().sum(1)>-600)[0]
# start_dict[i] = max(start_dict[i], idx[0] * 10000)
# end_dict[i] = min(end_dict[i], idx[-1] * 10000)
# Start and end indices of each chromosome
start_dict = {
"chr13": 0,
"chr21": 0,
"chr3": 0,
"chr20": 0,
"chrX": 0,
"chr8": 0,
"chr10": 0,
"chr15": 0,
"chr5": 0,
"chr2": 0,
"chr14": 0,
"chr6": 0,
"chr7": 0,
"chr17": 0,
"chr18": 0,
"chr16": 0,
"chr1": 0,
"chr11": 0,
"chr4": 0,
"chr9": 0,
"chr12": 0,
"chr19": 0,
}
end_dict = {
"chr13": 114360000,
"chr21": 46700000,
"chr3": 198020000,
"chr20": 63020000,
"chrX": 155270000,
"chr8": 145130000,
"chr10": 133790000,
"chr15": 101990000,
"chr5": 180910000,
"chr2": 242190000,
"chr14": 107040000,
"chr6": 170800000,
"chr7": 159130000,
"chr17": 81190000,
"chr18": 78070000,
"chr16": 90330000,
"chr1": 248950000,
"chr11": 135000000,
"chr4": 190210000,
"chr9": 138390000,
"chr12": 133270000,
"chr19": 58610000,
}
# Specify efective genome size for normalization
if "hg" in genome:
effective_genome_size = 2913022398
elif "mm" in genome:
effective_genome_size = 2652783500
else:
raise ValueError(
"Please compute effective_genome_size manuallly and modify this function accordingly."
)
# LOAD DATA
print("Loading training data for these cell types:")
print(ct_list)
print(args)
hic_dict = {}
hic_qval_dict = {}
pbulk_dict = {}
scatac_dict = {}
n_cells = {}
n_metacells = {}
libsize_cell = {}
for ct in ct_list:
hic_dict[ct], hic_qval_dict[ct] = load_hic(data_path, ct)
pbulk_dict[ct], scatac_dict[ct] = load_atac(data_path, ct, pbulk_only=False)
n_cells[ct] = get_num_cells(pbulk_dict[ct])
n_metacells[ct] = get_num_cells(scatac_dict[ct], dim=1)
libsize_cell[ct] = get_libsize(pbulk_dict[ct])
# ctcf_dict = load_ctcf_motif(data_path, genome)
if use_chip == 'True':
ctcf_dict = load_ctcf_motif(data_path, ct, use_chip = True)
else:
ctcf_dict = load_ctcf_motif(data_path, genome)
# Create dataset objects for training and validation
train_dataset = Dataset(
input_size,
effective_genome_size,
hic_dict,
pbulk_dict,
scatac_dict,
ctcf_dict,
n_cells,
n_metacells,
libsize_cell,
*get_starts(
chrom_list=train_chrom,
start_dict=start_dict,
end_dict=end_dict,
chrom_start_offset=4000000,
chrom_end_offset=5000000,
multi_ct=True,
ct_list=ct_list,
step=train_step,
),
transform=normalize,
is_training=True
)
val_dataset = Dataset(
input_size,
effective_genome_size,
hic_dict,
pbulk_dict,
scatac_dict,
ctcf_dict,
n_cells,
n_metacells,
libsize_cell,
*get_starts(
chrom_list=val_chrom,
start_dict=start_dict,
end_dict=end_dict,
chrom_start_offset=4000000,
chrom_end_offset=5000000,
multi_ct=True,
ct_list=ct_list,
step=val_step,
),
transform=normalize
)
# Create dataloader objects for training and validation
train_loader = DataLoader(
dataset=train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=args.workers,
)
val_loader = DataLoader(
dataset=val_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=args.workers,
)
# Define Model
mod_branch_pbulk = nn.DataParallel(branch_pbulk(), device_ids=[0])
PATH_branch_pbulk = args.bulk_checkpoint
mod_branch_pbulk.load_state_dict(torch.load(PATH_branch_pbulk), strict=True)
mod_branch_cov = nn.DataParallel(branch_cov(), device_ids=[0])
model = nn.DataParallel(trunk(mod_branch_pbulk, mod_branch_cov), device_ids=[0])
optimizer = torch.optim.SGD(
model.parameters(), lr=initial_rate, momentum=0.9, weight_decay=wd
)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[N_EPOCHS / 10 * 4, N_EPOCHS / 10 * 8], gamma=0.1
)
criterion = weighted_mse_loss
# Train
print("Training...")
model, optimizer, _ = training_loop(
train,
validate,
model,
ct_list,
args.seed,
criterion,
optimizer,
scheduler,
train_loader,
val_loader,
N_EPOCHS,
args.patience,
save_path,
args.device,
args.min_stripe_signal,
)
if __name__ == "__main__":
main()
| Python |
3D | viannegao/ChromaFold | chromafold/R_env.sh | .sh | 1,713 | 61 | #/bin/bash
mkdir -p /chromafold/packages
cd /chromafold/packages
# /miniconda3/condabin/conda install -c r r-base
#1. Create conda environment
cd /chromafold/packages
/miniconda3/bin/conda create -n chromafold_env
#2. activate conda env and install essential packages
source /miniconda3/etc/profile.d/conda.sh
conda init bash
conda activate chromafold_env
conda install -c r r-essentials
#3. going into R, and install packages
# https://github.com/GreenleafLab/ArchR
R
install.packages("Rcpp", dependencies = TRUE)
install.packages('git2r')
q()
# exit R, in bash
conda install conda-forge::r-rcpp
conda install -c conda-forge binutils
conda install -c conda-forge libgit2
conda install -c conda-forge r-gert
conda install -c conda-forge r-usethis
conda install -c conda-forge r-devtools
conda install -c conda-forge r-biocmanager
conda install conda-forge::r-rcpp
conda install -c conda-forge r-devtools
conda install bioconda::bioconductor-chromvar
conda install bioconda::bioconductor-dirichlemialtinom
conda install bioconda::bioconductor-motifmatchrols
conda install -c bioconda bioconductor-tfbstools
#4. Install ArchR (in R, the R installed from conda r, not independent R)
R
if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
Sys.setenv(CONDA_BUILD_SYSROOT="/")
devtools::install_github("GreenleafLab/ArchR", ref="master", repos = BiocManager::repositories())
library(ArchR)
ArchR::installExtraPackages()
# If you need to run HiC-DC+, install here:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("HiCDCPlus")
| Shell |
3D | viannegao/ChromaFold | chromafold/testing.py | .py | 4,019 | 136 | import os
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from datetime import datetime
from utils import *
"""
Dataset for testing with both aggregated accessibility and coaccessibility
"""
class test_Dataset(torch.utils.data.Dataset):
def __init__(
self,
input_size,
pbulk_dict,
scatac_dict,
ctcf_dict,
list_cts,
list_starts,
list_chroms,
transform,
is_training=False,
):
self.input_size = input_size
self.pbulk_dict = pbulk_dict
self.scatac_dict = scatac_dict
self.ctcf_dict = ctcf_dict
self.list_cts = list_cts
self.list_starts = list_starts
self.list_chroms = list_chroms
self.is_training = is_training
self.transform = transform
def __len__(self):
return len(self.list_starts)
def __getitem__(self, index):
chrom = self.list_chroms[index]
start = self.list_starts[index]
shift_input_by = 0
pbulk_res = 50
scatac_res = 500
input_window = int(self.input_size / pbulk_res)
atac_start_ind = int(start / pbulk_res)
sc_input_window = int(self.input_size / scatac_res)
sc_atac_start_ind = int(start / scatac_res)
X1 = self.pbulk_dict[chrom]
X1 = X1[
max(0, atac_start_ind + shift_input_by) : atac_start_ind
+ shift_input_by
+ input_window
]
X1 = self.transform(X1.toarray().astype(np.float32).T)
Z1 = self.scatac_dict[chrom]
Z1 = Z1[
max(0, sc_atac_start_ind + shift_input_by) : sc_atac_start_ind
+ shift_input_by
+ sc_input_window,
:,
]
Z1 = Z1.toarray().astype(np.float32).T
Z1 = torch.tensor(Z1)
X2 = self.ctcf_dict[chrom]
X2 = X2[max(0, atac_start_ind) : atac_start_ind + input_window]
X2 = torch.tensor(X2.toarray().astype(np.float32).T)
if start < 0:
desired_shape = (1, 80200)
# Determine the number of zeros to add on the left side
num_zeros = desired_shape[1] - X1.shape[1]
# Pad the tensor with zeros on the left side
X1 = torch.cat([torch.zeros((X1.shape[0], num_zeros)), X1], dim=1)
X2 = torch.cat([torch.zeros((X2.shape[0], num_zeros)), X2], dim=1)
desired_shape = (Z1.shape[0], 8020)
num_zeros = desired_shape[1] - Z1.shape[1]
Z1 = torch.cat([torch.zeros((Z1.shape[0], num_zeros)), Z1], dim=1)
Z1 = cpu_batch_corcoeff_vstripe(Z1)
X1_rev = torch.empty_like(X1).copy_(X1)
X1_rev = torch.flip(X1_rev, [1])
X2_rev = torch.empty_like(X2).copy_(X2)
X2_rev = torch.flip(X2_rev, [1])
Z1_rev = torch.empty_like(Z1).copy_(Z1)
Z1_rev = torch.flip(Z1_rev, [1])
return Z1, Z1_rev, X1, X1_rev, X2, X2_rev
def test(test_loader, model, device):
'''
Function for the validation step of the training loop
'''
y_hat_list = []
y_true_list = []
model.eval()
with tqdm(test_loader, unit="batch") as tepoch:
for Z2, Z2_rev, X1, X1_rev, X2, X2_rev in tepoch:
# left interactions
X1 = X1.to(device)
X2 = X2.to(device)
X = torch.cat((X1, X2), axis = 1)
Z2 = Z2.to(device)
y_hat = model(Z2, X)
# right interactions
X1_rev = X1_rev.to(device)
X2_rev = X2_rev.to(device)
X_rev = torch.cat((X1_rev, X2_rev), axis = 1)
Z2_rev = Z2_rev.to(device)
y_hat_rev = model(Z2_rev, X_rev)
y_hat = torch.cat((y_hat, torch.flip(y_hat_rev, [1])), 1)
y_hat_list.append(y_hat.detach().cpu())
return y_hat_list
def test_model(model, test_loader, device):
with torch.no_grad():
y_hat_list = test(test_loader, model, device)
return y_hat_list
| Python |
3D | viannegao/ChromaFold | chromafold/model.py | .py | 9,594 | 346 | import numpy as np
import torch
import torch.nn as nn
class resblock(nn.Module):
def __init__(self, ni):
super(resblock, self).__init__()
self.blocks = nn.Sequential(
nn.Conv1d(ni, ni, 3, 1, 1),
nn.BatchNorm1d(ni),
nn.ReLU(),
nn.Conv1d(ni, ni, 3, 1, 1),
nn.BatchNorm1d(ni),
nn.ReLU(),
)
def forward(self, x):
residual = x
out = self.blocks(x)
out = out + residual
return out
class symmetrize_bulk(nn.Module):
def __init__(self):
super(symmetrize_bulk, self).__init__()
def forward(self, x):
if len(x.shape) == 2:
print("not implemented")
return None
else:
if len(x.shape) == 3:
a, b, c = x.shape
x = x.reshape(a, b, 1, c)
x = x.repeat(1, 1, c, 1)
x_t = x.permute(0, 1, 3, 2)
x_sym = torch.concat((x, x_t), axis=1) # (x+x_t)/2
return x_sym
else:
return None
class branch_pbulk(nn.Module):
def __init__(self):
super(branch_pbulk, self).__init__()
pbulk_res = 50
scatac_res = 500
self.bulk_summed_2d = nn.Sequential(
nn.AvgPool1d(kernel_size=np.int64(1e04 / pbulk_res)), symmetrize_bulk()
)
self.bulk_extractor_2d = nn.Sequential(
nn.Conv1d(
in_channels=2,
out_channels=16,
kernel_size=11,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=16,
out_channels=32,
kernel_size=7,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=2,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=3,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=5,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=5,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=7,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=11,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=11,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.MaxPool1d(kernel_size=5),
nn.Conv1d(
in_channels=32,
out_channels=16,
kernel_size=3,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.MaxPool1d(kernel_size=5),
nn.Conv1d(
in_channels=16,
out_channels=16,
kernel_size=3,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.Conv1d(
in_channels=16,
out_channels=16,
kernel_size=3,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(16),
nn.ReLU(),
symmetrize_bulk(),
)
self.total_extractor_2d = nn.Sequential(
nn.Conv2d(in_channels=36, out_channels=64, kernel_size=3, stride=2),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, stride=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=32, out_channels=16, kernel_size=3, stride=2),
nn.BatchNorm2d(16),
nn.ReLU(),
)
self.classifier = nn.Sequential(
nn.Linear(in_features=(1936), out_features=512),
)
self.classifier2 = nn.Sequential(nn.Linear(in_features=(512), out_features=200))
def forward(self, x2):
x3_2d = self.bulk_summed_2d(x2)
x2_2d = self.bulk_extractor_2d(x2)
x4 = torch.cat((x3_2d, x2_2d), 1)
x4 = self.total_extractor_2d(x4)
x4 = torch.flatten(x4, 1)
x4 = self.classifier(x4)
return x4
class branch_cov(nn.Module):
def __init__(self):
super(branch_cov, self).__init__()
self.cov_extractor = nn.Sequential(
nn.Conv1d(
in_channels=20, out_channels=16, kernel_size=5, stride=1, padding=2
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=16, out_channels=16, kernel_size=5, stride=1, padding=2
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=16,
out_channels=16,
kernel_size=3,
stride=1,
dilation=1,
padding=1,
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
resblock(16),
nn.MaxPool1d(kernel_size=2),
resblock(16),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=16,
out_channels=16,
kernel_size=3,
stride=1,
dilation=1,
padding=1,
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=16,
out_channels=16,
kernel_size=3,
stride=1,
dilation=1,
padding=1,
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=16,
out_channels=16,
kernel_size=3,
stride=1,
dilation=1,
padding=1,
),
nn.BatchNorm1d(16),
nn.ReLU(),
)
self.classifier = nn.Sequential(
nn.Linear(in_features=(992), out_features=512),
)
def forward(self, x, x_pb):
x = self.cov_extractor(x)
x = torch.flatten(x, 1)
x_out = self.classifier(x)
return x_out
class trunk(nn.Module):
def __init__(self, branch_pbulk, branch_cov):
super(trunk, self).__init__()
self.branch_pbulk = branch_pbulk
self.branch_cov = branch_cov
self.out = nn.Sequential(
nn.Linear(in_features=(512 * 2), out_features=512),
nn.Linear(in_features=(512), out_features=200),
)
def forward(self, x, x2):
x = self.branch_cov(x, x2)
with torch.no_grad():
x2 = self.branch_pbulk(x2)
x = self.out(torch.cat((x, x2), 1))
return x
| Python |
3D | viannegao/ChromaFold | chromafold/train_bulkOnly.py | .py | 8,587 | 312 | import argparse
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import numpy as np
from tqdm import tqdm
import h5py
from utils import *
from dataloader import *
from model_bulk_only import *
from training_utils import *
# DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
parser = argparse.ArgumentParser(description="PyTorch chromaFold")
parser.add_argument(
"--data-path", metavar="DIR", default="./datasets", help="path to dataset"
)
parser.add_argument(
"--save-path", default="./checkpoints", help="path to save model chekpoints"
)
parser.add_argument("-ct", "--ct-list", nargs="+", default=[])
parser.add_argument("--genome", default="hg38", help="genome of training cell type")
parser.add_argument(
"--min-stripe-signal",
default=-170,
type=int,
help="Stripe signal cutoff; avoid training on low-signal stripes",
)
parser.add_argument(
"-j",
"--workers",
default=8,
type=int,
help="number of data loading workers (default: 8)",
)
parser.add_argument(
"--epochs", default=30, type=int, help="number of total epochs to run"
)
parser.add_argument(
"-b", "--batch-size", default=64, type=int, help="batch size (default: 64)"
)
parser.add_argument(
"--patience", default=10, type=int, help="training patience in epochs (default: 10)"
)
parser.add_argument(
"--lr",
"--learning-rate",
default=1e-6,
type=float,
metavar="LR",
help="initial learning rate",
dest="lr",
)
parser.add_argument(
"--wd",
"--weight-decay",
default=1e-3,
type=float,
metavar="W",
help="weight decay (default: 1e-3)",
dest="weight_decay",
)
parser.add_argument(
"--seed", default=1229, type=int, help="seed for initializing training. "
)
parser.add_argument("--disable-cuda", action="store_true", help="Disable CUDA")
parser.add_argument("--deterministic", action="store_true", help="cudnn deterministic")
parser.add_argument("--mod-name", default="", help="model name")
def main():
args = parser.parse_args()
# check if gpu training is available
if not args.disable_cuda and torch.cuda.is_available():
args.device = torch.device("cuda")
if args.deterministic:
cudnn.deterministic = True
logging.warning(
"You have chosen to seed training. This will turn on the CUDNN deterministic setting, which can slow down "
"your training considerably! You may see unexpected behavior when restarting from checkpoints."
)
# cudnn.benchmark = True
else:
args.device = torch.device("cpu")
torch.manual_seed(args.seed)
data_path = args.data_path
save_path = args.save_path
BATCH_SIZE = args.batch_size
N_EPOCHS = args.epochs
initial_rate = args.lr
wd = args.weight_decay
ct_list = args.ct_list
genome = args.genome
# Verify validity of input and output paths.
assert os.path.isdir(
data_path
), "data_path does not exist. Please create directory first."
assert os.path.isdir(
save_path
), "save_path does not exist. Please create directory first."
if args.mod_name:
save_path = os.path.join(save_path, args.mod_name)
os.mkdir(save_path)
print("Saving to {}".format(save_path))
# Training specifications.
train_chrom = [1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 19]
val_chrom = [3, 15]
test_chrom = [5, 18, 20, 21]
# The following should not be changed unless the user modifies the dimensions in the model accordingly.
input_size = 4010000
train_step = 5e04
val_step = 5e04
# start_dict = {}
# end_dict = {}
# for i in hic_dict[ct_list[1]].keys():
# idx = np.where(hic_dict[ct_list[0]][i].toarray().sum(1)>-600)[0]
# start_dict[i] = idx[0] * 10000
# end_dict[i] = idx[-1] * 10000
# for ct in ct_list[1:]:
# for i in hic_dict[ct_list[1]].keys():
# idx = np.where(hic_dict[ct][i].toarray().sum(1)>-600)[0]
# start_dict[i] = max(start_dict[i], idx[0] * 10000)
# end_dict[i] = min(end_dict[i], idx[-1] * 10000)
# Start and end indices of each chromosome
start_dict = {
"chr13": 0,
"chr21": 0,
"chr3": 0,
"chr20": 0,
"chrX": 0,
"chr8": 0,
"chr10": 0,
"chr15": 0,
"chr5": 0,
"chr2": 0,
"chr14": 0,
"chr6": 0,
"chr7": 0,
"chr17": 0,
"chr18": 0,
"chr16": 0,
"chr1": 0,
"chr11": 0,
"chr4": 0,
"chr9": 0,
"chr12": 0,
"chr19": 0,
}
end_dict = {
"chr13": 114360000,
"chr21": 46700000,
"chr3": 198020000,
"chr20": 63020000,
"chrX": 155270000,
"chr8": 145130000,
"chr10": 133790000,
"chr15": 101990000,
"chr5": 180910000,
"chr2": 242190000,
"chr14": 107040000,
"chr6": 170800000,
"chr7": 159130000,
"chr17": 81190000,
"chr18": 78070000,
"chr16": 90330000,
"chr1": 248950000,
"chr11": 135000000,
"chr4": 190210000,
"chr9": 138390000,
"chr12": 133270000,
"chr19": 58610000,
}
# Specify efective genome size for normalization
if "hg" in genome:
effective_genome_size = 2913022398
elif "mm" in genome:
effective_genome_size = 2652783500
else:
raise ValueError(
"Please compute effective_genome_size manuallly and modify this function accordingly."
)
# LOAD DATA
print("Loading training data for these cell types:")
print(ct_list)
print(args)
hic_dict = {}
hic_qval_dict = {}
pbulk_dict = {}
scatac_dict = {}
n_cells = {}
libsize_cell = {}
for ct in ct_list:
hic_dict[ct], hic_qval_dict[ct] = load_hic(data_path, ct)
pbulk_dict[ct], scatac_dict[ct] = load_atac(data_path, ct, pbulk_only=True)
n_cells[ct] = get_num_cells(pbulk_dict[ct])
libsize_cell[ct] = get_libsize(pbulk_dict[ct])
ctcf_dict = load_ctcf_motif(data_path, genome)
# Create dataset objects for training and validation
train_dataset = Dataset_bulk_only(
input_size,
effective_genome_size,
hic_dict,
pbulk_dict,
ctcf_dict,
n_cells,
libsize_cell,
*get_starts(
chrom_list=train_chrom,
start_dict=start_dict,
end_dict=end_dict,
chrom_start_offset=4000000,
chrom_end_offset=5000000,
multi_ct=True,
ct_list=ct_list,
step=train_step,
),
transform=normalize,
is_training=True
)
val_dataset = Dataset_bulk_only(
input_size,
effective_genome_size,
hic_dict,
pbulk_dict,
ctcf_dict,
n_cells,
libsize_cell,
*get_starts(
chrom_list=val_chrom,
start_dict=start_dict,
end_dict=end_dict,
chrom_start_offset=4000000,
chrom_end_offset=5000000,
multi_ct=True,
ct_list=ct_list,
step=val_step,
),
transform=normalize
)
# Create dataloader objects for training and validation
train_loader = DataLoader(
dataset=train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=args.workers,
)
val_loader = DataLoader(
dataset=val_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=args.workers,
)
# Define Model
model = nn.DataParallel(branch_pbulk(), device_ids=[0]).to(args.device)
optimizer = torch.optim.SGD(
model.parameters(), lr=initial_rate, momentum=0.9, weight_decay=wd
)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[N_EPOCHS / 10 * 4, N_EPOCHS / 10 * 8], gamma=0.1
)
criterion = weighted_mse_loss
# Train
print("Training...")
model, optimizer, _ = training_loop(
train_bulk_only,
validate_bulk_only,
model,
ct_list,
args.seed,
criterion,
optimizer,
scheduler,
train_loader,
val_loader,
N_EPOCHS,
args.patience,
save_path,
args.device,
args.min_stripe_signal,
)
if __name__ == "__main__":
main()
| Python |
3D | viannegao/ChromaFold | chromafold/model_bulk_only.py | .py | 7,102 | 251 | import numpy as np
import torch
import torch.nn as nn
class resblock(nn.Module):
def __init__(self, ni):
super(resblock, self).__init__()
self.blocks = nn.Sequential(
nn.Conv1d(ni, ni, 3, 1, 1),
nn.BatchNorm1d(ni),
nn.ReLU(),
nn.Conv1d(ni, ni, 3, 1, 1),
nn.BatchNorm1d(ni),
nn.ReLU(),
)
def forward(self, x):
residual = x
out = self.blocks(x)
out = out + residual
return out
class symmetrize_bulk(nn.Module):
def __init__(self):
super(symmetrize_bulk, self).__init__()
def forward(self, x):
if len(x.shape) == 2:
print("not implemented")
return None
else:
if len(x.shape) == 3:
a, b, c = x.shape
x = x.reshape(a, b, 1, c)
x = x.repeat(1, 1, c, 1)
x_t = x.permute(0, 1, 3, 2)
x_sym = torch.concat((x, x_t), axis=1) # (x+x_t)/2
return x_sym
else:
return None
# Pseudo-bulk branch
class branch_pbulk(nn.Module):
def __init__(self):
super(branch_pbulk, self).__init__()
scatac_res = 50
self.bulk_summed_2d = nn.Sequential(
nn.AvgPool1d(kernel_size=np.int64(1e04 / scatac_res)), symmetrize_bulk()
)
self.bulk_extractor_2d = nn.Sequential(
nn.Conv1d(
in_channels=2,
out_channels=16,
kernel_size=11,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=16,
out_channels=32,
kernel_size=7,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
# nn.MaxPool1d(kernel_size = 2),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
# nn.MaxPool1d(kernel_size = 2),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=2,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
# nn.MaxPool1d(kernel_size = 2),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=3,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
# nn.MaxPool1d(kernel_size = 2),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=5,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=5,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=7,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=11,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.Conv1d(
in_channels=32,
out_channels=32,
kernel_size=5,
stride=1,
dilation=11,
padding="same",
),
nn.BatchNorm1d(32),
nn.ReLU(),
nn.MaxPool1d(kernel_size=5),
nn.Conv1d(
in_channels=32,
out_channels=16,
kernel_size=3,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.MaxPool1d(kernel_size=5),
nn.Conv1d(
in_channels=16,
out_channels=16,
kernel_size=3,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.Conv1d(
in_channels=16,
out_channels=16,
kernel_size=3,
stride=1,
dilation=1,
padding="same",
),
nn.BatchNorm1d(16),
nn.ReLU(),
symmetrize_bulk(),
)
self.total_extractor_2d = nn.Sequential(
nn.Conv2d(in_channels=36, out_channels=64, kernel_size=3, stride=2),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, stride=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=32, out_channels=16, kernel_size=3, stride=2),
nn.BatchNorm2d(16),
nn.ReLU(),
)
self.classifier = nn.Sequential(
nn.Linear(in_features=(1936), out_features=512),
)
self.classifier2 = nn.Sequential(nn.Linear(in_features=(512), out_features=200))
def forward(self, x2):
x3_2d = self.bulk_summed_2d(x2)
x2_2d = self.bulk_extractor_2d(x2)
x4 = torch.cat((x3_2d, x2_2d), 1)
x4 = self.total_extractor_2d(x4)
x4 = torch.flatten(x4, 1)
x4 = self.classifier(x4)
out = self.classifier2(x4)
return out
| Python |
3D | viannegao/ChromaFold | chromafold/training_utils.py | .py | 6,358 | 230 | import os
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from datetime import datetime
def train_bulk_only(
train_loader, model, criterion, optimizer, device, min_stripe_signal
):
model.train()
running_loss = 0
with tqdm(train_loader, unit="batch") as tepoch:
for X1, X1_rev, X2, X2_rev, y_true, y_true_rev in tepoch:
optimizer.zero_grad()
# left interactions
X1 = X1.to(device)
X2 = X2.to(device)
X = torch.cat((X1, X2), axis=1)
y_true = y_true.to(device)
y_hat = model(X)
loss = criterion(y_hat, y_true, min_stripe_signal)
# right interactions
X1_rev = X1_rev.to(device)
X2_rev = X2_rev.to(device)
X_rev = torch.cat((X1_rev, X2_rev), axis=1)
y_true_rev = y_true_rev.to(device)
y_hat_rev = model(X_rev)
loss += criterion(y_hat_rev, y_true_rev, min_stripe_signal)
running_loss += loss.item()
# Backward pass
loss.backward()
optimizer.step()
epoch_loss = running_loss / len(train_loader.dataset)
return model, optimizer, epoch_loss
def validate_bulk_only(val_loader, model, criterion, device, min_stripe_signal):
model.eval()
running_loss = 0
for X1, X1_rev, X2, X2_rev, y_true, y_true_rev in val_loader:
# left interactions
X1 = X1.to(device)
X2 = X2.to(device)
X = torch.cat((X1, X2), axis=1)
y_true = y_true.to(device)
y_hat = model(X)
loss = criterion(y_hat, y_true, min_stripe_signal)
# right interactions
X1_rev = X1_rev.to(device)
X2_rev = X2_rev.to(device)
X_rev = torch.cat((X1_rev, X2_rev), axis=1)
y_true_rev = y_true_rev.to(device)
y_hat_rev = model(X_rev)
loss += criterion(y_hat_rev, y_true_rev, min_stripe_signal)
running_loss += loss.item()
epoch_loss = running_loss / len(val_loader.dataset)
return model, epoch_loss
def train(train_loader, model, criterion, optimizer, device, min_stripe_signal):
model.train()
running_loss = 0
with tqdm(train_loader, unit="batch") as tepoch:
for Z2, Z2_rev, X1, X1_rev, X2, X2_rev, y_true, y_true_rev in tepoch:
optimizer.zero_grad()
# left interactions
X1 = X1.to(device)
X2 = X2.to(device)
X = torch.cat((X1, X2), axis=1)
Z2 = Z2.to(device)
y_true = y_true.to(device)
y_hat = model(Z2, X)
loss = criterion(y_hat, y_true, min_stripe_signal)
# right interactions
X1_rev = X1_rev.to(device)
X2_rev = X2_rev.to(device)
X_rev = torch.cat((X1_rev, X2_rev), axis=1)
Z2_rev = Z2_rev.to(device)
y_true_rev = y_true_rev.to(device)
y_hat_rev = model(Z2_rev, X_rev)
loss += criterion(y_hat_rev, y_true_rev, min_stripe_signal)
running_loss += loss.item() # * X.size(0)
# Backward pass
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 100000)
optimizer.step()
epoch_loss = running_loss / len(train_loader.dataset)
return model, optimizer, epoch_loss
def validate(val_loader, model, criterion, device, min_stripe_signal):
"""
Function for the validation step of the training loop
"""
model.eval()
running_loss = 0
for Z2, Z2_rev, X1, X1_rev, X2, X2_rev, y_true, y_true_rev in val_loader:
# left interactions
X1 = X1.to(device)
X2 = X2.to(device)
X = torch.cat((X1, X2), axis=1)
Z2 = Z2.to(device)
y_true = y_true.to(device)
y_hat = model(Z2, X)
loss = criterion(y_hat, y_true, min_stripe_signal)
# right interactions
X1_rev = X1_rev.to(device)
X2_rev = X2_rev.to(device)
X_rev = torch.cat((X1_rev, X2_rev), axis=1)
Z2_rev = Z2_rev.to(device)
y_true_rev = y_true_rev.to(device)
y_hat_rev = model(Z2_rev, X_rev)
loss += criterion(y_hat_rev, y_true_rev, min_stripe_signal)
running_loss += loss.item() # * X.size(0)
epoch_loss = running_loss / len(val_loader.dataset)
return model, epoch_loss
def training_loop(
train,
validate,
model,
ct_list,
seed,
criterion,
optimizer,
scheduler,
train_loader,
valid_loader,
epochs,
patience,
save_path,
device,
min_stripe_signal,
print_every=1,
):
"""
Function defining the entire training loop
"""
# set objects for storing metrics
counter = 0
best_loss = 1e10
train_losses = []
valid_losses = []
SAVEPATH = os.path.join(
save_path, "{}_CTCFmotifScore_seed{}.pth.tar".format("_".join(ct_list), seed)
)
# Train model
for epoch in range(epochs):
# training
model, optimizer, train_loss = train(
train_loader, model, criterion, optimizer, device, min_stripe_signal
)
train_losses.append(train_loss)
# validation
with torch.no_grad():
model, valid_loss = validate(
valid_loader, model, criterion, device, min_stripe_signal
)
valid_losses.append(valid_loss)
counter += 1
if valid_loss < best_loss:
counter = 0
best_loss = valid_loss
print("++++saving+++++")
torch.save(model.state_dict(), SAVEPATH)
if epoch % print_every == (print_every - 1):
print(
f"{datetime.now().time().replace(microsecond=0)} --- "
f"Epoch: {epoch}\t"
f"Train loss: {train_loss:.4f}\t"
f"Valid loss: {valid_loss:.4f}\t"
)
if counter > patience:
print("Early stopping.")
break
scheduler.step()
return model, optimizer, (train_losses, valid_losses)
def weighted_mse_loss(input, target, min_stripe_signal=-99999):
# do not compute loss on low-mappability regions
weight = target.sum(1) > min_stripe_signal
loss = (input - target) ** 2
loss = (weight * loss.T).T
loss = torch.sum(loss)
return loss
| Python |
3D | viannegao/ChromaFold | chromafold/inference.py | .py | 6,029 | 184 | import argparse
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import numpy as np
from numpy import savez_compressed
from tqdm import tqdm
import h5py
from utils import *
from dataloader import *
from model import *
from testing import *
from util_chrom_start import *
# DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
parser = argparse.ArgumentParser(description="PyTorch chromaFold")
parser.add_argument(
"--data-path", metavar="DIR", default="./datasets", help="path to dataset"
)
parser.add_argument(
"--model-path", default="./checkpoints/chromafold_CTCFmotif.pth.tar", help="path to model checkpoint"
)
parser.add_argument(
"--save-path", default="./predictions", help="path to save model predictions"
)
parser.add_argument("-ct", default="umb_endo")
parser.add_argument("-chrom", "--test-chrom", nargs="+", default=[])
parser.add_argument("--genome", default="hg38", help="genome of training cell type")
parser.add_argument(
"-j",
"--workers",
default=8,
type=int,
help="number of data loading workers (default: 8)",
)
parser.add_argument(
"-b", "--batch-size", default=64, type=int, help="batch size (default: 64)"
)
parser.add_argument("-offset", default=0, type = int, help="Chromosome start index offset (bp). For training, we use 4,000,000 to skip over the low-signal regions. For testing, use 0 to start at 2Mb and ensure all regions have full input context for prediction. we can use -2,000,000 to make prediction starting from the beginning; input will be zero-padded in this case.")
parser.add_argument("--use-ctcf-chip", action="store_true", help="Use CTCF ChIP seq data instead of motif score")
parser.add_argument("--disable-cuda", action="store_true", help="Disable CUDA")
parser.add_argument("--deterministic", action="store_true", help="cudnn deterministic")
def main():
args = parser.parse_args()
# check if gpu training is available
if not args.disable_cuda and torch.cuda.is_available():
args.device = torch.device("cuda")
if args.deterministic:
cudnn.deterministic = True
logging.warning(
"You have chosen to seed training. This will turn on the CUDNN deterministic setting, which can slow down "
"your training considerably! You may see unexpected behavior when restarting from checkpoints."
)
# cudnn.benchmark = True
else:
args.device = torch.device("cpu")
data_path = args.data_path
save_path = args.save_path
model_path = args.model_path
BATCH_SIZE = args.batch_size
ct = args.ct
genome = args.genome
assert os.path.isdir(
data_path
), "data_path does not exist. Please create directory first."
assert os.path.exists(
model_path
), "model_path does not exist. Please double check path."
print("============================ Running ChromaFold ============================")
print("\n-> Predicting Hi-C interactions in {}".format(ct))
print("\n-> Using checkpoint {}".format(model_path))
if args.use_ctcf_chip:
print('\n-> Using CTCF ChIP seq data. Please make sure you are using the CTCF ChIP seq model.')
test_chrom = args.test_chrom
input_size = 4010000
test_step = 1e04
start_dict, end_dict = get_chrom_starts(genome)
# LOAD DATA
print("\n-> Params:")
print(args)
pbulk_dict = {}
scatac_dict = {}
n_cells = {}
n_metacells = {}
libsize_cell = {}
pbulk_dict, scatac_dict = load_atac_eval(data_path, ct, pbulk_only=False)
n_cells = get_num_cells(pbulk_dict)
effective_genome_size = get_effective_genome_size(genome)
normalized_pbulk_dict = normalize_bulk_dict(pbulk_dict, effective_genome_size)
del pbulk_dict
ctcf_dict = load_ctcf_motif(data_path, genome, args.use_ctcf_chip)
test_dataset = test_Dataset(
input_size,
normalized_pbulk_dict,
scatac_dict,
ctcf_dict,
*get_starts(
chrom_list=test_chrom,
start_dict=start_dict,
end_dict=end_dict,
chrom_start_offset=args.offset,
chrom_end_offset=5000000,
multi_ct=False,
ct_list=[ct],
step=test_step,
),
transform=normalize
)
test_loader = DataLoader(
dataset=test_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=args.workers,
)
# Run inference
print("Running Inference...")
mod_branch_pbulk = nn.DataParallel(branch_pbulk(), device_ids=[0])
mod_branch_cov = nn.DataParallel(branch_cov(), device_ids=[0])
model = nn.DataParallel(trunk(mod_branch_pbulk, mod_branch_cov), device_ids=[0])
model.to(args.device)
model.load_state_dict(torch.load(model_path))
model.eval()
y_hat_list = test_model(model, test_loader, args.device)
y_hat_list = [x.detach().cpu() for x in y_hat_list]
y_hat_list = np.concatenate([x for x in
y_hat_list], axis = 0)
# Save predictions
test_chrom_df = get_starts(
chrom_list=test_chrom,
start_dict=start_dict,
end_dict=end_dict,
chrom_start_offset=args.offset,
chrom_end_offset=5000000,
multi_ct=False,
ct_list=[ct],
step=test_step,
)
test_chrom_df = pd.DataFrame(test_chrom_df[2],
test_chrom_df[2])
for i in range(len(test_chrom)):
start_end = np.where(test_chrom_df[0]=='chr{}'.format(test_chrom[i]))[0]
print(start_end[0], start_end[-1]+1, test_chrom[i])
savez_compressed('{}/prediction_{}_chr{}.npz'.format(save_path, ct, test_chrom[i]),
y_hat_list[start_end[0] : start_end[-1] + 1, :])
print("Inference done and predictions saved to {}.".format(save_path))
if __name__ == "__main__":
main()
| Python |
3D | viannegao/ChromaFold | chromafold/utils.py | .py | 7,626 | 251 | import pickle
import os
import numpy as np
import pandas as pd
import scipy
from scipy import sparse
import torch
def get_effective_genome_size(genome):
if "hg" in genome:
effective_genome_size = 2913022398
elif "mm" in genome:
effective_genome_size = 2652783500
else:
raise ValueError(
"Please compute effective_genome_size manuallly and modify this function accordingly."
)
return effective_genome_size
def get_metacell_profile(sc_atac_dict, nbrs):
metacell_sc_atac_dict = {}
metacell = nbrs
for chrom in list(sc_atac_dict.keys()):
metacell_sc_atac_dict[chrom] = (
sparse.csr_matrix(metacell) * sc_atac_dict[chrom]
)
metacell_sc_atac_dict[chrom] = sparse.csr_matrix(
metacell_sc_atac_dict[chrom].toarray().transpose()
)
return metacell_sc_atac_dict
def load_hic(data_path, ct):
zscore_dict = pickle.load(
open(os.path.join(data_path, "hic/{}_hic_zscore_dict.p".format(ct)), "rb")
)
# qvalue_dict = pickle.load(
# open(os.path.join(data_path, "hic/{}_hic_qvalue_dict.p".format(ct)), "rb")
# )
qvalue_dict = pickle.load(
open(os.path.join(data_path, "hic/{}_hic_zscore_dict.p".format(ct)), "rb")
)
return zscore_dict, qvalue_dict
def load_atac(data_path, ct, pbulk_only=False):
bulk_atac_dict = pickle.load(
open(os.path.join(data_path, "atac/{}_tile_50bp_dict.p".format(ct)), "rb")
)
if pbulk_only:
sc_atac_dict = None
else:
sc_atac_dict = pickle.load(
open(os.path.join(data_path, "atac/{}_tile_500bp_dict.p".format(ct)), "rb")
)
metacell_mask = pd.read_csv(
os.path.join(data_path, "atac/{}_metacell_mask.csv".format(ct)), index_col=0
).values
sc_atac_dict = get_metacell_profile(sc_atac_dict, metacell_mask)
return bulk_atac_dict, sc_atac_dict
def load_atac_eval(data_path, ct, pbulk_only=False):
bulk_atac_dict = pickle.load(
open(os.path.join(data_path, "atac/{}_tile_pbulk_50bp_dict.p".format(ct)), "rb")
)
for chrom in bulk_atac_dict.keys():
bulk_atac_dict[chrom] = sparse.csr_matrix(bulk_atac_dict[chrom])
if pbulk_only:
sc_atac_dict = None
else:
sc_atac_dict = pickle.load(
open(os.path.join(data_path, "atac/{}_tile_500bp_dict.p".format(ct)), "rb")
)
metacell_mask = pd.read_csv(
os.path.join(data_path, "atac/{}_metacell_mask.csv".format(ct)), index_col=0
).values
sc_atac_dict = get_metacell_profile(sc_atac_dict, metacell_mask)
return bulk_atac_dict, sc_atac_dict
def load_ctcf_motif(data_path, genome, use_chip = False):
if not use_chip:
ctcf_dict = pickle.load(
open(os.path.join(data_path, "dna/{}_ctcf_motif_score.p".format(genome)), "rb")
)
else:
ctcf_dict = pickle.load(
open(os.path.join(data_path, "dna/{}_CTCF_50bp_dict.p".format(genome)), "rb")
)
for chrom in ctcf_dict.keys():
ctcf_dict[chrom] = ctcf_dict[chrom].T
return ctcf_dict
def get_num_cells(atac_dict, dim=0):
return atac_dict["chr1"].shape[dim]
def get_libsize(bulk_atac_dict):
libsize_cell = np.zeros(get_num_cells(bulk_atac_dict))
for chrom in bulk_atac_dict.keys():
libsize_cell += np.array(bulk_atac_dict[chrom].sum(1).T)[0]
return libsize_cell
def normalize(tensor, effective_genome_size=0, libsize=0, training=False):
tensor = torch.tensor(tensor)
if training:
assert libsize != 0, "Libsize cannot be 0 during training."
scale_factor = (libsize * 150) / effective_genome_size
tensor.div_(torch.as_tensor(scale_factor))
tensor = torch.log(tensor + 1)
return tensor
def normalize_bulk_dict(pbulk_dict, effective_genome_size):
normalized_pbulk_dict = {}
libsize = 0
for chrom in pbulk_dict.keys():
libsize += pbulk_dict[chrom].toarray().sum()
scale_factor = (libsize * 150) / (effective_genome_size)
for chrom in pbulk_dict.keys():
y = pbulk_dict[chrom].toarray()
y_t = y / scale_factor
normalized_pbulk_dict[chrom] = sparse.csr_matrix(y_t)
return normalized_pbulk_dict
def get_y_vstripe(start, hic, input_size, resolution=1e04):
hic_start_row = start
hic_start_col = start + 200 * np.int64(resolution)
ind_row = np.int64(hic_start_row // resolution)
ind_col = np.int64(hic_start_col // resolution)
vert = hic[ind_row : ind_row + 200, ind_col].toarray()
hic_start_row = start + 200 * np.int64(resolution)
hic_start_col = start + 201 * np.int64(resolution)
ind_row = np.int64(hic_start_row // resolution)
ind_col = np.int64(hic_start_col // resolution)
hori = hic[ind_row, ind_col : ind_col + 200].toarray()
y = np.append(vert, hori).astype(np.float32)
y = y.clip(-16, 16)
return y
def get_y_vstripe_eval(start, hic, input_size, resolution=1e04):
hic_start_row = start
hic_start_col = start + 200 * np.int64(resolution)
ind_row = np.int64(hic_start_row // resolution)
ind_col = np.int64(hic_start_col // resolution)
vert = hic[max(0, ind_row) : ind_row + 200, ind_col].toarray()
desired_length = 200
num_zeros = desired_length - vert.shape[0]
vert = np.pad(vert, ((num_zeros, 0), (0, 0)), "constant")
hic_start_row = start + 200 * np.int64(resolution)
hic_start_col = start + 201 * np.int64(resolution)
ind_row = np.int64(hic_start_row // resolution)
ind_col = np.int64(hic_start_col // resolution)
hori = hic[ind_row, ind_col : ind_col + 200].toarray()
num_zeros = desired_length - hori.shape[1]
hori = np.pad(hori, ((0, 0), (0, num_zeros)), "constant")
y = np.append(vert, hori).astype(np.float32)
return y
def cpu_jaccard_vstripe(x):
# calculate jaccard similarity of rows
scatac_res = 500
size = x.shape[1]
eps = 1e-8
i = np.int16(1000 / scatac_res)
x = torch.where(x > 0.0, torch.tensor([1.0]), torch.tensor([0.0]))
num = torch.mm(
x[2000 * i : 2010 * i, :],
x[np.r_[:, 0 : 2000 * i, 2010 * i : 4010 * i]].transpose(0, 1),
)
x = torch.where(x == 0.0, torch.tensor([1.0]), torch.tensor([0.0]))
denom = torch.mm(
x[2000 * i : 2010 * i, :],
x[np.r_[:, 0 : 2000 * i, 2010 * i : 4010 * i]].transpose(0, 1),
)
denom = size - denom
num = torch.div(num, torch.max(denom, eps * torch.ones_like(denom)))
return num
def cpu_batch_corcoeff_vstripe(x):
c = cpu_jaccard_vstripe(x.permute(1, 0))
c[c != c] = 0
return c
def get_starts(
chrom_list,
start_dict,
end_dict,
chrom_start_offset,
chrom_end_offset,
multi_ct=False,
ct_list=[],
step=5e4,
):
"""
Function to get the start and chrom list for train/test/val
"""
ctl = []
startl = []
chroml = []
for chrom in chrom_list:
chrom = "chr{}".format(chrom)
cur_starts = list(
np.arange(
start_dict[chrom] + chrom_start_offset,
end_dict[chrom] - chrom_end_offset,
step,
).astype(int)
)
startl = startl + cur_starts
chroml = chroml + list(np.repeat(chrom, len(cur_starts)))
if multi_ct:
for ct in ct_list:
ctl += list(np.repeat(ct, len(chroml)))
startl = startl * len(ct_list)
chroml = chroml * len(ct_list)
assert (len(startl) == len(chroml)) & (len(ctl) == len(chroml))
return ctl, startl, chroml
| Python |
3D | viannegao/ChromaFold | chromafold/dataloader.py | .py | 10,059 | 346 | import numpy as np
import torch
import torch.nn as nn
from utils import *
"""
Dataset with only aggregated accessibility
"""
class Dataset_bulk_only(torch.utils.data.Dataset):
def __init__(
self,
input_size,
effective_genome_size,
hic_dict,
pbulk_dict,
ctcf_dict,
n_cells,
libsize_cell,
list_cts,
list_starts,
list_chroms,
transform,
is_training=False,
):
self.input_size = input_size
self.effective_genome_size = effective_genome_size
self.hic_dict = hic_dict
self.pbulk_dict = pbulk_dict
self.ctcf_dict = ctcf_dict
self.n_cells = n_cells
self.libsize_cell = libsize_cell
self.list_cts = list_cts
self.list_starts = list_starts
self.list_chroms = list_chroms
self.is_training = is_training
self.transform = transform
def __len__(self):
return len(self.list_starts)
def __getitem__(self, index):
ct = self.list_cts[index]
chrom = self.list_chroms[index]
start = self.list_starts[index]
shift_input_by = 0
train_step = 5e04
pbulk_res = 50
if self.is_training:
start = (
np.int64(np.random.randint(0, np.int64(train_step / 1e4)) * 1e4) + start
)
# inject noise by adding shifts to the input
shift_input_by = np.random.randint(-1, 2)
input_window = np.int64(self.input_size / pbulk_res)
atac_start_ind = np.int64(start / pbulk_res)
hic = self.hic_dict[ct][chrom]
X1 = self.pbulk_dict[ct][chrom]
X1 = X1[
:,
atac_start_ind
+ shift_input_by : atac_start_ind
+ shift_input_by
+ input_window,
]
X2 = self.ctcf_dict[chrom]
X2 = X2[atac_start_ind : atac_start_ind + input_window]
X2 = torch.tensor(X2.toarray().astype(np.float32).T)
if self.is_training:
# Choose the number of cells to sample
size = np.random.randint(500, 5000)
mask = np.random.choice(
np.arange(self.n_cells[ct]), size=size, replace=True
)
X1 = X1[mask]
cur_libsize = self.libsize_cell[ct][mask].sum()
else:
cur_libsize = self.libsize_cell[ct].sum()
X1 = np.array(X1.sum(0)).astype(np.float32)
X1 = self.transform(
X1,
effective_genome_size=self.effective_genome_size,
libsize=cur_libsize,
training=True,
)
y = get_y_vstripe(start, hic, self.input_size)
y = torch.tensor(y)
X1_rev = torch.empty_like(X1).copy_(X1)
X1_rev = torch.flip(X1_rev, [1])
X2_rev = torch.empty_like(X2).copy_(X2)
X2_rev = torch.flip(X2_rev, [1])
y_rev = torch.flip(y, [0])
y = y[0:200]
y_rev = y_rev[0:200]
return X1, X1_rev, X2, X2_rev, y, y_rev
"""
Dataset with both aggregated accessibility and coaccessibility
"""
class Dataset(torch.utils.data.Dataset):
def __init__(
self,
input_size,
effective_genome_size,
hic_dict,
pbulk_dict,
scatac_dict,
ctcf_dict,
n_cells,
n_metacells,
libsize_cell,
list_cts,
list_starts,
list_chroms,
transform,
is_training=False,
):
self.input_size = input_size
self.effective_genome_size = effective_genome_size
self.hic_dict = hic_dict
self.pbulk_dict = pbulk_dict
self.scatac_dict = scatac_dict
self.ctcf_dict = ctcf_dict
self.n_cells = n_cells
self.n_metacells = n_metacells
self.libsize_cell = libsize_cell
self.list_cts = list_cts
self.list_starts = list_starts
self.list_chroms = list_chroms
self.is_training = is_training
self.transform = transform
def __len__(self):
return len(self.list_starts)
def __getitem__(self, index):
ct = self.list_cts[index]
chrom = self.list_chroms[index]
start = self.list_starts[index]
shift_input_by = 0
train_step = 5e04
pbulk_res = 50
scatac_res = 500
if self.is_training:
start = (
np.int32(np.random.randint(0, np.int32(train_step / 1e4)) * 1e4) + start
)
# inject noise by adding shifts to the input
shift_input_by = np.random.randint(-1, 2)
hic = self.hic_dict[ct][chrom]
input_window = np.int32(self.input_size / pbulk_res)
atac_start_ind = np.int32(start / pbulk_res)
sc_input_window = np.int32(self.input_size / scatac_res)
sc_atac_start_ind = np.int32(start / scatac_res)
X1 = self.pbulk_dict[ct][chrom]
X1 = X1[
:,
atac_start_ind
+ shift_input_by : atac_start_ind
+ shift_input_by
+ input_window,
]
Z1 = self.scatac_dict[ct][chrom]
Z1 = Z1[
sc_atac_start_ind
+ shift_input_by : sc_atac_start_ind
+ shift_input_by
+ sc_input_window
]
X2 = self.ctcf_dict[chrom]
X2 = X2[atac_start_ind : atac_start_ind + input_window]
X2 = torch.tensor(X2.toarray().astype(np.float32).T)
if self.is_training:
# Choose the number of cells to sample
size = np.random.randint(500, 5000)
mask = np.random.choice(
np.arange(self.n_cells[ct]), size=size, replace=True
)
X1 = X1[mask]
cur_libsize = self.libsize_cell[ct][mask].sum()
# Choose the number of metacells to sample
size2 = np.random.randint(400, 1000)
mask2 = np.random.choice(
np.arange(self.n_metacells[ct]), size=size2, replace=True
)
Z1 = Z1.T[mask2]
else:
Z1 = Z1.T
cur_libsize = self.libsize_cell[ct].sum()
X1 = np.array(X1.sum(0)).astype(np.float32)
X1 = self.transform(
X1,
effective_genome_size=self.effective_genome_size,
libsize=cur_libsize,
training=True,
)
Z1 = Z1.toarray().astype(np.float32)
Z1 = torch.tensor(Z1)
Z1 = cpu_batch_corcoeff_vstripe(Z1)
y = get_y_vstripe(start, hic, self.input_size)
y = torch.tensor(y)
X1_rev = torch.empty_like(X1).copy_(X1)
X1_rev = torch.flip(X1_rev, [1])
Z1_rev = torch.empty_like(Z1).copy_(Z1)
Z1_rev = torch.flip(Z1_rev, [1])
X2_rev = torch.empty_like(X2).copy_(X2)
X2_rev = torch.flip(X2_rev, [1])
y_rev = torch.flip(y, [0])
y = y[0:200]
y_rev = y_rev[0:200]
return Z1, Z1_rev, X1, X1_rev, X2, X2_rev, y, y_rev
"""
Dataset for testing with both aggregated accessibility and coaccessibility
"""
class test_Dataset(torch.utils.data.Dataset):
def __init__(
self,
input_size,
hic_dict,
pbulk_dict,
scatac_dict,
ctcf_dict,
list_cts,
list_starts,
list_chroms,
transform,
is_training=False,
):
self.input_size = input_size
self.hic_dict = hic_dict
self.pbulk_dict = pbulk_dict
self.scatac_dict = scatac_dict
self.ctcf_dict = ctcf_dict
self.list_cts = list_cts
self.list_starts = list_starts
self.list_chroms = list_chroms
self.is_training = is_training
self.transform = transform
def __len__(self):
return len(self.list_starts)
def __getitem__(self, index):
chrom = self.list_chroms[index]
start = self.list_starts[index]
shift_input_by = 0
pbulk_res = 50
scatac_res = 500
hic = hic_dict[chrom]
input_window = int(input_size / pbulk_res)
atac_start_ind = int(start / pbulk_res)
sc_input_window = int(input_size / scatac_res)
sc_atac_start_ind = int(start / scatac_res)
X1 = lib_normalizer[chrom]
X1 = X1[
max(0, atac_start_ind + shift_input_by) : atac_start_ind
+ shift_input_by
+ input_window
]
X1 = self.transform(X1.toarray().astype(np.float32).T)
Z1 = tile_dict[chrom]
Z1 = Z1[
max(0, sc_atac_start_ind + shift_input_by) : sc_atac_start_ind
+ shift_input_by
+ sc_input_window,
:,
]
Z1 = Z1.toarray().astype(np.float32).T
Z1 = torch.tensor(Z1)
X2 = ctcf_dict[chrom]
X2 = X2[max(0, atac_start_ind) : atac_start_ind + input_window]
X2 = torch.tensor(X2.toarray().astype(np.float32).T)
if start < 0:
desired_shape = (1, 80200)
# Determine the number of zeros to add on the left side
num_zeros = desired_shape[1] - X1.shape[1]
# Pad the tensor with zeros on the left side
X1 = torch.cat([torch.zeros((X1.shape[0], num_zeros)), X1], dim=1)
X2 = torch.cat([torch.zeros((X2.shape[0], num_zeros)), X2], dim=1)
desired_shape = (Z1.shape[0], 8020)
num_zeros = desired_shape[1] - Z1.shape[1]
Z1 = torch.cat([torch.zeros((Z1.shape[0], num_zeros)), Z1], dim=1)
Z1 = cpu_batch_corcoeff_vstripe(Z1)
y = get_y_vstripe_eval(start, hic, input_size)
y = torch.tensor(y)
X1_rev = torch.empty_like(X1).copy_(X1)
X1_rev = torch.flip(X1_rev, [1])
X2_rev = torch.empty_like(X2).copy_(X2)
X2_rev = torch.flip(X2_rev, [1])
Z1_rev = torch.empty_like(Z1).copy_(Z1)
Z1_rev = torch.flip(Z1_rev, [1])
y_rev = torch.flip(y, [0])
y = y[0:200]
y_rev = y_rev[0:200]
return Z1, Z1_rev, X1, X1_rev, X2, X2_rev, y, y_rev
| Python |
3D | viannegao/ChromaFold | chromafold/util_chrom_start.py | .py | 4,111 | 159 | def get_chrom_starts(genome):
if genome == "hg38":
start_dict = {
"chr1": 0,
"chr2": 0,
"chr3": 0,
"chr4": 0,
"chr5": 0,
"chr6": 0,
"chr7": 0,
"chr8": 0,
"chr9": 0,
"chr10": 0,
"chr11": 0,
"chr12": 0,
"chr13": 0,
"chr14": 0,
"chr15": 0,
"chr16": 0,
"chr17": 0,
"chr18": 0,
"chr19": 0,
"chr20": 0,
"chr21": 0,
"chr22": 0,
"chrX": 0,
}
end_dict = {
"chr1": 248950000,
"chr2": 242190000,
"chr3": 198020000,
"chr4": 190210000,
"chr5": 180910000,
"chr6": 170800000,
"chr7": 159130000,
"chr8": 145130000,
"chr9": 138390000,
"chr10": 133790000,
"chr11": 135000000,
"chr12": 133270000,
"chr13": 114360000,
"chr14": 107040000,
"chr15": 101990000,
"chr16": 90330000,
"chr17": 81190000,
"chr18": 78070000,
"chr19": 58610000,
"chr20": 63020000,
"chr21": 46700000,
"chr22": 51300000,
"chrX": 155270000,
}
elif genome == "hg19":
start_dict = {
"chr1": 0,
"chr2": 0,
"chr3": 0,
"chr4": 0,
"chr5": 0,
"chr6": 0,
"chr7": 0,
"chr8": 0,
"chr9": 0,
"chr10": 0,
"chr11": 0,
"chr12": 0,
"chr13": 0,
"chr14": 0,
"chr15": 0,
"chr16": 0,
"chr17": 0,
"chr18": 0,
"chr19": 0,
"chr20": 0,
"chr21": 0,
"chr22": 0,
"chrX": 0,
}
end_dict = {
"chr1": 249250000,
"chr2": 243190000,
"chr3": 198020000,
"chr4": 191150000,
"chr5": 180910000,
"chr6": 171110000,
"chr7": 159130000,
"chr8": 146360000,
"chr9": 141210000,
"chr10": 135530000,
"chr11": 135000000,
"chr12": 133850000,
"chr13": 115160000,
"chr14": 107340000,
"chr15": 102530000,
"chr16": 90350000,
"chr17": 81190000,
"chr18": 78070000,
"chr19": 59120000,
"chr20": 63020000,
"chr21": 48120000,
"chr22": 51300000,
"chrX": 155270000,
}
elif genome == "mm10":
start_dict = {
"chr1": 0,
"chr2": 0,
"chr3": 0,
"chr4": 0,
"chr5": 0,
"chr6": 0,
"chr7": 0,
"chr8": 0,
"chr9": 0,
"chr10": 0,
"chr11": 0,
"chr12": 0,
"chr13": 0,
"chr14": 0,
"chr15": 0,
"chr16": 0,
"chr17": 0,
"chr18": 0,
"chr19": 0,
"chrX": 0,
}
end_dict = {
"chr1": 195470000,
"chr2": 182110000,
"chr3": 160030000,
"chr4": 156500000,
"chr5": 151830000,
"chr6": 149730000,
"chr7": 145440000,
"chr8": 129400000,
"chr9": 124590000,
"chr10": 130690000,
"chr11": 122080000,
"chr12": 120120000,
"chr13": 120420000,
"chr14": 124900000,
"chr15": 104040000,
"chr16": 98200000,
"chr17": 94980000,
"chr18": 90700000,
"chr19": 61430000,
"chrX": 171030000,
}
else:
raise Exception(
"Chromosome info not provided for given genome version. Please compute manually and update here."
)
return start_dict, end_dict
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/setup.py | .py | 1,252 | 45 | #!/usr/bin/env python
import os
import setuptools
def readme():
with open('README.md') as f:
return f.read()
def get_requirements_filename():
if 'READTHEDOCS' in os.environ:
return "REQUIREMENTS-RTD.txt"
elif 'DOCKER' in os.environ:
return "REQUIREMENTS-DOCKER.txt"
else:
return "REQUIREMENTS.txt"
install_requires = [
line.rstrip() for line in open(os.path.join(os.path.dirname(__file__), get_requirements_filename()))
]
setuptools.setup(
name='pytorch_synapse',
version='0.1.0',
description='A PyTorch package for contrastive representation learning of 3D electron microscopy image chunks',
long_description=readme(),
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Science/Research'
'License :: OSI Approved :: BSD License',
'Programming Language :: Python :: 3.7',
'Topic :: Scientific/Engineering',
],
url='http://github.com/broadinstitute/pytorch_synapse',
author='Mehrtash Babadi, Alyssa Wilson',
license='BSD (3-Clause)',
packages=setuptools.find_packages(),
package_data={'': ['config/*.yaml']},
install_requires=install_requires,
include_package_data=True,
zip_safe=False
) | Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_augmenter/synapse_augmenter.py | .py | 47,797 | 1,075 | import torch
import numpy as np
from synapse_augmenter import consts
from typing import Tuple, List, Union, Optional
from boltons.cacheutils import cachedmethod
from scipy.spatial.transform import Rotation
import cc3d
# for cachedmethod
_cache = dict()
# constants
GAUSSIAN_BLUR_KERNEL_SIZE = 3 # must be an odd number
@cachedmethod(_cache)
def get_volume_for_structure(radius: int):
x = torch.arange(-radius, radius + 1)[:, None, None]
y = torch.arange(-radius, radius + 1)[None, :, None]
z = torch.arange(-radius, radius + 1)[None, None, :]
return ((x.pow(2) + y.pow(2) + z.pow(2)) <= (radius ** 2)).sum().item()
@cachedmethod(_cache)
def get_hole_volume_l(radii: Tuple[int]) -> np.ndarray:
return np.asarray([get_volume_for_structure(radius) for radius in radii])
@cachedmethod(_cache)
def get_corner_cutout_masks(size: int) -> np.ndarray:
"""Get corner cutout masks.
.. note:: the first mask (index 0) is identity (i.e. no cutout).
"""
assert size % 4 == 0
ranges = [
(0, size // 2),
(size // 4, 3 * size // 2),
(size // 2, size)]
ignore_range_set = {(1, 1, 1)}
n_ranges = len(ranges)
n_masks = n_ranges ** 3 - len(ignore_range_set) + 1
masks_jxyz = np.ones((n_masks, size, size, size), dtype=np.bool)
i_mask = 1
for i_x in range(n_ranges):
for i_y in range(n_ranges):
for i_z in range(n_ranges):
if (i_x, i_y, i_z) in ignore_range_set:
continue
masks_jxyz[
i_mask,
ranges[i_x][0]:ranges[i_x][1],
ranges[i_y][0]:ranges[i_y][1],
ranges[i_z][0]:ranges[i_z][1]] = 0
i_mask += 1
return masks_jxyz
@cachedmethod(_cache)
def get_gaussian_blur_kernels(sigmas: Tuple[float]) -> np.ndarray:
kernel_half_size = (GAUSSIAN_BLUR_KERNEL_SIZE - 1) // 2
x = torch.arange(-kernel_half_size, kernel_half_size + 1, dtype=torch.float)[:, None, None]
y = torch.arange(-kernel_half_size, kernel_half_size + 1, dtype=torch.float)[None, :, None]
z = torch.arange(-kernel_half_size, kernel_half_size + 1, dtype=torch.float)[None, None, :]
r2_xyz = x.pow(2) + y.pow(2) + z.pow(2)
kernel_11xyz_list = []
for sigma in sigmas:
kernel_xyz = torch.exp(- r2_xyz / (2. * sigma ** 2))
kernel_xyz = kernel_xyz / kernel_xyz.sum()
kernel_11xyz_list.append(kernel_xyz[None, None, ...])
kernel_r1xyz = torch.cat(kernel_11xyz_list, dim=0)
return kernel_r1xyz.cpu().numpy()
class SynapseAugmenter:
def __init__(self, **kwargs):
self.norm_scale = kwargs['norm_scale']
self.intensity_mean = kwargs['intensity_mean']
self.intensity_std = kwargs['intensity_std']
self.batch_mode = kwargs['batch_mode']
self.debug_mode = kwargs['debug_mode']
self.axial_to_sagittal_spacing = kwargs['axial_to_sagittal_spacing']
self.enable_section_cutout = kwargs['enable_section_cutout']
self.section_cutout_prob = kwargs['section_cutout_prob']
self.cutout_poisson_rate = kwargs['cutout_poisson_rate']
self.enable_sectional_intensity = kwargs['enable_sectional_intensity']
self.sectional_intensity_intercept_min = kwargs['sectional_intensity_intercept_min']
self.sectional_intensity_intercept_max = kwargs['sectional_intensity_intercept_max']
self.sectional_intensity_slope_min = kwargs['sectional_intensity_slope_min']
self.sectional_intensity_slope_max = kwargs['sectional_intensity_slope_max']
self.sectional_intensity_gamma_min = kwargs['sectional_intensity_gamma_min']
self.sectional_intensity_gamma_max = kwargs['sectional_intensity_gamma_max']
self.enable_global_intensity = kwargs['enable_global_intensity']
self.global_intensity_intercept_min = kwargs['global_intensity_intercept_min']
self.global_intensity_intercept_max = kwargs['global_intensity_intercept_max']
self.global_intensity_slope_min = kwargs['global_intensity_slope_min']
self.global_intensity_slope_max = kwargs['global_intensity_slope_max']
self.global_intensity_gamma_min = kwargs['global_intensity_gamma_min']
self.global_intensity_gamma_max = kwargs['global_intensity_gamma_max']
self.enable_pixel_noise = kwargs['enable_pixel_noise']
self.global_additive_gaussian_noise_scale_min = kwargs['global_additive_gaussian_noise_scale_min']
self.global_additive_gaussian_noise_scale_max = kwargs['global_additive_gaussian_noise_scale_max']
self.global_multiplicative_lognormal_noise_scale_min = kwargs['global_multiplicative_lognormal_noise_scale_min']
self.global_multiplicative_lognormal_noise_scale_max = kwargs['global_multiplicative_lognormal_noise_scale_max']
self.enable_gaussian_blur = kwargs['enable_gaussian_blur']
self.gaussian_blur_prob = kwargs['gaussian_blur_prob']
self.gaussian_blur_sigmas = tuple(kwargs['gaussian_blur_sigmas'])
self.enable_swiss_cheese = kwargs['enable_swiss_cheese']
self.swiss_cheese_max_vol_fraction = kwargs['swiss_cheese_max_vol_fraction']
self.swiss_cheese_radii = tuple(kwargs['swiss_cheese_radii'])
self.enable_corner_cutout = kwargs['enable_corner_cutout']
self.corner_cutout_prob = kwargs['corner_cutout_prob']
self.n_corner_cutout_applications = kwargs['n_corner_cutout_applications']
self.enable_random_cutout = kwargs['enable_random_cutout']
self.random_cutout_prob = kwargs['random_cutout_prob']
self.preserve_center_unmasked = kwargs['preserve_center_unmasked']
self.cutout_size_min = kwargs['cutout_size_min']
self.cutout_size_max = kwargs['cutout_size_max']
self.n_random_cutout_applications = kwargs['n_random_cutout_applications']
self.enable_random_periphery_cutout = kwargs['enable_random_periphery_cutout']
self.periphery_cutout_center_max_displacement_fraction = kwargs['periphery_cutout_center_max_displacement_fraction']
self.periphery_cutout_center_fraction_min = kwargs['periphery_cutout_center_fraction_min']
self.periphery_cutout_center_fraction_max = kwargs['periphery_cutout_center_fraction_max']
self.random_periphery_cutout_prob = kwargs['random_periphery_cutout_prob']
self.enable_displacement = kwargs['enable_displacement']
self.enable_rotation = kwargs['enable_rotation']
self.global_max_relative_displacement = kwargs['global_max_relative_displacement']
self.enable_x_flip = kwargs['enable_x_flip']
self.enable_y_flip = kwargs['enable_y_flip']
self.enable_z_flip = kwargs['enable_z_flip']
self.enable_xy_swap = kwargs['enable_xy_swap']
self.enable_active_zone = kwargs['enable_active_zone']
self.connectivity = kwargs['connectivity']
self.active_zone_inflation_radii = tuple(kwargs['active_zone_inflation_radii'])
self.final_crop_size = kwargs['final_crop_size']
self.final_img_size = kwargs['final_img_size']
self.channel_organization = kwargs['channel_organization']
if self.channel_organization == 'merged':
self.n_intensity_output_channels = 1
elif self.channel_organization == 'separate':
self.n_intensity_output_channels = 2
else:
raise ValueError
self.max_inflation_radius = 0
if self.enable_active_zone:
self.max_inflation_radius = max(self.max_inflation_radius, max(self.active_zone_inflation_radii))
if self.enable_swiss_cheese:
self.max_inflation_radius = max(self.max_inflation_radius, max(self.swiss_cheese_radii))
if self.debug_mode:
# print the random state
print(f'maximum inflation radius: {self.max_inflation_radius}')
self.dtype = kwargs['dtype']
self.device = kwargs['device']
@staticmethod
def apply_random_section_cutout(
intensity_bcxyz: torch.Tensor,
section_cutout_prob: float,
cutout_poisson_rate: float):
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
batch_size = intensity_bcxyz.shape[0]
n_total_sections = intensity_bcxyz.shape[-1]
do_section_cutout_b = torch.distributions.Bernoulli(
probs=torch.tensor(section_cutout_prob, device=device, dtype=dtype)).sample([batch_size]).type(torch.bool)
cutout_bernoulli_prob_bz = torch.tensor(
cutout_poisson_rate / n_total_sections,
device=device, dtype=dtype).expand([batch_size, n_total_sections])
cutout_bernoulli_prob_bz = cutout_bernoulli_prob_bz * do_section_cutout_b[:, None]
cutout_sections_bz = torch.distributions.Bernoulli(cutout_bernoulli_prob_bz).sample().type(torch.bool)
intensity_bcxyz.permute(0, 4, 1, 2, 3)[cutout_sections_bz] = 0.
@staticmethod
def apply_random_sectional_intensity_distortion(
intensity_bcxyz: torch.Tensor,
sectional_intensity_intercept_min: float,
sectional_intensity_intercept_max: float,
sectional_intensity_slope_min: float,
sectional_intensity_slope_max: float,
sectional_intensity_gamma_min: float,
sectional_intensity_gamma_max: float):
"""Applies a sectional intensity distortion.
.. note:: The following transformation is applied to every section:
I_out = intercept + slope * I_in ^ gamma,
where intercept, mean, and gamma are i.i.d random numbers for each section.
.. note:: Masked values will remain masked.
"""
batch_size = intensity_bcxyz.shape[0]
n_sections = intensity_bcxyz.shape[-1]
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
intercept_bz = (
sectional_intensity_intercept_min +
(sectional_intensity_intercept_max - sectional_intensity_intercept_min) *
torch.rand((batch_size, n_sections), device=device, dtype=dtype))
slope_bz = (
sectional_intensity_slope_min +
(sectional_intensity_slope_max - sectional_intensity_slope_min) *
torch.rand((batch_size, n_sections), device=device, dtype=dtype))
gamma_bz = (
sectional_intensity_gamma_min +
(sectional_intensity_gamma_max - sectional_intensity_gamma_min) *
torch.rand((batch_size, n_sections), device=device, dtype=dtype))
intensity_bcxyz \
.pow_(gamma_bz[:, None, None, None, :]) \
.mul_(slope_bz[:, None, None, None, :]) \
.add_(intercept_bz[:, None, None, None, :]) \
.clamp_(0., 1.)
@staticmethod
def apply_random_global_intensity_distortion(
intensity_bcxyz: torch.Tensor,
global_intensity_intercept_min: float,
global_intensity_intercept_max: float,
global_intensity_slope_min: float,
global_intensity_slope_max: float,
global_intensity_gamma_min: float,
global_intensity_gamma_max: float):
"""Applies a global intensity distortion.
.. note:: The following transformation is applied to all pixels:
I_out = intercept + slope * I_in ^ gamma,
where intercept, mean, and gamma are global random numbers for the
entire volume.
.. note:: Masked values will remain masked.
"""
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
batch_size = intensity_bcxyz.shape[0]
intercept_b = (
global_intensity_intercept_min +
(global_intensity_intercept_max - global_intensity_intercept_min) *
torch.rand((batch_size,), device=device, dtype=dtype))
slope_b = (
global_intensity_slope_min +
(global_intensity_slope_max - global_intensity_slope_min) *
torch.rand((batch_size,), device=device, dtype=dtype))
gamma_b = (
global_intensity_gamma_min +
(global_intensity_gamma_max - global_intensity_gamma_min) *
torch.rand((batch_size,), device=device, dtype=dtype))
intensity_bcxyz \
.pow_(gamma_b[:, None, None, None, None]) \
.mul_(slope_b[:, None, None, None, None]) \
.add_(intercept_b[:, None, None, None, None]) \
.clamp_(0., 1.)
@staticmethod
def apply_pixel_noise(
intensity_bcxyz: torch.Tensor,
global_additive_gaussian_noise_scale_min: float,
global_additive_gaussian_noise_scale_max: float,
global_multiplicative_lognormal_noise_scale_min: float,
global_multiplicative_lognormal_noise_scale_max: float):
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
batch_size = intensity_bcxyz.shape[0]
additive_noise_scale_b = (
global_additive_gaussian_noise_scale_min +
(global_additive_gaussian_noise_scale_max -
global_additive_gaussian_noise_scale_min) * torch.rand(
(batch_size,), device=device, dtype=dtype))
multiplicative_noise_scale_b = (
global_multiplicative_lognormal_noise_scale_min +
(global_multiplicative_lognormal_noise_scale_max -
global_multiplicative_lognormal_noise_scale_min) * torch.rand(
(batch_size,), device=device, dtype=dtype))
additive_noise_bcxyz = additive_noise_scale_b[:, None, None, None, None] * torch.randn_like(intensity_bcxyz)
multiplicative_noise_bcxyz = torch.exp(
multiplicative_noise_scale_b[:, None, None, None, None] * torch.randn_like(intensity_bcxyz))
multiplicative_noise_bcxyz = (
multiplicative_noise_bcxyz /
multiplicative_noise_bcxyz.mean(dim=(-1, -2, -3, -4))[:, None, None, None, None])
intensity_bcxyz \
.mul_(multiplicative_noise_bcxyz) \
.add_(additive_noise_bcxyz) \
.clamp_(0., 1.)
@staticmethod
def get_random_3d_affine_matrix_batch(
batch_size: int,
batch_mode: str,
enable_displacement: bool,
enable_rotation: bool,
global_max_relative_displacement: float,
scale_factors_i: np.ndarray,
device: torch.device,
dtype: torch.dtype) -> torch.Tensor:
"""
.. note:: only 3D rotations and translation; no scaling
"""
if batch_mode == 'coupled':
_batch_size = batch_size
batch_size = 1
affine_bik = torch.zeros((batch_size, 3, 4), device=device, dtype=dtype)
if enable_rotation:
# random 3D rotation
rot_matrices_bij = torch.tensor(
Rotation.random(batch_size).as_matrix(), device=device, dtype=dtype)
else:
rot_matrices_bij = torch.eye(3, device=device, dtype=dtype).expand(batch_size, 3, 3).contiguous()
# scale factors along the 3 axes
for i in range(3):
rot_matrices_bij[:, i, :] = rot_matrices_bij[:, i, :] / scale_factors_i[i]
# random displacement of the origin
if enable_displacement:
r_bi = 2. * torch.rand((batch_size, 3), device=device, dtype=dtype) - 1.
r_bi = global_max_relative_displacement * r_bi
else:
r_bi = torch.zeros((batch_size, 3), device=device, dtype=dtype)
affine_bik[:, :, :3] = rot_matrices_bij
affine_bik[:, :, 3] = r_bi
if batch_mode == 'coupled':
affine_bik = affine_bik.expand((_batch_size, 3, 4))
return affine_bik
@staticmethod
def apply_random_3d_affine_transformation_to_volume(
intensity_bcxyz: torch.Tensor,
mask_bcxyz: torch.Tensor,
enable_displacement: bool,
enable_rotation: bool,
final_crop_size: int,
final_img_size: int,
axial_to_sagittal_spacing: int,
global_max_relative_displacement: float,
batch_mode: str,
align_corners: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:
"""TBW."""
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
batch_size, n_channels, orig_x_size, orig_y_size, orig_z_size = intensity_bcxyz.shape
assert orig_x_size == orig_y_size
# global scale factor is for cropping
global_scale_factor = orig_x_size / final_crop_size
# per_axis_scale_factor_i is for correcting for too many/few sections
z_scale_factor = (axial_to_sagittal_spacing * orig_z_size) / orig_x_size
per_axis_scale_factor_i = np.asarray([1., 1., z_scale_factor])
scale_factors_i = global_scale_factor * per_axis_scale_factor_i
final_shape = (
batch_size, n_channels,
final_img_size, final_img_size, final_img_size)
# generate random affine theta
theta_bik = SynapseAugmenter.get_random_3d_affine_matrix_batch(
batch_size, batch_mode, enable_displacement, enable_rotation,
global_max_relative_displacement, scale_factors_i, device, dtype)
# make the affine grid
grid = torch.nn.functional.affine_grid(
theta_bik, final_shape, align_corners=align_corners)
# transform intensity
affine_intensity_bcxyz = torch.nn.functional.grid_sample(
intensity_bcxyz, grid, align_corners=align_corners)
affine_intensity_bcxyz.clamp_(0., 1.)
# transform mask
affine_mask_bcxyz = torch.nn.functional.grid_sample(
mask_bcxyz.to(dtype), grid, align_corners=align_corners) > 0.5
return affine_intensity_bcxyz, affine_mask_bcxyz
@staticmethod
def inflate_binary_mask(mask_bxyz: torch.Tensor, radius: int, max_radius: Optional[int] = None) -> torch.Tensor:
if radius == 0:
return mask_bxyz
if max_radius is None:
max_radius = radius
assert max_radius >= radius >= 0
device = mask_bxyz.device
x = torch.arange(-max_radius, max_radius + 1, dtype=torch.float, device=device)[:, None, None]
y = torch.arange(-max_radius, max_radius + 1, dtype=torch.float, device=device)[None, :, None]
z = torch.arange(-max_radius, max_radius + 1, dtype=torch.float, device=device)[None, None, :]
struct = ((x.pow(2) + y.pow(2) + z.pow(2)) <= (radius ** 2)).float()
kern = struct[None, None, ...]
return (torch.nn.functional.conv3d(
mask_bxyz[:, None, :, :, :].float(), kern, padding=max_radius) > 0)[:, 0, :, :, :].type(torch.bool)
@staticmethod
def get_active_zone_binary_mask(
mask_bcxyz: torch.Tensor,
connectivity: int,
active_zone_inflation_radii: Tuple[int],
batch_mode: str,
max_radius: int) -> Tuple[torch.Tensor, int]:
"""
.. note:: this method always has a "coupled" behavior
"""
assert mask_bcxyz.ndim == 5
batch_size = mask_bcxyz.shape[0]
n_radii = len(active_zone_inflation_radii)
device = mask_bcxyz.device
# bitwise_or of all non-background pixels
mask_bcxyz_np = mask_bcxyz.cpu().numpy()
pre_and_cleft_binary_mask_bxyz_np = (
mask_bcxyz_np[:, consts.MASK_PRE_SYNAPTIC_NEURON - 1] |
mask_bcxyz_np[:, consts.MASK_SYNAPTIC_CLEFT - 1])
post_and_cleft_binary_mask_bxyz_np = (
mask_bcxyz_np[:, consts.MASK_POST_SYNAPTIC_NEURON - 1] |
mask_bcxyz_np[:, consts.MASK_SYNAPTIC_CLEFT - 1])
cleft_binary_mask_bxyz_np = mask_bcxyz_np[:, consts.MASK_SYNAPTIC_CLEFT - 1]
active_zone_mask_1xyz_np_list = []
for i_batch in range(batch_size):
if connectivity > 0:
pre_and_cleft_binary_mask_xyz_np = pre_and_cleft_binary_mask_bxyz_np[i_batch]
post_and_cleft_binary_mask_xyz_np = post_and_cleft_binary_mask_bxyz_np[i_batch]
cleft_mask_xyz_np = cleft_binary_mask_bxyz_np[i_batch]
pre_and_cleft_labels_xyz_np = cc3d.connected_components(
pre_and_cleft_binary_mask_xyz_np,
connectivity=connectivity)
post_and_cleft_labels_xyz_np = cc3d.connected_components(
post_and_cleft_binary_mask_xyz_np,
connectivity=connectivity)
# identify all labels that overlap with the cleft
pre_cleft_overlapping_labels = np.unique(
(pre_and_cleft_labels_xyz_np * cleft_mask_xyz_np)[cleft_mask_xyz_np > 0])
post_cleft_overlapping_labels = np.unique(
(post_and_cleft_labels_xyz_np * cleft_mask_xyz_np)[cleft_mask_xyz_np > 0])
active_zone_mask_xyz_np = np.zeros_like(cleft_mask_xyz_np)
for component_label in pre_cleft_overlapping_labels:
active_zone_mask_xyz_np = (
active_zone_mask_xyz_np |
(pre_and_cleft_labels_xyz_np == component_label))
for component_label in post_cleft_overlapping_labels:
active_zone_mask_xyz_np = (
active_zone_mask_xyz_np |
(post_and_cleft_labels_xyz_np == component_label))
else: # do not do connected components
active_zone_mask_xyz_np = np.sum(mask_bcxyz_np[i_batch], 0) > 0
# add to list
active_zone_mask_1xyz_np_list.append(active_zone_mask_xyz_np[None, ...])
# inflate
active_zone_inflation_radius_idx = torch.distributions.Categorical(
torch.ones(n_radii, device=device, dtype=torch.float32)).sample([1]).item()
active_zone_inflation_radius = active_zone_inflation_radii[active_zone_inflation_radius_idx]
active_zone_mask_bxyz = SynapseAugmenter.inflate_binary_mask(
torch.tensor(
np.concatenate(active_zone_mask_1xyz_np_list, axis=0),
device=mask_bcxyz.device, dtype=torch.bool),
radius=active_zone_inflation_radius,
max_radius=max_radius)
return active_zone_mask_bxyz, active_zone_inflation_radius
@staticmethod
def apply_swiss_cheese_hollow_out(
intensity_bcxyz: torch.Tensor,
swiss_cheese_max_vol_fraction: float,
swiss_cheese_radii: Tuple[int],
batch_mode: str,
max_radius: int):
assert all(radius > 0 for radius in swiss_cheese_radii)
assert swiss_cheese_max_vol_fraction >= 0.
assert max(swiss_cheese_radii) <= max_radius
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
batch_size = intensity_bcxyz.shape[0]
if batch_mode == 'coupled':
_batch_size = batch_size
batch_size = 1
# sample to-be-hollowed-out volume fraction for the batch
vol_fraction_b = swiss_cheese_max_vol_fraction * torch.rand(
batch_size, dtype=dtype, device=device)
# assuming uniform distribution of holes of various sizes, sample
# a Bernoulli field for the position of the holes for each element
# in the batch
total_volume = intensity_bcxyz.shape[-1] * intensity_bcxyz.shape[-2] * intensity_bcxyz.shape[-3]
hole_volume_l = torch.tensor(
get_hole_volume_l(swiss_cheese_radii),
device=device,
dtype=dtype)
hole_count_bl = vol_fraction_b[:, None] * total_volume / (hole_volume_l * hole_volume_l.shape[0])
bernoulli_prob_bl = hole_count_bl / total_volume
hole_indicator_blxyz = torch.distributions.Bernoulli(
probs=bernoulli_prob_bl[:, :, None, None, None].expand(
hole_count_bl.shape + intensity_bcxyz.shape[-3:])).sample([])
# inflate the hole indicators by their respective radii and reduce to a single mask
swiss_cheese_bxyz = torch.sum(
torch.cat([
SynapseAugmenter.inflate_binary_mask(
hole_indicator_blxyz[:, l, ...],
radius=radius,
max_radius=max_radius)[:, None, ...]
for l, radius in enumerate(swiss_cheese_radii)],
dim=1),
dim=1) > 0
if batch_mode == 'coupled':
swiss_cheese_bxyz = swiss_cheese_bxyz.expand((_batch_size,) + intensity_bcxyz.shape[-3:])
# apply swiss cheese in-place
intensity_bcxyz.mul_(~swiss_cheese_bxyz[:, None, ...])
@staticmethod
def apply_corner_cutout(
intensity_bcxyz: torch.Tensor,
mask_bcxyz: torch.Tensor,
corner_cutout_prob: float,
preserve_center_unmasked: bool,
batch_mode: str):
batch_size = intensity_bcxyz.shape[0]
size = intensity_bcxyz.shape[-1]
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
if batch_mode == 'coupled':
_batch_size = batch_size
batch_size = 1
masks_jxyz = torch.tensor(get_corner_cutout_masks(size), device=device, dtype=torch.bool)
n_masks = masks_jxyz.shape[0]
do_corner_cutout_b = torch.distributions.Bernoulli(
probs=torch.tensor(corner_cutout_prob, device=device, dtype=dtype)).sample([batch_size]).type(torch.bool)
corner_index_b = torch.distributions.Categorical(
probs=torch.ones(n_masks - 1, device=device, dtype=dtype)).sample([batch_size]) + 1
corner_index_b[~do_corner_cutout_b] = 0
masks_bxyz = masks_jxyz[corner_index_b, :, :, :]
if preserve_center_unmasked:
center_lo = size // 4
center_hi = 3 * size // 4
masks_bxyz[:, center_lo:center_hi, center_lo:center_hi, center_lo:center_hi] = 1
if batch_mode == 'coupled':
masks_bxyz = masks_bxyz.expand((_batch_size,) + intensity_bcxyz.shape[-3:])
intensity_bcxyz.mul_(masks_bxyz[:, None, :, :, :])
mask_bcxyz.mul_(masks_bxyz[:, None, :, :, :])
@staticmethod
def apply_random_periphery_cutout(
intensity_bcxyz: torch.Tensor,
mask_bcxyz: torch.Tensor,
center_max_displacement_fraction: float,
center_fraction_min: float,
center_fraction_max: float,
random_periphery_cutout_prob: bool,
batch_mode: str):
assert 0. < center_max_displacement_fraction < 1.
assert 0. < center_fraction_min < 1.
assert 0. < center_fraction_max < 1.
assert 0. <= random_periphery_cutout_prob <= 1.
batch_size = intensity_bcxyz.shape[0]
size = intensity_bcxyz.shape[-1]
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
if batch_mode == 'coupled':
_batch_size = batch_size
batch_size = 1
center_max_displacement_size = 0.5 * center_max_displacement_fraction * size
c_x_b = torch.randint(
max(int(0.5 * size - center_max_displacement_size), 0),
min(int(0.5 * size + center_max_displacement_size), size),
[batch_size], device=device)
c_y_b = torch.randint(
max(int(0.5 * size - center_max_displacement_size), 0),
min(int(0.5 * size + center_max_displacement_size), size),
[batch_size], device=device)
c_z_b = torch.randint(
max(int(0.5 * size - center_max_displacement_size), 0),
min(int(0.5 * size + center_max_displacement_size), size),
[batch_size], device=device)
half_center_size_b = torch.randint(
int(0.5 * size * center_fraction_min),
int(0.5 * size * center_fraction_max),
[batch_size], device=device)
i_x_b = torch.maximum(c_x_b - half_center_size_b, torch.tensor(0, device=device))
i_y_b = torch.maximum(c_y_b - half_center_size_b, torch.tensor(0, device=device))
i_z_b = torch.maximum(c_z_b - half_center_size_b, torch.tensor(0, device=device))
j_x_b = torch.minimum(c_x_b + half_center_size_b, torch.tensor(size, device=device))
j_y_b = torch.minimum(c_y_b + half_center_size_b, torch.tensor(size, device=device))
j_z_b = torch.minimum(c_z_b + half_center_size_b, torch.tensor(size, device=device))
do_random_cutout_b = torch.distributions.Bernoulli(
probs=torch.tensor(random_periphery_cutout_prob, device=device, dtype=dtype)).sample([batch_size]).type(torch.bool)
do_batch_indices = torch.arange(0, batch_size, device=device, dtype=torch.long)[do_random_cutout_b]
mask_bxyz = torch.ones((batch_size, size, size, size), device=device, dtype=torch.bool)
for i_batch in do_batch_indices:
mask_bxyz[i_batch, ...] = 0
mask_bxyz[
i_batch,
i_x_b[i_batch]:j_x_b[i_batch],
i_y_b[i_batch]:j_y_b[i_batch],
i_z_b[i_batch]:j_z_b[i_batch]] = 1
if batch_mode == 'coupled':
mask_bxyz = mask_bxyz.expand((_batch_size,) + intensity_bcxyz.shape[-3:])
intensity_bcxyz.mul_(mask_bxyz[:, None, :, :, :])
mask_bcxyz.mul_(mask_bxyz[:, None, :, :, :])
@staticmethod
def apply_random_cutout(
intensity_bcxyz: torch.Tensor,
mask_bcxyz: torch.Tensor,
preserve_center_unmasked: bool,
cutout_size_min: int,
cutout_size_max: int,
random_cutout_prob: bool,
batch_mode: str):
batch_size = intensity_bcxyz.shape[0]
size = intensity_bcxyz.shape[-1]
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
if batch_mode == 'coupled':
_batch_size = batch_size
batch_size = 1
assert cutout_size_min > 0
assert cutout_size_max > 0
assert cutout_size_max >= cutout_size_min
assert cutout_size_max < size
i_x_b = torch.randint(0, size - cutout_size_min, [batch_size], device=device)
i_y_b = torch.randint(0, size - cutout_size_min, [batch_size], device=device)
i_z_b = torch.randint(0, size - cutout_size_min, [batch_size], device=device)
j_x_b = torch.minimum(
i_x_b + torch.randint(cutout_size_min, cutout_size_max + 1, [batch_size], device=device),
torch.tensor(size, device=device))
j_y_b = torch.minimum(
i_y_b + torch.randint(cutout_size_min, cutout_size_max + 1, [batch_size], device=device),
torch.tensor(size, device=device))
j_z_b = torch.minimum(
i_z_b + torch.randint(cutout_size_min, cutout_size_max + 1, [batch_size], device=device),
torch.tensor(size, device=device))
do_random_cutout_b = torch.distributions.Bernoulli(
probs=torch.tensor(random_cutout_prob, device=device, dtype=dtype)).sample([batch_size]).type(torch.bool)
do_batch_indices = torch.arange(0, batch_size, device=device, dtype=torch.long)[do_random_cutout_b]
mask_bxyz = torch.ones((batch_size, size, size, size), device=device, dtype=torch.bool)
for i_batch in do_batch_indices:
mask_bxyz[
i_batch,
i_x_b[i_batch]:j_x_b[i_batch],
i_y_b[i_batch]:j_y_b[i_batch],
i_z_b[i_batch]:j_z_b[i_batch]] = 0
if preserve_center_unmasked:
center_lo = size // 4
center_hi = 3 * size // 4
mask_bxyz[:, center_lo:center_hi, center_lo:center_hi, center_lo:center_hi] = 1
if batch_mode == 'coupled':
mask_bxyz = mask_bxyz.expand((_batch_size,) + intensity_bcxyz.shape[-3:])
intensity_bcxyz.mul_(mask_bxyz[:, None, :, :, :])
mask_bcxyz.mul_(mask_bxyz[:, None, :, :, :])
@staticmethod
def apply_x_flip(
intensity_bcxyz: torch.Tensor,
mask_bcxyz: torch.Tensor,
batch_mode: str):
batch_size = intensity_bcxyz.shape[0]
x_size, y_size, z_size = intensity_bcxyz.shape[2:]
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
x_reverse_range = torch.arange(x_size - 1, -1, -1, device=device, dtype=torch.long)
if batch_mode == 'decoupled':
do_flip_b = torch.rand(batch_size, device=device) > 0.5
else:
do_flip_b = (torch.rand(1, device=device) > 0.5).expand((batch_size,))
do_batch_indices = torch.arange(0, batch_size, device=device, dtype=torch.long)[do_flip_b]
for i_batch in do_batch_indices:
intensity_bcxyz[i_batch, ...] = intensity_bcxyz[i_batch, :, x_reverse_range, :, :]
mask_bcxyz[i_batch, ...] = mask_bcxyz[i_batch, :, x_reverse_range, :, :]
@staticmethod
def apply_y_flip(
intensity_bcxyz: torch.Tensor,
mask_bcxyz: torch.Tensor,
batch_mode: str):
batch_size = intensity_bcxyz.shape[0]
x_size, y_size, z_size = intensity_bcxyz.shape[2:]
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
y_reverse_range = torch.arange(y_size - 1, -1, -1, device=device, dtype=torch.long)
if batch_mode == 'decoupled':
do_flip_b = torch.rand(batch_size, device=device) > 0.5
else:
do_flip_b = (torch.rand(1, device=device) > 0.5).expand((batch_size,))
do_batch_indices = torch.arange(0, batch_size, device=device, dtype=torch.long)[do_flip_b]
for i_batch in do_batch_indices:
intensity_bcxyz[i_batch, ...] = intensity_bcxyz[i_batch, :, :, y_reverse_range, :]
mask_bcxyz[i_batch, ...] = mask_bcxyz[i_batch, :, :, y_reverse_range, :]
@staticmethod
def apply_z_flip(
intensity_bcxyz: torch.Tensor,
mask_bcxyz: torch.Tensor,
batch_mode: str):
batch_size = intensity_bcxyz.shape[0]
x_size, y_size, z_size = intensity_bcxyz.shape[2:]
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
z_reverse_range = torch.arange(z_size - 1, -1, -1, device=device, dtype=torch.long)
if batch_mode == 'decoupled':
do_flip_b = torch.rand(batch_size, device=device) > 0.5
else:
do_flip_b = (torch.rand(1, device=device) > 0.5).expand((batch_size,))
do_batch_indices = torch.arange(0, batch_size, device=device, dtype=torch.long)[do_flip_b]
for i_batch in do_batch_indices:
intensity_bcxyz[i_batch, ...] = intensity_bcxyz[i_batch, :, :, :, z_reverse_range]
mask_bcxyz[i_batch, ...] = mask_bcxyz[i_batch, :, :, :, z_reverse_range]
@staticmethod
def apply_xy_swap(
intensity_bcxyz: torch.Tensor,
mask_bcxyz: torch.Tensor,
batch_mode: str):
batch_size = intensity_bcxyz.shape[0]
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
if batch_mode == 'decoupled':
do_swap_b = torch.rand(batch_size, device=device) > 0.5
else:
do_swap_b = (torch.rand(1, device=device) > 0.5).expand((batch_size,))
do_batch_indices = torch.arange(0, batch_size, device=device, dtype=torch.long)[do_swap_b]
intensity_bcxyz[do_batch_indices, ...] = intensity_bcxyz[do_batch_indices, ...].permute(0, 1, 3, 2, 4).clone()
mask_bcxyz[do_batch_indices, ...] = mask_bcxyz[do_batch_indices, ...].permute(0, 1, 3, 2, 4).clone()
@staticmethod
def apply_gaussian_blur(
intensity_bcxyz: torch.Tensor,
gaussian_blur_prob: float,
gaussian_blur_sigmas: Tuple[float],
batch_mode: str):
batch_size = intensity_bcxyz.shape[0]
n_input_channels = intensity_bcxyz.shape[1]
device = intensity_bcxyz.device
dtype = intensity_bcxyz.dtype
# select samples to actually apply blur on
if batch_mode == 'decoupled':
do_blur_b = torch.rand(batch_size, device=device) < gaussian_blur_prob
else:
do_blur_b = (torch.rand(1, device=device) < gaussian_blur_prob).expand((batch_size,))
do_batch_indices = torch.arange(0, batch_size, device=device, dtype=torch.long)[do_blur_b]
n_blur_apply = do_batch_indices.shape[0]
if n_blur_apply == 0:
return
kern_r1xyz = torch.tensor(get_gaussian_blur_kernels(gaussian_blur_sigmas), device=device, dtype=dtype)
n_sigmas = kern_r1xyz.shape[0]
kernel_size = kern_r1xyz.shape[-1]
# select blur sigmas for each sample
if batch_mode == 'decoupled':
blur_radius_idx_b = torch.distributions.Categorical(
torch.ones(n_sigmas, device=device, dtype=dtype)).sample([batch_size])
else:
blur_radius_idx_b = torch.distributions.Categorical(
torch.ones(n_sigmas, device=device, dtype=dtype)).sample([1]).expand([batch_size])
# we blur only the selected batch indices
for i_input_channel in range(n_input_channels):
blurred_intensity_drxyz = torch.nn.functional.conv3d(
intensity_bcxyz[do_batch_indices, i_input_channel, ...][:, None, ...],
kern_r1xyz,
padding=(kernel_size - 1) // 2)
intensity_bcxyz[do_batch_indices, i_input_channel, ...] = blurred_intensity_drxyz[
torch.arange(0, n_blur_apply), blur_radius_idx_b[do_batch_indices], ...]
def __call__(
self,
intensity_mask_bcxyz_np: np.ndarray) -> Tuple[torch.Tensor, torch.Tensor]:
# generate intensity tensor
intensity_bcxyz = torch.clamp(
torch.tensor(
intensity_mask_bcxyz_np[:, consts.INPUT_DATA_INTENSITY_CHANNEL, ...],
device=self.device, dtype=self.dtype) / self.norm_scale,
min=0., max=1.)[:, None, ...]
# generate binary mask tensor
integer_mask_bxyz = torch.tensor(
intensity_mask_bcxyz_np[:, consts.INPUT_DATA_MASK_CHANNEL, ...],
device=self.device)
mask_bcxyz = torch.cat(
[(integer_mask_bxyz == mask_int_value)[:, None, ...]
for mask_int_value in consts.MASK_INTEGER_VALUES], dim=-4)
batch_size = intensity_bcxyz.shape[0]
if self.enable_section_cutout:
self.apply_random_section_cutout(
intensity_bcxyz,
section_cutout_prob=self.section_cutout_prob,
cutout_poisson_rate=self.cutout_poisson_rate)
if self.enable_sectional_intensity:
self.apply_random_sectional_intensity_distortion(
intensity_bcxyz,
sectional_intensity_intercept_min=self.sectional_intensity_intercept_min,
sectional_intensity_intercept_max=self.sectional_intensity_intercept_max,
sectional_intensity_slope_min=self.sectional_intensity_slope_min,
sectional_intensity_slope_max=self.sectional_intensity_slope_max,
sectional_intensity_gamma_min=self.sectional_intensity_gamma_min,
sectional_intensity_gamma_max=self.sectional_intensity_gamma_max)
if self.enable_global_intensity:
self.apply_random_global_intensity_distortion(
intensity_bcxyz,
global_intensity_intercept_min=self.global_intensity_intercept_min,
global_intensity_intercept_max=self.global_intensity_intercept_max,
global_intensity_slope_min=self.global_intensity_slope_min,
global_intensity_slope_max=self.global_intensity_slope_max,
global_intensity_gamma_min=self.global_intensity_gamma_min,
global_intensity_gamma_max=self.global_intensity_gamma_max)
if self.enable_pixel_noise:
self.apply_pixel_noise(
intensity_bcxyz,
global_additive_gaussian_noise_scale_min=self.global_additive_gaussian_noise_scale_min,
global_additive_gaussian_noise_scale_max=self.global_additive_gaussian_noise_scale_max,
global_multiplicative_lognormal_noise_scale_min=self.global_multiplicative_lognormal_noise_scale_min,
global_multiplicative_lognormal_noise_scale_max=self.global_multiplicative_lognormal_noise_scale_max)
intensity_bcxyz, mask_bcxyz = self.apply_random_3d_affine_transformation_to_volume(
intensity_bcxyz,
mask_bcxyz,
enable_displacement=self.enable_displacement,
enable_rotation=self.enable_rotation,
final_crop_size=self.final_crop_size,
final_img_size=self.final_img_size,
axial_to_sagittal_spacing=self.axial_to_sagittal_spacing,
global_max_relative_displacement=self.global_max_relative_displacement,
batch_mode=self.batch_mode)
if self.enable_gaussian_blur:
self.apply_gaussian_blur(
intensity_bcxyz,
gaussian_blur_prob=self.gaussian_blur_prob,
gaussian_blur_sigmas=self.gaussian_blur_sigmas,
batch_mode=self.batch_mode)
if self.enable_swiss_cheese:
self.apply_swiss_cheese_hollow_out(
intensity_bcxyz,
swiss_cheese_max_vol_fraction=self.swiss_cheese_max_vol_fraction,
swiss_cheese_radii=self.swiss_cheese_radii,
batch_mode=self.batch_mode,
max_radius=self.max_inflation_radius)
if self.enable_corner_cutout:
for _ in range(self.n_corner_cutout_applications):
self.apply_corner_cutout(
intensity_bcxyz,
mask_bcxyz,
corner_cutout_prob=self.corner_cutout_prob,
preserve_center_unmasked=self.preserve_center_unmasked,
batch_mode=self.batch_mode)
if self.enable_random_cutout:
for _ in range(self.n_random_cutout_applications):
self.apply_random_cutout(
intensity_bcxyz,
mask_bcxyz,
preserve_center_unmasked=self.preserve_center_unmasked,
cutout_size_min=self.cutout_size_min,
cutout_size_max=self.cutout_size_max,
random_cutout_prob=self.random_cutout_prob,
batch_mode=self.batch_mode)
if self.enable_random_periphery_cutout:
self.apply_random_periphery_cutout(
intensity_bcxyz=intensity_bcxyz,
mask_bcxyz=mask_bcxyz,
center_max_displacement_fraction=self.periphery_cutout_center_max_displacement_fraction,
center_fraction_min=self.periphery_cutout_center_fraction_min,
center_fraction_max=self.periphery_cutout_center_fraction_max,
random_periphery_cutout_prob=self.random_periphery_cutout_prob,
batch_mode=self.batch_mode)
if self.enable_x_flip:
self.apply_x_flip(intensity_bcxyz, mask_bcxyz, batch_mode=self.batch_mode)
if self.enable_y_flip:
self.apply_y_flip(intensity_bcxyz, mask_bcxyz, batch_mode=self.batch_mode)
if self.enable_z_flip:
self.apply_z_flip(intensity_bcxyz, mask_bcxyz, batch_mode=self.batch_mode)
if self.enable_xy_swap:
self.apply_xy_swap(intensity_bcxyz, mask_bcxyz, batch_mode=self.batch_mode)
if self.enable_active_zone:
# get the active zone
active_zone_mask_bxyz, chosen_inflation_radius = self.get_active_zone_binary_mask(
mask_bcxyz,
connectivity=self.connectivity,
active_zone_inflation_radii=self.active_zone_inflation_radii,
batch_mode=self.batch_mode,
max_radius=self.max_inflation_radius)
else:
active_zone_mask_bxyz = torch.ones(
(batch_size,) + intensity_bcxyz.shape[2:], device=intensity_bcxyz.device, dtype=torch.bool)
chosen_inflation_radius = 0
if self.channel_organization == 'merged':
# cut both the mask and the intensity tensor with the active zone mask
mask_bcxyz.mul_(active_zone_mask_bxyz[:, None, ...])
intensity_bcxyz.mul_(active_zone_mask_bxyz[:, None, ...])
elif self.channel_organization == 'separate':
pre_and_cleft_binary_mask_bxyz = SynapseAugmenter.inflate_binary_mask(
mask_bcxyz[:, consts.MASK_PRE_SYNAPTIC_NEURON - 1] | mask_bcxyz[:, consts.MASK_SYNAPTIC_CLEFT - 1],
radius=chosen_inflation_radius,
max_radius=self.max_inflation_radius) & active_zone_mask_bxyz
post_and_cleft_binary_mask_bxyz = SynapseAugmenter.inflate_binary_mask(
mask_bcxyz[:, consts.MASK_POST_SYNAPTIC_NEURON - 1] | mask_bcxyz[:, consts.MASK_SYNAPTIC_CLEFT - 1],
radius=chosen_inflation_radius,
max_radius=self.max_inflation_radius) & active_zone_mask_bxyz
intensity_bcxyz = torch.cat([
(intensity_bcxyz[:, 0, ...] * pre_and_cleft_binary_mask_bxyz)[:, None, ...],
(intensity_bcxyz[:, 0, ...] * post_and_cleft_binary_mask_bxyz)[:, None, ...]], dim=1)
else:
raise ValueError
# z-score intensities
intensity_bcxyz = (intensity_bcxyz - self.intensity_mean) / self.intensity_std
if self.debug_mode:
# print the random state
print(f'pytorch random: {torch.rand(1).item():.3f}, numpy random: {np.random.rand():.3f}')
return intensity_bcxyz, mask_bcxyz
def augment_raw_data(
self,
loaded_npy_data: Union[List[np.ndarray], np.ndarray]) -> Tuple[torch.Tensor, torch.Tensor]:
# basic check of input data
if isinstance(loaded_npy_data, np.ndarray):
loaded_npy_data = [loaded_npy_data]
elif isinstance(loaded_npy_data, list):
assert all(isinstance(a, np.ndarray) for a in loaded_npy_data)
else:
raise ValueError
assert all(a.ndim == 4 for a in loaded_npy_data)
assert all(a.shape[0] == 2 for a in loaded_npy_data)
intensity_mask_bcxyz = np.concatenate(
[intensity_mask_cxyz[None, ...] for intensity_mask_cxyz in loaded_npy_data],
axis=0)
return self(intensity_mask_bcxyz)
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_augmenter/__init__.py | .py | 48 | 2 | from .synapse_augmenter import SynapseAugmenter
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_augmenter/consts.py | .py | 256 | 12 | MASK_PRE_SYNAPTIC_NEURON = 1
MASK_SYNAPTIC_CLEFT = 2
MASK_POST_SYNAPTIC_NEURON = 3
MASK_INTEGER_VALUES = [
MASK_PRE_SYNAPTIC_NEURON,
MASK_SYNAPTIC_CLEFT,
MASK_POST_SYNAPTIC_NEURON]
INPUT_DATA_INTENSITY_CHANNEL = 0
INPUT_DATA_MASK_CHANNEL = 1
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_dataset/__init__.py | .py | 43 | 1 | from .synapse_dataset import SynapseDataset | Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_dataset/synapse_dataset.py | .py | 4,981 | 119 | import os
import numpy as np
import pandas as pd
from typing import List, Tuple, Optional, Union
from torch.utils.data.dataset import Dataset
from boltons.cacheutils import cachedmethod
_cache = dict()
class SynapseDataset(Dataset):
def __init__(
self,
dataset_path: str,
head: Optional[int] = None,
supervised_mode: bool = False,
drop_annotated_synapses: bool = False,
supervised_column_names: List[str] = [],
supervised_types: List[str] = [],
metadata_table: Optional[pd.DataFrame] = None):
self.dataset_path = dataset_path
self.supervised_mode = supervised_mode
self.supervised_column_names = supervised_column_names
self.supervised_types = supervised_types
if supervised_mode:
assert len(supervised_column_names) == len(supervised_types)
assert all(entry in {'continuous', 'categorical'} for entry in self.supervised_types)
# metadata w/ extended annotations
if metadata_table is not None:
self.meta_df = metadata_table
else:
self.meta_df = pd.read_csv(os.path.join(dataset_path, 'meta_ext.csv'))
column_names_set = set(self.meta_df.columns.values)
assert all(column_name in column_names_set for column_name in supervised_column_names)
self.supervised_column_name_to_type_map = {
column_name: data_type
for column_name, data_type in zip(supervised_column_names, supervised_types)}
else:
# metadata w/o extended annotations
if metadata_table is not None:
self.meta_df = metadata_table
else:
self.meta_df = pd.read_csv(os.path.join(dataset_path, 'meta.csv'))
if drop_annotated_synapses:
meta_ext_df = pd.read_csv(os.path.join(dataset_path, 'meta_ext.csv'))
self.meta_df = self.meta_df[~self.meta_df['synapse_id'].isin(meta_ext_df['synapse_id'])]
assert len(self.meta_df) > 0
# truncate the dataset to the first `head` entries? (used for debugging)
if head is not None:
self.meta_df = self.meta_df.head(head)
@cachedmethod(cache=_cache)
def get_num_categories(self, column_name: str) -> int:
assert column_name in self.supervised_column_name_to_type_map
if self.supervised_column_name_to_type_map[column_name] == 'continuous':
return 1
else:
return len(np.unique(self.meta_df[column_name].values))
@cachedmethod(cache=_cache)
def get_data_type(self, column_name: str) -> str:
assert column_name in self.supervised_column_name_to_type_map
return self.supervised_column_name_to_type_map[column_name]
def __getitem__(self, index: int) -> Union[Tuple[int, np.ndarray], Tuple[int, np.ndarray, np.ndarray]]:
synapse_meta = self.meta_df.iloc[index]
if self.supervised_mode:
annotations_ndarray = np.asarray(
[synapse_meta[column_name] for column_name in self.supervised_column_names])
return index, np.load(os.path.join(self.dataset_path, synapse_meta.filename)), annotations_ndarray
else:
return index, np.load(os.path.join(self.dataset_path, synapse_meta.filename))
def __len__(self):
return len(self.meta_df)
def split(
self,
train_fraction: float,
seed: int) -> Tuple['SynapseDataset', 'SynapseDataset']:
assert 0. < train_fraction < 1.
total_entries = len(self.meta_df)
train_entries = int(train_fraction * total_entries)
random_indices = np.random.RandomState(seed).permutation(total_entries)
train_indices = random_indices[:train_entries]
test_indices = random_indices[train_entries:]
assert len(train_indices) > 0
assert len(test_indices) > 0
train_meta = self.meta_df.iloc[train_indices].copy().reset_index(drop=True)
test_meta = self.meta_df.iloc[test_indices].copy().reset_index(drop=True)
train_dataset = SynapseDataset(
dataset_path=self.dataset_path,
head=None,
supervised_mode=self.supervised_mode,
supervised_column_names=self.supervised_column_names,
supervised_types=self.supervised_types,
metadata_table=train_meta)
test_dataset = SynapseDataset(
dataset_path=self.dataset_path,
head=None,
supervised_mode=self.supervised_mode,
supervised_column_names=self.supervised_column_names,
supervised_types=self.supervised_types,
metadata_table=test_meta)
return train_dataset, test_dataset
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_utils/vis.py | .py | 18,928 | 546 | import torch
import numpy as np
import matplotlib.pylab as plt
import pandas as pd
from synapse_dataset import SynapseDataset
from synapse_simclr import utils
from synapse_utils import vis
from synapse_augmenter import SynapseAugmenter
from synapse_augmenter import consts as syn_consts
from scipy.ndimage import binary_erosion
import plotly.graph_objects as go
import pyvista as pv
from skimage.filters import threshold_otsu
from sklearn.decomposition import PCA as SKPCA
import warnings
from ipywidgets import interactive
from typing import Optional
# constants
color_pre = np.asarray((138,43,226)) / 255
color_cleft = np.asarray((255,128,0)) / 255
color_post = np.asarray((52,235,128)) / 255
def plot_section_1_channel(
intensity_xyz: np.ndarray,
mask_cxyz: np.ndarray,
section_idx: int,
slider_axis: int,
figsize = (3, 3),
mask_alpha: float = 0.25,
cleft_alpha: float = 0.9,
show: bool = True,
ax = None,
fig = None,
**imshow_kwargs):
if fig is None or ax is None:
fig, ax = plt.subplots(figsize=figsize)
section_idx = int(np.round(section_idx))
if slider_axis == 0:
img_2d = intensity_xyz[section_idx, :, :]
mask_2d = mask_cxyz[:, section_idx, :, :]
elif slider_axis == 1:
img_2d = intensity_xyz[:, section_idx, :]
mask_2d = mask_cxyz[:, :, section_idx, :]
elif slider_axis == 2:
img_2d = intensity_xyz[:, :, section_idx]
mask_2d = mask_cxyz[:, :, :, section_idx]
ax.imshow(
img_2d,
aspect=1.0,
interpolation='nearest',
cmap=plt.cm.Greys_r,
**imshow_kwargs)
color_pre_alpha = np.asarray(color_pre.tolist() + [mask_alpha])
color_cleft_alpha = np.asarray(color_cleft.tolist() + [cleft_alpha])
color_post_alpha = np.asarray(color_post.tolist() + [mask_alpha])
colorized_mask_2d = (
mask_2d[0][..., None] * color_pre_alpha[None, None, :] +
mask_2d[1][..., None] * color_cleft_alpha[None, None, :] +
mask_2d[2][..., None] * color_post_alpha[None, None, :])
ax.imshow(
colorized_mask_2d,
aspect=1.0,
interpolation='nearest',
cmap=plt.cm.Greys_r,
**imshow_kwargs)
ax.axis('off')
if show:
plt.show()
return fig, ax
def animate_synapse(synapse_index: int):
# get the intensity data
intensity_bcxyz, mask_bcxyz = aug.augment_raw_data([synapse_dataset[synapse_index][1]])
intensity_xyz = intensity_bcxyz[0, 0, ...].cpu().numpy()
panel_size = 2
max_z = 95
fig, ax = plt.subplots(
nrows=1,
ncols=1,
figsize=(panel_size, panel_size))
ax.clear()
ax.set_xticks([])
ax.set_yticks([])
fig.tight_layout()
def plot_section(z_idx, channel_idx=0):
if z_idx > max_z:
z_idx = 2 * max_z - z_idx
ax.clear()
ax.imshow(
intensity_xyz[:, :, z_idx],
cmap=plt.cm.Greys_r)
ax.set_xticks([])
ax.set_yticks([])
anim = matplotlib.animation.FuncAnimation(
fig, plot_section, frames=2 * max_z - 1, interval=50);
plt.close()
return anim.to_html5_video()
def plot_mask_3d(synapse_index: int, seg_mask_channels=[0, 1, 2], skip_every=5):
# get the mask data
intensity_bcxyz, mask_bcxyz = aug.augment_raw_data([synapse_dataset[synapse_index][1]])
mask_cxyz = mask_bcxyz[0, :, :, :]
final_img_size = mask_cxyz.shape[-1]
X, Y, Z = np.mgrid[:final_img_size, :final_img_size, :final_img_size]
data = []
for seg_mask_channel in seg_mask_channels:
mask_xyz = mask_cxyz[seg_mask_channel, ...].cpu().numpy()
data.append(
go.Scatter3d(
x=X[mask_xyz].flatten()[::skip_every],
y=Y[mask_xyz].flatten()[::skip_every],
z=Z[mask_xyz].flatten()[::skip_every],
mode='markers',
marker={'size': 2}))
fig = go.Figure(data)
fig.update_layout(
scene = dict(
xaxis_title='x',
yaxis_title='y',
zaxis_title='z',
aspectratio=dict(x=1, y=1, z=1),
xaxis=dict(range=[0, final_img_size - 1],),
yaxis=dict(range=[0, final_img_size - 1],),
zaxis=dict(range=[0, final_img_size - 1],)
),
autosize=False,
width=500,
height=500,
margin=dict(
l=50,
r=50,
b=100,
t=100,
pad=4
))
fig.show()
class SynapseVisContext:
def __init__(
self,
dataset_path: str,
aug_yaml_path: str,
meta_df_override: Optional[pd.DataFrame],
device: str = 'cpu'):
# dataset
self.synapse_dataset = SynapseDataset(dataset_path)
if meta_df_override is not None:
self.synapse_dataset.meta_df = meta_df_override
# augmenter
synapse_augmenter_kwargs = utils.yaml_config_hook(aug_yaml_path)
self.aug = SynapseAugmenter(
**synapse_augmenter_kwargs,
device=torch.device(device),
dtype=torch.float32)
def erode_mask(mask_xyz, rad=1):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
sz = mask_xyz.shape[-1]
for iz in range(sz):
mask_xyz[:, :, iz] = binary_erosion(mask_xyz[:, :, iz], iterations=rad)
return mask_xyz
def whiteout_missing_planes(intensity_xyz, inside_mask_xyz, threshold=0.05):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
sz = intensity_xyz.shape[-1]
for iz in range(sz):
m = np.mean(intensity_xyz[:, :, iz].flatten()[inside_mask_xyz[:, :, iz].flatten()])
if m < threshold:
intensity_xyz[:, :, iz] = 1.
return intensity_xyz
def get_dark_mask(intensity_xyz, inside_mask_xyz, otsu_prefactor):
dark_mask_xyz = inside_mask_xyz.copy()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
sz = intensity_xyz.shape[-1]
for iz in range(sz):
try:
intensities = intensity_xyz[:, :, iz].flatten()[inside_mask_xyz[:, :, iz].flatten()]
t = otsu_prefactor * threshold_otsu(intensities)
dark_mask_xyz[:, :, iz] = intensity_xyz[:, :, iz] < t
except:
continue
dark_mask_xyz = dark_mask_xyz & inside_mask_xyz
return dark_mask_xyz
def get_optimal_camera_props(mask_cxyz):
# generate grid
final_img_size = mask_cxyz.shape[-1]
X, Y, Z = np.mgrid[:final_img_size, :final_img_size, :final_img_size]
# mask components
mask_pre = mask_cxyz[syn_consts.MASK_PRE_SYNAPTIC_NEURON - 1].astype(float)
mask_post = mask_cxyz[syn_consts.MASK_POST_SYNAPTIC_NEURON - 1].astype(float)
mask_cleft = mask_cxyz[syn_consts.MASK_SYNAPTIC_CLEFT - 1].astype(float)
mask_all = (np.sum(mask_cxyz, 0) > 0).astype(float)
# cleft COM
cleft_com_x = np.sum(mask_cleft * X) / (1e-6 + np.sum(mask_cleft))
cleft_com_y = np.sum(mask_cleft * Y) / (1e-6 + np.sum(mask_cleft))
cleft_com_z = np.sum(mask_cleft * Z) / (1e-6 + np.sum(mask_cleft))
# cleft normal direction
cleft_x = X[mask_cleft > 0].flatten()
cleft_y = Y[mask_cleft > 0].flatten()
cleft_z = Z[mask_cleft > 0].flatten()
cleft_points_n3 = np.vstack((cleft_x, cleft_y, cleft_z)).T
try:
cleft_pca = SKPCA(n_components=3).fit(cleft_points_n3 - np.mean(cleft_points_n3, axis=0, keepdims=True))
cleft_normal = cleft_pca.components_[2]
cleft_inplane_1 = cleft_pca.components_[0]
cleft_inplane_2 = cleft_pca.components_[1]
except:
cleft_normal = np.asarray([0., 0., 1.])
cleft_inplane_1 = np.asarray([1., 0., 0.])
cleft_inplane_2 = np.asarray([0., 1., 0.])
# good in-plane directions
mask_all_x = X[mask_all > 0].flatten()
mask_all_y = Y[mask_all > 0].flatten()
mask_all_z = Z[mask_all > 0].flatten()
mask_all_points_n3 = np.vstack((mask_all_x, mask_all_y, mask_all_z)).T
mask_all_points_n3 = mask_all_points_n3 - np.mean(mask_all_points_n3, axis=0, keepdims=True)
mask_all_points_inplane_1_n = np.einsum("ni,i->n", mask_all_points_n3, cleft_inplane_1)
mask_all_points_inplane_2_n = np.einsum("ni,i->n", mask_all_points_n3, cleft_inplane_2)
mask_all_points_n2 = np.vstack((mask_all_points_inplane_1_n, mask_all_points_inplane_2_n)).T
mask_all_pca = SKPCA(n_components=2).fit(mask_all_points_n2)
mask_all_inplane_dir_1 = mask_all_pca.components_[0]
mask_all_inplane_dir_2 = mask_all_pca.components_[1]
view_1 = mask_all_inplane_dir_1[0] * cleft_inplane_1 + mask_all_inplane_dir_1[1] * cleft_inplane_2
view_2 = mask_all_inplane_dir_2[0] * cleft_inplane_1 + mask_all_inplane_dir_2[1] * cleft_inplane_2
mask_pre_vol = 1e-6 + np.sum(mask_pre > 0)
mask_post_vol = 1e-6 + np.sum(mask_post > 0)
polarity = (
(np.sum(X[mask_pre > 0]) / mask_pre_vol - np.sum(X[mask_post > 0]) / mask_post_vol) * cleft_normal[0] +
(np.sum(Y[mask_pre > 0]) / mask_pre_vol - np.sum(Y[mask_post > 0]) / mask_post_vol) * cleft_normal[1] +
(np.sum(Z[mask_pre > 0]) / mask_pre_vol - np.sum(Z[mask_post > 0]) / mask_post_vol) * cleft_normal[2])
if polarity < 0.:
cleft_normal = (-1.) * cleft_normal
return {
'cleft_com_x': cleft_com_x,
'cleft_com_y': cleft_com_y,
'cleft_com_z': cleft_com_z,
'cleft_normal': cleft_normal,
'cleft_inplane_1': view_1,
'cleft_inplane_2': view_2,
}
def make_3d_synapse_figure(
ctx: SynapseVisContext,
synapse_index: int,
inside_erosion_radius = 3,
mask_inflation_radis = 7,
max_points = 200_000,
max_triangles = 100_000,
otsu_prefactor = 0.3,
point_cloud_opacity = 0.1,
point_cloud_size = 0.8,
surface_opacity = 0.03,
surface_point_cloud_opacity = 0.04,
surface_point_cloud_size = 1.0,
surface_max_points = 100_000,
tri_alpha = 0.05,
zoom_out = 1.75,
fig_width = 500,
fig_height = 500,
view_plane = 1,
fix_cleft_issue = True,
show_points = False,
fig = None,
cam_props = None):
# get data
intensity_bcxyz, mask_bcxyz = ctx.aug.augment_raw_data([ctx.synapse_dataset[synapse_index][1]])
mask_cxyz = mask_bcxyz[0, :, :, :].cpu().numpy()
intensity_xyz = intensity_bcxyz[0, 0, ...].cpu().numpy()
if fix_cleft_issue and np.sum(mask_cxyz[syn_consts.MASK_POST_SYNAPTIC_NEURON - 1]) < 1.0:
mid = mask_cxyz.shape[-1] // 2
mask_cxyz[syn_consts.MASK_SYNAPTIC_CLEFT - 1, mid, mid, mid] = 1
# camera props
if not cam_props:
cam_props = get_optimal_camera_props(mask_cxyz)
# generate grid
final_img_size = mask_cxyz.shape[-1]
X, Y, Z = np.mgrid[:final_img_size, :final_img_size, :final_img_size]
X = (X - cam_props['cleft_com_x']) / final_img_size
Y = (Y - cam_props['cleft_com_y']) / final_img_size
Z = (Z - cam_props['cleft_com_z']) / final_img_size
# preprocess
inside_mask_xyz = np.sum(mask_cxyz, 0) > 0
inside_mask_xyz = erode_mask(inside_mask_xyz, inside_erosion_radius)
dark_mask_xyz = get_dark_mask(intensity_xyz, inside_mask_xyz, otsu_prefactor)
plot_mask_xyz = dark_mask_xyz
indices = np.random.permutation(X[plot_mask_xyz].flatten().shape[0])[:max_points]
x = X[plot_mask_xyz].flatten()[indices]
y = Y[plot_mask_xyz].flatten()[indices]
z = Z[plot_mask_xyz].flatten()[indices]
c = intensity_xyz[plot_mask_xyz].flatten()[indices]
color = plt.cm.Greys_r(c)
# make the figure from scratch
if fig is None:
data = []
data.append(
go.Scatter3d(
x=x,
y=y,
z=z,
mode='markers',
marker={
'size': point_cloud_size,
'color': color,
'opacity': point_cloud_opacity
}))
mask_bcxyz[0, 0] = ctx.aug.inflate_binary_mask(mask_bcxyz[:, 0], radius=mask_inflation_radis)[0]
mask_bcxyz[0, 2] = ctx.aug.inflate_binary_mask(mask_bcxyz[:, 2], radius=mask_inflation_radis)[0]
mask_cxyz = mask_bcxyz[0, :, :, :].cpu().numpy()
for mask_int, color, plot_type in zip(
[0, 1, 2],
['rgb(138,43,226)',
'rgb(255,128,0)',
'rgb(52,235,128)'],
['surface',
'points',
'surface']):
if plot_type == 'surface':
if show_points:
pre_mask_xyz = mask_cxyz[mask_int]
x = X[pre_mask_xyz].flatten()
y = Y[pre_mask_xyz].flatten()
z = Z[pre_mask_xyz].flatten()
indices = np.random.permutation(len(x))[:surface_max_points]
x = x[indices]
y = y[indices]
z = z[indices]
data.append(
go.Scatter3d(
x=x,
y=y,
z=z,
mode='markers',
marker={
'size': surface_point_cloud_size,
'color': color,
'opacity': surface_point_cloud_opacity
}))
pre_mask_xyz = mask_cxyz[mask_int] ^ erode_mask(mask_cxyz[mask_int].copy(), 1)
x = X[pre_mask_xyz].flatten()
y = Y[pre_mask_xyz].flatten()
z = Z[pre_mask_xyz].flatten()
indices = np.random.permutation(len(x))[:surface_max_points]
x = x[indices]
y = y[indices]
z = z[indices]
data.append(
go.Scatter3d(
x=x,
y=y,
z=z,
mode='markers',
marker={
'size': surface_point_cloud_size,
'color': color,
'opacity': surface_point_cloud_opacity
}))
if show_points:
points = np.vstack((x, y, z)).T
cloud = pv.PolyData(points)
volume = cloud.delaunay_3d(alpha=tri_alpha)
shell = volume.extract_geometry()
x = shell.points[:, 0]
y = shell.points[:, 1]
z = shell.points[:, 2]
i = shell.faces.reshape((-1, 4))[:, 1]
j = shell.faces.reshape((-1, 4))[:, 2]
k = shell.faces.reshape((-1, 4))[:, 3]
indices = np.random.permutation(len(i))[:max_triangles]
i = i[indices]
j = j[indices]
k = k[indices]
triangles = np.vstack((i, j, k)).T
data.append(
go.Mesh3d(
x=x,
y=y,
z=z,
i=i,
j=j,
k=k,
opacity=surface_opacity,
color=color,
lighting=dict(ambient=0.9)
))
elif plot_type == 'points':
pre_mask_xyz = mask_cxyz[mask_int]
x = X[pre_mask_xyz].flatten()
y = Y[pre_mask_xyz].flatten()
z = Z[pre_mask_xyz].flatten()
indices = np.random.permutation(len(x))[:surface_max_points]
x = x[indices]
y = y[indices]
z = z[indices]
data.append(
go.Scatter3d(
x=x,
y=y,
z=z,
mode='markers',
marker={
'size': point_cloud_size,
'color': color,
'opacity': point_cloud_opacity
}))
fig = go.Figure(data);
# setup the scene
camera_dict = dict(
up=dict(
x=cam_props['cleft_normal'][0],
y=cam_props['cleft_normal'][1],
z=cam_props['cleft_normal'][2],
),
eye=dict(
x=zoom_out * (cam_props[f'cleft_inplane_{view_plane}'][0] - 0. * cam_props['cleft_normal'][0]),
y=zoom_out * (cam_props[f'cleft_inplane_{view_plane}'][1] - 0. * cam_props['cleft_normal'][1]),
z=zoom_out * (cam_props[f'cleft_inplane_{view_plane}'][2] - 0. * cam_props['cleft_normal'][2])
)
)
scene_dict = dict(
xaxis_title='x',
yaxis_title='y',
zaxis_title='z',
aspectratio=dict(x=1, y=1, z=1),
xaxis=dict(range=[np.min(X), np.max(X)], visible=False),
yaxis=dict(range=[np.min(Y), np.max(Y)], visible=False),
zaxis=dict(range=[np.min(Z), np.max(Z)], visible=False)
)
fig.update_layout(
scene=scene_dict,
scene_camera=camera_dict,
autosize=False,
width=fig_width,
height=fig_height,
showlegend=False,
margin=dict(
l=0,
r=0,
b=0,
t=0,
pad=4
));
return fig
def interact_synapse_sections(
ctx: SynapseVisContext,
synapse_index: int,
slider_axis=2,
figsize=(4, 4)):
intensity_bcxyz, mask_bcxyz = ctx.aug.augment_raw_data([ctx.synapse_dataset[synapse_index][1]])
intensity_bcxyz = intensity_bcxyz.cpu().numpy()
mask_bcxyz = mask_bcxyz.cpu().numpy()
interactive_plot = interactive(
lambda section_idx: plot_section_1_channel(
intensity_bcxyz[0, 0, ...],
mask_bcxyz[0, ...],
slider_axis=slider_axis,
section_idx=section_idx,
figsize=figsize),
section_idx=(0, intensity_bcxyz.shape[-1] - 1))
return interactive_plot
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_utils/__init__.py | .py | 0 | 0 | null | Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_utils/io.py | .py | 2,831 | 75 | import os
import numpy as np
import pandas as pd
from typing import List, Tuple, Optional
def load_features(
checkpoint_path: str,
node_idx_list: List[int],
reload_epoch: int,
feature_hook: str,
dataset_path: str,
l2_normalize: bool,
contamination_indices_path: Optional[str] = None) -> Tuple[np.ndarray, pd.DataFrame]:
# load feaetures
features_nf_list = []
index_n_list = []
for node_idx in node_idx_list:
features_nf_list.append(
np.load(
os.path.join(
checkpoint_path,
'features',
f'extracted_features__node.{node_idx}__epoch.{reload_epoch}__{feature_hook}.npy')))
index_n_list.append(
np.load(
os.path.join(
checkpoint_path,
'features',
f'extracted_features__node.{node_idx}__epoch.{reload_epoch}__index_array.npy')))
features_nf = np.concatenate(features_nf_list, axis=0)
index_n = np.concatenate(index_n_list, axis=0)
# remove repeat entries (if any; this can happen if the features are extracted in parallel)
actual_idx_to_cat_idx_map = {actual_idx: cat_idx for cat_idx, actual_idx in enumerate(index_n)}
cat_keep_indices = list(map(actual_idx_to_cat_idx_map.get, np.unique(index_n)))
index_n = index_n[cat_keep_indices]
features_nf = features_nf[cat_keep_indices]
# sort
order = np.argsort(index_n)
index_n = index_n[order]
features_nf = features_nf[order]
# l2 normalize?
if l2_normalize:
features_nf = features_nf / np.linalg.norm(features_nf, axis=1, keepdims=True)
# load metadata
meta_df = pd.read_csv(os.path.join(dataset_path, 'meta.csv'))
# load annotations
meta_ext_df = pd.read_csv(os.path.join(dataset_path, 'meta_ext.csv'))
# load contamination indices
if contamination_indices_path is not None:
contamination_indices = np.load(contamination_indices_path)
contamination_synapse_ids = set(meta_df.iloc[contamination_indices]['synapse_id'].values)
# remove contamination from features
retained_indices = sorted(list(set(np.arange(features_nf.shape[0])).difference(contamination_indices)))
features_nf = features_nf[retained_indices]
# remove contamination from metadata
meta_df = meta_df.iloc[retained_indices]
# remove contamination from annotations
retained_indices_meta_ext = [
row_idx for row_idx, synapse_id in enumerate(meta_ext_df['synapse_id'].values)
if synapse_id not in contamination_synapse_ids]
meta_ext_df = meta_ext_df.iloc[retained_indices_meta_ext]
return features_nf, meta_df, meta_ext_df | Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_utils/commons.py | .py | 6,419 | 129 | import pandas as pd
import numpy as np
import torch
def log1p_zscore(a):
a = np.log1p(a)
m = np.mean(a)
s = np.std(a)
return (a - m) / s
def load_imputed_annotations(
meta_df: pd.DataFrame,
imputed_cell_types_df_path: str,
imputed_meta_ext_df_path: str,
cell_type_strategy: str = 'consensus'):
# MLE consensus cell types
consensus_cell_types_df = pd.read_csv(imputed_cell_types_df_path, index_col=0).set_index('synapse_id')
# imputed annotations
original_imputed_meta_ext_df = pd.read_csv(imputed_meta_ext_df_path, index_col=0).set_index('synapse_id')
# assert that rows from all tables correspond to the same synapses
assert(np.all(meta_df['synapse_id'].values == original_imputed_meta_ext_df.index.values))
assert(np.all(meta_df['synapse_id'].values == consensus_cell_types_df.index.values))
imputed_meta_ext_df = meta_df.copy()
imputed_meta_ext_df['cleft_size_log1p_zscore'] = original_imputed_meta_ext_df['imputed__cleft_size_log1p_zscore__mean'].values
imputed_meta_ext_df['presyn_soma_dist_log1p_zscore'] = original_imputed_meta_ext_df['imputed__presyn_soma_dist_log1p_zscore__mean'].values
imputed_meta_ext_df['postsyn_soma_dist_log1p_zscore'] = original_imputed_meta_ext_df['imputed__postsyn_soma_dist_log1p_zscore__mean'].values
imputed_meta_ext_df['mito_size_pre_vx_log1p_zscore_zi'] = original_imputed_meta_ext_df['imputed__mito_size_pre_vx_log1p_zscore_zi__mean'].values
imputed_meta_ext_df['mito_size_post_vx_log1p_zscore_zi'] = original_imputed_meta_ext_df['imputed__mito_size_post_vx_log1p_zscore_zi__mean'].values
imputed_meta_ext_df['has_mito_pre'] = original_imputed_meta_ext_df['imputed__has_mito_pre__class_1'].values > 0.5
imputed_meta_ext_df['has_mito_post'] = original_imputed_meta_ext_df['imputed__has_mito_post__class_1'].values > 0.5
if cell_type_strategy == 'consensus':
imputed_meta_ext_df['pre_cell_type'] = consensus_cell_types_df['pre_cell_type__consensus'].values
imputed_meta_ext_df['post_cell_type'] = consensus_cell_types_df['post_cell_type__consensus'].values
elif cell_type_strategy == 'single':
imputed_meta_ext_df['pre_cell_type'] = consensus_cell_types_df['imputed__pre_cell_type__class_1'] > 0.5
imputed_meta_ext_df['post_cell_type'] = consensus_cell_types_df['imputed__post_cell_type__class_1'] > 0.5
else:
raise ValueError
# misc.
imputed_meta_ext_df['pre_synaptic_volume_log1p_zscore'] = log1p_zscore(meta_df['pre_synaptic_volume'].values)
imputed_meta_ext_df['post_synaptic_volume_log1p_zscore'] = log1p_zscore(meta_df['post_synaptic_volume'].values)
return imputed_meta_ext_df
def get_normal_sample(loc: np.ndarray, scale: np.ndarray):
return torch.distributions.Normal(
loc=torch.tensor(loc),
scale=torch.tensor(scale)).sample([]).cpu().numpy()
def get_bernoulli_sample(probs: np.ndarray):
return torch.distributions.Bernoulli(
probs=torch.tensor(probs)).sample([]).cpu().numpy()
def load_imputed_annotations_stochastic(
meta_df: pd.DataFrame,
imputed_cell_types_df_path: str,
imputed_meta_ext_df_path: str,
cell_type_strategy: str = 'consensus'):
# MLE consensus cell types
consensus_cell_types_df = pd.read_csv(imputed_cell_types_df_path, index_col=0).set_index('synapse_id')
# imputed annotations
original_imputed_meta_ext_df = pd.read_csv(imputed_meta_ext_df_path, index_col=0).set_index('synapse_id')
# assert that rows from all tables correspond to the same synapses
assert(np.all(meta_df['synapse_id'].values == original_imputed_meta_ext_df.index.values))
assert(np.all(meta_df['synapse_id'].values == consensus_cell_types_df.index.values))
imputed_meta_ext_df = meta_df.copy()
imputed_meta_ext_df['cleft_size_log1p_zscore'] = get_normal_sample(
original_imputed_meta_ext_df['imputed__cleft_size_log1p_zscore__mean'].values,
original_imputed_meta_ext_df['imputed__cleft_size_log1p_zscore__std'].values)
imputed_meta_ext_df['presyn_soma_dist_log1p_zscore'] = get_normal_sample(
original_imputed_meta_ext_df['imputed__presyn_soma_dist_log1p_zscore__mean'].values,
original_imputed_meta_ext_df['imputed__presyn_soma_dist_log1p_zscore__std'].values)
imputed_meta_ext_df['postsyn_soma_dist_log1p_zscore'] = get_normal_sample(
original_imputed_meta_ext_df['imputed__postsyn_soma_dist_log1p_zscore__mean'].values,
original_imputed_meta_ext_df['imputed__postsyn_soma_dist_log1p_zscore__std'].values)
imputed_meta_ext_df['mito_size_pre_vx_log1p_zscore_zi'] = get_normal_sample(
original_imputed_meta_ext_df['imputed__mito_size_pre_vx_log1p_zscore_zi__mean'].values,
original_imputed_meta_ext_df['imputed__mito_size_pre_vx_log1p_zscore_zi__std'].values)
imputed_meta_ext_df['mito_size_post_vx_log1p_zscore_zi'] = get_normal_sample(
original_imputed_meta_ext_df['imputed__mito_size_post_vx_log1p_zscore_zi__mean'].values,
original_imputed_meta_ext_df['imputed__mito_size_post_vx_log1p_zscore_zi__std'].values)
imputed_meta_ext_df['has_mito_pre'] = get_bernoulli_sample(
original_imputed_meta_ext_df['imputed__has_mito_pre__class_1'].values)
imputed_meta_ext_df['has_mito_post'] = get_bernoulli_sample(
original_imputed_meta_ext_df['imputed__has_mito_post__class_1'].values)
if cell_type_strategy == 'consensus':
imputed_meta_ext_df['pre_cell_type'] = consensus_cell_types_df['pre_cell_type__consensus'].values
imputed_meta_ext_df['post_cell_type'] = consensus_cell_types_df['post_cell_type__consensus'].values
elif cell_type_strategy == 'single':
imputed_meta_ext_df['pre_cell_type'] = get_bernoulli_sample(
consensus_cell_types_df['imputed__pre_cell_type__class_1'].values)
imputed_meta_ext_df['post_cell_type'] = get_bernoulli_sample(
consensus_cell_types_df['imputed__post_cell_type__class_1'].values)
else:
raise ValueError
# misc.
imputed_meta_ext_df['pre_synaptic_volume_log1p_zscore'] = log1p_zscore(meta_df['pre_synaptic_volume'].values)
imputed_meta_ext_df['post_synaptic_volume_log1p_zscore'] = log1p_zscore(meta_df['post_synaptic_volume'].values)
return imputed_meta_ext_df
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/synapse_simclr_pretrain.py | .py | 7,778 | 232 | import os
import numpy as np
import torch
import argparse
import pkg_resources
from operator import itemgetter
from typing import Union, List
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from synapse_simclr import SynapseSimCLRWorkspace
import synapse_simclr.utils as utils
torch.backends.cudnn.benchmark = True
# if in notebook mode, use default arguments
notebook_mode = False
# how to log?
log_info = print
def run_synapse_simclr_pretrain(
gpu_index: int,
args: argparse.Namespace):
"""Stages and runs the Synapse SimCLR pretraining loop."""
# instantiate the workspace
ws = SynapseSimCLRWorkspace(gpu_index, args)
log_info(f"Hashes before starting pretraining (make sure different nodes have the same hash) ...\n")
utils.print_hash(
model=ws.model,
optimizer=ws.optimizer,
scheduler=ws.scheduler)
log_info(f"\n")
# the training loop
log_info(f"Starting the training loop ...")
for i_epoch in range(ws.start_epoch, args.epochs):
if ws.train_sampler is not None:
ws.train_sampler.set_epoch(i_epoch)
ws.model.train()
lr = ws.optimizer.param_groups[0]["lr"]
log_info(f"Start epoch [{i_epoch}/{args.epochs}]\t lr: {lr:.7f}")
loss_list = run_synapse_simclr_pretrain_single_epoch(i_epoch, args, ws)
loss_epoch = np.sum(loss_list)
if ws.scheduler is not None:
ws.scheduler.step()
if i_epoch % args.checkpoint_frequency == 0:
utils.print_hash(
model=ws.model,
optimizer=ws.optimizer,
scheduler=ws.scheduler)
if args.nr == 0:
log_info(f"Checkpointing ...")
utils.checkpoint_state(
model=ws.model,
optimizer=ws.optimizer,
scheduler=ws.scheduler,
output_path=args.checkpoint_path,
current_epoch=i_epoch)
torch.cuda.synchronize()
torch.cuda.empty_cache()
if i_epoch % args.summary_frequency == 0:
if args.nr == 0:
log_info(f"Writing summary ...")
utils.write_summary(
output_path=args.checkpoint_path,
loss_list=loss_list,
current_epoch=i_epoch)
log_info(
f"End epoch [{i_epoch}/{args.epochs}]\t "
f"Loss: {loss_epoch / len(ws.train_loader)}\t lr: {lr:.7f}")
# end training
if args.nr == 0:
log_info(f"Checkpointing ...")
utils.checkpoint_state(
model=ws.model,
optimizer=ws.optimizer,
scheduler=ws.scheduler,
output_path=args.checkpoint_path,
current_epoch=i_epoch)
utils.print_hash(
model=ws.model,
optimizer=ws.optimizer,
scheduler=ws.scheduler)
log_info(f"Training finished!")
def run_synapse_simclr_pretrain_single_epoch(
i_epoch: int,
args: argparse.Namespace,
ws: SynapseSimCLRWorkspace) -> List[float]:
"""Runs a single epoch of Synapse SimCLR pretraining and returns the minibatch loss list."""
loss_list = []
for i_minibatch, minibatch_data_bundle in enumerate(ws.train_loader):
# obtain two independent augmentations from the loaded data
# .. note:: minibatch_data_bundle is a list of Tuple[int, np.ndarray]
# (i.e. index and data) we drop the index
# .. note:: the second return argument is the mask and is ignored
if args.print_minibatch_indices:
index_list = list(map(itemgetter(0), minibatch_data_bundle))
info_string = f"Node rank: {args.nr}, epoch index: {i_epoch}, minibatch index: {i_minibatch}"
separator = "=" * len(info_string)
log_info(info_string)
log_info(separator)
log_info(', '.join(list(map(str, index_list))))
log_info(separator)
intensity_mask_cxyz_list = list(map(itemgetter(1), minibatch_data_bundle))
x_i, _ = ws.augmenter.augment_raw_data(intensity_mask_cxyz_list)
x_j, _ = ws.augmenter.augment_raw_data(intensity_mask_cxyz_list)
# SimCLR step
ws.optimizer.zero_grad(set_to_none=True)
z_list_i = ws.model(x_i)
z_list_j = ws.model(x_j)
z_projector_head_i = z_list_i[-1]
z_projector_head_j = z_list_j[-1]
loss = ws.criterion(z_projector_head_i, z_projector_head_j)
loss.backward()
ws.optimizer.step()
if dist.is_available() and dist.is_initialized():
loss = loss.data.clone()
dist.all_reduce(loss.div_(dist.get_world_size()))
if args.nr == 0 and i_minibatch % args.loss_logging_frequency == 0:
log_info(
f"Minibatch [{i_minibatch}/{len(ws.train_loader)}]\t"
f"Loss: {loss.item()}")
loss_list.append(loss.item())
torch.cuda.empty_cache()
torch.cuda.synchronize()
return loss_list
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Synapse SimCLR")
parser.add_argument(f"--config-yaml-path", type=str, required=True)
# for DDP
parser.add_argument(f"--master-addr", default="127.0.0.1", type=str)
parser.add_argument(f"--master-port", default="8000", type=str)
parser.add_argument(f"--nodes", default=1, type=int)
parser.add_argument(f"--gpus", default=1, type=int)
parser.add_argument(f"--nr", default=0, required=True, type=int)
primary_args = parser.parse_args(args=[] if notebook_mode else None)
simclr_config_yaml_path = os.path.join(
primary_args.config_yaml_path, 'config.yaml')
augmenter_config_yaml_path = os.path.join(
primary_args.config_yaml_path, 'augmenter_pretrain.yaml')
log_info(f"Loading base SimCLR configuration from {simclr_config_yaml_path} ...")
log_info(f"Loading base SynapseAugmenter configuration from {augmenter_config_yaml_path} ...")
simclr_config = utils.yaml_config_hook(simclr_config_yaml_path)
augmenter_config = utils.yaml_config_hook(augmenter_config_yaml_path)
for key, value in simclr_config.items():
parser.add_argument(f"--{key}", default=value, type=type(value))
for key, value in augmenter_config.items():
parser.add_argument(f"--{key}", default=value, type=type(value))
args = parser.parse_args(args=[] if notebook_mode else None)
# Master address for distributed data parallel
os.environ["MASTER_ADDR"] = args.master_addr
os.environ["MASTER_PORT"] = args.master_port
# inject additional keys into the args Namespace
args.augmenter_config_yaml_path = augmenter_config_yaml_path
args.nr = primary_args.nr
args.run_mode = 'pretrain'
args.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
args.num_gpus = torch.cuda.device_count()
args.world_size = args.gpus * args.nodes
args.dtype = utils.parse_dtype(args.dtype)
if args.nodes > 1:
log_info(
f"Training with {args.nodes} nodes, waiting until all nodes "
f"join before starting training ...")
mp.spawn(
run_synapse_simclr_pretrain,
args=(args,),
nprocs=args.gpus,
join=True)
else:
run_synapse_simclr_pretrain(0, args)
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/synapse_simclr_extract.py | .py | 8,583 | 240 | import os
import numpy as np
import torch
import argparse
import pkg_resources
from operator import itemgetter
from typing import Union, List, Dict, Iterable, Callable
from collections import defaultdict
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from synapse_simclr import SynapseSimCLRWorkspace
import synapse_simclr.utils as utils
# if in notebook mode, use default arguments
notebook_mode = False
# how to log?
log_info = print
class FeatureExtractor(torch.nn.Module):
def __init__(
self,
model: torch.nn.Module,
args: argparse.Namespace,
layers: Iterable[str]):
super().__init__()
self.model = model
self.layers = layers
self._features = {layer: torch.empty(0) for layer in layers}
for layer_id in layers:
if args.world_size > 1:
fetch_layer_id = "module." + layer_id
else:
fetch_layer_id = layer_id
layer = dict([*self.model.named_modules()])[fetch_layer_id]
layer.register_forward_hook(self.save_outputs_hook(layer_id))
def save_outputs_hook(self, layer_id: str) -> Callable:
def fn(_, __, output):
self._features[layer_id] = output
return fn
def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
_ = self.model(x)
return self._features
class FeatureBuffer:
def __init__(self):
self._buffer_dict = defaultdict(list)
def accumulate(
self,
index_tensor: torch.Tensor,
feature_dict: Dict[str, torch.Tensor]):
self._buffer_dict['index_array'].append(index_tensor.cpu().numpy())
for key, value in feature_dict.items():
self._buffer_dict[key].append(value.cpu().numpy())
def _validate(self):
full_index_array_n = np.concatenate(self._buffer_dict['index_array'])
assert full_index_array_n.ndim == 1
n_samples = full_index_array_n.shape[0]
for key in self._buffer_dict.keys():
n_samples_for_key = 0
for value in self._buffer_dict[key]:
n_samples_for_key += value.shape[0]
assert n_samples_for_key == n_samples, \
f'Key {key} has {n_samples_for_key} buffered values; expected {n_samples}'
def save(
self,
output_path: str,
suffix: str,
validate: bool = True):
if validate:
self._validate()
for key, values in self._buffer_dict.items():
concat_values = np.concatenate(values, axis=0)
output_file_path = os.path.join(
output_path,
f'extracted_features__{suffix}__{key}.npy')
np.save(output_file_path, concat_values)
def get_feature_dict_list_mean(feature_dict_list: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
keys = feature_dict_list[0].keys()
mean_feature_dict = dict()
for key in keys:
mean_feature_value = torch.mean(
torch.cat(
[feature_dict[key][None, :] for feature_dict in feature_dict_list],
dim=0),
dim=0)
mean_feature_dict[key] = mean_feature_value
return mean_feature_dict
def run_synapse_simclr_extract_features(
gpu_index: int,
args: argparse.Namespace):
"""Stages and runs the Synapse SimCLR feature extraction loop."""
# instantiate the workspace
ws = SynapseSimCLRWorkspace(gpu_index, args)
# assert that output path exists
if not os.path.exists(args.feature_output_path):
os.makedirs(args.feature_output_path, exist_ok=True)
# forward feture extraction wrapper
module = ws.model.module if isinstance(ws.model, torch.torch.nn.DataParallel) else ws.model
feature_extractor = FeatureExtractor(
model=module,
args=args,
layers=args.feature_extraction_hooks)
# a buffer for extracted features
feature_buffer = FeatureBuffer()
# the training loop
log_info(f"Starting the feature extraction loop ...")
if ws.train_sampler is not None:
ws.train_sampler.set_epoch(0)
ws.model.eval()
for i_minibatch, minibatch_data_bundle in enumerate(ws.train_loader):
# obtain two independent augmentations from the loaded data
# .. note:: minibatch_data_bundle is a list of Tuple[int, np.ndarray]
# (i.e. index and data) we drop the index
# .. note:: the second return argument is the mask and is ignored
index_tensor = torch.tensor(list(map(itemgetter(0), minibatch_data_bundle)))
if args.print_minibatch_indices:
index_list = index_tensor.numpy().tolist()
info_string = f"Node rank: {args.nr}, minibatch index: {i_minibatch}"
separator = "=" * len(info_string)
log_info(info_string)
log_info(separator)
log_info(', '.join(list(map(str, index_list))))
log_info(separator)
# extract features
intensity_mask_cxyz_list = list(map(itemgetter(1), minibatch_data_bundle))
feature_dict_list = []
with torch.no_grad():
for _ in range(args.n_extract_augmentations):
x, _ = ws.augmenter.augment_raw_data(intensity_mask_cxyz_list)
feature_dict_list.append(feature_extractor(x))
feature_dict = get_feature_dict_list_mean(feature_dict_list)
# send to buffer
feature_buffer.accumulate(
index_tensor=index_tensor,
feature_dict=feature_dict)
if args.nr == 0 and i_minibatch % args.extract_logging_frequency == 0:
log_info(f"Minibatch [{i_minibatch}/{len(ws.train_loader)}]")
# save buffer
log_info("Saving features ...")
feature_buffer.save(
output_path=args.feature_output_path,
suffix=f'node.{args.nr}__epoch.{args.reload_epoch_num}')
log_info(f"Feature extraction finished!")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Synapse SimCLR")
parser.add_argument(f"--config-yaml-path", type=str, required=True)
# for DDP
parser.add_argument(f"--master-addr", default="127.0.0.1", type=str)
parser.add_argument(f"--master-port", default="8000", type=str)
parser.add_argument(f"--nodes", default=1, type=int)
parser.add_argument(f"--gpus", default=1, type=int)
parser.add_argument(f"--nr", default=0, required=True, type=int)
primary_args = parser.parse_args(args=[] if notebook_mode else None)
simclr_config_yaml_path = os.path.join(
primary_args.config_yaml_path, 'config.yaml')
augmenter_config_yaml_path = os.path.join(
primary_args.config_yaml_path, 'augmenter_extract.yaml')
log_info(f"Loading base SimCLR configuration from {simclr_config_yaml_path} ...")
log_info(f"Loading base SynapseAugmenter configuration from {augmenter_config_yaml_path} ...")
simclr_config = utils.yaml_config_hook(simclr_config_yaml_path)
augmenter_config = utils.yaml_config_hook(augmenter_config_yaml_path)
for key, value in simclr_config.items():
parser.add_argument(f"--{key}", default=value, type=type(value))
for key, value in augmenter_config.items():
parser.add_argument(f"--{key}", default=value, type=type(value))
args = parser.parse_args(args=[] if notebook_mode else None)
# Master address for distributed data parallel
os.environ["MASTER_ADDR"] = args.master_addr
os.environ["MASTER_PORT"] = args.master_port
# inject additional keys into the args Namespace
args.augmenter_config_yaml_path = augmenter_config_yaml_path
args.nr = primary_args.nr
args.run_mode = 'extract'
args.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
args.num_gpus = torch.cuda.device_count()
args.world_size = args.gpus * args.nodes
args.dtype = utils.parse_dtype(args.dtype)
if args.nodes > 1:
log_info(
f"Training with {args.nodes} nodes, waiting until all nodes "
f"join before starting training ...")
mp.spawn(
run_synapse_simclr_extract_features,
args=(args,),
nprocs=args.gpus,
join=True)
else:
run_synapse_simclr_extract_features(0, args)
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/__init__.py | .py | 61 | 2 | from .synapse_simclr_workspace import SynapseSimCLRWorkspace
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/synapse_simclr_workspace.py | .py | 9,285 | 248 | import os
import numpy as np
import torch
import argparse
import pkg_resources
from typing import Union, List
from operator import itemgetter
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from synapse_augmenter import SynapseAugmenter
from synapse_dataset import SynapseDataset
from synapse_simclr.modules import SynapseSimCLR, NT_Xent, generate_resnet_3d
import synapse_simclr.utils as utils
# how to log?
log_info = print
class SynapseSimCLRWorkspace:
def __init__(
self,
gpu_index: int,
args: argparse.Namespace):
assert args.run_mode in {'pretrain', 'extract'}
log_info(f"Instantiating the Synapse SimCLR workspace in {args.run_mode} mode ...")
if args.run_mode == 'extract':
shuffle = False
drop_last = False
train_mode = False
elif args.run_mode == 'pretrain':
shuffle = True
drop_last = True
train_mode = True
else:
raise ValueError
# rank of the process
rank = args.nr * args.gpus + gpu_index
if args.nodes > 1:
dist.init_process_group("nccl", rank=rank, world_size=args.world_size)
torch.cuda.set_device(gpu_index)
# make a dictionary of augmenter options after argparse
augmenter_config_keys = utils.yaml_config_hook(args.augmenter_config_yaml_path).keys()
args_dict = vars(args)
augmenter_kwargs = {key: args_dict[key] for key in augmenter_config_keys}
# set the random seed for determinism
if augmenter_kwargs['batch_mode'] == 'coupled':
torch.manual_seed(args.seed)
np.random.seed(args.seed)
else:
torch.manual_seed(args.seed + args.nr)
np.random.seed(args.seed + args.nr)
# load the dataset
log_info("Instantiating synapse dataset ...")
train_dataset = SynapseDataset(
dataset_path=args.dataset_path,
head=args.dataset_head if args.dataset_head > 0 else None,
drop_annotated_synapses=(args.drop_annotated_synapses > 0))
log_info(f'- Training dataset size: {len(train_dataset)}')
# instantiate data sampler and loader
if args.nodes > 1:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=args.world_size,
rank=rank,
shuffle=shuffle)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
drop_last=drop_last,
collate_fn=lambda x: x, # do not collate; return as a list of np.ndarray
num_workers=args.dataloader_workers,
sampler=train_sampler,
pin_memory=True)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=shuffle,
drop_last=drop_last,
collate_fn=lambda x: x, # do not collate; return as a list of np.ndarray
num_workers=args.dataloader_workers,
sampler=None,
pin_memory=True)
# instantiate the augmenter
augmenter = SynapseAugmenter(
**augmenter_kwargs,
device=args.device,
dtype=args.dtype)
# instantiate the encoder
log_info("Instantiating the encoder ...")
encoder = utils.instantiate_encoder(
encoder_type=args.encoder_type,
n_input_channels=augmenter.n_intensity_output_channels)
# instantiate the SynapseSimCLR model
log_info("Instantiating SimCLR ...")
model = SynapseSimCLR(encoder, args.projection_dims).to(args.device).type(args.dtype)
model.train(mode=train_mode)
# instantiate the optimizer and LR scheduler
log_info("Instantiating the optimizer and scheduler ...")
optimizer, scheduler = utils.instantiate_optimizer(
optimizer_type=args.optimizer_type,
model=model,
adam_lr=args.adam_lr,
batch_size=args.batch_size,
weight_decay=args.weight_decay,
epochs=args.epochs)
# make sure checkpoint path exists
if not os.path.exists(args.checkpoint_path):
os.makedirs(args.checkpoint_path, exist_ok=True)
# reload from a checkpoint?
if int(args.reload):
# load model
model_checkpoint_path = os.path.join(
args.checkpoint_path,
f"model_checkpoint_{args.reload_epoch_num}.pt")
log_info(f"Loading model checkpoint from {model_checkpoint_path} ...")
model_state_dict = torch.load(model_checkpoint_path, map_location=args.device.type)
# ..note:: we need to drop "module." prefix from model state dict keys
# for some reason, saving the module attribute of DDP models adds this extra prefix ...
fixed_model_state_dict = dict()
for key, value in model_state_dict.items():
fixed_model_state_dict[key[7:] if key.find("module.") == 0 else key] = value
model_keys = set(map(itemgetter(0), model.named_parameters()))
loaded_model_keys = set(fixed_model_state_dict.keys())
missing_keys = model_keys.difference(loaded_model_keys)
if len(missing_keys) == 0:
log_info("All model parameters are available in the loaded state dictionary.")
else:
log_info("WARNING: The following model parameters are not available in the loaded state dictionary:")
for key in missing_keys:
log_info(f'* {key}')
model.load_state_dict(fixed_model_state_dict, strict=False)
# load optimizer state?
if int(args.reload_optimizer_state):
optimizer_checkpoint_path = os.path.join(
args.checkpoint_path,
f"optimizer_checkpoint_{args.reload_epoch_num}.pt")
log_info(f"Loading optimizer checkpoint from {optimizer_checkpoint_path} ...")
optimizer_state_dict = torch.load(optimizer_checkpoint_path, map_location=args.device.type)
optimizer.load_state_dict(optimizer_state_dict)
scheduler_checkpoint_path = os.path.join(
args.checkpoint_path,
f"scheduler_checkpoint_{args.reload_epoch_num}.pt")
log_info(f"Loading scheduler checkpoint from {scheduler_checkpoint_path} ...")
scheduler_checkpoint_path = torch.load(scheduler_checkpoint_path, map_location=args.device.type)
scheduler.load_state_dict(scheduler_checkpoint_path)
if args.optimizer_type == "Adam":
lr = scheduler.get_last_lr()[0]
for g in optimizer.param_groups:
g['lr'] = lr
else:
raise NotImplementedError("Only reloading Adam optimizer state is supported!")
else:
log_info("Optimizer state NOT reloaded.")
self.start_epoch = args.reload_epoch_num + 1
else:
assert args.run_mode == 'pretrain', \
'If not pretraining, you must reload a pretrained model from a checkpoint!'
self.start_epoch = 0
# loss
criterion = NT_Xent(args.batch_size, args.temperature, args.world_size)
# DDP
if args.nodes > 1:
# only sync batch norm if need to train; otherwise, no need
if train_mode:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = DDP(model, device_ids=[gpu_index])
model = model.to(args.device)
model.train(mode=train_mode)
if args.nr == 0:
# todo: placeholder for instantiating a summary writer
pass
if args.nodes > 1:
module = model.module
else:
module = model
if args.nr == 0:
log_info("\n")
log_info("Encoder architecture:\n")
log_info(module.encoder)
log_info("\n")
log_info("Projector architecture:\n")
log_info(module.projector)
log_info("\n")
# references to useful variables
self.nr = args.nr
self.train_dataset = train_dataset
self.train_loader = train_loader
self.train_sampler = train_sampler
self.augmenter = augmenter
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.criterion = criterion
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/identity.py | .py | 156 | 9 | import torch
class Identity(torch.nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/nt_xent.py | .py | 2,080 | 52 | import torch
import torch.distributed as dist
from .gather import GatherLayer
class NT_Xent(torch.nn.Module):
def __init__(self, batch_size, temperature, world_size):
super(NT_Xent, self).__init__()
self.batch_size = batch_size
self.temperature = temperature
self.world_size = world_size
self.full_batch_size = world_size * batch_size
self.mask = self.get_self_excluding_mask()
self.criterion = torch.nn.CrossEntropyLoss(reduction="sum")
self.similarity_f = torch.nn.CosineSimilarity(dim=2)
def get_self_excluding_mask(self) -> torch.Tensor:
mask = torch.ones((2 * self.full_batch_size, 2 * self.full_batch_size), dtype=bool)
mask = mask.fill_diagonal_(0)
for i in range(self.full_batch_size):
mask[i, i + self.full_batch_size] = 0
mask[i + self.full_batch_size, i] = 0
return mask
def forward(self, z_i: torch.Tensor, z_j: torch.Tensor) -> torch.Tensor:
if self.world_size > 1:
z_i = torch.cat(GatherLayer.apply(z_i), dim=0)
z_j = torch.cat(GatherLayer.apply(z_j), dim=0)
# first M entries of z will be the first augmentation; last M entries will
# be the second augmentation
z = torch.cat((z_i, z_j), dim=0)
# the dimension of sim is (2 * self.full_batch_size) x (2 * self.full_batch_size)
sim = self.similarity_f(z.unsqueeze(1), z.unsqueeze(0)) / self.temperature
sim_i_j = torch.diag(sim, self.full_batch_size)
sim_j_i = torch.diag(sim, -self.full_batch_size)
positive_samples = torch.cat((sim_i_j, sim_j_i), dim=0).reshape(2 * self.full_batch_size, 1)
negative_samples = sim[self.mask].reshape(2 * self.full_batch_size, -1)
labels = torch.zeros(2 * self.full_batch_size).to(positive_samples.device).long()
logits = torch.cat((positive_samples, negative_samples), dim=1)
loss = self.criterion(logits, labels) / (2 * self.full_batch_size)
return loss
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/synapse_simclr.py | .py | 2,548 | 78 | import os
from scipy.stats import truncnorm
import numpy as np
from typing import List, Tuple, Union, Optional
import torch
from synapse_simclr.modules import Identity, Projector
class SynapseSimCLR(torch.nn.Module):
"""TBW.
"""
def __init__(
self,
encoder: torch.nn.Module,
projection_dims: List[int]):
"""
.. note::
encoder must have the following attributes:
- 'fc': a fully connected readout layer (that we strip out)
- 'n_features': number of features just after the final pooling and before 'fc'
"""
super(SynapseSimCLR, self).__init__()
self.encoder = encoder
self.projection_dims = projection_dims
# Replace the fc layer with an Identity function
self.encoder.fc = Identity()
# We use a MLP with ReLU non-linearity as the projection head
self.projector = Projector(
input_features=self.encoder.n_features,
channel_dims=projection_dims)
self.reset_parameters()
def reset_parameters(self):
def conv3d_weight_truncated_normal_init(p):
fan_in = p.shape[1]
stddev = np.sqrt(1. / fan_in) / .87962566103423978
r = truncnorm.rvs(-2, 2, loc=0, scale=1., size=p.shape)
r = stddev * r
with torch.no_grad():
p.copy_(torch.FloatTensor(r))
def linear_normal_init(p):
with torch.no_grad():
p.normal_(std=0.01)
for m in self.modules():
if isinstance(m, torch.nn.Conv3d):
conv3d_weight_truncated_normal_init(m.weight)
elif isinstance(m, torch.nn.Linear):
linear_normal_init(m.weight)
@property
def output_dims(self) -> List[int]:
encoder_output_dim = self.encoder.n_features
projector_output_dims = self.projection_dims
return [encoder_output_dim] + projector_output_dims
def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
"""
.. note: returns a list of activations; the first entry on the list is the output from
the backbone (e.g. ResNet), and the last entry is the output from the last layer of the
project.
"""
# generate representations for the two sets of augmentations
h = self.encoder(x)
# project to the "cosine similarity space"
z_list = self.projector(h)
return [h] + z_list
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/lars.py | .py | 5,961 | 166 | """
LARS: Layer-wise Adaptive Rate Scaling
Converted from TensorFlow to PyTorch
https://github.com/google-research/simclr/blob/master/lars_optimizer.py
"""
import torch
from torch.optim.optimizer import Optimizer, required
import re
EETA_DEFAULT = 0.001
class LARS(Optimizer):
"""
Layer-wise Adaptive Rate Scaling for large batch training.
Introduced by "Large Batch Training of Convolutional Networks" by Y. You,
I. Gitman, and B. Ginsburg. (https://arxiv.org/abs/1708.03888)
"""
def __init__(
self,
params,
lr=required,
momentum=0.9,
use_nesterov=False,
weight_decay=0.0,
exclude_from_weight_decay=None,
exclude_from_layer_adaptation=None,
classic_momentum=True,
eeta=EETA_DEFAULT,
):
"""Constructs a LARSOptimizer.
Args:
lr: A `float` for learning rate.
momentum: A `float` for momentum.
use_nesterov: A 'Boolean' for whether to use nesterov momentum.
weight_decay: A `float` for weight decay.
exclude_from_weight_decay: A list of `string` for variable screening, if
any of the string appears in a variable's name, the variable will be
excluded for computing weight decay. For example, one could specify
the list like ['batch_normalization', 'bias'] to exclude BN and bias
from weight decay.
exclude_from_layer_adaptation: Similar to exclude_from_weight_decay, but
for layer adaptation. If it is None, it will be defaulted the same as
exclude_from_weight_decay.
classic_momentum: A `boolean` for whether to use classic (or popular)
momentum. The learning rate is applied during momeuntum update in
classic momentum, but after momentum for popular momentum.
eeta: A `float` for scaling of learning rate when computing trust ratio.
name: The name for the scope.
"""
self.epoch = 0
defaults = dict(
lr=lr,
momentum=momentum,
use_nesterov=use_nesterov,
weight_decay=weight_decay,
exclude_from_weight_decay=exclude_from_weight_decay,
exclude_from_layer_adaptation=exclude_from_layer_adaptation,
classic_momentum=classic_momentum,
eeta=eeta,
)
super(LARS, self).__init__(params, defaults)
self.lr = lr
self.momentum = momentum
self.weight_decay = weight_decay
self.use_nesterov = use_nesterov
self.classic_momentum = classic_momentum
self.eeta = eeta
self.exclude_from_weight_decay = exclude_from_weight_decay
# exclude_from_layer_adaptation is set to exclude_from_weight_decay if the
# arg is None.
if exclude_from_layer_adaptation:
self.exclude_from_layer_adaptation = exclude_from_layer_adaptation
else:
self.exclude_from_layer_adaptation = exclude_from_weight_decay
def step(self, epoch=None, closure=None):
loss = None
if closure is not None:
loss = closure()
if epoch is None:
epoch = self.epoch
self.epoch += 1
for group in self.param_groups:
weight_decay = group["weight_decay"]
momentum = group["momentum"]
eeta = group["eeta"]
lr = group["lr"]
for p in group["params"]:
if p.grad is None:
continue
param = p.data
grad = p.grad.data
param_state = self.state[p]
# TODO: get param names
# if self._use_weight_decay(param_name):
grad += self.weight_decay * param
if self.classic_momentum:
trust_ratio = 1.0
# TODO: get param names
# if self._do_layer_adaptation(param_name):
w_norm = torch.norm(param)
g_norm = torch.norm(grad)
device = g_norm.get_device()
trust_ratio = torch.where(
w_norm.ge(0),
torch.where(
g_norm.ge(0),
(self.eeta * w_norm / g_norm),
torch.Tensor([1.0]).to(device),
),
torch.Tensor([1.0]).to(device),
).item()
scaled_lr = lr * trust_ratio
if "momentum_buffer" not in param_state:
next_v = param_state["momentum_buffer"] = torch.zeros_like(
p.data
)
else:
next_v = param_state["momentum_buffer"]
next_v.mul_(momentum).add_(scaled_lr, grad)
if self.use_nesterov:
update = (self.momentum * next_v) + (scaled_lr * grad)
else:
update = next_v
p.data.add_(-update)
else:
raise NotImplementedError
return loss
def _use_weight_decay(self, param_name):
"""Whether to use L2 weight decay for `param_name`."""
if not self.weight_decay:
return False
if self.exclude_from_weight_decay:
for r in self.exclude_from_weight_decay:
if re.search(r, param_name) is not None:
return False
return True
def _do_layer_adaptation(self, param_name):
"""Whether to do layer-wise learning rate adaptation for `param_name`."""
if self.exclude_from_layer_adaptation:
for r in self.exclude_from_layer_adaptation:
if re.search(r, param_name) is not None:
return False
return True
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/resnet_3d.py | .py | 8,198 | 260 | #################################################################################
# code is adapted from: #
# https://github.com/kenshohara/3D-ResNets-PyTorch/blob/master/models/resnet.py #
#################################################################################
import math
from functools import partial
import torch
import torch.nn as nn
def get_inplanes():
return [64, 128, 256, 512]
def conv3x3x3(in_planes, out_planes, stride=1, dilation=1):
return torch.nn.Conv3d(
in_planes,
out_planes,
kernel_size=3,
dilation=dilation,
stride=stride,
padding=dilation,
bias=False)
def conv1x1x1(in_planes, out_planes, stride=1):
return torch.nn.Conv3d(
in_planes,
out_planes,
kernel_size=1,
stride=stride,
bias=False)
class BasicBlock(torch.nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, dilation=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3x3(in_planes, planes, stride=stride, dilation=dilation)
self.bn1 = nn.BatchNorm3d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3x3(planes, planes, dilation=dilation)
self.bn2 = nn.BatchNorm3d(planes)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(torch.nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1, dilation=1, downsample=None):
super().__init__()
self.conv1 = conv1x1x1(in_planes, planes)
self.bn1 = torch.nn.BatchNorm3d(planes)
self.conv2 = conv3x3x3(planes, planes, stride, dilation)
self.bn2 = torch.nn.BatchNorm3d(planes)
self.conv3 = conv1x1x1(planes, planes * self.expansion)
self.bn3 = torch.nn.BatchNorm3d(planes * self.expansion)
self.relu = torch.nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(torch.nn.Module):
def __init__(self,
block,
layers,
block_inplanes,
block_strides=[1, 2, 2, 2],
block_dilations=[1, 1, 1, 1],
n_input_channels=3,
conv1_kernel_size=7,
conv1_stride=2,
conv1_padding=3,
no_max_pool=False,
shortcut_type='B',
widen_factor=1.0,
n_classes=400):
super().__init__()
block_inplanes = [int(x * widen_factor) for x in block_inplanes]
self.in_planes = block_inplanes[0]
self.no_max_pool = no_max_pool
self.conv1 = torch.nn.Conv3d(
n_input_channels,
self.in_planes,
kernel_size=conv1_kernel_size,
stride=conv1_stride,
padding=conv1_padding,
bias=False)
self.bn1 = torch.nn.BatchNorm3d(self.in_planes)
self.relu = torch.nn.ReLU(inplace=True)
self.maxpool = torch.nn.MaxPool3d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(
block,
block_inplanes[0],
layers[0],
shortcut_type,
stride=block_strides[0],
dilation=block_dilations[0])
self.layer2 = self._make_layer(
block,
block_inplanes[1],
layers[1],
shortcut_type,
stride=block_strides[1],
dilation=block_dilations[1])
self.layer3 = self._make_layer(
block,
block_inplanes[2],
layers[2],
shortcut_type,
stride=block_strides[2],
dilation=block_dilations[2])
self.layer4 = self._make_layer(
block,
block_inplanes[3],
layers[3],
shortcut_type,
stride=block_strides[3],
dilation=block_dilations[3])
self.avgpool = torch.nn.AdaptiveAvgPool3d((1, 1, 1))
self.n_features = block_inplanes[3] * block.expansion
self.fc = torch.nn.Linear(self.n_features, n_classes)
for m in self.modules():
if isinstance(m, torch.nn.Conv3d):
torch.nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, torch.nn.BatchNorm3d):
torch.nn.init.constant_(m.weight, 1)
torch.nn.init.constant_(m.bias, 0)
def _downsample_basic_block(self, x, planes, stride):
out = torch.nn.functional.avg_pool3d(x, kernel_size=1, stride=stride)
zero_pads = torch.zeros(out.size(0), planes - out.size(1), out.size(2),
out.size(3), out.size(4))
if isinstance(out.data, torch.cuda.FloatTensor):
zero_pads = zero_pads.cuda()
out = torch.cat([out.data, zero_pads], dim=1)
return out
def _make_layer(self, block, planes, blocks, shortcut_type, stride=1, dilation=1):
downsample = None
if stride != 1 or self.in_planes != planes * block.expansion:
if shortcut_type == 'A':
downsample = partial(
self._downsample_basic_block,
planes=planes * block.expansion,
stride=stride)
else:
downsample = torch.nn.Sequential(
conv1x1x1(self.in_planes, planes * block.expansion, stride),
torch.nn.BatchNorm3d(planes * block.expansion))
layers = []
layers.append(
block(in_planes=self.in_planes,
planes=planes,
stride=stride,
dilation=dilation,
downsample=downsample))
self.in_planes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.in_planes, planes, dilation=dilation))
return torch.nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
if not self.no_max_pool:
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def generate_model(model_depth, **kwargs):
assert model_depth in [10, 18, 34, 50, 101, 152, 200]
if model_depth == 10:
model = ResNet(BasicBlock, [1, 1, 1, 1], get_inplanes(), **kwargs)
elif model_depth == 18:
model = ResNet(BasicBlock, [2, 2, 2, 2], get_inplanes(), **kwargs)
elif model_depth == 34:
model = ResNet(BasicBlock, [3, 4, 6, 3], get_inplanes(), **kwargs)
elif model_depth == 50:
model = ResNet(Bottleneck, [3, 4, 6, 3], get_inplanes(), **kwargs)
elif model_depth == 101:
model = ResNet(Bottleneck, [3, 4, 23, 3], get_inplanes(), **kwargs)
elif model_depth == 152:
model = ResNet(Bottleneck, [3, 8, 36, 3], get_inplanes(), **kwargs)
elif model_depth == 200:
model = ResNet(Bottleneck, [3, 24, 36, 3], get_inplanes(), **kwargs)
return model | Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/projector.py | .py | 1,296 | 38 | from typing import List
import torch
class Projector(torch.nn.Module):
def __init__(
self,
input_features: int,
channel_dims: List[int],
bias: bool = False):
"""MLP projection head for SimCLR."""
super(Projector, self).__init__()
# generate a basic MLP
blocks = []
prev_channel = input_features
for channel_dim in channel_dims[:-1]:
blocks.append(torch.nn.Linear(prev_channel, channel_dim, bias=bias))
blocks.append(torch.nn.BatchNorm1d(channel_dim))
blocks.append(torch.nn.ReLU())
prev_channel = channel_dim
blocks.append(torch.nn.Linear(prev_channel, channel_dims[-1], bias=bias))
self.mlp = torch.nn.Sequential(*blocks)
def forward(self, x) -> List[torch.Tensor]:
"""
.. note: returns a list of activations from all linear layers; the last entry
on the list is the one that is used in SimCLR.
"""
layers = list(self.mlp.children())
linear_activations = []
for layer in layers:
x = layer(x)
if isinstance(layer, torch.nn.Linear):
linear_activations.append(x)
return linear_activations | Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/__init__.py | .py | 409 | 13 | from .nt_xent import NT_Xent
from .lars import LARS
from .gather import GatherLayer
from .identity import Identity
from .projector import Projector
from .resnet_3d import generate_model as generate_resnet_3d
from .pre_act_resnet_3d import generate_model as generate_pre_act_resnet_3d
from .synapse_simclr import SynapseSimCLR
# [emphemeral]
from .resnet_3d_medicalnet import resnet18 as resnet18_medicalnet
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/pre_act_resnet_3d.py | .py | 3,404 | 108 | #########################################################################################
# code is based on: #
# https://github.com/kenshohara/3D-ResNets-PyTorch/blob/master/models/pre_act_resnet.py #
#########################################################################################
import torch
from .resnet_3d import conv3x3x3, conv1x1x1, get_inplanes, ResNet
class PreActivationBasicBlock(torch.nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super().__init__()
self.bn1 = torch.nn.BatchNorm3d(inplanes)
self.conv1 = conv3x3x3(inplanes, planes, stride)
self.bn2 = torch.nn.BatchNorm3d(planes)
self.conv2 = conv3x3x3(planes, planes)
self.relu = torch.nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.bn1(x)
out = self.relu(out)
out = self.conv1(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
return out
class PreActivationBottleneck(torch.nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super().__init__()
self.bn1 = torch.nn.BatchNorm3d(inplanes)
self.conv1 = conv1x1x1(inplanes, planes)
self.bn2 = torch.nn.BatchNorm3d(planes)
self.conv2 = conv3x3x3(planes, planes, stride)
self.bn3 = torch.nn.BatchNorm3d(planes)
self.conv3 = conv1x1x1(planes, planes * self.expansion)
self.relu = torch.nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.bn1(x)
out = self.relu(out)
out = self.conv1(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn3(out)
out = self.relu(out)
out = self.conv3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
return out
def generate_model(model_depth, **kwargs):
assert model_depth in [10, 18, 34, 50, 101, 152, 200]
if model_depth == 10:
model = ResNet(PreActivationBasicBlock, [1, 1, 1, 1], get_inplanes(),
**kwargs)
elif model_depth == 18:
model = ResNet(PreActivationBasicBlock, [2, 2, 2, 2], get_inplanes(),
**kwargs)
elif model_depth == 34:
model = ResNet(PreActivationBasicBlock, [3, 4, 6, 3], get_inplanes(),
**kwargs)
elif model_depth == 50:
model = ResNet(PreActivationBottleneck, [3, 4, 6, 3], get_inplanes(),
**kwargs)
elif model_depth == 101:
model = ResNet(PreActivationBottleneck, [3, 4, 23, 3], get_inplanes(),
**kwargs)
elif model_depth == 152:
model = ResNet(PreActivationBottleneck, [3, 8, 36, 3], get_inplanes(),
**kwargs)
elif model_depth == 200:
model = ResNet(PreActivationBottleneck, [3, 24, 36, 3], get_inplanes(),
**kwargs)
return model | Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/synapse_supervised.py | .py | 1,469 | 46 | import os
from scipy.stats import truncnorm
import numpy as np
from typing import List, Tuple, Union, Optional
import torch
from synapse_simclr.modules import SynapseSimCLR
class SynapseSupervised(torch.nn.Module):
"""TBW.
"""
def __init__(
self,
synapse_simclr: SynapseSimCLR,
synapse_simclr_output_layer_index: int,
linear_readout_type: List[str],
linear_readout_num_categories: List[int]):
"""
:param synapse_simclr_output_layer_index: which layer of the SimCLR model to construct
linear predictors from?
:param linear_readout_type: 'continous' or 'categorical'
:param linear_readout_num_categories: for 'continuous', must be 1; for categorical, however many
categories there are
"""
super(SynapseSupervised, self).__init__()
self.synapse_simclr = synapse_simclr
def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
"""
.. note: returns a list of activations; the first entry on the list is the output from
the backbone (e.g. ResNet), and the last entry is the output from the last layer of the
project.
"""
# generate representations for the two sets of augmentations
h = self.encoder(x)
# project to the "cosine similarity space"
z_list = self.projector(h)
return [h] + z_list
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/gather.py | .py | 601 | 21 | import torch
import torch.distributed as dist
class GatherLayer(torch.autograd.Function):
"""Gather tensors from all process, supporting backward propagation."""
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
output = [torch.zeros_like(input) for _ in range(dist.get_world_size())]
dist.all_gather(output, input)
return tuple(output)
@staticmethod
def backward(ctx, *grads):
(input,) = ctx.saved_tensors
grad_out = torch.zeros_like(input)
grad_out[:] = grads[dist.get_rank()]
return grad_out
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/modules/resnet_3d_medicalnet.py | .py | 7,027 | 243 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
from functools import partial
__all__ = [
'ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnet200'
]
def conv3x3x3(in_planes, out_planes, stride=1, dilation=1):
# 3x3x3 convolution with padding
return nn.Conv3d(
in_planes,
out_planes,
kernel_size=3,
dilation=dilation,
stride=stride,
padding=dilation,
bias=False)
def downsample_basic_block(x, planes, stride, no_cuda=False):
out = F.avg_pool3d(x, kernel_size=1, stride=stride)
zero_pads = torch.Tensor(
out.size(0), planes - out.size(1), out.size(2), out.size(3),
out.size(4)).zero_()
if not no_cuda:
if isinstance(out.data, torch.cuda.FloatTensor):
zero_pads = zero_pads.cuda()
out = Variable(torch.cat([out.data, zero_pads], dim=1))
return out
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3x3(inplanes, planes, stride=stride, dilation=dilation)
self.bn1 = nn.BatchNorm3d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3x3(planes, planes, dilation=dilation)
self.bn2 = nn.BatchNorm3d(planes)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm3d(planes)
self.conv2 = nn.Conv3d(
planes, planes, kernel_size=3, stride=stride, dilation=dilation, padding=dilation, bias=False)
self.bn2 = nn.BatchNorm3d(planes)
self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm3d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self,
block,
layers,
shortcut_type='B',
no_cuda=False,
n_classes=400):
self.inplanes = 64
self.no_cuda = no_cuda
super(ResNet, self).__init__()
self.conv1 = nn.Conv3d(
1,
64,
kernel_size=7,
stride=(2, 2, 2),
padding=(3, 3, 3),
bias=False)
self.bn1 = nn.BatchNorm3d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], shortcut_type)
self.layer2 = self._make_layer(
block, 128, layers[1], shortcut_type, stride=2)
self.layer3 = self._make_layer(
block, 256, layers[2], shortcut_type, stride=1, dilation=2)
self.layer4 = self._make_layer(
block, 512, layers[3], shortcut_type, stride=1, dilation=4)
self.avgpool = torch.nn.AdaptiveAvgPool3d((1, 1, 1))
self.n_features = 512 * block.expansion
self.fc = torch.nn.Linear(self.n_features, n_classes)
for m in self.modules():
if isinstance(m, nn.Conv3d):
m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')
elif isinstance(m, nn.BatchNorm3d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, shortcut_type, stride=1, dilation=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
if shortcut_type == 'A':
downsample = partial(
downsample_basic_block,
planes=planes * block.expansion,
stride=stride,
no_cuda=self.no_cuda)
else:
downsample = nn.Sequential(
nn.Conv3d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False), nn.BatchNorm3d(planes * block.expansion))
layers = []
layers.append(block(self.inplanes, planes, stride=stride, dilation=dilation, downsample=downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=dilation))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnet10(**kwargs):
"""Constructs a ResNet-18 model.
"""
model = ResNet(BasicBlock, [1, 1, 1, 1], **kwargs)
return model
def resnet18(**kwargs):
"""Constructs a ResNet-18 model.
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
return model
def resnet34(**kwargs):
"""Constructs a ResNet-34 model.
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
return model
def resnet50(**kwargs):
"""Constructs a ResNet-50 model.
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
return model
def resnet101(**kwargs):
"""Constructs a ResNet-101 model.
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
return model
def resnet152(**kwargs):
"""Constructs a ResNet-101 model.
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
return model
def resnet200(**kwargs):
"""Constructs a ResNet-101 model.
"""
model = ResNet(Bottleneck, [3, 24, 36, 3], **kwargs)
return model
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/utils/train_helper.py | .py | 8,338 | 270 | from typing import List, Tuple, Union, Optional
import os
import hashlib
import pickle
import numpy as np
import torch
from torch.optim.optimizer import Optimizer
from torch.optim.lr_scheduler import CosineAnnealingLR
from synapse_simclr.modules import \
LARS, \
generate_resnet_3d
_SUPPORTED_ENCODERS = [
'resnet18',
'resnet34',
'resnet50',
'resnet18_no_max_pool',
'resnet34_no_max_pool',
'resnet50_no_max_pool',
'resnet18_no_max_pool_cifar_stem',
'resnet34_no_max_pool_cifar_stem',
'resnet50_no_max_pool_cifar_stem',
'resnet18_medicalnet'
]
def instantiate_encoder(
encoder_type: str,
n_input_channels: int,
**kwargs) -> torch.nn.Module:
if encoder_type == 'resnet18':
return generate_resnet_3d(
model_depth=18,
n_input_channels=n_input_channels,
no_max_pool=False,
conv1_kernel_size=7,
conv1_stride=2,
conv1_padding=3,
**kwargs)
if encoder_type == 'resnet34':
return generate_resnet_3d(
model_depth=34,
n_input_channels=n_input_channels,
no_max_pool=False,
conv1_kernel_size=7,
conv1_stride=2,
conv1_padding=3,
**kwargs)
elif encoder_type == 'resnet50':
return generate_resnet_3d(
model_depth=50,
n_input_channels=n_input_channels,
no_max_pool=False,
conv1_kernel_size=7,
conv1_stride=2,
conv1_padding=3,
**kwargs)
elif encoder_type == 'resnet18_no_max_pool':
return generate_resnet_3d(
model_depth=18,
n_input_channels=n_input_channels,
no_max_pool=True,
conv1_kernel_size=7,
conv1_stride=2,
conv1_padding=3,
**kwargs)
elif encoder_type == 'resnet34_no_max_pool':
return generate_resnet_3d(
model_depth=34,
n_input_channels=n_input_channels,
no_max_pool=True,
conv1_kernel_size=7,
conv1_stride=2,
conv1_padding=3,
**kwargs)
elif encoder_type == 'resnet50_no_max_pool':
return generate_resnet_3d(
model_depth=50,
n_input_channels=n_input_channels,
no_max_pool=True,
conv1_kernel_size=7,
conv1_stride=2,
conv1_padding=3,
**kwargs)
elif encoder_type == 'resnet18_no_max_pool_cifar_stem':
return generate_resnet_3d(
model_depth=18,
n_input_channels=n_input_channels,
no_max_pool=True,
conv1_kernel_size=3,
conv1_stride=1,
conv1_padding=1,
**kwargs)
elif encoder_type == 'resnet34_no_max_pool_cifar_stem':
return generate_resnet_3d(
model_depth=34,
n_input_channels=n_input_channels,
no_max_pool=True,
conv1_kernel_size=3,
conv1_stride=1,
conv1_padding=1,
**kwargs)
elif encoder_type == 'resnet50_no_max_pool_cifar_stem':
return generate_resnet_3d(
model_depth=50,
n_input_channels=n_input_channels,
no_max_pool=True,
conv1_kernel_size=3,
conv1_stride=1,
conv1_padding=1,
**kwargs)
elif encoder_type == 'resnet18_medicalnet':
return generate_resnet_3d(
model_depth=18,
n_input_channels=n_input_channels,
no_max_pool=False,
conv1_kernel_size=7,
conv1_stride=2,
conv1_padding=3,
shortcut_type='A',
block_dilations=[1, 1, 2, 4],
block_strides=[1, 2, 1, 1])
else:
raise ValueError(
f'Only the following encoders are currently supported: '
f'{", ".join(_SUPPORTED_ENCODERS)}')
def instantiate_optimizer(
optimizer_type: str,
model: Union[torch.nn.Module, torch.nn.DataParallel],
adam_lr: Optional[float] = 3e-4,
batch_size: Optional[int] = None,
weight_decay: Optional[float] = None,
epochs: Optional[int] = None) -> Tuple[Optimizer, Optional[CosineAnnealingLR]]:
assert optimizer_type in {"Adam", "LARS"}
if optimizer_type == "Adam":
assert adam_lr is not None
optimizer = torch.optim.Adam(model.parameters(), lr=adam_lr)
# decay the learning rate with the cosine decay schedule without restarts
eta_min = 0.1 * adam_lr
scheduler = CosineAnnealingLR(
optimizer, epochs, eta_min=eta_min, last_epoch=-1)
elif optimizer_type == "LARS":
assert batch_size is not None
assert weight_decay is not None
assert epochs is not None
# optimized using LARS with linear learning rate scaling
# (i.e. LearningRate = 0.3 × BatchSize/256) and weight decay of 10−6.
learning_rate = 0.3 * batch_size / 256
optimizer = LARS(
model.parameters(),
lr=learning_rate,
weight_decay=weight_decay,
exclude_from_weight_decay=["batch_normalization", "bias"])
# decay the learning rate with the cosine decay schedule without restarts
eta_min = 0.
scheduler = CosineAnnealingLR(
optimizer, epochs, eta_min=eta_min, last_epoch=-1)
else:
raise ValueError
return optimizer, scheduler
def checkpoint_state(
model: Union[torch.nn.Module, torch.nn.DataParallel],
optimizer: Optimizer,
scheduler: CosineAnnealingLR,
output_path: str,
current_epoch: int):
# To save a DataParallel model generically, save the model.module.state_dict().
# This way, you have the flexibility to load the model any way you want to any device you want.
module = model.module if isinstance(model, torch.torch.nn.DataParallel) else model
torch.save(
module.state_dict(),
os.path.join(output_path, f"model_checkpoint_{current_epoch}.pt"))
torch.save(
optimizer.state_dict(),
os.path.join(output_path, f"optimizer_checkpoint_{current_epoch}.pt"))
torch.save(
scheduler.state_dict(),
os.path.join(output_path, f"scheduler_checkpoint_{current_epoch}.pt"))
def get_cpu_state_dict(state_dict: dict) -> dict:
cpu_state_dict = dict()
for k, v in state_dict.items():
if isinstance(v, torch.Tensor):
cpu_state_dict[k] = v.detach().cpu().numpy()
elif isinstance(v, dict):
cpu_state_dict[k] = get_cpu_state_dict(v)
else:
cpu_state_dict[k] = v
return cpu_state_dict
def print_hash(
model: Union[torch.nn.Module, torch.nn.DataParallel],
optimizer: Optimizer,
scheduler: CosineAnnealingLR):
# To save a DataParallel model generically, save the model.module.state_dict().
# This way, you have the flexibility to load the model any way you want to any device you want.
module = model.module if isinstance(model, torch.torch.nn.DataParallel) else model
model_md5_hash = hashlib.md5(pickle.dumps(get_cpu_state_dict(module.state_dict()))).hexdigest()
optimizer_md5_hash = hashlib.md5(pickle.dumps(get_cpu_state_dict(optimizer.state_dict()))).hexdigest()
scheduler_md5_hash = hashlib.md5(pickle.dumps(get_cpu_state_dict(scheduler.state_dict()))).hexdigest()
print(f'model MD5 hash: {model_md5_hash}')
print(f'optimizer MD5 hash: {optimizer_md5_hash}')
print(f'scheduler MD5 hash: {scheduler_md5_hash}')
def write_summary(
output_path: str,
loss_list: List[float],
current_epoch: int):
np.save(
os.path.join(output_path, f"loss_list_{current_epoch}.npy"),
np.asarray(loss_list))
def parse_dtype(dtype_str: str) -> torch.dtype:
if dtype_str == 'float16':
return torch.float16
elif dtype_str == 'float32':
return torch.float32
elif dtype_str == 'float64':
return torch.float64
else:
raise ValueError
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/utils/__init__.py | .py | 211 | 10 | from .yaml_config_hook import yaml_config_hook
from .train_helper import \
instantiate_encoder, \
instantiate_optimizer, \
checkpoint_state, \
print_hash, \
write_summary, \
parse_dtype
| Python |
3D | cellarium-ai/SynapseCLR | pytorch_synapse/synapse_simclr/utils/yaml_config_hook.py | .py | 709 | 25 | import os
import yaml
def yaml_config_hook(config_file):
"""
Custom YAML config loader, which can include other yaml files (I like using config files
insteaad of using argparser)
"""
# load yaml files in the nested 'defaults' section, which include defaults for experiments
with open(config_file) as f:
cfg = yaml.safe_load(f)
for d in cfg.get("defaults", []):
config_dir, cf = d.popitem()
cf = os.path.join(os.path.dirname(config_file), config_dir, cf + ".yaml")
with open(cf) as f:
l = yaml.safe_load(f)
cfg.update(l)
if "defaults" in cfg.keys():
del cfg["defaults"]
return cfg
| Python |
3D | cellarium-ai/SynapseCLR | notebooks/misc/01_medicalnet_model_adapt.ipynb | .ipynb | 32,670 | 825 | {
"cells": [
{
"cell_type": "markdown",
"id": "clinical-trust",
"metadata": {},
"source": [
"## Pretrained MedicalNet to SynapseCLR adaptation\n",
"\n",
"Generate a SynapseCLR checkpoint initialized to MedicalNet pre-trained 3D-ResNet18."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "illegal-factor",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"\n",
"import synapse_simclr\n",
"import torchinfo"
]
},
{
"cell_type": "markdown",
"id": "nasty-planner",
"metadata": {},
"source": [
"First, we need to ascertain that we get the exact same output with our ResNet-3D implementation (w/ the right args) as the MedicalNet ResNet-3D implementation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "electoral-genealogy",
"metadata": {},
"outputs": [],
"source": [
"state_dict_medical = torch.load(\n",
" '../../ext/MedicalNet/pretrain/resnet_18_23dataset.pth')\n",
"\n",
"# fix the state dict key names\n",
"fixed_state_dict_medical = dict()\n",
"for key, value in state_dict_medical['state_dict'].items():\n",
" fixed_state_dict_medical[key[7:] if key.find(\"module.\") == 0 else key] = value"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "statutory-theme",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"conv1.weight\n",
"bn1.weight\n",
"bn1.bias\n",
"bn1.running_mean\n",
"bn1.running_var\n",
"bn1.num_batches_tracked\n",
"layer1.0.conv1.weight\n",
"layer1.0.bn1.weight\n",
"layer1.0.bn1.bias\n",
"layer1.0.bn1.running_mean\n",
"layer1.0.bn1.running_var\n",
"layer1.0.bn1.num_batches_tracked\n",
"layer1.0.conv2.weight\n",
"layer1.0.bn2.weight\n",
"layer1.0.bn2.bias\n",
"layer1.0.bn2.running_mean\n",
"layer1.0.bn2.running_var\n",
"layer1.0.bn2.num_batches_tracked\n",
"layer1.1.conv1.weight\n",
"layer1.1.bn1.weight\n",
"layer1.1.bn1.bias\n",
"layer1.1.bn1.running_mean\n",
"layer1.1.bn1.running_var\n",
"layer1.1.bn1.num_batches_tracked\n",
"layer1.1.conv2.weight\n",
"layer1.1.bn2.weight\n",
"layer1.1.bn2.bias\n",
"layer1.1.bn2.running_mean\n",
"layer1.1.bn2.running_var\n",
"layer1.1.bn2.num_batches_tracked\n",
"layer2.0.conv1.weight\n",
"layer2.0.bn1.weight\n",
"layer2.0.bn1.bias\n",
"layer2.0.bn1.running_mean\n",
"layer2.0.bn1.running_var\n",
"layer2.0.bn1.num_batches_tracked\n",
"layer2.0.conv2.weight\n",
"layer2.0.bn2.weight\n",
"layer2.0.bn2.bias\n",
"layer2.0.bn2.running_mean\n",
"layer2.0.bn2.running_var\n",
"layer2.0.bn2.num_batches_tracked\n",
"layer2.1.conv1.weight\n",
"layer2.1.bn1.weight\n",
"layer2.1.bn1.bias\n",
"layer2.1.bn1.running_mean\n",
"layer2.1.bn1.running_var\n",
"layer2.1.bn1.num_batches_tracked\n",
"layer2.1.conv2.weight\n",
"layer2.1.bn2.weight\n",
"layer2.1.bn2.bias\n",
"layer2.1.bn2.running_mean\n",
"layer2.1.bn2.running_var\n",
"layer2.1.bn2.num_batches_tracked\n",
"layer3.0.conv1.weight\n",
"layer3.0.bn1.weight\n",
"layer3.0.bn1.bias\n",
"layer3.0.bn1.running_mean\n",
"layer3.0.bn1.running_var\n",
"layer3.0.bn1.num_batches_tracked\n",
"layer3.0.conv2.weight\n",
"layer3.0.bn2.weight\n",
"layer3.0.bn2.bias\n",
"layer3.0.bn2.running_mean\n",
"layer3.0.bn2.running_var\n",
"layer3.0.bn2.num_batches_tracked\n",
"layer3.1.conv1.weight\n",
"layer3.1.bn1.weight\n",
"layer3.1.bn1.bias\n",
"layer3.1.bn1.running_mean\n",
"layer3.1.bn1.running_var\n",
"layer3.1.bn1.num_batches_tracked\n",
"layer3.1.conv2.weight\n",
"layer3.1.bn2.weight\n",
"layer3.1.bn2.bias\n",
"layer3.1.bn2.running_mean\n",
"layer3.1.bn2.running_var\n",
"layer3.1.bn2.num_batches_tracked\n",
"layer4.0.conv1.weight\n",
"layer4.0.bn1.weight\n",
"layer4.0.bn1.bias\n",
"layer4.0.bn1.running_mean\n",
"layer4.0.bn1.running_var\n",
"layer4.0.bn1.num_batches_tracked\n",
"layer4.0.conv2.weight\n",
"layer4.0.bn2.weight\n",
"layer4.0.bn2.bias\n",
"layer4.0.bn2.running_mean\n",
"layer4.0.bn2.running_var\n",
"layer4.0.bn2.num_batches_tracked\n",
"layer4.1.conv1.weight\n",
"layer4.1.bn1.weight\n",
"layer4.1.bn1.bias\n",
"layer4.1.bn1.running_mean\n",
"layer4.1.bn1.running_var\n",
"layer4.1.bn1.num_batches_tracked\n",
"layer4.1.conv2.weight\n",
"layer4.1.bn2.weight\n",
"layer4.1.bn2.bias\n",
"layer4.1.bn2.running_mean\n",
"layer4.1.bn2.running_var\n",
"layer4.1.bn2.num_batches_tracked\n"
]
}
],
"source": [
"for key in fixed_state_dict_medical.keys():\n",
" print(key)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "derived-madness",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyter/dev/SynapseCLR/pytorch_synapse/synapse_simclr/modules/resnet_3d_medicalnet.py:148: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
" m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')\n"
]
}
],
"source": [
"from synapse_simclr.modules import Identity\n",
"\n",
"resnet18_medicalnet = synapse_simclr.modules.resnet18_medicalnet(\n",
" shortcut_type='A')\n",
"\n",
"# drop the fully connected layer\n",
"resnet18_medicalnet.fc = Identity()\n",
"resnet18_medicalnet.load_state_dict(fixed_state_dict_medical)\n",
"resnet18_medicalnet.eval();"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "hydraulic-ordering",
"metadata": {},
"outputs": [],
"source": [
"# generate our own ResNet\n",
"resnet18_syn = synapse_simclr.modules.generate_resnet_3d(\n",
" model_depth=18,\n",
" n_input_channels=1,\n",
" no_max_pool=False,\n",
" conv1_kernel_size=7,\n",
" conv1_stride=2,\n",
" conv1_padding=3,\n",
" shortcut_type='A',\n",
" block_dilations=[1, 1, 2, 4],\n",
" block_strides=[1, 2, 1, 1])\n",
"\n",
"# drop the fully connected layer\n",
"resnet18_syn.fc = Identity()\n",
"resnet18_syn.load_state_dict(fixed_state_dict_medical)\n",
"resnet18_syn.eval();"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "welsh-bracket",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"=================================================================\n",
"Layer (type:depth-idx) Param #\n",
"=================================================================\n",
"ResNet --\n",
"├─Conv3d: 1-1 21,952\n",
"├─BatchNorm3d: 1-2 128\n",
"├─ReLU: 1-3 --\n",
"├─MaxPool3d: 1-4 --\n",
"├─Sequential: 1-5 --\n",
"│ └─BasicBlock: 2-1 --\n",
"│ │ └─Conv3d: 3-1 110,592\n",
"│ │ └─BatchNorm3d: 3-2 128\n",
"│ │ └─ReLU: 3-3 --\n",
"│ │ └─Conv3d: 3-4 110,592\n",
"│ │ └─BatchNorm3d: 3-5 128\n",
"│ └─BasicBlock: 2-2 --\n",
"│ │ └─Conv3d: 3-6 110,592\n",
"│ │ └─BatchNorm3d: 3-7 128\n",
"│ │ └─ReLU: 3-8 --\n",
"│ │ └─Conv3d: 3-9 110,592\n",
"│ │ └─BatchNorm3d: 3-10 128\n",
"├─Sequential: 1-6 --\n",
"│ └─BasicBlock: 2-3 --\n",
"│ │ └─Conv3d: 3-11 221,184\n",
"│ │ └─BatchNorm3d: 3-12 256\n",
"│ │ └─ReLU: 3-13 --\n",
"│ │ └─Conv3d: 3-14 442,368\n",
"│ │ └─BatchNorm3d: 3-15 256\n",
"│ └─BasicBlock: 2-4 --\n",
"│ │ └─Conv3d: 3-16 442,368\n",
"│ │ └─BatchNorm3d: 3-17 256\n",
"│ │ └─ReLU: 3-18 --\n",
"│ │ └─Conv3d: 3-19 442,368\n",
"│ │ └─BatchNorm3d: 3-20 256\n",
"├─Sequential: 1-7 --\n",
"│ └─BasicBlock: 2-5 --\n",
"│ │ └─Conv3d: 3-21 884,736\n",
"│ │ └─BatchNorm3d: 3-22 512\n",
"│ │ └─ReLU: 3-23 --\n",
"│ │ └─Conv3d: 3-24 1,769,472\n",
"│ │ └─BatchNorm3d: 3-25 512\n",
"│ └─BasicBlock: 2-6 --\n",
"│ │ └─Conv3d: 3-26 1,769,472\n",
"│ │ └─BatchNorm3d: 3-27 512\n",
"│ │ └─ReLU: 3-28 --\n",
"│ │ └─Conv3d: 3-29 1,769,472\n",
"│ │ └─BatchNorm3d: 3-30 512\n",
"├─Sequential: 1-8 --\n",
"│ └─BasicBlock: 2-7 --\n",
"│ │ └─Conv3d: 3-31 3,538,944\n",
"│ │ └─BatchNorm3d: 3-32 1,024\n",
"│ │ └─ReLU: 3-33 --\n",
"│ │ └─Conv3d: 3-34 7,077,888\n",
"│ │ └─BatchNorm3d: 3-35 1,024\n",
"│ └─BasicBlock: 2-8 --\n",
"│ │ └─Conv3d: 3-36 7,077,888\n",
"│ │ └─BatchNorm3d: 3-37 1,024\n",
"│ │ └─ReLU: 3-38 --\n",
"│ │ └─Conv3d: 3-39 7,077,888\n",
"│ │ └─BatchNorm3d: 3-40 1,024\n",
"├─AdaptiveAvgPool3d: 1-9 --\n",
"├─Identity: 1-10 --\n",
"=================================================================\n",
"Total params: 32,986,176\n",
"Trainable params: 32,986,176\n",
"Non-trainable params: 0\n",
"================================================================="
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torchinfo.summary(resnet18_medicalnet)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "pregnant-alert",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"=================================================================\n",
"Layer (type:depth-idx) Param #\n",
"=================================================================\n",
"ResNet --\n",
"├─Conv3d: 1-1 21,952\n",
"├─BatchNorm3d: 1-2 128\n",
"├─ReLU: 1-3 --\n",
"├─MaxPool3d: 1-4 --\n",
"├─Sequential: 1-5 --\n",
"│ └─BasicBlock: 2-1 --\n",
"│ │ └─Conv3d: 3-1 110,592\n",
"│ │ └─BatchNorm3d: 3-2 128\n",
"│ │ └─ReLU: 3-3 --\n",
"│ │ └─Conv3d: 3-4 110,592\n",
"│ │ └─BatchNorm3d: 3-5 128\n",
"│ └─BasicBlock: 2-2 --\n",
"│ │ └─Conv3d: 3-6 110,592\n",
"│ │ └─BatchNorm3d: 3-7 128\n",
"│ │ └─ReLU: 3-8 --\n",
"│ │ └─Conv3d: 3-9 110,592\n",
"│ │ └─BatchNorm3d: 3-10 128\n",
"├─Sequential: 1-6 --\n",
"│ └─BasicBlock: 2-3 --\n",
"│ │ └─Conv3d: 3-11 221,184\n",
"│ │ └─BatchNorm3d: 3-12 256\n",
"│ │ └─ReLU: 3-13 --\n",
"│ │ └─Conv3d: 3-14 442,368\n",
"│ │ └─BatchNorm3d: 3-15 256\n",
"│ └─BasicBlock: 2-4 --\n",
"│ │ └─Conv3d: 3-16 442,368\n",
"│ │ └─BatchNorm3d: 3-17 256\n",
"│ │ └─ReLU: 3-18 --\n",
"│ │ └─Conv3d: 3-19 442,368\n",
"│ │ └─BatchNorm3d: 3-20 256\n",
"├─Sequential: 1-7 --\n",
"│ └─BasicBlock: 2-5 --\n",
"│ │ └─Conv3d: 3-21 884,736\n",
"│ │ └─BatchNorm3d: 3-22 512\n",
"│ │ └─ReLU: 3-23 --\n",
"│ │ └─Conv3d: 3-24 1,769,472\n",
"│ │ └─BatchNorm3d: 3-25 512\n",
"│ └─BasicBlock: 2-6 --\n",
"│ │ └─Conv3d: 3-26 1,769,472\n",
"│ │ └─BatchNorm3d: 3-27 512\n",
"│ │ └─ReLU: 3-28 --\n",
"│ │ └─Conv3d: 3-29 1,769,472\n",
"│ │ └─BatchNorm3d: 3-30 512\n",
"├─Sequential: 1-8 --\n",
"│ └─BasicBlock: 2-7 --\n",
"│ │ └─Conv3d: 3-31 3,538,944\n",
"│ │ └─BatchNorm3d: 3-32 1,024\n",
"│ │ └─ReLU: 3-33 --\n",
"│ │ └─Conv3d: 3-34 7,077,888\n",
"│ │ └─BatchNorm3d: 3-35 1,024\n",
"│ └─BasicBlock: 2-8 --\n",
"│ │ └─Conv3d: 3-36 7,077,888\n",
"│ │ └─BatchNorm3d: 3-37 1,024\n",
"│ │ └─ReLU: 3-38 --\n",
"│ │ └─Conv3d: 3-39 7,077,888\n",
"│ │ └─BatchNorm3d: 3-40 1,024\n",
"├─AdaptiveAvgPool3d: 1-9 --\n",
"├─Identity: 1-10 --\n",
"=================================================================\n",
"Total params: 32,986,176\n",
"Trainable params: 32,986,176\n",
"Non-trainable params: 0\n",
"================================================================="
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torchinfo.summary(resnet18_syn)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "fantastic-medicine",
"metadata": {},
"outputs": [],
"source": [
"x = torch.rand((1, 1, 64, 64, 64))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "concrete-concept",
"metadata": {},
"outputs": [],
"source": [
"resnet18_syn_out = resnet18_syn(x)\n",
"resnet18_medicalnet_out = resnet18_medicalnet(x)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "nutritional-selling",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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" 0., 0., 0., 0., 0., 0., 0., 0.]], grad_fn=<SubBackward0>)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resnet18_syn_out - resnet18_medicalnet_out"
]
},
{
"cell_type": "markdown",
"id": "dying-captain",
"metadata": {},
"source": [
"If all zeros, we're good!"
]
},
{
"cell_type": "markdown",
"id": "funded-programmer",
"metadata": {},
"source": [
"## Adaptation"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "wired-psychiatry",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"module.encoder.conv1.weight\n",
"module.encoder.bn1.weight\n",
"module.encoder.bn1.bias\n",
"module.encoder.bn1.running_mean\n",
"module.encoder.bn1.running_var\n",
"module.encoder.bn1.num_batches_tracked\n",
"module.encoder.layer1.0.conv1.weight\n",
"module.encoder.layer1.0.bn1.weight\n",
"module.encoder.layer1.0.bn1.bias\n",
"module.encoder.layer1.0.bn1.running_mean\n",
"module.encoder.layer1.0.bn1.running_var\n",
"module.encoder.layer1.0.bn1.num_batches_tracked\n",
"module.encoder.layer1.0.conv2.weight\n",
"module.encoder.layer1.0.bn2.weight\n",
"module.encoder.layer1.0.bn2.bias\n",
"module.encoder.layer1.0.bn2.running_mean\n",
"module.encoder.layer1.0.bn2.running_var\n",
"module.encoder.layer1.0.bn2.num_batches_tracked\n",
"module.encoder.layer1.1.conv1.weight\n",
"module.encoder.layer1.1.bn1.weight\n",
"module.encoder.layer1.1.bn1.bias\n",
"module.encoder.layer1.1.bn1.running_mean\n",
"module.encoder.layer1.1.bn1.running_var\n",
"module.encoder.layer1.1.bn1.num_batches_tracked\n",
"module.encoder.layer1.1.conv2.weight\n",
"module.encoder.layer1.1.bn2.weight\n",
"module.encoder.layer1.1.bn2.bias\n",
"module.encoder.layer1.1.bn2.running_mean\n",
"module.encoder.layer1.1.bn2.running_var\n",
"module.encoder.layer1.1.bn2.num_batches_tracked\n",
"module.encoder.layer2.0.conv1.weight\n",
"module.encoder.layer2.0.bn1.weight\n",
"module.encoder.layer2.0.bn1.bias\n",
"module.encoder.layer2.0.bn1.running_mean\n",
"module.encoder.layer2.0.bn1.running_var\n",
"module.encoder.layer2.0.bn1.num_batches_tracked\n",
"module.encoder.layer2.0.conv2.weight\n",
"module.encoder.layer2.0.bn2.weight\n",
"module.encoder.layer2.0.bn2.bias\n",
"module.encoder.layer2.0.bn2.running_mean\n",
"module.encoder.layer2.0.bn2.running_var\n",
"module.encoder.layer2.0.bn2.num_batches_tracked\n",
"module.encoder.layer2.0.downsample.0.weight\n",
"module.encoder.layer2.0.downsample.1.weight\n",
"module.encoder.layer2.0.downsample.1.bias\n",
"module.encoder.layer2.0.downsample.1.running_mean\n",
"module.encoder.layer2.0.downsample.1.running_var\n",
"module.encoder.layer2.0.downsample.1.num_batches_tracked\n",
"module.encoder.layer2.1.conv1.weight\n",
"module.encoder.layer2.1.bn1.weight\n",
"module.encoder.layer2.1.bn1.bias\n",
"module.encoder.layer2.1.bn1.running_mean\n",
"module.encoder.layer2.1.bn1.running_var\n",
"module.encoder.layer2.1.bn1.num_batches_tracked\n",
"module.encoder.layer2.1.conv2.weight\n",
"module.encoder.layer2.1.bn2.weight\n",
"module.encoder.layer2.1.bn2.bias\n",
"module.encoder.layer2.1.bn2.running_mean\n",
"module.encoder.layer2.1.bn2.running_var\n",
"module.encoder.layer2.1.bn2.num_batches_tracked\n",
"module.encoder.layer3.0.conv1.weight\n",
"module.encoder.layer3.0.bn1.weight\n",
"module.encoder.layer3.0.bn1.bias\n",
"module.encoder.layer3.0.bn1.running_mean\n",
"module.encoder.layer3.0.bn1.running_var\n",
"module.encoder.layer3.0.bn1.num_batches_tracked\n",
"module.encoder.layer3.0.conv2.weight\n",
"module.encoder.layer3.0.bn2.weight\n",
"module.encoder.layer3.0.bn2.bias\n",
"module.encoder.layer3.0.bn2.running_mean\n",
"module.encoder.layer3.0.bn2.running_var\n",
"module.encoder.layer3.0.bn2.num_batches_tracked\n",
"module.encoder.layer3.0.downsample.0.weight\n",
"module.encoder.layer3.0.downsample.1.weight\n",
"module.encoder.layer3.0.downsample.1.bias\n",
"module.encoder.layer3.0.downsample.1.running_mean\n",
"module.encoder.layer3.0.downsample.1.running_var\n",
"module.encoder.layer3.0.downsample.1.num_batches_tracked\n",
"module.encoder.layer3.1.conv1.weight\n",
"module.encoder.layer3.1.bn1.weight\n",
"module.encoder.layer3.1.bn1.bias\n",
"module.encoder.layer3.1.bn1.running_mean\n",
"module.encoder.layer3.1.bn1.running_var\n",
"module.encoder.layer3.1.bn1.num_batches_tracked\n",
"module.encoder.layer3.1.conv2.weight\n",
"module.encoder.layer3.1.bn2.weight\n",
"module.encoder.layer3.1.bn2.bias\n",
"module.encoder.layer3.1.bn2.running_mean\n",
"module.encoder.layer3.1.bn2.running_var\n",
"module.encoder.layer3.1.bn2.num_batches_tracked\n",
"module.encoder.layer4.0.conv1.weight\n",
"module.encoder.layer4.0.bn1.weight\n",
"module.encoder.layer4.0.bn1.bias\n",
"module.encoder.layer4.0.bn1.running_mean\n",
"module.encoder.layer4.0.bn1.running_var\n",
"module.encoder.layer4.0.bn1.num_batches_tracked\n",
"module.encoder.layer4.0.conv2.weight\n",
"module.encoder.layer4.0.bn2.weight\n",
"module.encoder.layer4.0.bn2.bias\n",
"module.encoder.layer4.0.bn2.running_mean\n",
"module.encoder.layer4.0.bn2.running_var\n",
"module.encoder.layer4.0.bn2.num_batches_tracked\n",
"module.encoder.layer4.0.downsample.0.weight\n",
"module.encoder.layer4.0.downsample.1.weight\n",
"module.encoder.layer4.0.downsample.1.bias\n",
"module.encoder.layer4.0.downsample.1.running_mean\n",
"module.encoder.layer4.0.downsample.1.running_var\n",
"module.encoder.layer4.0.downsample.1.num_batches_tracked\n",
"module.encoder.layer4.1.conv1.weight\n",
"module.encoder.layer4.1.bn1.weight\n",
"module.encoder.layer4.1.bn1.bias\n",
"module.encoder.layer4.1.bn1.running_mean\n",
"module.encoder.layer4.1.bn1.running_var\n",
"module.encoder.layer4.1.bn1.num_batches_tracked\n",
"module.encoder.layer4.1.conv2.weight\n",
"module.encoder.layer4.1.bn2.weight\n",
"module.encoder.layer4.1.bn2.bias\n",
"module.encoder.layer4.1.bn2.running_mean\n",
"module.encoder.layer4.1.bn2.running_var\n",
"module.encoder.layer4.1.bn2.num_batches_tracked\n",
"module.projector.mlp.0.weight\n",
"module.projector.mlp.1.weight\n",
"module.projector.mlp.1.bias\n",
"module.projector.mlp.1.running_mean\n",
"module.projector.mlp.1.running_var\n",
"module.projector.mlp.1.num_batches_tracked\n",
"module.projector.mlp.3.weight\n"
]
}
],
"source": [
"# load a \"prototype\" Synapse-SimCLR checkpoint\n",
"state_dict_syn = torch.load(\n",
" '../../output/checkpoint__synapseclr__so3__second_stage/model_checkpoint_99.pt')\n",
"\n",
"for key in state_dict_syn.keys():\n",
" print(key)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "solved-cooperation",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"conv1.weight\n",
"bn1.weight\n",
"bn1.bias\n",
"bn1.running_mean\n",
"bn1.running_var\n",
"bn1.num_batches_tracked\n",
"layer1.0.conv1.weight\n",
"layer1.0.bn1.weight\n",
"layer1.0.bn1.bias\n",
"layer1.0.bn1.running_mean\n",
"layer1.0.bn1.running_var\n",
"layer1.0.bn1.num_batches_tracked\n",
"layer1.0.conv2.weight\n",
"layer1.0.bn2.weight\n",
"layer1.0.bn2.bias\n",
"layer1.0.bn2.running_mean\n",
"layer1.0.bn2.running_var\n",
"layer1.0.bn2.num_batches_tracked\n",
"layer1.1.conv1.weight\n",
"layer1.1.bn1.weight\n",
"layer1.1.bn1.bias\n",
"layer1.1.bn1.running_mean\n",
"layer1.1.bn1.running_var\n",
"layer1.1.bn1.num_batches_tracked\n",
"layer1.1.conv2.weight\n",
"layer1.1.bn2.weight\n",
"layer1.1.bn2.bias\n",
"layer1.1.bn2.running_mean\n",
"layer1.1.bn2.running_var\n",
"layer1.1.bn2.num_batches_tracked\n",
"layer2.0.conv1.weight\n",
"layer2.0.bn1.weight\n",
"layer2.0.bn1.bias\n",
"layer2.0.bn1.running_mean\n",
"layer2.0.bn1.running_var\n",
"layer2.0.bn1.num_batches_tracked\n",
"layer2.0.conv2.weight\n",
"layer2.0.bn2.weight\n",
"layer2.0.bn2.bias\n",
"layer2.0.bn2.running_mean\n",
"layer2.0.bn2.running_var\n",
"layer2.0.bn2.num_batches_tracked\n",
"layer2.1.conv1.weight\n",
"layer2.1.bn1.weight\n",
"layer2.1.bn1.bias\n",
"layer2.1.bn1.running_mean\n",
"layer2.1.bn1.running_var\n",
"layer2.1.bn1.num_batches_tracked\n",
"layer2.1.conv2.weight\n",
"layer2.1.bn2.weight\n",
"layer2.1.bn2.bias\n",
"layer2.1.bn2.running_mean\n",
"layer2.1.bn2.running_var\n",
"layer2.1.bn2.num_batches_tracked\n",
"layer3.0.conv1.weight\n",
"layer3.0.bn1.weight\n",
"layer3.0.bn1.bias\n",
"layer3.0.bn1.running_mean\n",
"layer3.0.bn1.running_var\n",
"layer3.0.bn1.num_batches_tracked\n",
"layer3.0.conv2.weight\n",
"layer3.0.bn2.weight\n",
"layer3.0.bn2.bias\n",
"layer3.0.bn2.running_mean\n",
"layer3.0.bn2.running_var\n",
"layer3.0.bn2.num_batches_tracked\n",
"layer3.1.conv1.weight\n",
"layer3.1.bn1.weight\n",
"layer3.1.bn1.bias\n",
"layer3.1.bn1.running_mean\n",
"layer3.1.bn1.running_var\n",
"layer3.1.bn1.num_batches_tracked\n",
"layer3.1.conv2.weight\n",
"layer3.1.bn2.weight\n",
"layer3.1.bn2.bias\n",
"layer3.1.bn2.running_mean\n",
"layer3.1.bn2.running_var\n",
"layer3.1.bn2.num_batches_tracked\n",
"layer4.0.conv1.weight\n",
"layer4.0.bn1.weight\n",
"layer4.0.bn1.bias\n",
"layer4.0.bn1.running_mean\n",
"layer4.0.bn1.running_var\n",
"layer4.0.bn1.num_batches_tracked\n",
"layer4.0.conv2.weight\n",
"layer4.0.bn2.weight\n",
"layer4.0.bn2.bias\n",
"layer4.0.bn2.running_mean\n",
"layer4.0.bn2.running_var\n",
"layer4.0.bn2.num_batches_tracked\n",
"layer4.1.conv1.weight\n",
"layer4.1.bn1.weight\n",
"layer4.1.bn1.bias\n",
"layer4.1.bn1.running_mean\n",
"layer4.1.bn1.running_var\n",
"layer4.1.bn1.num_batches_tracked\n",
"layer4.1.conv2.weight\n",
"layer4.1.bn2.weight\n",
"layer4.1.bn2.bias\n",
"layer4.1.bn2.running_mean\n",
"layer4.1.bn2.running_var\n",
"layer4.1.bn2.num_batches_tracked\n"
]
}
],
"source": [
"# Load MedicalNet pretrained weights\n",
"state_dict_medical = torch.load(\n",
" '../../ext/MedicalNet/pretrain/resnet_18_23dataset.pth')\n",
"\n",
"# fix the state dict key names\n",
"fixed_state_dict_medical = dict()\n",
"for key, value in state_dict_medical['state_dict'].items():\n",
" fixed_state_dict_medical[key[7:] if key.find(\"module.\") == 0 else key] = value\n",
" \n",
"for key in fixed_state_dict_medical.keys():\n",
" print(key)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "dressed-terrorist",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from collections import OrderedDict\n",
"\n",
"output_checkpoint_path = '../../output/checkpoint__medicalnet'\n",
"copy_projector_params = False\n",
"\n",
"encoder_prefix = 'module.encoder.'\n",
"projector_prefix = 'module.projector.'\n",
"\n",
"adapated_state_dict_medical = OrderedDict()\n",
"\n",
"# copy encoder parameters\n",
"encoder_keys = list(resnet18_syn.state_dict().keys())\n",
"for key in encoder_keys:\n",
" adapated_state_dict_medical[encoder_prefix + key] = fixed_state_dict_medical[key]\n",
"\n",
"# copy projector parameters\n",
"if copy_projector_params:\n",
" for key in state_dict_syn.keys():\n",
" if key.find(projector_prefix) == 0:\n",
" adapated_state_dict_medical[key] = state_dict_syn[key]\n",
" \n",
"torch.save(\n",
" adapated_state_dict_medical,\n",
" os.path.join(output_checkpoint_path, 'model_checkpoint_0.pt'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "roman-roman",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"environment": {
"kernel": "python3",
"name": "common-cu113.m91",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/base-cu113:m91"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | cellarium-ai/SynapseCLR | notebooks/misc/02_preprocess_annotations.ipynb | .ipynb | 39,271 | 1,094 | {
"cells": [
{
"cell_type": "markdown",
"id": "340d2c22",
"metadata": {},
"source": [
"## Preprocess annotations"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "347cd1c5",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import matplotlib as mpl\n",
"import matplotlib.pylab as plt\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"import colorcet as cc\n",
"from ipywidgets import interactive\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import logging\n",
"import pickle\n",
"import pickle\n",
"\n",
"SMALL_SIZE = 12\n",
"MEDIUM_SIZE = 14\n",
"BIGGER_SIZE = 16\n",
"\n",
"plt.rc('font', size=SMALL_SIZE) # controls default text sizes\n",
"plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title\n",
"plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels\n",
"plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n",
"plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n",
"plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize\n",
"plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title\n",
"plt.style.use('dark_background')\n",
"\n",
"logger = logging.getLogger()\n",
"logger.setLevel(logging.INFO)\n",
"log_info = print"
]
},
{
"cell_type": "markdown",
"id": "829a2734",
"metadata": {},
"source": [
"## Configuration"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0e1eb3f8",
"metadata": {},
"outputs": [],
"source": [
"proc_data_path = '../../data/MICrONS__L23__8_8_40__processed'\n",
"tables_path = '../../tables'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a184e5bb",
"metadata": {},
"outputs": [],
"source": [
"# load meta.csv\n",
"meta_df = pd.read_csv(os.path.join(proc_data_path, 'meta.csv'))\n",
"annotations_df = pd.read_csv(os.path.join(tables_path, 'annotated_synapse_features.csv'), index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "49f1e82a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"syn_id 0\n",
"cleft_size_vx 0\n",
"presyn_soma_dist_um 25\n",
"postsyn_soma_dist_um 151\n",
"n_mitos_pre 90\n",
"n_mitos_post 90\n",
"pre_cell_type 0\n",
"post_cell_type 0\n",
"mito_size_pre_vx 90\n",
"mito_size_post_vx 90\n"
]
}
],
"source": [
"# drop NaN columns\n",
"bad_mask = np.zeros((len(annotations_df),), dtype=bool)\n",
"for column in annotations_df.columns:\n",
" c_mask = np.isnan(annotations_df[column].values)\n",
" bad_mask = bad_mask | c_mask\n",
" print(column, np.sum(c_mask))\n",
"annotations_df = annotations_df[~bad_mask]\n",
"annotations_df.reset_index(drop=True, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e4b4db23",
"metadata": {},
"outputs": [],
"source": [
"kept_annotations_list = [\n",
" 'cleft_size_vx',\n",
" 'presyn_soma_dist_um',\n",
" 'postsyn_soma_dist_um',\n",
" 'pre_cell_type',\n",
" 'post_cell_type',\n",
" 'n_mitos_pre',\n",
" 'n_mitos_post',\n",
" 'mito_size_pre_vx',\n",
" 'mito_size_post_vx'\n",
"]\n",
"\n",
"kept_annotations_type = [\n",
" 'continuous',\n",
" 'continuous',\n",
" 'continuous',\n",
" 'categorical',\n",
" 'categorical',\n",
" 'continuous',\n",
" 'continuous',\n",
" 'continuous',\n",
" 'continuous'\n",
"]\n",
"\n",
"final_annotations_column_names = [\n",
" 'cleft_size_log1p_zscore',\n",
" 'presyn_soma_dist_log1p_zscore',\n",
" 'postsyn_soma_dist_log1p_zscore',\n",
" 'pre_cell_type',\n",
" 'post_cell_type',\n",
" 'n_mitos_pre',\n",
" 'n_mitos_post',\n",
" 'mito_size_pre_vx_log1p_zscore_zi',\n",
" 'mito_size_post_vx_log1p_zscore_zi'\n",
"]\n",
"\n",
"identity = lambda x: x\n",
"\n",
"def log1p_zscore(values):\n",
" v = np.log1p(values)\n",
" m, s = np.mean(v), np.std(v)\n",
" return (v - m) / s\n",
"\n",
"def log1p_zscore_zero_inflated(values):\n",
" zero_mask = (values == 0.)\n",
" v = np.log1p(values[~zero_mask])\n",
" m, s = np.mean(v), np.std(v)\n",
" z = (v - m) / s\n",
" out = np.zeros((len(values),))\n",
" out[~zero_mask] = z[:]\n",
" out[zero_mask] = float(\"-inf\")\n",
" return out\n",
"\n",
"def log1p_zscore_meta(values):\n",
" v = np.log1p(values)\n",
" m, s = np.mean(v), np.std(v)\n",
" return m, s\n",
"\n",
"def log1p_zscore_zero_inflated_meta(values):\n",
" zero_mask = (values == 0.)\n",
" v = np.log1p(values[~zero_mask])\n",
" m, s = np.mean(v), np.std(v)\n",
" return m, s\n",
"\n",
"transforms = [\n",
" log1p_zscore,\n",
" log1p_zscore,\n",
" log1p_zscore,\n",
" identity,\n",
" identity,\n",
" identity,\n",
" identity,\n",
" log1p_zscore_zero_inflated,\n",
" log1p_zscore_zero_inflated\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d5980c00-2bf9-4d75-98fb-c30672df43ca",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>syn_id</th>\n",
" <th>cleft_size_vx</th>\n",
" <th>presyn_soma_dist_um</th>\n",
" <th>postsyn_soma_dist_um</th>\n",
" <th>n_mitos_pre</th>\n",
" <th>n_mitos_post</th>\n",
" <th>pre_cell_type</th>\n",
" <th>post_cell_type</th>\n",
" <th>mito_size_pre_vx</th>\n",
" <th>mito_size_post_vx</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1484</td>\n",
" <td>798</td>\n",
" <td>221.313279</td>\n",
" <td>85.491890</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <th>1</th>\n",
" <td>2254</td>\n",
" <td>129</td>\n",
" <td>279.652318</td>\n",
" <td>84.258586</td>\n",
" <td>0.0</td>\n",
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" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>563600.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3785</td>\n",
" <td>62</td>\n",
" <td>130.542692</td>\n",
" <td>90.183479</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>3863</td>\n",
" <td>62</td>\n",
" <td>336.784967</td>\n",
" <td>241.501515</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>4264516.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4075</td>\n",
" <td>62</td>\n",
" <td>154.577558</td>\n",
" <td>135.108827</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6178</th>\n",
" <td>1533469</td>\n",
" <td>603</td>\n",
" <td>117.594227</td>\n",
" <td>162.934700</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>66332.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6179</th>\n",
" <td>287537</td>\n",
" <td>276</td>\n",
" <td>202.799738</td>\n",
" <td>111.211712</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>157080.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6180</th>\n",
" <td>3032966</td>\n",
" <td>181</td>\n",
" <td>119.304778</td>\n",
" <td>13.846323</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>528440.0</td>\n",
" <td>2785792.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6181</th>\n",
" <td>531494</td>\n",
" <td>107</td>\n",
" <td>217.056124</td>\n",
" <td>24.085480</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>375388.0</td>\n",
" <td>395452.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6182</th>\n",
" <td>2633096</td>\n",
" <td>873</td>\n",
" <td>181.643810</td>\n",
" <td>0.437892</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>25172.0</td>\n",
" <td>36872.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6183 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" syn_id cleft_size_vx presyn_soma_dist_um postsyn_soma_dist_um \\\n",
"0 1484 798 221.313279 85.491890 \n",
"1 2254 129 279.652318 84.258586 \n",
"2 3785 62 130.542692 90.183479 \n",
"3 3863 62 336.784967 241.501515 \n",
"4 4075 62 154.577558 135.108827 \n",
"... ... ... ... ... \n",
"6178 1533469 603 117.594227 162.934700 \n",
"6179 287537 276 202.799738 111.211712 \n",
"6180 3032966 181 119.304778 13.846323 \n",
"6181 531494 107 217.056124 24.085480 \n",
"6182 2633096 873 181.643810 0.437892 \n",
"\n",
" n_mitos_pre n_mitos_post pre_cell_type post_cell_type \\\n",
"0 0.0 0.0 0 0 \n",
"1 0.0 1.0 0 0 \n",
"2 0.0 0.0 0 0 \n",
"3 0.0 1.0 0 0 \n",
"4 0.0 0.0 0 0 \n",
"... ... ... ... ... \n",
"6178 1.0 0.0 1 1 \n",
"6179 0.0 1.0 1 1 \n",
"6180 3.0 3.0 1 1 \n",
"6181 2.0 3.0 1 1 \n",
"6182 1.0 1.0 1 1 \n",
"\n",
" mito_size_pre_vx mito_size_post_vx \n",
"0 0.0 0.0 \n",
"1 0.0 563600.0 \n",
"2 0.0 0.0 \n",
"3 0.0 4264516.0 \n",
"4 0.0 0.0 \n",
"... ... ... \n",
"6178 66332.0 0.0 \n",
"6179 0.0 157080.0 \n",
"6180 528440.0 2785792.0 \n",
"6181 375388.0 395452.0 \n",
"6182 25172.0 36872.0 \n",
"\n",
"[6183 rows x 10 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"annotations_df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9a27f18b",
"metadata": {},
"outputs": [],
"source": [
"# drop nans\n",
"bad_indices = []\n",
"for col_name in kept_annotations_list:\n",
" bad_indices += list(annotations_df[np.isnan(annotations_df[col_name].values)].index)\n",
"good_indices = list(set(list(annotations_df.index)).difference(set(bad_indices)))\n",
"annotations_df = annotations_df.iloc[good_indices]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1a255ec2",
"metadata": {},
"outputs": [],
"source": [
"final_annotations_dict = dict()\n",
"\n",
"for orig_column_name, annotation_type, final_column_name, transform in zip(\n",
" kept_annotations_list, kept_annotations_type, final_annotations_column_names, transforms):\n",
"\n",
" assert annotation_type in {'continuous', 'categorical'}\n",
" \n",
" orig_values = annotations_df[orig_column_name].values\n",
"\n",
" if annotation_type == 'continuous':\n",
" final_values = transform(orig_values)\n",
" \n",
" elif annotation_type == 'categorical':\n",
" final_values = orig_values.astype(int)\n",
" \n",
" final_annotations_dict[final_column_name] = final_values"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "7cc3da61-2269-4dcc-ae0f-dc36b7155828",
"metadata": {},
"outputs": [],
"source": [
"final_annotations_dict['has_mito_pre'] = np.isfinite(final_annotations_dict['mito_size_pre_vx_log1p_zscore_zi']).astype(int)\n",
"final_annotations_dict['has_mito_post'] = np.isfinite(final_annotations_dict['mito_size_post_vx_log1p_zscore_zi']).astype(int)\n",
"assert np.all(final_annotations_dict['has_mito_pre'] == (final_annotations_dict['n_mitos_pre'] > 0).astype(int))\n",
"assert np.all(final_annotations_dict['has_mito_post'] == (final_annotations_dict['n_mitos_post'] > 0).astype(int))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b1fbf602",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"type: (0, 0), fraction: 0.31\n",
"type: (1, 0), fraction: 0.49\n",
"type: (0, 1), fraction: 0.17\n",
"type: (1, 1), fraction: 0.03\n"
]
}
],
"source": [
"from collections import Counter\n",
"counts = Counter(list(zip(final_annotations_dict['pre_cell_type'], final_annotations_dict['post_cell_type'])))\n",
"for key, value in counts.items():\n",
" print(f'type: {key}, fraction: {value / len(annotations_df):.2f}')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "77b51c43",
"metadata": {},
"outputs": [],
"source": [
"em_syn_ids = meta_df['synapse_id'].values\n",
"annotated_syn_ids = annotations_df['syn_id'].values\n",
"mutual_syn_ids = sorted(list(set(em_syn_ids).intersection(set(annotated_syn_ids))))\n",
"mutual_meta_df = meta_df[meta_df['synapse_id'].isin(mutual_syn_ids)]\n",
"mutual_meta_df_synapse_ids = mutual_meta_df['synapse_id'].values\n",
"synapse_id_to_annotations_df_row_index = {synapse_id: index for index, synapse_id in enumerate(annotations_df['syn_id'])}\n",
"annotations_df_row_indices = list(map(synapse_id_to_annotations_df_row_index.get, mutual_meta_df_synapse_ids))\n",
"final_annotations_df = pd.DataFrame(data=dict(**{'synapse_id': annotations_df['syn_id'].values}, **final_annotations_dict))\n",
"mutual_meta_df_aligned_final_annotations_df = final_annotations_df.iloc[annotations_df_row_indices]\n",
"first_df = mutual_meta_df.set_index('synapse_id')\n",
"second_df = mutual_meta_df_aligned_final_annotations_df.set_index('synapse_id')\n",
"meta_ext_df = pd.concat((first_df, second_df), axis=1)\n",
"meta_ext_df = meta_ext_df.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4b231789",
"metadata": {},
"outputs": [],
"source": [
"meta_ext_df.to_csv(os.path.join(proc_data_path, 'meta_ext.csv'), index=False)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ad2218b2-9d27-4965-ad7a-434b64d236c1",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>synapse_id</th>\n",
" <th>filename</th>\n",
" <th>n_cutout_sections</th>\n",
" <th>post_synaptic_volume</th>\n",
" <th>pre_synaptic_volume</th>\n",
" <th>synaptic_cleft_volume</th>\n",
" <th>cleft_size_log1p_zscore</th>\n",
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" <th>mito_size_pre_vx_log1p_zscore_zi</th>\n",
" <th>mito_size_post_vx_log1p_zscore_zi</th>\n",
" <th>has_mito_pre</th>\n",
" <th>has_mito_post</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5659</th>\n",
" <td>995738</td>\n",
" <td>995738__2_256_256_52.npy</td>\n",
" <td>1</td>\n",
" <td>1090620.0</td>\n",
" <td>283245.0</td>\n",
" <td>3060.0</td>\n",
" <td>-0.549512</td>\n",
" <td>0.285049</td>\n",
" <td>0.785046</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>-inf</td>\n",
" <td>1.833237</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5660</th>\n",
" <td>998414</td>\n",
" <td>998414__2_256_256_52.npy</td>\n",
" <td>0</td>\n",
" <td>656790.0</td>\n",
" <td>861390.0</td>\n",
" <td>2365.0</td>\n",
" <td>-0.900531</td>\n",
" <td>-0.427890</td>\n",
" <td>0.545759</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.172815</td>\n",
" <td>-inf</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5661</th>\n",
" <td>998624</td>\n",
" <td>998624__2_256_256_52.npy</td>\n",
" <td>2</td>\n",
" <td>1744330.0</td>\n",
" <td>1349180.0</td>\n",
" <td>3110.0</td>\n",
" <td>-0.549512</td>\n",
" <td>1.018483</td>\n",
" <td>0.531163</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.571826</td>\n",
" <td>1.677105</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5662</th>\n",
" <td>998959</td>\n",
" <td>998959__2_256_256_52.npy</td>\n",
" <td>2</td>\n",
" <td>6834865.0</td>\n",
" <td>1799490.0</td>\n",
" <td>2750.0</td>\n",
" <td>-0.722024</td>\n",
" <td>-0.162076</td>\n",
" <td>-0.831938</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.927955</td>\n",
" <td>-0.830449</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5663</th>\n",
" <td>999623</td>\n",
" <td>999623__2_256_256_52.npy</td>\n",
" <td>2</td>\n",
" <td>2223405.0</td>\n",
" <td>648425.0</td>\n",
" <td>3285.0</td>\n",
" <td>-0.549512</td>\n",
" <td>0.170969</td>\n",
" <td>0.518517</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>-inf</td>\n",
" <td>0.693126</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5664 rows × 17 columns</p>\n",
"</div>"
],
"text/plain": [
" synapse_id filename n_cutout_sections \\\n",
"0 1000004 1000004__2_256_256_52.npy 1 \n",
"1 1001064 1001064__2_256_256_52.npy 7 \n",
"2 1001959 1001959__2_256_256_52.npy 3 \n",
"3 1003796 1003796__2_256_256_52.npy 2 \n",
"4 100426 100426__2_256_256_52.npy 0 \n",
"... ... ... ... \n",
"5659 995738 995738__2_256_256_52.npy 1 \n",
"5660 998414 998414__2_256_256_52.npy 0 \n",
"5661 998624 998624__2_256_256_52.npy 2 \n",
"5662 998959 998959__2_256_256_52.npy 2 \n",
"5663 999623 999623__2_256_256_52.npy 2 \n",
"\n",
" post_synaptic_volume pre_synaptic_volume synaptic_cleft_volume \\\n",
"0 2855510.0 1382710.0 5805.0 \n",
"1 757705.0 1228365.0 35695.0 \n",
"2 7581970.0 1306600.0 10230.0 \n",
"3 131530.0 302620.0 3070.0 \n",
"4 443370.0 344255.0 1410.0 \n",
"... ... ... ... \n",
"5659 1090620.0 283245.0 3060.0 \n",
"5660 656790.0 861390.0 2365.0 \n",
"5661 1744330.0 1349180.0 3110.0 \n",
"5662 6834865.0 1799490.0 2750.0 \n",
"5663 2223405.0 648425.0 3285.0 \n",
"\n",
" cleft_size_log1p_zscore presyn_soma_dist_log1p_zscore \\\n",
"0 0.221381 -0.087308 \n",
"1 2.527689 -0.955527 \n",
"2 0.932481 -0.033998 \n",
"3 -0.541478 1.540747 \n",
"4 -1.557111 -1.791284 \n",
"... ... ... \n",
"5659 -0.549512 0.285049 \n",
"5660 -0.900531 -0.427890 \n",
"5661 -0.549512 1.018483 \n",
"5662 -0.722024 -0.162076 \n",
"5663 -0.549512 0.170969 \n",
"\n",
" postsyn_soma_dist_log1p_zscore pre_cell_type post_cell_type \\\n",
"0 0.110199 1 0 \n",
"1 0.452097 0 0 \n",
"2 -1.494799 1 0 \n",
"3 1.279091 0 0 \n",
"4 0.243304 0 0 \n",
"... ... ... ... \n",
"5659 0.785046 0 0 \n",
"5660 0.545759 1 0 \n",
"5661 0.531163 1 0 \n",
"5662 -0.831938 1 0 \n",
"5663 0.518517 1 0 \n",
"\n",
" n_mitos_pre n_mitos_post mito_size_pre_vx_log1p_zscore_zi \\\n",
"0 1.0 1.0 0.361396 \n",
"1 1.0 0.0 -1.579014 \n",
"2 1.0 2.0 0.306555 \n",
"3 0.0 0.0 -inf \n",
"4 0.0 1.0 -inf \n",
"... ... ... ... \n",
"5659 0.0 1.0 -inf \n",
"5660 1.0 0.0 1.172815 \n",
"5661 1.0 1.0 0.571826 \n",
"5662 1.0 1.0 0.927955 \n",
"5663 0.0 1.0 -inf \n",
"\n",
" mito_size_post_vx_log1p_zscore_zi has_mito_pre has_mito_post \n",
"0 2.663820 1 1 \n",
"1 -inf 1 0 \n",
"2 0.365641 1 1 \n",
"3 -inf 0 0 \n",
"4 -0.591276 0 1 \n",
"... ... ... ... \n",
"5659 1.833237 0 1 \n",
"5660 -inf 1 0 \n",
"5661 1.677105 1 1 \n",
"5662 -0.830449 1 1 \n",
"5663 0.693126 0 1 \n",
"\n",
"[5664 rows x 17 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"meta_ext_df"
]
},
{
"cell_type": "markdown",
"id": "b9eafcc7-51f5-420c-87f5-6ccf089ff05c",
"metadata": {},
"source": [
"## Transformation metadata"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "1b3b8116-967c-4e12-8d2a-57704074cba7",
"metadata": {},
"outputs": [],
"source": [
"trans_meta_dict = dict()\n",
"\n",
"for orig_column_name, annotation_type, final_column_name, transform in zip(\n",
" kept_annotations_list, kept_annotations_type, final_annotations_column_names, transforms):\n",
"\n",
" assert annotation_type in {'continuous', 'categorical'}\n",
" \n",
" orig_values = annotations_df[orig_column_name].values\n",
"\n",
" if annotation_type == 'continuous':\n",
" if transform == log1p_zscore:\n",
" loc, scale = log1p_zscore_meta(orig_values)\n",
" elif transform == log1p_zscore_zero_inflated:\n",
" loc, scale = log1p_zscore_zero_inflated_meta(orig_values)\n",
" else:\n",
" loc, scale = 0., 1.\n",
" trans_meta_dict[final_column_name + \"__loc\"] = [loc]\n",
" trans_meta_dict[final_column_name + \"__scale\"] = [scale]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "99871e4c-b7c4-4865-a51e-58303529ecab",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cleft_size_log1p_zscore__loc</th>\n",
" <th>cleft_size_log1p_zscore__scale</th>\n",
" <th>presyn_soma_dist_log1p_zscore__loc</th>\n",
" <th>presyn_soma_dist_log1p_zscore__scale</th>\n",
" <th>postsyn_soma_dist_log1p_zscore__loc</th>\n",
" <th>postsyn_soma_dist_log1p_zscore__scale</th>\n",
" <th>n_mitos_pre__loc</th>\n",
" <th>n_mitos_pre__scale</th>\n",
" <th>n_mitos_post__loc</th>\n",
" <th>n_mitos_post__scale</th>\n",
" <th>mito_size_pre_vx_log1p_zscore_zi__loc</th>\n",
" <th>mito_size_pre_vx_log1p_zscore_zi__scale</th>\n",
" <th>mito_size_post_vx_log1p_zscore_zi__loc</th>\n",
" <th>mito_size_post_vx_log1p_zscore_zi__scale</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.486893</td>\n",
" <td>0.795317</td>\n",
" <td>5.193572</td>\n",
" <td>0.370629</td>\n",
" <td>3.816027</td>\n",
" <td>0.754296</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>11.628451</td>\n",
" <td>1.018936</td>\n",
" <td>13.295805</td>\n",
" <td>1.109729</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cleft_size_log1p_zscore__loc cleft_size_log1p_zscore__scale \\\n",
"0 5.486893 0.795317 \n",
"\n",
" presyn_soma_dist_log1p_zscore__loc presyn_soma_dist_log1p_zscore__scale \\\n",
"0 5.193572 0.370629 \n",
"\n",
" postsyn_soma_dist_log1p_zscore__loc postsyn_soma_dist_log1p_zscore__scale \\\n",
"0 3.816027 0.754296 \n",
"\n",
" n_mitos_pre__loc n_mitos_pre__scale n_mitos_post__loc \\\n",
"0 0.0 1.0 0.0 \n",
"\n",
" n_mitos_post__scale mito_size_pre_vx_log1p_zscore_zi__loc \\\n",
"0 1.0 11.628451 \n",
"\n",
" mito_size_pre_vx_log1p_zscore_zi__scale \\\n",
"0 1.018936 \n",
"\n",
" mito_size_post_vx_log1p_zscore_zi__loc \\\n",
"0 13.295805 \n",
"\n",
" mito_size_post_vx_log1p_zscore_zi__scale \n",
"0 1.109729 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trans_meta_df = pd.DataFrame(data=trans_meta_dict)\n",
"trans_meta_df"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6cb1f13d-ac40-4149-a80a-1dbdcd5b119c",
"metadata": {},
"outputs": [],
"source": [
"trans_meta_df.to_csv(os.path.join(tables_path, 'meta_ext_loc_scale.csv'), index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07680ff3-efb5-4dcd-9e52-084cd0a048bb",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"environment": {
"kernel": "python3",
"name": "common-cu113.m91",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/base-cu113:m91"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
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