python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from http.client import RemoteDisconnected
import json
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
class NBLoss(torch.nn.Module):
def __init__(self):
super(NBLoss, self).__init__()
def for... | CPA-main | cpa/model.py |
import copy
import itertools
import os
import pprint
import time
from collections import defaultdict
from typing import Optional, Union, Tuple
import numpy as np
import pandas as pd
import scanpy as sc
import torch
from torch.distributions import (
NegativeBinomial,
Normal
)
from cpa.train import evaluate, pre... | CPA-main | cpa/api.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import warnings
import numpy as np
import pandas as pd
import scanpy as sc
from sklearn.metrics import r2_score
from scipy.sparse import issparse
from scipy.stats import wasserstein_distance
import torch
warnings.filterwarnings("ignore")
import ... | CPA-main | cpa/helper.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import json
import os
import time
from collections import defaultdict
import numpy as np
import torch
from cpa.data import load_dataset_splits
from cpa.model import CPA, MLP
from sklearn.metrics import r2_score
from torch.autograd ... | CPA-main | cpa/train.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import warnings
import numpy as np
import torch
warnings.simplefilter(action="ignore", category=FutureWarning)
from typing import Union
import pandas as pd
import scanpy as sc
import scipy
from cpa.helper import rank_genes_groups
from sklearn.pr... | CPA-main | cpa/data.py |
import sys
sys.path.append("../")
import cpa
import scanpy as sc
import scvi
from cpa.helper import rank_genes_groups_by_cov
def sim_adata():
adata = scvi.data.synthetic_iid(run_setup_anndata=False)
sc.pp.filter_cells(adata, min_counts=0)
sc.pp.log1p(adata)
adata.obs["condition"] = "drugA"
adata... | CPA-main | tests/test.py |
"""For pip."""
from setuptools import setup
exec(open("unagi/_version.py").read())
setup(
name="unagi",
version=__version__,
description="Official repo for the paper 'Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning'",
long_description=open("README.md").read(),
... | thanos-code-main | setup.py |
import logging
from typing import Any, Dict, Sequence
import hydra
import torch
from torch import nn
from unagi.trainer import MODULE_REGISTRY
from unagi.trainer.task import UnagiTask
logger = logging.getLogger(__name__)
def _instantiate_modules(module_config: Dict[str, Any], type: str):
# Assert type with use... | thanos-code-main | unagi/task.py |
import logging
from types import SimpleNamespace
from unagi.datasets import DATASET_CLASSES
logger = logging.getLogger(__name__)
def get_data(dataflow_config):
"""
Builds datasets and dataloaders from config file.
# Inputs
:param config: (dict) dictionary representation of experiment config file
... | thanos-code-main | unagi/data_driver.py |
"""Unagi version."""
__version__ = "0.0.1+dev"
| thanos-code-main | unagi/_version.py |
import logging
import pytorch_lightning as pl
from unagi.data_driver import get_data
from unagi.trainer.trainer import UnagiModule
logger = logging.getLogger(__name__)
def main(config):
# Create dataloaders
data = get_data(config.dataflow)
# set seed
if (
"random_seed" in config.dataflow.k... | thanos-code-main | unagi/unagi.py |
TEXT = "text"
TIMESERIES = "timeseries"
IMAGE = "image"
TYPE = "type"
AUGMENTATIONS = "augmentations"
CONTRASTIVE = "contrastive"
MASKED = "masked"
NAME = "name"
DATASET = "dataset"
TASKS = "tasks"
RAW = "raw"
PATCH = "patch"
FEATURE = "feature"
| thanos-code-main | unagi/constants.py |
import logging
from functools import partial
from emmental.scorer import Scorer
from emmental.task import EmmentalTask
from torch import nn
logger = logging.getLogger(__name__)
def create_unagi_task(
model_name,
model,
dataset_name,
task_flow,
loss_module,
loss_fns,
output_classification... | thanos-code-main | unagi/tasks/unagi_task_template.py |
from torch.nn import (
BCEWithLogitsLoss as BCELoss,
CrossEntropyLoss as CELoss,
L1Loss as L1Loss,
MSELoss as MSELoss,
)
from unagi.tasks.loss_fns.ce_loss import LabelSmoothing, SoftCrossEntropyLoss
from unagi.tasks.loss_fns.contrastive_loss import ContrastiveLoss
from unagi.tasks.loss_fns.mask_loss im... | thanos-code-main | unagi/tasks/__init__.py |
import torch
from einops import rearrange
def mask_loss(
loss_fns,
aug_type,
n_views,
module_names,
task_name,
intermediate_output_dict,
Y,
):
# TODO: MODIFY THIS TO SUPPORT NEW FLOW
total_loss = 0
pre_encoding_embs = intermediate_output_dict["pre_encoder"][0]
decoder_ouput... | thanos-code-main | unagi/tasks/loss_modules.py |
from torch.nn import functional as F
def multiclass_classification(module_name, immediate_output_dict):
return F.softmax(
immediate_output_dict[module_name][len(immediate_output_dict[module_name]) - 1],
dim=1,
)
def multilabel_classification(module_name, immediate_output_dict):
return F.... | thanos-code-main | unagi/tasks/output_layer_modules.py |
import copy
import torch
import torch.nn as nn
from einops import rearrange
class MaskInputTransforms(nn.Module):
def __init__(self):
super().__init__()
self.name = "masked_input_transform"
def forward(self, x_batch):
temp_x_batch = copy.deepcopy(x_batch)
is_train = temp_x_ba... | thanos-code-main | unagi/tasks/task_preprocessing_layer.py |
import torch
import torch.nn.functional as F
from unagi.tasks.loss_fns.base_loss import UnagiLoss
class BatchMask(UnagiLoss):
def __init__(self):
super().__init__()
def forward(self, last_layer, embs):
# embs == output of embedding layers
# last_layer == output of the decoder
... | thanos-code-main | unagi/tasks/loss_fns/mask_loss.py |
from typing import List
import torch
import torch.nn.functional as F
from torch import Tensor
from unagi.tasks.loss_fns.base_loss import UnagiLoss
class SoftCrossEntropyLoss(UnagiLoss):
"""Calculate the CrossEntropyLoss with soft targets.
:param weight: Weight to assign to each of the classes. Default: None... | thanos-code-main | unagi/tasks/loss_fns/ce_loss.py |
import numpy as np
import torch
from einops import rearrange
from unagi.tasks.loss_fns.base_loss import UnagiLoss
class UnagiContrastiveLoss(UnagiLoss):
def __init__(self, views):
super().__init__()
self.views = views
def combine_views(self, *views):
all_views = [view for view in vie... | thanos-code-main | unagi/tasks/loss_fns/contrastive_loss.py |
import torch.nn as nn
class UnagiLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self):
raise NotImplementedError
| thanos-code-main | unagi/tasks/loss_fns/base_loss.py |
import copy
from collections import defaultdict
from functools import partial
from pathlib import Path
from typing import Any, Dict, List, Sequence, Tuple, Union
import torch
from einops import rearrange
from unagi.data.data_utils.transform_util import get_transforms
from unagi.utils.misc import list_to_tensor
def ... | thanos-code-main | unagi/datasets/base_dataset.py |
from unagi.datasets.celeba.celeba_dataset import CelebA
from unagi.datasets.cifar.cifar_dataset import CIFAR10, CIFAR100
from unagi.datasets.mnist.mnist_dataset import MNIST
from unagi.datasets.tiny_imagenet.tinyimagenet_dataset import TinyImageNet
DATASET_CLASSES = {
"cifar10": CIFAR10,
"cifar100": CIFAR100,
... | thanos-code-main | unagi/datasets/__init__.py |
from torch.utils.data import Dataset
class MeerkatDataset(Dataset):
"""Torch dataset wrapper around meerkat dp"""
def __init__(self, datapanel, xs, ys):
self.dataset = datapanel
self.x_names = xs
self.y_names = ys
def __len__(self):
return len(self.dataset)
def __get... | thanos-code-main | unagi/datasets/meerkat_dataset.py |
import meerkat as mk
import torchvision
from unagi.datasets.base_dataset import UnagiDatasetBuilder
from unagi.datasets.meerkat_dataset import MeerkatDataset
from unagi.datasets.mnist.utils import sparse2coarse
class MNIST(UnagiDatasetBuilder):
"""Dataset to load MNIST dataset."""
_name_ = "mnist"
# TOD... | thanos-code-main | unagi/datasets/mnist/mnist_dataset.py |
import numpy as np
def sparse2coarse(targets, scramble=False, dataset="mnist"):
"""Convert Pytorch MNIST sparse targets.
trainset = torchvision.datasets.CIFAR100(path)
trainset.targets = sparse2coarse(trainset.targets)
"""
if dataset == "mnist":
sparse_coarse_array = [
0,
... | thanos-code-main | unagi/datasets/mnist/utils.py |
import os
import numpy as np
def sparse2coarse(targets, scramble=False):
"""Convert Pytorch CIFAR100 sparse targets to coarse targets.
Usage:
trainset = torchvision.datasets.CIFAR100(path)
trainset.targets = sparse2coarse(trainset.targets)
"""
sparse_coarse_array = [
14,
... | thanos-code-main | unagi/datasets/tiny_imagenet/utils.py |
import os
import meerkat as mk
import torchvision
from unagi.datasets.base_dataset import UnagiDatasetBuilder
from unagi.datasets.meerkat_dataset import MeerkatDataset
from unagi.datasets.tiny_imagenet.utils import create_val_img_folder, sparse2coarse
class TinyImageNet(UnagiDatasetBuilder):
"""Dataset to load ... | thanos-code-main | unagi/datasets/tiny_imagenet/tinyimagenet_dataset.py |
import os
import meerkat as mk
import numpy as np
import pandas as pd
import torch
from unagi.datasets.base_dataset import UnagiDatasetBuilder
from unagi.datasets.meerkat_dataset import MeerkatDataset
class CelebA(UnagiDatasetBuilder):
"""Dataset to load CelebA dataset."""
_name_ = "celeba"
# TODO: the... | thanos-code-main | unagi/datasets/celeba/celeba_dataset.py |
import meerkat as mk
import numpy as np
import torch
import torchvision
from unagi.datasets.base_dataset import UnagiDatasetBuilder
from unagi.datasets.cifar.utils import get_superclass_subclass_mapping, sparse2coarse
from unagi.datasets.meerkat_dataset import MeerkatDataset
class CIFAR10(UnagiDatasetBuilder):
"... | thanos-code-main | unagi/datasets/cifar/cifar_dataset.py |
import numpy as np
# https://github.com/ryanchankh/cifar100coarse/blob/master/sparse2coarse.py
def sparse2coarse(targets, scramble=False, dataset="cifar10"):
"""Convert Pytorch CIFAR100 sparse targets to coarse targets.
Usage:
trainset = torchvision.datasets.CIFAR100(path)
trainset.targets = s... | thanos-code-main | unagi/datasets/cifar/utils.py |
"""Unagi utils. Credit: Emmental """
import json
import random
import string
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from numpy import ndarray
from torch import Tensor
def list_to_tensor(
item_list: List[Tensor], min_len: int = 0, max_len: int = 0
) -> Tuple[Ten... | thanos-code-main | unagi/utils/misc.py |
import numpy as np
from PIL import Image
def relu_model(X, E, W, classification=False, delta=0.1):
"""Relu model."""
Y = W.dot(np.maximum((E.T @ X.T), 0.0)).reshape(-1)
if classification:
idx = abs(Y) >= delta
X = X[idx]
Y = Y[idx]
return X, ((Y < 0).astype(int)).reshape(-1... | thanos-code-main | unagi/utils/image_utils.py |
thanos-code-main | unagi/utils/__init__.py | |
import json
import os
import numpy as np
import yaml
def load_yaml(file_path):
with open(file_path, "r") as f:
return yaml.load(f)
def write_to_file(file_path, value):
"""Write value to file."""
directory = os.path.dirname(file_path)
os.makedirs(directory, exist_ok=True)
if not isinstan... | thanos-code-main | unagi/utils/file_utils.py |
import copy
import importlib
import logging
from collections.abc import Mapping
from transformers import AutoModel, AutoTokenizer
# from unagi.configs import MODEL_DEFAULT_PATHS
from unagi.datasets import DATASET_CONFIG_REGISTRY
from unagi.models import AUGMENTATION_LAYERS, MODULE_DICTS
from unagi.tasks import ( # L... | thanos-code-main | unagi/utils/task_utils.py |
import copy
import os
from collections.abc import Mapping
from unagi.configs import (
BASE_CONFIG_PATH,
BASE_DATASET_PATH,
BASE_INPUT_FEATURE_PATH,
BASE_OUTPUT_FEATURE_PATH,
DATASET_DEFAULT_PATHS,
)
from unagi.utils.file_utils import load_yaml
def get_feature_to_type(config):
dataset_config =... | thanos-code-main | unagi/utils/config_utils.py |
from unagi.models.decoders.classifier import ClassificationDecoder
from unagi.models.decoders.image.resnet import ResnetDecoder
from unagi.models.decoders.image.resnet_autoencoder import (
Resnet18Decoder,
Resnet50Decoder,
)
from unagi.models.decoders.sequence.mixer import MixerDecoder
from unagi.models.decoder... | thanos-code-main | unagi/models/__init__.py |
from math import sqrt
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import nn
from transformers import AutoModel
from unagi.models.embeddings.base_embedding import EmbeddingModule
from unagi.models.layers.blocks import Transpose
class SquarePatchEmbed(EmbeddingModule):
def... | thanos-code-main | unagi/models/embeddings/embeddings.py |
from torch import nn
class EmbeddingModule(nn.Module):
def __init__(
self,
d_input: int,
d_model: int,
):
super().__init__()
self.d_input = d_input
self.d_model = d_model
| thanos-code-main | unagi/models/embeddings/base_embedding.py |
from math import sqrt
from einops import rearrange
from torch import nn
class Transpose(nn.Module):
def __init__(self, i, j):
super().__init__()
self.i = i
self.j = j
def forward(self, x):
return x.transpose(self.i, self.j)
class SquareEmb(nn.Module):
def __init__(self,... | thanos-code-main | unagi/models/layers/embeds.py |
import torch
import torch.nn.functional as F
from torch import nn
class Transpose(nn.Module):
def __init__(self, i, j):
super().__init__()
self.i = i
self.j = j
def forward(self, x):
return x.transpose(self.i, self.j)
class Truncate(nn.Module):
def __init__(self, max_seq... | thanos-code-main | unagi/models/layers/blocks.py |
import random
from typing import Dict
import numpy as np
import torch
import torchvision.transforms.functional as TF
from torch import nn
from torch.functional import Tensor
class MixUpAugmentation(nn.Module):
"""
Inter-image augmentation: Computes augmentations on an individual sample
"""
def __ini... | thanos-code-main | unagi/models/layers/patch_augmentations.py |
from torch import nn
class SequenceModule(nn.Module):
"""Abstract sequence model class. All layers that the backbones
use must adhere to this
A sequence model is a layer that transforms an input of shape
(n_batch, l_sequence, d_input) to (n_batch, l_sequence, d_output)
Additionally, it returns a ... | thanos-code-main | unagi/models/encoders/base_sequence.py |
import torch
import torch.nn as nn
from torchvision.models import resnet18, resnet34, resnet50 # noqa: F401
class ResnetEncoder(nn.Module):
def __init__(
self,
model="resnet18",
use_pretrained=True,
**kwargs,
):
super().__init__()
encoder = eval(model)(pretrai... | thanos-code-main | unagi/models/encoders/image/resnet/resnet.py |
import torch
from torch import nn
from unagi.models.encoders.base_sequence import SequenceModule
from unagi.models.encoders.sequence.transformer.transformer_modules import (
MHA_Encoder,
MHA_Encoder_Cat,
)
class TransformerEncoder(SequenceModule):
def __init__(
self,
d_model,
n_he... | thanos-code-main | unagi/models/encoders/sequence/transformer/transformer.py |
import torch
from einops import rearrange
from torch import nn
from unagi.models.layers.blocks import FFN, Cat, PreNorm, Residual
class MHA(nn.Module):
def __init__(self, d_model, n_heads, dropout=0.1, head_dropout=None):
super().__init__()
self.n_heads = n_heads
self.head_dim = d_model /... | thanos-code-main | unagi/models/encoders/sequence/transformer/transformer_modules.py |
import torch
from transformers import AutoTokenizer, BertModel
from unagi.models.encoders.base_sequence import SequenceModule
class BertEncoder(SequenceModule):
def __init__(
self,
freeze_layers=True,
pretrained_lm_name="bert-base-uncased",
use_cls_token=True,
use_all_toke... | thanos-code-main | unagi/models/encoders/sequence/bert/bert.py |
from torch import nn
from unagi.models.layers.blocks import FFN, PreNorm, Residual
class mixer(nn.Module):
def __init__(self, d, n=64, dropout=0.0):
super().__init__()
self.f = FFN(n, n << 1)
def forward(self, x):
# b x p x c
return self.f(x.transpose(1, 2)).transpose(1, 2)
... | thanos-code-main | unagi/models/encoders/sequence/mixer/mixer_modules.py |
from torch import nn
from unagi.models.encoders.base_sequence import SequenceModule
from unagi.models.encoders.sequence.mixer.mixer_modules import mixer_encoder
class MixerEncoder(SequenceModule):
def __init__(
self,
d_model,
n_heads,
l_max, # can be computed based on embedding
... | thanos-code-main | unagi/models/encoders/sequence/mixer/mixer.py |
import torch
from torch import nn
class ClassificationDecoder(nn.Module):
def __init__(self, d_input, d_output, **kwargs):
super().__init__()
# NOTE: compute d_input as module instantiation time
# d_input = sum(d_model of all encoders being fed to Classifier)
self.classification_la... | thanos-code-main | unagi/models/decoders/classifier.py |
import torch
from torch import nn
class ViewConcat(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.name = "view_concat"
def forward(self, *args):
return torch.stack(args, dim=1)
| thanos-code-main | unagi/models/decoders/view_concat.py |
"""
Modified from PyTorch Lightning Bolts implementation.
https://github.com/PyTorchLightning/lightning-bolts/blob/master/pl_bolts/models/autoencoders/components.py
"""
import torch
from torch import nn
from torch.nn import functional as F
class Interpolate(nn.Module):
"""nn.Module wrapper for F.interpolate."""
... | thanos-code-main | unagi/models/decoders/image/resnet_autoencoder.py |
import torch.nn as nn
from torchvision.models import resnet18, resnet34, resnet50 # noqa: F401
class ResnetDecoder(nn.Module):
def __init__(
self,
decoder_hidden_dim,
decoder_projection_dim,
model="resnet18",
d_model=None,
**kwargs,
):
super().__init__(... | thanos-code-main | unagi/models/decoders/image/resnet.py |
from torch import nn
from unagi.models.encoders.base_sequence import SequenceModule
from unagi.models.encoders.sequence.transformer.transformer_modules import MHA_Decoder
class TransformerDecoder(SequenceModule):
def __init__(self, d_model, n_heads, dropout=0.1, head_dropout=0.1, **kwargs):
super().__ini... | thanos-code-main | unagi/models/decoders/sequence/transformer.py |
import torch
from einops import rearrange
from torch import nn
from unagi.models.encoders.base_sequence import SequenceModule
from unagi.models.encoders.sequence.mixer.mixer_modules import mixer_encoder
class MixerDecoder(SequenceModule):
def __init__(
self,
d_model,
n_heads,
l_ma... | thanos-code-main | unagi/models/decoders/sequence/mixer.py |
import torch
from torch import nn
class EinsumReduceDecoder(nn.Module):
def __init__(self, d_model, **kwargs):
super().__init__()
# NOTE: compute d_input as module instantiation time
# d_input = sum(d_model of all encoders being fed to Classifier)
self.attend = nn.Linear(d_model, 1... | thanos-code-main | unagi/models/ops/einsum_reduce.py |
import torch
from torch import nn
from torchvision import transforms as transforms
class Grayscale(nn.Module):
def __init__(self, dim=1, resize=None, **kwargs):
super().__init__()
self.dim = dim
self.resize = resize
if self.resize:
self.resize_func = transforms.Resize(
... | thanos-code-main | unagi/models/ops/grayscale.py |
import torch
from torch import nn
class SequenceConcat(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.name = "sequence_concat"
def forward(self, *args):
return torch.cat(args, dim=1)
| thanos-code-main | unagi/models/ops/sequence_concat.py |
from einops import rearrange
from torch import nn
class ViewSelect(nn.Module):
def __init__(self, view_idx, n_views, **kwargs):
super().__init__()
self.name = "view_select"
self.view_idx = view_idx
self.n_views = n_views
def forward(self, input):
embs = rearrange(input... | thanos-code-main | unagi/models/ops/view_select.py |
import torch
from torch import nn
class ViewConcat(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.name = "view_concat"
def forward(self, *args):
return torch.stack(args, dim=1)
| thanos-code-main | unagi/models/ops/view_concat.py |
from torch import nn
class LinearProj(nn.Module):
def __init__(self, d_input, d_output, **kwargs):
super().__init__()
self.linear_proj = nn.Linear(d_input, d_output)
def forward(self, x):
return self.linear_proj(x)
| thanos-code-main | unagi/models/ops/linear_proj.py |
from torch import nn
class PoolDecoder(nn.Module):
def __init__(self, **kwargs):
super().__init__()
# NOTE: compute d_input as module instantiation time
# d_input = sum(d_model of all encoders being fed to Classifier)
def forward(self, x):
"""
x: intermediate outpus fr... | thanos-code-main | unagi/models/ops/pool.py |
from einops import rearrange
from torch import nn
class ImageReshape(nn.Module):
def __init__(self, d_input, output_height, output_width, **kwargs):
super().__init__()
self.name = "view_select"
self.d_input = d_input
self.output_height = output_height
self.output_width = ou... | thanos-code-main | unagi/models/ops/image_reshape.py |
from copy import deepcopy
from unagi.data.transforms import ALL_TRANSFORMS
from unagi.data.transforms.image.compose import Compose
def get_transforms(
input_features: dict,
dataset_split: str,
augmentations: dict,
default_transforms: dict = {},
):
"""
Gets list of transforms for each input fe... | thanos-code-main | unagi/data/data_utils/transform_util.py |
thanos-code-main | unagi/data/data_utils/__init__.py | |
# flake8: noqa
# from __future__ import annotation
import logging
from typing import Collection, List, Sequence, Tuple
import meerkat as mk
import pandas as pd
import torch
from meerkat.columns.lambda_column import LambdaColumn
from meerkat.tools.lazy_loader import LazyLoader
from unagi.data.transforms.task import G... | thanos-code-main | unagi/data/data_utils/meerkat_processors.py |
from typing import Any, Dict, List, Tuple, Union
from einops import rearrange
from torch import Tensor
from unagi.trainer.data import default_unagi_collate_fn
def unagi_collate_fn(
# is_train,
# feature_type_map,
# feature_view_map,
batch: Union[List[Tuple[Dict[str, Any], Dict[str, Tensor]]], List[D... | thanos-code-main | unagi/data/data_utils/collate_fns.py |
from unagi.data.transforms.image import ALL_TRANSFORMS as ALL_IMAGE_TRANSFORMS
from unagi.data.transforms.task import ALL_TRANSFORMS as ALL_TASK_TRANSFORMS
from unagi.data.transforms.text import ALL_TRANSFORMS as ALL_TEXT_TRANSFORMS
ALL_TRANSFORMS = {
"text": ALL_TEXT_TRANSFORMS,
"image": ALL_IMAGE_TRANSFORMS,... | thanos-code-main | unagi/data/transforms/__init__.py |
from unagi.data.transforms.image.transform import UnagiTransform
class Reshape2D(UnagiTransform):
def __init__(self, h_dim, w_dim, name=None, prob=1.0, level=0):
self.h_dim = h_dim
self.w_dim = w_dim
super().__init__(name, prob, level)
def transform(self, pil_img, label, **kwargs):
... | thanos-code-main | unagi/data/transforms/image/reshape2d.py |
from PIL import ImageOps
from unagi.data.transforms.image.transform import UnagiTransform
from unagi.data.transforms.image.utils import categorize_value
class Solarize(UnagiTransform):
value_range = (0, 256)
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
... | thanos-code-main | unagi/data/transforms/image/solarize.py |
from PIL import ImageOps
from unagi.data.transforms.image.transform import UnagiTransform
class AutoContrast(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
def transform(self, pil_img, label, **kwargs):
return ImageOps.autocontrast(pil_... | thanos-code-main | unagi/data/transforms/image/auto_contrast.py |
from torchvision import transforms
from unagi.data.transforms.image.transform import UnagiTransform
class GaussianBlur(UnagiTransform):
def __init__(self, kernel_size, sigma=(0.1, 2.0), name=None, prob=1.0, level=0):
self.kernel_size = kernel_size
self.sigma = sigma
self.transform_func = ... | thanos-code-main | unagi/data/transforms/image/gaussian_blur.py |
from PIL import ImageEnhance
from unagi.data.transforms.image.transform import UnagiTransform
from unagi.data.transforms.image.utils import categorize_value
class Color(UnagiTransform):
value_range = (0.1, 1.9)
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
... | thanos-code-main | unagi/data/transforms/image/color.py |
from PIL import Image
from unagi.data.transforms.image.transform import UnagiTransform
class HorizontalFlip(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
def transform(self, pil_img, label, **kwargs):
return pil_img.transpose(Image.FLI... | thanos-code-main | unagi/data/transforms/image/horizontal_filp.py |
from torchvision import transforms as transforms
from unagi.data.transforms.image.transform import UnagiTransform
class Grayscale(UnagiTransform):
def __init__(self, num_output_channels=1):
self.num_output_channels = num_output_channels
self.transform_func = transforms.Grayscale(self.num_output_c... | thanos-code-main | unagi/data/transforms/image/grayscale.py |
class Compose(object):
"""Composes several transforms together.
Originally from:
https://pytorch.org/docs/stable/_modules/torchvision/transforms/transforms.html#Compose
Args:
transforms (list of ``Transform`` objects): list of transforms to compose.
"""
def __init__(self, transforms... | thanos-code-main | unagi/data/transforms/image/compose.py |
from torchvision import transforms
from unagi.data.transforms.image.transform import UnagiTransform
class ColorDistortion(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0, strength=0.5):
super().__init__(name, prob, level)
self.strength = strength
self.color_jitter = trans... | thanos-code-main | unagi/data/transforms/image/color_distortion.py |
from torchvision import transforms as transforms
from unagi.data.transforms.image.transform import UnagiTransform
class RandomGrayscale(UnagiTransform):
def __init__(self, p=0.1, name=None, prob=1.0, level=0):
self.p = p
self.transform_func = transforms.RandomGrayscale(self.p)
super().__... | thanos-code-main | unagi/data/transforms/image/random_grayscale.py |
import numpy as np
from PIL import ImageDraw
from unagi.data.transforms.image.transform import UnagiTransform
from unagi.data.transforms.image.utils import categorize_value
class Cutout(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0, max_pixel=20, color=None):
self.max_pixel = max_pixel... | thanos-code-main | unagi/data/transforms/image/cutout.py |
from torchvision import transforms as transforms
from unagi.data.transforms.image.transform import UnagiTransform
class Normalize(UnagiTransform):
def __init__(self, mean, std, name=None, prob=1.0, level=0):
self.mean = mean
self.std = std
self.transform_func = transforms.Normalize(mean, ... | thanos-code-main | unagi/data/transforms/image/normalize.py |
from torchvision import transforms as transforms
from unagi.data.transforms.image.transform import UnagiTransform
class CenterCrop(UnagiTransform):
def __init__(self, size, name=None, prob=1.0, level=0):
self.size = size
self.transform_func = transforms.CenterCrop(self.size)
super().__ini... | thanos-code-main | unagi/data/transforms/image/center_crop.py |
from unagi.data.transforms.image.auto_contrast import AutoContrast
from unagi.data.transforms.image.blur import Blur
from unagi.data.transforms.image.brightness import Brightness
from unagi.data.transforms.image.center_crop import CenterCrop
from unagi.data.transforms.image.color import Color
from unagi.data.transforms... | thanos-code-main | unagi/data/transforms/image/__init__.py |
from PIL import ImageOps
from unagi.data.transforms.image.transform import UnagiTransform
class Equalize(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
def transform(self, pil_img, label, **kwargs):
return ImageOps.equalize(pil_img), la... | thanos-code-main | unagi/data/transforms/image/equalize.py |
from torchvision import transforms as transforms
from unagi.data.transforms.image.transform import UnagiTransform
class ColorJitter(UnagiTransform):
def __init__(
self,
brightness=0.0,
contrast=0.0,
saturation=0.0,
hue=0.0,
name=None,
prob=1.0,
leve... | thanos-code-main | unagi/data/transforms/image/color_jitter.py |
from PIL import ImageFilter
from unagi.data.transforms.image.transform import UnagiTransform
class Blur(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
def transform(self, pil_img, label, **kwargs):
return pil_img.filter(ImageFilter.BLUR... | thanos-code-main | unagi/data/transforms/image/blur.py |
from torchvision import transforms as transforms
from unagi.data.transforms.image.transform import UnagiTransform
class Resize(UnagiTransform):
def __init__(
self,
size,
name=None,
prob=1.0,
level=0,
interpolation=transforms.InterpolationMode.BILINEAR,
):
... | thanos-code-main | unagi/data/transforms/image/resize.py |
import random
from PIL import Image
from unagi.data.transforms.image.transform import UnagiTransform
from unagi.data.transforms.image.utils import categorize_value
class TranslateX(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0, max_degree=10):
self.max_degree = max_degree
self... | thanos-code-main | unagi/data/transforms/image/translate_x.py |
def categorize_value(level, value_range, type="int"):
val = value_range[0] + level * (value_range[1] - value_range[0])
return int(val) if type == "int" else float(val)
| thanos-code-main | unagi/data/transforms/image/utils.py |
import random
class UnagiTransform(object):
"""Base UnagiTransform transfrom class.
Args:
name(str): Transformation name.
prob(float): Transformation probability.
level(int): Transformation level.
"""
def __init__(self, name=None, prob=1.0, level=0):
self.name = name if nam... | thanos-code-main | unagi/data/transforms/image/transform.py |
import random
from PIL import Image
from unagi.data.transforms.image.transform import UnagiTransform
from unagi.data.transforms.image.utils import categorize_value
class TranslateY(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0, max_degree=10):
self.max_degree = max_degree
self... | thanos-code-main | unagi/data/transforms/image/translate_y.py |
from PIL import ImageFilter
from unagi.data.transforms.image.transform import UnagiTransform
class Smooth(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
def transform(self, pil_img, label, **kwargs):
return pil_img.filter(ImageFilter.SM... | thanos-code-main | unagi/data/transforms/image/smooth.py |
from PIL import ImageOps
from unagi.data.transforms.image.transform import UnagiTransform
from unagi.data.transforms.image.utils import categorize_value
class Posterize(UnagiTransform):
value_range = (0, 4)
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
d... | thanos-code-main | unagi/data/transforms/image/posterize.py |
from PIL import ImageEnhance
from unagi.data.transforms.image.transform import UnagiTransform
from unagi.data.transforms.image.utils import categorize_value
class Sharpness(UnagiTransform):
value_range = (0.1, 1.9)
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level... | thanos-code-main | unagi/data/transforms/image/sharpness.py |
from PIL import ImageOps
from unagi.data.transforms.image.transform import UnagiTransform
class Invert(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
def transform(self, pil_img, label, **kwargs):
return ImageOps.invert(pil_img), label
| thanos-code-main | unagi/data/transforms/image/invert.py |
from PIL import Image, ImageOps
from torchvision import transforms as transforms
from unagi.data.transforms.image.transform import UnagiTransform
class ResizeAndPad(UnagiTransform):
def __init__(
self,
resized_width,
resized_height,
name=None,
prob=1.0,
level=0,
... | thanos-code-main | unagi/data/transforms/image/resize_and_pad.py |
from PIL import ImageEnhance
from unagi.data.transforms.image.transform import UnagiTransform
from unagi.data.transforms.image.utils import categorize_value
class Brightness(UnagiTransform):
value_range = (0.1, 1.9)
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, leve... | thanos-code-main | unagi/data/transforms/image/brightness.py |
import random
from PIL import Image
from unagi.data.transforms.image.transform import UnagiTransform
from unagi.data.transforms.image.utils import categorize_value
class ShearY(UnagiTransform):
value_range = (0.0, 0.3)
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, ... | thanos-code-main | unagi/data/transforms/image/shear_y.py |
import random
from unagi.data.transforms.image.transform import UnagiTransform
from unagi.data.transforms.image.utils import categorize_value
class Rotate(UnagiTransform):
value_range = (0, 30)
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
def transform(... | thanos-code-main | unagi/data/transforms/image/rotate.py |
from unagi.data.transforms.image.transform import UnagiTransform
class Identity(UnagiTransform):
def __init__(self, name=None, prob=1.0, level=0):
super().__init__(name, prob, level)
def transform(self, pil_img, label, **kwargs):
return pil_img, label
| thanos-code-main | unagi/data/transforms/image/identity.py |
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