repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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OpenGlue | OpenGlue-main/utils/losses.py | import numpy as np
import torch
from .misc import pairwise_cosine_dist
def criterion(y_true, y_pred, margin=None):
gt_matches0, gt_matches1 = y_true['gt_matches0'], y_true['gt_matches1']
gdesc0, gdesc1, scores = y_pred['context_descriptors0'], y_pred['context_descriptors1'], y_pred['scores']
if margin is... | 3,935 | 40.87234 | 112 | py |
OpenGlue | OpenGlue-main/utils/augmentations.py | import kornia.augmentation as KA
def get_augmentation_transform(config):
method_name = config['name']
allowed_methods = ['none', 'weak_color_aug']
if method_name not in allowed_methods:
raise NameError('{} module was not found among local descriptors. Please choose one of the following '
... | 903 | 33.769231 | 110 | py |
OpenGlue | OpenGlue-main/utils/misc.py | import torch
import torch.nn.functional as F
def normalize_with_intrinsics(kpts: torch.tensor, K: torch.tensor):
kpts0_calibrated = (kpts - K[:2, 2].unsqueeze(0)) / K[[0, 1], [0, 1]].unsqueeze(0)
return kpts0_calibrated
def data_to_device(data, device):
"""Recursively transfers all tensors in dictionary... | 4,513 | 37.254237 | 108 | py |
OpenGlue | OpenGlue-main/utils/train_utils.py | from datetime import datetime
import os
import shutil
from omegaconf import OmegaConf
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks.lr_monitor import LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger
from utils.lightning_callbacks impor... | 2,398 | 38.327869 | 117 | py |
OpenGlue | OpenGlue-main/utils/lightning_callbacks.py | import pytorch_lightning as pl
from models.superglue.attention import FavorAttention
class FavorAttentionProjectionRedrawCallback(pl.callbacks.Callback):
def __init__(self, redraw_every_n_steps=100):
self.redraw_every_n_steps = redraw_every_n_steps
def on_train_batch_start(self, trainer: pl.Trainer,... | 597 | 38.866667 | 115 | py |
OpenGlue | OpenGlue-main/utils/metrics.py | import cv2
import kornia
import numpy as np
import torch
import torchmetrics
from .misc import normalize_with_intrinsics
class AccuracyUsingEpipolarDist(torchmetrics.Metric):
def __init__(self, threshold=5e-4):
super(AccuracyUsingEpipolarDist, self).__init__(dist_sync_on_step=True, compute_on_step=False)... | 5,821 | 40 | 110 | py |
OpenGlue | OpenGlue-main/data/oxford_paris_datamodule.py | from typing import Optional, Tuple
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from data.oxford_paris_dataset import OxfordParis1MDataset
class OxfordParis1MDataModule(pl.LightningDataModule):
def __init__(self, root_path, resize_shape: Tuple[int, int], warp_offset: int, batch_size: i... | 1,563 | 29.666667 | 118 | py |
OpenGlue | OpenGlue-main/data/megadepth_datamodule.py | from typing import Optional
import pytorch_lightning as pl
import torch
from functools import partial
from torch.utils.data import DataLoader
from data.megadepth_dataset import MegaDepthPairsDataset, MegaDepthPairsDatasetFeatures
from data.megadepth_balanced_sampler import MegaDepthBalancedSampler
class BaseMegaDep... | 9,367 | 44.475728 | 120 | py |
OpenGlue | OpenGlue-main/data/megadepth_balanced_sampler.py | import torch
import torch.distributed as dist
class MegaDepthBalancedSampler(torch.utils.data.distributed.DistributedSampler):
"""Derive from DistributedSampler for compatability with PytorchLightning.
Can sample the same indices at each process"""
def __init__(self, dataset, seed: int = 0):
self.... | 1,396 | 34.820513 | 106 | py |
OpenGlue | OpenGlue-main/data/megadepth_dataset.py | import glob
from collections import OrderedDict
from itertools import chain
from pathlib import Path
import cv2
import deepdish as dd
import numpy as np
import torch
def array_to_tensor(img_array):
return torch.FloatTensor(img_array / 255.).unsqueeze(0)
class MegaDepthWarpingDataset(torch.utils.data.Dataset):
... | 13,239 | 43.42953 | 120 | py |
OpenGlue | OpenGlue-main/data/oxford_paris_dataset.py | import glob
import pathlib
from typing import Tuple
import albumentations as A
import cv2
import numpy as np
import torch
class OxfordParis1MDataset(torch.utils.data.Dataset):
def __init__(self, root_path: pathlib.Path, resize_shape: Tuple[int, int], offset: int):
self.root_path = root_path
self.... | 3,450 | 35.712766 | 113 | py |
image_harmonization | image_harmonization-master/train.py | import argparse
import importlib.util
import torch
from iharm.utils.exp import init_experiment
def main():
args = parse_args()
model_script = load_module(args.model_path)
cfg = init_experiment(args)
torch.backends.cudnn.benchmark = True
torch.multiprocessing.set_sharing_strategy('file_system')
... | 2,652 | 36.9 | 120 | py |
image_harmonization | image_harmonization-master/models/crop512/hrnet18_ssam.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, PadIfNeeded, RandomCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose, Lon... | 4,324 | 33.325397 | 106 | py |
image_harmonization | image_harmonization-master/models/crop512/hrnet18_sedih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, PadIfNeeded, RandomCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose, Lon... | 4,323 | 33.31746 | 106 | py |
image_harmonization | image_harmonization-master/models/crop512/improved_dih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, PadIfNeeded, RandomCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose, Lon... | 4,187 | 32.504 | 106 | py |
image_harmonization | image_harmonization-master/models/crop512/hrnet18_idih_no_mask.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, PadIfNeeded, RandomCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose, Lon... | 4,388 | 33.023256 | 110 | py |
image_harmonization | image_harmonization-master/models/crop512/dih.py | import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, PadIfNeeded, RandomCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
from iharm.engine.simple_trainer im... | 3,835 | 31.235294 | 106 | py |
image_harmonization | image_harmonization-master/models/crop512/hrnet18_idih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, PadIfNeeded, RandomCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose, Lon... | 4,327 | 33.349206 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/hrnet18_issam.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,130 | 32.314516 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/ssam.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,009 | 31.08 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/hrnet18s_issam.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,123 | 32.258065 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/iseunet_v1.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,015 | 31.128 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/hrnet18_v2p_idih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,151 | 32.483871 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/hrnet18s_sedih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,124 | 32.266129 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/improved_ssam.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,103 | 31.0625 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/improved_dih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 3,981 | 31.909091 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/hrnet32_idih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,139 | 32.387097 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/hrnet18s_no_mask_idih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,096 | 32.308943 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/hrnet18s_idih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,122 | 32.25 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/hrnet18_idih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,129 | 32.306452 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/improved_sedih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 3,996 | 32.033058 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/hrnet18s_v2p_idih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,144 | 32.427419 | 106 | py |
image_harmonization | image_harmonization-master/models/fixed256/deeplab_idih.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose
fr... | 4,078 | 31.895161 | 106 | py |
image_harmonization | image_harmonization-master/scripts/evaluate_model_fg_ratios.py | import argparse
import sys
sys.path.insert(0, '.')
import torch
from pathlib import Path
from tqdm import trange
from albumentations import Resize, NoOp
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose, LongestMaxSizeIfLarger
from iharm.inference.predictor import Predictor
from iha... | 5,213 | 42.45 | 116 | py |
image_harmonization | image_harmonization-master/scripts/predict_for_dir.py | import argparse
import os
import os.path as osp
from pathlib import Path
import sys
import cv2
import numpy as np
import torch
from tqdm import tqdm
sys.path.insert(0, '.')
from iharm.inference.predictor import Predictor
from iharm.inference.utils import load_model, find_checkpoint
from iharm.mconfigs import ALL_MCON... | 3,768 | 34.556604 | 119 | py |
image_harmonization | image_harmonization-master/scripts/evaluate_model.py | import argparse
import sys
sys.path.insert(0, '.')
import torch
from pathlib import Path
from albumentations import Resize, NoOp
from iharm.data.hdataset import HDataset
from iharm.data.transforms import HCompose, LongestMaxSizeIfLarger
from iharm.inference.predictor import Predictor
from iharm.inference.evaluation i... | 3,598 | 37.698925 | 108 | py |
image_harmonization | image_harmonization-master/iharm/engine/simple_trainer.py | import os
import logging
from copy import deepcopy
from collections import defaultdict
import cv2
import torch
import numpy as np
from tqdm import tqdm
from torch.utils.data import DataLoader
from torchvision.transforms import Normalize
from iharm.utils.log import logger, TqdmToLogger, SummaryWriterAvg
from iharm.uti... | 11,897 | 40.601399 | 119 | py |
image_harmonization | image_harmonization-master/iharm/engine/optimizer.py | import torch
import math
from iharm.utils.log import logger
def get_optimizer(model, opt_name, opt_kwargs):
params = []
base_lr = opt_kwargs['lr']
for name, param in model.named_parameters():
param_group = {'params': [param]}
if not param.requires_grad:
params.append(param_grou... | 797 | 27.5 | 82 | py |
image_harmonization | image_harmonization-master/iharm/utils/exp.py | import os
import sys
import shutil
import pprint
from pathlib import Path
from datetime import datetime
import yaml
import torch
from easydict import EasyDict as edict
from .log import logger, add_new_file_output_to_logger
def init_experiment(args):
model_path = Path(args.model_path)
ftree = get_model_famil... | 4,632 | 27.95625 | 113 | py |
image_harmonization | image_harmonization-master/iharm/utils/misc.py | import torch
from .log import logger
def get_dims_with_exclusion(dim, exclude=None):
dims = list(range(dim))
if exclude is not None:
dims.remove(exclude)
return dims
def save_checkpoint(net, checkpoints_path, epoch=None, prefix='', verbose=True, multi_gpu=False):
if epoch is None:
... | 1,192 | 27.404762 | 97 | py |
image_harmonization | image_harmonization-master/iharm/utils/log.py | import io
import time
import logging
from datetime import datetime
import numpy as np
from torch.utils.tensorboard import SummaryWriter
LOGGER_NAME = 'root'
LOGGER_DATEFMT = '%Y-%m-%d %H:%M:%S'
handler = logging.StreamHandler()
logger = logging.getLogger(LOGGER_NAME)
logger.setLevel(logging.INFO)
logger.addHandler(... | 2,809 | 27.1 | 89 | py |
image_harmonization | image_harmonization-master/iharm/data/base.py | import random
import numpy as np
import torch
class BaseHDataset(torch.utils.data.dataset.Dataset):
def __init__(self,
augmentator=None,
input_transform=None,
keep_background_prob=0.0,
with_image_info=False,
epoch_len=-1):
... | 2,768 | 31.964286 | 92 | py |
image_harmonization | image_harmonization-master/iharm/model/losses.py | import torch
import torch.nn as nn
from iharm.utils import misc
class Loss(nn.Module):
def __init__(self, pred_outputs, gt_outputs):
super().__init__()
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
class MSE(Loss):
def __init__(self, pred_name='images', gt_image_name... | 1,347 | 32.7 | 99 | py |
image_harmonization | image_harmonization-master/iharm/model/metrics.py | import torch
import torch.nn.functional as F
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs, epsilon=1e-6):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
self.epsilon = epsilon
self._last_batch_metric = 0.0
self._epoch_metric_sum = 0.... | 3,379 | 32.8 | 95 | py |
image_harmonization | image_harmonization-master/iharm/model/ops.py | import torch
from torch import nn as nn
class SimpleInputFusion(nn.Module):
def __init__(self, add_ch=1, rgb_ch=3, ch=8, norm_layer=nn.BatchNorm2d):
super(SimpleInputFusion, self).__init__()
self.fusion_conv = nn.Sequential(
nn.Conv2d(in_channels=add_ch + rgb_ch, out_channels=ch, kern... | 4,695 | 32.784173 | 101 | py |
image_harmonization | image_harmonization-master/iharm/model/initializer.py | import torch
import torch.nn as nn
import numpy as np
class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, ... | 3,408 | 31.160377 | 98 | py |
image_harmonization | image_harmonization-master/iharm/model/syncbn/modules/nn/syncbn.py | """
/*****************************************************************************/
BatchNorm2dSync with multi-gpu
/*****************************************************************************/
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
try:
... | 5,187 | 33.818792 | 79 | py |
image_harmonization | image_harmonization-master/iharm/model/syncbn/modules/functional/syncbn.py | """
/*****************************************************************************/
BatchNorm2dSync with multi-gpu
code referenced from : https://github.com/mapillary/inplace_abn
/*****************************************************************************/
"""
from __future__ import absolute_import
from __future__... | 5,291 | 37.347826 | 79 | py |
image_harmonization | image_harmonization-master/iharm/model/syncbn/modules/functional/_csrc.py | """
/*****************************************************************************/
Extension module loader
code referenced from : https://github.com/facebookresearch/maskrcnn-benchmark
/*****************************************************************************/
"""
from __future__ import absolute_import
from __f... | 1,586 | 27.854545 | 79 | py |
image_harmonization | image_harmonization-master/iharm/model/backboned/hrnet.py | import torch.nn as nn
from iharm.model.modeling.hrnet_ocr import HighResolutionNet
from iharm.model.backboned.ih_model import IHModelWithBackbone
from iharm.model.modifiers import LRMult
from iharm.model.modeling.basic_blocks import MaxPoolDownSize
class HRNetIHModel(IHModelWithBackbone):
def __init__(
s... | 5,787 | 43.523077 | 117 | py |
image_harmonization | image_harmonization-master/iharm/model/backboned/ih_model.py | import torch
import torch.nn as nn
from iharm.model.ops import SimpleInputFusion, ScaleLayer
class IHModelWithBackbone(nn.Module):
def __init__(
self,
model, backbone,
downsize_backbone_input=False,
mask_fusion='sum',
backbone_conv1_channels=64,
):
"""
... | 3,309 | 37.045977 | 118 | py |
image_harmonization | image_harmonization-master/iharm/model/backboned/deeplab.py | from torch import nn as nn
from iharm.model.modeling.deeplab_v3 import DeepLabV3Plus
from iharm.model.backboned.ih_model import IHModelWithBackbone
from iharm.model.modifiers import LRMult
from iharm.model.modeling.basic_blocks import MaxPoolDownSize
class DeepLabIHModel(IHModelWithBackbone):
def __init__(
... | 4,474 | 41.619048 | 117 | py |
image_harmonization | image_harmonization-master/iharm/model/base/dih_model.py | import torch
import torch.nn as nn
from iharm.model.modeling.conv_autoencoder import ConvEncoder, DeconvDecoder
class DeepImageHarmonization(nn.Module):
def __init__(
self,
depth,
norm_layer=nn.BatchNorm2d, batchnorm_from=0,
attend_from=-1,
image_fusion=False,
ch=6... | 1,049 | 32.870968 | 112 | py |
image_harmonization | image_harmonization-master/iharm/model/base/iseunet_v1.py | import torch
import torch.nn as nn
from iharm.model.modeling.unet import UNetEncoder, UNetDecoder
from iharm.model.ops import MaskedChannelAttention
class ISEUNetV1(nn.Module):
def __init__(
self,
depth,
norm_layer=nn.BatchNorm2d, batchnorm_from=2,
attend_from=3,
image_fus... | 1,191 | 30.368421 | 66 | py |
image_harmonization | image_harmonization-master/iharm/model/base/ssam_model.py | import torch
from functools import partial
from torch import nn as nn
from iharm.model.modeling.basic_blocks import ConvBlock, GaussianSmoothing
from iharm.model.modeling.unet import UNetEncoder, UNetDecoder
from iharm.model.ops import ChannelAttention
class SSAMImageHarmonization(nn.Module):
def __init__(
... | 2,771 | 35 | 86 | py |
image_harmonization | image_harmonization-master/iharm/model/modeling/basic_blocks.py | import math
import numbers
import torch
import torch.nn.functional as F
from torch import nn as nn
class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init... | 6,923 | 36.225806 | 117 | py |
image_harmonization | image_harmonization-master/iharm/model/modeling/deeplab_v3.py | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from iharm.model.modeling.basic_blocks import select_activation_function
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='re... | 6,392 | 35.118644 | 103 | py |
image_harmonization | image_harmonization-master/iharm/model/modeling/resnet.py | import torch
from .resnetv1b import resnet34_v1b, resnet50_v1s, resnet101_v1s, resnet152_v1s
class ResNetBackbone(torch.nn.Module):
def __init__(self, backbone='resnet50', pretrained_base=True, dilated=True, **kwargs):
super(ResNetBackbone, self).__init__()
if backbone == 'resnet34':
... | 1,552 | 35.97619 | 93 | py |
image_harmonization | image_harmonization-master/iharm/model/modeling/hrnet_ocr.py | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._utils
from .ocr import SpatialOCR_Module, SpatialGather_Module
from .resnetv1b import BasicBlockV1b, BottleneckV1b
from iharm.utils.log import logger
relu_inplace = True
class HighResolutionModule(nn.Module... | 17,393 | 42.376559 | 111 | py |
image_harmonization | image_harmonization-master/iharm/model/modeling/ocr.py | import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
... | 5,740 | 39.429577 | 100 | py |
image_harmonization | image_harmonization-master/iharm/model/modeling/conv_autoencoder.py | import torch
from torch import nn as nn
from iharm.model.modeling.basic_blocks import ConvBlock
from iharm.model.ops import MaskedChannelAttention, FeaturesConnector
class ConvEncoder(nn.Module):
def __init__(
self,
depth, ch,
norm_layer, batchnorm_from, max_channels,
backbone_fro... | 4,940 | 37.601563 | 120 | py |
image_harmonization | image_harmonization-master/iharm/model/modeling/unet.py | import torch
from torch import nn as nn
from functools import partial
from iharm.model.modeling.basic_blocks import ConvBlock
from iharm.model.ops import FeaturesConnector
class UNetEncoder(nn.Module):
def __init__(
self,
depth, ch,
norm_layer, batchnorm_from, max_channels,
backbo... | 7,240 | 38.140541 | 110 | py |
image_harmonization | image_harmonization-master/iharm/model/modeling/resnetv1b.py | import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = 'rwightman/pytorch-pretrained-gluonresnet'
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(Basi... | 10,805 | 38.01083 | 112 | py |
image_harmonization | image_harmonization-master/iharm/inference/predictor.py | import torch
from iharm.inference.transforms import NormalizeTensor, PadToDivisor, ToTensor, AddFlippedTensor
class Predictor(object):
def __init__(self, net, device, with_flip=False,
mean=(.485, .456, .406), std=(.229, .224, .225)):
self.device = device
self.net = net.to(self.dev... | 1,432 | 32.325581 | 96 | py |
image_harmonization | image_harmonization-master/iharm/inference/evaluation.py | from time import time
from tqdm import trange
import torch
def evaluate_dataset(dataset, predictor, metrics_hub):
for sample_i in trange(len(dataset), desc=f'Testing on {metrics_hub.name}'):
sample = dataset.get_sample(sample_i)
sample = dataset.augment_sample(sample)
sample_mask = sample... | 841 | 39.095238 | 104 | py |
image_harmonization | image_harmonization-master/iharm/inference/transforms.py | import cv2
import torch
from collections import namedtuple
class EvalTransform:
def __init__(self):
pass
def transform(self, image, mask):
raise NotImplementedError
def inv_transform(self, image):
raise NotImplementedError
class PadToDivisor(EvalTransform):
"""
Pad side... | 3,462 | 31.980952 | 114 | py |
ACDC | ACDC-main/main.py | """
File for main compression experiment
Main usage: invoke from bash script in run/ folder
accompanied by the corresponding config from config/ folder
TODO:
* add a controller for using butterfly convs as 1x1 convs.
"""
import logging
import argparse
import time
import os
from policies import Manager
from utils.pars... | 10,499 | 49.239234 | 147 | py |
ACDC | ACDC-main/policies/recyclers.py | """
Implementations for reintroducing policies for weight/kernels
"""
import numpy as np
from policies.policy import PolicyBase
from utils import (get_total_sparsity,
recompute_bn_stats,
percentile,
is_wrapped_layer,
get_normal_stats)
imp... | 13,802 | 42.134375 | 132 | py |
ACDC | ACDC-main/policies/pruners.py | """
Implement Pruners here.
"""
import numpy as np
from policies.policy import PolicyBase
from utils import (get_total_sparsity,
recompute_bn_stats,
percentile,
get_prunable_children)
import torch
import torch.optim as optim
from torch.utils.data.sampler i... | 27,490 | 43.555916 | 136 | py |
ACDC | ACDC-main/policies/trainers.py | """
This module implements training policies.
For most usecases, only one trainer instance is needed for training and pruning
with a single model. Several trainers can be used for training with knowledge distillation.
"""
import numpy as np
import torch
import torch.nn as nn
from optimization.sgd import SGD
# from tor... | 7,055 | 37.347826 | 98 | py |
ACDC | ACDC-main/policies/freezers.py | """
Implement weight freezing policies here
WARNING: We currently do not support using both freezers
and pruners in one run to simplify the code logics
TODO: THINK OF BETTER REALIZATION USE THIS OR CUSTOM OPTIMIZERS?!
THE CODE BELOW IS PROTOTYPE!!!
"""
import torch
import torch.nn as nn
from policies.policy import ... | 4,255 | 31.48855 | 84 | py |
ACDC | ACDC-main/policies/policy.py | """
This is a base class for:
* Pruners
* Optimizers
Each type subclasses PolicyBase by another unifying class
and has a separate config_reader.
TODO: think! is this an extensible decision?
"""
import torch
class PolicyBase(object):
def __init__(self, *args, **kwargs):
pass
def on_epoch_begin(self,... | 829 | 19.243902 | 61 | py |
ACDC | ACDC-main/policies/regularizers.py | """
Implement regularization policies here
"""
import torch
import torch.nn as nn
from policies.policy import PolicyBase
import logging
def build_reg_from_config(model, reg_config):
"""
This function build regularizer given the model (only need for weigths typically)
and regularizer configuration.
""... | 3,633 | 32.33945 | 93 | py |
ACDC | ACDC-main/policies/manager.py | """
Managing class for different Policies.
"""
from models import get_model
from policies.pruners import build_pruners_from_config
from policies.trainers import build_training_policy_from_config
from policies.recyclers import build_recyclers_from_config
from policies.regularizers import build_regs_from_config
from u... | 27,840 | 48.276106 | 165 | py |
ACDC | ACDC-main/models/resnet_imagenet.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/r... | 7,366 | 30.216102 | 78 | py |
ACDC | ACDC-main/models/resnet_mixed_imagenet.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
from models.layers import Conv2dMixedSize, drop_connect, SELayer
__all__ = ['resnet50_mixed']
KERNEL_SIZES = [3,5,7,9]
def mixed_conv(in_planes, out_planes, stride=1):
return Conv2dMixedSize(in_planes, out_planes, KERNEL_SIZES, stride... | 6,809 | 30.82243 | 131 | py |
ACDC | ACDC-main/models/wide_resnet_cifar.py | """
Code credit: https://github.com/meliketoy/wide-resnet.pytorch/blob/master/networks/wide_resnet.py
"""
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
import sys
import numpy as np
def conv3x3(in_planes, out_planes, stride=1):
... | 3,185 | 33.258065 | 98 | py |
ACDC | ACDC-main/models/resnet_mixed_imagenet_static.py | import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import math
from models.layers import drop_connect, SELayer, Conv2dMixedSizeStatic
__all__ = ['resnet50_mixed_static' , 'get_resnet50_mixed_onnx_version']
PADS = [(2, 2),(4, 4),(6, 6),(8, 8),(2, 2),(4, 4),(6, 6),(8, 8),(2, 2),(4, 4),(6, ... | 6,637 | 32.695431 | 131 | py |
ACDC | ACDC-main/models/resnet_cifar10_swish.py | import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['resnet20_sw', 'resnet32_sw', 'resnet44_sw', 'resnet56_sw']
NUM_CLASSES = 10
def swish(x):
return torch.sigmoid(x) * x
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
retur... | 4,326 | 31.291045 | 96 | py |
ACDC | ACDC-main/models/efficientnet.py | """
EfficientNet model
To use an original implementation, install from here:
https://github.com/lukemelas/EfficientNet-PyTorch
as `pip install efficientnet_pytorch`
"""
import re
import logging
import collections
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import model_zoo
... | 14,670 | 39.194521 | 111 | py |
ACDC | ACDC-main/models/utils.py | """
Utilities for models
"""
import torch
import torch.nn as nn
from models.blocks import get_2d_conv, round_to_power
from models.efficientnet import construct_effnet_for_dataset
def get_num_params(module):
""" Compute number of parameters """
s = 0
for parameter in module.parameters():
s += para... | 3,797 | 30.38843 | 82 | py |
ACDC | ACDC-main/models/wide_resnet_imagenet.py | import torch
import torch.nn as nn
from models.layers import Conv2dMixedSize, SELayer
__all__ = ['wide_resnet50_2_mixed']
KERNEL_SIZES = [3, 5, 7]
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, str... | 6,762 | 34.973404 | 99 | py |
ACDC | ACDC-main/models/resnet_cifar10.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['resnet20', 'resnet32', 'resnet44', 'resnet56']
NUM_CLASSES = 10
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
... | 4,272 | 32.124031 | 96 | py |
ACDC | ACDC-main/models/logistic_regression.py | import torch.nn as nn
class LogisticRegression(nn.Module):
def __init__(self):
super(LogisticRegression, self).__init__()
self.fc = nn.Linear(5,3)
def forward(self, input_tensor):
return self.fc(input_tensor) | 243 | 23.4 | 50 | py |
ACDC | ACDC-main/models/__init__.py | """
Example models to train and prune.
Interface is provided by the get_model function.
"""
import torch
from models.resnet_imagenet import *
from models.resnet_cifar10 import *
from models.resnet_cifar10_swish import *
from models.efficientnet import *
from models.simplenet import *
from models.logistic_regression im... | 4,525 | 40.522936 | 143 | py |
ACDC | ACDC-main/models/butterfly.py | """
Butterfly convolution implementation.
TODO:
* The asserts here assume that the output channel sizes are 2^k.
Check if it is necessary.
"""
from typing import Callable
import math
import numpy as np
import torch
from torch import Tensor
from torch.nn import (
init, Module, Sequential, Conv2d, BatchNorm2d, R... | 5,166 | 35.64539 | 130 | py |
ACDC | ACDC-main/models/simplenet.py | """
Just a mock net for testing.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.blocks import get_2d_conv
class SimpleNet(nn.Module):
def __init__(self, dataset, use_butterfly):
super().__init__()
self.conv1 = get_2d_conv(1, 2**5, 3, 1, use_butterfly=use_butterf... | 1,868 | 27.318182 | 94 | py |
ACDC | ACDC-main/models/resnet_mixed_cifar10.py | """
Implementation of ResNets for cifar with regular convs replaced
by mixed kernel size convolutions (with dynamic same padding)
"""
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
from models.layers import Conv2dMixedSize, drop_connect, SELayer
__all__ = ['resnet20_mixed']
NUM_CLASSES ... | 7,451 | 32.12 | 139 | py |
ACDC | ACDC-main/models/mobilenet.py | #
# Copyright (c) 2018 Intel Corporation
#
# 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... | 3,117 | 35.255814 | 112 | py |
ACDC | ACDC-main/models/blocks.py | """
Building blocks for constructing custom PyTorch models.
In particular, an efficient pointwise convolution
throught Butterfly transform is implemented in this module.
"""
from typing import Callable
import re
import math
import collections
from functools import partial
import logging
import torch
from torch impor... | 10,313 | 39.289063 | 112 | py |
ACDC | ACDC-main/models/layers/mixed_conv.py | """
File implementing mixed convolution operations
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy
class _Conv2dSamePadding(nn.Conv2d):
""" Class implementing 2d adaptively padded convolutions """
def __init__(self, in_channels, out_channels, kernel_size,
... | 3,771 | 38.291667 | 95 | py |
ACDC | ACDC-main/models/layers/mixed_conv_static.py | """
File implementing static mixed convolution operations
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy
from copy import deepcopy
class _Conv2dSamePaddingStatic(nn.Conv2d):
""" Class implementing 2d adaptively padded convolutions """
def __init__(self, in_c... | 3,285 | 35.10989 | 95 | py |
ACDC | ACDC-main/models/layers/squeeze_exitation_layer.py | """
Impementation of squeeze and excitation operation
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.layers.mixed_conv import _Conv2dSamePadding
from models.layers.utils import swish, composite_swish
class SELayer(nn.Module):
"""
Class impements the squeeze and excitation... | 981 | 34.071429 | 83 | py |
ACDC | ACDC-main/models/layers/utils.py | """
Implementing utility functional for layers
"""
import torch
def drop_connect(inputs, drop_rate, training):
if not training: return inputs
batch_size = inputs.size(0)
keep_rates = (1 - drop_rate) * torch.ones(batch_size)[:,None,None,None]
normalized_mask = torch.bernoulli(keep_rates) / keep_rates
... | 599 | 25.086957 | 75 | py |
ACDC | ACDC-main/optimization/gradual_norm_reduction_pruner.py | import torch
from torch.optim import Optimizer
from utils import percentile
__all__ = ['_preprocess_params_for_pruner_optim', 'GradualNormPrunerSGD']
DEFAULTS = ['conv', 'fc', 'bn', 'downsample.']
def _preprocess_params_for_pruner_optim(model, modules):
prefix = ''
if isinstance(model, torch.nn.DataParall... | 6,704 | 39.149701 | 91 | py |
ACDC | ACDC-main/optimization/sgd.py | """
Exactly like the official SGD optimizer but lets you zero out momentum.
"""
import torch
from torch.optim import Optimizer
class SGD(Optimizer):
r"""Implements stochastic gradient descent (optionally with momentum).
Nesterov momentum is based on the formula from
`On the importance of initialization a... | 4,951 | 38.935484 | 88 | py |
ACDC | ACDC-main/optimization/lr_schedulers.py | import numpy as np
from torch.optim.lr_scheduler import _LRScheduler
class StageExponentialLR(_LRScheduler):
def __init__(self, optimizer, init_gamma, final_gamma, freq_reset, last_epoch=-1):
self.init_gamma = init_gamma
self.final_gamma = final_gamma
self.freq_reset = freq_reset
s... | 2,309 | 32.478261 | 97 | py |
ACDC | ACDC-main/utils/approximation.py | """
This .py contains various approximation schemes for pruning
differences. We assume that only weights are sparsified
"""
import torch
import torch.nn.functional as F
from utils import percentile
from copy import deepcopy
def magnitude(delta):
raise NotImplementedError
def diag_hess(delta):
raise NotImple... | 2,724 | 27.989362 | 95 | py |
ACDC | ACDC-main/utils/checkpoints.py | import os
import errno
import torch
import shutil
import logging
import inspect
from models import get_model
from utils.utils import normalize_module_name, _fix_double_wrap_dict
from utils.masking_utils import (is_wrapped_layer,
WrappedLayer,
get_wrap... | 8,877 | 43.39 | 154 | py |
ACDC | ACDC-main/utils/utils.py | """
Miscellaneous utilities
"""
import torch
import functools
import inspect
import warnings
import logging
import wandb
from math import ceil
from utils.masking_utils import is_wrapped_layer
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
def _fix_double_wrap_dict(chkpt):
return {
... | 11,661 | 36.986971 | 134 | py |
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