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
value |
|---|---|---|---|---|---|---|
DIVA-DAF | DIVA-DAF-main/src/datamodules/SSLTiles/datamodule_prebuilt.py | from pathlib import Path
from typing import Union, List, Optional
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import ImageFolder
from src.datamodules.SSLTiles.datasets.dataset import DatasetSSLTiles
from src.datamodules.SSLTiles.utils.image_analytics import get... | 4,664 | 39.921053 | 110 | py |
DIVA-DAF | DIVA-DAF-main/src/datamodules/SSLTiles/datasets/dataset.py | from pathlib import Path
from typing import Optional, Union, List, Tuple
import numpy as np
from PIL import Image
from omegaconf import ListConfig
from torch import Tensor
from torchvision.datasets.folder import pil_loader, has_file_allowed_extension
from torchvision.transforms import ToTensor, ToPILImage
from src.d... | 5,959 | 39.821918 | 118 | py |
DIVA-DAF | DIVA-DAF-main/src/datamodules/utils/twin_transforms.py | import random
from torchvision.transforms import functional as F
class TwinCompose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img, gt):
for t in self.transforms:
img, gt = t(img, gt)
return img, gt
class TwinRandomCrop(objec... | 2,368 | 28.246914 | 103 | py |
DIVA-DAF | DIVA-DAF-main/src/datamodules/utils/dataset_predict.py | from glob import glob
from pathlib import Path
from typing import List
import torch.utils.data as data
from torch import is_tensor
from torchvision.datasets.folder import pil_loader
from torchvision.transforms import ToTensor
from src.datamodules.utils.misc import ImageDimensions, get_output_file_list
from src.utils ... | 3,133 | 30.656566 | 109 | py |
DIVA-DAF | DIVA-DAF-main/src/datamodules/utils/functional.py | import torch
def argmax_onehot(tensor: torch.Tensor):
return torch.LongTensor(torch.argmax(tensor, dim=0)) | 112 | 21.6 | 56 | py |
DIVA-DAF | DIVA-DAF-main/src/datamodules/utils/misc.py | from dataclasses import dataclass
from pathlib import Path
from typing import Union, List
import numpy as np
import torch
from PIL import Image
from omegaconf import ListConfig
from src.datamodules.utils.exceptions import PathNone, PathNotDir, PathMissingSplitDir, PathMissingDirinSplitDir
from src.utils import utils
... | 6,264 | 33.61326 | 120 | py |
DIVA-DAF | DIVA-DAF-main/src/datamodules/utils/wrapper_transforms.py | from typing import Callable
class OnlyImage(object):
"""Wrapper function around a single parameter transform. It will be cast only on image"""
def __init__(self, transform: Callable):
"""Initialize the transformation with the transformation to be called.
Could be a compose.
Parameter... | 1,097 | 28.675676 | 94 | py |
DIVA-DAF | DIVA-DAF-main/src/datamodules/DivaHisDB/datamodule_cropped.py | from pathlib import Path
from typing import Union, List, Optional
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from src.datamodules.DivaHisDB.utils.single_transform import IntegerEncoding
from src.datamodules.base_datamodule import AbstractDatamodule
from src.datamodules.Div... | 7,683 | 50.226667 | 120 | py |
DIVA-DAF | DIVA-DAF-main/src/datamodules/DivaHisDB/datasets/cropped_dataset.py | """
Load a dataset of historic documents by specifying the folder where its located.
"""
# Utils
import re
from pathlib import Path
from typing import List, Tuple, Union, Optional
import torch.utils.data as data
from omegaconf import ListConfig
from torch import is_tensor
from torchvision.datasets.folder import pil_l... | 7,703 | 35.339623 | 118 | py |
DIVA-DAF | DIVA-DAF-main/src/datamodules/DivaHisDB/utils/image_analytics.py | # Utils
import errno
import json
import logging
import os
from pathlib import Path
import numpy as np
# Torch related stuff
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from PIL import Image
from src.datamodules.utils.image_analytics import compute_mean_std
def ge... | 6,824 | 36.295082 | 119 | py |
DIVA-DAF | DIVA-DAF-main/src/datamodules/DivaHisDB/utils/functional.py | from typing import List
import numpy as np
import torch
from sklearn.preprocessing import OneHotEncoder
def gt_to_int_encoding(matrix: torch.Tensor, class_encodings: List[int]):
matrix = (matrix * 255)
# take only blue channel
img_blue = matrix[2, :, :]
# change border pixels to background
bor... | 2,237 | 32.909091 | 108 | py |
DIVA-DAF | DIVA-DAF-main/src/callbacks/model_callbacks.py | import logging
import os
import sys
import traceback
from typing import Optional, OrderedDict
import pytorch_lightning as pl
import torch
from pytorch_lightning import Callback
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from pytorch_lightning.utilitie... | 4,475 | 42.882353 | 115 | py |
DIVA-DAF | DIVA-DAF-main/src/callbacks/wandb_callbacks.py | from pytorch_lightning import Callback, Trainer
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.utilities import rank_zero_only
from src.utils import utils
def get_wandb_logger(trainer: Trainer) -> WandbLogger:
if isinstance(trainer.logger, WandbLogger):
return trainer.logger
... | 1,225 | 33.055556 | 101 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbone_header_model.py | from typing import Union, Optional, OrderedDict
import pytorch_lightning as pl
import torch.nn
from torchvision.models._utils import IntermediateLayerGetter
class BackboneHeaderModel(pl.LightningModule):
"""A generic model class to provide the possibility to create different backbone/header combinations"""
... | 1,087 | 35.266667 | 114 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/deeplabv3_resnet.py | # Adapted from https://github.com/fregu856/deeplabv3
# NOTE! OS: output stride, the ratio of input image resolution to final output resolution (OS16: output size is (img_h/16, img_w/16)) (OS8: output size is (img_h/8, img_w/8))
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.models.backbon... | 8,284 | 37.534884 | 174 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/resnet.py | """
Model definition adapted from: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import math
from typing import Optional, List, Union, Type
import torch.nn as nn
from torchvision.models.resnet import Bottleneck, BasicBlock
model_urls = {
'resnet18': 'https://download.pytorch.org/m... | 4,433 | 37.224138 | 112 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/doc_ufcn.py | import torch
from torch import nn
def dil_block(in_c, out_c):
conv = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3, stride=1, padding=1, dilation=1),
nn.BatchNorm2d(out_c),
nn.ReLU(inplace=True),
nn.Dropout(0.4),
nn.Conv2d(out_c, out_c, kernel_size=3, stride=1, paddin... | 4,565 | 34.123077 | 99 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/baby_cnn.py | """
CNN with 3 conv layers and a fully connected classification layer
"""
import torch.nn as nn
class CNN_basic(nn.Module):
"""
Simple feed forward convolutional neural network
Attributes
----------
expected_input_size : tuple(int,int)
Expected input size (width, height)
conv1 : torch... | 1,757 | 23.760563 | 65 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/backboned_unet.py | import torch
import torch.nn as nn
from torchvision import models
from torch.nn import functional as F
from src.models.backbones.resnet import ResNet50, ResNet18, ResNet34, ResNet152, ResNet101
# The whole class is from https://github.com/mkisantal/backboned-unet/blob/master/backboned_unet/unet.py
def get_backbone(... | 10,342 | 37.593284 | 124 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/resnetdd.py | """
Model definition adapted from: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import logging
import math
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'htt... | 7,306 | 30.632035 | 105 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/unet.py | import torch
from torch import nn
from torch.nn import functional as F
class OldUNet(nn.Module):
"""
Paper: `U-Net: Convolutional Networks for Biomedical Image Segmentation
<https://arxiv.org/abs/1505.04597>`_
Paper authors: Olaf Ronneberger, Philipp Fischer, Thomas Brox
Implemented by:
- ... | 9,887 | 29.9 | 115 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/segnet.py | # Adapted from https://github.com/zijundeng/pytorch-semantic-segmentation
import torch
from torch import nn
from src.models.backbones.VGG import vgg19_bn
class SegNet(nn.Module):
def __init__(self, num_classes, pretrained=False, **kwargs):
super(SegNet, self).__init__()
vgg = vgg19_bn(pretrained... | 3,095 | 35.857143 | 92 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/deeplabv3.py | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import urllib
import os
from src.models.backbones.deeplabv3_resnet import ResNet18_OS16, ResNet34_OS16, ResNet50_OS16, ResNet101_OS16, \
ResNet152_OS16, ResNet18_OS8, ResNet34_OS8
from src.models.backbones.deeplabv3_aspp import ASP... | 4,893 | 40.82906 | 298 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/VGG.py | """
Model definition adapted from: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
"""
import logging
import math
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://downloa... | 6,554 | 31.450495 | 113 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/deeplabv3_aspp.py | # Adapted from https://github.com/fregu856/deeplabv3
import torch
import torch.nn as nn
import torch.nn.functional as F
class ASPP(nn.Module):
def __init__(self, num_classes):
super(ASPP, self).__init__()
self.conv_1x1_1 = nn.Conv2d(512, 256, kernel_size=1)
self.bn_conv_1x1_1 = nn.BatchN... | 5,079 | 49.29703 | 216 | py |
DIVA-DAF | DIVA-DAF-main/src/models/backbones/adaptive_unet.py | import torch
from torch import nn
def encoding_block(in_c, out_c):
conv = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3, stride=1, padding=1, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(out_c, out_c, kernel_size=3, stride=1, padding=1, bias=True),
nn.ReLU(inplace=True)
)... | 4,344 | 32.945313 | 79 | py |
DIVA-DAF | DIVA-DAF-main/src/models/headers/fully_convolution.py | from typing import Tuple, OrderedDict
from torch import nn
class ResNetFCNHead(nn.Sequential):
"""
FCN header for resnets. The in_channels are fixed for the different resnet architectures:
resnet18, 34 = 512
resnet50, 101, 152 = 2048
"""
def __init__(self, in_channels: int, num_classes: int,... | 1,188 | 35.030303 | 111 | py |
DIVA-DAF | DIVA-DAF-main/src/models/headers/fully_connected.py | import torch
from torch import nn
class FCNHead(nn.Sequential):
# taken from https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/fcn.py
def __init__(self, in_channels: int, channels: int) -> None:
inter_channels = in_channels // 4
layers = [
nn.Conv2d(in_cha... | 1,301 | 26.702128 | 99 | py |
DIVA-DAF | DIVA-DAF-main/src/models/headers/unet.py | import torch
from torch import nn
class ConvPoolHeader(nn.Module):
def __init__(self, in_channels: int = 8, num_conv_channels: int = 32, num_classes: int = 4):
super(ConvPoolHeader, self).__init__()
self.fc = nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=num_conv_chan... | 1,343 | 27.595745 | 104 | py |
DIVA-DAF | DIVA-DAF-main/src/metrics/divahisdb.py | from typing import Any, Optional, Callable
import numpy as np
import torch
from torchmetrics import Metric
class HisDBIoU(Metric):
def __init__(self, num_classes: int = None, mask_modifies_prediction: bool = True, compute_on_step: bool = True,
dist_sync_on_step: bool = False, process_group: Opt... | 3,416 | 47.126761 | 171 | py |
DIVA-DAF | DIVA-DAF-main/src/utils/utils.py | import logging
import random
import sys
import warnings
from typing import List, Sequence
import numpy as np
import pytorch_lightning as pl
import rich
import wandb
from omegaconf import DictConfig, OmegaConf
from pytorch_lightning import seed_everything
from pytorch_lightning.loggers.wandb import WandbLogger
from pyt... | 7,533 | 33.245455 | 116 | py |
DIVA-DAF | DIVA-DAF-main/src/tasks/base_task.py | import os
from abc import ABCMeta
from pathlib import Path
from typing import Optional, Union, Type, Mapping, Sequence, Callable, Dict, Any, Tuple, List
import numpy as np
import pandas as pd
import seaborn as sn
import torch
import wandb
from torchmetrics import MetricCollection
from torchmetrics.classification impor... | 15,373 | 43.822157 | 121 | py |
DIVA-DAF | DIVA-DAF-main/src/tasks/classification/classification.py | from typing import Optional, Callable
import torch.nn as nn
import torch.optim
import torchmetrics
from src.tasks.base_task import AbstractTask
from src.utils import utils
from src.tasks.utils.outputs import OutputKeys, reduce_dict
log = utils.get_logger(__name__)
class Classification(AbstractTask):
def __ini... | 3,568 | 41.488095 | 107 | py |
DIVA-DAF | DIVA-DAF-main/src/tasks/RGB/semantic_segmentation.py | from pathlib import Path
from typing import Optional, Callable, Union, Any, List
import numpy as np
import torch.nn as nn
import torch.optim
import torchmetrics
from pytorch_lightning.utilities import rank_zero_only
from src.datamodules.RGB.utils.output_tools import save_output_page_image
from src.datamodules.utils.m... | 8,540 | 46.983146 | 117 | py |
DIVA-DAF | DIVA-DAF-main/src/tasks/RGB/semantic_segmentation_cropped.py | from pathlib import Path
from typing import Optional, Callable, Union
import numpy as np
import torch.nn as nn
import torch.optim
import torchmetrics
from src.datamodules.utils.misc import _get_argmax
from src.tasks.base_task import AbstractTask
from src.utils import utils
from src.tasks.utils.outputs import OutputKe... | 5,615 | 44.290323 | 120 | py |
DIVA-DAF | DIVA-DAF-main/src/tasks/utils/outputs.py | from typing import Dict, List
from pytorch_lightning.utilities import LightningEnum
class OutputKeys(LightningEnum):
PREDICTION = 'pred'
TARGET = 'target'
LOG = 'logs'
LOSS = 'loss'
def __hash__(self):
return hash(self.value)
def reduce_dict(input_dict: Dict, key_list: List) -> Dict:
... | 394 | 20.944444 | 74 | py |
DIVA-DAF | DIVA-DAF-main/src/tasks/DivaHisDB/semantic_segmentation_cropped.py | from pathlib import Path
from typing import Optional, Callable, Union
import numpy as np
import torch.nn as nn
import torch.optim
import torchmetrics
from src.datamodules.utils.misc import _get_argmax
from src.tasks.base_task import AbstractTask
from src.utils import utils
from src.tasks.utils.outputs import OutputKe... | 5,995 | 45.48062 | 119 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/RolfFormat/test_datamodule.py | import pytest
from omegaconf import OmegaConf
from pytorch_lightning import Trainer
from tests.datamodules.RolfFormat.datasets.test_full_page_dataset import _get_dataspecs
from src.datamodules.RolfFormat.datamodule import DataModuleRolfFormat
from tests.test_data.dummy_data_rolf.dummy_data import data_dir
NUM_WORKERS... | 4,175 | 41.181818 | 113 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/IndexedFormats/test_datamodule.py | import pytest
from omegaconf import OmegaConf
from pytorch_lightning import Trainer
from src.datamodules.IndexedFormats.datamodule import DataModuleIndexed
from tests.test_data.dummy_fixed_gif.dummy_data import data_dir
@pytest.fixture
def datamodule_indexed(data_dir):
OmegaConf.clear_resolvers()
datamodules... | 3,194 | 40.493506 | 113 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/IndexedFormats/datasets/test_full_page_dataset.py | import pytest
from torch import is_tensor
from src.datamodules.IndexedFormats.datasets.full_page_dataset import DatasetIndexed
from src.datamodules.utils.misc import ImageDimensions
from tests.test_data.dummy_fixed_gif.dummy_data import data_dir
@pytest.fixture
def dataset_train(data_dir):
return DatasetIndexed(... | 2,545 | 37 | 128 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/RGB/test_datamodule_cropped.py | import numpy as np
import pytest
import torch
from omegaconf import OmegaConf
from pytorch_lightning import Trainer
from src.datamodules.RGB.datamodule_cropped import DataModuleCroppedRGB
from tests.test_data.dummy_data_hisdb.dummy_data import data_dir_cropped
from tests.datamodules.DivaHisDB.datasets.test_cropped_his... | 3,782 | 45.134146 | 107 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/RGB/test_datamodule.py | import pytest
from omegaconf import OmegaConf
from pytorch_lightning import Trainer
from src.datamodules.RGB.datamodule import DataModuleRGB
from tests.test_data.dummy_data_hisdb.dummy_data import data_dir
NUM_WORKERS = 4
@pytest.fixture
def data_module_rgb(data_dir):
OmegaConf.clear_resolvers()
datamodules... | 3,119 | 39 | 116 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/RGB/datasets/test_cropped_dataset.py | from pathlib import PosixPath
import pytest
import torch
from src.datamodules.RGB.datasets.cropped_dataset import CroppedDatasetRGB
from tests.test_data.dummy_data_hisdb.dummy_data import data_dir_cropped
DATA_FOLDER_NAME = 'data'
GT_FOLDER_NAME = 'gt'
DATASET_PREFIX = 'e-codices_fmb-cb-0055_0098v_max/e-codices_fmb-... | 8,842 | 44.582474 | 109 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/RGB/utils/test_output_tools.py | import numpy as np
import pytest
import torch
from PIL import Image
from src.datamodules.RGB.utils.output_tools import output_to_class_encodings, save_output_page_image
@pytest.fixture()
def input_image():
return torch.tensor([[[0., 0.3], [4., 2.]],
[[1., 4.1], [-0.2, 1.9]],
... | 1,228 | 34.114286 | 104 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/RGB/utils/test_functional.py | import pytest
import torch
from src.datamodules.RGB.utils.functional import gt_to_int_encoding, gt_to_one_hot
@pytest.fixture()
def input_matrix():
return torch.as_tensor([[[255, 255, 255], [255, 255, 0], [255, 0, 255]],
[[255, 255, 0], [255, 0, 0], [0, 0, 255]],
... | 1,551 | 39.842105 | 85 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/util/test_misc.py | from pathlib import Path
import numpy as np
import pytest
import torch
from src.datamodules.utils.exceptions import PathNone, PathNotDir, PathMissingSplitDir, PathMissingDirinSplitDir
from src.datamodules.utils.misc import validate_path_for_segmentation, _get_argmax, get_output_file_list, \
find_new_filename, sel... | 9,297 | 37.263374 | 117 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/util/test_single_transforms.py | import torch
from src.datamodules.utils.single_transforms import OneHotToPixelLabelling
def test_one_hot_to_pixel_labelling():
transformation = OneHotToPixelLabelling()
tensor_input = torch.tensor([[[0.6999015212, 0.4833144546],
[0.8329959512, 0.1569360495]],
... | 731 | 42.058824 | 74 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/util/test_twin_transforms.py | import random
import numpy as np
import pytest
import torch
from torchvision.datasets.folder import pil_loader
from src.datamodules.utils.twin_transforms import TwinRandomCrop, TwinImageToTensor, TwinCompose, \
ToTensorSlidingWindowCrop
from tests.test_data.dummy_data_hisdb.dummy_data import data_dir_cropped
de... | 3,066 | 42.814286 | 111 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/util/test_functional.py | import torch
from src.datamodules.utils.functional import argmax_onehot
def test_argmax_onehot():
input_tensor = torch.tensor([[[0.3143, 0.0669, 0.1640],
[0.0879, 0.5411, 0.6898],
[0.6721, 0.0067, 0.8442]],
[[0.... | 1,017 | 39.72 | 86 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/util/test_predict_dataset.py | import pytest
import torch
from torchvision.transforms import ToTensor
from src.datamodules.utils.dataset_predict import DatasetPredict
from src.datamodules.utils.misc import ImageDimensions
from tests.test_data.dummy_data_hisdb.dummy_data import data_dir
@pytest.fixture
def file_path_list(data_dir):
test_data_p... | 1,077 | 31.666667 | 108 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/RotNet/test_datamodule_cropped.py | import numpy as np
import pytest
import torch
from omegaconf import OmegaConf
from src.datamodules.RotNet.datamodule_cropped import RotNetDivaHisDBDataModuleCropped
from tests.test_data.dummy_data_hisdb.dummy_data import data_dir_cropped
NUM_WORKERS = 4
DATA_FOLDER_NAME = 'data'
@pytest.fixture
def data_module_crop... | 1,723 | 41.04878 | 106 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/RotNet/datasets/test_cropped_dataset.py | from pathlib import PosixPath
import numpy as np
import pytest
import torch
from torchvision.transforms import ToTensor
from torchvision.transforms.functional import rotate
from src.datamodules.RotNet.datasets.cropped_dataset import CroppedRotNet, ROTATION_ANGLES
from tests.test_data.dummy_data_hisdb.dummy_data impor... | 7,673 | 40.934426 | 96 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/SSLTiles/datasets/test_dataset.py | import numpy as np
import pytest
import torch
from PIL import ImageChops
from torchvision.datasets.folder import pil_loader
from torchvision.transforms import ToTensor
from src.datamodules.SSLTiles.datasets.dataset import DatasetSSLTiles
from src.datamodules.SSLTiles.utils.misc import GTType
from src.datamodules.utils... | 4,358 | 45.870968 | 119 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/DivaHisDB/test_hisDBDataModule.py | import pytest
import torch
from omegaconf import OmegaConf
from src.datamodules.DivaHisDB.datamodule_cropped import DivaHisDBDataModuleCropped
from tests.test_data.dummy_data_hisdb.dummy_data import data_dir_cropped
from tests.datamodules.DivaHisDB.datasets.test_cropped_hisdb_dataset import dataset_test
NUM_WORKERS =... | 2,480 | 47.647059 | 113 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/DivaHisDB/datasets/test_cropped_hisdb_dataset.py | from pathlib import PosixPath
import pytest
import torch
from src.datamodules.DivaHisDB.datasets.cropped_dataset import CroppedHisDBDataset
from tests.test_data.dummy_data_hisdb.dummy_data import data_dir_cropped
DATA_FOLDER_NAME = 'data'
GT_FOLDER_NAME = 'gt'
DATASET_PREFIX = 'e-codices_fmb-cb-0055_0098v_max/e-codi... | 16,064 | 51.845395 | 120 | py |
DIVA-DAF | DIVA-DAF-main/tests/datamodules/DivaHisDB/utils/test_output_tools.py | import numpy as np
from PIL import Image
from torch import tensor, equal
from src.datamodules.DivaHisDB.utils.output_tools import output_to_class_encodings, \
save_output_page_image
from src.datamodules.utils.output_tools import merge_patches
# batchsize (2) x classes (4) x W (2) x H (2)
from src.datamodules.utils... | 2,830 | 37.256757 | 119 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/backbones/test_segnet.py | from src.models.backbones.segnet import SegNet
import torch
def test_forward():
model = SegNet(num_classes=5)
model.eval()
output_tensor = model(torch.rand(1, 3, 32, 32))
assert output_tensor.shape == torch.Size([1, 5, 32, 32])
assert not output_tensor.isnan().any()
| 289 | 25.363636 | 60 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/backbones/test_unet.py | import torch
from src.models.backbones.unet import UNet, Baby_UNet, UNet16, UNet32, UNet64, OldUNet
def test_unet():
model = UNet()
model.eval()
output_tensor = model(torch.rand(1, 3, 32, 32))
assert output_tensor.shape == torch.Size([1, 64, 32, 32])
assert not output_tensor.isnan().any()
def t... | 3,047 | 30.42268 | 86 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/backbones/test_resnetdd.py | from src.models.backbones.resnetdd import resnet18, resnet34, resnet50, resnet101, resnet152
import torch
def test_ResNet18_dd():
model = resnet18(num_classes=5)
model.eval()
output_tensor = model(torch.rand(1, 3, 224, 224))
assert output_tensor.shape == torch.Size([1, 5])
assert not output_tensor... | 1,255 | 28.209302 | 92 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/backbones/test_doc_ufcn.py | import torch
from src.models.backbones.doc_ufcn import Doc_ufcn
def test_forward():
model = Doc_ufcn(out_channels=3)
model.eval()
output_tensor = model(torch.rand(1, 3, 32, 32))
assert output_tensor.shape == torch.Size([1, 3, 32, 32])
assert not output_tensor.isnan().any()
| 297 | 23.833333 | 60 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/backbones/test_adaptive_unet.py | import torch
from src.models.backbones.adaptive_unet import Adaptive_Unet
def test_forward():
model = Adaptive_Unet(out_channels=3)
model.eval()
output_tensor = model(torch.rand(1, 3, 32, 32))
assert output_tensor.shape == torch.Size([1, 3, 32, 32])
assert not output_tensor.isnan().any()
| 312 | 25.083333 | 60 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/backbones/test_resnet.py | import torch
from src.models.backbones.resnet import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
def test_res_net18():
model = ResNet18()
model.eval()
output_tensor = model(torch.rand(1, 3, 32, 32))
assert output_tensor.shape == torch.Size([1, 512, 1, 1])
assert not output_tensor.isnan().a... | 1,212 | 26.568182 | 90 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/backbones/test_deeplabv3.py | import torch
from src.models.backbones.deeplabv3 import deeplabv3, deeplabv3_resnet18_os16, deeplabv3_resnet34_os16, \
deeplabv3_resnet50_os16, deeplabv3_resnet101_os16, deeplabv3_resnet152_os16, deeplabv3_resnet18_os8, \
deeplabv3_resnet34_os8
def test_deeplabv3():
model = deeplabv3(num_classes=5)
m... | 2,322 | 32.185714 | 106 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/backbones/test_baby_cnn.py | import torch
from src.models.backbones.baby_cnn import CNN_basic
def test_forward():
model = CNN_basic()
model.eval()
output_model = model(torch.rand(1, 3, 24, 24)) # B, C, W, H
assert output_model.shape == torch.Size([1, 72, 1, 1])
assert not output_model.isnan().any() # checks if there are an... | 341 | 27.5 | 87 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/backbones/test_VGG.py | from src.models.backbones.VGG import vgg11, vgg19_bn, vgg19, vgg16_bn, vgg16, vgg13_bn, vgg13, vgg11_bn
import torch
def test_vgg11():
model = vgg11(num_classes=5)
model.eval()
output_tensor = model(torch.rand(1, 3, 224, 224))
assert output_tensor.shape == torch.Size([1, 5])
assert not output_tens... | 3,907 | 28.832061 | 103 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/headers/test_fully_convolution.py | import torch
from src.models.headers.fully_convolution import ResNetFCNHead
def test_res_net_fcnhead():
model = ResNetFCNHead(in_channels=512, num_classes=4, output_dims=(12, 12))
model.eval()
output_tensor = model(torch.rand(1, 512, 1, 1))
assert output_tensor.shape == torch.Size([1, 4, 12, 12])
... | 360 | 29.083333 | 79 | py |
DIVA-DAF | DIVA-DAF-main/tests/models/headers/test_fully_connected.py | import torch
from src.models.headers.fully_connected import ResNetHeader, SingleLinear
def test_res_net_header():
model = ResNetHeader(in_channels=512, num_classes=4)
model.eval()
output_tensor = model(torch.rand(1, 512, 1, 1))
assert output_tensor.shape == torch.Size([1, 4])
assert not output_te... | 589 | 28.5 | 73 | py |
DIVA-DAF | DIVA-DAF-main/tests/metrics/test_accuracy.py | import numpy as np
import torch
from src.metrics.divahisdb import HisDBIoU
def test_iou_boundary_mask_modifies_prediction_identical():
label_preds, label_trues, num_classes, mask = _get_test_data(with_boundary=True, identical=True)
metric = HisDBIoU(num_classes=num_classes)
metric.update(pred=label_preds... | 4,853 | 38.145161 | 102 | py |
DIVA-DAF | DIVA-DAF-main/tests/test_data/result_data_ssltiles/result_data.py | import os
import pytest
from pathlib import Path
import shutil
from PIL import Image
from torchvision.datasets.folder import pil_loader
def _get_result_imgs(tmp_path, filename: str) -> Image:
"""
Moves the test data into the tmp path of the testing environment.
:param tmp_path:
:return:
"""
... | 969 | 23.25 | 86 | py |
DIVA-DAF | DIVA-DAF-main/tests/utils/test_utils.py | import pytest
import io
import sys
from omegaconf import DictConfig
from src.utils.utils import _check_if_in_config, REQUIRED_CONFIGS, check_config, print_config
@pytest.fixture
def get_dict():
return DictConfig({'plugins': {
'ddp_plugin': {'_target_': 'pytorch_lightning.plugins.DDPPlugin', 'find_unused_... | 9,582 | 41.591111 | 118 | py |
DIVA-DAF | DIVA-DAF-main/tests/tasks/test_base_task.py | import os
import numpy as np
import pytest
import torch
import torchmetrics
from omegaconf import OmegaConf
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.trainer.states import TrainerState, RunningStage
from torch.nn import Identity, CrossEntropyLoss
from torchmetrics import MetricColle... | 15,221 | 43.508772 | 124 | py |
DIVA-DAF | DIVA-DAF-main/tests/tasks/classification/test_classification.py | import os
import numpy as np
import pytest
import pytorch_lightning as pl
import torch.optim.optimizer
from omegaconf import OmegaConf
from pytorch_lightning import seed_everything, Trainer
from src.datamodules.RotNet.datamodule_cropped import RotNetDivaHisDBDataModuleCropped
from src.models.backbones.baby_cnn import... | 5,353 | 37.242857 | 92 | py |
DIVA-DAF | DIVA-DAF-main/tests/tasks/RGB/test_semantic_segmentation.py | import os
import numpy as np
import pytest
import pytorch_lightning as pl
import torch.optim.optimizer
from omegaconf import OmegaConf
from pytorch_lightning import seed_everything, Trainer
from src.models.backbone_header_model import BackboneHeaderModel
from src.models.backbones.unet import UNet
from src.models.head... | 6,856 | 40.557576 | 113 | py |
DIVA-DAF | DIVA-DAF-main/tests/tasks/utils/test_functional.py | import pytest
import torch
from src.datamodules.DivaHisDB.utils.functional import gt_to_one_hot
@pytest.fixture
def get_class_encodings():
return [1, 2]
@pytest.fixture
def get_input_tensor():
return torch.tensor(
[[[0.01, 0.1], [0.001, 0.01], [0.01, 0.1]], [[0.01, 0.1], [0.01, 0.1], [3.01, 0.1]],
... | 1,030 | 28.457143 | 92 | py |
DIVA-DAF | DIVA-DAF-main/tests/tasks/DivaHisDB/test_semantic_segmentation_cropped.py | import os
import numpy as np
import pytest
import pytorch_lightning as pl
import torch.optim.optimizer
from omegaconf import OmegaConf
from pytorch_lightning import seed_everything, Trainer
from src.datamodules.DivaHisDB.datamodule_cropped import DivaHisDBDataModuleCropped
from src.models.backbone_header_model import... | 5,362 | 39.323308 | 116 | py |
SINBAD | SINBAD-master/load_mvtec_loco.py | import torchvision
import torchvision.transforms as transforms
import torch
from torch.utils.data import Dataset,DataLoader
import numpy as np
import PIL.Image as Image
import os
def default_loader(path):
return Image.open(path).convert('RGB')
def find_classes(dir):
classes = [d for d in os.listdir(dir) if o... | 3,417 | 30.072727 | 164 | py |
SINBAD | SINBAD-master/ResNet.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from copy import deepcopy
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101'... | 18,005 | 38.573626 | 121 | py |
SINBAD | SINBAD-master/data_to_matrices.py | import os
import load_mvtec_loco as mvt
import argparse
import pathlib
import torch
import numpy as np
import torch.nn.functional as F
def resize_array(new_img_size, in_array):
array_new = torch.zeros(in_array.shape)
array_interp = F.interpolate(in_array, (int(new_img_size[0]), int(new_img_size[1])))
arra... | 3,735 | 39.172043 | 142 | py |
SINBAD | SINBAD-master/set_features.py | import torch
import torch.nn.functional as F
class CumulativeSetFeatures(torch.nn.Module):
def __init__(self, n_channels, n_projections=100, n_quantiles=20, is_projection=True):
self.n_channels = n_channels
self.n_projections = n_projections
self.n_quantiles = n_quantiles
self.proj... | 1,611 | 35.636364 | 106 | py |
SINBAD | SINBAD-master/sinbad_single_layer.py |
from __future__ import print_function
import os
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import roc_auc_score
import argparse
import ResNet as resnet
from utils import kNN_shrunk
from set_features import CumulativeSetFeatures
import wandb
from torchvision import transforms... | 9,526 | 40.064655 | 166 | py |
cotta | cotta-main/imagenet/cotta.py | from copy import deepcopy
import torch
import torch.nn as nn
import torch.jit
import PIL
import torchvision.transforms as transforms
import my_transforms as my_transforms
from time import time
import logging
def get_tta_transforms(gaussian_std: float=0.005, soft=False, clip_inputs=False):
img_shape = (224, 224,... | 7,817 | 37.136585 | 114 | py |
cotta | cotta-main/imagenet/tent.py | from copy import deepcopy
import torch
import torch.nn as nn
import torch.jit
class Tent(nn.Module):
"""Tent adapts a model by entropy minimization during testing.
Once tented, a model adapts itself by updating on every forward.
"""
def __init__(self, model, optimizer, steps=1, episodic=False):
... | 4,403 | 33.952381 | 79 | py |
cotta | cotta-main/imagenet/norm.py | from copy import deepcopy
import torch
import torch.nn as nn
class Norm(nn.Module):
"""Norm adapts a model by estimating feature statistics during testing.
Once equipped with Norm, the model normalizes its features during testing
with batch-wise statistics, just like batch norm does during training.
... | 2,159 | 31.727273 | 77 | py |
cotta | cotta-main/imagenet/my_transforms.py | import torch
import torchvision.transforms.functional as F
from torchvision.transforms import ColorJitter, Compose, Lambda
from numpy import random
class GaussianNoise(torch.nn.Module):
def __init__(self, mean=0., std=1.):
super().__init__()
self.std = std
self.mean = mean
def forward(... | 4,847 | 38.096774 | 111 | py |
cotta | cotta-main/imagenet/imagenetc.py | import logging
import torch
import torch.optim as optim
from robustbench.data import load_imagenetc
from robustbench.model_zoo.enums import ThreatModel
from robustbench.utils import load_model
from robustbench.utils import clean_accuracy as accuracy
import tent
import norm
import cotta
from conf import cfg, load_cf... | 5,419 | 35.621622 | 78 | py |
cotta | cotta-main/imagenet/conf.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Configuration file (powered by YACS)."""
import argparse
import os
import sys
import logging
import random
import torch
import numpy as np... | 6,183 | 28.032864 | 146 | py |
cotta | cotta-main/imagenet/robustbench/loaders.py | """
This file is based on the code from https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py.
"""
from torchvision.datasets.vision import VisionDataset
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image
import os
import os.path
impor... | 7,147 | 37.021277 | 113 | py |
cotta | cotta-main/imagenet/robustbench/utils.py | import argparse
import dataclasses
import json
import math
import os
import warnings
from collections import OrderedDict
from pathlib import Path
from typing import Dict, Optional, Union
import requests
import torch
from torch import nn
from robustbench.model_zoo import model_dicts as all_models
from robustbench.mode... | 18,143 | 38.103448 | 172 | py |
cotta | cotta-main/imagenet/robustbench/data.py | import os
from pathlib import Path
from typing import Callable, Dict, Optional, Sequence, Set, Tuple
import numpy as np
import torch
import torch.utils.data as data
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from robustbench.model_zoo.enums... | 8,743 | 36.050847 | 122 | py |
cotta | cotta-main/imagenet/robustbench/eval.py | import warnings
from argparse import Namespace
from pathlib import Path
from typing import Dict, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import torch
import random
from autoattack import AutoAttack
from torch import nn
from tqdm import tqdm
from robustbench.data import CORRUPTIONS, loa... | 8,723 | 38.475113 | 107 | py |
cotta | cotta-main/imagenet/robustbench/model_zoo/cifar100.py | from collections import OrderedDict
import torch
from robustbench.model_zoo.architectures.dm_wide_resnet import CIFAR100_MEAN, CIFAR100_STD, \
DMWideResNet, Swish, DMPreActResNet
from robustbench.model_zoo.architectures.resnet import PreActBlock, PreActResNet
from robustbench.model_zoo.architectures.resnext impor... | 9,269 | 35.210938 | 134 | py |
cotta | cotta-main/imagenet/robustbench/model_zoo/cifar10.py | from collections import OrderedDict
import torch
import torch.nn.functional as F
from torch import nn
from robustbench.model_zoo.architectures.dm_wide_resnet import CIFAR10_MEAN, CIFAR10_STD, \
DMWideResNet, Swish, DMPreActResNet
from robustbench.model_zoo.architectures.resnet import Bottleneck, BottleneckChen202... | 28,007 | 36.645161 | 102 | py |
cotta | cotta-main/imagenet/robustbench/model_zoo/imagenet.py | from collections import OrderedDict
from torchvision import models as pt_models
from robustbench.model_zoo.enums import ThreatModel
from robustbench.model_zoo.architectures.utils_architectures import normalize_model
mu = (0.485, 0.456, 0.406)
sigma = (0.229, 0.224, 0.225)
linf = OrderedDict(
[
('Wong2... | 3,493 | 37.822222 | 93 | py |
cotta | cotta-main/imagenet/robustbench/model_zoo/architectures/resnet.py | import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 ... | 9,823 | 36.639847 | 104 | py |
cotta | cotta-main/imagenet/robustbench/model_zoo/architectures/dm_wide_resnet.py | # Copyright 2020 Deepmind Technologies Limited.
#
# 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 agr... | 10,748 | 35.561224 | 101 | py |
cotta | cotta-main/imagenet/robustbench/model_zoo/architectures/resnext.py | """ResNeXt implementation (https://arxiv.org/abs/1611.05431).
MIT License
Copyright (c) 2017 Xuanyi Dong
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without li... | 5,799 | 32.918129 | 113 | py |
cotta | cotta-main/imagenet/robustbench/model_zoo/architectures/wide_resnet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
se... | 4,070 | 41.40625 | 116 | py |
cotta | cotta-main/imagenet/robustbench/model_zoo/architectures/utils_architectures.py | import torch
import torch.nn as nn
from collections import OrderedDict
from typing import Tuple
from torch import Tensor
class ImageNormalizer(nn.Module):
def __init__(self, mean: Tuple[float, float, float],
std: Tuple[float, float, float]) -> None:
super(ImageNormalizer, self).__init__()
... | 829 | 28.642857 | 76 | py |
cotta | cotta-main/cifar/cifar100c.py | import logging
import torch
import torch.optim as optim
from robustbench.data import load_cifar100c
from robustbench.model_zoo.enums import ThreatModel
from robustbench.utils import load_model
from robustbench.utils import clean_accuracy as accuracy
import tent
import norm
import cotta
from conf import cfg, load_cf... | 5,503 | 35.450331 | 78 | py |
cotta | cotta-main/cifar/cotta.py | from copy import deepcopy
import torch
import torch.nn as nn
import torch.jit
import PIL
import torchvision.transforms as transforms
import my_transforms as my_transforms
from time import time
import logging
def get_tta_transforms(gaussian_std: float=0.005, soft=False, clip_inputs=False):
img_shape = (32, 32, 3... | 7,641 | 36.64532 | 116 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.