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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OLD3S | OLD3S-main/model/train.py | import random
import numpy as np
import argparse
from model import *
from loaddatasets import *
from model_vae import *
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def main():
pa... | 4,771 | 41.990991 | 111 | py |
OLD3S | OLD3S-main/model/metric.py | import numpy as np
import torch
from matplotlib import pyplot as plt
from numpy import *
from scipy.interpolate import make_interp_spline
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
def plot_reuter(y_axi_1, x, path, a, b):
fig = plt.figure()
ax = fig.add_subplot(111)
# ax.plot(range(20))
ax.ax... | 1,544 | 29.9 | 89 | py |
CTC2021 | CTC2021-main/ctc_gector/train.py | import argparse
import os
from random import seed
import torch
from allennlp.data.iterators import BucketIterator
from allennlp.data.vocabulary import DEFAULT_OOV_TOKEN, DEFAULT_PADDING_TOKEN
from allennlp.data.vocabulary import Vocabulary
from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
from ... | 14,433 | 43.687307 | 119 | py |
CTC2021 | CTC2021-main/ctc_gector/gector/seq2labels_model.py | """Basic model. Predicts tags for every token"""
from typing import Dict, Optional, List, Any
import numpy
import torch
import torch.nn.functional as F
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import TimeDistributed, TextFieldEmbedder
from allennlp.nn import In... | 9,880 | 49.671795 | 107 | py |
CTC2021 | CTC2021-main/ctc_gector/gector/wordpiece_indexer.py | """Tweaked version of corresponding AllenNLP file"""
import logging
from collections import defaultdict
from typing import Dict, List, Callable
from allennlp.common.util import pad_sequence_to_length
from allennlp.data.token_indexers.token_indexer import TokenIndexer
from allennlp.data.tokenizers.token import Token
fr... | 21,046 | 46.296629 | 119 | py |
CTC2021 | CTC2021-main/ctc_gector/gector/bert_token_embedder.py | """Tweaked version of corresponding AllenNLP file"""
import logging
from copy import deepcopy
from typing import Dict
import torch
import torch.nn.functional as F
from allennlp.modules.token_embedders.token_embedder import TokenEmbedder
from allennlp.nn import util
#from transformers import AutoModel, PreTrainedModel
... | 12,469 | 44.677656 | 115 | py |
CTC2021 | CTC2021-main/ctc_gector/gector/trainer.py | """Tweaked version of corresponding AllenNLP file"""
import datetime
import logging
import math
import os
import time
import traceback
from typing import Dict, Optional, List, Tuple, Union, Iterable, Any
import torch
import torch.optim.lr_scheduler
from allennlp.common import Params
from allennlp.common.checks import ... | 42,210 | 48.139697 | 113 | py |
CTC2021 | CTC2021-main/ctc_gector/gector/gec_model.py | """Wrapper of AllenNLP model. Fixes errors based on model predictions"""
import logging
import os
import sys
from time import time
import torch
from allennlp.data.dataset import Batch
from allennlp.data.fields import TextField
from allennlp.data.instance import Instance
from allennlp.data.tokenizers import Token
from ... | 13,208 | 39.148936 | 115 | py |
MetaCat | MetaCat-master/main.py | # The code structure is adapted from the WeSTClass implementation
# https://github.com/yumeng5/WeSTClass
import numpy as np
np.random.seed(1234)
from time import time
from model import WSTC, f1
from keras.optimizers import SGD
from gen import augment, pseudodocs
from load_data import load_dataset
from gensim.models im... | 7,965 | 35.541284 | 137 | py |
MetaCat | MetaCat-master/model.py | import numpy as np
np.random.seed(1234)
import os
from time import time
import csv
import keras.backend as K
# K.set_session(K.tf.Session(config=K.tf.ConfigProto(intra_op_parallelism_threads=30, inter_op_parallelism_threads=30)))
from keras.engine.topology import Layer
from keras.layers import Dense, Input, Convolution... | 9,046 | 32.383764 | 124 | py |
drn | drn-master/classify.py | import argparse
import shutil
import time
import numpy as np
import os
from os.path import exists, split, join, splitext
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
im... | 12,237 | 34.6793 | 83 | py |
drn | drn-master/drn.py | import pdb
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
BatchNorm = nn.BatchNorm2d
# __all__ = ['DRN', 'drn26', 'drn42', 'drn58']
webroot = 'http://dl.yf.io/drn/'
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'drn-c-26': webroot + '... | 14,175 | 33.241546 | 88 | py |
drn | drn-master/segment.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import json
import logging
import math
import os
from os.path import exists, join, split
import threading
import time
import numpy as np
import shutil
import sys
from PIL import Image
import torch
from torch import nn
import torch.backends.cudnn as cudnn... | 26,909 | 34.927904 | 102 | py |
drn | drn-master/data_transforms.py | import numbers
import random
import numpy as np
from PIL import Image, ImageOps
import torch
class RandomCrop(object):
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, image, la... | 8,825 | 32.05618 | 82 | py |
drn | drn-master/lib/test.py | import pdb
import time
import logging
import torch
from torch.autograd import Variable
from torch.autograd import gradcheck
from modules import batchnormsync
FORMAT = "[%(asctime)-15s %(filename)s:%(lineno)d %(funcName)s] %(message)s"
logging.basicConfig(format=FORMAT)
logger = logging.getLogger(__name__)
logger.set... | 1,481 | 25.945455 | 89 | py |
drn | drn-master/lib/build.py | import glob
import os
import torch
from torch.utils.ffi import create_extension
this_file = os.path.dirname(__file__)
sources = ['src/batchnormp.c']
headers = ['src/batchnormp.h']
defines = []
with_cuda = False
abs_path = os.path.dirname(os.path.realpath(__file__))
extra_objects = [os.path.join(abs_path, 'dense/batc... | 846 | 23.911765 | 70 | py |
drn | drn-master/lib/functions/batchnormp.py | import pdb
import numpy as np
import torch
from torch.autograd import Function
from dense import batch_norm
from queue import Queue
from threading import Condition
cum_queue = Queue()
broadcast_queue = Queue()
broadcast_cv = Condition()
class BatchNormPFunction(Function):
def __init__(self, running_mean, runn... | 7,692 | 41.977654 | 82 | py |
drn | drn-master/lib/modules/batchnormsync.py | from queue import Queue
import torch
from torch.nn import Module
from torch.nn.parameter import Parameter
from functions.batchnormp import BatchNormPFunction
class BatchNormSync(Module):
sync = True
checking_mode = False
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
... | 2,268 | 34.453125 | 76 | py |
drn | drn-master/lib/dense/batch_norm/__init__.py |
from torch.utils.ffi import _wrap_function
from ._batch_norm import lib as _lib, ffi as _ffi
__all__ = []
def _import_symbols(locals):
for symbol in dir(_lib):
fn = getattr(_lib, symbol)
locals[symbol] = _wrap_function(fn, _ffi)
__all__.append(symbol)
_import_symbols(locals())
| 309 | 22.846154 | 49 | py |
Reweight-CC | Reweight-CC-master/visualization.py | import numpy as np
from PIL import Image, ImageFont, ImageDraw
import keras.backend as K
from utils import color_correction
BACKGROUND_COLOR = (10, 10, 10, 160)
TEXT_COLOR = BOX_COLOR = (230, 230, 230)
FONT = ImageFont.truetype(font='arial', size=24)
EPSILON = 1E-9
def white_balance(input_img,
glo... | 9,917 | 50.388601 | 123 | py |
Reweight-CC | Reweight-CC-master/cc.py | # -*- coding: utf-8 -*-
import os
import argparse
parser = argparse.ArgumentParser(description="Read image(s) and perform computational color constancy. "
"See README and paper Color Constancy by Image Feature Maps Reweighting for more details.",
... | 14,919 | 51.167832 | 141 | py |
Reweight-CC | Reweight-CC-master/utils.py | import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import path
from PIL import Image
from keras.utils import Sequence
from keras.preprocessing.image import img_to_array, load_img
class DataGenerator(Sequence):
"""Generates data for Keras"""
def __init__(self, list_imgs, labels, batc... | 19,374 | 46.839506 | 167 | py |
Reweight-CC | Reweight-CC-master/model.py | import numpy as np
import tensorflow as tf
import keras.backend as K
from keras.models import Model
from keras.engine.topology import Layer
from keras.layers.convolutional import Conv2D
from keras.layers.normalization import BatchNormalization
from keras.layers import (
Input,
Activation,
MaxPooling2D,
... | 21,691 | 39.928302 | 112 | py |
Reweight-CC | Reweight-CC-master/normalization.py | import keras.backend as K
from keras.engine import Layer, InputSpec
from keras import initializers, regularizers, constraints
from keras.utils.generic_utils import get_custom_objects
class ChannellNormalization(Layer):
"""Channel normalization layer
Normalize the activations of the previous layer at each step... | 5,866 | 42.459259 | 88 | py |
Reweight-CC | Reweight-CC-master/train.py | import os
import argparse
parser = argparse.ArgumentParser(description="Training networks on RECommended dataset.",
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("-l", "--level", type=int, choices=[1, 2, 3], default=3,
help="Select how many hier... | 16,729 | 48.643917 | 158 | py |
CG-Detection | CG-Detection-main/train1.py | import math
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.autograd import Variable
from torch.optim import lr_scheduler
import torch.nn.functional as F
from torchvision import transforms, datasets, utils
# import matplotlib.pyplot as plt
import numpy as np
import torch.optim a... | 6,999 | 34.532995 | 110 | py |
CG-Detection | CG-Detection-main/DualNet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from srm_filter_kernel import *
class SoftPooling2D(torch.nn.Module):
def __init__(self,kernel_size,strides=None,padding=0,ceil_mode = False,count_include_pad = True,divisor_override = None):
super(SoftPooling2D, self).__init__()
se... | 4,463 | 30.216783 | 125 | py |
tiatoolbox | tiatoolbox-master/setup.py | #!/usr/bin/env python
"""The setup script."""
import sys
from pathlib import Path
from setuptools import find_packages, setup
with open("README.md") as readme_file:
readme = readme_file.read()
with open("HISTORY.md") as history_file:
history = history_file.read()
install_requires = [
line
for line ... | 1,860 | 25.971014 | 78 | py |
tiatoolbox | tiatoolbox-master/tests/conftest.py | """pytest fixtures."""
import pathlib
import shutil
from pathlib import Path
from typing import Callable
import pytest
from _pytest.tmpdir import TempPathFactory
from tiatoolbox.data import _fetch_remote_sample
# -------------------------------------------------------------------------------------
# Generate Parame... | 13,658 | 29.01978 | 91 | py |
tiatoolbox | tiatoolbox-master/tests/test_graph.py | """Tests for graph construction tools."""
import numpy as np
import pytest
import torch
from matplotlib import pyplot as plt
from tiatoolbox.tools.graph import (
SlideGraphConstructor,
affinity_to_edge_index,
delaunay_adjacency,
edge_index_to_triangles,
triangle_signed_area,
)
def test_delaunay_... | 9,810 | 32.03367 | 85 | py |
tiatoolbox | tiatoolbox-master/tests/test_utils.py | """Tests for utils."""
import hashlib
import os
import random
import shutil
from pathlib import Path
from typing import Tuple
import cv2
import joblib
import numpy as np
import pandas as pd
import pytest
from PIL import Image
from shapely.geometry import Polygon
from tests.test_annotation_stores import cell_polygon
... | 49,055 | 32.280868 | 88 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_arch_nuclick.py | """Unit test package for NuClick."""
import pathlib
import numpy as np
import torch
from tiatoolbox.models import NuClick
from tiatoolbox.models.architecture import fetch_pretrained_weights
from tiatoolbox.utils.misc import imread
ON_GPU = False
# Test pretrained Model =============================
def test_func... | 2,264 | 30.027397 | 87 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_arch_micronet.py | """Unit test package for MicroNet."""
import pathlib
import numpy as np
import pytest
import torch
from tiatoolbox import utils
from tiatoolbox.models import MicroNet
from tiatoolbox.models.architecture import fetch_pretrained_weights
from tiatoolbox.models.engine.semantic_segmentor import SemanticSegmentor
from tia... | 2,651 | 32.56962 | 81 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_patch_predictor.py | """Tests for Patch Predictor."""
import copy
import os
import pathlib
import shutil
import cv2
import numpy as np
import pytest
import torch
from click.testing import CliRunner
from tiatoolbox import cli, rcParam
from tiatoolbox.models.architecture.vanilla import CNNModel
from tiatoolbox.models.dataset import (
... | 39,420 | 32.779777 | 88 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_arch_unet.py | """Unit test package for Unet."""
import pathlib
import numpy as np
import pytest
import torch
from tiatoolbox.models.architecture import fetch_pretrained_weights
from tiatoolbox.models.architecture.unet import UNetModel
from tiatoolbox.wsicore.wsireader import WSIReader
ON_GPU = False
# Test pretrained Model ====... | 2,041 | 30.415385 | 82 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_arch_sccnn.py | """Unit test package for SCCNN."""
import numpy as np
import torch
from tiatoolbox import utils
from tiatoolbox.models import SCCNN
from tiatoolbox.models.architecture import fetch_pretrained_weights
from tiatoolbox.wsicore.wsireader import WSIReader
def _load_sccnn(tmp_path, name):
"""Loads SCCNN model with spe... | 1,556 | 32.847826 | 81 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_feature_extractor.py | """Tests for feature extractor."""
import os
import pathlib
import shutil
import numpy as np
import torch
from tiatoolbox.models.architecture.vanilla import CNNBackbone
from tiatoolbox.models.engine.semantic_segmentor import (
DeepFeatureExtractor,
IOSegmentorConfig,
)
from tiatoolbox.utils import env_detect... | 3,572 | 30.619469 | 87 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_hovernet.py | """Unit test package for HoVerNet."""
import numpy as np
import pytest
import torch
import torch.nn as nn
from tiatoolbox.models import HoVerNet
from tiatoolbox.models.architecture import fetch_pretrained_weights
from tiatoolbox.models.architecture.hovernet import (
DenseBlock,
ResidualBlock,
TFSamepaddin... | 4,878 | 38.666667 | 85 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_arch_mapde.py | """Unit test package for SCCNN."""
import numpy as np
import torch
from tiatoolbox import utils
from tiatoolbox.models import MapDe
from tiatoolbox.models.architecture import fetch_pretrained_weights
from tiatoolbox.wsicore.wsireader import WSIReader
def _load_mapde(tmp_path, name):
"""Loads MapDe model with spe... | 1,627 | 32.916667 | 81 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_semantic_segmentation.py | """Tests for Semantic Segmentor."""
import copy
# ! The garbage collector
import gc
import multiprocessing
import os
import pathlib
import shutil
import numpy as np
import pytest
import torch
import torch.multiprocessing as torch_mp
import torch.nn as nn
import torch.nn.functional as F # noqa: N812
import yaml
from... | 29,697 | 33.174914 | 88 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_hovernetplus.py | """Unit test package for HoVerNet+."""
import torch
from tiatoolbox.models import HoVerNetPlus
from tiatoolbox.models.architecture import fetch_pretrained_weights
from tiatoolbox.utils.misc import imread
from tiatoolbox.utils.transforms import imresize
def test_functionality(remote_sample, tmp_path):
"""Functio... | 1,542 | 41.861111 | 88 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_arch_idars.py | """Functional unit test package for IDARS."""
import torch
from tiatoolbox.models import IDaRS
def test_functional():
"""Functional test for architectures."""
# test forward
samples = torch.rand(4, 3, 224, 224, dtype=torch.float32)
model = IDaRS("resnet18")
model(samples)
model = IDaRS("res... | 723 | 25.814815 | 61 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_arch_vanilla.py | """Unit test package for vanilla CNN within toolbox."""
import numpy as np
import pytest
import torch
from tiatoolbox.models.architecture.vanilla import CNNModel
from tiatoolbox.utils.misc import model_to
ON_GPU = False
def test_functional():
"""Test for creating backbone."""
backbones = [
"alexnet... | 1,296 | 24.94 | 73 | py |
tiatoolbox | tiatoolbox-master/tests/models/test_arch_utils.py | """Unit test package for architecture utilities"""
import numpy as np
import pytest
import torch
from tiatoolbox.models.architecture.utils import (
UpSample2x,
centre_crop,
centre_crop_to_shape,
)
def test_all():
"""Contains all tests for now."""
layer = UpSample2x()
sample = np.array([[1, 2... | 2,272 | 31.942029 | 79 | py |
tiatoolbox | tiatoolbox-master/docs/conf.py | #!/usr/bin/env python
# flake8: noqa
#
# tiatoolbox documentation build configuration file, created by
# sphinx-quickstart on Fri Jun 9 13:47:02 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# auto gene... | 170,772 | 83.332346 | 641 | py |
tiatoolbox | tiatoolbox-master/pre-commit/missing_imports.py | """Static analysis of requirements files and import statements.
Imports which are not found in the requirements files are considered bad.
Any found bad imports will be printed and the script will exit with a non-zero
status.
"""
import argparse
import ast
import importlib
import os
import sys
import tokenize
from pat... | 7,134 | 28.605809 | 87 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/tools/graph.py | """Construction and visualisation of graphs for WSI prediction."""
from __future__ import annotations
from collections import defaultdict
from numbers import Number
from typing import Callable, Dict, Optional, Union
import numpy as np
import torch
import umap
from matplotlib import pyplot as plt
from matplotlib.axes... | 19,823 | 38.807229 | 106 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/tools/registration/wsi_registration.py | import itertools
from numbers import Number
from typing import Dict, Tuple, Union
import cv2
import numpy as np
import SimpleITK as sitk # noqa: N813
import torch
import torchvision
from numpy.linalg import inv
from skimage import exposure, filters
from skimage.registration import phase_cross_correlation
from skimage... | 57,079 | 36.016861 | 88 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/models_abc.py | """Defines Abstract Base Class for Models defined in tiatoolbox."""
from abc import ABC, abstractmethod
import torch.nn as nn
class IOConfigABC(ABC):
"""Define an abstract class for holding predictor I/O information.
Enforcing such that following attributes must always be defined by
the subclass.
"... | 3,623 | 27.761905 | 81 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/dataset/classification.py | import os
import pathlib
import cv2
import numpy as np
import PIL
import torchvision.transforms as transforms
from tiatoolbox import logger
from tiatoolbox.models.dataset import dataset_abc
from tiatoolbox.tools.patchextraction import PatchExtractor
from tiatoolbox.utils.misc import imread
from tiatoolbox.wsicore.wsi... | 12,103 | 34.6 | 87 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/dataset/dataset_abc.py | import os
import pathlib
from abc import ABC, abstractmethod
import numpy as np
import torch
from tiatoolbox.utils.misc import imread
class PatchDatasetABC(ABC, torch.utils.data.Dataset):
"""Defines abstract base class for patch dataset."""
def __init__(
self,
):
super().__init__()
... | 5,005 | 31.506494 | 87 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/micronet.py | """Defines MicroNet architecture.
Raza, SEA et al., “Micro-Net: A unified model for segmentation of
various objects in microscopy images,” Medical Image Analysis,
Dec. 2018, vol. 52, p. 160–173.
"""
from collections import OrderedDict
from typing import Tuple
import numpy as np
import torch
import torch.nn as nn
im... | 17,442 | 26.775478 | 85 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/mapde.py | """Defines MapDe architecture.
Raza, Shan E Ahmed, et al. "Deconvolving convolutional neural network
for cell detection." 2019 IEEE 16th International Symposium on Biomedical
Imaging (ISBI 2019). IEEE, 2019.
"""
import numpy as np
import torch
import torch.nn.functional as F # noqa: N812
from skimage.feature import... | 8,693 | 28.571429 | 90 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/utils.py | """Defines utility layers and operators for models in tiatoolbox."""
from typing import Union
import numpy as np
import torch
import torch.nn as nn
def centre_crop(
img: Union[np.ndarray, torch.tensor],
crop_shape: Union[np.ndarray, torch.tensor],
data_format: str = "NCHW",
):
"""A function to cent... | 3,972 | 28.213235 | 88 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/idars.py | """Defines CNNs as used in IDaRS for prediction of molecular pathways and mutations."""
import numpy as np
from torchvision import transforms
from tiatoolbox.models.architecture.vanilla import CNNModel
TRANSFORM = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0... | 2,075 | 24.317073 | 87 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/vanilla.py | """Defines vanilla CNNs with torch backbones, mainly for patch classification."""
import numpy as np
import torch
import torch.nn as nn
import torchvision.models as torch_models
from tiatoolbox.models.models_abc import ModelABC
from tiatoolbox.utils.misc import select_device
def _get_architecture(arch_name, pretrai... | 7,968 | 31.794239 | 85 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/unet.py | """Defines a set of UNet variants to be used within tiatoolbox."""
from typing import List, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F # noqa: N812
from torchvision.models.resnet import Bottleneck as ResNetBottleneck
from torchvision.models.resnet import ResNet
from tiatoolbox.models.a... | 14,209 | 32.356808 | 88 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/__init__.py | """Defines a set of models to be used within tiatoolbox."""
import os
import pathlib
from pydoc import locate
from typing import Union
import torch
from tiatoolbox import rcParam
from tiatoolbox.models.architecture.vanilla import CNNBackbone, CNNModel
from tiatoolbox.models.dataset.classification import predefined_p... | 4,777 | 35.753846 | 88 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/hovernet.py | import math
from collections import OrderedDict
from typing import List
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F # noqa: N812
from scipy import ndimage
from skimage.morphology import remove_small_objects
from skimage.segmentation import watershed
from tiatoolbo... | 28,016 | 33.588889 | 88 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/hovernetplus.py | from collections import OrderedDict
from typing import List
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F # noqa: N812
from skimage import morphology
from tiatoolbox.models.architecture.hovernet import HoVerNet
from tiatoolbox.models.architecture.utils import UpSamp... | 12,044 | 33.414286 | 88 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/nuclick.py | """Defines original NuClick architecture
Koohbanani, N. A., Jahanifar, M., Tajadin, N. Z., & Rajpoot, N. (2020).
NuClick: a deep learning framework for interactive segmentation of microscopic images.
Medical Image Analysis, 65, 101771.
"""
from typing import Tuple, Union
import numpy as np
import torch
import torch.... | 20,609 | 30.904025 | 87 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/architecture/sccnn.py | """Defines SCCNN architecture.
Sirinukunwattana, Korsuk, et al.
"Locality sensitive deep learning for detection and classification
of nuclei in routine colon cancer histology images."
IEEE transactions on medical imaging 35.5 (2016): 1196-1206.
"""
from __future__ import annotations
from collections import OrderedDi... | 13,123 | 33 | 90 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/engine/multi_task_segmentor.py | # ***** BEGIN GPL LICENSE BLOCK *****
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed ... | 18,210 | 41.155093 | 86 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/engine/nucleus_instance_segmentor.py | """This module enables nucleus instance segmentation."""
import uuid
from collections import deque
from typing import Callable, List, Union
# replace with the sql database once the PR in place
import joblib
import numpy as np
import torch
import tqdm
from shapely.geometry import box as shapely_box
from shapely.strtre... | 31,587 | 38.93426 | 87 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/engine/patch_predictor.py | """This module implements patch level prediction."""
import copy
import os
import pathlib
from collections import OrderedDict
from typing import Callable, Tuple, Union
import numpy as np
import torch
import tqdm
from tiatoolbox import logger
from tiatoolbox.models.architecture import get_pretrained_model
from tiatoo... | 33,563 | 36.376392 | 88 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/models/engine/semantic_segmentor.py | """This module implements semantic segmentation."""
import copy
import logging
import os
import pathlib
import shutil
from concurrent.futures import ProcessPoolExecutor
from multiprocessing.managers import Namespace
from typing import Callable, List, Tuple, Union
import cv2
import joblib
import numpy as np
import to... | 64,566 | 39.633732 | 88 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/utils/misc.py | """Miscellaneous small functions repeatedly used in tiatoolbox."""
import copy
import json
import os
import pathlib
import zipfile
from typing import IO, Dict, Optional, Tuple, Union
import cv2
import joblib
import numpy as np
import pandas as pd
import requests
import torch
import yaml
from shapely.affinity import tr... | 31,597 | 29.093333 | 110 | py |
tiatoolbox | tiatoolbox-master/tiatoolbox/utils/env_detection.py | """Detection methods for the current environment.
This module contains methods for detecting aspects of the current
environment.
Some things which this module can detect are:
- Whether the current environment is interactive.
- Whether the current environment is a conda environment.
- Whether the current e... | 12,448 | 29.437653 | 86 | py |
Keras-FCN | Keras-FCN-master/inference.py | import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import os
import sys
import cv2
from PIL import Image
from keras.preprocessing.image import *
from keras.models import load_model
import keras.backend as K
from keras.applications.imagenet_utils import preprocess_input
from models import *
def in... | 3,546 | 37.978022 | 131 | py |
Keras-FCN | Keras-FCN-master/evaluate.py | import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import os
import sys
import time
import cv2
from PIL import Image
from keras.preprocessing.image import *
from keras.utils.np_utils import to_categorical
from keras.models import load_model
import keras.backend as K
from models import *
from infere... | 4,607 | 42.471698 | 170 | py |
Keras-FCN | Keras-FCN-master/train_coco.py | import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import os
import sys
import pickle
import time
from keras.optimizers import SGD, Adam
from keras.callbacks import *
from keras.objectives import *
from keras.models import load_model
import keras.backend as K
#import keras.utils.visualize_util as vi... | 3,586 | 51.75 | 170 | py |
Keras-FCN | Keras-FCN-master/models.py | import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import os
import sys
from keras_contrib.applications import densenet
from keras.models import Model
from keras.regularizers import l2
from keras.layers import *
from keras.engine import Layer
from keras.applications.vgg16 import *
from keras.models ... | 17,472 | 52.271341 | 176 | py |
Keras-FCN | Keras-FCN-master/train.py | import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import os
import sys
import pickle
from keras.optimizers import SGD, Adam, Nadam
from keras.callbacks import *
from keras.objectives import *
from keras.metrics import binary_accuracy
from keras.models import load_model
import keras.backend as K
#im... | 10,131 | 42.672414 | 170 | py |
Keras-FCN | Keras-FCN-master/test/test_preprocessing.py | from keras.preprocessing.image import img_to_array, array_to_img
from utils import SegDataGenerator
from PIL import Image as PILImage
import numpy as np
def test_crop(crop_function):
arr = np.random.random(500, 800)
img = PILImage.fromarray(arr)
crop_width = img.width / 5
crop_height = img.height / ... | 1,415 | 27.897959 | 89 | py |
Keras-FCN | Keras-FCN-master/utils/BilinearUpSampling.py | import keras.backend as K
import tensorflow as tf
from keras.layers import *
def resize_images_bilinear(X, height_factor=1, width_factor=1, target_height=None, target_width=None, data_format='default'):
'''Resizes the images contained in a 4D tensor of shape
- [batch, channels, height, width] (for 'channels_fi... | 4,452 | 46.88172 | 143 | py |
Keras-FCN | Keras-FCN-master/utils/transfer_FCN.py | import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import os
import sys
from keras.models import Model
from keras.regularizers import l2
from keras.layers import *
from keras.models import model_from_json
from keras.utils import np_utils
from keras.applications.vgg16 import *
from keras.applications... | 6,302 | 41.302013 | 124 | py |
Keras-FCN | Keras-FCN-master/utils/resnet_helpers.py | from keras.layers import *
from keras.layers.merge import Add
from keras.regularizers import l2
# The original help functions from keras does not have weight regularizers, so I modified them.
# Also, I changed these two functions into functional style
def identity_block(kernel_size, filters, stage, block, weight_decay... | 7,977 | 50.470968 | 132 | py |
Keras-FCN | Keras-FCN-master/utils/basics.py | from keras.models import Model
from keras.layers import *
from keras.regularizers import l2
import tensorflow as tf
def conv_relu(nb_filter, nb_row, nb_col, subsample=(1, 1), border_mode='same', bias = True, w_decay = 0.01):
def f(x):
with tf.name_scope('conv_relu'):
x = Conv2D(filters=nb_filte... | 3,168 | 50.112903 | 138 | py |
Keras-FCN | Keras-FCN-master/utils/SegDataGenerator.py | from keras.preprocessing.image import *
from keras.applications.imagenet_utils import preprocess_input
from keras import backend as K
from PIL import Image
import numpy as np
import os
def center_crop(x, center_crop_size, data_format, **kwargs):
if data_format == 'channels_first':
centerh, centerw = x.sha... | 22,399 | 42.159923 | 164 | py |
Keras-FCN | Keras-FCN-master/utils/loss_function.py | from keras.objectives import *
from keras.metrics import binary_crossentropy
import keras.backend as K
import tensorflow as tf
# Softmax cross-entropy loss function for pascal voc segmentation
# and models which do not perform softmax.
# tensorlow only
def softmax_sparse_crossentropy_ignoring_last_label(y_true, y_pre... | 1,139 | 34.625 | 81 | py |
Keras-FCN | Keras-FCN-master/utils/metrics.py | import keras.backend as K
import tensorflow as tf
from tensorflow.contrib.metrics import streaming_mean_iou
def sparse_accuracy_ignoring_last_label(y_true, y_pred):
nb_classes = K.int_shape(y_pred)[-1]
y_pred = K.reshape(y_pred, (-1, nb_classes))
y_true = K.one_hot(tf.to_int32(K.flatten(y_true)),
... | 2,044 | 41.604167 | 142 | py |
Keras-FCN | Keras-FCN-master/utils/get_weights_path.py | from keras.utils.data_utils import get_file
def get_weights_path_vgg16():
TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',TF_WEIGHTS_PATH,cache_subdir='m... | 771 | 44.411765 | 142 | py |
irm-empirical-study | irm-empirical-study-master/colored_mnist/main.py | # Copyright (c) Facebook, Inc. and its affiliates and Kakao Brain.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
from typing import Union, List, Tuple
import numpy as np
import torch
from torchvision i... | 10,756 | 37.14539 | 105 | py |
irm-empirical-study | irm-empirical-study-master/punctuated_sst2/main.py | """
Minimal IRM for ColoredSST-2 with a bag-of-words model
"""
import argparse
import itertools as it
from typing import List
import numpy as np
import torch
from torch import nn, optim, autograd
import torch.nn.functional as F
import torchtext
from data_processors import get_train_examples, get_test_examples
cla... | 10,008 | 37.794574 | 117 | py |
dwave-tabu | dwave-tabu-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# tabu documentation build configuration file
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commente... | 4,053 | 31.174603 | 83 | py |
tunit | tunit-master/main.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import argparse
import warnings
from datetime import datetime
from glob import glob
from shutil import copyfile
from collections import OrderedDict
import torch.nn
import torch.nn.parallel
import torch.back... | 21,710 | 40.354286 | 183 | py |
tunit | tunit-master/tools/utils.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import os
import torch
class Logger(object):
def __init__(self, log_dir):
self.last = None
def scalar_summary(self, tag, value, step):
if self.last and self.last['step'] != step:
... | 2,081 | 25.35443 | 88 | py |
tunit | tunit-master/tools/ops.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
from torch import autograd
import torch
import torch.distributed as dist
from torch.nn import functional as F
def compute_grad_gp(d_out, x_in, is_patch=False):
batch_size = x_in.size(0)
grad_dout =... | 4,752 | 28.159509 | 97 | py |
tunit | tunit-master/models/guidingNet.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
from torch import nn
import torch.nn.functional as F
try:
from models.blocks import Conv2dBlock, FRN
except:
from blocks import Conv2dBlock, FRN
cfg = {
'vgg11': [64, 'M', 128, 'M', 256, 256, ... | 3,090 | 31.197917 | 120 | py |
tunit | tunit-master/models/discriminator.py | """
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, USA... | 3,458 | 34.659794 | 87 | py |
tunit | tunit-master/models/inception.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torch.ut... | 11,736 | 36.142405 | 126 | py |
tunit | tunit-master/models/generator.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license
(https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from torch import nn
import torch
import torch.nn.functional as F
import torch.nn.init as init
import math
import numpy as np
try:
from mod... | 6,164 | 33.830508 | 135 | py |
tunit | tunit-master/models/blocks.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license
(https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
import torch.nn.functional as F
from torch import nn
class ResBlocks(nn.Module):
def __init__(self, num_blocks, dim, norm, act... | 7,693 | 33.657658 | 118 | py |
tunit | tunit-master/datasets/custom_dataset.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import torch.utils.data as data
from PIL import Image
import os
import os.path
import sys
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:... | 9,301 | 32.948905 | 131 | py |
tunit | tunit-master/datasets/datasetgetter.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import torch
from torchvision.datasets import ImageFolder
import os
import torchvision.transforms as transforms
from datasets.custom_dataset import ImageFolerRemap, CrossdomainFolder
class DuplicatedCompos... | 14,535 | 40.295455 | 116 | py |
tunit | tunit-master/train/train_unsupervised.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
from tqdm import trange
from torch.nn import functional as F
import torch.nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from tools.utils import *
... | 7,511 | 31.102564 | 102 | py |
tunit | tunit-master/train/train_supervised.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
from tqdm import trange
import torch.nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from tools.utils import *
from tools.ops import compute_grad_gp... | 6,545 | 29.877358 | 110 | py |
tunit | tunit-master/train/train_semisupervised.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
from tqdm import trange
from torch.nn import functional as F
import torch.nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from tools.utils import *
... | 12,719 | 31.615385 | 110 | py |
tunit | tunit-master/validation/eval_metrics.py | """
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
Copyright (c) 2019 Xu Ji
MIT license
"""
from __future__ import print_function
import numpy as np
import torch
from sklearn import metrics
from scipy.optimize import linear_sum_assignment
def _original_match(flat_preds, flat_... | 2,818 | 30.322222 | 102 | py |
tunit | tunit-master/validation/validation.py | """
TUNIT: Truly Unsupervised Image-to-Image Translation
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import torch.nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.utils as vutils
import torch.nn.functional as F
import numpy as np
... | 16,853 | 42.215385 | 175 | py |
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