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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L-MCL | L-MCL-main/models/lmcl_wrn_cifar.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['lmcl_wrn_16_2_cifar', 'lmcl_wrn_40_2_cifar', 'lmcl_wrn_28_4_cifar']
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1... | 8,109 | 39.55 | 121 | py |
L-MCL | L-MCL-main/models/lmcl_resnet_imagenet.py | import torch
import torch.nn as nn
__all__ = ['lmcl_resnet18_imagenet', 'lmcl_resnet34_imagenet',
'lmcl_resnet50_imagenet']
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, stride=stride... | 12,976 | 37.393491 | 135 | py |
L-MCL | L-MCL-main/models/resnet_cifar.py | import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['resnet32_cifar', 'resnet56_cifar', 'resnet110_cifar']
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, stride=stride,
... | 7,591 | 33.666667 | 97 | py |
L-MCL | L-MCL-main/models/meta_network.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class LossWeightNetwork(nn.Module):
def __init__(self, number_features):
super(LossWeightNetwork, self).__init__()
self.proj = nn.ModuleList([])
self.number_features = number_features
self.number_net = len(number_fea... | 1,527 | 34.534884 | 105 | py |
L-MCL | L-MCL-main/models/lmcl_shufflenetv2_cifar.py | import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['lmcl_ShuffleNetV2_05x_cifar', 'lmcl_ShuffleNetV2_1x_cifar']
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
'''C... | 9,458 | 36.535714 | 99 | py |
L-MCL | L-MCL-main/losses/imagenet_sup_layer_mcl_meta_loss.py | import torch
from torch import nn
import math
import torch.nn.functional as F
class ContrastMemory(nn.Module):
"""
memory buffer that supplies large amount of negative samples.
"""
def __init__(self, args):
super(ContrastMemory, self).__init__()
self.number_net = args.number_net
... | 8,259 | 37.418605 | 119 | py |
L-MCL | L-MCL-main/losses/meta_optimizers.py | import torch, copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
def _copy(state):
if isinstance(state, torch.Tensor):
return state.cpu().clone()
elif isinstance(state, dict):
new_state = {}
for key in state:
new_state[key] =... | 6,416 | 34.65 | 103 | py |
L-MCL | L-MCL-main/losses/cifar_sup_layer_mcl_meta_loss.py | import torch
from torch import nn
import math
import torch.nn.functional as F
class SupMCL(nn.Module):
def __init__(self, args):
super(SupMCL, self).__init__()
self.number_net = args.number_net
self.feat_dim = args.feat_dim
self.args = args
self.kl = KLDiv(T=args.kd_T)
... | 5,531 | 33.792453 | 97 | py |
EdMIPS | EdMIPS-master/main.py | import argparse
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distr... | 17,650 | 38.137472 | 100 | py |
EdMIPS | EdMIPS-master/search.py | import argparse
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distr... | 19,674 | 38.989837 | 103 | py |
EdMIPS | EdMIPS-master/models/quant_resnet.py | import torch
import torch.nn as nn
import math
from . import quant_module as qm
__all__ = [
'quantres18_2w2a', 'quantres18_cfg', 'quantres18_pretrained_cfg',
'quantres50_2w2a', 'quantres50_cfg', 'quantres50_pretrained_cfg',
]
class BasicBlock(nn.Module):
expansion = 1
num_layers = 2
def __init__... | 10,921 | 38.007143 | 107 | py |
EdMIPS | EdMIPS-master/models/quant_googlenet.py | from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import quant_module as qm
__all__ = [
'quantgoogle_2w2a', 'quantgoogle_cfg', 'quantgoogle_pretrained_cfg',
]
_GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
... | 11,205 | 36.353333 | 105 | py |
EdMIPS | EdMIPS-master/models/mixgoogle.py | from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import quant_module as qm
__all__ = [
'mixgoogle_w1234a234',
]
_GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
class BasicConv2d(nn.Module):
def __init__... | 9,025 | 34.535433 | 106 | py |
EdMIPS | EdMIPS-master/models/quant_inception.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from . import quant_module as qm
__all__ = [
'quantinception_2w2a', 'quantinception_cfg', 'quantinception_pretrained_cfg',
]
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, s... | 19,103 | 39.050314 | 102 | py |
EdMIPS | EdMIPS-master/models/quant_module.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
gaussian_steps = {1: 1.596, 2: 0.996, 3: 0.586, 4: 0.336}
hwgq_steps = {1: 0.799, 2: 0.538, 3: 0.3217, 4: 0.185}
class _gauss_quantize(torch.autograd.Function):
@stat... | 17,329 | 37.94382 | 102 | py |
EdMIPS | EdMIPS-master/models/mixresnet.py | import torch.nn as nn
import math
from . import quant_module as qm
__all__ = [
'mixres18_w1234a234', 'mixres50_w1234a234',
]
def conv3x3(conv_func, in_planes, out_planes, stride=1, **kwargs):
"3x3 convolution with padding"
return conv_func(in_planes, out_planes, kernel_size=3, stride=stride,
... | 7,231 | 33.937198 | 106 | py |
EdMIPS | EdMIPS-master/models/mixinception.py | from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import quant_module as qm
__all__ = [
'mixinception_w1234a234',
]
_GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
class BasicConv2d(nn.Module):
def __ini... | 15,689 | 35.744731 | 106 | py |
TranSVAE | TranSVAE-main/dataset_preparation/video2I3D_ucf_hmdb.py | from pytorch_i3d import InceptionI3d
import argparse
import imageio
import os
import re
import time
from colorama import init
from colorama import Fore, Back
import numpy as np
from multiprocessing.dummy import Pool as ThreadPool
import torch
import torch.backends.cudnn as cudnn
import torchvision.transforms as transfo... | 7,430 | 35.072816 | 105 | py |
TranSVAE | TranSVAE-main/dataset_preparation/video2I3D_jester.py | import os
import imageio
import numpy as np
from multiprocessing.dummy import Pool as ThreadPool
from PIL import Image
import torchvision.transforms as transforms
import torch
import time
num_thread = 4
print('thread #:', num_thread)
pool = ThreadPool(num_thread)
batch_size = 16
def im2tensor(im):
im = Image.fr... | 3,179 | 29.285714 | 104 | py |
TranSVAE | TranSVAE-main/dataset_preparation/video2I3D_epic_kitchens.py | import os
import imageio.v2 as imageio
import numpy as np
from multiprocessing.dummy import Pool as ThreadPool
from PIL import Image
import torchvision.transforms as transforms
import torch
import time
import pickle
batch_size = 16
def im2tensor(im):
im = Image.fromarray(im)
t_im = data_transform(im)
re... | 4,242 | 29.092199 | 108 | py |
TranSVAE | TranSVAE-main/dataset_preparation/pytorch_i3d.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPool3dSamePadding(nn.MaxPool3d):
def compute_pad(self, dim, s):
if s % self.stride[dim] == 0:
return max(self.kernel_size[dim] - self.stride[dim], 0)
else:
return max(self.kernel_size[dim] - (s % s... | 13,247 | 37.736842 | 118 | py |
TranSVAE | TranSVAE-main/dataset_preparation/jester_dataset.py | import os
import math
import pandas as pd
import numpy as np
import torch
class VideoDataset_Jester(Dataset):
'''
Input :
csv_file : Path to file where path to videos is stored - <path>, label
frequency : Sampling frequency for the i3d.
num_nodes : Number of graph nodes. Set to 16... | 3,231 | 37.47619 | 141 | py |
TranSVAE | TranSVAE-main/models/base.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class TransposeLast(nn.Module):
def __init__(self, deconstruct_idx=None):
super().__init__()
self.deconstruct_idx = deconstruct_idx
def forward(self, x):
if self.deconstruct_idx is not None:
x = x[self.deco... | 2,514 | 29.301205 | 98 | py |
TranSVAE | TranSVAE-main/models/dcgan_64.py | import torch.nn.functional as F
import torch.nn as nn
class dcgan_conv(nn.Module):
def __init__(self, nin, nout):
super(dcgan_conv, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(nin, nout, 4, 2, 1),
nn.BatchNorm2d(nout),
nn.LeakyReLU(0.2, inplace=True),
... | 4,436 | 28.58 | 94 | py |
TranSVAE | TranSVAE-main/exp/train_hmdb_ucf.py | import torch
import torch.nn as nn
import argparse
import os
import json
import random
import utils
import numpy as np
import torch.nn.functional as F
import math
import time
import TranSVAE
from dataset import TSNDataSet
from torch.nn.utils import clip_grad_norm_
parser = argparse.ArgumentParser()
# ================... | 34,045 | 49.141384 | 382 | py |
TranSVAE | TranSVAE-main/exp/train_epic_kitchens.py | import torch
import torch.nn as nn
import argparse
import os
import json
import random
import utils
import numpy as np
import torch.nn.functional as F
import math
import time
import TranSVAE
from dataset import TSNDataSet
from torch.nn.utils import clip_grad_norm_
parser = argparse.ArgumentParser()
# ================... | 33,508 | 47.704942 | 382 | py |
TranSVAE | TranSVAE-main/exp/TranSVAE.py | import torch
import torch.nn.functional as F
import torch.nn as nn
import TRNmodule
import torchvision
from torch.autograd import Function
class GradReverse(Function):
@staticmethod
def forward(ctx, x, beta):
ctx.beta = beta
return x.view_as(x)
@staticmethod
def backward(ctx, grad_out... | 21,158 | 48.436916 | 158 | py |
TranSVAE | TranSVAE-main/exp/utils.py | import torch.nn as nn
from torch.autograd import Variable
import shutil
import torch.nn.functional as F
from PIL import Image, ImageDraw
import torch
import socket
import numpy as np
import scipy.misc
hostname = socket.gethostname()
def sprites_loaddata(path, Src_domain, Tar_domain, seed=0):
directions = ['front... | 8,256 | 32.42915 | 100 | py |
TranSVAE | TranSVAE-main/exp/dataset.py | import os
import os.path
import numpy as np
from numpy.random import randint
import torch
import torch.utils.data as data
class VideoRecord(object):
def __init__(self, row):
self._data = row
@property
def path(self):
return self._data[0]
@property
def num_frames(self):
re... | 5,717 | 34.079755 | 129 | py |
TranSVAE | TranSVAE-main/exp/TRNmodule.py | import torch
import torch.nn as nn
from math import ceil
class RelationModule(torch.nn.Module):
def __init__(self, img_feature_dim, num_bottleneck, num_frames):
super(RelationModule, self).__init__()
self.num_frames = num_frames
self.img_feature_dim = img_feature_dim
self.num_bottl... | 3,456 | 37.842697 | 102 | py |
TranSVAE | TranSVAE-main/exp/train_sprites.py | import torch
import torch.nn as nn
import argparse
import os
import json
import random
import utils
import numpy as np
import torch.nn.functional as F
import math
import time
import TranSVAE
from torch.utils.data import DataLoader
from torch.nn.utils import clip_grad_norm_
parser = argparse.ArgumentParser()
# =======... | 33,003 | 47.181022 | 382 | py |
TranSVAE | TranSVAE-main/exp/train_jester.py | import torch
import torch.nn as nn
import argparse
import os
import json
import random
import utils
import numpy as np
import torch.nn.functional as F
import math
import time
import TranSVAE
from dataset import TSNDataSet
from torch.nn.utils import clip_grad_norm_
parser = argparse.ArgumentParser()
# ================... | 33,492 | 48.692878 | 382 | py |
DNN-Models-for-Chemical-Kinetics | DNN-Models-for-Chemical-Kinetics-main/modelclass.py | # -*- coding: utf-8 -*-
"""
Created on Thu Sep 24 22:34:47 2020
@author: Tianhan Zhang
@email:
"""
import cantera as ct
from torch.nn.modules import Module
from torch import nn
import torch
import matplotlib.pyplot as plt
import os
import math
import json
import re
import numpy as np
from copy import deepcopy
# plot
i... | 11,912 | 35.431193 | 79 | py |
BriVL | BriVL-main/BriVL-code-inference/evaluation/XYB_box_extract.py | import sys
import os
sys.path.append(os.path.abspath(os.path.dirname(os.path.realpath(__file__))+'/'+'..'))
import os
import time
import argparse
import torch
import json
from tqdm import tqdm
import math
import numpy as np
import random
from utils import getLanMask
from models import build_network
from dataset impor... | 3,497 | 32 | 103 | py |
BriVL | BriVL-main/BriVL-code-inference/evaluation/cal_xyb_retrieval.py | import os
import numpy as np
import random
from tqdm import tqdm
import argparse
import torch
import sys
sys.path.append(os.path.abspath(os.path.dirname(os.path.realpath(__file__))+'/'+'..'))
parser = argparse.ArgumentParser()
parser.add_argument('--feat_load_dir', type=str, default='./logs/feature/ance_trip')
pars... | 2,028 | 29.283582 | 89 | py |
BriVL | BriVL-main/BriVL-code-inference/dataset/__init__.py | import torch
from .xybDataset import XYBDataset_all
import os
__all__ = {
'XYBDataset_all': XYBDataset_all,
}
def build_moco_dataset(args, cfg=None, is_training=True):
Dataset = __all__[cfg.DATASET.NAME]
dataset_val = Dataset(cfg, args, 'val')
dataloader_val = torch.utils.data.DataLoader(
... | 502 | 18.346154 | 58 | py |
BriVL | BriVL-main/BriVL-code-inference/dataset/xybDataset.py | import os
import numpy as np
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import json
from transformers import AutoTokenizer
import random
from PIL import ImageFilter
import msgpack
import... | 4,080 | 36.787037 | 145 | py |
BriVL | BriVL-main/BriVL-code-inference/models/bert.py | import torch
import torch.nn as nn
from transformers import AutoModel
class Bert(nn.Module):
def __init__(self, args):
super(Bert, self).__init__()
self.args = args
self.bert = AutoModel.from_pretrained(args.ENCODER)
def forward(self, x):
y = self.bert(x, return_dict=True)... | 356 | 21.3125 | 61 | py |
BriVL | BriVL-main/BriVL-code-inference/models/fakeTransformer.py | import torch
import torch.nn as nn
class FakeTransformer(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(FakeTransformer, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_dim, output_dim)
... | 445 | 21.3 | 58 | py |
BriVL | BriVL-main/BriVL-code-inference/models/vl_model.py | import torch
import torch.nn as nn
from .fakeTransformer import FakeTransformer
from .bert import Bert
from utils import pairLoss, alignmentLoss, attAlignmentLoss, AlignTripLoss, SimpTripLoss, NCELoss
import torch.nn.functional as F
import timm
import numpy as np
import sys
class ImgLearnableEncoder(nn.Module):
de... | 16,669 | 40.059113 | 152 | py |
BriVL | BriVL-main/BriVL-code-inference/models/__init__.py | from .vl_model import VL_model
import torch
__all__ = {
'VL': VL_model
}
def build_network(model_cfg=None):
model = __all__[model_cfg.NAME](
model_cfg=model_cfg
)
return model
| 204 | 12.666667 | 36 | py |
BriVL | BriVL-main/BriVL-code-inference/utils/loss.py | import torch
import torch.nn.functional as F
def pairLoss(fea1, fea2, mask):
fea1 = F.normalize(fea1, p=2, dim=-1)
fea2 = F.normalize(fea2, p=2, dim=-1)
fea_sim = (fea1 * fea2).sum(dim=-1) # (bs, max_len)
fea_sim = torch.masked_select(fea_sim, mask == 0)
loss = 1.0 - torch.mean(fea_sim)
retur... | 7,329 | 40.179775 | 104 | py |
triangle | triangle-master/doc/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 2,341 | 33.955224 | 79 | py |
histocartography | histocartography-main/setup.py | """Install package."""
import re
import os
import sys
import subprocess
import traceback
from setuptools import setup, find_packages, Command
from setuptools.command.bdist_egg import bdist_egg as _bdist_egg
from setuptools.command.develop import develop as _develop
from distutils.command.build import build as _build
V... | 5,217 | 30.624242 | 104 | py |
histocartography | histocartography-main/examples/masked_patch_feature_extraction_from_layer.py | """
Example: Extract patch features on an image using a tissue mask.
"""
import os
from glob import glob
from PIL import Image
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from histocartography.preprocessing import MaskedGridDeepFeatureExtractor, GaussianTissueMask
from histocartography.u... | 2,538 | 34.760563 | 93 | py |
histocartography | histocartography-main/examples/cell_graph_explainer.py | """
Example: Explain a cell graph (CG) prediction using a pretrained CG-GNN
and a graph explainer: GraphGradCAM.
As used in:
"Quantifying Explainers of Graph Neural Networks in Computational Pathology", Jaume et al, CVPR, 2021.
"""
import os
from glob import glob
from PIL import Image
import yaml
import nump... | 3,062 | 28.451923 | 102 | py |
histocartography | histocartography-main/histocartography/metrics/metrics.py | from functools import partial
import logging
from abc import abstractmethod
from typing import Any, List, Union
import numpy as np
import sklearn.metrics
import torch
def fast_confusion_matrix(y_true: Union[np.ndarray,
torch.Tensor],
y_pred: Union[np.... | 6,585 | 30.361905 | 97 | py |
histocartography | histocartography-main/histocartography/interpretability/base_explainer.py | """Base explainer."""
from abc import abstractmethod
from typing import Optional, Tuple
import dgl
import numpy as np
import torch
import os
from ..pipeline import PipelineStep
class BaseExplainer(PipelineStep):
"""Base pipelines step"""
def __init__(
self,
model: Optional[torch.nn.Module] ... | 1,006 | 24.820513 | 140 | py |
histocartography | histocartography-main/histocartography/interpretability/graph_pruning_explainer.py | from tqdm import tqdm
from copy import deepcopy
import dgl
import math
from scipy.stats import entropy
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import importlib
from ..ml.layers.constants import GNN_NODE_FEAT_IN
from .base_explainer import BaseExplainer
f... | 14,195 | 33.456311 | 111 | py |
histocartography | histocartography-main/histocartography/interpretability/lrp_gnn_explainer.py | import torch
from copy import deepcopy
import dgl
from .base_explainer import BaseExplainer
from ..utils.torch import torch_to_numpy
class GraphLRPExplainer(BaseExplainer):
"""
Layerwise-Relevance Propagation. This module will only work
if the model was built with the ml library provided.
"""
de... | 1,385 | 28.489362 | 67 | py |
histocartography | histocartography-main/histocartography/interpretability/grad_cam.py | from typing import List, Optional, Tuple, Union
import dgl
import numpy as np
import torch
import torch.nn.functional as F
from .base_explainer import BaseExplainer
from ..utils.graph import copy_graph
EPS = 10e-7
class BaseCAM(object):
def __init__(self, model: torch.nn.Module, conv_layers: List[str]) -> Non... | 10,845 | 33.322785 | 130 | py |
histocartography | histocartography-main/histocartography/ml/models/tissue_graph_model.py | import dgl
from typing import Dict, Union, Tuple
import torch
import os
from ..layers.mlp import MLP
from .base_model import BaseModel
from .. import MultiLayerGNN
from ..layers.constants import GNN_NODE_FEAT_IN
from .zoo import MODEL_NAME_TO_URL, MODEL_NAME_TO_CONFIG
from ...utils import download_box_link
class Tis... | 4,759 | 30.523179 | 100 | py |
histocartography | histocartography-main/histocartography/ml/models/hact_model.py | from typing import Dict, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import os
from .base_model import BaseModel
from ..layers.constants import GNN_NODE_FEAT_IN
from ..layers.mlp import MLP
from .. import MultiLayerGNN
from .zoo import MODEL_NAME_TO_URL, MODEL_NAME_TO_CONFIG
fr... | 7,969 | 34.110132 | 149 | py |
histocartography | histocartography-main/histocartography/ml/models/base_model.py | import os
import torch
from torch.nn import Module
from abc import abstractmethod
from ..layers.multi_layer_gnn import MultiLayerGNN
from .zoo import MODEL_NAME_TO_URL, MODEL_NAME_TO_CONFIG
from ...utils import download_box_link
def get_number_of_classes(class_split):
return len(class_split.split('VS'))
class B... | 2,598 | 30.695122 | 128 | py |
histocartography | histocartography-main/histocartography/ml/models/cell_graph_model.py | import dgl
import os
import torch
from typing import Tuple, Union, List
from ..layers.mlp import MLP
from .base_model import BaseModel
from .. import MultiLayerGNN
from ..layers.constants import GNN_NODE_FEAT_IN
from .zoo import MODEL_NAME_TO_URL, MODEL_NAME_TO_CONFIG
from ...utils import download_box_link
class Cel... | 4,802 | 29.788462 | 98 | py |
histocartography | histocartography-main/histocartography/ml/models/hovernet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class HoverNet(nn.Module):
def __init__(self):
"""
HoverNet PyTorch re-implementation based:
`HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images`.
... | 11,407 | 27.52 | 109 | py |
histocartography | histocartography-main/histocartography/ml/layers/dense_gin_layer.py | """
Implementation of a Dense GIN (Graph Isomorphism Network) layer. This implementation should be used
when the input graph(s) can only be represented as an adjacency (typically when dealing with dense
adjacency matrices).
Original paper:
- How Powerful are Graph Neural Networks: https://arxiv.org/abs/1810.00826
... | 3,176 | 30.455446 | 99 | py |
histocartography | histocartography-main/histocartography/ml/layers/constants.py | import torch
from torch.nn import ReLU, Tanh, Sigmoid, ELU, LeakyReLU, PReLU
import dgl
import numpy as np
ACTIVATIONS = {
'relu': ReLU(),
'tanh': Tanh(),
'sigmoid': Sigmoid(),
'elu': ELU(),
'PReLU': PReLU(),
'leaky_relu': LeakyReLU()
}
GNN_MSG = 'gnn_msg'
GNN_NODE_FEAT_IN = 'feat'
GNN_NODE_... | 2,533 | 19.111111 | 102 | py |
histocartography | histocartography-main/histocartography/ml/layers/mlp.py | import torch.nn as nn
from torch.nn import Sequential, Linear
import torch
from .constants import ACTIVATIONS
class MLP(nn.Module):
def __init__(
self,
in_dim,
hidden_dim,
out_dim,
num_layers=1,
act="relu",
use_bn=False,
bias=True,
verbose=... | 6,506 | 32.19898 | 118 | py |
histocartography | histocartography-main/histocartography/ml/layers/gin_layer.py | """
Implementation of a GIN (Graph Isomorphism Network) layer.
Original paper:
- How Powerful are Graph Neural Networks: https://arxiv.org/abs/1810.00826
- Author's public implementation: https://github.com/weihua916/powerful-gnns
"""
import itertools
import numpy as np
import torch
import torch.nn as nn
impor... | 5,536 | 31.19186 | 103 | py |
histocartography | histocartography-main/histocartography/ml/layers/multi_layer_gnn.py | import torch
import torch.nn as nn
import importlib
import dgl
from histocartography.ml.layers.constants import (
AVAILABLE_LAYER_TYPES, GNN_MODULE,
GNN_NODE_FEAT_OUT, READOUT_TYPES,
REDUCE_TYPES
)
class MultiLayerGNN(nn.Module):
"""
MultiLayer network that concatenates several gnn layers.
""... | 5,223 | 31.855346 | 103 | py |
histocartography | histocartography-main/histocartography/ml/layers/pna_layer.py | """
PNA: Principal Neighbourhood Aggregation
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Lio, Petar Velickovic
https://arxiv.org/abs/2004.05718
"""
import itertools
import math
import numpy as np
import dgl
import torch
import torch.nn as nn
import torch.nn.functional as F
from .constants imp... | 7,695 | 34.302752 | 122 | py |
histocartography | histocartography-main/histocartography/utils/torch.py | import torch
def torch_to_numpy(x):
return x.cpu().detach().numpy()
| 74 | 11.5 | 35 | py |
histocartography | histocartography-main/histocartography/utils/graph.py | import networkx as nx
import numpy as np
import dgl
import copy
import torch
def adj_to_networkx(
adj,
feat,
node_importance=None,
threshold=0.1,
max_component=False,
rm_iso_nodes=False,
centroids=None,
nuclei_labels=None):
"""Cleaning a graph by thre... | 4,384 | 28.233333 | 79 | py |
histocartography | histocartography-main/histocartography/utils/io.py | import json
import os
import torch
import numpy as np
import PIL
from PIL import Image
import io
import pickle
import csv
import requests
def is_box_url(candidate):
# check if IBM box static link
if 'https://ibm.box.com/shared/static/' in candidate:
return True
return False
def buffer_plot_and_g... | 4,278 | 26.429487 | 81 | py |
histocartography | histocartography-main/histocartography/preprocessing/graph_builders.py | """This module handles all the graph building"""
import logging
from abc import abstractmethod
from pathlib import Path
from typing import Any, Optional, Tuple, Union
import cv2
import dgl
import networkx as nx
import numpy as np
import pandas as pd
import torch
from dgl.data.utils import load_graphs, save_graphs
fro... | 14,201 | 35.229592 | 118 | py |
histocartography | histocartography-main/histocartography/preprocessing/nuclei_extraction.py | """Detect and Classify nuclei from an image with the HoverNet model."""
import os
from pathlib import Path
from typing import Tuple, Union
import cv2
import numpy as np
import torch
from PIL import Image
import os
from typing import Optional
from skimage.measure import regionprops
from skimage.morphology import remo... | 11,039 | 32.761468 | 142 | py |
histocartography | histocartography-main/histocartography/preprocessing/feature_extraction.py | """Extract features from images for a given structure"""
import copy
import math
import warnings
from abc import abstractmethod
from pathlib import Path
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Callable, List, Optional, Tuple, Union
import cv2
import numpy as np
import pan... | 54,704 | 37.255245 | 128 | py |
histocartography | histocartography-main/histocartography/preprocessing/nuclei_concept_extraction.py | """Extract features from images for a given structure"""
import numpy as np
import torch
from ..pipeline import PipelineStep
from .feature_extraction import HANDCRAFTED_FEATURES_NAMES, HandcraftedFeatureExtractor
class NucleiConceptExtractor(PipelineStep):
"""Class for Nuclei concept extraction.
Extract nuc... | 2,045 | 33.1 | 89 | py |
histocartography | histocartography-main/test/metrics/test_segmentation_metrics.py | """Unit test for metrics"""
import unittest
import numpy as np
import cv2
import torch
import dgl
import os
from PIL import Image
import shutil
from histocartography import PipelineRunner
from histocartography.metrics import IoU, Dice, MeanIoU, MeanDice
class SegmentationMetricsTestCase(unittest.TestCase):
"""Se... | 7,023 | 31.518519 | 75 | py |
histocartography | histocartography-main/test/interpretability/test_graphgradcam.py | """Unit test for interpretability.gradcam"""
import unittest
import numpy as np
import cv2
import torch
import yaml
from copy import deepcopy
import h5py
import os
import shutil
from dgl.data.utils import load_graphs
from histocartography.interpretability import GraphGradCAMExplainer, GraphGradCAMPPExplainer
from hist... | 7,510 | 30.961702 | 92 | py |
histocartography | histocartography-main/test/interpretability/test_graphlrp.py | """Unit test for interpretability.lrp_gnn_explainer"""
import unittest
import numpy as np
import cv2
import torch
import yaml
import os
from copy import deepcopy
import shutil
from dgl.data.utils import load_graphs
from histocartography.interpretability import GraphLRPExplainer
from histocartography.ml import CellGrap... | 2,334 | 29.324675 | 78 | py |
histocartography | histocartography-main/test/interpretability/test_gnnexplainer.py | """Unit test for interpretability.graph_pruning_explainer"""
import unittest
import numpy as np
import cv2
import torch
import yaml
from copy import deepcopy
import os
import shutil
from dgl.data.utils import load_graphs
from histocartography.interpretability import GraphPruningExplainer
from histocartography.ml impor... | 2,394 | 29.705128 | 78 | py |
histocartography | histocartography-main/test/ml/test_tissue_graph_model.py | """Unit test for ml.models.tissue_graph_model"""
import unittest
import torch
import dgl
import os
import yaml
from dgl.data.utils import load_graphs
from histocartography.ml import TissueGraphModel
from histocartography.utils import set_graph_on_cuda, download_box_link, download_test_data
IS_CUDA = torch.cuda.is_av... | 5,247 | 31.8 | 91 | py |
histocartography | histocartography-main/test/ml/test_cell_graph_model.py | """Unit test for ml.models.cell_graph_model"""
import unittest
import torch
import dgl
import os
import yaml
from dgl.data.utils import load_graphs
from histocartography.ml import CellGraphModel
from histocartography.utils import set_graph_on_cuda, download_box_link, download_test_data
IS_CUDA = torch.cuda.is_availab... | 6,136 | 31.470899 | 91 | py |
histocartography | histocartography-main/test/ml/test_multi_layer_gnn.py | """Unit test for ml.layers.multi_layer_gnn"""
import unittest
import torch
import dgl
import yaml
import os
from histocartography.ml import MultiLayerGNN
class MultiLayerGNNTestCase(unittest.TestCase):
"""MultiLayerGNN class."""
@classmethod
def setUpClass(self):
self.current_path = os.path.dirn... | 2,879 | 26.961165 | 76 | py |
histocartography | histocartography-main/test/ml/test_hact_model.py | """Unit test for ml.models.hact_model"""
import unittest
import torch
import dgl
import os
import yaml
from dgl.data.utils import load_graphs
from histocartography.ml import HACTModel
from histocartography.utils import set_graph_on_cuda, download_box_link, download_test_data
IS_CUDA = torch.cuda.is_available()
DEVIC... | 4,826 | 35.847328 | 91 | py |
histocartography | histocartography-main/test/preprocessing/test_feature_extraction.py | """Unit test for preprocessing.feature_extraction"""
import unittest
import numpy as np
import pandas as pd
import yaml
import os
import torch
import shutil
from histocartography import PipelineRunner
from histocartography.utils import download_test_data
class FeatureExtractionTestCase(unittest.TestCase):
"""Fea... | 13,924 | 39.479651 | 95 | py |
histocartography | histocartography-main/test/preprocessing/test_io.py | """Unit test for preprocessing.io"""
import unittest
import numpy as np
import cv2
import torch
import yaml
import dgl
import os
from PIL import Image
import shutil
from histocartography import PipelineRunner
from histocartography.preprocessing import ImageLoader, DGLGraphLoader
from histocartography.utils import down... | 4,208 | 31.882813 | 79 | py |
histocartography | histocartography-main/test/preprocessing/test_graph_builders.py | """Unit test for preprocessing.graph_builders"""
import unittest
import numpy as np
import yaml
import os
import torch
from PIL import Image
import shutil
import dgl
from histocartography import PipelineRunner
from histocartography.preprocessing import DeepFeatureExtractor
from histocartography.preprocessing import Au... | 8,683 | 35.334728 | 84 | py |
aitiaexplorer | aitiaexplorer-master/src/aitia_explorer/feature_selection_runner.py | #
# This file is part of AitiaExplorer and is released under the FreeBSD License.
#
# Copyright (c) 2020, Seamus Brady <seamus@corvideon.ie>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of sour... | 5,662 | 50.954128 | 113 | py |
aitiaexplorer | aitiaexplorer-master/src/aitia_explorer/feature_reduction/xgboost_feature_reduction.py | #
# This file is part of AitiaExplorer and is released under the FreeBSD License.
#
# Copyright (c) 2020, Seamus Brady <seamus@corvideon.ie>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of sour... | 2,984 | 38.276316 | 109 | py |
aitiaexplorer | aitiaexplorer-master/src/test/test_app.py | #
# This file is part of AitiaExplorer and is released under the FreeBSD License.
#
# Copyright (c) 2020, Seamus Brady <seamus@corvideon.ie>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of sour... | 6,566 | 44.604167 | 109 | py |
aitiaexplorer | aitiaexplorer-master/src/test/test_feature_reduction.py | #
# This file is part of AitiaExplorer and is released under the FreeBSD License.
#
# Copyright (c) 2020, Seamus Brady <seamus@corvideon.ie>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of sour... | 4,211 | 45.8 | 109 | py |
unet-vda | unet-vda-main/src/dealias.py | """
dealias model
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those o... | 4,480 | 34.563492 | 392 | py |
unet-vda | unet-vda-main/src/layers.py | """
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s)... | 2,864 | 39.352113 | 392 | py |
unet-vda | unet-vda-main/src/feature_extraction.py | """
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s)... | 5,738 | 39.702128 | 392 | py |
Wasserstein_Patch_Prior | Wasserstein_Patch_Prior-main/run_FS.py | # This code belongs to the paper
#
# J. Hertrich, A. Houdard and C. Redenbach.
# Wasserstein Patch Prior for Image Superresolution.
# IEEE Transactions on Computational Imaging, 2022.
#
# Please cite the paper, if you use this code.
#
# This script applies the Wasserstein Patch Prior reconstruction onto the 2D Fontaine... | 3,278 | 32.121212 | 114 | py |
Wasserstein_Patch_Prior | Wasserstein_Patch_Prior-main/wgenpatex.py | # This code belongs to the paper
#
# J. Hertrich, A. Houdard and C. Redenbach.
# Wasserstein Patch Prior for Image Superresolution.
# IEEE Transactions on Computational Imaging, 2022.
#
# Please cite the paper, if you use this code.
#
# This file contains the core functions for the reconstruction using the Wasserstein ... | 16,653 | 40.635 | 167 | py |
Wasserstein_Patch_Prior | Wasserstein_Patch_Prior-main/run_diam.py | # This code belongs to the paper
#
# J. Hertrich, A. Houdard and C. Redenbach.
# Wasserstein Patch Prior for Image Superresolution.
# IEEE Transactions on Computational Imaging, 2022.
#
# Please cite the paper, if you use this code.
#
# This script applies the Wasserstein Patch Prior reconstruction onto the 2D SiC Diam... | 3,278 | 30.528846 | 114 | py |
AttGAN-Tensorflow | AttGAN-Tensorflow-master/tfprob/gan/loss.py | import tensorflow as tf
def get_gan_losses_fn():
bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def d_loss_fn(r_logit, f_logit):
r_loss = bce(tf.ones_like(r_logit), r_logit)
f_loss = bce(tf.zeros_like(f_logit), f_logit)
return r_loss, f_loss
def g_loss_fn(f_logit):
... | 2,185 | 25.02381 | 62 | py |
SGNet | SGNet-master/pytorch-detection-SGNet/trainval_net.py | # --------------------------------------------------------
# Pytorch multi-GPU Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
... | 15,131 | 36.362963 | 135 | py |
SGNet | SGNet-master/pytorch-detection-SGNet/test_net.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __... | 12,756 | 33.478378 | 136 | py |
SGNet | SGNet-master/pytorch-detection-SGNet/demo.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __... | 13,700 | 34.96063 | 115 | py |
SGNet | SGNet-master/pytorch-detection-SGNet/lib/roi_data_layer/roibatchLoader.py |
"""The data layer used during training to train a Fast R-CNN network.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.utils.data as data
from PIL import Image
import torch
from model.utils.config import cfg
from roi_data_layer.minibatch i... | 8,888 | 39.775229 | 150 | py |
SGNet | SGNet-master/pytorch-detection-SGNet/lib/roi_data_layer/minibatch.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
"""Compute minibatch blobs for training a Fast R-CNN ne... | 2,948 | 32.511364 | 115 | py |
SGNet | SGNet-master/pytorch-detection-SGNet/lib/model/roi_crop/build.py | from __future__ import print_function
import os
import torch
from torch.utils.ffi import create_extension
#this_file = os.path.dirname(__file__)
sources = ['src/roi_crop.c']
headers = ['src/roi_crop.h']
defines = []
with_cuda = False
if torch.cuda.is_available():
print('Including CUDA code.')
sources += ['sr... | 881 | 22.837838 | 75 | py |
SGNet | SGNet-master/pytorch-detection-SGNet/lib/model/roi_crop/functions/gridgen.py | # functions/add.py
import torch
from torch.autograd import Function
import numpy as np
class AffineGridGenFunction(Function):
def __init__(self, height, width,lr=1):
super(AffineGridGenFunction, self).__init__()
self.lr = lr
self.height, self.width = height, width
self.grid = np.ze... | 2,233 | 46.531915 | 171 | py |
SGNet | SGNet-master/pytorch-detection-SGNet/lib/model/roi_crop/functions/crop_resize.py | # functions/add.py
import torch
from torch.autograd import Function
from .._ext import roi_crop
from cffi import FFI
ffi = FFI()
class RoICropFunction(Function):
def forward(self, input1, input2):
self.input1 = input1
self.input2 = input2
self.device_c = ffi.new("int *")
output = to... | 1,545 | 39.684211 | 126 | py |
SGNet | SGNet-master/pytorch-detection-SGNet/lib/model/roi_crop/functions/roi_crop.py | # functions/add.py
import torch
from torch.autograd import Function
from .._ext import roi_crop
import pdb
class RoICropFunction(Function):
def forward(self, input1, input2):
self.input1 = input1.clone()
self.input2 = input2.clone()
output = input2.new(input2.size()[0], input1.size()[1], in... | 1,002 | 44.590909 | 122 | py |
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