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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MedicalZooPytorch | MedicalZooPytorch-master/lib/train/BaseTrainer.py | import torch
from abc import abstractmethod
from numpy import inf
from lib.visual3D_temp import TensorboardWriter
class BaseTrainer:
"""
Base class for all trainers
"""
def __init__(self, model, criterion, metric_ftns, optimizer, config):
self.config = config
self.logger = config.get_l... | 4,619 | 37.5 | 115 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/train/train_covid.py | import torch
import torch.nn as nn
from lib.utils.covid_utils import MetricTracker, accuracy
def train(args, model, trainloader, optimizer, epoch, writer):
model.train()
criterion = nn.CrossEntropyLoss(reduction='mean')
metric_ftns = ['loss', 'accuracy']
train_metrics = MetricTracker(*[m for m in me... | 2,759 | 37.873239 | 97 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/train/train_old.py | import torch
import lib.utils as utils
"""
Unified train script that keep train/val statistics in Tensorboard
Currently works for 4-class segmentations
"""
def train_dice(args, epoch, model, trainLoader, optimizer, criterion):
model.train()
n_processed = 0
train_loss = 0
dice_avg_coeff = 0
avg_ai... | 3,706 | 35.343137 | 146 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/train/trainer.py | import numpy as np
import torch
from lib.utils.general import prepare_input
from lib.visual3D_temp.BaseWriter import TensorboardWriter
class Trainer:
"""
Trainer class
"""
def __init__(self, args, model, criterion, optimizer, train_data_loader,
valid_data_loader=None, lr_scheduler=N... | 3,337 | 36.088889 | 98 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/Unet3D.py | import torch.nn as nn
import torch
from torchsummary import summary
import torchsummaryX
from lib.medzoo.BaseModelClass import BaseModel
class UNet3D(BaseModel):
"""
Implementations based on the Unet3D paper: https://arxiv.org/abs/1606.06650
"""
def __init__(self, in_channels, n_classes, base_n_filte... | 9,760 | 43.775229 | 119 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/Unet2D.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
from lib.medzoo.BaseModelClass import BaseModel
# 2D-Unet Model taken from https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_model.py
class DoubleConv(nn.Module):
'''(conv => BN => ReLU) * 2'''
def... | 3,523 | 27.419355 | 98 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/DenseVoxelNet.py | import torch
import torch.nn as nn
from torchsummary import summary
from lib.medzoo.BaseModelClass import BaseModel
"""
Implementation od DenseVoxelNet based on https://arxiv.org/abs/1708.00573
Hyperparameters used:
batch size = 3
weight decay = 0.0005
momentum = 0.9
lr = 0.05
"""
def init_weights(m):
"""
Th... | 6,019 | 38.605263 | 118 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/Vnet.py | import torch.nn as nn
import torch
from torchsummary import summary
from lib.medzoo.BaseModelClass import BaseModel
"""
Implementation of this model is borrowed and modified
(to support multi-channels and latest pytorch version)
from here:
https://github.com/Dawn90/V-Net.pytorch
"""
def passthrough(x, **kwargs):
... | 7,239 | 32.364055 | 110 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/BaseModelClass.py | """
Implementation of BaseModel taken and modified from here
https://github.com/kwotsin/mimicry/blob/master/torch_mimicry/nets/basemodel/basemodel.py
"""
import os
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
class BaseModel(nn.Module, ABC):
r"""
BaseModel with basic functionalities... | 4,153 | 29.544118 | 88 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/SkipDenseNet3D.py | from collections import OrderedDict
import torch
import torch.nn as nn
from torchsummary import summary
from lib.medzoo.BaseModelClass import BaseModel
"""
Based on the implementation of https://github.com/tbuikr/3D-SkipDenseSeg
Paper here : https://arxiv.org/pdf/1709.03199.pdf
"""
class _DenseLayer(nn.Sequential)... | 8,204 | 43.592391 | 128 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/HyperDensenet.py | import torch
import torch.nn as nn
from torchsummary import summary
from lib.medzoo.BaseModelClass import BaseModel
"""
Code was borrowed and modified from this repo: https://github.com/josedolz/HyperDenseNet_pytorch
"""
def conv(nin, nout, kernel_size=3, stride=1, padding=1, bias=False, layer=nn.Conv2d,
B... | 22,253 | 35.362745 | 115 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/HighResNet3D.py | import torch
import torch.nn as nn
from lib.medzoo.BaseModelClass import BaseModel
"""
Implementation based on the paper:
https://arxiv.org/pdf/1707.01992.pdf
"""
class ConvInit(nn.Module):
def __init__(self, in_channels):
super(ConvInit, self).__init__()
self.num_features = 16
self.in_ch... | 7,645 | 32.243478 | 100 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/Densenet3D.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from torchsummary import summary
from lib.medzoo.BaseModelClass import BaseModel
"""
Implementations based on the HyperDenseNet paper: https://arxiv.org/pdf/1804.02967.pdf
"""
class _HyperDenseLayer(nn.Sequential):
def __init__(self, num_input_fe... | 13,939 | 43.967742 | 116 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/ResNet3D_VAE.py | import torch
import torch.nn as nn
from lib.medzoo.BaseModelClass import BaseModel
"""
Implementation based on the original paper https://arxiv.org/pdf/1810.11654.pdf
"""
class GreenBlock(nn.Module):
def __init__(self, in_channels, out_channels=32, norm="group"):
super(GreenBlock, self).__init__()
... | 10,758 | 34.391447 | 122 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/__init__.py | import torch.optim as optim
from .COVIDNet import CovidNet, CNN
from .DenseVoxelNet import DenseVoxelNet
from .Densenet3D import DualPathDenseNet, DualSingleDenseNet, SinglePathDenseNet
from .HighResNet3D import HighResNet3D
from .HyperDensenet import HyperDenseNet, HyperDenseNet_2Mod
from .ResNet3DMedNet import gener... | 3,514 | 42.9375 | 110 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/COVIDNet.py | import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
class PEPX(nn.Module):
def __init__(self, n_input, n_out):
super(PEPX, self).__init__()
'''
• First-... | 8,386 | 42.455959 | 121 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/medzoo/ResNet3DMedNet.py | from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.medzoo.BaseModelClass import BaseModel
"""
Original paper here: https://arxiv.org/abs/1904.00625
Implementation is strongly and modified from here: https://github.com/kenshohara/3D-ResNets-PyTorch
Network architec... | 12,369 | 36.828746 | 125 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/utils/save_old.py | import os
import shutil
import torch
"""
Not used anymore. Will be removed soon.
They now exist in the base class of the models.
"""
def save_checkpoint(state, is_best, path, prefix, filename='checkpoint.pth.tar'):
prefix_save = os.path.join(path, prefix)
name = prefix_save + '_' + filename
torch.save(sta... | 1,553 | 32.782609 | 81 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/utils/covid_utils.py | import torch
def accuracy(output, target):
with torch.no_grad():
pred = torch.argmax(output, dim=1)
assert pred.shape[0] == len(target)
correct = 0
correct += torch.sum(pred == target).item()
return correct, len(target), correct / len(target)
def print_stats(args, epoch, num_... | 4,123 | 38.653846 | 118 | py |
MedicalZooPytorch | MedicalZooPytorch-master/lib/utils/general.py | import json
import os
import random
import shutil
import time
import pickle
import numpy as np
import torch
import torch.backends.cudnn as cudnn
def reproducibility(args, seed):
torch.manual_seed(seed)
if args.cuda:
torch.cuda.manual_seed(seed)
np.random.seed(seed)
cudnn.deterministic = True
... | 3,297 | 25.596774 | 95 | py |
JPerceiver | JPerceiver-master/run.py | import os
if __name__ == '__main__':
# os.system('/home/user/software/anaconda/envs/py37t11/bin/python -m torch.distributed.launch --master_port=9900 --nproc_per_node=1 train.py')
# os.system('/home/hadoop-wallemnl/cephfs/data/shuchang/envs/py37t11/bin/python -m torch.distributed.launch --master_port=9900 --np... | 541 | 76.428571 | 194 | py |
JPerceiver | JPerceiver-master/net.py | from __future__ import absolute_import, division, print_function
import torch
import torch.nn.functional as F
import torch.nn as nn
from .dice_loss import IoULoss, TverskyLoss, SoftDiceLoss
from .focal_loss import FocalLoss
from .boundary_loss import BDLoss
from .layers import SSIM, Backproject, Project, disp_to_depth,... | 53,313 | 52.798184 | 150 | py |
JPerceiver | JPerceiver-master/train.py | from __future__ import division
import argparse
from mmcv import Config
from mmcv.runner import load_checkpoint
from mono.datasets.get_dataset import get_dataset
from mono.apis import (train_mono,
init_dist,
get_root_logger,
set_random_seed)
from mo... | 3,501 | 31.728972 | 77 | py |
JPerceiver | JPerceiver-master/mono/apis/env.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Duanzhixiang(zhixiangduan@deepmotion.ai)
import logging
import os
import random
import subprocess
import numpy as np
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from mmcv.runner import get_dist_info
def init_dist(launcher, ... | 2,329 | 28.871795 | 70 | py |
JPerceiver | JPerceiver-master/mono/apis/trainer.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Duanzhixiang(zhixiangduan@deepmotion.ai)
from __future__ import division
import re
from collections import OrderedDict
import torch
from mmcv.runner import Runner, DistSamplerSeedHook, obj_from_dict
from mmcv.parallel import MMDataParallel, MMDistributedDataPar... | 9,063 | 37.40678 | 120 | py |
JPerceiver | JPerceiver-master/mono/tools/transformations.py | # -*- coding: utf-8 -*-
# transformations.py
# Copyright (c) 2006-2015, Christoph Gohlke
# Copyright (c) 2006-2015, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modifica... | 66,201 | 33.390649 | 85 | py |
JPerceiver | JPerceiver-master/mono/core/evaluation/pixel_error.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Duanzhixiang(zhixiangduan@deepmotion.ai)
import numpy as np
import math
class AverageMeter(object):
"""
Computes and stores the average and current value
"""
def __init__(self):
self.reset()
def reset(self):
self.val=0
... | 4,451 | 23.733333 | 78 | py |
JPerceiver | JPerceiver-master/mono/core/evaluation/eval_hooks.py | import os
import os.path as osp
import cv2
import matplotlib.pyplot as plt
import mmcv
import torch
import torch.distributed as dist
from mmcv.runner import Hook
from mmcv.parallel import scatter, collate
from torch.utils.data import Dataset
from .pixel_error import *
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
def change_input... | 12,347 | 36.877301 | 113 | py |
JPerceiver | JPerceiver-master/mono/core/utils/dist_utils.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Duanzhixiang(zhixiangduan@deepmotion.ai)
from collections import OrderedDict
import torch.distributed as dist
from torch._utils import (_flatten_dense_tensors, _unflatten_dense_tensors,
_take_tensors)
from mmcv.runner import OptimizerHoo... | 2,042 | 32.491803 | 75 | py |
JPerceiver | JPerceiver-master/mono/datasets/cityscape_dataset.py | # from __future__ import absolute_import, division, print_function
# import random
# import numpy as np
# from PIL import Image # using pillow-simd for increased speed
# import os
# import zipfile
#
# import torch
# import torch.utils.data as data
# from torchvision import transforms
#
#
# def pil_loader(archive, file... | 11,079 | 33.842767 | 137 | py |
JPerceiver | JPerceiver-master/mono/datasets/argoverse_dataset.py | from __future__ import absolute_import, division, print_function
import math
import os
import random
import PIL
from PIL import Image as pil
import matplotlib.pyplot as PLT
import cv2
import numpy as np
import shutil
import torch
import torch.utils.data as data
from scipy.ndimage.filters import gaussian_filter
from... | 5,595 | 40.147059 | 110 | py |
JPerceiver | JPerceiver-master/mono/datasets/nuscenes_dataset.py | from __future__ import absolute_import, division, print_function
import math
import os
import random
import PIL
from PIL import Image as pil
import matplotlib.pyplot as PLT
import cv2
import numpy as np
import shutil
import torch
import torch.utils.data as data
from scipy.ndimage.filters import gaussian_filter
from... | 2,419 | 29.632911 | 71 | py |
JPerceiver | JPerceiver-master/mono/datasets/utils.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Duanzhixiang(zhixiangduan@deepmotion.ai)
from __future__ import absolute_import, division, print_function
import torch
import numpy as np
import cv2
def readlines(filename):
"""Read all the lines in a text file and return as a list
"""
with open(fil... | 5,009 | 27.793103 | 79 | py |
JPerceiver | JPerceiver-master/mono/datasets/folder_dataset.py | from __future__ import absolute_import, division, print_function
import random
import numpy as np
from PIL import Image # using pillow-simd for increased speed
import os
import torch
import torch.utils.data as data
from torchvision import transforms
def pil_loader(filename):
# open path as file to avoid Resourc... | 5,526 | 33.329193 | 116 | py |
JPerceiver | JPerceiver-master/mono/datasets/mono_dataset.py | #
from __future__ import absolute_import, division, print_function
import random
import math
import os
import numpy as np
from PIL import Image as pil # using pillow-simd for increased speed
from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import torch
import torch.utils.data as da... | 20,413 | 44.7713 | 124 | py |
JPerceiver | JPerceiver-master/mono/datasets/euroc_dataset.py | from __future__ import absolute_import, division, print_function
import random
import numpy as np
from PIL import Image # using pillow-simd for increased speed
import os
import torch
import torch.utils.data as data
from torchvision import transforms
def pil_loader(filename):
# open path as file to avoid Resourc... | 6,088 | 33.994253 | 116 | py |
JPerceiver | JPerceiver-master/mono/datasets/eth3d_dataset.py | from __future__ import absolute_import, division, print_function
import random
import numpy as np
from PIL import Image # using pillow-simd for increased speed
import os
import torch
import torch.utils.data as data
from torchvision import transforms
def pil_loader(filename):
# open path as file to avoid Resourc... | 6,000 | 33.889535 | 116 | py |
JPerceiver | JPerceiver-master/mono/datasets/loader/sampler.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Duanzhixiang(zhixiangduan@deepmotion.ai)
from __future__ import division
import math
import torch
import numpy as np
from torch.distributed import get_world_size, get_rank
from torch.utils.data import Sampler
from torch.utils.data import DistributedSampler as _... | 5,755 | 34.097561 | 78 | py |
JPerceiver | JPerceiver-master/mono/datasets/loader/build_loader.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Duanzhixiang(zhixiangduan@deepmotion.ai)
from functools import partial
from mmcv.runner import get_dist_info
from mmcv.parallel import collate
from torch.utils.data import DataLoader
from .sampler import GroupSampler, DistributedGroupSampler, DistributedSampler
... | 1,979 | 35 | 87 | py |
JPerceiver | JPerceiver-master/mono/model/registry.py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Duanzhixiang(zhixiangduan@deepmotion.ai)
import torch
import torch.nn as nn
class Registry(object):
def __init__(self, name):
self._name = name
self._module_dict = dict()
@property
def name(self):
return self._name
@prop... | 1,111 | 25.47619 | 73 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/dice_loss.py | """
get_tp_fp_fn, SoftDiceLoss, and DC_and_CE/TopK_loss are from https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/training/loss_functions
"""
import torch
# from ND_Crossentropy import CrossentropyND, TopKLoss, WeightedCrossEntropyLoss
from torch import nn
from torch.autograd import Variable
from torch import ein... | 17,462 | 33.375984 | 138 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/pose_decoder.py | from __future__ import absolute_import, division, print_function
import torch.nn as nn
class PoseDecoder(nn.Module):
def __init__(self, num_ch_enc, stride=1):
super(PoseDecoder, self).__init__()
self.reduce = nn.Conv2d(num_ch_enc[-1], 256, 1)
self.conv1 = nn.Conv2d(256, 256, 3, stride, 1)... | 855 | 30.703704 | 64 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/CrossViewTransformer.py | import os
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from PIL import Image
import matplotlib.pyplot as PLT
import matplotlib.cm as mpl_color_map
from .layout_model import Conv3x3
def feature_selection(input, dim, index):
# feature selection
# input: [N, ?... | 5,665 | 48.701754 | 125 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/depth_decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .layers import Conv1x1, Conv3x3, CRPBlock, upsample
class DepthDecoder(nn.Module):
def __init__(self, num_ch_enc):
super(DepthDecoder, self).__init__()
bottleneck = 256
stage = 4
self.do = nn.Dropout(p=0.5)
... | 5,367 | 37.898551 | 83 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/resnet.py | import os.path as osp
import torch
import torch.nn as nn
from torch.nn import BatchNorm2d as bn
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
def conv1x1(in_planes, out_planes, stri... | 5,737 | 30.016216 | 96 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/CycledViewProjection.py | import os
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class CycledViewProjection(nn.Module):
def __init__(self, in_dim):
super(CycledViewProjection, self).__init__()
self.transform_module = TransformModule(dim=in_dim)
self.retransform_... | 2,877 | 36.868421 | 95 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/boundary_loss.py | import torch
# from nnunet.training.loss_functions.TopK_loss import TopKLoss
# from nnunet.utilities.nd_softmax import softmax_helper
# from nnunet.training.loss_functions.ND_Crossentropy import CrossentropyND
# from nnunet.utilities.tensor_utilities import sum_tensor
from torch import nn
from scipy.ndimage import dist... | 11,267 | 33.885449 | 114 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/ResnetEncoder.py | from __future__ import absolute_import, division, print_function
import numpy as np
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torchvision.models as models
import matplotlib.pyplot as PLT
class ResNetMultiImageInput(models.ResNet):
"""Constructs a resnet model with vary... | 4,043 | 35.432432 | 79 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/layers.py | from __future__ import absolute_import, division, print_function
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class PyramidPooling(nn.Module):
def __init__(self, in_channels, norm_layer):
super(PyramidPooling, self).__init__()
self.pool1 = nn.AdaptiveAvgP... | 11,871 | 38.052632 | 128 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/depth_encoder.py | from __future__ import absolute_import, division, print_function
import numpy as np
import torch
import torch.nn as nn
from .resnet import resnet18, resnet34, resnet50, resnet101
class DepthEncoder(nn.Module):
def __init__(self, num_layers, pretrained_path=None):
super(DepthEncoder, self).__init__()
... | 1,565 | 33.8 | 92 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/pose_encoder.py | from __future__ import absolute_import, division, print_function
import numpy as np
import torch
import torch.nn as nn
from .resnet import ResNet, BasicBlock, resnet18, resnet34, resnet50, resnet101, Bottleneck
from torch.nn import BatchNorm2d as bn
class ResNetMultiImageInput(ResNet):
def __init__(self, block,... | 3,705 | 38.849462 | 109 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/net.py | from __future__ import absolute_import, division, print_function
import torch
import torch.nn.functional as F
import torch.nn as nn
from .dice_loss import IoULoss, TverskyLoss, SoftDiceLoss
from .focal_loss import FocalLoss
from .boundary_loss import BDLoss
from .layers import SSIM, Backproject, Project, disp_to_depth,... | 40,349 | 50.270648 | 150 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/focal_loss.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
"""
copy from: https://github.com/Hsuxu/Loss_ToolBox-PyTorch/blob/master/FocalLoss/FocalLoss.py
This is a implementation of Focal Loss with smooth label cross entropy supported which is propos... | 3,551 | 36.389474 | 118 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/net_testcomplexity.py | from __future__ import absolute_import, division, print_function
import torch
import torch.nn.functional as F
import torch.nn as nn
from .dice_loss import IoULoss, TverskyLoss, SoftDiceLoss
from .focal_loss import FocalLoss
from .boundary_loss import BDLoss
from .layers import SSIM, Backproject, Project, disp_to_depth,... | 55,641 | 53.021359 | 150 | py |
JPerceiver | JPerceiver-master/mono/model/mono_baseline/layout_model.py | from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import spectral_norm
from .ResnetEncoder import ResnetEncoder
import matplotlib.pyplot as PLT
# Utils
class ConvBlock(nn.Module):
"""Layer to perform a convolution follo... | 6,202 | 29.707921 | 74 | py |
JPerceiver | JPerceiver-master/scripts/eval_depth_eigen.py | from __future__ import absolute_import, division, print_function
import cv2
import sys
import numpy as np
from mmcv import Config
import torch
from torch.utils.data import DataLoader
sys.path.append('.')
from mono.model.registry import MONO
from mono.model.mono_baseline.layers import disp_to_depth
from mono.datasets.... | 4,394 | 36.245763 | 196 | py |
JPerceiver | JPerceiver-master/scripts/eval_argo_both_video.py | from __future__ import absolute_import, division, print_function
import os
import cv2
import sys
import matplotlib.pyplot as plt
import numpy as np
from mmcv import Config
import glob
import torch
import io
from torchvision import transforms
from torch.utils.data import DataLoader
import PIL.Image as pil
sys.path.appen... | 14,825 | 41.726225 | 204 | py |
JPerceiver | JPerceiver-master/scripts/draw_odometry.py | from __future__ import absolute_import, division, print_function
import os
import sys
import argparse
import numpy as np
import torch
from torch.utils.data import DataLoader
sys.path.append('.')
sys.path.append('..')
from mono.datasets.euroc_dataset import FolderDataset
from mono.datasets.kitti_dataset import KITTIOd... | 5,250 | 48.537736 | 210 | py |
JPerceiver | JPerceiver-master/scripts/eval_kitti_video.py | from __future__ import absolute_import, division, print_function
import os
import cv2
import sys
import matplotlib.pyplot as plt
import numpy as np
from mmcv import Config
import glob
import torch
import io
from torchvision import transforms
from torch.utils.data import DataLoader
import PIL.Image as pil
sys.path.appen... | 16,330 | 41.976316 | 200 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_uncertainty_aware_mean_teacher_ViT_2D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... | 14,772 | 43.230539 | 127 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_adversarial_consistency_ViT_2D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... | 15,884 | 42.04878 | 143 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/test_3D.py | import argparse
import os
import shutil
from glob import glob
import torch
from networks.unet_3D import unet_3D
from test_3D_util import test_all_case
parser = argparse.ArgumentParser()
parser.add_argument('--root_path', type=str,
default='../data/BraTS2019', help='Name of Experiment')
parser.add... | 1,495 | 34.619048 | 128 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_Fixmatch_CNN_2D.py | import argparse
import logging
import os
import re
import random
import shutil
import sys
import time
from xml.etree.ElementInclude import default_loader
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.d... | 15,924 | 37.746959 | 119 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_mean_teacher_3D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... | 11,142 | 40.890977 | 126 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/val_3D.py | import math
from glob import glob
import h5py
import nibabel as nib
import numpy as np
import SimpleITK as sitk
import torch
import torch.nn.functional as F
from medpy import metric
from tqdm import tqdm
def test_single_case(net, image, stride_xy, stride_z, patch_size, num_classes=1):
w, h, d = image.shape
... | 4,073 | 36.722222 | 130 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_fully_supervised_2D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... | 8,999 | 40.09589 | 117 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_cross_pseudo_supervision_3D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... | 14,030 | 42.574534 | 120 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_exam_student_teacher_3D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... | 12,245 | 39.415842 | 126 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_cnn_meet_vit_2D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms
from tensorboardX import SummaryWriter
from to... | 22,537 | 42.425819 | 126 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/test_2D_fully.py | import argparse
import os
import shutil
import h5py
import nibabel as nib
import numpy as np
import SimpleITK as sitk
import torch
from medpy import metric
from scipy.ndimage import zoom
from scipy.ndimage.interpolation import zoom
from tqdm import tqdm
# from networks.efficientunet import UNet
from networks.net_fact... | 4,595 | 35.188976 | 76 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_Contrastive_Cross_CNN_ViT_2D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
from datetime import datetime
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from to... | 20,290 | 45.011338 | 275 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/gridmask.py | # -*- coding: utf-8 -*-
"""
Created on Tue Mar 15 14:38:27 2022
@author: loua2
"""
import torch
import torch.nn as nn
import numpy as np
from PIL import Image
import pdb
import math
class Grid(object):
def __init__(self, d1, d2, rotate=1, ratio=0.5, mode=0, prob=1.):
self.d1 = d1
self.d2 = d2
... | 3,113 | 27.833333 | 105 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_cross_pseudo_supervision_2D_ViT.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn.modules.loss import Cro... | 17,267 | 42.606061 | 128 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_fully_supervised_3D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... | 8,444 | 40.397059 | 126 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/test_CNNVIT.py | import argparse
import os
import shutil
import h5py
import nibabel as nib
import numpy as np
import SimpleITK as sitk
import torch
from medpy import metric
from scipy.ndimage import zoom
from scipy.ndimage.interpolation import zoom
from tqdm import tqdm
from config import get_config
from networks.vision_transformer i... | 5,551 | 38.375887 | 210 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_cross_teaching_between_cnn_transformer_2D.py | # -*- coding: utf-8 -*-
# Author: Xiangde Luo
# Date: 16 Dec. 2021
# Implementation for Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer.
# # Reference:
# @article{luo2021ctbct,
# title={Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transfor... | 18,005 | 43.569307 | 142 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_Contrastive_Fixmatch_CNN_ViT_2D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
from datetime import datetime
# from info_nce import *
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX imp... | 30,430 | 43.751471 | 129 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/val_2D.py | import numpy as np
import torch
from medpy import metric
from scipy.ndimage import zoom
def calculate_metric_percase(pred, gt):
pred[pred > 0] = 1
gt[gt > 0] = 1
if pred.sum() > 0:
dice = metric.binary.dc(pred, gt)
hd95 = metric.binary.hd95(pred, gt)
return dice, hd95
else:
... | 2,359 | 35.307692 | 77 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_cross_pseudo_supervision_2D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... | 15,388 | 42.471751 | 129 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_uncertainty_aware_mean_teacher_3D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... | 12,302 | 41.570934 | 126 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_uncertainty_aware_mean_teacher_2D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... | 12,858 | 42.006689 | 108 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_mean_teacher_2D.py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms
from tensorboardX import SummaryWriter
from to... | 14,295 | 41.047059 | 118 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/train_tripleview_2D(demo).py | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms
from tensorboardX import SummaryWriter
from to... | 22,945 | 42.957854 | 158 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/efficient_encoder.py | import re
from typing import List
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch.utils import get_model_params, url_map
from torchvision.models.densenet import DenseNet
from torchvision.models.resnet import BasicBlock... | 17,641 | 31.975701 | 110 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/pnet.py |
# -*- coding: utf-8 -*-
"""
An PyTorch implementation of the DeepIGeoS paper:
Wang, Guotai and Zuluaga, Maria A and Li, Wenqi and Pratt, Rosalind and Patel, Premal A and Aertsen, Michael and Doel, Tom and David, Anna L and Deprest, Jan and Ourselin, S{\'e}bastien and others:
DeepIGeoS: a deep interactive geo... | 4,200 | 33.154472 | 202 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/pretrained_unet.py | # -*- coding: utf-8 -*-
"""
Created on Mon Feb 14 20:08:11 2022
@author: loua2
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from networks.Res2Net_v1b import res2net50_v1b_26w_4s, res2net101_v1b_26w_4s
import math
import torchvision.models as models
import os
class CONV_Block(nn.Module):
... | 2,618 | 30.178571 | 82 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/projector.py | # -*- coding: utf-8 -*-
"""
Created on Thu Feb 3 11:21:36 2022
@author: loua2
"""
import functools
import torch
import torch.nn as nn
class CONV_Block(nn.Module):
def __init__(self, in_channels, middle_channels, out_channels):
super().__init__()
# self.relu = nn.ReLU(inplace = True)
sel... | 2,851 | 29.021053 | 77 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/grid_attention_layer.py | import torch
from torch import nn
from torch.nn import functional as F
from networks.networks_other import init_weights
class _GridAttentionBlockND(nn.Module):
def __init__(self, in_channels, gating_channels, inter_channels=None, dimension=3, mode='concatenation',
sub_sample_factor=(2,2,2)):
... | 16,619 | 40.446384 | 137 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/attention_unet.py | import torch.nn as nn
import torch
from networks.utils import UnetConv3, UnetUp3_CT, UnetGridGatingSignal3, UnetDsv3
import torch.nn.functional as F
from networks.networks_other import init_weights
from networks.grid_attention_layer import GridAttentionBlock3D
class Attention_UNet(nn.Module):
def __init__(self, ... | 6,336 | 45.595588 | 122 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/discriminator.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class FC3DDiscriminator(nn.Module):
def __init__(self, num_classes, ndf=64, n_channel=1):
super(FC3DDiscriminator, self).__init__()
# downsample 16
self.conv0 = nn.Conv3d(
num_classes, ndf, kernel_size=4, strid... | 3,133 | 30.029703 | 78 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/encoder_tool.py | from typing import List
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch.utils import get_model_params, url_map
class EncoderMixin:
"""Add encoder functionality such as:
- output channels specification of ... | 6,765 | 30.765258 | 87 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from networks.networks_other import init_weights
class conv2DBatchNorm(nn.Module):
def __init__(self, in_channels, n_filters, k_size, stride, padding, bias=True):
super(conv2DBatchNorm, self).__init__()
self.cb_unit = nn.Sequent... | 18,130 | 38.159827 | 120 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/neural_network.py | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... | 45,370 | 49.189159 | 137 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/VoxResNet.py | # -*- coding: utf-8 -*-
from __future__ import print_function, division
import torch
import torch.nn as nn
class SEBlock(nn.Module):
def __init__(self, in_channels, r):
super(SEBlock, self).__init__()
redu_chns = int(in_channels / r)
self.se_layers = nn.Sequential(
nn.Adapti... | 3,637 | 30.094017 | 79 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/vision_transformer.py | # coding=utf-8
# This file borrowed from Swin-UNet: https://github.com/HuCaoFighting/Swin-Unet
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import logging
import math
from os.path import join as pjoin
import torch
import torch.nn as nn
impor... | 3,981 | 43.244444 | 113 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/swin_transformer_unet_skip_expand_decoder_sys.py | # This file borrowed from Swin-UNet: https://github.com/HuCaoFighting/Swin-Unet
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class Mlp(nn.Module):
def __init__(self, in_features, hidden... | 33,208 | 40.253416 | 209 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/unet.py | # -*- coding: utf-8 -*-
"""
The implementation is borrowed from: https://github.com/HiLab-git/PyMIC
"""
from __future__ import division, print_function
import numpy as np
import torch
import torch.nn as nn
from torch.distributions.uniform import Uniform
def kaiming_normal_init_weight(model):
for m in model.module... | 13,801 | 34.030457 | 79 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/efficientunet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from networks.attention import *
from networks.efficient_encoder import get_encoder
def initialize_decoder(module):
for m in module.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, mode="fan_in", non... | 7,930 | 34.725225 | 117 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/Res2Net_v1b.py | # -*- coding: utf-8 -*-
"""
Created on Tue Jun 22 16:13:07 2021
@author: angelou
"""
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import torch.nn.functional as F
__all__ = ['Res2Net', 'res2net50_v1b', 'res2net101_v1b', 'res2net50_v1b_26w_4s']
model_urls = {
'res2net50... | 8,203 | 34.059829 | 122 | py |
CV-SSL-MIS | CV-SSL-MIS-main/code/networks/nnunet.py | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... | 23,919 | 43.71028 | 177 | py |
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