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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TraBS | TraBS-main/scripts/main_train.py |
from pathlib import Path
from datetime import datetime
import torch
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
import numpy as np
import torchio as tio
from breaststudies.data import BreastDataModule, BreastDataModuleLR, BreastDataModule2D,... | 6,986 | 42.12963 | 191 | py |
TraBS | TraBS-main/scripts/main_predict.py | from pathlib import Path
from datetime import datetime
from shutil import copyfile
import logging
import numpy as np
import torch
import torch.nn.functional as F
import SimpleITK as sitk
import torchio as tio
from breaststudies.data import BreastDatasetCreator
from breaststudies.models import UNet, nnUNet, SwinUN... | 6,468 | 41.559211 | 146 | py |
TraBS | TraBS-main/scripts/main_predict_kfold.py | from pathlib import Path
from shutil import copyfile
import logging
import sys
import numpy as np
import torch
import torchio as tio
import SimpleITK as sitk
from monai.metrics import compute_meandice
from breaststudies.augmentation.augmentations import Resample2, ZNormalization, ToOrientation, RandomDisableChan... | 8,639 | 44.235602 | 213 | py |
TraBS | TraBS-main/breaststudies/models/basic_model.py |
from pathlib import Path
import json
import torch
import torch.nn.functional as F
import pytorch_lightning as pl
from torchvision.utils import save_image
from pytorch_lightning.utilities.cloud_io import load as pl_load
from pytorch_lightning.utilities.migration import pl_legacy_patch
from pytorch_msssim import ssim
f... | 9,994 | 43.820628 | 169 | py |
TraBS | TraBS-main/breaststudies/models/monai_mods/swin_unetr.py | # Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, so... | 38,039 | 39.947255 | 473 | py |
TraBS | TraBS-main/breaststudies/models/monai_mods/blocks.py | from typing import Sequence, Type, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import LayerNorm
from monai.networks.layers import Conv, trunc_normal_
from monai.utils import ensure_tuple_rep, optional_import
from monai.utils.module import look_up_option
R... | 3,877 | 36.650485 | 111 | py |
TraBS | TraBS-main/breaststudies/augmentation/augmentations.py | from typing import Iterable, Tuple, Union, List, Optional, Sequence, Dict
from numbers import Number
from pathlib import Path
import warnings
from tqdm import tqdm
import numpy as np
import nibabel as nib
import torch
import torchio as tio
from torchio import Subject, RandomAffine, IntensityTransform, CropOrPad, Re... | 31,025 | 40.983762 | 167 | py |
TraBS | TraBS-main/breaststudies/utils/prediction.py | import torch
import torch.nn.functional as F
import torchio as tio
import time
import logging
logger = logging.getLogger(__name__)
def series_pred(item_pointers, load_item, model, test_time_flipping=False, device=None):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None els... | 2,908 | 44.453125 | 141 | py |
TraBS | TraBS-main/breaststudies/utils/functions.py | import torch
import torch.nn.functional as F
import numpy as np
from torchvision.utils import draw_segmentation_masks
def heaviside(input, threshold=0.5):
"""Heaviside function
Arguments:
input {torch.Tensor} -- Input tensor
Keyword Arguments:
threshold {float} -- Input values... | 4,556 | 41.588785 | 239 | py |
TraBS | TraBS-main/breaststudies/utils/data.py | import numpy as np
import SimpleITK as sitk
from scipy.ndimage import zoom
def get_affine(image):
# Coppied from TorchIO:
# https://github.com/fepegar/torchio/blob/164a1bf3699863ef3a74f2a7694f6f4cf0fff361/torchio/data/io.py#L271
spacing = np.array(image.GetSpacing())
direction = np.array(image.GetDi... | 3,180 | 30.81 | 110 | py |
TraBS | TraBS-main/breaststudies/data/datamodule.py | from pathlib import Path
import yaml
import itertools
from tqdm import tqdm
import pytorch_lightning as pl
import torch
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import RandomSampler, WeightedRandomSampler
import torch.multiprocessing as mp
class BaseDataModule(pl.LightningDataModule... | 11,957 | 47.217742 | 191 | py |
TraBS | TraBS-main/breaststudies/data/datamodule_breast.py | import torch
from breaststudies.data import BaseDataModule, BreastDataset, BreastDatasetLR, BreastDataset2D, BreastUKADatasetLR
class BreastDataModule(BaseDataModule):
Dataset = BreastDataset
label2rgb = torch.tensor([
[0,0,0], # Background
... | 976 | 31.566667 | 115 | py |
TraBS | TraBS-main/breaststudies/data/dataset_breast.py | import logging
from pathlib import Path
import json
import torchio as tio
import SimpleITK as sitk
import numpy as np
from breaststudies.augmentation import ZNormalization, CropOrPadFixed
from breaststudies.data import BaseDataset
from breaststudies.utils import get_affine
logger = logging.getLogger(__name__)
c... | 27,146 | 45.325939 | 208 | py |
LearningToSelect | LearningToSelect-main/UFET/parallel_TE_UFET.py | """BERT finetuning runner."""
# from __future__ import absolute_import, division, print_function
import bi_bert as db
import numpy as np
import torch
import random
import wandb
import argparse
from scipy.special import softmax
import torch.nn as nn
from torch.nn import CrossEntropyLoss, BCEWithLogitsLoss
from torc... | 36,027 | 46.405263 | 263 | py |
LearningToSelect | LearningToSelect-main/UFET/context_TE_UFET.py | import argparse
import csv
import logging
import json
import random
import sys
import numpy as np
import torch
import torch.nn as nn
from collections import defaultdict
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
... | 35,757 | 41.117786 | 156 | py |
LearningToSelect | LearningToSelect-main/UFET/bi_bert.py |
import numpy as np
import torch
import json
import wandb
import argparse
from sklearn.metrics import pairwise
from torch.nn import CosineEmbeddingLoss
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from tqdm import tqdm
from transformers impo... | 24,039 | 45.953125 | 258 | py |
LearningToSelect | LearningToSelect-main/BANKING77/context_TE_BANKING77.py |
import argparse
import csv
import logging
import json
import random
import sys
import codecs
import numpy as np
import torch
import torch.nn as nn
from collections import defaultdict
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
... | 48,248 | 47.008955 | 218 | py |
LearningToSelect | LearningToSelect-main/BANKING77/parallel_TE_BANKING77.py | """BERT finetuning runner."""
# from __future__ import absolute_import, division, print_function
import numpy as np
import torch
import random
import argparse
import csv
import json
from collections import defaultdict
from scipy.special import softmax
from scipy import stats
from sklearn.metrics import accuracy_score... | 27,865 | 43.5856 | 252 | py |
LearningToSelect | LearningToSelect-main/MCTest/parallel_TE_MCTest.py | """BERT finetuning runner."""
# from __future__ import absolute_import, division, print_function
import codecs
import numpy as np
import torch
import random
import argparse
import json
from scipy.special import softmax
from sklearn.metrics import accuracy_score
from collections import defaultdict
import torch.nn as n... | 25,150 | 42.893543 | 283 | py |
LearningToSelect | LearningToSelect-main/MCTest/context_TE_MCTest.py | # Copyright (c) 2018, salesforce.com, inc.
# All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""BERT finetuning runner."""
from __future__ import absolute_import, division, print_function
import ... | 39,264 | 44.977752 | 218 | py |
MaskedDenoising | MaskedDenoising-main/main_test_swinir_x8.py | import argparse
import cv2
import glob
import numpy as np
from collections import OrderedDict
import os
import torch
import requests
from models.network_swinir import SwinIR as net
from utils import utils_image as util
from utils import utils_option as option
import lpips
import torch
def transform(v, op):
# if s... | 17,485 | 44.774869 | 133 | py |
MaskedDenoising | MaskedDenoising-main/main_test_swinir.py | import argparse
import cv2
import glob
import numpy as np
from collections import OrderedDict
import os
import torch
import requests
from models.network_swinir import SwinIR as net
from utils import utils_image as util
from utils import utils_option as option
import lpips
import torch
def main():
parser = argpars... | 15,007 | 45.9 | 133 | py |
MaskedDenoising | MaskedDenoising-main/main_train_psnr.py | import os.path
import math
import argparse
import time
import random
import numpy as np
from collections import OrderedDict
import logging
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
import torch
from utils import utils_logger
from utils import utils_image as uti... | 13,438 | 39.236527 | 225 | py |
MaskedDenoising | MaskedDenoising-main/models/network_cnn.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class MeanShift(nn.Conv2d):
def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1):
super(MeanShift, self).__init__(3, 3, kernel_size=1)
std = torch.Tensor(rgb_std)
self... | 2,591 | 25.181818 | 69 | py |
MaskedDenoising | MaskedDenoising-main/models/model_base.py | import os
import torch
import torch.nn as nn
from utils.utils_bnorm import merge_bn, tidy_sequential
from torch.nn.parallel import DataParallel, DistributedDataParallel
class ModelBase():
def __init__(self, opt):
self.opt = opt # opt
self.save_dir = opt['path']['models'] #... | 7,712 | 33.900452 | 148 | py |
MaskedDenoising | MaskedDenoising-main/models/network_rnan.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
# def make_model(args, parent=False):
# return RNAN(args)
### RNAN
def default_conv(in_channels, out_channels, kernel_size, bias=True):
return nn.Conv2d(
in_channels, out_channels, k... | 14,692 | 34.404819 | 134 | py |
MaskedDenoising | MaskedDenoising-main/models/select_network.py | import functools
import torch
from torch.nn import init
"""
# --------------------------------------------
# select the network of G, D and F
# --------------------------------------------
"""
# --------------------------------------------
# Generator, netG, G
# --------------------------------------------
def defi... | 22,764 | 39.435169 | 113 | py |
MaskedDenoising | MaskedDenoising-main/models/network_ridnet.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class MeanShift(nn.Module):
def __init__(self, mean_rgb, sub):
super(MeanShift, self).__init__()
sign = -1 if sub else 1
r = mean_rgb[0] * sign
g = mean_rgb[1] * s... | 10,852 | 28.815934 | 110 | py |
MaskedDenoising | MaskedDenoising-main/models/network_dncnn.py |
import torch.nn as nn
import models.basicblock as B
"""
# --------------------------------------------
# DnCNN (20 conv layers)
# FDnCNN (20 conv layers)
# IRCNN (7 conv layers)
# --------------------------------------------
# References:
@article{zhang2017beyond,
title={Beyond a gaussian denoiser: Residual learni... | 6,298 | 36.052941 | 123 | py |
MaskedDenoising | MaskedDenoising-main/models/model_plain.py | from collections import OrderedDict
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
from torch.optim import Adam
from models.select_network import define_G
from models.model_base import ModelBase
from models.loss import CharbonnierLoss
from models.loss_ssim import SSIMLoss
from utils.utils_mod... | 11,698 | 40.193662 | 146 | py |
MaskedDenoising | MaskedDenoising-main/models/loss.py | import torch
import torch.nn as nn
import torchvision
from torch.nn import functional as F
from torch import autograd as autograd
"""
Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace)
(2*): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(... | 11,137 | 37.673611 | 150 | py |
MaskedDenoising | MaskedDenoising-main/models/network_feature.py | import torch
import torch.nn as nn
import torchvision
"""
# --------------------------------------------
# VGG Feature Extractor
# --------------------------------------------
"""
# --------------------------------------------
# VGG features
# Assume input range is [0, 1]
# ------------------------------------------... | 1,594 | 32.93617 | 93 | py |
MaskedDenoising | MaskedDenoising-main/models/network_usrnet_v1.py | import torch
import torch.nn as nn
import models.basicblock as B
import numpy as np
from utils import utils_image as util
import torch.fft
# for pytorch version >= 1.8.1
"""
# --------------------------------------------
# Kai Zhang (cskaizhang@gmail.com)
@inproceedings{zhang2020deep,
title={Deep unfolding networ... | 8,627 | 31.681818 | 172 | py |
MaskedDenoising | MaskedDenoising-main/models/network_msrresnet.py | import math
import torch.nn as nn
import models.basicblock as B
import functools
import torch.nn.functional as F
import torch.nn.init as init
"""
# --------------------------------------------
# modified SRResNet
# -- MSRResNet0 (v0.0)
# -- MSRResNet1 (v0.1)
# --------------------------------------------
Referenc... | 6,718 | 35.715847 | 218 | py |
MaskedDenoising | MaskedDenoising-main/models/network_mirnet.py | """
## Learning Enriched Features for Real Image Restoration and Enhancement
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao
## ECCV 2020
## https://arxiv.org/abs/2003.06792
"""
# --- Imports --- #
import torch
import torch.nn as nn
import torch.nn.fun... | 15,375 | 35.961538 | 154 | py |
MaskedDenoising | MaskedDenoising-main/models/network_ffdnet.py | import numpy as np
import torch.nn as nn
import models.basicblock as B
import torch
"""
# --------------------------------------------
# FFDNet (15 or 12 conv layers)
# --------------------------------------------
Reference:
@article{zhang2018ffdnet,
title={FFDNet: Toward a fast and flexible solution for CNN-based i... | 2,593 | 29.517647 | 108 | py |
MaskedDenoising | MaskedDenoising-main/models/basicblock.py | from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
'''
# --------------------------------------------
# Advanced nn.Sequential
# https://github.com/xinntao/BasicSR
# --------------------------------------------
'''
def sequential(*args):
"""Advanced nn.Sequent... | 24,138 | 39.775338 | 160 | py |
MaskedDenoising | MaskedDenoising-main/models/common.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def default_conv(in_channels, out_channels, kernel_size, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2), bias=bias)
class MeanShift(n... | 10,839 | 33.74359 | 130 | py |
MaskedDenoising | MaskedDenoising-main/models/network_rrdbnet.py | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
... | 3,777 | 35.326923 | 94 | py |
MaskedDenoising | MaskedDenoising-main/models/network_faceenhancer.py | '''
@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
@author: yangxy (yangtao9009@gmail.com)
# 2021-06-03, modified by Kai
'''
import sys
op_path = 'models'
if op_path not in sys.path:
sys.path.insert(0, op_path)
from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
import m... | 19,199 | 26.906977 | 137 | py |
MaskedDenoising | MaskedDenoising-main/models/loss_ssim.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
"""
# ============================================
# SSIM loss
# https://github.com/Po-Hsun-Su/pytorch-ssim
# ============================================
"""
def gaussian(window_size, sigma):
... | 3,708 | 30.974138 | 104 | py |
MaskedDenoising | MaskedDenoising-main/models/network_dpsr.py | import math
import torch.nn as nn
import models.basicblock as B
"""
# --------------------------------------------
# modified SRResNet
# -- MSRResNet_prior (for DPSR)
# --------------------------------------------
References:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary B... | 4,331 | 37.678571 | 218 | py |
MaskedDenoising | MaskedDenoising-main/models/model_gan.py | from collections import OrderedDict
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
from torch.optim import Adam
from models.select_network import define_G, define_D
from models.model_base import ModelBase
from models.loss import GANLoss, PerceptualLoss
from models.loss_ssim import SSIMLoss
c... | 15,535 | 42.887006 | 205 | py |
MaskedDenoising | MaskedDenoising-main/models/network_unet.py | import torch
import torch.nn as nn
import models.basicblock as B
import numpy as np
'''
# ====================
# Residual U-Net
# ====================
citation:
@article{zhang2020plug,
title={Plug-and-Play Image Restoration with Deep Denoiser Prior},
author={Zhang, Kai and Li, Yawei and Zuo, Wangmeng and Zhang, Lei an... | 3,484 | 38.602273 | 170 | py |
MaskedDenoising | MaskedDenoising-main/models/network_srmd.py |
import torch.nn as nn
import models.basicblock as B
import torch
"""
# --------------------------------------------
# SRMD (15 conv layers)
# --------------------------------------------
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-resolution network for multiple degrada... | 2,804 | 33.207317 | 113 | py |
MaskedDenoising | MaskedDenoising-main/models/network_discriminator.py | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn.utils import spectral_norm
import models.basicblock as B
import functools
import numpy as np
"""
# --------------------------------------------
# Discriminator_PatchGAN
# Discriminator_UNet
# ----------------------------------------... | 13,231 | 38.032448 | 147 | py |
MaskedDenoising | MaskedDenoising-main/models/network_vrt.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import os
import warnings
import math
import torch
import torch.nn as nn
import torchvision
import torch.nn.functional as F
import torch.util... | 69,614 | 43.482428 | 175 | py |
MaskedDenoising | MaskedDenoising-main/models/network_swinir.py | # -----------------------------------------------------------------------------------
# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
# Originally Written by Ze Liu, Modified by Jingyun Liang.
# -----------------------------------------------------------------------------------
imp... | 46,138 | 42.733649 | 134 | py |
MaskedDenoising | MaskedDenoising-main/models/network_imdn.py | import math
import torch.nn as nn
import models.basicblock as B
"""
# --------------------------------------------
# simplified information multi-distillation
# network (IMDN) for SR
# --------------------------------------------
References:
@inproceedings{hui2019lightweight,
title={Lightweight Image Super-Resoluti... | 2,513 | 36.522388 | 131 | py |
MaskedDenoising | MaskedDenoising-main/models/model_vrt.py | from collections import OrderedDict
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
from torch.optim import Adam
from models.select_network import define_G
from models.model_plain import ModelPlain
from models.loss import CharbonnierLoss
from models.loss_ssim import SSIMLoss
from utils.utils_m... | 11,246 | 42.42471 | 127 | py |
MaskedDenoising | MaskedDenoising-main/models/network_usrnet.py | import torch
import torch.nn as nn
import models.basicblock as B
import numpy as np
from utils import utils_image as util
"""
# --------------------------------------------
# Kai Zhang (cskaizhang@gmail.com)
@inproceedings{zhang2020deep,
title={Deep unfolding network for image super-resolution},
author={Zhang, Ka... | 10,347 | 28.994203 | 172 | py |
MaskedDenoising | MaskedDenoising-main/models/network_rrdb.py | import math
import torch.nn as nn
import models.basicblock as B
"""
# --------------------------------------------
# SR network with Residual in Residual Dense Block (RRDB)
# "ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks"
# --------------------------------------------
"""
class RRDB(nn.Module):... | 1,828 | 32.254545 | 112 | py |
MaskedDenoising | MaskedDenoising-main/models/op/upfirdn2d.py | import os
import torch
from torch.autograd import Function
from torch.utils.cpp_extension import load, _import_module_from_library
module_path = os.path.dirname(__file__)
upfirdn2d_op = load(
'upfirdn2d',
sources=[
os.path.join(module_path, 'upfirdn2d.cpp'),
os.path.join(module_path, 'upfirdn... | 5,313 | 27.116402 | 108 | py |
MaskedDenoising | MaskedDenoising-main/models/op/fused_act.py | import os
import torch
from torch import nn
from torch.autograd import Function
from torch.utils.cpp_extension import load, _import_module_from_library
module_path = os.path.dirname(__file__)
fused = load(
'fused',
sources=[
os.path.join(module_path, 'fused_bias_act.cpp'),
os.path.join(module... | 2,492 | 27.011236 | 83 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_matconvnet.py | # -*- coding: utf-8 -*-
import numpy as np
import torch
from collections import OrderedDict
# import scipy.io as io
import hdf5storage
"""
# --------------------------------------------
# Convert matconvnet SimpleNN model into pytorch model
# --------------------------------------------
# Kai Zhang (cskaizhang@gmail.... | 6,804 | 33.368687 | 239 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_sisr.py | # -*- coding: utf-8 -*-
from utils import utils_image as util
import random
import scipy
import scipy.stats as ss
import scipy.io as io
from scipy import ndimage
from scipy.interpolate import interp2d
import numpy as np
import torch
"""
# --------------------------------------------
# Super-Resolution
# -----------... | 23,082 | 26.188457 | 138 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_image.py | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
# import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
'''
# -... | 33,132 | 31.579154 | 120 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_dist.py | # Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501
import functools
import os
import subprocess
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
# ----------------------------------
# init
# ----------------------------------
def init... | 5,275 | 25.118812 | 102 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_params.py | import torch
import torchvision
from models import basicblock as B
def show_kv(net):
for k, v in net.items():
print(k)
# should run train debug mode first to get an initial model
#crt_net = torch.load('../../experiments/debug_SRResNet_bicx4_in3nf64nb16/models/8_G.pth')
#
#for k, v in crt_net.items():
# ... | 4,039 | 28.705882 | 103 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_blindsr.py | # -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch
from utils import utils_image as util
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
"""
# --------------------------------------------
# Super-Res... | 21,023 | 31.85 | 147 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_deblur.py | # -*- coding: utf-8 -*-
import numpy as np
import scipy
from scipy import fftpack
import torch
from math import cos, sin
from numpy import zeros, ones, prod, array, pi, log, min, mod, arange, sum, mgrid, exp, pad, round
from numpy.random import randn, rand
from scipy.signal import convolve2d
import cv2
import random
#... | 19,609 | 28.893293 | 188 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_model.py | # -*- coding: utf-8 -*-
import numpy as np
import torch
from utils import utils_image as util
import re
import glob
import os
'''
# --------------------------------------------
# Model
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# ------------------------... | 9,982 | 29.160121 | 148 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_regularizers.py | import torch
import torch.nn as nn
'''
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# --------------------------------------------
'''
# --------------------------------------------
# SVD Orthogonal Regularization
# --------------------------------------... | 3,416 | 31.542857 | 87 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_mask.py | # -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch
from utils import utils_image as util
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
"""
# --------------------------------------------
# Super-Reso... | 19,881 | 31.119548 | 147 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_bnorm.py | import torch
import torch.nn as nn
"""
# --------------------------------------------
# Batch Normalization
# --------------------------------------------
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# 01/Jan/2019
# --------------------------------------------
"""
# --------------------------------... | 3,132 | 33.054348 | 187 | py |
MaskedDenoising | MaskedDenoising-main/utils/utils_modelsummary.py | import torch.nn as nn
import torch
import numpy as np
'''
---- 1) FLOPs: floating point operations
---- 2) #Activations: the number of elements of all ‘Conv2d’ outputs
---- 3) #Conv2d: the number of ‘Conv2d’ layers
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 21/July/2... | 16,097 | 32.123457 | 129 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_video_test.py | import glob
import torch
from os import path as osp
import torch.utils.data as data
import utils.utils_video as utils_video
class VideoRecurrentTestDataset(data.Dataset):
"""Video test dataset for recurrent architectures, which takes LR video
frames as input and output corresponding HR video frames. Modified... | 15,059 | 38.321149 | 97 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_sr.py | import random
import numpy as np
import torch.utils.data as data
import utils.utils_image as util
class DatasetSR(data.Dataset):
'''
# -----------------------------------------
# Get L/H for SISR.
# If only "paths_H" is provided, sythesize bicubicly downsampled L on-the-fly.
# --------------------... | 3,941 | 36.188679 | 128 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_jpeg.py | import random
import torch.utils.data as data
import utils.utils_image as util
import cv2
class DatasetJPEG(data.Dataset):
def __init__(self, opt):
super(DatasetJPEG, self).__init__()
print('Dataset: JPEG compression artifact reduction (deblocking) with quality factor. Only dataroot_H is needed.')... | 5,084 | 41.731092 | 122 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_blindsr.py | import random
import numpy as np
import torch.utils.data as data
import utils.utils_image as util
import os
from utils import utils_blindsr as blindsr
class DatasetBlindSR(data.Dataset):
'''
# -----------------------------------------
# dataset for BSRGAN
# -----------------------------------------
... | 3,515 | 37.217391 | 139 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_fdncnn.py | import random
import numpy as np
import torch
import torch.utils.data as data
import utils.utils_image as util
class DatasetFDnCNN(data.Dataset):
"""
# -----------------------------------------
# Get L/H/M for denosing on AWGN with a range of sigma.
# Only dataroot_H is needed.
# -----------------... | 4,290 | 38.009091 | 175 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_plain.py | import random
import numpy as np
import torch.utils.data as data
import utils.utils_image as util
class DatasetPlain(data.Dataset):
'''
# -----------------------------------------
# Get L/H for image-to-image mapping.
# Both "paths_L" and "paths_H" are needed.
# -----------------------------------... | 3,347 | 37.930233 | 128 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_usrnet.py | import random
import numpy as np
import torch
import torch.utils.data as data
import utils.utils_image as util
from utils import utils_deblur
from utils import utils_sisr
import os
from scipy import ndimage
from scipy.io import loadmat
# import hdf5storage
class DatasetUSRNet(data.Dataset):
'''
# ----------... | 5,037 | 38.669291 | 120 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_plainpatch.py | import os.path
import random
import numpy as np
import torch.utils.data as data
import utils.utils_image as util
class DatasetPlainPatch(data.Dataset):
'''
# -----------------------------------------
# Get L/H for image-to-image mapping.
# Both "paths_L" and "paths_H" are needed.
# --------------... | 5,097 | 37.621212 | 164 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_dncnn.py | import os.path
import random
import numpy as np
import torch
import torch.utils.data as data
import utils.utils_image as util
class DatasetDnCNN(data.Dataset):
"""
# -----------------------------------------
# Get L/H for denosing on AWGN with fixed sigma.
# Only dataroot_H is needed.
# ----------... | 3,505 | 33.372549 | 92 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_dpsr.py | import random
import numpy as np
import torch
import torch.utils.data as data
import utils.utils_image as util
class DatasetDPSR(data.Dataset):
'''
# -----------------------------------------
# Get L/H/M for noisy image SR.
# Only "paths_H" is needed, sythesize bicubicly downsampled L on-the-fly.
... | 4,930 | 36.356061 | 132 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_masked_denoising.py | import random
import numpy as np
import torch.utils.data as data
import utils.utils_image as util
import os
from utils import utils_mask
class DatasetMaskedDenoising(data.Dataset):
'''
# -----------------------------------------
# dataset for BSRGAN
# -----------------------------------------
'''
... | 3,340 | 38.77381 | 139 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_l.py | import torch.utils.data as data
import utils.utils_image as util
class DatasetL(data.Dataset):
'''
# -----------------------------------------
# Get L in testing.
# Only "dataroot_L" is needed.
# -----------------------------------------
# -----------------------------------------
'''
... | 1,337 | 29.409091 | 71 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_ffdnet.py | import random
import numpy as np
import torch
import torch.utils.data as data
import utils.utils_image as util
class DatasetFFDNet(data.Dataset):
"""
# -----------------------------------------
# Get L/H/M for denosing on AWGN with a range of sigma.
# Only dataroot_H is needed.
# -----------------... | 3,884 | 36.355769 | 104 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_srmd.py | import random
import numpy as np
import torch
import torch.utils.data as data
import utils.utils_image as util
from utils import utils_sisr
import hdf5storage
import os
class DatasetSRMD(data.Dataset):
'''
# -----------------------------------------
# Get L/H/M for noisy image SR with Gaussian kernels.
... | 6,011 | 37.538462 | 132 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_dnpatch.py | import random
import numpy as np
import torch
import torch.utils.data as data
import utils.utils_image as util
class DatasetDnPatch(data.Dataset):
"""
# -----------------------------------------
# Get L/H for denosing on AWGN with fixed sigma.
# ****Get all H patches first****
# Only dataroot_H is... | 4,808 | 34.88806 | 141 | py |
MaskedDenoising | MaskedDenoising-main/data/dataset_video_train.py | import numpy as np
import random
import torch
from pathlib import Path
import torch.utils.data as data
import utils.utils_video as utils_video
class VideoRecurrentTrainDataset(data.Dataset):
"""Video dataset for training recurrent networks.
The keys are generated from a meta info txt file.
basicsr/data/... | 15,730 | 39.648579 | 133 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/networks/equilibrium_u_net.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
class ConvBlock(nn.Module):
"""
A Convolutional Block that co... | 6,704 | 34.664894 | 98 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/networks/normalized_equilibrium_u_net.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
from utils.spectral_norm import conv_spectral_norm
import utils.spectra... | 7,553 | 38.34375 | 155 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/networks/twolayer_linear_net.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
class LinearNet(nn.Module):
def __init__(self, input_size, bottl... | 1,205 | 29.923077 | 66 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/networks/resnet.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
import torch
import torch.nn as nn
class nblock_resnet(nn.Module):
... | 1,833 | 30.62069 | 81 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/networks/u_net.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
class ConvBlock(nn.Module):
"""
A Convolutional Block that co... | 7,115 | 36.0625 | 135 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/training/denoiser_training.py | import torch
import numpy as np
from solvers import new_equilibrium_utils as eq_utils
from torch import autograd
from utils import cg_utils
import gc
def train_denoiser(denoising_net, train_dataloader, test_dataloader,
measurement_process, optimizer,
save_location, loss_function, n_ep... | 10,567 | 44.551724 | 130 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/training/standard_training.py | import torch
import numpy as np
def train_solver(solver, train_dataloader, test_dataloader,
measurement_process, optimizer,
save_location, loss_function, n_epochs, forward_model=None,
use_dataparallel=False, device='cpu', scheduler=None, n_blocks=10,
... | 6,265 | 40.496689 | 89 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/training/new_equilibrium_training.py | import torch
import numpy as np
from solvers import new_equilibrium_utils as eq_utils
from torch import autograd
def train_solver(single_iterate_solver, train_dataloader, test_dataloader,
measurement_process, optimizer,
save_location, loss_function, n_epochs, forward_iterator, iterato... | 10,141 | 45.1 | 113 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/training/refactor_equilibrium_training.py | import torch
import numpy as np
from solvers import new_equilibrium_utils as eq_utils
from torch import autograd
from utils import cg_utils
def train_solver(single_iterate_solver, train_dataloader, test_dataloader,
measurement_process, optimizer,
save_location, loss_function, n_epochs... | 16,122 | 47.272455 | 130 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/training/equilibrium_training.py | import torch
import numpy as np
from solvers import equilibrium_utils as eq_utils
from torch import autograd
def train_solver(single_iterate_solver, train_dataloader, test_dataloader,
measurement_process, optimizer,
save_location, loss_function, n_epochs,
use_datapara... | 14,024 | 44.684039 | 129 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/scripts/fixedpoint/deblur_proxgrad_fixedeta_pre.py | import torch
import os
import random
import sys
import argparse
sys.path.append('/home-nfs/gilton/learned_iterative_solvers')
# sys.path.append('/Users/dgilton/PycharmProjects/learned_iterative_solvers')
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
import operators.blurs as blu... | 6,779 | 38.418605 | 122 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/scripts/fixedpoint/mri_grad_fixedeta_pre_and4.py | import torch
import os
import random
import sys
import argparse
sys.path.append('/home-nfs/gilton/learned_iterative_solvers')
# sys.path.append('/Users/dgilton/PycharmProjects/learned_iterative_solvers')
import torch.nn as nn
import torch.optim as optim
import operators.singlecoil_mri as mrimodel
from operators.opera... | 6,531 | 39.320988 | 121 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/scripts/fixedpoint/mri_prox_fixedeta_pre_and.py | import torch
import os
import random
import sys
import argparse
sys.path.append('/home-nfs/gilton/learned_iterative_solvers')
# sys.path.append('/Users/dgilton/PycharmProjects/learned_iterative_solvers')
import torch.nn as nn
import torch.optim as optim
import operators.singlecoil_mri as mrimodel
from operators.opera... | 6,521 | 39.259259 | 121 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/scripts/denoising/gaussian_dncnn_norm_denoise.py | import torch
import os
import random
import sys
import argparse
sys.path.append('/home-nfs/gilton/learned_iterative_solvers')
# sys.path.append('/Users/dgilton/PycharmProjects/learned_iterative_solvers')
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
import operators.operator as ... | 5,275 | 35.638889 | 121 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/scripts/denoising/gaussian_unet_denoise.py | import torch
import os
import random
import sys
import argparse
sys.path.append('/home-nfs/gilton/learned_iterative_solvers')
# sys.path.append('/Users/dgilton/PycharmProjects/learned_iterative_solvers')
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
import operators.operator as ... | 4,901 | 35.044118 | 121 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/scripts/denoising/mri_unet_denoise.py | import torch
import os
import random
import sys
import argparse
sys.path.append('/home-nfs/gilton/learned_iterative_solvers')
# sys.path.append('/Users/dgilton/PycharmProjects/learned_iterative_solvers')
import torch.nn as nn
import torch.optim as optim
import operators.operator as lin_operator
from operators.operato... | 4,447 | 36.066667 | 121 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/scripts/denoising/mri_dncnn_denoise.py | import torch
import os
import random
import sys
import argparse
sys.path.append('/home-nfs/gilton/learned_iterative_solvers')
# sys.path.append('/Users/dgilton/PycharmProjects/learned_iterative_solvers')
import torch.nn as nn
import torch.optim as optim
import operators.operator as lin_operator
from operators.operato... | 5,199 | 36.681159 | 121 | py |
deep_equilibrium_inverse | deep_equilibrium_inverse-main/operators/operator.py | import torch
class LinearOperator(torch.nn.Module):
def __init__(self):
super(LinearOperator, self).__init__()
def forward(self, x):
pass
def adjoint(self, x):
pass
def gramian(self, x):
return self.adjoint(self.forward(x))
class SelfAdjointLinearOperator(LinearOpera... | 819 | 24.625 | 61 | py |
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