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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Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/main.py | import torch
import utility
import data
import model
import loss
from option import args
from trainer import Trainer
torch.manual_seed(args.seed)
checkpoint = utility.checkpoint(args)
def main():
global model
if args.data_test == ['video']:
from videotester import VideoTester
model = model.Mo... | 1,028 | 27.583333 | 98 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/utility.py | import os
import math
import time
import datetime
from multiprocessing import Process
from multiprocessing import Queue
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import imageio
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lrs
class time... | 7,480 | 30.432773 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/dataloader.py | import threading
import random
import torch
import torch.multiprocessing as multiprocessing
from torch.utils.data import DataLoader
from torch.utils.data import SequentialSampler
from torch.utils.data import RandomSampler
from torch.utils.data import BatchSampler
from torch.utils.data import _utils
from torch.utils.da... | 5,259 | 32.081761 | 104 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/videotester.py | import os
import math
import utility
from data import common
import torch
import cv2
from tqdm import tqdm
class VideoTester():
def __init__(self, args, my_model, ckp):
self.args = args
self.scale = args.scale
self.ckp = ckp
self.model = my_model
self.filename, _ = os.p... | 2,280 | 30.246575 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/trainer.py | import os
import math
from decimal import Decimal
import utility
import torch
import torch.nn.utils as utils
from tqdm import tqdm
class Trainer():
def __init__(self, args, loader, my_model, my_loss, ckp):
self.args = args
self.scale = args.scale
self.ckp = ckp
self.loader_train ... | 4,820 | 31.795918 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/loss/adversarial.py | import utility
from types import SimpleNamespace
from model import common
from loss import discriminator
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class Adversarial(nn.Module):
def __init__(self, args, gan_type):
super(Adversarial, self).__init__()
... | 4,393 | 37.884956 | 84 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/loss/discriminator.py | from model import common
import torch.nn as nn
class Discriminator(nn.Module):
'''
output is not normalized
'''
def __init__(self, args):
super(Discriminator, self).__init__()
in_channels = args.n_colors
out_channels = 64
depth = 7
def _block(_in_channels,... | 1,595 | 27.5 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/loss/vgg.py | from model import common
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class VGG(nn.Module):
def __init__(self, conv_index, rgb_range=1):
super(VGG, self).__init__()
vgg_features = models.vgg19(pretrained=True).features
modules = [m ... | 1,106 | 28.918919 | 75 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/loss/__init__.py | import os
from importlib import import_module
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Loss(nn.modules.loss._Loss):
def __init__(self, args, ckp):
super(Loss, self).__init__()
... | 4,628 | 31.598592 | 83 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/utils/tools.py | import os
import torch
import numpy as np
from PIL import Image
import torch.nn.functional as F
def normalize(x):
return x.mul_(2).add_(-1)
def same_padding(images, ksizes, strides, rates):
assert len(images.size()) == 4
batch_size, channel, rows, cols = images.size()
out_rows = (rows + strides[0] - ... | 2,777 | 32.878049 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/data/benchmark.py | import os
from data import common
from data import srdata
import numpy as np
import torch
import torch.utils.data as data
class Benchmark(srdata.SRData):
def __init__(self, args, name='', train=True, benchmark=True):
super(Benchmark, self).__init__(
args, name=name, train=train, benchmark=Tr... | 703 | 26.076923 | 67 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/data/video.py | import os
from data import common
import cv2
import numpy as np
import imageio
import torch
import torch.utils.data as data
class Video(data.Dataset):
def __init__(self, args, name='Video', train=False, benchmark=False):
self.args = args
self.name = name
self.scale = args.scale
s... | 1,207 | 25.844444 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/data/srdata.py | import os
import glob
import random
import pickle
from data import common
import numpy as np
import imageio
import torch
import torch.utils.data as data
class SRData(data.Dataset):
def __init__(self, args, name='', train=True, benchmark=False):
self.args = args
self.name = name
self.train... | 5,343 | 32.822785 | 73 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/data/demo.py | import os
from data import common
import numpy as np
import imageio
import torch
import torch.utils.data as data
class Demo(data.Dataset):
def __init__(self, args, name='Demo', train=False, benchmark=False):
self.args = args
self.name = name
self.scale = args.scale
self.idx_scale... | 1,075 | 25.9 | 76 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/data/common.py | import random
import numpy as np
import skimage.color as sc
import torch
def get_patch(*args, patch_size=96, scale=2, multi=False, input_large=False):
ih, iw = args[0].shape[:2]
if not input_large:
p = scale if multi else 1
tp = p * patch_size
ip = tp // scale
else:
tp = ... | 1,786 | 23.479452 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/data/__init__.py | from importlib import import_module
#from dataloader import MSDataLoader
from torch.utils.data import dataloader
from torch.utils.data import ConcatDataset
# This is a simple wrapper function for ConcatDataset
class MyConcatDataset(ConcatDataset):
def __init__(self, datasets):
super(MyConcatDataset, self).... | 1,949 | 35.792453 | 76 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/rcan.py | ## ECCV-2018-Image Super-Resolution Using Very Deep Residual Channel Attention Networks
## https://arxiv.org/abs/1807.02758
from model import common
from model.attention import ContextualAttention
import torch.nn as nn
import torch
def make_model(args, parent=False):
return RCAN(args)
## Channel Attention (CA) Lay... | 5,662 | 36.256579 | 139 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/ddbpn.py | # Deep Back-Projection Networks For Super-Resolution
# https://arxiv.org/abs/1803.02735
from model import common
import torch
import torch.nn as nn
def make_model(args, parent=False):
return DDBPN(args)
def projection_conv(in_channels, out_channels, scale, up=True):
kernel_size, stride, padding = {
... | 3,629 | 26.5 | 78 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/rdn.py | # Residual Dense Network for Image Super-Resolution
# https://arxiv.org/abs/1802.08797
from model import common
import torch
import torch.nn as nn
def make_model(args, parent=False):
return RDN(args)
class RDB_Conv(nn.Module):
def __init__(self, inChannels, growRate, kSize=3):
super(RDB_Conv, self)... | 3,202 | 29.216981 | 90 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/mdsr.py | from model import common
import torch.nn as nn
def make_model(args, parent=False):
return MDSR(args)
class MDSR(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(MDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
... | 1,837 | 25.637681 | 78 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/common.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def default_conv(in_channels, out_channels, kernel_size,stride=1, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2),stride=stride, bias=bias)
class MeanShift(nn.Conv2d):
... | 2,799 | 30.460674 | 80 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/__init__.py | import os
from importlib import import_module
import torch
import torch.nn as nn
from torch.autograd import Variable
class Model(nn.Module):
def __init__(self, args, ckp):
super(Model, self).__init__()
print('Making model...')
self.scale = args.scale
self.idx_scale = 0
sel... | 6,243 | 31.520833 | 90 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/mssr.py | from model import common
import torch.nn as nn
import torch
from model.attention import ContextualAttention,NonLocalAttention
def make_model(args, parent=False):
return MSSR(args)
class MultisourceProjection(nn.Module):
def __init__(self, in_channel,kernel_size = 3, conv=common.default_conv):
super(Mul... | 4,174 | 38.761905 | 122 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/edsr.py | from model import common
from model import attention
import torch.nn as nn
def make_model(args, parent=False):
if args.dilation:
from model import dilated
return PAEDSR(args, dilated.dilated_conv)
else:
return PAEDSR(args)
class PAEDSR(nn.Module):
def __init__(self, args, conv=comm... | 2,989 | 32.977273 | 106 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from torchvision import utils as vutils
from model import common
from utils.tools import extract_image_patches,\
reduce_mean, reduce_sum, same_padding
class PyramidAttention(nn.Module):
def __init__(self, leve... | 4,427 | 46.106383 | 147 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/vdsr.py | from model import common
import torch.nn as nn
import torch.nn.init as init
url = {
'r20f64': ''
}
def make_model(args, parent=False):
return VDSR(args)
class VDSR(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(VDSR, self).__init__()
n_resblocks = args.n_resblocks... | 1,275 | 26.148936 | 73 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/paedsr.py | from model import common
from model import attention
import torch.nn as nn
def make_model(args, parent=False):
if args.dilation:
from model import dilated
return PAEDSR(args, dilated.dilated_conv)
else:
return PAEDSR(args)
class PAEDSR(nn.Module):
def __init__(self, args, conv=comm... | 2,989 | 32.977273 | 106 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/model/utils/tools.py | import os
import torch
import numpy as np
from PIL import Image
import torch.nn.functional as F
def normalize(x):
return x.mul_(2).add_(-1)
def same_padding(images, ksizes, strides, rates):
assert len(images.size()) == 4
batch_size, channel, rows, cols = images.size()
out_rows = (rows + strides[0] - ... | 2,777 | 32.878049 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/main.py | import torch
import utility
import data
import model
import loss
from option import args
from trainer import Trainer
torch.manual_seed(args.seed)
checkpoint = utility.checkpoint(args)
def main():
global model
if args.data_test == ['video']:
from videotester import VideoTester
model = model.Mo... | 1,026 | 27.527778 | 97 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/utility.py | import os
import math
import time
import datetime
from multiprocessing import Process
from multiprocessing import Queue
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import imageio
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lrs
class time... | 7,458 | 30.340336 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/dataloader.py | import threading
import random
import torch
import torch.multiprocessing as multiprocessing
from torch.utils.data import DataLoader
from torch.utils.data import SequentialSampler
from torch.utils.data import RandomSampler
from torch.utils.data import BatchSampler
from torch.utils.data import _utils
from torch.utils.da... | 5,259 | 32.081761 | 104 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/videotester.py | import os
import math
import utility
from data import common
import torch
import cv2
from tqdm import tqdm
class VideoTester():
def __init__(self, args, my_model, ckp):
self.args = args
self.scale = args.scale
self.ckp = ckp
self.model = my_model
self.filename, _ = os.p... | 2,280 | 30.246575 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/trainer.py | import os
import math
from decimal import Decimal
import utility
import torch
import torch.nn.utils as utils
from tqdm import tqdm
class Trainer():
def __init__(self, args, loader, my_model, my_loss, ckp):
self.args = args
self.scale = args.scale
self.ckp = ckp
self.loader_train ... | 4,820 | 31.795918 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/loss/adversarial.py | import utility
from types import SimpleNamespace
from model import common
from loss import discriminator
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class Adversarial(nn.Module):
def __init__(self, args, gan_type):
super(Adversarial, self).__init__()
... | 4,393 | 37.884956 | 84 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/loss/discriminator.py | from model import common
import torch.nn as nn
class Discriminator(nn.Module):
'''
output is not normalized
'''
def __init__(self, args):
super(Discriminator, self).__init__()
in_channels = args.n_colors
out_channels = 64
depth = 7
def _block(_in_channels,... | 1,595 | 27.5 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/loss/vgg.py | from model import common
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class VGG(nn.Module):
def __init__(self, conv_index, rgb_range=1):
super(VGG, self).__init__()
vgg_features = models.vgg19(pretrained=True).features
modules = [m ... | 1,106 | 28.918919 | 75 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/loss/__init__.py | import os
from importlib import import_module
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Loss(nn.modules.loss._Loss):
def __init__(self, args, ckp):
super(Loss, self).__init__()
... | 4,659 | 31.361111 | 80 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/utils/tools.py | import os
import torch
import numpy as np
from PIL import Image
import torch.nn.functional as F
def normalize(x):
return x.mul_(2).add_(-1)
def same_padding(images, ksizes, strides, rates):
assert len(images.size()) == 4
batch_size, channel, rows, cols = images.size()
out_rows = (rows + strides[0] - ... | 2,777 | 32.878049 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/data/benchmark.py | import os
from data import common
from data import srdata
import numpy as np
import torch
import torch.utils.data as data
class Benchmark(srdata.SRData):
def __init__(self, args, name='', train=True, benchmark=True):
super(Benchmark, self).__init__(
args, name=name, train=train, benchmark=Tr... | 703 | 26.076923 | 67 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/data/video.py | import os
from data import common
import cv2
import numpy as np
import imageio
import torch
import torch.utils.data as data
class Video(data.Dataset):
def __init__(self, args, name='Video', train=False, benchmark=False):
self.args = args
self.name = name
self.scale = args.scale
s... | 1,207 | 25.844444 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/data/srdata.py | import os
import glob
import random
import pickle
from data import common
import numpy as np
import imageio
import torch
import torch.utils.data as data
class SRData(data.Dataset):
def __init__(self, args, name='', train=True, benchmark=False):
self.args = args
self.name = name
self.train... | 5,337 | 32.78481 | 73 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/data/demo.py | import os
from data import common
import numpy as np
import imageio
import torch
import torch.utils.data as data
class Demo(data.Dataset):
def __init__(self, args, name='Demo', train=False, benchmark=False):
self.args = args
self.name = name
self.scale = args.scale
self.idx_scale... | 1,075 | 25.9 | 76 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/data/common.py | import random
import numpy as np
import skimage.color as sc
import torch
def get_patch(*args, patch_size=96, scale=1, multi=False, input_large=False):
ih, iw = args[0].shape[:2]
if not input_large:
p = 1 if multi else 1
tp = p * patch_size
ip = tp // 1
else:
tp = patch_si... | 1,770 | 23.260274 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/data/__init__.py | from importlib import import_module
#from dataloader import MSDataLoader
from torch.utils.data import dataloader
from torch.utils.data import ConcatDataset
# This is a simple wrapper function for ConcatDataset
class MyConcatDataset(ConcatDataset):
def __init__(self, datasets):
super(MyConcatDataset, self).... | 1,968 | 36.150943 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/model/rcan.py | ## ECCV-2018-Image Super-Resolution Using Very Deep Residual Channel Attention Networks
## https://arxiv.org/abs/1807.02758
from model import common
import torch.nn as nn
def make_model(args, parent=False):
return RCAN(args)
## Channel Attention (CA) Layer
class CALayer(nn.Module):
def __init__(self, channel... | 5,178 | 34.717241 | 116 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/model/ddbpn.py | # Deep Back-Projection Networks For Super-Resolution
# https://arxiv.org/abs/1803.02735
from model import common
import torch
import torch.nn as nn
def make_model(args, parent=False):
return DDBPN(args)
def projection_conv(in_channels, out_channels, scale, up=True):
kernel_size, stride, padding = {
... | 3,629 | 26.5 | 78 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/model/rdn.py | # Residual Dense Network for Image Super-Resolution
# https://arxiv.org/abs/1802.08797
from model import common
import torch
import torch.nn as nn
def make_model(args, parent=False):
return RDN(args)
class RDB_Conv(nn.Module):
def __init__(self, inChannels, growRate, kSize=3):
super(RDB_Conv, self)... | 3,202 | 29.216981 | 90 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/model/mdsr.py | from model import common
import torch.nn as nn
def make_model(args, parent=False):
return MDSR(args)
class MDSR(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(MDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
... | 1,837 | 25.637681 | 78 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/model/common.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def default_conv(in_channels, out_channels, kernel_size,stride=1, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2),stride=stride, bias=bias)
class MeanShift(nn.Conv2d):
... | 2,799 | 30.460674 | 80 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/model/__init__.py | import os
from importlib import import_module
import torch
import torch.nn as nn
from torch.autograd import Variable
class Model(nn.Module):
def __init__(self, args, ckp):
super(Model, self).__init__()
print('Making model...')
self.scale = args.scale
self.idx_scale = 0
sel... | 6,200 | 31.465969 | 90 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/model/panet.py | from model import common
from model import attention
import torch.nn as nn
def make_model(args, parent=False):
return PANET(args)
class PANET(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(PANET, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_f... | 2,779 | 32.493976 | 104 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/model/attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from torchvision import utils as vutils
from model import common
from utils.tools import extract_image_patches,\
reduce_mean, reduce_sum, same_padding
class PyramidAttention(nn.Module):
def __init__(self, leve... | 4,427 | 46.106383 | 147 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/model/vdsr.py | from model import common
import torch.nn as nn
import torch.nn.init as init
url = {
'r20f64': ''
}
def make_model(args, parent=False):
return VDSR(args)
class VDSR(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(VDSR, self).__init__()
n_resblocks = args.n_resblocks... | 1,275 | 26.148936 | 73 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/model/utils/tools.py | import os
import torch
import numpy as np
from PIL import Image
import torch.nn.functional as F
def normalize(x):
return x.mul_(2).add_(-1)
def same_padding(images, ksizes, strides, rates):
assert len(images.size()) == 4
batch_size, channel, rows, cols = images.size()
out_rows = (rows + strides[0] - ... | 2,777 | 32.878049 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/main.py | import torch
import utility
import data
import model
import loss
from option import args
from trainer import Trainer
torch.manual_seed(args.seed)
checkpoint = utility.checkpoint(args)
def main():
global model
if args.data_test == ['video']:
from videotester import VideoTester
model = model.Mo... | 1,026 | 27.527778 | 97 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/utility.py | import os
import math
import time
import datetime
from multiprocessing import Process
from multiprocessing import Queue
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import imageio
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lrs
class time... | 7,459 | 30.344538 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/dataloader.py | import threading
import random
import torch
import torch.multiprocessing as multiprocessing
from torch.utils.data import DataLoader
from torch.utils.data import SequentialSampler
from torch.utils.data import RandomSampler
from torch.utils.data import BatchSampler
from torch.utils.data import _utils
from torch.utils.da... | 5,259 | 32.081761 | 104 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/videotester.py | import os
import math
import utility
from data import common
import torch
import cv2
from tqdm import tqdm
class VideoTester():
def __init__(self, args, my_model, ckp):
self.args = args
self.scale = args.scale
self.ckp = ckp
self.model = my_model
self.filename, _ = os.p... | 2,280 | 30.246575 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/trainer.py | import os
import math
from decimal import Decimal
import utility
import torch
import torch.nn.utils as utils
from tqdm import tqdm
class Trainer():
def __init__(self, args, loader, my_model, my_loss, ckp):
self.args = args
self.scale = args.scale
self.ckp = ckp
self.loader_train ... | 4,820 | 31.795918 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/loss/adversarial.py | import utility
from types import SimpleNamespace
from model import common
from loss import discriminator
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class Adversarial(nn.Module):
def __init__(self, args, gan_type):
super(Adversarial, self).__init__()
... | 4,393 | 37.884956 | 84 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/loss/discriminator.py | from model import common
import torch.nn as nn
class Discriminator(nn.Module):
'''
output is not normalized
'''
def __init__(self, args):
super(Discriminator, self).__init__()
in_channels = args.n_colors
out_channels = 64
depth = 7
def _block(_in_channels,... | 1,595 | 27.5 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/loss/vgg.py | from model import common
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class VGG(nn.Module):
def __init__(self, conv_index, rgb_range=1):
super(VGG, self).__init__()
vgg_features = models.vgg19(pretrained=True).features
modules = [m ... | 1,106 | 28.918919 | 75 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/loss/__init__.py | import os
from importlib import import_module
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Loss(nn.modules.loss._Loss):
def __init__(self, args, ckp):
super(Loss, self).__init__()
... | 4,659 | 31.361111 | 80 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/utils/tools.py | import os
import torch
import numpy as np
from PIL import Image
import torch.nn.functional as F
def normalize(x):
return x.mul_(2).add_(-1)
def same_padding(images, ksizes, strides, rates):
assert len(images.size()) == 4
batch_size, channel, rows, cols = images.size()
out_rows = (rows + strides[0] - ... | 2,777 | 32.878049 | 79 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/data/benchmark.py | import os
from data import common
from data import srdata
import numpy as np
import torch
import torch.utils.data as data
class Benchmark(srdata.SRData):
def __init__(self, args, name='', train=True, benchmark=True):
super(Benchmark, self).__init__(
args, name=name, train=train, benchmark=Tr... | 702 | 26.038462 | 67 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/data/video.py | import os
from data import common
import cv2
import numpy as np
import imageio
import torch
import torch.utils.data as data
class Video(data.Dataset):
def __init__(self, args, name='Video', train=False, benchmark=False):
self.args = args
self.name = name
self.scale = args.scale
s... | 1,207 | 25.844444 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/data/srdata.py | import os
import glob
import random
import pickle
from data import common
import numpy as np
import imageio
import torch
import torch.utils.data as data
class SRData(data.Dataset):
def __init__(self, args, name='', train=True, benchmark=False):
self.args = args
self.name = name
self.train... | 5,337 | 32.78481 | 73 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/data/demo.py | import os
from data import common
import numpy as np
import imageio
import torch
import torch.utils.data as data
class Demo(data.Dataset):
def __init__(self, args, name='Demo', train=False, benchmark=False):
self.args = args
self.name = name
self.scale = args.scale
self.idx_scale... | 1,075 | 25.9 | 76 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/data/common.py | import random
import numpy as np
import skimage.color as sc
import torch
def get_patch(*args, patch_size=96, scale=1, multi=False, input_large=False):
ih, iw = args[0].shape[:2]
print('heelo')
print(args[0].shape)
if not input_large:
p = 1 if multi else 1
tp = p * patch_size
... | 1,799 | 23 | 77 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/data/__init__.py | from importlib import import_module
#from dataloader import MSDataLoader
from torch.utils.data import dataloader
from torch.utils.data import ConcatDataset
# This is a simple wrapper function for ConcatDataset
class MyConcatDataset(ConcatDataset):
def __init__(self, datasets):
super(MyConcatDataset, self).... | 1,987 | 36.509434 | 96 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/rcan.py | ## ECCV-2018-Image Super-Resolution Using Very Deep Residual Channel Attention Networks
## https://arxiv.org/abs/1807.02758
from model import common
import torch.nn as nn
def make_model(args, parent=False):
return RCAN(args)
## Channel Attention (CA) Layer
class CALayer(nn.Module):
def __init__(self, channel... | 5,178 | 34.717241 | 116 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/ddbpn.py | # Deep Back-Projection Networks For Super-Resolution
# https://arxiv.org/abs/1803.02735
from model import common
import torch
import torch.nn as nn
def make_model(args, parent=False):
return DDBPN(args)
def projection_conv(in_channels, out_channels, scale, up=True):
kernel_size, stride, padding = {
... | 3,629 | 26.5 | 78 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/rdn.py | # Residual Dense Network for Image Super-Resolution
# https://arxiv.org/abs/1802.08797
from model import common
import torch
import torch.nn as nn
def make_model(args, parent=False):
return RDN(args)
class RDB_Conv(nn.Module):
def __init__(self, inChannels, growRate, kSize=3):
super(RDB_Conv, self)... | 3,202 | 29.216981 | 90 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/mdsr.py | from model import common
import torch.nn as nn
def make_model(args, parent=False):
return MDSR(args)
class MDSR(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(MDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
... | 1,837 | 25.637681 | 78 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/common.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def default_conv(in_channels, out_channels, kernel_size,stride=1, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2),stride=stride, bias=bias)
class MeanShift(nn.Conv2d):
... | 2,799 | 30.460674 | 80 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/__init__.py | import os
from importlib import import_module
import torch
import torch.nn as nn
from torch.autograd import Variable
class Model(nn.Module):
def __init__(self, args, ckp):
super(Model, self).__init__()
print('Making model...')
self.scale = args.scale
self.idx_scale = 0
sel... | 6,199 | 31.460733 | 90 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/panet.py | from model import common
from model import attention
import torch.nn as nn
def make_model(args, parent=False):
return PANET(args)
class PANET(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(PANET, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_f... | 2,779 | 32.493976 | 104 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from torchvision import utils as vutils
from model import common
from utils.tools import extract_image_patches,\
reduce_mean, reduce_sum, same_padding
class PyramidAttention(nn.Module):
def __init__(self, leve... | 4,427 | 46.106383 | 147 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/vdsr.py | from model import common
import torch.nn as nn
import torch.nn.init as init
url = {
'r20f64': ''
}
def make_model(args, parent=False):
return VDSR(args)
class VDSR(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(VDSR, self).__init__()
n_resblocks = args.n_resblocks... | 1,275 | 26.148936 | 73 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/utils/tools.py | import os
import torch
import numpy as np
from PIL import Image
import torch.nn.functional as F
def normalize(x):
return x.mul_(2).add_(-1)
def same_padding(images, ksizes, strides, rates):
assert len(images.size()) == 4
batch_size, channel, rows, cols = images.size()
out_rows = (rows + strides[0] - ... | 2,777 | 32.878049 | 79 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noisier2Noise/models/unet_fastMRI.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
import math
from torch import nn
from torch.nn import functional as F
class unet_fastMRI(nn.Module):
"""
PyTorch imple... | 7,138 | 34.517413 | 131 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noisier2Noise/utils/utils_image.py | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
def modcrop(img, scale):
# img_in: BCHW or CHW or HW
#img = np.copy(img_in)
if img.ndim == 2:
H, W = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, ... | 10,256 | 37.852273 | 99 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noisier2Noise/utils/train_utils.py | import argparse
import os
import logging
import numpy as np
import random
import sys
import torch
from datetime import datetime
from torch.serialization import default_restore_location
def add_logging_arguments(parser):
parser.add_argument("--seed", default=0, type=int, help="random number generator seed")
p... | 7,573 | 52.716312 | 138 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noisier2Noise/utils/main_function_helpers.py | import torch
import argparse
import os
import yaml
import pathlib
import pickle
import logging
import sys
import time
from torch.utils.tensorboard import SummaryWriter
import torch.nn.functional as F
import torchvision
import glob
from torch.serialization import default_restore_location
from torch.utils.data import Dat... | 22,955 | 45.563895 | 182 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noisier2Noise/utils/util_calculate_psnr_ssim.py | import cv2
import numpy as np
import torch
# from https://github.com/JingyunLiang/SwinIR/blob/328dda0f4768772e6d8c5aa3d5aa8e24f1ad903b/utils/util_calculate_psnr_ssim.py#L80
def calculate_psnr(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
"""Calculate PSNR (Peak Signal-to-Noise Ratio).
Re... | 9,023 | 37.564103 | 129 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noisier2Noise/utils/test_metrics.py | import torch
import numpy as np
import matplotlib.pyplot as plt
import glob
import os
#import cv2
from utils.noise_model import get_noise
from utils.metrics import ssim,psnr
from utils.util_calculate_psnr_ssim import calculate_psnr,calculate_ssim
from skimage import color
import PIL.Image as Image
import torchvision.tr... | 4,784 | 31.331081 | 119 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noisier2Noise/utils/noise_model.py | import torch
def get_noise(data, noise_seed, fix_noise, noise_std = float(25)/255.0):
if fix_noise:
device = torch.device('cuda')
gen = torch.Generator(device=device)
batch_size = data.size(dim=0)
tensor_dim = list(data.size())[1:]
for i in range(0,batch_size)... | 880 | 31.62963 | 87 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noisier2Noise/utils/meters.py | import time
import torch
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
if isinstance(val, torch.Tensor):
val = val.item()
... | 1,321 | 20.322581 | 75 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noisier2Noise/utils/data_helpers/load_datasets_helpers.py | import os
import os.path
import numpy as np
import h5py
import torch
import torchvision.transforms as transforms
import PIL.Image as Image
from utils.utils_image import *
class ImagenetSubdataset(torch.utils.data.Dataset):
def __init__(self, size, path_to_ImageNet_train, mode='train', patch_size='128', val_crop=Tr... | 1,949 | 32.62069 | 131 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Neighbor2Neighbor/models/unet_fastMRI.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
import math
from torch import nn
from torch.nn import functional as F
class unet_fastMRI(nn.Module):
"""
PyTorch imple... | 7,138 | 34.517413 | 131 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Neighbor2Neighbor/utils/utils_image.py | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
def modcrop(img, scale):
# img_in: BCHW or CHW or HW
#img = np.copy(img_in)
if img.ndim == 2:
H, W = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, ... | 10,256 | 37.852273 | 99 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Neighbor2Neighbor/utils/train_utils.py | import argparse
import os
import logging
import numpy as np
import random
import sys
import torch
from datetime import datetime
from torch.serialization import default_restore_location
def add_logging_arguments(parser):
parser.add_argument("--seed", default=0, type=int, help="random number generator seed")
p... | 7,573 | 52.716312 | 138 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Neighbor2Neighbor/utils/main_function_helpers.py | import torch
import argparse
import os
import yaml
import pathlib
import pickle
import logging
import sys
import time
from torch.utils.tensorboard import SummaryWriter
import torch.nn.functional as F
import torchvision
import glob
from torch.serialization import default_restore_location
from torch.utils.data import Dat... | 29,827 | 45.244961 | 182 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Neighbor2Neighbor/utils/util_calculate_psnr_ssim.py | import cv2
import numpy as np
import torch
# from https://github.com/JingyunLiang/SwinIR/blob/328dda0f4768772e6d8c5aa3d5aa8e24f1ad903b/utils/util_calculate_psnr_ssim.py#L80
def calculate_psnr(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
"""Calculate PSNR (Peak Signal-to-Noise Ratio).
Re... | 13,571 | 40.631902 | 151 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Neighbor2Neighbor/utils/test_metrics.py | import torch
import numpy as np
import matplotlib.pyplot as plt
import glob
import os
#import cv2
from utils.noise_model import get_noise
from utils.metrics import ssim,psnr
from utils.util_calculate_psnr_ssim import calculate_psnr,calculate_ssim
from skimage import color
import PIL.Image as Image
import torchvision.tr... | 4,404 | 29.804196 | 119 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Neighbor2Neighbor/utils/noise_model.py | import torch
def get_noise(data, noise_seed, fix_noise, noise_std = float(25)/255.0):
if fix_noise:
device = torch.device('cuda')
gen = torch.Generator(device=device)
batch_size = data.size(dim=0)
tensor_dim = list(data.size())[1:]
for i in range(0,batch_size)... | 880 | 31.62963 | 87 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Neighbor2Neighbor/utils/meters.py | import time
import torch
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
if isinstance(val, torch.Tensor):
val = val.item()
... | 1,321 | 20.322581 | 75 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Neighbor2Neighbor/utils/data_helpers/load_datasets_helpers.py | import os
import os.path
import numpy as np
import h5py
import torch
import torchvision.transforms as transforms
import PIL.Image as Image
from utils.utils_image import *
class ImagenetSubdataset(torch.utils.data.Dataset):
def __init__(self, size, path_to_ImageNet_train, mode='train', patch_size='128', val_crop=Tr... | 1,949 | 32.62069 | 131 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noise2Noise/models/unet_fastMRI.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
import math
from torch import nn
from torch.nn import functional as F
class unet_fastMRI(nn.Module):
"""
PyTorch imple... | 7,138 | 34.517413 | 131 | py |
sample_complexity_ss_recon | sample_complexity_ss_recon-main/Image_denoising_figure2/Noise2Noise/utils/utils_image.py | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
def modcrop(img, scale):
# img_in: BCHW or CHW or HW
#img = np.copy(img_in)
if img.ndim == 2:
H, W = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, ... | 9,403 | 36.466135 | 99 | py |
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