repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/data/div2kjpeg.py | import os
from data import srdata
from data import div2k
class DIV2KJPEG(div2k.DIV2K):
def __init__(self, args, name='', train=True, benchmark=False):
self.q_factor = int(name.replace('DIV2K-Q', ''))
super(DIV2KJPEG, self).__init__(
args, name=name, train=train, benchmark=benchmark
... | 675 | 31.190476 | 67 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/SR/code/data/sr291.py | from data import srdata
class SR291(srdata.SRData):
def __init__(self, args, name='SR291', train=True, benchmark=False):
super(SR291, self).__init__(args, name=name)
| 180 | 24.857143 | 72 | 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/data/div2k.py | import os
from data import srdata
class DIV2K(srdata.SRData):
def __init__(self, args, name='DIV2K', train=True, benchmark=False):
data_range = [r.split('-') for r in args.data_range.split('/')]
if train:
data_range = data_range[0]
else:
if args.test_only and len(dat... | 1,134 | 33.393939 | 72 | 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/SR/code/model/utils/__init__.py | 0 | 0 | 0 | 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/template.py | def set_template(args):
# Set the templates here
if args.template.find('jpeg') >= 0:
args.data_train = 'DIV2K_jpeg'
args.data_test = 'DIV2K_jpeg'
args.epochs = 200
args.decay = '100'
if args.template.find('EDSR_paper') >= 0:
args.model = 'EDSR'
args.n_resbloc... | 1,312 | 23.314815 | 45 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/option.py | import argparse
import template
parser = argparse.ArgumentParser(description='EDSR and MDSR')
parser.add_argument('--debug', action='store_true',
help='Enables debug mode')
parser.add_argument('--template', default='.',
help='You can set various templates in option.py')
# Hard... | 7,465 | 45.372671 | 86 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/__init__.py | 0 | 0 | 0 | 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/utils/__init__.py | 0 | 0 | 0 | py | |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/data/div2kjpeg.py | import os
from data import srdata
from data import div2k
class DIV2KJPEG(div2k.DIV2K):
def __init__(self, args, name='', train=True, benchmark=False):
self.q_factor = int(name.replace('DIV2K-Q', ''))
super(DIV2KJPEG, self).__init__(
args, name=name, train=train, benchmark=benchmark
... | 675 | 31.190476 | 67 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/DN_RGB/code/data/sr291.py | from data import srdata
class SR291(srdata.SRData):
def __init__(self, args, name='SR291', train=True, benchmark=False):
super(SR291, self).__init__(args, name=name)
| 180 | 24.857143 | 72 | 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/data/div2k.py | import os
from data import srdata
class DIV2K(srdata.SRData):
def __init__(self, args, name='DIV2K', train=True, benchmark=False):
data_range = [r.split('-') for r in args.data_range.split('/')]
if train:
data_range = data_range[0]
else:
if args.test_only and len(dat... | 1,134 | 33.393939 | 72 | 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/DN_RGB/code/model/utils/__init__.py | 0 | 0 | 0 | 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/template.py | def set_template(args):
# Set the templates here
if args.template.find('jpeg') >= 0:
args.data_train = 'DIV2K_jpeg'
args.data_test = 'DIV2K_jpeg'
args.epochs = 200
args.decay = '100'
if args.template.find('EDSR_paper') >= 0:
args.model = 'EDSR'
args.n_resbloc... | 1,312 | 23.314815 | 45 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/option.py | import argparse
import template
parser = argparse.ArgumentParser(description='EDSR and MDSR')
parser.add_argument('--debug', action='store_true',
help='Enables debug mode')
parser.add_argument('--template', default='.',
help='You can set various templates in option.py')
# Hard... | 7,467 | 45.385093 | 89 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/__init__.py | 0 | 0 | 0 | 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/utils/__init__.py | 0 | 0 | 0 | py | |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/data/div2kjpeg.py | import os
from data import srdata
from data import div2k
class DIV2KJPEG(div2k.DIV2K):
def __init__(self, args, name='', train=True, benchmark=False):
self.q_factor = int(name.replace('DIV2K-Q', ''))
super(DIV2KJPEG, self).__init__(
args, name=name, train=train, benchmark=benchmark
... | 675 | 31.190476 | 67 | py |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/data/sr291.py | from data import srdata
class SR291(srdata.SRData):
def __init__(self, args, name='SR291', train=True, benchmark=False):
super(SR291, self).__init__(args, name=name)
| 180 | 24.857143 | 72 | 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/data/div2k.py | import os
from data import srdata
class DIV2K(srdata.SRData):
def __init__(self, args, name='DIV2K', train=True, benchmark=False):
data_range = [r.split('-') for r in args.data_range.split('/')]
if train:
data_range = data_range[0]
else:
if args.test_only and len(dat... | 1,134 | 33.393939 | 72 | 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 |
Pyramid-Attention-Networks | Pyramid-Attention-Networks-master/CAR/code/model/utils/__init__.py | 0 | 0 | 0 | py | |
time-series-forecasting-release | time-series-forecasting-release/generic_model_trainer.py | import numpy as np
import tensorflow as tf
import argparse
from utility_scripts.persist_optimized_config_results import persist_results
from generic_model_tester import testing
from utility_scripts.hyperparameter_scripts.hyperparameter_config_reader import read_initial_hyperparameter_values
# import the config space a... | 17,829 | 51.908012 | 262 | py |
time-series-forecasting-release | time-series-forecasting-release/generic_model_tester.py | import csv
import tensorflow as tf
# import the different model types
# stacking model
from rnn_architectures.stacking_model.stacking_model_tester import \
StackingModelTester as StackingModelTester
# seq2seq model with decoder
from rnn_architectures.seq2seq_model.with_decoder.non_moving_window.unaccumulated_err... | 8,308 | 41.829897 | 269 | py |
time-series-forecasting-release | time-series-forecasting-release/external_packages/cocob_optimizer/cocob_optimizer.py | # Copyright 2017 Francesco Orabona. All Rights Reserved.
#
# 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 l... | 4,063 | 43.659341 | 138 | py |
time-series-forecasting-release | time-series-forecasting-release/external_packages/cocob_optimizer/__init__.py | from cocob_optimizer import * | 29 | 29 | 29 | py |
time-series-forecasting-release | time-series-forecasting-release/preprocess_scripts/CIF_2016/moving_window/create_o12_tfrecords.py | from tfrecords_handler.moving_window.tfrecord_writer import TFRecordWriter
import os
output_path = "../../../datasets/binary_data/CIF_2016/moving_window/"
if not os.path.exists(output_path):
os.makedirs(output_path)
if __name__ == '__main__':
tfrecord_writer = TFRecordWriter(
input_size = 15,
... | 1,052 | 44.782609 | 97 | py |
time-series-forecasting-release | time-series-forecasting-release/preprocess_scripts/CIF_2016/moving_window/create_o6_tfrecords.py | from tfrecords_handler.moving_window.tfrecord_writer import TFRecordWriter
import os
output_path = "../../../datasets/binary_data/CIF_2016/moving_window/"
if not os.path.exists(output_path):
os.makedirs(output_path)
if __name__ == '__main__':
tfrecord_writer = TFRecordWriter(
input_size = 7,
o... | 1,040 | 44.26087 | 95 | py |
time-series-forecasting-release | time-series-forecasting-release/preprocess_scripts/CIF_2016/moving_window/without_stl_decomposition/create_o12_tfrecords.py | from tfrecords_handler.moving_window.tfrecord_writer import TFRecordWriter
import os
output_path = "../../../../datasets/binary_data/CIF_2016/moving_window/without_stl_decomposition/"
if not os.path.exists(output_path):
os.makedirs(output_path)
if __name__ == '__main__':
tfrecord_writer = TFRecordWriter(
... | 1,172 | 50 | 126 | py |
time-series-forecasting-release | time-series-forecasting-release/preprocess_scripts/CIF_2016/moving_window/without_stl_decomposition/create_o6_tfrecords.py | from tfrecords_handler.moving_window.tfrecord_writer import TFRecordWriter
import os
output_path = "../../../../datasets/binary_data/CIF_2016/moving_window/without_stl_decomposition/"
if not os.path.exists(output_path):
os.makedirs(output_path)
if __name__ == '__main__':
tfrecord_writer = TFRecordWriter(
... | 1,156 | 49.304348 | 124 | py |
time-series-forecasting-release | time-series-forecasting-release/preprocess_scripts/CIF_2016/non_moving_window/create_o12_tfrecords.py | from tfrecords_handler.non_moving_window.tfrecord_writer import TFRecordWriter
import os
output_path = "../../../datasets/binary_data/CIF_2016/non_moving_window/"
if not os.path.exists(output_path):
os.makedirs(output_path)
if __name__ == '__main__':
tfrecord_writer = TFRecordWriter(
output_size = 12,... | 1,084 | 46.173913 | 100 | py |
time-series-forecasting-release | time-series-forecasting-release/preprocess_scripts/CIF_2016/non_moving_window/create_o6_tfrecords.py | from tfrecords_handler.non_moving_window.tfrecord_writer import TFRecordWriter
import os
output_path = "../../../datasets/binary_data/CIF_2016/non_moving_window/"
if not os.path.exists(output_path):
os.makedirs(output_path)
if __name__ == '__main__':
tfrecord_writer = TFRecordWriter(
output_size = 6,
... | 1,049 | 46.727273 | 101 | py |
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