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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ParaCNN | ParaCNN-main/train_off.py | import os
import os.path as osp
import argparse
import numpy as np
import json
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torchvisi... | 8,740 | 35.573222 | 190 | py |
ParaCNN | ParaCNN-main/train_reg.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torch.nn.functional as F
import time
import os
from six.moves import cPickle
import math
import opts
from models.AttMo... | 11,207 | 40.820896 | 144 | py |
ParaCNN | ParaCNN-main/rl_utils.py | import os
import os.path as osp
import argparse
import numpy as np
import json
import time
from nltk import ngrams
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import torchvision.datasets as dat... | 3,190 | 28.275229 | 109 | py |
ParaCNN | ParaCNN-main/train_imit_meshed.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torch.nn.functional as F
import time
import os
from six.moves import cPickle
import math
import opts
import models
from... | 12,162 | 37.612698 | 144 | py |
ParaCNN | ParaCNN-main/eval_utils_beamsearch.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import json
from json import encoder
import random
import string
import time
import os
import sys
import misc.utils as utils... | 8,777 | 38.719457 | 165 | py |
ParaCNN | ParaCNN-main/train_normal.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torch.nn.functional as F
import time
import os
from six.moves import cPickle
import math
import opts
from models.transfo... | 13,646 | 39.616071 | 122 | py |
ParaCNN | ParaCNN-main/vggfeats.py | import torch
import torch.nn as nn
from torchvision import models
from torch.autograd import Variable
pretrained_model = models.vgg16(pretrained=True)
class Vgg16Feats(nn.Module):
def __init__(self):
super(Vgg16Feats, self).__init__()
self.features_nopool = nn.Sequential(*list(pretrained_model.features.chil... | 686 | 30.227273 | 90 | py |
ParaCNN | ParaCNN-main/seq_model.py | from seq_auto import *
import torch
import random
import numpy as np
class Seq2Seq(nn.Module):
def __init__(self):
super().__init__()
self.attention = Attention(512, 512)
self.encoder = Encoder(8668, 512, 512, 512, 0.1)
self.decoder = Decoder(8668, 512, 512, 512, 0.1, self.attention)... | 14,882 | 45.949527 | 110 | py |
ParaCNN | ParaCNN-main/eval_utils.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import json
from json import encoder
import random
import string
import time
import os
import sys
import misc.utils as utils... | 6,906 | 37.586592 | 165 | py |
ParaCNN | ParaCNN-main/eval_utils_trigram.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import json
from json import encoder
import random
import string
import time
import os
import sys
import misc.utils as utils... | 7,865 | 40.840426 | 165 | py |
ParaCNN | ParaCNN-main/eval_transformer.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import numpy as np
import time
import os
from six.moves import cPickle
import opts
import models
from dataloader import *
from dataloaderraw import *
import eval_utils
import argparse
import misc.... | 1,894 | 21.831325 | 100 | py |
ParaCNN | ParaCNN-main/models/AttModel_vae.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,506 | 40.23904 | 179 | py |
ParaCNN | ParaCNN-main/models/AttModel_rnn.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 17,399 | 35.554622 | 130 | py |
ParaCNN | ParaCNN-main/models/AttModel_fusion.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,595 | 40.72392 | 179 | py |
ParaCNN | ParaCNN-main/models/AttModel_high_dim_use.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,450 | 41.148504 | 179 | py |
ParaCNN | ParaCNN-main/models/AttModel_reverse.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,920 | 40.802094 | 179 | py |
ParaCNN | ParaCNN-main/models/AttModel_meshed.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 10,432 | 35.225694 | 108 | py |
ParaCNN | ParaCNN-main/models/AttModel_use.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,484 | 40.47584 | 179 | py |
ParaCNN | ParaCNN-main/models/AttModel_cnn.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,222 | 40.461945 | 179 | py |
ParaCNN | ParaCNN-main/models/AttModel_vae1.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,732 | 40.345473 | 179 | py |
ParaCNN | ParaCNN-main/models/Attmodel_transformer.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 21,678 | 35.071547 | 109 | py |
ParaCNN | ParaCNN-main/models/AttModel_reconstruct.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,917 | 39.732653 | 179 | py |
ParaCNN | ParaCNN-main/models/AttModel_rnn_rnn.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 17,650 | 35.926778 | 130 | py |
ParaCNN | ParaCNN-main/models/CaptionModel.py | # This file contains ShowAttendTell and AllImg model
# ShowAttendTell is from Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
# https://arxiv.org/abs/1502.03044
# AllImg is a model where
# img feature is concatenated with word embedding at every time step as the input of lstm
from __futur... | 9,200 | 50.983051 | 142 | py |
ParaCNN | ParaCNN-main/models/captioning_model.py | import torch
from torch import distributions
import utils
from models.containers import Module
from models.beam_search import *
class CaptioningModel(Module):
def __init__(self):
super(CaptioningModel, self).__init__()
def init_weights(self):
raise NotImplementedError
def step(self, t, p... | 2,647 | 36.295775 | 132 | py |
ParaCNN | ParaCNN-main/models/AttModel_dimm_change.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,470 | 40.461134 | 179 | py |
ParaCNN | ParaCNN-main/models/containers.py | from contextlib import contextmanager
from torch import nn
from utils.typing import *
class Module(nn.Module):
def __init__(self):
super(Module, self).__init__()
self._is_stateful = False
self._state_names = []
self._state_defaults = dict()
def register_state(self, name: str, ... | 2,661 | 31.864198 | 118 | py |
ParaCNN | ParaCNN-main/models/AttModel.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 14,213 | 34.98481 | 100 | py |
ParaCNN | ParaCNN-main/models/AttModel_use_high_dimension.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,262 | 40.680467 | 179 | py |
ParaCNN | ParaCNN-main/models/AttModel_vis.py | #= This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning... | 39,827 | 40.836134 | 179 | py |
ParaCNN | ParaCNN-main/models/beam_search/beam_search.py | import torch
import utils
class BeamSearch(object):
def __init__(self, model, max_len: int, eos_idx: int, beam_size: int):
self.model = model
self.max_len = max_len
self.eos_idx = eos_idx
self.beam_size = beam_size
self.b_s = None
self.device = None
self.seq... | 7,313 | 49.441379 | 120 | py |
ParaCNN | ParaCNN-main/models/transformer2/transformer.py | import torch
from torch import nn
import copy
from models.containers import ModuleList
from ..captioning_model import CaptioningModel
class Transformer(CaptioningModel):
def __init__(self, bos_idx, encoder, decoder):
super(Transformer, self).__init__()
self.bos_idx = bos_idx
self.encoder =... | 2,472 | 34.84058 | 94 | py |
ParaCNN | ParaCNN-main/models/transformer2/attention.py | import numpy as np
import torch
from torch import nn
from models.containers import Module
class ScaledDotProductAttention(nn.Module):
'''
Scaled dot-product attention
'''
def __init__(self, d_model, d_k, d_v, h):
'''
:param d_model: Output dimensionality of the model
:param d_... | 7,773 | 41.25 | 132 | py |
ParaCNN | ParaCNN-main/models/transformer2/decoders.py | import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
from models.transformer2.attention import MultiHeadAttention
from models.transformer2.utils import sinusoid_encoding_table, PositionWiseFeedForward
from models.containers import Module, ModuleList
class MeshedDecoderLayer(Modul... | 5,351 | 51.990099 | 120 | py |
ParaCNN | ParaCNN-main/models/transformer2/encoders.py | from torch.nn import functional as F
from models.transformer2.utils import PositionWiseFeedForward
import torch
from torch import nn
from models.transformer2.attention import MultiHeadAttention
class EncoderLayer(nn.Module):
def __init__(self, d_model=512, d_k=64, d_v=64, h=8, d_ff=2048, dropout=.1, identity_map_... | 3,126 | 47.107692 | 119 | py |
ParaCNN | ParaCNN-main/models/transformer/utils.py | import torch
from torch import nn
from torch.nn import functional as F
def position_embedding(input, d_model):
input = input.view(-1, 1)
dim = torch.arange(d_model // 2, dtype=torch.float32, device=input.device).view(1, -1)
sin = torch.sin(input / 10000 ** (2 * dim / d_model))
cos = torch.cos(input / ... | 1,675 | 32.52 | 90 | py |
ParaCNN | ParaCNN-main/models/transformer/transformer.py | import torch
from torch import nn
import copy
from models.containers import ModuleList
from ..captioning_model import CaptioningModel
class Transformer(CaptioningModel):
def __init__(self, bos_idx, encoder, decoder):
super(Transformer, self).__init__()
self.bos_idx = bos_idx
self.encoder =... | 2,464 | 34.724638 | 94 | py |
ParaCNN | ParaCNN-main/models/transformer/attention.py | import numpy as np
import torch
from torch import nn
from models.containers import Module
class ScaledDotProductAttention(nn.Module):
'''
Scaled dot-product attention
'''
def __init__(self, d_model, d_k, d_v, h):
'''
:param d_model: Output dimensionality of the model
:param d_... | 7,773 | 41.25 | 132 | py |
ParaCNN | ParaCNN-main/models/transformer/decoders.py | import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
from models.transformer.attention import MultiHeadAttention
from models.transformer.utils import sinusoid_encoding_table, PositionWiseFeedForward
from models.containers import Module, ModuleList
class MeshedDecoderLayer(Module)... | 5,299 | 51.475248 | 130 | py |
ParaCNN | ParaCNN-main/models/transformer/encoders.py | from torch.nn import functional as F
from models.transformer.utils import PositionWiseFeedForward
import torch
from torch import nn
from models.transformer.attention import MultiHeadAttention
class EncoderLayer(nn.Module):
def __init__(self, d_model=512, d_k=64, d_v=64, h=8, d_ff=2048, dropout=.1, identity_map_re... | 3,014 | 46.857143 | 119 | py |
ParaCNN | ParaCNN-main/misc/resnet.py | import torch
import torch.nn as nn
import torchvision.models.resnet
from torchvision.models.resnet import BasicBlock, Bottleneck
class ResNet(torchvision.models.resnet.ResNet):
def __init__(self, block, layers, num_classes=1000):
super(ResNet, self).__init__(block, layers, num_classes)
self.maxpool... | 2,164 | 29.492958 | 96 | py |
ParaCNN | ParaCNN-main/misc/rewards.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import time
import misc.utils as utils
from collections import OrderedDict
import torch
import sys
sys.path.append("cider")
from pyciderevalcap.cider.cider_scorer import CiderScorer
sys.path... | 2,468 | 30.653846 | 91 | py |
ParaCNN | ParaCNN-main/misc/utils.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
def if_use_att(caption_model):
# Decide if load attention feature according to caption model
if capti... | 3,568 | 34.336634 | 137 | py |
ParaCNN | ParaCNN-main/misc/utils_revise2.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
from torch.autograd import Variable
def if_use_att(caption_model):
# Decide if load attention feature ac... | 4,083 | 30.658915 | 137 | py |
ParaCNN | ParaCNN-main/misc/resnet_utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class myResnet(nn.Module):
def __init__(self, resnet):
super(myResnet, self).__init__()
self.resnet = resnet
def forward(self, img, att_size=14):
x = img.unsqueeze(0)
x = self.resnet.conv1(x)
x = self.r... | 698 | 23.964286 | 85 | py |
ParaCNN | ParaCNN-main/utils/typing.py | from typing import Union, Sequence, Tuple
import torch
TensorOrSequence = Union[Sequence[torch.Tensor], torch.Tensor]
TensorOrNone = Union[torch.Tensor, None]
| 160 | 25.833333 | 62 | py |
ParaCNN | ParaCNN-main/utils/__init__.py | from .utils import download_from_url
from .typing import *
def get_batch_size(x: TensorOrSequence) -> int:
if isinstance(x, torch.Tensor):
b_s = x.size(0)
else:
b_s = x[0].size(0)
return b_s
def get_device(x: TensorOrSequence) -> int:
if isinstance(x, torch.Tensor):
b_s = x.de... | 376 | 19.944444 | 47 | py |
JoPEQ | JoPEQ-main/main.py | import gc
import sys
from statistics import mean
import time
import torch
from configurations import args_parser
from tqdm import tqdm
import utils
import models
import federated_utils
from torchinfo import summary
import numpy as np
if __name__ == '__main__':
start_time = time.time()
args = args_parser()
... | 3,523 | 34.959184 | 104 | py |
JoPEQ | JoPEQ-main/quantization.py | import torch
import numpy as np
class LatticeQuantization:
def __init__(self, args):
self.gamma = args.gamma
# lattice generating matrix
hex_mat = np.array([[np.sqrt(3) / 2, 0], [1 / 2, 1]])
gen_mat = hex_mat/np.linalg.det(hex_mat)
self.gen_mat = torch.from_numpy(gen_mat).... | 2,060 | 35.803571 | 112 | py |
JoPEQ | JoPEQ-main/federated_utils.py | import torch
import torch.optim as optim
import copy
import math
from quantization import LatticeQuantization, ScalarQuantization
from privacy import Privacy
def federated_setup(global_model, train_data, args):
# create a dict of dict s (local users), i.e. {'1': {'data':..., 'model':..., 'opt':...}, ...}
index... | 4,208 | 38.336449 | 120 | py |
JoPEQ | JoPEQ-main/utils.py | import os
from statistics import mean
import torch
from tensorboardX import SummaryWriter
from torchvision import datasets, transforms
import numpy as np
def data(args):
if args.data == 'mnist':
train_data = datasets.MNIST('./data', train=True, download=True,
transform=... | 4,399 | 33.375 | 102 | py |
JoPEQ | JoPEQ-main/privacy.py | import torch
from torch.distributions.laplace import Laplace
import numpy as np
from scipy import stats
class Privacy:
def __init__(self, args, dither_var):
self.privacy_noise = args.privacy_noise
if self.privacy_noise == 'laplace' or self.privacy_noise == 'jopeq_scalar':
b_lap = 2 / ... | 1,370 | 44.7 | 113 | py |
JoPEQ | JoPEQ-main/models.py | import torch.nn
import torch.nn as nn
import torch.nn.functional as F
class Linear(torch.nn.Module):
def __init__(self, input_size, output_size):
super(Linear, self).__init__()
self.input_size = input_size
self.linear = torch.nn.Linear(input_size, output_size)
def forward(self, x):
... | 2,800 | 31.569767 | 124 | py |
language-models | language-models-master/nlputils2.py | from fastai.basics import *
import re
import urllib.request
def get_wiki_download(path,lang):
name = f'{lang}wiki'
xml_fn = f"{lang}wiki-latest-pages-articles.xml"
zip_fn = f"{xml_fn}.bz2"
if (path/zip_fn).exists():
print(f"{path/zip_fn} already exists; not downloading")
r... | 5,997 | 33.274286 | 128 | py |
language-models | language-models-master/adapters/question-answering/run_qa_adapter.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Team 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-... | 35,745 | 45.243208 | 228 | py |
language-models | language-models-master/adapters/token-classification/run_ner_adapter.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Team 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-... | 28,070 | 41.531818 | 212 | py |
FuSS | FuSS-main/base.py | import os
import sys
import numpy as np
import torch
from torch.utils import data
from skimage import io
from skimage import color
from skimage import measure
from skimage import transform
from skimage import util
from sklearn import metrics
from matplotlib import pyplot as plt
from matplotlib import lines
from matp... | 29,247 | 39.67872 | 228 | py |
FuSS | FuSS-main/models/fcn_wideresnet50.py | import torch
from torch import nn
from torchvision import models
import torch.nn.functional as F
from utils import get_upsampling_weight
from utils import initialize_weights
class FCNWideResNet50(nn.Module):
def __init__(self, input_channels, num_classes, pretrained=True, skip=True, hidden_classes=None):
... | 4,017 | 33.637931 | 103 | py |
FuSS | FuSS-main/models/fcn_densenet121.py | import torch
from torch import nn
from torchvision import models
import torch.nn.functional as F
from utils import get_upsampling_weight
from utils import initialize_weights
class FCNDenseNet121(nn.Module):
def __init__(self, input_channels, num_classes, pretrained=True, skip=True, hidden_classes=None):
... | 3,884 | 33.380531 | 103 | py |
FuSS | FuSS-main/models/unet.py | import torch
import torch.nn.functional as F
from torch import nn
from utils import initialize_weights
class _EncoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, dropout=False):
super(_EncoderBlock, self).__init__()
layers = [
nn.Conv2d(in_channels, out_channels, ... | 4,086 | 31.696 | 115 | py |
FuSS | FuSS-main/utils/misc.py | import os
from math import ceil
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable
def check_mkdir(dir_name):
if not os.path.exists(dir_name):
os.mkdir(dir_name)
def initialize_weights(*models):
for model in models:
for m... | 21,129 | 38.057301 | 120 | py |
llvm-xposit-xposit | llvm-xposit-xposit-main/clang/docs/conf.py | # -*- coding: utf-8 -*-
#
# Clang documentation build configuration file, created by
# sphinx-quickstart on Sun Dec 9 20:01:55 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All c... | 9,591 | 31.849315 | 83 | py |
llvm-xposit-xposit | llvm-xposit-xposit-main/clang/docs/analyzer/conf.py | # -*- coding: utf-8 -*-
#
# Clang Static Analyzer documentation build configuration file, created by
# sphinx-quickstart on Wed Jan 2 15:54:28 2013.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated... | 8,064 | 31.520161 | 80 | py |
llvm-xposit-xposit | llvm-xposit-xposit-main/openmp/docs/conf.py | # -*- coding: utf-8 -*-
#
# LLDB documentation build configuration file, created by
# sphinx-quickstart on Sun Dec 9 20:01:55 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All co... | 8,212 | 32.386179 | 81 | py |
llvm-xposit-xposit | llvm-xposit-xposit-main/lldb/docs/conf.py | # -*- coding: utf-8 -*-
#
# LLDB documentation build configuration file, created by
# sphinx-quickstart on Sun Dec 9 20:01:55 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All co... | 11,380 | 34.018462 | 105 | py |
llvm-xposit-xposit | llvm-xposit-xposit-main/clang-tools-extra/docs/conf.py | # -*- coding: utf-8 -*-
#
# Extra Clang Tools documentation build configuration file, created by
# sphinx-quickstart on Wed Feb 13 10:00:18 2013.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated fil... | 7,902 | 31.389344 | 80 | py |
llvm-xposit-xposit | llvm-xposit-xposit-main/libcxx/docs/conf.py | # -*- coding: utf-8 -*-
#
# libc++ documentation build configuration file.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are comment... | 8,008 | 30.656126 | 80 | py |
llvm-xposit-xposit | llvm-xposit-xposit-main/polly/docs/conf.py | # -*- coding: utf-8 -*-
#
# Polly documentation build configuration file, created by
# sphinx-quickstart on Sun Dec 9 20:01:55 2013.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All c... | 7,686 | 30.896266 | 80 | py |
llvm-xposit-xposit | llvm-xposit-xposit-main/mlir/python/mlir/runtime/np_to_memref.py | # Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# This file contains functions to convert between Memrefs and NumPy arrays and vice-versa.
import numpy as np
import... | 3,836 | 30.975 | 91 | py |
llvm-xposit-xposit | llvm-xposit-xposit-main/flang/docs/conf.py | # -*- coding: utf-8 -*-
# Flang documentation build configuration file.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented ... | 9,782 | 31.180921 | 80 | py |
llvm-xposit-xposit | llvm-xposit-xposit-main/libunwind/docs/conf.py | # -*- coding: utf-8 -*-
#
# libunwind documentation build configuration file.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are comm... | 8,007 | 30.652174 | 80 | py |
AnimeSR | AnimeSR-main/setup.py | #!/usr/bin/env python
from setuptools import find_packages, setup
import os
import subprocess
import time
version_file = 'animesr/version.py'
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return content
def get_git_hash():
def _minimal_ext_cmd(cmd):
... | 3,354 | 28.429825 | 112 | py |
AnimeSR | AnimeSR-main/predict.py | import os
import shutil
import tempfile
from subprocess import call
from zipfile import ZipFile
from typing import Optional
import mimetypes
import torch
from cog import BasePredictor, Input, Path, BaseModel
call("python setup.py develop", shell=True)
class ModelOutput(BaseModel):
video: Path
sr_frames: Op... | 3,853 | 33.720721 | 89 | py |
AnimeSR | AnimeSR-main/animesr/models/video_recurrent_model.py | import cv2
import os
import torch
from collections import OrderedDict
from os import path as osp
from torch import distributed as dist
from tqdm import tqdm
from basicsr.models.video_base_model import VideoBaseModel
from basicsr.utils import USMSharp, get_root_logger, imwrite, tensor2img
from basicsr.utils.dist_util i... | 9,915 | 38.193676 | 116 | py |
AnimeSR | AnimeSR-main/animesr/utils/inference_base.py | import argparse
import os.path
import torch
from animesr.archs.vsr_arch import MSRSWVSR
def get_base_argument_parser() -> argparse.ArgumentParser:
"""get the base argument parser for inference scripts"""
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, default='inputs', h... | 2,568 | 37.924242 | 119 | py |
AnimeSR | AnimeSR-main/animesr/data/paired_image_dataset.py | import glob
import os
from torch.utils import data as data
from torchvision.transforms.functional import normalize
from basicsr.data.transforms import augment, mod_crop, paired_random_crop
from basicsr.utils import FileClient, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
@DATASET_REGIS... | 3,970 | 41.698925 | 104 | py |
AnimeSR | AnimeSR-main/animesr/data/ffmpeg_anime_lbo_dataset.py | import numpy as np
import random
import torch
from torch.nn import functional as F
from animesr.archs.simple_degradation_arch import SimpleDegradationArch
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_mixed_kernels
from basicsr.utils import FileClient, get_root_logger, img2tensor
from basi... | 4,858 | 43.172727 | 115 | py |
AnimeSR | AnimeSR-main/animesr/data/data_utils.py | import random
import torch
def random_crop(imgs, patch_size, top=None, left=None):
"""
randomly crop patches from imgs
:param imgs: can be (list of) tensor, cv2 img
:param patch_size: patch size, usually 256
:param top: will sample if is None
:param left: will sample if is None
:return: cr... | 1,103 | 28.052632 | 84 | py |
AnimeSR | AnimeSR-main/animesr/data/ffmpeg_anime_dataset.py | import cv2
import ffmpeg
import glob
import numpy as np
import os
import random
import torch
from os import path as osp
from torch.utils import data as data
from basicsr.data.degradations import random_add_gaussian_noise, random_mixed_kernels
from basicsr.data.transforms import augment
from basicsr.utils import FileCl... | 8,740 | 40.42654 | 120 | py |
AnimeSR | AnimeSR-main/animesr/archs/discriminator_arch.py | import functools
from torch import nn as nn
from torch.nn import functional as F
from torch.nn.utils import spectral_norm
from basicsr.utils.registry import ARCH_REGISTRY
def get_conv_layer(input_nc, ndf, kernel_size, stride, padding, bias=True, use_sn=False):
if not use_sn:
return nn.Conv2d(input_nc, nd... | 8,048 | 36.263889 | 118 | py |
AnimeSR | AnimeSR-main/animesr/archs/vsr_arch.py | import torch
from torch import nn as nn
from torch.nn import functional as F
from basicsr.archs.arch_util import ResidualBlockNoBN, pixel_unshuffle
from basicsr.utils.registry import ARCH_REGISTRY
class RightAlignMSConvResidualBlocks(nn.Module):
"""right align multi-scale ConvResidualBlocks, currently only suppo... | 5,574 | 43.246032 | 118 | py |
AnimeSR | AnimeSR-main/animesr/archs/simple_degradation_arch.py | from torch import nn as nn
from basicsr.archs.arch_util import default_init_weights, pixel_unshuffle
from basicsr.utils.registry import ARCH_REGISTRY
@ARCH_REGISTRY.register()
class SimpleDegradationArch(nn.Module):
"""simple degradation architecture which consists several conv and non-linear layer
it learns... | 1,755 | 41.829268 | 110 | py |
AnimeSR | AnimeSR-main/scripts/inference_animesr_video.py | import cv2
import ffmpeg
import glob
import mimetypes
import numpy as np
import os
import shutil
import subprocess
import torch
from os import path as osp
from tqdm import tqdm
from animesr.utils import video_util
from animesr.utils.inference_base import get_base_argument_parser, get_inference_model
from basicsr.data.... | 14,080 | 36.251323 | 119 | py |
AnimeSR | AnimeSR-main/scripts/inference_animesr_frames.py | """inference AnimeSR on frames"""
import argparse
import cv2
import glob
import numpy as np
import os
import psutil
import queue
import threading
import time
import torch
from os import path as osp
from tqdm import tqdm
from animesr.utils.inference_base import get_base_argument_parser, get_inference_model
from animesr... | 7,046 | 33.714286 | 120 | py |
AnimeSR | AnimeSR-main/scripts/anime_videos_preprocessing.py | import argparse
import cv2
import glob
import numpy as np
import os
import shutil
import torch
import torchvision
from multiprocessing import Pool
from os import path as osp
from PIL import Image
from tqdm import tqdm
from animesr.utils import video_util
from animesr.utils.shot_detector import ShotDetector
from basics... | 23,504 | 35.050613 | 116 | py |
AnimeSR | AnimeSR-main/scripts/metrics/MANIQA/inference_MANIQA.py | import argparse
import os
import random
import torch
from pipal_data import NTIRE2022
from torch.utils.data import DataLoader
from torchvision import transforms
from tqdm import tqdm
from utils import Normalize, ToTensor, crop_image
def parse_args():
parser = argparse.ArgumentParser(description='Inference script ... | 3,844 | 35.971154 | 134 | py |
AnimeSR | AnimeSR-main/scripts/metrics/MANIQA/utils.py | import numpy as np
import torch
def crop_image(top, left, patch_size, img=None):
tmp_img = img[:, :, top:top + patch_size, left:left + patch_size]
return tmp_img
class RandCrop(object):
def __init__(self, patch_size, num_crop):
self.patch_size = patch_size
self.num_crop = num_crop
... | 2,619 | 28.111111 | 83 | py |
AnimeSR | AnimeSR-main/scripts/metrics/MANIQA/pipal_data.py | import cv2
import numpy as np
import os
import torch
class NTIRE2022(torch.utils.data.Dataset):
def __init__(self, ref_path, dis_path, transform):
super(NTIRE2022, self).__init__()
self.ref_path = ref_path
self.dis_path = dis_path
self.transform = transform
ref_files_data... | 1,491 | 34.52381 | 85 | py |
AnimeSR | AnimeSR-main/scripts/metrics/MANIQA/models/swin.py | """
isort:skip_file
"""
# flake8: noqa
import torch
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from torch import nn
""" attention decoder mask """
def get_attn_decoder_mask(seq):
subsequ... | 31,957 | 40.396373 | 119 | py |
AnimeSR | AnimeSR-main/scripts/metrics/MANIQA/models/model_attentionIQA2.py | # flake8: noqa
import timm
import torch
from einops import rearrange
from models.swin import SwinTransformer
from timm.models.vision_transformer import Block
from torch import nn
class ChannelAttn(nn.Module):
def __init__(self, dim, drop=0.1):
super().__init__()
self.c_q = nn.Linear(dim, dim)
... | 4,630 | 32.80292 | 111 | py |
motion_adaptation | motion_adaptation-master/Util.py | import cPickle
import numpy
import tensorflow as tf
from Log import log
# from https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10/cifar10_multi_gpu_train.py
def average_gradients(tower_grads):
"""Calculate the average gradient for each shared variable across all towers.
Note that this func... | 6,117 | 35.416667 | 114 | py |
motion_adaptation | motion_adaptation-master/Engine.py | import glob
import time
import tensorflow as tf
from tensorflow.contrib.framework import list_variables
import Constants
import Measures
from Log import log
from Network import Network
from Trainer import Trainer
from Util import load_wider_or_deeper_mxnet_model
from datasets.Forward import forward, online_forward, b... | 9,506 | 42.610092 | 120 | py |
Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/demo.py | #!/usr/bin/env python3
"""Process an image with the trained neural network
Usage:
demo.py [options] <yaml-config> <checkpoint> <images>...
demo.py (-h | --help )
Arguments:
<yaml-config> Path to the yaml hyper-parameter file
<checkpoint> Path to the checkpoint
<images>... | 5,387 | 32.886792 | 86 | py |
Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/process.py | #!/usr/bin/env python3
"""Process a dataset with the trained neural network
Usage:
process.py [options] <yaml-config> <checkpoint>
process.py (-h | --help )
Arguments:
<yaml-config> Path to the yaml hyper-parameter file
<checkpoint> Path to the checkpoint
Options:
... | 4,765 | 31.202703 | 81 | py |
Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/train.py | #!/usr/bin/env python3
"""Train L-CNN
Usage:
train.py [options] <yaml-config>
train.py (-h | --help )
Arguments:
<yaml-config> Path to the yaml hyper-parameter file
Options:
-h --help Show this screen.
-d --devices <devices> Comma seperated GPU devices... | 5,730 | 28.694301 | 85 | py |
Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/lcnn/utils.py | import math
import os.path as osp
import multiprocessing
from timeit import default_timer as timer
import numpy as np
import torch
import matplotlib.pyplot as plt
class benchmark(object):
def __init__(self, msg, enable=True, fmt="%0.3g"):
self.msg = msg
self.fmt = fmt
self.enable = enable... | 2,533 | 23.843137 | 80 | py |
Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/lcnn/datasets.py | import glob
import json
import math
import os
import random
import numpy as np
import numpy.linalg as LA
import torch
from skimage import io
from torch.utils.data import Dataset
from torch.utils.data.dataloader import default_collate
from lcnn.config import M
class WireframeDataset(Dataset):
def __init__(self, ... | 3,809 | 36.722772 | 88 | py |
Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/lcnn/trainer.py | import atexit
import os
import os.path as osp
import shutil
import signal
import subprocess
import threading
import time
from timeit import default_timer as timer
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
from skimage import io
from tensorb... | 12,174 | 34.808824 | 89 | py |
Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/lcnn/models/multitask_learner.py | from collections import OrderedDict, defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from lcnn.config import M
class MultitaskHead(nn.Module):
def __init__(self, input_channels, num_class):
super(MultitaskHead, self).__init__()
m = int(input_cha... | 3,691 | 32.261261 | 87 | py |
Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/lcnn/models/line_vectorizer.py | import itertools
import random
from collections import defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from lcnn.config import M
### no line pre-featuress required
FEATURE_DIM = 0
class LineVectorizer(nn.Module):
def __init__(self, backbone):
super().__... | 9,510 | 36.007782 | 87 | py |
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