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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RAML | RAML-master/incremental/network/utils.py | from re import M
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from collections import OrderedDict
import json
class DeepLabHeadV3Plus_Metric(nn.Module):
def __init__(self, in_channels, low_level_channels, num_classes, aspp_dilate=[12, 24, 36], finetune=False):
super... | 21,245 | 39.701149 | 136 | py |
RAML | RAML-master/incremental/network/backbone/resnet.py | import torch
import torch.nn as nn
#from torchvision.models.utils import load_state_dict_from_url
from torch.hub import load_state_dict_from_url
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resne... | 13,621 | 38.598837 | 107 | py |
RAML | RAML-master/incremental/network/backbone/mobilenetv2.py | from torch import nn
#from torchvision.models.utils import load_state_dict_from_url
from torch.hub import load_state_dict_from_url
import torch.nn.functional as F
__all__ = ['MobileNetV2', 'mobilenet_v2']
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}
def _mak... | 6,970 | 35.883598 | 123 | py |
RAML | RAML-master/incremental/network/.ipynb_checkpoints/utils-checkpoint.py | from re import M
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from collections import OrderedDict
import json
class DeepLabHeadV3Plus_Metric(nn.Module):
def __init__(self, in_channels, low_level_channels, num_classes, aspp_dilate=[12, 24, 36], finetune=False):
super... | 21,245 | 39.701149 | 136 | py |
RAML | RAML-master/incremental/network/.ipynb_checkpoints/_deeplab-checkpoint.py | import torch
from torch import nn
from torch.nn import functional as F
from .utils import _SimpleSegmentationModel, _SimpleSegmentationModel_embedding, _SimpleSegmentationModel_embedding_self_distillation,_SimpleSegmentationModel_Metric
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
... | 8,740 | 39.281106 | 165 | py |
RAML | RAML-master/incremental/.ipynb_checkpoints/main-checkpoint.py | from tqdm import tqdm
import network
import utils
import os
import random
import argparse
import numpy as np
import torch.nn.functional as F
from torch.utils import data
from datasets import VOCSegmentation, Cityscapes, cityscapes
from utils import ext_transforms as et
from metrics import StreamSegMetrics
import torc... | 28,621 | 42.170437 | 171 | py |
RAML | RAML-master/incremental/.ipynb_checkpoints/main_metric-checkpoint.py | from tqdm import tqdm
import network
import utils
import os
import random
import argparse
import numpy as np
import torch.nn.functional as F
from torch.utils import data
from datasets import VOCSegmentation, Cityscapes, cityscapes, Cityscapes_Novel
from utils import ext_transforms as et
from metrics import StreamSegMe... | 43,558 | 44.092133 | 152 | py |
RAML | RAML-master/incremental/utils/loss.py | import torch.nn as nn
import torch.nn.functional as F
import torch
import numpy as np
from torch.autograd import Variable
class FocalLoss(nn.Module):
def __init__(self, alpha=1, gamma=0, size_average=True, ignore_index=255):
super(FocalLoss, self).__init__()
self.alpha = alpha
self.gamma = ... | 10,333 | 39.84585 | 177 | py |
RAML | RAML-master/incremental/utils/utils.py | from torchvision.transforms.functional import normalize
import torch.nn as nn
import numpy as np
import os
def denormalize(tensor, mean, std):
mean = np.array(mean)
std = np.array(std)
_mean = -mean/std
_std = 1/std
return normalize(tensor, _mean, _std)
class Denormalize(object):
def __init_... | 2,850 | 28.391753 | 84 | py |
RAML | RAML-master/incremental/utils/scheduler.py | from torch.optim.lr_scheduler import _LRScheduler, StepLR
class PolyLR(_LRScheduler):
def __init__(self, optimizer, max_iters, power=0.9, last_epoch=-1, min_lr=1e-6):
self.power = power
self.max_iters = max_iters # avoid zero lr
self.min_lr = min_lr
super(PolyLR, self).__init__(opt... | 509 | 41.5 | 96 | py |
RAML | RAML-master/incremental/utils/ext_transforms.py | import torchvision
import torch
import torchvision.transforms.functional as F
import random
import numbers
import numpy as np
from PIL import Image
#
# Extended Transforms for Semantic Segmentation
#
class ExtRandomHorizontalFlip(object):
"""Horizontally flip the given PIL Image randomly with a given probabilit... | 20,817 | 35.458844 | 150 | py |
LLP-VAT | LLP-VAT-main/llp_vat/main.py | import argparse
import os
import uuid
from tqdm.auto import tqdm
import arrow
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from llp_vat.lib.llp import (BagMiniBatch, load_llp_datase... | 14,895 | 39.150943 | 79 | py |
LLP-VAT | LLP-VAT-main/llp_vat/lib/losses.py | import contextlib
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.constraints import simplex
from llp_vat.lib.networks import GaussianNoise
def compute_soft_kl(inputs, targets):
with torch.no_grad():
loss = cross_entropy_loss(inputs, targets)
loss = to... | 4,292 | 30.8 | 78 | py |
LLP-VAT | LLP-VAT-main/llp_vat/lib/run_experiment.py | import glob
import os
import pathlib
import warnings
import logzero
import torch
import torch.nn as nn
import yaml
from torch.utils.tensorboard import SummaryWriter
def write_meters(epoch, tag, tb_writer, meters):
for name, value in meters.averages("").items():
tb_writer.add_scalar("{}/{}".format(tag, na... | 3,101 | 31.652632 | 76 | py |
LLP-VAT | LLP-VAT-main/llp_vat/lib/networks.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def wide_resnet28_2(**kwargs):
net = WideResNet(28, 2, **kwargs)
net.apply(conv_init)
return net
class GaussianNoise(nn.Module):
""" add gasussian noise into feature """
def __init__(self, std):
super(G... | 4,260 | 31.526718 | 78 | py |
LLP-VAT | LLP-VAT-main/llp_vat/lib/llp.py | import os
import pathlib
import time
from itertools import groupby
import numpy as np
import torch
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import PCA
from torch.utils.data import Sampler, BatchSampler, RandomSampler
from llp_vat.lib.datasets import load_dataset
class Iteration:
de... | 5,463 | 31.141176 | 78 | py |
LLP-VAT | LLP-VAT-main/llp_vat/lib/datasets.py | import torch
import torch.nn.functional as F
from torchvision import transforms
from torchvision.datasets import CIFAR10, CIFAR100, SVHN
class ToOneHot:
def __init__(self, num_classes):
self.num_classes = num_classes
def __call__(self, y: int) -> torch.Tensor:
one_hot = F.one_hot(torch.tensor... | 3,819 | 30.570248 | 75 | py |
ADLD | ADLD-master/test.py | import argparse
import os
import torch.optim as optim
import torch.utils.data as util_data
import itertools
import network
import pre_process as prep
import lr_schedule
from util import *
from data_list import ImageList_au, ImageList_land_au
optim_dict = {'SGD': optim.SGD, 'Adam': optim.Adam}
def main(config):
... | 5,813 | 43.381679 | 135 | py |
ADLD | ADLD-master/network.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Feat_Enc(nn.Module):
def __init__(self):
super(Feat_Enc, self).__init__()
self.align_attention_features = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32),
... | 8,607 | 32.235521 | 109 | py |
ADLD | ADLD-master/util.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import sklearn
from sklearn.metrics import accuracy_score, f1_score
def AU_detection_eval_src(loader, base_net, au_enc, use_gpu=True):
missing_label = 999
for i, batch in enumerate(loader):
input, label = batch
... | 5,630 | 33.335366 | 103 | py |
ADLD | ADLD-master/pre_process.py | import numpy as np
from torchvision import transforms
from PIL import Image
class PlaceCrop(object):
"""Crops the given PIL.Image at the particular index.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (w, h), a square crop (size, ... | 3,931 | 32.322034 | 91 | py |
ADLD | ADLD-master/train.py | import argparse
import os
import torch.optim as optim
import torch.utils.data as util_data
import itertools
import network
import pre_process as prep
import lr_schedule
from util import *
from data_list import ImageList_au, ImageList_land_au
optim_dict = {'SGD': optim.SGD, 'Adam': optim.Adam}
def main(config):
... | 26,351 | 58.485327 | 272 | py |
MICO | MICO-main/training/train_purchase100.py | import os
import argparse
import warnings
import git
import csv
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchcsprng import create_mt19937_generator, create_random_device_generator
from torch.utils.data import DataLoader
from opacus import PrivacyEngine
from opacus.valid... | 17,940 | 41.921053 | 149 | py |
MICO | MICO-main/training/train_sst2.py | import numpy as np
import pandas as pd
import os
import torch
import sys
import csv
import yaml
import warnings
import datasets
from opacus import PrivacyEngine
from dp_transformers import TrainingArguments, PrivacyArguments, PrivacyEngineCallback
from prv_accountant.dpsgd import find_noise_multiplier, DPSGDAccounta... | 7,676 | 35.042254 | 124 | py |
MICO | MICO-main/training/train_cifar10.py | import os
import argparse
import warnings
import git
import csv
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchcsprng import create_mt19937_generator, create_random_device_generator
from torch.utils.data import DataLoader
from opacus import PrivacyEngine
from opacus.valid... | 17,963 | 41.976077 | 149 | py |
MICO | MICO-main/src/mico-competition/mico.py | from __future__ import annotations
import os
import torch
import torch.nn as nn
from collections import OrderedDict
from typing import List, Optional, Union, Type, TypeVar
from torch.utils.data import Dataset, ConcatDataset, random_split
D = TypeVar("D", bound="ChallengeDataset")
LEN_CHALLENGE = 100
class Challeng... | 10,705 | 39.55303 | 139 | py |
MICO | MICO-main/src/mico-competition/challenge_datasets.py | import os
import numpy as np
import torch
from torch.utils.data import Dataset, ConcatDataset
def load_cifar10(dataset_dir: str = ".", download=True) -> Dataset:
"""Loads the CIFAR10 dataset.
"""
from torchvision.datasets import CIFAR10
import torchvision.transforms as transforms
# Precomputed s... | 3,560 | 34.61 | 120 | py |
pineko | pineko-main/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 3,189 | 30.27451 | 79 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/exp_elas.py | import torch.nn.functional as F
import matplotlib.pyplot as plt
from timeit import default_timer
from utils.utilities3 import *
from utils.adam import Adam
from utils.params import get_args
from model_dict import get_model
import math
import os
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.bac... | 4,143 | 33.823529 | 103 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/exp_airfoils.py | import torch.nn.functional as F
import matplotlib.pyplot as plt
from timeit import default_timer
from utils.utilities3 import *
from utils.adam import Adam
from utils.params import get_args
from model_dict import get_model
import math
import os
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.bac... | 4,794 | 32.767606 | 115 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/exp_elas_interp.py | import torch.nn.functional as F
import matplotlib.pyplot as plt
from timeit import default_timer
from utils.utilities3 import *
from utils.adam import Adam
from utils.params import get_args
from model_dict import get_model
import math
import os
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.bac... | 3,753 | 30.283333 | 115 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/exp_pipe.py | import torch.nn.functional as F
import matplotlib.pyplot as plt
from timeit import default_timer
from utils.utilities3 import *
from utils.adam import Adam
from utils.params import get_args
from model_dict import get_model
import math
import os
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.bac... | 4,190 | 31.238462 | 115 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/exp_darcy.py | import torch.nn.functional as F
import matplotlib.pyplot as plt
from timeit import default_timer
from utils.utilities3 import *
from utils.adam import Adam
from utils.params import get_args
from model_dict import get_model
import math
import os
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.bac... | 3,958 | 30.927419 | 115 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/exp_ns.py | import torch.nn.functional as F
import matplotlib.pyplot as plt
from timeit import default_timer
from utils.utilities3 import *
from utils.params import get_args
from model_dict import get_model
from utils.adam import Adam
import math
import os
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.bac... | 4,624 | 31.118056 | 115 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/exp_plas.py | import torch.nn.functional as F
import matplotlib.pyplot as plt
from timeit import default_timer
from utils.utilities3 import *
from utils.adam import Adam
from utils.params import get_args
from model_dict import get_model
import math
import os
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.bac... | 5,411 | 37.935252 | 115 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/models/LSM_Irregular_Geo.py | """
@author: Haixu Wu
"""
import torch.nn.functional as F
import torch.nn as nn
import torch
import numpy as np
import math
################################################################
# Multiscale modules 2D
################################################################
class DoubleConv(nn.Module):
"""(con... | 17,899 | 39.134529 | 130 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/models/FNO_Irregular_Geo.py | """
@author: Zongyi Li
modified by Haixu Wu to adapt to this code base
"""
import torch.nn.functional as F
import torch.nn as nn
import torch
import numpy as np
import math
################################################################
# fourier layer
################################################################... | 11,055 | 36.733788 | 130 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/models/FNO_3D.py | """
@author: Zongyi Li
modified by Haixu Wu to adapt to this code base
"""
import torch.nn.functional as F
import torch.nn as nn
import torch
import numpy as np
import math
################################################################
# 3d fourier layers
############################################################... | 6,128 | 41.86014 | 103 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/models/FNO_2D.py | """
@author: Zongyi Li
modified by Haixu Wu to adapt to this code base
"""
import torch.nn.functional as F
import torch.nn as nn
import torch
import numpy as np
import math
################################################################
# fourier layer
################################################################... | 4,586 | 36.598361 | 111 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/models/LSM_2D.py | """
@author: Haixu Wu
"""
import torch.nn.functional as F
import torch.nn as nn
import torch
import numpy as np
import math
################################################################
# Multiscale modules 2D
################################################################
class DoubleConv(nn.Module):
"""(con... | 9,905 | 40.103734 | 122 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/models/LSM_3D.py | """
@author: Haixu Wu
"""
import torch.nn.functional as F
import torch.nn as nn
import torch
import numpy as np
import math
################################################################
# Multiscale modules 3D
################################################################
class DoubleConv(nn.Module):
"""(co... | 9,849 | 41.094017 | 126 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/utils/adam.py | import math
import torch
from torch import Tensor
from typing import List, Optional
from torch.optim.optimizer import Optimizer
def adam(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
max_exp_avg_sqs: List[Tensor],
state_steps... | 6,563 | 39.02439 | 120 | py |
Latent-Spectral-Models | Latent-Spectral-Models-main/utils/utilities3.py | import torch
import numpy as np
import scipy.io
import h5py
import torch.nn as nn
import operator
from functools import reduce
#################################################
# Utilities
#################################################
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# readi... | 10,440 | 28.246499 | 116 | py |
FaceChat | FaceChat-main/app.py | async_mode = None
if async_mode is None:
try:
import eventlet
async_mode = "eventlet"
except ImportError:
pass
if async_mode is None:
try:
from gevent import monkey
async_mode = "gevent"
except ImportError:
pass
if async_mo... | 21,580 | 31.748103 | 194 | py |
GraphCAD | GraphCAD-main/gin_conv_weight.py | from typing import Callable, Optional, Union
import torch
from torch import Tensor
from torch_sparse import SparseTensor, matmul
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size
from ..inits im... | 3,471 | 35.166667 | 102 | py |
GraphCAD | GraphCAD-main/MAG/main.py | import os
import argparse
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
import random
import json
import pickle
from collections import defaultdict
from operator import itemgetter
import logging
from torch_geometric.data import Data, DataLoader
from torch.optim.lr_scheduler import _LRSch... | 9,336 | 46.637755 | 213 | py |
GraphCAD | GraphCAD-main/MAG/utils.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math, copy, time
from torch.autograd import Variable
import multiprocessing
from sklearn.metrics import roc_auc_score, auc, roc_curve
from torch_geometric.utils import add_self_loops, degree, softmax, to_dense_adj, dense_to_spa... | 2,237 | 27.329114 | 96 | py |
GraphCAD | GraphCAD-main/MAG/models.py | from random import sample
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics.pairwise import euclidean_distances, cosine_similarity
import pickle
from torch_geometric.nn import GCNConv, MessagePassing, GINConv, GATConv
from torch_geometric.utils import add_self_loops, degree, softm... | 9,780 | 37.507874 | 189 | py |
GraphCAD | GraphCAD-main/AMiner/main.py | import os
import argparse
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
import random
import json
import pickle
from collections import defaultdict
from operator import itemgetter
import logging
from torch_geometric.data import Data, DataLoader
from torch.optim.lr_scheduler import _LRSch... | 9,342 | 46.668367 | 213 | py |
GraphCAD | GraphCAD-main/AMiner/utils.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math, copy, time
from torch.autograd import Variable
import multiprocessing
from sklearn.metrics import roc_auc_score, auc, roc_curve
from torch_geometric.utils import add_self_loops, degree, softmax, to_dense_adj, dense_to_spa... | 2,237 | 27.329114 | 96 | py |
GraphCAD | GraphCAD-main/AMiner/models.py | from random import sample
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics.pairwise import euclidean_distances, cosine_similarity
import pickle
from torch_geometric.nn import GCNConv, MessagePassing, GINConv, GATConv
from torch_geometric.utils import add_self_loops, degree, softm... | 9,780 | 37.507874 | 189 | py |
GraphCAD | GraphCAD-main/Yelp/main.py | import os
import argparse
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
import random
import json
import pickle
from collections import defaultdict
from operator import itemgetter
import logging
from torch_geometric.data import Data, DataLoader
from torch.optim.lr_scheduler import _LRSch... | 9,996 | 46.379147 | 213 | py |
GraphCAD | GraphCAD-main/Yelp/utils.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math, copy, time
from torch.autograd import Variable
import multiprocessing
from sklearn.metrics import roc_auc_score, auc, roc_curve
from torch_geometric.utils import add_self_loops, degree, softmax, to_dense_adj, dense_to_spa... | 2,237 | 27.329114 | 96 | py |
GraphCAD | GraphCAD-main/Yelp/models.py | from random import sample
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics.pairwise import euclidean_distances, cosine_similarity
import pickle
from torch_geometric.nn import GINConv_w as GINConv
from torch_geometric.utils import add_self_loops, degree, softmax, to_dense_adj, den... | 9,770 | 37.317647 | 189 | py |
GraphCAD | GraphCAD-main/Alpha/main.py | import os
import argparse
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
import random
import json
import pickle
from collections import defaultdict
from operator import itemgetter
import logging
from torch_geometric.data import Data, DataLoader
from torch.optim.lr_scheduler import _LRSch... | 10,052 | 46.419811 | 213 | py |
GraphCAD | GraphCAD-main/Alpha/utils.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math, copy, time
from torch.autograd import Variable
import multiprocessing
from sklearn.metrics import roc_auc_score, auc, roc_curve
from torch_geometric.utils import add_self_loops, degree, softmax, to_dense_adj, dense_to_spa... | 2,237 | 27.329114 | 96 | py |
GraphCAD | GraphCAD-main/Alpha/models.py | from random import sample
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics.pairwise import euclidean_distances, cosine_similarity
import pickle
from torch_geometric.nn import GINConv_w as GINConv
from torch_geometric.utils import add_self_loops, degree, softmax, to_dense_adj, den... | 9,763 | 37.290196 | 189 | py |
CoordFill | CoordFill-master/test.py | import argparse
import os
import math
from functools import partial
import yaml
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
import datasets
import models
import utils
from PIL import Image
from torchvision import transforms
from torchsummary import summary
import numpy as np
def batch... | 4,752 | 31.333333 | 90 | py |
CoordFill | CoordFill-master/utils.py | import os
import time
import shutil
import math
import torch
import numpy as np
from torch.optim import SGD, Adam
from tensorboardX import SummaryWriter
from skimage.measure import compare_ssim
from skimage.measure import compare_psnr
class Averager():
def __init__(self):
self.n = 0.0
self.v = 0... | 3,801 | 24.346667 | 87 | py |
CoordFill | CoordFill-master/train_parallel.py | import argparse
import os
import yaml
import torch
import torch.nn as nn
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torch.optim.lr_scheduler import MultiStepLR, LambdaLR
from torchvision import transforms
import random
import dataset... | 7,851 | 35.52093 | 202 | py |
CoordFill | CoordFill-master/demo.py | import argparse
import os
from PIL import Image
import torch
from torchvision import transforms
import models
def resize_fn(img, size):
return transforms.ToTensor()(
transforms.Resize(size)(
transforms.ToPILImage()(img)))
def to_mask(mask):
return transforms.ToTensor()(
transfor... | 1,668 | 28.280702 | 94 | py |
CoordFill | CoordFill-master/train.py | import argparse
import os
import yaml
import torch
import torch.nn as nn
from tqdm import tqdm
from torch.utils.data import DataLoader
import datasets
import models
import utils
from test import eval_psnr, batched_predict
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def make_data_loader(spe... | 6,360 | 33.570652 | 105 | py |
CoordFill | CoordFill-master/models/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.dat... | 3,218 | 35.579545 | 115 | py |
CoordFill | CoordFill-master/models/modules.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .networks import BaseNetwork
from .networks import get_nonspade_norm_layer
from .networks import MySeparableBilinearDownsample as BilinearDownsample
import torch.nn.utils.spectral_norm as spectral_norm
import torch as th
from math import pi
from ma... | 12,294 | 36.257576 | 143 | py |
CoordFill | CoordFill-master/models/misc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import models
from models import register
from utils import make_coord
@register('metasr')
class MetaSR(nn.Module):
def __init__(self, encoder_spec):
super().__init__()
self.encoder = models.make(encoder_spec)
imnet_spec... | 2,303 | 31.450704 | 78 | py |
CoordFill | CoordFill-master/models/gan.py | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import models
from models import register
import math
import numpy as np
from torch.autograd import Variable
import os
import logging
logger = logging.getLogger(__name__)
from .coordfill import CoordFill
from .ffc_baseline import FFC
fro... | 6,804 | 36.185792 | 162 | py |
CoordFill | CoordFill-master/models/networks.py | import torch.nn as nn
from torch.nn import init
import torch.nn.utils.spectral_norm as spectral_norm
import torch
import torch.nn.functional as F
import functools
import numpy as np
class MySeparableBilinearDownsample(torch.nn.Module):
def __init__(self, stride, channels, use_gpu):
super().__init__()
... | 7,259 | 43 | 120 | py |
CoordFill | CoordFill-master/models/ffc.py | # Fast Fourier Convolution NeurIPS 2020
# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import ... | 22,247 | 39.014388 | 125 | py |
CoordFill | CoordFill-master/models/sync_batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import contextlib
import... | 16,476 | 43.05615 | 135 | py |
CoordFill | CoordFill-master/models/adv_loss.py | import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch import autograd
import torchvision.models as models
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class AdversarialLoss(nn.Module):
"""
Adversarial loss
https://arxiv.org/abs/1711... | 1,484 | 28.7 | 89 | py |
CoordFill | CoordFill-master/models/coordfill.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from scipy import ndimage
import numpy as np
from .ffc import FFCResNetGenerator
from .modules import CoordFillGenerator
from .ffc import FFCResNetGenerator, FFCResnetBlock, ConcatTupleLayer, FFC_BN_ACT
class AttFFC(nn.Module):
"""Convolutional LR... | 7,669 | 36.598039 | 135 | py |
CoordFill | CoordFill-master/models/bn_helper.py | import torch
import functools
if torch.__version__.startswith('0'):
from .sync_bn.inplace_abn.bn import InPlaceABNSync
BatchNorm2d = functools.partial(InPlaceABNSync, activation='none')
BatchNorm2d_class = InPlaceABNSync
relu_inplace = False
else:
BatchNorm2d_class = BatchNorm2d = torch.nn.SyncBatc... | 451 | 27.25 | 70 | py |
CoordFill | CoordFill-master/models/ffc_baseline.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from scipy import ndimage
import numpy as np
class ResnetBlock_remove_IN(nn.Module):
def __init__(self, dim, dilation=1, use_spectral_norm=True):
super(ResnetBlock_remove_IN, self).__init__()
self.conv_block = nn.Sequential(
... | 4,182 | 32.464 | 164 | py |
CoordFill | CoordFill-master/models/LPIPS/models/base_model.py | import os
import torch
import sys
sys.path.insert(1, './LPIPS/')
# import util.util as util
from torch.autograd import Variable
from pdb import set_trace as st
from IPython import embed
class BaseModel():
def __init__(self):
pass;
def name(self):
return 'BaseModel'
def initializ... | 1,794 | 26.19697 | 78 | py |
CoordFill | CoordFill-master/models/LPIPS/models/pretrained_networks.py | from collections import namedtuple
import torch
from torchvision import models
from IPython import embed
class squeezenet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(squeezenet, self).__init__()
pretrained_features = models.squeezenet1_1(pretrained=pretrained).... | 6,788 | 35.5 | 121 | py |
CoordFill | CoordFill-master/models/LPIPS/models/networks_basic.py |
from __future__ import absolute_import
import sys
sys.path.append('..')
sys.path.append('.')
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable
import numpy as np
from pdb import set_trace as st
from skimage import color
from IPython import embed
from . import pretrain... | 10,730 | 37.188612 | 136 | py |
CoordFill | CoordFill-master/models/LPIPS/models/dist_model.py |
from __future__ import absolute_import
import sys
sys.path.append('..')
sys.path.append('.')
import numpy as np
import torch
from torch import nn
import os
from collections import OrderedDict
from torch.autograd import Variable
import itertools
from .base_model import BaseModel
from scipy.ndimage import zoom
import f... | 13,452 | 39.521084 | 278 | py |
CoordFill | CoordFill-master/models/LPIPS/util/util.py | from __future__ import print_function
import numpy as np
from PIL import Image
import inspect
import re
import numpy as np
import os
import collections
import matplotlib.pyplot as plt
from scipy.ndimage.interpolation import zoom
from skimage.measure import compare_ssim
# from skimage.metrics import
from skimage import... | 14,095 | 29.912281 | 153 | py |
CoordFill | CoordFill-master/datasets/wrappers.py | import functools
import random
import math
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from datasets import register
def to_mask(mask):
return transforms.ToTensor()(
transforms.Grayscale(num_output_channels=1)(
... | 2,575 | 22.851852 | 77 | py |
CoordFill | CoordFill-master/datasets/image_folder.py | import os
import json
from PIL import Image
import pickle
import imageio
import numpy as np
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from datasets import register
@register('image-folder')
class ImageFolder(Dataset):
def __init__(self, path, split_file=None, split_key... | 1,885 | 27.575758 | 107 | py |
cycle-transformer | cycle-transformer-main/test.py | # This code is released under the CC BY-SA 4.0 license.
import glob
import os
import numpy as np
import pandas as pd
import pydicom
import torch
from skimage.metrics import structural_similarity as ssim
from models import create_model
from options.train_options import TrainOptions
@torch.no_grad()
def compute_eval_... | 2,738 | 29.775281 | 86 | py |
cycle-transformer | cycle-transformer-main/options/base_options.py | import argparse
import os
from util import util
import torch
import models as models
class BaseOptions:
"""This class defines options used during both training and test time.
It also implements several helper functions such as parsing, printing, and saving the options.
It also gathers additional options ... | 8,414 | 58.680851 | 235 | py |
cycle-transformer | cycle-transformer-main/models/base_model.py | import os
import torch
from collections import OrderedDict
from abc import ABC, abstractmethod
from . import networks
class BaseModel(ABC):
"""This class is an abstract base class (ABC) for models.
To create a subclass, you need to implement the following five functions:
-- <__init__>: ... | 10,583 | 44.038298 | 260 | py |
cycle-transformer | cycle-transformer-main/models/cytran_model.py | # This code is released under the CC BY-SA 4.0 license.
import torch
import itertools
from util import ImagePool
from models.conv_transformer import ConvTransformer
from .base_model import BaseModel
from . import networks
class CyTranModel(BaseModel):
@staticmethod
def modify_commandline_options(parser, is_t... | 10,350 | 54.352941 | 362 | py |
cycle-transformer | cycle-transformer-main/models/conv_transformer.py | # This code is released under the CC BY-SA 4.0 license.
from einops import rearrange
from torch import nn, einsum
import functools
class Encoder(nn.Module):
def __init__(self, input_nc, ngf=16, norm_layer=nn.BatchNorm2d, n_downsampling=3):
super(Encoder, self).__init__()
if type(norm_layer) == fu... | 6,016 | 34.187135 | 116 | py |
cycle-transformer | cycle-transformer-main/models/networks.py | # This code is released under the CC BY-SA 4.0 license.
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
###############################################################################
# Helper Functions
###############################################... | 28,452 | 45.115073 | 167 | py |
cycle-transformer | cycle-transformer-main/models/cycle_gan_model.py | # This code is released under the CC BY-SA 4.0 license.
import torch
import itertools
from util import ImagePool
from .base_model import BaseModel
from . import networks
class CycleGANModel(BaseModel):
"""
This class implements the CycleGAN model, for learning image-to-image translation without paired data.
... | 10,621 | 52.918782 | 362 | py |
cycle-transformer | cycle-transformer-main/util/image_pool.py | import random
import torch
class ImagePool:
"""This class implements an image buffer that stores previously generated images.
This buffer enables us to update discriminators using a history of generated images
rather than the ones produced by the latest generators.
"""
def __init__(self, pool_si... | 2,224 | 39.454545 | 140 | py |
cycle-transformer | cycle-transformer-main/util/util.py | """This module contains simple helper functions """
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input i... | 3,175 | 29.538462 | 119 | py |
cycle-transformer | cycle-transformer-main/data/colorization_dataset.py | import os
from data.base_dataset import BaseDataset, get_transform
from data import make_dataset
from skimage import color # require skimage
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
class ColorizationDataset(BaseDataset):
"""This dataset class can load a set of natural... | 2,704 | 38.202899 | 141 | py |
cycle-transformer | cycle-transformer-main/data/base_dataset.py | """This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
"""
import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.... | 5,400 | 33.183544 | 141 | py |
cycle-transformer | cycle-transformer-main/data/image_folder.py | """A modified image folder class
We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
so that this class can load images from both current directory and its subdirectories.
"""
import torch.utils.data as data
from PIL import Image
import os
IMG_E... | 1,885 | 27.575758 | 122 | py |
cycle-transformer | cycle-transformer-main/data/__init__.py | """This package includes all the modules related to data loading and preprocessing
To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
You need to implement four functions:
-- <__init__>: ... | 3,270 | 37.034884 | 176 | py |
GreedyAC | GreedyAC-master/utils/experience_replay.py | # Import modules
import numpy as np
import torch
from abc import ABC, abstractmethod
# Class definitions
class ExperienceReplay(ABC):
"""
Abstract base class ExperienceReplay implements an experience replay
buffer. The specific kind of buffer is determined by classes which
implement this base class. F... | 9,362 | 33.422794 | 79 | py |
GreedyAC | GreedyAC-master/agent/Random.py | #!/usr/bin/env python3
# Adapted from https://github.com/pranz24/pytorch-soft-actor-critic
# Import modules
import torch
import numpy as np
from agent.baseAgent import BaseAgent
class Random(BaseAgent):
"""
Random implement a random agent, which is one which samples uniformly from
all available actions.... | 2,248 | 24.556818 | 78 | py |
GreedyAC | GreedyAC-master/agent/baseAgent.py | #!/usr/bin/env python3
# Import modules
from abc import ABC, abstractmethod
# TODO: Given a data dictionary generated by main, create a static
# function to initialize any agent based on this dict. Note that since the
# dict has the agent name, only one function is needed to create ANY agent
# we could also use the e... | 3,716 | 28.975806 | 77 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/VACDiscrete.py | # Import modules
import torch
import inspect
import time
from gym.spaces import Box, Discrete
import numpy as np
import torch.nn.functional as F
from torch.optim import Adam
from agent.baseAgent import BaseAgent
import agent.nonlinear.nn_utils as nn_utils
from agent.nonlinear.policy.MLP import Softmax
from agent.nonlin... | 9,344 | 37.29918 | 78 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/GreedyAC.py | # Import modules
from gym.spaces import Box, Discrete
import torch
import torch.nn.functional as F
from torch.optim import Adam
import numpy as np
from agent.baseAgent import BaseAgent
from utils.experience_replay import TorchBuffer as ExperienceReplay
from agent.nonlinear.value_function.MLP import Q as QMLP
from agent... | 11,905 | 40.340278 | 79 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/GreedyACDiscrete.py | # Import modules
from gym.spaces import Box, Discrete
import inspect
import torch
import torch.nn.functional as F
from torch.optim import Adam
import numpy as np
from agent.baseAgent import BaseAgent
from utils.experience_replay import TorchBuffer as ExperienceReplay
from agent.nonlinear.value_function.MLP import Q as ... | 8,572 | 36.436681 | 78 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/SAC.py | # Import modules
import torch
import numpy as np
import torch.nn.functional as F
from torch.optim import Adam
from agent.baseAgent import BaseAgent
import agent.nonlinear.nn_utils as nn_utils
from agent.nonlinear.policy.MLP import SquashedGaussian, Gaussian
from agent.nonlinear.value_function.MLP import DoubleQ, Q
from... | 20,671 | 35.587611 | 79 | py |
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