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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DPSN | DPSN-master/time_series_proto/lib/models/utils.py | import torch
def euclidean_dist(x, y, transform=True):
bs = x.shape[0]
if transform:
num_proto = y.shape[0]
query_lst = []
for i in range(bs):
ext_query = x[i, :].repeat(num_proto, 1)
query_lst.append(ext_query)
x = torch.cat(query_lst, dim=0)
y = y.repeat(bs, 1)
return torch.po... | 340 | 21.733333 | 46 | py |
DPSN | DPSN-master/time_series_proto/lib/models/initialization.py | import torch
import torch.nn as nn
def init_resnet(m):
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_... | 744 | 31.391304 | 74 | py |
DPSN | DPSN-master/time_series_proto/lib/datasets/time_series_dataset.py | import random
import numpy as np
import torch
import torch.utils.data as data
from pathlib import Path
from collections import defaultdict
import sys
class timeSeriesDataset(data.Dataset):
def __init__(self, dataset_dir, mode, name, ratio_number, ind_number):
super(timeSeriesDataset, self).__init__()
self.dat... | 2,035 | 36.703704 | 108 | py |
DPSN | DPSN-master/time_series_proto/lib/configs/utils.py | import time
import torch
import torch.nn as nn
def calculate_loss(c_logits, f_logits, c_ys, f_ys, c2f, f2c):
# bs, n_c; bs, n_f
batch_size = c_logits.size(0)
select_x = [bs for bs in range(0, batch_size)]
select_cy = c_ys.tolist()
select_fy = f_ys.tolist()
c_max, _ = torch.max(c_logits, dim=1)
c_logit ... | 3,127 | 31.926316 | 138 | py |
DPSN | DPSN-master/time_series_proto/exp/train_full.py | import os, sys, time
import math
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from pathlib import Path
import numpy as np
from copy import deepcopy
from collections import defaultdict, OrderedDict
lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
if str(lib_dir) not in sys.pa... | 12,750 | 40.265372 | 212 | py |
DPSN | DPSN-master/time_series_proto/exp/test_load_model_shapelet.py | from sklearn.metrics import accuracy_score
import shutil
import os, sys, time
import math
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from pathlib import Path
import numpy as np
from copy import deepcopy
from collections import defaultdict, OrderedDict
sys.path.append(os.getcwd()[:-3... | 4,953 | 34.898551 | 110 | py |
DPSN | DPSN-master/time_series_proto/exp/train.py | import os, sys, time
import math
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from pathlib import Path
import numpy as np
from copy import deepcopy
from collections import defaultdict, OrderedDict
lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
if str(lib_dir) not in sys.pa... | 12,543 | 41.958904 | 212 | py |
LRI | LRI-main/src/get_model.py | import torch
import torch.nn as nn
from torch_geometric.nn import knn_graph, radius_graph
from torch_geometric.nn import global_mean_pool, global_add_pool, global_max_pool
from backbones import DGCNN, PointTransformer, EGNN
from utils import ExtractorMLP, MLP, CoorsNorm
class Model(nn.Module):
def __init__(self,... | 8,481 | 50.406061 | 216 | py |
LRI | LRI-main/src/trainer.py | import yaml
import json
import shutil
import argparse
from tqdm import tqdm
from pathlib import Path
from copy import deepcopy
from datetime import datetime
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.tensorboard import SummaryWriter
from get_model impor... | 12,327 | 55.81106 | 235 | py |
LRI | LRI-main/src/baselines/bernmask_p.py | # https://github.com/pyg-team/pytorch_geometric/blob/72eb1b38f60124d4d700060a56f7aa9a4adb7bb0/torch_geometric/nn/models/pg_explainer.py
import torch
import torch.nn as nn
from torch_scatter import scatter_mean
class BernMaskP(nn.Module):
def __init__(self, clf, extractor, criterion, config):
super().__i... | 3,696 | 44.641975 | 186 | py |
LRI | LRI-main/src/baselines/lri_gaussian.py | import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from torch_scatter import scatter, scatter_max
class LRIGaussian(nn.Module):
def __init__(self, clf, extractor, criterion, config):
super().__init__()
self.clf = clf
self.extractor = extractor
se... | 4,302 | 41.60396 | 166 | py |
LRI | LRI-main/src/baselines/pointmask.py | # https://github.com/asgsaeid/PointMask/blob/main/model_cls.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter_add
class PointMask(nn.Module):
def __init__(self, clf, extractor, criterion, config):
super().__init__()
self.clf = clf
se... | 2,471 | 34.826087 | 118 | py |
LRI | LRI-main/src/baselines/bernmask.py | # https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/models/gnn_explainer.html
# https://github.com/RexYing/gnn-model-explainer/blob/master/explainer/explain.py
import torch
import torch.nn as nn
from math import sqrt
class BernMask(nn.Module):
def __init__(self, clf, extractor, crit... | 3,305 | 38.831325 | 134 | py |
LRI | LRI-main/src/baselines/grad.py | # https://github.com/jacobgil/pytorch-grad-cam/blob/master/pytorch_grad_cam/base_cam.py
import numpy as np
import torch
import torch.nn as nn
class Grad(nn.Module):
def __init__(self, clf, signal_class, criterion, config) -> None:
super().__init__()
self.clf = clf
self.target_layers = [cl... | 5,999 | 35.585366 | 112 | py |
LRI | LRI-main/src/baselines/lri_bern.py | import torch
import torch.nn as nn
from torch_scatter import scatter
import numpy as np
class LRIBern(nn.Module):
def __init__(self, clf, extractor, criterion, config):
super().__init__()
self.clf = clf
self.extractor = extractor
self.criterion = criterion
self.device = ne... | 3,574 | 40.569767 | 135 | py |
LRI | LRI-main/src/datasets/tau3mu.py | import sys
sys.path.append('../')
import os
import yaml
import shutil
import os.path as osp
from tqdm import tqdm
import numpy as np
import pandas as pd
import torch
from torch_geometric.data import Data, InMemoryDataset
from utils import log, get_random_idx_split, download_url, extract_zip, decide_download
class ... | 5,160 | 35.602837 | 112 | py |
LRI | LRI-main/src/datasets/synmol.py | import sys
sys.path.append('../')
import os
import yaml
import shutil
import os.path as osp
from tqdm import tqdm
import numpy as np
import pandas as pd
import torch
from torch_geometric.data import InMemoryDataset, Data
import rdkit.Chem as Chem
import rdkit.Chem.AllChem as AllChem
from utils import download_url, ... | 5,997 | 38.202614 | 144 | py |
LRI | LRI-main/src/datasets/actstrack.py | import sys
sys.path.append('../')
import os
import yaml
import shutil
import pickle
import os.path as osp
from tqdm import tqdm
from itertools import combinations
import numpy as np
import torch
from torch_geometric.data import Data, InMemoryDataset
from utils import get_random_idx_split, download_url, extract_zip, ... | 8,513 | 40.130435 | 197 | py |
LRI | LRI-main/src/datasets/plbind.py | # Adopted from EquiBind
# https://github.com/HannesStark/EquiBind/blob/main/datasets/pdbbind.py
import sys
sys.path.append('../')
import os
import yaml
import shutil
import pickle
import warnings
import os.path as osp
from tqdm import tqdm
from pathlib import Path
from copy import deepcopy
import math
import numpy a... | 27,891 | 47.006885 | 205 | py |
LRI | LRI-main/src/utils/model_utils.py | import torch
import torch.nn as nn
from torch_geometric.nn import BatchNorm
class MLP(nn.Sequential):
def __init__(self, channels, dropout_p, norm_type, act_type):
norm = self.get_norm(norm_type)
act = self.get_act(act_type)
m = []
for i in range(1, len(channels)):
m.a... | 4,970 | 36.946565 | 170 | py |
LRI | LRI-main/src/utils/get_data_loaders.py | from pathlib import Path
from torch_geometric.loader import DataLoader
from datasets import ActsTrack, PLBind, Tau3Mu, SynMol
def get_data_loaders(dataset_name, batch_size, data_config, seed):
data_dir = Path(data_config['data_dir'])
assert dataset_name in ['tau3mu', 'plbind', 'synmol'] or 'acts' in dataset_n... | 2,069 | 53.473684 | 137 | py |
LRI | LRI-main/src/utils/utils.py | import os
import sys
import random
from tqdm import tqdm
from joblib import Parallel, delayed
import torch
import numpy as np
from Bio.PDB import ShrakeRupley
from .logger import log
init_metric_dict = {'metric/best_clf_epoch': 0, 'metric/best_clf_valid_loss': float('inf'),
'metric/best_clf_trai... | 4,972 | 38.784 | 186 | py |
LRI | LRI-main/src/utils/logger.py | import torch
import numpy as np
from datetime import datetime
from sklearn.metrics import roc_auc_score
import torch.nn.functional as F
def to_item(tensor):
if tensor is None:
return None
elif isinstance(tensor, torch.Tensor):
return tensor.item()
else:
return tensor
def log_epoc... | 13,705 | 51.51341 | 202 | py |
LRI | LRI-main/src/backbones/dgcnn.py | # https://github.com/pyg-team/pytorch_geometric/blob/master/examples/dgcnn_classification.py
from torch import Tensor
from typing import Callable, Union
from torch_geometric.typing import OptTensor, PairOptTensor, PairTensor
import torch
from torch.nn import Linear
import torch.nn.functional as F
from torch_geometri... | 5,574 | 39.992647 | 168 | py |
LRI | LRI-main/src/backbones/egnn.py | # https://github.com/vgsatorras/egnn/blob/main/qm9/models.py
# https://github.com/vgsatorras/egnn/blob/main/models/gcl.py
import torch
from torch import nn
import torch.nn.functional as F
from utils import FeatEncoder, MLP
class EGNN(nn.Module):
def __init__(self, x_dim, pos_dim, model_config, feat_info, n_categ... | 8,783 | 39.855814 | 199 | py |
LRI | LRI-main/src/backbones/pointtrans.py | # https://github.com/pyg-team/pytorch_geometric/blob/master/examples/point_transformer_classification.py
from torch import Tensor
from typing import Callable, Optional, Tuple, Union
from torch_geometric.typing import Adj, OptTensor, PairTensor
import torch
import torch.nn.functional as F
from torch.nn import Linear a... | 8,771 | 39.423963 | 168 | py |
nbclient | nbclient-main/docs/conf.py | #!/usr/bin/env python3
#
# nbclient documentation build configuration file, created by
# sphinx-quickstart on Mon Jan 26 16:00:00 2020.
#
# 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.
#
# A... | 6,008 | 29.19598 | 100 | py |
gossipnet | gossipnet-master/nms_net/network.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.slim.nets import resnet_v1
from nms_net import cfg
from nms_net.roi_pooling_layer impo... | 28,831 | 43.356923 | 161 | py |
Neural-SCL-Domain-Adaptation | Neural-SCL-Domain-Adaptation-master/AE-SCL-SR/tr.py | from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD
import pre
import w2v
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
import os
import numpy as np
from numpy.random import seed
from keras.models import load_model
seed(... | 2,255 | 37.237288 | 129 | py |
Neural-SCL-Domain-Adaptation | Neural-SCL-Domain-Adaptation-master/AE-SCL/tr.py | from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD
import pre
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
import os
import numpy as np
from numpy.random import seed
from keras.models import load_model
seed(1)
def ... | 1,759 | 35.666667 | 129 | py |
TTV2Fast2Furious | TTV2Fast2Furious-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 6,030 | 28.70936 | 79 | py |
cuml-branch-23.08 | cuml-branch-23.08/python/cuml/tests/test_fil.py | # Copyright (c) 2019-2023, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agre... | 25,175 | 31.276923 | 79 | py |
cuml-branch-23.08 | cuml-branch-23.08/python/cuml/tests/test_benchmark.py | # Copyright (c) 2019-2023, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agre... | 6,760 | 27.770213 | 79 | py |
cuml-branch-23.08 | cuml-branch-23.08/python/cuml/tests/explainer/test_gpu_treeshap.py | #
# Copyright (c) 2021-2023, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 32,453 | 31.421578 | 79 | py |
cuml-branch-23.08 | cuml-branch-23.08/python/cuml/tests/experimental/test_filex.py | # Copyright (c) 2023, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 30,167 | 33.556701 | 79 | py |
cuml-branch-23.08 | cuml-branch-23.08/python/cuml/benchmark/bench_helper_funcs.py | #
# Copyright (c) 2019-2023, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 14,358 | 30.627753 | 79 | py |
cuml-branch-23.08 | cuml-branch-23.08/python/cuml/internals/array.py | #
# Copyright (c) 2020-2023, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 43,609 | 33.749004 | 79 | py |
cuml-branch-23.08 | cuml-branch-23.08/python/cuml/internals/import_utils.py | #
# Copyright (c) 2019-2023, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 4,358 | 19.180556 | 79 | py |
cuml-branch-23.08 | cuml-branch-23.08/python/cuml/internals/input_utils.py | #
# Copyright (c) 2019-2023, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 15,862 | 27.685353 | 79 | py |
cuml-branch-23.08 | cuml-branch-23.08/ci/wheel_smoke_test_cuml.py | """
A simple test for cuML based on scikit-learn.
Adapted from xgboost:
https://raw.githubusercontent.com/rapidsai/xgboost-conda/branch-23.02/recipes/xgboost/test-py-xgboost.py
"""
from cuml.ensemble import RandomForestClassifier
import sklearn.datasets
import sklearn.model_selection
import sklearn.metrics
X, y = skl... | 765 | 27.37037 | 104 | py |
BFSNNOAI | BFSNNOAI-main/lossfns/alosses.py | import torch
# ---------------------------------------------------------------------------
class FBMBLoss(torch.nn.Module):
"""Frequency-band model-based loss"""
def __init__(self, L1, L2, etaS, eta):
super(FBMBLoss, self).__init__()
self.L1 = L1
self.L2 = L2
self.etaS = etaS
... | 593 | 28.7 | 83 | py |
BFSNNOAI | BFSNNOAI-main/fbfdunet_2bands/predict2bands.py | # Importing the necessary libraries:
import numpy as np
import time
import torch
from models.fbfdunetln import FDUNet
from fbfdunet_2bands.train2bands import expsetupparam
from utils.funmatrix import createForwMat, applyInvMat
from utils.OATdataloader import gettestdata
from utils.comparison import comparisonSOTA... | 2,386 | 30.407895 | 119 | py |
BFSNNOAI | BFSNNOAI-main/fbfdunet_2bands/train2bands.py | # Importing the necessary libraries:
#import os
import logging
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt
from utils.funmatrix import createForwMat, applyInvMat, applyForwMat
from utils.filtros import CreateFilterMatrix, applyFilter
from utils.OATdataloader import gettraindata, get_train... | 12,625 | 37.493902 | 131 | py |
BFSNNOAI | BFSNNOAI-main/fdunetln/predict.py | # Importing the necessary libraries:
import numpy as np
import torch
from models.fdunetln import FDUNet
from fdunetln.train import expsetupparam
from utils.funmatrix import createForwMat, applyInvMat
from utils.OATdataloader import gettestdata
from utils.comparison import comparisonSOTA
# -----------------------... | 1,894 | 28.153846 | 112 | py |
BFSNNOAI | BFSNNOAI-main/fdunetln/train.py | # Importing the necessary libraries:
#import os
import logging
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.optim import Adam
from utils.funmatrix import createForwMat, applyInvMat
from utils.OATdataloader import gettraindata, get_trainloader
... | 10,735 | 36.936396 | 131 | py |
BFSNNOAI | BFSNNOAI-main/models/fdunetln.py | # Importing the necessary libraries:
import torch
from torch import nn
import numpy as np
#import math
# ---------------------------------------------------------------------------
# Set up a random generator see so that the experiment
# can be replicated identically on any machine:
# torch.manual_seed(111)
# ----... | 4,692 | 38.771186 | 107 | py |
BFSNNOAI | BFSNNOAI-main/models/fbfdunetln.py | # Importing the necessary libraries:
import torch
from torch import nn
import numpy as np
#import math
# ---------------------------------------------------------------------------
# Set up a random generator see so that the experiment
# can be replicated identically on any machine:
# torch.manual_seed(111)
# ----... | 4,801 | 39.352941 | 107 | py |
BFSNNOAI | BFSNNOAI-main/utils/SLcheckpoint.py | import shutil
import torch
# ---------------------------------------------------------------------------
def save_ckp(state, is_best, checkpoint_path, best_model_path):
"""
state: checkpoint we want to save
is_best: is this the best checkpoint; min validation loss
checkpoint_path: path to save checkpoi... | 1,545 | 39.684211 | 77 | py |
BFSNNOAI | BFSNNOAI-main/utils/filtros.py | import numpy as np
from scipy import signal
from scipy.linalg import convolution_matrix
import torch
# ---------------------------------------------------------------------------
def CreateFilterMatrix(Nt,dx,nx,dsa,arco,vs,to,tf):
t = np.linspace(to, tf, Nt) # (Nt,)
# FILTER DESIGN
Fs = 1/(t[1]-t... | 2,667 | 30.023256 | 84 | py |
BFSNNOAI | BFSNNOAI-main/utils/OATdataloader.py | import numpy as np
import os
import torch
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import Dataset
from torch.utils.data import random_split
# ---------------------------------------------------------------------------
def gettraindata(cache_dir, n_name):
print('Obtaining data f... | 4,194 | 32.830645 | 175 | py |
BFSNNOAI | BFSNNOAI-main/utils/funmatrix.py | import numpy as np
import torch
from utils.model_based_matrix import build_matrix, SensorMaskCartCircleArc
# ---------------------------------------------------------------------------
def createForwMat(Ns,Nt,dx,nx,dsa,arco,vs,to,tf): #
print('Creating Forward Model-based Matrix')
t = np.linspace(to, ... | 1,474 | 41.142857 | 103 | py |
SAXS_py3 | SAXS_py3-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# SAXS documentation build configuration file, created by
# sphinx-quickstart on Wed Jun 4 10:53:22 2014.
#
# 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 ... | 10,826 | 30.382609 | 87 | py |
ReGO-Pytorch | ReGO-Pytorch-master/val.py | import argparse
import os
from PIL import Image
import numpy as np
import torch
from torchvision.utils import save_image
from torch.autograd import Variable
import glob
import random
from datasets import *
from models import *
from tqdm import trange
parser = argparse.ArgumentParser()
parser.add_argument("--image-p... | 5,704 | 40.948529 | 138 | py |
ReGO-Pytorch | ReGO-Pytorch-master/test.py | import argparse
import os
from PIL import Image
import numpy as np
import torch
from torchvision.utils import save_image
from torch.autograd import Variable
import json
from models import *
from tqdm import trange
parser = argparse.ArgumentParser()
parser.add_argument("--image-path", default='../Boundless/data/raw_t... | 4,667 | 39.241379 | 125 | py |
ReGO-Pytorch | ReGO-Pytorch-master/temp.py | import argparse
from datetime import datetime
import os
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torch.autograd import Variable
from datasets import *
from models import *
import time
import pdb
import time
from network import adjust_learning_rate, Preceptual_Loss
import t... | 9,495 | 39.237288 | 186 | py |
ReGO-Pytorch | ReGO-Pytorch-master/eval_fid_is_score.py | #import torch
#import torch.utils.data as data
from fid import calculate_fid_given_paths
#import torchvision.transforms as transforms
from inception_score.model import get_inception_score
from PIL import Image
import pathlib
import argparse
import os
import numpy as np
import random
parser = argparse.ArgumentParser(de... | 1,623 | 25.622951 | 103 | py |
ReGO-Pytorch | ReGO-Pytorch-master/val_detail_guide.py | import argparse
import os
from PIL import Image
import numpy as np
import torch
from torchvision.utils import save_image
from torch.autograd import Variable
import glob
import random
from datasets import *
from models import *
from tqdm import trange
parser = argparse.ArgumentParser()
parser.add_argument("--image-p... | 7,505 | 43.678571 | 138 | py |
ReGO-Pytorch | ReGO-Pytorch-master/network.py | import torch.nn as nn
import torch
from torchvision.models import resnet152, inception_v3
import torchvision.models as models
import pdb
from torchvision.models.resnet import model_urls
from torchvision.models.inception import model_urls as model_urls1
from places365.run_placesCNN_unified import load_model
def adjust_... | 9,520 | 33.003571 | 100 | py |
ReGO-Pytorch | ReGO-Pytorch-master/datasets.py | import glob
import random
import numpy as np
import torch
from torch.utils.data import Dataset
from PIL import Image
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from prefetch_generator import BackgroundGenerator
from sketch_mask import mask_sketch_basic
import json
import sys
im... | 6,631 | 35.043478 | 141 | py |
ReGO-Pytorch | ReGO-Pytorch-master/models.py | import torch.nn as nn
import torch
from torchvision.models import resnet152, inception_v3
import torch.nn.functional as F
import pdb
from torchvision.models.resnet import model_urls
from torchvision.models.inception import model_urls as model_urls1
from network import Conditional_Network
from network import ResBlock, F... | 16,136 | 38.167476 | 140 | py |
ReGO-Pytorch | ReGO-Pytorch-master/train.py | #--------------------------------
#author: xxx
#code for our paper: ReGO: Reference-Guided Outpainting for Scenery Image
#Our code is built on the top of this project: https://github.com/recong/Boundless-in-Pytorch,
#Thanks the author's contribution
#--------------------------------
import argparse
from datetime impo... | 9,864 | 39.764463 | 221 | py |
ReGO-Pytorch | ReGO-Pytorch-master/wideresnet.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/r... | 6,702 | 30.032407 | 95 | py |
ultramnist | ultramnist-main/test.py | # importing required modules
import sys
import os
import numpy as np
import pandas as pd
import cv2
import torch
import torch.nn as nn
from efficientnet_pytorch import EfficientNet
from tqdm.notebook import tqdm
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from tqdm import tqdm
fr... | 4,688 | 32.492857 | 95 | py |
ultramnist | ultramnist-main/train.py | # Importing required modules
from calendar import EPOCH
from random import seed
import warnings
import gc
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
from efficientnet_pytorch import ... | 8,300 | 34.935065 | 163 | py |
ultramnist | ultramnist-main/data_generation/add_noise.py | import os
import cv2
import argparse
import numpy as np
from glob import glob
from tqdm import tqdm
import multiprocessing as mp
from PIL import Image, ImageDraw
from torchvision.transforms import RandomAffine, CenterCrop, Compose, Resize
from joblib import Parallel, delayed
ap = argparse.ArgumentParser()
ap.add_argu... | 3,186 | 39.341772 | 119 | py |
ultramnist | ultramnist-main/data_generation/ultramnist.py | import os
import cv2
import random
import string
import numpy as np
from tqdm import tqdm
from PIL import Image
from torchvision import transforms
from collections import defaultdict
import torchvision.datasets as datasets
from .image_metrics import get_iou
class CreateUltraMNIST:
def __init__(self, root: str, ba... | 6,434 | 38.968944 | 146 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/neighbor-sampler-pyg-ppi.py | import os
import torch
from create_pyg import PPI
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_available() else '... | 1,378 | 27.729167 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-dgl-flickr.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from sklearn.metrics import f1_score
from load_graph import load_flickr
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_available() else 'c... | 1,654 | 32.77551 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/neighbor-sampler-dgl-yelp.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from sklearn.metrics import f1_score
from load_graph import load_yelp
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu... | 1,600 | 31.673469 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/create_dgl.py | import dgl
from dgl.data import DGLDataset
import torch
import json
import numpy as np
import scipy.sparse
import os
class PPIDataset(DGLDataset):
def __init__(self):
super().__init__(name='ppi')
def process(self):
path = os.path.abspath(os.path.join(os.getcwd(), ".."))
adj_full = scip... | 4,636 | 29.506579 | 89 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-pyg-ppi.py | import os
import torch
from create_pyg import PPI
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_available() else '... | 1,503 | 27.923077 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-pyg-flickr.py | import os
import torch
from torch_geometric.datasets import Flickr
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_a... | 1,450 | 28.02 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/neighbor-sampler-pyg-reddit.py | import os
import torch
from torch_geometric.datasets import Reddit
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
from torch_sparse import SparseTensor
import time
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_a... | 1,403 | 28.87234 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-pyg-ppi.py | import os
import torch
from create_pyg import PPI
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_available() else '... | 1,426 | 26.442308 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/neighbor-sampler-pyg-flickr.py | import os
import torch
from torch_geometric.datasets import Flickr
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_a... | 1,402 | 29.5 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-pyg-products.py | import os
import torch
from ogb.nodeproppred import PygNodePropPredDataset
from torch_geometric.utils import to_undirected, add_self_loops
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
import torch_geometric
# device = torch.device('cu... | 1,709 | 28.482759 | 124 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-pyg-flickr.py | import os
import torch
from torch_geometric.datasets import Flickr
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_a... | 1,528 | 28.980392 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-dgl-ppi.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from sklearn.metrics import f1_score
from load_graph import load_ppi
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_available() else 'c... | 1,795 | 32.259259 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-pyg-products.py | import os
import torch
from ogb.nodeproppred import PygNodePropPredDataset
from torch_geometric.utils import to_undirected, add_self_loops
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
import torch_geometric
# device = torch.device('cu... | 1,777 | 29.135593 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-dgl-reddit.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from sklearn.metrics import f1_score
from load_graph import load_reddit
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_available() else '... | 1,657 | 30.884615 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-dgl-arxiv.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from ogb.nodeproppred import DglNodePropPredDataset
from sklearn.metrics import f1_score
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
# from experiment_impact_tracker.compute_tracker import ImpactT... | 2,045 | 34.275862 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-pyg-reddit.py | import os
import torch
from torch_geometric.datasets import Reddit
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
from torch_sparse import SparseTensor
import time
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_a... | 1,451 | 27.470588 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-dgl-yelp.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from sklearn.metrics import f1_score
from load_graph import load_yelp
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu... | 1,648 | 32.653061 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/neighbor-sampler-pyg-arxiv.py | import os
import torch
from ogb.nodeproppred import PygNodePropPredDataset
from torch_geometric.utils import to_undirected, add_self_loops
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
import torch_geometric
# device = torch.device('cu... | 1,737 | 30.6 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-dgl-products.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from ogb.nodeproppred import DglNodePropPredDataset
from sklearn.metrics import f1_score
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_a... | 1,784 | 33.326923 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/neighbor-sampler-pyg-yelp.py | import os
import torch
from torch_geometric.datasets import Yelp
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_ava... | 1,395 | 28.702128 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-pyg-yelp.py | import os
import torch
from torch_geometric.datasets import Yelp
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_ava... | 1,521 | 28.269231 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-dgl-reddit.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from sklearn.metrics import f1_score
from load_graph import load_reddit
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_available() else '... | 1,811 | 30.789474 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/neighbor-sampler-dgl-arxiv.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from ogb.nodeproppred import DglNodePropPredDataset
from sklearn.metrics import f1_score
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
# from experiment_impact_tracker.compute_tracker import ImpactT... | 1,839 | 33.716981 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-pyg-arxiv.py | import os
import torch
from ogb.nodeproppred import PygNodePropPredDataset
from torch_geometric.utils import to_undirected, add_self_loops
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
import torch_geometric
# device = torch.device('cu... | 1,785 | 29.271186 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/neighbor-sampler-dgl-flickr.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from sklearn.metrics import f1_score
from load_graph import load_flickr
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_available() else 'c... | 1,602 | 31.714286 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/create_pyg.py | import json
import os
import os.path as osp
from typing import Callable, List, Optional
import numpy as np
import scipy.sparse as sp
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
class PPI(InMemoryDataset):
def __init__(self, root: str, transform: Optional[Callable] = None,
... | 2,745 | 35.131579 | 79 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-dgl-products.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from ogb.nodeproppred import DglNodePropPredDataset
from sklearn.metrics import f1_score
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_a... | 1,942 | 33.087719 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-pyg-reddit.py | import os
import torch
from torch_geometric.datasets import Reddit
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
from torch_sparse import SparseTensor
import time
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_a... | 1,529 | 28.423077 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-dgl-arxiv.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from ogb.nodeproppred import DglNodePropPredDataset
from sklearn.metrics import f1_score
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
# from experiment_impact_tracker.compute_tracker import ImpactT... | 1,891 | 34.698113 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-dgl-yelp.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from sklearn.metrics import f1_score
from load_graph import load_yelp
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu... | 1,798 | 32.314815 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/neighbor-sampler-dgl-reddit.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from sklearn.metrics import f1_score
from load_graph import load_reddit
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_available() else '... | 1,605 | 29.884615 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/saint-sampler-pyg-yelp.py | import os
import torch
from torch_geometric.datasets import Yelp
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
from torch_geometric.utils import add_self_loops
import torch_geometric
# device = torch.device('cuda' if torch.cuda.is_ava... | 1,443 | 27.313725 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-pyg-arxiv.py | import os
import torch
from ogb.nodeproppred import PygNodePropPredDataset
from torch_geometric.utils import to_undirected, add_self_loops
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
from torch_sparse import SparseTensor
import torch_geometric
# device = torch.device('cu... | 1,861 | 30.033333 | 122 | py |
GNN-Benchmark | GNN-Benchmark-main/code/Functional/sampler/cluster-sampler-dgl-flickr.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from sklearn.metrics import f1_score
from load_graph import load_flickr
from codecarbon import EmissionsTracker
from energy_logger import energy_logger
import time
# device = torch.device('cuda' if torch.cuda.is_available() else 'c... | 1,808 | 32.5 | 122 | py |
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