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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ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/models/hrnet/pose_hrnet.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Minor Modifications made for ActiveLearningForHumanPose code base
# --------------------------------------------------------------... | 20,055 | 37.274809 | 120 | py |
ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/models/auxiliary/AuxiliaryNet.py | import logging
import torch
import numpy as np
import torch.nn as nn
from torch.nn.parameter import Parameter
class AuxNet(nn.Module):
def __init__(self, arch):#num_feat, pose_num_channels, convolution=False, pose_feat_shape=(64, 32, 16, 8, 4)):
"""
:param num_feat:
:param pose_num_chann... | 3,943 | 33.295652 | 133 | py |
ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/models/learning_loss/LearningLoss.py | import logging
import torch
import numpy as np
import torch.nn as nn
from torch.nn.parameter import Parameter
class LearnLossActive(nn.Module):
def __init__(self, num_feat, hg_feat, hg_depth, original=False, hg_feat_shape=(64, 32, 16, 8, 4)):
'''
:param num_feat:
:param hg_feat:
... | 3,801 | 31.775862 | 114 | py |
assimp | assimp-master/port/PyAssimp/scripts/transformations.py | # -*- coding: utf-8 -*-
# transformations.py
# Copyright (c) 2006, Christoph Gohlke
# Copyright (c) 2006-2009, The Regents of the University of California
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions a... | 57,695 | 32.819461 | 79 | py |
sVAE | sVAE-main/svae/_utils.py | import torch
class GumbelSigmoid(torch.nn.Module):
def __init__(self, num_action, num_latent, freeze=False, drawhard=True, tau=1):
super(GumbelSigmoid, self).__init__()
self.shape = (num_action, num_latent)
self.freeze = freeze
self.drawhard = drawhard
self.log_alpha = torc... | 2,200 | 32.861538 | 88 | py |
sVAE | sVAE-main/svae/_module.py | # -*- coding: utf-8 -*-
"""Main module."""
from typing import Callable, Iterable, Optional
import numpy as np
import torch
from scvi import REGISTRY_KEYS
from scvi._compat import Literal
from scvi.distributions import NegativeBinomial
from scvi.module.base import BaseModuleClass, LossRecorder, auto_move_data
from scvi... | 14,243 | 34.969697 | 149 | py |
sVAE | sVAE-main/svae/_model.py | import logging
from typing import List, Optional, Sequence
import numpy as np
import pandas as pd
import torch
from anndata import AnnData
from scvi import REGISTRY_KEYS
from scvi._compat import Literal
from scvi.data import AnnDataManager
from scvi.data.fields import (
CategoricalJointObsField,
CategoricalObs... | 10,309 | 32.803279 | 118 | py |
sVAE | sVAE-main/svae/metrics.py | import torch
import numpy as np
from scipy.stats import spearmanr
from scipy.optimize import linear_sum_assignment
from sklearn.linear_model import LinearRegression
def get_linear_score(x, y):
reg = LinearRegression().fit(x, y)
return reg.score(x, y)
def linear_regression_metric(z, z_hat, num_samples=int(1... | 2,000 | 29.784615 | 78 | py |
sVAE | sVAE-main/svae/baselines/_module.py | # -*- coding: utf-8 -*-
"""Main module."""
from typing import Callable, Iterable, Optional
import numpy as np
import torch
from scvi import REGISTRY_KEYS
from scvi._compat import Literal
from scvi.distributions import NegativeBinomial
from scvi.module.base import BaseModuleClass, LossRecorder, auto_move_data
from scvi... | 12,419 | 33.985915 | 133 | py |
sVAE | sVAE-main/svae/baselines/_model.py | import logging
from typing import List, Optional, Sequence
import numpy as np
import pandas as pd
import torch
from anndata import AnnData
from scvi import REGISTRY_KEYS
from scvi._compat import Literal
from scvi.data import AnnDataManager
from scvi.data.fields import (
CategoricalJointObsField,
CategoricalObs... | 10,291 | 32.744262 | 110 | py |
sVAE | sVAE-main/entry_points/run_real_data_replogle_wandb.py | import argparse
import logging
import numpy as np
import pandas as pd
import scanpy as sc
import torch
import wandb
from pytorch_lightning.loggers import WandbLogger
logger = logging.getLogger("scvi")
settings = wandb.Settings(start_method="fork")
from svae import SpikeSlabVAE, sVAE
EXOSOME = [
"ZC3H3",
"ZF... | 13,055 | 20.723794 | 149 | py |
sVAE | sVAE-main/entry_points/demo.py | import argparse
import logging
import os
import numpy as np
import torch
import wandb
from pytorch_lightning.loggers import WandbLogger
logger = logging.getLogger("scvi")
settings = wandb.Settings(start_method="fork")
from svae import SpikeSlabVAE, metrics, sparse_shift, sVAE
def reinit_model(model):
# create ... | 8,242 | 34.995633 | 89 | py |
RGTSI | RGTSI-main/test.py | import time
import pdb
from options.test_options import TestOptions
from data.dataprocess import DataProcess
from models.model import create_model
import torchvision
from torch.utils import data
from torch.utils.tensorboard import SummaryWriter
import os
import torch
from PIL import Image
import numpy as np
from glob i... | 3,552 | 36.010417 | 103 | py |
RGTSI | RGTSI-main/train.py | import time
from options.train_options import TrainOptions
from data.dataprocess import DataProcess
from models.model import create_model
import torchvision
from torch.utils import data
from torch.utils.tensorboard import SummaryWriter
import os
import torch
if __name__ == "__main__":
opt = TrainOptions().parse()
... | 2,859 | 47.474576 | 151 | py |
RGTSI | RGTSI-main/options/base_options.py | import argparse
import os
from util import util
import torch
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize(self, parser):
parser.add_argument('--st_root', type=str, default=r'./data/datasets/structure', help='path to structure images')
parser.add_arg... | 4,787 | 49.4 | 163 | py |
RGTSI | RGTSI-main/models/base_model.py | import os
import torch
class BaseModel():
def __init__(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if se... | 3,444 | 34.885417 | 116 | py |
RGTSI | RGTSI-main/models/Decoder.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from models import model
class UnetSkipConnectionDBlock(nn.Module):
def __init__(self, inner_nc, outer_nc, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d,
use_dropout=False):
super(UnetSkipConnectionDBlock, sel... | 2,677 | 37.811594 | 128 | py |
RGTSI | RGTSI-main/models/Discriminator.py | import torch.nn as nn
import functools
def spectral_norm(module, mode=True):
if mode:
return nn.utils.spectral_norm(module)
return module
class NLayerDiscriminator(nn.Module):
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
super(NLayerDiscri... | 1,756 | 31.537037 | 99 | py |
RGTSI | RGTSI-main/models/loss.py |
import torch
import torch.nn as nn
import torchvision.models as models
import torch.nn.functional as F
class VGG16(torch.nn.Module):
def __init__(self):
super(VGG16, self).__init__()
features = models.vgg16(pretrained=True).features
self.relu1_1 = torch.nn.Sequential()
self.relu1_2 ... | 7,644 | 32.384279 | 110 | py |
RGTSI | RGTSI-main/models/model.py | from models.RGTSI import RGTSI
import torch
def create_model(opt):
model = RGTSI(opt)
#model = torch.nn.DataParallel(model.to(opt.device), device_ids=opt.gpu_ids, output_device=opt.gpu_ids[0])
print("model [%s] was created" % (model.name()))
return model
| 274 | 24 | 110 | py |
RGTSI | RGTSI-main/models/networks.py | # Define networks, init networks
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
from models.PCconv import PCconv
from models.InnerCos import InnerCos
from models.Encoder import Encoder, RefEncoder
from models.Discriminator import NLayerDiscriminator
fr... | 4,649 | 35.614173 | 216 | py |
RGTSI | RGTSI-main/models/Encoder.py | import torch.nn as nn
# Define the resnet block
class ResnetBlock(nn.Module):
def __init__(self, dim, dilation=1):
super(ResnetBlock, self).__init__()
self.conv_block = nn.Sequential(
nn.ReflectionPad2d(dilation),
nn.Conv2d(in_channels=dim, out_channels=dim, kernel_size=3, ... | 4,895 | 38.483871 | 126 | py |
RGTSI | RGTSI-main/models/PCconv.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.nn.functional as F
import torch
import torch.nn as nn
from models.FAM.FeatureAlignment import FAM
import util.util as util
from util.Selfpatch import Selfpatch
from util.util import saveoffset, sh... | 14,147 | 35.748052 | 119 | py |
RGTSI | RGTSI-main/models/RGTSI.py | import torch
import random
from collections import OrderedDict
from torch.autograd import Variable
from PIL import Image
import torch.nn.functional as F
from models.base_model import BaseModel
from models import networks
from .loss import VGG16, PerceptualLoss, StyleLoss, GANLoss
class RGTSI(BaseModel):
def __i... | 12,491 | 45.438662 | 144 | py |
RGTSI | RGTSI-main/models/InnerCos.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import util.util as util
class InnerCos(nn.Module):
def __init__(self):
super(InnerCos, self).__init__()
self.criterion = nn.L1Loss()
self.target = None
self.down_model = nn.Sequent... | 1,212 | 31.783784 | 97 | py |
RGTSI | RGTSI-main/models/FAM/non_local_embedded_gaussian.py | import torch
from torch import nn
from torch.nn import functional as F
class _NonLocalBlockND(nn.Module):
def __init__(self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True):
"""
:param in_channels:
:param inter_channels:
:param dimension:
:par... | 5,241 | 35.657343 | 102 | py |
RGTSI | RGTSI-main/models/FAM/FeatureAlignment.py | import torch.nn as nn
import torch
from models.FAM.DeformableBlock import DeformableConvBlock
from util.util import showpatch
class FAM(nn.Module):
def __init__(self,in_channels):
super(FAM, self).__init__()
self.deformblock = DeformableConvBlock(input_channels = in_channels*2)
def fo... | 502 | 28.588235 | 78 | py |
RGTSI | RGTSI-main/models/FAM/Model_utils.py | import torch
import torch.nn as nn
class L1_Charbonnier_loss(nn.Module):
"""L1 Charbonnierloss."""
def __init__(self):
super(L1_Charbonnier_loss, self).__init__()
self.eps = 1e-6
def forward(self, X, Y):
diff = torch.add(X, -Y)
error = torch.sqrt( diff * diff + self.eps)
... | 2,849 | 38.041096 | 132 | py |
RGTSI | RGTSI-main/models/FAM/Dynamic_offset_estimator.py | import torch.nn as nn
import torch
from models.FAM.non_local_embedded_gaussian import NONLocalBlock2D
from models.FAM.Model_utils import DOE_downsample_block, DOE_upsample_block
class Dynamic_offset_estimator(nn.Module):
def __init__(self,input_channelsize):
super(Dynamic_offset_estimator, self).__init__(... | 1,981 | 41.170213 | 131 | py |
RGTSI | RGTSI-main/models/FAM/DeformableBlock.py | import torch
import torch.nn as nn
from models.FAM.Dynamic_offset_estimator import Dynamic_offset_estimator
from mmcv.ops.deform_conv import DeformConv2d
from util.util import saveoffset, showpatch
class DeformableConvBlock(nn.Module):
def __init__(self, input_channels):
super(DeformableConvBlock, self)._... | 1,112 | 40.222222 | 125 | py |
RGTSI | RGTSI-main/util/Selfpatch.py | import torch
import torch.nn as nn
class Selfpatch(object):
def buildAutoencoder(self, target_img, target_img_2, target_img_3, patch_size=1, stride=1):
nDim = 3
assert target_img.dim() == nDim, 'target image must be of dimension 3.'
C = target_img.size(0)
self.Tensor = torch.cuda.... | 2,597 | 37.776119 | 147 | py |
RGTSI | RGTSI-main/util/se_module.py | from torch import nn
import torch
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Conv2d(channel, channel // reduction, kernel_size=1,stride=1, padding=0),
... | 950 | 28.71875 | 89 | py |
RGTSI | RGTSI-main/util/util.py | from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import random
import inspect, re
import numpy as np
import os
import collections
import math
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn as nn
import matplotlib.pyplot as plt
# Converts a... | 8,839 | 29.909091 | 97 | py |
RGTSI | RGTSI-main/data/dataprocess.py | import random
import torch
import torch.utils.data
from PIL import Image
from glob import glob
import numpy as np
import torchvision.transforms as transforms
class DataProcess(torch.utils.data.Dataset):
def __init__(self, de_root, st_root, input_mask_root,ref_root,opt, train=True):
super(DataProcess, self)... | 1,920 | 36.666667 | 92 | py |
pygsp | pygsp-master/doc/conf.py | # -*- coding: utf-8 -*-
import pygsp
extensions = [
'sphinx.ext.viewcode',
'sphinx.ext.autosummary',
'sphinx.ext.mathjax',
'sphinx.ext.inheritance_diagram',
]
extensions.append('sphinx.ext.autodoc')
autodoc_default_options = {
'members': True,
'undoc-members': True,
'member-order': 'group... | 2,259 | 25.27907 | 77 | py |
vad-sli-asr | vad-sli-asr-master/scripts/exp_asr-eval.py | import os
import pandas as pd
import torchaudio
from datasets import Dataset
from helpers.asr import configure_w2v2_for_inference
from jiwer import wer, cer
EVAL_MODELS_DATASETS = [
# Evaluation on the same test set using model trained using different amounts of data
("data/exps/asr/checkpoints/train-100", "d... | 5,752 | 58.309278 | 105 | py |
vad-sli-asr | vad-sli-asr-master/scripts/run_asr-by-w2v2.py | from argparse import ArgumentParser
from datasets import Dataset
from helpers.asr import configure_w2v2_for_inference
from transformers import logging
import pandas as pd
import pympi.Elan as Elan
import os
import re
import torchaudio
parser = ArgumentParser(
prog='run_asr-by-w2v2',
description='Run automatic... | 3,610 | 35.11 | 132 | py |
vad-sli-asr | vad-sli-asr-master/scripts/run_sli-by-sblr.py | from argparse import ArgumentParser
from speechbrain.pretrained import EncoderClassifier
from tqdm import tqdm
import pickle
import pympi.Elan as Elan
import os
import torch
import torchaudio
parser = ArgumentParser(
prog='run_sli-by-sblr',
description='Spoken language identification (SLI) using SpeechBrain e... | 3,440 | 36.813187 | 132 | py |
vad-sli-asr | vad-sli-asr-master/scripts/train_asr-by-w2v2-ft.py | import json
import math
import os
import torch
from argparse import ArgumentParser
from datasets import load_metric
from helpers.asr import (
configure_lm,
configure_w2v2_for_training,
DataCollatorCTCWithPadding,
dataset_from_dict,
get_metrics_computer,
preprocess_text,
process_data
)
from ... | 4,228 | 31.037879 | 150 | py |
vad-sli-asr | vad-sli-asr-master/scripts/run_vad-by-pyannote.py | from pyannote.audio.pipelines import VoiceActivityDetection
from argparse import ArgumentParser
import pympi.Elan as Elan
import os
import sys
import torch
from helpers.eaf import get_eaf_file
parser = ArgumentParser(
prog='run_vad-by-pyannote',
description='Voice activity detection with PyAnnote. Writes inte... | 2,545 | 32.064935 | 132 | py |
vad-sli-asr | vad-sli-asr-master/scripts/run_vad-by-silero.py | from argparse import ArgumentParser
import pympi.Elan as Elan
import os
import sys
import torch
import torchaudio
from helpers.eaf import get_eaf_file
parser = ArgumentParser(
prog='run_vad-by-silero',
description='Voice activity detection with Silero. Writes intervals onto _vad tier in sidecar file.',
)
par... | 3,051 | 34.488372 | 132 | py |
vad-sli-asr | vad-sli-asr-master/scripts/helpers/asr.py | import json
import numpy as np
import glob
import os
import pandas as pd
import re
import torch
from dataclasses import dataclass
from datasets import (
Audio,
Dataset,
DatasetDict,
disable_progress_bar,
enable_progress_bar,
load_metric
)
from typing import Dict, List, Union
from pyctcdecode im... | 8,823 | 31.20438 | 133 | py |
vad-sli-asr | vad-sli-asr-master/scripts/helpers/sli.py | import glob
import os
import numpy as np
import pandas as pd
import torch
import torchaudio
from sklearn.exceptions import ConvergenceWarning
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.utils import shuffle
from sklearn.utils._testing import ignore... | 2,451 | 30.037975 | 101 | py |
mipGNN | mipGNN-master/gnn_models/train_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import os
import os.path as osp
import networkx as nx
from sklearn.model_selection import train_test_split
from torchmetrics import F1, Precision, Recall, Accuracy
from torch_geometric.data import (InMemoryDataset, Data)
from to... | 17,218 | 39.325527 | 117 | py |
mipGNN | mipGNN-master/gnn_models/Sage/mip_bipartite_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch_geometric.utils.softmax
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU, Sigmoid
from torch_geometric.nn import MessagePassing
device ... | 8,206 | 36.646789 | 118 | py |
mipGNN | mipGNN-master/gnn_models/Sage/mip_bipartite_simple_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import MessagePassing
from torch_sparse import matmul
device = torch.device... | 4,248 | 34.408333 | 110 | py |
mipGNN | mipGNN-master/gnn_models/GIN/mip_bipartite_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch_geometric.utils.softmax
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU, Sigmoid
from torch_geometric.nn import MessagePassing
devic... | 8,207 | 36.309091 | 118 | py |
mipGNN | mipGNN-master/gnn_models/GIN/mip_bipartite_simple_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import MessagePassing
device = torch.device('cuda' if torch.cuda.is_availab... | 4,082 | 35.132743 | 110 | py |
mipGNN | mipGNN-master/gnn_models/EdgeConv/mip_bipartite_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch_geometric.utils.softmax
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU, Sigmoid
from torch_geometric.nn import MessagePassing
device... | 7,303 | 35.52 | 118 | py |
mipGNN | mipGNN-master/gnn_models/EdgeConv/mip_bipartite_simple_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import MessagePassing
device = torch.device('cuda' if torch.cuda.is_availab... | 4,148 | 35.078261 | 107 | py |
mipGNN | mipGNN-master/gisp_generator/read_data.py | import networkx as nx
import torch_geometric
# pickle file containing the bipartite graph corresponding to a single GISP instance
# the last integer in the filename refers to the random seed that generated this instance
data_path = "DATA/test/C125.9.clq_SET2_0.75_100_0.pk"
# vcg is the Variable-Constraint bipartite g... | 1,607 | 52.6 | 184 | py |
mipGNN | mipGNN-master/model_execution/inference.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import os
import os.path as osp
import numpy as np
import networkx as nx
import argparse
import io
import heapq
from pathlib import Path
import time
import math
import torch
from torch_geometric.data import (InMemoryDataset, Data... | 16,424 | 40.582278 | 205 | py |
mipGNN | mipGNN-master/model_execution/spo_train.py | # todo: check CPLEX status
# todo: solve LP instead of MIP
import os
import sys
import numpy as np
import argparse
from pathlib import Path
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
from sklearn import svm
from sklearn import neural_network
from sklearn.linear_... | 20,676 | 41.370902 | 199 | py |
mipGNN | mipGNN-master/model_execution/spo_torch.py | import torch
import torch.nn as nn
import cplex
import os
import numpy as np
import argparse
def solveIP(instance_cpx):
instance_cpx.solve()
optval = instance_cpx.solution.get_objective_value()
solution = np.array(instance_cpx.solution.get_values())
return solution, optval
def solveIP_obj(instance_cp... | 4,260 | 35.110169 | 123 | py |
mipGNN | mipGNN-master/model_execution/slurm_train.py | import spo_train
import spo_utils
import submitit
import random
from random import sample
output_dir = 'spo_torch_polydeg2_warmstart_hypersearch'
num_cpus = 25
executor = submitit.AutoExecutor(folder="log_%s" % output_dir)
print(executor.which())
executor.update_parameters(
additional_parameters={"account":... | 2,949 | 39.410959 | 383 | py |
mipGNN | mipGNN-master/model_execution/predict.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import os
import os.path as osp
import numpy as np
import networkx as nx
from pathlib import Path
import torch
from torch_geometric.data import (InMemoryDataset, Data)
from torch_geometric.data import DataLoader
#from gnn_models.... | 7,296 | 34.081731 | 102 | py |
mipGNN | mipGNN-master/model_execution/spo_test.py | import os
import numpy as np
import argparse
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd
import networkx as nx
import cplex
import pickle
import time
import re
import torch
import spo_utils
... | 6,383 | 41 | 127 | py |
mipGNN | mipGNN-master/code/gnn_models/train_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import os
import os.path as osp
import networkx as nx
from sklearn.model_selection import train_test_split
from torchmetrics import F1, Precision, Recall, Accuracy
from torch_geometric.data import (InMemoryDataset, Data)
from to... | 16,583 | 38.021176 | 113 | py |
mipGNN | mipGNN-master/code/gnn_models/Sage/mip_bipartite_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch_geometric.utils.softmax
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU, Sigmoid
from torch_geometric.nn import MessagePassing
device ... | 8,206 | 36.646789 | 118 | py |
mipGNN | mipGNN-master/code/gnn_models/Sage/mip_bipartite_simple_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import MessagePassing
from torch_sparse import matmul
device = torch.device... | 4,248 | 34.408333 | 110 | py |
mipGNN | mipGNN-master/code/gnn_models/GIN/mip_bipartite_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch_geometric.utils.softmax
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU, Sigmoid
from torch_geometric.nn import MessagePassing
devic... | 8,207 | 36.309091 | 118 | py |
mipGNN | mipGNN-master/code/gnn_models/GIN/mip_bipartite_simple_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import MessagePassing
device = torch.device('cuda' if torch.cuda.is_availab... | 4,082 | 35.132743 | 110 | py |
mipGNN | mipGNN-master/code/gnn_models/EdgeConv/mip_bipartite_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch_geometric.utils.softmax
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU, Sigmoid
from torch_geometric.nn import MessagePassing
device... | 7,303 | 35.52 | 118 | py |
mipGNN | mipGNN-master/code/gnn_models/EdgeConv/mip_bipartite_simple_class.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BN
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import MessagePassing
device = torch.device('cuda' if torch.cuda.is_availab... | 4,148 | 35.078261 | 107 | py |
mipGNN | mipGNN-master/code/model_execution/inference.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import os
import os.path as osp
import numpy as np
import networkx as nx
import argparse
import io
import heapq
from pathlib import Path
import time
import math
import torch
from torch_geometric.data import (InMemoryDataset, Data... | 15,580 | 40.328912 | 205 | py |
mipGNN | mipGNN-master/code/model_execution/predict.py | import sys
sys.path.insert(0, '..')
sys.path.insert(0, '../..')
sys.path.insert(0, '.')
import os
import os.path as osp
import numpy as np
import networkx as nx
from pathlib import Path
import torch
from torch_geometric.data import (InMemoryDataset, Data)
from torch_geometric.data import DataLoader
#from gnn_models.... | 6,527 | 35.066298 | 102 | py |
eegnet-based-embedded-bci | eegnet-based-embedded-bci-master/main_global.py | #*----------------------------------------------------------------------------*
#* Copyright (C) 2020 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... | 6,501 | 37.473373 | 134 | py |
eegnet-based-embedded-bci | eegnet-based-embedded-bci-master/models.py | #*----------------------------------------------------------------------------*
#* Copyright (C) 2020 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... | 4,269 | 43.479167 | 79 | py |
eegnet-based-embedded-bci | eegnet-based-embedded-bci-master/main_ss.py | #*----------------------------------------------------------------------------*
#* Copyright (C) 2020 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... | 6,798 | 37.196629 | 128 | py |
ConvoSource | ConvoSource-master/generate_pybdsf_solutions2.py | """
This script outputs the PyBDSF results in the same format as the AutoSource results, again assuming the images have size 50x50 pixels and are spaced 50 pixels apart. The following command gets the PyBDSF results on the 560MHz data at 8h exposure time, at an SNR of 1.
Usage:
python generate_pybdsf_solutions2.py --... | 25,031 | 31.807339 | 598 | py |
ConvoSource | ConvoSource-master/generate_real_data_and_solutions_Bx_yh_v1.py | """
This script generates the segmented real maps and solutions at a chosen exposure time, frequency and SNR on the simulated SKA data. The script command as it is generates 50x50 pixel images that are each spaced 50 pixels apart. The following commands segment the 560MHz at 8h exposure time dataset, at an SNR of 1. Ru... | 6,962 | 32.637681 | 409 | py |
ConvoSource | ConvoSource-master/source_finding_DNN_Bx_yh_v3.py |
"""
This script trains and tests AutoSource on the segmented real maps and solutions at a chosen exposure time, frequency and SNR on the simulated SKA data. Run 'generate_real_data_and_solutions_Bx_yh_v1.py' first before running this script. The script commands as they are currently assume there are 50x50 pixel segmen... | 36,730 | 36.328252 | 598 | py |
STM-Evaluation | STM-Evaluation-main/classification/main.py | """
Modified from DeiT official training and evaluation code.
"""
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import json
import time
import argparse
impor... | 23,878 | 46.285149 | 121 | py |
STM-Evaluation | STM-Evaluation-main/classification/engine.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from logging import critical
import math
from typing import Iterable, Optional
import torch
import torch.nn.functional a... | 10,886 | 38.879121 | 114 | py |
STM-Evaluation | STM-Evaluation-main/classification/invariance_eval_all.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import sys
import argparse
from pathlib import Path
from xml.sax import default_parser_list
import torch
impo... | 10,598 | 45.69163 | 317 | py |
STM-Evaluation | STM-Evaluation-main/classification/utils.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import sys
import math
import time
import datetime
import subprocess
from pathlib import Path
from collections... | 18,383 | 32.981516 | 128 | py |
STM-Evaluation | STM-Evaluation-main/classification/datasets.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import os.path as osp
from torchvision import datasets, transforms
import torch
import math
from tqdm import tqdm... | 10,490 | 34.562712 | 109 | py |
STM-Evaluation | STM-Evaluation-main/classification/samplers.py | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import torch
import torch.distributed as dist
import math
class RASampler(torch.utils.data.Sampler):
"""Sampler that restricts data loading to a subset of the dataset for distributed,
with repeated augmentation.
It ensures that different ... | 2,584 | 38.769231 | 103 | py |
STM-Evaluation | STM-Evaluation-main/classification/optim_factory.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import optim as optim
from timm.optim.adafactor import Adafactor
from timm.optim.adahessian impor... | 7,412 | 36.251256 | 117 | py |
STM-Evaluation | STM-Evaluation-main/classification/tools/variance_transforms.py | """
data transform modules for invariance analysis
"""
import torch
from torchvision import transforms
from torchvision.transforms.functional import rotate
import numpy as np
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from PIL import Image
def standard_transform(img_size=224, crop_ra... | 5,303 | 39.181818 | 96 | py |
STM-Evaluation | STM-Evaluation-main/classification/models/meta_arch.py | import torch
import torch.nn.functional as F
from torch import nn
from timm.models.layers import to_2tuple, trunc_normal_
class LayerNorm2d(nn.LayerNorm):
""" LayerNorm for channels of '2D' spatial NCHW tensors """
def __init__(self, num_channels, eps=1e-6, affine=True):
super().__init__(num_channel... | 7,604 | 34.537383 | 132 | py |
STM-Evaluation | STM-Evaluation-main/classification/models/blocks/pvt.py | # --------------------------------------------------------
# Modified from original PVT block implementation.
# https://github.com/whai362/PVT
# --------------------------------------------------------
import torch
import torch.nn as nn
from timm.models.layers import DropPath, trunc_normal_
class Mlp(nn.Module):
... | 4,728 | 35.376923 | 112 | py |
STM-Evaluation | STM-Evaluation-main/classification/models/blocks/pvt_v2.py | # --------------------------------------------------------
# Modified from original PVT block v2 implementation.
# https://github.com/whai362/PVT
# --------------------------------------------------------
import math
import torch
from torch import nn
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
... | 9,466 | 36.717131 | 126 | py |
STM-Evaluation | STM-Evaluation-main/classification/models/blocks/swin.py | # Modified from official swin-transformer implementation
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import torch
fro... | 7,149 | 39.39548 | 119 | py |
STM-Evaluation | STM-Evaluation-main/classification/models/blocks/halonet.py | """
Modified from Timm lib's implementation of Halo-Attention
https://github.com/rwightman/pytorch-image-models/blob/main/timm/models/layers/halo_attn.py
Following modifications are made:
1. A query-free related positional embedding (PE) is added. This PE runs faster but slightly
decrease the performance.
... | 15,562 | 40.723861 | 112 | py |
STM-Evaluation | STM-Evaluation-main/classification/models/blocks/convnext.py | '''
Modified from official ConvNeXt implementation
Note that, the unified ConvNeXt block is very different
from the official implementation. In the unified ConvNeXt,
depth-wise convolution with input&output projection is used
as the spatial token mixer, but the block design still follows
the original transformer's bl... | 4,985 | 35.661765 | 95 | py |
STM-Evaluation | STM-Evaluation-main/detection/mmdet_test.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp... | 10,515 | 36.827338 | 79 | py |
STM-Evaluation | STM-Evaluation-main/detection/mmdet_train.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
import torch.distributed as dist
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash
fro... | 9,169 | 36.125506 | 79 | py |
STM-Evaluation | STM-Evaluation-main/detection/mmdet_custom/models/backbones/meta_arch.py | import torch
import torch.nn.functional as F
from torch import nn
from mmdet.utils import get_root_logger
from timm.models.layers import to_2tuple, trunc_normal_
class LayerNorm2d(nn.LayerNorm):
""" LayerNorm for channels of '2D' spatial NCHW tensors """
def __init__(self, num_channels, eps=1e-6, affine=Tru... | 9,234 | 34.656371 | 132 | py |
STM-Evaluation | STM-Evaluation-main/detection/mmdet_custom/models/backbones/blocks/pvt.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.builder import BACKBONES
from timm.models.layers import DropPath, trunc_normal_
from ..meta_arch import MetaArch
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, dr... | 5,115 | 35.542857 | 112 | py |
STM-Evaluation | STM-Evaluation-main/detection/mmdet_custom/models/backbones/blocks/swin.py | import torch
import torch.nn.functional as F
from torch import nn
from mmdet.models.builder import BACKBONES
from timm.models.layers import DropPath, Mlp, to_2tuple, _assert
from timm.models.swin_transformer import WindowAttention, window_partition, window_reverse
from ..meta_arch import LayerNorm2d, MetaArch
class S... | 8,506 | 40.70098 | 119 | py |
STM-Evaluation | STM-Evaluation-main/detection/mmdet_custom/models/backbones/blocks/halonet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.builder import BACKBONES
from timm.models.layers import DropPath, Mlp
from timm.models.layers.halo_attn import rel_logits_1d
from ..meta_arch import MetaArch
def make_divisible(v, divisor=8, min_value=None, round_limit=.9):
min_v... | 15,537 | 40.10582 | 112 | py |
STM-Evaluation | STM-Evaluation-main/detection/mmdet_custom/models/backbones/blocks/convnext.py | import torch
from torch import nn
from mmdet.models.builder import BACKBONES
from timm.models.layers import DropPath, to_2tuple
from ..meta_arch import LayerNorm2d, MetaArch
class ConvNeXtBlock(nn.Module):
def __init__(self, dim, drop_path, layer_scale_init_value, kernel_size=7, **kwargs):
super().__init_... | 4,654 | 34.534351 | 109 | py |
STM-Evaluation | STM-Evaluation-main/detection/configs/_base_/models/mask_rcnn_r50_fpn.py | # model settings
model = dict(
type='MaskRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(ty... | 4,054 | 32.512397 | 79 | py |
loop | loop-master/generate.py | # Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import argparse
import numpy as np
import phonemizer
import string
import torch
from torch.autograd import Variable
from ... | 5,316 | 30.461538 | 83 | py |
loop | loop-master/utils.py | # Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import print_function
import os
import logging
import numpy
import subprocess
import time
from datetime import timede... | 11,826 | 32.036313 | 112 | py |
loop | loop-master/model.py | # Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.utils.rnn import pad_packed_sequence as unpack
f... | 8,810 | 34.103586 | 83 | py |
loop | loop-master/data.py | # Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from functools import partial
from collections import defaultdict
import numpy as np
import os
import torch
import torch.utils.data ... | 6,273 | 31.174359 | 79 | py |
loop | loop-master/train.py | # Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import argparse
import visdom
import numpy as np
from tqdm import tqdm
import torch
import torch.optim as optim
from data... | 7,591 | 34.811321 | 80 | py |
ParallelWaveGAN | ParallelWaveGAN-master/setup.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Setup Parallel WaveGAN libarary."""
import os
import pip
import sys
from distutils.version import LooseVersion
from setuptools import find_packages
from setuptools import setup
if LooseVersion(sys.version) < LooseVersion("3.7"):
raise RuntimeError(
"para... | 3,056 | 29.878788 | 106 | py |
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