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
value |
|---|---|---|---|---|---|---|
RMI | RMI-master/model/sync_bn/src/gpu/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='syncbn_gpu',
ext_modules=[
CUDAExtension('syncbn_gpu', [
'operator.cpp',
'syncbn_kernel.cu',
]),
],
cmdclass={
'build_ext': BuildExtension
... | 325 | 20.733333 | 67 | py |
RMI | RMI-master/model/sync_bn/src/cpu/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CppExtension
setup(
name='syncbn_cpu',
ext_modules=[
CppExtension('syncbn_cpu', [
'operator.cpp',
'syncbn_cpu.cpp',
]),
],
cmdclass={
'build_ext': BuildExtension
})... | 321 | 20.466667 | 66 | py |
RMI | RMI-master/losses/normal_loss.py | #coding=utf-8
"""
Implementation of some commonly used losses.
"""
# python 2.X, 3.X compatibility
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
#import os
#import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class BC... | 1,606 | 28.218182 | 106 | py |
RMI | RMI-master/losses/loss_factory.py | # coding=utf-8
# python 2.X, 3.X compatibility
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
#import torch
import torch.nn as nn
from RMI.losses import normal_loss
from RMI.losses import pyramid_loss
from RMI.losses.rmi import rmi
from RMI.losses.affinit... | 1,638 | 30.519231 | 92 | py |
RMI | RMI-master/losses/pyramid_loss.py | #coding=utf-8
# python 2.X, 3.X compatibility
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
#import os
#import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class PyramidLoss(nn.Module):
"""
Pyramid Loss.
"""
def __i... | 1,578 | 25.316667 | 78 | py |
RMI | RMI-master/losses/rmi/rmi.py | #coding=utf-8
"""
The implementation of the paper:
Region Mutual Information Loss for Semantic Segmentation.
"""
# python 2.X, 3.X compatibility
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
import torch
import torch.nn as nn
import torch.nn.functional a... | 9,039 | 39 | 115 | py |
RMI | RMI-master/losses/rmi/rmi_utils.py | #coding=utf-8
# python 2.X, 3.X compatibility
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
#import os
#import numpy as np
import torch
import torch.nn.functional as F
__all__ = ['map_get_pairs', 'log_det_by_cholesky']
def map_get_pairs(labels_4D, pro... | 5,201 | 26.670213 | 97 | py |
RMI | RMI-master/losses/affinity/utils.py | #coding=utf-8
"""
The pytorch implementation of the paper:
@inproceedings{aaf2018,
author = {Ke, Tsung-Wei and Hwang, Jyh-Jing and Liu, Ziwei and Yu, Stella X.},
title = {Adaptive Affinity Fields for Semantic Segmentation},
booktitle = {European Conference on Computer Vision (ECCV)},
month = {September},
year = {... | 2,729 | 30.022727 | 81 | py |
RMI | RMI-master/losses/affinity/aaf.py | #coding=utf-8
"""
The pytorch implementation of the paper:
@inproceedings{aaf2018,
author = {Ke, Tsung-Wei and Hwang, Jyh-Jing and Liu, Ziwei and Yu, Stella X.},
title = {Adaptive Affinity Fields for Semantic Segmentation},
booktitle = {European Conference on Computer Vision (ECCV)},
month = {September},
year = {... | 4,077 | 31.624 | 115 | py |
apps | apps-master/notebooks/riemann_tools.py | """
This version of riemann_tools.py was adapted from the
version in clawpack/riemann/src from Clawpack V5.3.1 to
add some new features and improve the plots.
This may be modified further as the notebooks in this
directory are improved and expanded. Eventually a
stable version of this should be moved back to
clawpa... | 11,161 | 32.51952 | 134 | py |
indic_nlp_library | indic_nlp_library-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Indic NLP Library documentation build configuration file, created by
# sphinx-quickstart on Tue Nov 3 01:50:37 2015.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated fil... | 7,932 | 31.646091 | 120 | py |
BarkBeetle-Damage-Classification-DL | BarkBeetle-Damage-Classification-DL-main/train.py | # Main training and testing file for carrying out experiments.
import os
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from augment_data import augment_data, get_options_dir
from crop_data import crop_images, read_data, save_image_crops
from df_repo.deepforest import main, model
from df_repo.... | 18,251 | 38.336207 | 197 | py |
BarkBeetle-Damage-Classification-DL | BarkBeetle-Damage-Classification-DL-main/df_repo/setup.py | from setuptools import setup, find_packages
import setuptools
from distutils.command.build_ext import build_ext as DistUtilsBuildExt
NAME = 'deepforest'
VERSION = '1.2.1'
DESCRIPTION = 'Tree crown prediction using deep learning retinanets'
URL = 'https://github.com/Weecology/DeepForest'
AUTHOR = 'Ben Weinstein'
LICENC... | 1,953 | 31.566667 | 183 | py |
BarkBeetle-Damage-Classification-DL | BarkBeetle-Damage-Classification-DL-main/df_repo/deepforest/main.py | # entry point for deepforest model
import os
import pandas as pd
from PIL import Image
import torch
import pytorch_lightning as pl
from torch import optim
import numpy as np
from ..deepforest import utilities
from ..deepforest import dataset
from ..deepforest import get_data
from ..deepforest import model
from ..deep... | 23,330 | 44.927165 | 252 | py |
BarkBeetle-Damage-Classification-DL | BarkBeetle-Damage-Classification-DL-main/df_repo/deepforest/callbacks.py | """
A deepforest callback
Callbacks must have the following methods on_epoch_begin, on_epoch_end, on_fit_end, on_fit_begin methods and inject model and epoch kwargs.
"""
from deepforest import visualize
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
import glob
import tempfile
from pyto... | 2,736 | 39.25 | 156 | py |
BarkBeetle-Damage-Classification-DL | BarkBeetle-Damage-Classification-DL-main/df_repo/deepforest/model.py | # Model
import torchvision
from torchvision.models.detection.retinanet import RetinaNet
from torchvision.models.detection.retinanet import AnchorGenerator
def load_backbone():
"""A torch vision retinanet model"""
backbone = torchvision.models.detection.retinanet_resnet50_fpn(pretrained=True)
# load the m... | 2,029 | 34 | 142 | py |
BarkBeetle-Damage-Classification-DL | BarkBeetle-Damage-Classification-DL-main/df_repo/deepforest/dataset.py | """
Dataset model
https://pytorch.org/docs/stable/torchvision/models.html#object-detection-instance-segmentation-and-person-keypoint-detection
During training, the model expects both the input tensors, as well as a targets (list of dictionary), containing:
boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1... | 5,048 | 38.445313 | 124 | py |
BarkBeetle-Damage-Classification-DL | BarkBeetle-Damage-Classification-DL-main/df_repo/deepforest/predict.py | # Prediction utilities
import cv2
import pandas as pd
from PIL import Image
import numpy as np
import os
from tqdm import tqdm
import warnings
import torch
import rasterio as rio
from torchvision.ops import nms
from torchvision.utils import save_image
from ..deepforest import preprocess
from ..deepforest import visu... | 14,956 | 40.090659 | 218 | py |
BarkBeetle-Damage-Classification-DL | BarkBeetle-Damage-Classification-DL-main/df_repo/deepforest/preprocess.py | # Deepforest Preprocessing model
"""The preprocessing module is used to reshape data into format suitable for
training or prediction.
For example cutting large tiles into smaller images.
"""
import os
import numpy as np
import pandas as pd
import slidingwindow
from PIL import Image
import torch
import warnings
import... | 10,792 | 39.272388 | 140 | py |
BarkBeetle-Damage-Classification-DL | BarkBeetle-Damage-Classification-DL-main/df_repo/deepforest/visualize.py | # Visualize module for plotting and handling predictions
import os
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
import numpy as np
import pandas.api.types as ptypes
import cv2
import random
import warnings
def view_dataset(ds, savedir=... | 6,126 | 39.576159 | 177 | py |
ARIL | ARIL-master/eval_time.py | import scipy.io as sio
from torch.utils.data import TensorDataset, DataLoader
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
import math
import time
import torch
from torch import nn
from torch.autograd import Var... | 5,656 | 33.919753 | 112 | py |
ARIL | ARIL-master/test.py | import scipy.io as sio
from torch.utils.data import TensorDataset, DataLoader
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
import math
import time
import torch
from torch import nn
from torch.autograd import Var... | 3,732 | 39.139785 | 111 | py |
ARIL | ARIL-master/train.py | import scipy.io as sio
from torch.utils.data import TensorDataset, DataLoader
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
import math
import time
import torch
from torch import nn
from torch.autograd import Var... | 10,730 | 40.754864 | 124 | py |
ARIL | ARIL-master/models/apl_plus.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import torch
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv1d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def... | 7,221 | 33.555024 | 116 | py |
ARIL | ARIL-master/models/apl.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import torch
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv1d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def... | 7,018 | 33.072816 | 116 | py |
HRank | HRank-master/main.py |
import torch
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from data import imagenet
from models import *
from utils import progress_bar
from mask import *
import utils
parser = argparse.ArgumentParser(desc... | 10,016 | 30.699367 | 160 | py |
HRank | HRank-master/evaluate.py |
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import argparse
from data import imagenet
from models import *
from mask import *
import utils
parser = argparse.ArgumentParser(description='... | 3,734 | 27.295455 | 118 | py |
HRank | HRank-master/get_flops.py | from __future__ import absolute_import
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
import torch
from torch.autograd import Variable
from functools import reduce
import operator
count_ops = 0
count_params = 0
def get_num_gen(gen):
return sum(1 for... | 5,553 | 34.151899 | 204 | py |
HRank | HRank-master/utils.py |
from __future__ import absolute_import
import os
import sys
import time
import logging
import datetime
import torch
from pathlib import Path
def get_logger(file_path):
""" Make python logger """
# [!] Since tensorboardX use default logger (e.g. logging.info()), we should use custom logger
logger = loggi... | 4,693 | 25.223464 | 98 | py |
HRank | HRank-master/mask.py |
import torch
import numpy as np
import pickle
class mask_vgg_16_bn:
def __init__(self, model=None, compress_rate=[0.50], job_dir='',device=None):
self.model = model
self.compress_rate = compress_rate
self.mask = {}
self.job_dir=job_dir
self.device = device
def layer_... | 13,437 | 37.067989 | 123 | py |
HRank | HRank-master/rank_generation.py |
import torch
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
import data.imagenet as imagenet
from models import *
from utils import progress_bar
import numpy as np
parser = argparse.ArgumentParser(description='Rank extraction')
parser.... | 16,289 | 33.439746 | 135 | py |
HRank | HRank-master/cal_flops_params.py |
import torch
import argparse
import get_flops
from models import *
parser = argparse.ArgumentParser(description='Calculating flops and params')
parser.add_argument(
'--input_image_size',
type=int,
default=32,
help='The input_image_size')
parser.add_argument(
'--arch',
type=str,
default='v... | 1,749 | 25.515152 | 111 | py |
HRank | HRank-master/models/resnet_imagenet.py |
import torch.nn as nn
norm_mean, norm_var = 1.0, 0.1
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class ResBottleneck(nn.Module):
expansion = 4
de... | 5,749 | 33.431138 | 146 | py |
HRank | HRank-master/models/vgg.py |
import math
import torch.nn as nn
from collections import OrderedDict
norm_mean, norm_var = 0.0, 1.0
defaultcfg = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 512]
relucfg = [2, 6, 9, 13, 16, 19, 23, 26, 29, 33, 36, 39]
convcfg = [0, 3, 7, 10, 14, 17, 20, 24, 27, 30, 34, 37]
... | 2,614 | 31.283951 | 101 | py |
HRank | HRank-master/models/resnet_cifar.py |
import torch.nn as nn
import torch.nn.functional as F
norm_mean, norm_var = 0.0, 1.0
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class LambdaLayer(nn.Module):
def __init__(self, lambd):
... | 4,325 | 30.808824 | 106 | py |
HRank | HRank-master/models/densenet_cifar.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
norm_mean, norm_var = 0.0, 1.0
cov_cfg=[(3*i+1) for i in range(12*3+2+1)]
class DenseBasicBlock(nn.Module):
def __init__(self, inplanes, filters, index, expansion=1, growthRate=12, dropRate=0, compress_rate=0., t... | 5,858 | 37.294118 | 156 | py |
HRank | HRank-master/models/googlenet_cifar.py | '''GoogLeNet with PyTorch.'''
import torch
import torch.nn as nn
norm_mean, norm_var = 0.0, 1.0
cov_cfg=[(22*i+2) for i in range(1+2+5+2)]
class Inception(nn.Module):
def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes, cp_rate, tmp_name):
super(Inception, self).__init__()
... | 6,582 | 32.247475 | 148 | py |
HRank | HRank-master/data/cifar10.py | from torchvision.datasets import CIFAR10
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
class Data:
def __init__(self, args):
# pin_memory = False
# if args.gpu is not None:
pin_memory = True
transform_train = transforms.Compose([
... | 1,258 | 38.34375 | 100 | py |
HRank | HRank-master/data/imagenet.py | import os
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
class Data:
def __init__(self, args, is_evaluate=False):
pin_memory = False
if args.gpu is not None:
pin_memory = True
scale_size = 224
... | 1,655 | 29.666667 | 70 | py |
Rce-KGQA | Rce-KGQA-main/answer_filtering_module/dataloader.py | import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
class MetaQADataSet(Dataset):
def __init__(self, entity_embed_path, entity_dict_path, relation_embed_path, relation_dict_path, qa_dataset_path,
split):
"""
create MetaQADataSet
:param enti... | 9,612 | 45.665049 | 131 | py |
Rce-KGQA | Rce-KGQA-main/answer_filtering_module/utils.py | import torch
import numpy as np
from typing import Optional
def create_src_lengths_mask(batch_size: int, src_lengths: torch.Tensor, max_src_len: Optional[int] = None):
"""
Generate boolean mask to prevent attention beyond the end of source
Inputs:
batch_size : int
src_lengths : [batch_size] of... | 2,392 | 41.732143 | 115 | py |
Rce-KGQA | Rce-KGQA-main/answer_filtering_module/model.py | import torch
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from utils import Attention_layer
import numpy as np
class Answer_filtering_module(torch.nn.Module):
def __init__(self, entity_embeddings, embedding_dim, vocab_size, word_dim, hidden_dim, fc_hidden_dim, relation_dim,
... | 4,546 | 61.287671 | 119 | py |
Rce-KGQA | Rce-KGQA-main/answer_filtering_module/train.py | import time
from dataloader import MetaQADataLoader, DEV_MetaQADataLoader
from model import Answer_filtering_module
import torch
import logging
from tqdm import tqdm
import os
from collections import OrderedDict
import numpy as np
if not torch.cuda.is_available:
print('Sorry, you should buy an NVIDIA Graphic Proce... | 8,804 | 48.189944 | 149 | py |
Rce-KGQA | Rce-KGQA-main/relational_chain_reasoning_module/dataloader.py | import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
class MetaQADataSet(Dataset):
def __init__(self, entity_embed_path, entity_dict_path, relation_embed_path, relation_dict_path, qa_dataset_path,
split):
"""
create MetaQADataSet
:param enti... | 9,693 | 45.605769 | 131 | py |
Rce-KGQA | Rce-KGQA-main/relational_chain_reasoning_module/utils.py | import torch
import numpy as np
from typing import Optional
def create_src_lengths_mask(batch_size: int, src_lengths: torch.Tensor, max_src_len: Optional[int] = None):
"""
Generate boolean mask to prevent attention beyond the end of source
Inputs:
batch_size : int
src_lengths : [batch_size] of... | 3,379 | 41.25 | 117 | py |
Rce-KGQA | Rce-KGQA-main/relational_chain_reasoning_module/model.py | import torch
from utils import ContrastiveLoss, Attention_layer
from transformers import RobertaModel, RobertaTokenizer
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
class Relational_chain_reasoning_module(torch.nn.Module):
def __init__(self, relation_dim, dim_l1, dim_l2, lstm_hidden_di... | 3,914 | 63.180328 | 152 | py |
Rce-KGQA | Rce-KGQA-main/relational_chain_reasoning_module/train.py | import torch
import networkx
import os
import logging
import time
from dataloader import DEV_MetaQADataLoader
from model import Relational_chain_reasoning_module
import numpy as np
from tqdm import tqdm
from collections import OrderedDict
# from answer_filtering_module.model import Answer_filtering_module
# ====datal... | 14,872 | 50.463668 | 155 | py |
Rce-KGQA | Rce-KGQA-main/knowledge_graph_embedding_module/model.py | import torch
class ComplEx_KGE(torch.nn.Module):
def __init__(self, d, entity_dim, do_batch_norm, input_dropout, hidden_dropout1, hidden_dropout2):
super(ComplEx_KGE, self).__init__()
self.E = torch.nn.Embedding(len(d.entities), entity_dim * 2, padding_idx=0)
self.R = torch.nn.Embedding(l... | 2,452 | 39.213115 | 113 | py |
Rce-KGQA | Rce-KGQA-main/knowledge_graph_embedding_module/train.py | from collections import defaultdict
from model import ComplEx_KGE
import numpy as np
import os
import time
import torch
from torch.optim.lr_scheduler import ExponentialLR
from tqdm import tqdm
class DataSet:
def __init__(self, data_dir, reverse):
self.train_data = self.load_data(data_dir, "train", revers... | 12,211 | 50.527426 | 128 | py |
sven | sven-master/scripts/sec_eval.py | import os
import csv
import json
import torch
import shutil
import argparse
import subprocess
import libcst as cst
from libcst.metadata import PositionProvider
from libcst._position import CodePosition
from collections import OrderedDict
from sven.evaler import LMEvaler, PrefixEvaler, TextPromptEvaler
from sven.utils ... | 10,740 | 41.121569 | 132 | py |
sven | sven-master/scripts/human_eval_gen.py | import os
import sys
import torch
import numpy
import random
import shutil
import argparse
from tqdm import tqdm
from pathlib import Path
from sven.utils import set_seed
from sven.model import load_model
from sven.constant import PROMPTS, MODEL_DIRS
from sven.human_eval.problem_yaml import Problem
def get_args():
... | 4,692 | 36.544 | 116 | py |
sven | sven-master/scripts/train.py |
import os
import torch
import logging
import argparse
from sven.trainer import PrefixTrainer, TextPromptTrainer
from sven.utils import set_seed, set_logging, set_devices
from sven.constant import MODEL_DIRS
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--output_name', type=str, requ... | 4,174 | 37.302752 | 105 | py |
sven | sven-master/sven/utils.py | import os
import sys
import ast
import time
import torch
import random
import lizard
import logging
import subprocess
import numpy as np
from urllib.request import Request, urlopen
from urllib.error import HTTPError
from diff_match_patch import diff_match_patch
from sven.constant import ALL_VUL_TYPES, PY_VUL_TYPES, CP... | 11,251 | 33.515337 | 180 | py |
sven | sven-master/sven/model.py | import os
import torch
from typing import Optional, Tuple, Union, List
from transformers import AutoTokenizer, AutoConfig, logging
from transformers.modeling_outputs import CausalLMOutputWithPast
from sven.codegen import CodeGenForCausalLM
class CodeGenPrefixCausalLM(CodeGenForCausalLM):
def __init__(self, config)... | 6,838 | 39.467456 | 108 | py |
sven | sven-master/sven/dataset.py | import os
import abc
import json
import torch
import random
from torch.utils.data import Dataset
from sven.constant import BINARY_LABELS, SEC_LABEL, VUL_LABEL, ALL_VUL_TYPES, PROMPTS
from sven.utils import get_indent
class DatasetBase(Dataset):
def __init__(self, args, tokenizer, mode):
self.args = args
... | 4,767 | 39.40678 | 96 | py |
sven | sven-master/sven/evaler.py | import os
import re
import abc
import torch
import numpy as np
from sven.model import CodeGenPrefixCausalLM, load_model
from sven.constant import PROMPTS
from sven.utils import try_parse
class EvalerBase:
def __init__(self, args):
self.args = args
self.load_model()
@abc.abstractclassmethod
... | 6,450 | 38.576687 | 143 | py |
sven | sven-master/sven/trainer.py | import os
import abc
import torch
import torch.nn.functional as F
import numpy as np
from collections import OrderedDict
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from transformers import AdamW, get_linear_schedule_with_warmup
from sven.model import save_model, parallelize_model, load_m... | 12,643 | 44.978182 | 161 | py |
sven | sven-master/sven/codegen/modeling_codegen.py | # coding=utf-8
# Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/l... | 34,701 | 41.319512 | 127 | py |
UrbanPy | UrbanPy-master/UrbanPy/test.py | import os
import h5py
import numpy as np
import argparse
import sys
import warnings
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from .utils.metrics import get_RMSE, get_MAE, get_MSE, get_MRE
from .utils.data_process import... | 4,000 | 40.247423 | 103 | py |
UrbanPy | UrbanPy-master/UrbanPy/model.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from .layers import LocalConv
import math
def n2_normalization_func(x, scale_factor):
out = F.avg_pool2d(x, scale_factor) * scale_factor ** 2
out = F.upsample(out, scale_factor=scale_factor)
return torch.div(x, out + 1e-5)
def recover_func... | 8,955 | 38.628319 | 202 | py |
UrbanPy | UrbanPy-master/UrbanPy/layers.py | import torch.nn as nn
import torch
import torch.nn.functional as F
cuda = True if torch.cuda.is_available() else False
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
import numpy as np
class LocalConv(nn.Module):
def __init__(self, width, block_size, in_chn, out_chn):
super(LocalConv, self)... | 3,147 | 41.540541 | 132 | py |
UrbanPy | UrbanPy-master/UrbanPy/train.py | import os
import sys
import warnings
import numpy as np
import random
import argparse
import warnings
from datetime import datetime
from PIL import Image
import pickle
import json
import time
import h5py
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.auto... | 9,749 | 41.763158 | 111 | py |
UrbanPy | UrbanPy-master/UrbanPy/utils/data_process.py | import numpy as np
import os
import math
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import json
def save_args(args, path):
with open(os.path.join(path, 'args.json'), 'w') as f:
json.dump(vars(args), f, sort_keys=True, indent=4)
print('Saved args to {}'.format(f... | 2,575 | 33.810811 | 121 | py |
SecurityPatchDetection | SecurityPatchDetection-main/helper.py | import contextlib
from functools import wraps
from time import time
import itertools
import requests
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from numpy import array
from sklearn.metrics import precision_recall_fscore_support as f_score
from sklearn.metrics import accuracy_score as a_... | 14,550 | 33.727924 | 99 | py |
SecurityPatchDetection | SecurityPatchDetection-main/models.py | import tensorflow as tf
from tensorflow import keras
from keras.layers import BatchNormalization
from keras.layers import Dense, LSTM, Input, Flatten, MaxPool1D
from keras.layers import Dense, LSTM, GRU, BatchNormalization
from keras.layers import Convolution1D
from keras.optimizers import SGD, Adam, RMSprop, Adagra... | 25,233 | 39.897893 | 170 | py |
SecurityPatchDetection | SecurityPatchDetection-main/train.py | #!/usr/bin/env python
# coding: utf-8
from importlib.metadata import metadata
from tensorflow import keras
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
from keras.optimizers import SGD, Adam
from data_collection.create_dataset import gh_cve_dir, repo_metadata_filename
from dat... | 29,503 | 34.04038 | 129 | py |
SecurityPatchDetection | SecurityPatchDetection-main/data_collection/graphql.py | import csv
import itertools
import logging
import time
import collections
import requests
import os
from .utils import safe_mkdir
class RepoNotFoundError(BaseException):
pass
less_than_10_vulns = [
'01org_opa-ff', '01org_opa-fm', '01org_tpm2.0-tools',
'10gen-archive_mongo-c-driver-legacy', '1up-lab_o... | 27,303 | 41.070878 | 92 | py |
simba | simba-master/setup.py | """
SimBA (Simple Behavioral Analysis)
https://github.com/sgoldenlab/simba
Contributors.
https://github.com/sgoldenlab/simba#contributors-
Licensed under GNU Lesser General Public License v3.0
"""
import setuptools
setuptools.setup(
name="Simba-UW-tf-dev",
version="1.59.3",
author="Simon Nilsson, Jia Jie ... | 1,861 | 49.324324 | 124 | py |
FGNM | FGNM-main/utils.py | # coding: utf-8
import os
import numpy as np
from PIL import Image
import tensorflow as tf
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch
import cv2
import matplotlib.pyplot as plt
def get_val_loder(data_path, batch_size):
normalize = transforms.Normalize(mean=[0.... | 4,576 | 34.207692 | 105 | py |
FGNM | FGNM-main/frequence_domain/frequency_analysis.py | """Implementation of sample attack."""
import os
from matplotlib import image
from numpy.testing._private.utils import requires_memory
import torch
import torchvision.models as models
from torch.autograd import Variable as V
from torch import nn
import torch.nn.functional as F
from torch.autograd.gradcheck import zero_... | 6,243 | 35.729412 | 118 | py |
FGNM | FGNM-main/nets/resnet_utils.py | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 10,449 | 40.967871 | 80 | py |
residual_adapters | residual_adapters-master/imdbfolder_coco.py | # imdbfolder_coco.py
# created by Sylvestre-Alvise Rebuffi [srebuffi@robots.ox.ac.uk]
# Copyright © The University of Oxford, 2017-2020
# This code is made available under the Apache v2.0 licence, see LICENSE.txt for details
import torch.utils.data as data
import torchvision
import torchvision.transforms as transforms... | 5,455 | 34.660131 | 203 | py |
residual_adapters | residual_adapters-master/sgd.py | # sgd.py
# created by Sylvestre-Alvise Rebuffi [srebuffi@robots.ox.ac.uk]
# Copyright © The University of Oxford, 2017-2020
# This code is made available under the Apache v2.0 licence, see LICENSE.txt for details
import torch
import math
import torch.nn.functional as F
import config_task
class SGD(torch.optim.Optimiz... | 2,406 | 36.609375 | 88 | py |
residual_adapters | residual_adapters-master/utils_pytorch.py | # imdbfolder_coco.py
# created by Sylvestre-Alvise Rebuffi [srebuffi@robots.ox.ac.uk]
# Copyright © The University of Oxford, 2017-2020
# This code is made available under the Apache v2.0 licence, see LICENSE.txt for details
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F... | 5,315 | 35.662069 | 113 | py |
residual_adapters | residual_adapters-master/models.py | # models.py
# created by Sylvestre-Alvise Rebuffi [srebuffi@robots.ox.ac.uk]
# Copyright © The University of Oxford, 2017-2020
# This code is made available under the Apache v2.0 licence, see LICENSE.txt for details
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable... | 5,832 | 38.952055 | 133 | py |
residual_adapters | residual_adapters-master/train_new_task_finetuning.py | # train_new_task_finetuning.py
# created by Sylvestre-Alvise Rebuffi [srebuffi@robots.ox.ac.uk]
# Copyright © The University of Oxford, 2017-2020
# This code is made available under the Apache v2.0 licence, see LICENSE.txt for details
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.funct... | 6,473 | 39.4625 | 237 | py |
residual_adapters | residual_adapters-master/train_new_task_adapters.py | # train_new_task_adapters.py
# created by Sylvestre-Alvise Rebuffi [srebuffi@robots.ox.ac.uk]
# Copyright © The University of Oxford, 2017-2020
# This code is made available under the Apache v2.0 licence, see LICENSE.txt for details
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functio... | 6,968 | 39.051724 | 237 | py |
residual_adapters | residual_adapters-master/train_new_task_from_scratch.py | # train_new_task_from_scratch.py
# created by Sylvestre-Alvise Rebuffi [srebuffi@robots.ox.ac.uk]
# Copyright © The University of Oxford, 2017-2020
# This code is made available under the Apache v2.0 licence, see LICENSE.txt for details
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.fun... | 5,045 | 40.702479 | 237 | py |
NKF-AEC-gh-pages | NKF-AEC-gh-pages/src/utils.py | import torch
import numpy as np
def gcc_phat(sig, refsig, fs=1, max_tau=None, interp=16):
'''
This function computes the offset between the signal sig and the reference signal refsig
using the Generalized Cross Correlation - Phase Transform (GCC-PHAT)method.
Code src: https://github.com/xiongyihui/tdo... | 1,142 | 28.307692 | 92 | py |
NKF-AEC-gh-pages | NKF-AEC-gh-pages/src/nkf.py | '''
Tencent is pleased to support the open source community by making NKF-AEC available.
Copyright (C) 2022 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
in compliance with the License. You may obtain a copy of the Li... | 6,917 | 37.865169 | 128 | py |
autonialm | autonialm-master/bayesian_optimization/automl_hyperopt_cli.py | import warnings; warnings.filterwarnings("ignore")
from hyperopt import fmin, tpe, hp, STATUS_OK, STATUS_FAIL, Trials, space_eval
# For surpressing print
import os, sys
print (sys.version)
class HiddenPrints:
def __enter__(self):
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, 'w... | 21,897 | 42.621514 | 380 | py |
autonialm | autonialm-master/bayesian_optimization/automl_hyperopt.py | import warnings; warnings.filterwarnings("ignore")
from hyperopt import fmin, tpe, hp, STATUS_OK, STATUS_FAIL, Trials, space_eval
# For surpressing print
import os, sys
print(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(os.path.abspath('bayesian_optimization/'))
prepend_path = "bayesian_optimization/"... | 19,514 | 41.702407 | 252 | py |
autonialm | autonialm-master/bayesian_optimization/algorithms/fcnn.py | from __future__ import print_function, division
import warnings; warnings.filterwarnings("ignore")
from nilmtk import DataSet
import pandas as pd
import numpy as np
import datetime
import time
import math
import glob
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
fro... | 11,452 | 38.905923 | 227 | py |
autonialm | autonialm-master/bayesian_optimization/algorithms/LSTM/lstmdisaggregator.py | from __future__ import print_function, division
from warnings import warn, filterwarnings
from matplotlib import rcParams
import matplotlib.pyplot as plt
import random
import sys
import pandas as pd
import numpy as np
import h5py
from keras.models import load_model
from keras.models import Sequential
from keras.laye... | 13,953 | 34.779487 | 145 | py |
autonialm | autonialm-master/bayesian_optimization/algorithms/DAE/daedisaggregator.py | from __future__ import print_function, division
from warnings import warn, filterwarnings
from matplotlib import rcParams
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import h5py
import random
import sys
from keras.models import load_model
from keras.models import Sequential
from keras.laye... | 13,966 | 34.270202 | 140 | py |
autonialm | autonialm-master/bayesian_optimization/algorithms/GRU/grudisaggregator.py | from __future__ import print_function, division
from warnings import warn, filterwarnings
from matplotlib import rcParams
import matplotlib.pyplot as plt
import random
import sys
import pandas as pd
import numpy as np
import h5py
from keras.models import load_model
from keras.models import Sequential
from keras.laye... | 13,981 | 34.759591 | 145 | py |
TokenMixup | TokenMixup-main/tokenmixup/horizontal.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.transforms.functional import resize
from scipy.optimize import linear_sum_assignment
import numpy as np
class HorizontalTokenMixupLayer(nn.Module):
def __init__(self, layer,
tau,
rho,... | 10,952 | 46.008584 | 132 | py |
TokenMixup | TokenMixup-main/tokenmixup/vertical.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import warnings
class VTM_ATTN(nn.Module):
"""
Obtained from timm: github.com:rwightman/pytorch-image-models
"""
def __init__(self, dim, num_heads=8, attention_dropout=0.1, projection_dropout=0.1):
super().__init__()
sel... | 7,146 | 41.289941 | 159 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/setup.py | import sys
import warnings
import os
from setuptools import setup, find_packages
import subprocess
import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME, load
# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__... | 31,830 | 40.01933 | 287 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/examples/dcgan/main_amp.py | from __future__ import print_function
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchv... | 10,518 | 37.250909 | 114 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/examples/simple/distributed/distributed_data_parallel.py | import torch
import argparse
import os
from apex import amp
# FOR DISTRIBUTED: (can also use torch.nn.parallel.DistributedDataParallel instead)
from apex.parallel import DistributedDataParallel
parser = argparse.ArgumentParser()
# FOR DISTRIBUTED: Parse for the local_rank argument, which will be supplied
# automatica... | 2,548 | 37.621212 | 88 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/examples/imagenet/main_amp.py | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvi... | 21,164 | 37.90625 | 239 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/tests/L0/run_optimizers/test_fused_optimizer.py | from itertools import product
import random
import unittest
import torch
import apex
class TestFusedOptimizer(unittest.TestCase):
def setUp(self, max_abs_diff=1e-3, max_rel_diff=1, iters=7):
self.max_abs_diff = max_abs_diff
self.max_rel_diff = max_rel_diff
self.iters = iters
torc... | 11,511 | 38.560137 | 104 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/tests/L0/run_optimizers/test_lamb.py | import unittest
import os
import torch
from torch.optim import Optimizer
import apex
from apex.multi_tensor_apply import multi_tensor_applier
from itertools import product
class RefLAMB(Optimizer):
r"""Implements Lamb algorithm.
It has been proposed in `Large Batch Optimization for Deep Learning: Training BE... | 13,823 | 40.020772 | 122 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/tests/L0/run_optimizers/test_fused_novograd.py | import torch
from torch.optim import Optimizer
import math
import apex
import unittest
from test_fused_optimizer import TestFusedOptimizer
from itertools import product
class Novograd(Optimizer):
"""
Implements Novograd algorithm.
Args:
params (iterable): iterable of parameters to optimize or dic... | 6,750 | 38.479532 | 93 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/tests/L0/run_amp/test_larc.py | import unittest
import torch
from torch import nn
from torch.nn import Parameter
from apex import amp
from apex.parallel.LARC import LARC
from utils import common_init
class MyModel(torch.nn.Module):
def __init__(self, unique):
super(MyModel, self).__init__()
self.weight0 = Parameter(
... | 1,339 | 23.814815 | 80 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/tests/L0/run_amp/test_multi_tensor_scale.py | import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
try:
import amp_C
from amp_C import multi_tensor_scale
from apex.multi_t... | 4,573 | 35.015748 | 109 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/tests/L0/run_amp/test_cache.py | import unittest
import functools as ft
import itertools as it
from apex import amp
from apex.amp import _amp_state
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
def get_reference_grad(i, w, ops):
# Creati... | 4,833 | 34.028986 | 98 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/tests/L0/run_amp/test_multi_tensor_axpby.py | import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from math import floor
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
try:
import amp_C
from amp_C import multi_tensor_axp... | 7,231 | 38.955801 | 111 | py |
TokenMixup | TokenMixup-main/experiments/apex_copy/tests/L0/run_amp/test_basic_casts.py | import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
def run_layer_test(test_case, fns, expected, input_shape, test_backward=True):
... | 5,085 | 34.319444 | 92 | py |
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