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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Analyzing-the-Generalization-Capability-of-SGLD-Using-Properties-of-Gaussian-Channels | Analyzing-the-Generalization-Capability-of-SGLD-Using-Properties-of-Gaussian-Channels-main/code/models/cnn.py | import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self, nchannels, nclasses):
super(Network, self).__init__()
self.conv1 = nn.Conv2d(nchannels, 32, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 32, 5)
self.fc1 = nn.L... | 680 | 29.954545 | 48 | py |
LiDAL | LiDAL-main/evaluate.py | import argparse
import numpy as np
import random
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torchsparse import SparseTensor
import time
import utils.iou_sk as iou_sk
import utils.iou_nu as iou_nu
from dataset.sk_dataloader import SK_Dataloader
from dataset.nu_dataloader impor... | 6,118 | 36.084848 | 139 | py |
LiDAL | LiDAL-main/train.py | import os
import argparse
import numpy as np
import random
import torch
import torch.optim as optim
import torch.distributed as dist
import torch.multiprocessing as mp
from torchsparse import SparseTensor
from dataset.sk_dataloader import SK_Dataloader
from dataset.nu_dataloader import NU_Dataloader
from network.spvcn... | 9,130 | 39.22467 | 200 | py |
LiDAL | LiDAL-main/dataset/nu_dataset.py | import os
import pickle
import math
import numpy as np
import torch
from torch.utils import data
####################################### Meta ###############################################
# labels:
# 0: 'noise'
# 1: 'animal'
# 2: 'human.pedestrian.adult'
# 3: 'human.pedestrian.child'
# 4: 'human.pedestria... | 8,431 | 33 | 208 | py |
LiDAL | LiDAL-main/dataset/sk_dataloader.py | import os
import numpy as np
import torch
import glob
import pickle
import tqdm
import torch.utils.data
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from dataset.sk_dataset import SK_Dataset
####################################### Meta ################################... | 12,755 | 41.805369 | 157 | py |
LiDAL | LiDAL-main/dataset/sk_dataset.py | import os
import pickle
import math
import numpy as np
import torch
from torch.utils import data
####################################### Meta ###############################################
label_name_mapping = {
0: 'unlabeled',
1: 'outlier',
10: 'car',
11: 'bicycle',
13: 'bus',
15: 'motorcycl... | 9,618 | 38.584362 | 208 | py |
LiDAL | LiDAL-main/dataset/nu_dataloader.py | import os
import numpy as np
import torch
import glob
import pickle
import torch.utils.data
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from nuscenes import NuScenes
from nuscenes.utils.splits import create_splits_scenes
from dataset.nu_dataset import NU_Dataset
cla... | 18,999 | 44.346062 | 174 | py |
LiDAL | LiDAL-main/score/prob_inference.py | from http.client import ImproperConnectionState
import os
import random
import numpy as np
import argparse
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torchsparse import SparseTensor
from nuscenes.utils.splits import create_splits_scenes
from dataset.sk_dataloader import SK... | 11,519 | 44.896414 | 165 | py |
LiDAL | LiDAL-main/network/utils.py | import torch
import torchsparse.nn.functional as F
import torchsparse.nn as spnn
from torch import nn
from torchsparse import PointTensor, SparseTensor
from torchsparse.nn.utils import get_kernel_offsets
__all__ = ['initial_voxelize', 'point_to_voxel', 'voxel_to_point']
# z: PointTensor
# return: SparseTensor
def in... | 5,866 | 33.110465 | 80 | py |
LiDAL | LiDAL-main/network/spvcnn.py | import torchsparse
import torchsparse.nn as spnn
from torch import nn
from torchsparse import PointTensor
from network.utils import initial_voxelize, point_to_voxel, voxel_to_point, BasicConvolutionBlock, BasicDeconvolutionBlock, ResidualBlock
class SPVCNN(nn.Module):
def __init__(self, class_num):
supe... | 5,207 | 32.384615 | 137 | py |
LiDAL | LiDAL-main/network/minkunet.py | import time
from collections import OrderedDict
import torch
import torchsparse
import torch.nn as nn
import torchsparse.nn as spnn
from network.utils import BasicConvolutionBlock, BasicDeconvolutionBlock, ResidualBlock
__all__ = ['MinkUNet']
class MinkUNet(nn.Module):
def __init__(self, class_num):
su... | 4,067 | 32.073171 | 87 | py |
THUMT | THUMT-master/setup.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from setuptools import find_packages
from setuptools import setup
setup(
name="thumt",
version="1.2.0",
author="The THUMT Authors",
author_email="thumt17@gmail.com",
description="THUMT: An open-source toolkit for neural ... | 1,076 | 29.771429 | 79 | py |
THUMT | THUMT-master/thumt/modules/embedding.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import torch
class PositionalEmbedding(torch.nn.Module):
def __init__(self):
super(PositionalEmbedding, self).__init__()
d... | 1,341 | 30.209302 | 76 | py |
THUMT | THUMT-master/thumt/modules/losses.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import torch
class SmoothedCrossEntropyLoss(torch.nn.Module):
def __init__(self, smoothing=0.0, normalize=True):
super(Smoothed... | 1,620 | 30.173077 | 74 | py |
THUMT | THUMT-master/thumt/modules/affine.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import torch
import torch.nn as nn
import thumt.utils as utils
from thumt.modules.module import Module
class Affine(Module):
def __ini... | 1,491 | 30.083333 | 79 | py |
THUMT | THUMT-master/thumt/modules/module.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import thumt.utils as utils
class Module(nn.Module):
def __init__(self, name=""):
super(Module, self)._... | 576 | 20.37037 | 58 | py |
THUMT | THUMT-master/thumt/modules/feed_forward.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import thumt.utils as utils
from thumt.modules.module import Module
from thumt.modules.affine import Affine
class Fe... | 1,475 | 31.8 | 78 | py |
THUMT | THUMT-master/thumt/modules/recurrent.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import thumt.utils as utils
from thumt.modules.module import Module
from thumt.modules.affine import Affine
from thum... | 4,484 | 32.721805 | 76 | py |
THUMT | THUMT-master/thumt/modules/layer_norm.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numbers
import torch
import torch.nn as nn
import thumt.utils as utils
from thumt.modules.module import Module
class LayerNorm(Module):
def... | 1,654 | 32.1 | 77 | py |
THUMT | THUMT-master/thumt/modules/attention.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import thumt.utils as utils
from thumt.modules.module import Module
from thumt.modules.affine import Affine
class At... | 9,955 | 34.942238 | 78 | py |
THUMT | THUMT-master/thumt/models/transformer.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import torch
import torch.nn as nn
import thumt.utils as utils
import thumt.modules as modules
class AttentionSubLayer(modules.Module):
... | 14,385 | 32.61215 | 79 | py |
THUMT | THUMT-master/thumt/bin/scorer.py | #! /usr/bin python
# coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import re
import six
import time
import copy
import torch
import socket
import logging
import argparse
import numpy as np
im... | 8,317 | 28.185965 | 78 | py |
THUMT | THUMT-master/thumt/bin/trainer.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import copy
import glob
import logging
import os
import re
import six
import socket
import time
import torch
import thumt.data as data
im... | 15,969 | 29.770713 | 79 | py |
THUMT | THUMT-master/thumt/bin/translator.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import copy
import logging
import os
import re
import six
import socket
import time
import torch
import thumt.data as data
import torch.d... | 10,260 | 28.317143 | 77 | py |
THUMT | THUMT-master/thumt/scripts/average_checkpoints.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import argparse
import collections
import torch
import shutil
def parse_args():
parser = argparse.Argume... | 2,121 | 24.878049 | 79 | py |
THUMT | THUMT-master/thumt/scripts/convert_checkpoint.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import numpy as np
import tensorflow as tf
import torch
def convert_tensor(variables, name, ... | 3,860 | 36.125 | 71 | py |
THUMT | THUMT-master/thumt/optimizers/optimizers.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
import math
import torch
import torch.distributed as dist
import thumt.utils as utils
import thumt.utils.summary as summary
from thumt.optimize... | 15,912 | 30.636183 | 79 | py |
THUMT | THUMT-master/thumt/utils/inference.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import torch
from collections import namedtuple
from thumt.utils.nest import map_structure
def _merge_first_two_dims(tensor):
shape = l... | 11,062 | 36.375 | 79 | py |
THUMT | THUMT-master/thumt/utils/convert_params.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
# Modified from torch.nn.utils.convert_parameters.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
def params_to_vec(parameters):
r"""Convert parameters to one vector
Arguments... | 3,165 | 28.867925 | 81 | py |
THUMT | THUMT-master/thumt/utils/checkpoint.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import torch
def oldest_checkpoint(path):
names = glob.glob(os.path.join(path, "*.pt"))
if not names:
return None... | 1,738 | 21.584416 | 66 | py |
THUMT | THUMT-master/thumt/utils/evaluation.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import datetime
import glob
import operator
import os
import shutil
import time
import torch
import torch.distributed as dist
from thumt.utils.checkpoin... | 7,411 | 27.398467 | 79 | py |
THUMT | THUMT-master/thumt/utils/summary.py | # coding=utf-8
# Copyright 2017-2020 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import queue
import threading
import torch
import torch.distributed as dist
import torch.utils.tensorboard as tensorboard
_SUMMARY_WRITER = None
_QUEUE ... | 2,224 | 25.176471 | 78 | py |
THUMT | THUMT-master/thumt/data/dataset.py | # coding=utf-8
# Copyright 2017-Present The THUMT Authors
import abc
import torch
from collections.abc import Sequence
from torch.utils.data import IterableDataset
from thumt.data.iterator import Iterator
from thumt.data.vocab import Vocabulary
from thumt.tokenizers import Tokenizer
from typing import Any, Dict, NoRe... | 18,929 | 27.338323 | 79 | py |
THUMT | THUMT-master/thumt/data/vocab.py | # coding=utf-8
# Copyright 2017-Present The THUMT Authors
import numpy as np
import six
import torch
from typing import Union
class Vocabulary(object):
def __init__(self, filename):
self._idx2word = {}
self._word2idx = {}
cnt = 0
with open(filename, "rb") as fd:
for... | 1,123 | 23.434783 | 60 | py |
THUMT | THUMT-master/thumt/data/pipeline.py | # coding=utf-8
# Copyright 2017-Present The THUMT Authors
import torch
from thumt.data.dataset import Dataset, ElementSpec, MapFunc, TextLineDataset
from thumt.data.vocab import Vocabulary
from thumt.tokenizers import WhiteSpaceTokenizer
def _sort_input_file(filename, reverse=True):
with open(filename, "rb") as... | 7,894 | 36.240566 | 79 | py |
adaptive_template_systems | adaptive_template_systems-master/docs/source/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 ------------------------------------------------------------... | 5,204 | 29.982143 | 88 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/general/multi_categorical.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.one_hot_categorical import OneHotCategorical
class MultiCategorical(nn.Module):
def __init__(self, input_size, variable_sizes):
super(MultiCategorical, self).__init__()
... | 1,637 | 31.76 | 96 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/general/discriminator.py | from __future__ import print_function
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, input_size, hidden_sizes=(256, 128), bn_decay=0.01, critic=False):
super(Discriminator, self).__init__()
hidden_activation = nn.LeakyReLU(0.2)
previous_layer_size = input_size... | 983 | 28.818182 | 89 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/general/encoder.py | from __future__ import print_function
import torch.nn as nn
class Encoder(nn.Module):
def __init__(self, input_size, code_size, hidden_sizes=[]):
super(Encoder, self).__init__()
hidden_activation = nn.Tanh()
previous_layer_size = input_size
layer_sizes = list(hidden_sizes) + [... | 678 | 24.148148 | 69 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/general/wgan_gp.py | from __future__ import print_function
import torch
from torch.autograd.variable import Variable
from multi_categorical_gans.utils.cuda import to_cuda_if_available
def calculate_gradient_penalty(discriminator, penalty, real_data, fake_data):
real_data = real_data.data
fake_data = fake_data.data
alpha =... | 1,031 | 35.857143 | 115 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/general/autoencoder.py | from __future__ import print_function
import torch
import torch.nn as nn
from multi_categorical_gans.methods.general.decoder import Decoder
from multi_categorical_gans.methods.general.encoder import Encoder
class AutoEncoder(nn.Module):
def __init__(self, input_size, code_size=128, encoder_hidden_sizes=[], dec... | 1,446 | 34.292683 | 99 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/general/single_output.py | from __future__ import print_function
import torch.nn as nn
class SingleOutput(nn.Module):
def __init__(self, previous_layer_size, output_size, activation=None):
super(SingleOutput, self).__init__()
if activation is None:
self.model = nn.Linear(previous_layer_size, output_size)
... | 525 | 29.941176 | 95 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/general/decoder.py | from __future__ import print_function
import torch.nn as nn
from multi_categorical_gans.methods.general.multi_categorical import MultiCategorical
from multi_categorical_gans.methods.general.single_output import SingleOutput
class Decoder(nn.Module):
def __init__(self, code_size, output_size, hidden_sizes=[]):
... | 1,439 | 32.488372 | 103 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/general/generator.py | from __future__ import print_function
import torch.nn as nn
from multi_categorical_gans.methods.general.multi_categorical import MultiCategorical
from multi_categorical_gans.methods.general.single_output import SingleOutput
class Generator(nn.Module):
def __init__(self, noise_size, output_size, hidden_sizes=[]... | 1,585 | 34.244444 | 89 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/medgan/discriminator.py | from __future__ import print_function
import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, input_size, hidden_sizes=(256, 128)):
super(Discriminator, self).__init__()
hidden_activation = nn.LeakyReLU()
previous_layer_size = input_size * 2
layers... | 1,103 | 28.052632 | 75 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/medgan/sampler.py | from __future__ import print_function
import argparse
import torch
import numpy as np
from torch.autograd.variable import Variable
from multi_categorical_gans.methods.general.autoencoder import AutoEncoder
from multi_categorical_gans.methods.medgan.generator import Generator
from multi_categorical_gans.utils.categ... | 4,969 | 30.858974 | 110 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/medgan/pre_trainer.py | from __future__ import print_function
import argparse
import torch
import numpy as np
from torch.autograd.variable import Variable
from torch.optim import Adam
from multi_categorical_gans.datasets.dataset import Dataset
from multi_categorical_gans.datasets.formats import data_formats, loaders
from multi_categorica... | 7,066 | 31.122727 | 117 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/medgan/generator.py | from __future__ import print_function
import torch.nn as nn
class Generator(nn.Module):
def __init__(self, code_size=128, num_hidden_layers=2, bn_decay=0.01):
super(Generator, self).__init__()
self.modules = []
self.batch_norms = []
for layer_number in range(num_hidden_layers):... | 1,404 | 34.125 | 116 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/medgan/trainer.py | from __future__ import division
from __future__ import print_function
import argparse
import torch
import numpy as np
from torch.autograd.variable import Variable
from torch.optim import Adam
from torch.nn import BCELoss
from multi_categorical_gans.datasets.dataset import Dataset
from multi_categorical_gans.dataset... | 13,686 | 34.18509 | 115 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/arae/sampler.py | from __future__ import print_function
import argparse
import torch
import numpy as np
from torch.autograd.variable import Variable
from multi_categorical_gans.methods.general.autoencoder import AutoEncoder
from multi_categorical_gans.methods.general.generator import Generator
from multi_categorical_gans.utils.cate... | 5,188 | 30.640244 | 111 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/arae/trainer.py | from __future__ import print_function
import argparse
import torch
import numpy as np
from torch.autograd.variable import Variable
from torch.optim import Adam
from multi_categorical_gans.datasets.dataset import Dataset
from multi_categorical_gans.datasets.formats import data_formats, loaders
from multi_categorica... | 14,552 | 32.6875 | 114 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/mc_wgan_gp/sampler.py | from __future__ import print_function
import argparse
import torch
import numpy as np
from torch.autograd.variable import Variable
from multi_categorical_gans.methods.general.generator import Generator
from multi_categorical_gans.utils.categorical import load_variable_sizes_from_metadata
from multi_categorical_gan... | 3,285 | 29.71028 | 106 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/mc_wgan_gp/trainer.py | from __future__ import print_function
import argparse
import torch
import numpy as np
from torch.autograd.variable import Variable
from torch.optim import Adam
from multi_categorical_gans.datasets.dataset import Dataset
from multi_categorical_gans.datasets.formats import data_formats, loaders
from multi_categorica... | 10,134 | 32.013029 | 115 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/mc_gumbel/sampler.py | from __future__ import print_function
import argparse
import torch
import numpy as np
from torch.autograd.variable import Variable
from multi_categorical_gans.methods.general.generator import Generator
from multi_categorical_gans.utils.categorical import load_variable_sizes_from_metadata
from multi_categorical_gan... | 3,504 | 29.478261 | 106 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/methods/mc_gumbel/trainer.py | from __future__ import division
from __future__ import print_function
import argparse
import torch
import numpy as np
from torch.autograd.variable import Variable
from torch.nn import BCELoss
from torch.optim import Adam
from multi_categorical_gans.datasets.dataset import Dataset
from multi_categorical_gans.dataset... | 11,645 | 33.052632 | 114 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/datasets/synthetic/generate.py | from __future__ import division
from __future__ import print_function
import argparse
import json
import torch
import numpy as np
from scipy.sparse import csr_matrix, save_npz
from torch.distributions.one_hot_categorical import OneHotCategorical
distribution_types = ["probs", "logits", "uniform"]
class Variable(... | 7,152 | 37.251337 | 118 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/utils/cuda.py | import torch
def to_cuda_if_available(*tensors):
if torch.cuda.is_available():
tensors = [tensor.cuda() if tensor is not None else None for tensor in tensors]
if len(tensors) == 1:
return tensors[0]
return tensors
def to_cpu_if_available(*tensors):
if torch.cuda.is_available():
... | 620 | 27.227273 | 97 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/utils/categorical.py | import json
import numpy as np
import torch
import torch.nn.functional as F
def load_variable_sizes_from_metadata(metadata_path):
with open(metadata_path, "r") as metadata_file:
metadata = json.load(metadata_file)
return metadata["variable_sizes"]
def categorical_variable_loss(reconstructed, origi... | 2,835 | 37.324324 | 83 | py |
multi-categorical-gans | multi-categorical-gans-master/multi_categorical_gans/utils/initialization.py | import torch.nn as nn
from multi_categorical_gans.utils.cuda import load_without_cuda
def initialize_weights(module):
if type(module) == nn.Linear:
nn.init.xavier_normal_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0.0)
elif type(module) == nn.BatchNo... | 595 | 27.380952 | 63 | py |
pytorch-hessian-eigenthings | pytorch-hessian-eigenthings-master/setup.py | """setup.py for hessian_eigenthings"""
from setuptools import setup, find_packages
install_requires = [
'numpy>=0.14',
'torch>=0.4',
'scipy>=1.2.1'
]
setup(name="hessian_eigenthings",
author="Noah Golmant",
install_requires=install_requires,
packages=find_packages(),
description='... | 394 | 22.235294 | 69 | py |
pytorch-hessian-eigenthings | pytorch-hessian-eigenthings-master/tests/random_matrix_tests.py | """
This file tests the accuracy of the power iteration methods by comparing
against np.linalg.eig results for various random matrix configurations
"""
import argparse
import functools
import numpy as np
import torch
from hessian_eigenthings.operator import LambdaOperator
from hessian_eigenthings.power_iter import def... | 4,091 | 35.212389 | 115 | py |
pytorch-hessian-eigenthings | pytorch-hessian-eigenthings-master/tests/principle_eigenvec_tests.py | import argparse
import numpy as np
import torch
from hessian_eigenthings import compute_hessian_eigenthings
from utils import plot_eigenval_estimates, plot_eigenvec_errors
from torch.utils.data import DataLoader
from torch import nn
import matplotlib.pyplot as plt
from variance_tests import get_full_hessian
import sc... | 2,769 | 28.784946 | 81 | py |
pytorch-hessian-eigenthings | pytorch-hessian-eigenthings-master/tests/variance_tests.py | """
This test looks at the variance of eigenvalue/eigenvector estimates
(1) Full dataset should have deterministic results
(2) Compute variance of repeated trials and the effect of averaging, error
relative to full dataset
(3) Compute variance of full power iteration on a fixed mini-batch (vs.
... | 7,988 | 31.741803 | 103 | py |
pytorch-hessian-eigenthings | pytorch-hessian-eigenthings-master/hessian_eigenthings/lanczos.py | """ Use scipy/ARPACK implicitly restarted lanczos to find top k eigenthings """
from typing import Tuple
import numpy as np
import torch
import scipy.sparse.linalg as linalg
from scipy.sparse.linalg import LinearOperator as ScipyLinearOperator
from warnings import warn
import hessian_eigenthings.utils as utils
from ... | 3,048 | 29.49 | 87 | py |
pytorch-hessian-eigenthings | pytorch-hessian-eigenthings-master/hessian_eigenthings/hvp_operator.py | """
This module defines a linear operator to compute the hessian-vector product
for a given pytorch model using subsampled data.
"""
from typing import Callable
import torch
import torch.nn as nn
import torch.utils.data as data
import hessian_eigenthings.utils as utils
from hessian_eigenthings.operator import Ope... | 5,275 | 35.136986 | 94 | py |
pytorch-hessian-eigenthings | pytorch-hessian-eigenthings-master/hessian_eigenthings/power_iter.py | """
This module contains functions to perform power iteration with deflation
to compute the top eigenvalues and eigenvectors of a linear operator
"""
from typing import Tuple
import numpy as np
import torch
from hessian_eigenthings.operator import Operator, LambdaOperator
import hessian_eigenthings.utils as utils
d... | 4,190 | 32.528 | 84 | py |
pytorch-hessian-eigenthings | pytorch-hessian-eigenthings-master/hessian_eigenthings/__init__.py | """ Top-level module for hessian eigenvec computation """
from hessian_eigenthings.power_iter import power_iteration, deflated_power_iteration
from hessian_eigenthings.lanczos import lanczos
from hessian_eigenthings.hvp_operator import HVPOperator
name = "hessian_eigenthings"
def compute_hessian_eigenthings(
mod... | 2,968 | 33.126437 | 86 | py |
pytorch-hessian-eigenthings | pytorch-hessian-eigenthings-master/example/main.py | """
A simple example to calculate the top eigenvectors for the hessian of
ResNet18 network for CIFAR-10
"""
import track
import skeletor
from skeletor.datasets import build_dataset
from skeletor.models import build_model
import torch
from hessian_eigenthings import compute_hessian_eigenthings
def extra_args(parser... | 2,460 | 28.650602 | 85 | py |
TSCC2019 | TSCC2019-master/dqn_agent.py | import random
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
class DQNAgent:
def __init__(self, config):
self.state_size = config['state_size']
self.action_size = config['action_size']
sel... | 2,469 | 36.424242 | 77 | py |
TTE | TTE-main/main.py | import sys
import torch
import random
import argparse
import numpy as np
import os.path as osp
import torch.backends.cudnn as cudnn
from utils.utils import (AugWrapper, get_model, print_to_log, eval_chunk,
eval_files)
# For deterministic behavior
cudnn.benchmark = False
cudnn.deterministic = ... | 2,176 | 28.026667 | 80 | py |
TTE | TTE-main/experiments/gowal.py | # Copyright 2020 Deepmind Technologies Limited.
#
# 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 agr... | 6,440 | 32.201031 | 80 | py |
TTE | TTE-main/experiments/unlabeled_pretraining.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.utils import NormalizedWrapper
"""Based on code from https://github.com/yaodongyu/TRADES"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_... | 4,907 | 38.264 | 116 | py |
TTE | TTE-main/experiments/mart.py | # Taken from MART repo https://github.com/YisenWang/MART/blob/master/wideresnet.py
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.utils import NormalizedWrapper
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(B... | 4,554 | 41.175926 | 116 | py |
TTE | TTE-main/experiments/hydra.py | ## Make sure to first download the model_best_dense.pth.tar from https://www.dropbox.com/sh/56yyfy16elwbnr8/AADmr7bXgFkrNdoHjKWwIFKqa?dl=0
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.utils import NormalizedWrapper
class BasicBlock(nn.Module):
def __init__(self, conv_l... | 5,521 | 33.949367 | 138 | py |
TTE | TTE-main/experiments/imagenet_pretraining.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.utils import NormalizedWrapper
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
sel... | 4,385 | 39.990654 | 116 | py |
TTE | TTE-main/experiments/ates.py | # We took this code from
# https://github.com/chawins/ates-minimal/blob/master/lib/wideresnet.py
'''
This code is taken from
https://github.com/yaodongyu/TRADES/blob/master/models/wideresnet.py
'''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.utils import NormalizedWrap... | 5,123 | 38.415385 | 116 | py |
TTE | TTE-main/experiments/adv_weight_pert_cif100.py | # Taken from AWP repo
# https://github.com/csdongxian/AWP/blob/main/AT_AWP/wideresnet.py
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.utils import NormalizedWrapper
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
s... | 5,199 | 41.276423 | 116 | py |
TTE | TTE-main/experiments/adv_weight_pert.py | # Taken from AWP repo
# https://github.com/csdongxian/AWP/blob/main/AT_AWP/wideresnet.py
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.utils import NormalizedWrapper
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
s... | 5,066 | 40.876033 | 116 | py |
TTE | TTE-main/experiments/eval.py | # Copyright 2020 Deepmind Technologies Limited.
#
# 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 agr... | 3,712 | 33.700935 | 80 | py |
TTE | TTE-main/experiments/trades.py | # Taken from TRADES repo
# https://github.com/yaodongyu/TRADES/blob/master/models/wideresnet.py
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.utils import NormalizedWrapper
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
... | 4,910 | 40.974359 | 116 | py |
TTE | TTE-main/utils/resnet.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class FakeReLU(torch.autograd.Function):
... | 5,715 | 33.433735 | 79 | py |
TTE | TTE-main/utils/utils.py | import os
import random
import argparse
import numpy as np
import os.path as osp
from tqdm import tqdm
from scipy import ndimage
from autoattack import AutoAttack
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import CIFAR10, CIFAR100, ImageFolder
from torch.utils.data imp... | 14,696 | 38.087766 | 95 | py |
GFocalV2 | GFocalV2-master/setup.py | #!/usr/bin/env python
import os
from setuptools import find_packages, setup
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return co... | 5,864 | 35.203704 | 125 | py |
GFocalV2 | GFocalV2-master/tools/test.py | import argparse
import os
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
fro... | 7,967 | 37.679612 | 79 | py |
GFocalV2 | GFocalV2-master/tools/benchmark.py | import argparse
import time
import torch
from mmcv import Config
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model
from mmdet.datasets import (build_dataloader, build_dataset,
replace_ImageToTensor)
from mmde... | 3,176 | 30.455446 | 79 | py |
GFocalV2 | GFocalV2-master/tools/get_flops.py | import argparse
import torch
from mmcv import Config
from mmdet.models import build_detector
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
... | 1,932 | 27.426471 | 79 | py |
GFocalV2 | GFocalV2-master/tools/publish_model.py | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = par... | 1,125 | 27.15 | 77 | py |
GFocalV2 | GFocalV2-master/tools/regnet2mmdet.py | import argparse
from collections import OrderedDict
import torch
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = model_weight
converted_names.add(model_key)
... | 3,015 | 32.511111 | 77 | py |
GFocalV2 | GFocalV2-master/tools/pytorch2onnx.py | import argparse
import os.path as osp
import numpy as np
import onnx
import onnxruntime as rt
import torch
from mmdet.core import (build_model_from_cfg, generate_inputs_and_wrap_model,
preprocess_example_input)
def pytorch2onnx(config_path,
checkpoint_path,
... | 4,585 | 31.06993 | 78 | py |
GFocalV2 | GFocalV2-master/tools/upgrade_model_version.py | import argparse
import re
import tempfile
from collections import OrderedDict
import torch
from mmcv import Config
def is_head(key):
valid_head_list = [
'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head'
]
return any(key.startswith(h) for h in valid_head_list)
def parse_co... | 6,794 | 31.357143 | 79 | py |
GFocalV2 | GFocalV2-master/tools/test_robustness.py | import argparse
import copy
import os
import os.path as osp
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
from pycocotools.coco import COCO
from pycocotools.cocoe... | 14,711 | 37.920635 | 79 | py |
GFocalV2 | GFocalV2-master/tools/train.py | import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import init_dist
from mmcv.utils import get_git_hash
from mmdet import __version__
from mmdet.apis import set_random_seed, train_detector
from mmdet.dat... | 6,435 | 34.955307 | 79 | py |
GFocalV2 | GFocalV2-master/tools/detectron2pytorch.py | import argparse
from collections import OrderedDict
import mmcv
import torch
arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)}
def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names):
# detectron replace bn with affine channel layer
state_dict[torch_name + '.bias'] = torch.from_numpy... | 3,530 | 41.542169 | 78 | py |
GFocalV2 | GFocalV2-master/tests/async_benchmark.py | import asyncio
import os
import shutil
import urllib
import mmcv
import torch
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector, show_result)
from mmdet.utils.contextmanagers import concurrent
from mmdet.utils.profiling import profile_time
async def main():
... | 3,126 | 29.960396 | 79 | py |
GFocalV2 | GFocalV2-master/tests/test_anchor.py | """
CommandLine:
pytest tests/test_anchor.py
xdoctest tests/test_anchor.py zero
"""
import torch
def test_standard_anchor_generator():
from mmdet.core.anchor import build_anchor_generator
anchor_generator_cfg = dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
... | 17,722 | 42.121655 | 79 | py |
GFocalV2 | GFocalV2-master/tests/test_async.py | """Tests for async interface."""
import asyncio
import os
import sys
import asynctest
import mmcv
import torch
from mmdet.apis import async_inference_detector, init_detector
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import concurrent
class AsyncTestCase(asynctest.TestCase):
use_defau... | 2,560 | 29.855422 | 75 | py |
GFocalV2 | GFocalV2-master/tests/test_config.py | from os.path import dirname, exists, join, relpath
import pytest
import torch
from mmcv.runner import build_optimizer
from mmdet.core import BitmapMasks, PolygonMasks
def _get_config_directory():
"""Find the predefined detector config directory."""
try:
# Assume we are running in the source mmdetect... | 14,537 | 38.505435 | 79 | py |
GFocalV2 | GFocalV2-master/tests/test_coder.py | import torch
from mmdet.core.bbox.coder import YOLOBBoxCoder
def test_yolo_bbox_coder():
coder = YOLOBBoxCoder()
bboxes = torch.Tensor([[-42., -29., 74., 61.], [-10., -29., 106., 61.],
[22., -29., 138., 61.], [54., -29., 170., 61.]])
pred_bboxes = torch.Tensor([[0.4709, 0.6152,... | 896 | 39.772727 | 75 | py |
GFocalV2 | GFocalV2-master/tests/test_masks.py | import numpy as np
import pytest
import torch
from mmdet.core import BitmapMasks, PolygonMasks
def dummy_raw_bitmap_masks(size):
"""
Args:
size (tuple): expected shape of dummy masks, (H, W) or (N, H, W)
Return:
ndarray: dummy mask
"""
return np.random.randint(0, 2, size, dtype=n... | 24,825 | 38.343899 | 79 | py |
GFocalV2 | GFocalV2-master/tests/test_iou2d_calculator.py | import numpy as np
import pytest
import torch
from mmdet.core import BboxOverlaps2D, bbox_overlaps
def test_bbox_overlaps_2d(eps=1e-7):
def _construct_bbox(num_bbox=None):
img_h = int(np.random.randint(3, 1000))
img_w = int(np.random.randint(3, 1000))
if num_bbox is None:
num... | 4,230 | 38.915094 | 77 | py |
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