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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | pytorch_lightning/plugins/training_type/single_tpu.py | Apache-2.0 | 4,327 | f9fa415e67090bc637b3b21cf836133ec83413ab | Python | py | |
# torch imports
import torch.nn.functional as F
import torch.nn as nn
## TODO: Complete this classifier
class BinaryClassifier(nn.Module):
"""
Define a neural network that performs binary classification.
The network should accept your number of features as input, and produce
a single sigmoid value, t... | Project_Plagiarism_Detection/source_pytorch/model.py | MIT | 1,953 | b2ac8b0fbe6ddc4188042674058092066dcb3bce | Python | py | |
# PROGRAMMER: Abdullah Bahi
# DATE CREATED: 6/20/2020
# PURPOSE: Contains configurations for loading data and training the model
#
# VARIABLES:
# - transforms_dict : dictionary of desired transformations on training and validation sets
# - train_batch_size : training dataset batch size
# ... | config.py | MIT | 2,604 | 3a7bceb4a924e285a14adde02942d67054cf3041 | Python | py | |
# Copyright 2021, Crepaldi Michele.
#
# Developed as a thesis project at the TORSEC research group of the Polytechnic of Turin (Italy) under the supervision
# of professor Antonio Lioy and engineer Andrea Atzeni and with the support of engineer Andrea Marcelli.
#
# Licensed under the Apache License, Version 2.0 (the "L... | src/Model/gen3_speed_evaluation.py | Apache-2.0 | 32,507 | fd7858295596b0db6d000678131cd13adaad2f01 | Python | py | |
import os
from setuptools import setup
from torch.utils.cpp_extension import CUDAExtension, BuildExtension, _find_cuda_home
os.system('pip uninstall -y tcmm')
#os.system('make clean; make -j%d' % os.cpu_count())
CUDA_DIR = _find_cuda_home()
#NCCL_DIR= '/home/comp/15485625/downloads/nccl_2.4.8-1+cuda10.1_x86_64'
#MPI... | packages/tcmm/setup.py | MIT | 1,504 | f96b832f284efb30e6dd2fd12ab8212fe741e4a8 | Python | py | |
import pytest
import torch
import torch.testing
from rllib.policy import EpsGreedy
from rllib.util.parameter_decay import ExponentialDecay
from rllib.util.utilities import tensor_to_distribution
from rllib.value_function import NNQFunction
@pytest.fixture(params=[0.0, 0.9])
def eps_start(request):
return request... | rllib/policy/q_function_policy/tests/test_epsilon_greedy.py | MIT | 1,337 | b230c848f295a329120d60e201bd785c17a3d8a7 | Python | py | |
import torch
import torch.nn as nn
from options import HiDDenConfiguration
from model.conv_bn_relu import ConvBNRelu
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
class Decoder(nn.Module):
"""
Decoder module. Receives a watermarked image and extracts the watermark.
The input ima... | model/decoder.py | MIT | 3,035 | 3a7c20e6633fef2271501aa4308092abe541e757 | Python | py | |
""" The main file to train a JKNet model using a full graph """
import argparse
import copy
import torch
import torch.optim as optim
import torch.nn as nn
import numpy as np
from dgl.data import CoraGraphDataset, CiteseerGraphDataset
from tqdm import trange
from sklearn.model_selection import train_test_split
from mo... | examples/pytorch/jknet/main.py | Apache-2.0 | 4,923 | 3a7c32ccb37a7633b5c7f5c88a67edeae2631a6e | Python | py | |
from __future__ import division
import torch
import math
import random
from PIL import Image, ImageOps
try:
import accimage
except ImportError:
accimage = None
import numpy as np
import numbers
import types
import collections
def _is_pil_image(img):
if accimage is not None:
return isinstance(img, ... | torchvision/transforms.py | BSD-3-Clause | 19,002 | 4b0675a644fc9cfcd6a0d2473a5cf622c926d3f5 | Python | py | |
import math
import torch
from enum import Enum
from torch import nn
from typing import Optional, cast
__all__ = ["MultiheadInitType", "ElementwiseTransformType", "SetTransformer", "ISAB", "PMA"]
class MultiheadInitType(Enum):
"""Initialization type for multihead attetion."""
XAVIER = 0
KAIMING = 1
cla... | mi_module_zoo/settransformer.py | MIT | 17,865 | 3a7c4dfc6b5026d5ba0442042e46299529047a51 | Python | py | |
import math
import sys
import time
import torch
import torchvision.models.detection.mask_rcnn
from .coco_utils import get_coco_api_from_dataset
from .coco_eval import CocoEvaluator
from . import utils
def train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq):
model.train()
metric_logger ... | utils_cv/detection/references/engine.py | MIT | 3,791 | 3a7c51c68ebc336b9c2595dfb01d0dfeb0c79d74 | Python | py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
from common_testing import TestCaseMixin
from pytorch3d.commo... | tests/test_rendering_utils.py | MIT,BSD-3-Clause | 9,334 | 3a7c668d91d4b15259b0b38ceb450d322550885e | Python | py | |
from torch.utils.trainer.plugins.plugin import Plugin
import matplotlib.pyplot as plt
import math
import os
class HistoryPlugin(Plugin):
def __init__(self, parameters):
super(HistoryPlugin, self).__init__([(1, 'epoch')])
self.history = {}
self.trainer = None
for parameter in para... | src/utils/HistoryPlugin.py | MIT | 1,965 | 3a7c69d7000e0fc6c37733b3e4e2a8d7bea4035e | Python | py | |
import os
import numpy as np
import onnxruntime
import pytest
import torch
import tests.base.develop_pipelines as tpipes
import tests.base.develop_utils as tutils
from pytorch_lightning import Trainer
from tests.base import EvalModelTemplate
def test_model_saves_with_input_sample(tmpdir):
"""Test that ONNX mode... | tests/models/test_onnx.py | Apache-2.0 | 3,859 | d7cc7cffaec3f78aa3eb0db89e0dbf152c208cd7 | Python | py | |
#!/usr/bin/env python3
"""
Preprocesses datasets
"""
import json
import logging
import os
import numpy as np
import pathlib
import PIL.Image
from moviepy.video.io.VideoFileClip import VideoFileClip
import platalea.hardware
import soundfile
import torch
import torch.nn as nn
import torchvision.models as models
impor... | platalea/utils/preprocessing.py | Apache-2.0 | 18,858 | 3a7c8c189a298eb81350b0d09849d3a37b931159 | Python | py | |
'''
Reference:
https://github.com/khurramjaved96/incremental-learning/blob/autoencoders/model/resnet32.py
https://github.com/hshustc/CVPR19_Incremental_Learning/blob/master/cifar100-class-incremental/modified_resnet_cifar.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from convs.modified_line... | convs/modified_cifar_resnet.py | MIT | 6,161 | 3a7c9c2d341ea52eeb439fc90b46e8438b56a054 | Python | py | |
import torch
import torchvision
#import skimage.io as io
import numpy as np
import torchvision.transforms as t
import torch.nn as nn
import os
import matplotlib.pyplot as plt
import torchvision.models as model
#from sklearn.metrics import accuracy_score
torch.cuda.set_device(0)
#device=torch.device(#"cuda" i... | DeepFeatureExtraction/EXTRACTOR_ResNet50.py | MIT | 12,171 | 1e0f78c23e82116f27c1f4c9b9d0f7c04001dcaa | Python | py | |
import json
import os
import time
from datetime import timedelta
import numpy as np
import torch
from guacamol.utils.data import remove_duplicates
from torch.utils.data import TensorDataset
from .rnn_model import SmilesRnn
from .smiles_char_dict import SmilesCharDictionary
def get_tensor_dataset(numpy_array):
... | guacamol_baselines/smiles_lstm_hc/rnn_utils.py | MIT | 6,165 | 3a7ca51c7bae8b586fe02bad14a0f4accd482f9f | Python | py | |
# =============================================================================
# IMPORTS
# =============================================================================
import argparse
import os
import numpy as np
import torch
import espaloma as esp
from torch.nn.parallel import DistributedDataParallel as DDP
from ... | scripts/phalkethoh_boltzmann/test.py | MIT | 3,174 | 5e3a0ec82bc1120ad7510bcda504b492d1ed1e79 | Python | py | |
import torch
class TestInfo(object):
def test_info(self):
if not torch.cuda.is_available():
return
from rflib.ops import get_compiler_version, get_compiling_cuda_version
cv = get_compiler_version()
ccv = get_compiling_cuda_version()
assert cv is not None
... | tests/test_ops/test_info.py | Apache-2.0 | 345 | 12d61093e75bf0109d3b559fd9188d4033c8fa0a | Python | py | |
import torch
import itertools
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import numpy as np
import copy
class InstaGANModel(BaseModel):
def name(self):
return "InstaGANModel"
@staticmethod
def modify_commandline_options(parser, is_train=True):
... | instagan/models/insta_depth_model.py | Apache-2.0 | 17,892 | 3a7cc5d3b06cedc819450eb5d581489da7b886e2 | Python | py | |
import os, sys
import pydiffvg
import argparse
import torch
# import torch as th
import scipy.ndimage.filters as filters
# import numba
import numpy as np
from skimage import io
sys.path.append('./textureSyn')
from patchBasedTextureSynthesis import *
from make_gif import make_gif
import random
import ttools.modules
fr... | apps/texture_synthesis.py | Apache-2.0 | 6,998 | 3a7ccce083bdccfa56a61bfc27ec049742ed117f | Python | py | |
"""
BertCapModel is using huggingface transformer bert model as seq2seq model.
The result is not as goog as original transformer.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import... | captioning/models/BertCapModel.py | MIT | 4,001 | 3a7ccec2c40b2a171393059ec1a3af511163c246 | Python | py | |
# Copyright (c) 2017-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | lib/core/test_engine.py | MIT | 20,060 | 3a7ce96dc9526d0dad0dc5060086021159eb3ca5 | Python | py | |
import pytest
import torch
from mmcls.models import build_loss
def test_asymmetric_loss():
# test asymmetric_loss
cls_score = torch.Tensor([[5, -5, 0], [5, -5, 0]])
label = torch.Tensor([[1, 0, 1], [0, 1, 0]])
weight = torch.tensor([0.5, 0.5])
loss_cfg = dict(
type='AsymmetricLoss',
... | tests/test_losses.py | Apache-2.0 | 6,545 | fdc7c27600c921e791f17eafc7ec0dffbd104b4a | Python | py | |
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/... | training/resnet.py | BSD-3-Clause | 8,970 | 52b0876191c22f148d3d0e92d5108e48a53bdeae | Python | py | |
import dgl
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.multiprocessing as mp
import dgl.nn.pytorch as dglnn
import time
import math
import argparse
from torch.nn.parallel import DistributedDataParallel
import tqdm
import os.path as... | benchmarks/ogbn-papers100M/train_dgl_multi_node.py | Apache-2.0 | 15,595 | 3a7cf84a5d2d444f18b4d90fd6bdb4fc01b1fbee | Python | py | |
import os
from data import common
import numpy as np
import imageio
import torch
import torch.utils.data as data
class Demo(data.Dataset):
def __init__(self, args, name='Demo', train=False, benchmark=False):
self.args = args
self.name = name
self.scale = args.scale
self.idx_scale... | src/data/demo.py | MIT | 1,076 | b29ce9fde18928468e93d7a79c1dad18f3b62738 | Python | py | |
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
import numpy as np
from src.Models.loss import losses_joint
from src.Models.models import Encoder
from src.Models.models import Imit... | get_test_stats.py | MIT | 6,229 | 5f9a236e6d19cad2ae599c49f32e09d6db36cf4d | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import mmcv
import pytest
import torch
from mmaction.models import build_localizer
def test_ssn_train():
train_cfg = mmcv.ConfigDict(
dict(
ssn=dict(
assigner=dict(
positive_iou_threshold=0.7,
... | tests/test_models/test_localizers/test_ssn.py | Apache-2.0 | 8,037 | 1e67455a32e0f46e951c9561a64ad558b2542533 | Python | py | |
#!/usr/bin/python3
# -*- coding: UTF-8 -*-
"""
@Time : 2/14/21 2:28 PM
@Author : uyplayer
@Site : uyplayer.pw
@contact : uyplayer@qq.com
@File : W2V.py
@Software : PyCharm
"""
# dependency library
from __future__ import print_function
import argparse
# sklearn
from sklearn.preprocessing import LabelEn... | TL/W2V.py | MIT | 3,086 | 6b2f8edaab74e4d54c87cb138486b9b65bd5255d | Python | py | |
import numpy as np
import torch
from collections import namedtuple
from torch.nn.parallel import DistributedDataParallel as DDP
# from torch.nn.parallel import DistributedDataParallelCPU as DDPC # Deprecated
from rlpyt.agents.base import BaseAgent, AgentStep
from rlpyt.models.qpg.mlp import QofMuMlpModel, PiMlpModel... | rlpyt/agents/qpg/sac_agent.py | MIT | 8,709 | 52fd7d950fef7a57d28ce624f6b5b0bc1a044025 | Python | py | |
import torch
from torch_geometric.nn import ARMAConv
def test_arma_conv():
in_channels, out_channels = (16, 32)
num_stacks, num_layers = 8, 4
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
num_nodes = edge_index.max().item() + 1
edge_weight = torch.rand(edge_index.size(1))
... | test/nn/conv/test_arma_conv.py | MIT | 932 | 1e542b63be7349884fb2a2f397beea6cc88c4eae | Python | py | |
from typing import Dict, List, Any
import torch
from allennlp.common.checks import check_dimensions_match
from allennlp.data import TextFieldTensors, Vocabulary
from allennlp.models.model import Model
from allennlp.modules import FeedForward, InputVariationalDropout
from allennlp.modules.matrix_attention.legacy_matri... | allennlp/allennlp/models/esim.py | MIT | 9,720 | 129952df3e374d2bdc73497d68f33e36f8202ef1 | Python | py | |
#!/usr/bin/env python
import logging
import argparse
import numpy
import torch
import pyro
import pyro.infer
import pyro.infer.mcmc
import pyro.distributions as dist
def get_arguments():
parser = argparse.ArgumentParser(description='Bayesian Calibration arguments')
parser.add_argument('--logging-level', def... | run.py | BSD-3-Clause | 2,941 | 3a7e05f46664d6bc32449c52a3bf1f392a493974 | Python | py | |
'''
Copyright@ Qiao-Mu(Albert) Ren.
All Rights Reserved.
This is the code to build a neural network.
'''
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from dataloader import HairNetDataset
class Net(nn.Modu... | src/model.py | MIT | 8,232 | d7949f8976ac55a7fdf3f3126d606abd67be2a21 | Python | py | |
#!/usr/bin/env python
""" Translator Class and builder """
from __future__ import print_function
#from sklearn.linear_model import LinearRegression
#from sklearn import linear_model
import argparse
import codecs
import os
import math
import numpy as np
import torch
from itertools import count
from onmt.utils.misc im... | onmt/translate/translator.py | MIT | 49,453 | 3a7e3ca0bf8de90ae174f40f281b1560b02601e5 | Python | py | |
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import ipdb
class Net(nn.Module):
def __init__(self, img_side):
super(Net, self).__init__()
self.img... | test_synch/optimize/mnist.py | MIT | 7,804 | 12a8e1ec77867f19d79c280b432a64aa1c774de3 | Python | py | |
# --------------------------------------------------------
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import os
import torch
import torch.nn.functional as F
from torch.optim import Adam
import numpy as np
from core import networks
from core.uti... | core/agent.py | MIT | 16,345 | f9ae51b00b3abd82e4b85b8687f4847287b5d049 | Python | py | |
import math
import pdb
import torch
from torch import nn, einsum
from einops import rearrange
from .mish_activ import Mish
# translated from tensorflow code
# https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
# positional embedding helpers
# 如果是元组，则直接返回。如果不是元组，则返回元组，并且元组内部有两个元素
def pair(x):
... | bottleneck_transformer_pytorch/bottleneck_transformer_pytorch.py | MIT | 18,394 | 3a7e5561b98bbe409e41272bd97616472a6e7f94 | Python | py | |
# AUTOGENERATED! DO NOT EDIT! File to edit: Rapids/01-Rapids-LendingClub-Benchmark-solution-power8.ipynb (unless otherwise specified).
__all__ = ['pgdf', 'time_command', 'COMPARE', 'run_times', 'filename', 'DATA_DOUBLE_FACTOR', 'loan_pdf', 'loan_rdf',
'describe_gpu', 'describe_cpu', 'ohe_cpu', 'ohe_gpu', 's... | ORNL/02_rapids.py | Apache-2.0 | 22,163 | 3a7e5b8f6b7a542e4b0c67781736597075e5336c | Python | py | |
from ..losses import GenericPairLoss
import torch
from ..utils import loss_and_miner_utils as lmu
from ..distances import CosineSimilarity
class IntraPairVarianceLoss(GenericPairLoss):
def __init__(self, pos_eps=0.01, neg_eps=0.01, **kwargs):
super().__init__(mat_based_loss=False, **kwargs)
... | src/pytorch_metric_learning/losses/intra_pair_variance_loss.py | MIT | 2,100 | a98582e0a5b9922e3bdc4d32589bfa1a8978ee0d | Python | py | |
# Copyright (c) 2021 Horizon Robotics and ALF Contributors. 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... | alf/algorithms/qrsac_algorithm.py | Apache-2.0 | 8,280 | 342c3b422f9c5db0c186a5d641ccec10591cff20 | Python | py | |
from typing import List, Optional
from pytorch_lightning import LightningModule, LightningDataModule, Callback, Trainer
from pytorch_lightning.loggers import LightningLoggerBase
from pytorch_lightning import seed_everything
import hydra
from omegaconf import DictConfig
from src.utils import template_utils
log = tem... | src/train.py | MIT | 3,179 | 3a7e9b5144aeabdcc8be88594c20f187df46c5ec | Python | py | |
import torch
import torch.nn.functional as F
import argparse
import cv2
import numpy as np
from glob import glob
import copy
num_classes = 2
img_height, img_width = 224, 224
channel = 3
GPU = False
torch.manual_seed(0)
class ResBlock(torch.nn.Module):
def __init__(self, in_f, f_1, out_f, stride=1):
super... | Question_model/answers/res50_pytorch.py | MIT | 8,622 | 3a7ea6509c51a0c471ab4da2e5a72b2a10bf5bb3 | Python | py | |
from machin.frame.buffers import DistributedPrioritizedBuffer
from test.util_run_multi import *
import random
import torch as t
import numpy as np
class TestDistributedPrioritizedBuffer(WorldTestBase):
BUFFER_SIZE = 1
SAMPLE_BUFFER_SIZE = 10
##############################################################... | test/frame/buffers/test_prioritized_buffer_d.py | MIT | 9,914 | 425b231c0b54d87fa3d6c15ed05dc3b89aabf04c | Python | py | |
# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0
import torch
def plot_conditional_samples_ssvae(ssvae, visdom_session):
"""
This is a method to do conditional sampling in visdom
"""
vis = visdom_session
ys = {}
for i in range(10):
ys[i] = torch.... | examples/vae/utils/vae_plots.py | Apache-2.0 | 3,263 | 3a7eaa4ce228b66eed78ea4d779639fce7596c0e | Python | py | |
# encoding: utf-8
"""
@author: xyliao
@contact: xyliao1993@qq.com
"""
import warnings
from copy import deepcopy
import numpy as np
import torch
import torch.nn.functional as F
from mxtorch import meter
from mxtorch.trainer import Trainer, ScheduledOptim
from mxtorch.vision.eval_tools import eval_semantic_segmentation
... | chapter9_Computer-Vision/segmentation/main.py | MIT | 7,027 | 28ae6dd1def1c4968f5d72df7c58412b2d5b8dc6 | Python | py | |
"""
Implementation of "Attention is All You Need"
"""
import torch
import torch.nn as nn
from c2nl.decoders.decoder import DecoderBase
from c2nl.modules.multi_head_attn import MultiHeadedAttention
from c2nl.modules.position_ffn import PositionwiseFeedForward
from c2nl.utils.misc import sequence_mask
from c2nl.modules... | c2nl/decoders/transformer.py | MIT | 8,008 | f9c1d7a6ec78d6d46dd2a6943ed71b8574f6632d | Python | py | |
#!/usr/bin/env python3
import os
from pathlib import Path
from torch.jit._decompositions import decomposition_table
# from torchgen.code_template import CodeTemplate
DECOMP_HEADER = r"""
/**
* @generated
* This is an auto-generated file. Please do not modify it by hand.
* To re-generate, please run:
* cd ~/pytor... | torchgen/decompositions/gen_jit_decompositions.py | Intel | 2,375 | 7cfbb803f9b8bf8fa07b96fdcf47b07dab9d2c6b | Python | py | |
"""Specific Pysyft exceptions."""
import syft as sy
import torch
from tblib import Traceback
import traceback
from six import reraise
from typing import Tuple
class PureTorchTensorFoundError(BaseException):
"""Exception raised for errors in the input.
This error is used in a recursive analysis of the args pr... | syft/exceptions.py | Apache-2.0 | 9,865 | 3a7ebc16392132f9122a5e1a61d86b7812d13091 | Python | py | |
from __future__ import print_function
import os
import pickle
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torch.optim as optim
from torchvision import datasets, transforms
# from models.wideresnet import *
from models import *
from losses import alp_loss... | AT/train_plain_cifar10_2.py | MIT | 10,994 | 3a7ed95b2ece314c7feecd97cf35be1364dffdf3 | Python | py | |
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import ... | onnxruntime/python/tools/transformers/benchmark_helper.py | MIT | 10,218 | 1e1eaf4800713e325ff1b632b3cd9f258d7a44d8 | Python | py | |
import os
from torchvision.datasets import voc
class Path(object):
@staticmethod
def db_root_dir(dataset):
if dataset == 'pascal':
base_dir = os.path.join(os.getcwd(), "data", "pascal")
trainval_dir = os.path.join(base_dir, "VOCdevkit", "VOC2012")
if not os.path.isdi... | mypath.py | MIT | 694 | 3a7ef2b7eeff777e9fb7e6abb4bd8b9e9f9cab8b | Python | py | |
"""
A collection of classes for calculation distance metrics on graphs.
"""
from abc import ABC
import networkx as nx
import numpy as np
import torch
from code_transformer.modeling.constants import BIN_PADDING
from code_transformer.preprocessing.graph.alg import (
next_sibling_shortest_paths,
all_pairs_short... | code_transformer/preprocessing/graph/distances.py | MIT | 11,235 | 5e9c7294a73a4c45905b1d4a747abb3dbb6c811a | Python | py | |
#encoding:utf-8
# from torch.utils import data
'''
专门为multimodel而写 生成pth
'''
import torch as t
import numpy as np
from config import opt
import models
import json
import fire
import csv
import tqdm
from torch.autograd import Variable
def load_data(type_='char'):
with open(opt.labels_path) as f:
labels_ = j... | test.3.py | MIT | 6,313 | 5f6f88acaa692dd1f6d876b13a75158c88d64762 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from typing import Dict, List, Tuple
import torch
from pytext.torchscript.tensorizer.tensorizer import ScriptTensorizer
def max_tokens(per_sentence_tokens: List[List[Tuple[str, int, int]]]) -> int:
"""receive the token... | pytext/torchscript/batchutils.py | BSD-3-Clause | 6,111 | 1e88e87e01c103b9819d0f98a077d3674b4b5125 | Python | py | |
import torch
import numpy as np
from torch import nn
from model.decoder import Decoder
from model.encoder import Encoder
class VariationalAutoencoder(nn.Module):
def __init__(self, latent_dim: int = 128, image_channels: int = 3):
super().__init__()
self.latent_dim = latent_dim
self.encoder = Encoder(in_channel... | model/vae.py | MIT | 1,033 | 3a7f56adf4c1f8649fce63c01a47432e1c63bee1 | Python | py | |
import os
import sys
import traceback
import numpy as np
import argparse
import threading
import codecs
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO, format="%(message)s")
from tensorboardX import SummaryWriter # install tensorboardX (pip install tensorboardX) before import... | search.py | Apache-2.0,MIT | 6,808 | 70727ea8d7cc9acef56e96ef064cecccb0792269 | Python | py | |
from torch import nn
from .optimizers import get_optimizer
import torch
from src import models
from torch.nn import functional as F
import itertools
import tqdm
from haven import haven_utils as hu
import os
import numpy as np
from src import utils as ut
import torch.optim as optim
from .base_netC import resnet_met... | src/models/netC.py | Apache-2.0 | 3,605 | 3a7f967450a18537611a89d062899d9b530cd9f8 | Python | py | |
import torch
import numpy as np
# 创建一个没有初始化的张量（5*3的矩阵）
x = torch.empty(5, 3)
print(x)
# 创建一个随机初始化（符合均匀分布的初始化）的张量
x = torch.rand(5, 3)
print(x)
# 初始化一个全为0的张量，并指定数据类型
x = torch.zeros(5, 3, dtype=torch.long)
print(x)
# 通过列表创建一个张量（是一个1维的）
x = torch.tensor([5.20, 10.24])
print(x)
# 创建一个三维的张量
x = torch.Tensor(2, 3, 4)
p... | PyTorch_01_Tensors/tensors.py | MIT | 1,504 | e631e0d8da7f9276a377f163df886b2771985581 | Python | py | |
"""Few-Shot_learning Segmentation"""
import numpy as np
import torch
import torch.nn as nn
from nn_common_modules import modules as sm
from data_utils import split_batch
# import torch.nn.functional as F
from squeeze_and_excitation import squeeze_and_excitation as se
class SDnetConditioner(nn.Module):
"""
A ... | other_experiments/more/few_shot_segmentor_model6.py | MIT | 13,297 | 3a7fd16cb7e0952b0609a1e7ff8119aff1d86072 | Python | py | |
### Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
import numpy as np
import torch
import os
import sys
from collections import OrderedDict
from torch.autograd import Variable
import util.util as ... | models/modelD_stage3.py | MIT | 21,396 | 42954fcf08c6b923da5f55c4dee71e0de6053652 | Python | py | |
# paper: ResT: An Efficient Transformer for Visual Recognition
# code: https://github.com/wofmanaf/ResT
import torch
import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from models.head import *
import torch.nn.functional as F
up_kwargs = {'mode': 'bilinear', 'align_corners': False... | models/resT.py | Apache-2.0 | 22,782 | 3a7fdfaf25aa7d1a0a79bd84702dc24c7e779b9f | Python | py | |
# Copyright (c) 2020 Huawei Technologies Co., Ltd
# Copyright (c) 2019, Facebook CORPORATION.
# 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 License at
#
# https://opensource.org/lice... | test/test_npu/test_network_ops/test_broadcastToD.py | BSD-3-Clause | 1,544 | 3a7ff68abd1c5954077359894ed5dc4e3820d1f1 | Python | py | |
import abc
import torch
from typing import Any, Callable, List, MutableMapping, Optional, Text, Tuple
class AttributionTechnique(abc.ABC):
"""Abstract class for an attribution technique."""
name: Text
sample_size: int # Number of graphs to hold in memory per input.
@abc.abstractmethod
def... | MolRep/Explainer/Attribution/CAM.py | MIT | 2,447 | f9ba332b07d5c20b81e6bc47b52842cc77e0e6a6 | Python | py | |
import torchtext.data as data
import os
import random
class MyDataset(data.Dataset):
def __init__(self, text_field, label_field, path='data', examples=None, **kwargs):
fields = [('text', text_field), ('label', label_field)]
if examples is None:
path = path
examples = []... | newdatasets.py | MIT | 1,219 | 24742f5be0699d08fae3155c9fc4b69a8b715e42 | Python | py | |
from bdb import set_trace
import imp
from operator import is_
import os
from re import I
import time
import csv
import numpy as np
from path import Path
import argparse
import matplotlib.pyplot as plt
from tensorboardX import SummaryWriter
import cv2
import torch
import torch.nn.functional as F
from core... | ddptrain.py | MIT | 18,279 | 3a802ad09ed52da27842f8bfa97f12329ac5ff0a | Python | py | |
# Copyright Amazon.com, Inc. or its affiliates. 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 ... | experiments/experiment_triplet.py | Apache-2.0 | 5,443 | e9133d74be24337b48803d45e8b1400a940342e3 | Python | py | |
import os
import glob
from torch.utils.data import Dataset
from PIL import Image
EXTENSION = 'JPEG'
NUM_IMAGES_PER_CLASS = 500
CLASS_LIST_FILE = 'wnids.txt'
VAL_ANNOTATION_FILE = 'val_annotations.txt'
class TinyImageNet(Dataset):
"""Tiny ImageNet data set available from `http://cs231n.stanford.edu/ti... | API/data_engine/TinyImagenetDataset.py | MIT | 4,208 | 4b968f5e1327ec9877d9ba351eba0d3e6303963b | Python | py | |
import os
import sys
import argparse
import torch.utils.data as data
FILE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(FILE_DIR, '../'))
DATA_ROOT = os.path.join(FILE_DIR, '../../data')
from datasets import prepare_texas, prepare_purchase
import models as models
from utils import mkdir... | source/nonimage/run_attacks.py | MIT | 3,651 | f9699201373d76d15cce26e957cac7169475a485 | Python | py | |
from torchtext.datasets import Multi30k
from torchtext.data import Field, BucketIterator, TabularDataset
import spacy
import numpy as np
import random
import math
def load_data(batch_size, device):
spacy_en = spacy.load('en_core_web_sm')
def tokenize_en(text):
return [tok.text for tok in spac... | Class6/data_reader.py | MIT | 2,277 | 4b49bae89cdf08823fa4946519a998ecddff4c75 | Python | py | |
import pickle
import numpy as np
import pytest
import torch
from torch import nn
from garage.tf.envs import TfEnv
from garage.torch.q_functions import ContinuousMLPQFunction
from tests.fixtures.envs.dummy import DummyBoxEnv
class TestContinuousNNQFunction:
# yapf: disable
@pytest.mark.parametr... | tests/garage/torch/q_functions/test_continuous_mlp_q_function.py | MIT | 3,315 | 10956049a727cc056c2b0e4ea9f67c7dc6370ed0 | Python | py | |
# _ _ _ _ _ _ _ _
# /\ \ /\ \ _ / /\ /\ \ /\_\/\_\ _ _\ \ /\ \
# / \ \ \ \ \ /_/ / / / \ \ / / / / //\_\/\__ \ \ \ \
# / /\ \ \ \ \ \ \___\/ / /\ \ \ /\ \/ \ \/ / / /_ \... | eve/utils/legacy.py | MIT | 4,011 | 3a8113b4634a39e3b1e49752a554478da73ef0d4 | Python | py | |
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. 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 re... | megatron/fused_kernels/__init__.py | MIT | 5,822 | 459bf600a8ce44c751707f518a2eb6104fa24945 | Python | py | |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.4.2
# kernelspec:
# display_name: main2
# language: python
# name: main2
# ---
# +
import glob
import matplot... | figures/feature_importance.py | Apache-2.0 | 9,782 | ff3bc56b72e4ced0287c01c862d682d60fffd482 | Python | py | |
"""
tester模块实现了 fastNLP 所需的Tester类，能在提供数据、模型以及metric的情况下进行性能测试。
.. code-block::
import numpy as np
import torch
from torch import nn
from fastNLP import Tester
from fastNLP import DataSet
from fastNLP import AccuracyMetric
class Model(nn.Module):
def __init__(self):
su... | phenobert/utils/fastNLP/core/tester.py | MIT | 9,054 | e92eb4220ada7a625d6ca37b94bf7b22cedcf321 | Python | py | |
'''
This script handles the training process.
'''
import argparse
import math
import time
import dill as pickle
from tqdm import tqdm
import torch
import torch.nn.functional as F
import torch.optim as optim
from torchtext.data import Field, Dataset, BucketIterator
from torchtext.datasets import TranslationDataset
im... | train.py | MIT | 11,964 | d715414960f02f543f5549248ca7169be9f80ab7 | Python | py | |
#!/usr/bin/python
#
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | scripts/train_layout6.py | Apache-2.0 | 46,261 | 5e287ec84c9791222c196944c6f6238c3845cfbe | Python | py | |
# This code is modified from https://github.com/jakesnell/prototypical-networks
import backbone
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
from methods.meta_template import MetaTemplate
class ProtoNet(MetaTemplate):
def __init__(self,... | methods/protonet.py | MIT | 4,489 | cc24ee4b0d0d82b815533e29f83d8fccc68026d3 | Python | py | |
#!/usr/bin/env python
import sys
sys.path.append("..")
from models import iresnet
from collections import OrderedDict
from termcolor import cprint
from torch.nn import Parameter
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
import math
import torch
import torch.nn as nn
import ... | models/magface_dist.py | Apache-2.0 | 3,154 | 5faac7cd7c32d1a2e2e2bb7961e12a68bf889990 | Python | py | |
## we conduct the DA methods following https://github.com/thuml/Transfer-Learning-Library, which gives better results than the common settings.
import torch
import os
import math
import time
from .base_solver import BaseSolver
import torch.nn.functional as F
from utils.utils import AverageMeter, to_cuda, accuracy, ac... | code/version3.0/solver/solver_entropy_minimization.py | MIT | 4,670 | df9f41a0592be96bcc9a19d43963316a3554334a | Python | py | |
"""
Author: Zhiyuan Zhang
Date: Dec 2021
Email: cszyzhang@gmail.com
Website: https://wwww.zhiyuanzhang.net
"""
import argparse
import os
import torch
import datetime
import logging
import sys
import importlib
import shutil
import provider
import numpy as np
from pathlib import Path
from tqdm import tqdm
from data_util... | train_partseg.py | MIT | 13,443 | 5f5bfa1432f6a41c800dad80b16e1d70f795cce5 | Python | py | |
import logging
import os
import re
import shutil
import unittest
from pathlib import Path
import numpy as np
import torch
import Resources.testing as test_resources
from Models.erfh5_DeconvModel import DeconvModelEfficient
from Pipeline.data_gather import get_filelist_within_folder_blacklisted
from Pipeline.data_load... | Tests/test_eval_train_deconv.py | MIT | 11,656 | 3a8201a4b37ee3786d61df1e111f2ac311401ca5 | Python | py | |
import numpy as np
import torch
from rlpyt.models.mlp import MlpModel
from rlpyt.utils.tensor import infer_leading_dims, restore_leading_dims
class MuMlpModel(torch.nn.Module):
"""MLP neural net for action mean (mu) output for DDPG agent."""
def __init__(
self,
observation_shape,
hid... | rlpyt/models/qpg/mlp.py | MIT | 3,451 | 3a8205a76b84a5b89462b388c784e00a76f6a088 | Python | py | |
import torch.nn as nn
import torch
class CRNN_FC(nn.Module):
def __init__(self, imgH, nc, nclass, leakyRelu=False):
super(CRNN_FC, self).__init__()
assert imgH % 16 == 0, 'imgH has to be a multiple of 16'
ks = [3, 3, 3, 3, 3, 3, 3, 2, 2]
ps = [1, 1, 1, 1, 1, 1, 1, 0, 0]
s... | recognition_model/model_crnn_fc.py | MIT | 3,615 | 3a8207b060ed84b96f3e2ca3a04096860426c608 | Python | py | |
import string
from itertools import chain
from tqdm import tqdm
import sys
import numpy as np
import random
from collections import defaultdict
import torch
import torch.nn as nn
# programming languages
from asdl.transition_system import TransitionSystem
from asdl.lang.csharp.csharp_grammar import CSharpASDLGrammar
f... | edit_model/editor.py | MIT | 117,956 | 3a8223b64fca5bdc00386b77676886a8b02ce109 | Python | py | |
#!/usr/bin/env python3
import os
import torch
from setuptools import setup, find_packages
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
cxx_args = ['-std=c++11']
nvcc_args = [
# '-gencode', 'arch=compute_50,code=sm_50',
# '-gencode', 'arch=compute_52,code=sm_52',
# '-gencode', 'arch... | my_package/DepthFlowProjection/setup.py | MIT | 831 | dfe392b6dcacc67fbc1297c505498a2f9ce4fb8d | Python | py | |
from scipy.optimize import linear_sum_assignment as hungarian
from sklearn.metrics.cluster import (
normalized_mutual_info_score,
adjusted_rand_score,
adjusted_mutual_info_score,
)
import torch
class Confusion(object):
"""
column of confusion matrix: predicted index
row of confusion matrix: ta... | cluster_evaluation.py | MIT | 4,858 | 3a826f536fccfb099b2697c6ca714b75eb61a3aa | Python | py | |
import numpy as np
import torch
class EarlyStopping:
"""Terminates training if validation loss doesn't improve after a given patience. As inspired from
https://github.com/Bjarten/early-stopping-pytorch"""
def __init__(self, patience=5, verbose=False, delta=0):
"""
Args:
patienc... | utils/early_stopping.py | MIT | 1,952 | 5e2cc2c738672277b97146ab6cf4dbbc2386acd9 | Python | py | |
from os.path import join
from torchvision.transforms import Compose, ToTensor
from dataset import DatasetFromFolderEval, DatasetFromFolder
def transform():
return Compose([
ToTensor(),
])
def get_training_set(data_dir, hr, upscale_factor, patch_size, data_augmentation):
hr_dir = join(data_dir, hr)... | data.py | MIT | 609 | 3a8271372f1e466b2c54743a4830c8a7c9a6bd73 | Python | py | |
from collections import OrderedDict
from typing import Dict, Optional
import copy
from torch import nn
class IntermediateLayerGetter(nn.ModuleDict):
"""
Module wrapper that returns intermediate layers from a model
It has a strong assumption that the modules have been registered
into the model in the ... | torchvision/models/_utils.py | BSD-3-Clause | 5,898 | 3a82831c5d13cc6cb0bbb31d97d3fdd9c34b3b74 | Python | py | |
import os
import requests
import torch
from flask import Flask, jsonify, request
from io import BytesIO
from PIL import Image
from shutil import copyfileobj
from tempfile import NamedTemporaryFile
from torchvision import transforms
from urllib.request import urlopen
from catfish_model import CatfishModel, CatfishClass... | chapter8/catfish_docker_cloud/catfish_server.py | MIT | 1,648 | d7147468d6becd9f24907223a76e71ff84d7d8ae | Python | py | |
from argparse import ArgumentParser
import pytorch_lightning as pl
import torch
from pytorch_lightning import LightningModule
from torch.utils.data import DataLoader
from transformers import (
BartForConditionalGeneration,
BartTokenizer,
get_linear_schedule_with_warmup,
)
import jsonlines
from src.data.seq... | src/models/bart_seq2seq_kilt.py | MIT | 7,320 | 0392762948f081581d76fd59a3f542253b0aa4cf | Python | py | |
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import math
from args import args as parser_args
DenseConv = nn.Conv2d
class GetSubnet(autograd.Function):
@staticmethod
def forward(ctx, scores, k):
# Get the subnetwork by sorting the scores and ... | utils/conv_type.py | Apache-2.0 | 4,164 | 3a829ca87d91bafa00ed69c53ac068dc8563c07e | Python | py | |
import os
import math
import argparse
import random
import logging
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from data.data_sampler import DistIterSampler
import options.options as option
from utils import util
from data import create_dataloader, create_dataset
from models impor... | codes/train.py | Apache-2.0 | 7,879 | 45bca701081e23e766da893227b009f9aaa5f0aa | Python | py | |
import torch
from mmcv.runner import force_fp32
from mmdet.core import images_to_levels
from ..builder import HEADS
from ..losses import carl_loss, isr_p
from .retina_head import RetinaHead
@HEADS.register_module()
class PISARetinaHead(RetinaHead):
"""PISA Retinanet Head.
The head owns the same structure wi... | mmdet/models/dense_heads/pisa_retinanet_head.py | Apache-2.0 | 6,220 | fde718904591639673323da27f5b3a46106b6a79 | Python | py | |
import gym
import torch
import tensorboardX
from ddpg import DDPG
import argparse
import os
def main(args):
env = gym.make(args['env_name'])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
action_dim = env.action_space.shape[0]
max_action = env.action_space.high[0]
state_... | evaluate.py | Apache-2.0 | 2,276 | 3a82b485c7dc63068f3f7de1b358260b3b28d018 | Python | py | |
# -*- coding: utf-8 -*-
import math
import torch
from torch import nn, Tensor
# pylint: disable=arguments-differ
class MultiHeadedAttention(nn.Module):
"""
Multi-Head Attention module from "Attention is All You Need"
Implementation modified from OpenNMT-py.
https://github.com/OpenNMT/OpenNMT-py
... | joeynmt/transformer_layers.py | Apache-2.0 | 9,136 | f96c93f824aaacf928e8909805c6f8f7bd553f4c | Python | py | |
#!/usr/bin/env python3
import logging
import math
import time
from typing import Optional
import numpy as np
import torch
from reagent.ope.estimators.types import PredictResults, Trainer, TrainingData
from sklearn.linear_model import Lasso, LogisticRegression, SGDClassifier
from sklearn.metrics import accuracy_score,... | reagent/ope/trainers/linear_trainers.py | BSD-3-Clause | 12,700 | 3a82f5c1fa6251fb75366c739dfcc97e9803b86c | Python | py |
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