content stringlengths 200 198k | repo stringlengths 4 186 | path stringclasses 1
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# --------- Link Prediction Model with both TAT and GAT contained -----------
import torch.nn as nn
import torch
from .GAT import GAT
from .TAT import TAT
class GATA(nn.Module):
def __init__(self, emb_dim, hid_dim, out_dim, num_voc, num_heads, num_ent, num_rel, dropout, alpha, **kwargs):
super(GA... | Existing paper reading/model/GATA.py | MIT | 1,782 | 105813c869a5edc310f98773b833ce8591293d86 | Python | py | |
import argparse
import datetime
import random
import time
from pathlib import Path
import torch
import torchvision.transforms as standard_transforms
import numpy as np
from PIL import Image
import cv2
from crowd_datasets import build_dataset
from engine import *
from models import build_model
import os
import warning... | test_video.py | BSD-3-Clause | 5,324 | 10861da00bfec7ae0a2c81190acfaca8ab56366a | Python | py | |
# Copyright (c) 2020 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/data_transformer_test.py | Apache-2.0 | 13,244 | 3aca8848c79a96835684431a9ec6d7653f72a16b | Python | py | |
# PyTorch
import torch
import torchvision.transforms as transforms
# Image managing
from PIL import Image
# Paths
import os
# Utils
from tqdm import tqdm
class HistopathologyImageMaker:
def __init__(self, model: torch.nn.Module) -> None:
"""
Constructor for the HistopathologyImageMaker
... | HistImageMaker.py | MIT | 6,860 | 5ea8a5beda7364afb8fcfccce5e4d2b80fe863e9 | Python | py | |
from torch.utils.data import Dataset
from torchvision.transforms import transforms
from sklearn.model_selection import train_test_split
import os
import glob
import torch
import numpy as np
from PIL import Image
import pdb
class LocalDataset(Dataset):
def __init__(
self,
root: str,
datase... | src/base/local_dataset.py | MIT | 3,356 | e9667bd424694f5af16378d0dfcd7bc9fa58a7a6 | Python | py | |
import numpy as np
import torch
import torch.nn.functional as F
import scipy.sparse as sps
from scipy.linalg import eigh, inv, det
from numpy import zeros
import math
from .source import Source
from utils import roll
class Lattice:
def __init__(self,L, d, BC='periodic'):
self.L = L
self.d = d
... | source/ising.py | Apache-2.0 | 2,517 | ff0be3d2fde851cd4b8ac7c3269abc018fb1bd0f | Python | py | |
import json
from typing import NamedTuple
import numpy
from numpy.testing import assert_array_almost_equal, assert_almost_equal
import torch
import pytest
from flaky import flaky
from allennlp.common.checks import ConfigurationError
from allennlp.common.testing import AllenNlpTestCase
from allennlp.nn import util
fro... | allennlp/tests/nn/util_test.py | Apache-2.0 | 56,551 | 3acaa78601c7e3136002abe0dfca3c23e2c12c31 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import numpy as np
import sys
import os
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = o... | models/loss_helper.py | MIT | 42,095 | 3acab136dea98d6a53ce2191d7ffcabf9d43c35e | Python | py | |
import os
import sys
sys.path.append("../../../../monk_v1/");
sys.path.append("../../../monk/");
import psutil
from pytorch_prototype import prototype
from compare_prototype import compare
from common import print_start
from common import print_status
import torch
import numpy as np
from pytorch.losses.return_loss im... | monk/system_unit_tests/pytorch/test_layer_transposed_convolution1d.py | Apache-2.0 | 1,422 | 10a26b4e1e3fd2d2851622f032540fc2101bd362 | Python | py | |
import torch.nn as nn
import torchvision.transforms as transforms
class AlexNetOWT_BN(nn.Module):
def __init__(self, num_classes=1000):
super(AlexNetOWT_BN, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 128, kernel_size=3, stride=1, padding=1,
bias... | models/vgg_cifar10.py | MIT | 2,206 | 1ec23cd157301e4a7712e8f863a4712aa9bcc93d | Python | py | |
'''Simplified version of DLA in PyTorch.
Note this implementation is not identical to the original paper version.
But it seems works fine.
See dla.py for the original paper version.
Reference:
Deep Layer Aggregation. https://arxiv.org/abs/1707.06484
'''
import torch
import torch.nn as nn
import torch.nn.function... | models/dla_simple.py | MIT | 4,084 | 3acaba68014993ed6235f6ce40272a226b32610a | Python | py | |
import pytest
import torch
from pytorch_lightning.metrics.functional.classification import dice_score
@pytest.mark.parametrize(['pred', 'target', 'expected'], [
pytest.param([[0, 0], [1, 1]], [[0, 0], [1, 1]], 1.),
pytest.param([[1, 1], [0, 0]], [[0, 0], [1, 1]], 0.),
pytest.param([[1, 1], [1, 1]], [[1, ... | tests/metrics/functional/test_classification.py | Apache-2.0 | 541 | f9f3033427b1bdfc89c70af188aaa8e6c69b802f | Python | py | |
# Copyright (C) 2020 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 License at
# https://opensource.org/licenses/BSD-3-Clause
# Unless required by appl... | benchmark/benchmark_helper.py | BSD-3-Clause | 6,220 | 8036164162942a77bc56f45ff438cc2d7af3d881 | Python | py | |
from torch.multiprocessing import Queue
import time
import tqdm
import torch
from typing import List
from ml_gym.multiprocessing.job import JobType, Job
from ml_gym.multiprocessing.worker import WorkerProcessWrapper
from ml_gym.util.logger import QueuedLogging
from ml_gym.util.logger import LogLevel, QLogger
class Po... | src/ml_gym/multiprocessing/pool.py | Apache-2.0 | 4,374 | b2d403fdde7c0ec43b23e0246a2521f141c95fba | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import copy
import math
from pathlib import Path
import utils
from encoder import make_encoder
LOG_FREQ = 10000
def gaussian_logprob(noise, log_std):
"""Compute Gaussian log probability."""
residual = (-0.5 * noise.pow(2) -... | curl_sac.py | MIT | 16,835 | 3acb1b896affae47f4ae6e3e9c040e1f009f0c1a | Python | py | |
# 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... | tests/callbacks/test_model_summary.py | Apache-2.0 | 3,479 | b2a57b4e192d095e2131a8de29e8b23a2dc8c1bd | Python | py | |
# Copyright 2020 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Conformer encoder block."""
import logging
import random
import torch
import torch.nn as nn
from neural_sp.models.modules.conformer_convolution import ConformerConvBlock
from neural_sp.models.modules.p... | neural_sp/models/seq2seq/encoders/conformer_block.py | Apache-2.0 | 7,261 | 7c93b3aa425b899cc7cb8c608bbfab1149d6f715 | Python | py | |
import argparse
import torch
from torch.nn import Linear
import torch.nn.functional as F
from torch_geometric.nn import APPNP
from citation import get_planetoid_dataset, random_planetoid_splits, run
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, required=True)
parser.add_argument('--ran... | benchmark/citation/appnp.py | MIT | 1,936 | 038d428692dfe8239e798eb6afb964efec68a6c6 | Python | py | |
#!/usr/bin/env python
"""
Main training workflow
"""
from __future__ import division
import argparse
import glob
import os
import random
import signal
import time
import torch
from transformers import BertTokenizer
import distributed
from models import data_loader, model_builder
from models.data_loader import lo... | temp/src/train_abstractive.py | MIT | 12,448 | 5fb91d65bd5499f69420f9d47c7049720026fc82 | Python | py | |
from functools import partial
import pytest
import torch
from sklearn.metrics import (
accuracy_score as sk_accuracy,
jaccard_score as sk_jaccard_score,
precision_score as sk_precision,
recall_score as sk_recall,
f1_score as sk_f1_score,
fbeta_score as sk_fbeta_score,
roc_curve as sk_roc_cu... | tests/metrics/functional/test_classification.py | Apache-2.0 | 23,322 | 12eb8555b10aa1d4831184d449abe3052c7c037a | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import os
import torch
from maskrcnn_benchmark.utils.model_serialization import load_state_dict
from maskrcnn_benchmark.utils.c2_model_loading import load_c2_format
from maskrcnn_benchmark.utils.imports import import_file
from mask... | maskrcnn_benchmark/utils/checkpoint.py | MIT | 4,956 | 12c47c407301b7133a266cb8030f2b36177b3a3d | Python | py | |
import csv
import numpy as np
from sklearn.neighbors import KernelDensity
from sklearn.model_selection import GridSearchCV
from Utils import cuda_utils
import torch
global cpu_kde, cpu_kde_conditional
cpu_kde = None
cpu_kde_conditional = {}
def build_cpu_kde(X):
# Cross-validation to find best bandwidth
param... | Models/Parzen/Parzen.py | MIT | 2,500 | 24c725a4fc41a15a939a7037cd023be80473ed23 | Python | py | |
import copy
import torch
import torch.nn as nn
from . import diffeq_layers
__all__ = ["ODEnet", "ODEfunc"]
def divergence_approx(f, y, e=None):
e_dzdx = torch.autograd.grad(f, y, e, create_graph=True)[0]
e_dzdx_e = e_dzdx.mul(e)
cnt = 0
while not e_dzdx_e.requires_grad and cnt < 10:
... | module/odefunc.py | MIT | 5,953 | 342563890d4f82aace4c607f76602c7d79fc1686 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class SegNet(nn.Module):
def __init__(self,input_nbr,label_nbr):
super(SegNet, self).__init__()
batchNorm_momentum = 0.1
self.conv11 = nn.Conv2d(input_nbr, 64, kernel_size=3, padding=1)
... | models/segnet.py | MIT | 6,168 | 80520868e3b6dbdc4500a19ef0e7ca4c2c886c8d | Python | py | |
"""
Modeling Relational Data with Graph Convolutional Networks
Paper: https://arxiv.org/abs/1703.06103
Code: https://github.com/tkipf/relational-gcn
Difference compared to tkipf/relation-gcn
* l2norm applied to all weights
* remove nodes that won't be touched
"""
import argparse, gc
import numpy as np
import time
impor... | examples/pytorch/rgcn/entity_classify_mp.py | Apache-2.0 | 25,849 | 3acb758b43c716045ef485d80a4422a185609781 | Python | py | |
"""This model shows an example of using dgl.metapath_reachable_graph on the original heterogeneous
graph.
Because the original HAN implementation only gives the preprocessed homogeneous graph, this model
could not reproduce the result in HAN as they did not provide the preprocessing code, and we
constructed another da... | examples/pytorch/han/model_hetero.py | Apache-2.0 | 3,815 | 2477b1711dfc61c1d278795f858d56730c4204b2 | Python | py | |
import cv2
import torch
from PIL import Image
from threading import Thread
def get_boxes(frame):
global box_data, ACTIVE_THREADS
ACTIVE_THREADS += 1
# Get prediction bounding boxes
results = model(frame, size=320)
box_data = results.xyxy
ACTIVE_THREADS -= 1
def draw_boxes(frame, box, line_th... | Computer_Vision/4_trained_yolov5.py | MIT | 2,476 | 3acb977c70bcad0242d61ff92ffa412b75664cc1 | Python | py | |
import os
from torchaudio.functional import magphase
from functools import partial
import torch
class OnlinePreprocessor(torch.nn.Module):
def __init__(self, sample_rate=16000, win_len=512, hop_len=256, n_freq=257, feat_list=None, **kwargs):
super(OnlinePreprocessor, self).__init__()
n_fft = (n_fr... | preprocess.py | MIT | 5,158 | d7f9644de985dd4dbd9deaec9e4f3cf28fa3e916 | Python | py | |
import flask
import torch
import torchvision
from typing import Tuple
from pathlib import Path
from dotenv import find_dotenv, load_dotenv
from urllib.request import urlretrieve
import shutil
import json
from mapillary_image_classification.config import MAPILLARY_TOKEN_v4
from mapillary_image_classification.models.mod... | mapillary_image_classification/models/predict_model.py | MIT | 4,464 | dfddb373ba1a9fbe856aa7b1f3b5c9329fb5ee2d | Python | py | |
import os
from unittest import TestCase
import torch
import yaml
from spectrainer import ToyNetTrainer
from toynet.toynetv1 import ToyNetV1
from utils.utils import cal_parameters, getConfig
class TrainerFunctionTest(TestCase):
def setUp(self):
conf = getConfig('./config/toynetv1.yml')
self.traine... | test/test_trainer.py | MIT | 890 | 70489ea23e5284b7b50f80e681b2ecdc0bd0434e | Python | py | |
# coding=utf-8
# Copyleft 2019 project LXRT.
import os
import collections
import torch
import torch.nn as nn
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
from param import args
from pretrain.qa_answer_table import load_lxmert_qa
from tasks.vqa_model import VQAModel
from tasks.vqa_data imp... | src/tasks/vqa.py | MIT | 7,842 | 3acbc4cce61d8aa68e67d9996d8a71c79c3ca50a | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class yoloLoss(nn.Module):
'''
定义一个torch.nn中并未实现的网络层，以使得代码更加模块化
torch.nn.Modules相当于是对网络某种层的封装，包括网络结构以及网络参数，和其他有用的操作如输出参数
继承Modules类，需实现__init__()方法，以及forward()方法
'''
def __init__(self,S,B,l_co... | Yolov1_pytorch/utils/yoloLoss.py | MIT | 7,117 | d7a01ec09803493de95726d413110e03730a31ba | Python | py | |
"""Federated datasets."""
import enum
import os
import random as rnd
from collections import Counter, defaultdict
from dataclasses import dataclass
from typing import (Callable, Dict, Generator, Iterable, List, Optional, Set, Tuple, Union)
import torch
from torch.utils.data import Dataset
from torchtext.data.utils im... | src/fl_simulation/utils/data/datasets.py | MIT | 21,418 | fce4e4acceb9771aa9614ad14dec7002ca2a278d | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import modules
class UnwarpNet_cmap(nn.Module):
def __init__(self, use_simple=False, combine_num=3, train_mod="all"):
super(UnwarpNet_cmap, self).__init__()
self.combine_num = combine_num
self.use_simple = use_simple... | models/misc/model_cmap.py | Apache-2.0 | 8,135 | 3acbeda11a3aa0a000785c272b86733b2ea6fc48 | Python | py | |
import math
import pdb
import torch.nn as nn
from torch.nn import functional as F
__all__ = ['BahdanauAttention']
class BahdanauAttention(nn.Module):
def __init__(self):
pass
def forward(self):
pass
| elvis/modeling/models/layers/attention.py | Apache-2.0 | 228 | 3acc11b577eeecc66dbe72d1a763e43e2d42317a | 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/model/multiple_choice.py | MIT | 5,700 | dfe289aad7ae0bc827aeec7981624f5ac0305807 | Python | py | |
from __future__ import absolute_import, division, print_function
import os
from transformers import BertTokenizer
import torch
import torch.nn as nn
import const
bert_tokenizer = BertTokenizer.from_pretrained(const.MODEL_TYPE, do_lower_case=True)
def get_bert_data(examples, tag2id, config):
bert_data = []
... | NER_BERT/bert_data_util.py | Apache-2.0 | 3,368 | 3acc6766366f5f27858c6ac0cd1edd9d3c12a19b | Python | py | |
import os
import pytest
import torch
import ignite.distributed as idist
from ignite.distributed.utils import has_hvd_support
from tests.ignite.distributed.utils import (
_test_distrib_all_gather,
_test_distrib_all_reduce,
_test_distrib_barrier,
_test_distrib_config,
_test_distrib_one_rank_only,
... | tests/ignite/distributed/utils/test_horovod.py | BSD-3-Clause | 8,086 | 1ecc4a0c96d8a5ab2067f9b0382d3e253b39ed64 | Python | py | |
# Copyright (c) 2022, 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 required by appli... | tests/collections/nlp/test_retrieval_module_inference.py | Apache-2.0 | 26,708 | 3acc6fdceb8478674c292b3c1f953e37219ee52f | Python | py | |
import os
import os.path as osp
import math
import sqlite3
from tqdm import tqdm
import pandas as pd
import numpy as np
import torch
import magnificat.observation_utils as obs_utils
class LSSTCadence:
min_mjd = 59580.139555
nside_in = 32
nested = False
fov_radius = 1.75 # deg, field of view
bp_to... | magnificat/cadence.py | MIT | 10,151 | 7c390999b5719b46ea7483792c682eb00a0ee176 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
import copy
from enum import Enum, auto
import functools
import logging
from math import inf
import time
import traceback
im... | fairscale/nn/data_parallel/fully_sharded_data_parallel.py | BSD-3-Clause,Apache-2.0,MIT | 104,357 | f9a75c8d0f1f0963746e685cb64a0de29181af60 | Python | py | |
# Copyright 2020 The HuggingFace Team. 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 applicabl... | src/transformers/commands/env.py | Apache-2.0 | 3,232 | 3acc85825d7e6d66e0f1591413a4cb828ea81063 | Python | py | |
import os
from collections import defaultdict
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from torchvision.datasets.utils import download_and_extract_archive
from src.datasets.specs import Input2dSpec
AIRCRAFT_RESOURCES = {
'aircraft': 'http://www.robots.ox.ac.uk... | src/datasets/natural_images/aircraft.py | MIT | 4,995 | 4b49505d80418eaa1e31dc592799072e651db00a | Python | py | |
"""
Useful functions related to the `ava.models` subpackage.
"""
__date__ = "July - November 2020"
from affinewarp import PiecewiseWarping
from affinewarp.crossval import paramsearch
import h5py
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import numpy as np
import os
from scipy.interpolate import inter... | ava/models/utils.py | MIT | 14,493 | dfe3af549a685c1c0434ba01719058c8e012362f | Python | py | |
# adapted from https://github.com/sthalles/SimCLR/blob/master/models/resnet_simclr.py
import torch.nn as nn
import torchvision.models as models
class Resnet(nn.Module):
def __init__(self, out_dim=128):
super(Resnet, self).__init__()
self.resnet = models.resnet18(pretrained=False, num_classes=out_... | model.py | Unlicense | 594 | a91b8c943a55d35cbd3b3c4fe1c7dec50055b46c | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytext.config import ConfigBase
from pytext.models.module import Module
from pytext.utils import cuda
from pyte... | pytext/models/representations/ordered_neuron_lstm.py | BSD-3-Clause | 7,014 | 3accd73402e2a6eeabd55b33aa622b3423f05212 | Python | py | |
import sys
import os
from transformers import AutoModel
from typing import List
from tfkit.utility.predictor import Predictor
dir_path = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.abspath(os.path.join(dir_path, os.pardir)))
import torch
from torch import nn
from tfkit.model.seq2seq.dataload... | tfkit/model/seq2seq/model.py | Apache-2.0 | 10,765 | fd6f9fc79ae45ce7f2f9bc870a2382ed25158ed3 | Python | py | |
# Copyright 2020 - 2021 MONAI Consortium
# 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 in wri... | monai/networks/nets/resnet.py | Apache-2.0 | 16,338 | 1086a21e46f37095d6583dbc71eac4e4e4607697 | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its 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 datetime
import json
import logging
import random
import s... | torch/distributed/elastic/rendezvous/etcd_rendezvous.py | Intel | 49,569 | 3accfc3b43b5fb95f6a10451b52f3a6c82b8e2da | Python | py | |
import os
import torch
from ucca.convert import to_text, xml2passage
from .instance import Instance
from .dataset import TensorDataSet
class Corpus(object):
def __init__(self, dic_name=None):
self.dic_name = dic_name
self.passages = self.read_passages(dic_name)
self.instances = [Instanc... | parser/utils/corpus.py | MIT | 3,769 | b256e8955294fab061c05ad4e29a8623547fb0c3 | Python | py | |
# -*- coding: utf-8 -*-
# @Author : William
# @Project : TextGAN-william
# @FileName : maligan_instructor.py
# @Time : Created at 2019/11/29
# @Blog : http://zhiweil.ml/
# @Description :
# Copyrights (C) 2018. All Rights Reserved.
import torch
import torch.optim as optim
import confi... | instructor/real_data/maligan_instructor.py | MIT | 6,228 | e65f0d7e75baa9b1aed9550a310d1de29adf621d | Python | py | |
#!/usr/bin/env python3
# Copyright 2020 Christian Henning
#
# 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 l... | ex02/tests/public/test_main.py | MIT | 2,934 | fccb72920c92c5034ffa3f579151d9b4a9115ac1 | Python | py | |
# OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
# Reference https://github.com/open-mmlab/OpenPCDet
# Written by Shaoshuai Shi, Chaoxu Guo
# All Rights Reserved 2019-2020.
import os
import pickle
import copy
import cv2
from cv2 import cvtColor
import numpy as np
import torch
import multiproc... | pcdet/datasets/waymo/waymo_dataset.py | MIT | 25,987 | dffe2d3890938ebd7c42429d58f0707cdb5381a0 | Python | py | |
import torch as T
import neuralnet_pytorch.ext as ext
__all__ = ['earth_mover_distance']
class EarthMoverDistanceFunction(T.autograd.Function):
@staticmethod
def forward(ctx, xyz1, xyz2):
xyz1 = xyz1.contiguous()
xyz2 = xyz2.contiguous()
assert xyz1.is_cuda and xyz2.is_cuda, "Only sup... | neuralnet_pytorch/extensions/dist_emd.py | MIT | 1,012 | 3acd87ec7f8276a911f71b457483ac4a20bc33b6 | Python | py | |
# Copyright (c) Facebook, Inc. and its 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 glob
import importlib
import logging
import os
import sys
import warnings
import torch
from mmf.common.reg... | mmf/utils/checkpoint.py | BSD-3-Clause | 17,750 | ff0d7df8571aec99c8dc3fae2f314cc4b1225229 | Python | py | |
"""
file: models/ada_lista.py
author: Xiaohan Chen
last modified: 2021.08.10
Implementation Ada-LISTA.
"""
import math
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from .utils import shrink, shrink_ss
class AdaLIS... | models/ada_lista.py | MIT | 6,545 | e9c746d248e5f8e7499a780bc9b7cdd51441b5d2 | Python | py | |
# 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... | torchmetrics/classification/roc.py | Apache-2.0 | 7,237 | 3acda6e64ff970636c03546a71d13fcfa44e4e1f | Python | py | |
"""Transducer decoder interface module."""
from espnet.nets.beam_search_transducer import Hypothesis
from typing import Any
from typing import Dict
from typing import List
from typing import Tuple
from typing import Union
import torch
class TransducerDecoderInterface:
"""Decoder interface for transducer models... | espnet/nets/transducer_decoder_interface.py | Apache-2.0 | 3,398 | 3acdaef51012a2a5e5b330599a45a7c89eac2f3b | Python | py | |
import os
from glob import glob
import numpy as np
import torch
from args import args
if args.dtype == 'float32':
default_dtype = np.float32
default_dtype_torch = torch.float32
elif args.dtype == 'float64':
default_dtype = np.float64
default_dtype_torch = torch.float64
else:
raise ValueError('Unk... | src_hop_sk/utils.py | MIT | 3,616 | 3406c95e24ebf99752835c8704b3ca6d2d4d10f9 | Python | py | |
import numpy as np
import os,sys,time
import torch
import torch.nn.functional as torch_F
import torchvision
import torchvision.transforms.functional as torchvision_F
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import PIL
import imageio
from easydict import EasyDict as edict
... | util_vis.py | MIT | 10,655 | 3acdd7f5aaff2a2ae51192c16f0eb184d92dccf2 | Python | py | |
import numpy as np
import torch
from .misc import process_outputs_gaussian
from .estimators import (
EnsembleGaussianCategoricals,
)
def compute_token_gaussian_uncertainties(args, outputs, extra):
"""
Function which computes token-level measures of uncertainty for Gaussian Categorical model.
:param a... | examples/self_distribution_distillation/self_distribution_distillation_src/uncertainty/gaussian.py | MIT | 2,838 | 3acded7e220980ae26bc4ee9ee1a4bd546cb5a2d | Python | py | |
import yaml
import torch.optim as op
from torch.nn import modules
import goodDataML.learning.horizontal.torch_model.process_data as processes
from typing import Callable
def convert_loss_name_to_func(loss):
# type: (str) -> modules.module
return getattr(modules.loss, loss)
def convert_optimizer_to_func(opt... | goodDataML/learning/utils/pytorch_utils.py | Apache-2.0 | 2,044 | 45474782da857d00c0a0a450b401d97c471803e7 | Python | py | |
# encoding: utf-8
"""
A script for running the following experiments:
* dataset: Overnight
* model: BART encoder + vanilla Transformer decoder for LF
* training: normal (CE on teacher forced target)
"""
import json
import random
import string
from copy import deepcopy
from functools import partial
from typing import C... | parseq/scripts_multling/geoquery_base_bert.py | MIT | 18,386 | 5f7b7482e18483988e3c6ca7f157d277232e4e5f | Python | py | |
"""
Copyright (c) 2021 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in wri... | torchreid/apis/export.py | MIT | 3,873 | 3ace0397813b0fccbe77c7961603b07923fc7a09 | Python | py | |
r"""RNN language model with residual connection."""
from typing import ClassVar, Dict, List, Optional
import torch
import torch.nn as nn
from lmp.model._rnn import RNNModel
from lmp.tknzr._base import BaseTknzr
class ResRNNBlock(nn.Module):
r"""Residual connected RNN blocks.
Each output of RNN will be add... | lmp/model/_res_rnn.py | Beerware | 12,483 | e91876fc13dc0fbb83c53fd0b0d5b81efd687abb | Python | py | |
import collections
import json
import logging
import os
from argparse import Namespace
from typing import (Dict, List, Optional)
import torch
from .tag_encoder import NERTagsEncoder, SCHEMES
from .tokenization import (
Token,
TokenizerWithAlignment,
reconstruct_text_from_tokens,
)
from pytorch_transforme... | portuguese_bert/preprocessing.py | MIT | 19,549 | 4b4faf3ce8c7375f30c3c442502042bb3bee6623 | Python | py | |
# -*- coding: utf-8 -*-
"""CIFAR_training.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1XcUVE2Bt2CVgmD9uHwATsBJ40MC9w0PC
"""
datasetName = "CIFAR-100"
# datasetName = "CIFAR-10"
if (datasetName == "CIFAR-100"):
tc = 100
if (datasetName == ... | cifar_training.py | MIT | 9,209 | 5f46c92f937a03792024b72f2a4723ecbb9f968b | Python | py | |
import torch.nn as nn
import torch
import torch.nn.functional as F
from ltr.models.layers.blocks import conv_block
from .conv_gru import ConvGRUCell
#import msvcrt
from visdom import Visdom
vis=Visdom(env="rnn")
class ResponsePredictor(nn.Module):
def __init__(self, state_dim=8, representation_predictor_dims=(64,... | ltr/models/kys/response_predictor_stark.py | MIT | 10,194 | 428f15369b0425521c8df806357a9d3f4fd8e46f | Python | py | |
import numpy as np
import torch
import time
from hydroDL.model import rnn, crit
from hydroDL.data import transform
def subsetRandom(dataLst, batchSize, sizeLst, opt='Random', matB=None):
"""get a random subset of training data
Arguments:
dataLst {list} -- see trainModel [x,xc,y,yc]
batchSize ... | hydroDL/model/trainBasin.py | MIT | 8,560 | 3ace3a30b9a2888d46edb6892dd79465c12de3e7 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import math
from itertools import groupby
import torch
import torch.nn.functional as F
from fairseq im... | examples/audio_visual_se_sr/criterions/RNNT_loss.py | MIT | 7,261 | 3ace69484cf3bd5c1695b37bbb9bf0d13cb3e6df | Python | py | |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import json
import logging
from tqdm import tqdm
import random
import torch
from torch.utils.data import DataLoader, SequentialSampler, Dataset
from torch.utils.data.distributed import DistributedSampler
from transformers import Rober... | utils_nlp/models/transformers/abstractive_summarization_seq2seq.py | MIT | 45,326 | 3ace6da3c92fee90f84bc7810c2db49e121ea69f | Python | py | |
import torch
import torch.utils.data
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
from torchvision.transforms.functional import rgb_to_grayscale
from utils import AttnLabelConverter
from model import Model
from dataset import NormalizePAD
from PIL import Image,ImageDraw,ImageFont
import math
i... | demo-opencv.py | Apache-2.0 | 6,692 | 3ace6f94bf35292c12959d70337879f936b5da67 | Python | py | |
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, k... | system/flcore/trainmodel/resnet.py | MIT | 6,372 | 5f80c293570422d15642b5972182158e9b935b0f | Python | py | |
# -*- coding: utf-8 -*-
import argparse
import os.path as osp
from copy import deepcopy
import yaml
from loguru import logger
import torch
from main.test import build_sat_tester, build_siamfcpp_tester
from videoanalyst.config.config import cfg as root_cfg
from videoanalyst.config.config import specify_task
from vid... | main/hpo.py | MIT | 2,412 | f93bfe6caaf65e9584ae0340edebb61c514ff726 | Python | py | |
import warnings
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from qd3dt.ops import DeformConvPack, ModulatedDeformConvPack
from .conv_ws import ConvWS2d
from .norm import build_norm_layer
conv_cfg = {
'Conv': nn.Conv2d,
'ConvWS': ConvWS2d,
'DCN': DeformConvPack,
'DCNv2': Mod... | qd3dt/models/utils/new_conv_module.py | BSD-3-Clause | 5,869 | 3acec4920a162be6827deea90c049de164c513b7 | Python | py | |
# Copyright (c) 2019 Graphcore Ltd. All rights reserved.
import numpy as np
import popart
import torch
import pytest
from op_tester import op_tester
# `import test_util` requires adding to sys.path
import sys
from pathlib import Path
sys.path.append(Path(__file__).resolve().parent.parent)
import test_util as tu
def ... | tests/integration/operators_test/matmul_test_broadcasting_base.py | MIT | 4,671 | 52a5294e14ee3836ad4277dd2c30343a02104ff4 | Python | py | |
import glob
import math
import os
import random
import cv2
import numpy as np
import torch
# from torch.utils.data import Dataset
from utils.utils import xyxy2xywh
class LoadImages: # for inference
def __init__(self, path, img_size=416):
if os.path.isdir(path):
image_format = ['.jpg', '.jpe... | utils/datasets.py | MIT | 12,227 | 7caf2df186cfa6a8e7ccf3d991e0a11708980339 | Python | py | |
import argparse
import numpy as np
import os
import Loader as Loader
import sys
import math
import torch
import matplotlib.pyplot as plt
import torch.nn.functional as F
from utils import CropImage, linear_schedule
from torch import nn, optim
from torch.nn import functional as F
from torch.autograd import Variable
from ... | vae/plotshitsogram.py | MIT | 1,877 | 3acf22f037ca8afc573af7bf58c7f58ff848431c | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 29 14:57:05 2019
@author: esat
"""
import os
import sys
import numpy as np
import math
import cv2
import scipy.io as sio
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import to... | MLP/VideoSpatialPrediction3D_bert_embedding.py | MIT | 10,430 | fccc13df9466234a086f18b04091953192733c6a | Python | py | |
from utils.utils import AUCMeter
from tqdm import tqdm
from models.resnet import resnet18, resnet50
import numpy as np
from utils.Logger import Logger
from optimizer.optimizer_helper import get_optim_and_scheduler, FocalLoss
from models import model_factory
from data.data_helper import available_datasets
from data impo... | Domain_Generalization/train.py | BSD-2-Clause | 19,249 | 10e5c6dc0a8c0488dfc376c38e13b45482be814b | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import ModuleList, force_fp32
from mmdet.core import (build_anchor_generator, build_assigner,
build_bbox_coder, build_sampler, multi_apply)
from ..builder import HEADS
from ..losses import smooth_l1_loss
from .a... | mmdet/models/dense_heads/ssd_head.py | MIT | 11,064 | 1e2151afd32849c1e7cd13ab38351ef6a6d670b6 | Python | py | |
import torch
import torchvision
import cv2
import random
import numpy as np
from PIL import Image
from torchvision import transforms
from sourcecode.configs.make_cfg import Struct
"""
- type: RandomRotation
prob: 0.3
angle: [-10, 10]
- type: RandomRotation
prob: 0.3
angle: [90, 90]
- typ... | sourcecode/dataset/transform.py | MIT | 5,061 | 3acf98942959c5f175e5b0d17233c7614d77a66a | Python | py | |
"""
A collection of utility functions for working with files, such as reading metadata from
demonstration datasets, loading model checkpoints, or downloading dataset files.
"""
import os
import h5py
import json
import time
import urllib.request
import numpy as np
from collections import OrderedDict
from tqdm import tqd... | robomimic/utils/file_utils.py | MIT | 13,479 | 7079b02d59ecd4d033002a386ae68031feef426c | Python | py | |
# Copyright (c) 2020, Fabio Muratore, Honda Research Institute Europe GmbH, and
# Technical University of Darmstadt.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source ... | Pyrado/pyrado/policies/base.py | DOC,Zlib,BSD-3-Clause | 9,665 | 129318ead4f2ed99d020a298dacd1191b3c2cb50 | Python | py | |
#!/usr/bin/env python
import argparse
from mmcls.apis import init_model
import torch
from list_dataset import ImageFilelist
import numpy as np
import pickle
from tqdm import tqdm
import mmcv
from os.path import dirname
import torchvision as tv
def parse_args():
parser = argparse.ArgumentParser(description='Say hel... | extract_feature_vit.py | Apache-2.0 | 2,525 | 3acfc1d1c4310e0d49095eaabb347a96e498b64d | Python | py | |
import torch
from torch import nn, optim
from torch.nn import functional as F
class FaderAE(nn.Module):
def __init__(self, num_attributes, num_channels=1):
super().__init__()
self.input_size = 256
self.latent_space = 50
self.num_channels = num_channels
self.num... | models/FaderAE.py | MIT | 2,925 | 104ca01ad391b9267ee561803224167acef0b60d | Python | py | |
"""
Copyright (c) 2019-2020 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in w... | tests/torch/test_backward_compat.py | Apache-2.0 | 7,890 | 3acfc52d4e803dbb9c4bd37ab6708087e88ce30a | Python | py | |
"""Tests for graph construction tools."""
import numpy as np
import pytest
import torch
from matplotlib import pyplot as plt
from tiatoolbox.tools.graph import (
SlideGraphConstructor,
affinity_to_edge_index,
delaunay_adjacency,
edge_index_to_triangles,
triangle_signed_area,
)
def test_delaunay_... | tests/test_graph.py | BSD-3-Clause | 10,494 | 10a6eb7f8c8a8f0a3005f8c270ac47acb04c72c5 | Python | py | |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, 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 cop... | examples/research_projects/distillation/run_squad_w_distillation.py | Apache-2.0 | 36,220 | 3acfd468640626f052fa66a9560d04ac46db03a3 | Python | py | |
#!/usr/bin/env python
# Copyright 2020-2021 OpenDR European Project
#
# 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 a... | projects/opendr_ws/src/perception/scripts/object_detection_2d_detr.py | Apache-2.0 | 4,316 | 52f7d31aa2123715a7816e8be06078e9915ddac0 | Python | py | |
# Import from third library
import torch
import torch.nn as nn
from up.utils.model.initializer import initialize_from_cfg
from up.utils.general.registry_factory import MODULE_ZOO_REGISTRY
from up.tasks.det.plugins.yolov5.models.components import ConvBnAct, Focus, SPP
from up.tasks.det.plugins.yolov5.models.components ... | up/tasks/det/plugins/yolox/models/backbone/cspdarknet.py | Apache-2.0 | 11,355 | 3ad01767b66d7c5201ea80cec7cc2dfb2f55d078 | Python | py | |
import torch
from torch import nn
from torch import optim
from torch.utils import data
from torch.backends import cudnn
from .trainer import Trainer
from ..utils import StepClass
from ..models.ExprAutoEncoders import ClassDiscriminator
class SAAETrainer(Trainer):
'''see readme'''
def __init__(self, parameter... | src/biopy/training/saae_trainer.py | MIT | 7,150 | dfe220f5159146fc3c030509fd11b4ce06b67d0f | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class LSTM_model(torch.nn.Module):
def __init__(self, embedding_dim, hidden_dim, target_size, batchsize, isTraining=True, use_dropout=False, gpu=0,
lstm_layers=1, att_dim=0):
super(LSTM_model,self).__init__()
self.e... | networks.py | MIT | 2,323 | 10c3113148eeac98aeb4b4de969e6229194e6ce1 | Python | py | |
"""
File Name: UnoPytorch/drug_qed_dataset.py
Author: Xiaotian Duan (xduan7)
Email: xduan7@uchicago.edu
Date: 9/4/18
Python Version: 3.6.6
File Description:
"""
import logging
import numpy as np
import pandas as pd
import torch
import torch.ut... | Pilot1/UnoMT/utils/datasets/drug_qed_dataset.py | MIT | 4,797 | 12e9de28fd1f35863e09ab306c564fb46f825d47 | Python | py | |
import pickle
import random
import os
import numpy as np
import torch
from torch.utils.data import TensorDataset
from options import opt
class FoldedDataset:
def __init__(self, baseclass, kfold, *args):
self.__class__ = type(self.__class__.__name__,
(baseclass, object),
di... | code/data_prep/folded_dataset.py | MIT | 2,596 | 3ad0b186d88a538bb7d8781c2091079bdb2de8b2 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# pylint: disable=missing-module-docstring
# pylint: disable=missing-class-docstring
# pylint: disable=missing-function-d... | tests/nn/data_parallel/test_fsdp_regnet.py | MIT,Apache-2.0,BSD-3-Clause | 13,553 | 10e6d351d6f039a918741da8ac24a326698d4d73 | Python | py | |
import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
from pathlib import Path
from timm.data import Mixup
from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.scheduler import cre... | search.py | MIT | 18,800 | 29f246c6fd7a541e9b72127b2cae7c9c9b2d68f1 | Python | py | |
import torch
from .autograd_impl import Autograd, AutogradFloat32
from torchaudio_unittest import common_utils
@common_utils.skipIfNoCuda
class TestAutogradLfilterCUDA(Autograd, common_utils.PytorchTestCase):
dtype = torch.float64
device = torch.device('cuda')
@common_utils.skipIfNoCuda
class TestAutogradRN... | test/torchaudio_unittest/functional/autograd_cuda_test.py | BSD-2-Clause | 435 | 341c575d87aadc0f6cdc59c9daed3eb5d94e84c4 | Python | py | |
from __future__ import print_function
import argparse
import json
import logging
import os
import sys
import horovod.torch as hvd
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data.distributed
from model_def import Net
from torchvision import datasets, transforms
logger = logging.get... | sagemaker-python-sdk/pytorch_horovod_mnist/code/mnist.py | Apache-2.0 | 7,337 | d7d3f474ced32a368607501f37f1446a7fdc8096 | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.ops import gather_points
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_gather_points():
features = torch.tensor([[[
-1.6095, -0.1029, -0.8876, -1.2447, -2.4031, 0.3708, ... | tests/test_ops/test_gather_points.py | Apache-2.0 | 2,188 | a93df692a58425140fc1fd73f5cefe9c07cf0d6b | Python | py |
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