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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2020 Patrick Lumban Tobing (Nagoya University)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import division
import argparse
import logging
import math
import os
import sys
import numpy as np
import torch
import torch.multiproce... | baseline/src/bin/calc_rec_gru-cyclevae-mult-mix-scpost_laplace.py | MIT | 13,262 | a987274e9342fa28707b8f9a202071dcfa08bd1e | Python | py | |
from __future__ import division
"""
Creates a ResNeXt Model as defined in:
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2016).
Aggregated residual transformations for deep neural networks.
arXiv preprint arXiv:1611.05431.
import from https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua
... | cifar10-code/models/imagenet/resnext.py | Apache-2.0 | 7,780 | fef3b86cc919e0605e17185511522d940bbf06b7 | 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_gpu_stats_monitor.py | Apache-2.0 | 4,874 | 4223a7e7b9e3fdb65cc3c9927d3379eeed3ae2ae | Python | py | |
import math
import torch
from torch import nn
class MultiheadAttention(nn.Module):
"""
A multihead attention implementation.
"""
def __init__(
self,
*,
num_heads: int,
input_state_dimension: int,
key_query_dimension: int,
value_dimension: int,
... | buglab/models/layers/multihead_attention.py | MIT | 3,681 | f93bc6460d145b72c63587aeb0e36ecfc2607db6 | Python | py | |
import math
from collections import OrderedDict
import numpy as np
import torch
from torch import nn as nn
from torch.nn import functional as F
from utils.misc import param_ndim_setup
import matplotlib.pyplot as plt
class LossFn(nn.Module):
def __init__(self,
sim_loss_fn,
reg_l... | model/loss.py | MIT | 16,224 | fef3edfbe5a391cb242c734504d8876e6b647268 | Python | py | |
#!/usr/bin/python
# coding:utf-8
"""
@author: yyhaker
@contact: 572176750@qq.com
@file: model.py
@time: 2019/3/9 15:40
"""
import torch
import torch.nn as nn
from myutils import GRU, SelfAttention, Linear, seq_mask
class RNNAttention(nn.Module):
"""Text RNN Attention model."""
def __init__(self, config, pret... | src/model/text_rnn_att.py | MIT | 1,632 | b2d125546c16b99e4bb6cf68d26d77a22fb3b787 | Python | py | |
import os
import torch
try:
import ujson as json
except ImportError:
import json
from tqdm import tqdm
if torch.__version__ >= '1.6.0':
from torch.cuda.amp import autocast
else:
from ..torch_amp_dummy import autocast
from .base import BaseTester
from ..seg_utils import ConfusionMatrix
from ..lane_det_u... | utils/runners/lane_det_tester.py | BSD-3-Clause | 5,668 | 7c567f8f52ad2ceee751c3ff64ce01627186bce2 | Python | py | |
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2017 Kai Arulkumaran
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use,... | agent.py | MIT | 8,779 | 7ceb9b498acb0a92209da0ab25ccefeb4d5af2f9 | Python | py | |
# Copyright (c) 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 writing, so... | tests/test_label_to_contour.py | Apache-2.0 | 6,691 | fef40af08dc3f260551662ce7dfc8f0174c0243a | Python | py | |
import torch.nn.functional as F
def get_activation(activation):
if callable(activation):
return activation
if activation is None:
return lambda x: x
if not isinstance(activation, str):
raise ValueError("'activation' is expected a 'string', "
f"but got {ty... | graphgallery/nn/layers/pytorch/get_activation.py | MIT | 557 | 65277db519562c89b0a2604bae593db0d237d4e7 | Python | py | |
"""
A simple walkthrough of how to code a convolutional neural network (CNN)
using the PyTorch library. For demonstration we train it on the very
common MNIST dataset of handwritten digits. In this code we go through
how to create the network as well as initialize a loss function, optimizer,
check accuracy and more.
P... | ML/Pytorch/Basics/pytorch_simple_CNN.py | MIT | 3,939 | e93c7e81a614c68da72e85fe851cafd7eb62f45e | Python | py | |
import copy
import datetime
from termcolor import colored
from tqdm import tqdm
import torch
import numpy as np
import training.utils as utils
import torch.nn.functional as F
from data_utils import EnvSampler
from torch.utils.data import DataLoader
def train_loop(train_loaders, model, opt, ep, args):
stats = {}
... | src/training/dro.py | MIT | 6,356 | d706efce0798b3683c474c0f008c123b28991b50 | Python | py | |
from typing import Dict, Union
from overrides import overrides
import torch
from pytorch_pretrained_bert.modeling import BertModel
from allennlp.data.vocabulary import Vocabulary
from allennlp.models.model import Model
from allennlp.modules.token_embedders.bert_token_embedder import PretrainedBertModel
from allennlp.... | bert_mcq/bert_mcq_model.py | MIT | 5,899 | 4b47c7ede80aa8d9bc3649c5fe8c9c38ba6e31eb | Python | py | |
import os
import sys
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import random
import argparse
import numpy as np
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.callbacks.early_s... | seq2seq/lightning/run_lightning.py | Apache-2.0 | 2,377 | 7c2c718f600a91695d023549874d1e13257a302f | Python | py | |
# Copyright (c) 2021, 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... | examples/nlp/language_modeling/megatron_gpt_test.py | Apache-2.0 | 2,737 | 24770fac0e3caffad794f1c247a1b5605ee1b40d | Python | py | |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os
import unittest
import pytest
import sys
import copy
import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
import onnx
import torch
import torch.nn as nn
import torch.nn.functional as F
f... | onnxruntime/test/python/onnxruntime_test_ort_trainer.py | MIT | 32,836 | d77cd6569988155d23d178f4809e362748c382fb | Python | py | |
from test_pytorch_common import TestCase, run_tests
import torch
import torch.onnx
from torch.onnx import utils, OperatorExportTypes, TrainingMode, register_custom_op_symbolic
from torch.onnx.symbolic_helper import _set_opset_version, _set_operator_export_type, _set_onnx_shape_inference
import torch.utils.cpp_extensio... | test/onnx/test_utility_funs.py | Intel | 46,908 | 28c2979ff2c11a5479ae7936e8bc6f4e1283fc88 | Python | py | |
"""BackPACK implementation of operations used in ``vivit.optim``."""
from test.implementation.backpack import BackpackExtensions
from typing import Any, Dict, List, Optional
from backpack import backpack
from torch import Tensor
from vivit.optim import GramComputations
from vivit.optim.computations import BaseComput... | test/implementation/optim_backpack.py | MIT | 8,130 | fef46454a07edd64a06f07a4c983be324f82205b | Python | py | |
# -*- coding: utf-8 -*-
"""Implementation of NTN."""
from typing import Any, ClassVar, Mapping, Optional
import torch
from torch import nn
from ..base import EntityEmbeddingModel
from ...constants import DEFAULT_EMBEDDING_HPO_EMBEDDING_DIM_RANGE
from ...nn.emb import EmbeddingSpecification
from ...typing import Hin... | src/pykeen/models/unimodal/ntn.py | MIT | 9,068 | 24e9e3a663c5a3a9196cd71bebbf05ef0f663399 | Python | py | |
import numpy as np
import torch
import os
from a2c_ppo_acktr.arguments import get_args
from eval import plot_env, play_env, benchmark_env, robustness_test
from a2c_ppo_acktr.envs import make_vec_envs
from a2c_ppo_acktr.utils import get_vec_normalize
# initialize
args = get_args(is_train=False)
device = args.device
... | test.py | MIT | 1,670 | e666e635b5e09bbc8171e46a888164b46350d9d9 | Python | py | |
# Here are some basic settings.
# It could be overwritten if you want to specify
# some configs. However, please check the correspoding
# codes in loadopts.py.
import torchvision.transforms as T
import random
from PIL import ImageFilter
from .dict2obj import Config
class _GaussBlur:
def __init__(self... | src/config.py | MIT | 4,599 | 7cdc6ef5cf40767c00b505a23eef1f3b85bcf297 | Python | py | |
"""
This subpackage was borrowed from `torchvision`__.
__ https://github.com/pytorch/vision
"""
import numpy as np
import torch
def to_tensor(pic: np.ndarray) -> torch.Tensor:
"""Convert ``numpy.ndarray`` to tensor.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Retu... | catalyst/contrib/data/cv/transforms/torch.py | Apache-2.0 | 4,848 | 28c96b4370af007e0775ffbe5b4fc2c6397cc8a5 | Python | py | |
# ------------------------------------------------------------------------------
# Portions of this code are from
# CornerNet (https://github.com/princeton-vl/CornerNet)
# Copyright (c) 2018, University of Michigan
# Licensed under the BSD 3-Clause License
# -------------------------------------------------------------... | src/lib/models/losses.py | MIT | 9,560 | fde55191762a0a3881e847173726fee2f62798ea | Python | py | |
import torch
import torch.nn as nn
from utils.global_variables import Global
from model.layers import embedding, outputLayer
class Bilstm(nn.Module):
def __init__(self, config):
super(Bilstm, self).__init__()
self.config = config
self.embedding = embedding.Embedding(config)
self.rnn... | baselines/DMCNN_BiLSTM_(CRF)/model/Bilstm.py | MIT | 3,216 | 7027ab409aad8a583780e1e7411abc5cff25724b | Python | py | |
import typing
from typing import Optional, Any
import gym
import gym_minigrid.minigrid
import numpy as np
import torch
from babyai.utils.format import InstructionsPreprocessor
from gym_minigrid.minigrid import MiniGridEnv
from core.base_abstractions.sensor import Sensor, prepare_locals_for_super
from core.base_abstra... | plugins/minigrid_plugin/minigrid_sensors.py | MIT | 4,254 | fef4b53f026f0879f8be13166b110a80e812f024 | Python | py | |
import math
import torch
from kge import Config, Dataset
from kge.model.kge_model import KgeEmbedder, KgeModel, RelationalScorer
class RescalScorer(RelationalScorer):
r"""Implementation of the RESCAL KGE scorer."""
def __init__(self, config: Config, dataset: Dataset, configuration_key=None):
super(... | kge/model/rescal.py | MIT | 2,890 | 6523968f59ad19fbfed17525fa511d86b82d40be | Python | py | |
import datetime
import logging
import sys
from collections import defaultdict
from copy import deepcopy
import joblib
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torch.utils.data import Dataset, DataLoader
SCRIPT_NAME = "fully-connected-e... | section2/fully-connected-ewc-si.py | MIT | 10,343 | b2dba0c096f88cfeed91c6edcfcf07029e5a1652 | Python | py | |
import uuid
import pandas as pd
from typing import *
import torch
from torch import nn
from fastai.vision.all import *
idx2lbl = {0:"Cassava Bacterial Blight (CBB)",
1:"Cassava Brown Streak Disease (CBSD)",
2:"Cassava Green Mottle (CGM)",
3:"Cassava Mosaic Disease (CMD)",
4:"... | experiments/cassava_utils.py | MIT | 4,379 | 4b24e9f8c0bee8c072b645b7593e5cb0e2841dc3 | Python | py | |
#!/usr/bin/env python3
import os
import random
import unittest
from math import pi
import gpytorch
import torch
from gpytorch.likelihoods import GaussianLikelihood
from gpytorch.models import AbstractVariationalGP
from gpytorch.test.utils import least_used_cuda_device
from gpytorch.variational import CholeskyVariatio... | test/examples/test_batch_whitened_svgp_gp_regression.py | MIT | 5,726 | 1283244c859ef5b0de3356dabeb9ce082eea420c | Python | py | |
################
#
# train.py
#
# Trains a FasterRCNN-based detector for the wildlife classification project.
#
# Most of the actual training is deferred to the FasterRCNNTrainer class in trainer.py.
#
################
import os
import matplotlib
from tqdm import tqdm
import time
import numpy as np
import tensorboardX... | FasterRCNNDetection/train.py | MIT | 12,305 | 1ee2db742c81f13422d0e823d3b5e7c5431976c2 | Python | py | |
import torch
from torch import nn
from collections import OrderedDict
class ChannelAttention(nn.Module):
def __init__(self, in_channels: int, out_channels: int, n_layers: int = 1) -> None:
super(ChannelAttention, self).__init__()
self.attn = nn.Sequential()
channels = [in_channels] + [out_... | torchop/conv/bam.py | MIT | 3,983 | dfc7073b7625c3152f2a738c7fd9f615dfa2790e | Python | py | |
# https://github.com/pranz24/pytorch-soft-actor-critic
import argparse
import collections
import os
import random
import time
from distutils.util import strtobool
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from gym.spaces import Box
fro... | cleanrl/sac_continuous_action.py | MIT | 15,610 | 7cdf40bc9194bb28d147d2844345d781e28e8eec | Python | py | |
"""
Copyright 2019-present NAVER Corp.
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 writing, so... | loader.py | Apache-2.0 | 6,556 | e9b5506dfdcd0698147a05f6f9aeeec4c6df5196 | Python | py | |
import sys
import inspect
import torch
import torch.nn as nn
import torch.nn.functional as F
class Base(nn.Module):
def __init__(self):
super(Base, self).__init__()
self.criterion = nn.CrossEntropyLoss()
def _initialize_weights(self):
for m in self.modules():
if isinstance... | lth/models/utils.py | MIT | 1,964 | 10beea9bec77d85ce9f950652deb58f1f687ef3a | 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 logging
from typing import List, Optional
import torch
from torch import nn
from fairseq.models import (
FairseqEncoder,
Fair... | fairseq/models/text_to_speech/tts_transformer.py | MIT | 17,002 | fef56af6d533ea904cd433db71188742a5cb3e6a | Python | py | |
"""This module defines a configurable SSTDataset class."""
import pytreebank
import torch
from loguru import logger
from pytorch_transformers import BertTokenizer
from torch.utils.data import Dataset
logger.info("Loading the tokenizer")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
logger.info("Load... | bert-sentiment-master/bert_sentiment/data.py | MIT | 2,817 | 455db84f5a93700ebb13130d5be7cc51989d0897 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3,
stride=2, padding=1, bias=False) # out: 64 x 64 x ... | models/encoder.py | Apache-2.0 | 2,183 | ff94326934dbef58cc09b873621bbef51d000111 | Python | py | |
import numpy as np
import torch
from mpi4py import MPI
def setup_MPI():
try:
comm = MPI.COMM_WORLD
# Convert the Object to a Class so that it is possible to add attributes later
class A(MPI.Intracomm):
pass
comm = A(comm)
except:
comm = None
return comm
... | mpi4pytorch.py | MIT | 3,115 | d78ee7a8a4c2763a365c12f3eeab155b68c4e61c | Python | py | |
from typing import Dict, Union, Set
import logging
from overrides import overrides
import torch
from allennlp.data.fields.field import Field
from allennlp.data.vocabulary import Vocabulary
from allennlp.common.checks import ConfigurationError
logger = logging.getLogger(__name__)
class LabelField(Field[torch.Tensor... | allennlp/data/fields/label_field.py | Apache-2.0 | 4,798 | fef5f20539df46b868e215bf05dca727e3a90fa5 | Python | py | |
import sys
import torch
from os.path import join
from args import get_argparser, parse_args, get_aligner, get_bbox
if __name__ == '__main__':
parser = get_argparser()
parser.add_argument('--block_size',
help='batch size for regularization; batches are necessary to prevent large vectors from accumulating durin... | inference/_archive/render_regularized.py | MIT | 1,418 | fef601df096b623bb9fe2ce497201f5eaa321ffc | Python | py | |
import os
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import torch
from PIL import Image
matplotlib.use('Agg')
def render(data, mode, name, colour_map=None, offset=0, out_dir=""):
if isinstance(data, torch.Tensor):
if data.is_cuda:
data = data.cpu()
data = dat... | IIC/utils/segmentation/render.py | MIT | 4,559 | fef609e3f1a6ea42e4702f5580b0a58826356123 | Python | py | |
import sys
import inspect
import torch
from torch_geometric.utils import scatter_
special_args = [
'edge_index', 'edge_index_i', 'edge_index_j', 'size', 'size_i', 'size_j'
]
__size_error_msg__ = ('All tensors which should get mapped to the same source '
'or target nodes must be of same size ... | models/conv/message_passing.py | MIT | 3,697 | dfe115e35b3ee13fbb740c1d991510eb184d9a02 | 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... | flash/text/question_answering/input_transform.py | Apache-2.0 | 1,601 | fef63ccabd69bf636d6ea0c413aba74c6ea85068 | Python | py | |
import os
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.models import densenet121
from wilds.common.data_loaders import get_eval_loader
from wilds.datasets.camelyon17_data... | src/models/camelyon.py | MIT | 2,514 | 7c24239f0760e58c951884869b4cdee3d1b594ab | Python | py | |
import numpy as np
import torch
import random
from model import metric
from base.base_trainer import base_trainer
from utils import h36m_utils, visualization
from utils.logger import Logger
class fk_trainer(base_trainer):
def __init__(self, model, resume, config, data_loader, test_data_loader):
super(fk_... | trainer/trainer.py | BSD-2-Clause | 6,074 | 804c58362514a841984c73a7cb203cfe1c1869a9 | Python | py | |
import logging
import os
import os.path as osp
import sys
import time
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
from ray import tune
from torch.utils.data import Subset
from cords.utils.config_utils import load_config_data
from cords.utils.data.data_utils import WeightedSubset
fr... | text_train_sl.py | MIT | 32,321 | fef66a944eb97da63ad4b3bd46600a038c0b2f81 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, inchannel, outchannel, stride=1):
super(ResidualBlock, self).__init__()
self.left = nn.Sequential(
nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride,... | nn.py | MIT | 2,369 | e964e9cfadff2cec35b6b9ec2e264ca2906e722e | 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/helpers/reference_metrics.py | Apache-2.0 | 10,818 | fef679e492487f89509949cb6abd3d571618e6d9 | 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/scripts/training/qq-su_sac.py | DOC,Zlib,BSD-3-Clause | 4,364 | e9cc5c266db6131282489c4871ca67d6722ea801 | Python | py | |
# 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 required by appli... | nemo/collections/asr/parts/rnnt_beam_decoding.py | Apache-2.0 | 33,319 | 24998391372600b5be44595acb8fcfb2a06a51ad | Python | py | |
# dataloader for KITTI / when training & testing F-Net and MaGNet
import os
import random
import glob
import numpy as np
import torch
import torch.utils.data.distributed
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import pykitti
class KittiLoader(object... | data/dataloader_kitti.py | MIT | 8,475 | e90fd383baf30a43c9c65dd2c49d510af0162236 | Python | py | |
import os
import re
import sys
sys.path.append('.')
import cv2
import math
import time
import scipy
import argparse
import matplotlib
import numpy as np
import pylab as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from sci... | demo/picture_demo.py | MIT | 1,844 | d7332f0b753589667bf18b78a5d566ff12e25f67 | Python | py | |
"""
****************** COPYRIGHT AND CONFIDENTIALITY INFORMATION ******************
Copyright (c) 2019 [Thomson Licensing]
All Rights Reserved
This program contains proprietary information which is a trade secret/business \
secret of [Thomson Licensing] and is protected, even if unpublished, under \
applicable Copyrigh... | sodeep_master/utils.py | BSD-3-Clause | 3,880 | fef6acbefe42eb7c2c21a287c479747eb3cd8469 | 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 math
from typing import Dict, Optional, Tuple
import logging
import torch
import torch.nn.functional as F
from fairseq import utils
fro... | fairseq/modules/multihead_attention.py | MIT | 51,684 | 4554996089516cf659f896b7914921e4f82442dd | Python | py | |
import torch
from transformers import BertPreTrainedModel, BertModel
class BertForMultiLabelSequenceClassification(BertPreTrainedModel):
"""BERT model for Multilabel classification.
This module is composed of the BERT model with a classifier (linear) layer on top of
the pooled output.
Params:
... | src/model.py | MIT | 2,851 | 52b631efcce4a8770336cb33a111eae5ad6564eb | Python | py | |
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import torch
from torchvision import datasets, transforms, models
from torch import nn , optim
from collections import OrderedDict
def process_image(image):
''' Scales, crops, and normalizes a PIL image for a PyTorch model,
returns an... | utils.py | MIT | 3,264 | 7c8976e38ab5d192b911752ffac9e33516d08153 | Python | py | |
import argparse
import glob
import logging
import os
import time
import torch
from torch.utils.data import DataLoader
from pathlib import Path
from transformer_base import BaseTransformer, add_generic_args, setup_trainer, get_linear_schedule_with_warmup
from utils import SummarizationDataset
logger = logging.getLogg... | examples/summarization/bart/run_bart_sum.py | Apache-2.0 | 8,237 | fef6d6d5e90c6efd674f922ce2906916477c5b7b | Python | py | |
""""
TextCNN模型训练
"""
import os
import sys
import time
import torch
from fastNLP import Const
from fastNLP.embeddings import BertEmbedding
from fastNLP.models import BiLSTMCRF
from fastNLP import SpanFPreRecMetric
from fastNLP import Trainer
from fastNLP import LossInForward
from torch.optim import Adam
from fastNLP i... | ner/weibo_NER/train_bilstm_crf_bert_embed.py | Apache-2.0 | 3,247 | 1e65c755c4e307259c55197aa105acee12c0ff9e | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorNetwork(nn.Module):
def __init__(self, input_dims, layer1_dims, layer2_dims, action_dim):
super(ActorNetwork, self).__init__()
self.fc1 = nn.Linear(input_dims, layer1_dims)
self.fc2 = nn.Linear(layer1_dims, layer... | network.py | MIT | 1,608 | fef7118e34fba83b0a9787bf9d41e5e032672f1d | Python | py | |
import unittest
import torch
from torch import nn
import sys
import kgbench as kg
from kgbench import tic, toc
class TestLoad(unittest.TestCase):
stats = {
'amfull' : {
'num edges' : 5988321,
'num relations' : 133,
'num entities' : 1666764,
... | tests/loadtests.py | MIT | 2,637 | 1e026134dd1c5842a1d4216f13130db88e1e5711 | Python | py | |
import mmcv
import numpy as np
import pycocotools.mask as mask_util
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from mmdet.core import auto_fp16, force_fp32, mask_target
from mmdet.ops import ConvModule, build_upsample_layer
from mmdet.ops.carafe import CARAFEPack
from ..builder import ... | mmdet/models/mask_heads/fcn_mask_head.py | Apache-2.0 | 8,248 | fef7603aba66c9bf78c5a1dabcc4daa6ea940eac | Python | py | |
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: dev_nb/SparseImageWarp.ipynb
import torch
import numpy as np
def sparse_image_warp(img_tensor,
source_control_point_locations,
... | exp.py | Apache-2.0 | 14,965 | fef76a35be1e54fbea9be99f8c3dc0e91be7b922 | Python | py | |
# Defined in Section 4.6.8
import sys
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset
from tqdm.auto import tqdm
from nlptoolkit.data.embeddings.pos_encding import PositionalEncoding
from nlptool... | examples/sentence_polarity/transformer_sent_polarity.py | Apache-2.0 | 4,650 | fef776f2a479fa169b887920839cb544c237f4e4 | Python | py | |
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... | train.py | BSD-Source-Code | 16,405 | d78f9f7649f2816a248e55d6639a7897fefb99bb | Python | py | |
# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
# model file: example-models/misc/irt/irt2.stan
import torch
import pyro
import pyro.distributions as dist
def init_vector(name, dims=None):
return pyro.sample(name, dist.Normal(torch.zeros(dims), 0.2 * torch.ones(dims)).to_event(... | pyro_models/misc/irt2.py | Apache-2.0 | 1,592 | fef78c6d6534cad2acc151d09a7194669e09ed83 | Python | py | |
import sys
sys.path.insert(0, "/home/igor/code/mestrado/REDN/opennre/framework")
sys.path.insert(0, "/home/igor/code/mestrado/REDN/opennre/dataset")
import torch
from ..dataset.utils import is_normal, is_multi_label, is_over_lapping
from .data_loader import SentenceREDataset
import random
import pandas as pd
CORRECT... | opennre/framework/f1_metric.py | MIT | 9,072 | a963484d23b6684cf9f6c6a6a16eee931f3316a0 | Python | py | |
import argparse
import sys
import os
import copy
import datetime
import socket
import cv2
import torchvision
from common.utils.yamlConfig import YamlConfig
from common.utils.logger import CreateLogger
import numpy as np
import pprint
import torch
from fvcore.nn.precise_bn import get_bn_modules, update_bn_stats
from ... | azure/run.py | Apache-2.0 | 4,892 | 7c1d2e4b3387f6e53867584f5afcdc05bc091a89 | Python | py | |
import torch
from torch.utils.data import DataLoader, Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from warmup_scheduler import GradualWarmupScheduler
import math
from torch.nn.modules.loss import _WeightedLoss
f... | src/utils.py | Apache-2.0 | 11,962 | 29f6aa16d9ff667e168e0d404137aa61c7ad3f3c | Python | py | |
# -*- coding: utf-8 -*-
"""
Torch version of hyperparams
TODO:
[ ] - need to extract relavent params from loaders
[ ] - need to extract relavent params from datasets
[ ] - ensure monitor is handled gracefully
[ ] - prevent non-relevant params from being used in the hash
CommandLine:
python ~/code/... | netharn/hyperparams.py | Apache-2.0 | 41,870 | fef7b35d848c5ae6ca43f38de0997765c39690ec | Python | py | |
# -*- coding: utf-8 -*-
# @Author: Jie Yang
# @Date: 2017-10-17 16:47:32
# @Last Modified by: Jie Yang, Contact: jieynlp@gmail.com
# @Last Modified time: 2019-02-13 11:49:38
from __future__ import print_function
from __future__ import absolute_import
import torch
import torch.nn as nn
import torch.nn.functiona... | model/.ipynb_checkpoints/seqlabel-checkpoint.py | Apache-2.0 | 5,275 | fef7c904b250d642a5cab459ebece245c755536f | Python | py | |
# help function
from transfer_learning import NeuralNet
from Functions.dataset_loader import data_loader, get_descriptors, one_filter, data_scaler
# modules
import torch
import torch.nn as nn
import torch.optim as optim
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_s... | Batch_runs/batch_run_32.py | MIT | 4,550 | fef7efed21c01b1307e958a3b4c73aac176078c5 | Python | py | |
# Copyright 2020 trueto
# 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 writi... | bertology_sklearn/models/focal_loss.py | Apache-2.0 | 2,810 | f9bba445c749a13203e51bf07761a0343d52ff59 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1. / np.sqrt(fan_in)
return (-lim, lim)
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed=0,... | p2_continuous-control/ddpg_model.py | MIT | 3,465 | fef7f86430641d513caa6017da27ef99e1ca94d3 | Python | py | |
import argparse
import math
import subprocess
import tempfile
from pathlib import Path
import numpy as np
import pretty_midi
import soundfile
import torch
import torch.nn.functional as F
from mir_eval.util import midi_to_hz
from constants import HOP_SIZE, MIN_MIDI, SAMPLE_RATE
from evaluate import extract_notes, save... | hw3/transcribe.py | MIT | 3,406 | 24a2d7d0fd59952b34007e2da0430ae64d63ea92 | Python | py | |
import numpy as np
import matplotlib.pyplot as plt
import cv2 as cv
from sklearn.metrics import f1_score
from tqdm import tqdm
from PIL import Image
from imutils.paths import list_images
import torch
import torch.nn as nn
from torchvision import transforms
from torch.utils.data import Dataset
import copy, random
impo... | pv_vision/defective_cell_detection/model/cnn_train_val.py | BSD-3-Clause | 9,538 | 80329e391d0e98372557b60bcc2187559bd77a51 | Python | py | |
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import os
class Linear_QNet(nn.Module):
def __init__(self,input_size,hidden_size,output_size):
super().__init__()
self.linear1 = nn.Linear(input_size,hidden_size).cpu()
self.linear2 = nn.Linear(hi... | model.py | MIT | 3,190 | 802313aa63ab185a25dd695c992543d28bb0800d | Python | py | |
import autograd.numpy as np
import tensorflow as tf
import torch
import pymanopt
from examples._tools import ExampleRunner
from pymanopt.manifolds import Sphere
from pymanopt.optimizers import SteepestDescent
SUPPORTED_BACKENDS = ("autograd", "numpy", "pytorch", "tensorflow")
def create_cost_and_derivates(manifold... | examples/dominant_eigenvector.py | BSD-3-Clause | 2,917 | fef814385deadc08a6485ee68437ad3e9e4aa1d0 | Python | py | |
"""
Copyright (c) Facebook, Inc. and its affiliates.
"""
import pickle
import numpy as np
import torch
from torch.utils import data as tds
from copy import deepcopy
def underdirt(schematic, labels=None, max_shift=0, nothing_id=0):
# todo fancier dirt!
# FIXME!!!! label as ground where appropriate
shift =... | droidlet/perception/craftassist/voxel_models/detection-transformer/datasets/house.py | MIT | 13,556 | b2fc895b66aae31d0e6b0676ef56a1c52478b2de | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
from torchvision.models.utils import load_state_dict_from_url
# Inception weights ported to Pytorch from
# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
FID_WEIGHTS_URL = ('https://github.c... | pytorch_gan_metrics/inception.py | Apache-2.0 | 12,415 | fef82562454e8718a9c5bc9eaed7d108dfee98ea | Python | py | |
import torch
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import remove_self_loops, add_self_loops
from ..inits import reset
def point_pair_features(pos_i, pos_j, norm_i, norm_j):
def get_angle(v1, v2):
return torch.atan2(
torch.cross(v1, v2, dim=1).norm(p=2, ... | torch_geometric/nn/conv/ppf_conv.py | MIT | 3,396 | fef833f447a07c652f9b0a170d9978ce0c402a8d | Python | py | |
import torch
def get_mask(attention_mask: torch.Tensor, # (b, seq)
ng_token_mask: torch.Tensor, # (b, seq, case, seq)
) -> torch.Tensor: # (b, seq, case, seq)
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) # (b, 1, 1, seq)
return extended_attention... | src/model/sub/mask.py | MIT | 342 | fef856039d748fcef87e6370d0cdbcc94e24155b | Python | py | |
import os
import random
from skimage import io
import torch
class DataLoader:
def __init__(self, root, phase, batch_size):
self.batch_size = batch_size
self.dataset_sizes = {'train': 800 * 2, 'val': 200 * 2}
self.root = root
self.phase = phase
self.path = os.path.join(r... | src/data_loader.py | Apache-2.0 | 5,651 | fef85ba25cd1e0ed97fe8bc3ccf6bdb24560ae5d | Python | py | |
from mylib.dataloader import DataLoader
import torch
from argparse import ArgumentParser
# Arguments
parser = ArgumentParser(description='Testing Model')
parser.add_argument('--model', type=str, default='models/model_cuda.pkl', help='Path of Previous Trained Model')
parser.add_argument('--imgs', type=str, default='d... | evaluation_cuda.py | MIT | 1,659 | 52b96849d9f5b3452876b6f2ad74fb46cc951451 | Python | py | |
#!/usr/bin/env python3
# Copyright 2021, Ludwig Kürzinger, Takaaki Saeki
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Perform CTC Re-Segmentation on japanese dataset.
Either start this program as a script or from the interactive python REPL.
# Recommended model:
# Japanese Transformer Model by Shinj... | scripts/align.py | Apache-2.0 | 17,049 | a9972578a25635e964e0bd2966a6cb22e764c2cd | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .bounding_box import BoxList
from maskrcnn.layers import nms as _box_nms
def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="score"):
"""
Performs non-maximum suppression on a boxlist, with scores speci... | maskrcnn/structures/boxlist_ops.py | MIT | 3,695 | fef8e7202b5b06191ddb2f367cee6444eb55b377 | Python | py | |
import copy
import json
import os
from pathlib import Path
import pickle
import shutil
import time
import re
import fire
import numpy as np
import torch
from google.protobuf import text_format
import second.data.kitti_common as kitti
import torchplus
from second.builder import target_assigner_builder, voxel_builder
f... | second/pytorch/train.py | MIT | 26,632 | fef8e89f531a08d4b5c986bf7b8d715d2bb9c763 | Python | py | |
import torch
from torch import nn
from ..backbone import *
class SegBaseModule(nn.Module):
def __init__(self, num_classes=1):
super(SegBaseModule, self).__init__()
self.num_classes = num_classes
def train_backbone(self):
self.backbone.train()
self.backbone.requires_grad_(True)... | blseg/model/base.py | MIT | 4,321 | fef903b7d68f34eac65ea0a85b107702e9d5173a | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
TensorRT Object detection.
Copyright (c) 2021 Nobuo Tsukamoto
This software is released under the MIT License.
See the LICENSE file in the project root for more information.
"""
import argparse
import colorsys
import os
import random
import time
impo... | python/segmantation/torchvision_segmantation_capture.py | MIT | 5,196 | a9698c0f31536742cdb0e278b456c88f9d00fbda | Python | py | |
# Copyright 2019 Ondrej Skopek.
#
# 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 writing... | mt/mvae/distributions/wrapped_distributions.py | Apache-2.0 | 1,692 | fef94cf7641426286f25604007ddceb57f310a48 | Python | py | |
# This file contains Att2in2, AdaAtt, AdaAttMO, TopDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning
... | models/TransformerModel.py | MIT | 23,140 | fef987e8a15a2b7d87732eaee8a7e2de88264d92 | Python | py | |
from fixtures import * # noqa: F401,F403
from lightning import RpcError
from utils import DEVELOPER, wait_for, only_one, sync_blockheight
import copy
import pytest
import random
import string
import time
import unittest
def test_pay(node_factory):
l1, l2 = node_factory.line_graph(2)
inv = l2.rpc.invoice(1... | tests/test_pay.py | MIT | 36,771 | 03db1708633c819ff9cdd7f661f9437c3de5c69e | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from seq2seq import utils
from seq2seq.models import Seq2SeqModel, Seq2SeqEncoder, Seq2SeqDecoder
from seq2seq.models import register_model, register_model_architecture
@register_model('lstm')
class LSTMModel(Seq2SeqModel):
""" Defines the sequen... | seq2seq/models/lstm.py | MIT | 17,558 | fef99e39ca9a9233c225516f7633494483e8ee1a | Python | py | |
import os
import torch
import torch.utils.data
from torchvision import transforms
import torch.nn.functional as F
from tqdm import tqdm
def test(dataloader, model, device, source_target, epoch = 0, tb_writer=None):
model = model.eval()
with torch.no_grad():
model = model.to(device)
target_iter = tqdm(enume... | vlcs-2sources/baseline_test_2sources.py | MIT | 1,244 | fef9a72b408a5b8927592824343c8181dbe5fbcf | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
... | save_load_17/model.py | Apache-2.0 | 771 | 129a125e6a468e4329e98c3d16c7b9864b4b2bb2 | Python | py | |
# -*- coding: utf-8 -*-
# @Author : William
# @Project : TextGAN-william
# @FileName : instructor.py
# @Time : Created at 2019-04-25
# @Blog : http://zhiweil.ml/
# @Description :
# Copyrights (C) 2018. All Rights Reserved.
import numpy as np
import os
import torch
import torch.nn as nn... | instructor/oracle_data/instructor.py | MIT | 9,793 | 70817174fabd852a1febae687220ff75d1502358 | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Callable, List, Optional, Union
import torch
from ..dist_utils import master_only
from .hook import HOOKS, Hook
@HOOKS.register_module()
class ProfilerHook(Hook):
"""Profiler to analyze performance during training.
PyTorch P... | mmcv/runner/hooks/profiler.py | Apache-2.0 | 8,059 | fef9adc13927df463c7ad37bc4c2f0b89c90a1f1 | Python | py | |
import os
import sys
# Append TensorBreeze root directory to sys
this_dir = os.path.dirname(os.path.realpath(__file__))
root_dir = os.path.abspath(os.path.join(this_dir, os.pardir))
sys.path.append(root_dir)
# import pytest
import numpy as np
import torch
import tensorflow as tf
from torchvision import models as tor... | test/test_resnet.py | MIT | 1,432 | 12782092080052a939a20ec7e4c9a66c3388aba6 | Python | py | |
import math
import os
import pytest
import torch
from torchvision.io import _HAS_GPU_VIDEO_DECODER, VideoReader
try:
import av
except ImportError:
av = None
VIDEO_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "videos")
@pytest.mark.skipif(_HAS_GPU_VIDEO_DECODER is False, reason="... | test/test_video_gpu_decoder.py | BSD-3-Clause | 3,658 | fef9bc6dc9eba9493002ae356d055d6cdbbf8846 | Python | py | |
from fusions.robotics.sensor_fusion import SensorFusionSelfSupervised, roboticsConcat
from torchvision import transforms
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data import DataLoader
from datasets.robotics.data_loader import get_data
from robotics_utils import set_seeds
from training_... | deprecated/dataloaders/deprecated_examples/robotics/unimodal_force-pred.py | MIT | 2,732 | fef9bcaf155be8d78600ebb11315a5efe7448500 | Python | py | |
import pytorch_lightning as pl
import torch
from torch import nn as nn
from models.mdn import mdn
class LitMDN(pl.LightningModule):
def __init__(self, in_features, out_features, hidden_size, gaussians, noise_regularization=0.0):
super().__init__()
self.save_hyperparameters()
self.noise_r... | models/mdn/lit_mdn.py | MIT | 2,127 | d7fa4ed39793178e26c0eb16e34bbe9f44bed3a2 | Python | py |
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