version stringclasses 24
values | code stringlengths 396 135k | apis list | full_version stringlengths 1 6 | repo_name stringlengths 6 64 | hexsha stringlengths 40 40 |
|---|---|---|---|---|---|
1.1 | import configargparse as cfargparse
import os
import torch
import onmt.opts as opts
from onmt.utils.logging import logger
class ArgumentParser(cfargparse.ArgumentParser):
def __init__(
self,
config_file_parser_class=cfargparse.YAMLConfigFileParser,
formatter_clas... | [
"torch.cuda.is_available"
] | 1.1 | ACL2020-Submission/ACL2020 | 2a3d6e26d22c650cad823c68b65ee315aa1fe22c |
1.4 | import time
from typing import Optional, Dict
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from torch.nn.utils.rnn import pack_padded_sequence
from utils import TensorboardWriter, AverageMeter, save_checkpoint, accuracy, \
clip_gradient, adjust_learning_rate
from metrics import ... | [
"torch.no_grad",
"torch.nn.utils.rnn.pack_padded_sequence",
"torch.max"
] | 1.4.0 | Renovamen/Image-Captioning | de8d4f553a22e967fa56a01d5b4a2206b9431771 |
0.3 | """
Baseline CNN, losss function and metrics
Also customizes knowledge distillation (KD) loss function here
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
"""
This is t... | [
"torch.nn.Linear",
"torch.nn.BatchNorm2d",
"torch.nn.functional.log_softmax",
"torch.nn.Conv2d",
"torch.nn.BatchNorm1d",
"torch.nn.functional.cross_entropy",
"torch.nn.KLDivLoss",
"torch.nn.functional.softmax",
"torch.nn.functional.max_pool2d",
"torch.nn.CrossEntropyLoss"
] | 0.3.0 | eungbean/knowledge-distillation-cifar10 | 683379804c8724d097a845cee85f130b6767dbd7 |
1.0 | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
class TripleSoftmaxLoss(nn.Module):
def __init__(self,
model: SentenceTransformer,
sentence_embedding_dimension: int... | [
"torch.nn.Linear",
"torch.cosine_similarity",
"torch.cat",
"torch.nn.ReLU",
"torch.abs",
"torch.nn.CrossEntropyLoss"
] | 1.0.1 | jaimeenahn/COVID-sentence-bert | 2f47d116f7d9b774946fbf3c0724b721d1b88225 |
1.6 |
import os
import sys
sys.path.append(os.getcwd())
import numpy as np
import torch
import flow
from utils import cdfDiscreteLogitstic, cdfMixDiscreteLogistic
from utils import logDiscreteLogistic, logMixDiscreteLogistic
nbins = 4096
_bins = torch.arange(-nbins // 2, nbins // 2).reshape(-1, 1, 1, 1, 1)
decimal = flow.... | [
"torch.arange",
"torch.tensor"
] | 1.6.0 | li012589/NeuralWavelet | 6e593ded5cb4ae80579cbf56eb9c346d808669cb |
1.6 | # Source: https://gist.github.com/redknightlois/c4023d393eb8f92bb44b2ab582d7ec20
from torch.optim.optimizer import Optimizer
import torch
import math
class Ralamb(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-4):
defaults = dict(lr=lr, betas=betas, eps=eps... | [
"torch.zeros_like"
] | 1.6.0 | achaiah/pywick | 9d663faf0c1660a9b8359a6472c164f658dfc8cb |
1.6 | """ PyTorch MADGRAD optimizer
MADGRAD: https://arxiv.org/abs/2101.11075
Code from: https://github.com/facebookresearch/madgrad
"""
# 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... | [
"torch.zeros_like",
"torch.no_grad",
"torch.clone",
"torch.enable_grad"
] | 1.6.0 | achaiah/pywick | 9d663faf0c1660a9b8359a6472c164f658dfc8cb |
1.4 | # 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... | [
"torch.isfinite"
] | 1.4 | neggert/pytorch-lightning | 8208c330eb1a4e8cca243ee525882854dd366921 |
1.4 | import os
import sys
import numpy as np
import random
import math
from PIL import Image, ImageOps, ImageFilter
import torch
import torch.utils.data as data
import torchvision.transforms as transform
from .base import BaseDataset
class NYUv2Segmentation(BaseDataset):
BASE_DIR = 'nyuv2'
NUM_CLAS... | [
"torch.from_numpy"
] | 1.4.0 | etmwb/cvsegmentation | c283a79f4cf4e78d057f598944b1c252f6533f00 |
1.8 | from __future__ import absolute_import
import os
from collections import namedtuple
import time
from torch.nn import functional as F
from baseline.fast_rcnn.model.utils.creator_tool import AnchorTargetCreator, ProposalTargetCreator
from torch import nn
import torch as t
from baseline.fast_rcnn.utils import array_tool ... | [
"torch.zeros",
"torch.device",
"torch.arange",
"torch.save",
"torch.load",
"torch.nn.CrossEntropyLoss"
] | 1.8.1 | ITMO-NSS-team/LightObjRecEnsembler | 1375400f0a681aefdd3ab484e828257fd7aed318 |
1.6 | import copy
from functools import wraps
import numpy as np
import wandb
import torchvision
import torch
import torch.nn.functional as F
from kornia import enhance, filters
from torchvision.transforms import RandomApply, RandomChoice
from atariari.methods.utils import EarlyStopping
from torch import nn
from torch.u... | [
"torch.nn.Linear",
"torch.nn.functional.normalize",
"torch.rand",
"torch.stack",
"torch.no_grad",
"torch.nn.ReLU",
"torch.nn.BatchNorm1d",
"torch.cuda.is_available"
] | 1.6 | mariodoebler/byol-pytorch | 4c1b6d27d86e0a9a39ecef6f6888038355943cd0 |
1.4 | # Copyright 2020 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, s... | [
"torch.load"
] | 1.4 | BRAINSia/MONAI | 04e1c345fc840f5a1b6504ee5857d5a9feb27d84 |
0.6 | import torch
import torch.nn as nn
import torch.nn.functional as F
from segmentation_models_pytorch.base import modules as md
class DecoderBlock(nn.Module):
def __init__(
self,
in_channels,
skip_channels,
out_channels,
use_batchnorm=True,
attention_type=None,
)... | [
"torch.nn.functional.interpolate",
"torch.cat",
"torch.nn.Identity",
"torch.nn.ModuleList"
] | 0.6.3 | navivokaj/segmentation_models.pytorch | 5dbb5f6733515097cecc93f078c09e59ccbeb0c0 |
1.6 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base import Loss
class AdaCos(Loss):
"""PyTorch implementation of AdaCos. See Ref[1] for paper
This implementation is different from the most open-source implementations in following ways:
1) expects raw logits of size ... | [
"torch.zeros",
"torch.cos",
"torch.nn.functional.normalize",
"torch.no_grad",
"torch.nn.init.xavier_uniform_",
"torch.zeros_like"
] | 1.6 | YevheniiSemendiak/pytorch-tools | 11f895ac7af796ca786a3d94bb46de70d7fce87a |
1.5 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from detr.models.backbone import Backbone, Joiner
from detr.models.detr import DETR, PostProcess
from detr.models.position_encoding import PositionEmbeddingSine
from detr.models.segmentation import DETRsegm, PostProcessPanoptic
from de... | [
"torch.hub.load_state_dict_from_url"
] | 1.5.0 | kcetskcaz/detr_package | 0f5cad16c72ec37d7b596d37e12dc32cfb5ef6aa |
1.5 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DETR model and criterion classes.
"""
import torch
import torch.nn.functional as F
from torch import nn
from detr.util import box_ops
from detr.util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get... | [
"torch.nn.Linear",
"torch.device",
"torch.cat",
"torch.stack",
"torch.nn.functional.l1_loss",
"torch.no_grad",
"torch.ones",
"torch.full_like",
"torch.nn.Conv2d",
"torch.full",
"torch.distributed.all_reduce",
"torch.nn.functional.softmax",
"torch.nn.Embedding"
] | 1.5.0 | kcetskcaz/detr_package | 0f5cad16c72ec37d7b596d37e12dc32cfb5ef6aa |
1.4 | import logging
import torch.nn as nn
from . import arch as archs
logger = logging.getLogger()
def build_model(cfg_model):
if cfg_model.get('pretrained', False):
info = "=> building pre-trained model {}".format(cfg_model['arch'])
model = archs.__dict__[cfg_model.arch](pretrained=True)
in_... | [
"torch.nn.Linear"
] | 1.4 | ChaseMonsterAway/vedacls | 91657f688dcaf3f9f4c58eb40a8f5c8f34a4bd73 |
1.10 | import os
import logging
from typing import Dict, Union
from datetime import timedelta
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
import mlflow
import torch
import pytorch_lightning as pl
import pprint
pp = pprint.PrettyPrinter(indent=4)
from arnet i... | [
"torch.cat",
"torch.stack",
"torch.isnan",
"torch.tensor"
] | 1.10.0 | ZeyuSun/flare-prediction-smarp | ad60163eb83b47ba39e898beb387031d349e2ed6 |
1.4 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as functional
from .noisy_linear import NoisyLinear
class Enet(nn.Module):
def __init__(self) -> None:
super(Enet, self).__init__()
return
def get_max_action(self, observation: torch.Tensor) -> int:
"""
... | [
"torch.nn.Linear",
"torch.nn.init.xavier_uniform_",
"torch.argmax"
] | 1.4.0 | hbutsuak95/iv_rl | 0f72a8f077a238237027ea96b7d1160c35ac9959 |
1.1 | """Trains a hypergraph machine on MNIST and generates Figure 1 panels b and c
of Discrete and continuous learning machines
"""
import numpy as np
import torch
import torch.nn.functional as F
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from hypergraph_machines.hypergraph_mach... | [
"torch.device"
] | 1.1.0 | Veos-Digital/hypergraph_machines | 0d24cd89766c45c6c1ffb2967438ef82288a5d3c |
1.4 | import time
import copy
import pickle
import warnings
import numpy as np
import scipy.sparse as sp
import torch
import torch.nn.functional as F
from sklearn.metrics import roc_auc_score, average_precision_score, precision_recall_curve, auc
def sparse_to_tuple(sparse_mx):
if not sp.isspmatrix_coo(sparse_mx):
... | [
"torch.nn.functional.binary_cross_entropy_with_logits",
"torch.sigmoid",
"torch.no_grad",
"torch.exp"
] | 1.4.0 | coodest/GAug | ef6ab307e3dfd3e9e0a653d385dc1f41963f9ba8 |
1.1 | import torch
import torch.nn as nn
import torch.nn.functional as F
from starter_code.modules.networks import MLP, MinigridCNN
from mnist.embedded_mnist import MNIST_CNN
class SimpleValueFn(nn.Module):
def __init__(self, state_dim, hdim):
super(SimpleValueFn, self).__init__()
self.value_net = MLP(d... | [
"torch.nn.Linear"
] | 1.1.0 | mbchang/societal-decision-making | 23fd6de4df33f985d360330a9d5a2c29faeb8e52 |
1.2 | # MIT License
#
# Copyright (C) IBM Corporation 2019
#
# 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, copy, modify, merge... | [
"torch.cuda.is_available",
"torch.nn.CrossEntropyLoss",
"torch.load"
] | 1.2.0 | virkt25/adversarial-robustness-toolbox | 3cfa6de196cb32a3efafab2ff6bbf44247c9ddbd |
1.8 | import numpy as np
from torch import nn
import torch.optim as optim
import torch
import matplotlib.pyplot as plt
import pandas as pd
import data_loader as dl
import time
import copy
import utility
import yaml
import trainer
from PIL import Image
from os import path
Image.MAX_IMAGE_PIXELS = None
from scipy.io import sav... | [
"torch.optim.SGD",
"torch.cuda.is_available",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.nn.CrossEntropyLoss"
] | 1.8.1 | micqu/hotel-challenge | 9373d5bd69a48e22b043b1410a57ec051f63dd45 |
1.5 | """
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... | [
"torch.zeros",
"torch.nn.BatchNorm2d",
"torch.ones",
"torch.nn.Conv2d",
"torch.load",
"torch.empty"
] | 1.5.0 | evgeniya-egupova/nncf | 39a3c5b2e5cc7d33723154d2e622d4d7882a99a4 |
1.5 | """
Copyright (c) 2019 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 writin... | [
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.BatchNorm2d",
"torch.load"
] | 1.5.0 | evgeniya-egupova/nncf | 39a3c5b2e5cc7d33723154d2e622d4d7882a99a4 |
1.7 | # 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... | [
"torch.load"
] | 1.7 | lemairecarl/pytorch-lightning | 85304d4672a9ed24a16f7f5b2abaa34148ab86f4 |
1.4 | #!/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 contextlib
import copy
import enum
import json
import logging
import math
import multiprocessing as mp
import ti... | [
"torch.cuda.amp.autocast",
"torch.nn.SyncBatchNorm.convert_sync_batchnorm",
"torch.no_grad",
"torch.enable_grad",
"torch.cuda.device_count",
"torch.cuda.is_available",
"torch.tensor",
"torch.cuda.amp.GradScaler",
"torch.distributed.broadcast"
] | 1.4 | shinianzhihou/ClassyVision | b3f714ef94275b3e9753ab3f3c8256cb852b96fc |
1.1 | #!/usr/bin/env python
""" Translator Class and builder """
from __future__ import print_function
import codecs
import os
import math
import torch
from tensorboardX import SummaryWriter
from others.utils import rouge_results_to_str, test_rouge, tile
from translate.beam import GNMTGlobalScorer
def build_predictor(ar... | [
"torch.no_grad",
"torch.full",
"torch.arange"
] | 1.1.0 | SebastianVeile/PreSumm | 780c340e04fd5911badb4a8b2af2284c5cdbb8b5 |
0.1 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Callable, List, NamedTuple, Tuple
import numpy as np
import plotly.graph_objs as go
import torch
from torch import Tenso... | [
"torch.var"
] | 0.1.0 | facebookresearch/beanmachine | 225114d9964b90c3a49adddc4387b4a47d1b4262 |
0.1 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import inspect
import math
import operator
from types import MethodType
from typing import Any, Callable, Dict, List, NoReturn, Optional, S... | [
"torch.__dict__.items",
"torch.tensor"
] | 0.1.0 | facebookresearch/beanmachine | 225114d9964b90c3a49adddc4387b4a47d1b4262 |
1.7 | from collections import OrderedDict
import torch
import torch.nn as nn
import numpy as np
from torch.nn import functional as F
from .SubLayers import MultiHeadAttention, PositionwiseFeedForward
class FFTBlock(torch.nn.Module):
"""FFT Block"""
def __init__(self, d_model, n_head, d_k, d_v, d_inner, kernel_si... | [
"torch.nn.BatchNorm1d",
"torch.nn.ModuleList",
"torch.nn.Conv1d"
] | 1.7.1 | richarai9/FastSpeech2 | d044c00a44cbfa3e1c89a22c8285a374a00e27a9 |
1.1 | import argparse
import os
import os.path as osp
import shutil
import tempfile
import json
import pdb
import numpy as np
import pickle
import pandas as pd
import mmcv
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, l... | [
"torch.norm",
"torch.no_grad",
"torch.pow",
"torch.full",
"torch.distributed.barrier",
"torch.distributed.broadcast"
] | 1.1 | ydiller/BalancedGroupSoftmax | 6fecf9fbb8ed1f54540787188e212ab39cd2b501 |
1.3 | """A training script of TD3 on OpenAI Gym Mujoco environments.
This script follows the settings of http://arxiv.org/abs/1802.09477 as much
as possible.
"""
import argparse
import logging
import sys
import gym
import gym.wrappers
import numpy as np
import torch
from torch import nn
import pfrl
from pfrl import exper... | [
"torch.nn.Linear",
"torch.nn.Tanh",
"torch.nn.ReLU"
] | 1.3.0 | yhisaki/pfrl | d89ddf66201bcfaaae6130bdee704d56ee4b7b76 |
1.10 | # Copyright 2021 solo-learn development team.
# 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,
# copy, modify, merge, publ... | [
"torch.randn",
"torch.mm"
] | 1.10.0 | xwyzsn/solo-learn | 16d021d8053439a3de205337ab2a11d191500b09 |
1.10 | # Copyright 2021 solo-learn development team.
# 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,
# copy, modify, merge, publ... | [
"torch.nn.Linear",
"torch.zeros",
"torch.nn.ReLU",
"torch.nn.BatchNorm1d",
"torch.randn"
] | 1.10.0 | xwyzsn/solo-learn | 16d021d8053439a3de205337ab2a11d191500b09 |
0.4 | import torch.nn as nn
import torch.nn.functional as F
class RNNAgent(nn.Module):
def __init__(self, input_shape, args):
super(RNNAgent, self).__init__()
self.args = args
self.fc1 = nn.Linear(input_shape, args.rnn_hidden_dim)
self.rnn = nn.GRUCell(args.rnn_hidden_dim, args.rnn_hidd... | [
"torch.nn.Linear",
"torch.nn.GRUCell"
] | 0.4.1 | halleanwoo/AGMA | a1c4980e05150a9cfa1be338e7c8cbd8ccd6b002 |
1.0 | import unittest
import torch
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
BertConfig,
BertForSequenceClassification,
GlueDataset,
GlueDataTrainingArguments,
Trainer,
TrainingArguments,
)
from transformers.adapters.composition import Fuse
from transforme... | [
"torch.equal"
] | 1.0 | AngadSethi/adapter-transformers | b147bba9107a5a561aca28c99f4e4ec2816a6e4f |
4 | import torch.nn as nn
import math
import torch
import torch.nn.functional as F
def conv_bn(inp, oup, stride, k_size=3):
return nn.Sequential(
nn.Conv2d(inp, oup, k_size, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.PReLU()
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
... | [
"torch.cat",
"torch.nn.MaxPool2d",
"torch.nn.Sequential",
"torch.nn.AvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.Conv2d",
"torch.nn.PReLU"
] | 4 | GzuPark/EXTD_Pytorch | e99af10f282d07054c1cf7c4b8c035084daaff78 |
1.2 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
#-------------------------------------------------#
# MISH激活函数
#-------------------------------------------------#
class Mish(nn.Module):
def __init__(self):
super(Mish, self).__init__()
def forward(self, x):
re... | [
"torch.cat",
"torch.nn.functional.softplus",
"torch.nn.BatchNorm2d",
"torch.nn.Conv2d",
"torch.load"
] | 1.2.0 | Arcofcosmos/MyYolov4_Pytorch | 14c445503d0fc69b8a8b64ecdc87256ac4c1fce1 |
1.2 | from collections import OrderedDict
import torch
import torch.nn as nn
from nets.CSPdarknet import darknet53
#---------------------------------------------------#
# 卷积块 -> 卷积 + 标准化 + 激活函数
# Conv2d + BatchNormalization + LeakyRelu
#---------------------------------------------------#
def CBL(filter_in, filter_o... | [
"torch.cat",
"torch.nn.MaxPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.LeakyReLU",
"torch.nn.Upsample",
"torch.nn.Conv2d"
] | 1.2.0 | Arcofcosmos/MyYolov4_Pytorch | 14c445503d0fc69b8a8b64ecdc87256ac4c1fce1 |
1.6 | import torch
from utils.distmat import compute_distmat
def init_feedback_indices(q, g, device=None):
return torch.zeros((q, g), dtype=torch.bool, device=device)
def init_feedback_indices_qg(q, g, positive=False, device=None):
indices = torch.zeros(q, q + g, dtype=torch.bool, device=device)
if positive:... | [
"torch.zeros",
"torch.arange"
] | 1.6.0 | itsnamgyu/reid-metric | 437e02ebad510b482f620a293fd8c7baa4f42ad6 |
1.4 | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class Actor(nn.Module):
"""For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(self, preprocess_net, action_shape, hidden_layer_size=128):
super().__i... | [
"torch.nn.Linear",
"torch.nn.BatchNorm2d",
"torch.nn.Conv2d",
"torch.tensor"
] | 1.4.0 | FightingSrain/tianshou | bd9c3c7f8d144448c44a350828b2c5222298bd8e |
1.10 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
sys.path.append('./')
from update import BasicUpdateBlock, SmallUpdateBlock
from extractor import BasicEncoder, SmallEncoder
from corr import CorrBlock, AlternateCorrBlock
from util import bilinear_sampler, coords_grid, u... | [
"torch.nn.functional.unfold",
"torch.relu",
"torch.split",
"torch.softmax",
"torch.tanh",
"torch.sum"
] | 1.10.0 | aharley/track_check_repeat | 564c3065a758deea11acdcaeea7a187ce376d564 |
1.3 | import torch
import os
import os.path
import shutil
import numpy as np
import soundfile as sf
from pathlib import PurePath
from torch import nn
from torch.utils.data import DataLoader, random_split
from asteroid.data import TimitDataset
from asteroid.data.utils import CachedWavSet, RandomMixtureSet, FixedMixtureSet
fr... | [
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.Generator",
"torch.utils.data.DataLoader"
] | 1.3.0 | flyingleafe/asteroid | 1c3c68ffc83f4b0bf7b00893083e4eff1f577b88 |
1.4 | """
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.
"""
from collections import defaultdict
from pathlib import Path
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import to... | [
"torch.utils.data.sampler.RandomSampler",
"torch.utils.data.DataLoader",
"torch.Tensor"
] | 1.4.0 | ygrepo/fastMRI | cb9a2019f1833bfffe4969023113189abcbad0f7 |
1.8 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 7 14:34:39 2021
@author: Eric
"""
#%%
from model import Unet
from utils import random_fliplr, random_crop
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torch.utils.data import DataLoader
from torch.utils.data impo... | [
"torch.cat",
"torch.nn.MSELoss",
"torch.no_grad",
"torch.cuda.empty_cache",
"torch.cuda.is_available",
"torch.utils.data.DataLoader",
"torch.utils.tensorboard.SummaryWriter"
] | 1.8.0 | yuchen071/Normal-map-generator | 40f92a38a75a35dcf4b8309517bf83b6a52b4fbb |
1.7 | """
---
title: Train Feedback Transformer
summary: This is training code with notes for a feedback transformer.
---
# Train Feedback Transformer
This trains a [feedback transformer](index.html) model for auto-regression.
You can pick the original feedback transformer or the new version
where the keys and values are p... | [
"torch.nn.Linear",
"torch.nn.Embedding"
] | 1.7 | lc0/nn | 0de7e343a11685de37a03ae4ee2510d18fc07369 |
1.0 | #!/usr/bin/env python3
import torch
import torch.cuda.profiler as profiler
from apex import pyprof
class Foo(torch.jit.ScriptModule):
def __init__(self, size):
super(Foo, self).__init__()
self.n = torch.nn.Parameter(torch.ones(size))
self.m = torch.nn.Parameter(torch.ones(size))
@torc... | [
"torch.cuda.profiler.start",
"torch.autograd.profiler.emit_nvtx",
"torch.cuda.profiler.stop",
"torch.ones"
] | 1.0 | oyj0594/apex | b66ffc1d952d0b20d6706ada783ae5b23e4ee734 |
1.0 | #!/usr/bin/env python3
"""
Example to run pyprof with imagenet models.
"""
import sys
import torch
import torch.nn as nn
import torchvision.models as models
import torch.cuda.profiler as profiler
import argparse
from apex import pyprof
from apex.optimizers import FusedAdam
def parseArgs():
parser = argparse.Argume... | [
"torch.rand",
"torch.autograd.profiler.emit_nvtx",
"torch.cuda.profiler.stop",
"torch.cuda.profiler.start",
"torch.empty",
"torch.nn.CrossEntropyLoss"
] | 1.0 | oyj0594/apex | b66ffc1d952d0b20d6706ada783ae5b23e4ee734 |
1.1 | #!/usr/bin/python
# -*- encoding: utf-8 -*-
from logger import setup_logger
from models.model_stages import BiSeNet
from cityscapes import CityScapes
from loss.loss import OhemCELoss
from loss.detail_loss import DetailAggregateLoss
from evaluation import MscEvalV0
from optimizer_loss import Optimizer
import torch
impo... | [
"torch.save",
"torch.no_grad",
"torch.nn.parallel.DistributedDataParallel",
"torch.cuda.device_count",
"torch.cuda.set_device",
"torch.squeeze",
"torch.utils.data.DataLoader",
"torch.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.distributed.get_rank"
] | 1.1.0 | Toby-SZZ/STDC-Seg | 9273e03b02241fda107962bfc7bd366310a8d23b |
1.3 | import string
import numpy as np
import torch as th
from ttools.training import ModelInterface
from . import utils
class VectorizerInterface(ModelInterface):
def __init__(self, model, lr, n_primitives, canvas_size, w_surface, w_alignment, csg, rounded, cuda=True):
self.model = model
self.cuda =... | [
"torch.sum",
"torch.cat",
"torch.mean",
"torch.max"
] | 1.3.1 | dmsm/DeepParametricShapes | 2e0de365191b29c61796f7cd6cbd2bdf631eae2c |
1.1 | ######################################################################################
#FSSNet: Fast Semantic Segmentation for Scene Perception
#Paper-Link: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8392426
######################################################################################
import to... | [
"torch.nn.MaxPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.functional.interpolate",
"torch.nn.ConvTranspose2d",
"torch.add",
"torch.nn.Conv2d",
"torch.cuda.is_available",
"torch.nn.Dropout2d"
] | 1.1.0 | ZAKAUDD/Segmentation-Networks | 7e006809a7345819ebc50326175df156beeca618 |
1.4 | from abc import ABC, abstractmethod
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
class mab_user(ABC):
def __init__(self, n_arms, lamb=1):
super(mab_user, self).__init__()
self.t = torch.tensor(1.0)
self.r = torch.zeros(n_arms)
self.n = torch.zero... | [
"torch.zeros",
"torch.ceil",
"torch.tensor",
"torch.log",
"torch.randn"
] | 1.4.0 | tginart/competing-ai | 75c456854e4770adf8be7cd56e58177d50f74a24 |
1.0 | import numpy as np
import tempfile
import os
import pytest
import torch
from anndata import AnnData
from scvi.dataset import (
AnnDatasetFromAnnData,
CortexDataset,
SyntheticDataset,
GeneExpressionDataset,
Dataset10X,
)
from scvi.inference import (
JointSemiSupervisedTrainer,
AlternateSemi... | [
"torch.rand",
"torch.manual_seed",
"torch.randn_like",
"torch.ones_like",
"torch.randint_like"
] | 1.0.1 | shaoxin0801/scVI | f439eeb7b696b01a281af2f0e2f49592318614cb |
1.0 | import argparse
import pandas as pd
from tqdm import tqdm
import torch
import torch.nn.parallel
from contextlib import suppress
import os
from effdet import create_model, create_loader
from effdet.data import resolve_input_config
from timm.utils import setup_default_logging
from timm.models.layers import set_layer_con... | [
"torch.flip",
"torch.save",
"torch.no_grad"
] | 1.0.3 | yellowdolphin/SIIM-COVID19-Detection | 31e8653b467ac35a8b1d92330ad5f15a12622676 |
1.3 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from torchvision import models
import numpy as np
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
... | [
"torch.nn.Linear",
"torch.cat",
"torch.nn.BatchNorm2d",
"torch.nn.init.kaiming_normal_",
"torch.inverse",
"torch.bmm",
"torch.cuda.is_available",
"torch.mul",
"torch.nn.init.constant_",
"torch.FloatTensor",
"torch.nn.init.normal_",
"torch.div",
"torch.nn.init.xavier_normal_",
"torch.nn.Seq... | 1.3.0 | levindabhi/SieveNet | a5e2263acf28b52a551d4e139328957cf454e7e8 |
1.7 | # -*- coding: utf-8 -*- #
"""*********************************************************************************************"""
# FileName [ split_long_utter_to_short.py ]
# Synopsis [ preprocess long audio / speech to shorter versions ]
# Author [ Andy T. Liu (Andi611) ]
# Copyright [ Copyleft(c... | [
"torch.split"
] | 1.7.0 | hhhaaahhhaa/s3prl | a469787f05c42196c4d989555082f5fd9dcbe8a6 |
1.10 | import os
from easydict import EasyDict
import torch
# architecture
from basicts.archs.DCRNN_arch import DCRNN
# runner
from basicts.runners.DCRNN_runner import DCRNNRunner
from basicts.data.base_dataset import BaseDataset
from basicts.metrics.mae import masked_mae
from basicts.metrics.mape import masked_mape
from bas... | [
"torch.tensor"
] | 1.10.0 | zezhishao/BasicTS | 584ca6f8215a6fc9976789b600996934ba2d499e |
1.2 | #!/usr/bin/env python
# coding: utf-8
import os
import yaml
import torch
import argparse
import numpy as np
from torch.distributed import get_rank, get_world_size
# For reproducibility, comment these may speed up training
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Arguments
par... | [
"torch.cuda.manual_seed_all",
"torch.distributed.init_process_group",
"torch.hub.set_dir",
"torch.manual_seed",
"torch.cuda.set_device",
"torch.cuda.is_available"
] | 1.2.0 | s3prl/End-to-end-ASR-Pytorch | 64e3d844cebca1eb442b9327f43145c95c9a6088 |
1.7 | import time
from pathlib import Path
from tqdm import tqdm
import hydra
from omegaconf import DictConfig
# 言語処理
# import fasttext
# import fasttext.util
from transformers import BertTokenizer, BertModel
# データ処理
import numpy as np
import torch
def extract_feats(config):
start = time.time()
# FPs
with ope... | [
"torch.no_grad",
"torch.Tensor"
] | 1.7.0 | ndkgit339/filledpause_prediction_group | db511c081f155ec2c23afe82bc44c03c38618590 |
1.7 | import pytest
from typing import Any
import torch
from torchtyping import TensorType
import typeguard
a = b = c = None
def test_fixed_int_dim():
@typeguard.typechecked
def _3_dim_checker(x: TensorType[3]):
pass
@typeguard.typechecked
def _3m1_dim_checker(x: TensorType[3, -1]):
pass
... | [
"torch.rand"
] | 1.7.0 | olliethomas/torchtyping | 81e1cffa841307d700b11e9a2c970a5face65020 |
1.7 | # -*- coding: utf-8 -*- #
"""*********************************************************************************************"""
# FileName [ model.py ]
# Synopsis [ the 1-hidden model ]
# Author [ S3PRL ]
# Copyright [ Copyleft(c), Speech Lab, NTU, Taiwan ]
"""************************************... | [
"torch.nn.Linear",
"torch.nn.Dropout",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Conv1d",
"torch.nn.functional.relu"
] | 1.7.1 | OlegJakushkin/s3prl | c0e41f07fa56f0f79b5bf3839b4d0a4cf7c421bf |
0.4 | #!/usr/bin/env python
import argparse
import logging
import os
from shutil import copyfile, rmtree
import numpy as np
import torch
import torch.nn as nn
from ase.data import atomic_numbers
from torch.optim import Adam
from torch.utils.data.sampler import RandomSampler
import schnetpack as spk
from schnetpack.datasets... | [
"torch.device",
"torch.utils.data.sampler.RandomSampler",
"torch.optim.Adam",
"torch.nn.DataParallel"
] | 0.4 | heytitle/schnetpack | 6facf724e6e220053f4ba8d5b81744744d1abef3 |
1.6 | """
2020.06.09-Changed for building GhostNet
Huawei Technologies Co., Ltd. <foss@huawei.com>
Creates a GhostNet Model as defined in:
GhostNet: More Features from Cheap Operations By Kai Han, Yunhe Wang,
Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu.
https://arxiv.org/abs/1911.11907
Modified from https://github.com/d-li1... | [
"torch.cat",
"torch.nn.functional.relu6",
"torch.nn.Sigmoid",
"torch.nn.Conv1d",
"torch.nn.Sequential",
"torch.nn.BatchNorm2d",
"torch.nn.init.constant_",
"torch.nn.ReLU6",
"torch.nn.Conv2d",
"torch.nn.init.normal_",
"torch.hub.load_state_dict_from_url",
"torch.nn.AdaptiveAvgPool2d"
] | 1.6 | samcw/nanodet | dc7c4f6021199d6988221b516d49af392a52d748 |
1.7 | import torch
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as dist
import logging
import pandas as pd
import traceback
from ...core import models
from ..misc import utils
class BaseManager:
"""
Manager all modules and computation devices. Support three kinds of comput... | [
"torch.distributed.get_world_size",
"torch.nn.parallel.DistributedDataParallel",
"torch.cuda.empty_cache",
"torch.cuda.is_available",
"torch.nn.DataParallel"
] | 1.7.0 | wangck20/GeDML | 1f76ac2094d7b88be7fd4eb6145e5586e547b9ca |
1.7 | import torch
import torchvision
import collections
import numpy as np
'''
_sparsity_ratios is a dictionary to save the sparsity ratios of each layer,
Key - the layer name
Value - list of sparsity ratios for the executed forward passes
'''
_sparsity_ratios_per_layer = collections.defaultdict(list)
_sparsity_ratios_per_... | [
"torch.abs",
"torch.numel"
] | 1.7.1 | scale-lab/BitTrain | 3a15f96cc32222e3d6fceb00a622521e31745d4c |
1.8 | import typing
from typing import Dict, Union, Tuple, Iterator, Any
from typing import Optional
import numpy as np
import torch
from gym.utils import seeding
from advisor_losses import AlphaScheduler, AdvisorWeightedStage
from allenact.algorithms.offpolicy_sync.losses.abstract_offpolicy_loss import (
AbstractOffPo... | [
"torch.ones",
"torch.from_numpy"
] | 1.8.0 | allenai/advisor | 6849755042c6dab1488f64cf21bde2322add3cc1 |
1.7 | # 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... | [
"torch.Size",
"torch.rand",
"torch.nn.Linear",
"torch.nn.MSELoss",
"torch.tensor"
] | 1.7 | charlesjhill/lightning-flash | 2b19acbb5d627c609f2f7e13b48006e157781718 |
1.7 | # 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... | [
"torch.nn.Linear",
"torch.nn.MSELoss",
"torch.arange"
] | 1.7 | charlesjhill/lightning-flash | 2b19acbb5d627c609f2f7e13b48006e157781718 |
1.5 | import torch as to
BASE_GRAPH = to.tensor([[0, 1, 1, 0],
[1, 0, 1, 0],
[1, 1, 0, 1],
[0, 0, 1, 0]])
BASE_GRAPH_NODE_FEATURES = to.tensor([[1, 2], [1, 1], [2, 0.5], [0.5, 0.5]])
BASE_GRAPH_EDGE_FEATURES = to.tensor([[[0.0, 0.0], [1.0, 2.0], [2.0, 0... | [
"torch.tensor"
] | 1.5.0 | kovanostra/message-passing-nn | 6617a4753173c8fffc60140b9d8d0f497b33aed4 |
1.3 | # 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... | [
"torch.tensor"
] | 1.3.1 | bibinwils/metrics | e1c3fda24f90367803c2b04315ad7c8bced719db |
1.0 | # 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... | [
"torch.nn.Linear",
"torch.zeros",
"torch.nn.Dropout",
"torch.nn.LayerNorm",
"torch.cat",
"torch.nn.MSELoss",
"torch.arange",
"torch.einsum",
"torch.nn.Tanh",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"torch.nn.BCEWithLogitsLoss",
"torch.nn.functional.softmax",
"torch.tanh",
"torch.matmu... | 1.0 | khoih-prog/transformers | 77321481247787c97568c3b9f64b19e22351bab8 |
1.2 | # encoding: utf-8
# Sample-based Monte Carlo Denoising using a Kernel-Splatting Network
# Michaël Gharbi Tzu-Mao Li Miika Aittala Jaakko Lehtinen Frédo Durand
# Siggraph 2019
#
# Copyright (c) 2019 Michaël Gharbi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in c... | [
"torch.abs",
"torch.mean",
"torch.clamp",
"torch.pow"
] | 1.2.0 | milebril/Temporal-SBMC-extension | 57c56b73786e49d233facffde4ba80f212a00fa8 |
1.6 | import os
import pandas as pd
import numpy as np
import torch
import json
import joblib
from ..scripts.compute_normalization_factors import annotate_kmer_information, create_kmer_mapping_df, create_norm_dict
from torch.utils.data import DataLoader, Dataset
from torch.utils.data._utils.collate import default_collate
fro... | [
"torch.utils.data._utils.collate.default_collate",
"torch.LongTensor",
"torch.Tensor"
] | 1.6.0 | GoekeLab/m6anet | be3148a6404bdd2a4e5e9544b3e618e836c6483c |
1.6 | import torch
from torch import nn
def get_activation(activation):
activation_func = None
if activation == 'tanh':
activation_func = nn.Tanh()
elif activation == 'sigmoid':
activation_func = nn.Sigmoid()
elif activation == 'relu':
activation_func = nn.ReLU()
elif activation ... | [
"torch.nn.Linear",
"torch.nn.Dropout",
"torch.nn.Softmax",
"torch.nn.Sigmoid",
"torch.nn.Sequential",
"torch.nn.Tanh",
"torch.nn.ReLU",
"torch.nn.BatchNorm1d",
"torch.nn.Embedding",
"torch.nn.Flatten"
] | 1.6.0 | GoekeLab/m6anet | be3148a6404bdd2a4e5e9544b3e618e836c6483c |
1.8 | import os
import torch
import numpy as np
class Exp_Basic(object):
def __init__(self, args):
self.args = args
self.device = self._acquire_device()
self.model = self._build_model().cuda()
def _build_model(self):
raise NotImplementedError
def _acquire_device(self):
i... | [
"torch.device"
] | 1.8.0 | MarcAntoineAlex/SCINet | 4ac582cd717ba1c0c6c6d31a9a824235d35563ed |
0.4 | # 새로운 참고링크: https://github.com/eriklindernoren/PyTorch-GAN/tree/master/implementations/context_encoder
# 우리랑 같은 3채널에 하고자하는 바도 비슷함. shape 참고하기 좋을듯하여 첨부함
# 나(소현)는 사진 11개 -> batch_size=11, num_classes=11이니 주의바람!
import argparse
import os
import numpy as np
from dataloader import OAGandataset
import math
import torchvisi... | [
"torch.nn.BatchNorm2d",
"torch.nn.LeakyReLU",
"torch.ones",
"torch.cuda.is_available",
"torch.nn.functional.pad",
"torch.nn.Softmax",
"torch.nn.MaxPool2d",
"torch.nn.init.constant_",
"torch.nn.ConvTranspose2d",
"torch.nn.init.normal_",
"torch.utils.data.DataLoader",
"torch.nn.BCELoss",
"torc... | 0.4.0 | gun8474/face-recognition-by-OAGAN | 54c67a29a22e25b14a24fb8aa3badba5444653ac |
0.4 | import argparse
import os
import numpy as np
from dataloader import OAGandataset
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch
import torchvision.m... | [
"torch.nn.BatchNorm2d",
"torch.nn.LeakyReLU",
"torch.ones",
"torch.cuda.is_available",
"torch.nn.functional.pad",
"torch.nn.Softmax",
"torch.nn.MaxPool2d",
"torch.nn.init.constant_",
"torch.nn.ConvTranspose2d",
"torch.nn.init.normal_",
"torch.utils.data.DataLoader",
"torch.nn.BCELoss",
"torc... | 0.4.0 | gun8474/face-recognition-by-OAGAN | 54c67a29a22e25b14a24fb8aa3badba5444653ac |
1.4 | # -*- coding: utf-8 -*-
__author__ = 'S.I. Mimilakis'
__copyright__ = 'MacSeNet'
# imports
import numpy as np
import torch
import torch.nn as nn
class ConvEncoder(nn.Module):
"""
Class for building the analysis part
of the Front-End ('Fe'), with randomly
initialized dictionaries.
"""
... | [
"torch.nn.init.kaiming_uniform_",
"torch.nn.ConvTranspose1d",
"torch.nn.Conv1d",
"torch.nn.Tanh",
"torch.nn.ReLU"
] | 1.4.0 | TUIlmenauAMS/rl_singing_voice | 60204c698d48f27b44588c9d6c8dd2c66a13fcd5 |
1.1 | import torch
import torch.nn as nn
from torchvision import models, transforms
class Resnet18(object):
'''
pretrained Resnet18 from torchvision
'''
def __init__(self, args, eval=True, share_memory=False, use_conv_feat=True):
self.model = models.resnet18(pretrained=True)
if args.gpu:
... | [
"torch.device",
"torch.cat",
"torch.set_grad_enabled"
] | 1.1.0 | jzhanson/alfred | d5b540e7c9b53d3f70cc2907503935fecff00018 |
1.4 | # -*- coding: utf-8 -*
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#... | [
"torch.cuda.manual_seed",
"torch.optim.lr_scheduler.StepLR",
"torch.no_grad",
"torch.cuda.device_count",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.load",
"torch.utils.data.distributed.DistributedSampler"
] | 1.4.1 | ruaruaruabick/waveglow | 636d2ba2bda4f4efd5f13f8e46aef23d8b7881bd |
1.5 | # 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... | [
"torch.jit.load",
"torch.load"
] | 1.5 | finalelement/MONAI | 8e8e1b391fa649d1227087164dba208008d00bc4 |
1.7 |
import torch
import torch.nn as nn
def soft_update(target, source, t):
for target_param, source_param in zip(target.parameters(), source.parameters()):
target_param.data.copy_( (1 - t) * target_param.data + t * source_param.data )
def hard_update(target, source):
for target_param, source_param in zi... | [
"torch.nn.init.orthogonal_",
"torch.no_grad",
"torch.nn.init.constant_",
"torch.svd"
] | 1.7.1 | zhangdongkun98/rl-lib | 50e36c18b130cff40abc6621923becd6cdc48e2b |
1.3 | """Compute the gradient with PyTorch and the variance with BackPACK."""
from torch.nn import CrossEntropyLoss, Flatten, Linear, Sequential
from backpack import backpack, extend, extensions
from backpack.utils.examples import load_mnist_data
B = 4
X, y = load_mnist_data(B)
print("# Gradient with PyTorch, individual ... | [
"torch.nn.Linear",
"torch.nn.CrossEntropyLoss",
"torch.nn.Flatten"
] | 1.3.0 | paulkogni/backpack | 3122de062d5bbcdcba8f8e02d24adb1bd2cdada6 |
1.6 | import errno
import os
import time
import urllib.request
import sys
import numpy as np
import pkg_resources
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from PIL import Image
from skimage import transform
from torchvision import transforms
from tqdm import tqdm
from . import d... | [
"torch.device",
"torch.min",
"torch.max",
"torch.no_grad",
"torch.cuda.is_available",
"torch.load"
] | 1.6.0 | 0xflotus/rembg | 7fb6683169d588f653281d53c3c258838194c950 |
0.4 | from collections import OrderedDict
import torch.nn as nn
import torchvision.models as models
class LinkNet(nn.Module):
def __init__(self, num_classes, resnet_size=18, pretrained_encoder=True):
super().__init__()
self.num_classes = num_classes
# The LinkNet encoder is a ResNet18 without t... | [
"torch.nn.ReLU",
"torch.nn.BatchNorm2d",
"torch.nn.ConvTranspose2d",
"torch.nn.Conv2d"
] | 0.4.1 | liyingben/kaggle-airbus-ship-detection | 21d89b2f1273b31a6ffafb4fe5f7e643ffbbc567 |
0.4 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import copy
import os
import pickle
import pandas as pd
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader
from utils.options import args_parser
from utils.train_utils import get_data, get_model
from models.U... | [
"torch.cuda.is_available",
"torch.nn.CrossEntropyLoss"
] | 0.4.1 | yhyeh/LG-FedAvg | f64a2943c7f1fed214412033e0fa0a63f3c03fb8 |
1.0 | """
Unit tests for various optimization related utilities.
"""
import unittest
import torch
from texar.core.optimization import *
class OptimizationTest(unittest.TestCase):
r"""Test optimization.
"""
def setUp(self):
N, D_in, H, D_out = 64, 100, 10, 1
self.x = torch.randn(N, D_in)
... | [
"torch.nn.Linear",
"torch.nn.MSELoss",
"torch.nn.ReLU",
"torch.tensor",
"torch.randn"
] | 1.0.0 | lunayach/texar-pytorch | ac3e334e491f524dd01654b07af030fa20c88b34 |
1.0 | # Copyright 2019 The Texar Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | [
"torch.zeros",
"torch.nn.Dropout",
"torch.nn.LayerNorm",
"torch.nn.ModuleList",
"torch.ones_like",
"torch.empty",
"torch.argmax",
"torch.where"
] | 1.0.0 | lunayach/texar-pytorch | ac3e334e491f524dd01654b07af030fa20c88b34 |
1.0 | """
Unit tests for attention mechanism.
"""
import unittest
import numpy as np
import torch
from texar.core.attention_mechanism import *
class AttentionMechanismTest(unittest.TestCase):
r"""Tests attention mechanism.
"""
def setUp(self):
self._batch_size = 8
self._max_time = 16
... | [
"torch.Size",
"torch.rand"
] | 1.0.0 | lunayach/texar-pytorch | ac3e334e491f524dd01654b07af030fa20c88b34 |
1.2 | import time
from typing import Any, Callable, Dict, List, Optional
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from captum._utils.models.linear_model.model import LinearModel
def l2_loss(x1, x2, weights=None):
if weights is None:
return torch.mean((x1 - x2) ** 2) / 2.0
... | [
"torch.stack",
"torch.norm",
"torch.FloatTensor",
"torch.no_grad",
"torch.nn.init.xavier_uniform_",
"torch.isclose",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.mean"
] | 1.2 | edward-io/captum | 8f959950baaad00f2f9a3404d583b9f9292e35c7 |
1.0 | import torch
from . import Metric
class MeanAbsoluteError(Metric):
def __init__(self):
super().__init__("mae", default_value=float('inf'))
self._absolute_error_sum = 0.
self._total = 0
def step(self, y: torch.Tensor, y_pred: torch.Tensor):
absolute_errors = torch.abs(y - y_pr... | [
"torch.abs",
"torch.sum"
] | 1.0.0 | benoitmartin88/pytorchtrainer | 7d73acd0802e00c3589d28bce6c42a489dcd46ea |
1.9 | """Convert the model to ONN format
"""
__author__ = "Likith Reddy"
__version__ = "1.0.0"
__email__ = "likith012@gmail.com"
import torch
import torch.nn as nn
import torch.onnx
import sys, os
sys.path.insert(0, os.path.join(sys.path[0], '../'))
from configs import config
from src.dataset import BERTDataset
if __n... | [
"torch.nn.DataParallel",
"torch.onnx.export",
"torch.cuda.device_count"
] | 1.9.1 | likith012/distill-grammar | 04ff5e07337789edfe57f21f85e30e7992ae90d9 |
1.1 | import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from foresight import ei
##################################
#### H ####
##################################
def test_H_0():
x = torch.zeros((4,))
x[1] = 1
assert ei.H(x).item() == 0
def test_H_1():
... | [
"torch.zeros",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.ones",
"torch.nn.Conv2d",
"torch.tensor",
"torch.flatten",
"torch.randn"
] | 1.1.0 | ejmichaud/torch-foresight | e36a8fdd65f0432b9fa25a5127412b081159956b |
1.4 | # Taken from https://github.com/psclklnk/spdl
# Copy of the license at TeachMyAgent/teachers/LICENSES/SPDL
import torch
import numpy as np
def set_weights(parameters, weights, use_cuda):
"""
Function used to set the value of a set of torch parameters given a
vector of values.
Args:
parameter... | [
"torch.cat",
"torch.from_numpy",
"torch.tensor"
] | 1.4.0 | flowersteam/TeachMyAgent | a8f71cbfce4cb8ca6da24d00ea690495e3afbd2e |
1.11 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
#!/usr/bin/env python3
# pyre-strict
import os
import time
import traceback
from dataclasses import dataclass
from typing import An... | [
"torch._C._log_api_usage_once"
] | 1.11.0 | laurencer/recipes | 60b7c5f0304c7eb44a39295eba78da02608ae858 |
0.4 | # From https://github.com/wjh720/QPLEX/, added here for convenience.
import copy
from components.episode_buffer import EpisodeBatch
from modules.mixers.dmaq_general import DMAQer
# from modules.mixers.dmaq_qatten import DMAQ_QattenMixer
import torch.nn.functional as F
import torch as th
from torch.optim import RMSprop
... | [
"torch.stack",
"torch.optim.RMSprop",
"torch.gather",
"torch.nn.utils.clip_grad_norm_",
"torch.mean"
] | 0.4.1 | HDUAIS/MARL_Bench | f592d20ddbcb2039453cf56221083d4ac64dee46 |
1.7 | """
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
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable
import torch.utils.data
import tor... | [
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.init.constant_",
"torch.nn.Sequential",
"torch.nn.init.kaiming_normal_",
"torch.nn.GroupNorm",
"torch.nn.Conv2d",
"torch.nn.functional.relu"
] | 1.7.1 | sbelenki/fastMRI | 9a359ffe340e9265491744e381d92241b36a6455 |
1.7 | """
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 h5py
import torch
from collections import OrderedDict
from fastmri.data import transforms
import numpy as np
import random
import pdb... | [
"torch.max",
"torch.Tensor",
"torch.mean"
] | 1.7.1 | sbelenki/fastMRI | 9a359ffe340e9265491744e381d92241b36a6455 |
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