content stringlengths 200 198k | repo stringlengths 4 186 | path stringclasses 1
value | license stringclasses 101
values | size int64 200 198k | sha stringlengths 40 40 | lang stringclasses 1
value | ext stringclasses 1
value |
|---|---|---|---|---|---|---|---|
import os
import argparse
from torch.backends import cudnn
from config import config, dataset_config, merge_cfg_arg
from dataloder import get_loader
from solver_cycle import Solver_cycleGAN
from solver_makeup import Solver_makeupGAN
from solver_makeup2 import Solver_makeupGAN2
from solver_makeup3 import Solver_makeu... | train.py | MIT | 5,075 | e61d5076bfa039937fdd7011cf3a5e493535584e | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 25 13:55:21 2021
@author: DELL
"""
import shutil
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import os
... | code_history/remove_code/my_classifier.py | MIT | 12,628 | 3a68b22830246e1cd564ceb742807135ec02a2f8 | Python | py | |
import torch
def validation(model, validation_loader, criterion, optimizer, gpu=True):
test_loss = 0
accuracy = 0
for ii, (inputs, labels) in enumerate(validation_loader):
optimizer.zero_grad()
if torch.cuda.is_available() and gpu:
inputs, labels = inputs.to('cuda:0'), labels... | trainer.py | MIT | 2,136 | d77206e644969b57c12affb2c3d6b84702830e2b | Python | py | |
import json
import torch
from torch.utils.data import Dataset
class ActivityDataset(Dataset):
"""
Dataloader for activity dataset
"""
def __init__(self, joints, phase):
"""
Load inputs and outputs from the pickles files from gt joints, mask joints or both
"""
assert(... | monoloco/monoloco/train/datasets.py | MIT | 2,868 | 3a68c17df686535bfdf81014f75c36be075b0dd6 | Python | py | |
"""
Test case for DQN pytroch!
CartPole-v0
"""
import numpy as np
import gym
import torch
from dqn_pytorch import DQN
from collections import deque
EPISODES = 20
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
env = gym.make('CartPole-v0')
state_size = env.observation_space.shape[0]
action_si... | DQL/pole_train.py | MIT | 2,073 | 28c2296df931d71b329a9c282ed41c4bd087e86d | Python | py | |
#!/usr/bin/env python3
import argparse
import os
import numpy as np
import time
import torch
from torch import optim
import visual_plt
import utils
import pandas as pd
from param_stamp import get_param_stamp, get_param_stamp_from_args
import evaluate
from data import get_multitask_experiment
from encoder import Classif... | main.py | MIT | 34,983 | 1ea8a08f37e57906c8b5488d05b3b63d6527e1a3 | Python | py | |
import os, sys
import logging
import torch
from pytorch_utils.trainers.train_utils import (
accuracy, distributed_average_gradients
)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(stream=sys.stdout))
def test_helper(test_loader, model, criterion, epo... | workshops/sagemaker/pytorch_utils/trainers/classifier_trainers.py | CC0-1.0 | 3,594 | 3a68cef1ab8204b101179e78cf1a7403a7cb86b7 | Python | py | |
import torch
import tqdm
from sklearn.metrics import roc_auc_score
from torch.utils.data import DataLoader
from torchfm.dataset.avazu import AvazuDataset
from torchfm.dataset.criteo import CriteoDataset
from torchfm.dataset.movielens import MovieLens1MDataset, MovieLens20MDataset
from torchfm.model.afi import Automati... | examples/main.py | MIT | 6,785 | ff2e9ca2350660db68d7d4a60601c687bdc5b78d | Python | py | |
"""
Usage:
main.py train [options] [--dataset-config=<dataset config path>] [--model-config=<model config path>] [--runner-config=<runner config path>]
main.py train (-h | --help)
Options:
--dataset-config <dataset config path> Path to YAML file for dataset configuration [default: conf/mlp/dataset/dataset... | train.py | MIT | 3,329 | f949d4102965674c1fed49268a9b41fff5abeb2d | Python | py | |
import os, sys, inspect, re
from collections import OrderedDict
from torch.optim import *
optimizer_dict = {}
naming_rule = lambda x: re.sub("([a-z])([A-Z])", r"\1_\2", x).lower()
for class_name, _class in inspect.getmembers(sys.modules[__name__], inspect.isclass):
optimizer_dict[naming_rule(class_name)] = _class... | jorldy/core/optimizer/__init__.py | Apache-2.0 | 1,177 | 3a68e4e20a4dd0992df1f91934f466b25070f53d | Python | py | |
"""
File defining the recurrent neural network models that we trained. They all inherit the class NNModel defined in
nnmodel.py.
"""
import torch.nn as nn
from torch.nn import functional as F
from nnmodel import NNModel
class LSTMNet1(NNModel):
def __init__(self, dim_input, dim_recurrent, num_layers, dim_outpu... | project1/src/rec_models.py | MIT | 2,747 | 5ec2de22ca0463700ebdf91bd58f8d61dc180828 | Python | py | |
from threading import Thread
import gtimer as gt
from torch.multiprocessing import Pipe, Process
import rlkit.torch.pytorch_util as ptu
import rlkit.torch.vae.vae_schedules as vae_schedules
from rlkit.core import logger
from rlkit.data_management.online_vae_replay_buffer import OnlineVaeRelabelingBuffer
from rlkit.da... | rlkit/rlkit/torch/skewfit/online_vae_algorithm.py | MIT | 7,458 | 7c992ba4354db4bbea5552855a71403712b15090 | Python | py | |
import time
import os
import argparse
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import seaborn as sns
parser = argparse.ArgumentParser()
parser.add_argument('--tol', type=float, default=1e-3)
parser.add_argument('--adjoint', type=eval, default=Fa... | experiments/function_fitting/double_function/double_func_anode_mixed.py | MIT | 7,258 | 4b2e6f18db0bed10d3c0f9340ac5ee62400051ea | Python | py | |
import torch
import numpy as np
from .strategy import Strategy
import torch.nn.functional as F
# use permutation to implement random
# seed is stable
class RandomSampling(Strategy):
def __init__(self, source, target_train, target_test, idxs_lb, net, cfg):
super(RandomSampling, self).__init__(source, target... | sample_strategy/RandomSample.py | Apache-2.0 | 591 | a914e71a0474ebd51adeb958f7e8d518171e7315 | Python | py | |
from torch.utils.data import Dataset
import numpy as np
import torch
from pathlib import Path
class TextDataset(Dataset):
def __init__(self,root_dir,mode):
self.root_dir=root_dir
self.mode=mode
file=Path(self.root_dir).joinpath(f"{self.mode}.txt")
self.data=np.loadtxt(file,dtype=... | lab4/dataset.py | MIT | 959 | 4b01582c4a5922a4ccb2d78b9a96e9214af70654 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
def initialize_weights(module):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight.data,
mode='fan_out',
nonlinearity='relu')
elif isinstance(module, ... | pytorch_image_classification/models/cifar/se_resnet_preact.py | MIT | 10,637 | d771e8aa5d5d6f3e63233a192a7ed48a65a3b1bf | Python | py | |
import numpy as np
import torch
from nltk.translate.bleu_score import sentence_bleu
import os
from metric import get_bleu
class _EVAL(object):
def __init__(self, vocab_obj, args, device):
super().__init__()
self.m_sos_idx = vocab_obj.sos_idx
self.m_eos_idx = vocab_obj.eos_idx
self.... | eval_rec.py | MIT | 7,455 | 343fff43482c92b2e7b78cc38e2e632f57e24ab2 | Python | py | |
"""Module to perform projective transformations to tensors."""
from typing import Tuple, List, Optional
import warnings
import torch
import kornia as K
from kornia.geometry.conversions import convert_affinematrix_to_homography3d
from kornia.geometry.transform.homography_warper import normalize_homography3d, homograph... | kornia/geometry/transform/projwarp.py | ECL-2.0,Apache-2.0 | 16,156 | b243c189314c2ce4f0dcf4b4478032be7294658a | 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/data/nifti_saver.py | Apache-2.0 | 9,877 | 427b2d29d5af4e115fd141e61623c9922b7bafd2 | Python | py | |
''' Handling the data io '''
import os
import argparse
import logging
import dill as pickle
import urllib
from tqdm import tqdm
import sys
import codecs
import spacy
import torch
import tarfile
import torchtext.data
import torchtext.datasets
from torchtext.datasets import TranslationDataset
import transformer.Constants... | preprocess.py | MIT | 15,284 | 3a69a3b52e49339d3fb9cb70e8c2df1c3e3bb4ff | Python | py | |
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import copy
class Data:
def __init__(self, dataset_name, dataset, dataloader):
self.dataset = dataset
self.dataloader = dataloader
self.name = dataset_name
if self.name == 'cifar':
# ... | lib/data.py | MIT | 2,989 | 3a69a4c8d5dfb9512d75e6c91e13dc3d8aee60ee | Python | py | |
# Copyright (c) 2017-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | utils/boxes.py | MIT | 15,773 | f93c7ec07b4de2414764da880c6ee92b6f9d6711 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Save some representative images from each dataset to disk.
"""
import random
import torch
import argparse
from domainbed import hparams_registry
from domainbed import datasets
import imageio
import os
from tqdm import tqdm
if __name__ == '__ma... | domainbed/scripts/save_images.py | MIT | 2,020 | 3a69cb806b8930be8d7edf6c609ac3e2c359fa17 | Python | py | |
import argparse
import torch
from torch.utils.data import DataLoader
from pytorch_transformers import GPT2LMHeadModel, AdamW, WarmupLinearSchedule
from utils.data import EpisodeSummaryTokenizer, create_datasets_from_jsons
from utils.gen_utils import set_random_seeds, generate_sequence
def make_train_state(save_path, ... | train.py | MIT | 11,407 | 45ac66a1bd22824043f39a840b07e718ef2ee031 | Python | py | |
# =========================================================================
# Copyright (C) 2021. Huawei Technologies Co., Ltd. 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 Lice... | fuxictr/pytorch/models/FFM.py | Apache-2.0 | 3,043 | ff1b734e471f9f81dd48b4a5130c2764645d88c1 | Python | py | |
import math
import random
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# from itertools import count
from collections import namedtuple
## Import function approximator for Q from other file
from Q_network import QNetwork
from whittle_network import Whittle
from whittle_network import New_Whittl... | src/whittle_old/FGDQN_Agent.py | MIT | 6,793 | d7fcd0ef07549417285910d5b72221d30395f33b | Python | py | |
# code is based on https://github.com/katerakelly/pytorch-maml
import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch
from torch.utils.data import DataLoader,Dataset
import random
import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
... | ucf101/task_generator_vid_8lian.py | MIT | 6,332 | 5f0ad0b2c0c832245025f78c4608eefab4fb6c6a | Python | py | |
#summary pytorch
from torchsummary import summary #pip install torchsummary
import torchvision.models as models
def summaryNet(net, inputSize):
summary(net, inputSize)
print(net)
def test():
alexnet = models.alexnet(pretrained=False)
#alexnet.cuda()
help(summary)
summary(alexnet, (3, 224, 224))
print(... | src/summaryModel.py | MIT | 3,617 | 3a6a3086f2a845b5f36ab1d0a800c65add67c56d | Python | py | |
import logging
import torch
import torch.nn as nn
import numpy as np
from pykp.masked_softmax import MaskedSoftmax
class Attention(nn.Module):
def __init__(self, decoder_size, memory_bank_size, coverage_attn, attn_mode):
super(Attention, self).__init__()
# attention
if attn_mode == "concat"... | pykp/attention.py | MIT | 9,362 | f986f53a68b2006003721d2b6879d2074d197081 | Python | py | |
'''This is the repo which contains the original code to the WACV 2022 paper
"Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection"
by Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn and Bastian Wandt.
For further information contact Marco Rudolph (rudolph@tnt.uni-hannover.de)'''
import config as c
from t... | baselines/differnet/main.py | MIT | 4,785 | 3a6a6e55dd911a5b4aa86da7d175e647050d080c | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from einops import rearrange, repeat
import torch
import torch.nn as nn
import torch.nn.functional as Fn
import math
def iterative_inv(mat, n_iter = 6, init_option="exact"):
I = torch.eye(mat.size(-2), device = mat.dev... | slowfast/models/nystrom_helper.py | Apache-2.0 | 5,098 | 804acc15678162491c0ecb81fde06ec858b1c5c7 | Python | py | |
import sys, os, time, math, random
from copy import deepcopy
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_
from sklearn.metrics import accuracy_score
from tensorboardX import SummaryWriter
from utils.data_loader imp... | model/trainer.py | MIT | 22,626 | 3a6aaa716c72e4f20f226fe2bcb68522b0b3b3fd | Python | py | |
import os, sys
import time
from torch import optim
import random as rand
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from PIL import Image
import torch.utils.data as data
import torchvision as tv
import torchvision.transforms as tvt
import math
from imsitu impo... | deepinspect/imsitu/baseline_crf_deepinspect.py | MIT | 52,767 | 347ea2261b2fcc2ffea930d45032ca1eb9e60059 | Python | py | |
#!/usr/bin/env python3
#
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See ../../../LICENSE for clarification regarding multiple authors
#
# 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... | k2/python/tests/nbest_test.py | Apache-2.0 | 3,565 | 3a6ae0e81e6ae88bf5ac120b44354ef1f3fb1742 | Python | py | |
import os
import shutil
import time
import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from torch.optim import SGD, Optimizer
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from architectures import ge... | code/train_utils.py | MIT | 7,662 | 3a6ae27e2aae2e9b83ee06789d6099091da33b86 | Python | py | |
import torch
from cogdl.tasks import build_task
from cogdl.datasets import build_dataset
from cogdl.models import build_model
from cogdl.utils import build_args_from_dict
def get_default_args():
default_dict = {'hidden_size': 16,
'num_shuffle': 1,
'cpu': True,
... | tests/tasks/test_unsupervised_node_classification.py | MIT | 7,927 | 343b91dc06923e51b9c4daa53704de335d26f245 | Python | py | |
# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
import logging
import pytest
import torch
from pyro.ops.indexing import Vindex
from tests.common import xfail_param
# put all funsor-related imports here, so test collection works without funsor
try:
import funsor
import py... | tests/contrib/funsor/test_valid_models_plate.py | Apache-2.0 | 7,546 | 4b2f75d844428eee2ac0f68e95798db279f62752 | Python | py | |
"""
Create train, valid, main iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.
[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""
import os
import numpy as np
# from utils import plot_images
from torchvision import datasets
from torchvision import tra... | train/dataset/cifar10_dataloader.py | MIT | 9,434 | 3a6b1214e8b819f73ecbb2ffb3304ff1b4e5deaf | Python | py | |
import sys
import torch.nn as nn
import time
import numpy as np
import torch
import copy
import ltprg.data.feature
from torch.autograd import Variable
from torch.optim import Adam
from mung.feature import MultiviewDataSet, Symbol
from mung.data import Partition
from mung.torch_ext.eval import Loss
from mung.torch_ext.l... | src/test/py/ltprg/game/shapeGrids/model/learn_seqS.py | MIT | 3,475 | 106f35133da694dd62ae0ea247fe8977d2e90257 | Python | py | |
from __future__ import print_function
import torch
from retinaface.data import cfg_mnet
from retinaface.models.retinaface import RetinaFace
def check_keys(model, pretrained_state_dict):
ckpt_keys = set(pretrained_state_dict.keys())
model_keys = set(model.state_dict().keys())
used_pretrained_keys = model... | retinaface/loader.py | Apache-2.0 | 1,769 | ff8b0ebac1c982e127ef2f85289e55a82cf9ea54 | Python | py | |
import os
import sys
BASE_DIR = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(
os.path.abspath(__file__)))))
sys.path.append(BASE_DIR)
from tools.path import ILSVRC2012_path
from simpleAICV.distillation.distillmodel import KDModel
from simpleAICV.distillation import losses
from simpleA... | distillation_training/imagenet/resnet_34_kd_18/train_config.py | MIT | 3,293 | 3a6b3bc1854bdf561f80e81f50a4169ac6b4cef8 | Python | py | |
import numpy as np
import torch.utils.data as data
import torchvision.datasets as datasets
from collections import defaultdict
class LabelNoise(data.Dataset):
def __init__(self, dataset, k, n_labels, p=1):
assert n_labels % k == 0
self.dataset = dataset
self.k = k
# random label... | smooth-topk/src/data/utils.py | MIT | 3,901 | 5ec6ec65cbd399da0eaa7e19d3361a7c647d022a | Python | py | |
import json
from data import load_data, get_epoch
import model
import torch
from torch import nn
import os
from random import shuffle
import argparse
import logging
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
logger.setLevel(logging.INFO)
def train_epoch(model, data, config):
model.train(... | train_model.py | MIT | 4,608 | e9822a29e6d122d16eafd07450245753dd8db735 | Python | py | |
"""
@author: JiXuan Xu, Jun Wang
@date: 20201015
@contact: jun21wangustc@gmail.com
"""
import numpy as np
import torch
from App.exts.face_sdk.api_usage.logFile import logger
from App.exts.face_sdk.core.model_handler.BaseModelHandler import BaseModelHandler
from App.exts.face_sdk.utils.BuzException import ... | App/exts/face_sdk/core/model_handler/face_recognition/FaceRecModelHandler.py | MIT | 2,384 | fdaebca5637d55377fab3ebf06a0cfa874426ddb | Python | py | |
import torch
import numpy as np
import time
import pdb
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
from rlkit.torch.policies.make_deterministic import MakeDeterministic
from rlkit.torch.policies.gumbel_softmax_policy import GumbelSoftmaxMlpPolicy
from rlkit.policies.argmax import ArgmaxDisc... | tests/RoboSumo/visualize_against.py | MIT | 3,227 | ff0ce03e68af3e8652470aa8ca279bf760d01930 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import TYPE_CHECKING, Optional, Tuple, Union
import torch
from .knn import knn_points
if TYPE_CHECKING:
from pytorch3d.structures import Pointclouds
def wmean(
x: torch.Tensor,
weight: Optional[torch.Tensor] = None,
... | pytorch3d/ops/utils.py | BSD-3-Clause | 5,119 | 3a6ba4a98e9db70b2f44c7be28e3f298cce9f225 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Misc functions, including distributed helpers.
Mostly copy-paste from torchvision references.
"""
import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from typing import Optional, List... | util/misc.py | Apache-2.0 | 13,420 | 45d055d919315eef682a856ab1888a64f316815f | Python | py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import sys
def read_files_batch(file_list):
"""Reads the provided files in batches"""
batch = [] # Keep batch for each file
fd_list = [] # File descriptor list
exit = False # Flag used for quitting the program in case of error
try:... | tools/create_vocabulary.py | MIT | 2,986 | 3a6bc4a889be57dc759f72e9d51f09434fde8149 | Python | py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Define the Gaussian Mixture Regression (GMR) value function approximators.
Define the GMR value that can be used.
"""
import numpy as np
import torch
from pyrobolearn.approximators.gmr import GMRApproximator
from pyrobolearn.values.value import ParametrizedValue, Para... | pyrobolearn/values/gmr.py | Apache-2.0 | 6,171 | a92b09cb097bce09afd9723c8dca3a475897cb75 | Python | py | |
# Copyright 2021, Crepaldi Michele.
#
# Developed as a thesis project at the TORSEC research group of the Polytechnic of Turin (Italy) under the supervision
# of professor Antonio Lioy and engineer Andrea Atzeni and with the support of engineer Andrea Marcelli.
#
# Licensed under the Apache License, Version 2.0 (the "L... | src/Model/nets/ALOHA_net.py | Apache-2.0 | 16,157 | 3a6bfb06a64ef15ed36a79540b7be220deb1410d | Python | py | |
import torch
from torchvision import datasets, transforms
import torch.utils.data.sampler as sampler
import torch.utils.data as data
import numpy as np
import argparse
import random
import os
from custom_datasets import *
import model
import vgg
import mnist_net
from solver import Solver
from utils import *
import a... | main.py | BSD-2-Clause | 5,955 | 5f6c8645914ed06777e38aac0d2ae12130365a30 | Python | py | |
"""Module Description.
- Author: Jongkuk Lim
- Contact: lim.jeikei@gmail.com
"""
import os
from typing import List, Union
import numpy as np
import torch
from torch import nn
from kindle import Model
from kindle.generator import GeneratorAbstract
from kindle.torch_utils import Activation, autopad, count_model_params... | tests/test_custom_module.py | MIT | 2,552 | 3a6c1e9927ef3ebaa4d627a5161e13a3cb794622 | Python | py | |
import argparse
import os
import numpy as np
import torch.nn as nn
from tqdm import tqdm
from mypath import Path
from dataloaders import make_data_loader
from modeling.sync_batchnorm.replicate import patch_replication_callback
# TODO: remove all wildcards
from modeling.deeplab import *
from modeling.fpn import FPN
fr... | lane_train.py | MIT | 18,369 | 3a6c2e102d2aa773ba85108eef1e10e22ecbec74 | Python | py | |
"""Gaussian-based Convolutional Restricted Boltzmann Machine.
"""
import time
from typing import Optional, Tuple
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from tqdm import tqdm
import learnergy.utils.constants as c
import learnergy.utils.exception as e
from lea... | learnergy/models/gaussian/gaussian_conv_rbm.py | MIT | 7,262 | 3a6c42c9f877a7a38598e8025b10958641c25dfb | Python | py | |
import sys
import torchpruner
import torchpruner.model_tools as model_tools
import torchvision
import torch
import torch.nn as nn
# model creator, the input shape, prune_modules
models = {
# "faster_rcnn": (torchvision.models.detection.fasterrcnn_resnet50_fpn,(1,3,300,400),
# [('self.backbone.body.l... | examples/support_model/detection.py | MIT | 1,438 | 707dae08c965ab0fb581a971d74b368852c037ae | Python | py | |
# -*- coding: utf-8 -*-
# @Date : 2/16/21
# @Author : Xinyu Gong (xinyu.gong@utexas.edu)
# @Link : None
# @Version : 0.0
import torch
import torch.nn as nn
from .modules import DisBlock, GenBlock, OptimizedDisBlock
class Generator(nn.Module):
def __init__(self, args, activation=nn.ReLU()):
super(... | GAN/models/sngan_small_128.py | MIT | 3,771 | 3a6c6331d64d1a20d5f4665ec2c887879291e164 | Python | py | |
# coding: utf-8
import gym
import torch.utils.data as data
from dynamics import *
from controller import *
from utils import *
from quanser_robots.common import GentlyTerminating
import time
def test(mpc, model):
reward_episodes = []
for i in range(data_fac.n_mpc_episodes):
data_tmp = []
label... | Quanser Robot/MPC/MPC-ball_balancer/test.py | MIT | 2,023 | 3a6c6fb39881f0cad53ca19b6d734638784a3551 | Python | py | |
import unidecode
import torch
from torch.autograd import Variable
from collections import Counter
import observations
import os
import pickle
cuda = torch.cuda.is_available()
def data_generator(args):
file, testfile, valfile = getattr(observations, args.dataset)('data/')
file_len = len(file)
valfile_len... | MCRM/char_ptb/utils.py | MIT | 2,995 | 106c8b1b2fd2179df5b4127dcb0bdd09e773056a | Python | py | |
import torch
from . import util
def cosine_similarity(a, b, normalize=True, inplace=False):
"""
Compute batched cosine similarity between 'a' and 'b'
a: torch.Tensor, shape : [l, d_vector, m]
b: torch.Tensor, shape : [l, d_vector, n]
normalize: bool, default : True
if True, a and b will be norm... | torchpq/metric.py | MIT | 3,503 | 03e601263e7c15a2ede3c8a06c141e2fd553f825 | Python | py | |
"""Data transformation test.
This test contains the following test:
- Transformations could be ``None``, ``yaml``, ``json`` or ``dict``.
- When it is ``None``, the script loads the default transforms
- When it is ``yaml``, ``json`` or ``dict``, `albumentations` package
deserializes the transformati... | tests/pre_merge/pre_processing/transforms/test_transforms.py | Apache-2.0 | 2,642 | 3a6c954435353d0c4ecf95c02dcb5a92fdb480fe | Python | py | |
import numpy as np
import wandb
from torch.utils.tensorboard import SummaryWriter
import omegaconf
class Writer(SummaryWriter):
def __init__(self, cfg, logdir):
self.cfg = cfg
if cfg.log.use_tensorboard:
self.tensorboard = SummaryWriter(logdir)
if cfg.log.use_wandb:
... | utils/writer.py | Apache-2.0 | 933 | d7f17136e4f9fbae4ce910f7214ba6d8a9d6a436 | Python | py | |
from __future__ import print_function, absolute_import
import time
from collections import OrderedDict
import pdb
import torch
import numpy as np
from .evaluation_metrics import cmc, mean_ap, map_cmc
from .utils.meters import AverageMeter
from torch.autograd import Variable
from .utils import to_torch
from .utils im... | reid/evaluators.py | Apache-2.0 | 12,347 | 5f280a42edc23de91b3193956546da8b40be94ae | 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/retrieval/recall.py | Apache-2.0 | 3,405 | 3a6cd0cc1b7b19f28f40a4e270ddcbcf3800127c | Python | py | |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Train and eval functions used in main.py
"""
import math
import sys
from typing import Iterable, Optional
import torch
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
from losses import DistillationLoss
import utils
def t... | engine.py | Apache-2.0 | 3,518 | a90f1f1a361800dbc989e78ca047df76568dae50 | Python | py | |
from io import BytesIO
import requests
import torch
from PIL import Image
from torchvision import models
from torchvision import transforms
class PythonPredictor:
def __init__(self, config):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"using device: {self.device}")
... | test/apis/pytorch/object-detector/predictor.py | Apache-2.0 | 1,629 | ff57e6c4e48386d49e422cc4b0e78d8807385c99 | Python | py | |
import torch as tr
class Hyperparams:
"""
Base Hyperparams class.
It uses base version of bcgan with 1D x space and z space
"""
dtype = float
# Trainer parameters:
n_iterations = 10000
show_visual_while_training = True
train_generator_adv = True
train_autoencoder = True
... | src/hyperparams/toy_gaussians.py | MIT | 1,466 | 3a6d3d0fac946a28c41345fbeaffaa3784b7a3fa | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from typing import Any, Dict, Optional, Type, Union
import torch
from accelerators.pytorch.lib.accelerator_lowering import (
lower_modules_to_accelerator,
lower_split_model_to_accelerator,
)
from accelerators.pytorc... | pytext/task/new_task.py | BSD-3-Clause | 19,202 | 3a6d4dff86f7f4cdb8aab11af8a265acd5995c12 | Python | py | |
"""
GraN-DAG
Copyright © 2019 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu
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... | dcdi/dag_optim.py | MIT | 5,015 | 03fc4b0de3d934899e1183040b495df02389a657 | Python | py | |
import os
import numpy as np
from torch.utils.data import Dataset
from utils import load_feature, tokenize
class TACOS(Dataset):
def __init__(self, feature_path, data, word2vec, is_training=True):
self.data = data
self.feature_path = feature_path
self.is_training = is_training
sel... | code/datasets/TACOS.py | MIT | 2,593 | 3a6d94dbc4dae7f0f0e528b2a3c55884a01ff0cf | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 30 10:07:33 2019
@author: Yang Xu
"""
import cv2
import numpy as np
import pandas as pd
import NaiveDE
##neural net
import torch
import torch.nn.functional as F
import umap
import scipy.stats
from scipy.special import softmax
from sklearn.cluster i... | CoSTA/MERFISH_by_CoSTA.py | MIT | 11,492 | 3a6d9ecc05194ffb399afc605c378dafa25a2f4a | Python | py | |
import torch
from synthesizer import audio
from synthesizer.hparams import hparams
from synthesizer.models.tacotron import Tacotron
from synthesizer.utils.symbols import symbols
from synthesizer.utils.text import text_to_sequence
from vocoder.display import simple_table
from pathlib import Path
from typing import Union... | synthesizer/inference.py | MIT | 7,691 | 3a6dc6c89702b44dccf66e7e8d0fbca9590dae8a | Python | py | |
import os
from pathlib import Path
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, models
from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor
import ignite.distributed as idi... | assets/tldr/teaser.py | BSD-3-Clause | 6,616 | 3a6dcd3fa58324414a618542beea62c26a553560 | Python | py | |
# Training DNC
import sys
import time
import torch
import random
import numpy as np
# torch.autograd.set_detect_anomaly(True) # Setting Anomaly Detection True for finding bad operations
def random_seed():
seed = int(time.time()*10000000)
random.seed(seed)
np.random.seed(int(seed/10000000)) # NumPy s... | train.py | MIT | 1,259 | 3a6de186c72e63907412fb086b2a8060d61daf7a | Python | py | |
import sys
sys.path.append('..')
from utils import *
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# from torchvision import datasets, transforms
class OthelloNNet(nn.Module):
def __init__(self, game, args):
# game params
self.board_... | trading/pytorch/TradingNNet.py | MIT | 3,913 | 3449179508f88166f0c467539ab16120b4683c8c | Python | py | |
#
# Copyright 2016 The BigDL Authors.
#
# 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 ... | python/nano/src/bigdl/nano/utils/inference/pytorch/model.py | Apache-2.0 | 1,954 | 3a6e28a902ec2969e8388197d4254eb90a98c2ba | Python | py | |
import torch
from torch.utils import data
from torchvision import datasets, transforms
import numpy as np
import os
from dataset_wrapper import DatasetWrapper
def get_loader(dataset_name, data_dir, batch_size, mode, shuffle, num_workers=1):
data_set = None
data_dir = os.path.join(data_dir, dataset_name.lower... | others/loader.py | MIT | 5,253 | 3a6e33ef5e771661dfb3049eb2aaa81d77fc9ec8 | Python | py | |
from enum import Enum
__all__ = ("DataType", "Algorithm", "Dataset", "VariationalDistribution")
class DataType(Enum):
single = 1
float32 = 2
double = 11
float64 = 12
def to_torch_dtype(self):
import torch
if self.value < 10:
return torch.float32
else:
... | benchmark/benchmark_utils.py | MIT | 1,691 | fcf200fb280ad4017aa711a1017fee2eb5904acf | Python | py | |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/config.ipynb (unless otherwise specified).
__all__ = ['create_path', 'DATA_PATH', 'MODEL_PATH', 'OUTPUT_PATH', 'LR', 'TRAIN_BATCH_SIZE', 'VALID_BATCH_SIZE',
'NUM_EPOCHS', 'MAX_SEQ_LEN', 'MODEL_NAME', 'DEVICE', 'TOKENIZER']
# Cell
import os
import torch
import... | Bert4NER/config.py | Apache-2.0 | 1,020 | 3a6e934f5f80bd807729620a819f11fc439cf575 | Python | py | |
import argparse
import time
import numpy as np
import torch
import torch.nn.functional as F
import dgl
from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset
from gcn import GCN
#from gcn_mp import GCN
#from gcn_spmv import GCN
def evaluate(model, features, labels, mask):
model.eval()
... | examples/pytorch/gcn_bit_random/evaluate.py | Apache-2.0 | 3,454 | 1e7313f14c30f04088908538ee569e86dda2ec4b | Python | py | |
#!/usr/bin/env python3
"""
part1.py
UNSW COMP9444 Neural Networks and Deep Learning
ONLY COMPLETE METHODS AND CLASSES MARKED "TODO".
DO NOT MODIFY IMPORTS. DO NOT ADD EXTRA FUNCTIONS.
DO NOT MODIFY EXISTING FUNCTION SIGNATURES.
DO NOT IMPORT ADDITIONAL LIBRARIES.
DOING SO MAY CAUSE YOUR CODE TO FAIL AUTOMATED TESTIN... | hw2/part1.py | MIT | 2,629 | 5e212b6ff4c6a7873783cef2ec2789c2281b7c6f | Python | py | |
import os, sys
import numpy as np
import json
import random
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm, trange
import scipy.io
import matplotlib.pyplot as plt
from run_netf_helpers import *
from MLP import *
from load_nlos import *
from math import ceil
device... | run_netf.py | MIT | 33,457 | 3a6f00d27af4d2b64694c7549f39178744e9fff1 | Python | py | |
import torch
import argparse
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.pyplot as plt
from IPython import embed
import numpy as np
from matplotlib.patches import Circle
import matplotlib.cm as cmap
class Cursor(object):
def __init__(self, sample_ax, draw_ax):
self.sample_ax =... | scripts/inspect_h36m_fusion.py | MIT | 2,873 | 3a6f01575e02d27027d7a7be0d3b5201dbe3685c | Python | py | |
from typing import Dict, Union, Callable, List, Optional
from pathlib import Path
import subprocess
import numpy as np
import pandas as pd
import random
import torch
import yaml
import json
import shutil
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.linear_model import Logist... | poison.py | BSD-3-Clause | 37,584 | b273d07be67763474ce0e955fb86a8ce1167a0a0 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from sqlnet.model.modules.word_embedding import WordEmbedding
from sqlnet.model.modules.aggregator_predict import AggPredictor
from sqlnet.model.modules.selection_predict import SelPredictor
from sq... | sqlnet1.py | BSD-3-Clause | 18,386 | 343174f20c5178e793daf3dddbabac99ef1d6aa2 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from UnarySim.sw.kernel.linear import UnaryLinear
from UnarySim.sw.kernel.relu import UnaryReLU
class NN_SC_Weight_Clipper(object):
"""
This is a clipper for weights and bias of neural networks
"""
def __init__(self, frequency=1, mode="... | sw/kernel/nn_utils.py | MIT | 7,361 | 3a6f231f9c9f77ccf647e2f77efea9c44c8a79cf | Python | py | |
import os
import numpy as np
from PIL import Image
from PIL import ImageFile
import torch
from torch.utils import data
ImageFile.LOAD_TRUNCATED_IMAGES = True
Image.MAX_IMAGE_PIXELS = None
def random_crop(img):
w, h = img.size
if w > h:
r = w/h*512
img = img.resize((int(r), 512))
w_in... | data.py | MIT | 1,996 | 3a6f2af15bd2659e796efb70e61129727f9af698 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from model_utils.resnet import Resnet18
# from modules.bn import InPlaceABNSync as BatchNorm2d
class ConvBNReLU(nn.Module):
def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
super(ConvBNReLU, self).__init_... | seg_mask_modifs/model_utils/model_celebmask.py | MIT | 10,873 | 707870763e5a9f5e863a3673e71e452a1084c103 | Python | py | |
import torch
import logging
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from Utils.constants import GEM
class GeMHeadNet(nn.Module):
''' module for the GeM network '''
def __init__(self, pool, whiten):
super(GeMHeadNet, self).__init__()
self.pool = pool
se... | src/Networks/GeMNet.py | MIT | 1,382 | 3a6f549102358a3ffedddeaf68ab239b8a179814 | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import math
import warnings
from functools import partial
from functools import wraps
import numpy
import torch
from torch._C import ListType, OptionalType
... | deeploader/x2onnx/converters/onnx_torch/symbolic_opset9.py | Apache-2.0 | 82,388 | 12ee9b128df90e0eae8f56a8a4798cc846a9f284 | Python | py | |
import colorsys
import os
import cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import tqdm
from PIL import Image, ImageDraw, ImageFont
from scipy.io import loadmat
from torch.autograd import Variable
from nets.retinaface import RetinaFace
from retinaface import Retinaf... | evaluation.py | MIT | 12,929 | 5e24df77f9e897eab1511d2444e0f7fc976ba4b0 | Python | py | |
import abc
import copy
import torch
import numpy as np
from rl_agents.agents.budgeted_ftq.greedy_policy import optimal_mixture, pareto_frontier_at
from rl_agents.agents.common.utils import sample_simplex
class BudgetedPolicy:
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def execute(self, state, beta)... | rl_agents/agents/budgeted_ftq/policies.py | MIT | 2,990 | 3a6f59dd42ee567aaf70b2026058a750ad51b7b3 | Python | py | |
import time
import logging
def timeit(f):
""" Decorator to time Any Function """
def timed(*args, **kwargs):
start_time = time.time()
result = f(*args, **kwargs)
end_time = time.time()
seconds = end_time - start_time
logging.getLogger("Timer").info(" [-] %s : %2.5f s... | utils/misc.py | MIT | 1,393 | d7a5cd00940e7f3d9410f008ac76fb4bdf7477ae | Python | py | |
from easydict import EasyDict
import torch
import os
config = EasyDict()
config.gpu = "1"
# dataloader jobs number
config.num_workers = 12
# batch_size
config.batch_size = 2
# training epoch number
config.max_epoch = 200
config.start_epoch = 0
# learning rate
config.lr = 1e-4
# using GPU
config.cuda = True
con... | util/config.py | MIT | 1,186 | 3a6f8ad3748f7dd532858c2b59e251bea9d1bf6e | Python | py | |
from typing import Tuple
import torch
from torch import nn
from models.vae import BaseVAE
class LargeVAESmallKernels(BaseVAE):
def __init__(self, model_parameters: dict, use_kld_warmup: bool, kld_weight: float = 1.0):
super().__init__(model_parameters, use_kld_warmup, kld_weight)
self.conv_1 =... | models/vae/large_vae_small_kernel.py | MIT | 8,834 | 343d4c175db1a609048f96830a3a79dcb3f4a943 | Python | py | |
import torch
from torch import nn
import torch.nn.functional as F
import pj3_emotion_car.config as conf
def optimize_step(model, input_tensors, category_tensor, optimizer):
model.zero_grad()
model.train()
output = model(input_tensors)
loss = F.cross_entropy(output, category_tensor)
optimizer.zero_... | pj3_emotion_car/model.py | Apache-2.0 | 6,703 | 3a6fbac38d56ebc92998cd05c3c92509c39718f9 | 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/accelerators/test_accelerator_connector.py | Apache-2.0 | 30,569 | 5f5d3a3877fa72f3a6a956c0668e803ae643859d | Python | py | |
import os
import pytest
import torch
import tests.models.utils as tutils
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import (
ModelCheckpoint,
)
from pytorch_lightning.core import memory
from tests.models import (
LightningTestModel,
)
from pytorch_lightning.trainer.distrib_parts im... | tests/test_gpu_models.py | Apache-2.0 | 13,427 | 5fd37870eee2327652e3c7ac7179db776dd3a7a1 | Python | py | |
import torch
from torch import nn
from einops.layers.torch import Reduce
from .utils import pair
class PreNormResidual(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.fn = fn
self.norm = nn.LayerNorm(dim)
def forward(self, x):
return self.fn(self.norm(x)) + x
... | models_pytorch/s2_mlp_v2.py | MIT | 4,532 | 3a704bb4e70e0cfe959de111d70ba30df4c3df1b | Python | py | |
import math
import numpy as np
import torch.nn as nn
from torch.nn.modules.utils import _pair
from .quantizable_ops import (
QuantizableConv2d,
QuantizableLinear
)
from utils.config import FLAGS
class DepthwiseSeparableConv(nn.Module):
def __init__(self, inp, outp, stride, input_size):
super(Dep... | quantization/lbq-v2/models/q_mobilenet_v1.py | MIT | 3,852 | 7cd7c1ba444332ea710834f2bd6efd1821214318 | Python | py | |
import logging
import math
from typing import Any, Dict, List, Tuple
import torch
import torch.nn.functional as F
from overrides import overrides
from allennlp.data import TextFieldTensors, Vocabulary
from allennlp.models.model import Model
from allennlp.modules.token_embedders import Embedding
from allennlp.modules ... | allennlp/models/coreference_resolution/coref.py | Apache-2.0 | 30,906 | 4b8cc0dead0f785e4fd61a6bc240b6b4a03c0e00 | Python | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.