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# -*- coding: utf-8 -*-
"""Tests for distributed multiplication operators."""
# Standard library imports
import functools
# PyTest imports
import pytest
# PyTorch imports
import torch
# Local imports
from distributed_dot_product.utils.comm import get_rank, get_world_size
from distributed_dot_product.multiplicatio... | distributed_dot_product/tests/test_multiplication.py | MIT | 6,470 | 10a3abf1c658dcf35069663a0d639bedf55df841 | Python | py | |
# -*- coding: utf-8 -*-
"""
@date: 2022/4/25 下午4:06
@file: tiny_autocoder.py
@author: zj
@description:
"""
import torch
from zcls2.config.key_word import KEY_OUTPUT
from simpleir.configs.key_words import KEY_FEAT
from simpleir.models.tiny_autocoder import TinyAutoCoder
if __name__ == '__main__':
img = torch.r... | tests/model/tiny_autocoder.py | Apache-2.0 | 629 | 3a01a8bec27e4bf406bfcb9fa8d3b0e28cf4bb1f | Python | py | |
# Copyright 2018-2020 Xanadu Quantum Technologies 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... | tests/test_measurements.py | Apache-2.0 | 51,356 | 3a01ad468fa98be1639c99483531c384392a9d9e | Python | py | |
import torch
import torch.nn as nn
from asym_rlpo.data import Episode
from .base import PO_A2C_ABC
class AsymA2C(PO_A2C_ABC):
model_keys = {
'agent': [
'action_model',
'observation_model',
'history_model',
'policy_model',
],
'critic': [
... | asym_rlpo/algorithms/a2c/asym_a2c.py | MIT | 1,067 | 4598390cb291c2400fbbbe0ab94439176bfe0d3f | Python | py | |
import torch
import numpy as np
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class ReplayBuffer:
"""Fixed-size buffer to store experience tuples."""
def __init__(self, state_size, action_size, buffer_size, batch_size, priority=False):
"""Initialize a ReplayBuffer object.
... | ReplayBuffer.py | MIT | 2,803 | 42888070da99f044f3ece1749ee0b3d6c561fe16 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from i2a.utils import get_conv_output_dims
from functools import reduce
class EncoderCNNNetwork(nn.Module):
def __init__(self, input_shape):
super(EncoderCNNNetwork, self).__init__()
input_channels = input_shape[0]
input_dim... | i2a/rollout_encoder.py | MIT | 5,994 | b29288adbac51f27ff23a7d0b80230959f2cd4bb | Python | py | |
import torch
import torch.utils.data
from torchvision import datasets
import numpy as np
# Assumes that tensor is (nchannels, height, width)
def tensor_rot_90(x):
return x.flip(2).transpose(1, 2)
def tensor_rot_180(x):
return x.flip(2).flip(1)
def tensor_rot_270(x):
return x.transpose(1, 2).flip(2)
def rotate_si... | code/digit/rotation.py | MIT | 1,964 | 106c9c1d74179285d512cd268021b0fcd5b3d548 | Python | py | |
import os
import pickle
import cv2
import numpy as np
import torch
from torch.utils.data import DataLoader
from collections import OrderedDict
import matplotlib.pyplot as plt
from tsnecuda import TSNE
from scipy.ndimage import gaussian_filter1d
from .abstract.abstract_hook import HookBase
from lib.datatool... | lib/core/hook/stae_hooks.py | Apache-2.0 | 6,159 | 3a020e2d66b6551b60bba9ac8c88d142d33a886e | Python | py | |
# Authors: YiFan Sun <peter@megvii.com>, Changmao Cheng <chengchangmao@megvii.com>
from typing import Tuple
import torch
from torch import nn
from torch.nn.functional import cross_entropy
class ClassificationCircleLoss(nn.Module):
"""Circle loss for class-level labels as described in the paper
`"Circle Loss:... | cvpods/modeling/losses/circle_loss.py | Apache-2.0 | 2,636 | 3a02126b25c63266f7ba91e8b43a23ad85504abe | Python | py | |
import torch as th
import torch.nn as nn
from leibniz.unet.senet import SELayer
class DirectBlocks(nn.Module):
extension = 1
least_required_dim = 1
def __init__(self, dim, step, relu, conv, reduction=16):
super(DirectBlocks, self).__init__()
self.dim = dim
self.step = step
... | src/blks/direct.py | Unlicense | 1,174 | 3a0252ad9ae269d4fdc006c8a697b413236bc9fe | Python | py | |
"""
Copyright (c) 2018 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... | pytorch_toolkit/face_recognition/datasets/vggface2.py | Apache-2.0 | 5,447 | 12f3afaa318a244c3a234735e5aeb33f3ad11435 | Python | py | |
import argparse
import os
import numpy as np
import math
import itertools
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from mnistm import MNISTM
import torch.nn as nn
... | .history/implementations/pixelda/pixelda_20190101223306.py | MIT | 19,208 | 5e54504c0f499aec31cf2392c5aa6b29e26b4ab7 | 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... | pytorch_lightning/utilities/apply_func.py | Apache-2.0 | 10,975 | d7d09251f60870daa3b6dc2ac1202cdb0bcada6c | Python | py | |
# Copyright (c) 2018-2021, 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:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of condit... | elegantrl/envs/isaac_integration/tasks/cartpole.py | Apache-2.0 | 9,286 | 3a0299d28d1437633bd4c0e5f3a29803402d6f6e | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
# from pycocotools.cocoeval import COCOeval
from utils.cocoeval import COCOeval
#from utils.voc_metric.pascalvoc import cal_voc_map
import numpy as np
import torch
import json
im... | src/lib/datasets/dataset/foodsoft2_100.py | MIT | 9,803 | 3a02d926ef164371e544c89299c5dacfc603562f | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
import gc
import math
import time
import random
import torch
from torch import optim
from tqdm import tqdm
from pythia.common.meter import Meter
from pythia.common.registry import registry
from pythia.common.report import Report
from pythia.common.dataset_loader impo... | pythia/trainers/base_trainer.py | BSD-3-Clause | 19,121 | fce1d74c5c3055a287c39f56a064116ef4b377d3 | Python | py | |
import torch
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
import torch.nn.functional as F # All functions that don't have any parameters
from torch.utils.data import (
DataLoader,... | BaseLine.py | MIT | 3,122 | 1098c885d74b0eddf7b1498fd8477df6129af6a3 | Python | py | |
"""Train a model on SQuAD.
Author:
Rohit Musti (rmusti@redhat.com)
Chris Chute (chute@stanford.edu)
"""
import numpy as np
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as sched
import torch.utils.data as torchdata
... | exp-x/exp3_train.py | MIT | 9,091 | 03cce0894afae1d81db800d92c87e32094e88ce3 | Python | py | |
import torch
import torch.nn as nn
from torch import optim
from multiprocessing import cpu_count
import numpy as np
import argparse
import os
import time
from networks import LightSpeech
from dataset import DataLoader, collate_fn
from dataset import LightSpeechDataset
from loss import LigthSpeechLoss
import utils
impo... | train.py | MIT | 7,034 | 42041606268ad302f087868f48ad7bc5cdd816ba | Python | py | |
# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | transfer_model/__main__.py | Xnet,X11,RSA-MD | 3,227 | d72d7164825aa591bd47096b978490df721d40bc | Python | py | |
from collections import defaultdict
from functools import partial
import logging
import os
import zipfile
import gzip
from urllib.request import urlretrieve
import torch
from tqdm import tqdm
import tarfile
from .utils import reporthook
from collections import Counter
logger = logging.getLogger(__name__)
class Vo... | torchtext/vocab.py | BSD-3-Clause | 22,463 | 3a03317b6931071b90bfbfd4e4061939a46ef6a0 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
class DoubleMnist:
def __init__(self, loader, label_mapping):
self.loader = loader
self.label_mapping = label... | lib/EGG_research/egg/zoo/language_bottleneck/mnist_classification/data.py | MIT | 3,937 | 28cd5a439c1b7a4bf42787cceecfd34f964888a1 | Python | py | |
import numpy as np
import cv2
import torch
import sys
sys.path.append('..')
import src.oflibpytorch as of
from src.oflibpytorch.utils import to_numpy, to_tensor, show_masked_image
# # # # Usage / Visualisation
# shape = (601, 601)
# flow = of.Flow.from_transforms([['rotation', 601, 601, -30]], shape)
# flow_def = of.... | docs/doc_code.py | MIT | 16,116 | 3a036e3e2268de948ccd5cd3caa23d801bfb1f12 | Python | py | |
import math
import time
import numpy
import ray
import torch
import models
#import asyncio
import time
@ray.remote
class SelfPlay:
"""
Class which run in a dedicated thread to play games and save them to the replay-buffer.
"""
def __init__(self, initial_checkpoint, Game, config, s... | self_play.py | MIT | 18,571 | 4bc41a402bf659aaf007ab766d8ee7205feb4c97 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 ... | eval_linear.py | Apache-2.0 | 13,339 | 5e465c1dd4af2f2e28b5c3adf536306d919b7a86 | Python | py | |
# Copyright (c) 2020 Horizon Robotics and ALF Contributors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | alf/data_structures.py | Apache-2.0 | 17,069 | f94c92c92f87db77e48f986b5291fcc3a8621bb4 | Python | py | |
import os
import torch
import numpy as np
import json
import h5py
__all__ = ['Checkpoint', 'RemoveCOMMotion', 'BiasPotential', 'FileLogger',
'TensorboardLogger', 'TemperatureLogger', 'MoleculeStream',
'PropertyStream']
class SimulationHook:
"""
Basic class for simulator hooks
"""
... | src/schnetpack/simulate/hooks.py | MIT | 13,448 | 3a03f0a9d45de3e95b9e81c3730fc1d5a8daad73 | Python | py | |
# -*- coding: utf8 -*-
# Copyright 2019 JSALT2019 Distant Supervision 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
#
# U... | fairseq/data/handwriting/aligner.py | MIT | 126,392 | 3a03f994ae06997794ab27fdc15e1904e5b2dcaa | Python | py | |
import pytest
import torch
import numpy as np
from bgflow import XTBEnergy, XTBBridge
try:
import xtb
xtb_imported = True
except ImportError:
xtb_imported = False
pytestmark = pytest.mark.skipif(not xtb_imported, reason="Test requires XTB")
@pytest.mark.parametrize("pos_shape", [(1, 3, 3), (1, 9)])
def ... | tests/distribution/energy/test_xtb.py | MIT | 2,495 | 3a03fb2091ad2f4baac62be920ed328b1c28a7c1 | Python | py | |
# train.py
import os
import pandas as pd
import albumentations
import torch
from sklearn import metrics
from sklearn.model_selection import train_test_split
import dataset
import engine
from model import get_model
if __name__ == "__main__":
data_path = "../input_image/"
device = "cuda"
epochs = 10
... | Image_Problem/Classification/src/train.py | MIT | 2,019 | 34176fe079d0064f465bd6b86e727ee9e43a6323 | Python | py | |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | src/accelerate/data_loader.py | Apache-2.0 | 19,589 | 24a7ad08af431fdc7cc492e6f7e7c438b7c9629b | Python | py | |
import time
from torch.utils.data import DataLoader, Dataset
from torch.optim import lr_scheduler
from modelZoo.K_FPN import load_preTrained_model
import torch.nn as nn
from modelZoo.DyanOF import creatRealDictionary
from modelZoo.K_FPN import keyframeProposalNet
from utils import *
from JHMDB_dloader import *
import s... | test_jhmdb.py | MIT | 5,731 | 3a0403c047095f2e40cb73f209def893f63b29d5 | Python | py | |
# Copyright Ping Luo and Jiamin Ren and Zhanglin Peng and Ruimao Zhang and Jingyu Li
# This file was taken from the SwitchNorm repository at
# https://github.com/switchablenorms/Switchable-Normalization/blob/5472286952ba5519a7d3229d9ad899a5c1b2e5b1/devkit/ops/switchable_norm.py
# by Ping Luo and Jiamin Ren and Zhangli... | c_unet/utils/normalization/SwitchNorm3d.py | MIT | 3,943 | 3a040d081e6f79ba5ff57856fab66d42aa2de089 | Python | py | |
from collections import namedtuple
import torch
AttrTensor_ = namedtuple('AttrTensor', ['data', 'lengths', 'word_probs', 'pc'])
class AttrTensor(AttrTensor_):
"""A wrapper around the batch tensor for a specific attribute.
The purpose of having a wrapper around the tensor is to include attribute specific
... | deepmatcher/batch.py | BSD-3-Clause | 4,255 | 3a0416951f2a7d5b9be5eac182fc33297d12da17 | Python | py | |
import os
import argparse
import torch
import numpy as np
import PIL.Image as Image
import torchvision.models as models
from torch import nn
from tqdm import tqdm
import torchvision.datasets as datasets
import torch.utils.data as data
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from to... | inference.py | MIT | 3,671 | 28fe002505ece2724f60bc0ac5ce64d98d207c35 | Python | py | |
import datetime
import os
import time
import torch
import torch.utils.data
import transforms as T
from torch import nn
import torchvision
import numpy as np
import scipy.io
import random
from PIL import Image
import matplotlib.image as mpimg
from tqdm import tqdm
from coco_utils import get_coco
import presets
import ut... | semseg/multimodel_train.py | MIT | 25,192 | 1e2b7172b7c6b44b2333421db842ebd9029e089b | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copyright (c) 2019, Linear Labs Technologies
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
Unles... | BLOX/DataSet/DataSet.py | Apache-2.0 | 5,057 | ff7fbeeb27176867e5317aed286f027f9e3ece30 | Python | py | |
import torch
import torch.nn as nn
from torch.distributions.normal import Normal
class Flow1d(nn.Module):
def __init__(self, n_components):
super(Flow1d, self).__init__()
self.mus = nn.Parameter(torch.randn(n_components), requires_grad=True)
self.log_sigmas = nn.Parameter(torch.zeros(n_com... | 1d/model.py | MIT | 771 | fcfd02f55e35d0081d961e1c741c60e6cebbe72e | Python | py | |
import numpy as np
import torch
import torch.utils.data as data
import torchvision
import torchvision.transforms as transforms
from PIL import Image, ImageFile
from random import random
from os.path import join, dirname
from data.dataset_utils import *
import pandas as pd
import os
Image.LOAD_TRUNCATED_IMAGE... | data/fgdr_dataset.py | MIT | 5,018 | 1077e2a792eef8b911992c4d7e9353fd9f74175d | Python | py | |
"""Classification and regression loss functions for object detection.
Localization losses:
* WeightedL2LocalizationLoss
* WeightedSmoothL1LocalizationLoss
Classification losses:
* WeightedSigmoidClassificationLoss
* WeightedSoftmaxClassificationLoss
* BootstrappedSigmoidClassificationLoss
"""
from abc import ABC... | second/pytorch/core/losses.py | MIT | 18,115 | 103574944a1703e5e9634de90536c0b5d6474e42 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 ... | cfvpy/models.py | Apache-2.0 | 2,631 | 65064c67e02746c32e42cab1930723641f7eba8e | Python | py | |
import numpy as np
import torch
def mask(shape, lengths, dim=-1):
assert dim != 0, 'Masking not available for batch dimension'
assert len(lengths) == shape[0], 'Lengths must contain as many elements as there are items in the batch'
lengths = torch.as_tensor(lengths)
to_expand = [1] * (len(shape)-1)... | utils/functional.py | MIT | 7,108 | b2a9fcbaee3bcb14979405678026bdbfe9962062 | Python | py | |
import os
from tqdm.auto import tqdm
from pathlib import Path
from random import shuffle
from math import isclose, ceil
from itertools import combinations_with_replacement
import gzip
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import skimage.transform
import torch
import tor... | experiments/runs/mnist25-6-6-6-5relu/run3.py | MIT | 9,383 | 3a04bbf5da32cc59714519e51ba40965efaf023e | Python | py | |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and 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.or... | transformers/modeling_distilbert.py | MIT | 39,828 | 7098529c9ea0299efceb77a70bd767020a1f3241 | Python | py | |
# ========================================================
# CONFIDENTIAL - Under development
# ========================================================
# Author: Kaicheng Yu with email kaicheng.yu@epfl.ch
# All Rights Reserved.
# Last modified: 2019/11/21 下午1:44
# NOTICE: All information contained herein is, a... | nasws/operations/elastic_utils.py | MIT | 4,482 | 3a04d60c28e0ec10eeca3a1d6a3ae303ae644d67 | Python | py | |
import torch
from codebase.utils.cluster.management.basic_dial_batching import ImagePathFolder, BasicDialTrainBatchLoader, \
list_collate_batcher
class MixedDomainTrainBatchLoader(BasicDialTrainBatchLoader):
'''
Batch loader that produces batches with images sampled from all domains
'''
def __in... | codebase/utils/cluster/management/mixed_domain_batching.py | MIT | 4,060 | 12954c01f1aace6c1a214c2a6159a6a3ce6d456b | Python | py | |
import torch
from sklearn.metrics import (
accuracy_score, precision_score,
recall_score, f1_score,
roc_auc_score
)
class BinaryBase:
def __init__(self, threshold=0.5, use_sigmoid=True):
self.threshold = threshold
self.use_sigmoid = use_sigmoid
def __call__(self, preds, target... | cv_utils/image_classification/metrics/binary.py | MIT | 1,799 | d7bfa18c700a1f96e27bbbc9dff3fc58412cea37 | Python | py | |
from __future__ import print_function
from PIL import Image, ImageFilter
import os
import os.path
import numpy as np
import sys
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
from torchvision.datasets.vision import VisionDataset
from torchvision.datasets import DatasetFolder,ImageFo... | utils/imagenet.py | BSD-3-Clause | 10,807 | 3a04eeaa2a406190c9310e2a88115b59f073231d | Python | py | |
import torch
import requests
import sys
import os
import logging
import torchvision.transforms as T
import torch.nn as nn
import numpy as np
from torch import nn
from dataclasses import dataclass
from functools import partial
from typing import Dict
from torch import Tensor
from .ModuleTransfer import ModuleTransfer
fr... | glasses/utils/PretrainedWeightsProvider.py | MIT | 11,003 | 3a050361f70bc91e45a0b203fd827720829996ec | Python | py | |
import torch
import pytorch_lightning as pl
import wandb
def get_trainer(cfg, engine):
callbacks = list()
callbacks.append(pl.callbacks.EarlyStopping(patience=cfg.training.early_stopping.stopping_patience,
monitor=cfg.training.early_stopping.monitor))
callba... | project_1_2/src/trainer.py | MIT | 1,448 | 5f170a4409021c27e785d20eca98422d9229f8aa | Python | py | |
from collections import namedtuple
import cv2
import numpy as np
import torch
from aabb import AABB
from coding import encode
from utils import compute_scale_down, prob_true
DataLoaderItem = namedtuple('DataLoaderItem', 'batch_imgs,batch_gt_maps,batch_aabbs')
class DataLoaderIAM:
"""loader for IAM dataset"""
... | src/dataloader.py | MIT | 6,182 | 3a05254b16a033819dabb2e41fef6a2d4b2104b7 | Python | py | |
import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.functional as F
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_f... | src/models/layers.py | MIT | 7,015 | 3a053a3ecbe0bca64e85bc5f218eecbbaa4b63be | Python | py | |
from typing import Dict
import torch
from torch import dtype
UNITS: Dict[str, int] = {
"b": 1,
"kb": 10 ** 3,
"kib": 2 ** 10,
"mb": 10 ** 6,
"mib": 2 ** 20,
"gb": 10 ** 9,
"gib": 2 ** 30,
"tb": 10 ** 4,
"tib": 2 ** 40,
}
MEMORY_BYTES: Dict[dtype, int] = {
torch.bool: 1,
to... | koila/constants.py | MIT | 598 | 3a053af792e72cd4fc74f011b50210e7d4541764 | Python | py | |
import copy
import unittest
import numpy as np
import torch
from pytorch_adapt.adapters import SymNets
from pytorch_adapt.containers import Models, Optimizers
from pytorch_adapt.hooks import (
SymNetsCategoryLossHook,
SymNetsDomainLossHook,
SymNetsEntropyHook,
SymNetsHook,
validate_hook,
)
from py... | tests/hooks/test_symnets.py | MIT | 9,131 | 3a053f3dea09918b2e2402a5503e2886c9a0da99 | 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... | pytorch_lightning/plugins/training_type/parallel.py | Apache-2.0 | 5,316 | 3a05455b87990032acd52d982de0a21589c2943d | Python | py | |
import argparse
import math
import os
import numpy as np
import pandas as pd
import torch
from torch import nn
from torch import optim
from torch.autograd import Variable
from torch.nn import functional as F
from torch.utils.data import DataLoader
from tqdm import tqdm
from build_vocab import WordVocab
from dataset i... | smiles_transformer/pretrain_trfm.py | MIT | 7,351 | 3a054a91258abde1e38ce732d4f3ff90e448d809 | Python | py | |
import torch
from NeuralBlocks.trainers import Trainer
from fastprogress import progress_bar
from NeuralBlocks.trainers.ganloss import GANLoss
from NeuralBlocks.trainers.logger import Logger
class GANTrainer(Trainer):
def __init__(self, G_model,
D_model,
dataloader,
... | trainers/gan_trainer.py | MIT | 2,534 | 52bd8aaadf830738ae2d2c33710d7b0c2094875d | Python | py | |
import torch
import torchvision
import torchvision.transforms as transforms
import torchvision.models as models
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import scipy.io as sio
from scipy.stats import ks_2samp
from scipy.stats im... | Perceptual_Optimization/compute_Perceptual_Differences.py | MIT | 4,999 | 2487ecfab4e6c5453c2b1f24b119d3ff8233f603 | Python | py | |
import os
import sys
from itertools import chain
import numpy as np
import pandas as pd
import torchvision.models
import torch.nn.functional as F
import torch.optim as optim
from torch import nn
from torch.utils.data import DataLoader
from torchsample.callbacks import CSVLogger, LearningRateScheduler
from callbacks... | nn_semisupervised_densenet_169.py | MIT | 6,367 | fce710bcd67d71248db9e87e1f370df5a95a2749 | Python | py | |
import torch
class MCTSvisualiser:
'''
Class that aims to visualize MCTS. It generates .gv file that describe the tree. Then dot from Graphviz can
be used to transform the .gv file into a pdf file with the tree grpahical representation on it.
'''
def __init__(self, env, indent = ' '):
se... | visualization/visualise_mcts.py | MIT | 5,552 | 3a057c94e0dee4072b33ad42851cd10b9c0c9ec2 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn.functional as F
from pytext.config import ConfigBase
from pytext.config.component import Component, ComponentType
from pytext.utils import loss as loss_utils, precision
from pytext.utils.cuda impo... | pytext/loss/loss.py | BSD-3-Clause | 17,249 | d7a925e527e38ba3519968e90a10c2c81bb4b486 | Python | py | |
from nn import NN
import random
import numpy as np
import torch
class Game:
def __init__(self, seed, n, K, K2, K3):
self.n = n
self.K = K
self.K2 = K2
self.K3 = K3
self.seed = seed
np.random.seed(seed)
torch.manual_seed(seed)
self.xhat = np.random.rand... | 1l/nnGame.py | MIT | 1,431 | 3a05d9a21e69b3b187245d208ef6b23b587fe679 | Python | py | |
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
###############################################################################
# Helper Functions
###############################################################################
def get_norm_layer(norm... | networks/gan_networks.py | MIT | 18,270 | e93bfbc475b5ad75cb58dd0845ab04983d7cc6ae | Python | py | |
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | pretrain_gpt2.py | MIT | 4,001 | 3a05e9ebd3f8d43d50ab028ef4d8e2066d67f9a2 | Python | py | |
import random
import math
import os
import numpy as np
import sys
from PIL import Image
from utils import show
from metrics import AEBatch, SEBatch
import time
import torch
import scipy.io as scio
class Estimator(object):
def __init__(self, setting, eval_loader, criterion=torch.nn.MSELoss(reduction="sum")):
... | eval/Estimator.py | MIT | 3,848 | fdfb57ee4a7b10a82b16fa818dcf93084714285c | Python | py | |
from .common import *
from torch.utils.data import DataLoader, Dataset
import torch
import random
class GabProcessor(DataProcessor):
"""
Data processor using DataProcessor class provided by BERT
"""
def __init__(self, configs, tokenizer=None):
super().__init__()
self.data_dir = configs.... | loader/gab.py | MIT | 4,905 | ffb64a8538a24d5a4a41edb4b715869a25557ad3 | Python | py | |
import fastai
from fastai.vision import *
from fastai.callbacks import *
from fastai.utils.mem import *
from torchvision.models import vgg16_bn
from glob import glob
import os
import sys
from PIL import Image
import nibabel as nib
def ensure_dir(file_path):
""" if the directory doesn't exist in the path given,
t... | src/file_converter.py | MIT | 4,891 | e9300c0e1e7e7493d28de2a5ae6c9d985c51212e | Python | py | |
import torch
import torch.nn as nn
from torch.nn import functional as F
from .retrieve import SEG_MODELS_REGISTRY
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(
in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
)
class Bottleneck(nn.Module):
expansion =... | realtime_hand_3d/segmentation/models/pspnet.py | MIT | 9,778 | 429f8b50d30b1d5343ba6ecf5b69449453f88e44 | Python | py | |
import argparse
import json
import os
from catalyst import dl
from catalyst.dl import utils
from catalyst.contrib.dl.callbacks.neptune_logger import NeptuneLogger
import numpy as np
import pandas as pd
from pathlib import Path
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import Stratifie... | nn_trainer.py | MIT | 6,909 | fcac9c7951ed185568080142121e5940713af0b1 | Python | py | |
# from:https://github.com/ildoonet/pytorch-gradual-warmup-lr
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.lr_scheduler import ReduceLROnPlateau
class GradualWarmupScheduler(_LRScheduler):
""" Gradually warm-up(increasing) learning rate in optimizer.
Proposed in 'Accurate, Large Minibatch ... | torchreid/optim/warm_up.py | MIT | 3,075 | 3a064f1f095bbe67edfbd3d2447f42c396eb6328 | Python | py | |
"""
run.py
Written by Eirik Vesterkjær, 2019
Apache License
Entry point for training or testing ESRDGAN
Sets up environment/logging, and starts training/testing
Usage:
python run.py < --train | --test | --use > [ --cfg path/to/config.ini ] [ -h ]
"""
import argparse
import logging
import os
import time
import t... | src/run.py | Apache-2.0 | 6,116 | 3a0650f12aaf3e1b3e20c62bc9e8cb77c49c1f20 | Python | py | |
import torch
# device on which to train
DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# train size
VAL_SIZE = 0.2
# embedding sizes
EMB_DIMS = 5
# number of epochs for Training:
N_EPOCHS = 24
# start learning rate
START_LR = 0.001
# start learning rate
END_LR = 0.001
# start learning ra... | config.py | MIT | 336 | 3a0668e398fd6d15409df0e4f4b434919771b4e2 | Python | py | |
import scipy.special
import torch
# Using exp(-x)I_v(k) for numerical stability (vanishes in ratios)
class ModifiedBesselFn(torch.autograd.Function):
@staticmethod
def forward(self, nu, inp):
self._nu = nu
self.save_for_backward(inp)
inp_cpu = inp.data.cpu().numpy()
return tor... | src/pythae/models/svae/svae_utils.py | Apache-2.0 | 611 | 3a06781e682fa81e8b5bae59532756701c83e14f | Python | py | |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | nemo/collections/nlp/models/language_modeling/megatron_finetune_model.py | Apache-2.0 | 25,964 | 65447d2c7966bf6ca168d2f652c91bb665380162 | Python | py | |
import os
from copy import deepcopy
import pytest
import torch
from torch.optim import SGD, Adadelta, Adagrad, Adam, RMSprop
from pythae.customexception import BadInheritanceError
from pythae.models.base.base_utils import ModelOutput
from pythae.models import WAE_MMD, WAE_MMD_Config
from pythae.trainers import BaseT... | tests/test_WAE_MMD.py | Apache-2.0 | 20,993 | 5f74ad88918954f448ebce8ee0b5f783031f126b | Python | py | |
import torch
import torch.nn as nn
from utils.config import *
from models.BertEncoder import BertEncoder
from models.GraphMemory import GraphMemory
from models.VisualMemory import VisualMemory
from models.Decoder import Decoder
from models.ContextRNN import ContextRNN
from models.ImageEncoder import ImageEncoder
from m... | s2s-ft/multimodalKB/models/MultimodalKB_GlobalLocal.py | MIT | 24,623 | 3a069e7c8bc4641f98e36c5b017f8115de5f0dcd | Python | py | |
# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | src/models/GPT_3/generate/generate_qa_desc_refined.py | MIT | 24,971 | 3a06a7ce2c512d7f913717a43659e63670935c39 | Python | py | |
# Copyright 2021 MosaicML. All Rights Reserved.
"""Logs metrics to dictionary objects that persist in memory throughout training.
Useful for collecting and plotting data inside notebooks.
"""
from __future__ import annotations
import copy
from typing import Any, Dict, List, Tuple, Union
import numpy as np
from tor... | composer/loggers/in_memory_logger.py | Apache-2.0 | 5,785 | e9c9febcd3e23a80eaad0327faf28f2d7aa84225 | Python | py | |
from torch import nn
from transformers import AutoModel, AutoTokenizer, AutoConfig, T5Config
import json
from typing import List, Dict, Optional, Union, Tuple
import os
class Transformer(nn.Module):
"""Huggingface AutoModel to generate token embeddings.
Loads the correct class, e.g. BERT / RoBERTa etc.
:... | sentence_transformers/models/Transformer.py | Apache-2.0 | 6,683 | 654516f9d6a2ee0ca8b1738a1fdabbe78452f954 | Python | py | |
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from mushroom_rl.algorithms.value import DQN
from mushroom_rl.core import Core
from mushroom_rl.environments import *
from mushroom_rl.policy import EpsGreedy
from mushroom_rl.approximators.parametric.torch_approximator import *
from mus... | examples/acrobot_dqn.py | MIT | 3,854 | 3a06ca720fced1cdc06da91cdbc337003bba52f3 | Python | py | |
import torch
from torch.optim.optimizer import Optimizer, required
from torch.autograd import Variable
import torch.nn.functional as F
from torch import nn
from torch import Tensor
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __... | spectral_normalization.py | MIT | 2,277 | 3a06e863ad69658984edc25e3a2ae2072b854bca | Python | py | |
import torch
import pytest
import torchaudio
from fastai.basic_data import Path
from audio import AudioConfig, AudioDataBunch, AudioList, audio_learner
from audio.learner import audio_predict
from audio.util import create_sin_wave
import pytest
@pytest.fixture(scope='session')
def audio_db():
sig, sr = create_... | tests/test_prediction.py | MIT | 1,166 | 3a06ec03d173e5cf7535c07bc6c0b9e45c460700 | Python | py | |
# Copyright (c) 2020 Horizon Robotics and ALF Contributors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | alf/algorithms/dynamic_action_repeat_agent.py | Apache-2.0 | 15,477 | 3a06ec55eb04e33dadca343b9311815e6a7b4c89 | Python | py | |
#!/usr/bin/env python3
import logging
import os
import tempfile
import unittest
import numpy as np
import onnx
import torch
from caffe2.python.onnx import backend as caffe2_backend
from fairseq import models
from pytorch_translate import char_source_hybrid # noqa
from pytorch_translate import char_source_model # no... | pytorch_translate/test/test_onnx.py | BSD-3-Clause | 21,189 | b2eb0c210dfab1e25d9211c9052d39fabffbb317 | Python | py | |
import torch
import torch_geometric
from torch import nn
from torch.nn import functional as F
from torch_geometric.nn import MessagePassing, global_sort_pool
from torch_geometric.utils import add_self_loops, degree
class DGCNN(nn.Module):
"""
Uses fixed architecture
"""
def __init__(self, dim_feature... | gnn-comparison/models/graph_classifiers/DGCNN.py | MIT | 5,157 | fce566de3400b7a7442011f779897cbac8268f0c | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .box_head.box_head import build_roi_box_head
from .mask_head.mask_head import build_roi_mask_head
from .keypoint_head.keypoint_head import build_roi_keypoint_head
from .ke_head.ke_head import build_roi_ke_head
class CombinedROIH... | maskrcnn_benchmark/modeling/roi_heads/roi_heads.py | BSD-2-Clause | 4,199 | 3a07252f0c23586e1ea39598ab4204ec4b156f82 | Python | py | |
# from __future__ import print_function
import os,sys,inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0,parentdir)
from rich import print
from rich import pretty
pretty.install()
from rich import traceback
traceback.i... | train_script/train_on_single_KITTI2015val.py | MIT | 12,034 | 3a0730617f9ebbc1200705b5ff0ea34c88dd1f63 | Python | py | |
import numpy as np
import torch.nn as nn
import torch
from base_layer import BaseLayer
import os
import pickle
class GeneratorLossPathReg(BaseLayer):
def __init__(self, pl_decay=0.01, pl_weight=2.0, opt=None):
super(GeneratorLossPathReg, self).__init__()
self.pl_decay = pl_decay
self.pl_wei... | chapter7/stylegan2_pytorch/generator_loss_pathreg.py | MIT | 1,668 | 3a07325533035867afe9d2d24dd47ad13c8c1a55 | Python | py | |
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | third_party/rlkit_library/rlkit/core/logging.py | Apache-2.0 | 12,103 | 5f8011dfb2929c0cfd11194f67950cde60ed606a | Python | py | |
import numpy as np
import scipy.signal
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.normal import Normal
from torch.distributions.utils import _standard_normal, broadcast_all
from torch.distributions.exp_family import ExponentialFamily
from numbers import Number
from torc... | spinup/algos/pytorch/mbfq/core.py | MIT | 17,230 | ff615304c0a196254799e59fc746ef3695a54be3 | Python | py | |
from pathlib import Path
import tqdm
import torch
import numpy as np
from cherry.envs import build_env
from cherry.agents import get_model, build_agent
from utils.helpers import add_verbosity_parser, read_yaml, copy_yaml, \
get_repo_hexsha, validate_config, get_logger, write_model
class Trainer:
def __init__... | cherry/runner/trainer.py | MIT | 2,349 | 3a0757c6ad35e8a32a663682154ff4d9ce9e6a1a | Python | py | |
import torch
import torch.nn as nn
class KeyPointModel(nn.Module):
def __init__(self):
super(KeyPointModel, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 3, 1, 1)
self.bn1 = nn.BatchNorm2d(6)
self.relu1 = nn.ReLU(True)
self.maxpool1 = nn.MaxPool2d((2, 2))
self.conv... | simple_keypoint/regression/model.py | Apache-2.0 | 1,356 | 700068b711cddb7b31b7a6f768a77efc02502e6a | Python | py | |
import os
import random
import torch
import imageio
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from mpl_toolkits.mplot3d import Axes3D
from pytorch3d.io import load_obj
from pytorch3d.structures import Meshes, Textures
from pytorch3d.re... | utils.py | MIT | 4,123 | 3a07737c05f084e5bae9bb8f327299d596c38f86 | Python | py | |
# code for training the LinearUNet model
import torch
import numpy as np
from learning.learning import Trainer
from utils.smooth_scaler import *
#from qso_fitting.models.utils import QuasarScaler
from utils.QuasarScaler import QuasarScaler
from torch.utils.data import DataLoader
from utils.errorfuncs import WavWeights
... | learning/learning_unet.py | BSD-3-Clause | 21,799 | 3a0789d63b60232a3d721e6d4391e67972acaa6c | Python | py | |
import os
from typing import Dict, List, Tuple
import gym
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from net.replay import ReplayBuffer
from net.utils import initial_seed
from net.noise import NoisyLinear
initial_... | 5_noise.py | MIT | 7,584 | 4b6e7faee1848aa872880cda9dbe4e5227fe0793 | Python | py | |
import torch.nn as nn
import torch
import math
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, 1, 0, b... | online_demo/mobilenet_v2_tsm.py | Apache-2.0 | 6,912 | 1e3f468e8bb8dfe1e89fcd336d30095a15be6ee0 | Python | py | |
from pytorch_lightning.callbacks.gpu_stats_monitor import GPUStatsMonitor
from pytorch_lightning.utilities import rank_zero_warn
class GpuUsageLogger(GPUStatsMonitor):
def __init__(self, *args, **kwargs):
rank_zero_warn("`GpuUsageLogger is now `GPUStatsMonitor`"
" and it will be rem... | pytorch_lightning/callbacks/gpu_usage_logger.py | Apache-2.0 | 400 | 3a0793887f7ab1445c15b8a9d056d32b0e1649d4 | Python | py | |
import torch
from transformers import AdamW
from utils.data import MyDataset
from model import *
from tqdm import tqdm
import os
Batch_size=16
model_name="hfl/chinese-xlnet-base"
#device=torch.device("cpu")
device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
best_accuracy = 0.0
#set random seed
def s... | BERT_BiLSTM/main.py | MIT | 4,100 | 3a07950e6b24710a37871e7bae7ec9ca6b8ca7f5 | Python | py | |
r"""Functional interface"""
import warnings
import math
import numpy as np
import torch
import torch.nn.functional as F
from torch.nn import ParameterList
from ..utils.signaltools import resample
# from torch._C import _infer_size, _add_docstr
# from torch.nn import _reduction as _Reduction
# from torch.nn.modules imp... | torchcomplex/nn/functional.py | MIT | 18,614 | 3a079789b72ab2451554952a951799f94ca2b778 | Python | py | |
import torch
# import torch.nn as nn
import argparse
# import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.autograd import Variable
import os
import time
import random
from utils.dataset import HyperDatasetValid, HyperDatasetTrain # Clean Data set
from mo... | main.py | Apache-2.0 | 16,087 | 7c94b2d787fd45b41d27ca373bbfd514937caa14 | Python | py |
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