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# 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.
#
# Description: generate inputs and targets for the dlrm benchmark
# The inpts and outputs are generated according to the following three opti... | dlrm_data_pytorch_class.py | MIT | 43,421 | 5ec3348822d02e4f6d957f7191cf8250b351d309 | Python | py | |
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import cv2
import matplotlib.pyplot as plt
try:
from util import count_parameters as count
from util import convert2cpu as cpu
from util import predic... | yolo/darknet.py | Apache-2.0 | 18,592 | 12d17ecd1665eb9e1f6dca5533a30b51f452aaae | Python | py | |
import torch.distributed as dist
def get_world_size():
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
return dist.get_world_size()
def reduce_mean(tensor):
'''
Reduce the tensor across all machines, the operation is in-place.
:param tensor: tenso... | src/CGDs/distributed.py | MIT | 597 | dfb0cc090a5ed5cfbcd0acda2a331cb4c96fb11a | Python | py | |
import torch as ch
import numpy as np
import torch.nn as nn
from torch.optim import SGD, lr_scheduler
from torchvision.utils import make_grid
from cox.utils import Parameters
from .tools import helpers
from .tools.helpers import AverageMeter, calc_fadein_eps, \
save_checkpoint, ckpt_at_epoch, has_attr
from .to... | robustness/train.py | MIT | 19,658 | 5fb0cdd5df81f01fb370970dacde6847f99cde27 | 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 io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse imp... | fairseq/distributed/utils.py | MIT | 29,693 | 29ddd75984bf45b5047b0f50e9d28471547bbacf | Python | py | |
import math
import torch
import stable_nalu
import argparse
# Parse arguments
parser = argparse.ArgumentParser(description='Runs the simple function static task')
parser.add_argument('--layer-type',
action='store',
default='NALU',
choices=list(stable_nalu.ne... | experiments/simple_function_recurrent.py | MIT | 5,447 | df9a05dd7004c639a372bd89b24f13f738f53f5f | Python | py | |
#!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Team and The HuggingFace Inc. 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.ap... | examples/pytorch/multiple-choice/run_swag.py | Apache-2.0 | 18,991 | 29ddd9475459640e7e650b4badbcd5e26e436cf3 | Python | py | |
# Code mainly from: https://github.com/lucidrains/stylegan2-pytorch/blob/master/stylegan2_pytorch/stylegan2_pytorch.py
from einops import rearrange
from functools import partial
from kornia.filters import filter2d
from math import log2, sqrt
from random import random
from stylegan2_deepspeed.diff_augment import DiffAu... | stylegan2_deepspeed/stylegan2.py | MIT | 16,053 | 1e57421be1d41e86b3749940447b5470c911ea0c | Python | py | |
# Compatability Imports
from __future__ import print_function
from os.path import join
try:
import tensorflow as tf
except:
print('Tensorflow could not be imported, therefore tensorboard cannot be used.')
from io import BytesIO
import matplotlib.pyplot as plt
import numpy as np
import torch
import datetime
c... | tb_logger.py | MIT | 4,617 | 10cf3ee9bd57bbb6729dc95240885521ee0dc747 | Python | py | |
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import os
import sys
model_urls = {
'imagenet': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth'
}
class BasicConv2d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):... | fastai/models/inceptionresnetv2.py | Apache-2.0 | 11,330 | 29de01ef44e89f03a970ff96b0b04ba932bec3a4 | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# PROGRAMMER: Johannes SB
# DATE CREATED: 05/20/21
# REVISED DATE:
# PURPOSE:
# Provides a image processing function to bring input image in the right format
# for the model
##
# Imports python modules
import torch
import numpy as np
from PIL import Image
def proces... | process_image.py | MIT | 2,003 | 29de04b1d3e20e7fc1d407b028f17e40c58c96b8 | Python | py | |
# Tensorflow solely for the Progress Bar (totally worth it)
from .utilities import ProgressBar
from .move_selection import get_next_move_training
import torch
import numpy as np
import re
from .utilities import (
config,
BOARD_SHAPE,
product
)
def training_loop(model, optimizer, num_games, device, off_policy = l... | pytorch-implementation/lib/training.py | MIT | 3,247 | 3437fc041194834886a5cb15379816ff5a03b037 | Python | py | |
from dataloaders.datasets import cityscapes, coco, combine_dbs, pascal, sbd, apollo, farsight
from torch.utils.data import DataLoader, random_split
def make_data_loader(args, **kwargs):
if args.dataset == 'pascal':
train_set = pascal.VOCSegmentation(args, split='train')
val_set = pascal.VOCSegment... | dataloaders/__init__.py | MIT | 4,571 | f9a05556d27c560e6d12e5844d5c076950fb8e1b | Python | py | |
import torch
import torch.nn as nn
from transformers.modeling_bart import BartForConditionalGeneration
from relogic.logickit.dataflow.semtransparse.grammar.keywords import SKETCH_KEYWORDS, KEYWORDS
import logging
import os
logger = logging.getLogger(__name__)
WEIGHTS_NAME = "pytorch_model.bin"
class SQL2TextModel(nn.... | relogic/pretrainkit/models/sql_to_text.py | Apache-2.0 | 2,972 | 29de2dcd5061c692574fd20150bf41d9d5826422 | Python | py | |
import os
import json
from pathlib import Path
from contextlib import nullcontext
# Set up logging
import sys
import logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
level=logging.WA... | seq2seq/eval_all.py | Apache-2.0 | 13,613 | 29de346b2fde7deb76f0f5f0bfc3a36d24ccbb37 | Python | py | |
from torch.utils.data.sampler import Sampler
from torch.utils.data import Dataset, DataLoader
from utils.dsp import *
from utils import hparams as hp
from utils.files import unpickle_binary
from utils.text import text_to_sequence
from pathlib import Path
import random
#################################################... | utils/dataset.py | MIT | 8,730 | 29de494c355dcf5615a02a17b434720aef667a1b | Python | py | |
import os
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import pickle
from tqdm import tqdm
import argparse
import operator
from torch.nn import functional as F
from dataloader import DatasetWebQSP, DataLoaderWebQSP
from model import RelationExtractor
from torch.... | KGQA/RoBERTa/main.py | Apache-2.0 | 20,945 | 28f1092a8395ad189c15820aa58ae2b3af5f65b3 | Python | py | |
import datetime
import os
import time
import warnings
import presets
import torch
import torch.utils.data
import torchvision
import transforms
import utils
from torch import nn
from torch.utils.data.dataloader import default_collate
from torchvision.transforms.functional import InterpolationMode
try:
from torchv... | references/classification/train.py | BSD-3-Clause | 21,499 | 29de4fce91ce201e5d4ce58c811c767f52b04e40 | Python | py | |
from typing import Callable, Sequence
from functools import partial
from operator import itemgetter
import torch
from keyedtensor import KeyedTensor
def _many_to_one(keyedtensors: Sequence[KeyedTensor], op: Callable, dim: int) -> KeyedTensor:
keys = list(keyedtensors[0])
getter = itemgetter(*keys)
op =... | src/keyedtensor/ops.py | MIT | 2,915 | 28f74e67d6feea0898eb88d25f1d7aa931e608d1 | Python | py | |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""SSD 300 utils adapted from MLCommons.
Based on MLCommons Reference Implementation `here`_
.. _here: https://github.com/mlcommons/training/tree/master/single_stage_detector/ssd
"""
import itertools
import random
from math import sqrt... | composer/models/ssd/utils.py | Apache-2.0 | 15,692 | 5ea8421475da1563850d9b23e9af8c73acde4ec3 | Python | py | |
# pip3 install transquest
# python3 evaluate_transquest.py --testset data/ro-en/dev --checkpoint TransQuest/monotransquest-da-ro_en-wiki
# python3 evaluate_transquest.py --testset data/et-en/dev --checkpoint TransQuest/monotransquest-da-et_en-wiki
import torch
from transquest.algo.sentence_level.monotransquest.run_mod... | scripts/evaluate_sentence_level_transquest.py | MIT | 1,512 | 45c3b88016c6bfd69a83e4d2d6f6a6f16cec0b4e | Python | py | |
# Owner(s): ["module: unknown"]
import torch
from torch.testing._internal.common_utils import TestCase, run_tests
class TestSetDefaultMobileCPUAllocator(TestCase):
def test_no_exception(self):
torch._C._set_default_mobile_cpu_allocator()
torch._C._unset_default_mobile_cpu_allocator()
def test... | test/test_set_default_mobile_cpu_allocator.py | Intel | 977 | 29dea32f5a88c0d032fd16a95b5c9766f1a6a907 | Python | py | |
import gym
import numpy as np
import time
import os
import cv2
import matplotlib.pyplot as plt
from IPython.display import clear_output
from collections import deque
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from qnetwork import QNetwork
from replay_buffer impor... | Value_Based/Double_Duel_Noisy_C51_MultiTD_1dim/agent.py | MIT | 13,330 | b2b941daee33d623f9154812a3dd24c455526dfc | Python | py | |
import collections
import sys
import os
this_path = os.path.dirname(os.path.realpath(__file__))
root_path = os.path.abspath(os.path.join(this_path, os.pardir))
sys.path.append(root_path)
from datasets import captioning, gpt2, bert, vggpt2, resgpt2, light
from utilities.evaluation import sanity
from utilities.evaluat... | src/models/eval.py | MIT | 29,349 | 29deb58b95bff98c4afa111e3a8550810717ef12 | Python | py | |
# python: 3.6
# encoding: utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_
from fastNLP.modules.utils import initial_parameter
class ConvMaxpool(nn.Module):
"""
Convolution and max-pooling module with multiple kernel sizes.
"""
def __i... | fastNLP/modules/encoder/conv_maxpool.py | Apache-2.0 | 1,890 | 42994bcd0168c4d572475c637d73cbd9e2c97517 | Python | py | |
from __future__ import absolute_import, division, print_function, unicode_literals
from abc import ABCMeta, abstractmethod
from six import add_metaclass
from itertools import combinations
from boltons.cacheutils import LRU
import numpy as np
from scipy.ndimage import zoom
from scipy.special import logsumexp
from tqd... | pysaliency/models.py | MIT | 27,276 | 5eb729b1d783753341ad90bed3ee7d69ced18d38 | Python | py | |
"""This module contains simple helper functions """
from __future__ import print_function
import os
import numpy as np
import torch
from PIL import Image
def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input... | util/util.py | MIT | 3,348 | 29df08fb2c0b85c4b1ffdb3c2e0c5d8e26209550 | Python | py | |
# 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... | tests/test_bilateral_approx_cuda.py | Apache-2.0 | 13,113 | 5ea0d997d1e1b68fd8c69912242ed57c4f5f2beb | Python | py | |
'''
Example shows how to compute combined softmax for multilabel multivalue setting.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
# in one output (6 dim vector) we want to encode 2 features and level of its expression
# each feature is represen... | softmax_accuracy.py | MIT | 1,632 | 45cecde7f3b1a08457731a4211a571f5babae117 | Python | py | |
#!/usr/bin/env python
"""Train models."""
import os
import signal
import torch
import onmt.opts as opts
import onmt.utils.distributed
from onmt.utils.misc import set_random_seed
from onmt.utils.logging import init_logger, logger
from onmt.train_single import main as single_main
from onmt.utils.parse import ArgumentPa... | train.py | MIT | 6,939 | 12cee32f3b41d4d04c81f85bb676ab654db510a7 | Python | py | |
import numpy as np
import torch
from torch import nn
import scipy
import tensorly as tl
from tensorly.decomposition import parafac
from tensorly.kruskal_tensor import kruskal_to_tensor
from musco.pytorch.compressor.rank_selection.estimator import estimate_rank_for_compression_rate
tl.set_backend('pytorch')
class ... | musco/pytorch/compressor/decompositions/cp4.py | BSD-3-Clause | 7,302 | 29df24f8efdf0c3f2c9b7dc6bffb965180869615 | Python | py | |
from __future__ import absolute_import
import math
import torch.nn as nn
__all__ = ['oresnet']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(n... | CIFAR10/models/cifar/oresnet.py | MIT | 4,493 | 4b76579eaa05a82be921f77501b8012a45df60fe | Python | py | |
import argparse
import os
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
fro... | detection/test.py | Apache-2.0 | 8,326 | 52c88faa31a7b997b0dc8fe36b01c49932c754be | Python | py | |
# AI for Self Driving Car
# Import libraries
import numpy as np
import random
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.autograd as autograd
from torch.autograd import Variable
# Create architecture of NN
class Network(nn.Mo... | AI - Virtual Self Driving Car/aicustom.py | MIT | 4,145 | 29df5576a93b6fc7dd7b509aefceb6a969070ce7 | Python | py | |
from gaussed.solver.base import Solver
from gaussed.utils.matrix_sqrt import sqrtm, svd_sqrtm
from gaussed.utils.safe_svd import svd_safe, svd_clip
from gaussed.utils.nearest_spd_matrix import nearestSPD
import torch
from torch import cholesky, cholesky_solve
class LinearSolver(Solver):
def __init__(self):
... | src/gaussed/utils/lin_alg_solvers.py | MIT | 6,358 | cc02954775328b649210ffc4595d258cf883a4b3 | Python | py | |
import torch
import torch.nn as nn
from collections import defaultdict
from models.yolo_layer import YOLOLayer
from torch.autograd import Variable
import numpy as np
def add_conv(in_ch, out_ch, ksize, stride):
"""
Add a conv2d / batchnorm / leaky ReLU block.
Args:
in_ch (int): number of input chan... | models/yolov3.py | MIT | 24,957 | 29df5f02f68f9ef90db915feb698a5786c0aac85 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from typing import Dict, Union
import torch
from pytext.common.constants import Stage
from pytext.config.component import create_trainer
from pytext.data.bert_tensorizer import BERTTensorizer
from pytext.data.data import Data... | pytext/task/tasks.py | BSD-3-Clause | 13,093 | 3448c270a8e2f2aead943a46b815dfd40d1d65f5 | Python | py | |
from DataLoad.Instance import *
from wt_DataUtils.UniversalData import char_pad
import torch
import re
import os
torch.manual_seed(seed_num)
random.seed(seed_num)
class DataLoadHelp(object):
@staticmethod
def _clean_str(string):
"""
Tokenization/string cleaning for all datasets except for SST... | DataLoad/DataLoad_NER.py | Apache-2.0 | 5,028 | 52bee45668ac72479b2cfc7502ef5850562eaa30 | Python | py | |
# --------------------------------------------------------
# Pytorch multi-GPU Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Thomas Balestri; based off Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ i... | trainval_net.py | MIT | 16,771 | 29dfb85d4b19d5486c8024cddb289c47bb092949 | Python | py | |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from a2c_ppo_acktr.utils import AddBias
# TODO: In order to make this code faster:
# 1) Implement _extract_patches as a single cuda kernel
# 2) Compute QR decomposition in a separate process
# 3) Actually make ... | a2c_ppo_acktr/algo/kfac.py | MIT | 7,731 | 12efb6f57c29330794946854508bf0fdb6455e11 | Python | py | |
# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of sourc... | rsl_rl/algorithms/ppo.py | BSD-3-Clause | 9,046 | 29dfbf74c9e6b1579857aca551d05f4f4a63c494 | Python | py | |
"""
This file contains specific functions for computing losses of FCOS
file
"""
import torch
from torch.nn import functional as F
from torch import nn
import os
from ..utils import concat_box_prediction_layers
from paa_core.layers import IOULoss
from paa_core.layers import SigmoidFocalLoss
from paa_core.modeling.match... | paa_core/modeling/rpn/fcos/loss.py | BSD-2-Clause | 11,302 | 29dfc9b5647fdba90bb3e35f7fcc30b0dd53e099 | Python | py | |
from src.utils import loaders
from src import algorithms, model
from . import cmd, document_parser
from runners.experiment_utils import load_experiment
import inspect
import numpy as np
import os
import matplotlib.pyplot as plt
from argparse import ArgumentParser
import logging
import torch
def visualize(path_to_yml_f... | scripts/visualize.py | MIT | 4,547 | 12c66ffc7c7278a0ffa42f0c5ad71ec6876113b4 | Python | py | |
import torch
import os
import PIL
from ncsnv2.datasets.vision import VisionDataset
from ncsnv2.datasets.utils import download_file_from_google_drive, check_integrity
class CelebA(VisionDataset):
"""`Large-scale CelebFaces Attributes (CelebA) Dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`_ Dataset.
... | ncsnv2/datasets/celeba.py | MIT | 7,886 | 5fb753a436d7fa9d75f8196eb9fe29d4b703023a | Python | py | |
import torch
import argparse
import sys
import logging
import time
from botorch.models.contextual_multioutput import LCEMGP
from gpytorch.mlls import ExactMarginalLogLikelihood
from botorch import fit_gpytorch_model
from botorch.optim.fit import fit_gpytorch_torch
from botorch.acquisition.objective import LinearMCObje... | contextualbo_experiments/cbo_experiment.py | Apache-2.0 | 10,020 | 29e00a1fe77362c1ac768a16ce5a9a64bf9be4ef | Python | py | |
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | nemo/collections/asr/data/audio_to_label.py | Apache-2.0 | 9,743 | 7c740f4bb57bc94bb35c22c6574aff272c51b46a | Python | py | |
from typing import Any
from typing import Dict
from typing import List
from typing import Optional
from typing import Union
import pandas as pd
from etna import SETTINGS
from etna.datasets.tsdataset import TSDataset
from etna.loggers import tslogger
from etna.models.base import Model
from etna.models.base import log_... | etna/models/nn/deepar.py | Apache-2.0 | 5,688 | 340a84059154bf2dac776ee552ca086eaa7a9b46 | Python | py | |
import os
import torch
import pytorch_lightning as pl
from torchvision.datasets import MNIST
from torchvision import transforms
from typing import Optional
from torch.utils.data import DataLoader, random_split
class MNISTDataModule(pl.LightningDataModule):
"""LightningDataModule implementation of MNIST dataset.
... | src/datasets/mnist_data.py | Apache-2.0 | 3,516 | 7054767d6d1bccf641881a9ca7b7e7a55049845d | Python | py | |
import os
import torch
import torchvision
import argparse
from tensorboardX import SummaryWriter
apex = False
try:
from apex import amp
apex = True
except ImportError:
print(
"Install the apex package from https://www.github.com/nvidia/apex to use fp16 for training"
)
from model import load_m... | main.py | MIT | 3,498 | 29e09865c4e75e8e34b1cef72cb7b4435d682388 | Python | py | |
import numpy as np
from torch import nn
from torch import autograd
import torch
import os
class Concat_embed(nn.Module):
def __init__(self, embed_dim, projected_embed_dim):
super(Concat_embed, self).__init__()
self.projection = nn.Sequential(
nn.Linear(in_features=embed_dim, out_featu... | utils.py | MIT | 3,386 | 5e78d33e6c81d08359f77d7f8c78267659cadae4 | Python | py | |
import torch
import torch.nn as nn
from dtln_utils.custom_layers import CausalConv1d
class DTLN(nn.Module):
""" Dual-Signal Transformation Long Short-Term Memory Network.
Similar architecture as presented by N. Westhausen et. al for the
DNS INTERSPEECH 2020:
https://www.isca-speech.org/archive/Inter... | recipes/experiments_dtln/leakage_rm/dtln_fix_aec.py | MIT | 17,714 | 29e0bb8ef6c60b05714b2a461f54a8d1cde1d7a4 | Python | py | |
import argparse
import time
import datetime
import torch
import torch_ac
import tensorboardX
import sys
import inspect
import utils
from model import ACModel
import gym_minigrid.envs as minigrid_envs
import random
# Parse arguments
parser = argparse.ArgumentParser()
## General parameters
parser.add_argument("--algo"... | scripts/train.py | MIT | 8,984 | 4b65746fe430f109c282c23a4023b8030459ca2a | Python | py | |
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Albert Berenguel
## Computer Vision Center (CVC). Universitat Autonoma de Barcelona
## Email: aberenguel@cvc.uab.es
## Copyright (c) 2017
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file... | model/matching-net-classifier.py | MIT | 3,167 | d7e46b7c26e4e3dbb94ac643edb8ae50ba32bbe1 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
## transpose
#FLIP_LEFT_RIGHT = 0
#FLIP_TOP_BOTTOM = 1
#class BoxList(object):
#"""
#This class represents a set of bounding boxes.
#The bounding boxes are r... | fcos_core/structures/bounding_box.py | BSD-2-Clause | 9,938 | 29e0dee21b46b2db7043e2efc0ad87c62733b379 | Python | py | |
import torch
import typing
from ..core.transforms_interface import BaseWaveformTransform
class PeakNormalization(BaseWaveformTransform):
"""
Apply a constant amount of gain, so that highest signal level present in each audio snippet
in the batch becomes 0 dBFS, i.e. the loudest level allowed if all sampl... | torch_audiomentations/augmentations/peak_normalization.py | MIT | 2,557 | 804b7f5254c254ff0320d281688898934e7ebe76 | Python | py | |
import glob
import os
from pathlib import Path
from typing import Callable, List, Optional
import torch
from torch_geometric.data import (Data, InMemoryDataset, download_url,
extract_tar)
from torch_geometric.utils import remove_isolated_nodes
class MalNetTiny(InMemoryDataset):
... | torch_geometric/datasets/malnet_tiny.py | MIT | 3,603 | 29e0f1942cd6c9e822ab4a8ce7ea577d17ae461d | Python | py | |
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical
from common.utils import init
class CategoricalAC(nn.Module):
def __init__(self, base, recurrent, num_actions):
"""
embedder: (torch.Tensor) model to extract the embedding for observation - probably a... | common/actor_critic.py | MIT | 1,766 | 703b2e178dfc582d43a9a15235386cc883ec6042 | Python | py | |
import argparse
import math
import os
import random as rn
import shutil
import subprocess
import h5py
import librosa
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.animation as manimation
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
import torch
import torch.nn ... | generate.py | MIT | 6,984 | 29e0fa067ec91a74bc0ec230b60356869b730043 | Python | py | |
import inspect
import torch.nn as nn
from .misc import is_tuple_of
from ..common.registry import NORM_LAYERS
NORM_LAYERS.register_module('BN', module=nn.BatchNorm2d)
NORM_LAYERS.register_module('BN1d', module=nn.BatchNorm1d)
NORM_LAYERS.register_module('BN2d', module=nn.BatchNorm2d)
NORM_LAYERS.register_module('BN3d... | examples/ASSA/module/common/norm.py | MIT | 6,238 | 29e1004dce3b1057f074e7cd4837cff024091a04 | Python | py | |
r"""
This package adds support for CUDA tensor types, that implement the same
function as CPU tensors, but they utilize GPUs for computation.
It is lazily initialized, so you can always import it, and use
:func:`is_available()` to determine if your system supports CUDA.
:ref:`cuda-semantics` has more details about wo... | torch/cuda/__init__.py | Intel | 23,938 | 29e112fc67abd9a43cb4ac78a8d434c48b14be16 | Python | py | |
# -*- coding: utf-8 -*-
"""
@date: 2020/9/20 下午4:23
@file: tsm_recognizer.py
@author: zj
@description:
"""
import torch
import torch.nn as nn
from tsn.model import registry
from tsn.model.backbones.build import build_backbone
from tsn.model.heads.build import build_head
from .temporal_shift import make_temporal_shi... | tsn/model/recognizers/tsm_recognizer.py | Apache-2.0 | 848 | 7c80f957dd45f526676b5003c5e70753c86844ae | Python | py | |
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
webroot = 'https://tigress-web.princeton.edu/~fy/drn/models/'
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'drn-c-26': we... | modeling/backbone/drn.py | MIT | 14,649 | 29e11e065b258e43d4a9ca1e671b8f5af4ae62c6 | Python | py | |
import itertools
import logging
import math
import os
from collections import OrderedDict
import torch
from torch import nn, optim
from torch.nn.functional import kl_div, softmax, log_softmax
from tqdm import tqdm
from theconf import Config as C, ConfigArgumentParser
from common import get_logger
from data import ge... | train.py | Apache-2.0 | 10,179 | fdfeb6a5d1bd254e511d5c696cb2a2205947e9e1 | Python | py | |
# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
#
# 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, ... | models/examples/indoor.py | MIT | 5,909 | 29e129c2a9e0d59ef823f86cfc389e23798f0bd3 | Python | py | |
from gehm.utils.np_distances import *
from tests.test_data import create_test_data
import pytest
import numpy as np
import torch
from numpy import cos, sin
@pytest.mark.distances
def test_nx_second_order_proximity(create_test_data):
G,G_undir=create_test_data
nodes=np.array(G.nodes)
# Te... | tests/test_np_distances.py | MIT | 7,605 | 29e12c7a6308a409cb7f250cbecb68ff20a73b65 | Python | py | |
import torch.nn as nn
class ResNetLoss(nn.Module):
def __init__(self, loss_type="ce"):
super().__init__()
self.loss_type = loss_type
if loss_type == "ce":
self.loss = nn.CrossEntropyLoss()
elif loss_type == "bce":
self.loss = nn.BCELoss()
def forward(s... | src/criterion.py | MIT | 631 | 10f2045edf620da4229737bba9da96377cc29ae7 | Python | py | |
import torch
from torch.utils.data import DataLoader, Dataset
class CategoryDataset(Dataset):
def __init__(self, id_list, digit1_list, digit2_list, digit3_list, text_obj_list, text_mthd_list, text_deal_list):
self.id_list = id_list
self.digit1_list = digit1_list
self.digit2_list = digit2_li... | main.py | MIT | 2,793 | 037fe9a1dbb19aed58b3e245b34f97e1fac20be1 | Python | py | |
import numpy as np
import torch
from common.logging.console_loger import ConsoleLogger
from services.text_extraction.application.ai.model import BERTBaseUncased
from services.text_extraction.application.ai.training.src.dataset import TextExtractionDataset
from services.text_extraction.application.ai.training.src.prepr... | backend/services/text_extraction/application/ai/inference/prediction_manager.py | MIT | 6,588 | 29e15e20b950dc5293e8ffe5672248baaf64aa21 | Python | py | |
import torch
from torch import nn, Tensor
from torch.nn import functional as F
from allennlp.data.vocabulary import Vocabulary
from allennlp.models import Model
from allennlp.modules import Seq2SeqEncoder, InputVariationalDropout, TimeDistributed
from allennlp.nn import InitializerApplicator
from allennlp.training.metr... | models/my_model/base_model.py | MIT | 17,864 | 4b3c6f3d75d686789d46d56d7e7d6d750fa4bb33 | Python | py | |
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
import numpy as np
from typing import Optional, Union, List, Dict, Iterator, Tuple
from lifelong_methods.models.resnetcifar import ResNetCIFAR
from lifelong_methods.models.resnet import ResNet
from lifelong_methods.utils import get_optimizer
from i... | lifelong_methods/methods/base_method.py | MIT | 13,163 | 29e165b83c21a6fce8f29036bfd1782a8a25cc55 | Python | py | |
import os
import random
import numpy as np
from PIL import Image, ImageFile
from torch.utils.data import Dataset
from torchvision import transforms
from s_data.MaskTheFace.augment_mask import AugmentMask
default_trans_list = [
transforms.Resize((112, 112)),
transforms.RandomHorizontalFlip(p=0.5),
transform... | s_data/dataset.py | MIT | 2,446 | 29e19e1e7829b720af4b55062727437ec3cf4f31 | Python | py | |
import torch.nn.functional as f
import torch.nn as nn
import torch
import numpy as np
def init_weights(m):
if type(m) == nn.Linear:
nn.init.xavier_uniform_(m.weight)
m.bias.data.fill_(0.01)
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, fc1_units, fc2_units, seed):
... | p3_collab-compet/model.py | MIT | 1,897 | 3480594052bb89c998512222674969e5ed340d40 | Python | py | |
# -*- coding: utf-8 -*-
# Copyright 2019 Pierre-Luc Delisle. All Rights Reserved.
#
# Licensed under the MIT License;
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/MIT
#
# Unless required by applicable law or agreed t... | deepNormalize/training/unet.py | MIT | 32,012 | 03ee6110005c84f3e475ec799fe153ce8a6364ff | Python | py | |
# 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... | solo/utils/pretrain_dataloader.py | MIT | 20,320 | 10b9c0c514207d82ed7fd2b8b38f3be72c5b99c2 | Python | py | |
# Owner(s): ["module: codegen"]
import torch
from torch.testing._internal.common_utils import TestCase, run_tests
from torch.testing._internal.logging_tensor import LoggingTensor, LoggingTensorReentrant, capture_logs, log_input
from torch.utils._pytree import tree_map
import logging
def are_aliased(x, y):
if x._... | test/test_functionalization.py | Intel | 26,557 | f9c425fb01a71300429c756eadca802a2f561b08 | Python | py | |
"""
Copyright 2021 The LightSeq Team
Copyright Facebook Fairseq
We use layers from Facebook Fairseq as our baseline for unit test
"""
from typing import Dict, List, Optional
import math
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.... | tests/fairseq_layers.py | Apache-2.0 | 20,684 | ff6573e0879a64fc6cb6b08ae556749cc12f7be9 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import inspect
import logging
import numpy as np
import os
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
import cv2
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.layers... | projects/WSL/wsl/modeling/roi_heads/roi_heads_oicr.py | Apache-2.0 | 26,859 | 29e1d795cc451bc439b5fe6f2eff504c2629a172 | Python | py | |
# Owner(s): ["oncall: fx"]
import torch
import torch.fx.experimental.fx_acc.acc_ops as acc_ops
from torch.testing._internal.common_fx2trt import AccTestCase
from parameterized import parameterized, param
from torch.testing._internal.common_utils import run_tests
class TestClampConverter(AccTestCase):
@parameteri... | test/fx2trt/converters/acc_op/test_clamp.py | Intel | 903 | e9be71edd7fb7e75312eef979bd13bf120df4a1d | Python | py | |
# Inspired by https://huggingface.co/transformers/perplexity.html
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
import torch
device = 'cuda'
model_id = 'gpt2-large'
model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
tokenizer = GPT2TokenizerFast.from_pretrained(model_id)
def get_perplexity(... | perplexity/compute_ppl.py | MIT | 1,215 | a96b34d6bc9e2acfd4f30f5ed5e3e2966b0fe17e | Python | py | |
from overrides import overrides
import torch
from claf.config.namespace import NestedNamespace
from claf.learn.optimization.learning_rate_scheduler import get_lr_schedulers
from claf.learn.optimization.learning_rate_scheduler import (
LearningRateWithoutMetricsWrapper,
LearningRateWithMetricsWrapper,
)
from c... | claf/factory/optimizer.py | MIT | 5,116 | ff8856b29d58de989892874326792dec4f0c28f6 | Python | py | |
import numpy as np
import platalea.dataset as D
import platalea.rank_eval as E
import platalea.xer as xer
import torch
def score(net, dataset):
data = dataset.evaluation()
correct = data['correct'].cpu().numpy()
image_e = net.embed_image(data['image'])
audio_e = net.embed_audio(data['audio'])
resu... | platalea/score.py | Apache-2.0 | 2,394 | fd6594f3ff978bc752ea48204045e74a112cda05 | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 25 09:55:24 2019
@author: edward
"""
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch.distributions import Categorical
import torch.nn.functional as F
from arguments import parse_a2c_args
from torchv... | 3dcdrl/models.py | MIT | 5,141 | 29e24f443ae3072c4f85487bfcb4ec3535eee3f6 | Python | py | |
#!/usr/bin/python
# -*- encoding: utf-8 -*-
from core.utils.logger import setup_logger
from core.models.cabinet import CABiNet
from core.datasets.cityscapes import CityScapes
import torch
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.distributed as dist
import logging
import nu... | scripts/evaluate.py | MIT | 5,053 | 29e2567d2aaaadd41a2196258d16b39d9d1e06bc | 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 | 4,588 | 29e2569a96f03a13f58b08bf2c4e8bcb9935c08f | Python | py | |
# execute mask_rcnn
# save txt file
# output : " frame_id, -1, x1, y1, w, h, scores, -1, -1, -1 "<- deep sort want this format
import torch
import numpy as np
import os
from PIL import Image
from PIL import ImageDraw
from maskrcnn_benchmark.config import cfg
from demo.predictor import COCODemo
def createFolder(direc... | excute_mask_rcnn_DAD.py | MIT | 4,135 | 29e267b1bf63f34617be8e2714d8e177d74a7eaa | Python | py | |
"""Define a dymamical system for TurtleBot3"""
from typing import Tuple, Optional, List
import torch
import numpy as np
from .control_affine_system import ControlAffineSystem
from neural_clbf.systems.utils import Scenario, ScenarioList
class TurtleBot(ControlAffineSystem):
"""
Represents a two wheeled diffe... | neural_clbf/systems/turtlebot.py | BSD-3-Clause | 9,445 | 7cccccae3c0f0cce8fad9d253a67eae3ccff070a | Python | py | |
# utils.py
import torch
from torchtext import data
import spacy
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
class Dataset(object):
def __init__(self, config):
self.config = config
self.train_iterator = None
self.test_iterator = None
self.val_it... | Model_Transformer/utils.py | MIT | 4,255 | b2abe0f98a6f15293cebbdd2f576fe743244469f | Python | py | |
from typing import Any, Dict, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.configuration_fsmt import FSMTConfig
from transformers.file_utils import is_torch_tpu_avail... | examples/seq2seq/seq2seq_trainer.py | Apache-2.0 | 9,810 | 805a73871fb22e9e6da6655f3e95655ccdda0260 | Python | py | |
import numpy as np
import pandas as pd
import os
import os.path as osp
import pickle
from pathlib import Path
import motmetrics as mm
from copy import deepcopy
from collections import OrderedDict
import torch
from torch_scatter import scatter_add
from pytorch_lightning import Callback
import re
from mot_neural_so... | src/mot_neural_solver/utils/evaluation.py | MIT | 18,236 | 29e2b868a582744dfa9b8ba57571e1c06edfff43 | Python | py | |
import warnings
from typing import Tuple, Optional
import torch
from torch import Tensor
from .linear import _LinearWithBias
from torch.nn.init import xavier_uniform_
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.parameter import Parameter
from .module import Module
from ..... | pytorch-frontend/torch/nn/modules/activation.py | MIT | 39,726 | fcaa84caa25d0ac275bc3076a6ac0323f47df4b5 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import itertools
import torch
import functorch
from functorch import vmap
import torch.utils._pytree as pytree
fr... | test/common_utils.py | BSD-3-Clause | 10,572 | b2db5b51664dec85f19f584a11ea51f4b8cd61d5 | Python | py | |
# code in this file is adpated from
# https://github.com/kekmodel/MPL-pytorch
import argparse
import logging
import math
import os
import random
import time
import numpy as np
import torch
from torch.cuda import amp
from torch import nn
from torch.nn import functional as F
from torch import optim
from torch.optim.lr_... | main.py | MIT | 28,751 | 102061ef6c1504201de60bfa4ec9a39c617cc6aa | Python | py | |
from ruamel.yaml import YAML, dump, RoundTripDumper
from raisimGymTorch.env.bin.rsg_ouzel_delta import RaisimGymEnv
from raisimGymTorch.env.bin.rsg_ouzel_delta import NormalSampler
from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv
from raisimGymTorch.helper.raisim_gym_helper import ConfigurationS... | raisimGymTorch/raisimGymTorch/env/envs/rsg_ouzel_delta/scripts/runner.py | Apache-2.0 | 6,810 | 24f490a9b39a1a8f78f84f2c469fc556663ae62c | Python | py | |
"""Evaluate the model"""
import argparse
import random
import logging
import os
import numpy as np
import torch
from SequenceTagger import BertForSequenceTagging, BertConfig
from metrics import f1_score
from metrics import classification_report
from data_loader import DataLoader
import utils
parser = argparse.Arg... | evaluate.py | MIT | 4,802 | 29e308ce54e736904eacc8e5f334fca093465c35 | Python | py | |
# -*- coding: utf-8 -*-
# @Time : 2018-8-2 9:20
# @Author : xylon
import time
import torch
def gct(f="l"):
"""
get current time
:param f: "l" for log, "f" for file name
:return: formatted time
"""
if f == "l":
return time.strftime("%m-%d %H:%M:%S", time.localtime(time.time()))
... | utils/common_utils.py | MIT | 2,119 | b2e17c2d52bafe44894ac725a42571945fa51dd9 | Python | py | |
import argparse
import os
import numpy as np
import torch
from torch.optim.lr_scheduler import ReduceLROnPlateau
from tqdm import tqdm
from dataloaders import make_data_loader
from modeling.panoptic_deeplab import PanopticDeepLab
from modeling.sync_batchnorm.replicate import patch_replication_callback
from mypath imp... | train_panoptic.py | MIT | 18,712 | 29e33a547e89474304b7d5e60011c96e723ab92b | Python | py | |
import torch.nn as nn
import numpy as np
from abc import abstractmethod
class BaseModel(nn.Module):
"""
Base class for all models
"""
@abstractmethod
def forward(self, *inputs):
"""
Forward pass logic
:return: Model output
"""
raise NotImplementedError
... | base/base_model.py | MIT | 838 | 29e3425afc22519f8ce853dc38e536951e60635c | Python | py | |
import argparse
from typing_extensions import IntVar
import torch
import time
from models.hardnext import HarDNeXt
from models.resnet import resnet50
def speed_test(full_model_name, model, height, width, batch, gpu):
# start = torch.cuda.Event(enable_timing=True)
# end = torch.cuda.Event(enable_timing=True... | src/speed_measure.py | MIT | 2,367 | d7f35802cad878e554a44a459d294416c47a0671 | Python | py | |
import torch
from pytorch_lightning.utilities.data import extract_batch_size
def test_extract_batch_size():
"""Tests the behavior of extracting the batch size."""
batch = "test string"
assert extract_batch_size(batch) == 11
batch = torch.zeros(11, 10, 9, 8)
assert extract_batch_size(batch) == 11... | tests/utilities/test_data.py | Apache-2.0 | 585 | 29e35a7b891166afb86fb0316c727ddb3f59f652 | Python | py | |
"""Custom decoder definition for transducer models."""
import torch
from espnet.nets.pytorch_backend.transducer.blocks_for_tt import build_blocks
from espnet.nets.pytorch_backend.transducer.utils import check_batch_state
from espnet.nets.pytorch_backend.transducer.utils import check_state
from espnet.nets.pytorch_bac... | espnet/nets/pytorch_backend/transducer/custom_decoder_for_tt.py | Apache-2.0 | 9,081 | b2a0693e6d2f034d34d04746b56e27ee05675cb3 | Python | py |
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