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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2021 Microsoft Corporation. Licensed under the MIT license.
import numpy as np
import torch
from torch.nn import functional as F
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.modeling.matcher i... | scene_graph_benchmark/relation_head/loss.py | MIT | 40,355 | fdffb1381c63b32d6efeaf8ee4a3eebdc8acf9a2 | Python | py | |
# original implementation: https://github.com/odegeasslbc/FastGAN-pytorch/blob/main/models.py
#
# modified by Axel Sauer for "Projected GANs Converge Faster"
#
import torch.nn as nn
from pg_modules.blocks import (InitLayer, UpBlockBig, UpBlockBigCond, UpBlockSmall, UpBlockSmallCond, SEBlock, conv2d)
def normalize_sec... | training/networks_fastgan.py | MIT | 6,139 | dfe0d0e14b9a9266dadc36ecf1ea111a203aa413 | Python | py | |
from models.mtcnn import MTCNN
from models.inception_resnet_v1 import InceptionResnetV1
import torch
from torch.utils.data import DataLoader
from torchvision import datasets
import numpy as np
import pandas as pd
import os
workers = 7
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('devi... | casia_test.py | MIT | 1,188 | e969ad4680b9af31ae1c86c0014f7abaeecf1cd3 | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2018 Nagoya University (Tomoki Hayashi)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Tacotron 2 related modules."""
import logging
import numpy as np
import torch
import torch.nn.functional as F
from espnet.nets.pytorch_backend.nets_utils... | espnet/nets/pytorch_backend/e2e_tts_tacotron2.py | Apache-2.0 | 33,030 | 10635ab9a7cde0dfa200f7a8dfb49f26103d1a42 | Python | py | |
import importlib
import math
import os
import warnings
from fractions import Fraction
from typing import List, Tuple
import numpy as np
import torch
_HAS_VIDEO_OPT = False
try:
lib_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
loader_details = (
importlib.machinery.Extension... | torchvision/io/_video_opt.py | BSD-3-Clause | 21,393 | e92ac1bd396b98e33e7483e70d257a8b28875c94 | Python | py | |
import time
import trimesh
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.nn.utils.rnn import pad_sequence
from options.train_options import TrainOptions
from options.test_options import TestOption... | unused_experiments/ST_global_content.py | MIT | 5,878 | f9b7d97fa575ea324bd20bbaebfa7f9d98f2bbc2 | Python | py | |
from typing import Any, List
from torch import nn, optim
from torch.optim.lr_scheduler import ExponentialLR
from torch.optim.optimizer import Optimizer
from pystiche import enc
from pystiche.image import transforms
__all__ = [
"multi_layer_encoder",
"preprocessor",
"postprocessor",
"optimizer",
"... | pystiche_papers/ulyanov_et_al_2016/_utils.py | BSD-3-Clause | 2,084 | 7097f2b295e5a4f45f0cfe3b3b94e4fd5e5e31b3 | Python | py | |
import gin
import torch
import torch.nn as nn
import torch.nn.functional as F
@gin.configurable
class GraphNetWeightedBCE(nn.Module):
"""Weighted binary cross entropy for RDGraphNet"""
def __init__(self, real_weight, fake_weight):
super().__init__()
self.real_weight = real_weight
self.... | ariadne/graph_net/loss.py | MIT | 746 | fcaf1aa5abb4242729b39ae26f4b06f744828207 | Python | py | |
from __future__ import print_function
import torch
import torch.nn.functional as F
from torchvision import datasets, transforms
from torch.autograd import Variable
import os
import keras
from keras import backend as K
import numpy as np
import dill as pickle
import numpy as np
def test_tf2torch(tf_model,torch_model,in... | util.py | BSD-3-Clause | 2,366 | d7b627b064b86444e83c47658f7c690d54954954 | Python | py | |
import json
import unittest
from shutil import rmtree
import numpy as np
import torch
from metal.end_model import EndModel
from metal.logging import LogWriter
from metal.tuners.random_tuner import RandomSearchTuner
class RandomSearchModelTunerTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
... | tests/metal/tuners/test_random_search_tuner.py | Apache-2.0 | 3,954 | fcc382bcd0f6a9b4eb0f8f41d64f092c43369bf8 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import numpy as np
import torch
from fairseq.data import FairseqDataset, data_utils
logger = logging.getLogger(__name__)
d... | fairseq/data/language_pair_dataset.py | MIT | 19,032 | ff3e14bf14770638524ef6067b558e455dbe5f2b | Python | py | |
import pytest
import torch
from caldera.blocks import Flex
def test_flex_block():
flex_linear = Flex(torch.nn.Linear)
model = flex_linear(Flex.d(), 11)
print(model.__str__())
print(model.__repr__())
x = torch.randn((30, 55))
model(x)
print(model.__str__())
print(model.__repr__())
@p... | tests/test_blocks/test_flex_block.py | MIT | 1,956 | 3a83a665cb40296884a26984a29093312b760bd0 | Python | py | |
import os
import numpy as np
import itertools
import collections
import torch
from .example import Example
from .utils import nostdout
class Dataset(object):
def __init__(self, examples, fields):
self.examples = examples
self.fields = dict(fields)
def collate_fn(self):
def collate(bat... | meshed-memory-transformer/data/dataset.py | MIT | 6,254 | b297f53d47e9ef9c05fce8376ce6890de9d413a1 | Python | py | |
import torch
import json
import os
import torch.nn.functional as F
import numpy as np
from utils.data_reader import SimpleQABatcher
from utils.args import get_args
from utils.util import save_model, clip_gradient_threshold, load_model, get_f1, threshold, get_f1_mit_span
from models.e2e_entity_linking import E2E_entity_... | train_e2e.py | MIT | 15,998 | fda3ccfc5b143814a20583a4fb8aaac7ce3b7a77 | Python | py | |
'''
REDS dataset
support reading images from lmdb, image folder and memcached
'''
import os.path as osp
import random
import pickle
import logging
import numpy as np
import cv2
import lmdb
import torch
import torch.utils.data as data
import data.util as util
try:
import mc # import memcached
except ImportError:
... | EDVR_data/REDS_dataset.py | Apache-2.0,MIT | 9,509 | 4b3e3ac7d8995d0dce8a284f460fad7c42bfc151 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from backbone import res50, bbn_res50, res32_cifar_mb, bbn_res32_cifar
from modules import GAP, Identity, FCNorm
class Network2(nn.Module):
def __init__(self, cfg, mode, num_classes):
super(Network2, self).__init__()
pret... | lib/net/network2.py | MIT | 3,769 | 7cdf4deedab208de2fa8ee9573c035bc4529153e | Python | py | |
#!/usr/bin/env python3
import threading
import typing
import warnings
from collections import defaultdict
from typing import Any, Callable, Dict, List, Tuple, Union, cast
import torch
from torch import Tensor, device
from torch.nn import Module
from captum._utils.common import (
_reduce_list,
_run_forward,
... | captum/_utils/gradient.py | BSD-3-Clause | 26,984 | 3a83ee38b79f90642474f841ef2c277c2594c9cb | Python | py | |
#!/usr/bin/env python
"""
Barebone Simulation without camera or extraneous sensors.
With navigation and relative coordinates
"""
from __future__ import print_function
# ==============================================================================
# -- find carla module --------------------------------------------... | relative_coordinates_simulation.py | Apache-2.0 | 14,220 | f99714ae02cf7d40f6c241a18254721389d2beae | Python | py | |
import torch
import gnn_fw as gfw
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
import os
from torch_geometric.utils import to_networkx
import importlib.util
from os import listdir
from os.path import isfile, join
"""
How the script works:
1) load the gfw.config.Config object
2) rea... | run.py | MIT | 12,226 | 1e3f04802a035e2f4e1995b10396fa71b3591d4b | Python | py | |
import argparse
import io
import pickle
import tarfile
import time
from timeit import default_timer as timer
import boto3
import numpy as np
import skimage.transform
import torch
import torchvision.models as models
from torch.autograd import Variable
from torch import nn
import candidate_data
import featurize
import ... | code/featurize_test.py | MIT | 2,873 | 5e6dbd2819e8834e6e7b11914c1f1a4578f1abec | Python | py | |
# Built on top of the original implementation at https://github.com/hila-chefer/Transformer-Explainability
#
# Modifications by Copyright 2022 Zilliz. 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 obt... | towhee/models/visualization/transformer_visualization.py | Apache-2.0 | 6,849 | 3a84498ed83fc1bbcb1ffbee19b00eaa9c840eb6 | Python | py | |
import os
import time
import argparse
import numpy as np
from tqdm import tqdm
from sklearn.metrics import roc_auc_score
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from torch.utils.data.sampler import RandomSampler
from utils.util import *
# import apex
# from... | AE_method_real_attack.py | MIT | 19,327 | 42257b7daba8a146bb3cc9e585f179aa03e597de | Python | py | |
# coding=utf-8
# Copyright 2021 The Google Research 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 applicab... | aloe/aloe/common/pytorch_util.py | Apache-2.0 | 12,840 | f9d31effb10247df24e7ca29ffb75eb32e91d4cb | Python | py | |
from copy import deepcopy
import numpy as np
import torch
from mvn.models.v2v import Basic3DBlock, Res3DBlock, Pool3DBlock
import torch.nn as nn
import torch.nn.functional as F
from mvn.utils import op, volumetric
from mvn.models import pose_resnet
class Encoder(nn.Module):
def __init__(self):
super()._... | mvn/models/fk.py | MIT | 11,826 | 1e3baa48332b1a40a38ca36c849ecce6a1cd6368 | Python | py | |
import unittest
import torch
from torch import nn
import torch.nn.functional as F
from asdfghjkl import FISHER_EXACT, FISHER_MC, COV
from asdfghjkl import SHAPE_FULL, SHAPE_BLOCK_DIAG
from asdfghjkl import fisher_free_for_cross_entropy
from asdfghjkl import NaturalGradient, LayerWiseNaturalGradient
def _relative_er... | tests/test_conjugate.py | MIT | 4,901 | 3a8490d703fe5b7ecebf1eb0f7181f778d0f47a9 | Python | py | |
"""
In this module **precision**, **recall** and **F1 score**
calculations are defined in separate functions.
:py:class:`PrecisionRecallF1ScoreMeter` can keep track for all three of these.
"""
from collections import defaultdict
import torch
from catalyst.tools.meters import meter
def f1score(precision_value, reca... | catalyst/tools/meters/ppv_tpr_f1_meter.py | Apache-2.0 | 4,036 | 7ca3ec1fbfe7338320e2d78db5947c78f719a363 | Python | py | |
import torch
import numpy as np
import random
import os
def iou_xyxy_torch(boxes1, boxes2):
"""
[N, 4] with [M, 4] return [N, M]
"""
boxes1_area = torch.prod(boxes1[:, 2:] - boxes1[:, :2], -1)
boxes2_area = torch.prod(boxes2[:, 2:] - boxes2[:, :2], -1)
# 计算出boxes1与boxes2相交部分的... | utils/tools.py | MIT | 10,551 | 3a84d9a5562a40c973c8a7f120c6f98373cf90b4 | Python | py | |
"""
Copyright (c) 2022 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... | nncf/torch/pruning/base_algo.py | Apache-2.0 | 13,277 | e6534f5eea53887dc2de9df4dab4161a6e78e450 | Python | py | |
###################################################################################################
#FPENet:Feature Pyramid Encoding Network for Real-time Semantic Segmentation
#Paper-Link: https://arxiv.org/pdf/1909.08599v1.pdf
###########################################################################################... | model/FPENet.py | MIT | 9,253 | ffa3809694b1b0373a6010eb43d6ee390e88bdc8 | Python | py | |
import unittest
from unittest.util import safe_repr
from warnings import warn
import torch
from scipy.stats import truncnorm
from TruncatedNormal import TruncatedNormal as TruncatedNormalPT
class TruncatedNormalSC:
def __init__(self, loc, scale, a, b):
self.loc = loc
self.scale = scale
se... | tests/test.py | BSD-3-Clause | 4,783 | e634e4206ce3e30c5f70a007727bd5b1e41c5cf3 | Python | py | |
#!/usr/bin/python3 -m unittest
#
# basic shelf assignment tests
#
import unittest
import pymm
import numpy as np
import math
import torch
def colored(r, g, b, text):
return "\033[38;2;{};{};{}m{} \033[38;2;255;255;255m".format(r, g, b, text)
def log(*args):
print(colored(0,255,255,*args))
force_new=True
... | src/python/pymm/testing/assignment.py | Apache-2.0 | 2,304 | 3418a8fdfa34858e36dd1e2759b417070b1d9766 | 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/metrics/metric.py | Apache-2.0 | 10,431 | 3a853be0ebdd50c1792fb4c53e215e6ef4e445d0 | Python | py | |
"""
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
distributed API using Horovod
Modified from OpenNMT's native pytorch distributed utils
(https://github.com/OpenNMT/OpenNMT-py)
"""
import math
import pickle
import torch
from horovod import torch as hvd
def all_reduce_and_rescale_tensors(tenso... | utils/distributed.py | MIT | 6,296 | fce69ea97322ec78a07fb2c8b3bc2f0398836e83 | Python | py | |
import torch
import torch.nn as nn
from CVAE_testbed.models.weight_init import weight_init
class CVAE(nn.Module):
def __init__(self, x_dim, c_dim, enc_layers, dec_layers):
super(CVAE, self).__init__()
self.xdim = x_dim
self.cdim = c_dim
self.enc_layer1 = enc_layers[0]
# en... | CVAE_testbed/models/CVAE_baseline.py | Xnet,X11 | 2,508 | 429ea1019eb574ca626aaf0d89f26daad6ed4718 | Python | py | |
# MIT License
#
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# 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 restricti... | venv/Lib/site-packages/transformers/models/mobilebert/modeling_mobilebert.py | MIT | 66,074 | 3a855ba4fb75ef20ee0df558ad83406e74fa5c09 | Python | py | |
""" Common training and validation utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
from copy import deepcopy
import torch
import math
import os
import re
import shutil
import glob
import csv
import operator
import logging
import logging.handlers
import numpy as np
from collections import OrderedDict
... | timm/utils.py | Apache-2.0 | 13,326 | 65255b532eba78c1a938bfeee791f641c89312b7 | Python | py | |
import numpy as np
import torch
from torch.nn import functional as F
from nflows.transforms.base import InputOutsideDomain
from nflows.utils import torchutils
DEFAULT_MIN_BIN_WIDTH = 1e-3
DEFAULT_MIN_BIN_HEIGHT = 1e-3
DEFAULT_MIN_DERIVATIVE = 1e-3
def unconstrained_rational_quadratic_spline(
inputs,
unnorma... | nflows/transforms/splines/rational_quadratic.py | MIT | 6,254 | 3a85986a100c65cb84aa270df33829d84fe0a87f | Python | py | |
import pytest
import torch
from pytorch_lightning import Trainer
from tests.base import EvalModelTemplate
def test_optimizer_with_scheduling(tmpdir):
""" Verify that learning rate scheduling is working """
hparams = EvalModelTemplate.get_default_hparams()
model = EvalModelTemplate(**hparams)
model.c... | tests/trainer/test_optimizers.py | Apache-2.0 | 8,509 | 3a85a756fc7855c17d06da9864c4363592e3e0fc | Python | py | |
# Pytorch Tabular
# Author: Manu Joseph <manujoseph@gmail.com>
# For license information, see LICENSE.TXT
"""Tabular Data Module"""
import logging
import re
from typing import Callable, Iterable, List, Optional, Tuple, Union
import category_encoders as ce
import numpy as np
import pandas as pd
import pytorch_lightning... | pytorch_tabular/tabular_datamodule.py | MIT | 23,749 | 3a85b0eb5e5beef536285fb61473dc1e765386ed | Python | py | |
import hparams
import torchvision.transforms.functional as tvf
import torchvision.transforms as transforms
import pathlib
import torch
from torch.utils.data import Dataset
from PIL import Image
class PetData(Dataset):
def __init__(self, transforms=None):
self.path = hparams.DATASET_PATH
self.tran... | nn/data.py | MIT | 1,401 | 4288de3b6f8f2fade59fca4338bb24c639ab9653 | Python | py | |
import abc
import os
import sys
import numpy as np
from torch._C import device
from torch.serialization import validate_cuda_device
import tqdm
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from typing import Callable, Any
from pathlib import Path
from src.utils... | src/train.py | MIT | 81,136 | 8051c1bbb00b5cda40dda0ff302ab050e82d13ce | Python | py | |
import torch
import cv2
import numpy as np
import torch.optim as optim
import torchvision.models as models
import torch.nn as nn
from PIL import Image
import scipy.misc
def loadim(path):
im = cv2.imread(path, cv2.IMREAD_COLOR)
im = np.array([im[:, :, 2], im[:, :, 1], im[:, :, 0]])
im = torch.from_numpy(im)... | benet_style.py | MIT | 4,415 | 3a86015406f249cab4fbdd2940f7b1278d260ba4 | Python | py | |
# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... | nnUNet/nnunet/inference/predict.py | Apache-2.0 | 39,256 | e96b4fcb2a23ef19917c92917fef21fd1abe5041 | 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... | examples/segmentation_3d/unet_training_dict.py | Apache-2.0 | 7,706 | a92c006c9f18f0eb377ef93ab33c34cba637897f | Python | py | |
import torch.nn as nn
import torch
import torch.nn.functional as F
from sklearn.metrics import roc_auc_score
import numpy as np
import sys
import imp
import molgrid
import argparse
import os
import time
import wandb
from sklearn.metrics import precision_recall_fscore_support
torch.backends.cudnn.benchmark = True
def p... | train.py | MIT | 15,295 | 10ba41b97090dfb1332072fe19de0473c1431f75 | Python | py | |
import torch
import torch.nn as nn
from .layers import DynamicLSTM
from .env import multienv
class TextRNN(nn.Module):
def __init__(self, rnn_type, num_layers, hidden_dim, configs):
super(TextRNN, self).__init__()
WN, WD = configs['embedding_matrix'].shape
HD = hidden_dim
C = con... | code/models/textrnn.py | MIT | 2,443 | 3410ea1eff1caa43cefdd9e3adf86e81b2e3f80d | Python | py | |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions... | loss_functions.py | BSD-3-Clause | 2,324 | ff5834696bb68b22075673ba48572074a03e46cb | Python | py | |
"""Model-Agnostic Meta-Learning (MAML) algorithm implementation for RL."""
import collections
import copy
from dowel import tabular
import numpy as np
import torch
from garage import log_multitask_performance
from garage import TrajectoryBatch
from garage.misc import tensor_utils
from garage.sampler import OnPolicyVe... | src/garage/torch/algos/maml.py | MIT | 21,079 | a901258c1848378768612959ef15f4d58aeb67ab | Python | py | |
# -*- coding: utf-8 -*-
# --------------------------------------------------
#
# validation.py
#
# Validation phase. Model tuning of attribute label embedding (ALE) method on APY dataset
# 15 seen classes : bird - cat - mug - bus - diningtable - bottle - car - boat
# dog - zebra - monkey - cen... | validation.py | MIT | 10,083 | 3a86508292d5159f58efc3570b388bf60819dae4 | Python | py | |
import argparse
import os
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from models.alexnet import alexnet
from models.vgg import ... | main.py | MIT | 16,825 | cc09fff7aa503acc5a99a3fdfe4649b721f4026a | Python | py | |
import math
import warnings
from typing import List
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
class WarmupCosineLR(_LRScheduler):
"""
Sets the learning rate of each parameter group to follow a linear warmup schedule
between warmup_start_lr and base_lr followed by... | src/hiding_adversarial_attacks/classifiers/pytorch_cifar10/scheduler.py | MIT | 5,422 | 3a866577356e1657f8578cefc47d09a25b963418 | Python | py | |
from os.path import join
import os
import sys
import time
import argparse
import numpy as np
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import roc_auc_score
from sklearn.metrics import precision_recall_curve
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data i... | train_rnn_match.py | MIT | 12,861 | fd6767d3e4cb11b49c33380284204423e5155fc5 | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
from torch.utils.data import Dataset
import pandas as pd
import os
import numpy as np
import pickle
import re
import random
import io
class Youcook_Caption... | end-2-end/UniVL/dataloaders/dataloader_youcook_caption.py | MIT | 9,932 | f952d16213069b17ca70e417ef3d1c5765cdffe7 | Python | py | |
import torch
import torch.nn as nn
from torchvision import models
import kornia.filters
# borrowed from https://github.com/usuyama/pytorch-unet/blob/master/pytorch_resnet18_unet.ipynb and edited
def convrelu(in_channels, out_channels, kernel, padding):
return nn.Sequential(
nn.Conv2d(in_channels, in_chan... | DRACO/models/resnet.py | MIT | 15,180 | 347c7e8fe476b813db5c0852686b4e27ce7fd607 | Python | py | |
#! /usr/bin/python3
# -*- coding: utf8 -*-
import os, time, random, re, glob
from pathlib import Path
import math
import random
import numpy as np
from PIL import Image, ImageMath, ImageFilter, ImageOps
from io import BytesIO
import torch
from torch.utils.data import DataLoader, Dataset
from torchvision import transfo... | main.py | Apache-2.0 | 16,915 | 24f0b053ad646495b68eae6d69a80518ecad2f3e | Python | py | |
# Copyright (c) 2019-present, Francesco Croce
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ i... | pytorch_ares/third_party/autoattack/autoattack/fab_base.py | MIT | 15,372 | 3a86bba87120557c0a68581468eea784a4fa7408 | Python | py | |
import math
import torch
from torch.distributions.kl import register_kl
_EPS = 1e-7
class _TTransform(torch.distributions.Transform):
domain = torch.distributions.constraints.real
codomain = torch.distributions.constraints.real
def _call(self, x):
t = x[..., 0].unsqueeze(-1)
v ... | power_spherical/distributions.py | MIT | 6,734 | 3a86d3cb187f3bb763f12ba0811aa48da38c3e1c | Python | py | |
import torch
import argparse
from albert_modeling import AlBertJointForNQ, AlbertConfig
from torch.utils.data import TensorDataset, SequentialSampler, DataLoader
import utils
from tqdm import tqdm
import os
import json
import collections
import pickle
import pandas as pd
from albert_preprocess import InputLSFeatures
fr... | tf2qa/predict.py | MIT | 8,526 | 3a86de7b740f0d496b5fa1b83578e18778b38e20 | Python | py | |
import torch
import torch.nn as nn
from transformers import BertModel, BertPreTrainedModel, BertLayer
from transformers.models.bert.modeling_bert import BertPooler
class PSUM(nn.Module):
def __init__(self, count, config, num_labels):
super(PSUM, self).__init__()
self.count = count
self.num_... | models/berts/bpsum_asc.py | Apache-2.0 | 2,375 | 5f81c875804482865d603bd3fe37adae8bfa4b50 | Python | py | |
import torch
from tensorboardX import SummaryWriter
class AverageMeterSet: # Nawid - Calculates averages
def __init__(self):
self.sums = {}
self.counts = {}
self.avgs = {}
def _compute_avgs(self): # Nawid - Calculate average
for name in self.sums:
self.avgs[name] =... | stats.py | MIT | 3,294 | 038043a9246d80bd7680079945858c5419b753d0 | Python | py | |
import torch
import torch.nn.functional as F
from torch.optim import Adam
from sac.utils import soft_update, hard_update
from sac.model import TransferQNetwork, TransferGaussianPolicy, Encoder, DynamicsActionEncoder, RewardActionEncoder
from sac.dynamics_model import DynamicsModel, RewardModel
import numpy as np
impor... | sac/sac_easy.py | MIT | 11,159 | 3a86f386bc965294648ecb0aa6797cb121adc324 | Python | py | |
"""CURRENTLY OUTDATED. WILL UPDATE IN FUTURE"""
"""
import torch
from torch import Tensor
from torch.autograd import Variable as Var
from torch.utils.data import DataLoader
from rl.utils import ProgBar, RealtimePlot
from rl.envs import controller
class DAgger():
def __init__(self, env, learner, expert):
... | rl/algos/dagger.py | MIT | 2,489 | 3a86f84ad86c83c7dc1d8695947adfbadd2e54ed | Python | py | |
import torch
from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
from espnet.nets.pytorch_backend.transformer.encoder_layer import EncoderLayer
from espnet.nets.pytorch_backend.transformer.layer_norm import ... | training/finetuneav/ctcencoder.py | Apache-2.0 | 4,313 | 28deff1b62c75ebf66293721ca91946b300979d9 | Python | py | |
from __future__ import absolute_import
from __future__ import division
import torch
import torch.nn as nn
class CrossEntropyLoss(nn.Module):
r"""Cross entropy loss with label smoothing regularizer.
Reference:
Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
... | torchreid/losses/cross_entropy_loss.py | MIT | 2,212 | 12d5eb9a948e5c640c89784b8a250b55806fe8a0 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class YoloLoss(nn.Module):
def __init__(self, S, B, l_coord, l_noobj):
super(YoloLoss,self).__init__()
self.S = S
self.B = B
self.l_coord = l_coord
self.l_noobj = l_noobj
... | yolo_loss.py | MIT | 11,588 | 3a870b3b4f8b064b87a70a4b5cb1cba39dbec4a9 | Python | py | |
import torch
import torch.nn as nn
from vgg import vgg
from decoder import decoder
def calc_mean_std(feat, eps=1e-5):
# eps is a small value added to the variance to avoid divide-by-zero.
size = feat.size()
assert (len(size) == 4)
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2... | model/SANet.py | MIT | 6,763 | 3a870d50e1cb0696b28eefbfa54ed5df5f3417d6 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
import math
import pdb
# -------- Vanilla ---------#
# nn.Conv3d(3, 64, kernel_size=3, padding=1)
# -------- 3DCDC ---------#
# CDC_ST(3, 64, kernel_size=3, padding=1, theta=0.6)
# CDC_T(3, 64, kernel_size=3, paddi... | 3DCDC.py | MIT | 4,955 | 24943597e58a0e7040af19461d98a5dd335b6111 | Python | py | |
import torch
import torch.nn as nn
from torchviz import make_dot
class MyNetwork2(nn.Module):
def __init__(self):
super().__init__()
# define the layers
self.layers = nn.Sequential(
nn.Linear(16, 12),
nn.ReLU(),
nn.Linear(12, 10),
... | 7_ref_how_to_build_network/test5.py | MIT | 802 | 7020aaba75230b99b9e0c38859b2693fe3c3c8a0 | Python | py | |
import torch
import torch.nn.functional as F
from tqdm import tqdm
from dice_loss import dice_coeff
from easydict import EasyDict as edict
import yaml
with open("cfgs/train.yml", 'r', encoding="utf-8") as f:
args = edict(yaml.load(f, Loader=yaml.FullLoader))
def eval_net(net, loader, device):
"""Evaluation w... | UNet_pytorch/eval.py | BSD-3-Clause | 1,238 | d71064e4aeac2f402a4fcaf3e519d0101ea5a30e | Python | py | |
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | flash/pointcloud/detection/data.py | Apache-2.0 | 5,465 | 3a87369f97a06a9fe5178e1b5fc470ea8840cc4a | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class KIMCNN1D(nn.Module):
def __init__(self, opt):
super(KIMCNN1D, self).__init__()
self.embedding_type = opt.embedding_type
self.batch_size = opt.batch_size
self.max_seq_len = opt.max_seq_len
self.embeddi... | models/CNNKim.py | MIT | 5,574 | 345a2004705a3c58adf9750eebba3c4e1d24e3ba | Python | py | |
# coding: utf-8
#############################################
# Cross Entropy with ResNet-34
#############################################
# Imports
import os
import time
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
import sys
from torch.utils.data import Da... | experiment-logs/cacd/cacd-ce.py | MIT | 12,795 | 3467e704a7dd250e818b182d1d4d9b088f309f25 | Python | py | |
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
#####################################################
import torch.nn as nn
import torch.nn.functional as F
from ..initialization import initialize_resnet
class ConvBNReLU(nn.Module):
def __init__(self, nI... | lib/models/shape_infers/InferCifarResNet_depth.py | MIT | 5,898 | d773fc5035054af8d22390b10250b7b295d52f79 | Python | py | |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
# from Discriminator import Discriminator
class Adversarial_Loss(nn.Module):
def __init__(self, lambda_adv):
super(Adversarial_Loss, self).__init__()
self.lambda_adv = lambda_adv
pass
def forward(self, input... | Adversarial_Loss.py | MIT | 788 | 3a875daa5f3115086f0543e16ba3da04468740a1 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and 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.
from dataclasses import dataclass, field
from enum import Enum, unique
from math import ... | torchrec/modules/embedding_configs.py | BSD-3-Clause | 3,588 | 7cc3270041944eec2adc0f5441e9b8e5c1b7e7cd | Python | py | |
import torch
import torch.nn as nn
from sandstone.models.pools.abstract_pool import AbstractPool
from sandstone.models.pools.factory import RegisterPool
@RegisterPool('GlobalMaxPool')
class GlobalMaxPool(AbstractPool):
def replaces_fc(self):
return False
def forward(self, x, batch=None):
# X ... | sandstone/models/pools/max_pool.py | MIT | 385 | 6b47974fc79c38d35af5116efc4fde6b2c7e43e5 | Python | py | |
from torch import nn
from ..functions.sigmoid_focal_loss import sigmoid_focal_loss
# TODO: remove this module
class SigmoidFocalLoss(nn.Module):
def __init__(self, gamma, alpha):
super(SigmoidFocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
def forward(self, logits,... | mmdet/ops/sigmoid_focal_loss/modules/sigmoid_focal_loss.py | Apache-2.0 | 670 | 34202b566437a4d4c6fee5b0cf70f630cac29b3f | Python | py | |
import argparse
import collections
import os
import re
import time
from collections import OrderedDict
from distutils.util import strtobool
import numpy as np
import scipy.stats
import torch
import torch.nn as nn
import torchvision
from torch._six import int_classes, string_classes
from torch.autograd import Variable
... | test_model.py | BlueOak-1.0.0,Apache-2.0 | 13,064 | 5fb78e916a09f0b0669ba5512210aced6a259294 | Python | py | |
import collections
import inspect
import io
import types
import typing
import warnings
import torch
from . import (_dev_utils, activations, convolution, normalization, pooling,
preprocessing, regularization, upsample)
from ._version import __version__
from .module import InferDimension
__all__ = ["sum... | torchlayers/__init__.py | MIT | 24,781 | e9ac09094bd5e14224a8cfb9e17c0ba476057827 | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmflow.models.decoders.pwcnet_decoder import PWCModule, PWCNetDecoder
from mmflow.models.decoders.utils import CorrBlock
def _get_test_data():
input_feat1 = dict(
level6=torch.randn(1, 196, 8, 8).cuda(),
level5=torch... | tests/test_models/test_decoder/test_pwcnet_decoder.py | Apache-2.0 | 3,228 | 10cae689e85d367d227f5e243e5c19ecba00acb5 | Python | py | |
import torch
import torch.nn as nn
class GRUNet(nn.Module):
def __init__(self, in_channels, hid_channels, out_channels, num_layers, drop_prob=0.2):
super(GRUNet, self).__init__()
self.relu = nn.ReLU()
self.gru = nn.GRU(in_channels, hid_channels, num_layers, batch_first=True, dropo... | Models/gru.py | MIT | 749 | d79e1dd284dcd0a5e56f274fad62e58b38b17423 | Python | py | |
from logging import NOTSET
from absl import app, flags, logging
from pytorch_lightning import trainer
import torch as th
import pytorch_lightning as pl
import nlp
from torch.optim import optimizer
import transformers
import sh
import math
import torch.nn as nn
import torch.nn.functional as F
from model.bert import BE... | demo.py | MIT | 4,002 | d78e378dc0266a072309d2d12865a515e7af9034 | Python | py | |
import torch
from torch.nn import Linear as Lin
from torch.nn import ReLU
from torch.nn import Sequential as Seq
from torch_sparse import SparseTensor
from torch_geometric.nn import DynamicEdgeConv, EdgeConv
def test_edge_conv_conv():
x1 = torch.randn(4, 16)
x2 = torch.randn(2, 16)
edge_index = torch.ten... | test/nn/conv/test_edge_conv.py | MIT | 3,234 | ff1e94d21f809c7f939f51770936d935eece978f | Python | py | |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""PyTorch Dataset implementation for Iterative CLEVR dataset"""
import json
import cv2
import h5py
import numpy as np
import torch
from torch.utils.data import Dataset
from ..utils.config import keys
class ICLEVERDataset(... | irgan/data/clevr_dataset.py | MIT | 4,615 | 5f7fd201cc4432d225e141549089d95ec0cb8142 | Python | py | |
import torch
import torch.nn as nn
from entmax import entmax15, sparsemax
from .sparsesoftmax import sparse_softmax
def entropy(p: torch.Tensor):
"""Numerically stable computation of Shannon's entropy
for probability distributions with zero-valued elements.
Arguments:
p {torch.Tensor} -- tensor o... | lvmhelpers/marg.py | MIT | 4,809 | 42a8acc323e86e386793935087c71e05496f789a | Python | py | |
import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np
import tensorrt as trt
import torch
import time
from cv2 import imwrite
from packnet_sfm.utils.image import load_image
from packnet_sfm.datasets.augmentations import resize_image, to_tensor
from packnet_sfm.utils.depth import write_depth, inv2dept... | src/trt_packnet/src/trt_engine.py | MIT | 4,243 | f9c70d6464cb4674ba7705b49ed5dc1712fff1ca | Python | py | |
from __future__ import print_function
import os
import math
import random
import numpy as np
import sys
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import CosineAnnealingLR
import t... | pruning/main.py | Apache-2.0 | 5,548 | 3a883431edaa642a8b96cac6844270b437843b04 | Python | py | |
import numpy as np
import torch
import time
import os
os.environ['NPY_MKL_FORCE_INTEL'] = 'GNU'
num_params = 4010
k = torch.tensor(np.load('./tests/utils/numpy_params/function_0_param_k.npy'), requires_grad=True, dtype=torch.float)
torch.set_num_threads(1)
y = (((k*k+3*k)-k/4)/k+k*k*k*k+k*k*(22/7*k)+k*k*k*k*k*k*k*k*k)... | tests/utils/pytorch.py | MIT | 559 | 3a883ace2c8a57d959b75113f054cd9ab1688032 | Python | py | |
#-*- coding:utf-8 -*-
import torch
import numpy as np
def point_form(boxes):
""" Convert prior_boxes to (xmin, ymin, xmax, ymax)
representation for comparison to point form ground truth data.
Args:
boxes: (tensor) center-size default boxes from priorbox layers.
Return:
boxes: (tensor)... | src/utils.py | MIT | 11,479 | 3a884478cdcaf49ff35d9f11dc688104cb4a59c2 | Python | py | |
from PIL import Image
import random
import math
from typing import ClassVar, Sequence, List, Tuple
from torch import Tensor
import torch
import torchvision.transforms.functional as F
import torchvision.transforms.transforms as T
import torch.nn as nn
from . import MultipleApply as MultipleApplyBase, NormalizeAndTranspo... | common/vision/transforms/segmentation.py | MIT | 9,176 | e648c1ab2afab85c34fc3961fa19aade61612b34 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
"""
The metrics module contains implementations of various metrics used commonly to
understand how well our models are performing. For e.g. accuracy, vqa_accuracy,
r@1 etc.
For implementing your own metric, you need to follow these steps:
1. Create your own metric cl... | pythia/modules/metrics.py | BSD-3-Clause | 18,463 | 3a8872a629677f847a25547cf2f7feb38a037c74 | Python | py | |
from collections import OrderedDict
import numpy as np
import torch.optim as optim
import rlkit.torch.utils.pytorch_util as ptu
import torch
from rlkit.core.trainer import Trainer
from rlkit.core.eval_util import create_stats_ordered_dict
from rlkit.policies.simple import RandomPolicy
from rlkit.samplers import rollo... | ILSwiss/rlkit/torch/algorithms/ddpg/ddpg.py | MIT | 10,115 | 247c44ce2d12a7a21944115fac3ff29d137f7ff3 | Python | py | |
"""Test the multi-output GP implementations."""
import pytest
import numpy as np
try:
import torch
from safe_exploration.ssm_pytorch import BatchMean, BatchKernel, MultiOutputGP, LinearMean, GPyTorchSSM
import gpytorch
from torch.nn.functional import softplus
except:
pass
@pytest.fixture(autous... | safe_exploration/test/test_gaussian_process.py | MIT | 7,472 | f9fa01e3675628d8fecb29506ec0d9a60d1a1c66 | Python | py | |
#-------------#
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pad_sequence
#-------------#
from .decoar import Decoar
from .audio import create_transform
from collections import OrderedDict
############
# CONSTANT #
############
EXAMPLE_FEAT_SEQLEN = 1000
###################
# UPSTREAM EXPERT #
#... | upstream/decoar/expert.py | MIT | 1,976 | 45f9f8a40b79babe8b4cf7b7586ce92fc295f97a | Python | py | |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _triple
from network.model.graph_front.graphFront import _graphFront
from network.model.tgcn.gcn import ConvTemporalGraphical
from network.model.roi_align.modules.roi_align import RoIAlignAvg
class Spat... | network/R3D_model.py | MIT | 16,195 | 3a88d7d1a6cf6a68d76ed92404bdba4d417b181e | Python | py | |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.
from loguru import logger
import apex
import torch
from apex import amp
from torch.utils.tensorboard import SummaryWriter
from yolox.data import DataPrefetcher
from yolox.utils import (
MeterBuffer,
ModelEMA,
a... | yolox/core/trainer.py | Apache-2.0 | 11,417 | 3a88e2011794df552f7c1515ea5b5666904d048d | Python | py | |
# coding=utf-8
# Copyright 2018 Salesforce and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# h... | transformers/modeling_ctrl.py | Apache-2.0 | 24,667 | 1ed9e6ebb111997b503a0938748a97539ae856a4 | Python | py | |
import torch
from tqdm import tqdm
def evaluate(model, data_loader, metrics, device):
if model.training:
model.eval()
summary = {metric: 0 for metric in metrics}
for step, mb in tqdm(enumerate(data_loader), desc='steps', total=len(data_loader)):
x_mb, y_mb = map(lambda elm: elm.to(device... | senCNN/model/metric.py | MIT | 741 | f96f3a8bf59867d30182ee8c83986bf197687406 | Python | py | |
import torchvision
import torch.utils.data as torch_data
from torch.nn import CrossEntropyLoss
from models.simple import SimpleNet
from tasks.task import Task
from torchvision.transforms import transforms
class MNISTTask(Task):
normalize = transforms.Normalize((0.1307,), (0.3081,))
def load_data(self):
... | tasks/mnist_task.py | MIT | 1,633 | 3a8914437c4e6ca740dfbe37406a5e5acde2212a | Python | py | |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch
from tests import utils
class SimpleFlattenModule(torch.nn.Module):
def __init__(self, start_dim=0, end_dim=-1):
super(SimpleFlattenModule, self).__init__()
self.start_dim = start_dim
self.end_... | torch_glow/tests/nodes/flatten_test.py | Apache-2.0 | 1,412 | 3a8914f8967c736d379482a362b9c6d22d241130 | Python | py |
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