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import torch
import numpy as np
import pandas as pd
def load_data(file_path, len_train, len_val):
df = pd.read_csv(file_path, header=None).values.astype(float)
train = df[: len_train]
val = df[len_train: len_train + len_val]
test = df[len_train + len_val:]
return train, val, test
def data_trans... | examples/pytorch/stgcn_wave/load_data.py | Apache-2.0 | 842 | 3a53409f54fab58339e880076dc2d244f73b4dda | Python | py | |
import logging
from logging.handlers import RotatingFileHandler
import pandas as pd
import torch
import torch.nn.functional as F
import torchmetrics
from datasets import Dataset
from pytorch_lightning import LightningModule
from transformers import GPT2Config, GPT2DoubleHeadsModel, GPT2Tokenizer
from .basic_model imp... | kirby/qa_model.py | Apache-2.0 | 9,686 | 701aa31db08f0dc560cdeecc6c22cebd8e093b7d | Python | py | |
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Transformer speech recognition model (pytorch)."""
from argparse import Namespace
from distutils.util import strtobool
from itertools import groupby
import logging
import math
import numpy
import torch
from espnet.nets.as... | espnet/nets/pytorch_backend/e2e_asr_transformer_recons.py | Apache-2.0 | 31,557 | 3a53a344b92b75db9092590525a40c84740e91d6 | Python | py | |
import os
import random
import cv2
import numpy as np
import torch
import torchvision.datasets as datasets
import tqdm
from dg_util.python_utils import drawing
from dg_util.python_utils import pytorch_util as pt_util
from dg_util.python_utils.persistent_dataloader import PersistentDataLoader
from sklearn.decomposition... | visualizations/view_nearest_neighbors.py | Apache-2.0 | 6,770 | a95a39e7f516df07ec51fd5e3d0992ab9654e961 | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 29 10:52:58 2021
@author: Mohammed Amine
"""
import pickle
import numpy as np
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib
import torch
#import region_labels
def extract_weights_single(dataset, v... | extract_results.py | MIT | 19,664 | 3a53b339b5947564ede02388a26be20545954a6f | 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 itertools
import json
import os
import re
import torch
import torchaudio
import torchvision
def log_normalize(x):
x.add_(1e-6).l... | datasets/audioset.py | MIT | 6,104 | 03ed5799803b2bb56efda7312c40c549c74788b3 | Python | py | |
#encoding:utf-8
import random
import numpy as np
import glob
import os
import itertools
import time
import sys
import re
import pyopenjtalk
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torchvision
from torchvision import models,transforms
import torchvision.uti... | vits_text_to_speech.py | MIT | 3,330 | 3a53d053f222e12d7eaf4144db17fafa2e62cfdc | Python | py | |
# 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 typing import Tuple
import torch
import torch.nn.functional as F
class LinearWithRepeat(torch.nn.Linear... | projects/nerf/nerf/linear_with_repeat.py | BSD-3-Clause | 1,496 | 3a53db5643fa77101ab11d09cd5abb73d14b6898 | Python | py | |
import torch.nn as nn
import torch.nn.functional as F
import sys
# stop python from writing so much bytecode
#sys.dont_write_bytecode = True
# -----------------------------------------
# custom modules
# -----------------------------------------
from lib.rpn_util import *
class RPN_3D_loss(nn.Module):
def __in... | lib/loss/rpn_3d.py | MIT | 67,261 | 3a53e1d6ff27ef96c964cdb18f1165ab3319b0c0 | Python | py | |
import os
import torch
import cv2
import numpy as np
from easydict import EasyDict
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from tqdm import tqdm
import time
from haco.DIDrive_core.policy import LBCBirdviewPolicy
from haco.DIDrive_core.data import LBCBirdViewDataset
from haco.DIDr... | haco/DIDrive_core/demo/lbc/lbc_birdview_train.py | Apache-2.0 | 5,092 | 3a53ff64477a5f72f1f9134968cfce260a145718 | Python | py | |
# Written by Seonwoo Min, Seoul National University (mswzeus@gmail.com)
""" Utility functions """
import os
import sys
import math
import random
import numpy as np
from datetime import datetime
from sklearn.metrics import auc, roc_auc_score
from scipy.stats import pearsonr, spearmanr, t
import torch
import torch.nn ... | plus/utils.py | MIT | 5,494 | 3a540a58f6e33fadeffee5f00a22845f9644c6fb | Python | py | |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from models.networks.base_network import BaseNetwork
from models.networks.normalization import get_nonspade_norm_layer
class ConvEncoder(BaseNetwork):
""" Same archite... | Face_Enhancement/models/networks/encoder.py | MIT | 1,914 | 3a540e06cf584cb7ad932e498a219f52171c9a81 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import torch
from PIL import Image, ImageFile
from torchvision import transforms
import torchvision.datasets.folder
from torch.utils.data import TensorDataset, Subset
from torchvision.datasets import MNIST, ImageFolder
from torchvision.tr... | domainbed/datasets.py | MIT | 12,652 | 3a54145da4ae502c82cbe2e8252699057b7400f9 | Python | py | |
"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
import copy
import gzip
import json
import lzma
import os
import re
import shutil
import sys
import tarfile
import tempfile
import time
im... | src/datasets/utils/file_utils.py | Apache-2.0 | 26,747 | 1e5cf1c70785867216750f84efea6b3a049eea4e | Python | py | |
from numpy import random
from numpy.core.numeric import full
import torch
import numpy as np
import os
import scipy.ndimage
import torchvision.transforms as transforms
import torch.nn.functional as F
from PIL import Image
import random
from torch._C import dtype, set_flush_denormal
# from detectron2.structures.masks i... | COTR/datasets/tracking_datasets.py | Apache-2.0 | 93,775 | 454f7cdc5862a7a3416b5256258ba3bead5b4599 | Python | py | |
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt
import torch
from utilities import *
from VoxelGrid import *
import os
import argparse
parser = argparse.ArgumentParser(description='Compute a movie from a model.')
parser.add_argument('-model', default="chair", help='model')
args = parser.parse... | make_movie.py | MIT | 1,175 | dfc4f8c81645e7a51d43efee753f3ee4e642d7df | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
class CrowdCounter(nn.Module):
def __init__(self,gpus,model_name):
super(CrowdCounter, self).__init__()
if model_name == 'AlexNet':
from .SCC_Model.AlexNet import AlexNet as net
elif m... | models/CC.py | MIT | 2,629 | 3a546196d164e9649583ed24e253efcdb9870db0 | Python | py | |
import copy
from abc import ABCMeta, abstractmethod
import mmcv
import numpy as np
from torch.utils.data import Dataset
from mmcls.core.evaluation import precision_recall_f1, support
from mmcls.models.losses import accuracy
from .pipelines import Compose
class BaseDataset(Dataset, metaclass=ABCMeta):
"""Base da... | mmcls/datasets/base_dataset.py | Apache-2.0 | 6,714 | fda3fb7042a67f15b36e1d753af5b16670321300 | Python | py | |
import torch
from torch import nn
from torch.nn import functional as F
class Encoder(nn.Module):
"""Maps an (x_i, y_i) pair to a representation r_i.
Parameters
----------
x_dim : int
Dimension of x values.
y_dim : int
Dimension of y values.
h_dim : int
Dimension of h... | models.py | MIT | 4,653 | 3a54979f0a57bd8292ab13828f4d2d8e09dce6fa | Python | py | |
import json
import os
import sys
import torch
from pathlib import Path
from nni.retiarii.experiment import RetiariiExperiment, RetiariiExeConfig
from nni.retiarii.strategies import TPEStrategy, RandomStrategy
from nni.retiarii.trainer import PyTorchImageClassificationTrainer
from darts_model import CNN
if __name__ =... | test/retiarii_test/darts/test.py | MIT | 1,300 | 3a549b76cd7ac83e1d54ba8d753424b09f37bf4d | Python | py | |
# loaders.py
import torch
import numpy as np
import scipy.io as sio
from skimage import io
from PIL import Image
def loader_skimage(path):
return io.imread(path)
def loader_image(path):
return Image.open(path).convert('RGB')
def loader_torch(path):
return torch.load(path)
def loader_numpy(path):
... | extraction/msu_latentafis/datasets/loaders.py | MIT | 396 | 3a549f58467a74f9a1741f47aee32cc4442303aa | 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 | 27,007 | d782c300c332a83a68668ddd6af98cfa8bf5d0dc | Python | py | |
r"""Functional interface"""
from typing import Callable, List, Optional, Tuple
import math
import warnings
import torch
from torch import _VF
from torch._C import _infer_size, _add_docstr
from torch._torch_docs import reproducibility_notes, tf32_notes
from .._jit_internal import boolean_dispatch, _overload, Broadcast... | torch/nn/functional.py | Intel | 197,724 | 3a54a8df58538dc5d602334cda8201d0fd36e344 | Python | py | |
from __future__ import print_function, division
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision.models as models
import numpy as np
import numpy.matlib
import pickle
from lib.torch_util import Softmax1D
from lib.conv4d import Conv4d
from lib... | lib/model_train/trash/model_pixelCT_mask_both_FB_check_differ.py | MIT | 36,198 | 7cad0609de1b77b3e514b7c5914f3b229396c763 | Python | py | |
from torch import nn
from torch.autograd import Function
import ocnn
class OctreeMaxPoolFunction(Function):
@staticmethod
def forward(ctx, data_in, octree, depth):
data_in = data_in.contiguous()
data_out, mask = ocnn.nn.octree_max_pool(data_in, octree, depth)
ctx.depth = depth
ctx.save_for_backw... | pytorch/ocnn/octree_pool.py | MIT | 3,126 | 3a54d971fab1d5bbfcd88d4ecd81eeeae98bbb16 | Python | py | |
from __future__ import print_function, division, absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
import os
import sys
__all__ = ['InceptionV4', 'inceptionv4']
pretrained_settings = {
'inceptionv4': {
'imagenet': {
'ur... | inception_v4.py | MIT | 10,999 | 10de4ec790880ca7f2822bb06d423aef2764dd4c | Python | py | |
import unittest
from transformers import AutoTokenizer, is_torch_available
from transformers.testing_utils import require_torch, slow
if is_torch_available():
import torch
from transformers import (
DataCollatorForLanguageModeling,
DataCollatorForNextSentencePrediction,
DataCollatorF... | tests/test_data_collator.py | Apache-2.0 | 9,294 | ff45e87d6ce695175c2298a3b2c1c2ae6b19c084 | Python | py | |
import random
import torch
import os
import numpy as np
from rendering import render_path
from dataset import load_data
from inputs import config_parser
from model import create_nerf
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
np.random.seed(0)
def test():
parser = config_parser()
... | test_nerf.py | MIT | 2,564 | 1ec4706d1f8a23ce752d88d708d7b1c037bdc109 | Python | py | |
from agent import Agent
import argparse
from collections import deque
from env import Environment
import numpy as np
import torch
from model import QNetwork, Small, Large, Dropout
import matplotlib
matplotlib.use("TkAgg")
from matplotlib import pyplot as plt
def main(args):
if args.examine:
examine()
... | main.py | MIT | 7,213 | f9686dc9ff2b89a0df7aed64052c25f7bda29a89 | Python | py | |
from __future__ import absolute_import, division, print_function
from torch.nn import functional as F
import argparse
#import csv
import logging
import os
import random
import sys
import json
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
... | run_xlm.py | MIT | 34,111 | 3a559a7b4d3df48475c8c1c2f827fb1bc7c46c5b | Python | py | |
import torch.nn.functional as F
import torch.nn as nn
from torch2trt.torch2trt import *
from torch2trt.module_test import add_module_test
import collections
def has_interpolate_plugin():
try:
from torch2trt.plugins import InterpolatePlugin
return True
except:
... | torch2trt/converters/interpolate.py | MIT | 7,562 | dfa20d1918c5df125e88527604e183fd71d272e3 | Python | py | |
"""
The ConvNeXt architecture is similar to previous convnet but was updated with many of the advances
found in recent papers (as of early 2022).
Networks are expanded by increasing the numbers of channel (denoted with C) or block (denoted with
B). Configurations suggested in the ConvNeXt paper are:
ConvNeXt-T: C =... | models/convnext.py | MIT | 22,776 | 7c2a07aaf5d92980a1f2a16e2ea87fb92e8f9a14 | Python | py | |
"""
Created by Edward Li at 10/7/20
Using a CNN structure from this paper (https://www.diva-portal.org/smash/get/diva2:1342302/FULLTEXT01.pdf)
and Google Deep mind
"""
import torch.nn.functional as F
import torch.nn as nn
class DQNCNN(nn.Module):
def __init__(self, side_length, outputs, in_channels = 1, features=... | DQN.py | MIT | 2,473 | 3a55adc947f77f29348907d83944b9ab51e3a831 | Python | py | |
#!/usr/bin/env python3
import math
import pickle
import unittest
import torch
from gpytorch.kernels import RBFKernel
from gpytorch.priors import NormalPrior
from gpytorch.test.base_kernel_test_case import BaseKernelTestCase
class TestRBFKernel(unittest.TestCase, BaseKernelTestCase):
def create_kernel_no_ard(se... | test/kernels/test_rbf_kernel.py | MIT | 8,740 | 3a55c3bef794e8b3182f77af5dd55f21ff19a3eb | Python | py | |
""" CLIP Model
Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
"""
from collections import OrderedDict
from dataclasses import dataclass
from typing import Tuple, Union, Callable, Optional
import numpy as np
import torch
import torch.nn.functional as F
from torch impor... | src/open_clip/model.py | MIT | 20,574 | 3a55f17311c1e13f6eba4ef8a69863a7464cc1dc | Python | py | |
import torch
from torch.utils.data import DataLoader, Dataset
import pickle
import h5py
from random import shuffle
from common import Common
from config import Config
class Dictionaries:
def __init__(self, conf):
self.config = conf
with open(self.config.DICT_PATH, 'rb') as f:
self.s... | loader.py | MIT | 8,483 | ffaadd5b3de17406db275c413770dfaba3274694 | Python | py | |
import torch
import numpy as np
from copy import deepcopy
import torch.nn.functional as F
from tianshou.data import Batch
from tianshou.policy import DDPGPolicy
class SACPolicy(DDPGPolicy):
"""Implementation of Soft Actor-Critic. arXiv:1812.05905
:param torch.nn.Module actor: the actor network following the... | tianshou/policy/modelfree/sac.py | MIT | 5,644 | 3a55fbafcb5f5b6a957ae773b4157267c27f99ba | Python | py | |
import glob
import math
import random
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.distributions as tdist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import torchvision.transforms.functi... | inf.py | MIT | 2,211 | 5e74daeb9deeafa0fc74757e3d7fc9bcbc14df6f | Python | py | |
# MIT License
#
# Copyright (c) 2017 Max W. Y. Lam
#
# 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, mer... | run.py | MIT | 6,875 | ff4b21ce954bf05d55c3a7d9edbcb0222a9e6b25 | Python | py | |
import torch
from torch import nn
import torch.nn.functional as F
import models
from torch.utils.data import DataLoader
import os.path as osp
from tqdm import tqdm
from torch.autograd import Variable
import numpy as np
from utils.logger import AverageMeter as meter
from data_loader import Visda_Dataset, Office_Dataset... | model_trainer.py | MIT | 25,854 | 457a9cc2ecab393aa0ad701a3fbc15b30b68006b | Python | py | |
# -*- coding:utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the ... | zeus/networks/pytorch/customs/modnas/arch_space/ops.py | MIT | 9,047 | 34458ac76ceb7763746666f8d5e28094854d2872 | Python | py | |
# YOLOv5 common modules
import logging
from copy import copy
from pathlib import Path, PosixPath
import math
import numpy as np
import pandas as pd
import requests
import torch
import torch.nn as nn
from PIL import Image
from torch.cuda import amp
from .datasets import exif_transpose, letterbox
from .general import ... | packages/yolov5/models/common.py | MIT | 16,648 | 3a5668b72cd35f4c8ad959a22604ef6fd371e123 | Python | py | |
import sys, time, random, argparse
from copy import deepcopy
import torch
from pathlib import Path
import numpy as np
import torch.nn as nn
import math
import os
import torch.nn.init as init
import torch.nn.functional as F
import warnings
warnings.filterwarnings("ignore")
lib_dir = (Path(__file__).parent / ".." / ".."... | exps/algos/Block_wise_TANAS.py | MIT | 24,534 | 3a566a598d46ef3bca6de25963ffae888cd1d384 | Python | py | |
from collections import defaultdict, deque
import datetime
import pickle
import time
import torch
import torch.distributed as dist
import errno
import os
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
... | torchvision-object-detection-finetuning-tutorial/utils.py | MIT | 9,758 | 3a566e434c66eada4efbfd3bc119fdc743505a1d | Python | py | |
import torch
import torch.nn as nn
from lib.utils import Configure, decoding, detection
class MultiDetector(nn.Module):
def __init__(self, block, in_planes, kernel_size=(16, 2, 2), num_classes=3, extra_layers=False,
phase='train', data_type='normal', policy='first', conf_thresh=0.01):
sup... | modules/layers/multi_detector.py | MIT | 15,747 | 45538dbe4f9a9073ebc9889b40b5d2ba96212fd9 | Python | py | |
import librosa
import torch
from typing import Tuple
import numpy as np
from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
class LogMel(torch.nn.Module):
"""Convert STFT to fbank feats
The arguments is same as librosa.filters.mel
Args:
fs: number > 0 [scalar] sampling rate of the ... | espnet2/layers/log_mel.py | Apache-2.0 | 2,756 | 3a569c6d2a4b057fc8e15f3a503ee3d47dfdefdb | Python | py | |
import collections
from contextlib import contextmanager
from dataclasses import dataclass, field
from enum import Enum
from typing import (
Dict,
List,
Optional,
Union
)
import threading
import torch
import torch.distributed as dist
from torch.distributed import rpc
from torch.distributed import distr... | torch/distributed/_sharded_tensor/api.py | Intel | 33,240 | 42a7eaf327eaa0c7e3f8a6de7d86cc0067345ad8 | Python | py | |
#! /usr/bin/python3
"""
Reference: https://github.com/pytorch/examples/blob/master/mnist/main.py
"""
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from net import LeNe... | train.py | MIT | 5,172 | 1e9365ba1ad28247a19e10b2de9297c3dfac8169 | Python | py | |
import functools
import logging
import random
import re
import warnings
from concurrent.futures import ProcessPoolExecutor
from contextlib import contextmanager
from dataclasses import dataclass
from decimal import ROUND_HALF_UP
from functools import lru_cache, partial
from io import BytesIO, IOBase
from itertools impo... | lhotse/audio.py | Apache-2.0 | 73,177 | d7da4698a100ee231e17b85d2bd1a2506bf34e4a | Python | py | |
import glob
import os
import re
import setuptools
from pkg_resources import DistributionNotFound, get_distribution
from setuptools import dist, find_packages, setup
dist.Distribution().fetch_build_eggs(['Cython', 'numpy>=1.11.1'])
import numpy # NOQA: E402 # isort:skip
from Cython.Build import cythonize # NOQA: E4... | mmcv-full-1.2.0/setup.py | Apache-2.0 | 8,106 | 3a56de8aa0bc50ef2e25291283fcf53738617791 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
from collections import Counter
import numpy as np
import torch
from pytorchvideo.data.utils import thwc_to_cthw
from pytorchvideo.transforms import (
ApplyTransformToKey,
MixUp,
Normalize,
OpSampler,
RandomSho... | tests/test_transforms.py | Apache-2.0 | 14,848 | 5f615ceb91178f50bd6476afe9e24dfaf6e0f26f | Python | py | |
from torch.utils.data import Dataset, DataLoader
import os
import json
import sys
import csv
import itertools
import numpy as np
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
UTILS_DIR = os.path.abspath(os.path.join(BASE_DIR, '..', 'utils'))
sys.path.append(UTILS_DIR)
from data_helper import *
from coord_helpe... | src/lin_my/classifier_dataset_torch.py | MIT | 11,370 | 3a570834f83c9f535df334cdcb39e0a8931d2881 | Python | py | |
import torch
import torch.nn as nn
import numpy as np
from collections import OrderedDict
from modules.my_modules import MyNetwork, MyModule
from modules.operations import ConvLayer, FactorizedIncrease, FactorizedReduce, \
MobileInvertedResidualBlock, MBInvertedConvLayer, Identity, ASPP
from modules.mixed_... | models/new_model.py | MIT | 14,971 | 4b3cb2f8d40646d0d38e792313306f8fd92f1ce0 | Python | py | |
# coding=utf-8
# Copyright 2021 Intel 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 ... | pretraining/optimizers.py | Apache-2.0 | 2,343 | 3a57109da508e64f161b6e792e77fa3d5ddba875 | Python | py | |
#!/usr/bin/env python
# coding: utf-8
# # This script allows you to run the model on the cluster.
# runall.py is not on an actual version right now but runall_onO2.py is
# Both scripts will be merged eventually to one.
# # Code starts here
#######################################################################
# IM... | misc/runallskorch.py | Apache-2.0 | 12,058 | 3a572769175ab9a26bd59f85408b6002f9852010 | Python | py | |
import argparse
import time
import math
import numpy as np
np.random.seed(331)
import torch
import torch.nn as nn
import tcn
import data
import model
from utils import batchify, get_batch, repackage_hidden
parser = argparse.ArgumentParser(description='PyTorch PennTreeBank RNN/LSTM Language Model')
parser.add_argument... | finetune_span.py | BSD-3-Clause | 13,467 | cc14a09ef974053294aa489cce47d26c3667c60c | Python | py | |
import numpy as np
import torch
from torch import nn
from torchvision.utils import make_grid
import visdom
class Visualizer():
def __init__(self, name, display_id=1):
self.display_id = display_id
self.vis = visdom.Visdom(env=name)
self.name = name
def throw_visdom_connection_error(sel... | utils/visualizer.py | MIT | 1,959 | 3a57449917a814901e97566773f58ab82330cbce | Python | py | |
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this... | src/transformers/pipelines/__init__.py | Apache-2.0 | 19,326 | 24a5b176335a71d8b718f4cbb587d54bae53c04b | Python | py | |
from __future__ import print_function, division
import os
import torch
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from PIL import Image
import glob
from sklearn import... | datasets/wavelets/wavelet_dset.py | MIT | 2,952 | 5f323386cd472e99d65e3ab68380b2c842492120 | Python | py | |
"""
Taken from https://github.com/TUM-LMF/MTLCC-pytorch/blob/master/src/models/convlstm/convlstm.py
authors: TUM-LMF
"""
import torch.nn as nn
from torch.autograd import Variable
import torch
class ConvLSTMCell(nn.Module):
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias):
"""
... | src/backbones/convlstm.py | MIT | 10,503 | 7c21d36bb4c33d5cdecf78376c8ad4e404bbf09e | Python | py | |
import logging
import os
import setproctitle
import shutil
import subprocess
import sys
import click
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.multiprocessing as multiprocessing
from revolver.data import datasets, datatypes, prepare_loader
from revolver.model impo... | train.py | BSD-2-Clause | 6,386 | 45ce9fdc17cc0872b93fa1958deec0248099c067 | Python | py | |
import os
import glob
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader, random_split
from skimage import io, transform
class DataHandler():
def __init__(self, dataset, batch_size=64, shuffle=True, validation_prop=0.2, validation_cache=64):
self.__val_... | src/data_handle/data_handler.py | MIT | 12,896 | fcc25483aadb115bbb479863291dd9d7b473ae8f | Python | py | |
import torch.nn as nn
class DeepSurv(nn.Module):
def __init__(self, n_features, hidden_size=64):
super().__init__()
self.survival = nn.Sequential(
nn.Linear(n_features, hidden_size),
nn.ReLU(),
nn.Dropout(0.2),
nn.BatchNorm1d(hidden_s... | Models/MFPCA_DeepSurv/DeepSurv.py | MIT | 633 | 3a579845c530fb5c5d0b960be1184bd0e446d7bc | Python | py | |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@File : main.py
@Time : 2020/03/09
@Author : jhhuang96
@Mail : hjh096@126.com
@Version : 1.0
@Description:
"""
from comet_ml import Experiment
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from encoder import Encoder
from decoder import De... | main.py | MIT | 9,361 | a940cf87f847651f323d59050fd850c978e0b9fa | Python | py | |
# -*- Python -*-
# This file is licensed under a pytorch-style license
# See frontends/pytorch/LICENSE for license information.
import typing
import torch
import torch_mlir
# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
mb = torch_mlir.ModuleBuilder()
class Submodule(torch.nn.Module):
def __init__(self, n):
... | frontends/pytorch/test/module_import/submodules.py | Apache-2.0 | 1,596 | 3a57c085defd12a35156ca8aa66901a02bbd4c9d | Python | py | |
"""Deformable 3D convolution
Adapted from https://github.com/oeway/pytorch-deform-conv/
"""
import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
def flatten(a):
return a.contiguous().view(a.nelement())
def repeat(a, repeats, axis=0):
assert len(a.size()) == 1
return... | deformable_conv_3d.py | MIT | 8,617 | f9a29b8779ca57d673408754c9d0d1fd5f015dbe | Python | py | |
import math
import torch
import torch.nn as nn
4
from models import register
def default_conv(in_channels, out_channels, kernel_size, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size // 2), bias=bias)
class BasicBlock(nn.Module):
def __init__(
... | models/common.py | Apache-2.0 | 2,833 | cc18a6a9e2623949e38eae3c94ef16e0301d1d63 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import random
from typing import Tuple
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import CfgNode
from densepose2.structures.mesh import create_mesh
from .utils import sample_random_indices
cla... | projects/DensePose/densepose/modeling/losses/cycle_shape2shape.py | Apache-2.0 | 4,667 | 10fb13dc6901245541ecb8f69344eca9b86f5a9c | Python | py | |
import torch.nn as nn
import torch.nn.functional as F
import torch
from functools import partial
def conv3x3(in_planes, out_planes, stride=1, bias=False):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=bias)
def dilat... | src/models/modules/refinenet.py | MIT | 13,612 | f9a13967862c1365ae821d2f3b967d3f83bd627a | Python | py | |
from thop import profile
import torch
import argparse
import os
import pprint
# import pose_resnet
import _init_paths
from config import cfg
from config import update_config
import models
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument(... | tools/calculate.py | MIT | 1,667 | 425f0cd14831f3480a078ed8ee37469e1578da73 | Python | py | |
#!/usr/bin/env python3
import copy
import inspect
import itertools
import operator
from collections import OrderedDict
import torch
from torch import nn
from torch.distributions import Distribution
from .constraints import Interval
from .lazy import LazyTensor
class Module(nn.Module):
def __init__(self):
... | gpytorch/module.py | MIT | 25,703 | 3a583798e2ffdaabba7d4a785880c458fc8a7ee7 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from loss.dice_loss import DiceLoss,ShiftDiceLoss
from loss.cross_entropy import CrossentropyLoss, TopKLoss, DynamicTopKLoss
class BCEPlusDice(nn.Module):
"""Dice loss, need one hot encode input
Args:
weight: An array of shape [num_cl... | loss/combine_loss.py | MIT | 8,494 | 24e96985b8161b9a6fc50adf91ffe5f3da556940 | Python | py | |
# -*- coding: utf-8 -*-
"""
@date: 2020/4/26 下午3:38
@file: squeeze_net.py
@author: zj
@description:
"""
import torch.nn as nn
from models.squeezenet.basic_conv2d import BasicConv2d
from models.squeezenet.fire import Fire
class SqueezeNet(nn.Module):
def __init__(self, num_classes=1000):
super(Squeeze... | py/lib/models/squeezenet/squeeze_net.py | Apache-2.0 | 1,926 | 3a587b65901fef5fc45c4b349763133908fe5cf5 | Python | py | |
import io
import os
import sys
import unittest
import torch
import torch.nn as nn
from torch.testing import FileCheck
from typing import Any
# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing.... | test/jit/test_class_type.py | Intel | 43,672 | 3a5881f365c168de569192ccc02a9cbbaca86fe3 | Python | py | |
from __future__ import division
from tools.model import load_model
from config.configs_kf import *
from lib.utils.visual import *
import torchvision.transforms as st
from sklearn.metrics import confusion_matrix
import numpy as np
# from tqdm import tqdm_notebook as tqdm
from tqdm import tqdm
from tools.ckpt import ... | MSCG-Net/test_submission.py | MIT | 9,111 | ff33d0ce6b545371dc987ee3de50f64ddfaf7165 | Python | py | |
import torch
import torch.distributed as dist
import argparse
import collections
import datetime
import json
import os
import _jsonnet
import attr
import random
import numpy
# These imports are needed for registry.lookup
# noinspection PyUnresolvedReferences
from ratsql import ast_util
# noinspection PyUnresolvedRe... | ratsql/commands/train.py | MIT | 15,553 | 3a589e8fae44c3a44971f37b0d8026ecbca31da2 | Python | py | |
import os
import argparse
import copy
from functools import partial
from easydict import EasyDict
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
import numpy as np
from carla_env import ImplicitCarlaEnv
from models import ImplicitDQN
from core.utils.others.tcp_helper import parse_carla_tcp
... | demo/implicit/train_rl.py | Apache-2.0 | 7,816 | 3a58a64f0501d41a31f24e6668eaad98de1ba6ca | Python | py | |
import torch
def flipud(tensor):
"""
Flips a given tensor along the first dimension (up to down)
Parameters
----------
tensor
a tensor at least two-dimensional
Returns
-------
Tensor
the flipped tensor
"""
return torch.flip(tensor, dims=[0])
| ACME/utility/flipud.py | MIT | 303 | 3a58ae95d719e650ef8251214e5592bff3f6c967 | Python | py | |
import os
import math
import json
import logging
import torch
import torch.optim as optim
from tensorboardX import SummaryWriter
from ..utils.util import ensure_dir
class BaseTrainer:
"""
Base class for all trainers
"""
def __init__(self, model, loss, metrics, resume, config, train_logger=None):
... | FOTS/base/base_trainer.py | BSD-3-Clause | 7,418 | 6b2ba80cecb4bb3bf407def805dd43d468b8913d | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 27 11:58:26 2018
file_utils
@author: GEO
"""
import os
import sys
import torch
import shutil
from datetime import datetime
import pandas as pd
from matplotlib import pyplot as plt
def init_folders(base_output_dir, model_name, resume, logging):
output_dir = os.path.j... | utils/file_utils.py | MIT | 10,852 | 3a58cfee04cd808757c8a0372cee0049c1b420b5 | Python | py | |
import numpy as np
from typing import NamedTuple, Mapping, Dict
from enum import Enum
from dataclasses import dataclass
import torch
import torch.utils.data.dataloader as dataloader
from jiant.utils.python.datastructures import ExtendedDataClassMixin, combine_dicts
@dataclass
class FeaturizationSpec:
"""Tokeniz... | jiant/tasks/core.py | MIT | 7,397 | 28bd8b051ace8f55c78f0682a6d1c314842a7de7 | Python | py | |
"""
Description: This library contains all the class model for building ml architectures
Please refer to each of the functions/classes for a full description of what they do.
Functions ([first,second] "first bool" notes the implemented functions "second bool" notes the implemented documentation):
n... | SP/ml/archs/build.py | MIT | 2,388 | 651acc487827fca4cb44a3f2e4e1321a4f6238cc | Python | py | |
import pytest
import time
from itertools import product
import numpy as np
import torch
from ding.rl_utils import ppg_data, ppg_joint_error
use_value_clip_args = [True, False]
random_weight = torch.rand(4) + 1
weight_args = [None, random_weight]
args = [item for item in product(*[use_value_clip_args, weight_args])]
... | ding/rl_utils/tests/test_ppg.py | Apache-2.0 | 1,712 | ffa881662e5f13ff101948279df38a5a3bc9c506 | Python | py | |
# This file is based on the BiaffineDependencyParser implemented in AllenNLP 0.8.4,
# which is licensed under the Apache License, Version 2.0.
# see https://github.com/allenai/allennlp/blob/030ef755aeeee7d05d119dcfe367f81bb26aed53/allennlp/models/biaffine_dependency_parser.py
#
# Copyright (c) 2020 Saarland University... | graph_dependency_parser/components/edge_models/DM.py | Apache-2.0 | 9,892 | 3a5904c85cfb29f13735ed7a21a1ba6f49c91d04 | Python | py | |
import torch
import torch.nn as nn
import torch.autograd as autograd
import torch.cuda.comm as comm
import torch.nn.functional as F
from torch.autograd.function import once_differentiable
import time
import functools
#from libs import InPlaceABN, InPlaceABNSync
#BatchNorm2d = functools.partial(InPlaceABNSync, activat... | cc_attention/functions.py | MIT | 5,093 | 3a59085e87b260d8f4cdb3bd0870cdb1d884b794 | Python | py | |
# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
This file contains components with some default boilerplate logic user may need
in training / testing. They will not work for everyone, but many users may find them useful.
The behavior of functions/classes in this file... | detectron2/engine/defaults.py | Apache-2.0 | 18,818 | 3a5913f02a4a2b71339d0a27ca1254a777434a55 | Python | py | |
# coding=utf-8
# Copyright 2018 the HuggingFace Inc. 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... | tests/test_trainer.py | Apache-2.0 | 37,364 | 3a5916d19a9423e050c346702933bb3647517a2a | Python | py | |
"""
Copyright 2022 Vanessa Boehm
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, software
d... | pytorch_pae/data_loader.py | Apache-2.0 | 1,863 | ff03e5b32be61f7c369110b0ee6295f7cf203780 | Python | py | |
import torch.optim as optim
import torch
from utils.train_utils import *
import logging
import math
import importlib
import datetime
import random
import munch
import yaml
import os
import sys
import argparse
from dataset import ShapeNetH5
def train():
logging.info(str(args))
metrics = ['cd_p', 'cd_t', 'emd',... | train.py | MIT | 8,621 | 3a593d8ae8e5f8dd5c879ef4750e874c9e22b130 | Python | py | |
import copy
from typing import Optional, Any
import torch, pickle
from torch import Tensor,nn
import torch.nn.functional as F
from torch.nn.modules import Module
from torch.nn.modules.activation import MultiheadAttention
from torch.nn.modules.container import ModuleList
from torch.nn.init import xavier_uniform_
from t... | HackTransformer.py | MIT | 38,912 | d7e062b0bdf874ce53260ae03cf6dbe999bcf708 | Python | py | |
import torch.nn as nn
class PairNorm(nn.Module):
def __init__(self, mode='PN', scale=1):
"""
mode:
'None' : No normalization
'PN' : Original version
'PN-SI' : Scale-Individually version
'PN-SCS' : Scale-and-Center-Simultaneously versio... | src/mylib/torch/nn/modules/pair_norm.py | MIT | 1,584 | 3a594b5d8d475e431e1886e8834f5080fc0e706e | Python | py | |
from __future__ import print_function
import torch
import torchvision.transforms as transforms
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.nn as nn
import argparse
import os
import random
import sys
import pprint
import datetime
import dateutil.tz
import time
import cloudpickle
impor... | code/auto-encoder-pokemon/gridsearch.py | MIT | 5,980 | 1e5f54221c0cf6fd9f3f3eef4d073800655dae17 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detec... | backend/MaskFormer/mask_former/modeling/transformer/transformer_predictor.py | MIT | 6,381 | 45d04451444dc5532d60dd7a6b6072ba5b87357f | Python | py | |
import cubes.torch.functional as C
import torch
def main():
x = torch.ones(1, 40, 5).cuda()
x.requires_grad = True
t = C.graded_dropout(x, a=10, b=50, training=True).sum()
t.backward()
print(x.grad)
if __name__ == "__main__":
main()
| examples/graded_dropout.py | MIT | 261 | 3a597f73a915909b2685989e96000ce3feba2806 | Python | py | |
# -*- coding: utf-8 -*-
"""basic_DDQN.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/107IqlTumlwGkd_ijA3PBnT9VJwoF4nF_
# README
based on: https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
##Requirements
## DONE
- ma... | basic_ddqn.py | MIT | 16,402 | 45b61ef908fc45578fa4cf40541a700710f3fd52 | Python | py | |
# get_objects.py
from os import path
import numpy as np
import pickle
import torch
from torch.autograd import Variable
import cv2
from WP5.KU.Algorithms.frame_analyser import FrameAnalyser
from WP5.KU.Algorithms.object_detection.SSD.data import BaseTransform, VOC_CLASSES as LABEL_MAP
from WP5.KU.Algorithms.object_detec... | WP5/KU/Algorithms/object_detection/get_objects.py | Apache-2.0 | 3,253 | 802f99d9d4c75a54fefb81bef9daacbb6f4381f2 | Python | py | |
import os
import glob
import numpy as np
import albumentations
from torch.utils.data import Dataset
from taming.data.base import ImagePaths, NumpyPaths, ConcatDatasetWithIndex
class ObjectsBase(Dataset):
def __init__(self, *args, **kwargs):
super().__init__()
self.data = None
self.keys = ... | taming/data/objects.py | MIT | 8,523 | 45ae3677356da3a1d10e7003aaa4d20b59d3658f | 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/core/integrations/icevision/adapter.py | Apache-2.0 | 7,470 | 1e6c7d48a98a657a59a768866b86d766361c9d2a | Python | py | |
import os
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
from models import ResNet as resnet_cifar
import pandas as pd
import argparse
import csv
from torch.optim.lr_scheduler import MultiStepLR
from dataLoader import DataLoader
from summaries import TensorboardSummary
# parameters... | Old_Codes_For_Exploration/train_js.py | MIT | 8,386 | 5fd1e458f2bbe264d3174d77470398e191a2f19b | Python | py | |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT license.
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
import os
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from la... | examples/pytorch/vision/Face_Detection/models/RPool_Face_C.py | MIT | 12,493 | 4b7dcb89ac44d66b027c20abf005f9390ba18210 | Python | py |
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