content stringlengths 200 198k | repo stringlengths 4 186 | path stringclasses 1
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import torch
from torch.autograd import Variable
import time
import os
import sys
from utils import AverageMeter, calculate_accuracy
def train_epoch(epoch, data_loader, model, criterion, optimizer, opt,
epoch_logger, batch_logger):
print('train at epoch {}'.format(epoch))
model.train()
... | train.py | MIT | 2,637 | 652643d091cb787a83ec58ec0e878fa5e6b7d1cd | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Sun Jul 4 12:59:27 2021
@author: talha
"""
import torch.nn as nn
import torch.nn.functional as F
import torch
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
keep_rate=0.50
#contracting
... | code/Model.py | MIT | 6,174 | 3aa99d6dbce3ee0f8e36344e97d9873102bd9b64 | Python | py | |
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and 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
#
# U... | src/transformers/modeling_albert.py | Apache-2.0 | 58,075 | 4598b73ca8f46381106c8989d62ef47a9dd8633d | Python | py | |
# coding=utf-8
import numpy as np
import scipy.sparse as sp
import json
import torch
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import f1_score
from util.nlp_utils import *
def normalize(mx):
"""Row-normalize sparse matrix"""
rowsum = np.array(mx.sum(1))
r_inv = np.power(rowsum, ... | src/models/CCIG/loader.py | BSD-3-Clause | 7,174 | 45c5e0cc488f4cb3a94e9e673ef2c399b0c261e1 | Python | py | |
import time
import collections
import os
from typing import Optional, List, Tuple
import torchvision
import numpy as np
import torch
from trw.train import SequenceArray, SamplerRandom
from ..basic_typing import ShapeX, Datasets, DatasetsInfo
from ..transforms import Transform
def _clutter(images, cluttered_size, cl... | src/trw/datasets/mnist_cluttered.py | MIT | 5,600 | 1ec3d0370ca4d5f635510bfa43d7a36a98712c82 | Python | py | |
import torch
import logging
import csv
import os
import gzip
from torch.utils.data import Dataset
from typing import List
from transformers import GPT2TokenizerFast
from pkg_resources import resource_filename
import itertools
from tqdm.auto import tqdm
import numpy as np
import sys
csv.field_size_limit(sys.maxsize)
l... | aitextgen/TokenDataset.py | MIT | 14,461 | 3aa9b4d949d2f3218fd2b486d3ca341741d2ace8 | Python | py | |
# -*- coding: utf-8 -*-
"""
# @file name : cifar_train.py
# @author : zjgbz https://github.com/zjgbz
# @date : 2021-04-22
# @brief : 模型训练主代码
"""
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(BASE_DIR, '..'))
import argparse
import torch.optim as... | src/cifar_train.py | MIT | 5,907 | b2bef807547a7b3a16f3f29261a9461722be4146 | Python | py | |
import torch
from .degree import degree_hyedge
def contiguous_hyedge_idx(H):
node_idx, hyedge_idx = H
unorder_pairs = [(hyedge_id, sequence_id) for sequence_id, hyedge_id in enumerate(hyedge_idx.numpy().tolist())]
unorder_pairs.sort(key=lambda x: x[0])
new_hyedge_id = -1
pre_hyedge_id = None
... | SuperMoon/hyedge/utils/verify.py | MIT | 1,025 | 3aa9bacd09d28fff5ce8d6f30fef424f3fd955be | Python | py | |
import copy
import datetime
import random
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from loguru import logger
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from pathlib import Path
from iva... | ivadomed/training.py | MIT | 21,804 | 1e890c838e3a3ecb6529c0d701f4187ac7163f25 | Python | py | |
"""
Agent - this is a class that has a unique id, and a step function which runs every cycle.
It is in this step() function where their lifecycle exists.
Things like check life state, buy product, make product, get paid, move to position... all happens here.
Custom Classes which build on Agent:
- BananaWorldAgents - ... | agents.py | Apache-2.0 | 27,628 | 7059fc5e5bde7d0a0fbcde3be479d1c373576b3e | Python | py | |
import os
import numpy as np
import random
import copy
import torch
import torch.nn as nn
from .system import prefix
def setup_seed(seed):
print(prefix(__name__) + 'seed is: ', seed)
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
tor... | rllib/basic/tools.py | MIT | 5,875 | 3aa9da80182f6d44781d33a88e020b0460216abb | Python | py | |
from __future__ import division
from __future__ import absolute_import
#typing
import torch
from torch.nn import ParameterList, Parameter
from allennlp.common.checks import ConfigurationError
try:
from itertools import izip
except:
izip = zip
class ScalarMix(torch.nn.Module):
u"""
Computes a param... | pymagnitude/third_party/allennlp/modules/scalar_mix.py | MIT | 3,392 | 12e6e7fee9cdc1f672859f6cac54842198b51391 | Python | py | |
import warnings
from functools import partial
from typing import Any, Optional
from torchvision.transforms.functional import InterpolationMode
from ....models.segmentation.fcn import FCN, _fcn_resnet
from ...transforms.presets import VocEval
from .._api import Weights, WeightEntry
from .._meta import _VOC_CATEGORIES
... | torchvision/prototype/models/segmentation/fcn.py | BSD-3-Clause | 3,984 | 3aa9e533d62d738b1b27838ff4e1a195cb35e07d | Python | py | |
from math import pi
import subprocess
import logging
from argparse import ArgumentParser, Namespace, ArgumentDefaultsHelpFormatter
from pathlib import Path
from typing import Tuple, Union
import torch
from torch import nn
from torch.utils.data import DataLoader
import pytorch_lightning as pl
import transformers
from t... | cort/model.py | MIT | 18,767 | e9ace96cb2f774e6bf40ac3da0398e0b194b6c2a | Python | py | |
#!/usr/bin/python3.6
''' Trains a model. '''
import argparse, hashlib, logging, math, os, pprint, random, sys, time
import multiprocessing
from typing import *
from collections import defaultdict, Counter
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import tor... | old_models/train_3d_se_resnext50__fixed_oversmp.py | Apache-2.0 | 18,478 | d7529c439b8786e84f2f7bbf8befaef3db58c868 | Python | py | |
import torch
import time
import musa_torch_extension
from utils.diff import diff_cpu
from utils.save import save_torch_to_txt
def make_data():
seq_len = 200
batch_size = 32
feature_num = 512
hidden_size = 256
num_layers = 4
inp = torch.rand([seq_len, batch_size, feature_num])
bidirectional... | mtPytorchTest/test_bilstm_multilayer.py | MIT | 3,092 | fce764caa3b3ae3a5815db4aa875ee04cbcd51c7 | Python | py | |
"""
---
title: Relative Multi-Headed Attention
summary: >
Documented implementation with explanations of
Relative Multi-Headed Attention from paper Transformer-XL.
---
# Relative Multi-Headed Attention
This is an implementation of relative multi-headed attention from paper
[Transformer-XL: Attentive Language Mode... | labml_nn/transformers/xl/relative_mha.py | MIT | 6,390 | 3aaa318944c4dcb311de3612c4039c9af3d9404a | Python | py | |
# Copyright 2018-2019 Open-MMLab. All rights reserved.
import torch
import os
import mmcv
from mmcv.runner import load_checkpoint
from mmcv.parallel import MMDistributedDataParallel
from .env import get_root_logger
from .test import multi_gpu_test
from ..datasets.dataloader import build_dataloader
def test_network(... | pyvrl/apis/inference.py | MIT | 2,398 | e65fba7632e5d4d61af35d0d1ea90b0ccf104da1 | Python | py | |
import cv2
import math
import json
import pickle
import random
import numpy as np
from matplotlib import cm
from pathlib import Path
from torchvision.transforms import ToTensor
from .utils import interp_l2_distance
from .utils import l2_distance_to_trajectory_set, angle_to_trajectory_set
from lidardet.datasets.base im... | lidardet/datasets/trajectory/covernet_dataset.py | Apache-2.0 | 16,047 | 8044e5682ed9c0f8bff493d45c3a4fe5e3b9e0c3 | Python | py | |
import unittest
import numpy as np
import torch
from torch.autograd import Variable, grad, gradcheck
from qmctorch.wavefunction.jastrows.elec_elec_nuclei.jastrow_factor_electron_electron_nuclei import JastrowFactorElectronElectronNuclei
from qmctorch.wavefunction.jastrows.elec_elec_nuclei.kernels.boys_handy_jastrow_ke... | tests/wavefunction/jastrows/elec_elec_nuc/test_three_body_jastrow_boys_handy.py | Apache-2.0 | 4,359 | b2d31fef1c497064ac8254e5ff8ad35b26f38b7c | Python | py | |
import contextlib
import sys
import os
import torch
import unittest
from torchvision import io
from torchvision.datasets.video_utils import VideoClips, unfold
from common_utils import get_tmp_dir
@contextlib.contextmanager
def get_list_of_videos(num_videos=5, sizes=None, fps=None):
with get_tmp_dir() as tmp_dir... | test/test_datasets_video_utils.py | BSD-3-Clause | 4,944 | 2488edc613d55b1a0c808685681648620b789b51 | 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/trainer/logging.py | Apache-2.0 | 6,628 | 3aaa84468eaa4542bc70a58faa46a68253c2a7b4 | Python | py | |
from __future__ import absolute_import
from __future__ import print_function
import os
import sys
import functools
import math
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd
# the next line can... | tests/onnx_matrix_mul/onnx_matrix_mul.py | Apache-2.0 | 8,956 | 3aaaa49953d34a8f5f8b462b084dea4006f620ee | Python | py | |
# ------------------------------------------------------------------------------------
# BaSSL
# Copyright (c) 2021 KakaoBrain. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------
import c... | bassl/dataset/base.py | Apache-2.0 | 4,405 | 3aaab19e92b9231de82a55ef7b3f28a4dbd74186 | Python | py | |
#!/usr/bin/env python3
from math import exp, pi
import os
import random
import torch
import unittest
import gpytorch
from torch import optim
from gpytorch.kernels import SpectralMixtureKernel
from gpytorch.likelihoods import GaussianLikelihood
from gpytorch.means import ConstantMean
from gpytorch.priors import Smooth... | test/examples/test_spectral_mixture_gp_regression.py | MIT | 4,164 | d7581c6c3193e1305efdd9400986fb4092fea375 | Python | py | |
# -*- coding: utf-8 -*-
"""
This is a simple example of DNN using Pytorch's nn package and optim package
"""
import torch
from torch.autograd import Variable
# data type: CPU or GPU
# dtype = torch.FloatTensor
dtype = torch.cuda.FloatTensor
# N batch: size
# D_in: input size
# D_out: output size
# H: hidden size
N, ... | tutorial/09_simple_dnn_optim.py | MIT | 1,182 | 6515cf81f20c7c243acbbf86c883644e76f74bf2 | Python | py | |
import torch
import torch.nn as nn
from torch import cuda
from sentence_transformers import SentenceTransformer
torch.manual_seed(0)
cuda.manual_seed_all(0)
class Encoder(nn.Module):
def __init__(self, emb_size, hidden_size, dropout_rate):
super(Encoder, self).__init__()
self.hidden_layer = nn.Li... | MoRT/mort/adaptBias/model.py | MIT | 3,164 | 7c2889c376512973856618a6a76fd7af20938796 | Python | py | |
# ***************************************************************************************
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. *
# *
# Permission is hereby granted, free of charge, to... | src/dbpedia_dataset.py | MIT-0 | 2,330 | 5e8dfd72f4c9a8418881c6b7927dd61c6966bdc9 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1. / np.sqrt(fan_in)
return (-lim, lim)
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | ddpg_model.py | MIT | 2,853 | 3aaaf25b9fe0bfdc85d3f28baccac5beecd221d3 | Python | py | |
import os
import torch
import pickle
from itertools import chain
import pandas as pd
import numpy as np
from rdkit.Chem import Descriptors
from rdkit.Chem import AllChem
from rdkit.Chem.rdMolDescriptors import GetMorganFingerprintAsBitVect
from torch.utils import data
from torch_geometric.data import Data
from torch_g... | loader.py | MIT | 31,298 | 652b0a110c064aa39254d41bfa11c287b45cc331 | Python | py | |
import os
import shutil
import random
import math
import time
import argparse
import yaml
from collections import OrderedDict
import sys
# for import parent utils
sys.path.append('../')
import utils
import modules
import datasets
import models
import numpy as np
import torch
import torch.nn as nn
import torch.optim... | AttentionRouting/trainer.py | Apache-2.0 | 15,294 | 6b16493e233bc5bc5ae0622f930b3ff851b4ab9a | Python | py | |
import os
import glob
import json
import random
import os.path as osp
from timeit import default_timer as timer
from collections import deque
import cv2
import numpy as np
import torch
import matplotlib.pylab as plt
from skimage import io, transform
from torch.utils.data import Dataset
import wireframe.utils as utils... | wireframe/datasets.py | MIT | 2,245 | 1e98fdc5a0155f56129246ac66e179702ac2528b | Python | py | |
from collections import OrderedDict
import torch.distributed as dist
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
"""Allreduce parameters as a whole."""
if bucket_size_mb ... | mmpose/core/utils/dist_utils.py | Apache-2.0 | 1,703 | 3aab2af8593b7bcf7d540b0109d81994561b3b8e | Python | py | |
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
# helper functions
def exists(val):
return val is not None
def batched_index_select(values, indices, dim = 1):
value_dims = values.shape[(dim + 1):]
va... | en_transformer/en_transformer.py | MIT | 8,943 | fda283cb600a8a47a4b90f08c7a1cc23001c05ad | Python | py | |
from transformers import *
import torch
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from keras.preprocessing.sequence import pad_sequences
from utils import normalize_text, flat_accuracy
import tempfile
import pandas as pd
import numpy as np
import random
import os
import time
class BertTex... | text_classifier/bert_text_classifier.py | MIT | 15,668 | 03fccff4c4d29d7488f97fe9be2d19f034b28e95 | Python | py | |
from functools import partial
import multiprocessing
from pathlib import Path
from PIL import Image
from random import random
import torch
from torch import nn
from torch.optim import Adam
from torch.utils import data
from torch.utils.data.distributed import DistributedSampler
from torchvision import transforms
EXTS =... | stylegan2_deepspeed/dataset.py | MIT | 3,058 | 3aab3e20f81a350a827c4ee7a43091979ef3d0a4 | Python | py | |
import numpy as np
from skimage import io, color
import torch
from PIL import Image
from torch.utils.data import Dataset, DataLoader, TensorDataset
import torchvision.transforms as T
batch_size = 128
offsets = (47.5, 2.4, 7.4)
scales = (25.6, 11.2, 16.8)
def norm_lab_to_rgb(L, ab, norm=True):
'''given an Nx1xWxH... | experiments/colorization_minimal_example/data.py | MIT | 2,479 | 3aab4bdf06d4aae13de4d0ca70fa1a3c7450fdd6 | Python | py | |
from collections import defaultdict
import os
import numpy as np
import matplotlib.pyplot as plt
import torch
def one_cold(i, n):
"""Inverse one-hot encoding."""
x = torch.ones(n, dtype=torch.bool)
x[i] = 0
return x
class GridBasedPooling(torch.nn.Module):
def __init__(self, cell_side=2.0, n=4, ... | Social-NCE_U-LSTM/gridbased_pooling.py | MIT | 17,386 | 12bff2b81008eeb8dacf328a367ce18336b5a37f | Python | py | |
# -*- encoding: utf-8 -*-
# @File : base.py
# @Time : 2021/05/20 11:55:32
# @Author : Ugur Gudelek
# @Contact : ugurgudelek@gmail.com
# @Desc : None
import torch
from torch.utils.data import Dataset, Subset, ConcatDataset
class BaseTorchDataset(Dataset):
def __init__(self):
super(Base... | berries/datasets/base.py | MIT | 773 | 3aaba77b886b237b5d8a012096cdc65790b41f3d | Python | py | |
import numpy as np
import torch
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import roc_curve, auc
class Scale(object):
def __call__(self, sample):
heartbeat, label = sample['cardiac_cycle'], ... | codes/utils/transform.py | MIT | 4,586 | 45d48cbde8e5254bfca738b8f3d7d9e75bdffd35 | Python | py | |
# -*- coding: utf-8 -*-
"""
@date: 2020/8/22 下午8:50
@file: step_lr.py
@author: zj
@description:
"""
import torch.optim as optim
from torch.optim.optimizer import Optimizer
from .. import registry
@registry.LR_SCHEDULERS.register('StepLR')
def build_step_lr(cfg, optimizer):
assert isinstance(optimizer, Optimiz... | tsn/optim/lr_schedulers/step_lr.py | Apache-2.0 | 494 | 4247d49cf7c36ff1c40c479e0e403bd91143d45b | Python | py | |
# -*- coding: utf-8 -*-
"""
@date: 2020/4/3 下午8:07
@file: custom_bbox_regression_dataset.py
@author: zj
@description:
"""
import os
import cv2
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import utils.util as ... | py/utils/data/custom_bbox_regression_dataset.py | Apache-2.0 | 4,418 | 3aabd90cbbc923c235a758020f70fb11e0dc05f2 | Python | py | |
"""
`Model` is an abstract class representing
an AllenNLP model.
"""
import logging
import os
from typing import Dict, Union, List, Set, Type, Optional
try:
from apex import amp
except ImportError:
amp = None
import numpy
import torch
from allennlp.common.checks import ConfigurationError
from allennlp.common... | allennlp/models/model.py | Apache-2.0 | 21,230 | f9c6496fe5361c8a44fa09e2b03f601cdd248e1a | Python | py | |
# encoding: utf-8
import os
import torch
import torch.distributed as dist
import sys
paths = os.getcwd().split("/")[0:-1]
base_path = "/".join(paths)
nets_path = os.path.join(base_path, "netspresso-compression-toolkit")
sys.path.append(nets_path)
from yolox.data import get_yolox_datadir
from yolox.exp import Exp as My... | exps/example/yolox_voc/yolox_voc_m.py | Apache-2.0 | 4,436 | 3aabe325f2dec9adfad60be1c05c5afe8d8831b3 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
import numpy as np
from copy import deepcopy
import sys
try:
#sys.path.append('../torchkit/torchkit') # Chin-Wei's NAF code for NAF
from torchkit import nn as nn_, flows, utils
from torchkit.transform... | model.py | MIT | 24,559 | 6517381b519aa2c7307f0a0988c41873df749f84 | Python | py | |
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
class Residual(nn.Module):
def __init__(self, in_channels, num_hiddens, num_residual_hiddens):
super(Residual, self).__init__()
self._block = nn.Sequential(
nn.ReLU(True),
nn.Conv2d(in_channels... | query_old/graph/vae_v3.py | MIT | 9,218 | 6501b7d40506f809a2f3cbb47e328b17c6e6f0d9 | Python | py | |
import torch
from syft.generic import object_storage
def test_clear_objects():
"""
Checks the clear_objects method
"""
obj_storage = (
object_storage.ObjectStore()
) # obj_storage is a wrapper object to a collection of objects
x = torch.tensor(1)
obj_storage.set_obj(x)
obj... | test/generic/test_object_storage.py | Apache-2.0 | 919 | 5e69516da451569c1cf67ee1d374d042b88830d4 | 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_array.py | Apache-2.0 | 6,942 | 3aac22bff7588a9698dc3aa91ff006bc7366f69c | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from lib.layers import ROIAlign
from .utils import cat
class LevelMapper(object):
"""Determine which FPN level each RoI in a set of RoIs should map to based
on the heuris... | lib/modeling/poolers.py | MIT | 4,536 | 3aac3e3a2589556c7be0d67c6e3e2e68afb7d005 | Python | py | |
from __future__ import print_function
import os
import os.path
import h5py
import numpy as np
import arrow
import json
import torch
class H5Logger(object):
Default_Auto_Incr_Format = '{0:06d}'
def __init__(
self,
filename='data.hdf5',
auto_incr=False,
auto_i... | h5_logger/h5_logger.py | MIT | 4,449 | 3aac3faa70ee43003276f343e6de4719bcec1520 | Python | py | |
# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0
import math
import warnings
import torch
from pyro.distributions.util import is_identically_zero
from pyro.infer.elbo import ELBO
from pyro.infer.enum import get_importance_trace
from pyro.infer.util import get_dependent_plate_di... | pyro/infer/renyi_elbo.py | Apache-2.0 | 9,308 | 3aac4854ba897751515f137cb6303c8e38dc909f | Python | py | |
import os
import streamlit as st
import joblib
import torch
import json
import base64
import uuid
import re
import jupytext
from bokeh.models.widgets import Div
import math
import importlib.util
def import_from_file(module_name: str, filepath: str):
"""
Imports a module from file.
Args:
module_na... | app/util/utils.py | MIT | 8,819 | 039c3d802405f3b542775f055fd2f32bd4118b2d | Python | py | |
import math
import tree
import torch
import sympy
import random
import graph_utils
import numpy as np
import pickle as pkl
from random import randint
from operator import itemgetter
from sympy.parsing.sympy_parser import parse_expr
class SymbolsManager():
def __init__(self, whether_add_special_tags):
se... | src/data_utils.py | Apache-2.0 | 6,654 | 5f3454d577b0e794992f037daa15c597e27893b7 | Python | py | |
"""
##################################################################################################
# Copyright Info : Copyright (c) Davar Lab @ Hikvision Research Institute. All rights reserved.
# Filename : gaspin_transformation.py
# Abstract : Implementations of the gaspin transformation
# C... | davarocr/davarocr/davar_rcg/models/transformations/gaspin_transformation.py | Apache-2.0 | 11,675 | 3aac5e7dcadf6973872c4f2178e02339db0769c0 | Python | py | |
import torch
import numpy as np
from torch import nn
from torch.nn import BatchNorm1d
from torch.autograd import Function
import torch.nn.functional as F
def _make_ix_like(input, dim=0):
d = input.size(dim)
rho = torch.arange(1, d + 1, device=input.device, dtype=input.dtype)
view = [1] * input.dim()
v... | src/nn_utils.py | MIT | 8,268 | 3aac8a7a88684f371bc8c3b31eec060de7d9a832 | Python | py | |
import transformers
import torch
from lm_eval import utils
from lm_eval.base import BaseLM
from tqdm import tqdm
class XGLM(BaseLM):
def __init__(self, device='cuda', pretrained='facebook/xglm-1.7B', revision='main', subfolder=None, tokenizer=None, batch_size=1):
super().__init__()
assert isinstan... | lm_eval/models/xglm.py | MIT | 4,183 | 3aac8f9ead27978623a7f751081aec5ec2c5255d | Python | py | |
import torch
import torch.nn as nn
class DenseLayer(nn.Sequential):
def __init__(self, in_channels, growth_rate):
super().__init__()
self.add_module('norm', nn.BatchNorm2d(in_channels))
self.add_module('relu', nn.ReLU(True))
self.add_module('conv', nn.Conv2d(in_channels, g... | network/layers.py | Apache-2.0 | 3,092 | 5fbdbceeab7332d44aebff3d6439158a6796fc30 | Python | py | |
from typing import Callable
import torch
from autoPyTorch.pipeline.components.setup.network_initializer.base_network_initializer import (
BaseNetworkInitializerComponent
)
class SparseInit(BaseNetworkInitializerComponent):
"""
Fills the 2D input Tensor as a sparse matrix
"""
def weights_init(se... | autoPyTorch/pipeline/components/setup/network_initializer/SparseInit.py | Apache-2.0 | 1,077 | 1e6dbdbf3c41a6fac83a2f09abfe5a37bc3cd2b0 | Python | py | |
from functools import partial
from typing import Any, Optional
from torchvision.prototype.transforms import VocEval
from torchvision.transforms.functional import InterpolationMode
from ....models.segmentation.fcn import FCN, _fcn_resnet
from .._api import WeightsEnum, Weights
from .._meta import _VOC_CATEGORIES
from ... | torchvision/prototype/models/segmentation/fcn.py | BSD-3-Clause | 3,823 | 108e6c13860189d08684974e47df428a0888e26c | Python | py | |
"""
Implementation of EfficientDet object detection network
Reference: EfficientDet: Scalable and Efficient Object Detection - https://arxiv.org/abs/1911.09070
This version supports init from pretrained weights and easy changing of many hyper-parameters
hacked by @bonlime
"""
import logging
from copy import deepcopy
fr... | pytorch_tools/detection_models/efficientdet.py | MIT | 14,227 | 3aad21d42567eb65fb536225e0628db569550515 | Python | py | |
# Copyright (c) 2021, 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/tts/modules/fastspeech2_submodules.py | Apache-2.0 | 5,170 | b2403d98ea0ed3f184edaf6fe6717f67c34061e6 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import kaiming_init, xavier_init, normal_init
from mmdet.core import auto_fp16, force_fp32, sanitize_coordinates
from ..registry import HEADS
from ..utils import ConvModule
@HEADS.register_module
class RdsMaskHead(nn.Module):
"""Mul... | mmdet/models/mask_heads/rdsnet_mask_head.py | Apache-2.0 | 13,827 | 5e4874a351198af4caf30aa2cc89dfb70b5d7881 | Python | py | |
"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
"""
import random
import numpy as np
import torch
import torch.utils.data as data
from PIL import Image
import... | data/base3D_dataset.py | BSD-3-Clause | 3,700 | 3aad641d2fc3991a29adbf3aff101a8ba0df1ba1 | Python | py | |
# coding: utf-8
import datetime
import os
import random
import re
import string
import time
from collections import Counter
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim
import torch.tensor as tensor
import torch.utils.data
from keras.preprocessing.sequence import pad_s... | quora/kernels/quora-6-milestone-2.py | MIT | 18,574 | 6b3a24620961bb950a48133180b8c7e4d97b5131 | Python | py | |
import torch
import torch.nn.functional as F
import os
import argparse
from tqdm import trange
from transformers import GPT2LMHeadModel, GPT2Config, BertTokenizer
def is_word(word):
for item in list(word):
if item not in "qwertyuiopasdfghjklzxcvbnm":
return False
return True
def _is_chin... | generate.py | MIT | 8,745 | 10729edde9aa51459078e133a014bf6698c6225e | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class BaseModel(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1)
self.conv3 = nn.Conv2d(... | code/Resnet50/model.py | MIT | 1,361 | d75e7390cffe8dcad0554745731a090b0a51ab97 | Python | py | |
# Owner(s): ["module: dataloader"]
import math
import sys
import errno
import multiprocessing
import os
import ctypes
import faulthandler
import torch
import gc
import time
import signal
import unittest
import itertools
import warnings
import tempfile
from torch import multiprocessing as mp
from torch.utils.data impor... | test/test_dataloader.py | Intel | 107,000 | 5e9415afc7a72fd5fbf26bb5d8f0aee96a1d5783 | Python | py | |
#!/usr/bin/env python3
# encoding: utf-8
# @Time : 2018/8/2 下午3:23
# @Author : yuchangqian
# @Contact : changqian_yu@163.com
# @File : engine.py
import os
import os.path as osp
import time
import argparse
import torch
import torch.distributed as dist
from utils.logger import get_logger
from utils.pyt_utils imp... | engine.py | MIT | 4,796 | 3aada45843daeffb1036888f95a2b3baf476d3be | Python | py | |
# %%
import os
import torch
from pytorch_lightning import LightningModule, Trainer
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader, random_split
from torchmetrics import Accuracy
from torchvision import transforms
from torchvision.datasets import MNIST
PATH_DATASETS =... | lightning_examples/mnist-hello-world/hello-world.py | Apache-2.0 | 7,425 | 03e234816c0bab46744e26153b122680196e83bf | Python | py | |
import torch
import numpy as np
from torch import nn
from typing import Any, Dict, List, Type, Optional
from tianshou.policy import A2CPolicy
from tianshou.data import Batch, ReplayBuffer, to_torch_as
class PPOPolicy(A2CPolicy):
r"""Implementation of Proximal Policy Optimization. arXiv:1707.06347.
:param to... | tianshou/policy/modelfree/ppo.py | MIT | 7,676 | e66a5f88249c815a14e6b76d0270bb6b2e58a3b2 | Python | py | |
import logging
import torch
from tools.registry import registry
logger = logging.getLogger(__name__)
class ScaledSupConLoss(torch.nn.Module):
"""Scaled Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
It also supports the unsupervised contrastive loss in SimCLR"""
def __init__(
... | mmt/losses.py | MIT | 7,910 | d71b98c999f7e2a851984f5cd0ae9bf5c2d3e34f | Python | py | |
import pytest
import torch
from toma import cpu_memory
@pytest.mark.forked
def test_cpu_mem_limit():
# 128 MB (128/4M float32)
tensor = torch.empty((128, 1024, 1024 // 4), dtype=torch.float32)
tensor.resize_(1)
cpu_memory.set_cpu_memory_limit(0.25)
# 512 MB (128/4M float32)
with pytest.rais... | tests/test_cpu_mem_limit.py | MIT | 403 | dfa3043cc44c166e37bb3ed7a6c976e1b9cd0393 | Python | py | |
"""
Main model implementation
"""
import torch
from .encoder import ImageEncoder
from .code import PositionalEncoding
from .model_util import make_encoder, make_mlp
import torch.autograd.profiler as profiler
from util import repeat_interleave
import os
import os.path as osp
import warnings
import torch.nn.functional as... | src/model/models.py | BSD-2-Clause | 13,841 | b2a6fa01d352b45f64bd8cef314424791d93d56f | Python | py | |
import base
import torch
import torchvision
import json
# 声明模型路径
PATH = './data/AlexNet_animals.pth'
# 读取部分测试数据
dataiter = iter(base.testloader)
images, labels, image_path = dataiter.next()
# 实例化网络并加载训练完成的模型
# net = base.MyCustomNet()
print(len(base.classes))
net = base.AlexNet(len(base.classes))
net.load_state_dic... | MY_ANIMALS/evaluate.py | MIT | 2,553 | 5f0d54b75044cd5d542a5a85294676c26a67d44a | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
from progress.bar import Bar
import time
import torch
from external.nms import soft_nms
from models.decode import ctdet_angle_decode
from models.utils import flip_tensor
from util... | src/lib/detectors/ctdet_angle.py | MIT | 3,949 | 3aae08f13ad0fe0c9bbc26e1ef9641905bed4336 | Python | py | |
#!/usr/bin/env python3
#使用交集融合而不是简单相加
import argparse
import logging
from pathlib import Path
import sys
import time
from typing import Any
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import Union
import humanfriendly
import numpy as np
import torch
from typeguard import... | espnet2/bin/asr_inference_ilme_intersect.py | Apache-2.0 | 22,471 | 3aae109724af351f57be114e38f53a63b96ea669 | Python | py | |
import os
import shutil
import subprocess
import time
from autonmt.bundle import utils
from autonmt.toolkits.base import BaseTranslator
from fairseq import options
import torch
from fairseq import options
from fairseq_cli import preprocess, train, generate
from fairseq.distributed import utils as distributed_utils
f... | autonmt/toolkits/fairseq.py | MIT | 13,302 | 12eaa7a5c5a3af205442fbac90290dc91f1243c5 | Python | py | |
#!/usr/bin/env python3
import time
import unittest
from itertools import product
from typing import NamedTuple
import torch
import torch.nn.functional as F
from pytorch_translate.dictionary import Dictionary
from pytorch_translate.research.adversarial import adversaries
from pytorch_translate.tasks.pytorch_translate_... | pytorch_translate/research/test/test_adversaries.py | BSD-3-Clause | 10,126 | fd66139846b03f3ed4432490b35151527143faf2 | Python | py | |
# coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors and 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/... | src/transformers/models/layoutlm/modeling_layoutlm.py | Apache-2.0 | 51,816 | 3aae3c62dd057565a8649cbe509a74cee1450dfe | Python | py | |
"""Copyright (c) Facebook, Inc. and its affiliates.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
Portions of the source code are from the OLTR project which
notice below and in LICENSE in the root directory of
this source tree... | loss/SoftmaxLoss.py | BSD-3-Clause | 495 | 3aae4354508f2305582aa2f41d6aca02af8712b5 | Python | py | |
import os
import numpy as np
import torch
from torch.utils.data import DataLoader
from data import task_a, task_b, task_c, all_tasks, read_test_file, read_test_file_all
from config import OLID_PATH
from cli import get_args
from utils import load
from datasets import HuggingfaceDataset, HuggingfaceMTDataset, ImbalancedD... | train.py | CC-BY-4.0 | 5,321 | 28f2b0edf036ff3c49f5635fe22ca94827573893 | 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... | training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_GN.py | Apache-2.0 | 7,404 | 70a14d025bc1a5af12a3aeb2f4b6586956d700f3 | Python | py | |
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the BSD 3-Clause License (the "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/BSD-... | built-in/PyTorch/Official/cv/image_classification/ResNet50_for_PyTorch/DistributedResnet50/image_classification/mixup.py | Apache-2.0 | 2,172 | ff9830430654da662372bca99bc32e9f60614148 | Python | py | |
import glob
import logging
import math
import os
import platform
import random
import shutil
import subprocess
import time
from contextlib import contextmanager
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as... | YOLOV5+DEEPSORT/yolov5/utils/general.py | MIT | 53,153 | fdd2f47233fcf918dcb42b9a9810c6d7824a00f2 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import os
im... | examples/interactive_asr/utils.py | BSD-2-Clause | 6,127 | 3aae6c6864b405648104214f839c1a7eb979c3a7 | Python | py | |
"""Example of classifying data using topological layers."""
from torch_topological.datasets import SphereVsTorus
from torch_topological.nn.data import make_tensor
from torch_topological.nn import VietorisRipsComplex
from torch_topological.nn.layers import StructureElementLayer
from torch.utils.data import DataLoade... | torch_topological/examples/classification.py | BSD-3-Clause | 1,916 | 3aae771d1ab865cb5ecab07c7fb989bc911bf757 | Python | py | |
import torch
from torch import nn as nn
try:
from inplace_abn.functions import inplace_abn, inplace_abn_sync
has_iabn = True
except ImportError:
has_iabn = False
def inplace_abn(x, weight, bias, running_mean, running_var,
training=True, momentum=0.1, eps=1e-05, activation="leaky_re... | timm/models/layers/inplace_abn.py | Apache-2.0 | 3,353 | 3aae7cf563edfe6c9d2bf1a9f3994d911aacea23 | Python | py | |
import itertools
from collections import OrderedDict
import torch.nn as nn
import torch
import torch.nn.functional as F
__all__ = ['bLVNet_TAM_BACKBONE', 'blvnet_tam_backbone']
model_urls = {
'blresnet50': 'pretrained/ImageNet-bLResNet-50-a2-b4.pth.tar',
'blresnet101': 'pretrained/ImageNet-bLResNet-101-a2-b... | core/models/blvnet_tam_backbone.py | Apache-2.0 | 15,804 | 45627f0064ec7c1d59c0e35c6f6233d284167451 | Python | py | |
import matplotlib.pyplot as plt
import numpy as np
import torch
def decode_seg_map_sequence(label_masks, dataset='pascal'):
rgb_masks = []
for label_mask in label_masks:
rgb_mask = decode_segmap(label_mask, dataset)
rgb_masks.append(rgb_mask)
rgb_masks = torch.from_numpy(np.array(rgb_masks)... | dataloaders/utils.py | MIT | 3,696 | 3aaeabe920faac4470ce7c6a09594f3898f7c939 | Python | py | |
from torch.utils.data import Dataset
import numpy as np
from h5py import File
import scipy.io as sio
from utils import data_utils
from matplotlib import pyplot as plt
class H36motion3D(Dataset):
def __init__(self, path_to_data, actions, input_n=20, output_n=10, dct_used=15, split=0, sample_rate=2):
"""
... | utils/h36motion3d.py | MIT | 2,317 | 3aaeb1a8f1191b41fe98886ed2529890a931fcec | Python | py | |
import math
import itertools
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from .utils import get_knn_faiss, cartesian_product, bool_flag
from .utils import get_gaussian_keys, get_uniform_keys
from .query import QueryIdentity, QueryMLP, QueryConv
MemoryDefaultDict = {
"... | pytorch_transformers/memory_pkm/memory.py | Apache-2.0 | 32,455 | e6346df6951dec4a806c92b90cbeb4f945cc65eb | Python | py | |
from __future__ import print_function, division
import argparse
import logging
import numpy as np
from pathlib import Path
from tqdm import tqdm
from torch.utils.tensorboard import SummaryWriter
import torch
import torch.nn as nn
import torch.optim as optim
from core.raft_stereo import RAFTStereo
from evaluate_stere... | train_stereo.py | MIT | 10,170 | 7c3897f6c15287aa2d486cef1114632dcc6b89bc | Python | py | |
"""Implements a Mixture Density LSTM Network for predicting the future latents
based on past latents
"""
import torch
import torch.nn as nn
import torch.nn.functional as f
from torch.distributions.normal import Normal
class _MixtureDensityLSTMBase(nn.Module):
def __init__(self, NUM_LATENTS: int, NUM_ACTIONS: ... | mdrnn/MixtureDensityLSTM.py | MIT | 6,245 | b24778b37fb97bfba23594088d5a384212445abb | Python | py | |
# Software Name: AIVC
# SPDX-FileCopyrightText: Copyright (c) 2021 Orange
# SPDX-License-Identifier: BSD 3-Clause "New"
#
# This software is distributed under the BSD-3-Clause license.
#
# Authors: Theo Ladune <theo.ladune@orange.com>
# Pierrick Philippe <pierrick.philippe@orange.com>
import torch
import torc... | src/layers/entropy_coding/entropy_coder.py | BSD-3-Clause | 887 | 3aaed44b270aac12480011277d819e2c18bf5d3c | Python | py | |
# This file contains experimental modules
import numpy as np
import torch
import torch.nn as nn
from models.common import Conv, DWConv
class CrossConv(nn.Module):
# Cross Convolution Downsample
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
# ch_in, ch_out, kernel, stride, groups, ... | intelligencelayer/shared/models/experimental.py | Apache-2.0 | 5,744 | 3aaf48590dcfebe18039aab53e2b279fb379504d | Python | py | |
import math
import numpy as np
import pytest
import torch
from sklearn.metrics import average_precision_score as sk_average_precision
from torchmetrics.functional.retrieval.average_precision import retrieval_average_precision
@pytest.mark.parametrize(['sklearn_metric', 'torch_metric'], [
pytest.param(sk_average... | tests/functional/test_retrieval.py | Apache-2.0 | 1,020 | 3aaf60d607d5a090f5cd55f2a7766feb1f0607d0 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
# Note: this file is modeled after td3.py
import logging
from typing import Dict, Optional
import reagent.core.types as rlt
import torch
from reagent.core.dataclasses import dataclass, field
from reagent.core.parameters imp... | reagent/model_managers/discrete/discrete_crr.py | BSD-3-Clause | 12,552 | 3aaf649f978a7617da4d24d6dd6c898f0724147a | 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/network_architecture/generic_modular_UNet.py | Apache-2.0 | 20,009 | 3aaf7360b245861c37736f6f2f157bdb0ba1ba46 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import torch
from fvcore.common.benchmark import benchmark
from pytorch3d.renderer.points.rasterize_points import (
rasterize_points,
rasterize_points_python,
)
from pytorch3d.structures.pointclouds import Pointclouds
def _bm_python_wi... | tests/bm_rasterize_points.py | BSD-3-Clause | 1,901 | 3aaf77c3b5ef71d390737772acad4790800ef0d8 | Python | py | |
from torch import nn
class ConvWithBatchNorm(nn.Module):
def __init__(
self,
in_channels,
out_channels,
spacetime_ndim,
kernel_size=3,
normalization=None, # "batch",
activation="ReLU",
):
super(ConvWithBatchNorm, self).__init__()
self.i... | aydin/nn/layers/conv_with_batch_norm.py | BSD-3-Clause | 1,408 | e9058ae88751851005c7cc812438446a30e51edc | Python | py |
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