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import torch
from scripts.study_case.ID_4.torch_geometric.nn.conv import MessagePassing
from scripts.study_case.ID_4.torch_geometric.utils import remove_self_loops, add_self_loops
from ..inits import reset
class PointConv(MessagePassing):
r"""The PointNet set layer from the `"PointNet: Deep Learning on Point Set... | scripts/study_case/ID_4/torch_geometric/nn/conv/point_conv.py | Apache-2.0 | 3,377 | f957c1e9f0e1ab30dfe883a67179675a8c10a487 | Python | py | |
#!/usr/bin/python
# -*- encoding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as modelzoo
# from modules.bn import InPlaceABNSync as BatchNorm2d
resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
def conv3x3(in_planes, out_planes... | models/face_part_seg/resnet.py | BSD-3-Clause | 3,761 | dfa114960c5103c47ef06e906ab20e44c67cfce5 | Python | py | |
import torch
from .module import Module
from torch.nn.parameter import Parameter
from .. import functional as F
from .. import init
# TODO: check contiguous in THNN
# TODO: use separate backend functions?
class _BatchNorm(Module):
_version = 2
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=T... | torch/nn/modules/batchnorm.py | BSD-3-Clause | 13,649 | cc1d0f1a142018e119dbf1df1b475546ee186c96 | Python | py | |
"""model.py - Model and module class for EfficientNet.
They are built to mirror those in the official TensorFlow implementation.
"""
# Author: lukemelas (github username)
# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
# With adjustments and added comments by workingcoder (github username).
import... | efficientnet_pytorch/model.py | Apache-2.0 | 15,066 | 7c6f6551375f2ceaa0ccf8af8b5a239d07e9d2eb | Python | py | |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import numpy as np
import torch
class CutoutCustom:
"""
Reference : https://github.com/quark0/darts/blob/master/cnn/utils.py
"""
def __init__(self, length):
self.length = length
def __call__(self, img)... | archai/datasets/transforms/cutout_custom.py | MIT | 829 | fed4cc65f3dd55574cb8cc15c4d7de77a0e1a745 | Python | py | |
import os
import argparse
import numpy
import pyro_models
from pyro_models.utils import json_file_to_mem_format
from utils import save, load
from model_constants import model_constants
import pyro.poutine as poutine
from pyro.infer.mcmc import MCMC, NUTS
from pyro.infer.abstract_infer import TracePredictive
import t... | run_nuts.py | MIT | 4,355 | fed4d343a72656944f5765aca0a6b8d449882816 | Python | py | |
from torch import nn
class ShapeClassMaskRCNNC4Predictor(nn.Module):
def __init__(self, config, pretrained=None):
super(ShapeClassMaskRCNNC4Predictor, self).__init__()
stage_index = 4
stage2_relative_factor = 2 ** (stage_index - 1)
res2_out_channels = config.MODEL.RESNETS.RES2_OUT... | maskrcnn_benchmark/modeling/roi_heads/shape_class_head/roi_shape_class_predictors.py | MIT | 1,161 | fed4ebb80de208864986a1e79bbf0464dd3ffd03 | Python | py | |
# Copyright 2020 - 2021 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 wri... | sample-apps/segmentation_spleen/lib/train.py | Apache-2.0 | 3,490 | 5e81394192f585b5b9be923fa1ac503f0b500069 | Python | py | |
import numpy as np
import torch
from ignite.engine import Engine
def mixup_data(x, y, alpha=1.0, use_cuda=True):
'''Returns mixed inputs, pairs of targets, and lambda'''
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = x.size()[0]
if use_cuda:
... | oneshot/mixup.py | BSD-3-Clause | 3,227 | 458f2395a325673aac7e399bed22ecb16b14f728 | Python | py | |
# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
# 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... | openspeech/models/deepspeech2/model.py | Apache-2.0,MIT | 5,042 | fed53d0d97c574dfdd44e8701e15a8eea3f574b2 | Python | py | |
from __future__ import absolute_import, division, print_function
import logging
import numpy as np
import pytest
import torch
import torch.optim
import pyro
import pyro.distributions as dist
import pyro.poutine as poutine
from pyro.distributions.testing import fakes
from pyro.infer import (SVI, JitTrace_ELBO, JitTra... | tests/infer/test_gradient.py | MIT | 9,875 | fed5464441475e6771861cb634003024dbe75e95 | Python | py | |
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Also available under a BSD-style license. See LICENSE.
import torch
from torch_mlir_e2e_test.torchscript.framework imp... | python/torch_mlir_e2e_test/test_suite/elementwise_comparison.py | Apache-2.0 | 14,342 | 247dd8bc1659db9ba1e5ff356bce5f868e3895a1 | Python | py | |
import torch.nn as nn
from layers.autogcn_layer import AUTOGCNLayer
from dgl.nn.pytorch.conv import SGConv
from layers.mlp_readout_layer import MLPReadout
import dgl
import torch.nn.functional as F
import torch
class AUTOGCNNet(nn.Module):
def __init__(self, net_params):
super().__init__()
in_di... | nets/SBMs_node_classification/autogcn_net.py | MIT | 2,384 | fed5a822d3275c52007b0d43aadbb243e5324784 | Python | py | |
import torch
import pandas as pd
import numpy as np
import os
from torch.utils.data import Dataset
from time import time
__author__ = "Junhao Wen"
__copyright__ = "Copyright 2018 The Aramis Lab Team"
__credits__ = ["Junhao Wen"]
__license__ = "See LICENSE.txt file"
__version__ = "0.1.0"
__maintainer__ = "Junhao Wen"
_... | clinicadl/clinicadl/patch_level/utils.py | MIT | 32,572 | fed5b6c265d4c01260bfee7b4ec9529185da617f | Python | py | |
from collections import Counter, OrderedDict
import torch
import torch.utils.data as data_utils
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim
from allennlp.modules import FeedForward
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils.clip_grad... | src/modules/hierarchical_document_classifier.py | BSD-3-Clause | 14,936 | fed5b839299a714a6a4fc920e07f92772c6150be | Python | py | |
import unittest
import torch
from leanai.core.indexed_tensor_helpers import *
class TestIndexedTensorHelpers(unittest.TestCase):
def setUp(self) -> None:
pass
def test_map_per_batch(self):
values = torch.tensor([1, 2, 3, 4, 5])
indices = torch.tensor([0, 0, 1, 1, 2])
def fun(s... | tests/core/test_indexed_tensor_helpers.py | MIT | 1,272 | d7b7b6eac41f9b094bfc19b39139444a897e3c55 | Python | py | |
import sys
sys.path.append('../../')
import time
import torch
from dataGen import train_loader, test_loader
from neurasp import NeurASP
from network import Sudoku_Net
startTime = time.time()
######################################
# The NeurASP program can be written in the scope of ''' Rules '''
# It can also be wr... | NeurASP-master/examples/solvingSudoku_70k/test.py | Apache-2.0 | 1,609 | 340e262d2e981281c9d45f1e0e83706a8dc7d9cf | Python | py | |
import itertools
import os
import re
from abc import ABC, abstractmethod
from glob import glob
from pathlib import Path
import numpy as np
import torch
from PIL import Image
from ..io.image import _read_png_16
from .utils import verify_str_arg
from .vision import VisionDataset
__all__ = (
"KittiFlow",
"Sint... | torchvision/datasets/_optical_flow.py | BSD-3-Clause | 15,376 | 7c728a5af8fd96e25db4a29229717d34756296fb | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
import torch
import numpy as np
import sys
import os
import ml3
from ml3.optimizee import ShapedSineModel
from ml3.ml3_train import meta_train_shaped_sine as meta_train
from ml3.learnable_losses import Ml3_loss_shaped_sine as Ml3_loss
from ml3.ml3_test import test_ml3_... | ml3/experiments/run_shaped_sine_exp.py | MIT | 3,015 | 4bc22de903100272385d41a91cfcba72214bc254 | Python | py | |
import time
import json
import torch
import logging
import argparse
from tqdm import tqdm
from modules.multi_gras import MultiGraS
from utils import test_rouge, rouge_results_to_str, output2doc_extractive
from data_management.vocabulary import Vocabulary
from data_management.data_loader import DataLoader
logging.basi... | evaluate.py | MIT | 3,790 | 5ebde0bdabb970b55059b80a7ec4318fbdb5eb8c | Python | py | |
from PIL import Image
import numpy as np
import torch
import torchvision.transforms as transforms
import torchvision.transforms.functional as F
import torch.nn.functional as functional
import torch.utils.data as data
import random
import time
import scipy.io as scio
import h5py
import math
import glob
import os
class ... | Phase2_Get_Weight_Fast/Dataset/DatasetConstructor.py | MIT | 9,435 | 80525c1fa1fbfb0a08f77cb30b3461d6f6192591 | Python | py | |
import torch.nn as nn
class BidirectionalLSTM(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(BidirectionalLSTM, self).__init__()
self.rnn = nn.LSTM(input_size, hidden_size, bidirectional=True, batch_first=True)
self.linear = nn.Linear(hidden_size * 2, output_s... | modules/sequence_modeling.py | MIT | 762 | e9d1f23a8899546b448e7efc503205fb808ecfdc | Python | py | |
from dataset import Dataset
from PIL import Image, ImageFilter
import torchvision.transforms as transforms
import torch.utils.data as tdata
import torch
import numpy as np
from os.path import join, dirname, realpath
import os
from matplotlib import pyplot as plt
class HeartDataset(Dataset):
def __init__(self,... | dataset/dataset_HEART.py | MIT | 3,007 | 45b94fd4399c3b8f39ab7e122d7e25d440181d7b | Python | py | |
import torch
import math
import random
from PIL import Image
try:
import accimage
except ImportError:
accimage = None
import numpy as np
import numbers
import types
from collections.abc import Sequence, Iterable
import warnings
from . import functional as F
__all__ = ["Compose", "ToTensor", "ToPILImage", "No... | torchvision/transforms/transforms.py | BSD-3-Clause | 49,459 | 10783c8e53db6ec8cae3048922dd87777fa92662 | Python | py | |
# coding=utf-8
# Copyright 2021 The IDEA Authors. 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 a... | fengshen/models/megatron_t5/modeling_megatron_t5.py | Apache-2.0 | 90,005 | 340099cea7552e22ec76a764e1822991b36a6d7f | Python | py | |
from typing import List, Tuple
import torch
from pytorch_lightning import LightningModule
from torch import nn
import neodroidaudition.audio_utilities.torch_transforms
class UNetConvBlock(nn.Module):
def __init__(self, in_channels: int, out_channels: int, kernel_size: int):
super().__init__()
self.conv = ... | neodroidaudition/classification/seperation/audio_unet.py | Apache-2.0 | 8,506 | b25f2ec5b15699c5b713f2b03ea02fad6c492659 | Python | py | |
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from tests.base.datasets import TrialMNIST
try:
from test_tube import HyperOptArgumentParser
except ImportError:
# TODO: this should be discussed a... | tests/base/models.py | Apache-2.0 | 4,840 | fed694891c2913afb8a4e8a2cc16619b0369835c | Python | py | |
# -*- coding: utf8 -*-
"""
======================================
Project Name: NLP
File Name: tplinker_plus_utils
Author: czh
Create Date: 2021/8/16
--------------------------------------
Change Activity:
======================================
"""
import re
import copy
import json
import os
import... | nlp/utils/tplinker_plus_utils.py | MIT | 42,927 | e902692155e2a0c38304dfd0e89884250aab9b31 | Python | py | |
import torch
from tqdm import tqdm
@torch.no_grad()
def predict(test_loader, model, n_samples=20, apply_softmax=True, return_targets=False, delta=1, n_data=None):
py = []
targets = []
count = 0
for x, y in test_loader:
if n_data is not None and count >= n_data:
break
x, y... | laplace/util.py | MIT | 763 | d7b35997a7c7daba80545f3034352d485c28ee61 | 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 os
import pathlib
from argparse import ArgumentParser
import pytorch_lightning as pl
from fastmri.data.mri_data import fetch_dir
from... | fastmri_examples/unet/unet_knee_mc_leaderboard_20201111.py | Fair,MIT | 6,022 | fed6e7f42cd0922561aa5b72298ed318686e921c | Python | py | |
import itertools
from collections import Counter
import coloredlogs
import logging
import torch
from sklearn import metrics
import numpy as np
logger = logging.getLogger('MajorityClassifier Log')
coloredlogs.install(logger=logger, level='DEBUG', fmt='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
class Maj... | models/majority_classifier.py | MIT | 2,655 | 7c56d8a500c80c1ff813c226c3994e9f00063016 | Python | py | |
# standard library imports
import os
import math
from math import sqrt, pi
# package imports
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.normal import Normal
from torch.distributions.gamma import Gamma
from torch.distributions.multivariate_normal impor... | src/bnn/networks/sparse.py | MIT | 64,370 | 5f8629c6a6222648d5b64256004f7a29aac3eca9 | Python | py | |
"""Discrete CNN Q Function."""
import torch
from torch import nn
from garage.torch.modules import DiscreteCNNModule
# pytorch v1.6 issue, see https://github.com/pytorch/pytorch/issues/42305
# pylint: disable=abstract-method
class DiscreteCNNQFunction(DiscreteCNNModule):
"""Discrete CNN Q Function.
A Q netwo... | src/garage/torch/q_functions/discrete_cnn_q_function.py | MIT | 5,605 | 6b27d77a9857d9f3c2b45f607e9b9db5258c8171 | Python | py | |
from torch.nn import GRU, LSTM
import torch
from torch import nn
import numpy as np
from speechjoey.encoders import RecurrentEncoder
from .test_helpers import TensorTestCase
from speechjoey.model import build_model
from speechjoey.vocabulary import Vocabulary
import copy
class TestModelInit(TensorTestCase):
def... | test/unit/test_model_init.py | MIT | 1,971 | 03ac0ff38cceea2d04d255909a7c274fc6a000f8 | Python | py | |
import torch
import torch.nn as nn
import torchvision.transforms.functional as T
from torchvision.transforms import ToTensor, Lambda, Normalize, Compose, Resize
from ..transforms import transform, scale_factor, scale_size
from ..utils import bbox2result
from PIL import Image
class RetinaNet(nn.Module):
def __ini... | models/detectors/retinanet.py | Apache-2.0 | 1,320 | 3441c442448bd948cd3beb1c14c7f2dd9096a9e1 | Python | py | |
from torch import nn
from torch.nn import functional as F
from collections import OrderedDict
__all__ = ["FCN"]
class _SimpleSegmentationModel(nn.Module):
__constants__ = ['aux_classifier']
def __init__(self, backbone, classifier, aux_classifier=None):
super(_SimpleSegmentationModel, self).__init__()... | segmentation/fcn.py | MIT | 2,501 | 4268404a11a24570abbd723332358f20cc0ec88d | 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... | monai/networks/blocks/aspp.py | Apache-2.0 | 3,774 | d7dedbf3097623dd77094f3d9f7fc3fba9b321c9 | Python | py | |
import os
from datetime import timedelta
import torch.distributed as c10d
from torch.autograd import Variable
import torch
torch.manual_seed(2048)
import random
random.seed(2048)
import numpy as np
np.random.seed(2048)
from distseq.training import LSTransformerEncoderLayer
local_rank = int(os.environ.get("LOCAL_RAN... | test_dist.py | Apache-2.0 | 1,459 | d713434cc442c6533243d720f3425767fb04dab6 | Python | py | |
import torch
import albumentations as A
from albumentations.pytorch import ToTensorV2
from tqdm import tqdm
import torch.nn as nn
import torch.optim as optim
from model import UNET
from utils import (
load_checkpoint,
save_checkpoint,
get_loaders,
check_accuracy,
save_predictions_as_imgs,
)
# Hyper... | ML/Pytorch/image_segmentation/semantic_segmentation_unet/train.py | MIT | 3,204 | fed782af579a792ffec18ac191557516e2f68d81 | Python | py | |
#! /usr/bin/env python
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | ludwig/features/vector_feature.py | Apache-2.0 | 8,177 | fed78620712d86a0b2df5d0f25173dd32bf59597 | Python | py | |
from __future__ import print_function, absolute_import
import argparse
import os.path as osp
import numpy as np
import sys
sys.path.append('./')
import torch
import torch.nn.functional as F
from torch import nn
from torch.backends import cudnn
from torch.utils.data import DataLoader
from torch.autograd import Variabl... | examples/main.py | MIT | 11,740 | 429df41f98ceb49d9671e5dae508d073f48d750a | Python | py | |
# --------------------------------------------------------
# Pytorch Multi-GPU Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
... | testoid_net.py | MIT | 13,803 | 7c6d0be929ed046a0ca912709300057cff347765 | Python | py | |
import contextlib
import json
import logging
import os
from typing import Any, Dict, Optional
from unittest import mock
import pytest
import torch
import torch.nn.functional as F
from torch import nn, Tensor
from torch.optim import Optimizer
from torch.utils.data import DataLoader
from torchmetrics import Accuracy
fr... | tests/plugins/test_deepspeed_plugin.py | Apache-2.0 | 40,659 | fed7a11b3fae061333463432e7cfc85d8bf1d27c | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import math
import warnings
from typing import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import (build_activation_layer, build_conv_layer,
build_norm_layer, xavier_init)
from mmcv.cnn.bricks.... | mmdet/models/utils/transformer.py | Apache-2.0 | 45,983 | fd71966f1b7f9cd266b43852fc18f79a945d4ecc | Python | py | |
"""Discrete Dueling CNN Module."""
import torch
from torch import nn
from garage.torch.modules import CNNModule, MLPModule
# pytorch v1.6 issue, see https://github.com/pytorch/pytorch/issues/42305
# pylint: disable=abstract-method
class DiscreteDuelingCNNModule(nn.Module):
"""Discrete Dueling CNN Module.
A ... | src/garage/torch/modules/discrete_dueling_cnn_module.py | MIT | 7,359 | fed7eed4767ec84e33d0ec9facfe895bbb2081a1 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
import math
import torch
import torch.nn.functional as F
from detectron2.layers import cat
from detectron2.layers.roi_align_rotated import ROIAlignRotated
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.m... | detectron2/export/c10.py | Apache-2.0 | 20,860 | ffb47c6cf19ae07f334b751ccadd071ebbd25e2e | Python | py | |
# DRL models from ElegantRL: https://github.com/AI4Finance-Foundation/ElegantRL
import torch
from elegantrl.agent import AgentDDPG
from elegantrl.agent import AgentPPO
from elegantrl.agent import AgentSAC
from elegantrl.agent import AgentTD3
from elegantrl.config import Arguments
# from elegantrl.agent import AgentA2C
... | finrl/drl_agents/elegantrl/models.py | MIT | 4,762 | 420f7524d51b83430d79fb4cd2e0317448aaf23b | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import numpy as np
import scipy as scp
import logging
import matplotlib.pyplot as plt
from pyvision.logger import Logger
from ast import literal_eval
import torch
import torch.nn as nn... | torchlab/evaluation/legacy/geoprod.py | MIT | 20,842 | 4ba8df14b66429be04fa3378b41466c31910fefe | Python | py | |
import torch
import torch.nn as nn
from torchvision import models
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
class FashionClassifier(nn.Module):
"""
A model that will be used to classify the fashion images.
... | src/models/fashion_classifier.py | MIT | 3,867 | d768ba279bac215171eb3c327cb5552553d89ba6 | Python | py | |
import torch
import torch.nn as nn
from torch.nn import functional as F
import mmcv
from mmcv.cnn import normal_init
from mmdet.core import distance2bbox, force_fp32, multi_apply, matrix_nms
from mmdet.ops import ConvModule, Scale
from ..builder import build_loss
from ..registry import HEADS
from ..utils import bias_i... | mmdet/models/anchor_heads/condinst_head.py | Apache-2.0 | 24,862 | 706a823af6674828e6c3acefdbf53843febb0eb8 | Python | py | |
# SOME AUXILIARY FUNCTION FOR CLEANER NOTEBOOKS
import numpy as np
import torch
import matplotlib.pyplot as plt
import seaborn as sns
def top10_accuracy_scorer(gt_idx, top10_idx):
aciertos = 0
for arr, gt in zip(top10_idx,gt_idx):
if gt in arr:
aciertos+=1
top_10_... | src/utils.py | MIT | 1,430 | fed84ef8fa6819d3296056df92acfe2b1e3d0385 | Python | py | |
from pathlib import Path
import torchvision.models as models
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
import torch
from sklearn.metrics import f1_score
from time import perf_counter
import argparse
import os
import sys
import config
import losses as L
from ... | train.py | MIT | 13,834 | 1e763642dfe80c328009d58a8d5eb6c67c3ebb27 | Python | py | |
from __future__ import division
from __future__ import print_function
import argparse
import random
import importlib
import numpy as np
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
import tools.utils as utils
#############################################################################... | infer.py | MIT | 3,100 | 70035f32b21747771ecc78e3e84e06b1b6cb2062 | Python | py | |
import unittest
import cvxpy as cp
import numpy as np
import numpy.random as npr
import torch
from torch.autograd import grad
from cvxpylayers.torch import CvxpyLayer
import diffcp
def set_seed(x):
npr.seed(x)
torch.manual_seed(x)
def sigmoid(z):
return 1 / (1 + np.exp(-z))
class TestCvxpyLayer(unit... | cvxpylayers/torch/test_cvxpylayer.py | Apache-2.0 | 11,523 | 2474c6a88b547711251e88477a062a6a99b6c7ae | Python | py | |
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | examples/speaker_recognition/speaker_reco.py | Apache-2.0 | 3,176 | fed887a98dd9ed50cfea821817e1dcf63ca68f4a | Python | py | |
import argparse
import torch
from torchvision import transforms
from retinanet import model
from retinanet.dataloader import CSVDataset, Resizer, Normalizer
from retinanet import csv_eval
assert torch.__version__.split('.')[0] == '1'
print('CUDA available: {}'.format(torch.cuda.is_available()))
def main(args=None)... | csv_validation.py | Apache-2.0 | 1,942 | 6b3495c74da6a223a1b720c870479cda44a56824 | Python | py | |
import sys
import argparse
import os
import numpy as np
import torch
import torch.nn as nn
from dataset import UrbanPlanningDataset
from metrics import compute_mae, compute_mape, \
compute_ssi, compute_geh, compute_cpl, \
compute_cpc, compute_binned_metric, compute_macro_metric, mae_metric, \
mape_metric, ... | fc_regression_baseline.py | MIT | 7,719 | fed88e5cbd0ea5aa3659450b015b32edfcd9c5a6 | Python | py | |
from vocoder.models.fatchord_version import WaveRNN
from vocoder.vocoder_dataset import VocoderDataset, collate_vocoder
from vocoder.distribution import discretized_mix_logistic_loss
from vocoder.display import stream, simple_table
from vocoder.gen_wavernn import gen_testset
from torch.utils.data import DataLoader
from... | vocoder/train.py | MIT | 4,376 | b2449f66ffe8fcb05e7c7aeccb502f70c6c114b5 | Python | py | |
import copy
import numpy as np
import pytest
from sklearn.datasets import load_digits
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch_shallow_neural_classifier import TorchShallowNeuralClassifier
import utils
from torch_model... | test/test_torch_model_base.py | Apache-2.0 | 4,901 | 7c2fc715c58e93d9f7647062721f93b0ff8bffff | Python | py | |
import os
import torch
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets, transforms
def train(rank, args, model, device, dataloader_kwargs):
torch.manual_seed(args.seed + rank)
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(
"../data... | mnist_hogwild/train.py | BSD-3-Clause | 3,026 | 12d151b61e7e363a10c9f8adb30281afb4923c76 | Python | py | |
from ray_on_aml.core import Ray_On_AML
from azureml.core import Run
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch.nn.functional as F
import ray.train.torch
from ray import train
from ra... | examples/distributed_dl/distributed_ml.py | MIT | 2,320 | fed8c18c3d1339c960c8aeae780ff666f6ab45b2 | Python | py | |
import numpy as np
# from numpy.lib.function_base import gradient
import torch
from torchvision import datasets, transforms, utils
from models.Nets import CNNMnist
from options import args_parser
from client import *
from server import *
import copy
import time
def load_dataset():
trans_mnist = transforms.Compose(... | Lab2/ex2_v2/ex2_code/main.py | MIT | 2,873 | fed8c7e2d3f9641958b5ec1ebf51172ea4cbf780 | Python | py | |
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from config import LABEL_MAP
from model import Criterion, ISRScheduler
from utils import convert_ids_to_tokens, safe_loss, process_outputs, get_bow_labels
def train_generator(config, vocab, batch, model_G, model_D, optim_G, optim_D, seq_crite... | train.py | MIT | 7,027 | f9ded2c9f84904547b4a8d191d9a0b23d4cd067b | Python | py | |
import numpy as np
import torch
import cv2
import torch.nn as nn
from dataloader import DataProcessor, ImbalancedDatasetSampler, threedpreprocess, ModDataProcessor, ModDataProcessor3slices, unsharp_transform
from dataloader import remove_calcs, ModDataProcessor3slicesNoCalcs
from torch.utils.data import DataLoader, ran... | Faster-RCNN/vocdata/model_class.py | MIT | 28,311 | fed8f47738e3a26543367a21114a1ac0b6041d94 | Python | py | |
from collections import defaultdict
from syft.frameworks.torch.mpc.primitives import PrimitiveStorage
from syft.generic.utils import remote, allow_command
import torch
import syft
RING_SIZE = 2 ** 32
ERR_MSG = "You must call PRZS.setup because the seeds where not shared between workers"
class PRZS:
def __init__... | syft/frameworks/torch/mpc/przs.py | Apache-2.0 | 4,884 | 7cae055494e6e901025c9454702b307ab7db0ecf | Python | py | |
import torch
import numpy as np
class ROptions:
"""
When search for the Lyapunov function, we use the 1-norm of |R*(x-x*)|₁.
This class specificies the options to search for R.
"""
def __init__(self):
pass
def set_variable_value(self, R_val: np.ndarray):
pass
def R(self) ... | neural_network_lyapunov/r_options.py | MIT | 8,895 | fcb3d7cd719a268106a3c6bde97d553164d9dc13 | Python | py | |
""" timg_discrim.py
"""
import torch
import torch.nn as nn
class Timg_Discrim(nn.Module):
def __init__(self):
super(Timg_Discrim, self).__init__()
self.C = 64
# input is expected to be 512 x 512
# 5 layers of downsampling gets it down to 16 x 16
# self.conv1 = nn.Conv2d(1,... | scripts/experiment/__unfinished_gan_like_model/pytorch_stuff/models/timg_discrim.py | Apache-2.0 | 1,644 | a93989b4fed3c7f9391870652bfd7e059bedf15b | Python | py | |
import copy
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image
class Normalization(nn.Module):
def __init__(self, m... | neuralartstudio/image_style.py | MIT | 11,484 | 5fb1aecb3f3c465cddea3126ede7d708bb6af772 | Python | py | |
r"""Definition of the DataLoader and associated iterators that subclass _BaseDataLoaderIter
To support these two classes, in `./_utils` we define many utility methods and
functions to be run in multiprocessing. E.g., the data loading worker loop is
in `./_utils/worker.py`.
"""
import os
import threading
import iterto... | torch/utils/data/dataloader.py | Intel | 67,430 | fed94f7d8ee05e43c729343b090ee3eac1ddcd9f | Python | py | |
'''
Script for training models.
'''
from torch import optim
import torch
import torch.utils.data
import argparse
import torch.backends.cudnn as cudnn
import random
import json
import sys
# Import dataloaders
import Data.cifar10 as cifar10
import Data.cifar100 as cifar100
import Data.tiny_imagenet as tiny_imagenet
# ... | train.py | MIT | 13,922 | 03f6b1a8d58d5bd8f7c3a838fc759f34132eec42 | Python | py | |
from hydroDL.master import basins
from hydroDL.app import waterQuality
from hydroDL import kPath, utils
from hydroDL.model import trainTS
from hydroDL.data import gageII, usgs
from hydroDL.post import axplot, figplot
import torch
import os
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as ... | app/waterQual/basinAll/basinRef_tsWRTDS.py | MIT | 5,036 | 28bce4344300577b31a3ce6aba44c9f76c49134b | Python | py | |
import os
import numpy as np
import torch
import lws
from scipy import signal
from autokeras.pretrained.base import Pretrained
from autokeras.constant import Constant
from autokeras.utils import temp_path_generator, ensure_dir, get_device
import librosa
from autokeras.pretrained.voice_generator.deepvoice3_pytorch im... | autokeras/pretrained/voice_generator/voice_generator.py | MIT | 10,828 | 03e190fc6b85aa30afea327b92353f9e11a3765e | Python | py | |
import torch
import numpy as np
import os
import sys
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.dirname(BASE_DIR))
class get_transform(nn.Module):
def __init__(self, num_classes=16):
... | models/model_seg.py | Apache-2.0 | 7,856 | 28e97d75a9055593f94a44ee5c20ca4b1d58c8aa | Python | py | |
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import os
import random
import pandas as pd
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch... | utils.py | MIT | 4,016 | 1e2583fd6f92c89a9a11c2fd8701a340aaa26548 | Python | py | |
r"""
Naive gate
"""
from .base_gate import BaseGate
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import os.path as osp
import errno
# from megatron import print_rank_0
def mkdir_if_missing(dirname):
"""Creates dirname if it is missing."""
if not osp.exists(dirname):
tr... | fmoe/gates/naive_gate.py | Apache-2.0 | 4,421 | fed9bdd614767e5012374c58ea08735764dfd5c0 | Python | py | |
"""
Color space conversions
=======================
In this tutorial we are going to learn how to convert image from different image spaces using `kornia.color`.
"""
from matplotlib import pyplot as plt
import cv2
import numpy as np
import torch
import kornia
import torchvision
#############################
# We u... | examples/color_conversions.py | Apache-2.0 | 1,721 | 7c1550ada00ac4025155d44c1000910ab755cb21 | Python | py | |
import torch
import math
def gelu_new(x):
"""
Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
"""
return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (... | ablang/extra_fns.py | BSD-3-Clause | 749 | fed9c50484e9908e7694b78bc009a3f0868fea9b | Python | py | |
import torch
from torch import nn
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from loss_scaler import DynamicLossScaler, LossScaler
FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
HALF_TYPES = (torch.H... | fp16_optimizer.py | BSD-3-Clause | 17,381 | 5e0cef18a0d4f08af790a6c7d01c9a0b8eef8a2d | Python | py | |
import cv2
import torch
import numpy as np
MEANS = (104, 117, 123)
def caffe_preprocessing(image, size, mean=MEANS):
x = cv2.resize(image, (size, size)).astype(np.float32)
x -= np.array(mean)
x = x.astype(np.float32)
return x
def np_to_tensor(image):
return torch.from_numpy(image).permute(2, ... | ssd/transforms/functional.py | MIT | 395 | 12d0b3c4d51b656f61d7eb414f74a08816369a9c | Python | py | |
import warnings
from typing import Dict, Tuple, Union
import numpy as np
import torch as th
from gym import spaces
from torch.nn import functional as F
def is_image_space_channels_first(observation_space: spaces.Box) -> bool:
"""
Check if an image observation space (see ``is_image_space``)
is channels-fi... | stable_baselines3/common/preprocessing.py | MIT | 8,300 | fed9ee6d5dc597d65e3bc7ab40a7972f8f840dff | Python | py | |
# Author - Abhinand --> https://github.com/abhinand5
# =====================================================================
# IMPORTS
# ======================================================================
import torch
from torchvision import transforms
import os
import sys
from PIL import Image
from model import ... | facemask_detector/detect_face_mask.py | MIT | 1,547 | a9173b74867d3fa61160f9c117be63d896860504 | Python | py | |
import os
import sys
import torch
import numpy as np
from losses import lpips
def to_scaled_tensor(imA):
# convert to RGB, scale images and cast to pytorch Tensor
# expected shape is Nx3xHxW
# first convet to fake RGB
imA = np.moveaxis(np.transpose(np.tile(np.expand_dims(imA,axis=-1), (1,1,1,3))),-1,0)... | src/metrics/lpips_metric.py | MIT | 1,443 | 5e6227058ee95b5fc3e71dff610be7846ceb6970 | Python | py | |
import torch.nn.functional as F
import torch.nn as nn
import torch
from torch.autograd import Variable
from base.common import *
class BasePolicy(nn.Module):
"""
Base policy class
"""
def __init__(self, opts):
# ---- Settings for policy network ----
super(BasePolicy, self).__init__()
... | base/base_policy.py | MIT | 12,789 | feda172bad66773da1079b69f970c038c7e8c3f1 | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import json
import cv2
import numpy as np
import time
from progress.bar import Bar
import torch
from opts import opts
from logger import Logger
from utils.utils import AverageMete... | src/test.py | MIT | 4,324 | feda211d616ef451ee9e02277244b6b1a69f683a | Python | py | |
# Copyright 2018 Uber Technologies, Inc. All Rights Reserved.
# Modifications copyright (C) 2019 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.or... | test/test_torch.py | Apache-2.0 | 64,843 | 701c65b3ea74f18c75735bc16850ee0896041960 | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import numpy as np
import json
import os
import torch.utils.data as data
class COCOHP(data.Dataset):
# num_classes =15#33 #1#24 chan... | src/lib/datasets/dataset/coco_hp.py | MIT | 4,540 | 700faacd3caf01bcd5f077afae561eae4ab32517 | Python | py | |
from datetime import timedelta
import logging
import threading
import warnings
from typing import Generator, Tuple
import torch
import torch.distributed as dist
logger = logging.getLogger(__name__)
_init_counter = 0
_init_counter_lock = threading.Lock()
def is_available():
return hasattr(torch._C, "_rpc_init... | torch/distributed/rpc/__init__.py | Intel | 9,041 | 1ed3b48939ae283582a238e4503c3cafadc1aed4 | Python | py | |
"""
Evaluation for mvtec_ad dataset.
Reference from https://github.com/denguir/student-teacher-anomaly-detection.
Author: Luyao Chen
Date: 2020.10
"""
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import transforms, datasets
import cv2
import matplotlib.pyplot a... | evaluate.py | MIT | 11,220 | d7e22430d0afb02262aa469cb9a606bef037fd7b | Python | py | |
from dreamcoder.task import Task, EvaluationTimeout
import gc
from dreamcoder.utilities import *
from collections import Counter
import math
from dreamcoder.domains.regex.groundtruthRegexes import gt_dict
gt_dict = {"Data column no. " + str(num): r_str for num, r_str in gt_dict.items()}
class AllOrNothingLikelihood... | dreamcoder/likelihoodModel.py | Unlicense | 14,937 | 803ad8d3773251283be09bd95029d58d2a64675a | Python | py | |
from datetime import datetime
from os import makedirs, path
import torch.nn as nn
import torch.utils.data
from sklearn.metrics import (accuracy_score, balanced_accuracy_score,
confusion_matrix, f1_score)
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from constrai... | code/experiments/_train_encoder_original.py | MIT | 7,802 | e9a2f85837d4a62b644dff7212ba5e37abaa2907 | Python | py | |
from typing import Tuple, Union, List, Callable, Optional, cast
import torch
from torch.distributions import Uniform, Beta
from functools import wraps
from kornia.utils import _extract_device_dtype
def _validate_input(f: Callable) -> Callable:
r"""Validates the 2D input of the wrapped function.
Args:
... | kornia/augmentation/utils/helpers.py | ECL-2.0,Apache-2.0 | 9,751 | fcb6bcdcac27ae8733ec76a3a466b846b211a415 | Python | py | |
#!/usr/bin/env python3
"""PyTorch Inference Script
An example inference script that outputs top-k class ids for images in a folder into a csv.
Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
"""
import os
import time
import argparse
import logging
import numpy as np
import torch
from... | inference.py | Apache-2.0 | 5,236 | 1e99ea5bb482617886f99732129b39a16ff7543c | Python | py | |
import os
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from core.config import cfg
import nn as mynn
import utils.net as net_utils
from utils.resnet_weights_helper import convert_state_dict
# -------------------------------------------------------------------... | lib/modeling/ResNet_bat.py | MIT | 14,832 | fedabaabc7e05f9b3cfbaed3a479d6cded47636a | Python | py | |
import numbers
from typing import Callable, Optional, Sequence, Tuple, Union
import torch
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
from ignite.metrics.metrics_lambda import MetricsLambda
__all__ = ["ConfusionMatrix", "mIoU", "IoU",... | ignite/metrics/confusion_matrix.py | BSD-3-Clause | 16,766 | fedabd1f1010a51bfb6ea380051f37ec68bfd971 | Python | py | |
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
import argparse
import glob
import numpy as np
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from sklearn.model_selection imp... | lab/wind_turbine.py | MIT-0 | 6,022 | fedabea01fe484b96f6e950f799d0f9de339e927 | Python | py | |
from torch import nn
import torch
import torch.nn.functional as F
import math
from einops import rearrange
def multi_head_attention(Q, K, V, mask):
"""Multi-head attention on a batch of Q, K, V
Arguments:
Q: torch.Tensor shape (B, N, n_head, d_k)
K: torch.Tensor shape (B, M, n_head, d_k)
... | src/architectures/transformer.py | MIT | 8,441 | 45c39c4360e39b2361978f18e0f9cd88fc9578a8 | Python | py | |
import shaDow.layers as layers
from shaDow import TRAIN, VALID, TEST
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
class DeepGNN(nn.Module):
NAME2CLS = {"mlp" : layers.MLP,
"gcn" : layers.GCN,
"gin" : layers.GIN,
"sage" ... | shaDow/models.py | MIT | 9,306 | fedaeffe331e4968a872b41ea4a9c63daa0857b9 | Python | py | |
import torch.nn as nn
class B(nn.Module):
def __init__(self):
super(B, self).__init__()
a= nn.Sequential(nn.Conv2d(3,3,3),nn.Conv2d(3,3,3),nn.Conv2d(3,3,3))
b= nn.ReLU()
c= nn.Conv2d(3,3,3)
self.a=a
self.b= b
self.c=... | deblurgan/test.py | MIT | 450 | e99a84b67710569f3420b344d700e9111f4b1a9b | Python | py | |
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import Conv2d, Linear, MaxPool2d, kaiming_init, normal_init
from mmcv.runner import force_fp32
from torch.nn.modules.utils import _pair
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class MaskIoUHead(nn.Module):
"""... | mmdet/models/roi_heads/mask_heads/maskiou_head.py | Apache-2.0 | 7,342 | 104d9b3186cbd17773e3b92884df11c5b6d966f2 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import subprocess
from typing import ClassVar
from unittest import TestCase
from etcd import EtcdKeyNotFound
f... | test/distributed/elastic/rendezvous/etcd_rendezvous_backend_test.py | Intel | 4,220 | fedafdab7c5dd9554258fc9802181ee1a9228abe | Python | py |
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