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import math
from pathlib import Path
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
MODEL_URLS = {"mobilenetv2": "https://github.com/rdroste/unisal/raw/master/unisal/models/weights/mobilenet_v2.pth.tar"}
# Source: https://github.com/tonylins/pytorch-mobilenet-v2
def conv_bn(inp, oup, ... | gazenet/models/shared_components/mobilenetv2/model.py | MIT | 6,922 | 28ee80b33cbd53d97fac86f4158410ff64a85371 | Python | py | |
import numpy as np
import torch
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
'''
Scaled dot-product attention
'''
def __init__(self, d_model, h,dropout=.1):
'''
:param d_model: Output dimensionality of the model
... | model/attention/SimplifiedSelfAttention.py | MIT | 2,937 | 29a671925fcc602beef43d74cb59ec06feef3153 | 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... | monai/transforms/io/array.py | Apache-2.0 | 13,031 | 5f236452ce867c9381528132e85892fff3149823 | Python | py | |
import typing as _typing
import torch
import torch_geometric
from autogl.data import Dataset, InMemoryDataset
from autogl.data.graph import GeneralStaticGraph
from autogl.data.graph.utils import conversion
def to_pyg_dataset(
dataset: _typing.Union[Dataset, _typing.Iterable[GeneralStaticGraph]]
) -> Dataset[t... | autogl/datasets/utils/conversion/_to_pyg_dataset.py | MIT | 1,435 | 29a6cd1e4b82c2134e0e5a92ef8cf7560332c3f1 | Python | py | |
""" CUDA / AMP utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
try:
from apex import amp
has_apex = True
except ImportError:
amp = None
has_apex = False
from .clip_grad import dispatch_clip_grad
class ApexScaler:
state_dict_key = "amp"
def __call... | timm/utils/cuda.py | Apache-2.0 | 1,758 | 5e8e7efc9c11e823428039dab3e73b8bc7b1a16a | Python | py | |
from data import config
from train import bilstm
from models.NER import NER
from build_dataloader import corpus, test_iter
import numpy as np
import torch
from torch import nn
from torch.optim import Adam
checkpoint_path = config.CHECKPOINT3
def accuracy(preds, y):
max_preds = preds.argmax(dim=1, ... | src/evaluate.py | MIT | 1,753 | b2bdfa533c1c58f2407c611a30145ec74f1dd1c5 | Python | py | |
import torch
from nanodet.util import util_mixins
class SamplingResult(util_mixins.NiceRepr):
"""
Example:
>>> # xdoctest: +IGNORE_WANT
>>> self = SamplingResult.random(rng=10)
>>> print('self = {}'.format(self))
self = <SamplingResult({
'neg_bboxes': torch.Size([1... | nanodet/model/head/sampler/sampling_result.py | Apache-2.0 | 3,025 | f9bf11f60276664f7ee7a7e95947506a977b9f40 | Python | py | |
import os
import sys
import unittest
sys.path.append(os.getcwd())
import numpy as np
import torch
from iqa import psnr
from libs.basicsr_psnr_ssim import calculate_psnr
IMAGE_SHAPES = (
('CHW', (3, 128, 128)),
('CHW', (1, 128, 128)),
('HWC', (128, 128, 3)),
('HWC', (128, 128, 1)),
)
class PSNRTestC... | tests/test_psnr.py | Apache-2.0 | 2,890 | 106ae37c43676469895062025e120357cfafbb13 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import functools
import os
from typing import Callable, Dict, List, Optional, Tuple
import torch
import torch.distributed as dist
from torch ... | oslo/torch/nn/parallel/data_parallel/_reduce_scatter_bucketer.py | Apache-2.0 | 8,448 | ff604db5eae2fd0db21777589cae3fd3a30573db | Python | py | |
import argparse
import time
import torch
from rl_credit.utils.penv import ParallelEnv
import script_utils as utils
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument("--env", required=True,
help="name of the environment (REQUIRED)")
parser.add_argument("--model", required=T... | rl_credit/scripts/evaluate.py | MIT | 3,846 | d7577f96beb52d105b0661f4e33db1be19c3d17c | Python | py | |
import os
import json
import torch
import numpy as np
import hifigan
from model import FastSpeech2, ScheduledOptim
def get_model(args, configs, device, train=False):
(preprocess_config, model_config, train_config) = configs
model = FastSpeech2(preprocess_config, model_config).to(device)
if args.restore... | utils/model.py | MIT | 3,325 | b2576aed77c0e4ea5393f8c207b408a13596d672 | Python | py | |
import os
# import torchsummary
from functools import partial
import pickle
import fire
import torchaudio
from pl_bolts.models.self_supervised.resnets import resnet18
from pl_bolts.optimizers.lars_scheduling import LARSWrapper
from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR
from pytorch_ligh... | semi_supervised.py | MIT | 27,602 | 29a713d05cdc7a6e6b072f256a90c0e7aaf6b43b | Python | py | |
# Copyright 2021 TUNiB Inc.
import os
import random
import numpy
import torch
import torch.distributed as dist
from datasets import load_dataset
from torch.optim import Adam
from torch.utils.data import DataLoader
from transformers import GPT2Tokenizer
from oslo.models.gpt2.modeling_gpt2 import (
GPT2ForSequence... | tests/gpt2/gpt2_test_3d_training_with_fusion.py | Apache-2.0 | 7,758 | 1e70498a8759e260e7509bd75387c903b129ee1d | Python | py | |
# -*- coding: utf-8 -*- #
"""*********************************************************************************************"""
# FileName [ evaluate.py ]
# Synopsis [ main objective evaluation script for any-to-any voice conversion ]
# Author [ Wen-Chin Huang (https://github.com/unilight) ]
# Copyr... | s3prl/downstream/a2a-vc-vctk/evaluate.py | Apache-2.0 | 10,261 | d7377c9127df9b3a3c1b8cd49511b4ca115f4f17 | Python | py | |
#
# export a trained model to huggingface format
# -------------------------------
import argparse
import os
import sys
sys.path.append(os.getcwd())
import torch
from matchmaker.utils import *
from matchmaker.models.all import get_model, get_word_embedder, build_model
from typing import Dict, Tuple, List
from ma... | matchmaker/utils/export_to_huggingface.py | Apache-2.0 | 2,950 | 29a72be7f9efb343dcb197cae7f66086b9521cb1 | Python | py | |
import pytest
import torch
from pytorch_lightning.metrics.utils import class_reduce, reduce
def test_reduce():
start_tensor = torch.rand(50, 40, 30)
assert torch.allclose(reduce(start_tensor, 'elementwise_mean'), torch.mean(start_tensor))
assert torch.allclose(reduce(start_tensor, 'sum'), torch.sum(star... | tests/metrics/functional/test_reduction.py | Apache-2.0 | 1,092 | 03a34f6c5a25b867b2d627bf3c514ceb37dd3f63 | Python | py | |
import argparse
import os
import torch
import torch.backends.cudnn as cudnn
import torchvision
from model import Net
parser = argparse.ArgumentParser(description="Train on market1501")
parser.add_argument("--data-dir",default='data',type=str)
parser.add_argument("--no-cuda",action="store_true")
parser.ad... | traffic_monitoring/deep_sort/deep/test.py | Apache-2.0 | 2,493 | 29a7370acce4061e8bc77f621b550f269c2751c9 | Python | py | |
"""lstm_utils.py contains utility functions for running LSTM Baselines."""
import os
from typing import Any, Dict, List, Tuple
import numpy as np
import torch
from torch.autograd import Variable
from torch.utils.data import Dataset
import utils.baseline_config as config
use_cuda = torch.cuda.is_available()
if use_c... | utils/lstm_utils.py | BSD-3-Clause-Clear | 7,373 | b26760f618ba8ff28a869ce4decd3e87096ee7bd | Python | py | |
""" Robust loss functions for training accurate deep neural networks (DNNs) in the presence of noisy (incorrect) labels.
Extracted [Normalized Loss Functions for Deep Learning with Noisy Labels](https://arxiv.org/abs/2006.13554) paper implemented on https://github.com/HanxunH/Active-Passive-Losses/blob/master/loss.py
... | train/losses/APL_losses.py | MIT | 18,276 | 1e0ba3ef47c0b99513b5576906c273f7454ea0cc | Python | py | |
import argparse
import os
import os.path as osp
import pickle
import shutil
import tempfile
import mmcv
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from mmdet.core import wrap_fp16_m... | test.py | Apache-2.0 | 15,527 | 29a779e315a36f00f2ebd348d3c54b022f0ccf87 | Python | py | |
import time
import torch
# Project imports
from utils import *
from options import TrainOptions
from data_loaders import create_dataloader
from methods import create_method
class Trainer:
"""
Trains any of the model methods in methods/ using any of the losses
in losses/.
Initialize the t... | train.py | MIT | 3,637 | 24a6ceaa6ad3a63b821c739a4e853b4bfbbe8d5a | 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future_... | projects/continual-lm/old_util.py | BSD-3-Clause | 3,269 | 29a797de701c771bfe426d3700b8cbb55166369c | Python | py | |
import torch
class TokenEmbedding(torch.nn.Module):
"""
Token Embedding
It can be embedding matrix, language model (ELMo), neural machine translation model (CoVe) and features.
* Args:
vocab: Vocab (rqa.tokens.vocab)
"""
def __init__(self, vocab):
super(TokenEmbedding, self... | claf/tokens/embedding/base.py | MIT | 628 | 5f97ebc44f51040c1e10fe702ea9cad06d5df67e | Python | py | |
# These operations are based on the implementation of https://github.com/JiahuiYu/slimmable_networks
import torch.nn as nn
from utils.config import FLAGS
def make_divisible(v, divisor=1, min_value=1):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * d... | models/randwidth_ops.py | MIT | 5,110 | 7c119fb209e5ea3c12d849e24bd65c0b74cd188e | Python | py | |
import os
import sys
import re
import six
import math
import lmdb
import torch
from augmentation.weather import Fog, Snow, Frost
from augmentation.warp import Curve, Distort, Stretch
from augmentation.geometry import Rotate, Perspective, Shrink, TranslateX, TranslateY
from augmentation.pattern import VGrid, HGrid, Gri... | dataset2.py | Apache-2.0 | 20,656 | 101d89c7fd01255d36778bc3aa1a768821610a56 | Python | py | |
import argparse
from collections import defaultdict
import logging
import copy
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from tqdm import trange
from torchsummary import summary
from experiments.nyuv2.data import nyu_dataloaders
from experiments.nyuv2.metrics imp... | experiments/nyuv2/trainer_cnn.py | MIT | 8,751 | 347e78355e79ddb2b0f9db6b2ec8236fe0733295 | Python | py | |
import numpy as np
import torch
from torch.autograd import Variable
from pytorch2keras.converter import pytorch_to_keras
import torchvision
class VGG(torchvision.models.vgg.VGG):
def __init__(self, *args, **kwargs):
super(VGG, self).__init__(*args, **kwargs)
def forward(self, x):
x = self.fea... | tests/models/vgg11.py | MIT | 1,287 | f9da7c744975883ded7dbbf14218730113444138 | Python | py | |
import os
import re
import yaml
import textwrap
import torch
from torch.testing._internal.common_utils import TestCase, run_tests
from collections import namedtuple
path = os.path.dirname(os.path.realpath(__file__))
aten_native_yaml = os.path.join(path, '../aten/src/ATen/native/native_functions.yaml')
all_operators_... | test/test_namedtuple_return_api.py | Intel | 7,765 | 345431d5b250827d287ab5dd71d6c8df3205bcf3 | Python | py | |
#!/usr/bin/python
# encoding: utf-8
from PyQt5 import QtWidgets
from multiprocessing import Process, Manager, freeze_support
from PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QSlider, QPushButton, QDesktopWidget, QVBoxLayout, \
QHBoxLayout, QComboBox, QTextBrowser, QTextEdit, QLabel, QDialog, QFileDia... | entrance.py | MIT | 4,863 | 29a840f6948dfd1d02a1847d9c1a74ce5ed3fc35 | Python | py | |
import torch
import os
import glob
from torch.utils.data import Dataset
import numpy as np
from PIL import Image
from torchvision import transforms
from xmuda.data.utils.preprocess import vox2pix, compute_local_frustums, compute_CP_mega_matrix
from xmuda.data.semantic_kitti.params import kitti_class_cluster_4, kitti_cl... | xmuda/data/semantic_kitti/kitti_dataset.py | Apache-2.0 | 12,195 | 4282f765c26cc45ae82090a2cdb3fdd24d495f77 | Python | py | |
"""
Learning to Count Objects in Natural Images for Visual Question Answering
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
ICLR 2018
This code is from Yan Zhang's repository.
https://github.com/Cyanogenoid/vqa-counting/blob/master/vqa-v2/counting.py
MIT License
"""
import torch
import torch.nn as nn
from torch.autogr... | counting.py | MIT | 7,594 | ff86e2c8d850a71f0dc2aeea435aa53eb0c2891e | Python | py | |
# -*- coding: utf-8 -*-
"""
PROJECT 1 - DEEP LEARNING
models.py
Authors: Lucia Montero Sanchis, Ada Pozo and Milica Novakovic
Deep Learning models
"""
import torch
import torch.nn as nn
from bci_dataset import *
#torch.set_default_tensor_type('torch.FloatTensor')
#################################################... | Projects/Project1/Submission_code_DL1/models.py | MIT | 9,576 | d7a7ee7a0113aceecfc6b01b2a6ddde2150f2a2f | Python | py | |
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
import sys
sys.path.append("./expansion_penalty/")
import expansion_penalty_module as expansion
sys.path.append... | model.py | Apache-2.0 | 6,414 | d79c38f34ed5f7c07d68790f3fd4322f35adef4a | Python | py | |
# Dataset utils and dataloaders
import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional ... | detector/yolov5/utils/datasets.py | MIT | 43,872 | 29a8812a20a2c32c4957977ffe74b8f8eeea0035 | Python | py | |
#!/usr/bin/env python3
import logging
import numpy as np
import time
import torch
import cv2
logger = logging.getLogger(__name__)
def retry_load_images(image_paths, retry=10, backend="pytorch"):
"""
This function is to load images with support of retrying for failed load.
Args:
image_paths (li... | slowfast/slowfast/datasets/utils.py | MIT | 2,946 | 29a897468616f4f503639ee310ae49a3ddf2ec71 | Python | py | |
import torch
import torch.nn as nn
OPS = {
'none' : lambda C, stride, affine: Zero(stride),
'avg_pool_3x3' : lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
'max_pool_3x3' : lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
'skip_connect' : lambd... | cnn/operations.py | Apache-2.0 | 4,587 | 29a8a4590de7d38913a3f23cc11df162708cd02b | Python | py | |
import os
import time
from collections import OrderedDict
import logging
import argparse
import numpy as np
import random
import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
import torch.backends.cudnn
import torchvision.utils
from torch.autograd import Variable
import torchvision.utils as vu... | generative_models/cGAN_PyTorch/main.py | MIT | 6,285 | d7829595b3d509e0e8f75fe4daa3832b40bfcbee | Python | py | |
import copy
import math
import glob
import os
import time
from collections import deque
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import algo
from arguments import get_args
from envs import make_vec_envs
from model_nomodulation import ... | main_nomodulation.py | MIT | 5,672 | 29a8b77da0f09a6dce858c5ceda9ddb79e9a6add | Python | py | |
'''Train CIFAR100 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from models import *
from utils import progress_bar
from mode... | eval_ablation3.py | MIT | 8,018 | 29a8c35dd56ca8bd519cdd3cadc2e85f72f3007d | Python | py | |
# coding=utf-8
# Copyright 2021 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... | src/transformers/modeling_flax_pytorch_utils.py | Apache-2.0 | 12,341 | a91d41b9d6d91baa8a909ad24b9c2fb3c3c33ce5 | Python | py | |
import torch
import torch.nn as nn
from dmb.modeling.stereo.layers.basic_layers import conv3d_bn, conv3d_bn_relu, conv_bn_relu, conv_bn
from dmb.modeling.stereo.cost_processors.utils.hourglass import Hourglass
class AcfAggregator(nn.Module):
"""
Args:
max_disp (int): max disparity
in_planes (... | dmb/modeling/stereo/cost_processors/aggregators/AcfNet.py | MIT | 3,356 | d777d6bf72369a49cec1819dc1dff78c3763fd52 | Python | py | |
from torch.utils.data import Sampler
import numpy as np
from random import shuffle
class GroupLengthBatchSampler(Sampler):
def __init__(self, data_source, batch_size, batches_per_group=20):
super().__init__(data_source)
ind_n_len = []
for i, p in enumerate(data_source):
ind_n_l... | hw_asr/batch_sampler/group_sort_batch_sampler.py | MIT | 1,892 | 29a8e4f031b88c913dd95d7e9e8efe682bdc96ab | Python | py | |
#This file regroups the methods used to load the data and preprocess images
from torchvision import datasets, transforms
import torch
import json
from PIL import Image
import numpy as np
class Utils:
@staticmethod
def LoadData(data_dir, folders):
# We define the transforms used on each image of t... | utility.py | MIT | 5,693 | 29a9040bac5086d3813d9235cb3f2f09e80c745d | Python | py | |
import torch.nn as nn
import math
"""
Imported by https://github.com/marvis/pytorch-mobilenet/blob/master/main.py
"""
class MobileNetV1(nn.Module):
def __init__(self, dropout_keep_prob=0.999):
super(MobileNetV1, self).__init__()
self.dropout_keep_prob = dropout_keep_prob
self.dropout = nn... | solutions/pedestrian_search/webserver/src/service/models/mobilenet.py | Apache-2.0 | 1,834 | 29a9117224979bd85b9882ef589d710d8dba2215 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 applica... | stego/dino/vision_transformer.py | Apache-2.0 | 12,222 | 10cf5b585e0b6cb3ada97e977ca9f30db005ec6d | Python | py | |
"""
Classes to be used for postprocessing data after a simulation.
"""
import warnings
import numpy as np
import torch
from scipy.stats import beta
from perm_hmm.util import ZERO
from perm_hmm.loss_functions import zero_one, log_zero_one
def clopper_pearson(alpha, num_successes, total_trials):
"""
Computes ... | perm_hmm/postprocessing.py | Apache-2.0 | 15,799 | fd79bfbf3ab2cb218182abbd353738ab15acadf9 | Python | py | |
# -*- coding=utf-8 -*
from __future__ import print_function, division
import os
import time
import torch
import mlflow
import argparse
import setproctitle
import numpy as np
from match import run_experiments, run_single_dataset_experiments
class CustomSettings(object):
def __init__(self, seed=None, data='weibo'... | traj_er/DPLink/codes/run_my_data.py | MIT | 6,652 | 29a93c1117a666e11be35b4d1557ca050d6fa8f2 | Python | py | |
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Guanglei Yang
## Department of Information Engineering and Computer Science, University of Trento
## Email: guanglei.yang@studenti.unitn.it or yangguanglei.phd@gmail.com
## Copyright (c) 2020
##
## This source code is licensed un... | depth_prediction/pytorch/multihead.py | MIT | 8,110 | 4bbc3896c88822ac784f71891404cf37ae7b4665 | Python | py | |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | src/transformers/training_args.py | Apache-2.0 | 44,357 | a91921d466a5db982c7e1e067ee8223c3cd90493 | Python | py | |
import random
from itertools import product
import torch
class CartesianSampler:
"""Random subset sampling from {0, 1, ..., M-1} X {0, 1, ..., L-2} where X is Cartesian product.
Attributes:
M: number of trajectories.
L: trajectory length.
batch_size: input batch size for training the... | misc/sampler.py | MIT | 5,474 | 10fc7c3c5c782b5be1e8cb43f8c4fb8feca75e4a | Python | py | |
import torch
from torch import nn
import numpy as np
import os
from .utils.detect_face import detect_face, extract_face
class PNet(nn.Module):
"""MTCNN PNet.
Keyword Arguments:
pretrained {bool} -- Whether or not to load saved pretrained weights (default: {True})
"""
def __init__(self, ... | models/mtcnn.py | MIT | 21,412 | 29a9534c518293c68f178cbb8abb1dd3da0841cc | Python | py | |
# System libs
import os
import time
import argparse
from distutils.version import LooseVersion
# Numerical libs
import numpy as np
import torch
import torch.nn as nn
from scipy.io import loadmat
import torch.utils.data as data
# Our libs
from data.augmentations import Compose, PaddingCenterCrop
from data.ac17_dataloade... | eval.py | BSD-3-Clause | 6,762 | ff04a77455d628cd6e79d080bafc01172e6cbeed | Python | py | |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
Detection Training Script.
This scripts reads a given config file and runs the training or evaluation.
It is an entry point that is made to train standard models in detectron2.
In order to let one script support training of many models,
this... | vehicle_detection/utils/trainer.py | MIT | 5,539 | 7c71302f503c85f9e31ef1a1d0b9e02628158036 | Python | py | |
# General
import torch
import torch.nn as nn
import torch.nn.functional as F
# Setup
from setups import CategoricalImageFlowSetup
# Data
from pixelflow.data import CategoricalCIFAR10
# Model
from pixelflow.flows import UniformlyDequantizedFlow
from pixelflow.transforms import LinearSplineAutoregressiveTransform2d
fr... | experiments/train/elbo_pixelcnn.py | MIT | 2,457 | 29a95e0be461696b804e990c2643bba59a6fe97b | Python | py | |
######################
# So you want to train a Neural CDE model?
# Let's get started!
######################
import math
import torch
import torchcde
######################
# A CDE model looks like
#
# z_t = z_0 + \int_0^t f_\theta(z_s) dX_s
#
# Where X is your data and f_\theta is a neural network. So the first th... | example/example.py | Apache-2.0 | 6,633 | 29a95eea6a2614a7daa5db7745229e20c96fc0fa | 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... | tests/trainer/test_dataloaders.py | Apache-2.0 | 39,173 | 03810309d91362cd65ea0fa1a140e0fe926deff4 | Python | py | |
# This file contains experimental modules
import numpy as np
import torch
import torch.nn as nn
from .common import Conv, DWConv
from .utils.google_utils import attempt_download
class CrossConv(nn.Module):
# Cross Convolution Downsample
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
... | models/experimental.py | MIT | 5,636 | 4b60543efd8b1a1fbd554a5cfcf8995621f6ee47 | Python | py | |
# -*-coding:utf-8-*-
import torch.nn as nn
import torch.nn.functional as F
__all__ = ["mynet"]
class MyNet(nn.Module):
def __init__(self, num_classes=10):
super(MyNet, self).__init__()
self.conv_1 = nn.Conv2d(3, 6, 5)
self.conv1_bn = nn.BatchNorm2d(6)
self.conv_2 = nn.Conv2d(6, 16... | models/mynet.py | MIT | 972 | 342a9e81140cc254a441a8b3d8068a82505f695f | Python | py | |
import pickle, logging
import numpy as np
import revtok # tokenizer
import torch
import torch.nn as nn
from gen.constants import *
class WordEmbedding(torch.nn.Module):
'''
inputs: x: batch x ...
outputs:embedding: batch x ... x emb
mask: batch x ...
'''
def __init_... | models/nn/embedding.py | MIT | 4,734 | 7c51aa7dad6502387fd7affb8dba06e35a05a26c | Python | py | |
# Expansion penalty module (based on minimum spanning tree)
# author: Minghua Liu
# Input:
# xyz: [#batch, #point]
# primitive_size: int, the number of points of sampled points for each surface elements, which should be no greater than 512
# in each point cloud, the points from the same surface element should be succ... | expansion_penalty/expansion_penalty_module.py | Apache-2.0 | 2,785 | 42130774caa412fd5a880aaaac1d8fda009be6fd | Python | py | |
"""
PrivacyRaven has support for user-defined Pytorch Lightning Bolt callbacks which may be passed as arguments
into the currently available attack functions.
A callback is just a function (or class in this case) that gets passed in as an argument to another
function, which should execute the callback function or cla... | examples/example_pytorch_callback.py | Apache-2.0 | 3,121 | 29a9c166fb3c2a53195e36849dc6cab2e95bef6c | Python | py | |
from __future__ import absolute_import
import itertools
import numpy as np
import torch
import unittest
from prob_phoc import cphoc, pphoc
class ProbPHOCTest(unittest.TestCase):
@staticmethod
def _run_cphoc_sum_prod(device, dtype, use_log):
x = np.asarray([[.7, .4], [.9, .6]], dtype=dtype)
y... | prob_phoc/test.py | MIT | 2,868 | 5e1683310cd736c80178367d4237b82dc467ef4c | Python | py | |
import torch
import torch.nn.functional as F
from torch import nn
from .layers import DownBlock, get_activation
# The number of filters used in the first conv layer.
# Each following layer will use twice filters (until reaching a predefined maximum filters).
cfg_conv1_nf = {'A': 32, }
# This is the maximum number of ... | models/stn/affine_stn.py | BSD-3-Clause | 6,230 | 29a9e1572efe2f50a2126564aca5d6f313ea87a2 | Python | py | |
import os
from pathlib import Path
from itertools import product
from functools import lru_cache
from ipdb import set_trace as breakpoint
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score, log_loss, hinge_loss
import requests
import plotly
import plotly.ex... | notebooks/cotrain_helper.py | MIT | 31,075 | 10e08f8f8a18efdc5d3e16d12cafb3f6305794fb | Python | py | |
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: RainbowSecret
## Microsoft Research
## yuyua@microsoft.com
## Copyright (c) 2018
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++... | Train/openseg/lib/models/nets/hrnet.py | MIT | 9,609 | 29a9f9e6a9c5dbd089d0ad03180185c0c7e08452 | Python | py | |
import torch
from torch import nn
from torch.nn import functional as F
from nets.layers import (
round_filters,
round_repeats,
drop_connect,
get_same_padding_conv2d,
get_model_params,
efficientnet_params,
load_pretrained_weights,
Swish,
MemoryEfficientSwish,
)
class MBConvBlock(nn... | nets/efficientnet.py | Apache-2.0 | 12,153 | 70937285665c51e172e9d8795fff24af949c4724 | Python | py | |
"""
@author: Junguang Jiang
@contact: JiangJunguang1123@outlook.com
"""
import random
import time
import warnings
import sys
import argparse
import os.path as osp
import shutil
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.optim import SGD
from torch.optim.lr_scheduler import Lambd... | examples/domain_adaptation/image_classification/mdd.py | MIT | 12,448 | 29aa1c84ae88036a721c706351bb1ad2357b0fc1 | Python | py | |
import os.path as osp
import mmcv
import numpy as np
import torch.nn as nn
from mmcv.parallel import MMDistributedDataParallel
from mmedit.core import tensor2img
from ..base import BaseModel
from ..builder import build_backbone, build_component, build_loss
from ..common import GANImageBuffer, set_requires_grad
from .... | mmedit/models/synthesizers/cycle_gan.py | Apache-2.0 | 17,710 | 29aa5d5ec0a5dacb9c325830c68f5613af330f19 | Python | py | |
""" torchinfo.py """
import sys
from typing import (
Any,
Callable,
Dict,
Iterable,
Iterator,
List,
Mapping,
Optional,
Sequence,
Tuple,
Union,
cast,
)
import torch
from torch import nn
from torch.jit import ScriptModule
from torch.utils.hooks import RemovableHandle
from... | torchinfo/torchinfo.py | MIT | 17,877 | 29aa60eba042659339f220bc016a1972ef003380 | Python | py | |
"""treelstm.py - TreeLSTM RNN models
Written by Riddhiman Dasgupta (https://github.com/dasguptar/treelstm.pytorch)
Rewritten in 2018 by Long-Huei Chen <longhuei@g.ecc.u-tokyo.ac.jp>
To the extent possible under law, the author(s) have dedicated all copyright
and related and neighboring rights to this software to the ... | onmt/modules/tree_lstm.py | CC0-1.0 | 6,052 | 7cfdf38d91fdd86f7c602016d31bb3c35ccc30e1 | Python | py | |
# encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import torch
from torch import nn
from torch.autograd import Variable
def normalize(x, axis=-1):
"""Normalizing to unit length along the specified dimension.
Args:
x: pytorch Variable
Returns:
x: pytorch Variable,... | solutions/1st-place/models/triplet_loss.py | BSD-3-Clause | 12,530 | 29aa781105a050f216b38eaaabb8cebf2187fb02 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) 2020 NVIDIA Corporation. All rights reserved.
# This work is licensed under the NVIDIA Source Code License - Non-commercial. Full
# text can be found in LICENSE.md
"""Test a DeepIM network on an image database."""
import torch
import torch.nn.parallel
import torch.backends.cudn... | tools/test_net.py | BSD-Source-Code | 6,454 | 801ce6e796492293c5eb8027632dd0aebeeb8918 | Python | py | |
"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import importlib
import torch
import logging
from .gscnn import GSCNN
def get_net(args, criterion):
net = get_model(network=args.arch, nu... | pywick/models/segmentation/testnets/gscnn/__init__.py | MIT | 956 | 29aab75936884b05b85aedbbff15932778b99fbb | Python | py | |
# coding: utf-8
import torchvision.transforms as tt
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
import time
import torch.nn as nn
import torch
import torchvision
import torch.nn as nn
from torch.autograd import Variable
import torchvision.transforms as transforms
from torchvisi... | manoah-farmer-individual-project/Code/7Layers_straight_4maxpool_10epochs_64batchsize_3kernelsize copy.py | MIT | 11,196 | 2494157458e7cd8c8b3293e09f7eca228cc5b4e6 | Python | py | |
import torch
import torch.nn as nn
def update_bn(model, s=1e-4):
for m in model.modules():
if isinstance(m, nn.modules.batchnorm._BatchNorm) or \
isinstance(m, nn.modules.instancenorm._InstanceNorm) or \
isinstance(m, nn.GroupNorm):
m.weight.grad.data.add_(s * torch.sig... | netslim/sparse.py | MIT | 748 | dff62958f5d14dc93ae98e223792e0b0a758f0bd | Python | py | |
import math
import torch
import numpy as np
from .. import utils
from .loss_surfaces import get_plane
def dataloader_loss_surface(basis, model,
dataloader,
loss=torch.nn.MSELoss(),
rng=0.1, n_pts=25, **kwargs):
start_pars = model.state_dict()
## get ... | hess/loss_surfaces/dataloader_loss_surface.py | Apache-2.0 | 1,191 | 29aae32bd290c974f1fd9d5d7df18c233741175b | Python | py | |
# Author: David Harwath, Wei-Ning Hsu
import datetime
import numpy as np
import pickle
import shutil
import time
import torch
import torch.nn as nn
from models.quantizers import compute_perplexity
from .util import *
def map_skip_none(fn, it):
"""
emulate list(map(fn, it)) but leave None as it is.
"""
... | steps/traintest.py | BSD-3-Clause | 12,155 | 4b7a0b848d2d2635572d7479ecb2379f7fbedffb | Python | py | |
# ------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (ht... | models/backbone.py | Apache-2.0 | 7,404 | 70821d2f4a703518ddae8f9517b578a564c42d0a | Python | py | |
import torch
from processor import RANGE_NOTE_ON, RANGE_NOTE_OFF, RANGE_VEL, RANGE_TIME_SHIFT
SEPERATOR = "========================="
# Taken from the paper
ADAM_BETA_1 = 0.9
ADAM_BETA_2 = 0.98
ADAM_EPSILON = 10e-9
LR_DEFAULT_START = 1.0
SCHEDULER_WARMUP_STEPS... | utilities/constants.py | MIT | 710 | e97940c2e5a2f738809078121d4314ff402222b0 | Python | py | |
import torch
DEVICE = torch.device("cuda")
SAVED_CHECKPOINTS = [32*1000, 100*1000, 150*1000, 200*1000, 300*1000, 400*1000]
SAVED_CHECKPOINTS += [10*1000, 20*1000, 30*1000, 40*1000, 45*1000, 50*1000, 60*1000, 70*1000, 80*1000, 90*1000]
SAVED_CHECKPOINTS += [25*1000, 50*1000, 75*1000]
SAVED_CHECKPOINTS = set(SAVED_CHE... | ColBERT_increase_cls_token_similarity/colbert/parameters.py | MIT | 329 | 34011d5bf8330323c5347e6baf82b53526e1a2b3 | Python | py | |
from tensorflow.keras.applications.imagenet_utils import preprocess_input as efficientnet_preprocess_input
from tensorflow.keras.layers import Activation
from tensorflow.keras.backend import sigmoid, constant
from tensorflow.keras.initializers import Initializer
from torch.nn import ConvTranspose2d, init
from torch im... | utils/helpers.py | CC-BY-4.0 | 16,042 | 29ab4b80413ce38004e662289a02c061dbe89b28 | Python | py | |
import shutil
from argparse import ArgumentParser
import optuna
import torch
import yaml
from build_problem import BuildProblem
from my_functions import out_size
from timeit import default_timer as timer
# Convert params from Optuna
def convert_params(params, length):
params["hidden_dims"] = 2 ** ... | xml-cnn/train.py | MIT | 4,031 | 34669bd51d41526382a03c32521a3f207526f56f | Python | py | |
import math
import torch
import torch.nn as nn
from mmcv.runner import load_checkpoint
from mmedit.models.common import (PixelShufflePack, ResidualBlockNoBN,
make_layer)
from mmedit.models.registry import BACKBONES
from mmedit.utils import get_root_logger
class UpsampleModule(nn.Seq... | mmedit/models/backbones/sr_backbones/edsr.py | Apache-2.0 | 4,603 | 038b7948643a15b073d20009531a950aaacf8d1b | Python | py | |
from torch import nn, Tensor
class Classifier(nn.Module):
"""
Simple model with linear layers for mnist
"""
def __init__(self):
super(Classifier, self).__init__()
self.fc1 = nn.Linear(784, 512)
self.relu1 = nn.LeakyReLU(negative_slope=0.1)
self.drop1 = nn.Dropout(p=0.5... | classifiers/MNIST.py | MIT | 803 | 03a97795ce8fb68401717a8e56f1f6b9eeed5570 | Python | py | |
#!/usr/bin/python
#
# Copyright 2020 Azade Farshad
#
# 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... | scripts/evaluate_robot_metrics.py | Apache-2.0 | 11,384 | 29ab7bb969e9e8dc3042d0193f791be8b28d39e5 | Python | py | |
from ..._fgsm import FGSM, FGM
from . import _plot_settings
import torch.nn as nn
import torch
import matplotlib.pyplot as plt
"""
Adversarial Evaluation functions for Pytorch neural models
"""
from tqdm import tqdm
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib
matplotlib.use('Agg')
__all__... | deepillusion/torchattacks/analysis/plot/_loss_landscape.py | MIT | 3,791 | 29ab7fad8ca6d355ce6296a3772bebd3f68ce000 | Python | py | |
# TODO:
# * Do not use test data during training
# * Add option to run on GPU
import argparse
import logging
import numpy as np
import os
import time
from sklearn.datasets import load_svmlight_file
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
# import torch.backends.c... | src/main/python/ncf/ncf.py | MIT | 6,365 | 4b45b5b013133f7fc80cd5e43cd401de8d7d5280 | Python | py | |
"""
Recurrent Neural Networks
-------------------------
"""
import torch.nn as nn
from numpy.random import RandomState
from typing import List, Optional, Union
from ..logging import raise_if_not, get_logger
from ..utils.torch import random_method
from .torch_forecasting_model import TorchForecastingModel
logger = ge... | darts/models/rnn_model.py | Apache-2.0 | 6,461 | 29ab8a5f9fd2b544dd64fc4c39b0b441590c5203 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .mwm import MWMLNet
from .layers import WordsCapsLayer,SelfAttnMatch
class MWMLNetLMFineGrind(nn.Module):
def __init__(self,**kwargs):
super(MWMLNetLMFineGrind,self).__init__()
self.n_class = kwargs["n_class"]
self.type... | src/model/lm.py | MIT | 3,877 | 29abd7916a8e2841444135a0210ab97065871d48 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Train and eval functions used in main.py
"""
import math
import os
import sys
from typing import Iterable
import torch
import util.misc as utils
from datasets.coco_eval import CocoEvaluator
from datasets.panoptic_eval import PanopticEvaluator
... | detr/engine_multi.py | MIT | 7,967 | 1e74bd00a78aa9aa7f16f14c729128fb711d5773 | Python | py | |
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import utils
import seaborn as sns
# Load the training sets
# The data is shuffled by the trainloader
transformCutout = transforms.Compose([
transforms.RandomCrop(32, padding=4),
tran... | data.py | MIT | 3,921 | 29ac1eaa732f8ba4d224e8d925a9fcbd1f098bb2 | Python | py | |
#!/usr/bin/env python
# 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 applica... | pytorch_lightning/setup_tools.py | Apache-2.0 | 7,362 | 29ac3b814b3c2e852e36611a34b9be054724bd6e | Python | py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import csv
import sys
from datetime import datetime
import pandas
import torch
sys.path.append('../')
from loglizer.models import AutoencoderConv
from loglizer import dataloader, preprocessing
import pickle
import numpy as np
import os
import time
# struct_log = '../data... | demo/Autoencoder_conv_demo.py | MIT | 5,065 | 3479dd8cacf78f7059e2bf4a3a338716543269ce | Python | py | |
""" Statistics calculation utility """
from __future__ import division
import time
import math
import sys
from onmt.utils.logging import logger
class Statistics(object):
"""
Accumulator for loss statistics.
Currently calculates:
* accuracy
* perplexity
* elapsed time
"""
def __init__... | onmt/utils/statistics.py | MIT | 4,401 | 703f3c4dfb629d7b546de328d66979f254478f98 | Python | py | |
"""this class build and run a trainer by a configuration"""
import os
import sys
import shutil
import datetime
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision.transforms import transforms
from torch.optim.lr_scheduler import ReduceLROnPla... | trainers/_fer2013_trainer.py | MIT | 13,648 | 12eca1fd942c0fe3c94e99fd7ca8997356694483 | Python | py | |
from .c_dataloader import CDataLoader
from .c_dataloader_sklearn import *
from .c_dataloader_svmlight import CDataLoaderSvmLight
from .c_dataloader_imgclients import CDataLoaderImgClients
from .c_dataloader_imgfolders import CDataLoaderImgFolders
from .c_dataloader_mnist import CDataLoaderMNIST
from .c_dataloader_lfw i... | src/secml/data/loader/__init__.py | Apache-2.0 | 709 | 29ac5827c029c098f991e6b578e11076a382593b | Python | py | |
#! /usr/bin/env python
from django.conf import settings
from django.core.management.base import BaseCommand
from django.db.models import Count, Q
from face_manager.models import Person, Face
from filepopulator.models import ImageFile
from scipy import stats
from scipy.spatial import distance
# from sklearn.cluster imp... | face_manager/management/commands/deprecated/classify_images_custom_snn.py | MIT | 21,632 | b24c43c2c4c0d04b17f898414f5fa4b48f6229d6 | Python | py | |
from rlpyt.envs.gym_isaac.isaacgym_env import IsaacGymEnv
from rlpyt.samplers.serial.isaac import IsaacSampler
from rlpyt.algos.pg.ppo import PPO
from rlpyt.agents.pg.mujoco import MujocoFfAgent
from rlpyt.runners.minibatch_rl import MinibatchRl
from rlpyt.utils.logging.context import logger_context
if __name__ == "_... | examples/example_2a_isaac.py | MIT | 1,797 | 29ac7d4997a521ffc22628e8aa8c206dd72a3967 | Python | py | |
import torch
class AliasMultinomial(torch.nn.Module):
'''Alias sampling method to speedup multinomial sampling
The alias method treats multinomial sampling as a combination of uniform sampling and
bernoulli sampling. It achieves significant acceleration when repeatedly sampling from
the save multinom... | models/loss/alias_multinomial.py | Apache-2.0 | 2,472 | 29ac7e76ebb1718964bdeb3f4eb32531195798f4 | Python | py | |
r"""Importing this file includes common utility methods and base clases for
checking quantization api and properties of resulting modules.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.intrinsic.quantized.dynamic as nniqd
import torch.nn.quantized as nnq
import torch.nn.quantiz... | torch/testing/_internal/common_quantization.py | Intel | 80,656 | e93273faa2781a94600999a897094f5a57a14a1f | Python | py |
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