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#Script to load GRU model trained on all datasets and test
# import relevant libraries
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib
import numpy as np
import trajectories
import loader
import argparse
import gc
import logging
import os
import sys
import time
from gru_prototype_v4_all... | RNN/data/gru_loadNtest.py | MIT | 8,870 | 34534850b62dcaf2706a524824e2fba8da6957d4 | Python | py | |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import torch
from utils.tools.logger import Logger as Log
class PafGenerator(object):
... | utils/layers/pose/paf_generator.py | Apache-2.0 | 3,078 | b26bab0f95d7e8f459132d04d8adb5d7bbce114a | Python | py | |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import numpy as np
import rasterio
from rasterio.errors import RasterioIOError
import rasterio.windows
from torch.utils.data.dataset import Dataset
class ZipDataset(Dataset):
def __init__(self, ds1, ds2):
assert ... | Damage Assessment Visualizer/utils/utils.py | MIT | 4,867 | 12a9291b25b8e00122fff245a469da98174aa0a1 | Python | py | |
import os
import torch
import numpy as np
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image
import binvox_rw
from point_transforms import *
class ShapeNet(data.Dataset):
def __init__(self,
img_root='/media/administrator/Dong1/ShapeNetCore.v1/renderin... | dataset.py | MIT | 5,662 | 3ac459c54f59ddbfd52c8985994ba52bf488513d | Python | py | |
from functools import partial
from itertools import repeat
import collections.abc as container_abcs
import logging
import os
from collections import OrderedDict
import numpy as np
import scipy
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from einops.layers.torch impo... | lib/models/cls_cvt/cls_cvt.py | MIT | 21,555 | 7c7e2748265158d9aae941ae2964de701d3e7f95 | Python | py | |
import warnings
from typing import List, Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from .regularizations import WeightDrop, LockedDropout, EmbeddingDropout
from .attentions import Attention
def repackage_var(h):
"""Wraps h in new Variables, to detach them from their history."""
... | nobita/modules.py | MIT | 9,101 | 3ac48a46c4d98e89f6959131bbcac9e9fadf11be | Python | py | |
import csv
import threading
from torch.utils.data.dataloader import DataLoader
from src.lib.word_operator import str_format
from src.data_process import get_dataloader
from src.train_process import DL_Model
class TaskThread(threading.Thread):
def __init__(
self,
model: DL_Model,
mode: str... | main.py | MIT | 4,374 | 3ac48abb25911ba385f7f05e68360d143c8798db | Python | py | |
# coding=utf-8
# Copyright 2019 project LXRT.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the ... | src/lxrt/modeling_big.py | MIT | 50,654 | 1ea16d9b6d7aa04c266480507de6f587dc121821 | Python | py | |
from __future__ import division
import argparse
import torch
from mmcv import Config
from mmcv.runner import load_checkpoint
from mmfashion.core import AttrPredictor, CatePredictor
from mmfashion.models import build_predictor
from mmfashion.utils import get_img_tensor
def parse_args():
parser = argparse.Argumen... | demo/test_cate_attr_predictor.py | Apache-2.0 | 1,917 | 3ac4a1322f4f614ebd0f4f20b3afe9309a8a03d7 | Python | py | |
import torch
import torch.nn as nn
from mmcv.cnn import constant_init, normal_init
from .conv_module import ConvModule
class NonLocal2D(nn.Module):
"""Non-local module.
See https://arxiv.org/abs/1711.07971 for details.
Args:
in_channels (int): Channels of the input feature map.
reductio... | SipMask-mmdetection/mmdet/ops/non_local.py | MIT | 3,593 | 3ac4bf9f529ef33a50c2ff1642c213f9f2a42531 | Python | py | |
import torch
import torch.utils.data as data
import random
import math
import os
import logging
from MIME.utils import config
import pickle
from tqdm import tqdm
import numpy as np
import pprint
pp = pprint.PrettyPrinter(indent=1)
import re
import time
import nltk
from constants import MIME_HOME
class Lang:
def... | utils/data_reader.py | MIT | 7,821 | d7cec9ae175c5aeb697eb79adae09f052d514681 | Python | py | |
# pylint: disable-all
import argparse
from animus import EarlyStoppingCallback, IExperiment
from animus.torch.callbacks import TorchCheckpointerCallback
from apto.utils.report import get_classification_report
from catalyst import utils
import numpy as np
import optuna
from sklearn.model_selection import train_test_spl... | src/scripts/tune_ts_lstm_oasis_cv_whole.py | Apache-2.0 | 9,173 | 3ac4d350db37631a50c583f2d20361269d3cdd5d | Python | py | |
from xmuda.models.quan_trainer_lmsc import RecNetLMSC
from xmuda.models.quan_trainer import RecNet
# from xmuda.models.quan_trainer_seg import RecNetSeg
from xmuda.models.SSC2d import SSC2d
from xmuda.data.semantic_kitti.semantic_kitti_quan import SemanticKITTISCN
from torch.utils.data.dataloader import DataLoader
impo... | xmuda/extract_results.py | Apache-2.0 | 2,508 | 3ac4f81f9cf4d5760c36022639311560ddb0af1a | Python | py | |
import torch
import numpy as np
from maskr.utils import box_utils
from maskr.lib.nms.nms_wrapper import nms
from maskr.utils.batch import batch_slice
from maskr.test.baseline import saveall
import logging
log = logging.getLogger()
@saveall
@batch_slice(2)
def proposals(rpn_class, rpn_bbox, proposal_count, anchors, co... | maskr/filters/proposals.py | MIT | 2,207 | b28ee69f293e0d91373cfd6c99a356300971f910 | Python | py | |
import numpy as np
import torch
import torchvision
from torch import nn
from core import build_graph, cat, to_numpy
torch.backends.cudnn.benchmark = True
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@cat.register(torch.Tensor)
def _(*xs):
return torch.cat(xs)
@to_numpy.register(torc... | examples/dist/CIFAR10-dawndist/torch_backend.py | BSD-2-Clause | 4,574 | 28bbf3503c050dde32342a7905bf4fb62f189812 | Python | py | |
"""
Created on Thu Oct 26 14:19:44 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import os
import numpy as np
from torch.optim import SGD
from torchvision import models
from misc_functions import preprocess_image, recreate_image, save_image
class ClassSpecificImageGeneration():
"""
Produces a... | src/generate_class_specific_samples.py | MIT | 2,187 | 12d526dd6a983cfe0fdbc6b387ef76b982621065 | Python | py | |
import torch.nn as nn
import math
import torch
from torch.autograd import Variable
from scipy.linalg import hadamard
class HadamardProj(nn.Module):
def __init__(self, input_size, output_size, bias=True, fixed_weights=True, fixed_scale=None):
super(HadamardProj, self).__init__()
self.output_size = ... | models/modules/fixed_proj.py | MIT | 2,828 | 3ac52d5faab36244a57b2c1a8d8fea00afde1627 | Python | py | |
'''
This code is mainly taken from the following github repositories:
1. parksunwoo/show_attend_and_tell_pytorch
Link: https://github.com/parksunwoo/show_attend_and_tell_pytorch/blob/master/prepro.py
2. sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning
Link: https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Capti... | KoBert_Based_Model/data_loader_kobert.py | Apache-2.0 | 3,107 | 456db50482489f82f7f6fb9f2e86d9e579ceda66 | Python | py | |
import torch.cuda
from LJ_surrogates.surrogates.collate_data import collate_physical_property_data
from LJ_surrogates.parameter_modification import vary_parameters_lhc, create_evaluation_grid
import time
from LJ_surrogates.sampling.likelihood import likelihood_function
from LJ_surrogates.surrogates.surrogate import co... | projects/examples/argon_single/argon_single.py | MIT | 7,475 | 3ac544fdb31a2ded0bc6f6132c5d9218489c3c55 | Python | py | |
"""
Provide quantilized form of torch.nn.modules.linear
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from .number import qsigned
class Linear(nn.Linear):
def __init__(self,
in_features,
out_features,
bias=True,
... | tqt/function/linear.py | BSD-3-Clause | 2,888 | 3ac55d8e4423ac7bb23b3580238d9a956a581f6d | Python | py | |
#!/usr/bin/env python3
# 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 argparse
import os
import random
import sys
import time
from math import pi
import numpy as np
import orbslam2
i... | habitat_baselines/agents/slam_agents_aimas.py | MIT | 22,958 | 5e1ff9de6d1924decdaefc6a28bfddcef4ca61f2 | Python | py | |
#!/usr/bin/env python3
import builtins as __builtin__
import collections
import datetime
import faulthandler
import math
import multiprocessing.queues as mp_queues
import os
import queue
import random
import time
from typing import Any, Callable, Dict, Optional, Tuple
import numpy as np
import torch
from fairseq impo... | pytorch_translate/train.py | BSD-3-Clause | 30,102 | 3ac5a57740f846dee854599d24d8044c9c946e6e | Python | py | |
import io, requests
import torch
import torch.nn as nn
from dall_e.encoder import Encoder
from dall_e.decoder import Decoder
from dall_e.utils import map_pixels, unmap_pixels
from dall_e._model_dl import models_path # Set up models directory
from dall_e import models # Load implicit namespace module
def load_model(... | dall_e/__init__.py | MIT | 721 | 03ba9a374135173c9a160cecf83d9afc3879d33d | Python | py | |
# import minpy.numpy as np
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torch.autograd import Function
import cv2
class UnNormalizer(object):
def __init__(self, mean=None, std=None):
if mean == None:
self.mean = [0.485, 0.456, 0.406]
... | Losses/Mask_bce_loss.py | MIT | 6,795 | 3ac5b0b2d84d906582785c76c0276a8d178bb35b | Python | py | |
import argparse
import os
import shutil
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data.dataloader import DataLoader
from dpp_nets.utils.io import make_embd, make_tensor_dataset
from dpp_nets.layers.layers import KernelVar, ReinforceSampler, PredNet, ReinforceTrainer
par... | train_reinforce_beer_cpu.py | MIT | 10,702 | 12c63dc06cf443c8dc394597ea859f4027611499 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
#__all__ = [ 'Conv', 'Deconv' ]
__all__ = [ 'Conv', 'Conv1d' ]
# Convolutional layer class
class Conv(nn.Conv2d):
'''
Convolutional layer::
conv0 = Conv([m,n,p], [c,r,s], [ystride,xstride], [ypadding, xpadding], bias, init)
creates ... | ummon/modules/conv.py | BSD-3-Clause | 25,457 | 5f356bd1d21855baa34f4c84b0b9d5da358a05be | Python | py | |
#copy of j25
import numpy as np
from collections import namedtuple
from functools import partial
from PIL import Image
import data_transforms
import data_iterators
import pathfinder
import utils
import app
import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional... | configs/f68_pt.py | MIT | 9,164 | 3ac629191c9a4574f1fa66bf0707ba461a0e6cfc | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
"""
Losses module contains implementations for various losses used generally
in vision and language space. One can register custom losses to be detected by
MMF using the following example.
.. code::
from mmf.common.registry import registry
from torch import nn
... | mmf/modules/losses.py | BSD-3-Clause | 38,637 | fcaca33a19799b4afa35cbe1eddf88bb307672a4 | Python | py | |
import numpy as np
import torch
import os.path as osp
import json
from core.config import cfg
from manopth.manolayer import ManoLayer
class MANO(object):
def __init__(self):
self.layer = self.get_layer()
self.vertex_num = 778
self.face = self.layer.th_faces.numpy()
self.joint_regr... | lib/_mano.py | MIT | 2,173 | 109c3f96ae5d13f4a4ad0689abca1cf5e11367a3 | Python | py | |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import unittest
import torch
import torch.nn.functional as F
from nni.algorithms.compression.v2.pytorch.pruning import (
LevelPruner,
L1NormPruner,
L2NormPruner,
SlimPruner,
FPGMPruner,
ActivationAPoZRankPruner,
Acti... | test/ut/compression/v2/test_pruner_torch.py | MIT | 7,687 | dfb7f0ba7c7deb6f3eb4250e56c710f40a4cb8ba | Python | py | |
from torch.utils.data import DataLoader
from .collate_batch import train_collate_fn, val_collate_fn
from .datasets import init_dataset, ImageDataset
from .samplers import RandomIdentitySampler, RandomIdentitySampler_alignedreid
from .transforms import build_transforms
def make_data_loader(cfg, is_train):
train_tra... | data/build.py | MIT | 1,789 | 3ac66edf20267eb3344ef470333563138878cf59 | Python | py | |
import torch
import torch.nn as nn
from . import utils
from . import layers
__all__ = ['rsunet_act']
nonlinearity = 'ReLU'
params = {}
def set_nonlinearity(act, **act_params):
global nonlinearity
assert act in ['LeakyReLU','PReLU','ELU','ReLU']
nonlinearity = act
global params
params = {}
... | emvision/models/rsunet_act.py | MIT | 4,712 | 3ac67344e1a2dd3ea200bc946035031919332593 | Python | py | |
# import os
# os.environ['CUDA_VISIBLE_DEVICES']="1"
from SOMDST_utils.eval_utils import compute_prf, compute_acc
from pytorch_transformers import BertTokenizer, BertConfig
from model.SOMDST import MGDST
import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import ... | MGL_SOMDST/SOMDST_evaluation.py | MIT | 12,608 | f9332319b1923433a55894c8f1e081b1efc4a1fb | Python | py | |
#!/usr/bin/env python3
from abc import ABC, abstractmethod
from typing import Any
from torch import Tensor
from torch.nn import Module
class Concept:
r"""
Concept features are human-friendly idea representations of target classes
in a Machine Learning task, in a tensor format: they can be images such as... | captum/concept/_core/concept.py | BSD-3-Clause | 2,645 | 3ac688aaf22ba459eda27adbbf22ee89b4f904f1 | Python | py | |
# src: https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/utils/misc.py
# -*- coding: utf-8 -*-
import string
import torch
import subprocess
from nltk.stem import PorterStemmer
from c2nl.inputters import constants
ps = PorterStemmer()
def normalize_string(s, dostem=False):
"""Lower text and rem... | c2nl/utils/misc.py | MIT | 5,415 | 3ac6975bb94f27cac1464bd59761becb3d58774d | Python | py | |
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, input_size, hidden_size, out_size, norm_out=False, dropout_rate=0.8):
super(MLP, self).__init__()
self.norm_out = norm_out
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
... | src/models/mlp.py | MIT | 754 | 3ac699c2f8cc1665ccb6d7477bd7db48a0df3275 | Python | py | |
'''
Retrieval Network, Written by Xiao
For robot localization in a dynamic environment.
'''
import numpy as np
import os, sys, random, scipy
import torch
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from scipy.sparse import find as find_sparse_idx
from torchvision import datasets, transforms
from Ne... | Network/retrieval_network/datasets.py | MIT | 11,178 | e94b17c50563204dd09753dfd2303516947decf0 | Python | py | |
import collections
import glob
import gzip
import pathlib
import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
from pylego import ops
from ..baseconditional import BaseGymTDVAE
class PreProcess(nn.Module):
""" The default preprocessing layer for 1d inputs.
"""
def ... | models/conditional/drqn.py | MIT | 13,609 | f99c1839d9a2ee0d4410229b65882ad3a9a85a35 | Python | py | |
import torch
import torch.nn as nn
import torchvision.models.detection.backbone_utils as backbone_utils
import torchvision.models.resnet as resnet
import torchvision.models._utils as _utils
import torch.nn.functional as F
from collections import OrderedDict
from models.mobilev1_width import MobileNetV1 as MobileNetV1
... | models/retinaface_width.py | Apache-2.0 | 5,878 | e602de1f9c8d65dfb97e7c0dfc281aa6280e7170 | Python | py | |
# coding=utf-8
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not u... | examples/run_finetune_cosmosqa_adapter.py | MIT | 80,025 | 1e02007b0abd35ee6dd6a7eae151e941b404742a | Python | py | |
#!/usr/bin/env python3
# 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.
from typing import Callable, Dict, List, Optional, Tuple, Type, Union
import numpy as np
import torch
from ax.core.dat... | ax/modelbridge/torch.py | MIT | 12,282 | b2d1e3cd3d0df5f4f3a0154959ba4a45f495185e | Python | py | |
"""Pytorch Dataset object that loads perfectly balanced MNIST dataset in bag form."""
import numpy as np
import torch
import torch.utils.data as data_utils
from PIL import Image
from sklearn.model_selection import KFold
from torch.utils.data import ConcatDataset
from torchvision import datasets, transforms
class Mni... | datasets/mnist_dataset.py | MIT | 4,670 | fdb098892c555c7d6b46cce4f31dad7526d18d0d | Python | py | |
import time
from enum import Enum
from functools import reduce
import contextlib
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
import torchplus
from second.pytorch.core import box_torch_ops
from second.pytorch.core.losses import (WeightedSigmoidClassificationLoss,
... | second/pytorch/models/voxelnet.py | MIT,BSD-3-Clause | 38,769 | e98277ddd0c19fdce4313023e1116645a2872ee0 | Python | py | |
# -*- coding: utf-8 -*-
import torch
import Net
import torch.nn as nn
import torch.optim as optim
from data_pipe import get_test_data, get_train_data
import matplotlib.pyplot as plt
from utils import visualize, visualize_cos, get_time,visualize_center
import random
import numpy as np
import os
from tqdm import tqdm
fro... | train.py | MIT | 4,704 | 3ac6e887ccf4b6d1faac7add00c93bd760b2bd1f | Python | py | |
import torch
import numpy as np
from torch_geometric.utils import (erdos_renyi_graph,
stochastic_blockmodel_graph,
barabasi_albert_graph)
def test_erdos_renyi_graph():
torch.manual_seed(1234)
edge_index = erdos_renyi_graph(5, 0.2, directed=... | test/utils/test_random.py | MIT | 1,495 | 3ac7058a5fa967d509068a2b5fe67f1fb1a6a532 | Python | py | |
import datetime
import os
import gym
import numpy
import torch
from games.abstract_game import AbstractGame
class MuZeroConfig:
def __init__(self):
self.seed = 0 # Seed for numpy, torch and the game
### Game
self.observation_shape = (1, 1,
4) # Dimens... | games/per_cartpole.py | MIT | 8,841 | 03b00dac126f744cfec71c79e25d2fd318f0653e | Python | py | |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import argparse
import time
import torch
import sys
sys.path.append("../..")
import communication
NUM_TRIALS = 20
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Test lightweight communica... | runtime/tests/communication/gloo_communication_handler.py | MIT | 4,830 | 3ac70eade0d5b4ac48e13d4ac0ca2c8ab01eff96 | Python | py | |
import re
import os
import torch
import pickle
import random
import numpy as np
from soynlp.hangle import decompose
from keras.preprocessing import text, sequence
# 결과 재생산을 위한 시드값 고정을 위한 함수
def seed_everything(seed=42):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
to... | utils.py | MIT | 2,546 | 4222c4deaf1739f723f7a374bfd9a12d590dc646 | Python | py | |
from __future__ import annotations
import copy
import attr
import torch
import numpy as np
import typing
from evocraft_ga.nn.linear import Linear
from evocraft_ga.nn.cppn import CPPN
model_dict = {"linear": Linear, "cppn": CPPN}
@attr.s
class Genome:
noise_dims: int = attr.ib()
cube_dims: int = attr.ib()
... | evocraft_ga/genome/genome.py | MIT | 2,368 | d7162fd4108dd4861f5ccc7ad70a2fd76d0f3072 | 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/loggers/test_neptune.py | Apache-2.0 | 5,131 | 3ac763cc87b4f555d0416b71f947281643dba996 | Python | py | |
# --------------------------------------------------------
# Tensorflow 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
from __... | test_net.py | MIT | 36,213 | 5e859492dc6949fec7fc5a986b10f2e999e8a097 | Python | py | |
# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0
import errno
import os
from functools import reduce
import numpy as np
import torch
from torch.utils.data import DataLoader
from pyro.contrib.examples.util import MNIST, get_data_directory
# This file contains utilities for cach... | examples/vae/utils/mnist_cached.py | Apache-2.0 | 8,858 | d7ad578fda8a5bb001699a012da197a0483eef52 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from cogdl.modules.conv import GATConv, SELayer
from . import BaseModel, register_model
@register_model("drgat")
class DrGAT(BaseModel):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the pa... | cogdl/models/drgat.py | MIT | 1,964 | 3ac78ac9b54ababe353d86ec0c4efee581c80986 | Python | py | |
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... | pytorch_transformers/modeling_xlnet.py | Apache-2.0 | 58,149 | 3ac791b12194e1ab59c2dd6df74917e2be7da2ba | Python | py | |
import torch as th
class Normalize(object):
def __init__(self, mean, std):
self.mean = th.FloatTensor(mean).view(1, 3, 1, 1)
self.std = th.FloatTensor(std).view(1, 3, 1, 1)
def __call__(self, tensor):
tensor = (tensor - self.mean) / (self.std + 1e-8)
return tensor
class Pre... | resnets/preprocessing.py | MIT | 1,323 | 4b6a5031e1439e173802bca534e7b21f76a21931 | Python | py | |
"""
kraken.lib.models
~~~~~~~~~~~~~~~~~
Wrapper around TorchVGSLModel including a variety of forward pass helpers for
sequence classification.
"""
from os.path import expandvars, expanduser, abspath
import torch
import numpy as np
import kraken.lib.lineest
import kraken.lib.ctc_decoder
from typing import List, Tuple... | kraken/lib/models.py | Apache-2.0 | 7,214 | 45d87b020fd86c8561ee575a8c6a7d37503aa267 | Python | py | |
import pandas as pd
import numpy as np
import torch.utils.data as data
from sklearn.preprocessing import MinMaxScaler
import config
class ElectricDataset(data.Dataset):
def __init__(self, preprocessor):
const = config.Constant
hp = config.Hyperparameter
df = pd.read_csv(const.RESERVE_MARG... | dataset/electric_dataset.py | MIT | 1,097 | 7c6dcda5a6f15f9baeeed3e08a398f49dbed276c | Python | py | |
#!/usr/bin/env python3
# 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.
from __future__ import annotations
from typing import Optional
import torch
from gpytorch.constraints import Interval... | botorch/models/kernels/downsampling.py | MIT | 4,143 | 3ac7c4ac16e6f3e2e320686a675075959a5e5460 | Python | py | |
# python imports
import signal, time, os, sys
import pickle as pkl
import argparse as ap
# numpy imports
import numpy as np
# torch imports
import torch
import torch.autograd as ta
import torch.nn.functional as F
import torch.nn as nn
from torch.utils import data
# code imports
import process_data as pd
import code.dat... | analysis.py | Apache-2.0 | 3,234 | 3ac7d9d372434d203ad08596f7149bce6a847144 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from typing import List, Optional
import torch
import torch.nn as nn
from pytorchvideo.layers.utils import set_attributes
from pytorchvideo.models.weight_init import init_net_weights
class Net(nn.Module):
"""
Build a general Net models ... | pytorchvideo/models/net.py | Apache-2.0 | 4,071 | e990d57195904068432bdeb7b0eae02cedf1b071 | Python | py | |
import argparse
import os
import torch
import numpy as np
from pgcn_dataset import PGCNDataSet
from pgcn_models import PGCN
from torch import multiprocessing
from torch.utils import model_zoo
from ops.utils import get_configs
from ops.I3D_Pooling import I3D_Pooling
from tqdm import tqdm
import random
parser = argparse... | pgcn_test.py | BSD-3-Clause | 8,363 | e92e45e26837b277c1801f6c1116ddf87873b272 | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import copy
import re
import pickle
import random
import sys
import urllib
import tarfile
import zipfile
import os.path as osp
from scipy.io import loadmat
import numpy as np
import h5py
i... | reid/data_manager/dukemtmcreid_tracklet.py | MIT | 12,257 | 7c89fdf5215422383bd7f4f95dc84df2be90e4d8 | Python | py | |
from daydayup_layer import GCN, My_APPNP, Cheb_Net, ARMA_Net, GAT_Net, SGC_Net, TAG_Net, DNA_Net
import torch
import torch.nn.functional as F
import lightgbm as lgb
from torch_geometric.nn import GCNConv, Node2Vec
from torch.nn import PReLU
from sklearn.linear_model import LogisticRegression
from torch.utils.da... | daydayup_submit/daydayup_model.py | MIT | 19,488 | 455cff1ee41050d4da2cdb44efe2cbea0850eeea | Python | py | |
# -*- coding: utf-8 -*-
"""
Pose models and loss functions for axis-angle representation
"""
import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from helperFunctions import eps
"""
Numpy functions
"""
# axis angle y = \theta v where theta is angle of rotation about axis v
# y = logm... | axisAngle.py | MIT | 3,286 | 5fbd044876babf33c265c4cb0afec031d78140f5 | Python | py | |
# pylint: disable=E1101, E0401, E1102
import time
import random
import argparse
import pickle
import numpy as np
import torch
import sys
from models import MNIST, CIFAR10, IMAGENET, load_model
from utils import load_mnist_data, load_cifar10_data, load_imagenet_data
from utils import proj, latent_proj, transform
from ... | attack.py | MIT | 13,234 | 3ac85b148500dddbc3e46e28ea21b014718f5506 | Python | py | |
import torch
from torch import nn
from fsdet.modeling.roi_heads import build_roi_heads
import logging
from detectron2.modeling.backbone import build_backbone
from detectron2.modeling.postprocessing import detector_postprocess
from detectron2.modeling.proposal_generator import build_proposal_generator
from detectron2.... | fsdet/modeling/meta_arch/rcnn.py | Apache-2.0 | 13,506 | a9098895175dd00dd5587f070536696f4e37db65 | Python | py | |
import sys
from itertools import product
from math import ceil, floor
import torch
import numpy as np
from seal import uIntVector, Plaintext, Ciphertext
from comm import CommBase, CommFheBuilder, BlobFheRawCts, PhaseProtocolServer, PhaseProtocolClient, init_communicate, \
torch_sync, end_communicate, BlobTorch, P... | src/fully_connected.py | MIT | 22,615 | 348154a17ddebae54cc407a7fe553b136d498b9e | Python | py | |
import logging
from collections import defaultdict
from typing import Dict, Any, List
from overrides import overrides
import numpy as np
import torch
from torch import Tensor
from allennlp.common import Lazy
from allennlp.data import Vocabulary
from allennlp.models import Model
from allennlp.modules import TextFiel... | zsee/models/sentence_level.py | Apache-2.0 | 11,787 | 652b6f2417286eed10e3f1e0c1ae6b553dea31cb | Python | py | |
import torch
# from torch import distributed
import torch.nn as nn
# from apex import amp
from torch.cuda import amp
from functools import reduce
from tqdm import tqdm
from utils.loss import KnowledgeDistillationLoss, BCEWithLogitsLossWithIgnoreIndex, \
UnbiasedKnowledgeDistillationLoss, UnbiasedCrossEntropy, Icarl... | train.py | MIT | 13,461 | d7a046bfb1c071c595e4cf60312a8070fdfc187a | Python | py | |
import os
from typing import Any, Callable, List
import torch
from ppq.core import (NetworkFramework, TargetPlatform, empty_ppq_cache,
ppq_warning)
from ppq.executor import TorchExecutor
from ppq.IR import (BaseGraph, GraphCommand, GraphCommandType, GraphFormatter,
GraphMerger... | ppq/api/interface.py | Apache-2.0 | 32,968 | e938b658921adfb168040e7079afd89cab07d0ee | Python | py | |
# ====================================================
# Library #
# ====================================================
import os
import gc
import sys
import math
import time
import random
import shutil
from requests import get
from pathlib import Path
from contextlib import contextmanager
from collections import def... | code/002/ver21/siim-002-train-effdet-ver21.py | CC-BY-4.0 | 35,782 | 7c153645b0762829101d42d8b37bd91903014504 | Python | py | |
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Experimental modules
"""
import math
import numpy as np
import torch
import torch.nn as nn
from .common import Conv
from video_streamer.utils.downloads import attempt_download
class CrossConv(nn.Module):
# Cross Convolution Downsample
def __init__(self, c1, c2... | video_streamer/models/experimental.py | MIT | 4,672 | 4baa00aac5c7c244eeda1947ed11361c3dd9f6e0 | Python | py | |
import math
import torch
from tqdm import tqdm
from torch.utils.data import DataLoader
from transformers import GPT2Config, GPT2Tokenizer, GPT2LMHeadModel
from core.utils.parser import get_test_parser
from core.models.huggingface.parser import add_cmdline_args_gen
from core.data.empdataset import EmpatheticDataset
fro... | experiments/baselines/test_gpt2.py | MIT | 3,847 | 3ac8be379038300757a79c4084c38ae1e50416ad | Python | py | |
from functools import partial
from collections import OrderedDict
import torch.nn.init as init
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from Models.layers import ConvBnRelu
from opts import _nostride2dilation
# ResNet34 Based
class PSPNet(nn.Module):
... | Models/PSPNet.py | MIT | 4,058 | 65178f11422cd2833a89773115b1edfed0ba2fe7 | Python | py | |
#!/usr/bin/python3
from __future__ import print_function
import os
import sys
import torch
import torch.backends.cudnn as cudnn
import argparse
import cv2
import numpy as np
from collections import OrderedDict
import time
sys.path.append(os.getcwd() + '/../../src')
from config import cfg
from prior_box import PriorB... | tasks/task1/inference.py | MIT | 10,208 | 24c703970fc1c3ea90ec59a79eaf8be20e04b3d9 | Python | py | |
import torch
import numpy as np
import pdb
class PatchAttacker:
def __init__(self, model, mean, std, kwargs):
std = torch.tensor(std)
mean = torch.tensor(mean)
self.epsilon = kwargs["epsilon"] / std
self.steps = kwargs["steps"]
self.step_size = kwargs["step_size"] / std
... | attacks/patch_attacker.py | BSD-2-Clause | 4,693 | b292278edbe85e5f3045bcff6fe45ff10d394210 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import logging
from collections import defaultdict
from enum import Enum
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union
import torch
from fvcore.common.param_scheduler import CosineParamScheduler, MultiSt... | detectron2/solver/build.py | Apache-2.0 | 11,664 | d79e23a60f24f1cbd0d2c98359d056bba419b1f1 | Python | py | |
import torch
import torch.nn as nn
class PolypDet(nn.Module):
def __init__(self):
super(PolypDet, self).__init__()
def CBR2d(
input_channels: int,
output_channels: int,
kernel_size: int = 3,
stride: int = 1,
padding: int = 1,
... | medimodule/Abdomen/models/polyp_det.py | Apache-2.0 | 5,107 | 3ac926cc99fe68a27993f582ed0630800e4ce9fb | Python | py | |
"""
@file : run_cls.py
@author : xiaolu
@email : luxiaonlp@163.com
@time : 2022-01-05
"""
import os
import copy
import torch
import json
import random
from tqdm import tqdm
import numpy as np
from torch import nn
from config import set_args
from model import Model
from sklearn import metrics
import torch.nn.functi... | Roberta+RDrop/run_cls_iflytek.py | Apache-2.0 | 6,400 | 45bd5080a59b82626449bb1306ee93d12879db46 | Python | py | |
"""Data processor for SMP-CAIL2020-Argmine.
Author: Tsinghuaboy (tsinghua9boy@sina.com)
In data file, each line contains 1 sc sentence and 5 bc sentences.
The data processor convert each line into 5 samples,
each sample with 1 sc sentence and 1 bc sentence.
Usage:
1. Tokenizer (used for RNN model):
from data imp... | CAIL2020/lbwjx/data.py | Apache-2.0 | 14,736 | 3ac933890a16d11871f262ac987aed299f7ea93f | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class BalancedPositiveNegativeSampler(object):
"""
This class samples batches, ensuring that they contain a fixed proportion of positives
"""
def __init__(self, batch_size_per_image, positive_fraction):
"""
... | maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py | BSD-2-Clause | 2,764 | 701d4e78d79b13e44c69cfb7bb93de0c407ea7d7 | Python | py | |
import argparse
import os
import numpy as np
import math
import itertools
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from mnistm import MNISTM
import torch.nn as nn
... | .history/implementations/pixelda/pixelda_try_20190106193210.py | MIT | 18,277 | fdabd5b70d1d5380a87819d468aa66e0632ba20b | Python | py | |
import yaml
import argparse
import os
import sys
import inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
print(sys.path)
import gym
import torch
from rlkit.envs import get_env, get_envs
from rlkit.envs... | run_scripts/ppo_exp_script.py | MIT | 3,535 | 3ac96760096e5ff5e6af6938d71b30abd517c05c | Python | py | |
import torch
import numpy as np
import load_data
import trainer
import models
import matplotlib.pyplot as plt
from hyperparameters import *
MODEL_PATH = './model.pt'
SAMPLES = 10
def show_model_result_enc_dec(model, loader):
image = load_data.get_dataset_image(loader)
(x, target) = next(iter(loader))
r... | code/utils.py | MIT | 3,364 | 5eb146b0795815e6d38f702c79e698e5cb80617e | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 29 11:39:53 2021
@author: ayman
"""
import joblib
import torch
import argparse
import random
import numpy as np
from lib import game, mcts, model
def get_action(game, net, mcts_, cur_state,cur_player, search_depth, batch_size, device):
if net is None:
action... | Connect4/play_human.py | MIT | 4,979 | 3ac9a0702646907e73f0b838d2f5839242d7b7e1 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.resnet import BasicBlock, conv1x1, resnet34
from argparse import Namespace
import sys
sys.path.append("flownet2_pytorch")
from flownet2_pytorch.networks import FlowNetC
class FlowNet2C(FlowNetC.FlowNetC):
def __init__(se... | models/flow_net_c.py | MIT | 3,248 | 4590ff05fcf396e954a6834f634133650873cdbf | Python | py | |
# Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
#
# 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 ... | wenet/transformer/label_smoothing_loss.py | Apache-2.0 | 3,459 | 428fedcb0eb4345cd1361c97008a9afcd94ac171 | Python | py | |
from __future__ import division, print_function, absolute_import
import torch
def accuracy(output: torch.Tensor, target: torch.LongTensor, topk=(1, )):
"""Computes the accuracy over the k top predictions for
the specified values of k.
Args:
output (torch.Tensor): prediction matrix with shape (bat... | torchreid/metrics/accuracy.py | MIT | 1,179 | 3ac9bf135ad98d304051451ffa1098026441a918 | Python | py | |
import math
import torch
import torch.nn as nn
from models.common_utils import normalization, inv_forward
__all__ = ['vgg11_inv', 'vgg13_inv', 'vgg16_inv', 'vgg19_inv']
cfg = {
'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512,... | models/imagenet_models/vgg_inv.py | Apache-2.0 | 4,630 | dfd3c0c124d7a3aef06e20093a03b9b78866bbe2 | Python | py | |
import argparse
import os
from typing import Dict, List, Any
import numpy as np
import torch
from torch.autograd import Variable
from policies.models.actor_critic import ACTOR_CRITIC
from policies.dqn import Policy as DQN_POLICY
class Policy(DQN_POLICY):
def __init__(self, params: argparse) -> None:
sup... | policies/a2c.py | MIT | 5,264 | d7932c9ac2b09c725c3d8cb9b69bc5560300fb45 | Python | py | |
import gym
import torch
import json
import os
import yaml
from tqdm import trange
import maml_rl.envs
from maml_rl.metalearners import MAMLTRPO
from maml_rl.baseline import LinearFeatureBaseline
from maml_rl.samplers import MultiTaskSampler
from maml_rl.utils.helpers import get_policy_for_env, get_input_size
from maml... | train.py | MIT | 4,735 | dfbb0104f91bd03cf012eb491b0bda92d843c110 | Python | py | |
# RAngerQH - @lessw2020 github
# 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.
# https://github.com/lessw2020/RangerQH-Testing/blob/master/rangerqh.py
import torch
from torch.optim.optimiz... | optimizers/rangerqh.py | MIT | 8,723 | 3aca097848702730540ca8ca884e2780d6cb955e | Python | py | |
import tensorflow as tf
import tensorboard as tb
tf.io.gfile = tb.compat.tensorflow_stub.io.gfile
import os
import sys
import time
import torch
import random
import numpy as np
from argparse import Namespace
from options.train_options import TrainOptions
from data import create_dataset
from models import create_mode... | train_aerial.py | MIT | 8,383 | 3aca0c519d1659b42bdbc7fcea2405f866c6c229 | Python | py | |
#-----------------------------------------------------------------------------
# Dataset and Dataloader related function and class
#-----------------------------------------------------------------------------
import torch, torchvision
from collections import Counter
from random import seed, choice, sample
import json... | DataLoader.py | MIT | 8,756 | 4263b5e80ed419dcb2078565a453be395657e7f5 | Python | py | |
# -*- encoding: utf-8 -*-
"""
@File : mbv3.py
@Time : 2021/5/11 15:35
@Author : Zhl
@Desc :
"""
"""
Creates a MobileNetV3 Model as defined in:
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. (20... | nets/mbv3.py | MIT | 7,852 | 1e619ad0629b291bb6d188a7f662b2c194deeb18 | Python | py | |
from tkinter import Place
from numpy import dtype
import park
from PlotUtils import PlotHelper
from stable_baselines3 import PPO, A2C, DDPG
import torch
from torch import nn
import torch.nn.functional as F
from torch_geometric.data import Data
from torch_geometric.nn import GCNConv
import numpy as np
from stable_base... | agents/tf_placement.py | MIT | 3,724 | 3aca4a6638201ff64f47000406ee39ec78f562b3 | Python | py | |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | GraphCodeBERT/refinement/run.py | MIT | 29,129 | e6135bb27f8dcb45c5e3bd45c5bc78ee344df2ef | Python | py | |
import pytorch_lightning as pl
import torch
from PIL import Image
from src.utils import transform_augmentation, transform_inference
from torch.utils.data import DataLoader, Dataset
class Covid19(Dataset):
def __init__(self, x, y, transforms):
self.x = x
self.y = y
self.transforms = transfo... | modeling/src/pl_datamodule.py | MIT | 1,870 | 3aca5432e7d33f139aee8551fdf7c22d2594fba8 | Python | py | |
import os
import shutil
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import pytorch_ssim
from config import Config as _config
from model.rgb_edsr imp... | train_rgb.py | MIT | 3,407 | 3aca6259b7db754f5b50832810181384d8d92f2f | Python | py | |
import torch
import random
import os
from PIL import Image
from dataset.clip_generator import CHARACTER_ENCODING
from training import BASE_SYMBOLS
from training.tacotron2_model.utils import get_mask_from_lengths
CHECKPOINT_SIZE_MB = 333
BATCH_SIZE_PER_GB = 2.5
LEARNING_RATE_PER_64 = 4e-4
MAXIMUM_LEARNING_RATE = 4e-4... | training/utils.py | BSD-3-Clause | 5,197 | 3aca6a8baab5d3a4738da449c101b476ed120cac | Python | py |
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