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from torch.nn.modules import Module
import torch
import math
class Log_Loss(Module):
def __init__(self, device):
super(Log_Loss, self).__init__()
self.device = device
def forward(self,outputs,targets,bins,batch_size):
loss = torch.zeros([batch_size],requires_grad= True).to(self.device)
... | optimization/log_loss.py | MIT | 1,483 | 3a74f0ae287cc393bcc9059ddc780e66da373901 | Python | py | |
from torch.utils.data import Dataset
class Episode:
def __init__(self, support_loader, query_loader, base_task, task_id, n_classes, tags):
self.support_loader = support_loader
self.query_loader = query_loader
self.base_task = base_task
self.task_id = task_id
self.n_classes... | datasets/episode.py | MIT | 589 | 3a74f2f4cceb4fe403c99b20c31e30888d8c2570 | Python | py | |
from __future__ import print_function, with_statement, division
import copy
import os
import torch
from tqdm.autonotebook import tqdm
from torch.optim.lr_scheduler import _LRScheduler
import matplotlib.pyplot as plt
try:
from apex import amp
IS_AMP_AVAILABLE = True
except ImportError:
import logging
... | pyTorchChitra/modelTraining/lrFinder.py | MIT | 18,311 | 3a74f494d75f98c8a9425b89b373f765df715797 | Python | py | |
# For Binary classification --> nn -- logits -- sigmoid -- BCE loss
# For multi-class classification --> nn -- logits -- softmax -- CrossEntropyLoss
import sys
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
# adding tenso... | pytorch-tuto/pytorch_rev_tensorboard.py | MIT | 4,373 | 3a74fbefa98328e6605d60f8249ceaaba926f9d4 | Python | py | |
# Adapted from SPFN
import torch
import torch.nn as nn
from .point_convolution_universal import TransitionDown, TransitionUp
from .model_util import construct_conv1d_modules, construct_conv_modules, CorrFlowPredNet, set_bn_not_training, set_grad_to_none
from .utils import farthest_point_sampling, get_knn_idx, batched_... | model/Poinetnet2.py | MIT | 8,255 | 7c783919a50a80ce2c3993da461e137edbd4900c | Python | py | |
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | flash/core/utilities/flash_cli.py | Apache-2.0 | 8,239 | 7cfb341342f5535282fd874619310f308938451e | Python | py | |
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch
class conv_block(nn.Module):
"""
Convolution Block
"""
def __init__(self, in_ch, out_ch):
super(conv_block, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, ker... | model/AttentionUnet.py | MIT | 4,714 | 3a75512255497d3256a3ed20adcc538c83508438 | Python | py | |
#-*-coding:utf-8-*-
# date:2021-06-15
# Author: Eric.Lee
# function: handpose 3D Inference
import os
import argparse
import torch
import torch.nn as nn
import numpy as np
import time
import datetime
import os
import math
from datetime import datetime
import cv2
import torch.nn.functional as F
from models.resnet impo... | inference.py | Apache-2.0 | 13,711 | 3a75559c95457a1c68943f942714a45ca7add161 | Python | py | |
import matplotlib.pyplot as plt
import torch, pandas as pd, numpy as np
import sys, os, json
from collections import Counter
base_path = os.path.dirname(os.path.realpath(__file__)).split('reproduce_results')[0]
sys.path.append(base_path)
from helper_classes.experiment import Experiment
from sklearn.model_selection im... | Method/reproduce_results/train_clp/reproduce_training_length_predictors_carcinogenesis_kb.py | MIT | 4,434 | 3a755ee1644841bd432014da9b76fbb7ea06fe31 | Python | py | |
# camera-ready
import torch
import torch.utils.data
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import math
import scipy.stats
import pickle
import cv2
# ############################################... | mdn_cell/datasets.py | MIT | 5,402 | 800dc239577f1a94e151bdb799a65dd6fbe74334 | Python | py | |
import os
import torch
import datetime
import numpy as np
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from .ppo import PPO
from .gail import GAIL
from . import utils
from .data import RolloutStorage, FakeDataRollout
class Trainer(object):
... | irl_dcb/trainer.py | MIT | 10,304 | ff222da4c74c3d0c680d1bff545cebdffe99088e | Python | py | |
import warnings
warnings.filterwarnings('ignore')
from loguru import logger
logger.add('mask_rcnn.log', rotation='100 MB')
import os
import cv2
import json
import random
import numpy as np
import pandas as pd
from tqdm import tqdm
from labelme import utils
import torch
import torch.nn as nn
import torch.utils.data as... | unet/train_points.py | Apache-2.0 | 5,175 | b2e39d18728f032edae91e94099d71395fada05f | Python | py | |
from typing import Sequence
import pytorch_lightning as pl
import torch
import editdistance
class CharacterErrorRate(pl.metrics.Metric):
"""Character error rate metric, computed using Levenshtein distance."""
def __init__(self, ignore_tokens: Sequence[int], *args):
super().__init__(*args)
se... | lab3/text_recognizer/lit_models/metrics.py | MIT | 1,970 | b241dc8cdeb18ccfecc4aa86c73eaa619ea401ef | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''Main OpenQA training and testing script.'''
'''
In order to train and test the model, please follow the following steps:
#0. cut uncessary gpu memory used by gnome-shell
reexecute gnome-shell: alt-f2 + r + 'enter'
systemctl isolate multi-user
... | main.py | MIT | 2,904 | 4241a2a853127b0e56d7be042fa60cfc5b4e60cb | Python | py | |
import torch
import torch.nn as nn
class SentencePlanner(nn.Module):
def __init__(self, opt):
super(SentencePlanner, self).__init__()
self.hidden_size = opt.hidden_size
self.batch_size = opt.batch_size
planner_hidden_size = 300
if opt.task == "arggen":
self.se... | src/modules/sentence_planner.py | MIT | 8,835 | e93e82f8ca8c1b47eb9d480266116f275a76bdd0 | Python | py | |
from typing import Any, ClassVar, Dict
import torch
from ..decorators import pretty_repr
@pretty_repr
class Augmentation:
TYPE: ClassVar[str] = "none"
def transform(self, x: torch.Tensor) -> torch.Tensor:
"""Returns augmented observation.
Args:
x: observation.
Returns... | d3rlpy-master/d3rlpy/augmentation/base.py | MIT | 830 | d72ccc372247a6d887b6771c9c2caaca2a9ac20e | Python | py | |
import torch
import torch.nn
import torch.nn.functional as nn
import torch.autograd as autograd
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
from torch.autograd import Variable
from tensorflow.examples.tutorials.mnist import in... | GAN/ebgan/ebgan_pytorch.py | Unlicense | 2,614 | 454c6670a85c8f7344ba1df8cd2f7b6e8f4d97e9 | Python | py | |
from dgllife.model import GCNPredictor
from dgllife.model.gnn import GCN, GAT
from dgllife.model.readout import WeightedSumAndMax
import dgl
from dgl import function as fn
from dgl.ops import edge_softmax
from dgl.base import DGLError
from dgl.utils import expand_as_pair
from dgl.nn.pytorch.utils import Identity
import... | model.py | CC0-1.0 | 22,759 | 3a760e49d4d5d0254b3944cb83e0289c6e77352f | Python | py | |
################################################################################
# Starlab RNN-compression with factorization method : Lowrank and group-lowrank rnn
#
# Author: Donghae Jang (jangdonghae@gmail.com), Seoul National University
# U Kang (ukang@snu.ac.kr), Seoul National University
#
# Version : 1.... | rnn_compression_factorization/src/module/compressed_deepConv.py | Apache-2.0 | 3,766 | 3a764acf8d1caabc98b09accb593cf81261352b2 | Python | py | |
from pytorch_composer import get_layer, Model
from torch import rand
from random import randint, random, choice, sample, shuffle
from pytorch_composer.get_layer import get_layer, layers_list
import pytorch_composer
from pytorch_composer.CodeSection import Vars, Vocab
import torch
pytorch_composer.warnings_off()
c... | test/layer_lab.py | MIT | 8,089 | 3a764f3668e8b245de2182685b607e6f1ecace35 | Python | py | |
import torch
import numpy as np
from model import Actor, Critic
from replay_buffer import ReplayBuffer
from her import HER_sampler
from normalizer import Normalizer
import utils
class Policy:
def __init__(self, params):
self.relative_goal = params['relative_goal']
env = params['cached_env']
... | policy.py | MIT | 7,182 | 3a765f51de8e8294831f308748cbdc8393319191 | Python | py | |
# -*- coding: utf-8 -*-
import os
import pdb
import torch
import torch.nn as nn
import torchvision.models as models
from . import util
# https://pytorch.org/docs/stable/torchvision/models.html
# models.resnet50((pretrained=True, progress=True)), resnet18, inception_v3
#-----------------------------------------... | src/models.py | Apache-2.0 | 7,802 | 3a7683c18b1cdffe687161f1df01030967b97054 | Python | py | |
from spinup.utils.run_utils import ExperimentGrid
from spinup import vpg
import torch
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument('--num_runs', type=int, default=3)
args = parser.parse_args()
... | spinup/examples/bench_vpg_cartpole.py | MIT | 687 | 45a62880b680160c3f30bf1a5df4070af7bf0f7f | Python | py | |
import torch.nn as nn
import torch.nn.functional as F
class TemporalConv(nn.Module):
"""Some Information about TCN"""
def __init__(self, in_dim, out_dim, kernel_size, dilation):
super(TemporalConv, self).__init__()
self.padding_size = dilation*(kernel_size-1)
self.temporal_conv = nn.C... | TCN/model.py | MIT | 1,343 | 3a76cb70c5d9ede5b1cf4e70d98ba5a747fa6f89 | Python | py | |
import torch
from torch import nn
from torch.nn import functional as F
from math import log, pi, exp
import numpy as np
from scipy import linalg as la
logabs = lambda x: torch.log(torch.abs(x))
class ActNorm(nn.Module):
def __init__(self, in_channel, logdet=True):
super().__init__()
... | model.py | MIT | 11,187 | 3a76d4419bdd47807ee200b2b7772746a9d18cec | Python | py | |
import numpy as np
import torch
from ..base import Storage as S
class ReplayBuffer(S):
"""
Storage class for Off-Policy algorithms.
Parameters
----------
size : int
Storage capacity along time axis.
device: torch.device
CPU or specific GPU where data tensors will be placed and... | pytorchrl/core/storages/off_policy/replay_buffer.py | MIT | 6,744 | dfeb7e14b0a1aaa18d2a554807b8c4e1ee72bb9d | Python | py | |
from __future__ import division
import os
import mock
import torch
import torchvision.transforms as transforms
import torchvision.transforms.functional as F
from torch._utils_internal import get_file_path_2
import unittest
import math
import random
import numpy as np
from PIL import Image
try:
import accimage
excep... | test/test_transforms.py | BSD-3-Clause | 60,688 | 3a76f3a1adb03d0bb2aa2b06779f231ca05893c9 | Python | py | |
import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import syft
from syft.frameworks.torch.tensors.interpreters.autograd import AutogradTensor
from syft.generic.pointers.pointer_tensor import PointerTensor
def test_wrap():
"""
Test the .on() wrap funct... | test/torch/tensors/test_autograd.py | Apache-2.0 | 27,434 | b29f1f5d756ed81058bff23d8d7675eaed8bbd86 | Python | py | |
import os
import json
import multiprocessing as mp
import time
import shutil
import torch.cuda
from utils import config
from data import data_path, load_embeddings
from probe import train, summarize, report
def run_layer(train_data, dev_data, test_data, task_name, model_name, mode, layer):
task_dir = os.path.jo... | probing/main.py | Apache-2.0 | 5,508 | 3a7719aadc1fd4820ec83b14b5ea9d7d69ca177d | Python | py | |
# Program trains the network 3000 episodes or until its good enough
# and then it shows the result by playing couple demo runs.
#
# Usage:
# cargo build --lib
# python python_pytorch.py
#
# You need Pytorch version 1.2.0
# Cargo builds lib to target/debug/librusted_cart_pole.so and it's used with FFI
# https://cf... | examples/python_pytorch.py | MIT | 6,958 | 3a7723838e34659c43e904a56634763d29048928 | Python | py | |
# -*- coding: utf-8 -*-
#
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
import dgl
import os
import torch
from dgl.data.utils import download, extract_archive, get_download_dir, _get_dgl_url
from torch.utils.data import Dataset
from .mol_tree import Vocab,... | examples/generative_models/jtnn/jtnn/datautils.py | Apache-2.0 | 9,335 | 425bf4287c9bbedabf2986751da5b7745dad4884 | Python | py | |
#!/usr/bin/env python3
import torch
from ..constraints import Positive
from ..distributions import base_distributions
from .likelihood import _OneDimensionalLikelihood
class LaplaceLikelihood(_OneDimensionalLikelihood):
r"""
A Laplace likelihood/noise model for GP regression.
It has one learnable parame... | gpytorch/likelihoods/laplace_likelihood.py | MIT | 1,458 | 3a7736cddb63c98fdcda330bf195a138917a5614 | 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... | tests/core/test_exp_manager.py | Apache-2.0 | 16,059 | 4284622edaa7fcc4581d55ba9ad05e6ac073ccdf | Python | py | |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. 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/LI... | examples/pytorch/speech-recognition/run_speech_recognition_ctc.py | Apache-2.0 | 27,168 | 3a7747e79117f7aabd6cf0584f549ac7fc051e08 | Python | py | |
#-*- coding: utf-8 -*-
import sys
import random
import re
import time
import torch
from torch.autograd import Variable
from torch import optim
import torch.nn as nn
import torch.nn.functional as F
from seq2seq_model import MemN2N_Seq2Seq
from data_utils import *
def init_command_line(argv):
from argparse import ... | MemN2N/MemN2N/predict.py | MIT | 6,234 | 3a775837fedc2eac6374351cae81b40ddcfa853c | Python | py | |
import math
import time
import numpy as np
from torch import cat, matmul
def batchify(*arrays, batch_size=10, shuffel=True):
"""Function that defines a generator that keeps outputting new batches
for the input arrays infinit times.
Arguments:
*arrays: a number of arrays all assume to have sam... | sggm/baselines/utils.py | Apache-2.0 | 2,423 | 6b3df68b75112608142c82d9e44a63d07b883e88 | Python | py | |
'''
Visualize the trained model's feature maps.
'''
import os
from tqdm import tqdm
import cv2
import numpy as np
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
import matplotlib.pyplot as plt
from load_dataset import LoadInputImages
from attention_cnn import AttentionCNN
from... | visualize.py | MIT | 3,850 | 1eaf9b00b0c96cb0249dcf9b197f4173fc57a500 | Python | py | |
import math
import os
import argparse
import yaml
import wandb
from math import sqrt
from pathlib import Path
from easydict import EasyDict
# torch
from PIL import Image
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import ExponentialLR
# vision imports
from torch.utils.data import DataLoad... | vqgan/train_vae.py | MIT | 6,533 | 702ea850f92d04553a3294e7e3bc9a506278b10f | Python | py | |
'''
load lottery tickets and evaluation
support datasets: cifar10, Fashionmnist, cifar100
'''
import os
import time
import random
import shutil
import argparse
import numpy as np
from copy import deepcopy
import matplotlib.pyplot as plt
import torch
import torch.optim
import torch.nn as nn
import torch.utils.data... | calculate_flops_regroup.py | MIT | 8,273 | 5e92b8a3dc4372d7ffdb01790ed00b82f626e77a | Python | py | |
import torch
from torch.nn import functional as F
from torch.nn.modules import loss
from mmcv.runner import load_checkpoint
from mmcv import Config
from ..builder import CLASSIFIERS, build_backbone, build_head, build_neck, build_classifier
from .image import ImageClassifier
from ..backbones.binary_utils.binary_blocks... | mmcls/models/classifiers/distiller.py | Apache-2.0 | 8,461 | b29677208e2ae0c476aeeaa899b786a2ac05798f | Python | py | |
# Modified by Microsoft Corporation.
# Licensed under the MIT license.
from convlab.modules.word_dst.multiwoz.trade.trade_utils import *
from convlab.modules.util.multiwoz.multiwoz_slot_trans import REF_SYS_DA, REF_USR_DA
from convlab.modules.dst.state_tracker import Tracker
from convlab.modules.dst.multiwoz.dst_util ... | convlab/modules/word_dst/multiwoz/trade/trade.py | MIT | 10,532 | 5e3db9c1b7bf28d18e3c531bfc3b5759e6d1a645 | Python | py | |
import torch
from torch.autograd import Function
from torch.autograd import Variable
class Linear(Function):
@staticmethod
def forward(ctx, input, weight, bias=None):
ctx.save_for_backward(input, weight, bias)
output = input.new(input.size(0), weight.size(0))
output.addmm_(0, 1, input... | torch/nn/_functions/linear.py | MIT | 3,117 | 708fb6b0d7859d873dec83dc1eaed8a6b797de0f | Python | py | |
import random
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple, cast
import torch
from allennlp.common.checks import check_dimensions_match
from allennlp.data.fields.text_field import TextFieldTensors
from allennlp.data.vocabulary import Vocabulary
from allennlp.models import Mod... | date/models/date.py | MIT | 10,130 | 3a77b5ec48207f79a65335eee243b901591dd4eb | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
"""
GAT: Graph Attention Network
Graph Attention Networks (Veličković et al., ICLR 2018)
https://arxiv.org/abs/1710.10903
"""
from layers.gat_layer import GATLayer
from layers.mlp_readout_layer import MLPReadout
c... | nets/molecules_graph_regression/gat_net.py | MIT | 2,435 | 24fed387e4abe78c3e442bac5bd0214faa459d23 | Python | py | |
from typing import Optional
from torch.utils.data import Dataset
import collections
from torch.utils.data._utils.collate import default_collate
import numpy as np
class DatasetOfSequences(Dataset):
"""
A Dataset which concatenates data into for a small window of frames
Takes a list of Datasets, each corr... | slam/dataset/sequence_dataset.py | MIT | 3,838 | 456398833ee1d883b16859ea45466ae8fbdde311 | Python | py | |
import numpy as np
import matplotlib.pyplot as plt
import torch
from torchvision import datasets, transforms, models
from torch import nn
from torch import optim
import torch.nn.functional as F
from PIL import Image
from fx_training import loading_data
from fx_training import build_and_train_model
impor... | train.py | MIT | 1,573 | 45c01996209e329c6fba315adf70e87e693c32f7 | Python | py | |
from __future__ import print_function
import sys
import os
import argparse
import numpy as np
from lib.dataset.voc import VOC_CLASSES
if '/data/software/opencv-3.4.0/prlib/python2.7/dist-packages' in sys.path:
sys.path.remove('/data/software/opencv-3.4.0/prlib/python2.7/dist-packages')
if '/data/software/opencv-... | inference_dir.py | MIT | 3,097 | a936f8f3cc9e0b319cc0dc87cc228bc686696d7e | Python | py | |
import torch
from catalyst.callbacks import BatchMetricCallback
from torch import nn
from dnn_cool.dsl import IFeaturesDict, IOut, ICondition, IFlowTaskResult
from dnn_cool.utils import any_value
class ReducedPerSample(nn.Module):
def __init__(self, loss, reduction):
super().__init__()
self.loss... | dnn_cool/losses.py | MIT | 15,503 | e9c5a9f0ed8d683487a727b37d15367d27f97bc4 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .box_head.box_head import build_roi_box_head
from .mask_head.mask_head import build_roi_mask_head
from .maskiou_head.maskiou_head import build_roi_maskiou_head
class CombinedROIHeads(torch.nn.ModuleDict):
"""
Combines a ... | maskrcnn_benchmark/modeling/roi_heads/roi_heads.py | MIT | 2,694 | 1e16c4f65676b125cdd91e7afd9959d2ccf1803d | Python | py | |
import math
import torch
from torch.optim.optimizer import Optimizer
from .types import Betas2, OptFloat, OptLossClosure, Params
__all__ = ('DiffGrad',)
class DiffGrad(Optimizer):
r"""Implements DiffGrad algorithm.
It has been proposed in `DiffGrad: An Optimization Method for
Convolutional Neural Netw... | torch_optimizer/diffgrad.py | Apache-2.0 | 5,635 | a989f8a286ce0a47f20a0e94d5bbf64206a9e2d9 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import warnings
from typing import Any, Union
import numpy as np
import torch
class TensorAccessor(object):
"""
A helper class to be used with the __getitem__ method. This can be used for
getting/setting the values for an attribute... | pytorch3d/renderer/utils.py | BSD-3-Clause | 11,776 | 3afd600353f1d0f41c591d93a64c07c23f045850 | Python | py | |
import torch
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
class Pix2PixModel(BaseModel):
def name(self):
return 'Pix2PixModel'
@staticmethod
def modify_commandline_options(parser, is_train=True):
# changing the default values to match the ... | models/pix2pix_model.py | BSD-3-Clause | 4,467 | 3a783e152499169b6bc450718fb768ec9d1e7f32 | Python | py | |
import torch
from torch import Tensor, cat
from torch.nn import functional as F
from models.building_blocks.covariates_feature_extractor import \
CovariatesFeatureExtractor
from models.building_blocks.mlp import MLP
from models.building_blocks.neural_network import NeuralNetworkEstimator
from models.building_block... | Structured/models/cat.py | MIT | 1,975 | 038858723904a693ca1ff4038268cf704d82c9e5 | Python | py | |
import argparse
import os
import sys
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import kornia as tgm
def load_data(root_path, sequence_name, frame_id):
# index paths
file_name = 'frame_%04d' % (frame_id)
image_file = os.pa... | examples/depth_regression/main.py | ECL-2.0,Apache-2.0 | 7,458 | 4b79e0fccfd35dbc5f89634350ff6820b9eaf3b6 | Python | py | |
""" script for generating samples from a trained model """
import torch as th
import argparse
import matplotlib.pyplot as plt
import os
from torch.backends import cudnn
# define the device for the training script
device = th.device("cuda" if th.cuda.is_available() else "cpu")
# th.manual_seed(3) # set manual seed ... | sourcecode/latent_space_interpolation.py | MIT | 4,751 | 1ed1c90b2f25fb4df3a36f9ff3d46d3dc1fbf693 | Python | py | |
# -*- coding: utf-8 -*-
import sys
import torch
from torch._C import _add_docstr, _linalg # type: ignore[attr-defined]
LinAlgError = torch._C._LinAlgError # type: ignore[attr-defined]
Tensor = torch.Tensor
common_notes = {
"experimental_warning": """This function is "experimental" and it may change in a futur... | torch/linalg/__init__.py | Intel | 94,089 | 3436b3fa4de744268faa9bae5025b4c0c797218b | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 6 16:45:16 2018
@author: landrieuloic
"""
from __future__ import division
from __future__ import print_function
from builtins import range
import random
import numpy as np
import os
import functools
import torch
import torchnet as tnt
import h5py
i... | learning/skitti_dataset.py | MIT | 7,313 | 3a7870e9a2e686337f7588a021caf0937f90e7d8 | Python | py | |
from __future__ import print_function
from __future__ import division
import argparse
import random
import torch
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable
import numpy as np
from warpctc_pytorch import CTCLoss
import os
import utils
imp... | train.py | MIT | 7,314 | ff1381bb24a27e9aa58fdf04083f6a38d382ad02 | Python | py | |
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader, random_split
from tqdm import tqdm
from reformer_pytorch import Reformer, ReformerLM
from transformers import BertTokenizer, PreTrainedTokenizer
from fairseq.optim.adafactor import Adafactor
... | pretraining/self-supervised.py | MIT | 15,486 | 3a788522a8416614056ceb421dcd1d60e6780500 | Python | py | |
import torch
from cleverhans.torch.utils import optimize_linear
def fgm(model_fn, x, eps, norm, loss_fn=None,
clip_min=None, clip_max=None, y=None, targeted=False):
x = x.clone().detach().to(torch.float).requires_grad_(True)
if y is None:
_, y = torch.max(model_fn(x), 1)
if loss_fn is Non... | adversarial/fgm.py | Apache-2.0 | 693 | 3a788ee59754bc7c6c8a87fc7c3ee73a7a4e23fd | Python | py | |
"""
Author: Parag Mali
Data reader for the GTDB dataset
Uses sliding windows to generate sub-images
"""
from .config import HOME
import os.path as osp
import sys
import torch
import torch.utils.data as data
import cv2
import numpy as np
from gtdb import box_utils
from gtdb import feature_extractor
import copy
import u... | data/gtdb_new.py | MIT | 9,726 | 4b38b1f0dc0323800cb95225238205bb55e5da98 | Python | py | |
from collections import OrderedDict
from torch import nn
class ConvBNReLU(nn.Sequential):
def __init__(self, in_planes, out_planes, kernel_size=3, stride=(1, 1), groups=1):
padding = (kernel_size - 1) // 2
super(ConvBNReLU, self).__init__(
nn.Conv2d(in_planes, out_planes, kernel_size, s... | recognition/src/models/modules/mobile_conv.py | MIT | 1,360 | 52aed2131ea29cfbebe713c95c693809c8371384 | Python | py | |
import numpy as np
import torch
import torch_geometric
import utils.metrics.metric as module_metric
from base.base_trainer import BaseTrainer
from datasets.scannetlabelgraph_dataloader import ScanNetGraphDataLoader
from models.singleconvmeshnet import SingleConvMeshNet
from utils import MetricTracker
from utils.metric... | trainers/segmentation_trainer.py | MIT | 12,701 | 3a78f6ec5d4823166943de8ebf9f4da2e60fc52b | Python | py | |
import argparse
import json
import logging
import os
import glob
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from tqdm import tqdm, trange
from attrdict import AttrDict
from transformers import (
BertConfig,
BertTokenizer,
AdamW,
get_linear... | run_goemotions.py | Apache-2.0 | 11,410 | fceec334a88de9e842d86400b54cb23cc045c188 | Python | py | |
import glob
import numpy as np
import os.path as osp
from PIL import Image
import random
import struct
from torch.utils.data import Dataset
import os
import scipy.ndimage as ndimage
import h5py
import cv2
import xml.etree.ElementTree as et
from skimage.transform import resize
import scipy.ndimage as ndimage
class Batc... | PointCloud/dataLoader.py | MIT | 20,232 | d72a98cc54841908b4ea29d4be0e8c73ae57c96b | Python | py | |
# --------------------------------------------------------
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import sys
sys.path.insert(0, "HCG")
import argparse
import datetime
import numpy as np
import torch
from core.bc import BC
from core.ddpg impo... | robot_clipseg.py | MIT | 16,802 | 3a7919fb2bf018f65db478a52562f1ca2a401400 | Python | py | |
"""
Created on Feb 26, 2018
@author: Siyuan Qi
Description of the file.
"""
import os
import random
import numpy as np
import scipy.misc
import torch
import torchvision
import cv2
import scipy.io as sio
import matplotlib.pyplot as plt
import metadata
import config
# TODO: Check if needed modification
def parse_c... | gpnn-master/src/python/datasets/HICO/roi_feature_model.py | MIT | 8,415 | 5e37652c32efe7b619d0f97dfb93e31b60e1cafb | Python | py | |
# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from torch import nn
from cvpods.structures import ImageList
from ..postprocessing import detector_postprocess, sem_seg_postprocess
__all__ = ["PanopticFPN"]
def build_roi_heads(cfg, input_shape):
pass
... | cvpods/modeling/meta_arch/panoptic_fpn.py | Apache-2.0 | 8,463 | d7ba58f43dcdc73c7ea048d2f3083ac8c24ef56f | Python | py | |
import torch
import torch.nn as nn
import torchvision
from models import resnet
from lib.nn import SynchronizedBatchNorm2d
import pandas as pd
import numpy as np
class SegmentationModuleBase(nn.Module):
def __init__(self):
super(SegmentationModuleBase, self).__init__()
def pixel_acc(self, pred, labe... | models/object_net.py | MIT | 11,232 | a971d06a91580393a1bce9b165211ee00d4145d3 | Python | py | |
# Copyright (c) 2022, Kwanhyung Lee. All rights reserved.
#
# Licensed under the MIT License;
# you may not use this file except in compliance with the License.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTI... | builder/models/detector_models/cnn2d_lstm_v8_6.py | MIT | 7,759 | 3a7944d4abb1945abcbc31977647229327acc31d | Python | py | |
import argparse
import os
import random
import shutil
import warnings
import numpy as np
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import to... | image_classification_experiments/train_base_init_network_from_scratch.py | MIT | 19,556 | 805296ce28673119c038b8726c7f11526bc71cf1 | Python | py | |
import torch
from allennlp.common.registrable import Registrable
class MatrixAttention(torch.nn.Module, Registrable):
"""
``MatrixAttention`` takes two matrices as input and returns a matrix of attentions.
We compute the similarity between each row in each matrix and return unnormalized similarity
s... | allennlp/modules/matrix_attention/matrix_attention.py | MIT | 885 | 24be3b11fe49d566368a0ea838adf79c38356390 | Python | py | |
# coding: utf-8
from __future__ import print_function
from __future__ import division
import torch
from torch.autograd import Variable
import numpy as np
import pickle
from collections import deque, Counter
import pdb
class RnnParameterData(object):
def __init__(self, loc_emb_size=500, uid_emb_size=40, voc_emb_s... | train_simple.py | MIT | 12,672 | 3a79711bdd49dc059a2cdded7a7b0faf886e8c25 | Python | py | |
import torch.nn as nn
import torch.nn.functional as F
from models.VAGCN_utils import PointNetSetAbstraction, knn
import torch
def sphg(pos, r, B, flow='source_to_target', max_num_neighbors=48, fpsi=None, resetFpsi=False,
random_replace=True):
# Make sure "batch" is ascending
assert flow in ['source_t... | models/VAGCN_part_seg.py | MIT | 11,655 | 5f667e835d1f37564ce0e801f2d4a0d23dece9e9 | Python | py | |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 ... | tests/test_modeling_xlnet.py | Apache-2.0 | 28,389 | 12b3e13f3f9e87aa643295c9caf5aef8b85173f2 | Python | py | |
# Copyright (c) 2020, Fabio Muratore, Honda Research Institute Europe GmbH, and
# Technical University of Darmstadt.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source ... | Pyrado/pyrado/exploration/stochastic_params.py | DOC,Zlib,BSD-3-Clause | 9,405 | 3a798b15994da076eee2f033832f290e47aa8649 | Python | py | |
"""
Copyright 2020 The Secure, Reliable, and Intelligent Systems Lab, ETH Zurich
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 appl... | Finance/common/utils.py | Apache-2.0 | 6,011 | 3a798ebecce81f66bb6373b17e9dfe04716b041e | Python | py | |
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | pytorch_lightning/utilities/__init__.py | Apache-2.0 | 2,311 | 5e6b99f1cea0caea05bfefa47682458229a1db3e | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from typing import cast, List
from unittest.mock import MagicMock
impor... | torchrec/distributed/planner/tests/test_proposers.py | BSD-3-Clause | 9,176 | 3a79be3660cd0af2d776f6113749dc3872710904 | Python | py | |
import torch
import torch.nn.functional as F
from torch.optim import Adam
from utils.misc import soft_update, hard_update, enable_gradients, disable_gradients
from utils.agents import AttentionAgent
from utils.critics import AttentionCritic
from utils.attention_medium import AttentionMedium
from utils.error_detector im... | algorithms/MAFTRL.py | MIT | 15,506 | 4237d22ca15473faf0716f88589012b77b56741e | Python | py | |
import torch
from BGNN import BGNNPredictor
import pandas as pd
import numpy as np
import json
import os
from dgl.data.utils import load_graphs
from dgl.nn.pytorch import GATConv as GATConvDGL, GraphConv, ChebConv as ChebConvDGL, \
AGNNConv as AGNNConvDGL, APPNPConv
from torch.nn import Dropout, ELU, Sequential, Li... | examples/pytorch/bgnn/run.py | Apache-2.0 | 6,834 | 3a79fee5113548ebf76460453a79c126501fa588 | Python | py | |
import warnings
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
import torch
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmdet.core import get_classes
from mmdet.datasets.pipelines import Compose
from mmdet.models import b... | mmdet/apis/inference.py | BSD-2-Clause | 10,262 | 3a7a07dc533d1936e2acae131b6ef3cd3bafd845 | Python | py | |
import argparse
import datetime
import os
import time
import sys
sys.path.append("/home1/machen/meta_perturbations_black_box_attack")
import glog as log
import numpy as np
import torch
import torch.nn as nn
from adversarial_defense.prototype_conformity_loss.contrastive_proximity import Con_Proximity
from adversarial_d... | adversarial_defense/prototype_conformity_loss/pcl_training_adversarial_pgd.py | Apache-2.0 | 14,421 | e9d3f5ed06fce0f52294606a861c30f2d06e4d56 | Python | py | |
"""
eureka_train.py
Zhiang Chen, Feb 2020
train mask rcnn with eureka data
"""
import transforms as T
from engine import train_one_epoch, evaluate
import utils
import torch
from rock_c3 import Dataset
from model import get_rock_model_instance_segmentation
import numpy as np
import time
class ToTensor(object):
def... | rock_c3_train2.py | Apache-2.0 | 4,495 | 3a7a4322e341bb95fceeb660e3e9fc345165c7f6 | Python | py | |
import numpy as np
import jax.numpy as jnp
from jax import config
import tensorflow as tf
import torch
import pytest
import tensornetwork
from tensornetwork import backends
config.update("jax_enable_x64", True)
np_real = [np.float32, np.float64]
np_complex = [np.complex64, np.complex128]
np_float_dtypes = np_real + np... | tensornetwork/tests/testing_utils.py | Apache-2.0 | 3,192 | 52b6f6cc3e926b608855354abaffcc924d7f61e1 | Python | py | |
from collections import namedtuple
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd.function import InplaceFunction, Function
QParams = namedtuple('QParams', ['range', 'zero_point', 'num_bits'])
_DEFAULT_FLATTEN = (1, -1)
_DEFAULT_FLATTEN_GRAD = (0, -1)
... | quantize.py | MIT | 16,209 | dfaa71f9bae8b6f6bb94b20b65d9856fd9f67b60 | 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/014/ver07/siim-014-train-eff-4class-step3-ver05.py | CC-BY-4.0 | 33,338 | 7c7cb9da4c401a1c1fd29c4589e5e259aa68b983 | Python | py | |
import torch
import torch.autograd as autograd
import torch.nn as nn
import pdb
import torch.nn.functional as F
import numpy as np
START_TAG = -2
STOP_TAG = -1
# Compute log sum exp in a numerically stable way for the forward algorithm
def log_sum_exp(vec, m_size):
"""
calculate log of exp sum
... | model/crf.py | Apache-2.0 | 14,106 | 3a7a6f9682c286b1f282fcec379a6e225c3a9cc2 | Python | py | |
from argparse import ArgumentParser
from typing import Tuple
import pytorch_lightning as pl
from torchvision import transforms
from transformers import T5Tokenizer
from .dataset import DocVQADataset
class DocVQADataModule(pl.LightningDataModule):
def __init__(self, hparams) -> None:
super().__init__()
... | src/data/docvqa/data_module.py | MIT | 3,561 | ff37a5932fd7dc46146d1b676a6bf776b80df8c4 | Python | py | |
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
from . import (_utils,
_io,
_logger,
... | orttraining/orttraining/python/training/ortmodule/_training_manager.py | MIT | 21,253 | 29ffb0dc7091fc123f818dc495787c2bcfd10d7d | Python | py | |
# camera-ready
from datasets import ToyDatasetEvalKL # (this needs to be imported before torch, because cv2 needs to be imported before torch for some reason)
from model import ToyNet
import torch
import torch.utils.data
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy a... | 1dregression/2/mlis_eval.py | MIT | 3,332 | e922ea8a1dc7ff0ad81bc604462209c71a35fd3e | Python | py | |
import json
import logging
import os
import shutil
import torch
class Params():
def __init__(self, json_path):
"""
### Función `__init__`
Almacena los parámetros contenidos en json_path sobre la instancia self.
#### Parámetros:
* `self`: instancia a inicializar.
... | code/ner/utils.py | CC-BY-4.0 | 5,611 | 3a7aacee366a32cd2aee71a9bace3c2e1b416307 | Python | py | |
import os
import sys
import json
import numpy as np
from PIL import Image
from datetime import datetime
from env.thor_env import ThorEnv
from eval import Eval
import torch
import constants
import torch.nn.functional as F
from torchvision.utils import save_image
from torchvision.transforms.functional import to_tensor
f... | alfred/models/eval_moca/eval_subgoals_seq.py | MIT | 15,358 | 34009656bf3093981d6cfaacca21f408a9482380 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class InitialConv(nn.Module):
def __init__(self, out_channels=16):
super().__init__()
self.out_channels = out_channels
self.conv = nn.Conv3d(in_channels=1, out_channels=out_channels, kernel_size=5, stride=1, padding=2)
... | models/vnet3d.py | MIT | 7,789 | 3a7b0398ea9f5d363f60d541c8b3295c85e2e4f8 | Python | py | |
from __future__ import annotations
import logging
from typing import Any, cast, Dict, List, Optional
import torch
from lightkit import ConfigurableBaseEstimator
from lightkit.data import collate_tensor, DataLoader, dataset_from_tensors, TensorLike
from lightkit.estimator import PredictorMixin, TransformerMixin
from .li... | pycave/clustering/kmeans/estimator.py | MIT | 9,849 | 5f5ece876bfcbb013d42184acc0b2303b770e134 | Python | py | |
import zipfile
from torch.hub import download_url_to_file
import argparse
import os
# If the link is broken you can download the MS COCO 2014 dataset manually from http://cocodataset.org/#download
MS_COCO_2014_TRAIN_DATASET_PATH = r'http://images.cocodataset.org/zips/train2014.zip' # ~13 GB after unzipping
PRETRAINE... | utils/resource_downloader.py | MIT | 2,029 | 3a7b3312fed3ce46ca9eb0a571c62d008f88ec69 | Python | py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2020 Patrick Lumban Tobing (Nagoya University)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import division
from __future__ import print_function
import argparse
from dateutil.relativedelta import relativedelta
from distutils.ut... | src/bin/train_nstages-sparse-wavernn_dualgru_compact_lpc_mband_16bit.py | Apache-2.0 | 76,198 | 3a7b468b00c0a90dfc58d4161c510dd4ff6f8960 | Python | py | |
#!/usr/bin/python3
import argparse
import os
import sys
import torch
# Aimet-related imports
from aimet_torch.model_validator.model_validator import ModelValidator
# TDDFA-related imports
import models
from utils.tddfa_util import load_model
def main():
parser = argparse.ArgumentParser(description='3DDFA Compr... | check_model.py | MIT | 855 | 3a7b49cd360e5765ea3adc01f5ea4f047340530e | Python | py | |
import random
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
class LSTM(nn.Module):
""" LSTM-RNN (text) network implementation """
def __init__(self,
n_classes=10,
vocabulary_size=26,
hidden_dim=2... | models/lstm.py | MIT | 2,820 | d7515d9f87e32fb4ac14e068724834534cd0ea62 | Python | py | |
import cvxpy as cp
import math
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
'''
Global Constants:
TASK_NAME: Name of the verification task (deprecated)
PATH: The path of the model file. (Initialized in... | verify/mnist.py | Apache-2.0 | 14,832 | 7cfb108a6570abbe0c923e6564963deaf8c48a7e | Python | py |
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