content stringlengths 200 198k | repo stringlengths 4 186 | path stringclasses 1
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import torch
import argparse
import datetime
from load_model import load_model
from networks import compressor, decompressor
from JPEGCompressor import jpeg_analysis
from QuantizedCompressor import quantization_analysis
from calculate_metrics import calculate_metrics
from utils import plot_image, save_image, metrics
... | src/main.py | MIT | 1,330 | e60ba06be0eb0dc2e0c0efc513c6b0ba5aa6f285 | Python | py | |
from BertFineTuning.utils import *
from BertFineTuning.model_config import*
import os
import sys
import numpy as np
import pandas as pd
from collections import OrderedDict
from pycm import *
import torch
if(torch.cuda.is_available()):
torch.cuda.current_device()
from torch import nn
from torch.utils.data import D... | BertFineTuning/model.py | BSD-2-Clause | 13,811 | fedb20a140f46949ba64a749a73857c2d0b72719 | Python | py | |
"""Tests for collecting layer histogram."""
from neural_compressor.utils.collect_layer_histogram import LayerHistogramCollector
from collections import OrderedDict
from neural_compressor.utils import logger
import numpy as np
import torch
import torch.nn as nn
import unittest
class InvertedResidual(nn.Module):
def... | test/test_layer_histogram.py | Apache-2.0 | 4,632 | fedb26f73dac358293482368ff624ab14eb2245d | Python | py | |
from collections import Counter
import os
import torch
from typing import List, Optional
import pandas as pd
from sklearn.model_selection import train_test_split
from shared.utils import save_dict_to_json
def create_train_val_split(
results_dir: str,
df: pd.DataFrame,
train_ratio: float,
random_state... | src/preprocessing/data_split.py | MIT | 3,186 | d779cc826d6f6e4b97bb95b2d7c14ed1720838dc | Python | py | |
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, so... | monai/networks/nets/netadapter.py | Apache-2.0 | 4,591 | 425c1d5820893273d37244fad5fa1ce97a131983 | Python | py | |
# ~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~
# MIT License
#
# Copyright (c) 2022 Nathan Juraj Michlo
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Softwar... | disent/util/lightning/callbacks/_callback_log_metrics.py | MIT | 6,203 | b2fafead7df3bf8615c4a69d33d3ee6c2b16bef2 | Python | py | |
import argparse
import os
import torch
import numpy as np
from model import wavefield_reconstruction
np.random.seed(19)
torch.manual_seed(19)
parser = argparse.ArgumentParser(description='')
parser.add_argument('--experiment', dest='experiment', default='interpolation', help='path of the dataset')
parser.add_argument(... | src/main.py | MIT | 3,042 | 4b60232cd16233370ae595755b07a39adc31768c | Python | py | |
import argparse
import collections
import datetime
import json
import os
import _jsonnet
import attr
import torch
# noinspection PyUnresolvedReferences
from seq2struct import ast_util
# noinspection PyUnresolvedReferences
from seq2struct import datasets
# noinspection PyUnresolvedReferences
from seq2struct import mod... | mrat-sql-gap/seq2struct/commands/train.py | Apache-2.0 | 11,269 | fedb3b35a63dcf15b647e503ae91a0c20a4157d4 | Python | py | |
"""
PyTorch utilities.
| Copyright 2017-2021, Voxel51, Inc.
| `voxel51.com <https://voxel51.com/>`_
|
"""
import logging
import itertools
import multiprocessing
import sys
import cv2
import numpy as np
from PIL import Image
import eta.core.image as etai
import eta.core.learning as etal
import eta.core.utils as etau
... | fiftyone/utils/torch.py | Apache-2.0 | 40,480 | 1e395b8a40f9b9974a9a66dc28ebf95cf185dc9e | Python | py | |
# code is based on https://github.com/katerakelly/pytorch-maml
import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch
from torch.utils.data import DataLoader,Dataset
import random
import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
... | miniimagenet/task_generator.py | MIT | 5,425 | fedb510a047ed656ae2c743faccab69deee1e5a6 | Python | py | |
import sys
from sklearn import preprocessing
sys.path.insert(0, '..')
from qptl.qpthlocal.qp import QPFunction
from qptl.qpthlocal.qp import QPSolvers
from qptl.qpthlocal.qp import make_gurobi_model
from SPO.sgd_learner import *
from torch import optim
import numpy as np
import pandas as pd
from sklearn.metrics i... | qptl/melding_knapsack.py | MIT | 10,599 | fedb726098d84eb27c2a8f16e2e187e20b1a2174 | Python | py | |
import pytest
import torch
from espnet2.main_funcs.average_nbest_models import average_nbest_models
from espnet2.train.reporter import Reporter
@pytest.fixture
def reporter():
_reporter = Reporter()
_reporter.set_epoch(1)
with _reporter.observe("valid") as sub:
sub.register({"acc": 0.4})
... | test/espnet2/main_funcs/test_average_nbest_models.py | Apache-2.0 | 1,927 | ff7fe506792f5ae1e82184b5ea72570d59ca3786 | Python | py | |
import torch
import numpy as np
from torch.utils.data import DataLoader
from argus import load_model
class StackPredictor:
def __init__(self, model_path,
batch_size, device='cuda'):
self.model = load_model(model_path, device=device)
self.batch_size = batch_size
def predict(s... | src/stacking/predictor.py | MIT | 781 | e64de69823003d5ce211eb76bf87309d189eb609 | Python | py | |
import torch.nn as nn
from pts.flowplusplus.models.flowplusplus import log_dist as logistic
from pts.flowplusplus.models.flowplusplus.nn import NN
class Coupling(nn.Module):
"""Mixture-of-Logistics Coupling layer in Flow++
Args:
in_channels (int): Number of channels in the input.
mid_channel... | pts/flowplusplus/models/flowplusplus/coupling.py | Apache-2.0,MIT | 1,900 | fedb7dc6dff82a317f6bf5bac129da8a2b763d77 | Python | py | |
import re
import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
import torch.nn.utils.spectral_norm as spectral_norm
from synchronised_batch_norm.batchnorm import SynchronizedBatchNorm2d
class BaseNetwork(nn.Module):
def __init__(self):
super(BaseNetwork, self).__ini... | architectures/SPADE.py | MIT | 7,357 | b2ea6529ee96b937f3e0c4cd44c629332bff9ef1 | Python | py | |
""" Pytorch Inception-V4 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENE... | timm/models/inception_v4.py | Apache-2.0 | 11,214 | 5f4e208f2c53e786197136ab31b18d380f73cbf1 | Python | py | |
import argparse
import os
from util import util
import torch
import models
import data
class BaseOptions():
"""This class defines options used during both training and test time.
It also implements several helper functions such as parsing, printing, and saving the options.
It also gathers additional opti... | options/base_options.py | BSD-3-Clause | 8,221 | 5f7973d44ccaa52f28a1a43355e2182ce679a85a | Python | py | |
import logging
import os
import matplotlib.pyplot as plt
import scipy
import torch
from scipy.stats import circmean
from flows.models import NormalizingFlowModel
from flows.prior_dist import *
from flows.flows import *
from slam.FactorGraphSolver import *
import torch.optim as optim
from slam.RunBatch im... | src/slam/NFiSAM.py | MIT | 29,220 | 5f8bccecb0b929abc51ad180c23c3b5464cf94d8 | Python | py | |
import time
import pytest
import torch.distributed as dist
from ignite.engine import Engine, Events
from ignite.metrics import Frequency
def test_nondistributed_average():
artificial_time = 1 # seconds
num_tokens = 100
average_upper_bound = num_tokens / artificial_time
average_lower_bound = average... | tests/ignite/metrics/test_frequency.py | BSD-3-Clause | 2,332 | fedb943a6b4f5e8fdab518b2413f0bc64edf075c | Python | py | |
#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple 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... | egs/librispeech/ASR/local/compute_fbank_librispeech.py | Apache-2.0 | 3,346 | b26034eb2017efe3893258a2d07e09acb70d60eb | Python | py | |
import torch
import torchvision
import torch.nn as nn
import numpy as np
import torchvision.transforms as transforms
# ================================================================== #
# Table of Contents #
# ========================================================... | tutorials/01-basics/pytorch_basics/main.py | MIT | 6,764 | fedba12087dfb334c3aa23b11697028068063485 | Python | py | |
import flair
import torch
import argparse
parser = argparse.ArgumentParser(description='Train flair model')
parser.add_argument('--input', '-i',
help='Name of the input folder containing train, dev and test files')
parser.add_argument('--output', '-o',
help='Name of the ... | ner/flair_train.py | Apache-2.0 | 2,220 | 10b62d2f0341bd1c9563c7434115fc70a18861b3 | Python | py | |
# Copyright (c) 2017-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | test_python/layers/test_avgpool.py | Apache-2.0 | 1,328 | fda711097816b60f97869a11f9b5703bd80f15bd | Python | py | |
import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.categorical import Categorical
from src.util import init_weights, init_gate
from src.module import VGGExtractor, CNN, ResCNN, RNNLayer, ScaleDotAttention, LocationAwareAttention
from src.audio imp... | src/asr.py | MIT | 16,239 | fedbc95e37b90d9882754cfaafb09ee812bad5d6 | Python | py | |
import os.path as osp
from mmcv.runner import Hook
from torch.utils.data import DataLoader
class EvalHook(Hook):
"""Evaluation hook.
Note that if new arguments are added for EvalHook, tools/test.py may be
effected.
Attributes:
dataloader (DataLoader): A PyTorch dataloader.
interval ... | mmdet/core/evaluation/eval_hooks.py | Apache-2.0 | 2,754 | fedbd96455da8034bfcc1d703ab5acda10ce66fb | Python | py | |
import torch
import torchvision
from torchvision import transforms as transforms
def load_CIFAR10(train_batch_size, test_batch_size):
train_transform = transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor(),transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
test_t... | datasets.py | Apache-2.0 | 1,775 | d7e77f0b43d40bb0a329cd1c68d6c9b9d56822e4 | Python | py | |
# -*- coding:utf-8 -*-
# Author: hankcs
# Date: 2021-01-06 16:12
from typing import List
from elit.common.dataset import SortingSamplerBuilder
from elit.common.transform import NormalizeToken
from elit.components.mtl.loss_balancer import MovingAverageBalancer
from elit.components.mtl.multi_task_learning import MultiTa... | stem_cell_hypothesis/en_bert_large/joint/dep_srl.py | Apache-2.0 | 4,739 | 5e1a68d0ae6544926731d65d4eb03f94316325c9 | Python | py | |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/data_loader.ipynb (unless otherwise specified).
__all__ = ['oversample', 'TextMelDataset', 'TextMelCollate', 'TextAudioSpeakerLoader', 'TextAudioSpeakerCollate',
'DistributedBucketSampler']
# Cell
import os
import random
import re
from pathlib import Path
fro... | uberduck_ml_dev/data_loader.py | Apache-2.0 | 19,331 | fedbf57b696be0107587ccad291f510caff7a869 | Python | py | |
"""
Instance-Segmentation-Projects
Nick Kaparinos
2021
"""
import math
import sys
import torchvision.models.detection.mask_rcnn
import wandb
from coco_eval import CocoEvaluator
from coco_utils import get_coco_api_from_dataset
from utilities import *
def train_one_epoch(model, optimizer, data_loader, device, epoch, p... | engine.py | MIT | 4,112 | fd933cb3a4b09ec5fb5dde02009c2a25b4d14c47 | Python | py | |
import numpy as np
import os
import torch
import torch.nn as nn
from models.helper import build_generator
PGGAN_Inter_Output_Layer_256 = [-1, 17, 14, 11, 8, 5, 2]
PGGAN_Inter_Output_Layer_1024 = [-1, 23, 20, 17, 14, 11, 8, 5, 2]
def standard_z_sample(size, depth, device=None):
'''
Generate a standard set o... | derivable_models/gan_utils.py | MIT | 1,590 | d79bb33531558a7f5b179887da5d95e0faf89766 | Python | py | |
"""
Compute the style and content loss using a VGG-16 model trained on ImageNet.
Uses perceptual loss to compute the style and content loss.
"""
import torch
import torchvision
from collections import namedtuple
LossOutput = namedtuple(
"LossOutput", ["relu1_2", "relu2_2", "relu3_3", "relu4_3"])
def gram_matrix... | src/perceptual_loss_net.py | MIT | 3,974 | 4b42fa2118f548456eff9ad799b75b043b4c4f17 | Python | py | |
import torch
# import hyperparams as hyp
import numpy as np
import utils.utils_geom as utils_geom
import utils.utils_samp as utils_samp
# import utils.utils_improc as utils_improc
import utils.utils_basic as utils_basic
import torch.nn.functional as F
from utils.utils_basic import *
class Vox_util(object):
def __i... | script/utils/utils_vox.py | MIT | 40,769 | fedc1ae4ea95ff4483052eaf81cc6b1bb37bf56f | Python | py | |
from colossalai.amp import AMP_TYPE
from model_zoo.gpt.gpt import GPTLMLoss, gpt2_small
from torch.optim import Adam
#from deepspeed.ops.adam import DeepSpeedCPUAdam
BATCH_SIZE = 1
NUM_EPOCHS = 60
SEQ_LEN = 1024
zero = dict(
level=3,
dynamic_loss_scale=True,
overlap_comm=True,
clip_grad=1.0,
offl... | language/gpt/gpt2_configs/gpt2_nvme.py | Apache-2.0 | 1,018 | e61e3fd56de58f0b1b1cd262d06512380717c4ab | Python | py | |
from PIL import Image
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import torch
import numpy as np
import cv2
label_names = {
0: "NORMAL",
1: "CNV",
2: "DME",
3: "DRUSEN",
}
CLS2IDX = label_names
# rule 5 from paper
def avg_heads(cam, grad):
cam = cam.reshape(-1, ca... | facebook_dino/mm_dino.py | Apache-2.0 | 4,726 | 34254408e7e49cbcf98c7527f8d99aa2ec191928 | Python | py | |
import torch
import math
import os
import sys
import logging
from graphgym.config import cfg
from graphgym.utils.io import dict_to_json, dict_to_tb
import pdb
from sklearn.metrics import *
from tensorboardX import SummaryWriter
def setup_printing():
logging.root.handlers = []
logging_cfg = {'level': logging.... | graphgym/logger.py | MIT | 6,402 | fedc56d1487618267ad1b580afc43463a685babb | Python | py | |
from networks import common
import torch
import torch.nn as nn
import math
import numpy as np
def make_model(args, parent=False):
return RCAN(args)
## Channel Attention (CA) Layer
class CALayer(nn.Module):
def __init__(self, channel, reduction=16):
super(CALayer, self).__init__()
# global avera... | networks/rcan_v3.py | MIT | 5,931 | 700b512d28655331bc4a1eb695e4cba7889e02d6 | Python | py | |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | nemo/collections/nlp/modules/common/megatron/megatron_encoders.py | Apache-2.0 | 4,350 | fedc5a6651ca346a8f3884bf3e7ed34e8ec898a0 | Python | py | |
import copy
import logging
import torch
import numpy as np
import wandb
from fedml_api.standalone.fednova.client import Client
class FedNovaTrainer(object):
def __init__(self, dataset, model, device, args):
self.device = device
self.args = args
[train_data_num, test_data_nu... | fedml_api/standalone/fednova/fednova_trainer.py | Apache-2.0 | 11,066 | 4578d794fb8fa2af257a512faad883a1ff85bc18 | Python | py | |
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/hshustc/CVPR19_Incremental_Learning
## Max Planck Institute for Informatics
## yaoyao.liu@mpi-inf.mpg.de
## Copyright (c) 2021
##
## This source code is licensed under the MIT-style... | classification/models/modified_resnet.py | MIT | 4,339 | fedc6eb22a66bae9798fdb590f8309b03d21aaa7 | Python | py | |
import torch
import matplotlib.pyplot as plt
import numpy as np
def saliency(img, model, save_path):
# We don't need gradients wrt weights for a trained model
for param in model.parameters():
param.requires_grad = False
model.eval()
input = torch.from_numpy(img).float().unsqueeze(0)
# Ge... | rlpyt/utils/saliency.py | MIT | 1,064 | fedc7dd7ef63afe3a402a3f621ad1a82ffa2f7e9 | Python | py | |
# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
# Copyright 2019 Shigeki Karita
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software... | applications/pytorch/conformer/src/layers/positionwise_feed_forward.py | MIT | 1,373 | fedc8309414cda583757b8a46d567ca53ffd5157 | Python | py | |
import torch
import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, scale_factor, mode="bilinear"):
super().__init__()
self.scale_factor = scale_factor
self.mode = mode
def forward(self, input):
return nn.functional.interpolate(input, scale_factor=self.scale_facto... | example_specs/models/unet2d_nuclei_broad/unet2d.py | MIT | 2,398 | fedc9be1055ac76da560a87d77c72328e339ab70 | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 11 10:28:06 2018
@author: herminarto.nugroho
"""
import pandas as pd
import numpy as np
from PIL import Image
import torch
from torchvision import transforms
from torch.utils.data.dataset import Dataset # For custom datasets
from torch.utils.data.sampler import SubsetR... | src/custom_datasets.py | MIT | 3,394 | fedca1dd8ece7252ca6c02bfeaff1eea9129094c | Python | py | |
"""
DRN for ImageNet-1K, implemented in PyTorch.
Original paper: 'Dilated Residual Networks,' https://arxiv.org/abs/1705.09914.
"""
__all__ = ['DRN', 'drnc26', 'drnc42', 'drnc58', 'drnd22', 'drnd38', 'drnd54', 'drnd105']
import os
import torch.nn as nn
import torch.nn.init as init
class DRNConv(nn.Module):
... | pytorch/pytorchcv/models/drn.py | MIT | 18,826 | fedca2c078c7106ab73f8fa22cf520ace6e86b39 | Python | py | |
import torch
from torch.nn import functional as F
__all__ = ["RoundSTE", "logit", "sum_except_batch", "to_discrete"]
class RoundSTE(torch.autograd.Function):
@staticmethod
def forward(ctx, inputs):
return inputs.round()
@staticmethod
def backward(ctx, grad_output):
"""Straight-throug... | dlfairness/original_code/nifr/nifr/utils/torch_ops.py | BSD-3-Clause | 804 | 5e66e0562c0b4887a59a1ba929a9ed42d43586f5 | Python | py | |
import torch
import torch.nn as nn
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = nn.Linear(hidden_size, hidden_size)
self.l3 = nn.Linear(hidden_size, n... | model.py | MIT | 602 | a95dfe2954338cc920e533ae54d7f4357a8fa1ea | Python | py | |
import yaml
import argparse
import numpy as np
import os
import sys
import time
import json
import pickle
import torch as th
import torch.nn as nn
from kaldi.util.table import MatrixWriter
from kaldi.util.io import read_matrix
from data import SpeechDataset, SeqDataloader
from models import LSTMStack, NnetAM
def ma... | decode.py | MIT | 5,218 | a98a8011ec768d378ac466f2d29a9c3d6f27ba70 | 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/connectors/test_signal_connector.py | Apache-2.0 | 1,907 | 1ec62f3d3082f3615fc3cea0c247c160a235099b | Python | py | |
import argparse
import os
import torch
import tempfile
from tqdm import tqdm
from mlprogram import logging
from mlprogram.entrypoint.configs import load_config, parse_config
from app import types
logger = logging.Logger(__name__)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("-... | tools/ideal_accuracy.py | MIT | 1,044 | 3474c13685edae8fba68c563b5cec28c3352e595 | Python | py | |
# coding=utf-8
# Copyright 2022 The OpenBMB 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 a... | model_center/model/config/vit_config.py | Apache-2.0 | 2,034 | 24715476ca402be15c2836e4c7e5e83b3cb02d96 | Python | py | |
import os
import sys
from torchtext import data, datasets
import sentencepiece as spm
import pandas as pd
import MeCab
def English_tokenizer():
df = pd.read_csv("test_sample.csv")
ENG_data = df['English']
f = open("eng.txt", "w", encoding = 'utf-8')
for row in ENG_data[:100000]:
f.wr... | tokenizer/English_tokenizer.py | Apache-2.0 | 725 | 6531670ebb1ba0a646eeeecf3d3df7fca8e4a1c3 | Python | py | |
import torch
import torch.nn as nn
class Conv6(nn.Module):
def __init__(self, in_ch, imgsz, num_classes=10):
super(Conv6, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 64, kernel_size=(3, 3), stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=(3, 3), stride=1, padding=1)
self.conv3 = nn... | models/bare/conv6.py | MIT | 1,263 | a94c5e885be267ec7fc943fb799b4b7e7444b6c7 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and 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 Any, Callable, Dict, List, Optional, Tuple
import torch
from ax.core.search_space import SearchSp... | ax/models/torch/rembo.py | MIT | 10,109 | e607aa90c37de00eebef257147b8cd7a99a64192 | Python | py | |
import json
import math
import os
import pickle
import random
from collections import defaultdict
import dgl
import numpy as np
import torch
from torch.utils.data import IterableDataset, DataLoader
from transformers import *
from models.GAIN import Bert
from utils import get_cuda
IGNORE_INDEX = -100
class DGLREDat... | code/.ipynb_checkpoints/data-checkpoint.py | MIT | 30,824 | fedd6e7046d576fd6df22124d28fd69b8e033b35 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_geometric
from torch_geometric.data import Data
from torch_geometric.nn import TransformerConv
from constant import *
from util_module import make_full_graph, get_seqsep
class UniMPBlock(nn.Module):
'''https://arxiv.org/pdf/2009.0350... | hallucination/models/rf_Nov05_2021/InitStrGenerator.py | MIT | 4,104 | fdd858e2e7d9cc0c0d9de8c8e7df4c376b8ceba7 | Python | py | |
###############################################################################
# Author: Wasi Ahmad
# Project: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/10/wwwfp0192-mitra.pdf
# Date Created: 7/23/2017
#
# File Description: This script is the entry point of the entire pipeline.
###########... | ranking_baselines/DUET/main.py | MIT | 3,130 | dfcfe3ffb1ded9df328c36b66e4bcb630fd74539 | Python | py | |
import os
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from scipy.misc import imread, imresize, imsave
class CustomDataSet(Dataset):
def __init__(self, input_dir, input_height, input_width):
self.input_dir = input_dir
self.input_size = [input_height, input_widt... | dataset.py | MIT | 2,323 | 342938eb7f643ba5ecbe803afee51322599d7fd8 | Python | py | |
from __future__ import absolute_import, division, print_function
from easydict import EasyDict as edict
import numpy as np
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import *
from data_utils import AmazonDataset
class KnowledgeEmbedding(nn.Module):
def __init__(s... | models/PGPR/transe_model.py | MIT | 22,485 | 10fa04db046137d09d91d869d03c97af712708f1 | Python | py | |
from dataclasses import dataclass, field
from typing import List, Optional
import torch
from omegaconf import DictConfig
from torch.optim.lr_scheduler import _LRScheduler
from ..dataclass.configurations import LearningRateSchedulerConfigs
from . import register_scheduler
class ExponentialLR(_LRScheduler):
"""Ex... | fulmo/schedulers/expontial.py | MIT | 2,858 | 1e7828e1d9c9c0febf8e57e381ca166c4b6ca8ec | Python | py | |
from __future__ import print_function
import argparse
import torch
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
from pointnet.dataset import ShapeNetDataset
from pointnet.model import PointNetCls
import torch.nn.functional as F
#showpoints(np.random.randn(2500,3), c1 = np.rando... | utils/show_cls.py | MIT | 1,485 | 6b23e0b1e6357dca0f36fb8e86db8090568da78a | Python | py | |
from starlette.applications import Starlette
from starlette.templating import Jinja2Templates
import uvicorn
import aiohttp
import asyncio
import sys
from io import BytesIO
from PIL import Image
import base64
import cv2
import numpy as np
import torch
from fastai import vision
from skimage.morphology import label
# Re... | airbus-ship-segmentation/app/server.py | MIT | 4,877 | 12f5aac9b29b9571977ce1f258c404d93e5cc925 | Python | py | |
"""
This code evaluates bert
"""
import argparse
import logging
from tqdm import tqdm
import numpy as np
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import torch
from torch.utils.data.dataloader import DataLoader
from transformers.data.processors import glue_processors, glue_output_modes
from transformers.dat... | WorkSpace/bert-eval.py | Apache-2.0 | 4,165 | 28bbbf0715de589841c13e03352a3ea56be4504b | Python | py | |
import argparse
import os
import sys
import torch
def parse_commandline_arguments():
parser = argparse.ArgumentParser(description='iTracker-pytorch-Trainer.')
#---------- Experimental Configuration ------------
parser.add_argument('--data_path',
help='Path to dataset folder contain... | utility_functions/argument_parser.py | RSA-MD | 8,603 | feddfbfcdaae377ae4aa4074a6a75ccafe752be2 | Python | py | |
import torch
import torch.nn as nn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
class PyBulletEncoder(nn.Module... | swae/models/custom.py | MIT | 5,071 | b2baf8e342b97e81f361c2b21fcba60993d50a76 | Python | py | |
"""Auxiliary dataset functions
This module provides generic functions related to all datasets
"""
from torch.utils.data import Dataset
import torch
NUM_WORKERS = 0
def get_dataset_with_index(dataset, offset):
class DatasetWithIndex(type(dataset)):
def __getitem__(self,index):
retu... | src/indirect_method/utils_dataset.py | MIT | 2,738 | 1ed460cf35d3dbc034d20608ab282a35c69916fb | Python | py | |
"""
This script is the agent implementation of a Deep Deterministic Policy Gradient agent on the Unity Reacher environment. The base model, agent
and training function were taken from the solution here:
https://github.com/udacity/deep-reinforcement-learning/tree/master/ddpg-pendulum.
It was modified to include paramet... | agent.py | MIT | 11,062 | 24d9b774106f7171b539c97ea54a0b2acab23d32 | Python | py | |
# ------------------------------------------------------------------------------
# Torchvision requires the mean and standard deviation to be calculated manually
# for normalization. This method can be used for that. However, this is mainly
# for colored 2D images and therefore rarely relevant for medical data.
# ---... | mp/utils/pytorch/compute_normalization_values.py | MIT | 1,064 | 03a619d8c268d47a14c60012f9e6c4c43c0a354c | Python | py | |
import argparse
import dgl
import math
import numpy as np
import os
import time
import torch
import torch.nn.functional as F
from torch.optim.lr_scheduler import LambdaLR
from tqdm import tqdm
import utils
from model import *
np.random.seed(12345)
torch.manual_seed(12345)
torch.cuda.manual_seed(12345)
class Cosin... | UnsupervisedNodeClassification/Model/DMPNN/src/main.py | MIT | 11,408 | fede6ca0966bb7e09cd157fdf51e46ae1b395c75 | Python | py | |
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch_geometric.utils import degree
from models_misc import mlp
from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
from ogb.utils.features import get_atom_feature_dims, get_bond_feature_dims
def multi_class_accuracy(y_hat, y, re... | utils_graph_learning.py | MIT | 9,658 | fede70154b56201c1e230f3415aa5c2c0a70bb2a | Python | py | |
import argparse
import os
import cv2
from tqdm import tqdm
import numpy as np
import torch
from torch.autograd import Variable
def main():
files = [
'car1.mp4',
'car2.mp4',
'ped1.mp4',
'ped2.mp4',
'ped3.mp4',
'ped4.mp4',
'ped5.mp4',
'ped6.mp4',
... | video_left_right.py | MIT | 2,998 | 7c6b53b630d5bb6d16460017e3b07954250034d5 | Python | py | |
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import math
import numpy as np
from scipy import linalg
from os import path as osp
import cv2
import random
import matplotlib.pyplot as plt
import pdb
#0. torch imports
import torch
from torch.utils.data import DataLoader,Dataset
from torch import optim,nn
import tor... | full_code/train_oneclass.py | MIT | 67,469 | fede821263881e01216e808271ca9231ff601815 | Python | py | |
# speed up the loading of the training data
import cv2
import numpy as np
import torch as th
import itertools
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from gan.src.model import NetG, NetD, NetA
from gan.src.data_set import LookbookDataset
import torch.optim as optim
import visd... | gan/src/train.py | MIT | 4,931 | fcc0def59a26241ff60e9531605b4e20a9362f3e | Python | py | |
import logging
import torch.nn as nn
from ..trainer import load_checkpoint
class AlexNet(nn.Module):
"""AlexNet backbone.
Args:
num_classes (int): number of classes for classification.
"""
def __init__(self, num_classes=-1):
super(AlexNet, self).__init__()
self.num_classes ... | det3d/torchie/cnn/alexnet.py | MIT | 1,932 | 28db64190d079b5416ad6bd4d20760069a4b3c12 | Python | py | |
import sys
import math
import functools
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.append('utils')
from proj_adaptive_softmax import ProjectedAdaptiveLogSoftmax
from log_uniform_sampler import LogUniformSampler, sample_logits
class PositionalEmbedding(nn.Module):
... | examples/enwik8_tf/mem_transformer.py | MIT | 30,964 | e6377264d8002f132e9cb1533af923b11dd3ac95 | Python | py | |
import os
import torch
import yaml
import numpy as np
from PIL import Image
import cv2
import torch.nn.functional as F
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
img = Image.open(f)
return... | utils/tools.py | MIT | 17,889 | 341324c7538e278b269a7b3a75b6a092bd861bbc | Python | py | |
import argparse
from typing import Any, List, Sequence
import torch
from torch import nn
from cassle.distillers.base import base_distill_wrapper
from cassle.losses.simclr import simclr_distill_loss_func
def contrastive_distill_wrapper(Method=object):
class ContrastiveDistillWrapper(base_distill_wrapper(Method)):... | cassle/distillers/contrastive.py | MIT | 2,687 | fedecd2636fb07cd2b25eadf1e34884e4f491de6 | Python | py | |
# modified from https://github.com/lioryariv/idr/blob/main/code/datasets/scene_dataset.py
import os
import json
import torch
import numpy as np
from tqdm import tqdm
from utils.io_util import load_mask, load_rgb, glob_imgs
from utils.rend_util import rot_to_quat, load_K_Rt_from_P
class SceneDataset(torch.utils.data.... | dataio/custom.py | MIT | 6,099 | a91412c1a8cd5937c2fd4ee6c79a9caa8000692a | Python | py | |
import torch
import torchvision
from torch.utils.mobile_optimizer import optimize_for_mobile
print(torch.__version__)
model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(
pretrained=True, box_score_thresh=0.7, rpn_post_nms_top_n_test=100, rpn_score_thresh=0.4, rpn_pre_nms_top_n_test=150
)
... | android/test_app/make_assets.py | BSD-3-Clause | 486 | fedee39fc52b50090dcbada630e753cb477d6a8b | Python | py | |
import io
import torch
from torchvision import ops
from torchvision import models
from torchvision.models.detection.image_list import ImageList
from torchvision.models.detection.transform import GeneralizedRCNNTransform
from torchvision.models.detection.rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
from to... | test/test_onnx.py | BSD-3-Clause | 21,051 | 03cd6ceb3a5ec9c708fcf30686db2d2dacb77296 | Python | py | |
import torch
import dgl
import numpy as np
from types import SimpleNamespace
from src.problem.tsptw.environment.tsptw import TSPTW
from src.problem.tsptw.learning.actor_critic import ActorCritic
from src.architecture.graph_attention_network import GATNetwork
class SolverBinding(object):
"""
Definition of th... | src/problem/tsptw/solving/solver_binding.py | MIT | 8,299 | 70a697060a4c451d27bdd9acbe94808c82311bbd | Python | py | |
try:
import cv2
except ImportError:
import sys
sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
import cv2
import torch, torchvision
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.resnet import Bottleneck
import numpy as np
from itertools import product
fro... | yolact.py | MIT | 30,595 | 03a89043f961af748adebcc12a7a79140f31b063 | Python | py | |
if __name__ == '__main__':
import os
import torch
from torch.utils.data import DataLoader
from networks import Discriminator, Generator, Loss
from options import TrainOption
from pipeline import CustomDataset
from utils import Manager, update_lr, weights_init
import numpy as np
from ... | train.py | MIT | 5,348 | fedf310cf2e632fcb4dd67b1be30693b83aba321 | Python | py | |
from functools import wraps
import pandas as pd
def from_dataframe_cache(key):
"""Returns a decorator that wraps a function with signature,
(params: dict, force: bool, kwargs) that returns a pandas
Dataframe.
The ``params[key]`` should be a :class:`pathlib.Path` to
save and load the cached datafr... | {{cookiecutter.repo_name}}/utils.py | MIT | 2,689 | 654da1c0029ebac0048f8765faf5b6d35beb5c5a | Python | py | |
from __future__ import absolute_import
import sys
import numpy as np
import torch
from torch import nn
import os
from collections import OrderedDict
from torch.autograd import Variable
import itertools
from .base_model import BaseModel
from scipy.ndimage import zoom
import fractions
import functools
import skimage.tr... | models/dist_model.py | BSD-2-Clause | 17,286 | fdd7bfec0af8486bac5351083be442b47c2a88ff | Python | py | |
import glob
import math
import os
import random
import shutil
import subprocess
import time
from copy import copy
from pathlib import Path
from sys import platform
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torchvision
import yaml
from scip... | utils/utils.py | MIT | 52,729 | 7cc1d9ca95d9d847d9ca0b31439bdb248e1e273e | Python | py | |
# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0
import math
import pytest
import torch
import pyro
import pyro.distributions as dist
import pyro.poutine as poutine
from pyro.infer.importance import psis_diagnostic
from pyro.infer.util import MultiFrameTensor
from tests.common ... | tests/infer/test_util.py | Apache-2.0 | 2,397 | 45f58072951d221d5e809fdaa88af04e294e87c3 | Python | py | |
import logging
import numpy as np
import torch
import torch.utils.data
class PairDataset(torch.utils.data.Dataset):
AUGMENT = None
def __init__(
self,
root,
phase,
voxel_size=0.05,
transform=None,
rotation_range=360,
random_rotation=True,
seed=... | src/data/base_loader.py | MIT | 927 | e659e7927798660e3a5d8de1cdaacb853b88d99b | Python | py | |
# -*- coding: utf-8 -*-
"""Untitled2.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1shr64TH78pZ1t28uNBguw-Ul2S5jhaCB
"""
from google.colab import auth
auth.authenticate_user()
from google.colab import drive
drive.mount('/content/gdrive')
cd /... | ai/KoBert/Model.py | MIT | 3,503 | 6b4026d8e6b203c977c2e063df35540101fc32fb | Python | py | |
# Owner(s): ["oncall: quantization"]
# Torch
import torch
from torch.ao.quantization import (
MinMaxObserver,
PerChannelMinMaxObserver,
MovingAverageMinMaxObserver,
MovingAveragePerChannelMinMaxObserver,
HistogramObserver,
RecordingObserver,
PlaceholderObserver,
NoopObserver,
FakeQu... | test/quantization/core/test_workflow_module.py | Intel | 53,244 | 1e910003a9c35f65782273f02aee973642c1b121 | Python | py | |
"""
3D IoU Calculation and Rotated NMS
Written by Shaoshuai Shi
All Rights Reserved 2019-2020.
"""
import torch
from ...utils import common_utils
from . import iou3d_nms_cuda
def boxes_bev_iou_cpu(boxes_a, boxes_b):
"""
Args:
boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
boxes_b: (N, 7) [x, ... | btcdet/ops/iou3d_nms/iou3d_nms_utils.py | Apache-2.0 | 4,276 | 7ce65fe48c8e0ad9a30e8e1ca4b8cb9f54eeab27 | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import _init_paths
import os
# os.environ['CUDA_VISIBLE_DEVICES'] = "0,1,2,3,4,5,6,7"
import torch
import torch.utils.data
from opts import opts
from models.model import create_model, load_model, sav... | src/main.py | MIT | 4,085 | fedff0781020c714f99d30d26af2c15dd9121112 | Python | py | |
# Copyright 2020 Lorna Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable l... | train.py | Apache-2.0 | 16,919 | 249eef54f4d2e92bdb11b9ddd263b5d2712ea053 | Python | py | |
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence
import numpy as np
import random
from .lrcn_trainer import LRCNTrainer
class GVETrainer(LRCNTrainer):
REQ_EVAL = True
def __init__(self, args, model, dataset, data_loader, logger... | train/gve_trainer.py | MIT | 3,665 | fedff6d8ff7e4cda598f5f39c090c802dfb29cea | Python | py | |
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## Email: zhanghang0704@gmail.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
##++++++++++++++++++++++++++... | encoding/functions/syncbn.py | MIT | 10,894 | e60f6b44a73481736bbd7c55e0e3ffa2214c0ae9 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from layers import *
from data import voc, coco
import os
from binarized_modules import BinarizeLinear,BinarizeConv2d
class SSD(nn.Module):
"""Single Shot Multibox Architecture
The network is composed of a... | ssd.py | MIT | 9,120 | fedfff5319b9db9f67e214ba82ef184c14ee4656 | Python | py | |
"""
Here, we revisit the 2D board env, since continuous-control with our prior RL-understanding refused to be solved, a few times already.
This time, to remove prediction-interference: the env's action needs 4 numbers, and is decided by which is higher.
Open-ended algorithms, to prepare for any goal, learn min-distan... | py/test_board_revisit.py | MIT | 19,117 | fee000293faf9b99cced78ff6961d04628bffa82 | Python | py | |
#!/usr/bin/env python
# coding: utf-8
import torch
import graphgallery
print("GraphGallery version: ", graphgallery.__version__)
print("Torch version: ", torch.__version__)
'''
Load Datasets
- cora/citeseer/pubmed
'''
from graphgallery.datasets import Planetoid
data = Planetoid('cora', root="~/GraphData/datasets/", ... | examples/Graph_Neural_Networks/PyTorch/SBVAT.py | MIT | 767 | fee00ac927df95c3524645994fa686e9381dd978 | Python | py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# author: yizhong
# created_at: 17-5-23 下午6:15
import torch
import torch.nn as nn
class BasicRecursiveNN(nn.Module):
def __init__(self, word_vocab_size, word_embed_dim, hidden_size, use_cuda):
super(BasicRecursiveNN, self).__init__()
self.use_cuda = us... | src/models/basic_recursive_nn.py | MIT | 1,734 | fee05302fb9e8dde01c985df7aa2f9750e0684da | Python | py | |
import glob
import os
import shutil
import unittest
import torch
from torch.utils.data import ConcatDataset
from transformers import TextDataset, AutoTokenizer, TrainingArguments, DataCollatorForLanguageModeling, \
PreTrainedTokenizer, BertForMaskedLM, AutoConfig, XLMRobertaForMaskedLM
from continued_pretraining.... | continued_pretraining/test/test_cont_trainer.py | MIT | 7,299 | 52eeee9107b7d75e115763c09fa8fd8f7d31e172 | Python | py | |
import os
from typing import List, Tuple
from torch.utils.data import Dataset
import data
import numpy as np
class InferenceDataset(Dataset):
def __init__(self, data_root: str, file_list: List[str], file_extension: str, data_type: str, transformation=None,
scan_dataset: str = "scannet", cad_data... | src/data/inference_dataset.py | MIT | 4,126 | 45463a0d3816f3b0c7619ba233e4e694c7decb5a | Python | py |
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