repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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pabst | pabst-main/pabst/run_pplm_discrim_train.py | #! /usr/bin/env python3
# coding=utf-8
# This code is licensed under a non-commercial license.
import argparse
import csv
import json
import math
import numpy as np
import pandas as pd
import os
import time
import torch
import torch.nn.functional as F
import torch.optim
import torch.optim as optim
import torch.utils.... | 19,877 | 32.748727 | 87 | py |
pabst | pabst-main/pabst/pplm_classification_head.py | import torch
class ClassificationHead(torch.nn.Module):
"""Classification Head for transformer encoders"""
def __init__(self, class_size, embed_size):
super(ClassificationHead, self).__init__()
self.class_size = class_size
self.embed_size = embed_size
# self.mlp1 = torch.nn.Li... | 678 | 34.736842 | 63 | py |
openXDATA | openXDATA-master/model.py | import tensorflow as tf
import numpy as np
from sklearn import metrics
import sys
"""
Variant of binary cross-entropy loss which is zero for missing labels.
"""
def multi_task_binary_classification_loss(y_true, y_pred):
label_flag = y_true[:,1]
labels = y_true[:,0]
return tf.keras.backend.binary_crossentropy(la... | 10,488 | 40.956 | 140 | py |
reclor | reclor-master/run_multiple_choice.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... | 33,880 | 42.83053 | 197 | py |
FAinASRtest | FAinASRtest-main/examples/utils.py | import random
import numpy as np
import json
import os, subprocess
import gc
import torch
from pydub import AudioSegment
import soundfile as sf
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
import torch
import requests
import time
from pool import asr_pool, tts_pool
from gtts import gTTS
from tts.rv imp... | 10,583 | 29.857143 | 166 | py |
FAinASRtest | FAinASRtest-main/examples/estimator/huggingface.py | import numpy as np
from crossasr.constant import NUM_LABELS, FAILED_TEST_CASE
from crossasr.estimator import Estimator
from crossasr.text import Text
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AdamW
from transformers import Trainer, TrainingArguments
from scipy.special im... | 3,656 | 31.945946 | 92 | py |
FAinASRtest | FAinASRtest-main/examples/tts/speedyspeech.py | import os
from crossasr.tts import TTS
import utils
#source: https://tts.readthedocs.io/en/latest/inference.html
# use this version of pytorch pyaudio: pip install torch==1.10.0+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
class Speedyspeech(TTS):
def __init__(self, ... | 497 | 34.571429 | 155 | py |
FAinASRtest | FAinASRtest-main/demo_issta/utils.py | import random
import numpy as np
import json
import os, subprocess
import gc
import torch
from pydub import AudioSegment
import soundfile as sf
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
import torch
import requests
import time
from pool import asr_pool, tts_pool
from tts.espeak import Espeak
from asr.... | 4,534 | 26.822086 | 166 | py |
FAinASRtest | FAinASRtest-main/demo_issta/estimator/huggingface.py | import numpy as np
from crossasr.constant import NUM_LABELS, FAILED_TEST_CASE
from crossasr.estimator import Estimator
from crossasr.text import Text
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AdamW
from transformers import Trainer, TrainingArguments
from scipy.special im... | 3,656 | 31.945946 | 92 | py |
FAinASRtest | FAinASRtest-main/crossasr/utils.py | import os, sys
import re, string
import random
import numpy as np
import json
from normalise import normalise, tokenize_basic
import torch
from crossasr.text import Text
def set_seed(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.bac... | 2,966 | 23.725 | 80 | py |
CycleGAN-Keras | CycleGAN-Keras-master/model.py | from keras.layers import Layer, Input, Conv2D, Activation, add, BatchNormalization, UpSampling2D, ZeroPadding2D, Conv2DTranspose, Flatten, MaxPooling2D, AveragePooling2D
from keras_contrib.layers.normalization import InstanceNormalization, InputSpec
from keras.layers.advanced_activations import LeakyReLU
from keras.lay... | 41,102 | 42.960428 | 180 | py |
CycleGAN-Keras | CycleGAN-Keras-master/load_data.py | import os
import numpy as np
from PIL import Image
from keras.utils import Sequence
#from skimage.io import imread
def load_data(nr_of_channels, batch_size=1, nr_A_train_imgs=None, nr_B_train_imgs=None,
nr_A_test_imgs=None, nr_B_test_imgs=None, subfolder='',
generator=False, D_model=None, ... | 5,190 | 45.348214 | 196 | py |
fgbuster | fgbuster-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# FGBuster documentation build configuration file, created by
# sphinx-quickstart on Thu Nov 15 11:15:34 2018.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# ... | 5,504 | 29.414365 | 79 | py |
UniTS | UniTS-main/app.py | import myeel as eel
from random import randint
import torch
import torch.multiprocessing as mp
import threading
import logging
import time
import random
import tkinter as tk
from tkinter import filedialog
from ts_url.controller import process_training_tasks
logging.basicConfig(format='%(asctime)s | %(levelname)s : %(... | 3,749 | 24 | 91 | py |
UniTS | UniTS-main/ts_url/process_data.py | from tsai.all import *
import numpy as np
import json
from torch.utils.data import Dataset
# import tfsnippet as spt
interfusion = ['omi-6', 'omi-9', 'omi-4', 'omi-7', 'machine-2-2', 'omi-10', 'omi-8', 'omi-11', 'machine-1-7',
'machine-2-8', 'omi-2', 'omi-3', 'machine-1-6', 'machine-3-3', 'machine-1-1', 'omi-12', 'm... | 23,327 | 38.472081 | 153 | py |
UniTS | UniTS-main/ts_url/controller.py | from .training_methods import Trainer
import json
import threading
import myeel as eel
from .models.default_configs.configues import optim_configures, task_configures
from tkinter.filedialog import (askdirectory, askopenfile, askopenfilename)
import torch
import numpy as np
import random
import time
import os
import l... | 12,016 | 28.525799 | 129 | py |
UniTS | UniTS-main/ts_url/process_model.py | try:
from .models.mvts_transformer.src.models.ts_transformer import TSTransformerEncoder
from .models.ts2vec.ts2vec import TS2Vec
from .models.ts_tcc.models.model import base_Model
from .models.ts_tcc.models.TC import TC
from .models.default_configs.configues import model_configures
from .models... | 9,103 | 41.542056 | 199 | py |
UniTS | UniTS-main/ts_url/training_methods.py | import logging
import time
import torch
from collections import OrderedDict
import os
import numpy as np
try:
from .utils import utils
from .utils.loss import *
from .process_data import *
from .utils.optimizers import get_optimizer
from .process_model import get_model, get_fusion_model
from .mo... | 39,347 | 50.978864 | 174 | py |
UniTS | UniTS-main/ts_url/models/UnsupervisedScalableRepresentationLearningTimeSeries/combine_uea.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 6,450 | 36.947059 | 79 | py |
UniTS | UniTS-main/ts_url/models/UnsupervisedScalableRepresentationLearningTimeSeries/uea.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 8,124 | 38.441748 | 134 | py |
UniTS | UniTS-main/ts_url/models/UnsupervisedScalableRepresentationLearningTimeSeries/utils.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 1,744 | 30.160714 | 74 | py |
UniTS | UniTS-main/ts_url/models/UnsupervisedScalableRepresentationLearningTimeSeries/transfer_ucr.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 2,862 | 35.705128 | 79 | py |
UniTS | UniTS-main/ts_url/models/UnsupervisedScalableRepresentationLearningTimeSeries/combine_ucr.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 6,450 | 36.947059 | 79 | py |
UniTS | UniTS-main/ts_url/models/UnsupervisedScalableRepresentationLearningTimeSeries/scikit_wrappers.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 30,496 | 41.122928 | 91 | py |
UniTS | UniTS-main/ts_url/models/UnsupervisedScalableRepresentationLearningTimeSeries/ucr.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 7,805 | 36.710145 | 130 | py |
UniTS | UniTS-main/ts_url/models/UnsupervisedScalableRepresentationLearningTimeSeries/networks/lstm.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 1,537 | 35.619048 | 78 | py |
UniTS | UniTS-main/ts_url/models/UnsupervisedScalableRepresentationLearningTimeSeries/networks/causal_cnn.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 8,314 | 37.85514 | 80 | py |
UniTS | UniTS-main/ts_url/models/UnsupervisedScalableRepresentationLearningTimeSeries/losses/triplet_loss.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 14,842 | 44.95356 | 79 | py |
UniTS | UniTS-main/ts_url/models/mvts_transformer/src/main.py | """
Written by George Zerveas
If you use any part of the code in this repository, please consider citing the following paper:
George Zerveas et al. A Transformer-based Framework for Multivariate Time Series Representation Learning, in
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining... | 15,168 | 47.932258 | 155 | py |
UniTS | UniTS-main/ts_url/models/mvts_transformer/src/running.py | import logging
import sys
import os
import traceback
import json
from datetime import datetime
import string
import random
from collections import OrderedDict
import time
import pickle
from functools import partial
import ipdb
import torch
from torch.utils.data import DataLoader
import numpy as np
import sklearn
from... | 22,729 | 43.920949 | 162 | py |
UniTS | UniTS-main/ts_url/models/mvts_transformer/src/optimizers.py | import math
import torch
from torch.optim.optimizer import Optimizer
def get_optimizer(name):
if name == "Adam":
return torch.optim.Adam
elif name == "RAdam":
return RAdam
# from https://github.com/LiyuanLucasLiu/RAdam/blob/master/radam/radam.py
class RAdam(Optimizer):
def __init__(sel... | 10,629 | 39.884615 | 116 | py |
UniTS | UniTS-main/ts_url/models/mvts_transformer/src/models/loss.py | import torch
import torch.nn as nn
from torch.nn import functional as F
def get_loss_module(config):
task = config['task']
if (task == "pretraining") or (task == "transduction"):
return MaskedMSELoss(reduction='none') # outputs loss for each batch element
if task == "classification":
r... | 2,466 | 31.893333 | 120 | py |
UniTS | UniTS-main/ts_url/models/mvts_transformer/src/models/ts_transformer.py | from typing import Optional, Any
import math
import torch
from torch import nn, Tensor
from torch.nn import functional as F
from torch.nn.modules import MultiheadAttention, Linear, Dropout, BatchNorm1d, TransformerEncoderLayer
def model_factory(config, data):
task = config['task']
feat_dim = data.feature_df.... | 16,669 | 49.978593 | 147 | py |
UniTS | UniTS-main/ts_url/models/mvts_transformer/src/utils/utils.py | import json
import os
import sys
import builtins
import functools
import time
import ipdb
from copy import deepcopy
import numpy as np
import torch
import xlrd
import xlwt
from xlutils.copy import copy
import logging
logging.basicConfig(format='%(asctime)s | %(levelname)s : %(message)s', level=logging.INFO)
logger = ... | 11,752 | 33.165698 | 121 | py |
UniTS | UniTS-main/ts_url/models/ts_tcc/get_performance.py | import os
import numpy as np
import torch
import sys
import torch
sys.path.append("../ts2vec")
from datautils import *
os.system("rm -rf " + "data")
datasets = "ArticularyWordRecognition AtrialFibrillation BasicMotions Epilepsy ERing HandMovementDirection Libras NATOPS PEMS-SF PenDigits StandWalkJump UWaveGestureLibra... | 1,231 | 43 | 173 | py |
UniTS | UniTS-main/ts_url/models/ts_tcc/main.py | import torch
import os
import numpy as np
from datetime import datetime
import argparse
from utils import _logger, set_requires_grad
from dataloader.dataloader import data_generator
from trainer.trainer import Trainer, model_evaluate
from models.TC import TC
from utils import _calc_metrics, copy_Files
from models.mode... | 6,282 | 38.515723 | 168 | py |
UniTS | UniTS-main/ts_url/models/ts_tcc/utils.py | import torch
import random
import numpy as np
import pandas as pd
import os
import sys
import logging
from sklearn.metrics import classification_report, cohen_kappa_score, confusion_matrix, accuracy_score
from shutil import copy
def set_requires_grad(model, dict_, requires_grad=True):
for param in model.named_para... | 3,644 | 36.57732 | 102 | py |
UniTS | UniTS-main/ts_url/models/ts_tcc/trainer/trainer.py | import os
import sys
sys.path.append("..")
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.loss import NTXentLoss
from sklearn.metrics import normalized_mutual_info_score
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics im... | 6,731 | 39.311377 | 167 | py |
UniTS | UniTS-main/ts_url/models/ts_tcc/models/TC.py | import torch
import torch.nn as nn
import numpy as np
from .attention import Seq_Transformer
class TC(nn.Module):
def __init__(self, device, final_out_channels, timesteps):
super(TC, self).__init__()
self.num_channels = final_out_channels
self.timestep = timesteps
self.Wk = nn.Mod... | 2,285 | 40.563636 | 130 | py |
UniTS | UniTS-main/ts_url/models/ts_tcc/models/loss.py | import torch
import numpy as np
class NTXentLoss(torch.nn.Module):
def __init__(self, device, batch_size, temperature, use_cosine_similarity):
super(NTXentLoss, self).__init__()
self.batch_size = batch_size
self.temperature = temperature
self.device = device
self.softmax = ... | 2,563 | 37.848485 | 97 | py |
UniTS | UniTS-main/ts_url/models/ts_tcc/models/model.py | from torch import nn
class base_Model(nn.Module):
def __init__(self, features_len, kernel_size, input_channels,
stride, dropout, final_out_channels, num_classes, timesteps):
super(base_Model, self).__init__()
feature_length = features_len
self.conv_block1 = nn.Sequential(
... | 1,855 | 36.12 | 94 | py |
UniTS | UniTS-main/ts_url/models/ts_tcc/models/attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
########################################################################################
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self... | 3,413 | 29.482143 | 91 | py |
UniTS | UniTS-main/ts_url/utils/optimizers.py | import math
import torch
from torch.optim.optimizer import Optimizer
def get_optimizer(name):
if name == "Adam":
return torch.optim.Adam
elif name == "RAdam":
return RAdam
# from https://github.com/LiyuanLucasLiu/RAdam/blob/master/radam/radam.py
class RAdam(Optimizer):
def __init__(sel... | 10,629 | 39.884615 | 116 | py |
UniTS | UniTS-main/ts_url/utils/loss.py | import torch
import torch.nn as nn
from torch.nn import functional as F
def hierarchical_contrastive_loss(z1, z2, alpha=0.5, temporal_unit=0):
loss = torch.tensor(0., device=z1.device)
d = 0
while z1.size(1) > 1:
if alpha != 0:
loss += alpha * instance_contrastive_loss(z1, z2)
i... | 4,279 | 34.081967 | 120 | py |
UniTS | UniTS-main/ts_url/utils/utils.py | import json
import os
import sys
import builtins
import functools
import time
import ipdb
from copy import deepcopy
import numpy as np
import torch
import xlrd
import xlwt
from xlutils.copy import copy
import logging
logging.basicConfig(format='%(asctime)s | %(levelname)s : %(message)s', level=logging.INFO)
logger = ... | 12,283 | 33.700565 | 121 | py |
SGRRN | SGRRN-master/test.py | from tqdm import tqdm
from utils import get_config, label2colormap
from trainer import Trainer
import argparse
from torch.autograd import Variable
import torch
import os
import cv2
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='configs/semantic_rr.yaml', help... | 4,539 | 43.509804 | 126 | py |
SGRRN | SGRRN-master/utils.py | import multiprocessing
from PIL import Image
from torch.utils.data import DataLoader
from torch.autograd import Variable
from torch.optim import lr_scheduler
from data import LowLevelImageFolder
import torch
import os
import math
import torchvision.utils as vutils
import yaml
import numpy as np
import torch.nn.init as... | 17,309 | 34.182927 | 120 | py |
SGRRN | SGRRN-master/data.py | """ data.py
data I/O for low-level vision tasks, ie, reflection removal
@autor: DreamTale
"""
import os.path
import random
import torch
import cv2
import numpy as np
import scipy.stats as st
import torch.utils.data as data
from torch.autograd import Variable
from PIL import Image
import pandas as pd
from torch.utils... | 27,220 | 42.904839 | 113 | py |
SGRRN | SGRRN-master/networks.py | """
Interface of DNN models for low level vision tasks
"""
import functools
from abc import ABC
import math
import numpy as np
from queue import Queue
from torch import nn
from torch.autograd import Variable
import torch
import collections
from torch.autograd import grad as ta_grad
import torch.nn.functional as F
from... | 73,716 | 37.798421 | 127 | py |
SGRRN | SGRRN-master/train.py | """ train.py
train entry for SGRRN
@autor: DreamTale
"""
import argparse
import os
import shutil
import cv2
import numpy as np
import tensorboardX
import torch
import torch.backends.cudnn as cudnn
import tqdm
from torchvision import transforms
from trainer import Trainer
from utils import get_local_time
from util... | 5,433 | 38.664234 | 119 | py |
SGRRN | SGRRN-master/trainer.py | from networks import get_generator, get_discriminator, RetinaLoss, VggLoss, LayerNorm, get_classifier, PerceptualLoss
from torchvision.models import vgg11, vgg19, resnet50, resnet101
from utils import weights_init, get_scheduler, get_model_list, label2colormap_batch, compute_miou, to_number
from torch.autograd import V... | 20,821 | 41.843621 | 129 | py |
DHIF-Net | DHIF-Net-main/CAVE/Test.py | import torch.utils.data as tud
import argparse
from Utils import *
from CAVE_Dataset import cave_dataset
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
parser = argparse.ArgumentParser(description="PyTorch Code for HSI Fusion")
parser.add_argument('--data_path', default='./D... | 1,829 | 33.528302 | 101 | py |
DHIF-Net | DHIF-Net-main/CAVE/Utils.py | import numpy as np
import scipy.io as sio
import os
import glob
import torch
import torch.nn as nn
import skimage.measure as measure
import torch.nn.functional as F
import cv2
import Pypher
import random
import re
import math
def _as_floats(im1, im2):
float_type = np.result_type(im1.dtype, im2.dtype, np.float32)
... | 11,913 | 34.670659 | 126 | py |
DHIF-Net | DHIF-Net-main/CAVE/CAVE_Dataset.py | import torch.utils.data as tud
from Utils import *
class cave_dataset(tud.Dataset):
def __init__(self, opt, HR_HSI, HR_MSI, istrain = True):
super(cave_dataset, self).__init__()
self.path = opt.data_path
self.istrain = istrain
self.factor = opt.sf
if istrain:
... | 3,750 | 38.904255 | 119 | py |
DHIF-Net | DHIF-Net-main/CAVE/Model.py | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.E1 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
... | 14,320 | 49.073427 | 148 | py |
DHIF-Net | DHIF-Net-main/CAVE/Train.py | from Model import HSI_Fusion
from CAVE_Dataset import cave_dataset
import torch.utils.data as tud
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR
import time
import datetime
import argparse
from torch.autograd import Variable
from Utils import *
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
... | 3,851 | 37.138614 | 129 | py |
SportTaskME22 | SportTaskME22-master/main_2_test_1.py | import os
import time
import datetime
import torch
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
import json
import xml.etree.ElementTree as ET
from utils import *
from model import *
from init_data import create_working_tree
import argparse
import sy... | 44,357 | 43.580905 | 282 | py |
SportTaskME22 | SportTaskME22-master/main_2_test_2.py | import os
import time
import datetime
import torch
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
import json
import xml.etree.ElementTree as ET
from utils import *
from model import *
from init_data import create_working_tree
import argparse
import sy... | 42,712 | 43.262176 | 282 | py |
SportTaskME22 | SportTaskME22-master/init_data.py | import os
import time
import os.path as osp
import gc
import cv2
import mmcv
import numpy as np
import torch
from mmdet.apis import inference_detector, init_detector
from mmpose.apis import inference_top_down_pose_model, init_pose_model, vis_pose_result
from utils import *
def frame_extraction(video_path, short_s... | 9,880 | 39.004049 | 177 | py |
SportTaskME22 | SportTaskME22-master/main_1.py | # Code dedicated to the Sport Task MediaEval22
__author__ = "Pierre-Etienne Martin"
__copyright__ = "Copyright (C) 2022 Pierre-Etienne Martin"
__license__ = "CC BY 4.0"
__version__ = "1.0"
import cv2
import datetime
import os
import platform
import time
import datetime
import torch
import xml.etree.ElementTree as ET
i... | 38,534 | 42.83959 | 223 | py |
SportTaskME22 | SportTaskME22-master/utils.py | # Code dedicated to the Sport Task MediaEval22
__author__ = "Pierre-Etienne Martin"
__copyright__ = "Copyright (C) 2022 Pierre-Etienne Martin"
__license__ = "CC BY 4.0"
__version__ = "1.0"
import gc
import numpy as np
import torch
import random
import platform
import os
import sys
from shutil import rmtree
import matp... | 7,952 | 30.188235 | 152 | py |
SportTaskME22 | SportTaskME22-master/model.py | # Code dedicated to the Sport Task MediaEval22
__author__ = "Pierre-Etienne Martin"
__copyright__ = "Copyright (C) 2022 Pierre-Etienne Martin"
__license__ = "CC BY 4.0"
__version__ = "1.0"
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from torch.nn.modules.batchnorm ... | 31,170 | 39.014121 | 153 | py |
SportTaskME22 | SportTaskME22-master/main_2.py | import os
import time
import datetime
import torch
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
import json
import xml.etree.ElementTree as ET
from utils import *
from model import *
from init_data import create_working_tree
import argparse
import sy... | 46,890 | 43.828872 | 282 | py |
SportTaskME22 | SportTaskME22-master/mmpose_utils/sports_skeleton_extract.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import shutil
import time
import cv2
import mmcv
import numpy as np
import torch
from mmcv import DictAction
from mmdet.apis import inference_detector, init_detector
from mmpose.apis import inference_top_down_pose_model, i... | 10,461 | 39.238462 | 160 | py |
SportTaskME22 | SportTaskME22-master/mmpose_utils/skeleton_extract.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import shutil
import cv2
import mmcv
import numpy as np
import torch
from mmdet.apis import inference_detector, init_detector
from mmpose.apis import inference_top_down_pose_model, init_pose_model, vis_pose_result
def frame_extraction(... | 5,370 | 35.290541 | 142 | py |
SportTaskME22 | SportTaskME22-master/mmpose_utils/demo/faster_rcnn_r50_fpn_2x_coco.py | # Copyright (c) OpenMMLab. All rights reserved.
# model config
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requi... | 5,793 | 30.661202 | 77 | py |
SportTaskME22 | SportTaskME22-master/hist/main_srgb_train.py | # Code dedicated to the Sport Task MediaEval22
__author__ = "Pierre-Etienne Martin"
__copyright__ = "Copyright (C) 2022 Pierre-Etienne Martin"
__license__ = "CC BY 4.0"
__version__ = "1.0"
import cv2
import datetime
import os
import platform
import time
import torch
import xml.etree.ElementTree as ET
import numpy as n... | 33,978 | 41.36783 | 223 | py |
SportTaskME22 | SportTaskME22-master/hist/main_rgb_train.py | # Code dedicated to the Sport Task MediaEval22
__author__ = "Pierre-Etienne Martin"
__copyright__ = "Copyright (C) 2022 Pierre-Etienne Martin"
__license__ = "CC BY 4.0"
__version__ = "1.0"
import cv2
import datetime
import os
import platform
import time
import torch
import xml.etree.ElementTree as ET
import numpy as n... | 33,987 | 41.379052 | 223 | py |
SportTaskME22 | SportTaskME22-master/hist/main_s_train.py | # Code dedicated to the Sport Task MediaEval22
__author__ = "Pierre-Etienne Martin"
__copyright__ = "Copyright (C) 2022 Pierre-Etienne Martin"
__license__ = "CC BY 4.0"
__version__ = "1.0"
import cv2
import datetime
import os
import platform
import time
import torch
import xml.etree.ElementTree as ET
import numpy as n... | 33,979 | 41.369077 | 223 | py |
RMI | RMI-master/inference.py | #coding=utf-8
import os
import time
import timeit
import argparse
import numpy as np
#import cv2
from PIL import Image
import torch
#import torch.nn.functional as F
from RMI import parser_params, full_model
from RMI.model import psp, deeplab
from RMI.dataloaders import factory
from RMI.utils.metrics import Evaluato... | 3,790 | 27.081481 | 94 | py |
RMI | RMI-master/parser_params.py | # coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import json
import argparse
#import argparse
import torch
def add_parser_params(parser):
"""add argument to the parser"""
# checkpoint
parser.add_argument('--resume', type=str, ... | 11,235 | 35.718954 | 107 | py |
RMI | RMI-master/eval.py | #coding=utf-8
import os
import time
import timeit
import argparse
import numpy as np
#import cv2
from PIL import Image
import torch
import torch.nn.functional as F
from RMI import parser_params, full_model
from RMI.model import psp, deeplab
from RMI.dataloaders import factory
from RMI.utils.metrics import Evaluator... | 4,875 | 29.475 | 100 | py |
RMI | RMI-master/full_model.py | # coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
#import torch
import torch.nn as nn
import torch.nn.functional as F
_PSP_AUX_WEIGHT = 0.4 # the weight of the auxiliary loss in PSPNet
class FullModel(nn.Module):
"""The full model wrapper."... | 2,338 | 32.414286 | 114 | py |
RMI | RMI-master/train.py | #coding=utf-8
"""
Training for the segmentation model.
reference:
https://github.com/zhanghang1989/PyTorch-Encoding
https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import ... | 11,755 | 36.43949 | 121 | py |
RMI | RMI-master/crf/crf_refine.py | #coding=utf-8
import os
import time
import timeit
import argparse
import numpy as np
#import cv2
from PIL import Image
import torch
import torch.nn.functional as F
from RMI import parser_params
from RMI.crf import crf
from RMI.model import psp, deeplab
from RMI.dataloaders import factory
from RMI.utils.metrics impo... | 5,264 | 30.716867 | 100 | py |
RMI | RMI-master/crf/crf_refine_test.py | #coding=utf-8
import os
import time
import timeit
import argparse
import numpy as np
#import cv2
from PIL import Image
import torch
import torch.nn.functional as F
from RMI import parser_params
from RMI.crf import crf
from RMI.model import psp, deeplab
from RMI.dataloaders import factory
from RMI.utils.metrics impo... | 4,364 | 28.693878 | 100 | py |
RMI | RMI-master/crf/crf.py | # coding=utf-8
"""
Function which returns the labelled image after applying CRF.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
#import cv2
import numpy as np
from PIL import Image
import pydensecrf.densecrf as dcrf
from pydensecrf.utils import unary_fr... | 3,623 | 36.360825 | 110 | py |
RMI | RMI-master/dataloaders/custom_transforms.py | # coding=utf-8
"""
some custom transforms
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
#from torchvision import transforms
import random
import numpy as np
from PIL import Image, ImageOps, ImageFilter
class RandomRescale(object):
... | 8,595 | 31.80916 | 100 | py |
RMI | RMI-master/dataloaders/utils.py | # coding=utf-8
import torch
import numpy as np
import matplotlib.pyplot as plt
def decode_seg_map_sequence(label_masks, dataset='pascal'):
rgb_masks = []
for label_mask in label_masks:
rgb_mask = decode_segmap(label_mask, dataset)
rgb_masks.append(rgb_mask)
rgb_masks = torch.from_numpy(np... | 3,368 | 31.394231 | 84 | py |
RMI | RMI-master/dataloaders/factory.py | # coding=utf-8
import torch
from torch.utils.data import DataLoader
from RMI.dataloaders.datasets import cityscapes, pascal, camvid
__all__ = ['get_data_loader', 'get_dataset']
def get_data_loader(data_dir,
batch_size=16,
crop_size=513,
dataset='pascal',
split="train",
num_workers=4,
... | 5,372 | 34.82 | 102 | py |
RMI | RMI-master/dataloaders/datasets/cityscapes.py | #coding=utf-8
"""
dataloader for Cityscapes dataset
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
from PIL import Image
from torch.utils import data
from torchvision import transforms
from RMI.dataloaders import custom_tr... | 5,429 | 30.387283 | 109 | py |
RMI | RMI-master/dataloaders/datasets/camvid.py | #coding=utf-8
"""
dataloader for CamVid dataset
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
from PIL import Image
from torch.utils import data
from torchvision import transforms
from RMI.dataloaders import custom_transf... | 4,825 | 28.248485 | 92 | py |
RMI | RMI-master/dataloaders/datasets/pascal.py | # coding=utf-8
"""
dataloader for PASCAL VOC 2012 dataset
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
from PIL import Image
from torchvision import transforms
from torch.utils.data import Dataset
from RMI.dataloaders i... | 5,013 | 27.327684 | 86 | py |
RMI | RMI-master/utils/loss.py | # coding=utf-8
import torch
import torch.nn as nn
class CrossEntropyLoss(object):
"""the normal cross entropy loss"""
def __init__(self, ignore_index=255, accumulation_steps=1):
self.ignore_index = ignore_index
self.criterion = torch.nn.CrossEntropyLoss(weight=None, ignore_index=self.ignore_i... | 1,174 | 30.756757 | 97 | py |
RMI | RMI-master/utils/train_utils.py | #coding=utf-8
"""
some training utils.
reference:
https://github.com/zhanghang1989/PyTorch-Encoding
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
#import os
import math
import torch
from torchvision.utils import make_grid
#from tensorboardX import Su... | 4,044 | 33.279661 | 100 | py |
RMI | RMI-master/utils/model_init.py | #coding=utf-8
"""
some training utils.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['init_weights', 'group_weight', 'seg_model_get_optim_params']
def init_weights(modul... | 3,231 | 30.076923 | 95 | py |
RMI | RMI-master/utils/files.py | # coding=utf-8
import os
import requests
import errno
import shutil
import hashlib
from tqdm import tqdm
import torch
__all__ = ['save_checkpoint', 'download', 'mkdir', 'check_sha1']
def save_checkpoint(state, args, is_best, filename='checkpoint.pth.tar'):
"""Saves checkpoint to disk"""
directory = "runs/%s... | 3,831 | 31.752137 | 98 | py |
RMI | RMI-master/model/psp.py | #coding=utf-8
"""
some training utils.
reference:
https://github.com/zhanghang1989/PyTorch-Encoding
https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torc... | 4,277 | 28.916084 | 101 | py |
RMI | RMI-master/model/net_factory.py | #coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.nn as nn
from RMI.model.backbone import resnet_v1
#from RMI.model.backbone import resnet_v1_beta
__all__ = ['get_backbone_net']
def get_backbone_net(backbone='resnet101',
o... | 1,236 | 28.452381 | 73 | py |
RMI | RMI-master/model/deeplab.py | # coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from RMI.model import net_factory
__all__ = ['DeepLabv3Plus', 'DeepLabv3', 'Decoder', 'ASPP']
# A dictionary from network na... | 9,210 | 30.013468 | 121 | py |
RMI | RMI-master/model/backbone/resnet_v1.py | # coding=utf-8
"""
Reference:
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from RMI.utils import... | 8,290 | 30.524715 | 118 | py |
RMI | RMI-master/model/sync_batchnorm/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.da... | 3,226 | 32.968421 | 115 | py |
RMI | RMI-master/model/sync_batchnorm/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import torch
class TorchTes... | 746 | 23.9 | 59 | py |
RMI | RMI-master/model/sync_batchnorm/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import torch
import torc... | 14,808 | 39.90884 | 116 | py |
RMI | RMI-master/model/sync_batchnorm/batchnorm_reimpl.py | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : batchnorm_reimpl.py
# Author : acgtyrant
# Date : 11/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import torch
import torch.nn as nn
import torch... | 2,383 | 30.786667 | 95 | py |
RMI | RMI-master/model/sync_bn/syncbn.py | #!/usr/bin/env python3
# encoding: utf-8
# @Time : 2018/10/3 下午1:45
# @Author : yuchangqian
# @Contact : changqian_yu@163.com from torch-encoding.nn.syncbn
# @File : syncbn.py.py
"""Synchronized Cross-GPU Batch Normalization Module"""
import collections
import threading
import torch
from torch.nn.modules.... | 9,377 | 37.592593 | 176 | py |
RMI | RMI-master/model/sync_bn/functions.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
##++++++++++++++++++++++++++... | 2,627 | 31.444444 | 79 | py |
RMI | RMI-master/model/sync_bn/parallel_apply.py | # import threading
import queue
import torch
import torch.multiprocessing as mp
# from pathos.multiprocessing import ProcessPool as Pool
from torch.cuda._utils import _get_device_index
#######貌似没什么用
def get_a_var(obj):
if isinstance(obj, torch.Tensor):
return obj
if isinstance(obj, list) or isinstanc... | 3,403 | 34.831579 | 82 | py |
RMI | RMI-master/model/sync_bn/parallel.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
import torch
from torch.n... | 5,961 | 36.031056 | 144 | py |
RMI | RMI-master/model/sync_bn/src/__init__.py | import os
import torch
from torch.utils.cpp_extension import load
cwd = os.path.dirname(os.path.realpath(__file__))
cpu_path = os.path.join(cwd, 'cpu')
gpu_path = os.path.join(cwd, 'gpu')
cpu = load('syncbn_cpu', [
os.path.join(cpu_path, 'operator.cpp'),
os.path.join(cpu_path, 'syncbn_cpu.cpp'),
]... | 585 | 29.842105 | 55 | py |
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