opts.py

import argparse


def parse_opts_offline():
    # Offline means not real time 
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--root_path',
        default='/root/data/ActivityNet',
        type=str,
        help='Root directory path of data')
    parser.add_argument(
        '--video_path',
        default='video_kinetics_jpg',
        type=str,
        help='Directory path of Videos')
    parser.add_argument(
        '--annotation_path',
        default='kinetics.json',
        type=str,
        help='Annotation file path')
    parser.add_argument(
        '--result_path',
        default='results',
        type=str,
        help='Result directory path')
    parser.add_argument(
        '--store_name', 
        default='model', 
        type=str, 
        help='Name to store checkpoints')
    parser.add_argument(
        '--modality', 
        default='RGB', 
        type=str, 
        help='Modality of input data. RGB, Depth, or RGB-D')
    parser.add_argument(
        '--dataset',
        default='kinetics',
        type=str,
        help='Used dataset (activitynet | kinetics | ucf101 | hmdb51)')
    parser.add_argument(
        '--n_classes',
        default=400,
        type=int,
        help=
        'Number of classes (activitynet: 200, kinetics: 400, ucf101: 101, hmdb51: 51)')
    parser.add_argument(
        '--n_finetune_classes',
        default=400,
        type=int,
        help=
        'Number of classes for fine-tuning. n_classes is set to the number when pretraining.')
    parser.add_argument(
        '--sample_size',
        default=112,
        type=int,
        help='Height and width of inputs')
    parser.add_argument(
        '--sample_duration',
        default=16,
        type=int,
        help='Temporal duration of inputs')
    parser.add_argument(
        '--initial_scale',
        default=1.0,
        type=float,
        help='Initial scale for multiscale cropping')
    parser.add_argument(
        '--n_scales',
        default=5,
        type=int,
        help='Number of scales for multiscale cropping')
    parser.add_argument(
        '--scale_step',
        default=0.84089641525,
        type=float,
        help='Scale step for multiscale cropping')
    parser.add_argument(
        '--train_crop',
        default='corner',
        type=str,
        help=
        'Spatial cropping method in training. random is uniform. corner is selection from 4 corners and 1 center.  (random | corner | center)')
    parser.add_argument(
        '--train_temporal',
        default='random',
        type=str,
        help=
        'Temporal transformation method in training. (random | ranpad)')
    parser.add_argument(
        '--temporal_pad',
        default=0,
        type=int,
        help='Pad number for temporal transformation method (ranpad)')
    parser.add_argument(
        '--learning_rate',
        default=0.1,
        type=float,
        help=
        'Initial learning rate (divided by 10 while training by lr scheduler)')
    parser.add_argument(
        '--lr_steps', 
        default=[10, 25, 50, 80, 100], 
        type=float, nargs="+", 
        metavar='LRSteps', 
        help='epochs to decay learning rate by 10')
    parser.add_argument(
        '--momentum', 
        default=0.9, 
        type=float, 
        help='Momentum')
    parser.add_argument(
        '--dampening', 
        default=0.9, 
        type=float,
         help='dampening of SGD')
    parser.add_argument(
        '--weight_decay', 
        default=1e-3, 
        type=float, 
        help='Weight Decay')
    parser.add_argument(
        '--mean_dataset',
        default='activitynet',
        type=str,
        help=
        'dataset for mean values of mean subtraction (activitynet | kinetics)')
    parser.add_argument(
        '--no_mean_norm',
        action='store_true',
        help='If true, inputs are not normalized by mean.')
    parser.set_defaults(no_mean_norm=False)
    parser.add_argument(
        '--std_norm',
        action='store_true',
        help='If true, inputs are normalized by standard deviation.')
    parser.set_defaults(std_norm=False)
    parser.add_argument(
        '--nesterov', action='store_true', help='Nesterov momentum')
    parser.set_defaults(nesterov=False)
    parser.add_argument(
        '--optimizer',
        default='sgd',
        type=str,
        help='Currently only support SGD')
    parser.add_argument(
        '--lr_patience',
        default=10,
        type=int,
        help='Patience of LR scheduler. See documentation of ReduceLROnPlateau.')
    parser.add_argument(
        '--batch_size', 
        default=128, 
        type=int, 
        help='Batch Size')
    parser.add_argument(
        '--n_epochs',
        default=200,
        type=int,
        help='Number of total epochs to run')
    parser.add_argument(
        '--begin_epoch',
        default=1,
        type=int,
        help=
        'Training begins at this epoch. Previous trained model indicated by resume_path is loaded.')
    parser.add_argument(
        '--n_val_samples',
        default=3,
        type=int,
        help='Number of validation samples for each activity')
    parser.add_argument(
        '--resume_path',
        default='',
        type=str,
        help='Save data (.pth) of previous training')
    parser.add_argument(
        '--pretrain_path', 
        default='', 
        type=str, 
        help='Pretrained model (.pth)')
    parser.add_argument(
        '--pretrain_dataset', 
        default='', 
        type=str, 
        help='dataset from pretrained model')
    parser.add_argument(
        '--ft_begin_index',
        default=0,
        type=int,
        help='Begin block index of fine-tuning')
    parser.add_argument(
        '--no_train',
        action='store_true',
        help='If true, training is not performed.')
    parser.set_defaults(no_train=False)
    parser.add_argument(
        '--fine_tuning',
        action='store_true',
        help='If true, fine-tuning starts from epoch 1.')
    parser.set_defaults(fine_tuning=False)
    parser.add_argument(
        '--no_val',
        action='store_true',
        help='If true, validation is not performed.')
    parser.set_defaults(no_val=False)
    parser.add_argument(
        '--true_valid',
        action='store_true',
        help='If true avg recognition per clip is performent (not only temporal center crop)')
    parser.set_defaults(true_valid=False)
    parser.add_argument(
        '--adap_temp',
        action='store_true',
        help='If true the input frames > x is reduced (adap temporal cropping)')
    parser.set_defaults(adap_temp=False)
    parser.add_argument(
        '--test', 
        action='store_true', 
        help='If true, test is performed.')
    parser.set_defaults(test=False)
    parser.add_argument(
        '--test_subset',
        default='val',
        type=str,
        help='Used subset in test (val | test)')
    parser.add_argument(
        '--train_validate', 
        action='store_true', 
        help='If true, test is performed.')
    parser.set_defaults(train_validate=False)
    parser.add_argument(
        '--scale_in_test',
        default=1.0,
        type=float,
        help='Spatial scale in test')
    parser.add_argument(
        '--crop_position_in_test',
        default='c',
        type=str,
        help='Cropping method (c | tl | tr | bl | br) in test')
    parser.add_argument(
        '--no_softmax_in_test',
        action='store_true',
        help='If true, output for each clip is not normalized using softmax.')
    parser.set_defaults(no_softmax_in_test=False)
    parser.add_argument(
        '--no_scrop',
        action='store_true',
        help='If true, denso images are not normalized cropped by subject')
    parser.set_defaults(no_scrop=False)
    parser.add_argument(
        '--no_cuda', action='store_true', help='If true, cuda is not used.')
    parser.set_defaults(no_cuda=False)
    parser.add_argument(
        '--n_threads',
        default=4,
        type=int,
        help='Number of threads for multi-thread loading')
    parser.add_argument(
        '--checkpoint',
        default=10,
        type=int,
        help='Trained model is saved at every this epochs.')
    parser.add_argument(
        '--no_hflip',
        action='store_true',
        help='If true holizontal flipping is not performed.')
    parser.set_defaults(no_hflip=False)
    parser.add_argument(
        '--norm_value',
        default=1,
        type=int,
        help=
        'If 1, range of inputs is [0-255]. If 255, range of inputs is [0-1].')
    parser.add_argument(
        '--model',
        default='resnet',
        type=str,
        help='(resnet | preresnet | wideresnet | resnext | densenet | ')
    parser.add_argument(
        '--model_depth',
        default=18,
        type=int,
        help='Depth of resnet (10 | 18 | 34 | 50 | 101)')
    parser.add_argument('--no_first_lay', action='store_true', help='If true, first conv layer is changed.')
    parser.set_defaults(no_first_lay=False)
    parser.add_argument(
        '--resnet_shortcut',
        default='B',
        type=str,
        help='Shortcut type of resnet (A | B)')
    parser.add_argument(
        '--wide_resnet_k', 
        default=2, 
        type=int,
        help='Wide resnet k')
    parser.add_argument(
        '--resnext_cardinality',
        default=32,
        type=int,
        help='ResNeXt cardinality')
    parser.add_argument(
        '--manual_seed', 
        default=1, 
        type=int, 
        help='Manually set random seed')
    parser.add_argument(
        '--weighted', 
        action='store_true', 
        help='If true, loss is weighted')
    parser.set_defaults(weighted=False)
    parser.add_argument(
        '--clf_threshold', 
        default=0.1, 
        type=float, 
        help='Threshold to predict none gestures')
    parser.add_argument(
        '--width_mult', 
        default=1.0, 
        type=float, 
        help='The applied width multiplier to scale number of filters')

    args = parser.parse_args()

    return args

def parse_opts_online():
    # Real-time test arguments with detector and classifier architecture
    parser = argparse.ArgumentParser()
    parser.add_argument('--root_path', default='/root/data/ActivityNet', type=str, help='Root directory path of data')
    parser.add_argument('--video_path', default='video_kinetics_jpg', type=str, help='Directory path of Videos')
    parser.add_argument('--whole_path', default='video_kinetics_jpg', type=str, help='The whole path of Videos')
    parser.add_argument('--annotation_path', default='kinetics.json', type=str, help='Annotation file path')
    parser.add_argument('--result_path', default='results', type=str, help='Result directory path')
    parser.add_argument('--store_name', default='model', type=str, help='Name to store checkpoints')
    parser.add_argument('--modality', default='RGB', type=str, help='Modality of input data. RGB, Flow or RGBFlow')
    parser.add_argument('--modality_det', default='RGB', type=str, help='Modality of input data. RGB, Flow or RGBFlow')
    parser.add_argument('--modality_clf', default='RGB', type=str, help='Modality of input data. RGB, Flow or RGBFlow')
    parser.add_argument('--dataset', default='kinetics', type=str, help='Used dataset (activitynet | kinetics | ucf101 | hmdb51)')
    parser.add_argument('--n_classes_det', default=400, type=int, help='Number of classes (activitynet: 200, kinetics: 400, ucf101: 101, hmdb51: 51)')
    parser.add_argument('--n_finetune_classes_det', default=400, type=int, help='Number of classes for fine-tuning. n_classes is set to the number when pretraining.')
    parser.add_argument('--n_classes_clf', default=400, type=int, help='Number of classes (activitynet: 200, kinetics: 400, ucf101: 101, hmdb51: 51)')
    parser.add_argument('--n_finetune_classes_clf', default=400, type=int, help='Number of classes for fine-tuning. n_classes is set to the number when pretraining.')
    parser.add_argument('--no_first_lay_det', action='store_true', help='If true, first conv layer is changed.')
    parser.set_defaults(no_first_lay_det=False)
    parser.add_argument('--no_first_lay_clf', action='store_true', help='If true, first conv layer is changed.')
    parser.set_defaults(no_first_lay_clf=False)

    
    parser.add_argument('--n_classes', default=400, type=int, help='Number of classes (activitynet: 200, kinetics: 400, ucf101: 101, hmdb51: 51)')
    parser.add_argument('--n_finetune_classes', default=400, type=int, help='Number of classes for fine-tuning. n_classes is set to the number when pretraining.')
    parser.add_argument('--sample_size', default=112, type=int, help='Height and width of inputs')
    parser.add_argument('--sample_duration_det', default=16, type=int, help='Temporal duration of inputs')
    parser.add_argument('--sample_duration_clf', default=16, type=int, help='Temporal duration of inputs')
    parser.add_argument('--sample_duration', default=16, type=int, help='Temporal duration of inputs')

    parser.add_argument('--initial_scale', default=1.0, type=float, help='Initial scale for multiscale cropping')
    parser.add_argument('--n_scales', default=5, type=int, help='Number of scales for multiscale cropping')
    parser.add_argument('--scale_step', default=0.84089641525, type=float, help='Scale step for multiscale cropping')
    parser.add_argument('--train_crop', default='corner', type=str, help='Spatial cropping method in training. random is uniform. corner is selection from 4 corners and 1 center.  (random | corner | center)')
    parser.add_argument('--learning_rate', default=0.1, type=float, help='Initial learning rate (divided by 10 while training by lr scheduler)')
    parser.add_argument('--lr_steps', default=[10, 20, 30, 40, 100], type=float, nargs="+", metavar='LRSteps', help='epochs to decay learning rate by 10')
    parser.add_argument('--momentum', default=0.9, type=float, help='Momentum')
    parser.add_argument('--dampening', default=0.9, type=float, help='dampening of SGD')
    parser.add_argument('--weight_decay', default=1e-3, type=float, help='Weight Decay')
    parser.add_argument('--mean_dataset', default='activitynet', type=str, help='dataset for mean values of mean subtraction (activitynet | kinetics)')
    parser.add_argument('--no_mean_norm', action='store_true', help='If true, inputs are not normalized by mean.')
    parser.set_defaults(no_mean_norm=False)
    parser.add_argument('--std_norm', action='store_true', help='If true, inputs are normalized by standard deviation.')
    parser.set_defaults(std_norm=False)
    parser.add_argument('--nesterov', action='store_true', help='Nesterov momentum')
    parser.set_defaults(nesterov=False)
    parser.add_argument('--optimizer', default='sgd', type=str, help='Currently only support SGD')
    parser.add_argument('--lr_patience', default=10, type=int, help='Patience of LR scheduler. See documentation of ReduceLROnPlateau.')
    parser.add_argument('--batch_size', default=128, type=int, help='Batch Size')
    parser.add_argument('--n_epochs', default=200, type=int, help='Number of total epochs to run')
    parser.add_argument('--begin_epoch', default=1, type=int, help='Training begins at this epoch. Previous trained model indicated by resume_path is loaded.')
    parser.add_argument('--n_val_samples', default=3, type=int, help='Number of validation samples for each activity')
    parser.add_argument('--resume_path_det', default='', type=str, help='Save data (.pth) of previous training')
    parser.add_argument('--resume_path_clf', default='', type=str, help='Save data (.pth) of previous training')
    parser.add_argument('--resume_path', default='', type=str, help='Save data (.pth) of previous training')
    parser.add_argument('--pretrain_path_det', default='', type=str, help='Pretrained model (.pth)')
    parser.add_argument('--pretrain_path_clf', default='', type=str, help='Pretrained model (.pth)')
    parser.add_argument('--pretrain_path', default='', type=str, help='Pretrained model (.pth)')
    parser.add_argument('--pretrain_dataset', default='', type=str, help='dataset from pretrained model')
    
    parser.add_argument('--adap_temp', action='store_true', help='If true the input frames > x is reduced (adap temporal cropping)')
    parser.set_defaults(adap_temp=False)
    parser.add_argument('--ft_begin_index', default=0, type=int, help='Begin block index of fine-tuning')
    parser.add_argument('--no_train', action='store_true', help='If true, training is not performed.')
    parser.set_defaults(no_train=False)
    parser.add_argument('--no_val', action='store_true', help='If true, validation is not performed.')
    parser.set_defaults(no_val=False)
    parser.add_argument('--test', action='store_true', help='If true, test is performed.')
    parser.set_defaults(test=True)
    parser.add_argument('--test_subset', default='val', type=str, help='Used subset in test (val | test)')
    parser.add_argument('--scale_in_test', default=1.0, type=float, help='Spatial scale in test')
    parser.add_argument('--crop_position_in_test', default='c', type=str, help='Cropping method (c | tl | tr | bl | br) in test')
    parser.add_argument('--no_softmax_in_test', action='store_true', help='If true, output for each clip is not normalized using softmax.')
    parser.set_defaults(no_softmax_in_test=False)
    parser.add_argument('--no_cuda', action='store_true', help='If true, cuda is not used.')
    parser.set_defaults(no_cuda=False)
    parser.add_argument('--n_threads', default=4, type=int, help='Number of threads for multi-thread loading')
    parser.add_argument('--checkpoint', default=10, type=int, help='Trained model is saved at every this epochs.')
    parser.add_argument('--no_hflip', action='store_true', help='If true holizontal flipping is not performed.')
    parser.set_defaults(no_hflip=False)
    parser.add_argument('--norm_value', default=1, type=int, help='If 1, range of inputs is [0-255]. If 255, range of inputs is [0-1].')
    parser.add_argument('--no_scrop', action='store_true', help='If true, denso images are not normalized cropped by subject')
    parser.set_defaults(no_scrop=False)

    parser.add_argument('--model_det', default='resnet', type=str, help='(resnet | preresnet | wideresnet | resnext | densenet | ')
    parser.add_argument('--model_depth_det', default=18, type=int, help='Depth of resnet (10 | 18 | 34 | 50 | 101)')
    parser.add_argument('--resnet_shortcut_det', default='B', type=str, help='Shortcut type of resnet (A | B)')
    parser.add_argument('--wide_resnet_k_det', default=2, type=int, help='Wide resnet k')
    parser.add_argument('--resnext_cardinality_det', default=32, type=int, help='ResNeXt cardinality')
    
    parser.add_argument('--model', default='resnet', type=str, help='(resnet | preresnet | wideresnet | resnext | densenet | ')
    parser.add_argument('--model_depth', default=18, type=int, help='Depth of resnet (10 | 18 | 34 | 50 | 101)')
    parser.add_argument('--resnet_shortcut', default='B', type=str, help='Shortcut type of resnet (A | B)')
    parser.add_argument('--wide_resnet_k', default=2, type=int, help='Wide resnet k')
    parser.add_argument('--resnext_cardinality', default=32, type=int, help='ResNeXt cardinality')
    
    parser.add_argument('--model_clf', default='resnet', type=str, help='(resnet | preresnet | wideresnet | resnext | densenet | ')
    parser.add_argument('--model_depth_clf', default=18, type=int, help='Depth of resnet (10 | 18 | 34 | 50 | 101)')
    parser.add_argument('--resnet_shortcut_clf', default='B', type=str, help='Shortcut type of resnet (A | B)')
    parser.add_argument('--wide_resnet_k_clf', default=2, type=int, help='Wide resnet k')
    parser.add_argument('--resnext_cardinality_clf', default=32, type=int, help='ResNeXt cardinality')
    
    parser.add_argument('--manual_seed', default=1, type=int, help='Manually set random seed')
    parser.add_argument('--det_strategy', default='raw', type=str, help='Detector filter (raw | median | ma | ewma)')
    parser.add_argument('--det_queue_size', default=1, type=int, help='Detector queue size')
    parser.add_argument('--det_counter', default=1, type=float, help='Number of consequtive detection')
    parser.add_argument('--clf_strategy', default='raw', type=str, help='Classifier filter (raw | median | ma | ewma)')
    parser.add_argument('--clf_queue_size', default=1, type=int, help='Classifier queue size')
    parser.add_argument('--clf_threshold_pre', default=1, type=float, help='Cumulative sum threshold to prepredict')
    parser.add_argument('--clf_threshold_final', default=1, type=float, help='Cumulative sum threshold to predict at the end')
    parser.add_argument('--clf_threshold', default=0.1, type=float, help='Threshold to predict none gestures')
    parser.add_argument('--stride_len', default=1, type=int, help='Stride Lenght of video loader window')
    parser.add_argument('--width_mult', default=1.0, type=float, help='The applied width multiplier to scale number of filters')

    args = parser.parse_args()

    return args