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opts.py
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opts.py
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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