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opts.py
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opts.py
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import argparse
def str_to_bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def parse_opts():
parser = argparse.ArgumentParser()
parser.add_argument(
'--timecycle_weight',
default=25,
type=int,
help='Weight to multiply TimeCycle loss with before adding to hmdb classification loss')
parser.add_argument(
'--binary_class_weight',
default=2,
type=int,
help='Weight to multiply binary classification loss with before adding to hmdb classification loss')
parser.add_argument(
'--root_path',
default='/home/martine/data',
type=str,
help='Root directory path of data')
parser.add_argument(
'--video_path',
default='hmdb_videos/jpg',
type=str,
help='Directory path of Videos')
parser.add_argument(
'--list',
default='hmdb_1.txt',
help='path to video list',
type=str)
parser.add_argument(
'--annotation_path',
default='hmdb51_1.json',
type=str,
help='Annotation file path')
parser.add_argument(
'--result_path',
default='test',
type=str,
help='Result directory path')
parser.add_argument(
'--batch_size',
default=4,
type=int,
help='Batch Size')
parser.add_argument(
'--n_epochs',
default=100,
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(
'--ft_begin_index',
default=0,
type=int,
help='Begin block index of fine-tuning')
parser.add_argument(
'--learning_rate',
default=2e-4,
type=float,
help=
'Initial learning rate (divided by 10 while training by lr scheduler)')
parser.add_argument(
'--momentum',
default=0.5,
type=float,
metavar='M',
help='momentum')
parser.add_argument(
'--weight_decay',
default=1e-4,
type=float,
metavar='W',
help='weight decay (default: 1e-4)')
parser.add_argument(
'--lr_patience',
default=10,
type=int,
help='Patience of LR scheduler. See documentation of ReduceLROnPlateau.'
)
parser.add_argument(
'--videoLen',
default=3,
type=int,
help='')
parser.add_argument(
'--frame_gap',
default=4,
type=int,
help='')
parser.add_argument(
'--hist',
default=1,
type=int,
help='')
parser.add_argument(
'--sample_size',
default=240,
type=int,
help='Height and width of inputs')
parser.add_argument(
'--sample_duration',
default=13,
type=int,
help='Temporal duration of inputs')
parser.add_argument(
'--predDistance',
default=0, type=int,
help='predict how many frames away')
parser.add_argument(
'--seperate2d',
type=int,
default=0,
help='manual seed')
parser.add_argument(
'--T',
default=512**-.5,
type=float,
help='temperature')
parser.add_argument(
'--gridSize',
default=9,
type=int,
help='temperature')
parser.add_argument(
'--lamda',
default=0.1,
type=float,
help='temperature')
parser.add_argument(
'--pretrained_imagenet',
type=str_to_bool,
nargs='?',
const=True,
default=False,
help='pretrained_imagenet')
# Miscs
parser.add_argument(
'--manualSeed', type=int, help='manual seed')
#Device options
parser.add_argument(
'--gpu_id',
default='0',
type=str,
help='id(s) for CUDA_VISIBLE_DEVICES')
# 3D-ResNets-PyTorch
parser.add_argument(
'--dataset',
default='hmdb51',
type=str,
help='Used dataset (activitynet | kinetics | ucf101 | hmdb51)')
parser.add_argument(
'--n_classes',
default=51,
type=int,
help=
'Number of classes (activitynet: 200, kinetics: 400, ucf101: 101, hmdb51: 51)'
)
parser.add_argument(
'--n_finetune_classes',
default=51,
type=int,
help=
'Number of classes for fine-tuning. n_classes is set to the number when pretraining.'
)
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, no validation step with n_val_samples is performed')
parser.set_defaults(no_val=False)
parser.add_argument(
'--no_test', action='store_true', help='If true, no test is performed')
parser.set_defaults(no_test=False)
parser.add_argument(
'--test_subset',
default='val',
type=str,
help='Used subset in test (val | test)')
parser.add_argument(
'--no_eval', action='store_true', help='If true, no evaluation is done.')
parser.set_defaults(no_eval=False)
parser.add_argument(
'--top_k', default=1, type=int, help='Top 1 or Top 5 accuracy')
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=12,
type=int,
help='Number of threads for multi-thread loading')
parser.add_argument(
'--checkpoint',
default=2,
type=int,
help='Trained model is saved at every this epochs.')
parser.add_argument(
'--model',
default='resnet',
type=str,
help='(resnet | preresnet | wideresnet | resnext | densenet | ')
parser.add_argument(
'--model_depth',
default=50,
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(
# '--manual_seed', default=1, type=int, help='Manually set random seed')
args = parser.parse_args()
return args