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opts.py
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import argparse
def parse_opt():
parser = argparse.ArgumentParser()
# Overall settings
parser.add_argument('--module', type=str, default='TEM')
parser.add_argument('--mode', type=str, default='train')
parser.add_argument('--sampler_mode', type=str, default='both',
help='for gymnastics, can be both, on, frames, or None')
parser.add_argument('--checkpoint_path', type=str, default='./checkpoint')
parser.add_argument('--checkpoint_epoch', type=int, default=None,
help="if none, use 'best'. else use that epoch.")
# Overall Dataset settings
parser.add_argument('--dataset', default='gymnastics', type=str,
help='gymnsatics, thumosfeatures, thumosimages')
parser.add_argument(
'--video_info',
type=str,
default="./data/activitynet_annotations/video_info_new.csv")
parser.add_argument(
'--video_anno',
type=str,
default="./data/activitynet_annotations/anet_anno_action.json")
parser.add_argument(
'--train_video_file_list',
type=str,
default="./data/activitynet_annotations/video_dataset_files/train_keys_split.24fps.txt",
help='for use with video_dataset.py. should be a space delinated txt of <path to file> <key into annotation dataframe>.')
parser.add_argument(
'--val_video_file_list',
type=str,
default="./data/activitynet_annotations/video_dataset_files/val_keys_split.24fps.txt",
help='for use with video_dataset.py. should be a space delinated txt of <path to file> <key into annotation dataframe>.')
parser.add_argument(
'--full_video_file_list',
type=str,
default="/private/home/cinjon/Code/BSN-boundary-sensitive-network.pytorch/data/activitynet_annotations/video_dataset_files/full_keys_split.24fps.txt",
help='for use with video_dataset.py. should be a space delinated txt of <path to file> <key into annotation dataframe>.')
parser.add_argument(
'--fps',
type=int,
default=24)
# TEM Dataset settings
parser.add_argument('--temporal_scale', type=int, default=100)
parser.add_argument('--boundary_ratio', type=float, default=0.1)
parser.add_argument('--feature_dirs', type=str, default=None,
help='comma delineated list of paths to feature directories')
# PEM Dataset settings
parser.add_argument('--pem_top_K', type=int, default=2500) # 500
parser.add_argument('--pem_top_K_inference', type=int, default=2500) # 1000
parser.add_argument('--pem_top_threshold', type=float, default=0,
help='instead of using pem_top_K, can do this to threshold the score and then use pem_top_K to randomly choose proposals from above this score.')
parser.add_argument('--pem_do_index', action='store_true')
parser.add_argument('--pem_max_zero_weight', type=float, default=0.1)
parser.add_argument('--postproc_width_init', type=int, default=300)
# TEM model settings
parser.add_argument('--tem_feat_dim', type=int, default=400)
parser.add_argument('--tem_hidden_dim', type=int, default=512)
parser.add_argument('--tem_nonlinear_factor', type=float, default=0.01)
parser.add_argument('--tem_reset_params', action='store_true')
parser.add_argument('--gym_image_dir', type=str, default='/checkpoint/cinjon/spaceofmotion/sep052019/rawframes.426x240.12')
parser.add_argument('--ccc_img_size', type=int, default=256)
parser.add_argument('--tsn_config', type=str, default='~/Code/BSN-boundary-sensitive-network.pytorch/representations/tsn/temp_tsn_rgb_bninception.py')
# PEM model settings
parser.add_argument('--pem_feat_dim', type=int, default=32)
parser.add_argument('--pem_hidden_dim', type=int, default=256)
# TEM Training settings
parser.add_argument('--tem_training_lr', type=float, default=0.001)
parser.add_argument('--tem_weight_decay', type=float, default=0.0)
parser.add_argument('--tem_l2_loss', type=float, default=0.005)
parser.add_argument('--tem_epoch', type=int, default=30) # NOTE: was 20
parser.add_argument('--tem_step_size', type=int, default=7)
parser.add_argument('--tem_step_gamma', type=float, default=0.1) # 0.1
parser.add_argument('--tem_lr_milestones', type=str, default='5')
parser.add_argument('--tem_batch_size', type=int, default=16)
parser.add_argument('--tem_match_thres', type=float, default=0.5)
parser.add_argument('--tem_compute_loss_interval', type=float, default=20)
parser.add_argument('--tem_train_subset', type=str, default='train', help='can be train or overfit.')
parser.add_argument('--tem_results_dir', type=str, default=None, help='used for inference to generate the results that PGM_proposals uses.')
parser.add_argument('--tem_results_subset', type=str, default='full', help='can be full, train, or overfit.')
# PEM Training settings
parser.add_argument('--pem_nonlinear_factor', type=float, default=0.1)
parser.add_argument('--pem_training_lr', type=float, default=0.01)
parser.add_argument('--pem_weight_decay', type=float, default=0)
parser.add_argument('--pem_l2_loss', type=float, default=0.000025)
parser.add_argument('--pem_lr_milestones', type=str, default='10')
parser.add_argument('--pem_epoch', type=int, default=20)
parser.add_argument('--pem_step_size', type=int, default=10)
parser.add_argument('--pem_step_gamma', type=float, default=0.1)
parser.add_argument('--pem_batch_size', type=int, default=16)
parser.add_argument('--pem_u_ratio_m', type=float, default=1)
parser.add_argument('--pem_u_ratio_l', type=float, default=2)
parser.add_argument('--pem_high_iou_thres', type=float, default=0.7)
parser.add_argument('--pem_low_iou_thres', type=float, default=0.3)
parser.add_argument('--pem_compute_loss_interval', type=float, default=20)
# PEM inference settings
parser.add_argument('--pem_inference_results_dir', type=str, default=None, help='where to save the pem_inference results.')
parser.add_argument('--pem_inference_subset',
type=str,
default="full")
# PGM settings
parser.add_argument('--pgm_threshold', type=float, default=0.5)
parser.add_argument('--pgm_thread', type=int, default=8)
# The original ahd it s.t. num_sample_start + end + action should equal to pem_feat_dim.
# However, using the Thumos one, it appears to be num_sample_start*2 + num_sample_end*2 + action beacuse the action stuff is included in the first two as well...
parser.add_argument('--num_sample_start', type=int, default=8)
parser.add_argument('--num_sample_end', type=int, default=8)
parser.add_argument(
'--num_sample_action', type=int, default=16
)
parser.add_argument('--num_sample_interpld', type=int, default=3)
parser.add_argument('--bsp_boundary_ratio', type=float, default=0.2)
parser.add_argument('--pgm_proposals_dir', type=str, default=None, help='used to save the pgm proposals.')
parser.add_argument('--pgm_features_dir', type=str, default=None, help='used to save the pgm features.')
parser.add_argument('--pgm_subset', type=str, default='full', help='can be full, train, or overfit.')
parser.add_argument('--pgm_score_threshold', type=float, default=0.5)
# Post processing
parser.add_argument('--post_process_top_K', type=int, default=100)
parser.add_argument('--post_process_thread', type=int, default=8)
parser.add_argument('--do_eval_after_postprocessing', action='store_true')
parser.add_argument('--soft_nms_alpha', type=float, default=0.75)
parser.add_argument('--soft_nms_low_thres', type=float, default=0.65)
parser.add_argument('--soft_nms_high_thres', type=float, default=0.9)
parser.add_argument('--postprocessed_results_dir',
type=str,
default="/checkpoint/cinjon/spaceofmotion/bsn/postprocessing")
parser.add_argument('--save_fig_path',
type=str,
default="./output/evaluation_result.jpg")
parser.add_argument('--do_augment', action='store_true')
parser.add_argument('--do_representation', action='store_true')
parser.add_argument('--do_feat_conversion', action='store_true')
parser.add_argument('--no_freeze', action='store_true', default=False)
parser.add_argument('--do_random_model', action='store_true')
parser.add_argument('--do_gradient_checkpointing', action='store_true', default=False)
parser.add_argument(
'--representation_module',
type=str,
default='corrflow',
help=
'the underlying representation module when using one. should have a forward call that yields a frozen repr and a linear transform func that get sthat representation into a manageable size.'
)
parser.add_argument(
'--representation_checkpoint',
type=str,
default=None,
help='the checkpoint for the underlying representation module.')
# '/checkpoint/cinjon/spaceofmotion/supercons/corrflow.kineticsmodel.pth',
parser.add_argument(
'--representation_tags',
type=str,
default=None,
help='the path to the tags. only used for amdim')
parser.add_argument('--num_videoframes', type=int, default=100)
parser.add_argument('--dist_videoframes', type=int, default=50, help='the frame interval between each sequence.')
parser.add_argument(
'--skip_videoframes',
type=int,
default=5,
help='the number of video frames to skip in between each one. using 1 means that there is no skip.'
)
parser.add_argument('--log_to_comet', action='store_true', default=False)
parser.add_argument('--log_to_comet_every', default=50, type=int)
parser.add_argument('--local_comet_dir',
type=str,
default=None,
help='local dir to process comet locally only. '
'primarily for fb, will stop remote calls.')
parser.add_argument('--name',
type=str,
help='the identifying name of this experiment.',
default=None)
parser.add_argument('--counter',
type=int,
help='the integer counter of this experiment. '
'defaults to None because Cinjon is likely the '
'only one who is going to use it.')
parser.add_argument(
'--data_workers',
type=int,
default=8,
help='the number of workers to pull data',
)
parser.add_argument(
'--seed',
type=int,
default=0,
help='the seed',
)
parser.add_argument(
'--num_gpus',
type=int,
default=1,
help='the seed',
)
parser.add_argument(
'--time',
type=float,
default=4,
help='the number of hours',
)
args = parser.parse_args()
return args