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opts.py
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import argparse
def get_args_parser():
parser = argparse.ArgumentParser('ReferFormer training and inference scripts.', add_help=False)
parser.add_argument('--lr', default=1e-4, type=float)
parser.add_argument('--lr_backbone', default=6e-6, type=float)
parser.add_argument('--lr_backbone_names', default=['backbone.0'], type=str, nargs='+')
parser.add_argument('--lr_text_encoder', default=1e-5, type=float)
parser.add_argument('--lr_text_encoder_names', default=['text_encoder'], type=str, nargs='+')
parser.add_argument('--lr_linear_proj_names', default=['reference_points', 'sampling_offsets'], type=str, nargs='+')
parser.add_argument('--lr_linear_proj_mult', default=1.0, type=float)
parser.add_argument('--batch_size', default=1, type=int)
parser.add_argument('--weight_decay', default=5e-4, type=float)
parser.add_argument('--epochs', default=10, type=int)
parser.add_argument('--lr_drop', default=[6, 8], type=int, nargs='+')
parser.add_argument('--clip_max_norm', default=0.1, type=float,
help='gradient clipping max norm')
# Model parameters
# load the pretrained weights
parser.add_argument('--pretrained_weights', type=str, default=None,
help="Path to the pretrained model.")
# Variants of Deformable DETR
parser.add_argument('--with_box_refine', default=False, action='store_true')
parser.add_argument('--two_stage', default=False, action='store_true') # NOTE: must be false
# * Backbone
# ["resnet50", "resnet101", "swin_t_p4w7", "swin_s_p4w7", "swin_b_p4w7", "swin_l_p4w7"]
# ["video_swin_t_p4w7", "video_swin_s_p4w7", "video_swin_b_p4w7"]
parser.add_argument('--backbone', default='resnet50', type=str,
help="Name of the convolutional backbone to use")
parser.add_argument('--backbone_pretrained', default=None, type=str,
help="if use swin backbone and train from scratch, the path to the pretrained weights")
parser.add_argument('--use_checkpoint', action='store_true', help='whether use checkpoint for swin/video swin backbone')
parser.add_argument('--dilation', action='store_true', # DC5
help="If true, we replace stride with dilation in the last convolutional block (DC5)")
parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),
help="Type of positional embedding to use on top of the image features")
parser.add_argument('--num_feature_levels', default=4, type=int, help='number of feature levels')
# * Transformer
parser.add_argument('--enc_layers', default=4, type=int,
help="Number of encoding layers in the transformer")
parser.add_argument('--dec_layers', default=4, type=int,
help="Number of decoding layers in the transformer")
parser.add_argument('--dim_feedforward', default=2048, type=int,
help="Intermediate size of the feedforward layers in the transformer blocks")
parser.add_argument('--hidden_dim', default=256, type=int,
help="Size of the embeddings (dimension of the transformer)")
parser.add_argument('--dropout', default=0.1, type=float,
help="Dropout applied in the transformer")
parser.add_argument('--nheads', default=8, type=int,
help="Number of attention heads inside the transformer's attentions")
parser.add_argument('--num_frames', default=5, type=int,
help="Number of clip frames for training")
parser.add_argument('--num_queries', default=5, type=int,
help="Number of query slots, all frames share the same queries")
parser.add_argument('--dec_n_points', default=4, type=int)
parser.add_argument('--enc_n_points', default=4, type=int)
parser.add_argument('--pre_norm', action='store_true')
# for text
parser.add_argument('--freeze_text_encoder', action='store_true') # default: False
# * Segmentation
parser.add_argument('--masks', action='store_true',
help="Train segmentation head if the flag is provided")
parser.add_argument('--mask_dim', default=256, type=int,
help="Size of the mask embeddings (dimension of the dynamic mask conv)")
parser.add_argument('--controller_layers', default=3, type=int,
help="Dynamic conv layer number")
parser.add_argument('--dynamic_mask_channels', default=8, type=int,
help="Dynamic conv final channel number")
parser.add_argument('--no_rel_coord', dest='rel_coord', action='store_false',
help="Disables relative coordinates")
# Loss
parser.add_argument('--no_aux_loss', dest='aux_loss', action='store_false',
help="Disables auxiliary decoding losses (loss at each layer)")
# * Matcher
parser.add_argument('--set_cost_class', default=2, type=float,
help="Class coefficient in the matching cost")
parser.add_argument('--set_cost_bbox', default=5, type=float,
help="L1 box coefficient in the matching cost")
parser.add_argument('--set_cost_giou', default=2, type=float,
help="giou box coefficient in the matching cost")
parser.add_argument('--set_cost_mask', default=2, type=float,
help="mask coefficient in the matching cost")
parser.add_argument('--set_cost_dice', default=5, type=float,
help="mask coefficient in the matching cost")
# * Loss coefficients
parser.add_argument('--mask_loss_coef', default=2, type=float)
parser.add_argument('--dice_loss_coef', default=5, type=float)
parser.add_argument('--cls_loss_coef', default=2, type=float)
parser.add_argument('--bbox_loss_coef', default=5, type=float)
parser.add_argument('--giou_loss_coef', default=2, type=float)
parser.add_argument('--eos_coef', default=0.1, type=float,
help="Relative classification weight of the no-object class")
parser.add_argument('--focal_alpha', default=0.25, type=float)
# dataset parameters
# ['ytvos', 'davis', 'refcoco', 'refcoco+', 'refcocog', 'all']
# 'all': using the three ref datasets for pretraining
parser.add_argument('--dataset_file', default='ytvos', help='Dataset name')
parser.add_argument('--coco_path', type=str, default='data/coco')
parser.add_argument('--ytvos_path', type=str, default='data/ref-youtube-vos')
parser.add_argument('--mevis_path', type=str, default='data/MeVIS')
parser.add_argument('--davis_path', type=str, default='data/ref-davis')
parser.add_argument('--max_skip', default=3, type=int, help="max skip frame number")
parser.add_argument('--max_size', default=640, type=int, help="max size for the frame")
parser.add_argument('--binary', action='store_true')
parser.add_argument('--remove_difficult', action='store_true')
parser.add_argument('--output_dir', default='output',
help='path where to save, empty for no saving')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--resume', default='', help='resume from checkpoint')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--eval', action='store_true')
parser.add_argument('--num_workers', default=4, type=int)
# test setting
parser.add_argument('--threshold', default=0.5, type=float) # binary threshold for mask
parser.add_argument('--ngpu', default=1, type=int, help='gpu number when inference for ref-ytvos and ref-davis')
parser.add_argument('--split', default='valid', type=str, choices=['valid', 'test'])
parser.add_argument('--visualize', action='store_true', help='whether visualize the masks during inference')
# distributed training parameters
parser.add_argument('--world_size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
parser.add_argument('--cache_mode', default=False, action='store_true', help='whether to cache images on memory')
parser.add_argument('--model_ckpt', type=str, default='checkpoint.pth')
parser.add_argument('--test_num_ckpt', default=-2, type=int)
# training technologies
parser.add_argument("--use_fp16", default=False, action="store_true")
# context setting
parser.add_argument("--no_sampling", default=False, action="store_true")
parser.add_argument('--sub_video_len', default=30, type=int)
return parser