| 2025-07-07 10:57:14,028 - PropVG - INFO - dataset = 'GRefCOCO' | |
| data_root = './data/seqtr_type/' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]) | |
| train_pipeline = [ | |
| dict( | |
| type='LoadImageAnnotationsFromFileGRES_TO', | |
| max_token=50, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='GRefCOCO', | |
| use_token_type='beit3', | |
| refer_file='data/seqtr_type/annotations/grefs/coco_annotations.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(320, 320), keep_ratio=False), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375]), | |
| dict(type='DefaultFormatBundle'), | |
| dict( | |
| type='CollectData', | |
| keys=[ | |
| 'img', 'ref_expr_inds', 'text_attention_mask', 'gt_mask_rle', | |
| 'gt_bbox', 'gt_mask_parts_rle' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', 'pad_shape', | |
| 'scale_factor', 'gt_ori_mask', 'target', 'empty', | |
| 'refer_target_index', 'tokenized_words' | |
| ]) | |
| ] | |
| val_pipeline = [ | |
| dict( | |
| type='LoadImageAnnotationsFromFileGRES_TO', | |
| max_token=50, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='GRefCOCO', | |
| use_token_type='beit3', | |
| refer_file='data/seqtr_type/annotations/grefs/coco_annotations.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(320, 320), keep_ratio=False), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375]), | |
| dict(type='DefaultFormatBundle'), | |
| dict( | |
| type='CollectData', | |
| keys=[ | |
| 'img', 'ref_expr_inds', 'text_attention_mask', 'gt_mask_rle', | |
| 'gt_bbox', 'gt_mask_parts_rle' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', 'pad_shape', | |
| 'scale_factor', 'gt_ori_mask', 'target', 'empty', | |
| 'refer_target_index', 'tokenized_words' | |
| ]) | |
| ] | |
| test_pipeline = [ | |
| dict( | |
| type='LoadImageAnnotationsFromFileGRES_TO', | |
| max_token=50, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='GRefCOCO', | |
| use_token_type='beit3', | |
| refer_file='data/seqtr_type/annotations/grefs/coco_annotations.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(320, 320), keep_ratio=False), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375]), | |
| dict(type='DefaultFormatBundle'), | |
| dict( | |
| type='CollectData', | |
| keys=[ | |
| 'img', 'ref_expr_inds', 'text_attention_mask', 'gt_mask_rle', | |
| 'gt_bbox', 'gt_mask_parts_rle' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', 'pad_shape', | |
| 'scale_factor', 'gt_ori_mask', 'target', 'empty', | |
| 'refer_target_index', 'tokenized_words' | |
| ]) | |
| ] | |
| word_emb_cfg = dict(type='GloVe') | |
| data = dict( | |
| samples_per_gpu=16, | |
| workers_per_gpu=4, | |
| train=dict( | |
| type='GRefCOCO', | |
| which_set='train', | |
| img_source=['coco'], | |
| annsfile='./data/seqtr_type/annotations/grefs/instances.json', | |
| imgsfile='./data/seqtr_type/images/mscoco/train2014', | |
| pipeline=[ | |
| dict( | |
| type='LoadImageAnnotationsFromFileGRES_TO', | |
| max_token=50, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='GRefCOCO', | |
| use_token_type='beit3', | |
| refer_file= | |
| 'data/seqtr_type/annotations/grefs/coco_annotations.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(320, 320), keep_ratio=False), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375]), | |
| dict(type='DefaultFormatBundle'), | |
| dict( | |
| type='CollectData', | |
| keys=[ | |
| 'img', 'ref_expr_inds', 'text_attention_mask', | |
| 'gt_mask_rle', 'gt_bbox', 'gt_mask_parts_rle' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', | |
| 'pad_shape', 'scale_factor', 'gt_ori_mask', 'target', | |
| 'empty', 'refer_target_index', 'tokenized_words' | |
| ]) | |
| ], | |
| word_emb_cfg=dict(type='GloVe')), | |
| val=dict( | |
| type='GRefCOCO', | |
| which_set='val', | |
| img_source=['coco'], | |
| annsfile='./data/seqtr_type/annotations/grefs/instances.json', | |
| imgsfile='./data/seqtr_type/images/mscoco/train2014', | |
| pipeline=[ | |
| dict( | |
| type='LoadImageAnnotationsFromFileGRES_TO', | |
| max_token=50, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='GRefCOCO', | |
| use_token_type='beit3', | |
| refer_file= | |
| 'data/seqtr_type/annotations/grefs/coco_annotations.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(320, 320), keep_ratio=False), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375]), | |
| dict(type='DefaultFormatBundle'), | |
| dict( | |
| type='CollectData', | |
| keys=[ | |
| 'img', 'ref_expr_inds', 'text_attention_mask', | |
| 'gt_mask_rle', 'gt_bbox', 'gt_mask_parts_rle' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', | |
| 'pad_shape', 'scale_factor', 'gt_ori_mask', 'target', | |
| 'empty', 'refer_target_index', 'tokenized_words' | |
| ]) | |
| ], | |
| word_emb_cfg=dict(type='GloVe')), | |
| testA=dict( | |
| type='GRefCOCO', | |
| which_set='testA', | |
| img_source=['coco'], | |
| annsfile='./data/seqtr_type/annotations/grefs/instances.json', | |
| imgsfile='./data/seqtr_type/images/mscoco/train2014', | |
| pipeline=[ | |
| dict( | |
| type='LoadImageAnnotationsFromFileGRES_TO', | |
| max_token=50, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='GRefCOCO', | |
| use_token_type='beit3', | |
| refer_file= | |
| 'data/seqtr_type/annotations/grefs/coco_annotations.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(320, 320), keep_ratio=False), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375]), | |
| dict(type='DefaultFormatBundle'), | |
| dict( | |
| type='CollectData', | |
| keys=[ | |
| 'img', 'ref_expr_inds', 'text_attention_mask', | |
| 'gt_mask_rle', 'gt_bbox', 'gt_mask_parts_rle' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', | |
| 'pad_shape', 'scale_factor', 'gt_ori_mask', 'target', | |
| 'empty', 'refer_target_index', 'tokenized_words' | |
| ]) | |
| ], | |
| word_emb_cfg=dict(type='GloVe')), | |
| testB=dict( | |
| type='GRefCOCO', | |
| which_set='testB', | |
| img_source=['coco'], | |
| annsfile='./data/seqtr_type/annotations/grefs/instances.json', | |
| imgsfile='./data/seqtr_type/images/mscoco/train2014', | |
| pipeline=[ | |
| dict( | |
| type='LoadImageAnnotationsFromFileGRES_TO', | |
| max_token=50, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='GRefCOCO', | |
| use_token_type='beit3', | |
| refer_file= | |
| 'data/seqtr_type/annotations/grefs/coco_annotations.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(320, 320), keep_ratio=False), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375]), | |
| dict(type='DefaultFormatBundle'), | |
| dict( | |
| type='CollectData', | |
| keys=[ | |
| 'img', 'ref_expr_inds', 'text_attention_mask', | |
| 'gt_mask_rle', 'gt_bbox', 'gt_mask_parts_rle' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', | |
| 'pad_shape', 'scale_factor', 'gt_ori_mask', 'target', | |
| 'empty', 'refer_target_index', 'tokenized_words' | |
| ]) | |
| ], | |
| word_emb_cfg=dict(type='GloVe'))) | |
| ema = False | |
| ema_factor = 0.999 | |
| use_fp16 = False | |
| seed = 6666 | |
| deterministic = True | |
| log_level = 'INFO' | |
| log_interval = 50 | |
| save_interval = -1 | |
| resume_from = None | |
| load_from = 'work_dir/gres/PropVG-grefcoco.pth' | |
| finetune_from = None | |
| evaluate_interval = 1 | |
| start_evaluate_epoch = 0 | |
| start_save_checkpoint = 7 | |
| max_token = 50 | |
| img_size = 320 | |
| patch_size = 16 | |
| model = dict( | |
| type='MIXGrefUniModel_OMG', | |
| vis_enc=dict( | |
| type='BEIT3', | |
| img_size=320, | |
| patch_size=16, | |
| vit_type='base', | |
| drop_path_rate=0.1, | |
| vocab_size=64010, | |
| freeze_layer=-1, | |
| vision_embed_proj_interpolate=False, | |
| pretrain='pretrain_weights/beit3_base_patch16_224.zip'), | |
| lan_enc=None, | |
| fusion=None, | |
| head=dict( | |
| type='GTMHead', | |
| input_channels=768, | |
| hidden_channels=256, | |
| num_queries=20, | |
| detr_loss=dict( | |
| criterion=dict(loss_class=1.0, loss_bbox=5.0, loss_giou=2.0), | |
| matcher=dict(cost_class=1.0, cost_bbox=5.0, cost_giou=2.0)), | |
| loss_weight=dict( | |
| mask=dict(dice=1.0, bce=1.0, nt=0.2, neg=0), | |
| bbox=0.1, | |
| allbbox=0.1, | |
| refer=1.0), | |
| MTD=dict(K=250)), | |
| post_params=dict( | |
| score_weighted=False, | |
| mask_threshold=0.5, | |
| score_threshold=0.7, | |
| with_nms=False, | |
| with_mask=True), | |
| process_visual=False, | |
| visualize_params=dict(row_columns=(4, 5)), | |
| visual_mode='test') | |
| grad_norm_clip = 0.15 | |
| lr = 0.0005 | |
| optimizer_config = dict( | |
| type='Adam', | |
| lr=0.0005, | |
| lr_vis_enc=5e-05, | |
| lr_lan_enc=0.0005, | |
| betas=(0.9, 0.98), | |
| eps=1e-09, | |
| weight_decay=0, | |
| amsgrad=True) | |
| scheduler_config = dict( | |
| type='MultiStepLRWarmUp', | |
| warmup_epochs=1, | |
| decay_steps=[7, 11], | |
| decay_ratio=0.1, | |
| max_epoch=12) | |
| launcher = 'pytorch' | |
| distributed = True | |
| rank = 0 | |
| world_size = 4 | |
| 2025-07-07 10:57:25,861 - PropVG - INFO - GRefCOCO-val size: 16870 | |
| 2025-07-07 10:57:37,626 - PropVG - INFO - GRefCOCO-testA size: 18712 | |
| 2025-07-07 10:57:49,703 - PropVG - INFO - GRefCOCO-testB size: 14933 | |
| 2025-07-07 10:57:55,300 - PropVG - INFO - loaded checkpoint from work_dir/gres/PropVG-grefcoco.pth | |
| 2025-07-07 10:57:55,323 - PropVG - INFO - PropVG - evaluating set val | |
| 2025-07-07 10:59:51,470 - PropVG - INFO - ------------ validate ------------ time: 116.14, F1score: 72.16, Nacc: 72.83, Tacc: 96.93, gIoU: 73.29, cIoU: 69.23, MaskACC@0.7-0.9: [74.74, 60.99, 23.42 | |
| 2025-07-07 10:59:52,918 - PropVG - INFO - PropVG - evaluating set testA | |
| 2025-07-07 11:01:57,887 - PropVG - INFO - ------------ validate ------------ time: 124.96, F1score: 68.77, Nacc: 69.87, Tacc: 96.56, gIoU: 74.43, cIoU: 74.20, MaskACC@0.7-0.9: [77.48, 65.93, 30.06 | |
| 2025-07-07 11:01:59,563 - PropVG - INFO - PropVG - evaluating set testB | |
| 2025-07-07 11:03:41,160 - PropVG - INFO - ------------ validate ------------ time: 101.59, F1score: 59.02, Nacc: 64.97, Tacc: 91.68, gIoU: 65.87, cIoU: 64.76, MaskACC@0.7-0.9: [62.03, 51.61, 28.43 | |
| 2025-07-07 11:03:42,844 - PropVG - INFO - sucessfully save the results to work_dir/gres/refer_output_thr0.7_no-nms_no-sw_0.5_250.xlsx !!! | |