| 2025-07-07 11:09:02,802 - PropVG - INFO - dataset = 'RefCOCOPlusUNC' | |
| 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='LoadImageAnnotationsFromFile_TO', | |
| max_token=20, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='RefCOCOPlusUNC', | |
| use_token_type='beit3', | |
| refer_file='data/seqtr_type/annotations/mixed-seg/coco_all.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(384, 384), 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' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', 'pad_shape', | |
| 'scale_factor', 'gt_ori_mask', 'target', 'empty', | |
| 'refer_target_index' | |
| ]) | |
| ] | |
| val_pipeline = [ | |
| dict( | |
| type='LoadImageAnnotationsFromFile_TO', | |
| max_token=20, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='RefCOCOPlusUNC', | |
| use_token_type='beit3', | |
| refer_file='data/seqtr_type/annotations/mixed-seg/coco_all.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(384, 384), 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' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', 'pad_shape', | |
| 'scale_factor', 'gt_ori_mask', 'target', 'empty', | |
| 'refer_target_index' | |
| ]) | |
| ] | |
| test_pipeline = [ | |
| dict( | |
| type='LoadImageAnnotationsFromFile_TO', | |
| max_token=20, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='RefCOCOPlusUNC', | |
| use_token_type='beit3', | |
| refer_file='data/seqtr_type/annotations/mixed-seg/coco_all.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(384, 384), 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' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', 'pad_shape', | |
| 'scale_factor', 'gt_ori_mask', 'target', 'empty', | |
| 'refer_target_index' | |
| ]) | |
| ] | |
| word_emb_cfg = dict(type='GloVe') | |
| data = dict( | |
| samples_per_gpu=8, | |
| workers_per_gpu=4, | |
| train=dict( | |
| type='RefCOCOPlusUNC', | |
| which_set='train', | |
| img_source=['coco'], | |
| annsfile= | |
| './data/seqtr_type/annotations/refcocoplus-unc/instances_withid.json', | |
| imgsfile='./data/seqtr_type/images/mscoco/train2014', | |
| pipeline=[ | |
| dict( | |
| type='LoadImageAnnotationsFromFile_TO', | |
| max_token=20, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='RefCOCOPlusUNC', | |
| use_token_type='beit3', | |
| refer_file= | |
| 'data/seqtr_type/annotations/mixed-seg/coco_all.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(384, 384), 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' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', | |
| 'pad_shape', 'scale_factor', 'gt_ori_mask', 'target', | |
| 'empty', 'refer_target_index' | |
| ]) | |
| ], | |
| word_emb_cfg=dict(type='GloVe')), | |
| val=dict( | |
| type='RefCOCOPlusUNC', | |
| which_set='val', | |
| img_source=['coco'], | |
| annsfile= | |
| './data/seqtr_type/annotations/refcocoplus-unc/instances_withid.json', | |
| imgsfile='./data/seqtr_type/images/mscoco/train2014', | |
| pipeline=[ | |
| dict( | |
| type='LoadImageAnnotationsFromFile_TO', | |
| max_token=20, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='RefCOCOPlusUNC', | |
| use_token_type='beit3', | |
| refer_file= | |
| 'data/seqtr_type/annotations/mixed-seg/coco_all.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(384, 384), 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' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', | |
| 'pad_shape', 'scale_factor', 'gt_ori_mask', 'target', | |
| 'empty', 'refer_target_index' | |
| ]) | |
| ], | |
| word_emb_cfg=dict(type='GloVe')), | |
| testA=dict( | |
| type='RefCOCOPlusUNC', | |
| which_set='testA', | |
| img_source=['coco'], | |
| annsfile= | |
| './data/seqtr_type/annotations/refcocoplus-unc/instances_withid.json', | |
| imgsfile='./data/seqtr_type/images/mscoco/train2014', | |
| pipeline=[ | |
| dict( | |
| type='LoadImageAnnotationsFromFile_TO', | |
| max_token=20, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='RefCOCOPlusUNC', | |
| use_token_type='beit3', | |
| refer_file= | |
| 'data/seqtr_type/annotations/mixed-seg/coco_all.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(384, 384), 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' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', | |
| 'pad_shape', 'scale_factor', 'gt_ori_mask', 'target', | |
| 'empty', 'refer_target_index' | |
| ]) | |
| ], | |
| word_emb_cfg=dict(type='GloVe')), | |
| testB=dict( | |
| type='RefCOCOPlusUNC', | |
| which_set='testB', | |
| img_source=['coco'], | |
| annsfile= | |
| './data/seqtr_type/annotations/refcocoplus-unc/instances_withid.json', | |
| imgsfile='./data/seqtr_type/images/mscoco/train2014', | |
| pipeline=[ | |
| dict( | |
| type='LoadImageAnnotationsFromFile_TO', | |
| max_token=20, | |
| with_mask=True, | |
| with_bbox=True, | |
| dataset='RefCOCOPlusUNC', | |
| use_token_type='beit3', | |
| refer_file= | |
| 'data/seqtr_type/annotations/mixed-seg/coco_all.json', | |
| object_area_filter=100, | |
| object_area_rate_filter=[0.05, 0.8]), | |
| dict(type='Resize', img_scale=(384, 384), 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' | |
| ], | |
| meta_keys=[ | |
| 'filename', 'expression', 'ori_shape', 'img_shape', | |
| 'pad_shape', 'scale_factor', 'gt_ori_mask', 'target', | |
| 'empty', 'refer_target_index' | |
| ]) | |
| ], | |
| 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/refcoco+/PropVG-refcoco+.pth' | |
| finetune_from = None | |
| evaluate_interval = 1 | |
| start_evaluate_epoch = 0 | |
| start_save_checkpoint = 20 | |
| max_token = 20 | |
| img_size = 384 | |
| patch_size = 16 | |
| model = dict( | |
| type='MIXRefUniModel_OMG', | |
| vis_enc=dict( | |
| type='BEIT3', | |
| img_size=384, | |
| 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='REFHead', | |
| 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=100)), | |
| post_params=dict( | |
| score_weighted=False, | |
| mask_threshold=0.5, | |
| score_threshold=0.7, | |
| with_nms=False, | |
| with_mask=True), | |
| process_visual=True, | |
| 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=[21, 27], | |
| decay_ratio=0.1, | |
| max_epoch=30) | |
| launcher = 'pytorch' | |
| distributed = True | |
| rank = 0 | |
| world_size = 4 | |
| 2025-07-07 11:09:07,978 - PropVG - INFO - RefCOCOPlusUNC-val size: 10758 | |
| 2025-07-07 11:09:13,867 - PropVG - INFO - RefCOCOPlusUNC-testA size: 5726 | |
| 2025-07-07 11:09:19,990 - PropVG - INFO - RefCOCOPlusUNC-testB size: 4889 | |
| 2025-07-07 11:09:24,879 - PropVG - INFO - loaded checkpoint from work_dir/refcoco+/PropVG-refcoco+.pth | |
| 2025-07-07 11:09:24,886 - PropVG - INFO - PropVG - evaluating set val | |
| 2025-07-07 11:11:17,140 - PropVG - INFO - ------------ validate ------------ time: 112.25, DetACC: 83.73, mIoU: 72.94, oIoU: 70.24, MaskACC@0.5-0.9: [83.12, 80.60, 76.04, 65.37, 33.26]DetACC@0.5-0.9: [83.73, 81.30, 77.10, 68.58, 42.65] | |
| 2025-07-07 11:11:18,910 - PropVG - INFO - PropVG - evaluating set testA | |
| 2025-07-07 11:12:32,835 - PropVG - INFO - ------------ validate ------------ time: 73.92, DetACC: 88.01, mIoU: 76.49, oIoU: 74.32, MaskACC@0.5-0.9: [88.04, 86.00, 81.37, 70.53, 33.52]DetACC@0.5-0.9: [88.01, 85.91, 82.12, 73.80, 47.14] | |
| 2025-07-07 11:12:34,541 - PropVG - INFO - PropVG - evaluating set testB | |
| 2025-07-07 11:13:39,576 - PropVG - INFO - ------------ validate ------------ time: 65.03, DetACC: 76.59, mIoU: 67.21, oIoU: 63.41, MaskACC@0.5-0.9: [75.57, 71.83, 66.95, 57.38, 33.87]DetACC@0.5-0.9: [76.59, 73.26, 68.11, 59.24, 36.12] | |
| 2025-07-07 11:13:41,507 - PropVG - INFO - sucessfully save the results to work_dir/refcoco+/refer_output_thr0.7_no-nms_no-sw_0.5_100.xlsx !!! | |