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| _base_ = [ | |
| './psgformer_r50.py', '../_base_/datasets/psg.py', | |
| '../_base_/custom_runtime.py' | |
| ] | |
| find_unused_parameters = True | |
| custom_imports = dict(imports=[ | |
| 'openpsg.models.frameworks.psgtr', 'openpsg.models.losses.seg_losses', | |
| 'openpsg.models.frameworks.dual_transformer', | |
| 'openpsg.models.relation_heads.psgformer_head', 'openpsg.datasets', | |
| 'openpsg.datasets.pipelines.loading', | |
| 'openpsg.datasets.pipelines.rel_randomcrop', | |
| 'openpsg.models.relation_heads.approaches.matcher', 'openpsg.utils' | |
| ], | |
| allow_failed_imports=False) | |
| dataset_type = 'PanopticSceneGraphDataset' | |
| # HACK: | |
| object_classes = [ | |
| 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', | |
| 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', | |
| 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', | |
| 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', | |
| 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', | |
| 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', | |
| 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', | |
| 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', | |
| 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', | |
| 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', | |
| 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', | |
| 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', | |
| 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', | |
| 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', | |
| 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', | |
| 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', | |
| 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', | |
| 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', | |
| 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', | |
| 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', | |
| 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', | |
| 'food-other-merged', 'building-other-merged', 'rock-merged', | |
| 'wall-other-merged', 'rug-merged' | |
| ] | |
| predicate_classes = [ | |
| 'over', | |
| 'in front of', | |
| 'beside', | |
| 'on', | |
| 'in', | |
| 'attached to', | |
| 'hanging from', | |
| 'on back of', | |
| 'falling off', | |
| 'going down', | |
| 'painted on', | |
| 'walking on', | |
| 'running on', | |
| 'crossing', | |
| 'standing on', | |
| 'lying on', | |
| 'sitting on', | |
| 'flying over', | |
| 'jumping over', | |
| 'jumping from', | |
| 'wearing', | |
| 'holding', | |
| 'carrying', | |
| 'looking at', | |
| 'guiding', | |
| 'kissing', | |
| 'eating', | |
| 'drinking', | |
| 'feeding', | |
| 'biting', | |
| 'catching', | |
| 'picking', | |
| 'playing with', | |
| 'chasing', | |
| 'climbing', | |
| 'cleaning', | |
| 'playing', | |
| 'touching', | |
| 'pushing', | |
| 'pulling', | |
| 'opening', | |
| 'cooking', | |
| 'talking to', | |
| 'throwing', | |
| 'slicing', | |
| 'driving', | |
| 'riding', | |
| 'parked on', | |
| 'driving on', | |
| 'about to hit', | |
| 'kicking', | |
| 'swinging', | |
| 'entering', | |
| 'exiting', | |
| 'enclosing', | |
| 'leaning on', | |
| ] | |
| model = dict(bbox_head=dict( | |
| num_classes=len(object_classes), | |
| num_relations=len(predicate_classes), | |
| object_classes=object_classes, | |
| predicate_classes=predicate_classes, | |
| num_obj_query=100, | |
| num_rel_query=100, | |
| ), ) | |
| img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True) | |
| # train_pipeline, NOTE the img_scale and the Pad's size_divisor is different | |
| # from the default setting in mmdet. | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadPanopticSceneGraphAnnotations', | |
| with_bbox=True, | |
| with_rel=True, | |
| with_mask=True, | |
| with_seg=True), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict( | |
| type='AutoAugment', | |
| policies=[ | |
| [ | |
| dict(type='Resize', | |
| img_scale=[(480, 1333), (512, 1333), (544, 1333), | |
| (576, 1333), (608, 1333), (640, 1333), | |
| (672, 1333), (704, 1333), (736, 1333), | |
| (768, 1333), (800, 1333)], | |
| multiscale_mode='value', | |
| keep_ratio=True) | |
| ], | |
| [ | |
| dict(type='Resize', | |
| img_scale=[(400, 1333), (500, 1333), (600, 1333)], | |
| multiscale_mode='value', | |
| keep_ratio=True), | |
| dict(type='RelRandomCrop', | |
| crop_type='absolute_range', | |
| crop_size=(384, 600), | |
| allow_negative_crop=False), # no empty relations | |
| dict(type='Resize', | |
| img_scale=[(480, 1333), (512, 1333), (544, 1333), | |
| (576, 1333), (608, 1333), (640, 1333), | |
| (672, 1333), (704, 1333), (736, 1333), | |
| (768, 1333), (800, 1333)], | |
| multiscale_mode='value', | |
| override=True, | |
| keep_ratio=True) | |
| ] | |
| ]), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=1), | |
| dict(type='RelsFormatBundle'), | |
| dict(type='Collect', | |
| keys=['img', 'gt_bboxes', 'gt_labels', 'gt_rels', 'gt_masks']) | |
| ] | |
| # test_pipeline, NOTE the Pad's size_divisor is different from the default | |
| # setting (size_divisor=32). While there is little effect on the performance | |
| # whether we use the default setting or use size_divisor=1. | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadSceneGraphAnnotations', with_bbox=True, with_rel=True), | |
| dict(type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=1), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='ToTensor', keys=['gt_bboxes', 'gt_labels']), | |
| dict(type='ToDataContainer', | |
| fields=(dict(key='gt_bboxes'), dict(key='gt_labels'))), | |
| dict(type='Collect', keys=['img']), | |
| ]) | |
| ] | |
| evaluation = dict( | |
| interval=1, | |
| metric='sgdet', | |
| relation_mode=True, | |
| classwise=True, | |
| iou_thrs=0.5, | |
| detection_method='pan_seg', | |
| ) | |
| data = dict(samples_per_gpu=1, | |
| workers_per_gpu=2, | |
| train=dict(pipeline=train_pipeline), | |
| val=dict(pipeline=test_pipeline), | |
| test=dict(pipeline=test_pipeline)) | |
| # optimizer | |
| optimizer = dict( | |
| type='AdamW', | |
| lr=0.001, | |
| weight_decay=0.0001, | |
| paramwise_cfg=dict( | |
| custom_keys={ | |
| 'backbone': dict(lr_mult=0.1, decay_mult=1.0), | |
| 'transformer.encoder': dict(lr_mult=0.1, decay_mult=1.0), | |
| 'transformer.decoder1': dict(lr_mult=0.1, decay_mult=1.0), | |
| 'obj_query_embed': dict(lr_mult=0.1, decay_mult=1.0), | |
| 'input_proj': dict(lr_mult=0.1, decay_mult=1.0), | |
| 'class_embed': dict(lr_mult=0.1, decay_mult=1.0), | |
| 'box_embed': dict(lr_mult=0.1, decay_mult=1.0), | |
| 'bbox_attention': dict(lr_mult=0.1, decay_mult=1.0), | |
| 'mask_head': dict(lr_mult=0.1, decay_mult=1.0), | |
| })) | |
| optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) | |
| # learning policy | |
| lr_config = dict(policy='step', step=40) | |
| runner = dict(type='EpochBasedRunner', max_epochs=60) | |
| project_name = 'psgformer' | |
| expt_name = 'psgformer_r50_psg' | |
| work_dir = f'./work_dirs/{expt_name}' | |
| checkpoint_config = dict(interval=1, max_keep_ckpts=15) | |
| log_config = dict( | |
| interval=50, | |
| hooks=[ | |
| dict(type='TextLoggerHook'), | |
| dict( | |
| type='WandbLoggerHook', | |
| init_kwargs=dict( | |
| project=project_name, | |
| name=expt_name, | |
| ), | |
| ) | |
| ], | |
| ) | |
| load_from = './work_dirs/checkpoints/detr4psgformer_r50.pth' | |