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| model = dict( | |
| type='CascadeRCNN', | |
| backbone=dict( | |
| type='SwinTransformer', | |
| embed_dims=96, | |
| depths=[2, 2, 6, 2], | |
| num_heads=[3, 6, 12, 24], | |
| window_size=7, | |
| mlp_ratio=4, | |
| qkv_bias=True, | |
| qk_scale=None, | |
| drop_rate=0.0, | |
| attn_drop_rate=0.0, | |
| drop_path_rate=0.2, | |
| patch_norm=True, | |
| out_indices=(0, 1, 2, 3), | |
| with_cp=False, | |
| convert_weights=True, | |
| init_cfg=None), | |
| neck=dict( | |
| type='FPN', | |
| in_channels=[96, 192, 384, 768], | |
| out_channels=256, | |
| num_outs=5), | |
| rpn_head=dict( | |
| type='RPNHead', | |
| in_channels=256, | |
| feat_channels=256, | |
| anchor_generator=dict( | |
| type='AnchorGenerator', | |
| scales=[8], | |
| ratios=[0.5, 1.0, 2.0], | |
| strides=[4, 8, 16, 32, 64]), | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[0.0, 0.0, 0.0, 0.0], | |
| target_stds=[1.0, 1.0, 1.0, 1.0]), | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | |
| loss_bbox=dict( | |
| type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)), | |
| roi_head=dict( | |
| type='CascadeRoIHead_LGF', | |
| num_stages=3, | |
| stage_loss_weights=[1, 1, 0.5], | |
| bbox_roi_extractor=dict( | |
| type='SingleRoIExtractor', | |
| roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), | |
| out_channels=256, | |
| featmap_strides=[4, 8, 16, 32]), | |
| bbox_head=[ | |
| dict( | |
| type='Shared3FCBBoxHead_with_BboxEncoding', | |
| in_channels=256, | |
| fc_out_channels=1024, | |
| bbox_encoding_dim=512, | |
| roi_feat_size=7, | |
| num_classes=18, | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[0.0, 0.0, 0.0, 0.0], | |
| target_stds=[0.1, 0.1, 0.2, 0.2]), | |
| reg_class_agnostic=True, | |
| loss_cls=dict(type='FocalLoss'), | |
| loss_bbox=dict( | |
| type='BalancedL1Loss', beta=1.0, loss_weight=1.0)), | |
| dict( | |
| type='Shared3FCBBoxHead_with_BboxEncoding', | |
| in_channels=256, | |
| fc_out_channels=1024, | |
| bbox_encoding_dim=512, | |
| roi_feat_size=7, | |
| num_classes=18, | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[0.0, 0.0, 0.0, 0.0], | |
| target_stds=[0.05, 0.05, 0.1, 0.1]), | |
| reg_class_agnostic=True, | |
| loss_cls=dict(type='FocalLoss'), | |
| loss_bbox=dict( | |
| type='BalancedL1Loss', beta=1.0, loss_weight=1.0)), | |
| dict( | |
| type='Shared3FCBBoxHead_with_BboxEncoding', | |
| in_channels=256, | |
| fc_out_channels=1024, | |
| bbox_encoding_dim=512, | |
| roi_feat_size=7, | |
| num_classes=18, | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[0.0, 0.0, 0.0, 0.0], | |
| target_stds=[0.033, 0.033, 0.067, 0.067]), | |
| reg_class_agnostic=True, | |
| loss_cls=dict(type='FocalLoss'), | |
| loss_bbox=dict( | |
| type='BalancedL1Loss', beta=1.0, loss_weight=1.0)) | |
| ], | |
| localglobal_fuser=dict( | |
| type='LocalGlobal_Context_Fuser', | |
| channels=256, | |
| roi_size=7, | |
| reduced_channels=256, | |
| lg_merge_layer=dict(type='SELayer', channels=256)), | |
| lgf_shared=False, | |
| bbox_encoder=dict( | |
| type='BboxEncoder', | |
| n_layer=4, | |
| n_head=4, | |
| n_embd=512, | |
| bbox_cord_dim=4, | |
| bbox_max_num=1024, | |
| embd_pdrop=0.1, | |
| attn_pdrop=0.1), | |
| bbox_encoder_shared=False), | |
| train_cfg=dict( | |
| rpn=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.7, | |
| neg_iou_thr=0.3, | |
| min_pos_iou=0.3, | |
| match_low_quality=True, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=256, | |
| pos_fraction=0.5, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=False), | |
| allowed_border=0, | |
| pos_weight=-1, | |
| debug=False), | |
| rpn_proposal=dict( | |
| nms_pre=2000, | |
| max_per_img=2000, | |
| nms=dict(type='nms', iou_threshold=0.7), | |
| min_bbox_size=0), | |
| rcnn=[ | |
| dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.5, | |
| neg_iou_thr=0.5, | |
| min_pos_iou=0.5, | |
| match_low_quality=False, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=512, | |
| pos_fraction=0.25, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=True), | |
| pos_weight=-1, | |
| debug=False), | |
| dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.6, | |
| neg_iou_thr=0.6, | |
| min_pos_iou=0.6, | |
| match_low_quality=False, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=512, | |
| pos_fraction=0.25, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=True), | |
| pos_weight=-1, | |
| debug=False), | |
| dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.7, | |
| neg_iou_thr=0.7, | |
| min_pos_iou=0.7, | |
| match_low_quality=False, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=512, | |
| pos_fraction=0.25, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=True), | |
| pos_weight=-1, | |
| debug=False) | |
| ]), | |
| test_cfg=dict( | |
| rpn=dict( | |
| nms_pre=1000, | |
| max_per_img=1000, | |
| nms=dict(type='nms', iou_threshold=0.7), | |
| min_bbox_size=0), | |
| rcnn=dict( | |
| score_thr=0., | |
| nms=dict(type='nms', iou_threshold=0.7), | |
| max_per_img=200))) | |
| dataset_type = 'CocoDataset' | |
| data_root = 'data/coco/' | |
| img_norm_cfg = dict( | |
| mean=[216.45, 212.36, 206.76], std=[55.82, 56.04, 55.56], to_rgb=True) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='AutoAugment', | |
| policies=[[{ | |
| '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 | |
| }], | |
| [{ | |
| 'type': 'Resize', | |
| 'img_scale': [(400, 1333), (500, 1333), (600, 1333)], | |
| 'multiscale_mode': 'value', | |
| 'keep_ratio': True | |
| }, { | |
| 'type': 'RandomCrop', | |
| 'crop_type': 'absolute_range', | |
| 'crop_size': (384, 600), | |
| 'allow_negative_crop': True | |
| }, { | |
| '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 | |
| }, { | |
| 'type': 'PhotoMetricDistortion', | |
| 'brightness_delta': 32, | |
| 'contrast_range': (0.5, 1.5), | |
| 'saturation_range': (0.5, 1.5), | |
| 'hue_delta': 18 | |
| }, { | |
| 'type': 'MinIoURandomCrop', | |
| 'min_ious': (0.4, 0.5, 0.6, 0.7, 0.8, 0.9), | |
| 'min_crop_size': 0.3 | |
| }, { | |
| 'type': | |
| 'CutOut', | |
| 'n_holes': (5, 10), | |
| 'cutout_shape': [(4, 4), (4, 8), (8, 4), (8, 8), | |
| (16, 32), (32, 16), (32, 32), (32, 48), | |
| (48, 32), (48, 48)] | |
| }]]), | |
| dict(type='RandomFlip', flip_ratio=0.1), | |
| dict( | |
| type='Normalize', | |
| mean=[216.45, 212.36, 206.76], | |
| std=[55.82, 56.04, 55.56], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip', flip_ratio=0.0), | |
| dict( | |
| type='Normalize', | |
| mean=[216.45, 212.36, 206.76], | |
| std=[55.82, 56.04, 55.56], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ] | |
| data = dict( | |
| samples_per_gpu=3, | |
| workers_per_gpu=4, | |
| train=dict( | |
| type='CocoDataset', | |
| ann_file= | |
| './data/pmc_2022/pmc_coco/element_detection/train.json', | |
| img_prefix= | |
| './data/pmc_2022/pmc_coco/element_detection/train/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='AutoAugment', | |
| policies=[[{ | |
| '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 | |
| }], | |
| [{ | |
| 'type': 'Resize', | |
| 'img_scale': [(400, 1333), (500, 1333), | |
| (600, 1333)], | |
| 'multiscale_mode': 'value', | |
| 'keep_ratio': True | |
| }, { | |
| 'type': 'RandomCrop', | |
| 'crop_type': 'absolute_range', | |
| 'crop_size': (384, 600), | |
| 'allow_negative_crop': True | |
| }, { | |
| '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 | |
| }, { | |
| 'type': 'PhotoMetricDistortion', | |
| 'brightness_delta': 32, | |
| 'contrast_range': (0.5, 1.5), | |
| 'saturation_range': (0.5, 1.5), | |
| 'hue_delta': 18 | |
| }, { | |
| 'type': 'MinIoURandomCrop', | |
| 'min_ious': (0.4, 0.5, 0.6, 0.7, 0.8, 0.9), | |
| 'min_crop_size': 0.3 | |
| }, { | |
| 'type': | |
| 'CutOut', | |
| 'n_holes': (5, 10), | |
| 'cutout_shape': [(4, 4), (4, 8), (8, 4), (8, 8), | |
| (16, 32), (32, 16), (32, 32), | |
| (32, 48), (48, 32), (48, 48)] | |
| }]]), | |
| dict(type='RandomFlip', flip_ratio=0.1), | |
| dict( | |
| type='Normalize', | |
| mean=[216.45, 212.36, 206.76], | |
| std=[55.82, 56.04, 55.56], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) | |
| ], | |
| classes=[ | |
| 'x_title', 'y_title', 'plot_area', 'other', 'xlabel', 'ylabel', | |
| 'chart_title', 'x_tick', 'y_tick', 'legend_patch', 'legend_label', | |
| 'legend_title', 'legend_area', 'mark_label', 'value_label', | |
| 'y_axis_area', 'x_axis_area', 'tick_grouping' | |
| ]), | |
| val=dict( | |
| type='CocoDataset', | |
| ann_file= | |
| './data/pmc_2022/pmc_coco/element_detection/val.json', | |
| img_prefix= | |
| './data/pmc_2022/pmc_coco/element_detection/val/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ], | |
| classes=[ | |
| 'x_title', 'y_title', 'plot_area', 'other', 'xlabel', 'ylabel', | |
| 'chart_title', 'x_tick', 'y_tick', 'legend_patch', 'legend_label', | |
| 'legend_title', 'legend_area', 'mark_label', 'value_label', | |
| 'y_axis_area', 'x_axis_area', 'tick_grouping' | |
| ]), | |
| test=dict( | |
| type='CocoDataset', | |
| ann_file= | |
| './data/pmc_2022/pmc_coco/element_detection/split3_test.json', | |
| img_prefix= | |
| './data/pmc_2022/pmc_coco/element_detection/split3_test/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ], | |
| classes=[ | |
| 'x_title', 'y_title', 'plot_area', 'other', 'xlabel', 'ylabel', | |
| 'chart_title', 'x_tick', 'y_tick', 'legend_patch', 'legend_label', | |
| 'legend_title', 'legend_area', 'mark_label', 'value_label', | |
| 'y_axis_area', 'x_axis_area', 'tick_grouping' | |
| ])) | |
| evaluation = dict(interval=1, metric=['bbox']) | |
| optimizer = dict( | |
| type='AdamW', | |
| lr=0.0002, | |
| betas=(0.9, 0.999), | |
| weight_decay=0.05, | |
| paramwise_cfg=dict( | |
| custom_keys=dict( | |
| absolute_pos_embed=dict(decay_mult=0.0), | |
| relative_position_bias_table=dict(decay_mult=0.0), | |
| norm=dict(decay_mult=0.0)))) | |
| optimizer_config = dict(grad_clip=None) | |
| lr_config = dict( | |
| policy='step', | |
| warmup='linear', | |
| warmup_iters=500, | |
| warmup_ratio=0.001, | |
| step=[8, 11]) | |
| runner = dict(type='EpochBasedRunner', max_epochs=150) | |
| checkpoint_config = dict(interval=1) | |
| log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) | |
| custom_hooks = [dict(type='NumClassCheckHook')] | |
| dist_params = dict(backend='nccl') | |
| log_level = 'INFO' | |
| load_from = None | |
| resume_from = None | |
| workflow = [('train', 1)] | |
| opencv_num_threads = 0 | |
| mp_start_method = 'fork' | |
| auto_scale_lr = dict(enable=False, base_batch_size=16) | |
| pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' | |
| classes = [ | |
| 'x_title', 'y_title', 'plot_area', 'other', 'xlabel', 'ylabel', | |
| 'chart_title', 'x_tick', 'y_tick', 'legend_patch', 'legend_label', | |
| 'legend_title', 'legend_area', 'mark_label', 'value_label', 'y_axis_area', | |
| 'x_axis_area', 'tick_grouping' | |
| ] | |
| auto_resume = False | |
| gpu_ids = range(0, 4) | |