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Merge pull request open-mmlab#121 from hellock/cascade-rcnn
Add Cascade R-CNN
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,224 @@ | ||
| # model settings | ||
| model = dict( | ||
| type='CascadeRCNN', | ||
| num_stages=3, | ||
| pretrained='modelzoo://resnet50', | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=50, | ||
| num_stages=4, | ||
| out_indices=(0, 1, 2, 3), | ||
| frozen_stages=1, | ||
| style='pytorch'), | ||
| neck=dict( | ||
| type='FPN', | ||
| in_channels=[256, 512, 1024, 2048], | ||
| out_channels=256, | ||
| num_outs=5), | ||
| rpn_head=dict( | ||
| type='RPNHead', | ||
| in_channels=256, | ||
| feat_channels=256, | ||
| anchor_scales=[8], | ||
| anchor_ratios=[0.5, 1.0, 2.0], | ||
| anchor_strides=[4, 8, 16, 32, 64], | ||
| target_means=[.0, .0, .0, .0], | ||
| target_stds=[1.0, 1.0, 1.0, 1.0], | ||
| use_sigmoid_cls=True), | ||
| bbox_roi_extractor=dict( | ||
| type='SingleRoIExtractor', | ||
| roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2), | ||
| out_channels=256, | ||
| featmap_strides=[4, 8, 16, 32]), | ||
| bbox_head=[ | ||
| dict( | ||
| type='SharedFCBBoxHead', | ||
| num_fcs=2, | ||
| in_channels=256, | ||
| fc_out_channels=1024, | ||
| roi_feat_size=7, | ||
| num_classes=81, | ||
| target_means=[0., 0., 0., 0.], | ||
| target_stds=[0.1, 0.1, 0.2, 0.2], | ||
| reg_class_agnostic=True), | ||
| dict( | ||
| type='SharedFCBBoxHead', | ||
| num_fcs=2, | ||
| in_channels=256, | ||
| fc_out_channels=1024, | ||
| roi_feat_size=7, | ||
| num_classes=81, | ||
| target_means=[0., 0., 0., 0.], | ||
| target_stds=[0.05, 0.05, 0.1, 0.1], | ||
| reg_class_agnostic=True), | ||
| dict( | ||
| type='SharedFCBBoxHead', | ||
| num_fcs=2, | ||
| in_channels=256, | ||
| fc_out_channels=1024, | ||
| roi_feat_size=7, | ||
| num_classes=81, | ||
| target_means=[0., 0., 0., 0.], | ||
| target_stds=[0.033, 0.033, 0.067, 0.067], | ||
| reg_class_agnostic=True) | ||
| ], | ||
| mask_roi_extractor=dict( | ||
| type='SingleRoIExtractor', | ||
| roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2), | ||
| out_channels=256, | ||
| featmap_strides=[4, 8, 16, 32]), | ||
| mask_head=dict( | ||
| type='FCNMaskHead', | ||
| num_convs=4, | ||
| in_channels=256, | ||
| conv_out_channels=256, | ||
| num_classes=81)) | ||
| # model training and testing settings | ||
| train_cfg = dict( | ||
| rpn=dict( | ||
| assigner=dict( | ||
| pos_iou_thr=0.7, | ||
| neg_iou_thr=0.3, | ||
| min_pos_iou=0.3, | ||
| ignore_iof_thr=-1), | ||
| sampler=dict( | ||
| num=256, | ||
| pos_fraction=0.5, | ||
| neg_pos_ub=-1, | ||
| add_gt_as_proposals=False, | ||
| pos_balance_sampling=False, | ||
| neg_balance_thr=0), | ||
| allowed_border=0, | ||
| pos_weight=-1, | ||
| smoothl1_beta=1 / 9.0, | ||
| debug=False), | ||
| rcnn=[ | ||
| dict( | ||
| assigner=dict( | ||
| pos_iou_thr=0.5, | ||
| neg_iou_thr=0.5, | ||
| min_pos_iou=0.5, | ||
| ignore_iof_thr=-1), | ||
| sampler=dict( | ||
| num=512, | ||
| pos_fraction=0.25, | ||
| neg_pos_ub=-1, | ||
| add_gt_as_proposals=True, | ||
| pos_balance_sampling=False, | ||
| neg_balance_thr=0), | ||
| mask_size=28, | ||
| pos_weight=-1, | ||
| debug=False), | ||
| dict( | ||
| assigner=dict( | ||
| pos_iou_thr=0.6, | ||
| neg_iou_thr=0.6, | ||
| min_pos_iou=0.6, | ||
| ignore_iof_thr=-1), | ||
| sampler=dict( | ||
| num=512, | ||
| pos_fraction=0.25, | ||
| neg_pos_ub=-1, | ||
| add_gt_as_proposals=True, | ||
| pos_balance_sampling=False, | ||
| neg_balance_thr=0), | ||
| mask_size=28, | ||
| pos_weight=-1, | ||
| debug=False), | ||
| dict( | ||
| assigner=dict( | ||
| pos_iou_thr=0.7, | ||
| neg_iou_thr=0.7, | ||
| min_pos_iou=0.7, | ||
| ignore_iof_thr=-1), | ||
| sampler=dict( | ||
| num=512, | ||
| pos_fraction=0.25, | ||
| neg_pos_ub=-1, | ||
| add_gt_as_proposals=True, | ||
| pos_balance_sampling=False, | ||
| neg_balance_thr=0), | ||
| mask_size=28, | ||
| pos_weight=-1, | ||
| debug=False) | ||
| ], | ||
| stage_loss_weights=[1, 0.5, 0.25]) | ||
| test_cfg = dict( | ||
| rpn=dict( | ||
| nms_across_levels=False, | ||
| nms_pre=2000, | ||
| nms_post=2000, | ||
| max_num=2000, | ||
| nms_thr=0.7, | ||
| min_bbox_size=0), | ||
| rcnn=dict( | ||
| score_thr=0.05, max_per_img=100, nms_thr=0.5, mask_thr_binary=0.5), | ||
| keep_all_stages=False) | ||
| # dataset settings | ||
| dataset_type = 'CocoDataset' | ||
| data_root = 'data/coco/' | ||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
| data = dict( | ||
| imgs_per_gpu=2, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_train2017.json', | ||
| img_prefix=data_root + 'train2017/', | ||
| img_scale=(1333, 800), | ||
| img_norm_cfg=img_norm_cfg, | ||
| size_divisor=32, | ||
| flip_ratio=0.5, | ||
| with_mask=True, | ||
| with_crowd=True, | ||
| with_label=True), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| img_scale=(1333, 800), | ||
| img_norm_cfg=img_norm_cfg, | ||
| size_divisor=32, | ||
| flip_ratio=0, | ||
| with_mask=True, | ||
| with_crowd=True, | ||
| with_label=True), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| img_scale=(1333, 800), | ||
| img_norm_cfg=img_norm_cfg, | ||
| size_divisor=32, | ||
| flip_ratio=0, | ||
| with_mask=True, | ||
| with_label=False, | ||
| test_mode=True)) | ||
| # optimizer | ||
| optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) | ||
| optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) | ||
| # learning policy | ||
| lr_config = dict( | ||
| policy='step', | ||
| warmup='linear', | ||
| warmup_iters=500, | ||
| warmup_ratio=1.0 / 3, | ||
| step=[8, 11]) | ||
| checkpoint_config = dict(interval=1) | ||
| # yapf:disable | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| # dict(type='TensorboardLoggerHook') | ||
| ]) | ||
| # yapf:enable | ||
| # runtime settings | ||
| total_epochs = 12 | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| work_dir = './work_dirs/cascade_mask_rcnn_r50_fpn_1x' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] |
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