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Runtime error
| # model settings | |
| model = dict( | |
| type='FasterRCNN', | |
| data_preprocessor=dict( | |
| type='DetDataPreprocessor', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| bgr_to_rgb=True, | |
| pad_size_divisor=32), | |
| backbone=dict( | |
| type='ResNet', | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(0, 1, 2, 3), | |
| frozen_stages=1, | |
| norm_cfg=dict(type='BN', requires_grad=True), | |
| norm_eval=True, | |
| style='pytorch', | |
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), | |
| 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_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], | |
| 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='L1Loss', loss_weight=1.0)), | |
| roi_head=dict( | |
| type='StandardRoIHead', | |
| 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='Shared2FCBBoxHead', | |
| in_channels=256, | |
| fc_out_channels=1024, | |
| roi_feat_size=7, | |
| num_classes=80, | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[0., 0., 0., 0.], | |
| target_stds=[0.1, 0.1, 0.2, 0.2]), | |
| reg_class_agnostic=False, | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | |
| loss_bbox=dict(type='L1Loss', loss_weight=1.0))), | |
| # model training and testing settings | |
| 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=-1, | |
| pos_weight=-1, | |
| debug=False), | |
| rpn_proposal=dict( | |
| nms_pre=2000, | |
| max_per_img=1000, | |
| 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)), | |
| 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=0.05, | |
| nms=dict(type='nms', iou_threshold=0.5), | |
| max_per_img=100) | |
| # soft-nms is also supported for rcnn testing | |
| # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05) | |
| )) | |