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model = dict(
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type='FasterRCNN',
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_scope_='mmdet',
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data_preprocessor=dict(
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type='DetDataPreprocessor',
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mean=[103.53, 116.28, 123.675],
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std=[1.0, 1.0, 1.0],
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bgr_to_rgb=False,
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pad_size_divisor=32),
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backbone=dict(
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type='ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=False),
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norm_eval=True,
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style='caffe',
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init_cfg=dict(
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type='Pretrained',
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checkpoint='open-mmlab://detectron2/resnet50_caffe')),
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neck=dict(
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type='FPN',
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in_channels=[256, 512, 1024, 2048],
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out_channels=256,
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num_outs=5),
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rpn_head=dict(
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type='RPNHead',
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in_channels=256,
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feat_channels=256,
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anchor_generator=dict(
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type='AnchorGenerator',
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scales=[8],
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ratios=[0.5, 1.0, 2.0],
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strides=[4, 8, 16, 32, 64]),
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[0.0, 0.0, 0.0, 0.0],
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target_stds=[1.0, 1.0, 1.0, 1.0]),
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
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roi_head=dict(
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type='StandardRoIHead',
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bbox_roi_extractor=dict(
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type='SingleRoIExtractor',
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roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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bbox_head=dict(
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type='Shared2FCBBoxHead',
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in_channels=256,
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fc_out_channels=1024,
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roi_feat_size=7,
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num_classes=1,
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[0.0, 0.0, 0.0, 0.0],
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target_stds=[0.1, 0.1, 0.2, 0.2]),
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reg_class_agnostic=False,
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
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train_cfg=dict(
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rpn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.7,
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neg_iou_thr=0.3,
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min_pos_iou=0.3,
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match_low_quality=True,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=256,
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pos_fraction=0.5,
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neg_pos_ub=-1,
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add_gt_as_proposals=False),
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allowed_border=-1,
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pos_weight=-1,
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debug=False),
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rpn_proposal=dict(
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nms_pre=2000,
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max_per_img=1000,
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nms=dict(type='nms', iou_threshold=0.7),
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min_bbox_size=0),
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rcnn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.5,
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neg_iou_thr=0.5,
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min_pos_iou=0.5,
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match_low_quality=False,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=512,
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pos_fraction=0.25,
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neg_pos_ub=-1,
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add_gt_as_proposals=True),
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pos_weight=-1,
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debug=False)),
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test_cfg=dict(
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rpn=dict(
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nms_pre=1000,
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max_per_img=1000,
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nms=dict(type='nms', iou_threshold=0.7),
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min_bbox_size=0),
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rcnn=dict(
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score_thr=0.05,
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nms=dict(type='nms', iou_threshold=0.5),
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max_per_img=100)))
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dataset_type = 'CocoDataset'
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data_root = 'data/coco/'
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file_client_args = dict(backend='disk')
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test_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(type='mmdet.Resize', scale=(1333, 800), keep_ratio=True),
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dict(
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type='mmdet.PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
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'scale_factor'))
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]
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test_dataloader = dict(
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batch_size=1,
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num_workers=2,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='CocoDataset',
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data_root='data/coco/',
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ann_file='annotations/instances_val2017.json',
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data_prefix=dict(img='val2017/'),
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test_mode=True,
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pipeline=test_pipeline,
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|
|
metainfo=dict(classes=('person', ), palette=[(220, 20, 60)])))
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|
