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model = dict(type='FasterRCNN', |
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backbone=dict(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=True), |
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norm_eval=True, |
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style='pytorch'), |
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neck=dict(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(type='RPNHead', |
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in_channels=256, |
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feat_channels=256, |
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anchor_generator=dict(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(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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roi_head=dict( |
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type='StandardRoIHead', |
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bbox_roi_extractor=dict(type='SingleRoIExtractor', |
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roi_layer=dict(type='RoIAlign', |
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output_size=7, |
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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(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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test_cfg=dict(rpn=dict(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(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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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='MultiScaleFlipAug', |
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img_scale=(1333, 800), |
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flip=False, |
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transforms=[ |
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dict(type='Resize', keep_ratio=True), |
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dict(type='RandomFlip'), |
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dict(type='Normalize', |
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mean=[123.675, 116.28, 103.53], |
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std=[58.395, 57.12, 57.375], |
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to_rgb=True), |
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dict(type='Pad', size_divisor=32), |
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dict(type='DefaultFormatBundle'), |
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dict(type='Collect', keys=['img']) |
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]) |
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] |
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data = dict(test=dict(pipeline=test_pipeline)) |
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|