Pointrender parsing

Model structure

It's using GeneralizedRCNN framework .

inference The overall process of is ：

1. Input image preprocessing . Data import specific device -> (x-mean)/std normalization -> Divisive treatment .
2. backbone The extracted features , Output characteristics list.
3. According to input images and features Generate proposals.
4. Input images, features, proposals, stay roi_heads Generate the final result .

backbone

For the original implementation bacbone yes resnet50-FPN.

bottom_up = build_resnet_backbone(cfg, input_shape)
backbone = FPN(bottom_up, in_features, out_channels, norm, top_block, fuse_type)
in_features: [“p2”, “p3”, “p4”, “p5”]
out_channels: 256
norm: “GN”, “SyncBN”, “FrozenBN”
top_block: LastLevelMaxPool()
fuse_type: sum

The output is [“p2”, “p3”, “p4”, “p5”] Features after four layers of fusion .

proposal_generator

RRN
proposal_generator = build_proposal_generator(cfg, backbone.output_shape())

roi_heads

PointRendROIHeads

roi_heads = build_roi_heads(cfg, backbone.output_shape())

box_pooler

box_head

box_predictor

mask_coarse_head

mask_point_head

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1. Explanation of footnotes ︎