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from detectron2.config import LazyCall as L
from detectron2.layers import ShapeSpec
from detectron2.modeling.poolers import ROIPooler
from detectron2.modeling.roi_heads import KRCNNConvDeconvUpsampleHead
from .mask_rcnn_fpn import model
[model.roi_heads.pop(x) for x in ["mask_in_features", "mask_pooler", "mask_head"]]
model.roi_heads.update(
num_classes=1,
keypoint_in_features=["p2", "p3", "p4", "p5"],
keypoint_pooler=L(ROIPooler)(
output_size=14,
scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32),
sampling_ratio=0,
pooler_type="ROIAlignV2",
),
keypoint_head=L(KRCNNConvDeconvUpsampleHead)(
input_shape=ShapeSpec(channels=256, width=14, height=14),
num_keypoints=17,
conv_dims=[512] * 8,
loss_normalizer="visible",
),
)
# Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2.
# 1000 proposals per-image is found to hurt box AP.
# Therefore we increase it to 1500 per-image.
model.proposal_generator.post_nms_topk = (1500, 1000)
# Keypoint AP degrades (though box AP improves) when using plain L1 loss
model.roi_heads.box_predictor.smooth_l1_beta = 0.5
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