768 lines
32 KiB
Python
768 lines
32 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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"""
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CommandLine:
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pytest tests/test_utils/test_anchor.py
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xdoctest tests/test_utils/test_anchor.py zero
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"""
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import pytest
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import torch
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def test_standard_points_generator():
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from mmdet.core.anchor import build_prior_generator
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# teat init
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anchor_generator_cfg = dict(
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type='MlvlPointGenerator', strides=[4, 8], offset=0)
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anchor_generator = build_prior_generator(anchor_generator_cfg)
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assert anchor_generator is not None
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assert anchor_generator.num_base_priors == [1, 1]
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# test_stride
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from mmdet.core.anchor import MlvlPointGenerator
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# Square strides
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mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0)
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mlvl_points_half_stride_generator = MlvlPointGenerator(
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strides=[4, 10], offset=0.5)
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assert mlvl_points.num_levels == 2
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# assert self.num_levels == len(featmap_sizes)
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with pytest.raises(AssertionError):
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mlvl_points.grid_priors(featmap_sizes=[(2, 2)], device='cpu')
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priors = mlvl_points.grid_priors(
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featmap_sizes=[(2, 2), (4, 8)], device='cpu')
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priors_with_stride = mlvl_points.grid_priors(
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featmap_sizes=[(2, 2), (4, 8)], with_stride=True, device='cpu')
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assert len(priors) == 2
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# assert last dimension is (coord_x, coord_y, stride_w, stride_h).
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assert priors_with_stride[0].size(1) == 4
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assert priors_with_stride[0][0][2] == 4
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assert priors_with_stride[0][0][3] == 4
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assert priors_with_stride[1][0][2] == 10
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assert priors_with_stride[1][0][3] == 10
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stride_4_feat_2_2 = priors[0]
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assert (stride_4_feat_2_2[1] - stride_4_feat_2_2[0]).sum() == 4
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assert stride_4_feat_2_2.size(0) == 4
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assert stride_4_feat_2_2.size(1) == 2
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stride_10_feat_4_8 = priors[1]
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assert (stride_10_feat_4_8[1] - stride_10_feat_4_8[0]).sum() == 10
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assert stride_10_feat_4_8.size(0) == 4 * 8
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assert stride_10_feat_4_8.size(1) == 2
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# assert the offset of 0.5 * stride
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priors_half_offset = mlvl_points_half_stride_generator.grid_priors(
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featmap_sizes=[(2, 2), (4, 8)], device='cpu')
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assert (priors_half_offset[0][0] - priors[0][0]).sum() == 4 * 0.5 * 2
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assert (priors_half_offset[1][0] - priors[1][0]).sum() == 10 * 0.5 * 2
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if torch.cuda.is_available():
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anchor_generator_cfg = dict(
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type='MlvlPointGenerator', strides=[4, 8], offset=0)
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anchor_generator = build_prior_generator(anchor_generator_cfg)
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assert anchor_generator is not None
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# Square strides
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mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0)
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mlvl_points_half_stride_generator = MlvlPointGenerator(
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strides=[4, 10], offset=0.5)
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assert mlvl_points.num_levels == 2
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# assert self.num_levels == len(featmap_sizes)
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with pytest.raises(AssertionError):
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mlvl_points.grid_priors(featmap_sizes=[(2, 2)], device='cuda')
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priors = mlvl_points.grid_priors(
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featmap_sizes=[(2, 2), (4, 8)], device='cuda')
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priors_with_stride = mlvl_points.grid_priors(
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featmap_sizes=[(2, 2), (4, 8)], with_stride=True, device='cuda')
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assert len(priors) == 2
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# assert last dimension is (coord_x, coord_y, stride_w, stride_h).
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assert priors_with_stride[0].size(1) == 4
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assert priors_with_stride[0][0][2] == 4
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assert priors_with_stride[0][0][3] == 4
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assert priors_with_stride[1][0][2] == 10
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assert priors_with_stride[1][0][3] == 10
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stride_4_feat_2_2 = priors[0]
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assert (stride_4_feat_2_2[1] - stride_4_feat_2_2[0]).sum() == 4
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assert stride_4_feat_2_2.size(0) == 4
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assert stride_4_feat_2_2.size(1) == 2
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stride_10_feat_4_8 = priors[1]
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assert (stride_10_feat_4_8[1] - stride_10_feat_4_8[0]).sum() == 10
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assert stride_10_feat_4_8.size(0) == 4 * 8
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assert stride_10_feat_4_8.size(1) == 2
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# assert the offset of 0.5 * stride
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priors_half_offset = mlvl_points_half_stride_generator.grid_priors(
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featmap_sizes=[(2, 2), (4, 8)], device='cuda')
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assert (priors_half_offset[0][0] - priors[0][0]).sum() == 4 * 0.5 * 2
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assert (priors_half_offset[1][0] - priors[1][0]).sum() == 10 * 0.5 * 2
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def test_sparse_prior():
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from mmdet.core.anchor import MlvlPointGenerator
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mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0)
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prior_indexs = torch.Tensor([0, 2, 4, 5, 6, 9]).long()
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featmap_sizes = [(3, 5), (6, 4)]
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grid_anchors = mlvl_points.grid_priors(
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featmap_sizes=featmap_sizes, with_stride=False, device='cpu')
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sparse_prior = mlvl_points.sparse_priors(
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prior_idxs=prior_indexs,
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featmap_size=featmap_sizes[0],
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level_idx=0,
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device='cpu')
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assert not sparse_prior.is_cuda
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assert (sparse_prior == grid_anchors[0][prior_indexs]).all()
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sparse_prior = mlvl_points.sparse_priors(
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prior_idxs=prior_indexs,
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featmap_size=featmap_sizes[1],
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level_idx=1,
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device='cpu')
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assert (sparse_prior == grid_anchors[1][prior_indexs]).all()
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from mmdet.core.anchor import AnchorGenerator
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mlvl_anchors = AnchorGenerator(
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strides=[16, 32], ratios=[1.], scales=[1.], base_sizes=[4, 8])
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prior_indexs = torch.Tensor([0, 2, 4, 5, 6, 9]).long()
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featmap_sizes = [(3, 5), (6, 4)]
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grid_anchors = mlvl_anchors.grid_priors(
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featmap_sizes=featmap_sizes, device='cpu')
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sparse_prior = mlvl_anchors.sparse_priors(
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prior_idxs=prior_indexs,
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featmap_size=featmap_sizes[0],
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level_idx=0,
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device='cpu')
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assert (sparse_prior == grid_anchors[0][prior_indexs]).all()
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sparse_prior = mlvl_anchors.sparse_priors(
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prior_idxs=prior_indexs,
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featmap_size=featmap_sizes[1],
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level_idx=1,
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device='cpu')
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assert (sparse_prior == grid_anchors[1][prior_indexs]).all()
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# for ssd
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from mmdet.core.anchor.anchor_generator import SSDAnchorGenerator
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featmap_sizes = [(38, 38), (19, 19), (10, 10)]
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anchor_generator = SSDAnchorGenerator(
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scale_major=False,
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input_size=300,
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basesize_ratio_range=(0.15, 0.9),
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strides=[8, 16, 32],
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ratios=[[2], [2, 3], [2, 3]])
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ssd_anchors = anchor_generator.grid_anchors(featmap_sizes, device='cpu')
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for i in range(len(featmap_sizes)):
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sparse_ssd_anchors = anchor_generator.sparse_priors(
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prior_idxs=prior_indexs,
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level_idx=i,
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featmap_size=featmap_sizes[i],
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device='cpu')
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assert (sparse_ssd_anchors == ssd_anchors[i][prior_indexs]).all()
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# for yolo
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from mmdet.core.anchor.anchor_generator import YOLOAnchorGenerator
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featmap_sizes = [(38, 38), (19, 19), (10, 10)]
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anchor_generator = YOLOAnchorGenerator(
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strides=[32, 16, 8],
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base_sizes=[
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[(116, 90), (156, 198), (373, 326)],
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[(30, 61), (62, 45), (59, 119)],
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[(10, 13), (16, 30), (33, 23)],
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])
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yolo_anchors = anchor_generator.grid_anchors(featmap_sizes, device='cpu')
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for i in range(len(featmap_sizes)):
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sparse_yolo_anchors = anchor_generator.sparse_priors(
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prior_idxs=prior_indexs,
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level_idx=i,
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featmap_size=featmap_sizes[i],
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device='cpu')
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assert (sparse_yolo_anchors == yolo_anchors[i][prior_indexs]).all()
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if torch.cuda.is_available():
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mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0)
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prior_indexs = torch.Tensor([0, 3, 4, 5, 6, 7, 1, 2, 4, 5, 6,
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9]).long().cuda()
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featmap_sizes = [(6, 8), (6, 4)]
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grid_anchors = mlvl_points.grid_priors(
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featmap_sizes=featmap_sizes, with_stride=False, device='cuda')
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sparse_prior = mlvl_points.sparse_priors(
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prior_idxs=prior_indexs,
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featmap_size=featmap_sizes[0],
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level_idx=0,
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device='cuda')
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assert (sparse_prior == grid_anchors[0][prior_indexs]).all()
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sparse_prior = mlvl_points.sparse_priors(
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prior_idxs=prior_indexs,
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featmap_size=featmap_sizes[1],
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level_idx=1,
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device='cuda')
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assert (sparse_prior == grid_anchors[1][prior_indexs]).all()
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assert sparse_prior.is_cuda
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mlvl_anchors = AnchorGenerator(
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strides=[16, 32],
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ratios=[1., 2.5],
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scales=[1., 5.],
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base_sizes=[4, 8])
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prior_indexs = torch.Tensor([4, 5, 6, 7, 0, 2, 50, 4, 5, 6,
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9]).long().cuda()
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featmap_sizes = [(13, 5), (16, 4)]
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grid_anchors = mlvl_anchors.grid_priors(
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featmap_sizes=featmap_sizes, device='cuda')
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sparse_prior = mlvl_anchors.sparse_priors(
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prior_idxs=prior_indexs,
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featmap_size=featmap_sizes[0],
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level_idx=0,
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device='cuda')
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assert (sparse_prior == grid_anchors[0][prior_indexs]).all()
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sparse_prior = mlvl_anchors.sparse_priors(
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prior_idxs=prior_indexs,
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featmap_size=featmap_sizes[1],
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level_idx=1,
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device='cuda')
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assert (sparse_prior == grid_anchors[1][prior_indexs]).all()
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# for ssd
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from mmdet.core.anchor.anchor_generator import SSDAnchorGenerator
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featmap_sizes = [(38, 38), (19, 19), (10, 10)]
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anchor_generator = SSDAnchorGenerator(
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scale_major=False,
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input_size=300,
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basesize_ratio_range=(0.15, 0.9),
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strides=[8, 16, 32],
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ratios=[[2], [2, 3], [2, 3]])
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ssd_anchors = anchor_generator.grid_anchors(
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featmap_sizes, device='cuda')
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for i in range(len(featmap_sizes)):
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sparse_ssd_anchors = anchor_generator.sparse_priors(
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prior_idxs=prior_indexs,
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level_idx=i,
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featmap_size=featmap_sizes[i],
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device='cuda')
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assert (sparse_ssd_anchors == ssd_anchors[i][prior_indexs]).all()
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# for yolo
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from mmdet.core.anchor.anchor_generator import YOLOAnchorGenerator
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featmap_sizes = [(38, 38), (19, 19), (10, 10)]
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anchor_generator = YOLOAnchorGenerator(
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strides=[32, 16, 8],
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base_sizes=[
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[(116, 90), (156, 198), (373, 326)],
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[(30, 61), (62, 45), (59, 119)],
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[(10, 13), (16, 30), (33, 23)],
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])
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yolo_anchors = anchor_generator.grid_anchors(
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featmap_sizes, device='cuda')
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for i in range(len(featmap_sizes)):
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sparse_yolo_anchors = anchor_generator.sparse_priors(
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prior_idxs=prior_indexs,
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level_idx=i,
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featmap_size=featmap_sizes[i],
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device='cuda')
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assert (sparse_yolo_anchors == yolo_anchors[i][prior_indexs]).all()
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def test_standard_anchor_generator():
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from mmdet.core.anchor import build_anchor_generator
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anchor_generator_cfg = 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])
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anchor_generator = build_anchor_generator(anchor_generator_cfg)
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assert anchor_generator.num_base_priors == \
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anchor_generator.num_base_anchors
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assert anchor_generator.num_base_priors == [3, 3]
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assert anchor_generator is not None
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def test_strides():
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from mmdet.core import AnchorGenerator
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# Square strides
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self = AnchorGenerator([10], [1.], [1.], [10])
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anchors = self.grid_anchors([(2, 2)], device='cpu')
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expected_anchors = torch.tensor([[-5., -5., 5., 5.], [5., -5., 15., 5.],
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[-5., 5., 5., 15.], [5., 5., 15., 15.]])
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assert torch.equal(anchors[0], expected_anchors)
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# Different strides in x and y direction
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self = AnchorGenerator([(10, 20)], [1.], [1.], [10])
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anchors = self.grid_anchors([(2, 2)], device='cpu')
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expected_anchors = torch.tensor([[-5., -5., 5., 5.], [5., -5., 15., 5.],
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[-5., 15., 5., 25.], [5., 15., 15., 25.]])
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assert torch.equal(anchors[0], expected_anchors)
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def test_ssd_anchor_generator():
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from mmdet.core.anchor import build_anchor_generator
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if torch.cuda.is_available():
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device = 'cuda'
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else:
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device = 'cpu'
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# min_sizes max_sizes must set at the same time
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with pytest.raises(AssertionError):
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anchor_generator_cfg = dict(
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type='SSDAnchorGenerator',
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scale_major=False,
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min_sizes=[48, 100, 150, 202, 253, 300],
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max_sizes=None,
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strides=[8, 16, 32, 64, 100, 300],
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ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
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build_anchor_generator(anchor_generator_cfg)
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# length of min_sizes max_sizes must be the same
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with pytest.raises(AssertionError):
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anchor_generator_cfg = dict(
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type='SSDAnchorGenerator',
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scale_major=False,
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min_sizes=[48, 100, 150, 202, 253, 300],
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max_sizes=[100, 150, 202, 253],
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strides=[8, 16, 32, 64, 100, 300],
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ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
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build_anchor_generator(anchor_generator_cfg)
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# test setting anchor size manually
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anchor_generator_cfg = dict(
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type='SSDAnchorGenerator',
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scale_major=False,
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min_sizes=[48, 100, 150, 202, 253, 304],
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max_sizes=[100, 150, 202, 253, 304, 320],
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strides=[16, 32, 64, 107, 160, 320],
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ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]])
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featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)]
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anchor_generator = build_anchor_generator(anchor_generator_cfg)
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expected_base_anchors = [
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torch.Tensor([[-16.0000, -16.0000, 32.0000, 32.0000],
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[-26.6410, -26.6410, 42.6410, 42.6410],
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[-25.9411, -8.9706, 41.9411, 24.9706],
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[-8.9706, -25.9411, 24.9706, 41.9411],
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[-33.5692, -5.8564, 49.5692, 21.8564],
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[-5.8564, -33.5692, 21.8564, 49.5692]]),
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torch.Tensor([[-34.0000, -34.0000, 66.0000, 66.0000],
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[-45.2372, -45.2372, 77.2372, 77.2372],
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[-54.7107, -19.3553, 86.7107, 51.3553],
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[-19.3553, -54.7107, 51.3553, 86.7107],
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[-70.6025, -12.8675, 102.6025, 44.8675],
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[-12.8675, -70.6025, 44.8675, 102.6025]]),
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torch.Tensor([[-43.0000, -43.0000, 107.0000, 107.0000],
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[-55.0345, -55.0345, 119.0345, 119.0345],
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[-74.0660, -21.0330, 138.0660, 85.0330],
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[-21.0330, -74.0660, 85.0330, 138.0660],
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[-97.9038, -11.3013, 161.9038, 75.3013],
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[-11.3013, -97.9038, 75.3013, 161.9038]]),
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torch.Tensor([[-47.5000, -47.5000, 154.5000, 154.5000],
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[-59.5332, -59.5332, 166.5332, 166.5332],
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[-89.3356, -17.9178, 196.3356, 124.9178],
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[-17.9178, -89.3356, 124.9178, 196.3356],
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[-121.4371, -4.8124, 228.4371, 111.8124],
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[-4.8124, -121.4371, 111.8124, 228.4371]]),
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torch.Tensor([[-46.5000, -46.5000, 206.5000, 206.5000],
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[-58.6651, -58.6651, 218.6651, 218.6651],
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[-98.8980, -9.4490, 258.8980, 169.4490],
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[-9.4490, -98.8980, 169.4490, 258.8980],
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[-139.1044, 6.9652, 299.1044, 153.0348],
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[6.9652, -139.1044, 153.0348, 299.1044]]),
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torch.Tensor([[8.0000, 8.0000, 312.0000, 312.0000],
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[4.0513, 4.0513, 315.9487, 315.9487],
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[-54.9605, 52.5198, 374.9604, 267.4802],
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[52.5198, -54.9605, 267.4802, 374.9604],
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[-103.2717, 72.2428, 423.2717, 247.7572],
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[72.2428, -103.2717, 247.7572, 423.2717]])
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]
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base_anchors = anchor_generator.base_anchors
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for i, base_anchor in enumerate(base_anchors):
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assert base_anchor.allclose(expected_base_anchors[i])
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# check valid flags
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expected_valid_pixels = [2400, 600, 150, 54, 24, 6]
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multi_level_valid_flags = anchor_generator.valid_flags(
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featmap_sizes, (320, 320), device)
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for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
|
|
assert single_level_valid_flag.sum() == expected_valid_pixels[i]
|
|
|
|
# check number of base anchors for each level
|
|
assert anchor_generator.num_base_anchors == [6, 6, 6, 6, 6, 6]
|
|
|
|
# check anchor generation
|
|
anchors = anchor_generator.grid_anchors(featmap_sizes, device)
|
|
assert len(anchors) == 6
|
|
|
|
# test vgg ssd anchor setting
|
|
anchor_generator_cfg = dict(
|
|
type='SSDAnchorGenerator',
|
|
scale_major=False,
|
|
input_size=300,
|
|
basesize_ratio_range=(0.15, 0.9),
|
|
strides=[8, 16, 32, 64, 100, 300],
|
|
ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
|
|
|
|
featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)]
|
|
anchor_generator = build_anchor_generator(anchor_generator_cfg)
|
|
|
|
# check base anchors
|
|
expected_base_anchors = [
|
|
torch.Tensor([[-6.5000, -6.5000, 14.5000, 14.5000],
|
|
[-11.3704, -11.3704, 19.3704, 19.3704],
|
|
[-10.8492, -3.4246, 18.8492, 11.4246],
|
|
[-3.4246, -10.8492, 11.4246, 18.8492]]),
|
|
torch.Tensor([[-14.5000, -14.5000, 30.5000, 30.5000],
|
|
[-25.3729, -25.3729, 41.3729, 41.3729],
|
|
[-23.8198, -7.9099, 39.8198, 23.9099],
|
|
[-7.9099, -23.8198, 23.9099, 39.8198],
|
|
[-30.9711, -4.9904, 46.9711, 20.9904],
|
|
[-4.9904, -30.9711, 20.9904, 46.9711]]),
|
|
torch.Tensor([[-33.5000, -33.5000, 65.5000, 65.5000],
|
|
[-45.5366, -45.5366, 77.5366, 77.5366],
|
|
[-54.0036, -19.0018, 86.0036, 51.0018],
|
|
[-19.0018, -54.0036, 51.0018, 86.0036],
|
|
[-69.7365, -12.5788, 101.7365, 44.5788],
|
|
[-12.5788, -69.7365, 44.5788, 101.7365]]),
|
|
torch.Tensor([[-44.5000, -44.5000, 108.5000, 108.5000],
|
|
[-56.9817, -56.9817, 120.9817, 120.9817],
|
|
[-76.1873, -22.0937, 140.1873, 86.0937],
|
|
[-22.0937, -76.1873, 86.0937, 140.1873],
|
|
[-100.5019, -12.1673, 164.5019, 76.1673],
|
|
[-12.1673, -100.5019, 76.1673, 164.5019]]),
|
|
torch.Tensor([[-53.5000, -53.5000, 153.5000, 153.5000],
|
|
[-66.2185, -66.2185, 166.2185, 166.2185],
|
|
[-96.3711, -23.1855, 196.3711, 123.1855],
|
|
[-23.1855, -96.3711, 123.1855, 196.3711]]),
|
|
torch.Tensor([[19.5000, 19.5000, 280.5000, 280.5000],
|
|
[6.6342, 6.6342, 293.3658, 293.3658],
|
|
[-34.5549, 57.7226, 334.5549, 242.2774],
|
|
[57.7226, -34.5549, 242.2774, 334.5549]]),
|
|
]
|
|
base_anchors = anchor_generator.base_anchors
|
|
for i, base_anchor in enumerate(base_anchors):
|
|
assert base_anchor.allclose(expected_base_anchors[i])
|
|
|
|
# check valid flags
|
|
expected_valid_pixels = [5776, 2166, 600, 150, 36, 4]
|
|
multi_level_valid_flags = anchor_generator.valid_flags(
|
|
featmap_sizes, (300, 300), device)
|
|
for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
|
|
assert single_level_valid_flag.sum() == expected_valid_pixels[i]
|
|
|
|
# check number of base anchors for each level
|
|
assert anchor_generator.num_base_anchors == [4, 6, 6, 6, 4, 4]
|
|
|
|
# check anchor generation
|
|
anchors = anchor_generator.grid_anchors(featmap_sizes, device)
|
|
assert len(anchors) == 6
|
|
|
|
|
|
def test_anchor_generator_with_tuples():
|
|
from mmdet.core.anchor import build_anchor_generator
|
|
if torch.cuda.is_available():
|
|
device = 'cuda'
|
|
else:
|
|
device = 'cpu'
|
|
|
|
anchor_generator_cfg = dict(
|
|
type='SSDAnchorGenerator',
|
|
scale_major=False,
|
|
input_size=300,
|
|
basesize_ratio_range=(0.15, 0.9),
|
|
strides=[8, 16, 32, 64, 100, 300],
|
|
ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
|
|
|
|
featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)]
|
|
anchor_generator = build_anchor_generator(anchor_generator_cfg)
|
|
anchors = anchor_generator.grid_anchors(featmap_sizes, device)
|
|
|
|
anchor_generator_cfg_tuples = dict(
|
|
type='SSDAnchorGenerator',
|
|
scale_major=False,
|
|
input_size=300,
|
|
basesize_ratio_range=(0.15, 0.9),
|
|
strides=[(8, 8), (16, 16), (32, 32), (64, 64), (100, 100), (300, 300)],
|
|
ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
|
|
|
|
anchor_generator_tuples = build_anchor_generator(
|
|
anchor_generator_cfg_tuples)
|
|
anchors_tuples = anchor_generator_tuples.grid_anchors(
|
|
featmap_sizes, device)
|
|
for anchor, anchor_tuples in zip(anchors, anchors_tuples):
|
|
assert torch.equal(anchor, anchor_tuples)
|
|
|
|
|
|
def test_yolo_anchor_generator():
|
|
from mmdet.core.anchor import build_anchor_generator
|
|
if torch.cuda.is_available():
|
|
device = 'cuda'
|
|
else:
|
|
device = 'cpu'
|
|
|
|
anchor_generator_cfg = dict(
|
|
type='YOLOAnchorGenerator',
|
|
strides=[32, 16, 8],
|
|
base_sizes=[
|
|
[(116, 90), (156, 198), (373, 326)],
|
|
[(30, 61), (62, 45), (59, 119)],
|
|
[(10, 13), (16, 30), (33, 23)],
|
|
])
|
|
|
|
featmap_sizes = [(14, 18), (28, 36), (56, 72)]
|
|
anchor_generator = build_anchor_generator(anchor_generator_cfg)
|
|
|
|
# check base anchors
|
|
expected_base_anchors = [
|
|
torch.Tensor([[-42.0000, -29.0000, 74.0000, 61.0000],
|
|
[-62.0000, -83.0000, 94.0000, 115.0000],
|
|
[-170.5000, -147.0000, 202.5000, 179.0000]]),
|
|
torch.Tensor([[-7.0000, -22.5000, 23.0000, 38.5000],
|
|
[-23.0000, -14.5000, 39.0000, 30.5000],
|
|
[-21.5000, -51.5000, 37.5000, 67.5000]]),
|
|
torch.Tensor([[-1.0000, -2.5000, 9.0000, 10.5000],
|
|
[-4.0000, -11.0000, 12.0000, 19.0000],
|
|
[-12.5000, -7.5000, 20.5000, 15.5000]])
|
|
]
|
|
base_anchors = anchor_generator.base_anchors
|
|
for i, base_anchor in enumerate(base_anchors):
|
|
assert base_anchor.allclose(expected_base_anchors[i])
|
|
|
|
# check number of base anchors for each level
|
|
assert anchor_generator.num_base_anchors == [3, 3, 3]
|
|
|
|
# check anchor generation
|
|
anchors = anchor_generator.grid_anchors(featmap_sizes, device)
|
|
assert len(anchors) == 3
|
|
|
|
|
|
def test_retina_anchor():
|
|
from mmdet.models import build_head
|
|
if torch.cuda.is_available():
|
|
device = 'cuda'
|
|
else:
|
|
device = 'cpu'
|
|
|
|
# head configs modified from
|
|
# configs/nas_fpn/retinanet_r50_fpn_crop640_50e.py
|
|
bbox_head = dict(
|
|
type='RetinaSepBNHead',
|
|
num_classes=4,
|
|
num_ins=5,
|
|
in_channels=4,
|
|
stacked_convs=1,
|
|
feat_channels=4,
|
|
anchor_generator=dict(
|
|
type='AnchorGenerator',
|
|
octave_base_scale=4,
|
|
scales_per_octave=3,
|
|
ratios=[0.5, 1.0, 2.0],
|
|
strides=[8, 16, 32, 64, 128]),
|
|
bbox_coder=dict(
|
|
type='DeltaXYWHBBoxCoder',
|
|
target_means=[.0, .0, .0, .0],
|
|
target_stds=[1.0, 1.0, 1.0, 1.0]))
|
|
|
|
retina_head = build_head(bbox_head)
|
|
assert retina_head.anchor_generator is not None
|
|
|
|
# use the featmap sizes in NASFPN setting to test retina head
|
|
featmap_sizes = [(80, 80), (40, 40), (20, 20), (10, 10), (5, 5)]
|
|
# check base anchors
|
|
expected_base_anchors = [
|
|
torch.Tensor([[-22.6274, -11.3137, 22.6274, 11.3137],
|
|
[-28.5088, -14.2544, 28.5088, 14.2544],
|
|
[-35.9188, -17.9594, 35.9188, 17.9594],
|
|
[-16.0000, -16.0000, 16.0000, 16.0000],
|
|
[-20.1587, -20.1587, 20.1587, 20.1587],
|
|
[-25.3984, -25.3984, 25.3984, 25.3984],
|
|
[-11.3137, -22.6274, 11.3137, 22.6274],
|
|
[-14.2544, -28.5088, 14.2544, 28.5088],
|
|
[-17.9594, -35.9188, 17.9594, 35.9188]]),
|
|
torch.Tensor([[-45.2548, -22.6274, 45.2548, 22.6274],
|
|
[-57.0175, -28.5088, 57.0175, 28.5088],
|
|
[-71.8376, -35.9188, 71.8376, 35.9188],
|
|
[-32.0000, -32.0000, 32.0000, 32.0000],
|
|
[-40.3175, -40.3175, 40.3175, 40.3175],
|
|
[-50.7968, -50.7968, 50.7968, 50.7968],
|
|
[-22.6274, -45.2548, 22.6274, 45.2548],
|
|
[-28.5088, -57.0175, 28.5088, 57.0175],
|
|
[-35.9188, -71.8376, 35.9188, 71.8376]]),
|
|
torch.Tensor([[-90.5097, -45.2548, 90.5097, 45.2548],
|
|
[-114.0350, -57.0175, 114.0350, 57.0175],
|
|
[-143.6751, -71.8376, 143.6751, 71.8376],
|
|
[-64.0000, -64.0000, 64.0000, 64.0000],
|
|
[-80.6349, -80.6349, 80.6349, 80.6349],
|
|
[-101.5937, -101.5937, 101.5937, 101.5937],
|
|
[-45.2548, -90.5097, 45.2548, 90.5097],
|
|
[-57.0175, -114.0350, 57.0175, 114.0350],
|
|
[-71.8376, -143.6751, 71.8376, 143.6751]]),
|
|
torch.Tensor([[-181.0193, -90.5097, 181.0193, 90.5097],
|
|
[-228.0701, -114.0350, 228.0701, 114.0350],
|
|
[-287.3503, -143.6751, 287.3503, 143.6751],
|
|
[-128.0000, -128.0000, 128.0000, 128.0000],
|
|
[-161.2699, -161.2699, 161.2699, 161.2699],
|
|
[-203.1873, -203.1873, 203.1873, 203.1873],
|
|
[-90.5097, -181.0193, 90.5097, 181.0193],
|
|
[-114.0350, -228.0701, 114.0350, 228.0701],
|
|
[-143.6751, -287.3503, 143.6751, 287.3503]]),
|
|
torch.Tensor([[-362.0387, -181.0193, 362.0387, 181.0193],
|
|
[-456.1401, -228.0701, 456.1401, 228.0701],
|
|
[-574.7006, -287.3503, 574.7006, 287.3503],
|
|
[-256.0000, -256.0000, 256.0000, 256.0000],
|
|
[-322.5398, -322.5398, 322.5398, 322.5398],
|
|
[-406.3747, -406.3747, 406.3747, 406.3747],
|
|
[-181.0193, -362.0387, 181.0193, 362.0387],
|
|
[-228.0701, -456.1401, 228.0701, 456.1401],
|
|
[-287.3503, -574.7006, 287.3503, 574.7006]])
|
|
]
|
|
base_anchors = retina_head.anchor_generator.base_anchors
|
|
for i, base_anchor in enumerate(base_anchors):
|
|
assert base_anchor.allclose(expected_base_anchors[i])
|
|
|
|
# check valid flags
|
|
expected_valid_pixels = [57600, 14400, 3600, 900, 225]
|
|
multi_level_valid_flags = retina_head.anchor_generator.valid_flags(
|
|
featmap_sizes, (640, 640), device)
|
|
for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
|
|
assert single_level_valid_flag.sum() == expected_valid_pixels[i]
|
|
|
|
# check number of base anchors for each level
|
|
assert retina_head.anchor_generator.num_base_anchors == [9, 9, 9, 9, 9]
|
|
|
|
# check anchor generation
|
|
anchors = retina_head.anchor_generator.grid_anchors(featmap_sizes, device)
|
|
assert len(anchors) == 5
|
|
|
|
|
|
def test_guided_anchor():
|
|
from mmdet.models import build_head
|
|
if torch.cuda.is_available():
|
|
device = 'cuda'
|
|
else:
|
|
device = 'cpu'
|
|
# head configs modified from
|
|
# configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py
|
|
bbox_head = dict(
|
|
type='GARetinaHead',
|
|
num_classes=8,
|
|
in_channels=4,
|
|
stacked_convs=1,
|
|
feat_channels=4,
|
|
approx_anchor_generator=dict(
|
|
type='AnchorGenerator',
|
|
octave_base_scale=4,
|
|
scales_per_octave=3,
|
|
ratios=[0.5, 1.0, 2.0],
|
|
strides=[8, 16, 32, 64, 128]),
|
|
square_anchor_generator=dict(
|
|
type='AnchorGenerator',
|
|
ratios=[1.0],
|
|
scales=[4],
|
|
strides=[8, 16, 32, 64, 128]))
|
|
|
|
ga_retina_head = build_head(bbox_head)
|
|
assert ga_retina_head.approx_anchor_generator is not None
|
|
|
|
# use the featmap sizes in NASFPN setting to test ga_retina_head
|
|
featmap_sizes = [(100, 152), (50, 76), (25, 38), (13, 19), (7, 10)]
|
|
# check base anchors
|
|
expected_approxs = [
|
|
torch.Tensor([[-22.6274, -11.3137, 22.6274, 11.3137],
|
|
[-28.5088, -14.2544, 28.5088, 14.2544],
|
|
[-35.9188, -17.9594, 35.9188, 17.9594],
|
|
[-16.0000, -16.0000, 16.0000, 16.0000],
|
|
[-20.1587, -20.1587, 20.1587, 20.1587],
|
|
[-25.3984, -25.3984, 25.3984, 25.3984],
|
|
[-11.3137, -22.6274, 11.3137, 22.6274],
|
|
[-14.2544, -28.5088, 14.2544, 28.5088],
|
|
[-17.9594, -35.9188, 17.9594, 35.9188]]),
|
|
torch.Tensor([[-45.2548, -22.6274, 45.2548, 22.6274],
|
|
[-57.0175, -28.5088, 57.0175, 28.5088],
|
|
[-71.8376, -35.9188, 71.8376, 35.9188],
|
|
[-32.0000, -32.0000, 32.0000, 32.0000],
|
|
[-40.3175, -40.3175, 40.3175, 40.3175],
|
|
[-50.7968, -50.7968, 50.7968, 50.7968],
|
|
[-22.6274, -45.2548, 22.6274, 45.2548],
|
|
[-28.5088, -57.0175, 28.5088, 57.0175],
|
|
[-35.9188, -71.8376, 35.9188, 71.8376]]),
|
|
torch.Tensor([[-90.5097, -45.2548, 90.5097, 45.2548],
|
|
[-114.0350, -57.0175, 114.0350, 57.0175],
|
|
[-143.6751, -71.8376, 143.6751, 71.8376],
|
|
[-64.0000, -64.0000, 64.0000, 64.0000],
|
|
[-80.6349, -80.6349, 80.6349, 80.6349],
|
|
[-101.5937, -101.5937, 101.5937, 101.5937],
|
|
[-45.2548, -90.5097, 45.2548, 90.5097],
|
|
[-57.0175, -114.0350, 57.0175, 114.0350],
|
|
[-71.8376, -143.6751, 71.8376, 143.6751]]),
|
|
torch.Tensor([[-181.0193, -90.5097, 181.0193, 90.5097],
|
|
[-228.0701, -114.0350, 228.0701, 114.0350],
|
|
[-287.3503, -143.6751, 287.3503, 143.6751],
|
|
[-128.0000, -128.0000, 128.0000, 128.0000],
|
|
[-161.2699, -161.2699, 161.2699, 161.2699],
|
|
[-203.1873, -203.1873, 203.1873, 203.1873],
|
|
[-90.5097, -181.0193, 90.5097, 181.0193],
|
|
[-114.0350, -228.0701, 114.0350, 228.0701],
|
|
[-143.6751, -287.3503, 143.6751, 287.3503]]),
|
|
torch.Tensor([[-362.0387, -181.0193, 362.0387, 181.0193],
|
|
[-456.1401, -228.0701, 456.1401, 228.0701],
|
|
[-574.7006, -287.3503, 574.7006, 287.3503],
|
|
[-256.0000, -256.0000, 256.0000, 256.0000],
|
|
[-322.5398, -322.5398, 322.5398, 322.5398],
|
|
[-406.3747, -406.3747, 406.3747, 406.3747],
|
|
[-181.0193, -362.0387, 181.0193, 362.0387],
|
|
[-228.0701, -456.1401, 228.0701, 456.1401],
|
|
[-287.3503, -574.7006, 287.3503, 574.7006]])
|
|
]
|
|
approxs = ga_retina_head.approx_anchor_generator.base_anchors
|
|
for i, base_anchor in enumerate(approxs):
|
|
assert base_anchor.allclose(expected_approxs[i])
|
|
|
|
# check valid flags
|
|
expected_valid_pixels = [136800, 34200, 8550, 2223, 630]
|
|
multi_level_valid_flags = ga_retina_head.approx_anchor_generator \
|
|
.valid_flags(featmap_sizes, (800, 1216), device)
|
|
for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
|
|
assert single_level_valid_flag.sum() == expected_valid_pixels[i]
|
|
|
|
# check number of base anchors for each level
|
|
assert ga_retina_head.approx_anchor_generator.num_base_anchors == [
|
|
9, 9, 9, 9, 9
|
|
]
|
|
|
|
# check approx generation
|
|
squares = ga_retina_head.square_anchor_generator.grid_anchors(
|
|
featmap_sizes, device)
|
|
assert len(squares) == 5
|
|
|
|
expected_squares = [
|
|
torch.Tensor([[-16., -16., 16., 16.]]),
|
|
torch.Tensor([[-32., -32., 32., 32]]),
|
|
torch.Tensor([[-64., -64., 64., 64.]]),
|
|
torch.Tensor([[-128., -128., 128., 128.]]),
|
|
torch.Tensor([[-256., -256., 256., 256.]])
|
|
]
|
|
squares = ga_retina_head.square_anchor_generator.base_anchors
|
|
for i, base_anchor in enumerate(squares):
|
|
assert base_anchor.allclose(expected_squares[i])
|
|
|
|
# square_anchor_generator does not check valid flags
|
|
# check number of base anchors for each level
|
|
assert (ga_retina_head.square_anchor_generator.num_base_anchors == [
|
|
1, 1, 1, 1, 1
|
|
])
|
|
|
|
# check square generation
|
|
anchors = ga_retina_head.square_anchor_generator.grid_anchors(
|
|
featmap_sizes, device)
|
|
assert len(anchors) == 5
|