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import pytest |
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import torch |
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from mmdet3d.registry import MODELS |
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def test_secfpn(): |
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neck_cfg = dict( |
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type='SECONDFPN', |
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in_channels=[2, 3], |
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upsample_strides=[1, 2], |
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out_channels=[4, 6], |
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) |
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neck = MODELS.build(neck_cfg) |
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assert neck.deblocks[0][0].in_channels == 2 |
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assert neck.deblocks[1][0].in_channels == 3 |
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assert neck.deblocks[0][0].out_channels == 4 |
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assert neck.deblocks[1][0].out_channels == 6 |
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assert neck.deblocks[0][0].stride == (1, 1) |
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assert neck.deblocks[1][0].stride == (2, 2) |
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assert neck is not None |
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neck_cfg = dict( |
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type='SECONDFPN', |
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in_channels=[2, 2], |
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upsample_strides=[1, 2, 4], |
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out_channels=[2, 2], |
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) |
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with pytest.raises(AssertionError): |
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MODELS.build(neck_cfg) |
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neck_cfg = dict( |
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type='SECONDFPN', |
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in_channels=[2, 2, 4], |
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upsample_strides=[1, 2, 4], |
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out_channels=[2, 2], |
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) |
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with pytest.raises(AssertionError): |
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MODELS.build(neck_cfg) |
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def test_centerpoint_fpn(): |
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second_cfg = dict( |
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type='SECOND', |
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in_channels=2, |
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out_channels=[2, 2, 2], |
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layer_nums=[3, 5, 5], |
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layer_strides=[2, 2, 2], |
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norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01), |
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conv_cfg=dict(type='Conv2d', bias=False)) |
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second = MODELS.build(second_cfg) |
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centerpoint_fpn_cfg = dict( |
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type='SECONDFPN', |
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in_channels=[2, 2, 2], |
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out_channels=[2, 2, 2], |
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upsample_strides=[0.5, 1, 2], |
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norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01), |
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upsample_cfg=dict(type='deconv', bias=False), |
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use_conv_for_no_stride=True) |
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fpn_cfg = dict( |
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type='SECONDFPN', |
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in_channels=[2, 2, 2], |
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upsample_strides=[1, 2, 4], |
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out_channels=[2, 2, 2]) |
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second_fpn = MODELS.build(fpn_cfg) |
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centerpoint_second_fpn = MODELS.build(centerpoint_fpn_cfg) |
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input = torch.rand([2, 2, 32, 32]) |
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sec_output = second(input) |
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centerpoint_output = centerpoint_second_fpn(sec_output) |
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second_output = second_fpn(sec_output) |
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assert centerpoint_output[0].shape == torch.Size([2, 6, 8, 8]) |
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assert second_output[0].shape == torch.Size([2, 6, 16, 16]) |
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