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import pytest |
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import torch |
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from mmdet.models.backbones import RegNet |
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regnet_test_data = [ |
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('regnetx_400mf', |
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dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, |
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bot_mul=1.0), [32, 64, 160, 384]), |
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('regnetx_800mf', |
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dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, |
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bot_mul=1.0), [64, 128, 288, 672]), |
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('regnetx_1.6gf', |
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dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, |
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bot_mul=1.0), [72, 168, 408, 912]), |
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('regnetx_3.2gf', |
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dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, |
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bot_mul=1.0), [96, 192, 432, 1008]), |
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('regnetx_4.0gf', |
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dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, |
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bot_mul=1.0), [80, 240, 560, 1360]), |
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('regnetx_6.4gf', |
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dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, |
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bot_mul=1.0), [168, 392, 784, 1624]), |
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('regnetx_8.0gf', |
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dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, |
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bot_mul=1.0), [80, 240, 720, 1920]), |
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('regnetx_12gf', |
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dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, |
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bot_mul=1.0), [224, 448, 896, 2240]), |
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] |
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@pytest.mark.parametrize('arch_name,arch,out_channels', regnet_test_data) |
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def test_regnet_backbone(arch_name, arch, out_channels): |
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with pytest.raises(AssertionError): |
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RegNet(arch_name + '233') |
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model = RegNet(arch_name) |
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model.train() |
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imgs = torch.randn(1, 3, 32, 32) |
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feat = model(imgs) |
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assert len(feat) == 4 |
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assert feat[0].shape == torch.Size([1, out_channels[0], 8, 8]) |
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assert feat[1].shape == torch.Size([1, out_channels[1], 4, 4]) |
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assert feat[2].shape == torch.Size([1, out_channels[2], 2, 2]) |
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assert feat[3].shape == torch.Size([1, out_channels[3], 1, 1]) |
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model = RegNet(arch) |
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assert feat[0].shape == torch.Size([1, out_channels[0], 8, 8]) |
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assert feat[1].shape == torch.Size([1, out_channels[1], 4, 4]) |
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assert feat[2].shape == torch.Size([1, out_channels[2], 2, 2]) |
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assert feat[3].shape == torch.Size([1, out_channels[3], 1, 1]) |
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