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