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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from unittest import TestCase | |
| import torch | |
| from torch.nn.modules.batchnorm import _BatchNorm | |
| from mmpose.models.backbones import HRNet | |
| from mmpose.models.backbones.hrnet import HRModule | |
| from mmpose.models.backbones.resnet import BasicBlock, Bottleneck | |
| class TestHrnet(TestCase): | |
| def is_block(modules): | |
| """Check if is HRModule building block.""" | |
| if isinstance(modules, (HRModule, )): | |
| return True | |
| return False | |
| def is_norm(modules): | |
| """Check if is one of the norms.""" | |
| if isinstance(modules, (_BatchNorm, )): | |
| return True | |
| return False | |
| def all_zeros(modules): | |
| """Check if the weight(and bias) is all zero.""" | |
| weight_zero = torch.equal(modules.weight.data, | |
| torch.zeros_like(modules.weight.data)) | |
| if hasattr(modules, 'bias'): | |
| bias_zero = torch.equal(modules.bias.data, | |
| torch.zeros_like(modules.bias.data)) | |
| else: | |
| bias_zero = True | |
| return weight_zero and bias_zero | |
| def test_hrmodule(self): | |
| # Test HRModule forward | |
| block = HRModule( | |
| num_branches=1, | |
| blocks=BasicBlock, | |
| num_blocks=(4, ), | |
| in_channels=[ | |
| 64, | |
| ], | |
| num_channels=(64, )) | |
| x = torch.randn(2, 64, 56, 56) | |
| x_out = block([x]) | |
| self.assertEqual(x_out[0].shape, torch.Size([2, 64, 56, 56])) | |
| def test_hrnet_backbone(self): | |
| extra = dict( | |
| stage1=dict( | |
| num_modules=1, | |
| num_branches=1, | |
| block='BOTTLENECK', | |
| num_blocks=(4, ), | |
| num_channels=(64, )), | |
| stage2=dict( | |
| num_modules=1, | |
| num_branches=2, | |
| block='BASIC', | |
| num_blocks=(4, 4), | |
| num_channels=(32, 64)), | |
| stage3=dict( | |
| num_modules=4, | |
| num_branches=3, | |
| block='BASIC', | |
| num_blocks=(4, 4, 4), | |
| num_channels=(32, 64, 128)), | |
| stage4=dict( | |
| num_modules=3, | |
| num_branches=4, | |
| block='BASIC', | |
| num_blocks=(4, 4, 4, 4), | |
| num_channels=(32, 64, 128, 256))) | |
| model = HRNet(extra, in_channels=3) | |
| imgs = torch.randn(2, 3, 224, 224) | |
| feat = model(imgs) | |
| self.assertIsInstance(feat, tuple) | |
| self.assertEqual(feat[-1].shape, torch.Size([2, 32, 56, 56])) | |
| # Test HRNet zero initialization of residual | |
| model = HRNet(extra, in_channels=3, zero_init_residual=True) | |
| model.init_weights() | |
| for m in model.modules(): | |
| if isinstance(m, Bottleneck): | |
| self.assertTrue(self.all_zeros(m.norm3)) | |
| model.train() | |
| imgs = torch.randn(2, 3, 224, 224) | |
| feat = model(imgs) | |
| self.assertIsInstance(feat, tuple) | |
| self.assertEqual(feat[-1].shape, torch.Size([2, 32, 56, 56])) | |
| # Test HRNet with the first three stages frozen | |
| frozen_stages = 3 | |
| model = HRNet(extra, in_channels=3, frozen_stages=frozen_stages) | |
| model.init_weights() | |
| model.train() | |
| if frozen_stages >= 0: | |
| self.assertFalse(model.norm1.training) | |
| self.assertFalse(model.norm2.training) | |
| for layer in [model.conv1, model.norm1, model.conv2, model.norm2]: | |
| for param in layer.parameters(): | |
| self.assertFalse(param.requires_grad) | |
| for i in range(1, frozen_stages + 1): | |
| if i == 1: | |
| layer = getattr(model, 'layer1') | |
| else: | |
| layer = getattr(model, f'stage{i}') | |
| for mod in layer.modules(): | |
| if isinstance(mod, _BatchNorm): | |
| self.assertFalse(mod.training) | |
| for param in layer.parameters(): | |
| self.assertFalse(param.requires_grad) | |
| if i < 4: | |
| layer = getattr(model, f'transition{i}') | |
| for mod in layer.modules(): | |
| if isinstance(mod, _BatchNorm): | |
| self.assertFalse(mod.training) | |
| for param in layer.parameters(): | |
| self.assertFalse(param.requires_grad) | |