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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from unittest import TestCase | |
| import pytest | |
| import torch | |
| from torch.nn.modules.batchnorm import _BatchNorm | |
| from mmyolo.models import PPYOLOECSPResNet | |
| from mmyolo.utils import register_all_modules | |
| from .utils import check_norm_state, is_norm | |
| register_all_modules() | |
| class TestPPYOLOECSPResNet(TestCase): | |
| def test_init(self): | |
| # out_indices in range(len(arch_setting) + 1) | |
| with pytest.raises(AssertionError): | |
| PPYOLOECSPResNet(out_indices=(6, )) | |
| with pytest.raises(ValueError): | |
| # frozen_stages must in range(-1, len(arch_setting) + 1) | |
| PPYOLOECSPResNet(frozen_stages=6) | |
| def test_forward(self): | |
| # Test PPYOLOECSPResNet with first stage frozen | |
| frozen_stages = 1 | |
| model = PPYOLOECSPResNet(frozen_stages=frozen_stages) | |
| model.init_weights() | |
| model.train() | |
| for mod in model.stem.modules(): | |
| for param in mod.parameters(): | |
| assert param.requires_grad is False | |
| for i in range(1, frozen_stages + 1): | |
| layer = getattr(model, f'stage{i}') | |
| for mod in layer.modules(): | |
| if isinstance(mod, _BatchNorm): | |
| assert mod.training is False | |
| for param in layer.parameters(): | |
| assert param.requires_grad is False | |
| # Test PPYOLOECSPResNet with norm_eval=True | |
| model = PPYOLOECSPResNet(norm_eval=True) | |
| model.train() | |
| assert check_norm_state(model.modules(), False) | |
| # Test PPYOLOECSPResNet-P5 forward with widen_factor=0.25 | |
| model = PPYOLOECSPResNet( | |
| arch='P5', widen_factor=0.25, out_indices=range(0, 5)) | |
| model.train() | |
| imgs = torch.randn(1, 3, 64, 64) | |
| feat = model(imgs) | |
| assert len(feat) == 5 | |
| assert feat[0].shape == torch.Size((1, 16, 32, 32)) | |
| assert feat[1].shape == torch.Size((1, 32, 16, 16)) | |
| assert feat[2].shape == torch.Size((1, 64, 8, 8)) | |
| assert feat[3].shape == torch.Size((1, 128, 4, 4)) | |
| assert feat[4].shape == torch.Size((1, 256, 2, 2)) | |
| # Test PPYOLOECSPResNet forward with dict(type='ReLU') | |
| model = PPYOLOECSPResNet( | |
| widen_factor=0.125, | |
| act_cfg=dict(type='ReLU'), | |
| out_indices=range(0, 5)) | |
| model.train() | |
| imgs = torch.randn(1, 3, 64, 64) | |
| feat = model(imgs) | |
| assert len(feat) == 5 | |
| assert feat[0].shape == torch.Size((1, 8, 32, 32)) | |
| assert feat[1].shape == torch.Size((1, 16, 16, 16)) | |
| assert feat[2].shape == torch.Size((1, 32, 8, 8)) | |
| assert feat[3].shape == torch.Size((1, 64, 4, 4)) | |
| assert feat[4].shape == torch.Size((1, 128, 2, 2)) | |
| # Test PPYOLOECSPResNet with BatchNorm forward | |
| model = PPYOLOECSPResNet(widen_factor=0.125, out_indices=range(0, 5)) | |
| for m in model.modules(): | |
| if is_norm(m): | |
| assert isinstance(m, _BatchNorm) | |
| model.train() | |
| imgs = torch.randn(1, 3, 64, 64) | |
| feat = model(imgs) | |
| assert len(feat) == 5 | |
| assert feat[0].shape == torch.Size((1, 8, 32, 32)) | |
| assert feat[1].shape == torch.Size((1, 16, 16, 16)) | |
| assert feat[2].shape == torch.Size((1, 32, 8, 8)) | |
| assert feat[3].shape == torch.Size((1, 64, 4, 4)) | |
| assert feat[4].shape == torch.Size((1, 128, 2, 2)) | |
| # Test PPYOLOECSPResNet with BatchNorm forward | |
| model = PPYOLOECSPResNet(plugins=[ | |
| dict( | |
| cfg=dict(type='mmdet.DropBlock', drop_prob=0.1, block_size=3), | |
| stages=(False, False, True, True)), | |
| ]) | |
| assert len(model.stage1) == 1 | |
| assert len(model.stage2) == 1 | |
| assert len(model.stage3) == 2 # +DropBlock | |
| assert len(model.stage4) == 2 # +DropBlock | |
| model.train() | |
| imgs = torch.randn(1, 3, 256, 256) | |
| feat = model(imgs) | |
| assert len(feat) == 3 | |
| assert feat[0].shape == torch.Size((1, 256, 32, 32)) | |
| assert feat[1].shape == torch.Size((1, 512, 16, 16)) | |
| assert feat[2].shape == torch.Size((1, 1024, 8, 8)) | |