mirror of https://github.com/open-mmlab/mmyolo.git
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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import pytest
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import torch
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from parameterized import parameterized
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmyolo.models.backbones import YOLOv5CSPDarknet, YOLOXCSPDarknet
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from mmyolo.utils import register_all_modules
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from .utils import check_norm_state, is_norm
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register_all_modules()
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class TestCSPDarknet(TestCase):
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@parameterized.expand([(YOLOv5CSPDarknet, ), (YOLOXCSPDarknet, )])
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def test_init(self, module_class):
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# out_indices in range(len(arch_setting) + 1)
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with pytest.raises(AssertionError):
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module_class(out_indices=(6, ))
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with pytest.raises(ValueError):
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# frozen_stages must in range(-1, len(arch_setting) + 1)
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module_class(frozen_stages=6)
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@parameterized.expand([(YOLOv5CSPDarknet, ), (YOLOXCSPDarknet, )])
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def test_forward(self, module_class):
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# Test CSPDarknet with first stage frozen
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frozen_stages = 1
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model = module_class(frozen_stages=frozen_stages)
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model.init_weights()
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model.train()
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for mod in model.stem.modules():
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for param in mod.parameters():
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assert param.requires_grad is False
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for i in range(1, frozen_stages + 1):
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layer = getattr(model, f'stage{i}')
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for mod in layer.modules():
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if isinstance(mod, _BatchNorm):
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assert mod.training is False
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for param in layer.parameters():
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assert param.requires_grad is False
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# Test CSPDarknet with norm_eval=True
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model = module_class(norm_eval=True)
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model.train()
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assert check_norm_state(model.modules(), False)
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# Test CSPDarknet-P5 forward with widen_factor=0.25
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model = module_class(
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arch='P5', widen_factor=0.25, out_indices=range(0, 5))
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model.train()
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imgs = torch.randn(1, 3, 64, 64)
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feat = model(imgs)
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assert len(feat) == 5
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assert feat[0].shape == torch.Size((1, 16, 32, 32))
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assert feat[1].shape == torch.Size((1, 32, 16, 16))
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assert feat[2].shape == torch.Size((1, 64, 8, 8))
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assert feat[3].shape == torch.Size((1, 128, 4, 4))
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assert feat[4].shape == torch.Size((1, 256, 2, 2))
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# Test CSPDarknet forward with dict(type='ReLU')
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model = module_class(
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widen_factor=0.125,
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act_cfg=dict(type='ReLU'),
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out_indices=range(0, 5))
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model.train()
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imgs = torch.randn(1, 3, 64, 64)
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feat = model(imgs)
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assert len(feat) == 5
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assert feat[0].shape == torch.Size((1, 8, 32, 32))
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assert feat[1].shape == torch.Size((1, 16, 16, 16))
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assert feat[2].shape == torch.Size((1, 32, 8, 8))
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assert feat[3].shape == torch.Size((1, 64, 4, 4))
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assert feat[4].shape == torch.Size((1, 128, 2, 2))
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# Test CSPDarknet with BatchNorm forward
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model = module_class(widen_factor=0.125, out_indices=range(0, 5))
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, _BatchNorm)
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model.train()
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imgs = torch.randn(1, 3, 64, 64)
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feat = model(imgs)
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assert len(feat) == 5
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assert feat[0].shape == torch.Size((1, 8, 32, 32))
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assert feat[1].shape == torch.Size((1, 16, 16, 16))
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assert feat[2].shape == torch.Size((1, 32, 8, 8))
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assert feat[3].shape == torch.Size((1, 64, 4, 4))
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assert feat[4].shape == torch.Size((1, 128, 2, 2))
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