mirror of https://github.com/open-mmlab/mmpose
126 lines
4.4 KiB
Python
126 lines
4.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import unittest
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import torch
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from torch.nn.modules import GroupNorm
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmpose.models.backbones.csp_darknet import CSPDarknet
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def is_norm(modules):
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"""Check if is one of the norms."""
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if isinstance(modules, (GroupNorm, _BatchNorm)):
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return True
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return False
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def check_norm_state(modules, train_state):
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"""Check if norm layer is in correct train state."""
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for mod in modules:
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if isinstance(mod, _BatchNorm):
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if mod.training != train_state:
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return False
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return True
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class TestCSPDarknetBackbone(unittest.TestCase):
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def test_invalid_frozen_stages(self):
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with self.assertRaises(ValueError):
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CSPDarknet(frozen_stages=6)
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def test_invalid_out_indices(self):
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with self.assertRaises(AssertionError):
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CSPDarknet(out_indices=[6])
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def test_frozen_stages(self):
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frozen_stages = 1
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model = CSPDarknet(frozen_stages=frozen_stages)
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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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self.assertFalse(param.requires_grad)
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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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self.assertFalse(mod.training)
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for param in layer.parameters():
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self.assertFalse(param.requires_grad)
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def test_norm_eval(self):
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model = CSPDarknet(norm_eval=True)
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model.train()
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self.assertFalse(check_norm_state(model.modules(), True))
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def test_csp_darknet_p5_forward(self):
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model = CSPDarknet(
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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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self.assertEqual(len(feat), 5)
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self.assertEqual(feat[0].shape, torch.Size((1, 16, 32, 32)))
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self.assertEqual(feat[1].shape, torch.Size((1, 32, 16, 16)))
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self.assertEqual(feat[2].shape, torch.Size((1, 64, 8, 8)))
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self.assertEqual(feat[3].shape, torch.Size((1, 128, 4, 4)))
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self.assertEqual(feat[4].shape, torch.Size((1, 256, 2, 2)))
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def test_csp_darknet_p6_forward(self):
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model = CSPDarknet(
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arch='P6',
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widen_factor=0.25,
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out_indices=range(0, 6),
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spp_kernal_sizes=(3, 5, 7))
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model.train()
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imgs = torch.randn(1, 3, 128, 128)
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feat = model(imgs)
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self.assertEqual(feat[0].shape, torch.Size((1, 16, 64, 64)))
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self.assertEqual(feat[1].shape, torch.Size((1, 32, 32, 32)))
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self.assertEqual(feat[2].shape, torch.Size((1, 64, 16, 16)))
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self.assertEqual(feat[3].shape, torch.Size((1, 128, 8, 8)))
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self.assertEqual(feat[4].shape, torch.Size((1, 192, 4, 4)))
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self.assertEqual(feat[5].shape, torch.Size((1, 256, 2, 2)))
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def test_csp_darknet_custom_arch_forward(self):
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arch_ovewrite = [[32, 56, 3, True, False], [56, 224, 2, True, False],
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[224, 512, 1, True, False]]
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model = CSPDarknet(
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arch_ovewrite=arch_ovewrite,
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widen_factor=0.25,
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out_indices=(0, 1, 2, 3))
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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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self.assertEqual(len(feat), 4)
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self.assertEqual(feat[0].shape, torch.Size((1, 8, 16, 16)))
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self.assertEqual(feat[1].shape, torch.Size((1, 14, 8, 8)))
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self.assertEqual(feat[2].shape, torch.Size((1, 56, 4, 4)))
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self.assertEqual(feat[3].shape, torch.Size((1, 128, 2, 2)))
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def test_csp_darknet_custom_arch_norm(self):
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model = CSPDarknet(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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self.assertIsInstance(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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self.assertEqual(len(feat), 5)
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self.assertEqual(feat[0].shape, torch.Size((1, 8, 32, 32)))
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self.assertEqual(feat[1].shape, torch.Size((1, 16, 16, 16)))
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self.assertEqual(feat[2].shape, torch.Size((1, 32, 8, 8)))
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self.assertEqual(feat[3].shape, torch.Size((1, 64, 4, 4)))
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self.assertEqual(feat[4].shape, torch.Size((1, 128, 2, 2)))
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if __name__ == '__main__':
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unittest.main()
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