mirror of https://github.com/open-mmlab/mmpose
103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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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 import ViPNAS_MobileNetV3
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from mmpose.models.backbones.utils import InvertedResidual
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class TestVipnasMbv3(TestCase):
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@staticmethod
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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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@staticmethod
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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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def test_mobilenetv3_backbone(self):
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with self.assertRaises(TypeError):
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# init_weights must have no parameter
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model = ViPNAS_MobileNetV3()
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model.init_weights(pretrained=0)
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with self.assertRaises(AttributeError):
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# frozen_stages must no more than 21
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model = ViPNAS_MobileNetV3(frozen_stages=22)
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model.train()
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# Test MobileNetv3
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model = ViPNAS_MobileNetV3()
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model.init_weights()
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model.train()
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# Test MobileNetv3 with first stage frozen
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frozen_stages = 1
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model = ViPNAS_MobileNetV3(frozen_stages=frozen_stages)
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model.init_weights()
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model.train()
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for param in model.conv1.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'layer{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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# Test MobileNetv3 with norm eval
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model = ViPNAS_MobileNetV3(norm_eval=True)
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model.init_weights()
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model.train()
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self.assertTrue(self.check_norm_state(model.modules(), False))
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# Test MobileNetv3 forward
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model = ViPNAS_MobileNetV3()
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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self.assertIsInstance(feat, tuple)
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self.assertEqual(feat[-1].shape, torch.Size([1, 160, 7, 7]))
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# Test MobileNetv3 forward with GroupNorm
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model = ViPNAS_MobileNetV3(
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norm_cfg=dict(type='GN', num_groups=2, requires_grad=True))
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for m in model.modules():
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if self.is_norm(m):
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self.assertIsInstance(m, GroupNorm)
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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self.assertIsInstance(feat, tuple)
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self.assertEqual(feat[-1].shape, torch.Size([1, 160, 7, 7]))
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# Test MobileNetv3 with checkpoint forward
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model = ViPNAS_MobileNetV3(with_cp=True)
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for m in model.modules():
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if isinstance(m, InvertedResidual):
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self.assertTrue(m.with_cp)
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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self.assertIsInstance(feat, tuple)
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self.assertEqual(feat[-1].shape, torch.Size([1, 160, 7, 7]))
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