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- # Copyright (c) OpenMMLab. All rights reserved.
- from unittest import TestCase
- import torch
- from torch.nn.modules import GroupNorm
- from torch.nn.modules.batchnorm import _BatchNorm
- from mmpose.models.backbones import ShuffleNetV2
- from mmpose.models.backbones.shufflenet_v2 import InvertedResidual
- class TestShufflenetV2(TestCase):
- @staticmethod
- def is_block(modules):
- """Check if is ResNet building block."""
- if isinstance(modules, (InvertedResidual, )):
- return True
- return False
- @staticmethod
- def is_norm(modules):
- """Check if is one of the norms."""
- if isinstance(modules, (GroupNorm, _BatchNorm)):
- return True
- return False
- @staticmethod
- def check_norm_state(modules, train_state):
- """Check if norm layer is in correct train state."""
- for mod in modules:
- if isinstance(mod, _BatchNorm):
- if mod.training != train_state:
- return False
- return True
- def test_shufflenetv2_invertedresidual(self):
- with self.assertRaises(AssertionError):
- # when stride==1, in_channels should be equal to
- # out_channels // 2 * 2
- InvertedResidual(24, 32, stride=1)
- with self.assertRaises(AssertionError):
- # when in_channels != out_channels // 2 * 2, stride should not be
- # equal to 1.
- InvertedResidual(24, 32, stride=1)
- # Test InvertedResidual forward
- block = InvertedResidual(24, 48, stride=2)
- x = torch.randn(1, 24, 56, 56)
- x_out = block(x)
- self.assertEqual(x_out.shape, torch.Size((1, 48, 28, 28)))
- # Test InvertedResidual with checkpoint forward
- block = InvertedResidual(48, 48, stride=1, with_cp=True)
- self.assertTrue(block.with_cp)
- x = torch.randn(1, 48, 56, 56)
- x.requires_grad = True
- x_out = block(x)
- self.assertEqual(x_out.shape, torch.Size((1, 48, 56, 56)))
- def test_shufflenetv2_backbone(self):
- with self.assertRaises(ValueError):
- # groups must be in 0.5, 1.0, 1.5, 2.0]
- ShuffleNetV2(widen_factor=3.0)
- with self.assertRaises(ValueError):
- # frozen_stages must be in [0, 1, 2, 3]
- ShuffleNetV2(widen_factor=1.0, frozen_stages=4)
- with self.assertRaises(ValueError):
- # out_indices must be in [0, 1, 2, 3]
- ShuffleNetV2(widen_factor=1.0, out_indices=(4, ))
- with self.assertRaises(TypeError):
- # init_weights must have no parameter
- model = ShuffleNetV2()
- model.init_weights(pretrained=1)
- # Test ShuffleNetV2 norm state
- model = ShuffleNetV2()
- model.init_weights()
- model.train()
- self.assertTrue(self.check_norm_state(model.modules(), True))
- # Test ShuffleNetV2 with first stage frozen
- frozen_stages = 1
- model = ShuffleNetV2(frozen_stages=frozen_stages)
- model.init_weights()
- model.train()
- for param in model.conv1.parameters():
- self.assertFalse(param.requires_grad)
- for i in range(0, frozen_stages):
- layer = model.layers[i]
- for mod in layer.modules():
- if isinstance(mod, _BatchNorm):
- self.assertFalse(mod.training)
- for param in layer.parameters():
- self.assertFalse(param.requires_grad)
- # Test ShuffleNetV2 with norm_eval
- model = ShuffleNetV2(norm_eval=True)
- model.init_weights()
- model.train()
- self.assertTrue(self.check_norm_state(model.modules(), False))
- # Test ShuffleNetV2 forward with widen_factor=0.5
- model = ShuffleNetV2(widen_factor=0.5, out_indices=(0, 1, 2, 3))
- model.init_weights()
- model.train()
- for m in model.modules():
- if self.is_norm(m):
- self.assertIsInstance(m, _BatchNorm)
- imgs = torch.randn(1, 3, 224, 224)
- feat = model(imgs)
- self.assertEqual(len(feat), 4)
- self.assertEqual(feat[0].shape, torch.Size((1, 48, 28, 28)))
- self.assertEqual(feat[1].shape, torch.Size((1, 96, 14, 14)))
- self.assertEqual(feat[2].shape, torch.Size((1, 192, 7, 7)))
- # Test ShuffleNetV2 forward with widen_factor=1.0
- model = ShuffleNetV2(widen_factor=1.0, out_indices=(0, 1, 2, 3))
- model.init_weights()
- model.train()
- for m in model.modules():
- if self.is_norm(m):
- self.assertIsInstance(m, _BatchNorm)
- imgs = torch.randn(1, 3, 224, 224)
- feat = model(imgs)
- self.assertEqual(len(feat), 4)
- self.assertEqual(feat[0].shape, torch.Size((1, 116, 28, 28)))
- self.assertEqual(feat[1].shape, torch.Size((1, 232, 14, 14)))
- self.assertEqual(feat[2].shape, torch.Size((1, 464, 7, 7)))
- # Test ShuffleNetV2 forward with widen_factor=1.5
- model = ShuffleNetV2(widen_factor=1.5, out_indices=(0, 1, 2, 3))
- model.init_weights()
- model.train()
- for m in model.modules():
- if self.is_norm(m):
- self.assertIsInstance(m, _BatchNorm)
- imgs = torch.randn(1, 3, 224, 224)
- feat = model(imgs)
- self.assertEqual(len(feat), 4)
- self.assertEqual(feat[0].shape, torch.Size((1, 176, 28, 28)))
- self.assertEqual(feat[1].shape, torch.Size((1, 352, 14, 14)))
- self.assertEqual(feat[2].shape, torch.Size((1, 704, 7, 7)))
- # Test ShuffleNetV2 forward with widen_factor=2.0
- model = ShuffleNetV2(widen_factor=2.0, out_indices=(0, 1, 2, 3))
- model.init_weights()
- model.train()
- for m in model.modules():
- if self.is_norm(m):
- self.assertIsInstance(m, _BatchNorm)
- imgs = torch.randn(1, 3, 224, 224)
- feat = model(imgs)
- self.assertEqual(len(feat), 4)
- self.assertEqual(feat[0].shape, torch.Size((1, 244, 28, 28)))
- self.assertEqual(feat[1].shape, torch.Size((1, 488, 14, 14)))
- self.assertEqual(feat[2].shape, torch.Size((1, 976, 7, 7)))
- # Test ShuffleNetV2 forward with layers 3 forward
- model = ShuffleNetV2(widen_factor=1.0, out_indices=(2, ))
- model.init_weights()
- model.train()
- for m in model.modules():
- if self.is_norm(m):
- self.assertIsInstance(m, _BatchNorm)
- imgs = torch.randn(1, 3, 224, 224)
- feat = model(imgs)
- self.assertIsInstance(feat, tuple)
- self.assertEqual(feat[-1].shape, torch.Size((1, 464, 7, 7)))
- # Test ShuffleNetV2 forward with layers 1 2 forward
- model = ShuffleNetV2(widen_factor=1.0, out_indices=(1, 2))
- model.init_weights()
- model.train()
- for m in model.modules():
- if self.is_norm(m):
- self.assertIsInstance(m, _BatchNorm)
- imgs = torch.randn(1, 3, 224, 224)
- feat = model(imgs)
- self.assertEqual(len(feat), 2)
- self.assertEqual(feat[0].shape, torch.Size((1, 232, 14, 14)))
- self.assertEqual(feat[1].shape, torch.Size((1, 464, 7, 7)))
- # Test ShuffleNetV2 forward with checkpoint forward
- model = ShuffleNetV2(widen_factor=1.0, with_cp=True)
- for m in model.modules():
- if self.is_block(m):
- self.assertTrue(m.with_cp)
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