# 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 MobileNetV3 from mmpose.models.backbones.utils import InvertedResidual class TestMobilenetV3(TestCase): @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_mobilenetv3_backbone(self): with self.assertRaises(AssertionError): # arch must in [small, big] MobileNetV3(arch='others') with self.assertRaises(ValueError): # frozen_stages must less than 12 when arch is small MobileNetV3(arch='small', frozen_stages=12) with self.assertRaises(ValueError): # frozen_stages must less than 16 when arch is big MobileNetV3(arch='big', frozen_stages=16) with self.assertRaises(ValueError): # max out_indices must less than 11 when arch is small MobileNetV3(arch='small', out_indices=(11, )) with self.assertRaises(ValueError): # max out_indices must less than 15 when arch is big MobileNetV3(arch='big', out_indices=(15, )) # Test MobileNetv3 model = MobileNetV3() model.init_weights() model.train() # Test MobileNetv3 with first stage frozen frozen_stages = 1 model = MobileNetV3(frozen_stages=frozen_stages) model.init_weights() model.train() for param in model.conv1.parameters(): self.assertFalse(param.requires_grad) for i in range(1, frozen_stages + 1): layer = getattr(model, f'layer{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 MobileNetv3 with norm eval model = MobileNetV3(norm_eval=True, out_indices=range(0, 11)) model.init_weights() model.train() self.assertTrue(self.check_norm_state(model.modules(), False)) # Test MobileNetv3 forward with small arch model = MobileNetV3(out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) self.assertEqual(len(feat), 11) self.assertEqual(feat[0].shape, torch.Size([1, 16, 56, 56])) self.assertEqual(feat[1].shape, torch.Size([1, 24, 28, 28])) self.assertEqual(feat[2].shape, torch.Size([1, 24, 28, 28])) self.assertEqual(feat[3].shape, torch.Size([1, 40, 14, 14])) self.assertEqual(feat[4].shape, torch.Size([1, 40, 14, 14])) self.assertEqual(feat[5].shape, torch.Size([1, 40, 14, 14])) self.assertEqual(feat[6].shape, torch.Size([1, 48, 14, 14])) self.assertEqual(feat[7].shape, torch.Size([1, 48, 14, 14])) self.assertEqual(feat[8].shape, torch.Size([1, 96, 7, 7])) self.assertEqual(feat[9].shape, torch.Size([1, 96, 7, 7])) self.assertEqual(feat[10].shape, torch.Size([1, 96, 7, 7])) # Test MobileNetv3 forward with small arch and GroupNorm model = MobileNetV3( out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), norm_cfg=dict(type='GN', num_groups=2, requires_grad=True)) for m in model.modules(): if self.is_norm(m): self.assertIsInstance(m, GroupNorm) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) self.assertEqual(len(feat), 11) self.assertEqual(feat[0].shape, torch.Size([1, 16, 56, 56])) self.assertEqual(feat[1].shape, torch.Size([1, 24, 28, 28])) self.assertEqual(feat[2].shape, torch.Size([1, 24, 28, 28])) self.assertEqual(feat[3].shape, torch.Size([1, 40, 14, 14])) self.assertEqual(feat[4].shape, torch.Size([1, 40, 14, 14])) self.assertEqual(feat[5].shape, torch.Size([1, 40, 14, 14])) self.assertEqual(feat[6].shape, torch.Size([1, 48, 14, 14])) self.assertEqual(feat[7].shape, torch.Size([1, 48, 14, 14])) self.assertEqual(feat[8].shape, torch.Size([1, 96, 7, 7])) self.assertEqual(feat[9].shape, torch.Size([1, 96, 7, 7])) self.assertEqual(feat[10].shape, torch.Size([1, 96, 7, 7])) # Test MobileNetv3 forward with big arch model = MobileNetV3( arch='big', out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) self.assertEqual(len(feat), 15) self.assertEqual(feat[0].shape, torch.Size([1, 16, 112, 112])) self.assertEqual(feat[1].shape, torch.Size([1, 24, 56, 56])) self.assertEqual(feat[2].shape, torch.Size([1, 24, 56, 56])) self.assertEqual(feat[3].shape, torch.Size([1, 40, 28, 28])) self.assertEqual(feat[4].shape, torch.Size([1, 40, 28, 28])) self.assertEqual(feat[5].shape, torch.Size([1, 40, 28, 28])) self.assertEqual(feat[6].shape, torch.Size([1, 80, 14, 14])) self.assertEqual(feat[7].shape, torch.Size([1, 80, 14, 14])) self.assertEqual(feat[8].shape, torch.Size([1, 80, 14, 14])) self.assertEqual(feat[9].shape, torch.Size([1, 80, 14, 14])) self.assertEqual(feat[10].shape, torch.Size([1, 112, 14, 14])) self.assertEqual(feat[11].shape, torch.Size([1, 112, 14, 14])) self.assertEqual(feat[12].shape, torch.Size([1, 160, 14, 14])) self.assertEqual(feat[13].shape, torch.Size([1, 160, 7, 7])) self.assertEqual(feat[14].shape, torch.Size([1, 160, 7, 7])) # Test MobileNetv3 forward with big arch model = MobileNetV3(arch='big', out_indices=(0, )) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) self.assertIsInstance(feat, tuple) self.assertEqual(feat[-1].shape, torch.Size([1, 16, 112, 112])) # Test MobileNetv3 with checkpoint forward model = MobileNetV3(with_cp=True) for m in model.modules(): if isinstance(m, InvertedResidual): self.assertTrue(m.with_cp) model.init_weights() model.train() imgs = torch.randn(1, 3, 224, 224) feat = model(imgs) self.assertIsInstance(feat, tuple) self.assertEqual(feat[-1].shape, torch.Size([1, 96, 7, 7]))