mmpose-0.29.0/tests/test_backbones/test_mobilenet_v3.py

# Copyright (c) OpenMMLab. All rights reserved.
import pytest
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


def is_norm(modules):
 """Check if is one of the norms."""
 if isinstance(modules, (GroupNorm, _BatchNorm)):
 return True
 return False


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():
 with pytest.raises(TypeError):
 # pretrained must be a string path
 model = MobileNetV3()
 model.init_weights(pretrained=0)

 with pytest.raises(AssertionError):
 # arch must in [small, big]
 MobileNetV3(arch='others')

 with pytest.raises(ValueError):
 # frozen_stages must less than 12 when arch is small
 MobileNetV3(arch='small', frozen_stages=12)

 with pytest.raises(ValueError):
 # frozen_stages must less than 16 when arch is big
 MobileNetV3(arch='big', frozen_stages=16)

 with pytest.raises(ValueError):
 # max out_indices must less than 11 when arch is small
 MobileNetV3(arch='small', out_indices=(11, ))

 with pytest.raises(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():
 assert param.requires_grad is False
 for i in range(1, frozen_stages + 1):
 layer = getattr(model, f'layer{i}')
 for mod in layer.modules():
 if isinstance(mod, _BatchNorm):
 assert mod.training is False
 for param in layer.parameters():
 assert param.requires_grad is False

 # Test MobileNetv3 with norm eval
 model = MobileNetV3(norm_eval=True, out_indices=range(0, 11))
 model.init_weights()
 model.train()
 assert 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)
 assert len(feat) == 11
 assert feat[0].shape == torch.Size([1, 16, 56, 56])
 assert feat[1].shape == torch.Size([1, 24, 28, 28])
 assert feat[2].shape == torch.Size([1, 24, 28, 28])
 assert feat[3].shape == torch.Size([1, 40, 14, 14])
 assert feat[4].shape == torch.Size([1, 40, 14, 14])
 assert feat[5].shape == torch.Size([1, 40, 14, 14])
 assert feat[6].shape == torch.Size([1, 48, 14, 14])
 assert feat[7].shape == torch.Size([1, 48, 14, 14])
 assert feat[8].shape == torch.Size([1, 96, 7, 7])
 assert feat[9].shape == torch.Size([1, 96, 7, 7])
 assert 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 is_norm(m):
 assert isinstance(m, GroupNorm)
 model.init_weights()
 model.train()

 imgs = torch.randn(1, 3, 224, 224)
 feat = model(imgs)
 assert len(feat) == 11
 assert feat[0].shape == torch.Size([1, 16, 56, 56])
 assert feat[1].shape == torch.Size([1, 24, 28, 28])
 assert feat[2].shape == torch.Size([1, 24, 28, 28])
 assert feat[3].shape == torch.Size([1, 40, 14, 14])
 assert feat[4].shape == torch.Size([1, 40, 14, 14])
 assert feat[5].shape == torch.Size([1, 40, 14, 14])
 assert feat[6].shape == torch.Size([1, 48, 14, 14])
 assert feat[7].shape == torch.Size([1, 48, 14, 14])
 assert feat[8].shape == torch.Size([1, 96, 7, 7])
 assert feat[9].shape == torch.Size([1, 96, 7, 7])
 assert 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)
 assert len(feat) == 15
 assert feat[0].shape == torch.Size([1, 16, 112, 112])
 assert feat[1].shape == torch.Size([1, 24, 56, 56])
 assert feat[2].shape == torch.Size([1, 24, 56, 56])
 assert feat[3].shape == torch.Size([1, 40, 28, 28])
 assert feat[4].shape == torch.Size([1, 40, 28, 28])
 assert feat[5].shape == torch.Size([1, 40, 28, 28])
 assert feat[6].shape == torch.Size([1, 80, 14, 14])
 assert feat[7].shape == torch.Size([1, 80, 14, 14])
 assert feat[8].shape == torch.Size([1, 80, 14, 14])
 assert feat[9].shape == torch.Size([1, 80, 14, 14])
 assert feat[10].shape == torch.Size([1, 112, 14, 14])
 assert feat[11].shape == torch.Size([1, 112, 14, 14])
 assert feat[12].shape == torch.Size([1, 160, 14, 14])
 assert feat[13].shape == torch.Size([1, 160, 7, 7])
 assert 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)
 assert feat.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):
 assert m.with_cp
 model.init_weights()
 model.train()

 imgs = torch.randn(1, 3, 224, 224)
 feat = model(imgs)
 assert feat.shape == torch.Size([1, 96, 7, 7])