mmpose-0.29.0/tests/test_backbones/test_vipnas_mbv3.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 ViPNAS_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 = ViPNAS_MobileNetV3()
 model.init_weights(pretrained=0)

 with pytest.raises(AttributeError):
 # frozen_stages must no more than 21
 model = ViPNAS_MobileNetV3(frozen_stages=22)
 model.train()

 # Test MobileNetv3
 model = ViPNAS_MobileNetV3()
 model.init_weights()
 model.train()

 # Test MobileNetv3 with first stage frozen
 frozen_stages = 1
 model = ViPNAS_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 = ViPNAS_MobileNetV3(norm_eval=True)
 model.init_weights()
 model.train()
 assert check_norm_state(model.modules(), False)

 # Test MobileNetv3 forward
 model = ViPNAS_MobileNetV3()
 model.init_weights()
 model.train()

 imgs = torch.randn(1, 3, 224, 224)
 feat = model(imgs)
 assert feat.shape == torch.Size([1, 160, 7, 7])

 # Test MobileNetv3 forward with GroupNorm
 model = ViPNAS_MobileNetV3(
 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 feat.shape == torch.Size([1, 160, 7, 7])

 # Test MobileNetv3 with checkpoint forward
 model = ViPNAS_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, 160, 7, 7])


test_mobilenetv3_backbone()