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# 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()
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