# 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 MobileNetV2
from mmpose.models.backbones.mobilenet_v2 import InvertedResidual
def is_block(modules):
"""Check if is ResNet building block."""
if isinstance(modules, (InvertedResidual, )):
return True
return False
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_mobilenetv2_invertedresidual():
with pytest.raises(AssertionError):
# stride must be in [1, 2]
InvertedResidual(16, 24, stride=3, expand_ratio=6)
# Test InvertedResidual with checkpoint forward, stride=1
block = InvertedResidual(16, 24, stride=1, expand_ratio=6)
x = torch.randn(1, 16, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size((1, 24, 56, 56))
# Test InvertedResidual with expand_ratio=1
block = InvertedResidual(16, 16, stride=1, expand_ratio=1)
assert len(block.conv) == 2
# Test InvertedResidual with use_res_connect
block = InvertedResidual(16, 16, stride=1, expand_ratio=6)
x = torch.randn(1, 16, 56, 56)
x_out = block(x)
assert block.use_res_connect is True
assert x_out.shape == torch.Size((1, 16, 56, 56))
# Test InvertedResidual with checkpoint forward, stride=2
block = InvertedResidual(16, 24, stride=2, expand_ratio=6)
x = torch.randn(1, 16, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size((1, 24, 28, 28))
# Test InvertedResidual with checkpoint forward
block = InvertedResidual(16, 24, stride=1, expand_ratio=6, with_cp=True)
assert block.with_cp
x = torch.randn(1, 16, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size((1, 24, 56, 56))
# Test InvertedResidual with act_cfg=dict(type='ReLU')
block = InvertedResidual(
16, 24, stride=1, expand_ratio=6, act_cfg=dict(type='ReLU'))
x = torch.randn(1, 16, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size((1, 24, 56, 56))
def test_mobilenetv2_backbone():
with pytest.raises(TypeError):
# pretrained must be a string path
model = MobileNetV2()
model.init_weights(pretrained=0)
with pytest.raises(ValueError):
# frozen_stages must in range(1, 8)
MobileNetV2(frozen_stages=8)
with pytest.raises(ValueError):
# tout_indices in range(-1, 8)
MobileNetV2(out_indices=[8])
# Test MobileNetV2 with first stage frozen
frozen_stages = 1
model = MobileNetV2(frozen_stages=frozen_stages)
model.init_weights()
model.train()
for mod in model.conv1.modules():
for param in mod.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 MobileNetV2 with norm_eval=True
model = MobileNetV2(norm_eval=True)
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
# Test MobileNetV2 forward with widen_factor=1.0
model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 8))
model.init_weights()
model.train()
assert check_norm_state(model.modules(), True)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 8
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, 32, 28, 28))
assert feat[3].shape == torch.Size((1, 64, 14, 14))
assert feat[4].shape == torch.Size((1, 96, 14, 14))
assert feat[5].shape == torch.Size((1, 160, 7, 7))
assert feat[6].shape == torch.Size((1, 320, 7, 7))
assert feat[7].shape == torch.Size((1, 1280, 7, 7))
# Test MobileNetV2 forward with widen_factor=0.5
model = MobileNetV2(widen_factor=0.5, out_indices=range(0, 7))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 7
assert feat[0].shape == torch.Size((1, 8, 112, 112))
assert feat[1].shape == torch.Size((1, 16, 56, 56))
assert feat[2].shape == torch.Size((1, 16, 28, 28))
assert feat[3].shape == torch.Size((1, 32, 14, 14))
assert feat[4].shape == torch.Size((1, 48, 14, 14))
assert feat[5].shape == torch.Size((1, 80, 7, 7))
assert feat[6].shape == torch.Size((1, 160, 7, 7))
# Test MobileNetV2 forward with widen_factor=2.0
model = MobileNetV2(widen_factor=2.0)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == torch.Size((1, 2560, 7, 7))
# Test MobileNetV2 forward with out_indices=None
model = MobileNetV2(widen_factor=1.0)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == torch.Size((1, 1280, 7, 7))
# Test MobileNetV2 forward with dict(type='ReLU')
model = MobileNetV2(
widen_factor=1.0, act_cfg=dict(type='ReLU'), out_indices=range(0, 7))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 7
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, 32, 28, 28))
assert feat[3].shape == torch.Size((1, 64, 14, 14))
assert feat[4].shape == torch.Size((1, 96, 14, 14))
assert feat[5].shape == torch.Size((1, 160, 7, 7))
assert feat[6].shape == torch.Size((1, 320, 7, 7))
# Test MobileNetV2 with GroupNorm forward
model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7))
for m in model.modules():
if is_norm(m):
assert isinstance(m, _BatchNorm)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 7
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, 32, 28, 28))
assert feat[3].shape == torch.Size((1, 64, 14, 14))
assert feat[4].shape == torch.Size((1, 96, 14, 14))
assert feat[5].shape == torch.Size((1, 160, 7, 7))
assert feat[6].shape == torch.Size((1, 320, 7, 7))
# Test MobileNetV2 with BatchNorm forward
model = MobileNetV2(
widen_factor=1.0,
norm_cfg=dict(type='GN', num_groups=2, requires_grad=True),
out_indices=range(0, 7))
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) == 7
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, 32, 28, 28))
assert feat[3].shape == torch.Size((1, 64, 14, 14))
assert feat[4].shape == torch.Size((1, 96, 14, 14))
assert feat[5].shape == torch.Size((1, 160, 7, 7))
assert feat[6].shape == torch.Size((1, 320, 7, 7))
# Test MobileNetV2 with layers 1, 3, 5 out forward
model = MobileNetV2(widen_factor=1.0, out_indices=(0, 2, 4))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == torch.Size((1, 16, 112, 112))
assert feat[1].shape == torch.Size((1, 32, 28, 28))
assert feat[2].shape == torch.Size((1, 96, 14, 14))
# Test MobileNetV2 with checkpoint forward
model = MobileNetV2(
widen_factor=1.0, with_cp=True, out_indices=range(0, 7))
for m in model.modules():
if is_block(m):
assert m.with_cp
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 7
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, 32, 28, 28))
assert feat[3].shape == torch.Size((1, 64, 14, 14))
assert feat[4].shape == torch.Size((1, 96, 14, 14))
assert feat[5].shape == torch.Size((1, 160, 7, 7))
assert feat[6].shape == torch.Size((1, 320, 7, 7))
