# 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 ShuffleNetV2
from mmpose.models.backbones.shufflenet_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_shufflenetv2_invertedresidual():
with pytest.raises(AssertionError):
# when stride==1, in_channels should be equal to out_channels // 2 * 2
InvertedResidual(24, 32, stride=1)
with pytest.raises(AssertionError):
# when in_channels != out_channels // 2 * 2, stride should not be
# equal to 1.
InvertedResidual(24, 32, stride=1)
# Test InvertedResidual forward
block = InvertedResidual(24, 48, stride=2)
x = torch.randn(1, 24, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size((1, 48, 28, 28))
# Test InvertedResidual with checkpoint forward
block = InvertedResidual(48, 48, stride=1, with_cp=True)
assert block.with_cp
x = torch.randn(1, 48, 56, 56)
x.requires_grad = True
x_out = block(x)
assert x_out.shape == torch.Size((1, 48, 56, 56))
def test_shufflenetv2_backbone():
with pytest.raises(ValueError):
# groups must be in 0.5, 1.0, 1.5, 2.0]
ShuffleNetV2(widen_factor=3.0)
with pytest.raises(ValueError):
# frozen_stages must be in [0, 1, 2, 3]
ShuffleNetV2(widen_factor=1.0, frozen_stages=4)
with pytest.raises(ValueError):
# out_indices must be in [0, 1, 2, 3]
ShuffleNetV2(widen_factor=1.0, out_indices=(4, ))
with pytest.raises(TypeError):
# pretrained must be str or None
model = ShuffleNetV2()
model.init_weights(pretrained=1)
# Test ShuffleNetV2 norm state
model = ShuffleNetV2()
model.init_weights()
model.train()
assert check_norm_state(model.modules(), True)
# Test ShuffleNetV2 with first stage frozen
frozen_stages = 1
model = ShuffleNetV2(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(0, frozen_stages):
layer = model.layers[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 ShuffleNetV2 with norm_eval
model = ShuffleNetV2(norm_eval=True)
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
# Test ShuffleNetV2 forward with widen_factor=0.5
model = ShuffleNetV2(widen_factor=0.5, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
for m in model.modules():
if is_norm(m):
assert isinstance(m, _BatchNorm)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == torch.Size((1, 48, 28, 28))
assert feat[1].shape == torch.Size((1, 96, 14, 14))
assert feat[2].shape == torch.Size((1, 192, 7, 7))
# Test ShuffleNetV2 forward with widen_factor=1.0
model = ShuffleNetV2(widen_factor=1.0, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
for m in model.modules():
if is_norm(m):
assert isinstance(m, _BatchNorm)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == torch.Size((1, 116, 28, 28))
assert feat[1].shape == torch.Size((1, 232, 14, 14))
assert feat[2].shape == torch.Size((1, 464, 7, 7))
# Test ShuffleNetV2 forward with widen_factor=1.5
model = ShuffleNetV2(widen_factor=1.5, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
for m in model.modules():
if is_norm(m):
assert isinstance(m, _BatchNorm)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == torch.Size((1, 176, 28, 28))
assert feat[1].shape == torch.Size((1, 352, 14, 14))
assert feat[2].shape == torch.Size((1, 704, 7, 7))
# Test ShuffleNetV2 forward with widen_factor=2.0
model = ShuffleNetV2(widen_factor=2.0, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
for m in model.modules():
if is_norm(m):
assert isinstance(m, _BatchNorm)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == torch.Size((1, 244, 28, 28))
assert feat[1].shape == torch.Size((1, 488, 14, 14))
assert feat[2].shape == torch.Size((1, 976, 7, 7))
# Test ShuffleNetV2 forward with layers 3 forward
model = ShuffleNetV2(widen_factor=1.0, out_indices=(2, ))
model.init_weights()
model.train()
for m in model.modules():
if is_norm(m):
assert isinstance(m, _BatchNorm)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert isinstance(feat, torch.Tensor)
assert feat.shape == torch.Size((1, 464, 7, 7))
# Test ShuffleNetV2 forward with layers 1 2 forward
model = ShuffleNetV2(widen_factor=1.0, out_indices=(1, 2))
model.init_weights()
model.train()
for m in model.modules():
if is_norm(m):
assert isinstance(m, _BatchNorm)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 2
assert feat[0].shape == torch.Size((1, 232, 14, 14))
assert feat[1].shape == torch.Size((1, 464, 7, 7))
# Test ShuffleNetV2 forward with checkpoint forward
model = ShuffleNetV2(widen_factor=1.0, with_cp=True)
for m in model.modules():
if is_block(m):
assert m.with_cp
