# 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 ShuffleNetV1
from mmpose.models.backbones.shufflenet_v1 import ShuffleUnit
def is_block(modules):
"""Check if is ResNet building block."""
if isinstance(modules, (ShuffleUnit, )):
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_shufflenetv1_shuffleuint():
with pytest.raises(ValueError):
# combine must be in ['add', 'concat']
ShuffleUnit(24, 16, groups=3, first_block=True, combine='test')
with pytest.raises(AssertionError):
# inplanes must be equal tp = outplanes when combine='add'
ShuffleUnit(64, 24, groups=4, first_block=True, combine='add')
# Test ShuffleUnit with combine='add'
block = ShuffleUnit(24, 24, groups=3, first_block=True, combine='add')
x = torch.randn(1, 24, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size((1, 24, 56, 56))
# Test ShuffleUnit with combine='concat'
block = ShuffleUnit(24, 240, groups=3, first_block=True, combine='concat')
x = torch.randn(1, 24, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size((1, 240, 28, 28))
# Test ShuffleUnit with checkpoint forward
block = ShuffleUnit(
24, 24, groups=3, first_block=True, combine='add', with_cp=True)
assert block.with_cp
x = torch.randn(1, 24, 56, 56)
x.requires_grad = True
x_out = block(x)
assert x_out.shape == torch.Size((1, 24, 56, 56))
def test_shufflenetv1_backbone():
with pytest.raises(ValueError):
# frozen_stages must be in range(-1, 4)
ShuffleNetV1(frozen_stages=10)
with pytest.raises(ValueError):
# the item in out_indices must be in range(0, 4)
ShuffleNetV1(out_indices=[5])
with pytest.raises(ValueError):
# groups must be in [1, 2, 3, 4, 8]
ShuffleNetV1(groups=10)
with pytest.raises(TypeError):
# pretrained must be str or None
model = ShuffleNetV1()
model.init_weights(pretrained=1)
# Test ShuffleNetV1 norm state
model = ShuffleNetV1()
model.init_weights()
model.train()
assert check_norm_state(model.modules(), True)
# Test ShuffleNetV1 with first stage frozen
frozen_stages = 1
model = ShuffleNetV1(frozen_stages=frozen_stages, out_indices=(0, 1, 2))
model.init_weights()
model.train()
for param in model.conv1.parameters():
assert param.requires_grad is False
for i in range(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 ShuffleNetV1 forward with groups=1
model = ShuffleNetV1(groups=1, out_indices=(0, 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) == 3
assert feat[0].shape == torch.Size((1, 144, 28, 28))
assert feat[1].shape == torch.Size((1, 288, 14, 14))
assert feat[2].shape == torch.Size((1, 576, 7, 7))
# Test ShuffleNetV1 forward with groups=2
model = ShuffleNetV1(groups=2, out_indices=(0, 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) == 3
assert feat[0].shape == torch.Size((1, 200, 28, 28))
assert feat[1].shape == torch.Size((1, 400, 14, 14))
assert feat[2].shape == torch.Size((1, 800, 7, 7))
# Test ShuffleNetV1 forward with groups=3
model = ShuffleNetV1(groups=3, out_indices=(0, 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) == 3
assert feat[0].shape == torch.Size((1, 240, 28, 28))
assert feat[1].shape == torch.Size((1, 480, 14, 14))
assert feat[2].shape == torch.Size((1, 960, 7, 7))
# Test ShuffleNetV1 forward with groups=4
model = ShuffleNetV1(groups=4, out_indices=(0, 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) == 3
assert feat[0].shape == torch.Size((1, 272, 28, 28))
assert feat[1].shape == torch.Size((1, 544, 14, 14))
assert feat[2].shape == torch.Size((1, 1088, 7, 7))
# Test ShuffleNetV1 forward with groups=8
model = ShuffleNetV1(groups=8, out_indices=(0, 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) == 3
assert feat[0].shape == torch.Size((1, 384, 28, 28))
assert feat[1].shape == torch.Size((1, 768, 14, 14))
assert feat[2].shape == torch.Size((1, 1536, 7, 7))
# Test ShuffleNetV1 forward with GroupNorm forward
model = ShuffleNetV1(
groups=3,
norm_cfg=dict(type='GN', num_groups=2, requires_grad=True),
out_indices=(0, 1, 2))
model.init_weights()
model.train()
for m in model.modules():
if is_norm(m):
assert isinstance(m, GroupNorm)
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == torch.Size((1, 240, 28, 28))
assert feat[1].shape == torch.Size((1, 480, 14, 14))
assert feat[2].shape == torch.Size((1, 960, 7, 7))
# Test ShuffleNetV1 forward with layers 1, 2 forward
model = ShuffleNetV1(groups=3, 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, 480, 14, 14))
assert feat[1].shape == torch.Size((1, 960, 7, 7))
# Test ShuffleNetV1 forward with layers 2 forward
model = ShuffleNetV1(groups=3, 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, 960, 7, 7))
# Test ShuffleNetV1 forward with checkpoint forward
model = ShuffleNetV1(groups=3, with_cp=True)
for m in model.modules():
if is_block(m):
assert m.with_cp
# Test ShuffleNetV1 with norm_eval
model = ShuffleNetV1(norm_eval=True)
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
