mmpose-0.29.0/tests/test_backbones/test_shufflenet_v1.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 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)