mmpose-0.29.0/tests/test_backbones/test_shufflenet_v2.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 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