mmpose-0.29.0/tests/test_backbones/test_scnet.py

# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm

from mmpose.models.backbones import SCNet
from mmpose.models.backbones.scnet import SCBottleneck, SCConv


def is_block(modules):
 """Check if is SCNet building block."""
 if isinstance(modules, (SCBottleneck, )):
 return True
 return False


def is_norm(modules):
 """Check if is one of the norms."""
 if isinstance(modules, (_BatchNorm, )):
 return True
 return False


def all_zeros(modules):
 """Check if the weight(and bias) is all zero."""
 weight_zero = torch.equal(modules.weight.data,
 torch.zeros_like(modules.weight.data))
 if hasattr(modules, 'bias'):
 bias_zero = torch.equal(modules.bias.data,
 torch.zeros_like(modules.bias.data))
 else:
 bias_zero = True

 return weight_zero and bias_zero


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_scnet_scconv():
 # Test scconv forward
 layer = SCConv(64, 64, 1, 4)
 x = torch.randn(1, 64, 56, 56)
 x_out = layer(x)
 assert x_out.shape == torch.Size([1, 64, 56, 56])


def test_scnet_bottleneck():
 # Test Bottleneck forward
 block = SCBottleneck(64, 64)
 x = torch.randn(1, 64, 56, 56)
 x_out = block(x)
 assert x_out.shape == torch.Size([1, 64, 56, 56])


def test_scnet_backbone():
 """Test scnet backbone."""
 with pytest.raises(KeyError):
 # SCNet depth should be in [50, 101]
 SCNet(20)

 with pytest.raises(TypeError):
 # pretrained must be a string path
 model = SCNet(50)
 model.init_weights(pretrained=0)

 # Test SCNet norm_eval=True
 model = SCNet(50, norm_eval=True)
 model.init_weights()
 model.train()
 assert check_norm_state(model.modules(), False)

 # Test SCNet50 with first stage frozen
 frozen_stages = 1
 model = SCNet(50, frozen_stages=frozen_stages)
 model.init_weights()
 model.train()
 assert model.norm1.training is False
 for layer in [model.conv1, model.norm1]:
 for param in layer.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 SCNet with BatchNorm forward
 model = SCNet(50, out_indices=(0, 1, 2, 3))
 for m in model.modules():
 if is_norm(m):
 assert isinstance(m, _BatchNorm)
 model.init_weights()
 model.train()

 imgs = torch.randn(2, 3, 224, 224)
 feat = model(imgs)
 assert len(feat) == 4
 assert feat[0].shape == torch.Size([2, 256, 56, 56])
 assert feat[1].shape == torch.Size([2, 512, 28, 28])
 assert feat[2].shape == torch.Size([2, 1024, 14, 14])
 assert feat[3].shape == torch.Size([2, 2048, 7, 7])

 # Test SCNet with layers 1, 2, 3 out forward
 model = SCNet(50, out_indices=(0, 1, 2))
 model.init_weights()
 model.train()

 imgs = torch.randn(2, 3, 224, 224)
 feat = model(imgs)
 assert len(feat) == 3
 assert feat[0].shape == torch.Size([2, 256, 56, 56])
 assert feat[1].shape == torch.Size([2, 512, 28, 28])
 assert feat[2].shape == torch.Size([2, 1024, 14, 14])

 # Test SEResNet50 with layers 3 (top feature maps) out forward
 model = SCNet(50, out_indices=(3, ))
 model.init_weights()
 model.train()

 imgs = torch.randn(2, 3, 224, 224)
 feat = model(imgs)
 assert feat.shape == torch.Size([2, 2048, 7, 7])

 # Test SEResNet50 with checkpoint forward
 model = SCNet(50, out_indices=(0, 1, 2, 3), with_cp=True)
 for m in model.modules():
 if is_block(m):
 assert m.with_cp
 model.init_weights()
 model.train()

 imgs = torch.randn(2, 3, 224, 224)
 feat = model(imgs)
 assert len(feat) == 4
 assert feat[0].shape == torch.Size([2, 256, 56, 56])
 assert feat[1].shape == torch.Size([2, 512, 28, 28])
 assert feat[2].shape == torch.Size([2, 1024, 14, 14])
 assert feat[3].shape == torch.Size([2, 2048, 7, 7])

 # Test SCNet zero initialization of residual
 model = SCNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=True)
 model.init_weights()
 for m in model.modules():
 if isinstance(m, SCBottleneck):
 assert all_zeros(m.norm3)
 model.train()

 imgs = torch.randn(2, 3, 224, 224)
 feat = model(imgs)
 assert len(feat) == 4
 assert feat[0].shape == torch.Size([2, 256, 56, 56])
 assert feat[1].shape == torch.Size([2, 512, 28, 28])
 assert feat[2].shape == torch.Size([2, 1024, 14, 14])
 assert feat[3].shape == torch.Size([2, 2048, 7, 7])