vendor/ViTPose/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])