# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.utils.parrots_wrapper import _BatchNorm

from mmpose.models.backbones import VGG


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_vgg():
    """Test VGG backbone."""
    with pytest.raises(KeyError):
        # VGG depth should be in [11, 13, 16, 19]
        VGG(18)

    with pytest.raises(AssertionError):
        # In VGG: 1 <= num_stages <= 5
        VGG(11, num_stages=0)

    with pytest.raises(AssertionError):
        # In VGG: 1 <= num_stages <= 5
        VGG(11, num_stages=6)

    with pytest.raises(AssertionError):
        # len(dilations) == num_stages
        VGG(11, dilations=(1, 1), num_stages=3)

    with pytest.raises(TypeError):
        # pretrained must be a string path
        model = VGG(11)
        model.init_weights(pretrained=0)

    # Test VGG11 norm_eval=True
    model = VGG(11, norm_eval=True)
    model.init_weights()
    model.train()
    assert check_norm_state(model.modules(), False)

    # Test VGG11 forward without classifiers
    model = VGG(11, out_indices=(0, 1, 2, 3, 4))
    model.init_weights()
    model.train()

    imgs = torch.randn(1, 3, 224, 224)
    feat = model(imgs)
    assert len(feat) == 5
    assert feat[0].shape == (1, 64, 112, 112)
    assert feat[1].shape == (1, 128, 56, 56)
    assert feat[2].shape == (1, 256, 28, 28)
    assert feat[3].shape == (1, 512, 14, 14)
    assert feat[4].shape == (1, 512, 7, 7)

    # Test VGG11 forward with classifiers
    model = VGG(11, num_classes=10, out_indices=(0, 1, 2, 3, 4, 5))
    model.init_weights()
    model.train()

    imgs = torch.randn(1, 3, 224, 224)
    feat = model(imgs)
    assert len(feat) == 6
    assert feat[0].shape == (1, 64, 112, 112)
    assert feat[1].shape == (1, 128, 56, 56)
    assert feat[2].shape == (1, 256, 28, 28)
    assert feat[3].shape == (1, 512, 14, 14)
    assert feat[4].shape == (1, 512, 7, 7)
    assert feat[5].shape == (1, 10)

    # Test VGG11BN forward
    model = VGG(11, norm_cfg=dict(type='BN'), out_indices=(0, 1, 2, 3, 4))
    model.init_weights()
    model.train()

    imgs = torch.randn(1, 3, 224, 224)
    feat = model(imgs)
    assert len(feat) == 5
    assert feat[0].shape == (1, 64, 112, 112)
    assert feat[1].shape == (1, 128, 56, 56)
    assert feat[2].shape == (1, 256, 28, 28)
    assert feat[3].shape == (1, 512, 14, 14)
    assert feat[4].shape == (1, 512, 7, 7)

    # Test VGG11BN forward with classifiers
    model = VGG(
        11,
        num_classes=10,
        norm_cfg=dict(type='BN'),
        out_indices=(0, 1, 2, 3, 4, 5))
    model.init_weights()
    model.train()

    imgs = torch.randn(1, 3, 224, 224)
    feat = model(imgs)
    assert len(feat) == 6
    assert feat[0].shape == (1, 64, 112, 112)
    assert feat[1].shape == (1, 128, 56, 56)
    assert feat[2].shape == (1, 256, 28, 28)
    assert feat[3].shape == (1, 512, 14, 14)
    assert feat[4].shape == (1, 512, 7, 7)
    assert feat[5].shape == (1, 10)

    # Test VGG13 with layers 1, 2, 3 out forward
    model = VGG(13, out_indices=(0, 1, 2))
    model.init_weights()
    model.train()

    imgs = torch.randn(1, 3, 224, 224)
    feat = model(imgs)
    assert len(feat) == 3
    assert feat[0].shape == (1, 64, 112, 112)
    assert feat[1].shape == (1, 128, 56, 56)
    assert feat[2].shape == (1, 256, 28, 28)

    # Test VGG16 with top feature maps out forward
    model = VGG(16)
    model.init_weights()
    model.train()

    imgs = torch.randn(1, 3, 224, 224)
    feat = model(imgs)
    assert feat.shape == (1, 512, 7, 7)

    # Test VGG19 with classification score out forward
    model = VGG(19, num_classes=10)
    model.init_weights()
    model.train()

    imgs = torch.randn(1, 3, 224, 224)
    feat = model(imgs)
    assert feat.shape == (1, 10)