mmdetection/tests/test_models/test_losses/test_loss.py

# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
import torch.nn.functional as F
from mmengine.utils import digit_version

from mmdet.models.losses import (BalancedL1Loss, CrossEntropyLoss, DiceLoss,
                                 DistributionFocalLoss, EQLV2Loss, FocalLoss,
                                 GaussianFocalLoss,
                                 KnowledgeDistillationKLDivLoss, L1Loss,
                                 MarginL2Loss, MSELoss, QualityFocalLoss,
                                 SeesawLoss, SmoothL1Loss, VarifocalLoss)
from mmdet.models.losses.ghm_loss import GHMC, GHMR
from mmdet.models.losses.iou_loss import (BoundedIoULoss, CIoULoss, DIoULoss,
                                          EIoULoss, GIoULoss, IoULoss,
                                          SIoULoss)


@pytest.mark.parametrize('loss_class', [
    IoULoss, BoundedIoULoss, GIoULoss, DIoULoss, CIoULoss, EIoULoss, SIoULoss
])
def test_iou_type_loss_zeros_weight(loss_class):
    pred = torch.rand((10, 4))
    target = torch.rand((10, 4))
    weight = torch.zeros(10)

    loss = loss_class()(pred, target, weight)
    assert loss == 0.


@pytest.mark.parametrize('loss_class', [
    BalancedL1Loss, BoundedIoULoss, CIoULoss, CrossEntropyLoss, DIoULoss,
    EIoULoss, SIoULoss, FocalLoss, DistributionFocalLoss, MSELoss, SeesawLoss,
    GaussianFocalLoss, GIoULoss, QualityFocalLoss, IoULoss, L1Loss,
    VarifocalLoss, GHMR, GHMC, SmoothL1Loss, KnowledgeDistillationKLDivLoss,
    DiceLoss
])
def test_loss_with_reduction_override(loss_class):
    pred = torch.rand((10, 4))
    target = torch.rand((10, 4)),
    weight = None

    with pytest.raises(AssertionError):
        # only reduction_override from [None, 'none', 'mean', 'sum']
        # is not allowed
        reduction_override = True
        loss_class()(
            pred, target, weight, reduction_override=reduction_override)


@pytest.mark.parametrize('loss_class', [QualityFocalLoss])
@pytest.mark.parametrize('activated', [False, True])
def test_QualityFocalLoss_Loss(loss_class, activated):
    input_shape = (4, 5)
    pred = torch.rand(input_shape)
    label = torch.Tensor([0, 1, 2, 0]).long()
    quality_label = torch.rand(input_shape[0])

    original_loss = loss_class(activated=activated)(pred,
                                                    (label, quality_label))
    assert isinstance(original_loss, torch.Tensor)

    target = torch.nn.functional.one_hot(label, 5)
    target = target * quality_label.reshape(input_shape[0], 1)

    new_loss = loss_class(activated=activated)(pred, target)
    assert isinstance(new_loss, torch.Tensor)
    assert new_loss == original_loss


@pytest.mark.parametrize('loss_class', [
    IoULoss, BoundedIoULoss, GIoULoss, DIoULoss, CIoULoss, EIoULoss, SIoULoss,
    MSELoss, L1Loss, SmoothL1Loss, BalancedL1Loss, MarginL2Loss
])
@pytest.mark.parametrize('input_shape', [(10, 4), (0, 4)])
def test_regression_losses(loss_class, input_shape):
    pred = torch.rand(input_shape)
    target = torch.rand(input_shape)
    weight = torch.rand(input_shape)

    # Test loss forward
    loss = loss_class()(pred, target)
    assert isinstance(loss, torch.Tensor)

    # Test loss forward with weight
    loss = loss_class()(pred, target, weight)
    assert isinstance(loss, torch.Tensor)

    # Test loss forward with reduction_override
    loss = loss_class()(pred, target, reduction_override='mean')
    assert isinstance(loss, torch.Tensor)

    # Test loss forward with avg_factor
    loss = loss_class()(pred, target, avg_factor=10)
    assert isinstance(loss, torch.Tensor)

    with pytest.raises(ValueError):
        # loss can evaluate with avg_factor only if
        # reduction is None, 'none' or 'mean'.
        reduction_override = 'sum'
        loss_class()(
            pred, target, avg_factor=10, reduction_override=reduction_override)

    # Test loss forward with avg_factor and reduction
    for reduction_override in [None, 'none', 'mean']:
        loss_class()(
            pred, target, avg_factor=10, reduction_override=reduction_override)
        assert isinstance(loss, torch.Tensor)


@pytest.mark.parametrize('loss_class', [CrossEntropyLoss])
@pytest.mark.parametrize('input_shape', [(10, 5), (0, 5)])
def test_classification_losses(loss_class, input_shape):
    if input_shape[0] == 0 and digit_version(
            torch.__version__) < digit_version('1.5.0'):
        pytest.skip(
            f'CELoss in PyTorch {torch.__version__} does not support empty'
            f'tensor.')

    pred = torch.rand(input_shape)
    target = torch.randint(0, 5, (input_shape[0], ))

    # Test loss forward
    loss = loss_class()(pred, target)
    assert isinstance(loss, torch.Tensor)

    # Test loss forward with reduction_override
    loss = loss_class()(pred, target, reduction_override='mean')
    assert isinstance(loss, torch.Tensor)

    # Test loss forward with avg_factor
    loss = loss_class()(pred, target, avg_factor=10)
    assert isinstance(loss, torch.Tensor)

    with pytest.raises(ValueError):
        # loss can evaluate with avg_factor only if
        # reduction is None, 'none' or 'mean'.
        reduction_override = 'sum'
        loss_class()(
            pred, target, avg_factor=10, reduction_override=reduction_override)

    # Test loss forward with avg_factor and reduction
    for reduction_override in [None, 'none', 'mean']:
        loss_class()(
            pred, target, avg_factor=10, reduction_override=reduction_override)
        assert isinstance(loss, torch.Tensor)


@pytest.mark.parametrize('loss_class', [FocalLoss])
@pytest.mark.parametrize('input_shape', [(10, 5), (3, 5, 40, 40)])
def test_FocalLoss_loss(loss_class, input_shape):
    pred = torch.rand(input_shape)
    target = torch.randint(0, 5, (input_shape[0], ))
    if len(input_shape) == 4:
        B, N, W, H = input_shape
        target = F.one_hot(torch.randint(0, 5, (B * W * H, )),
                           5).reshape(B, W, H, N).permute(0, 3, 1, 2)

    # Test loss forward
    loss = loss_class()(pred, target)
    assert isinstance(loss, torch.Tensor)

    # Test loss forward with reduction_override
    loss = loss_class()(pred, target, reduction_override='mean')
    assert isinstance(loss, torch.Tensor)

    # Test loss forward with avg_factor
    loss = loss_class()(pred, target, avg_factor=10)
    assert isinstance(loss, torch.Tensor)

    with pytest.raises(ValueError):
        # loss can evaluate with avg_factor only if
        # reduction is None, 'none' or 'mean'.
        reduction_override = 'sum'
        loss_class()(
            pred, target, avg_factor=10, reduction_override=reduction_override)

    # Test loss forward with avg_factor and reduction
    for reduction_override in [None, 'none', 'mean']:
        loss_class()(
            pred, target, avg_factor=10, reduction_override=reduction_override)
        assert isinstance(loss, torch.Tensor)


@pytest.mark.parametrize('loss_class', [GHMR])
@pytest.mark.parametrize('input_shape', [(10, 4), (0, 4)])
def test_GHMR_loss(loss_class, input_shape):
    pred = torch.rand(input_shape)
    target = torch.rand(input_shape)
    weight = torch.rand(input_shape)

    # Test loss forward
    loss = loss_class()(pred, target, weight)
    assert isinstance(loss, torch.Tensor)


@pytest.mark.parametrize('use_sigmoid', [True, False])
@pytest.mark.parametrize('reduction', ['sum', 'mean', None])
@pytest.mark.parametrize('avg_non_ignore', [True, False])
def test_loss_with_ignore_index(use_sigmoid, reduction, avg_non_ignore):
    # Test cross_entropy loss
    loss_class = CrossEntropyLoss(
        use_sigmoid=use_sigmoid,
        use_mask=False,
        ignore_index=255,
        avg_non_ignore=avg_non_ignore)
    pred = torch.rand((10, 5))
    target = torch.randint(0, 5, (10, ))

    ignored_indices = torch.randint(0, 10, (2, ), dtype=torch.long)
    target[ignored_indices] = 255

    # Test loss forward with default ignore
    loss_with_ignore = loss_class(pred, target, reduction_override=reduction)
    assert isinstance(loss_with_ignore, torch.Tensor)

    # Test loss forward with forward ignore
    target[ignored_indices] = 255
    loss_with_forward_ignore = loss_class(
        pred, target, ignore_index=255, reduction_override=reduction)
    assert isinstance(loss_with_forward_ignore, torch.Tensor)

    # Verify correctness
    if avg_non_ignore:
        # manually remove the ignored elements
        not_ignored_indices = (target != 255)
        pred = pred[not_ignored_indices]
        target = target[not_ignored_indices]
    loss = loss_class(pred, target, reduction_override=reduction)

    assert torch.allclose(loss, loss_with_ignore)
    assert torch.allclose(loss, loss_with_forward_ignore)

    # test ignore all target
    pred = torch.rand((10, 5))
    target = torch.ones((10, ), dtype=torch.long) * 255
    loss = loss_class(pred, target, reduction_override=reduction)
    assert loss == 0


@pytest.mark.parametrize('naive_dice', [True, False])
def test_dice_loss(naive_dice):
    loss_class = DiceLoss
    pred = torch.rand((10, 4, 4))
    target = torch.rand((10, 4, 4))
    weight = torch.rand((10))

    # Test loss forward
    loss = loss_class(naive_dice=naive_dice)(pred, target)
    assert isinstance(loss, torch.Tensor)

    # Test loss forward with weight
    loss = loss_class(naive_dice=naive_dice)(pred, target, weight)
    assert isinstance(loss, torch.Tensor)

    # Test loss forward with reduction_override
    loss = loss_class(naive_dice=naive_dice)(
        pred, target, reduction_override='mean')
    assert isinstance(loss, torch.Tensor)

    # Test loss forward with avg_factor
    loss = loss_class(naive_dice=naive_dice)(pred, target, avg_factor=10)
    assert isinstance(loss, torch.Tensor)

    with pytest.raises(ValueError):
        # loss can evaluate with avg_factor only if
        # reduction is None, 'none' or 'mean'.
        reduction_override = 'sum'
        loss_class(naive_dice=naive_dice)(
            pred, target, avg_factor=10, reduction_override=reduction_override)

    # Test loss forward with avg_factor and reduction
    for reduction_override in [None, 'none', 'mean']:
        loss_class(naive_dice=naive_dice)(
            pred, target, avg_factor=10, reduction_override=reduction_override)
        assert isinstance(loss, torch.Tensor)

    # Test loss forward with has_acted=False and use_sigmoid=False
    with pytest.raises(NotImplementedError):
        loss_class(
            use_sigmoid=False, activate=True, naive_dice=naive_dice)(pred,
                                                                     target)

    # Test loss forward with weight.ndim != loss.ndim
    with pytest.raises(AssertionError):
        weight = torch.rand((2, 8))
        loss_class(naive_dice=naive_dice)(pred, target, weight)

    # Test loss forward with len(weight) != len(pred)
    with pytest.raises(AssertionError):
        weight = torch.rand((8))
        loss_class(naive_dice=naive_dice)(pred, target, weight)


@pytest.mark.parametrize('loss_class', [EQLV2Loss])
@pytest.mark.parametrize('reduction', ['mean'])
def test_eqlv2_loss(loss_class, reduction):
    cls_score = torch.randn((1204, 1204))
    label = torch.randint(0, 2, (1204, ))
    weight = None

    loss = loss_class()(cls_score, label, weight)
    assert isinstance(loss, torch.Tensor)