def test_multi_class_dice_loss():
""" test_multi_class_dice_loss """
loss = nn.MultiClassDiceLoss(weights=None,
ignore_indiex=None,
activation="softmax")
y_pred = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]),
mstype.float32)
y = Tensor(np.array([[0, 1], [1, 0], [0, 1]]), mstype.float32)
loss(y_pred, y)
def test_multi_class_dice_loss_init_activation2():
""" test_multi_class_dice_loss """
with pytest.raises(KeyError):
loss = nn.MultiClassDiceLoss(weights=None,
ignore_indiex=None,
activation='www')
y_pred = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]),
mstype.float32)
y = Tensor(np.array([[1, 0], [0, 1]]), mstype.float32)
loss(y_pred, y)
def test_multi_class_dice_loss_check_shape():
""" test_multi_class_dice_loss """
loss = nn.MultiClassDiceLoss(weights=None,
ignore_indiex=None,
activation="softmax")
y_pred = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]),
mstype.float32)
y = Tensor(np.array([[1, 0], [0, 1]]), mstype.float32)
with pytest.raises(ValueError):
loss(y_pred, y)