kornia--kornia/tests/augmentation/_2d/mix/test_patchmix.py

# LICENSE HEADER MANAGED BY add-license-header
#
# Copyright 2018 Kornia Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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import pytest
import torch

from kornia.augmentation import PatchMix


@pytest.mark.parametrize(
    "batch_size,channels,height,width,patch_size",
    [
        (4, 3, 32, 32, 8),
        (2, 1, 16, 16, 4),
    ],
)
def test_patchmix_shape_and_type(batch_size, channels, height, width, patch_size):
    aug = PatchMix(alpha=1.0, patch_size=patch_size)
    x = torch.rand(batch_size, channels, height, width)
    params = aug.generate_parameters(x.shape)
    out = aug.apply_transform(x, params, {})
    assert out.shape == x.shape
    assert params["mix_pairs"].shape[0] == batch_size
    assert params["lam"].shape[0] == batch_size
    assert out.dtype == x.dtype


def test_patchmix_different_inputs():
    aug = PatchMix(alpha=1.0, patch_size=8)
    x = torch.rand(4, 3, 32, 32)
    y = torch.rand(4, 3, 32, 32)
    params = aug.generate_parameters(x.shape)
    out1 = aug.apply_transform(x, params, {})
    out2 = aug.apply_transform(y, params, {})
    assert not torch.allclose(out1, out2)


def test_patchmix_grad():
    aug = PatchMix(alpha=1.0, patch_size=8)
    x = torch.rand(2, 3, 16, 16, requires_grad=True)
    params = aug.generate_parameters(x.shape)
    out = aug.apply_transform(x, params, {})
    out.sum().backward()
    assert x.grad is not None