kornia--kornia/tests/feature/test_defmo.py

# LICENSE HEADER MANAGED BY add-license-header
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# 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.feature import DeFMO

from testing.base import BaseTester


class TestDeFMO(BaseTester):
    @pytest.mark.slow
    def test_shape(self, device, dtype):
        inp = torch.ones(1, 6, 128, 160, device=device, dtype=dtype)
        defmo = DeFMO().to(device, dtype)
        defmo.eval()  # batchnorm with size 1 is not allowed in train mode
        out = defmo(inp)
        assert out.shape == (1, 24, 4, 128, 160)

    @pytest.mark.slow
    def test_shape_batch(self, device, dtype):
        inp = torch.ones(2, 6, 128, 160, device=device, dtype=dtype)
        defmo = DeFMO().to(device, dtype)
        out = defmo(inp)
        with torch.no_grad():
            assert out.shape == (2, 24, 4, 128, 160)

    @pytest.mark.slow
    @pytest.mark.grad
    def test_gradcheck(self, device):
        patches = torch.rand(2, 6, 64, 64, device=device, dtype=torch.float64)
        defmo = DeFMO().to(patches.device, patches.dtype)
        self.gradcheck(defmo, (patches,), eps=1e-4, atol=1e-4, nondet_tol=1e-8)

    @pytest.mark.slow
    @pytest.mark.jit
    def test_jit(self, device, dtype):
        B, C, H, W = 1, 6, 128, 160
        patches = torch.rand(B, C, H, W, device=device, dtype=dtype)
        model = DeFMO(True).to(patches.device, patches.dtype).eval()
        model_jit = torch.jit.script(DeFMO(True).to(patches.device, patches.dtype).eval())
        with torch.no_grad():
            self.assert_close(model(patches), model_jit(patches))