# 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.
#

import pytest
import torch

from kornia.feature.sold2 import SOLD2, SOLD2_detector

from testing.base import BaseTester


class TestSOLD2_detector(BaseTester):
    @pytest.mark.slow
    @pytest.mark.parametrize("batch_size", [1, 2])
    def test_shape(self, device, batch_size, dtype):
        inp = torch.ones(batch_size, 1, 64, 64, device=device, dtype=dtype)
        sold2 = SOLD2_detector(pretrained=False).to(device, dtype)
        out = sold2(inp)
        assert out["junction_heatmap"].shape == (batch_size, 64, 64)
        assert out["line_heatmap"].shape == (batch_size, 64, 64)

    @pytest.mark.skip("Takes ages to run")
    def test_gradcheck(self, device):
        img = torch.rand(2, 1, 128, 128, device=device, dtype=torch.float64)
        sold2 = SOLD2_detector(pretrained=False).to(img.device, img.dtype)

        def proxy_forward(x):
            return sold2.forward(x)["junction_heatmap"]

        self.gradcheck(proxy_forward, (img,), eps=1e-4, atol=1e-4)

    @pytest.mark.skip("Does not like recursive definition of Hourglass in backbones.py l.134.")
    def test_jit(self, device, dtype):
        B, C, H, W = 2, 1, 128, 128
        img = torch.ones(B, C, H, W, device=device, dtype=dtype)
        model = SOLD2_detector().to(img.device, img.dtype).eval()
        model_jit = torch.jit.script(model)
        self.assert_close(model(img), model_jit(img))


class TestSOLD2(BaseTester):
    @pytest.mark.slow
    @pytest.mark.parametrize("batch_size", [1, 2])
    def test_shape(self, device, batch_size, dtype):
        inp = torch.ones(batch_size, 1, 64, 64, device=device, dtype=dtype)
        sold2 = SOLD2(pretrained=False).to(device, dtype)
        out = sold2(inp)
        assert out["dense_desc"].shape == (batch_size, 128, 16, 16)

    @pytest.mark.skip("Takes ages to run")
    def test_gradcheck(self, device):
        img = torch.rand(2, 1, 256, 256, device=device, dtype=torch.float64)
        sold2 = SOLD2(pretrained=False).to(img.device, img.dtype)

        def proxy_forward(x):
            return sold2.forward(x)["dense_desc"]

        self.gradcheck(proxy_forward, (img,), eps=1e-4, atol=1e-4)

    @pytest.mark.skip("Does not like recursive definition of Hourglass in backbones.py l.134.")
    def test_jit(self, device, dtype):
        B, C, H, W = 2, 1, 256, 256
        img = torch.ones(B, C, H, W, device=device, dtype=dtype)
        model = SOLD2().to(img.device, img.dtype).eval()
        model_jit = torch.jit.script(model)
        self.assert_close(model(img), model_jit(img))