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chore: import upstream snapshot with attribution
2026-07-13 12:49:27 +08:00

45 行
1.5 KiB
Python

# 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
import kornia
from testing.base import BaseTester
class TestEdgeDetector(BaseTester):
@pytest.mark.slow
def test_smoke(self, device, dtype):
img = torch.rand(2, 3, 64, 64, device=device, dtype=dtype)
net = kornia.contrib.EdgeDetectorBuilder.build(pretrained=False).to(device, dtype)
out = net(img)
# ResizePostProcessor returns a list, so we need to handle that
if isinstance(out, list):
assert len(out) == 2
assert all(item.shape == (1, 1, 64, 64) for item in out)
else:
assert out.shape == (2, 1, 64, 64)
@pytest.mark.slow
@pytest.mark.skip(reason="issue with `ClassVar[list[int]]`")
def test_jit(self, device, dtype):
op = kornia.contrib.EdgeDetectorBuilder.build(pretrained=False).to(device, dtype)
op_jit = torch.jit.script(op)
assert op_jit is not None