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329 lines
14 KiB
Python
329 lines
14 KiB
Python
# LICENSE HEADER MANAGED BY add-license-header
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#
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# Copyright 2018 Kornia Team
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import pytest
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import torch
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import kornia
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from testing.base import BaseTester
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class TestCropAndResize(BaseTester):
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def test_align_corners_true(self, device, dtype):
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inp = torch.tensor(
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[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]]],
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device=device,
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dtype=dtype,
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)
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height, width = 2, 3
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expected = torch.tensor([[[[6.0000, 6.5000, 7.0000], [10.0000, 10.5000, 11.0000]]]], device=device, dtype=dtype)
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boxes = torch.tensor([[[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]]], device=device, dtype=dtype) # 1x4x2
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# default should use align_coners True
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patches = kornia.geometry.transform.crop_and_resize(inp, boxes, (height, width))
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self.assert_close(patches, expected, rtol=1e-4, atol=1e-4)
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def test_align_corners_false(self, device, dtype):
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inp = torch.tensor(
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[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]]],
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device=device,
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dtype=dtype,
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)
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height, width = 2, 3
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expected = torch.tensor([[[[6.7222, 7.1667, 7.6111], [9.3889, 9.8333, 10.2778]]]], device=device, dtype=dtype)
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boxes = torch.tensor([[[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]]], device=device, dtype=dtype) # 1x4x2
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patches = kornia.geometry.transform.crop_and_resize(inp, boxes, (height, width), align_corners=False)
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self.assert_close(patches, expected, rtol=1e-4, atol=1e-4)
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def test_crop_batch(self, device, dtype):
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inp = torch.tensor(
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[
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[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]],
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[[[1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0], [4.0, 8.0, 12.0, 16.0]]],
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],
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device=device,
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dtype=dtype,
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)
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expected = torch.tensor(
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[[[[6.0, 7.0], [10.0, 11.0]]], [[[7.0, 15.0], [8.0, 16.0]]]], device=device, dtype=dtype
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)
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boxes = torch.tensor(
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[[[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]], [[1.0, 2.0], [3.0, 2.0], [3.0, 3.0], [1.0, 3.0]]],
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device=device,
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dtype=dtype,
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) # 2x4x2
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patches = kornia.geometry.transform.crop_and_resize(inp, boxes, (2, 2))
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self.assert_close(patches, expected, rtol=1e-4, atol=1e-4)
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def test_crop_batch_broadcast(self, device, dtype):
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inp = torch.tensor(
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[
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[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]],
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[[[1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0], [4.0, 8.0, 12.0, 16.0]]],
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],
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device=device,
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dtype=dtype,
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)
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expected = torch.tensor(
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[[[[6.0, 7.0], [10.0, 11.0]]], [[[6.0, 10.0], [7.0, 11.0]]]], device=device, dtype=dtype
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)
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boxes = torch.tensor([[[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]]], device=device, dtype=dtype) # 1x4x2
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patches = kornia.geometry.transform.crop_and_resize(inp, boxes, (2, 2))
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self.assert_close(patches, expected, rtol=1e-4, atol=1e-4)
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def test_gradcheck(self, device):
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img = torch.rand(1, 2, 5, 4, device=device, dtype=torch.float64)
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boxes = torch.tensor([[[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]]], device=device, dtype=torch.float64)
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self.gradcheck(
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kornia.geometry.transform.crop_and_resize, (img, boxes, (4, 2)), requires_grad=(True, False, False)
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)
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def test_dynamo(self, device, dtype, torch_optimizer):
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# Define script
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op = kornia.geometry.transform.crop_and_resize
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op_optimized = torch_optimizer(op)
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# Define input
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img = torch.tensor(
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[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]]],
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device=device,
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dtype=dtype,
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)
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boxes = torch.tensor([[[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]]], device=device, dtype=dtype) # 1x4x2
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crop_height, crop_width = 4, 2
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actual = op_optimized(img, boxes, (crop_height, crop_width))
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expected = op(img, boxes, (crop_height, crop_width))
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self.assert_close(actual, expected, rtol=1e-4, atol=1e-4)
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class TestCenterCrop(BaseTester):
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def test_center_crop_h2_w4(self, device, dtype):
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inp = torch.tensor(
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[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]]],
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device=device,
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dtype=dtype,
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)
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expected = torch.tensor([[[[5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0]]]], device=device, dtype=dtype)
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out_crop = kornia.geometry.transform.center_crop(inp, (2, 4))
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self.assert_close(out_crop, expected, rtol=1e-4, atol=1e-4)
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self.assert_close(kornia.geometry.transform.CenterCrop2D((2, 4))(inp), expected, rtol=1e-4, atol=1e-4)
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def test_center_crop_h4_w2(self, device, dtype):
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inp = torch.tensor(
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[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]]],
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device=device,
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dtype=dtype,
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)
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height, width = 4, 2
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expected = torch.tensor([[[[2.0, 3.0], [6.0, 7.0], [10.0, 11.0], [14.0, 15.0]]]], device=device, dtype=dtype)
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out_crop = kornia.geometry.transform.center_crop(inp, (height, width))
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self.assert_close(out_crop, expected, rtol=1e-4, atol=1e-4)
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self.assert_close(kornia.geometry.transform.CenterCrop2D((height, width))(inp), expected, rtol=1e-4, atol=1e-4)
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def test_center_crop_h4_w2_batch(self, device, dtype):
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inp = torch.tensor(
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[
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[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]],
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[[[1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0], [4.0, 8.0, 12.0, 16.0]]],
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],
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device=device,
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dtype=dtype,
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)
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expected = torch.tensor(
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[
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[[[2.0, 3.0], [6.0, 7.0], [10.0, 11.0], [14.0, 15.0]]],
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[[[5.0, 9.0], [6.0, 10.0], [7.0, 11.0], [8.0, 12.0]]],
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],
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device=device,
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dtype=dtype,
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)
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out_crop = kornia.geometry.transform.center_crop(inp, (4, 2))
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self.assert_close(out_crop, expected, rtol=1e-4, atol=1e-4)
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self.assert_close(kornia.geometry.transform.CenterCrop2D((4, 2))(inp), expected, rtol=1e-4, atol=1e-4)
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def test_gradcheck(self, device):
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img = torch.rand(1, 2, 5, 4, device=device, dtype=torch.float64)
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self.gradcheck(kornia.geometry.transform.center_crop, (img, (4, 2)))
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self.gradcheck(kornia.geometry.transform.CenterCrop2D((4, 2)), (img))
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def test_dynamo(self, device, dtype, torch_optimizer):
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# Define script
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op = kornia.geometry.transform.center_crop
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op_script = torch_optimizer(op)
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# Define input
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img = torch.ones(1, 2, 5, 4, device=device, dtype=dtype)
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actual = op_script(img, (4, 2))
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expected = op(img, (4, 2))
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self.assert_close(actual, expected, rtol=1e-4, atol=1e-4)
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class TestCropByBoxes(BaseTester):
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def test_crop_by_boxes_no_resizing(self, device, dtype):
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inp = torch.tensor(
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[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]]],
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device=device,
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dtype=dtype,
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)
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src = torch.tensor([[[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]]], device=device, dtype=dtype) # 1x4x2
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dst = torch.tensor([[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]]], device=device, dtype=dtype) # 1x4x2
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expected = torch.tensor([[[[6.0, 7.0], [10.0, 11.0]]]], device=device, dtype=dtype)
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patches = kornia.geometry.transform.crop_by_boxes(inp, src, dst)
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self.assert_close(patches, expected)
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def test_crop_by_boxes_resizing(self, device, dtype):
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inp = torch.tensor(
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[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]]],
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device=device,
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dtype=dtype,
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)
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src = torch.tensor([[[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]]], device=device, dtype=dtype) # 1x4x2
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dst = torch.tensor([[[0.0, 0.0], [2.0, 0.0], [2.0, 1.0], [0.0, 1.0]]], device=device, dtype=dtype) # 1x4x2
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expected = torch.tensor([[[[6.0, 6.5, 7.0], [10.0, 10.5, 11.0]]]], device=device, dtype=dtype)
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patches = kornia.geometry.transform.crop_by_boxes(inp, src, dst)
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self.assert_close(patches, expected, rtol=1e-4, atol=1e-4)
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def test_gradcheck(self, device):
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dtype = torch.float64
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inp = torch.randn((1, 1, 3, 3), device=device, dtype=dtype)
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src = torch.tensor([[[1.0, 0.0], [2.0, 0.0], [2.0, 1.0], [1.0, 1.0]]], device=device, dtype=dtype)
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dst = torch.tensor([[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]]], device=device, dtype=dtype)
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self.gradcheck(kornia.geometry.transform.crop_by_boxes, (inp, src, dst), requires_grad=(True, False, False))
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class TestCropByTransform(BaseTester):
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def test_crop_by_transform_no_resizing(self, device, dtype):
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inp = torch.tensor(
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[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]]],
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device=device,
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dtype=dtype,
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)
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transform = torch.tensor(
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[[[1.0, 0.0, -1.0], [0.0, 1.0, -1.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype
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) # 1x3x3
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expected = torch.tensor([[[[6.0, 7.0], [10.0, 11.0]]]], device=device, dtype=dtype)
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patches = kornia.geometry.transform.crop_by_transform_mat(inp, transform, (2, 2))
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self.assert_close(patches, expected)
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def test_crop_by_boxes_resizing(self, device, dtype):
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inp = torch.tensor(
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[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], [13.0, 14.0, 15.0, 16.0]]]],
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device=device,
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dtype=dtype,
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)
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transform = torch.tensor(
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[[[2.0, 0.0, -2.0], [0.0, 1.0, -1.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype
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) # 1x3x3
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expected = torch.tensor([[[[6.0, 6.5, 7.0], [10.0, 10.5, 11.0]]]], device=device, dtype=dtype)
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patches = kornia.geometry.transform.crop_by_transform_mat(inp, transform, (2, 3))
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self.assert_close(patches, expected, rtol=1e-4, atol=1e-4)
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def test_gradcheck(self, device):
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inp = torch.randn((1, 1, 3, 3), device=device, dtype=torch.float64)
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transform = torch.tensor(
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[[[2.0, 0.0, -2.0], [0.0, 1.0, -1.0], [0.0, 0.0, 1.0]]], device=device, dtype=torch.float64
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) # 1x3x3
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self.gradcheck(
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kornia.geometry.transform.crop_by_transform_mat,
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(inp, transform, (2, 2)),
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requires_grad=(True, False, False),
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)
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class TestCropByIndices(BaseTester):
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def test_crop_by_indices_no_resizing(self, device, dtype):
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inp = torch.tensor([[[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7, 8, 9]]]], device=device, dtype=dtype) # 1x3x3
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# provide the indices to crop as 4 points
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indices = torch.tensor([[[0, 0], [1, 0], [1, 1], [0, 1]]], device=device, dtype=torch.int64)
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expected = torch.tensor([[[[1.0, 2.0], [4.0, 5.0]]]], device=device, dtype=dtype)
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self.assert_close(kornia.geometry.transform.crop_by_indices(inp, indices), expected)
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def test_dynamo(self, device, dtype, torch_optimizer):
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# Define script
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op = kornia.geometry.transform.crop_by_indices
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op_script = torch_optimizer(op)
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# Define input
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img = torch.ones(1, 2, 5, 4, device=device, dtype=dtype)
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actual = op_script(img, torch.tensor([[[0, 0], [1, 0], [1, 1], [0, 1]]]))
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expected = op(img, torch.tensor([[[0, 0], [1, 0], [1, 1], [0, 1]]]))
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self.assert_close(actual, expected, rtol=1e-4, atol=1e-4)
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class TestCropSizeValidation:
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"""Tests that crop functions properly reject invalid size arguments."""
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def test_crop_and_resize_rejects_wrong_length(self, device, dtype):
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inp = torch.rand(1, 1, 4, 4, device=device, dtype=dtype)
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boxes = torch.tensor([[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]]], device=device, dtype=dtype)
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with pytest.raises(ValueError, match="tuple/list of length 2"):
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kornia.geometry.transform.crop_and_resize(inp, boxes, (2, 2, 2))
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def test_crop_and_resize_rejects_non_tuple(self, device, dtype):
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inp = torch.rand(1, 1, 4, 4, device=device, dtype=dtype)
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boxes = torch.tensor([[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]]], device=device, dtype=dtype)
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# Passing an int instead of a tuple can raise either ValueError (from an
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# explicit validation check) or TypeError (from calling len() on an int),
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# depending on which code path runs first inside crop_and_resize.
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with pytest.raises((ValueError, TypeError)):
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kornia.geometry.transform.crop_and_resize(inp, boxes, 2)
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def test_center_crop_rejects_wrong_length(self, device, dtype):
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inp = torch.rand(1, 1, 4, 4, device=device, dtype=dtype)
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with pytest.raises(ValueError, match="tuple/list of length 2"):
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kornia.geometry.transform.center_crop(inp, (2, 2, 2))
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