kornia--kornia
wehub-resource-sync
3a2c66702c
Tests on CPU (scheduled) / check-skip (push) Has been cancelled
Tests on CPU (scheduled) / pre-tests (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-ubuntu (float32) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-ubuntu (float64) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.11, float32, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.11, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.11, float64, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.11, float64, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.12, float32, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.12, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.12, float64, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.12, float64, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.13, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.13, float64, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-mac (3.11, float32, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-mac (3.11, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-mac (3.12, float32, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-mac (3.12, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-mac (3.13, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / coverage (push) Has been cancelled
Tests on CPU (scheduled) / typing (push) Has been cancelled
Tests on CPU (scheduled) / tutorials (push) Has been cancelled
Tests on CPU (scheduled) / docs (push) Has been cancelled
Lint / TOML Format (push) Has been cancelled
1113 行
42 KiB
Python
1113 行
42 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 TestDiffJPEG(BaseTester):
|
|
def test_smoke(self, device, dtype) -> None:
|
|
"""This test standard usage."""
|
|
B, H, W = 2, 32, 32
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B,), device=device, dtype=dtype)
|
|
img_jpeg = kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality)
|
|
assert img_jpeg is not None
|
|
assert img_jpeg.shape == img.shape
|
|
|
|
def test_keyword_argument(self, device, dtype) -> None:
|
|
"""Regression test for #3745: the image must be passable via the ``input=`` keyword."""
|
|
B, H, W = 2, 32, 32
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B,), device=device, dtype=dtype)
|
|
img_jpeg = kornia.enhance.jpeg_codec_differentiable(input=img, jpeg_quality=jpeg_quality)
|
|
self.assert_close(img_jpeg, kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality))
|
|
|
|
def test_smoke_not_div_by_16(self, device, dtype) -> None:
|
|
"""This test standard usage."""
|
|
B, H, W = 2, 33, 33
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B,), device=device, dtype=dtype)
|
|
img_jpeg = kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality)
|
|
assert img_jpeg is not None
|
|
assert img_jpeg.shape == img.shape
|
|
|
|
def test_multi_batch(self, device, dtype) -> None:
|
|
"""Here we test two batch dimensions."""
|
|
B, H, W = 4, 32, 32
|
|
img = torch.rand(B, B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(1,), device=device, dtype=dtype)
|
|
qt_y = torch.randint(low=1, high=255, size=(B * B, 8, 8), device=device, dtype=dtype)
|
|
qt_c = torch.randint(low=1, high=255, size=(B * B, 8, 8), device=device, dtype=dtype)
|
|
img_jpeg = kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality, qt_y, qt_c)
|
|
assert img_jpeg is not None
|
|
assert img_jpeg.shape == img.shape
|
|
|
|
def test_custom_qt(self, device, dtype) -> None:
|
|
"""Here we test if we can handle custom quantization tables."""
|
|
B, H, W = 4, 32, 32
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B,), device=device, dtype=dtype)
|
|
qt_y = torch.randint(low=1, high=255, size=(B, 8, 8), device=device, dtype=dtype)
|
|
qt_c = torch.randint(low=1, high=255, size=(B, 8, 8), device=device, dtype=dtype)
|
|
img_jpeg = kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality, qt_y, qt_c)
|
|
assert img_jpeg is not None
|
|
assert img_jpeg.shape == img.shape
|
|
|
|
def test_non_batch_param(self, device, dtype) -> None:
|
|
"""Here we test if we can handle non-batched JPEG parameters (JPEG quality and QT's)."""
|
|
B, H, W = 3, 32, 32
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(1,), device=device, dtype=dtype)
|
|
qt_y = torch.randint(low=1, high=255, size=(1, 8, 8), device=device, dtype=dtype)
|
|
qt_c = torch.randint(low=1, high=255, size=(1, 8, 8), device=device, dtype=dtype)
|
|
img_jpeg = kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality, qt_y, qt_c)
|
|
assert img_jpeg is not None
|
|
assert img_jpeg.shape == img.shape
|
|
|
|
def test_non_batch_inp(self, device, dtype) -> None:
|
|
"""Here we test if we can handle non-batched inputs (input image, JPEG quality, and QT's)."""
|
|
H, W = 32, 32
|
|
img = torch.rand(3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(1,), device=device, dtype=dtype)
|
|
qt_y = torch.randint(low=1, high=255, size=(8, 8), device=device, dtype=dtype)
|
|
qt_c = torch.randint(low=1, high=255, size=(8, 8), device=device, dtype=dtype)
|
|
img_jpeg = kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality, qt_y, qt_c)
|
|
assert img_jpeg is not None
|
|
assert img_jpeg.shape == img.shape
|
|
|
|
def test_exception(self, device, dtype) -> None:
|
|
"""Test exceptions (non-tensor input, wrong JPEG quality shape, wrong img shape, and wrong QT shape.)"""
|
|
with pytest.raises(TypeError) as errinfo:
|
|
B = 2
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B,), device=device, dtype=dtype)
|
|
kornia.enhance.jpeg_codec_differentiable(1904.0, jpeg_quality)
|
|
assert "Input input type is not a torch.Tensor" in str(errinfo.value)
|
|
|
|
from kornia.core.exceptions import TypeCheckError
|
|
|
|
with pytest.raises(TypeCheckError) as errinfo:
|
|
B, H, W = 2, 32, 32
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
kornia.enhance.jpeg_codec_differentiable(img, None)
|
|
assert "Type mismatch: expected Tensor" in str(errinfo.value)
|
|
|
|
from kornia.core.exceptions import BaseError
|
|
|
|
with pytest.raises(BaseError) as errinfo:
|
|
B, H, W = 2, 32, 32
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B, 3, 2, 1), device=device, dtype=dtype)
|
|
kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality)
|
|
assert "Shape dimension mismatch" in str(errinfo.value) or "Expected shape" in str(errinfo.value)
|
|
|
|
from kornia.core.exceptions import BaseError
|
|
|
|
with pytest.raises(BaseError) as errinfo:
|
|
B, H, W = 4, 32, 32
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B,), device=device, dtype=dtype)
|
|
qt_y = torch.randint(low=1, high=255, size=(B, 7, 8), device=device, dtype=dtype)
|
|
qt_c = torch.randint(low=1, high=255, size=(B, 8, 8), device=device, dtype=dtype)
|
|
kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality, qt_y, qt_c)
|
|
assert (
|
|
"Shape dimension mismatch" in str(errinfo.value)
|
|
or "Expected shape" in str(errinfo.value)
|
|
or "shape must be" in str(errinfo.value)
|
|
)
|
|
|
|
from kornia.core.exceptions import BaseError
|
|
|
|
with pytest.raises(BaseError) as errinfo:
|
|
B, H, W = 4, 32, 32
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B,), device=device, dtype=dtype)
|
|
qt_y = torch.randint(low=1, high=255, size=(B, 8, 8), device=device, dtype=dtype)
|
|
qt_c = torch.randint(low=1, high=255, size=(B, 8, 7), device=device, dtype=dtype)
|
|
kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality, qt_y, qt_c)
|
|
assert (
|
|
"Shape dimension mismatch" in str(errinfo.value)
|
|
or "Expected shape" in str(errinfo.value)
|
|
or "shape must be" in str(errinfo.value)
|
|
)
|
|
|
|
from kornia.core.exceptions import BaseError
|
|
|
|
with pytest.raises(BaseError) as errinfo:
|
|
B, H, W = 4, 32, 32
|
|
img = torch.rand(B, B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B * B,), device=device, dtype=dtype)
|
|
qt_y = torch.randint(low=1, high=255, size=(B * B, 8, 8), device=device, dtype=dtype)
|
|
qt_c = torch.randint(low=1, high=255, size=(B * 2, 8, 8), device=device, dtype=dtype)
|
|
kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality, qt_y, qt_c)
|
|
assert "Batch dimensions do not match" in str(errinfo.value)
|
|
|
|
from kornia.core.exceptions import BaseError
|
|
|
|
with pytest.raises(BaseError) as errinfo:
|
|
B, H, W = 4, 32, 32
|
|
img = torch.rand(B, B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B * B,), device=device, dtype=dtype)
|
|
qt_y = torch.randint(low=1, high=255, size=(B * 2, 8, 8), device=device, dtype=dtype)
|
|
qt_c = torch.randint(low=1, high=255, size=(B * B, 8, 8), device=device, dtype=dtype)
|
|
kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality, qt_y, qt_c)
|
|
assert "Batch dimensions do not match" in str(errinfo.value)
|
|
|
|
from kornia.core.exceptions import BaseError
|
|
|
|
with pytest.raises(BaseError) as errinfo:
|
|
B, H, W = 4, 32, 32
|
|
img = torch.rand(B, B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B * 2,), device=device, dtype=dtype)
|
|
qt_y = torch.randint(low=1, high=255, size=(B * B, 8, 8), device=device, dtype=dtype)
|
|
qt_c = torch.randint(low=1, high=255, size=(B * B, 8, 8), device=device, dtype=dtype)
|
|
kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality, qt_y, qt_c)
|
|
assert "Batch dimensions do not match" in str(errinfo.value)
|
|
|
|
def test_cardinality(self, device, dtype) -> None:
|
|
B, H, W = 1, 16, 16
|
|
img = torch.zeros(B, 3, H, W, device=device, dtype=dtype)
|
|
img[..., 4:-4, 4:-4] = 1.0
|
|
jpeg_quality = torch.tensor([2.0], device=device, dtype=dtype)
|
|
img_jpeg = kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality)
|
|
# Numbers generated based on reference implementation
|
|
img_jpeg_ref = torch.tensor(
|
|
[
|
|
[
|
|
[
|
|
[
|
|
-0.000,
|
|
0.002,
|
|
0.063,
|
|
0.060,
|
|
0.020,
|
|
0.017,
|
|
0.078,
|
|
0.146,
|
|
0.146,
|
|
0.078,
|
|
0.017,
|
|
0.020,
|
|
0.060,
|
|
0.063,
|
|
0.002,
|
|
-0.000,
|
|
],
|
|
[
|
|
0.002,
|
|
0.015,
|
|
0.008,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.008,
|
|
0.015,
|
|
0.002,
|
|
],
|
|
[
|
|
0.063,
|
|
0.009,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.009,
|
|
0.063,
|
|
],
|
|
[
|
|
0.060,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.246,
|
|
0.178,
|
|
0.080,
|
|
0.080,
|
|
0.178,
|
|
0.246,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.060,
|
|
],
|
|
[
|
|
0.020,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.694,
|
|
0.971,
|
|
0.960,
|
|
0.847,
|
|
0.847,
|
|
0.960,
|
|
0.971,
|
|
0.694,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
0.020,
|
|
],
|
|
[
|
|
0.017,
|
|
-0.000,
|
|
-0.000,
|
|
0.246,
|
|
0.971,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.971,
|
|
0.246,
|
|
-0.000,
|
|
-0.000,
|
|
0.017,
|
|
],
|
|
[
|
|
0.078,
|
|
-0.000,
|
|
-0.000,
|
|
0.178,
|
|
0.960,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.960,
|
|
0.178,
|
|
-0.000,
|
|
-0.000,
|
|
0.078,
|
|
],
|
|
[
|
|
0.146,
|
|
-0.000,
|
|
-0.000,
|
|
0.080,
|
|
0.847,
|
|
1.000,
|
|
1.000,
|
|
0.781,
|
|
0.781,
|
|
1.000,
|
|
1.000,
|
|
0.847,
|
|
0.080,
|
|
-0.000,
|
|
-0.000,
|
|
0.146,
|
|
],
|
|
[
|
|
0.146,
|
|
-0.000,
|
|
-0.000,
|
|
0.080,
|
|
0.847,
|
|
1.000,
|
|
1.000,
|
|
0.781,
|
|
0.781,
|
|
1.000,
|
|
1.000,
|
|
0.847,
|
|
0.080,
|
|
-0.000,
|
|
-0.000,
|
|
0.146,
|
|
],
|
|
[
|
|
0.078,
|
|
-0.000,
|
|
-0.000,
|
|
0.178,
|
|
0.960,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.960,
|
|
0.178,
|
|
-0.000,
|
|
-0.000,
|
|
0.078,
|
|
],
|
|
[
|
|
0.017,
|
|
-0.000,
|
|
-0.000,
|
|
0.246,
|
|
0.971,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.971,
|
|
0.246,
|
|
-0.000,
|
|
-0.000,
|
|
0.017,
|
|
],
|
|
[
|
|
0.020,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.694,
|
|
0.971,
|
|
0.960,
|
|
0.847,
|
|
0.847,
|
|
0.960,
|
|
0.971,
|
|
0.694,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
0.020,
|
|
],
|
|
[
|
|
0.060,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.246,
|
|
0.178,
|
|
0.080,
|
|
0.080,
|
|
0.178,
|
|
0.246,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.060,
|
|
],
|
|
[
|
|
0.063,
|
|
0.009,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.009,
|
|
0.063,
|
|
],
|
|
[
|
|
0.002,
|
|
0.015,
|
|
0.008,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.008,
|
|
0.015,
|
|
0.002,
|
|
],
|
|
[
|
|
-0.000,
|
|
0.002,
|
|
0.063,
|
|
0.060,
|
|
0.020,
|
|
0.017,
|
|
0.078,
|
|
0.146,
|
|
0.146,
|
|
0.078,
|
|
0.017,
|
|
0.020,
|
|
0.060,
|
|
0.063,
|
|
0.002,
|
|
-0.000,
|
|
],
|
|
],
|
|
[
|
|
[
|
|
-0.000,
|
|
0.002,
|
|
0.063,
|
|
0.060,
|
|
0.020,
|
|
0.017,
|
|
0.078,
|
|
0.146,
|
|
0.146,
|
|
0.078,
|
|
0.017,
|
|
0.020,
|
|
0.060,
|
|
0.063,
|
|
0.002,
|
|
-0.000,
|
|
],
|
|
[
|
|
0.002,
|
|
0.015,
|
|
0.008,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.008,
|
|
0.015,
|
|
0.002,
|
|
],
|
|
[
|
|
0.063,
|
|
0.009,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.009,
|
|
0.063,
|
|
],
|
|
[
|
|
0.060,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.246,
|
|
0.178,
|
|
0.080,
|
|
0.080,
|
|
0.178,
|
|
0.246,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.060,
|
|
],
|
|
[
|
|
0.020,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.694,
|
|
0.971,
|
|
0.960,
|
|
0.847,
|
|
0.847,
|
|
0.960,
|
|
0.971,
|
|
0.694,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
0.020,
|
|
],
|
|
[
|
|
0.017,
|
|
-0.000,
|
|
-0.000,
|
|
0.246,
|
|
0.971,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.971,
|
|
0.246,
|
|
-0.000,
|
|
-0.000,
|
|
0.017,
|
|
],
|
|
[
|
|
0.078,
|
|
-0.000,
|
|
-0.000,
|
|
0.178,
|
|
0.960,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.960,
|
|
0.178,
|
|
-0.000,
|
|
-0.000,
|
|
0.078,
|
|
],
|
|
[
|
|
0.146,
|
|
-0.000,
|
|
-0.000,
|
|
0.080,
|
|
0.847,
|
|
1.000,
|
|
1.000,
|
|
0.781,
|
|
0.781,
|
|
1.000,
|
|
1.000,
|
|
0.847,
|
|
0.080,
|
|
-0.000,
|
|
-0.000,
|
|
0.146,
|
|
],
|
|
[
|
|
0.146,
|
|
-0.000,
|
|
-0.000,
|
|
0.080,
|
|
0.847,
|
|
1.000,
|
|
1.000,
|
|
0.781,
|
|
0.781,
|
|
1.000,
|
|
1.000,
|
|
0.847,
|
|
0.080,
|
|
-0.000,
|
|
-0.000,
|
|
0.146,
|
|
],
|
|
[
|
|
0.078,
|
|
-0.000,
|
|
-0.000,
|
|
0.178,
|
|
0.960,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.960,
|
|
0.178,
|
|
-0.000,
|
|
-0.000,
|
|
0.078,
|
|
],
|
|
[
|
|
0.017,
|
|
-0.000,
|
|
-0.000,
|
|
0.246,
|
|
0.971,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.971,
|
|
0.246,
|
|
-0.000,
|
|
-0.000,
|
|
0.017,
|
|
],
|
|
[
|
|
0.020,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.694,
|
|
0.971,
|
|
0.960,
|
|
0.847,
|
|
0.847,
|
|
0.960,
|
|
0.971,
|
|
0.694,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
0.020,
|
|
],
|
|
[
|
|
0.060,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.246,
|
|
0.178,
|
|
0.080,
|
|
0.080,
|
|
0.178,
|
|
0.246,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.060,
|
|
],
|
|
[
|
|
0.063,
|
|
0.009,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.009,
|
|
0.063,
|
|
],
|
|
[
|
|
0.002,
|
|
0.015,
|
|
0.008,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.008,
|
|
0.015,
|
|
0.002,
|
|
],
|
|
[
|
|
-0.000,
|
|
0.002,
|
|
0.063,
|
|
0.060,
|
|
0.020,
|
|
0.017,
|
|
0.078,
|
|
0.146,
|
|
0.146,
|
|
0.078,
|
|
0.017,
|
|
0.020,
|
|
0.060,
|
|
0.063,
|
|
0.002,
|
|
-0.000,
|
|
],
|
|
],
|
|
[
|
|
[
|
|
-0.000,
|
|
0.002,
|
|
0.063,
|
|
0.060,
|
|
0.020,
|
|
0.017,
|
|
0.078,
|
|
0.146,
|
|
0.146,
|
|
0.078,
|
|
0.017,
|
|
0.020,
|
|
0.060,
|
|
0.063,
|
|
0.002,
|
|
-0.000,
|
|
],
|
|
[
|
|
0.002,
|
|
0.015,
|
|
0.008,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.008,
|
|
0.015,
|
|
0.002,
|
|
],
|
|
[
|
|
0.063,
|
|
0.009,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.009,
|
|
0.063,
|
|
],
|
|
[
|
|
0.060,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.246,
|
|
0.178,
|
|
0.080,
|
|
0.080,
|
|
0.178,
|
|
0.246,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.060,
|
|
],
|
|
[
|
|
0.020,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.694,
|
|
0.971,
|
|
0.960,
|
|
0.847,
|
|
0.847,
|
|
0.960,
|
|
0.971,
|
|
0.694,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
0.020,
|
|
],
|
|
[
|
|
0.017,
|
|
-0.000,
|
|
-0.000,
|
|
0.246,
|
|
0.971,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.971,
|
|
0.246,
|
|
-0.000,
|
|
-0.000,
|
|
0.017,
|
|
],
|
|
[
|
|
0.078,
|
|
-0.000,
|
|
-0.000,
|
|
0.178,
|
|
0.960,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.960,
|
|
0.178,
|
|
-0.000,
|
|
-0.000,
|
|
0.078,
|
|
],
|
|
[
|
|
0.146,
|
|
-0.000,
|
|
-0.000,
|
|
0.080,
|
|
0.847,
|
|
1.000,
|
|
1.000,
|
|
0.781,
|
|
0.781,
|
|
1.000,
|
|
1.000,
|
|
0.847,
|
|
0.080,
|
|
-0.000,
|
|
-0.000,
|
|
0.146,
|
|
],
|
|
[
|
|
0.146,
|
|
-0.000,
|
|
-0.000,
|
|
0.080,
|
|
0.847,
|
|
1.000,
|
|
1.000,
|
|
0.781,
|
|
0.781,
|
|
1.000,
|
|
1.000,
|
|
0.847,
|
|
0.080,
|
|
-0.000,
|
|
-0.000,
|
|
0.146,
|
|
],
|
|
[
|
|
0.078,
|
|
-0.000,
|
|
-0.000,
|
|
0.178,
|
|
0.960,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.960,
|
|
0.178,
|
|
-0.000,
|
|
-0.000,
|
|
0.078,
|
|
],
|
|
[
|
|
0.017,
|
|
-0.000,
|
|
-0.000,
|
|
0.246,
|
|
0.971,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
1.000,
|
|
0.971,
|
|
0.246,
|
|
-0.000,
|
|
-0.000,
|
|
0.017,
|
|
],
|
|
[
|
|
0.020,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.694,
|
|
0.971,
|
|
0.960,
|
|
0.847,
|
|
0.847,
|
|
0.960,
|
|
0.971,
|
|
0.694,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
0.020,
|
|
],
|
|
[
|
|
0.060,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.173,
|
|
0.246,
|
|
0.178,
|
|
0.080,
|
|
0.080,
|
|
0.178,
|
|
0.246,
|
|
0.173,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.060,
|
|
],
|
|
[
|
|
0.063,
|
|
0.009,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.009,
|
|
0.063,
|
|
],
|
|
[
|
|
0.002,
|
|
0.015,
|
|
0.008,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
-0.000,
|
|
0.008,
|
|
0.015,
|
|
0.002,
|
|
],
|
|
[
|
|
-0.000,
|
|
0.002,
|
|
0.063,
|
|
0.060,
|
|
0.020,
|
|
0.017,
|
|
0.078,
|
|
0.146,
|
|
0.146,
|
|
0.078,
|
|
0.017,
|
|
0.020,
|
|
0.060,
|
|
0.063,
|
|
0.002,
|
|
-0.000,
|
|
],
|
|
],
|
|
]
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
)
|
|
# We use a slightly higher tolerance since our implementation varies from the reference implementation
|
|
self.assert_close(img_jpeg, img_jpeg_ref, rtol=0.01, atol=0.01)
|
|
|
|
def test_module(self, device, dtype) -> None:
|
|
B, H, W = 4, 16, 16
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B,), device=device, dtype=dtype)
|
|
qt_y = torch.randint(low=1, high=255, size=(B, 8, 8), device=device, dtype=dtype)
|
|
qt_c = torch.randint(low=1, high=255, size=(B, 8, 8), device=device, dtype=dtype)
|
|
diff_jpeg_module = kornia.enhance.JPEGCodecDifferentiable(qt_y, qt_c)
|
|
img_jpeg = diff_jpeg_module(img, jpeg_quality)
|
|
assert img_jpeg is not None
|
|
assert img_jpeg.shape == img.shape
|
|
|
|
def test_module_with_param(self, device, dtype) -> None:
|
|
B, H, W = 4, 16, 16
|
|
img = torch.rand(B, 3, H, W, device=device, dtype=dtype)
|
|
jpeg_quality = torch.randint(low=0, high=100, size=(B,), device=device, dtype=dtype)
|
|
qt_y = torch.nn.Parameter(torch.randint(low=1, high=255, size=(B, 8, 8), device=device, dtype=dtype))
|
|
qt_c = torch.nn.Parameter(torch.randint(low=1, high=255, size=(B, 8, 8), device=device, dtype=dtype))
|
|
diff_jpeg_module = kornia.enhance.JPEGCodecDifferentiable(qt_y, qt_c)
|
|
img_jpeg = diff_jpeg_module(img, jpeg_quality)
|
|
assert img_jpeg is not None
|
|
assert img_jpeg.shape == img.shape
|
|
|
|
# @pytest.mark.slow
|
|
def test_gradcheck(self, device) -> None:
|
|
"""We test that the gradient matches the gradient of the reference implementation."""
|
|
B, H, W = 1, 16, 16
|
|
img = torch.zeros(B, 3, H, W, device=device, dtype=torch.float)
|
|
img[..., 0, 4:-4, 4:-4] = 1.0
|
|
img[..., 1, 4:-4, 4:-4] = 0.5
|
|
img[..., 2, 4:-4, 4:-4] = 0.5
|
|
img.requires_grad = True
|
|
jpeg_quality = torch.tensor([10.0], device=device, dtype=torch.float, requires_grad=True)
|
|
img_jpeg = kornia.enhance.jpeg_codec_differentiable(img, jpeg_quality)
|
|
(img_jpeg - torch.zeros_like(img_jpeg)).abs().sum().backward()
|
|
# Numbers generated based on reference implementation
|
|
img_jpeg_mean_grad_ref = torch.tensor([0.1919], device=device)
|
|
jpeg_quality_grad_ref = torch.tensor([0.0042], device=device)
|
|
# We use a slightly higher tolerance since our implementation varies from the reference implementation
|
|
self.assert_close(img.grad.mean().view(-1), img_jpeg_mean_grad_ref, rtol=0.01, atol=0.01)
|
|
self.assert_close(jpeg_quality.grad, jpeg_quality_grad_ref, rtol=0.01, atol=0.01)
|