197 lines
7.3 KiB
Python
197 lines
7.3 KiB
Python
# Copyright 2019-2022 Huawei Technologies Co., Ltd
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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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"""
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Testing RgbToHsv and HsvToRgb op in DE
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"""
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import colorsys
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import numpy as np
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from numpy.testing import assert_allclose
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import mindspore.dataset as ds
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import mindspore.dataset.transforms
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import mindspore.dataset.vision as vision
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import mindspore.dataset.vision.py_transforms_util as util
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DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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def generate_numpy_random_rgb(shape):
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# Only generate floating points that are fractions like n / 256, since they
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# are RGB pixels. Some low-precision floating point types in this test can't
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# handle arbitrary precision floating points well.
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return np.random.randint(0, 256, shape) / 255.
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def test_rgb_hsv_hwc():
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"""
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Feature: RgbToHsv and HsvToRgb ops
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Description: Test RgbToHsv and HsvToRgb utilities with an image in HWC format
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Expectation: Output is equal to the expected output
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"""
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rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.float32)
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rgb_np = rgb_flat.reshape((8, 8, 3))
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hsv_base = np.array([
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colorsys.rgb_to_hsv(
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r.astype(np.float64), g.astype(np.float64), b.astype(np.float64))
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for r, g, b in rgb_flat
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])
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hsv_base = hsv_base.reshape((8, 8, 3))
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hsv_de = util.rgb_to_hsvs(rgb_np, True)
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assert hsv_base.shape == hsv_de.shape
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assert_allclose(hsv_base.flatten(), hsv_de.flatten(), rtol=1e-5, atol=0)
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hsv_flat = hsv_base.reshape(64, 3)
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rgb_base = np.array([
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colorsys.hsv_to_rgb(
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h.astype(np.float64), s.astype(np.float64), v.astype(np.float64))
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for h, s, v in hsv_flat
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])
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rgb_base = rgb_base.reshape((8, 8, 3))
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rgb_de = util.hsv_to_rgbs(hsv_base, True)
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assert rgb_base.shape == rgb_de.shape
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assert_allclose(rgb_base.flatten(), rgb_de.flatten(), rtol=1e-5, atol=0)
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def test_rgb_hsv_batch_hwc():
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"""
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Feature: RgbToHsv and HsvToRgb ops
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Description: Test RgbToHsv and HsvToRgb utilities with a batch of images in HWC format
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Expectation: Output is equal to the expected output
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"""
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rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.float32)
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rgb_np = rgb_flat.reshape((4, 2, 8, 3))
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hsv_base = np.array([
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colorsys.rgb_to_hsv(
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r.astype(np.float64), g.astype(np.float64), b.astype(np.float64))
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for r, g, b in rgb_flat
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])
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hsv_base = hsv_base.reshape((4, 2, 8, 3))
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hsv_de = util.rgb_to_hsvs(rgb_np, True)
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assert hsv_base.shape == hsv_de.shape
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assert_allclose(hsv_base.flatten(), hsv_de.flatten(), rtol=1e-5, atol=0)
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hsv_flat = hsv_base.reshape((64, 3))
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rgb_base = np.array([
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colorsys.hsv_to_rgb(
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h.astype(np.float64), s.astype(np.float64), v.astype(np.float64))
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for h, s, v in hsv_flat
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])
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rgb_base = rgb_base.reshape((4, 2, 8, 3))
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rgb_de = util.hsv_to_rgbs(hsv_base, True)
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assert rgb_de.shape == rgb_base.shape
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assert_allclose(rgb_base.flatten(), rgb_de.flatten(), rtol=1e-5, atol=0)
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def test_rgb_hsv_chw():
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"""
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Feature: RgbToHsv and HsvToRgb ops
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Description: Test RgbToHsv and HsvToRgb utilities with an image in CHW format
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Expectation: Output is equal to the expected output
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"""
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rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.float32)
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rgb_np = rgb_flat.reshape((3, 8, 8))
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hsv_base = np.array([
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np.vectorize(colorsys.rgb_to_hsv)(
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rgb_np[0, :, :].astype(np.float64), rgb_np[1, :, :].astype(np.float64), rgb_np[2, :, :].astype(np.float64))
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])
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hsv_base = hsv_base.reshape((3, 8, 8))
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hsv_de = util.rgb_to_hsvs(rgb_np, False)
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assert hsv_base.shape == hsv_de.shape
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assert_allclose(hsv_base.flatten(), hsv_de.flatten(), rtol=1e-5, atol=0)
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rgb_base = np.array([
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np.vectorize(colorsys.hsv_to_rgb)(
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hsv_base[0, :, :].astype(np.float64), hsv_base[1, :, :].astype(np.float64),
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hsv_base[2, :, :].astype(np.float64))
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])
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rgb_base = rgb_base.reshape((3, 8, 8))
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rgb_de = util.hsv_to_rgbs(hsv_base, False)
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assert rgb_de.shape == rgb_base.shape
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assert_allclose(rgb_base.flatten(), rgb_de.flatten(), rtol=1e-5, atol=0)
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def test_rgb_hsv_batch_chw():
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"""
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Feature: RgbToHsv and HsvToRgb ops
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Description: Test RgbToHsv and HsvToRgb utilities with a batch of images in HWC format
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Expectation: Output is equal to the expected output
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"""
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rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.float32)
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rgb_imgs = rgb_flat.reshape((4, 3, 2, 8))
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hsv_base_imgs = np.array([
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np.vectorize(colorsys.rgb_to_hsv)(
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img[0, :, :].astype(np.float64), img[1, :, :].astype(np.float64), img[2, :, :].astype(np.float64))
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for img in rgb_imgs
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])
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hsv_de = util.rgb_to_hsvs(rgb_imgs, False)
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assert hsv_base_imgs.shape == hsv_de.shape
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assert_allclose(hsv_base_imgs.flatten(), hsv_de.flatten(), rtol=1e-5, atol=0)
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rgb_base = np.array([
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np.vectorize(colorsys.hsv_to_rgb)(
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img[0, :, :].astype(np.float64), img[1, :, :].astype(np.float64), img[2, :, :].astype(np.float64))
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for img in hsv_base_imgs
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])
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rgb_de = util.hsv_to_rgbs(hsv_base_imgs, False)
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assert rgb_base.shape == rgb_de.shape
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assert_allclose(rgb_base.flatten(), rgb_de.flatten(), rtol=1e-5, atol=0)
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def test_rgb_hsv_pipeline():
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"""
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Feature: RgbToHsv and HsvToRgb ops
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Description: Test RgbToHsv and HsvToRgb ops in data pipeline
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Expectation: Output is equal to the expected output
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"""
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# First dataset
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transforms1 = [
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vision.Decode(True),
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vision.Resize([64, 64]),
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vision.ToTensor()
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]
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transforms1 = mindspore.dataset.transforms.Compose(transforms1)
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ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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ds1 = ds1.map(operations=transforms1, input_columns=["image"])
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# Second dataset
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transforms2 = [
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vision.Decode(True),
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vision.Resize([64, 64]),
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vision.ToTensor(),
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vision.RgbToHsv(),
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vision.HsvToRgb()
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]
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transform2 = mindspore.dataset.transforms.Compose(transforms2)
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ds2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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ds2 = ds2.map(operations=transform2, input_columns=["image"])
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num_iter = 0
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for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1), ds2.create_dict_iterator(num_epochs=1)):
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num_iter += 1
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ori_img = data1["image"].asnumpy()
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cvt_img = data2["image"].asnumpy()
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assert_allclose(ori_img.flatten(), cvt_img.flatten(), rtol=1e-5, atol=0)
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assert ori_img.shape == cvt_img.shape
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if __name__ == "__main__":
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test_rgb_hsv_hwc()
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test_rgb_hsv_batch_hwc()
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test_rgb_hsv_chw()
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test_rgb_hsv_batch_chw()
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test_rgb_hsv_pipeline()
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