316 lines
12 KiB
Python
316 lines
12 KiB
Python
# Copyright 2021 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 AdjustGamma op in DE
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"""
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import numpy as np
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from numpy.testing import assert_allclose
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import PIL
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.py_transforms
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import mindspore.dataset.vision.py_transforms as F
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import mindspore.dataset.vision.c_transforms as C
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testImageNetData/train/"
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MNIST_DATA_DIR = "../data/dataset/testMnistData"
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DATA_DIR_2 = ["../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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"""
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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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"""
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return np.random.randint(0, 256, shape) / 255.
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def test_adjust_gamma_c_eager():
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# Eager 3-channel
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rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.float32)
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img_in = rgb_flat.reshape((8, 8, 3))
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adjustgamma_op = C.AdjustGamma(10, 1)
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img_out = adjustgamma_op(img_in)
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assert img_out is not None
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def test_adjust_gamma_py_eager():
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# Eager 3-channel
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rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.uint8)
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img_in = PIL.Image.fromarray(rgb_flat.reshape((8, 8, 3)))
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adjustgamma_op = F.AdjustGamma(10, 1)
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img_out = adjustgamma_op(img_in)
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assert img_out is not None
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def test_adjust_gamma_c_eager_gray():
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# Eager 3-channel
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rgb_flat = generate_numpy_random_rgb((64, 1)).astype(np.float32)
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img_in = rgb_flat.reshape((8, 8))
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adjustgamma_op = C.AdjustGamma(10, 1)
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img_out = adjustgamma_op(img_in)
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assert img_out is not None
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def test_adjust_gamma_py_eager_gray():
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# Eager 3-channel
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rgb_flat = generate_numpy_random_rgb((64, 1)).astype(np.uint8)
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img_in = PIL.Image.fromarray(rgb_flat.reshape((8, 8)))
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adjustgamma_op = F.AdjustGamma(10, 1)
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img_out = adjustgamma_op(img_in)
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assert img_out is not None
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def test_adjust_gamma_invalid_gamma_param_c():
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"""
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Test AdjustGamma C Op with invalid ignore parameter
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"""
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logger.info("Test AdjustGamma C Op with invalid ignore parameter")
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],
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input_columns=["image"])
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# invalid gamma
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data_set = data_set.map(operations=C.AdjustGamma(gamma=-10.0, gain=1.0),
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input_columns="image")
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except ValueError as error:
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logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
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assert "Input is not within the required interval of " in str(error)
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],
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input_columns=["image"])
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# invalid gamma
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data_set = data_set.map(operations=C.AdjustGamma(gamma=[1, 2], gain=1.0),
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input_columns="image")
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except TypeError as error:
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logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
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assert "is not of type [<class 'float'>, <class 'int'>], but got" in str(error)
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def test_adjust_gamma_invalid_gamma_param_py():
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"""
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Test AdjustGamma python Op with invalid ignore parameter
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"""
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logger.info("Test AdjustGamma python Op with invalid ignore parameter")
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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trans = mindspore.dataset.transforms.py_transforms.Compose([
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F.Decode(),
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F.Resize((224, 224)),
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F.AdjustGamma(gamma=-10.0),
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F.ToTensor()
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])
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data_set = data_set.map(operations=[trans], input_columns=["image"])
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except ValueError as error:
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logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
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assert "Input is not within the required interval of " in str(error)
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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trans = mindspore.dataset.transforms.py_transforms.Compose([
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F.Decode(),
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F.Resize((224, 224)),
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F.AdjustGamma(gamma=[1, 2]),
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F.ToTensor()
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])
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data_set = data_set.map(operations=[trans], input_columns=["image"])
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except TypeError as error:
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logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
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assert "is not of type [<class 'float'>, <class 'int'>], but got" in str(error)
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def test_adjust_gamma_invalid_gain_param_c():
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"""
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Test AdjustGamma C Op with invalid gain parameter
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"""
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logger.info("Test AdjustGamma C Op with invalid gain parameter")
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],
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input_columns=["image"])
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# invalid gain
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data_set = data_set.map(operations=C.AdjustGamma(gamma=10.0, gain=[1, 10]),
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input_columns="image")
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except TypeError as error:
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logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
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assert "is not of type [<class 'float'>, <class 'int'>], but got " in str(error)
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def test_adjust_gamma_invalid_gain_param_py():
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"""
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Test AdjustGamma python Op with invalid gain parameter
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"""
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logger.info("Test AdjustGamma python Op with invalid gain parameter")
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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trans = mindspore.dataset.transforms.py_transforms.Compose([
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F.Decode(),
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F.Resize((224, 224)),
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F.AdjustGamma(gamma=10.0, gain=[1, 10]),
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F.ToTensor()
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])
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data_set = data_set.map(operations=[trans], input_columns=["image"])
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except TypeError as error:
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logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
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assert "is not of type [<class 'float'>, <class 'int'>], but got " in str(error)
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def test_adjust_gamma_pipeline_c():
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"""
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Test AdjustGamma C Op Pipeline
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"""
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# First dataset
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transforms1 = [C.Decode(), C.Resize([64, 64])]
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transforms1 = mindspore.dataset.transforms.py_transforms.Compose(
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transforms1)
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ds1 = ds.TFRecordDataset(DATA_DIR_2,
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SCHEMA_DIR,
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columns_list=["image"],
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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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C.Decode(),
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C.Resize([64, 64]),
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C.AdjustGamma(1.0, 1.0)
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]
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transform2 = mindspore.dataset.transforms.py_transforms.Compose(
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transforms2)
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ds2 = ds.TFRecordDataset(DATA_DIR_2,
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SCHEMA_DIR,
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columns_list=["image"],
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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),
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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(),
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cvt_img.flatten(),
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rtol=1e-5,
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atol=0)
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assert ori_img.shape == cvt_img.shape
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def test_adjust_gamma_pipeline_py():
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"""
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Test AdjustGamma python Op Pipeline
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"""
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# First dataset
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transforms1 = [F.Decode(), F.Resize([64, 64]), F.ToTensor()]
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transforms1 = mindspore.dataset.transforms.py_transforms.Compose(
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transforms1)
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ds1 = ds.TFRecordDataset(DATA_DIR_2,
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SCHEMA_DIR,
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columns_list=["image"],
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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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F.Decode(),
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F.Resize([64, 64]),
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F.AdjustGamma(1.0, 1.0),
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F.ToTensor()
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]
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transform2 = mindspore.dataset.transforms.py_transforms.Compose(
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transforms2)
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ds2 = ds.TFRecordDataset(DATA_DIR_2,
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SCHEMA_DIR,
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columns_list=["image"],
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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),
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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(),
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cvt_img.flatten(),
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rtol=1e-5,
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atol=0)
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assert ori_img.shape == cvt_img.shape
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def test_adjust_gamma_pipeline_py_gray():
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"""
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Test AdjustGamma python Op Pipeline 1-channel
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"""
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# First dataset
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transforms1 = [F.Decode(), F.Resize([64, 64]), F.Grayscale(), F.ToTensor()]
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transforms1 = mindspore.dataset.transforms.py_transforms.Compose(
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transforms1)
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ds1 = ds.TFRecordDataset(DATA_DIR_2,
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SCHEMA_DIR,
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columns_list=["image"],
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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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F.Decode(),
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F.Resize([64, 64]),
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F.Grayscale(),
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F.AdjustGamma(1.0, 1.0),
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F.ToTensor()
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]
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transform2 = mindspore.dataset.transforms.py_transforms.Compose(
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transforms2)
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ds2 = ds.TFRecordDataset(DATA_DIR_2,
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SCHEMA_DIR,
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columns_list=["image"],
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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),
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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(),
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cvt_img.flatten(),
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rtol=1e-5,
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atol=0)
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if __name__ == "__main__":
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test_adjust_gamma_c_eager()
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test_adjust_gamma_py_eager()
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test_adjust_gamma_c_eager_gray()
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test_adjust_gamma_py_eager_gray()
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test_adjust_gamma_invalid_gamma_param_c()
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test_adjust_gamma_invalid_gamma_param_py()
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test_adjust_gamma_invalid_gain_param_c()
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test_adjust_gamma_invalid_gain_param_py()
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test_adjust_gamma_pipeline_c()
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test_adjust_gamma_pipeline_py()
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test_adjust_gamma_pipeline_py_gray()
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