forked from huawei/mindspore2022
255 lines
11 KiB
Python
255 lines
11 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 RandomAutoContrast op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.vision.c_transforms as c_vision
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from mindspore import log as logger
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from util import visualize_list, visualize_image, diff_mse
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image_file = "../data/dataset/testImageNetData/train/class1/1_1.jpg"
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data_dir = "../data/dataset/testImageNetData/train/"
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def test_random_auto_contrast_pipeline(plot=False):
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"""
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Test RandomAutoContrast pipeline
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"""
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logger.info("Test RandomAutoContrast pipeline")
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# Original Images
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data_set = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
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transforms_original = [c_vision.Decode(), c_vision.Resize(size=[224, 224])]
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ds_original = data_set.map(operations=transforms_original, input_columns="image")
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ds_original = ds_original.batch(512)
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = image.asnumpy()
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else:
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images_original = np.append(images_original,
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image.asnumpy(),
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axis=0)
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# Randomly Automatically Contrasted Images
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data_set1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
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transform_random_auto_contrast = [c_vision.Decode(),
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c_vision.Resize(size=[224, 224]),
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c_vision.RandomAutoContrast(prob=0.6)]
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ds_random_auto_contrast = data_set1.map(operations=transform_random_auto_contrast, input_columns="image")
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ds_random_auto_contrast = ds_random_auto_contrast.batch(512)
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for idx, (image, _) in enumerate(ds_random_auto_contrast):
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if idx == 0:
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images_random_auto_contrast = image.asnumpy()
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else:
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images_random_auto_contrast = np.append(images_random_auto_contrast,
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image.asnumpy(),
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axis=0)
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if plot:
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visualize_list(images_original, images_random_auto_contrast)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_random_auto_contrast[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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def test_random_auto_contrast_eager():
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"""
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Test RandomAutoContrast eager.
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"""
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img = np.fromfile(image_file, dtype=np.uint8)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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img = c_vision.Decode()(img)
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img_auto_contrast = c_vision.AutoContrast(1.0, None)(img)
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img_random_auto_contrast = c_vision.RandomAutoContrast(1.0, None, 1.0)(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img_auto_contrast), img_random_auto_contrast.shape))
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assert img_auto_contrast.all() == img_random_auto_contrast.all()
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def test_random_auto_contrast_comp(plot=False):
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"""
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Test RandomAutoContrast op compared with AutoContrast op.
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"""
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random_auto_contrast_op = c_vision.RandomAutoContrast(prob=1.0)
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auto_contrast_op = c_vision.AutoContrast()
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dataset1 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
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for item in dataset1.create_dict_iterator(num_epochs=1, output_numpy=True):
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image = item['image']
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dataset1.map(operations=random_auto_contrast_op, input_columns=['image'])
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dataset2 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
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dataset2.map(operations=auto_contrast_op, input_columns=['image'])
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for item1, item2 in zip(dataset1.create_dict_iterator(num_epochs=1, output_numpy=True),
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dataset2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image_random_auto_contrast = item1['image']
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image_auto_contrast = item2['image']
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mse = diff_mse(image_auto_contrast, image_random_auto_contrast)
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assert mse == 0
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logger.info("mse: {}".format(mse))
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if plot:
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visualize_image(image, image_random_auto_contrast, mse, image_auto_contrast)
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def test_random_auto_contrast_invalid_prob():
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"""
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Test RandomAutoContrast Op with invalid prob parameter.
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"""
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logger.info("test_random_auto_contrast_invalid_prob")
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dataset = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
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try:
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random_auto_contrast_op = c_vision.RandomAutoContrast(prob=1.5)
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dataset = dataset.map(operations=random_auto_contrast_op, input_columns=['image'])
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Input prob is not within the required interval of [0.0, 1.0]." in str(e)
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def test_random_auto_contrast_invalid_ignore():
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"""
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Test RandomAutoContrast Op with invalid ignore parameter.
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"""
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logger.info("test_random_auto_contrast_invalid_ignore")
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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_vision.Decode(),
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c_vision.Resize((224, 224)),
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lambda img: np.array(img[:, :, 0])], input_columns=["image"])
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# invalid ignore
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data_set = data_set.map(operations=c_vision.RandomAutoContrast(ignore=255.5), input_columns="image")
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except TypeError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Argument ignore with value 255.5 is not of type" 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_vision.Decode(), c_vision.Resize((224, 224)),
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lambda img: np.array(img[:, :, 0])], input_columns=["image"])
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# invalid ignore
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data_set = data_set.map(operations=c_vision.RandomAutoContrast(ignore=(10, 100)), input_columns="image")
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except TypeError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Argument ignore with value (10,100) is not of type" in str(error)
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def test_random_auto_contrast_invalid_cutoff():
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"""
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Test RandomAutoContrast Op with invalid cutoff parameter.
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"""
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logger.info("test_random_auto_contrast_invalid_cutoff")
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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_vision.Decode(),
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c_vision.Resize((224, 224)),
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lambda img: np.array(img[:, :, 0])], input_columns=["image"])
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# invalid cutoff
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data_set = data_set.map(operations=c_vision.RandomAutoContrast(cutoff=-10.0), input_columns="image")
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except ValueError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Input cutoff is not within the required interval of [0, 50)." 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_vision.Decode(),
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c_vision.Resize((224, 224)),
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lambda img: np.array(img[:, :, 0])], input_columns=["image"])
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# invalid cutoff
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data_set = data_set.map(operations=c_vision.RandomAutoContrast(cutoff=120.0), input_columns="image")
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except ValueError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Input cutoff is not within the required interval of [0, 50)." in str(error)
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def test_random_auto_contrast_one_channel():
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"""
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Feature: RandomAutoContrast
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Description: test with one channel images
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Expectation: raise errors as expected
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"""
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logger.info("test_random_auto_contrast_one_channel")
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c_op = c_vision.RandomAutoContrast()
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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_vision.Decode(), c_vision.Resize((224, 224)),
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lambda img: np.array(img[:, :, 0])], input_columns=["image"])
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data_set = data_set.map(operations=c_op, input_columns="image")
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except RuntimeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "image shape is incorrect, expected num of channels is 3." in str(e)
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def test_random_auto_contrast_four_dim():
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"""
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Feature: RandomAutoContrast
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Description: test with four dimension images
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Expectation: raise errors as expected
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"""
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logger.info("test_random_auto_contrast_four_dim")
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c_op = c_vision.RandomAutoContrast()
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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_vision.Decode(), c_vision.Resize((224, 224)),
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lambda img: np.array(img[2, 200, 10, 32])], input_columns=["image"])
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data_set = data_set.map(operations=c_op, input_columns="image")
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "image shape is not <H,W,C>" in str(e)
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def test_random_auto_contrast_invalid_input():
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"""
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Feature: RandomAutoContrast
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Description: test with images in uint32 type
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Expectation: raise errors as expected
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"""
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logger.info("test_random_invert_invalid_input")
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c_op = c_vision.RandomAutoContrast()
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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_vision.Decode(), c_vision.Resize((224, 224)),
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lambda img: np.array(img[2, 32, 3], dtype=uint32)], input_columns=["image"])
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data_set = data_set.map(operations=c_op, input_columns="image")
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Cannot convert from OpenCV type, unknown CV type" in str(e)
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if __name__ == "__main__":
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test_random_auto_contrast_pipeline(plot=True)
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test_random_auto_contrast_eager()
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test_random_auto_contrast_comp(plot=True)
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test_random_auto_contrast_invalid_prob()
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test_random_auto_contrast_invalid_ignore()
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test_random_auto_contrast_invalid_cutoff()
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test_random_auto_contrast_one_channel()
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test_random_auto_contrast_four_dim()
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test_random_auto_contrast_invalid_input()
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