!1636 Add TCs to 3 vision transformation ops and cleanup config settings
Merge pull request !1636 from Tinazhang/randomGraypull/1636/MERGE
mindspore-ci-bot
5 years ago
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Gitee
commit
15a196eba9
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# Copyright 2020 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 RandomGrayscale op in DE
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"""
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import numpy as np
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import mindspore.dataset.transforms.vision.py_transforms as py_vision
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import mindspore.dataset as ds
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from mindspore import log as logger
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from util import save_and_check_md5, visualize, \
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config_get_set_seed, config_get_set_num_parallel_workers
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GENERATE_GOLDEN = False
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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 test_random_grayscale_valid_prob(plot=False):
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"""
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Test RandomGrayscale Op: valid input, expect to pass
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"""
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logger.info("test_random_grayscale_valid_prob")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms1 = [
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py_vision.Decode(),
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# Note: prob is 1 so the output should always be grayscale images
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py_vision.RandomGrayscale(1),
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py_vision.ToTensor()
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]
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transform1 = py_vision.ComposeOp(transforms1)
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data1 = data1.map(input_columns=["image"], operations=transform1())
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms2 = [
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py_vision.Decode(),
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py_vision.ToTensor()
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]
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transform2 = py_vision.ComposeOp(transforms2)
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data2 = data2.map(input_columns=["image"], operations=transform2())
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image_gray = []
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image = []
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
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image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_gray.append(image1)
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image.append(image2)
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if plot:
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visualize(image, image_gray)
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def test_random_grayscale_input_grayscale_images():
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"""
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Test RandomGrayscale Op: valid parameter with grayscale images as input, expect to pass
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"""
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logger.info("test_random_grayscale_input_grayscale_images")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms1 = [
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py_vision.Decode(),
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py_vision.Grayscale(1),
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# Note: If the input images is grayscale image with 1 channel.
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py_vision.RandomGrayscale(0.5),
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py_vision.ToTensor()
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]
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transform1 = py_vision.ComposeOp(transforms1)
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data1 = data1.map(input_columns=["image"], operations=transform1())
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms2 = [
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py_vision.Decode(),
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py_vision.ToTensor()
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]
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transform2 = py_vision.ComposeOp(transforms2)
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data2 = data2.map(input_columns=["image"], operations=transform2())
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image_gray = []
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image = []
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
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image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_gray.append(image1)
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image.append(image2)
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assert len(image1.shape) == 3
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assert image1.shape[2] == 1
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assert len(image2.shape) == 3
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assert image2.shape[2] == 3
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# Restore config
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_grayscale_md5_valid_input():
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"""
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Test RandomGrayscale with md5 comparison: valid parameter, expect to pass
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"""
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logger.info("test_random_grayscale_md5_valid_input")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms = [
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py_vision.Decode(),
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py_vision.RandomGrayscale(0.8),
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py_vision.ToTensor()
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]
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transform = py_vision.ComposeOp(transforms)
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data = data.map(input_columns=["image"], operations=transform())
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# Check output images with md5 comparison
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filename = "random_grayscale_01_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_grayscale_md5_no_param():
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"""
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Test RandomGrayscale with md5 comparison: no parameter given, expect to pass
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"""
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logger.info("test_random_grayscale_md5_no_param")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms = [
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py_vision.Decode(),
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py_vision.RandomGrayscale(),
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py_vision.ToTensor()
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]
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transform = py_vision.ComposeOp(transforms)
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data = data.map(input_columns=["image"], operations=transform())
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# Check output images with md5 comparison
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filename = "random_grayscale_02_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_grayscale_invalid_param():
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"""
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Test RandomGrayscale: invalid parameter given, expect to raise error
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"""
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logger.info("test_random_grayscale_invalid_param")
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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try:
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transforms = [
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py_vision.Decode(),
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py_vision.RandomGrayscale(1.5),
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py_vision.ToTensor()
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]
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transform = py_vision.ComposeOp(transforms)
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data = data.map(input_columns=["image"], operations=transform())
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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 is not within the required range" in str(e)
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if __name__ == "__main__":
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test_random_grayscale_valid_prob(True)
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test_random_grayscale_input_grayscale_images()
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test_random_grayscale_md5_valid_input()
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test_random_grayscale_md5_no_param()
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test_random_grayscale_invalid_param()
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