forked from mindspore-Ecosystem/mindspore
135 lines
5.0 KiB
Python
135 lines
5.0 KiB
Python
# Copyright 2019 Huawei Technologies Co., Ltd
|
|
#
|
|
# 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.
|
|
# ==============================================================================
|
|
"""
|
|
Testing RandomSolarizeOp op in DE
|
|
"""
|
|
import pytest
|
|
import mindspore.dataset as ds
|
|
import mindspore.dataset.engine as de
|
|
import mindspore.dataset.vision.c_transforms as vision
|
|
from mindspore import log as logger
|
|
from util import visualize_list, save_and_check_md5, config_get_set_seed, config_get_set_num_parallel_workers, \
|
|
visualize_one_channel_dataset
|
|
|
|
GENERATE_GOLDEN = False
|
|
|
|
MNIST_DATA_DIR = "../data/dataset/testMnistData"
|
|
DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
|
|
SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
|
|
|
|
|
|
def test_random_solarize_op(threshold=(10, 150), plot=False, run_golden=True):
|
|
"""
|
|
Test RandomSolarize
|
|
"""
|
|
logger.info("Test RandomSolarize")
|
|
|
|
# First dataset
|
|
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
decode_op = vision.Decode()
|
|
|
|
original_seed = config_get_set_seed(0)
|
|
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
|
|
|
if threshold is None:
|
|
solarize_op = vision.RandomSolarize()
|
|
else:
|
|
solarize_op = vision.RandomSolarize(threshold)
|
|
|
|
data1 = data1.map(operations=decode_op, input_columns=["image"])
|
|
data1 = data1.map(operations=solarize_op, input_columns=["image"])
|
|
|
|
# Second dataset
|
|
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
data2 = data2.map(operations=decode_op, input_columns=["image"])
|
|
|
|
if run_golden:
|
|
filename = "random_solarize_01_result.npz"
|
|
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
|
|
|
|
image_solarized = []
|
|
image = []
|
|
|
|
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
|
|
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
|
|
image_solarized.append(item1["image"].copy())
|
|
image.append(item2["image"].copy())
|
|
if plot:
|
|
visualize_list(image, image_solarized)
|
|
|
|
ds.config.set_seed(original_seed)
|
|
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
|
|
|
|
|
def test_random_solarize_mnist(plot=False, run_golden=True):
|
|
"""
|
|
Test RandomSolarize op with MNIST dataset (Grayscale images)
|
|
"""
|
|
|
|
mnist_1 = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
|
|
mnist_2 = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
|
|
mnist_2 = mnist_2.map(operations=vision.RandomSolarize((0, 255)), input_columns="image")
|
|
|
|
images = []
|
|
images_trans = []
|
|
labels = []
|
|
|
|
for _, (data_orig, data_trans) in enumerate(zip(mnist_1, mnist_2)):
|
|
image_orig, label_orig = data_orig
|
|
image_trans, _ = data_trans
|
|
images.append(image_orig.asnumpy())
|
|
labels.append(label_orig.asnumpy())
|
|
images_trans.append(image_trans.asnumpy())
|
|
|
|
if plot:
|
|
visualize_one_channel_dataset(images, images_trans, labels)
|
|
|
|
if run_golden:
|
|
filename = "random_solarize_02_result.npz"
|
|
save_and_check_md5(mnist_2, filename, generate_golden=GENERATE_GOLDEN)
|
|
|
|
|
|
def test_random_solarize_errors():
|
|
"""
|
|
Test that RandomSolarize errors with bad input
|
|
"""
|
|
with pytest.raises(ValueError) as error_info:
|
|
vision.RandomSolarize((12, 1))
|
|
assert "threshold must be in min max format numbers" in str(error_info.value)
|
|
|
|
with pytest.raises(ValueError) as error_info:
|
|
vision.RandomSolarize((12, 1000))
|
|
assert "Input is not within the required interval of (0 to 255)." in str(error_info.value)
|
|
|
|
with pytest.raises(TypeError) as error_info:
|
|
vision.RandomSolarize((122.1, 140))
|
|
assert "Argument threshold[0] with value 122.1 is not of type (<class 'int'>,)." in str(error_info.value)
|
|
|
|
with pytest.raises(ValueError) as error_info:
|
|
vision.RandomSolarize((122, 100, 30))
|
|
assert "threshold must be a sequence of two numbers" in str(error_info.value)
|
|
|
|
with pytest.raises(ValueError) as error_info:
|
|
vision.RandomSolarize((120,))
|
|
assert "threshold must be a sequence of two numbers" in str(error_info.value)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_random_solarize_op((10, 150), plot=True, run_golden=True)
|
|
test_random_solarize_op((12, 120), plot=True, run_golden=False)
|
|
test_random_solarize_op(plot=True, run_golden=False)
|
|
test_random_solarize_mnist(plot=True, run_golden=True)
|
|
test_random_solarize_errors()
|