forked from huawei/mindspore2022
253 lines
9.8 KiB
Python
253 lines
9.8 KiB
Python
# Copyright 2021 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 RandomLighting op in DE
|
|
"""
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.dataset as ds
|
|
import mindspore.dataset.transforms.py_transforms
|
|
import mindspore.dataset.vision.py_transforms as F
|
|
import mindspore.dataset.vision.c_transforms as C
|
|
from mindspore import log as logger
|
|
from util import visualize_list, diff_mse, save_and_check_md5, \
|
|
config_get_set_seed, config_get_set_num_parallel_workers
|
|
|
|
DATA_DIR = "../data/dataset/testImageNetData/train/"
|
|
MNIST_DATA_DIR = "../data/dataset/testMnistData"
|
|
|
|
GENERATE_GOLDEN = False
|
|
|
|
|
|
def test_random_lighting_py(alpha=1, plot=False):
|
|
"""
|
|
Feature: RandomLighting
|
|
Description: test RandomLighting python op
|
|
Expectation: equal results
|
|
"""
|
|
logger.info("Test RandomLighting python op")
|
|
|
|
# Original Images
|
|
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
|
|
F.Resize((224, 224)),
|
|
F.ToTensor()])
|
|
|
|
ds_original = data.map(operations=transforms_original, input_columns="image")
|
|
|
|
ds_original = ds_original.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_original.create_tuple_iterator(num_epochs=1, output_numpy=True)):
|
|
if idx == 0:
|
|
images_original = np.transpose(image, (0, 2, 3, 1))
|
|
else:
|
|
images_original = np.append(images_original, np.transpose(image, (0, 2, 3, 1)), axis=0)
|
|
|
|
# Random Lighting Adjusted Images
|
|
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
alpha = alpha if alpha is not None else 0.05
|
|
py_op = F.RandomLighting(alpha)
|
|
|
|
transforms_random_lighting = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
|
|
F.Resize((224, 224)),
|
|
py_op,
|
|
F.ToTensor()])
|
|
ds_random_lighting = data.map(operations=transforms_random_lighting, input_columns="image")
|
|
|
|
ds_random_lighting = ds_random_lighting.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_random_lighting.create_tuple_iterator(num_epochs=1, output_numpy=True)):
|
|
if idx == 0:
|
|
images_random_lighting = np.transpose(image, (0, 2, 3, 1))
|
|
else:
|
|
images_random_lighting = np.append(images_random_lighting, np.transpose(image, (0, 2, 3, 1)), axis=0)
|
|
|
|
num_samples = images_original.shape[0]
|
|
mse = np.zeros(num_samples)
|
|
for i in range(num_samples):
|
|
mse[i] = diff_mse(images_random_lighting[i], images_original[i])
|
|
|
|
logger.info("MSE= {}".format(str(np.mean(mse))))
|
|
|
|
if plot:
|
|
visualize_list(images_original, images_random_lighting)
|
|
|
|
|
|
def test_random_lighting_py_md5():
|
|
"""
|
|
Feature: RandomLighting
|
|
Description: test RandomLighting python op with md5 comparison
|
|
Expectation: same MD5
|
|
"""
|
|
logger.info("Test RandomLighting python op with md5 comparison")
|
|
original_seed = config_get_set_seed(140)
|
|
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
|
|
|
# define map operations
|
|
transforms = [
|
|
F.Decode(),
|
|
F.Resize((224, 224)),
|
|
F.RandomLighting(1),
|
|
F.ToTensor()
|
|
]
|
|
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
|
|
|
|
# Generate dataset
|
|
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
data = data.map(operations=transform, input_columns=["image"])
|
|
|
|
# check results with md5 comparison
|
|
filename = "random_lighting_py_01_result.npz"
|
|
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
|
|
|
# Restore configuration
|
|
ds.config.set_seed(original_seed)
|
|
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
|
|
|
|
|
def test_random_lighting_c(alpha=1, plot=False):
|
|
"""
|
|
Feature: RandomLighting
|
|
Description: test RandomLighting cpp op
|
|
Expectation: equal results from Mindspore and benchmark
|
|
"""
|
|
logger.info("Test RandomLighting cpp op")
|
|
# Original Images
|
|
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
transforms_original = [C.Decode(), C.Resize((224, 224))]
|
|
|
|
ds_original = data.map(operations=transforms_original, input_columns="image")
|
|
|
|
ds_original = ds_original.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_original.create_tuple_iterator(num_epochs=1, output_numpy=True)):
|
|
if idx == 0:
|
|
images_original = image
|
|
else:
|
|
images_original = np.append(images_original, image, axis=0)
|
|
|
|
# Random Lighting Adjusted Images
|
|
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
alpha = alpha if alpha is not None else 0.05
|
|
c_op = C.RandomLighting(alpha)
|
|
|
|
transforms_random_lighting = [C.Decode(), C.Resize((224, 224)), c_op]
|
|
|
|
ds_random_lighting = data.map(operations=transforms_random_lighting, input_columns="image")
|
|
|
|
ds_random_lighting = ds_random_lighting.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_random_lighting.create_tuple_iterator(num_epochs=1, output_numpy=True)):
|
|
if idx == 0:
|
|
images_random_lighting = image
|
|
else:
|
|
images_random_lighting = np.append(images_random_lighting, image, axis=0)
|
|
|
|
num_samples = images_original.shape[0]
|
|
mse = np.zeros(num_samples)
|
|
for i in range(num_samples):
|
|
mse[i] = diff_mse(images_random_lighting[i], images_original[i])
|
|
|
|
logger.info("MSE= {}".format(str(np.mean(mse))))
|
|
|
|
if plot:
|
|
visualize_list(images_original, images_random_lighting)
|
|
|
|
|
|
def test_random_lighting_c_py(alpha=1, plot=False):
|
|
"""
|
|
Feature: RandomLighting
|
|
Description: test Random Lighting Cpp and Python Op
|
|
Expectation: equal results from Cpp and Python
|
|
"""
|
|
logger.info("Test RandomLighting Cpp and python Op")
|
|
|
|
# RandomLighting Images
|
|
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
data = data.map(operations=[C.Decode(), C.Resize((200, 300))], input_columns=["image"])
|
|
|
|
python_op = F.RandomLighting(alpha)
|
|
c_op = C.RandomLighting(alpha)
|
|
|
|
transforms_op = mindspore.dataset.transforms.py_transforms.Compose([lambda img: F.ToPIL()(img.astype(np.uint8)),
|
|
python_op,
|
|
np.array])
|
|
|
|
ds_random_lighting_py = data.map(operations=transforms_op, input_columns="image")
|
|
|
|
ds_random_lighting_py = ds_random_lighting_py.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_random_lighting_py.create_tuple_iterator(num_epochs=1, output_numpy=True)):
|
|
if idx == 0:
|
|
images_random_lighting_py = image
|
|
|
|
else:
|
|
images_random_lighting_py = np.append(images_random_lighting_py, image, axis=0)
|
|
|
|
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
data = data.map(operations=[C.Decode(), C.Resize((200, 300))], input_columns=["image"])
|
|
|
|
ds_images_random_lighting_c = data.map(operations=c_op, input_columns="image")
|
|
|
|
ds_random_lighting_c = ds_images_random_lighting_c.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_random_lighting_c.create_tuple_iterator(num_epochs=1, output_numpy=True)):
|
|
if idx == 0:
|
|
images_random_lighting_c = image
|
|
else:
|
|
images_random_lighting_c = np.append(images_random_lighting_c, image, axis=0)
|
|
|
|
num_samples = images_random_lighting_c.shape[0]
|
|
mse = np.zeros(num_samples)
|
|
for i in range(num_samples):
|
|
mse[i] = diff_mse(images_random_lighting_c[i], images_random_lighting_py[i])
|
|
logger.info("MSE= {}".format(str(np.mean(mse))))
|
|
if plot:
|
|
visualize_list(images_random_lighting_c, images_random_lighting_py, visualize_mode=2)
|
|
|
|
|
|
def test_random_lighting_invalid_params():
|
|
"""
|
|
Feature: RandomLighting
|
|
Description: test RandomLighting with invalid input parameters
|
|
Expectation: throw ValueError or TypeError
|
|
"""
|
|
logger.info("Test RandomLighting with invalid input parameters.")
|
|
with pytest.raises(ValueError) as error_info:
|
|
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
data = data.map(operations=[C.Decode(), C.Resize((224, 224)),
|
|
C.RandomLighting(-2)], input_columns=["image"])
|
|
assert "Input alpha is not within the required interval of [0, 16777216]." in str(error_info.value)
|
|
|
|
with pytest.raises(TypeError) as error_info:
|
|
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
data = data.map(operations=[C.Decode(), C.Resize((224, 224)),
|
|
C.RandomLighting('1')], input_columns=["image"])
|
|
err_msg = "Argument alpha with value 1 is not of type [<class 'float'>, <class 'int'>], but got <class 'str'>."
|
|
assert err_msg in str(error_info.value)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_random_lighting_py()
|
|
test_random_lighting_py_md5()
|
|
test_random_lighting_c()
|
|
test_random_lighting_c_py()
|
|
test_random_lighting_invalid_params()
|