You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
231 lines
8.0 KiB
231 lines
8.0 KiB
# Copyright 2020 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.
|
|
# ==============================================================================
|
|
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from mindspore import log as logger
|
|
import mindspore.dataset.engine as de
|
|
import mindspore.dataset.transforms.vision.py_transforms as F
|
|
import mindspore.dataset.transforms.vision.c_transforms as C
|
|
|
|
DATA_DIR = "../data/dataset/testImageNetData/train/"
|
|
|
|
def visualize(image_original, image_ua):
|
|
"""
|
|
visualizes the image using DE op and Numpy op
|
|
"""
|
|
num = len(image_ua)
|
|
for i in range(num):
|
|
plt.subplot(2, num, i + 1)
|
|
plt.imshow(image_original[i])
|
|
plt.title("Original image")
|
|
|
|
plt.subplot(2, num, i + num + 1)
|
|
plt.imshow(image_ua[i])
|
|
plt.title("DE UniformAugment image")
|
|
|
|
plt.show()
|
|
|
|
|
|
def test_uniform_augment(plot=False, num_ops=2):
|
|
"""
|
|
Test UniformAugment
|
|
"""
|
|
logger.info("Test UniformAugment")
|
|
|
|
# Original Images
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
transforms_original = F.ComposeOp([F.Decode(),
|
|
F.Resize((224,224)),
|
|
F.ToTensor()])
|
|
|
|
ds_original = ds.map(input_columns="image",
|
|
operations=transforms_original())
|
|
|
|
ds_original = ds_original.batch(512)
|
|
|
|
for idx, (image,label) in enumerate(ds_original):
|
|
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)
|
|
|
|
# UniformAugment Images
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
transform_list = [F.RandomRotation(45),
|
|
F.RandomColor(),
|
|
F.RandomSharpness(),
|
|
F.Invert(),
|
|
F.AutoContrast(),
|
|
F.Equalize()]
|
|
|
|
transforms_ua = F.ComposeOp([F.Decode(),
|
|
F.Resize((224,224)),
|
|
F.UniformAugment(transforms=transform_list, num_ops=num_ops),
|
|
F.ToTensor()])
|
|
|
|
ds_ua = ds.map(input_columns="image",
|
|
operations=transforms_ua())
|
|
|
|
ds_ua = ds_ua.batch(512)
|
|
|
|
for idx, (image,label) in enumerate(ds_ua):
|
|
if idx == 0:
|
|
images_ua = np.transpose(image, (0, 2,3,1))
|
|
else:
|
|
images_ua = np.append(images_ua,
|
|
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] = np.mean((images_ua[i]-images_original[i])**2)
|
|
logger.info("MSE= {}".format(str(np.mean(mse))))
|
|
|
|
if plot:
|
|
visualize(images_original, images_ua)
|
|
|
|
def test_cpp_uniform_augment(plot=False, num_ops=2):
|
|
"""
|
|
Test UniformAugment
|
|
"""
|
|
logger.info("Test CPP UniformAugment")
|
|
|
|
# Original Images
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
transforms_original = [C.Decode(), C.Resize(size=[224, 224]),
|
|
F.ToTensor()]
|
|
|
|
ds_original = ds.map(input_columns="image",
|
|
operations=transforms_original)
|
|
|
|
ds_original = ds_original.batch(512)
|
|
|
|
for idx, (image,label) in enumerate(ds_original):
|
|
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)
|
|
|
|
|
|
# UniformAugment Images
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
|
|
C.RandomHorizontalFlip(),
|
|
C.RandomVerticalFlip(),
|
|
C.RandomColorAdjust(),
|
|
C.RandomRotation(degrees=45)]
|
|
|
|
uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
|
|
|
|
transforms_all = [C.Decode(), C.Resize(size=[224, 224]),
|
|
uni_aug,
|
|
F.ToTensor()]
|
|
|
|
ds_ua = ds.map(input_columns="image",
|
|
operations=transforms_all, num_parallel_workers=1)
|
|
|
|
ds_ua = ds_ua.batch(512)
|
|
|
|
for idx, (image,label) in enumerate(ds_ua):
|
|
if idx == 0:
|
|
images_ua = np.transpose(image, (0, 2, 3, 1))
|
|
else:
|
|
images_ua = np.append(images_ua,
|
|
np.transpose(image, (0, 2, 3, 1)),
|
|
axis=0)
|
|
if plot:
|
|
visualize(images_original, images_ua)
|
|
|
|
num_samples = images_original.shape[0]
|
|
mse = np.zeros(num_samples)
|
|
for i in range(num_samples):
|
|
mse[i] = np.mean((images_ua[i] - images_original[i]) ** 2)
|
|
logger.info("MSE= {}".format(str(np.mean(mse))))
|
|
|
|
def test_cpp_uniform_augment_exception_pyops(num_ops=2):
|
|
"""
|
|
Test UniformAugment invalid op in operations
|
|
"""
|
|
logger.info("Test CPP UniformAugment invalid OP exception")
|
|
|
|
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
|
|
C.RandomHorizontalFlip(),
|
|
C.RandomVerticalFlip(),
|
|
C.RandomColorAdjust(),
|
|
C.RandomRotation(degrees=45),
|
|
F.Invert()]
|
|
|
|
try:
|
|
uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
|
|
|
|
except BaseException as e:
|
|
logger.info("Got an exception in DE: {}".format(str(e)))
|
|
assert "operations" in str(e)
|
|
|
|
def test_cpp_uniform_augment_exception_large_numops(num_ops=6):
|
|
"""
|
|
Test UniformAugment invalid large number of ops
|
|
"""
|
|
logger.info("Test CPP UniformAugment invalid large num_ops exception")
|
|
|
|
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
|
|
C.RandomHorizontalFlip(),
|
|
C.RandomVerticalFlip(),
|
|
C.RandomColorAdjust(),
|
|
C.RandomRotation(degrees=45)]
|
|
|
|
try:
|
|
uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
|
|
|
|
except BaseException as e:
|
|
logger.info("Got an exception in DE: {}".format(str(e)))
|
|
assert "num_ops" in str(e)
|
|
|
|
def test_cpp_uniform_augment_exception_nonpositive_numops(num_ops=0):
|
|
"""
|
|
Test UniformAugment invalid non-positive number of ops
|
|
"""
|
|
logger.info("Test CPP UniformAugment invalid non-positive num_ops exception")
|
|
|
|
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
|
|
C.RandomHorizontalFlip(),
|
|
C.RandomVerticalFlip(),
|
|
C.RandomColorAdjust(),
|
|
C.RandomRotation(degrees=45)]
|
|
|
|
try:
|
|
uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
|
|
|
|
except BaseException as e:
|
|
logger.info("Got an exception in DE: {}".format(str(e)))
|
|
assert "num_ops" in str(e)
|
|
|
|
if __name__ == "__main__":
|
|
test_uniform_augment(num_ops=1)
|
|
test_cpp_uniform_augment(num_ops=1)
|
|
test_cpp_uniform_augment_exception_pyops(num_ops=1)
|
|
test_cpp_uniform_augment_exception_large_numops(num_ops=6)
|
|
test_cpp_uniform_augment_exception_nonpositive_numops(num_ops=0)
|
|
|