forked from mindspore-Ecosystem/mindspore
266 lines
8.4 KiB
Python
266 lines
8.4 KiB
Python
# 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.
|
|
# ==============================================================================
|
|
"""
|
|
Testing Invert op in DE
|
|
"""
|
|
import numpy as np
|
|
|
|
import mindspore.dataset.engine as de
|
|
import mindspore.dataset.transforms.vision.py_transforms as F
|
|
import mindspore.dataset.transforms.vision.c_transforms as C
|
|
from mindspore import log as logger
|
|
from util import visualize_list, save_and_check_md5, diff_mse
|
|
|
|
DATA_DIR = "../data/dataset/testImageNetData/train/"
|
|
|
|
GENERATE_GOLDEN = False
|
|
|
|
|
|
def test_invert_py(plot=False):
|
|
"""
|
|
Test Invert python op
|
|
"""
|
|
logger.info("Test Invert Python op")
|
|
|
|
# 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, _) 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)
|
|
|
|
# Color Inverted Images
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
transforms_invert = F.ComposeOp([F.Decode(),
|
|
F.Resize((224, 224)),
|
|
F.Invert(),
|
|
F.ToTensor()])
|
|
|
|
ds_invert = ds.map(input_columns="image",
|
|
operations=transforms_invert())
|
|
|
|
ds_invert = ds_invert.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_invert):
|
|
if idx == 0:
|
|
images_invert = np.transpose(image, (0, 2, 3, 1))
|
|
else:
|
|
images_invert = np.append(images_invert,
|
|
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_invert[i] - images_original[i]) ** 2)
|
|
logger.info("MSE= {}".format(str(np.mean(mse))))
|
|
|
|
if plot:
|
|
visualize_list(images_original, images_invert)
|
|
|
|
|
|
def test_invert_c(plot=False):
|
|
"""
|
|
Test Invert Cpp op
|
|
"""
|
|
logger.info("Test Invert cpp op")
|
|
|
|
# Original Images
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
transforms_original = [C.Decode(), C.Resize(size=[224, 224])]
|
|
|
|
ds_original = ds.map(input_columns="image",
|
|
operations=transforms_original)
|
|
|
|
ds_original = ds_original.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_original):
|
|
if idx == 0:
|
|
images_original = image
|
|
else:
|
|
images_original = np.append(images_original,
|
|
image,
|
|
axis=0)
|
|
|
|
# Invert Images
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
transform_invert = [C.Decode(), C.Resize(size=[224, 224]),
|
|
C.Invert()]
|
|
|
|
ds_invert = ds.map(input_columns="image",
|
|
operations=transform_invert)
|
|
|
|
ds_invert = ds_invert.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_invert):
|
|
if idx == 0:
|
|
images_invert = image
|
|
else:
|
|
images_invert = np.append(images_invert,
|
|
image,
|
|
axis=0)
|
|
if plot:
|
|
visualize_list(images_original, images_invert)
|
|
|
|
num_samples = images_original.shape[0]
|
|
mse = np.zeros(num_samples)
|
|
for i in range(num_samples):
|
|
mse[i] = diff_mse(images_invert[i], images_original[i])
|
|
logger.info("MSE= {}".format(str(np.mean(mse))))
|
|
|
|
|
|
def test_invert_py_c(plot=False):
|
|
"""
|
|
Test Invert Cpp op and python op
|
|
"""
|
|
logger.info("Test Invert cpp and python op")
|
|
|
|
# Invert Images in cpp
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
ds = ds.map(input_columns=["image"],
|
|
operations=[C.Decode(), C.Resize((224, 224))])
|
|
|
|
ds_c_invert = ds.map(input_columns="image",
|
|
operations=C.Invert())
|
|
|
|
ds_c_invert = ds_c_invert.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_c_invert):
|
|
if idx == 0:
|
|
images_c_invert = image
|
|
else:
|
|
images_c_invert = np.append(images_c_invert,
|
|
image,
|
|
axis=0)
|
|
|
|
# invert images in python
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
ds = ds.map(input_columns=["image"],
|
|
operations=[C.Decode(), C.Resize((224, 224))])
|
|
|
|
transforms_p_invert = F.ComposeOp([lambda img: img.astype(np.uint8),
|
|
F.ToPIL(),
|
|
F.Invert(),
|
|
np.array])
|
|
|
|
ds_p_invert = ds.map(input_columns="image",
|
|
operations=transforms_p_invert())
|
|
|
|
ds_p_invert = ds_p_invert.batch(512)
|
|
|
|
for idx, (image, _) in enumerate(ds_p_invert):
|
|
if idx == 0:
|
|
images_p_invert = image
|
|
else:
|
|
images_p_invert = np.append(images_p_invert,
|
|
image,
|
|
axis=0)
|
|
|
|
num_samples = images_c_invert.shape[0]
|
|
mse = np.zeros(num_samples)
|
|
for i in range(num_samples):
|
|
mse[i] = diff_mse(images_p_invert[i], images_c_invert[i])
|
|
logger.info("MSE= {}".format(str(np.mean(mse))))
|
|
|
|
if plot:
|
|
visualize_list(images_c_invert, images_p_invert, visualize_mode=2)
|
|
|
|
|
|
def test_invert_one_channel():
|
|
"""
|
|
Test Invert cpp op with one channel image
|
|
"""
|
|
logger.info("Test Invert C Op With One Channel Images")
|
|
|
|
c_op = C.Invert()
|
|
|
|
try:
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
ds = ds.map(input_columns=["image"],
|
|
operations=[C.Decode(),
|
|
C.Resize((224, 224)),
|
|
lambda img: np.array(img[:, :, 0])])
|
|
|
|
ds.map(input_columns="image",
|
|
operations=c_op)
|
|
|
|
except RuntimeError as e:
|
|
logger.info("Got an exception in DE: {}".format(str(e)))
|
|
assert "The shape" in str(e)
|
|
|
|
|
|
def test_invert_md5_py():
|
|
"""
|
|
Test Invert python op with md5 check
|
|
"""
|
|
logger.info("Test Invert python op with md5 check")
|
|
|
|
# Generate dataset
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
transforms_invert = F.ComposeOp([F.Decode(),
|
|
F.Invert(),
|
|
F.ToTensor()])
|
|
|
|
data = ds.map(input_columns="image", operations=transforms_invert())
|
|
# Compare with expected md5 from images
|
|
filename = "invert_01_result_py.npz"
|
|
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
|
|
|
|
|
def test_invert_md5_c():
|
|
"""
|
|
Test Invert cpp op with md5 check
|
|
"""
|
|
logger.info("Test Invert cpp op with md5 check")
|
|
|
|
# Generate dataset
|
|
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
|
|
|
|
transforms_invert = [C.Decode(),
|
|
C.Resize(size=[224, 224]),
|
|
C.Invert(),
|
|
F.ToTensor()]
|
|
|
|
data = ds.map(input_columns="image", operations=transforms_invert)
|
|
# Compare with expected md5 from images
|
|
filename = "invert_01_result_c.npz"
|
|
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_invert_py(plot=False)
|
|
test_invert_c(plot=False)
|
|
test_invert_py_c(plot=False)
|
|
test_invert_one_channel()
|
|
test_invert_md5_py()
|
|
test_invert_md5_c()
|