forked from mindspore-Ecosystem/mindspore
442 lines
16 KiB
Python
442 lines
16 KiB
Python
# Copyright 2021-2022 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 AdjustGamma op in DE
|
|
"""
|
|
import cv2
|
|
import numpy as np
|
|
from numpy.testing import assert_allclose
|
|
from PIL import Image
|
|
import pytest
|
|
|
|
import mindspore.dataset as ds
|
|
import mindspore.dataset.transforms
|
|
import mindspore.dataset.vision as vision
|
|
from mindspore import log as logger
|
|
|
|
DATA_DIR = "../data/dataset/testImageNetData/train/"
|
|
MNIST_DATA_DIR = "../data/dataset/testMnistData"
|
|
|
|
DATA_DIR_2 = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
|
|
SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
|
|
|
|
|
|
def generate_numpy_random_rgb(shape):
|
|
"""
|
|
Only generate floating points that are fractions like n / 256, since they
|
|
are RGB pixels. Some low-precision floating point types in this test can't
|
|
handle arbitrary precision floating points well.
|
|
"""
|
|
return np.random.randint(0, 256, shape) / 255.
|
|
|
|
|
|
def test_adjust_gamma_c_eager():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test eager support for AdjustGamma Cpp implementation
|
|
Expectation: Receive non-None output image from op
|
|
"""
|
|
# Eager 3-channel
|
|
rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.float32)
|
|
img_in = rgb_flat.reshape((8, 8, 3))
|
|
|
|
adjustgamma_op = vision.AdjustGamma(10, 1)
|
|
img_out = adjustgamma_op(img_in)
|
|
assert img_out is not None
|
|
|
|
img_in2 = Image.open("../data/dataset/apple.jpg").convert("RGB")
|
|
|
|
adjustgamma_op2 = vision.AdjustGamma(10, 1)
|
|
img_out2 = adjustgamma_op2(img_in2)
|
|
assert img_out2 is not None
|
|
|
|
|
|
def test_adjust_gamma_py_eager():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test eager support for AdjustGamma Python implementation
|
|
Expectation: Receive non-None output image from op
|
|
"""
|
|
# Eager 3-channel
|
|
rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.uint8)
|
|
img_in = Image.fromarray(rgb_flat.reshape((8, 8, 3)))
|
|
|
|
adjustgamma_op = vision.AdjustGamma(10, 1)
|
|
img_out = adjustgamma_op(img_in)
|
|
assert img_out is not None
|
|
|
|
img_in2 = Image.open("../data/dataset/apple.jpg").convert("RGB")
|
|
|
|
adjustgamma_op2 = vision.AdjustGamma(10, 1)
|
|
img_out2 = adjustgamma_op2(img_in2)
|
|
assert img_out2 is not None
|
|
|
|
|
|
def test_adjust_gamma_c_eager_gray():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test eager support for AdjustGamma Cpp implementation 1-channel
|
|
Expectation: Receive non-None output image from op
|
|
"""
|
|
# Eager 1-channel
|
|
rgb_flat = generate_numpy_random_rgb((64, 1)).astype(np.float32)
|
|
img_in = rgb_flat.reshape((8, 8))
|
|
|
|
adjustgamma_op = vision.AdjustGamma(10, 1)
|
|
img_out = adjustgamma_op(img_in)
|
|
assert img_out is not None
|
|
|
|
|
|
def test_adjust_gamma_py_eager_gray():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test eager support for AdjustGamma Python implementation 1-channel
|
|
Expectation: Receive non-None output image from op
|
|
"""
|
|
# Eager 1-channel
|
|
rgb_flat = generate_numpy_random_rgb((64, 1)).astype(np.uint8)
|
|
img_in = Image.fromarray(rgb_flat.reshape((8, 8)))
|
|
|
|
adjustgamma_op = vision.AdjustGamma(10, 1)
|
|
img_out = adjustgamma_op(img_in)
|
|
assert img_out is not None
|
|
|
|
|
|
def test_adjust_gamma_invalid_gamma_param_c():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test AdjustGamma Cpp implementation with invalid ignore parameter
|
|
Expectation: Correct error is raised as expected
|
|
"""
|
|
logger.info(
|
|
"Test AdjustGamma C implementation with invalid ignore parameter")
|
|
try:
|
|
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
data_set = data_set.map(
|
|
operations=[vision.Decode(), vision.Resize((224, 224)),
|
|
lambda img: np.array(img[:, :, 0])],
|
|
input_columns=["image"])
|
|
# invalid gamma
|
|
data_set = data_set.map(operations=vision.AdjustGamma(gamma=-10.0, gain=1.0),
|
|
input_columns="image")
|
|
except ValueError as error:
|
|
logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
|
|
assert "Input is not within the required interval of " in str(error)
|
|
try:
|
|
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
data_set = data_set.map(
|
|
operations=[vision.Decode(), vision.Resize((224, 224)),
|
|
lambda img: np.array(img[:, :, 0])],
|
|
input_columns=["image"])
|
|
# invalid gamma
|
|
data_set = data_set.map(operations=vision.AdjustGamma(gamma=[1, 2], gain=1.0),
|
|
input_columns="image")
|
|
except TypeError as error:
|
|
logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
|
|
assert "is not of type [<class 'float'>, <class 'int'>], but got" in str(
|
|
error)
|
|
|
|
|
|
def test_adjust_gamma_invalid_gamma_param_py():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test AdjustGamma Python implementation with invalid ignore parameter
|
|
Expectation: Correct error is raised as expected
|
|
"""
|
|
logger.info(
|
|
"Test AdjustGamma Python implementation with invalid ignore parameter")
|
|
try:
|
|
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
trans = mindspore.dataset.transforms.Compose([
|
|
vision.Decode(True),
|
|
vision.Resize((224, 224)),
|
|
vision.AdjustGamma(gamma=-10.0),
|
|
vision.ToTensor()
|
|
])
|
|
data_set = data_set.map(operations=[trans], input_columns=["image"])
|
|
except ValueError as error:
|
|
logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
|
|
assert "Input is not within the required interval of " in str(error)
|
|
try:
|
|
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
trans = mindspore.dataset.transforms.Compose([
|
|
vision.Decode(True),
|
|
vision.Resize((224, 224)),
|
|
vision.AdjustGamma(gamma=[1, 2]),
|
|
vision.ToTensor()
|
|
])
|
|
data_set = data_set.map(operations=[trans], input_columns=["image"])
|
|
except TypeError as error:
|
|
logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
|
|
assert "is not of type [<class 'float'>, <class 'int'>], but got" in str(
|
|
error)
|
|
|
|
|
|
def test_adjust_gamma_invalid_gain_param_c():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test AdjustGamma Cpp implementation with invalid gain parameter
|
|
Expectation: Correct error is raised as expected
|
|
"""
|
|
logger.info("Test AdjustGamma C implementation with invalid gain parameter")
|
|
try:
|
|
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
data_set = data_set.map(
|
|
operations=[vision.Decode(), vision.Resize((224, 224)),
|
|
lambda img: np.array(img[:, :, 0])],
|
|
input_columns=["image"])
|
|
# invalid gain
|
|
data_set = data_set.map(operations=vision.AdjustGamma(gamma=10.0, gain=[1, 10]),
|
|
input_columns="image")
|
|
except TypeError as error:
|
|
logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
|
|
assert "is not of type [<class 'float'>, <class 'int'>], but got " in str(
|
|
error)
|
|
|
|
|
|
def test_adjust_gamma_invalid_gain_param_py():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test AdjustGamma Python implementation with invalid gain parameter
|
|
Expectation: Correct error is raised as expected
|
|
"""
|
|
logger.info(
|
|
"Test AdjustGamma Python implementation with invalid gain parameter")
|
|
try:
|
|
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
|
|
trans = mindspore.dataset.transforms.Compose([
|
|
vision.Decode(True),
|
|
vision.Resize((224, 224)),
|
|
vision.AdjustGamma(gamma=10.0, gain=[1, 10]),
|
|
vision.ToTensor()
|
|
])
|
|
data_set = data_set.map(operations=[trans], input_columns=["image"])
|
|
except TypeError as error:
|
|
logger.info("Got an exception in AdjustGamma: {}".format(str(error)))
|
|
assert "is not of type [<class 'float'>, <class 'int'>], but got " in str(
|
|
error)
|
|
|
|
|
|
def test_adjust_gamma_pipeline_c():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test AdjustGamma Cpp implementation Pipeline
|
|
Expectation: Runs successfully
|
|
"""
|
|
# First dataset
|
|
transforms1 = [vision.Decode(), vision.Resize([64, 64])]
|
|
transforms1 = mindspore.dataset.transforms.Compose(
|
|
transforms1)
|
|
ds1 = ds.TFRecordDataset(DATA_DIR_2,
|
|
SCHEMA_DIR,
|
|
columns_list=["image"],
|
|
shuffle=False)
|
|
ds1 = ds1.map(operations=transforms1, input_columns=["image"])
|
|
|
|
# Second dataset
|
|
transforms2 = [
|
|
vision.Decode(),
|
|
vision.Resize([64, 64]),
|
|
vision.AdjustGamma(1.0, 1.0)
|
|
]
|
|
transform2 = mindspore.dataset.transforms.Compose(
|
|
transforms2)
|
|
ds2 = ds.TFRecordDataset(DATA_DIR_2,
|
|
SCHEMA_DIR,
|
|
columns_list=["image"],
|
|
shuffle=False)
|
|
ds2 = ds2.map(operations=transform2, input_columns=["image"])
|
|
|
|
num_iter = 0
|
|
for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1),
|
|
ds2.create_dict_iterator(num_epochs=1)):
|
|
num_iter += 1
|
|
ori_img = data1["image"].asnumpy()
|
|
cvt_img = data2["image"].asnumpy()
|
|
assert_allclose(ori_img.flatten(),
|
|
cvt_img.flatten(),
|
|
rtol=1e-5,
|
|
atol=0)
|
|
assert ori_img.shape == cvt_img.shape
|
|
|
|
|
|
def test_adjust_gamma_pipeline_py():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test AdjustGamma Python implementation Pipeline
|
|
Expectation: Runs successfully
|
|
"""
|
|
# First dataset
|
|
transforms1 = [vision.Decode(True), vision.Resize(
|
|
[64, 64]), vision.ToTensor()]
|
|
transforms1 = mindspore.dataset.transforms.Compose(
|
|
transforms1)
|
|
ds1 = ds.TFRecordDataset(DATA_DIR_2,
|
|
SCHEMA_DIR,
|
|
columns_list=["image"],
|
|
shuffle=False)
|
|
ds1 = ds1.map(operations=transforms1, input_columns=["image"])
|
|
|
|
# Second dataset
|
|
transforms2 = [
|
|
vision.Decode(True),
|
|
vision.Resize([64, 64]),
|
|
vision.AdjustGamma(1.0, 1.0),
|
|
vision.ToTensor()
|
|
]
|
|
transform2 = mindspore.dataset.transforms.Compose(
|
|
transforms2)
|
|
ds2 = ds.TFRecordDataset(DATA_DIR_2,
|
|
SCHEMA_DIR,
|
|
columns_list=["image"],
|
|
shuffle=False)
|
|
ds2 = ds2.map(operations=transform2, input_columns=["image"])
|
|
|
|
num_iter = 0
|
|
for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1),
|
|
ds2.create_dict_iterator(num_epochs=1)):
|
|
num_iter += 1
|
|
ori_img = data1["image"].asnumpy()
|
|
cvt_img = data2["image"].asnumpy()
|
|
assert_allclose(ori_img.flatten(),
|
|
cvt_img.flatten(),
|
|
rtol=1e-5,
|
|
atol=0)
|
|
assert ori_img.shape == cvt_img.shape
|
|
|
|
|
|
def test_adjust_gamma_pipeline_py_gray():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test AdjustGamma Python implementation Pipeline 1-channel
|
|
Expectation: Runs successfully
|
|
"""
|
|
# First dataset
|
|
transforms1_list = [vision.Decode(True), vision.Resize(
|
|
[60, 60]), vision.Grayscale(), vision.ToTensor()]
|
|
transforms1 = mindspore.dataset.transforms.Compose(
|
|
transforms1_list)
|
|
ds1 = ds.TFRecordDataset(DATA_DIR_2,
|
|
SCHEMA_DIR,
|
|
columns_list=["image"],
|
|
shuffle=False)
|
|
ds1 = ds1.map(operations=transforms1, input_columns=["image"])
|
|
|
|
# Second dataset
|
|
transforms2_list = [
|
|
vision.Decode(True),
|
|
vision.Resize([60, 60]),
|
|
vision.Grayscale(),
|
|
vision.AdjustGamma(1.0, 1.0),
|
|
vision.ToTensor()
|
|
]
|
|
transform2 = mindspore.dataset.transforms.Compose(
|
|
transforms2_list)
|
|
ds2 = ds.TFRecordDataset(DATA_DIR_2,
|
|
SCHEMA_DIR,
|
|
columns_list=["image"],
|
|
shuffle=False)
|
|
ds2 = ds2.map(operations=transform2, input_columns=["image"])
|
|
|
|
num_iter = 0
|
|
for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1),
|
|
ds2.create_dict_iterator(num_epochs=1)):
|
|
num_iter += 1
|
|
ori_img = data1["image"].asnumpy()
|
|
cvt_img = data2["image"].asnumpy()
|
|
assert_allclose(ori_img.flatten(),
|
|
cvt_img.flatten(),
|
|
rtol=1e-5,
|
|
atol=0)
|
|
|
|
|
|
def test_adjust_gamma_eager_image_type():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Test AdjustGamma op eager support test for variety of image input types
|
|
Expectation: Receive non-None output image from op
|
|
"""
|
|
|
|
def test_config(my_input):
|
|
my_output = vision.AdjustGamma(gamma=1.2, gain=1.0)(my_input)
|
|
assert my_output is not None
|
|
|
|
# Test with OpenCV images
|
|
img = cv2.imread("../data/dataset/apple.jpg")
|
|
test_config(img)
|
|
|
|
# Test with NumPy array input
|
|
img = np.random.randint(0, 1, (100, 100, 3)).astype(np.uint8)
|
|
test_config(img)
|
|
|
|
# Test with PIL Image
|
|
img = Image.open("../data/dataset/apple.jpg").convert("RGB")
|
|
test_config(img)
|
|
|
|
|
|
def test_adjust_gamma_eager_invalid_image_types1():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Exception eager support test for error input type
|
|
Expectation: Error input image is detected
|
|
"""
|
|
|
|
def test_config(my_input, error_msg):
|
|
with pytest.raises(TypeError) as error_info:
|
|
_ = vision.AdjustGamma(gamma=1.2, gain=1.0)(my_input)
|
|
assert error_msg in str(error_info.value)
|
|
|
|
img = Image.open("../data/dataset/apple.jpg").convert("RGB")
|
|
test_config([img, img], "Input should be NumPy or PIL image, got <class 'list'>")
|
|
test_config((img, img), "Input should be NumPy or PIL image, got <class 'tuple'>")
|
|
|
|
img = cv2.imread("../data/dataset/apple.jpg")
|
|
test_config([img, img], "Input should be NumPy or PIL image, got <class 'list'>")
|
|
test_config((img, img), "Input should be NumPy or PIL image, got <class 'tuple'>")
|
|
|
|
|
|
def test_adjust_gamma_eager_invalid_image_types2():
|
|
"""
|
|
Feature: AdjustGamma op
|
|
Description: Exception eager support test for error input type
|
|
Expectation: Error input image is detected
|
|
"""
|
|
|
|
def test_config(my_input, error_msg):
|
|
with pytest.raises(TypeError) as error_info:
|
|
_ = vision.AdjustGamma(gamma=1.2, gain=1.0)(my_input)
|
|
assert error_msg in str(error_info.value)
|
|
|
|
test_config(1, "Input should be NumPy or PIL image, got <class 'int'>")
|
|
test_config(1.0, "Input should be NumPy or PIL image, got <class 'float'>")
|
|
test_config((1.0, 2.0), "Input should be NumPy or PIL image, got <class 'tuple'>")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_adjust_gamma_c_eager()
|
|
test_adjust_gamma_py_eager()
|
|
test_adjust_gamma_c_eager_gray()
|
|
test_adjust_gamma_py_eager_gray()
|
|
test_adjust_gamma_invalid_gamma_param_c()
|
|
test_adjust_gamma_invalid_gamma_param_py()
|
|
test_adjust_gamma_invalid_gain_param_c()
|
|
test_adjust_gamma_invalid_gain_param_py()
|
|
test_adjust_gamma_pipeline_c()
|
|
test_adjust_gamma_pipeline_py()
|
|
test_adjust_gamma_pipeline_py_gray()
|
|
test_adjust_gamma_eager_image_type()
|
|
test_adjust_gamma_eager_invalid_image_types1()
|
|
test_adjust_gamma_eager_invalid_image_types2()
|