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mindspore/tests/ut/python/dataset/test_normalizeOp.py

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# 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 Normalize op in DE
"""
import numpy as np
import mindspore.dataset as ds
import mindspore.dataset.transforms.py_transforms
import mindspore.dataset.vision.c_transforms as c_vision
import mindspore.dataset.vision.py_transforms as py_vision
from mindspore import log as logger
from util import diff_mse, save_and_check_md5, visualize_image
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"
GENERATE_GOLDEN = False
def normalize_np(image, mean, std):
"""
Apply the Normalization
"""
# DE decodes the image in RGB by default, hence
# the values here are in RGB
image = np.array(image, np.float32)
image = image - np.array(mean)
image = image * (1.0 / np.array(std))
return image
def util_test_normalize(mean, std, op_type):
"""
Utility function for testing Normalize. Input arguments are given by other tests
"""
if op_type == "cpp":
# define map operations
decode_op = c_vision.Decode()
normalize_op = c_vision.Normalize(mean, std)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data = data.map(operations=decode_op, input_columns=["image"])
data = data.map(operations=normalize_op, input_columns=["image"])
elif op_type == "python":
# define map operations
transforms = [
py_vision.Decode(),
py_vision.ToTensor(),
py_vision.Normalize(mean, std)
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data = data.map(operations=transform, input_columns=["image"])
else:
raise ValueError("Wrong parameter value")
return data
def util_test_normalize_grayscale(num_output_channels, mean, std):
"""
Utility function for testing Normalize. Input arguments are given by other tests
"""
transforms = [
py_vision.Decode(),
py_vision.Grayscale(num_output_channels),
py_vision.ToTensor(),
py_vision.Normalize(mean, std)
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data = data.map(operations=transform, input_columns=["image"])
return data
def test_normalize_op_c(plot=False):
"""
Test Normalize in cpp transformations
"""
logger.info("Test Normalize in cpp")
mean = [121.0, 115.0, 100.0]
std = [70.0, 68.0, 71.0]
# define map operations
decode_op = c_vision.Decode()
normalize_op = c_vision.Normalize(mean, std)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=normalize_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"])
num_iter = 0
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_de_normalized = item1["image"]
image_original = item2["image"]
image_np_normalized = normalize_np(image_original, mean, std)
mse = diff_mse(image_de_normalized, image_np_normalized)
logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
assert mse < 0.01
if plot:
visualize_image(image_original, image_de_normalized, mse, image_np_normalized)
num_iter += 1
def test_normalize_op_py(plot=False):
"""
Test Normalize in python transformations
"""
logger.info("Test Normalize in python")
mean = [0.475, 0.45, 0.392]
std = [0.275, 0.267, 0.278]
# define map operations
transforms = [
py_vision.Decode(),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
normalize_op = py_vision.Normalize(mean, std)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data1 = data1.map(operations=transform, input_columns=["image"])
data1 = data1.map(operations=normalize_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(operations=transform, input_columns=["image"])
num_iter = 0
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_de_normalized = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_np_normalized = (normalize_np(item2["image"].transpose(1, 2, 0), mean, std) * 255).astype(np.uint8)
image_original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
mse = diff_mse(image_de_normalized, image_np_normalized)
logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
assert mse < 0.01
if plot:
visualize_image(image_original, image_de_normalized, mse, image_np_normalized)
num_iter += 1
def test_decode_op():
"""
Test Decode op
"""
logger.info("Test Decode")
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image", "label"], num_parallel_workers=1,
shuffle=False)
# define map operations
decode_op = c_vision.Decode()
# apply map operations on images
data1 = data1.map(operations=decode_op, input_columns=["image"])
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1):
logger.info("Looping inside iterator {}".format(num_iter))
_ = item["image"]
num_iter += 1
def test_decode_normalize_op():
"""
Test Decode op followed by Normalize op
"""
logger.info("Test [Decode, Normalize] in one Map")
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image", "label"], num_parallel_workers=1,
shuffle=False)
# define map operations
decode_op = c_vision.Decode()
normalize_op = c_vision.Normalize([121.0, 115.0, 100.0], [70.0, 68.0, 71.0])
# apply map operations on images
data1 = data1.map(operations=[decode_op, normalize_op], input_columns=["image"])
num_iter = 0
for item in data1.create_dict_iterator(num_epochs=1):
logger.info("Looping inside iterator {}".format(num_iter))
_ = item["image"]
num_iter += 1
def test_normalize_md5_01():
"""
Test Normalize with md5 check: valid mean and std
expected to pass
"""
logger.info("test_normalize_md5_01")
data_c = util_test_normalize([121.0, 115.0, 100.0], [70.0, 68.0, 71.0], "cpp")
data_py = util_test_normalize([0.475, 0.45, 0.392], [0.275, 0.267, 0.278], "python")
# check results with md5 comparison
filename1 = "normalize_01_c_result.npz"
filename2 = "normalize_01_py_result.npz"
save_and_check_md5(data_c, filename1, generate_golden=GENERATE_GOLDEN)
save_and_check_md5(data_py, filename2, generate_golden=GENERATE_GOLDEN)
def test_normalize_md5_02():
"""
Test Normalize with md5 check: len(mean)=len(std)=1 with RGB images
expected to pass
"""
logger.info("test_normalize_md5_02")
data_py = util_test_normalize([0.475], [0.275], "python")
# check results with md5 comparison
filename2 = "normalize_02_py_result.npz"
save_and_check_md5(data_py, filename2, generate_golden=GENERATE_GOLDEN)
def test_normalize_exception_unequal_size_c():
"""
Test Normalize in c transformation: len(mean) != len(std)
expected to raise ValueError
"""
logger.info("test_normalize_exception_unequal_size_c")
try:
_ = c_vision.Normalize([100, 250, 125], [50, 50, 75, 75])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "Length of mean and std must be equal."
def test_normalize_exception_out_of_range_c():
"""
Test Normalize in c transformation: mean, std out of range
expected to raise ValueError
"""
logger.info("test_normalize_exception_out_of_range_c")
try:
_ = c_vision.Normalize([256, 250, 125], [50, 75, 75])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "not within the required interval" in str(e)
try:
_ = c_vision.Normalize([255, 250, 125], [0, 75, 75])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "not within the required interval" in str(e)
def test_normalize_exception_unequal_size_py():
"""
Test Normalize in python transformation: len(mean) != len(std)
expected to raise ValueError
"""
logger.info("test_normalize_exception_unequal_size_py")
try:
_ = py_vision.Normalize([0.50, 0.30, 0.75], [0.18, 0.32, 0.71, 0.72])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "Length of mean and std must be equal."
def test_normalize_exception_invalid_size_py():
"""
Test Normalize in python transformation: len(mean)=len(std)=2
expected to raise RuntimeError
"""
logger.info("test_normalize_exception_invalid_size_py")
data = util_test_normalize([0.75, 0.25], [0.18, 0.32], "python")
try:
_ = data.create_dict_iterator(num_epochs=1).__next__()
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Length of mean and std must both be 1 or" in str(e)
def test_normalize_exception_invalid_range_py():
"""
Test Normalize in python transformation: value is not in range [0,1]
expected to raise ValueError
"""
logger.info("test_normalize_exception_invalid_range_py")
try:
_ = py_vision.Normalize([0.75, 1.25, 0.5], [0.1, 0.18, 1.32])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Input mean_value is not within the required interval of [0.0, 1.0]." in str(e)
def test_normalize_grayscale_md5_01():
"""
Test Normalize with md5 check: len(mean)=len(std)=1 with 1 channel grayscale images
expected to pass
"""
logger.info("test_normalize_grayscale_md5_01")
data = util_test_normalize_grayscale(1, [0.5], [0.175])
# check results with md5 comparison
filename = "normalize_03_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
def test_normalize_grayscale_md5_02():
"""
Test Normalize with md5 check: len(mean)=len(std)=3 with 3 channel grayscale images
expected to pass
"""
logger.info("test_normalize_grayscale_md5_02")
data = util_test_normalize_grayscale(3, [0.5, 0.5, 0.5], [0.175, 0.235, 0.512])
# check results with md5 comparison
filename = "normalize_04_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
def test_normalize_grayscale_exception():
"""
Test Normalize: len(mean)=len(std)=3 with 1 channel grayscale images
expected to raise RuntimeError
"""
logger.info("test_normalize_grayscale_exception")
try:
_ = util_test_normalize_grayscale(1, [0.5, 0.5, 0.5], [0.175, 0.235, 0.512])
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Input is not within the required range" in str(e)
def test_multiple_channels():
logger.info("test_multiple_channels")
def util_test(item, mean, std):
data = ds.NumpySlicesDataset([item], shuffle=False)
data = data.map(c_vision.Normalize(mean, std))
for d in data.create_tuple_iterator(num_epochs=1, output_numpy=True):
actual = d[0]
mean = np.array(mean, dtype=item.dtype)
std = np.array(std, dtype=item.dtype)
expected = item
if len(item.shape) != 1 and len(mean) == 1:
mean = [mean[0]] * expected.shape[-1]
std = [std[0]] * expected.shape[-1]
if len(item.shape) == 2:
expected = np.expand_dims(expected, 2)
for c in range(expected.shape[-1]):
expected[:, :, c] = (expected[:, :, c] - mean[c]) / std[c]
expected = expected.squeeze()
np.testing.assert_almost_equal(actual, expected, decimal=6)
util_test(np.ones(shape=[2, 2, 3]), mean=[0.5, 0.6, 0.7], std=[0.1, 0.2, 0.3])
util_test(np.ones(shape=[20, 45, 3]) * 1.3, mean=[0.5, 0.6, 0.7], std=[0.1, 0.2, 0.3])
util_test(np.ones(shape=[20, 45, 4]) * 1.3, mean=[0.5, 0.6, 0.7, 0.8], std=[0.1, 0.2, 0.3, 0.4])
util_test(np.ones(shape=[2, 2]), mean=[0.5], std=[0.1])
util_test(np.ones(shape=[2, 2, 5]), mean=[0.5], std=[0.1])
util_test(np.ones(shape=[6, 6, 129]), mean=[0.5]*129, std=[0.1]*129)
util_test(np.ones(shape=[6, 6, 129]), mean=[0.5], std=[0.1])
if __name__ == "__main__":
test_decode_op()
test_decode_normalize_op()
test_normalize_op_c(plot=True)
test_normalize_op_py(plot=True)
test_normalize_md5_01()
test_normalize_md5_02()
test_normalize_exception_unequal_size_c()
test_normalize_exception_unequal_size_py()
test_normalize_exception_invalid_size_py()
test_normalize_exception_invalid_range_py()
test_normalize_grayscale_md5_01()
test_normalize_grayscale_md5_02()
test_normalize_grayscale_exception()
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