forked from mindspore-Ecosystem/mindspore
282 lines
11 KiB
Python
282 lines
11 KiB
Python
# Copyright 2019-2022 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 the random vertical flip op in DE
|
|
"""
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from mindspore import log as logger
|
|
import mindspore.dataset as ds
|
|
import mindspore.dataset.transforms as ops
|
|
import mindspore.dataset.vision as vision
|
|
from util import save_and_check_md5, save_and_check_md5_pil, visualize_list, visualize_image, diff_mse, \
|
|
config_get_set_seed, config_get_set_num_parallel_workers
|
|
|
|
GENERATE_GOLDEN = False
|
|
|
|
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"
|
|
|
|
|
|
def v_flip(image):
|
|
"""
|
|
Apply the random_vertical
|
|
"""
|
|
|
|
# with the seed provided in this test case, it will always flip.
|
|
# that's why we flip here too
|
|
image = image[::-1, :, :]
|
|
return image
|
|
|
|
|
|
def test_random_vertical_op(plot=False):
|
|
"""
|
|
Feature: RandomVerticalFlip op
|
|
Description: Test RandomVerticalFlip with default probability
|
|
Expectation: The dataset is processed as expected
|
|
"""
|
|
logger.info("Test random_vertical")
|
|
|
|
# First dataset
|
|
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
decode_op = vision.Decode()
|
|
random_vertical_op = vision.RandomVerticalFlip(1.0)
|
|
data1 = data1.map(operations=decode_op, input_columns=["image"])
|
|
data1 = data1.map(operations=random_vertical_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)):
|
|
|
|
# with the seed value, we can only guarantee the first number generated
|
|
if num_iter > 0:
|
|
break
|
|
|
|
image_v_flipped = item1["image"]
|
|
image = item2["image"]
|
|
image_v_flipped_2 = v_flip(image)
|
|
|
|
mse = diff_mse(image_v_flipped, image_v_flipped_2)
|
|
assert mse == 0
|
|
logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
|
|
num_iter += 1
|
|
if plot:
|
|
visualize_image(image, image_v_flipped, mse, image_v_flipped_2)
|
|
|
|
|
|
def test_random_vertical_valid_prob_c():
|
|
"""
|
|
Feature: RandomVerticalFlip op
|
|
Description: Test RandomVerticalFlip op with Cpp implementation using valid non-default input
|
|
Expectation: The dataset is processed as expected
|
|
"""
|
|
logger.info("test_random_vertical_valid_prob_c")
|
|
original_seed = config_get_set_seed(0)
|
|
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
|
|
|
# Generate dataset
|
|
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
decode_op = vision.Decode()
|
|
random_horizontal_op = vision.RandomVerticalFlip(0.8)
|
|
data = data.map(operations=decode_op, input_columns=["image"])
|
|
data = data.map(operations=random_horizontal_op, input_columns=["image"])
|
|
|
|
filename = "random_vertical_01_c_result.npz"
|
|
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
|
|
|
# Restore config setting
|
|
ds.config.set_seed(original_seed)
|
|
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
|
|
|
|
|
def test_random_vertical_valid_prob_py():
|
|
"""
|
|
Feature: RandomVerticalFlip op
|
|
Description: Test RandomVerticalFlip op with Python implementation using valid non-default input
|
|
Expectation: The dataset is processed as expected
|
|
"""
|
|
logger.info("test_random_vertical_valid_prob_py")
|
|
original_seed = config_get_set_seed(0)
|
|
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
|
|
|
# Generate dataset
|
|
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
transforms = [
|
|
vision.Decode(True),
|
|
vision.RandomVerticalFlip(0.8),
|
|
vision.ToTensor()
|
|
]
|
|
transform = ops.Compose(transforms)
|
|
data = data.map(operations=transform, input_columns=["image"])
|
|
|
|
filename = "random_vertical_01_py_result.npz"
|
|
save_and_check_md5_pil(data, filename, generate_golden=GENERATE_GOLDEN)
|
|
|
|
# Restore config setting
|
|
ds.config.set_seed(original_seed)
|
|
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
|
|
|
|
|
def test_random_vertical_invalid_prob_c():
|
|
"""
|
|
Feature: RandomVerticalFlip op
|
|
Description: Test RandomVerticalFlip op with Cpp implementation using invalid input
|
|
Expectation: Error is raised as expected
|
|
"""
|
|
logger.info("test_random_vertical_invalid_prob_c")
|
|
|
|
# Generate dataset
|
|
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
decode_op = vision.Decode()
|
|
try:
|
|
# Note: Valid range of prob should be [0.0, 1.0]
|
|
random_horizontal_op = vision.RandomVerticalFlip(1.5)
|
|
data = data.map(operations=decode_op, input_columns=["image"])
|
|
data = data.map(operations=random_horizontal_op, input_columns=["image"])
|
|
except ValueError as e:
|
|
logger.info("Got an exception in DE: {}".format(str(e)))
|
|
assert 'Input prob is not within the required interval of [0.0, 1.0].' in str(e)
|
|
|
|
|
|
def test_random_vertical_invalid_prob_py():
|
|
"""
|
|
Feature: RandomVerticalFlip op
|
|
Description: Test RandomVerticalFlip op with Python implementation using invalid input
|
|
Expectation: Error is raised as expected
|
|
"""
|
|
logger.info("test_random_vertical_invalid_prob_py")
|
|
|
|
# Generate dataset
|
|
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
try:
|
|
transforms = [
|
|
vision.Decode(True),
|
|
# Note: Valid range of prob should be [0.0, 1.0]
|
|
vision.RandomVerticalFlip(1.5),
|
|
vision.ToTensor()
|
|
]
|
|
transform = ops.Compose(transforms)
|
|
data = data.map(operations=transform, input_columns=["image"])
|
|
except ValueError as e:
|
|
logger.info("Got an exception in DE: {}".format(str(e)))
|
|
assert 'Input prob is not within the required interval of [0.0, 1.0].' in str(e)
|
|
|
|
|
|
def test_random_vertical_comp(plot=False):
|
|
"""
|
|
Feature: RandomVerticalFlip op
|
|
Description: Test RandomVerticalFlip and compare between Python and Cpp image augmentation ops
|
|
Expectation: Image outputs from both implementation are the same
|
|
"""
|
|
logger.info("test_random_vertical_comp")
|
|
|
|
# First dataset
|
|
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
decode_op = vision.Decode()
|
|
# Note: The image must be flipped if prob is set to be 1
|
|
random_horizontal_op = vision.RandomVerticalFlip(1)
|
|
data1 = data1.map(operations=decode_op, input_columns=["image"])
|
|
data1 = data1.map(operations=random_horizontal_op, input_columns=["image"])
|
|
|
|
# Second dataset
|
|
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
transforms = [
|
|
vision.Decode(True),
|
|
# Note: The image must be flipped if prob is set to be 1
|
|
vision.RandomVerticalFlip(1),
|
|
vision.ToTensor()
|
|
]
|
|
transform = ops.Compose(transforms)
|
|
data2 = data2.map(operations=transform, input_columns=["image"])
|
|
|
|
images_list_c = []
|
|
images_list_py = []
|
|
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_c = item1["image"]
|
|
image_py = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
|
|
images_list_c.append(image_c)
|
|
images_list_py.append(image_py)
|
|
|
|
# Check if the output images are the same
|
|
mse = diff_mse(image_c, image_py)
|
|
assert mse < 0.001
|
|
if plot:
|
|
visualize_list(images_list_c, images_list_py, visualize_mode=2)
|
|
|
|
|
|
def test_random_vertical_op_1():
|
|
"""
|
|
Feature: RandomVerticalFlip op
|
|
Description: Test RandomVerticalFlip with different fields
|
|
Expectation: The dataset is processed as expected
|
|
"""
|
|
logger.info("Test RandomVerticalFlip with different fields.")
|
|
|
|
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
data = data.map(operations=ops.Duplicate(), input_columns=["image"],
|
|
output_columns=["image", "image_copy"])
|
|
random_vertical_op = vision.RandomVerticalFlip(1.0)
|
|
decode_op = vision.Decode()
|
|
|
|
data = data.map(operations=decode_op, input_columns=["image"])
|
|
data = data.map(operations=decode_op, input_columns=["image_copy"])
|
|
data = data.map(operations=random_vertical_op, input_columns=["image", "image_copy"])
|
|
|
|
num_iter = 0
|
|
for data1 in data.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
image = data1["image"]
|
|
image_copy = data1["image_copy"]
|
|
mse = diff_mse(image, image_copy)
|
|
logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
|
|
assert mse == 0
|
|
num_iter += 1
|
|
|
|
|
|
def test_random_vertical_flip_invalid_data():
|
|
"""
|
|
Feature: RandomVerticalFlip
|
|
Description: Test RandomVerticalFlip with invalid data
|
|
Expectation: Error is raised as expected
|
|
"""
|
|
|
|
invalid_type_img = np.random.random((32, 32, 3)).astype(np.str_)
|
|
invalid_shape_img = np.random.random(32).astype(np.float32)
|
|
random_vertical_flip = vision.RandomVerticalFlip(0.1)
|
|
|
|
with pytest.raises(RuntimeError) as error_info:
|
|
random_vertical_flip(invalid_type_img)
|
|
assert "Currently unsupported data type: [uint32, int64, uint64, string]" in str(error_info.value)
|
|
|
|
with pytest.raises(RuntimeError) as error_info:
|
|
random_vertical_flip(invalid_shape_img)
|
|
assert "input tensor is not in shape of <H,W> or <H,W,C>" in str(error_info.value)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_random_vertical_op(plot=True)
|
|
test_random_vertical_valid_prob_c()
|
|
test_random_vertical_valid_prob_py()
|
|
test_random_vertical_invalid_prob_c()
|
|
test_random_vertical_invalid_prob_py()
|
|
test_random_vertical_comp(plot=True)
|
|
test_random_vertical_op_1()
|
|
test_random_vertical_flip_invalid_data()
|