forked from mindspore-Ecosystem/mindspore
270 lines
10 KiB
Python
270 lines
10 KiB
Python
# Copyright 2021-2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Testing RandomLighting op in DE
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"""
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import numpy as np
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import pytest
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import mindspore.dataset as ds
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import mindspore.dataset.transforms
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import mindspore.dataset.vision as vision
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from mindspore import log as logger
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from util import visualize_list, diff_mse, save_and_check_md5_pil, \
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config_get_set_seed, config_get_set_num_parallel_workers
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DATA_DIR = "../data/dataset/testImageNetData/train/"
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MNIST_DATA_DIR = "../data/dataset/testMnistData"
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GENERATE_GOLDEN = False
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def test_random_lighting_py(alpha=1, plot=False):
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"""
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Feature: RandomLighting
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Description: Test RandomLighting Python implementation
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Expectation: Equal results
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"""
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logger.info("Test RandomLighting Python implementation")
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# Original Images
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_original = mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.Resize((224, 224)),
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vision.ToTensor()])
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ds_original = data.map(
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operations=transforms_original, input_columns="image")
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ds_original = ds_original.batch(512)
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for idx, (image, _) in enumerate(ds_original.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_original = np.transpose(image, (0, 2, 3, 1))
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else:
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images_original = np.append(
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images_original, np.transpose(image, (0, 2, 3, 1)), axis=0)
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# Random Lighting Adjusted Images
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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alpha = alpha if alpha is not None else 0.05
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py_op = vision.RandomLighting(alpha)
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transforms_random_lighting = mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.Resize((224, 224)),
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py_op,
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vision.ToTensor()])
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ds_random_lighting = data.map(
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operations=transforms_random_lighting, input_columns="image")
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ds_random_lighting = ds_random_lighting.batch(512)
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for idx, (image, _) in enumerate(ds_random_lighting.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_random_lighting = np.transpose(image, (0, 2, 3, 1))
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else:
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images_random_lighting = np.append(
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images_random_lighting, np.transpose(image, (0, 2, 3, 1)), axis=0)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_random_lighting[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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if plot:
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visualize_list(images_original, images_random_lighting)
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def test_random_lighting_py_md5():
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"""
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Feature: RandomLighting
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Description: Test RandomLighting Python implementation with md5 comparison
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Expectation: Same MD5
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"""
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logger.info("Test RandomLighting Python implementation with md5 comparison")
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original_seed = config_get_set_seed(140)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# define map operations
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transforms = [
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vision.Decode(True),
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vision.Resize((224, 224)),
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vision.RandomLighting(1),
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vision.ToTensor()
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]
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transform = mindspore.dataset.transforms.Compose(transforms)
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# Generate dataset
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=transform, input_columns=["image"])
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# check results with md5 comparison
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filename = "random_lighting_py_01_result.npz"
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save_and_check_md5_pil(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore configuration
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_lighting_c(alpha=1, plot=False):
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"""
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Feature: RandomLighting
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Description: Test RandomLighting cpp op
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Expectation: Equal results from Mindspore and benchmark
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"""
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logger.info("Test RandomLighting cpp op")
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# Original Images
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_original = [vision.Decode(), vision.Resize((224, 224))]
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ds_original = data.map(
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operations=transforms_original, input_columns="image")
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ds_original = ds_original.batch(512)
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for idx, (image, _) in enumerate(ds_original.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original, image, axis=0)
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# Random Lighting Adjusted Images
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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alpha = alpha if alpha is not None else 0.05
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c_op = vision.RandomLighting(alpha)
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transforms_random_lighting = [
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vision.Decode(), vision.Resize((224, 224)), c_op]
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ds_random_lighting = data.map(
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operations=transforms_random_lighting, input_columns="image")
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ds_random_lighting = ds_random_lighting.batch(512)
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for idx, (image, _) in enumerate(ds_random_lighting.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_random_lighting = image
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else:
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images_random_lighting = np.append(
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images_random_lighting, image, axis=0)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_random_lighting[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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if plot:
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visualize_list(images_original, images_random_lighting)
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def test_random_lighting_c_py(alpha=1, plot=False):
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"""
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Feature: RandomLighting
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Description: Test Random Lighting Cpp and Python Op
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Expectation: Equal results from Cpp and Python
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"""
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logger.info("Test RandomLighting Cpp and python Op")
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# RandomLighting Images
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=[vision.Decode(), vision.Resize(
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(200, 300))], input_columns=["image"])
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python_op = vision.RandomLighting(alpha)
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c_op = vision.RandomLighting(alpha)
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transforms_op = mindspore.dataset.transforms.Compose([lambda img: vision.ToPIL()(img.astype(np.uint8)),
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python_op,
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np.array])
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ds_random_lighting_py = data.map(
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operations=transforms_op, input_columns="image")
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ds_random_lighting_py = ds_random_lighting_py.batch(512)
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for idx, (image, _) in enumerate(ds_random_lighting_py.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_random_lighting_py = image
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else:
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images_random_lighting_py = np.append(
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images_random_lighting_py, image, axis=0)
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=[vision.Decode(), vision.Resize(
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(200, 300))], input_columns=["image"])
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ds_images_random_lighting_c = data.map(
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operations=c_op, input_columns="image")
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ds_random_lighting_c = ds_images_random_lighting_c.batch(512)
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for idx, (image, _) in enumerate(ds_random_lighting_c.create_tuple_iterator(num_epochs=1, output_numpy=True)):
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if idx == 0:
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images_random_lighting_c = image
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else:
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images_random_lighting_c = np.append(
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images_random_lighting_c, image, axis=0)
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num_samples = images_random_lighting_c.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_random_lighting_c[i],
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images_random_lighting_py[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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if plot:
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visualize_list(images_random_lighting_c,
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images_random_lighting_py, visualize_mode=2)
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def test_random_lighting_invalid_params():
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"""
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Feature: RandomLighting
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Description: Test RandomLighting with invalid input parameters
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Expectation: Throw correct error and message
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"""
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logger.info("Test RandomLighting with invalid input parameters.")
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with pytest.raises(ValueError) as error_info:
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=[vision.Decode(), vision.Resize((224, 224)),
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vision.RandomLighting(-2)], input_columns=["image"])
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assert "Input alpha is not within the required interval of [0, 16777216]." in str(
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error_info.value)
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with pytest.raises(TypeError) as error_info:
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data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data = data.map(operations=[vision.Decode(), vision.Resize((224, 224)),
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vision.RandomLighting('1')], input_columns=["image"])
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err_msg = "Argument alpha with value 1 is not of type [<class 'float'>, <class 'int'>], but got <class 'str'>."
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assert err_msg in str(error_info.value)
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if __name__ == "__main__":
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test_random_lighting_py()
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test_random_lighting_py_md5()
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test_random_lighting_c()
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test_random_lighting_c_py()
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test_random_lighting_invalid_params()
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