283 lines
10 KiB
Python
283 lines
10 KiB
Python
# Copyright 2020-2022 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 Invert op in DE
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"""
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import numpy as np
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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, save_and_check_md5, diff_mse
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DATA_DIR = "../data/dataset/testImageNetData/train/"
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GENERATE_GOLDEN = False
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def test_invert_callable():
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"""
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Feature: Invert Op
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Description: Test op in eager mode
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Expectation: Output image shape from op is verified
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"""
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logger.info("Test Invert is callable")
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img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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img = vision.Decode()(img)
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img = vision.Invert()(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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assert img.shape == (2268, 4032, 3)
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def test_invert_py(plot=False):
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"""
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Feature: Invert Op
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Description: Test Python implementation
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Expectation: Dataset pipeline runs successfully and results are visually verified
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"""
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logger.info("Test Invert Python implementation")
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# Original Images
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data_set = 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_set.map(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):
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if idx == 0:
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images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
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else:
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images_original = np.append(images_original,
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np.transpose(image.asnumpy(), (0, 2, 3, 1)),
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axis=0)
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# Color Inverted Images
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_invert = mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.Resize((224, 224)),
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vision.Invert(),
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vision.ToTensor()])
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ds_invert = data_set.map(operations=transforms_invert, input_columns="image")
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ds_invert = ds_invert.batch(512)
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for idx, (image, _) in enumerate(ds_invert):
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if idx == 0:
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images_invert = np.transpose(image.asnumpy(), (0, 2, 3, 1))
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else:
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images_invert = np.append(images_invert,
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np.transpose(image.asnumpy(), (0, 2, 3, 1)),
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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] = np.mean((images_invert[i] - images_original[i]) ** 2)
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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_invert)
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def test_invert_c(plot=False):
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"""
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Feature: Invert Op
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Description: Test C++ implementation
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Expectation: Dataset pipeline runs successfully and results are verified
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"""
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logger.info("Test Invert C++ implementation")
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# Original Images
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_original = [vision.Decode(), vision.Resize(size=[224, 224])]
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ds_original = data_set.map(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):
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if idx == 0:
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images_original = image.asnumpy()
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else:
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images_original = np.append(images_original,
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image.asnumpy(),
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axis=0)
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# Invert Images
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transform_invert = [vision.Decode(), vision.Resize(size=[224, 224]),
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vision.Invert()]
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ds_invert = data_set.map(operations=transform_invert, input_columns="image")
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ds_invert = ds_invert.batch(512)
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for idx, (image, _) in enumerate(ds_invert):
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if idx == 0:
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images_invert = image.asnumpy()
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else:
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images_invert = np.append(images_invert,
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image.asnumpy(),
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axis=0)
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if plot:
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visualize_list(images_original, images_invert)
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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_invert[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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def test_invert_py_c(plot=False):
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"""
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Feature: Invert Op
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Description: Test C++ implementation and Python implementation
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Expectation: Dataset pipeline runs successfully and results are verified
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"""
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logger.info("Test Invert C++ and Python implementation")
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# Invert Images in cpp
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(operations=[vision.Decode(), vision.Resize((224, 224))], input_columns=["image"])
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ds_c_invert = data_set.map(operations=vision.Invert(), input_columns="image")
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ds_c_invert = ds_c_invert.batch(512)
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for idx, (image, _) in enumerate(ds_c_invert):
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if idx == 0:
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images_c_invert = image.asnumpy()
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else:
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images_c_invert = np.append(images_c_invert,
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image.asnumpy(),
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axis=0)
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# invert images in python
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(operations=[vision.Decode(), vision.Resize((224, 224))], input_columns=["image"])
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transforms_p_invert = mindspore.dataset.transforms.Compose([lambda img: img.astype(np.uint8),
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vision.ToPIL(),
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vision.Invert(),
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np.array])
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ds_p_invert = data_set.map(operations=transforms_p_invert, input_columns="image")
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ds_p_invert = ds_p_invert.batch(512)
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for idx, (image, _) in enumerate(ds_p_invert):
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if idx == 0:
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images_p_invert = image.asnumpy()
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else:
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images_p_invert = np.append(images_p_invert,
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image.asnumpy(),
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axis=0)
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num_samples = images_c_invert.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_p_invert[i], images_c_invert[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_c_invert, images_p_invert, visualize_mode=2)
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def test_invert_one_channel():
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"""
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Feature: Invert Op
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Description: Test Invert C++ implementation with one channel images
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Expectation: Invalid input is detected
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"""
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logger.info("Test Invert C++ implementation with One Channel Images")
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c_op = vision.Invert()
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(operations=[vision.Decode(), vision.Resize((224, 224)),
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lambda img: np.array(img[:, :, 0])], input_columns=["image"])
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data_set.map(operations=c_op, input_columns="image")
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except RuntimeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "The shape" in str(e)
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def test_invert_md5_py():
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"""
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Test Invert Python implementation with md5 check
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"""
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logger.info("Test Invert Python implementation with md5 check")
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# Generate dataset
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_invert = mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.Invert(),
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vision.ToTensor()])
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data = data_set.map(operations=transforms_invert, input_columns="image")
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# Compare with expected md5 from images
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filename = "invert_01_result_py_unified.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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def test_invert_md5_c():
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"""
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Feature: Invert Op
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Description: Test Invert C++ implementation with md5 check
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Expectation: Dataset pipeline runs successfully and md5 results are verified
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"""
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logger.info("Test Invert C++ implementation with md5 check")
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# Generate dataset
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_invert = [vision.Decode(),
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vision.Resize(size=[224, 224]),
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vision.Invert(),
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vision.ToTensor()]
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data = data_set.map(operations=transforms_invert, input_columns="image")
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# Compare with expected md5 from images
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filename = "invert_01_result_c_unified.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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if __name__ == "__main__":
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test_invert_callable()
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test_invert_py(plot=False)
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test_invert_c(plot=False)
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test_invert_py_c(plot=False)
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test_invert_one_channel()
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test_invert_md5_py()
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test_invert_md5_c()
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