forked from mindspore-Ecosystem/mindspore
280 lines
10 KiB
Python
280 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 UniformAugment 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, diff_mse
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DATA_DIR = "../data/dataset/testImageNetData/train/"
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def test_uniform_augment_callable(num_ops=2):
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"""
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Feature: UniformAugment
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Description: Test UniformAugment under normal test case
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Expectation: Output's shape is the same as expected output's shape
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"""
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logger.info("test_uniform_augment_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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decode_op = vision.Decode()
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img = decode_op(img)
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assert img.shape == (2268, 4032, 3)
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transforms_ua = [vision.RandomCrop(size=[400, 400], padding=[32, 32, 32, 32]),
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vision.RandomCrop(size=[400, 400], padding=[32, 32, 32, 32])]
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uni_aug = vision.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
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img = uni_aug(img)
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assert img.shape == (2268, 4032, 3) or img.shape == (400, 400, 3)
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def test_uniform_augment(plot=False, num_ops=2):
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"""
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Feature: UniformAugment
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Description: Test UniformAugment using Python implementation
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Expectation: Output is the same as expected output
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"""
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logger.info("Test UniformAugment")
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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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# UniformAugment Images
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transform_list = [vision.RandomRotation(45),
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vision.RandomColor(),
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vision.RandomSharpness(),
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vision.Invert(),
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vision.AutoContrast(),
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vision.Equalize()]
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transforms_ua = \
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mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.Resize((224, 224)),
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vision.UniformAugment(transforms=transform_list,
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num_ops=num_ops),
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vision.ToTensor()])
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ds_ua = data_set.map(operations=transforms_ua, input_columns="image")
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ds_ua = ds_ua.batch(512)
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for idx, (image, _) in enumerate(ds_ua):
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if idx == 0:
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images_ua = np.transpose(image.asnumpy(), (0, 2, 3, 1))
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else:
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images_ua = np.append(images_ua,
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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] = diff_mse(images_ua[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_ua)
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def test_cpp_uniform_augment(plot=False, num_ops=2):
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"""
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Feature: UniformAugment
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Description: Test UniformAugment using Cpp implementation
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Expectation: Output is the same as expected output
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"""
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logger.info("Test CPP UniformAugment")
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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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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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# UniformAugment Images
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_ua = [vision.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
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vision.RandomHorizontalFlip(),
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vision.RandomVerticalFlip(),
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vision.RandomColorAdjust(),
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vision.RandomRotation(degrees=45)]
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uni_aug = vision.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
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transforms_all = [vision.Decode(), vision.Resize(size=[224, 224]),
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uni_aug,
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vision.ToTensor()]
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ds_ua = data_set.map(operations=transforms_all, input_columns="image", num_parallel_workers=1)
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ds_ua = ds_ua.batch(512)
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for idx, (image, _) in enumerate(ds_ua):
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if idx == 0:
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images_ua = np.transpose(image.asnumpy(), (0, 2, 3, 1))
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else:
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images_ua = np.append(images_ua,
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np.transpose(image.asnumpy(), (0, 2, 3, 1)),
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axis=0)
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if plot:
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visualize_list(images_original, images_ua)
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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_ua[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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def test_cpp_uniform_augment_exception_large_numops(num_ops=6):
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"""
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Feature: UniformAugment
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Description: Test UniformAugment using invalid large number of ops
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Expectation: Exception is raised as expected
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"""
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logger.info("Test CPP UniformAugment invalid large num_ops exception")
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transforms_ua = [vision.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
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vision.RandomHorizontalFlip(),
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vision.RandomVerticalFlip(),
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vision.RandomColorAdjust(),
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vision.RandomRotation(degrees=45)]
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try:
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_ = vision.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
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except Exception as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "num_ops" in str(e)
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def test_cpp_uniform_augment_exception_nonpositive_numops(num_ops=0):
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"""
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Feature: UniformAugment
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Description: Test UniformAugment using invalid non-positive num_ops
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Expectation: Exception is raised as expected
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"""
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logger.info("Test CPP UniformAugment invalid non-positive num_ops exception")
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transforms_ua = [vision.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
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vision.RandomHorizontalFlip(),
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vision.RandomVerticalFlip(),
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vision.RandomColorAdjust(),
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vision.RandomRotation(degrees=45)]
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try:
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_ = vision.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
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except Exception as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Input num_ops must be greater than 0" in str(e)
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def test_cpp_uniform_augment_exception_float_numops(num_ops=2.5):
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"""
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Feature: UniformAugment
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Description: Test UniformAugment using invalid float num_ops
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Expectation: Exception is raised as expected
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"""
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logger.info("Test CPP UniformAugment invalid float num_ops exception")
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transforms_ua = [vision.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
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vision.RandomHorizontalFlip(),
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vision.RandomVerticalFlip(),
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vision.RandomColorAdjust(),
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vision.RandomRotation(degrees=45)]
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try:
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_ = vision.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
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except Exception as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Argument num_ops with value 2.5 is not of type [<class 'int'>]" in str(e)
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def test_cpp_uniform_augment_random_crop_badinput(num_ops=1):
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"""
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Feature: UniformAugment
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Description: Test UniformAugment with greater crop size
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Expectation: Exception is raised as expected
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"""
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logger.info("Test CPP UniformAugment with random_crop bad input")
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batch_size = 2
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cifar10_dir = "../data/dataset/testCifar10Data"
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ds1 = ds.Cifar10Dataset(cifar10_dir, shuffle=False) # shape = [32,32,3]
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transforms_ua = [
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# Note: crop size [224, 224] > image size [32, 32]
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vision.RandomCrop(size=[224, 224]),
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vision.RandomHorizontalFlip()
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]
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uni_aug = vision.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
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ds1 = ds1.map(operations=uni_aug, input_columns="image")
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# apply DatasetOps
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ds1 = ds1.batch(batch_size, drop_remainder=True, num_parallel_workers=1)
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num_batches = 0
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try:
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for _ in ds1.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_batches += 1
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except Exception as e:
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assert "crop size" in str(e)
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if __name__ == "__main__":
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test_uniform_augment_callable(num_ops=2)
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test_uniform_augment(num_ops=1, plot=True)
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test_cpp_uniform_augment(num_ops=1, plot=True)
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test_cpp_uniform_augment_exception_large_numops(num_ops=6)
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test_cpp_uniform_augment_exception_nonpositive_numops(num_ops=0)
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test_cpp_uniform_augment_exception_float_numops(num_ops=2.5)
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test_cpp_uniform_augment_random_crop_badinput(num_ops=1)
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