forked from huawei/mindspore2022
398 lines
15 KiB
Python
398 lines
15 KiB
Python
# Copyright 2021 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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Test STL10 dataset operators
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"""
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import os
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import matplotlib.pyplot as plt
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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.vision.c_transforms as vision
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testSTL10Data"
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WRONG_DIR = "../data/dataset/testMnistData"
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def loadfile(path_to_data, path_to_labels=None):
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"""
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Feature: loadfile.
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Description: parse stl10 file.
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Expectation: get image and label of stl10 dataset.
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"""
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labels = None
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if path_to_labels:
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with open(os.path.realpath(path_to_labels), 'rb') as f:
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labels = np.fromfile(f, dtype=np.uint8) - 1 # 0-based
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with open(path_to_data, 'rb') as f:
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# read whole file in uint8 chunks
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everything = np.fromfile(f, dtype=np.uint8)
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images = np.reshape(everything, (-1, 3, 96, 96))
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images = np.transpose(images, (0, 1, 3, 2))
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return images, labels
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def load_stl10(path, usage):
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"""
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Feature: load_stl10.
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Description: load stl10.
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Expectation: get data of stl10 dataset.
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"""
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assert usage in ["train", "test", "unlabeled", "train+unlabeled", "all"]
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if usage == "train":
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image_path = os.path.join(path, "train_X.bin")
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label_path = os.path.join(path, "train_y.bin")
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images, labels = loadfile(image_path, label_path)
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elif usage == "train+unlabeled":
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image_path = os.path.join(path, "train_X.bin")
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label_path = os.path.join(path, "train_y.bin")
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images, labels = loadfile(image_path, label_path)
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image_path = os.path.join(path, "unlabeled_X.bin")
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unlabeled_image, _ = loadfile(image_path)
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images = np.concatenate((images, unlabeled_image))
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labels = np.concatenate((labels, np.asarray([-1] * unlabeled_image.shape[0])))
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elif usage == "unlabeled":
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image_path = os.path.join(path, "unlabeled_X.bin")
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images, _ = loadfile(image_path)
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labels = np.asarray([-1] * images.shape[0])
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elif usage == "test":
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image_path = os.path.join(path, "test_X.bin")
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label_path = os.path.join(path, "test_y.bin")
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images, labels = loadfile(image_path, label_path)
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elif usage == "all":
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image_path = os.path.join(path, "test_X.bin")
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label_path = os.path.join(path, "test_y.bin")
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images, labels = loadfile(image_path, label_path)
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image_path = os.path.join(path, "train_X.bin")
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label_path = os.path.join(path, "train_y.bin")
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train_image, train_label = loadfile(image_path, label_path)
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images = np.concatenate((images, train_image))
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labels = np.concatenate((labels, train_label))
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image_path = os.path.join(path, "unlabeled_X.bin")
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unlabeled_image, _ = loadfile(image_path)
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images = np.concatenate((images, unlabeled_image))
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labels = np.concatenate((labels, np.asarray([-1] * unlabeled_image.shape[0])))
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return images, labels
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def visualize_dataset(images, labels):
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"""
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Feature: visualize_dataset.
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Description: visualize stl10 dataset.
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Expectation: plot images.
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"""
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num_samples = len(images)
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for i in range(num_samples):
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plt.subplot(1, num_samples, i + 1)
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plt.imshow(np.transpose(images[i], (1, 2, 0)))
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plt.title(labels[i])
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plt.show()
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def test_stl10_content_check():
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"""
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Feature: test_stl10_content_check.
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Description: validate STL10ataset image readings.
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Expectation: get correct number of data and correct content.
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"""
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logger.info("Test STL10Dataset Op with content check")
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# 1. train data.
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data1 = ds.STL10Dataset(DATA_DIR, usage="train", num_samples=1, shuffle=False)
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images, labels = load_stl10(DATA_DIR, "train")
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num_iter = 0
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# in this example, each dictionary has keys "image" and "label".
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for i, d in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(d["image"], np.transpose(images[i], (1, 2, 0)))
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np.testing.assert_array_equal(d["label"], labels[i])
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num_iter += 1
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assert num_iter == 1
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# 2. test data.
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data1 = ds.STL10Dataset(DATA_DIR, usage="test", num_samples=1, shuffle=False)
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images, labels = load_stl10(DATA_DIR, "test")
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num_iter = 0
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# in this example, each dictionary has keys "image" and "label".
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for i, d in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(d["image"], np.transpose(images[i], (1, 2, 0)))
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np.testing.assert_array_equal(d["label"], labels[i])
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num_iter += 1
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assert num_iter == 1
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# 3. unlabeled data.
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data1 = ds.STL10Dataset(DATA_DIR, usage="unlabeled", num_samples=1, shuffle=False)
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images, labels = load_stl10(DATA_DIR, "unlabeled")
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num_iter = 0
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# in this example, each dictionary has keys "image" and "label".
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for i, d in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(d["image"], np.transpose(images[i], (1, 2, 0)))
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np.testing.assert_array_equal(d["label"], labels[i])
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num_iter += 1
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assert num_iter == 1
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# 4. train+unlabeled data.
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data1 = ds.STL10Dataset(DATA_DIR, usage="train+unlabeled", num_samples=2, shuffle=False)
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images, labels = load_stl10(DATA_DIR, "train+unlabeled")
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num_iter = 0
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# in this example, each dictionary has keys "image" and "label".
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for i, d in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(d["image"], np.transpose(images[i], (1, 2, 0)))
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np.testing.assert_array_equal(d["label"], labels[i])
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num_iter += 1
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assert num_iter == 2
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# 4. all data.
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data1 = ds.STL10Dataset(DATA_DIR, usage="all", num_samples=3, shuffle=False)
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images, labels = load_stl10(DATA_DIR, "all")
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num_iter = 0
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# in this example, each dictionary has keys "image" and "label".
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for i, d in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(d["image"], np.transpose(images[i], (1, 2, 0)))
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np.testing.assert_array_equal(d["label"], labels[i])
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num_iter += 1
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assert num_iter == 3
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def test_stl10_basic():
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"""
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Feature: test_stl10_basic.
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Description: test basic usage of STL10Dataset.
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Expectation: get correct number of data.
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"""
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logger.info("Test STL10Dataset Op")
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# case 1: test loading whole dataset.
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all_data = ds.STL10Dataset(DATA_DIR, "all")
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num_iter = 0
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for _ in all_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 3
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# case 2: test num_samples.
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all_data = ds.STL10Dataset(DATA_DIR, "all", num_samples=1)
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num_iter = 0
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for _ in all_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 1
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# case 3: test repeat.
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all_data = ds.STL10Dataset(DATA_DIR, "all", num_samples=2)
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all_data = all_data.repeat(5)
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num_iter = 0
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for _ in all_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 10
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# case 4: test batch with drop_remainder=False.
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all_data = ds.STL10Dataset(DATA_DIR, "all", num_samples=2)
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assert all_data.get_dataset_size() == 2
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assert all_data.get_batch_size() == 1
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all_data = all_data.batch(batch_size=2) # drop_remainder is default to be False.
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assert all_data.get_batch_size() == 2
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assert all_data.get_dataset_size() == 1
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num_iter = 0
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for _ in all_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 1
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# case 5: test batch with drop_remainder=True.
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all_data = ds.STL10Dataset(DATA_DIR, "all", num_samples=2)
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assert all_data.get_dataset_size() == 2
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assert all_data.get_batch_size() == 1
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all_data = all_data.batch(batch_size=2, drop_remainder=True) # the rest of incomplete batch will be dropped.
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assert all_data.get_dataset_size() == 1
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assert all_data.get_batch_size() == 2
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num_iter = 0
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for _ in all_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 1
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def test_stl10_sequential_sampler():
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"""
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Feature: test_stl10_sequential_sampler.
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Description: test usage of STL10Dataset with SequentialSampler.
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Expectation: get correct number of data.
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"""
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logger.info("Test STL10Dataset Op with SequentialSampler")
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num_samples = 2
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sampler = ds.SequentialSampler(num_samples=num_samples)
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all_data_1 = ds.STL10Dataset(DATA_DIR, "all", sampler=sampler)
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all_data_2 = ds.STL10Dataset(DATA_DIR, "all", shuffle=False, num_samples=num_samples)
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label_list_1, label_list_2 = [], []
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num_iter = 0
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for item1, item2 in zip(all_data_1.create_dict_iterator(num_epochs=1),
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all_data_2.create_dict_iterator(num_epochs=1)):
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label_list_1.append(item1["label"].asnumpy())
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label_list_2.append(item2["label"].asnumpy())
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num_iter += 1
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np.testing.assert_array_equal(label_list_1, label_list_2)
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assert num_iter == num_samples
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def test_stl10_exception():
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"""
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Feature: test_stl10_exception.
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Description: test error cases for STL10Dataset.
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Expectation: raise exception.
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"""
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logger.info("Test error cases for STL10Dataset")
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error_msg_1 = "sampler and shuffle cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_1):
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ds.STL10Dataset(DATA_DIR, "all", shuffle=False, sampler=ds.PKSampler(3))
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error_msg_2 = "sampler and sharding cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_2):
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ds.STL10Dataset(DATA_DIR, "all", sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
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error_msg_3 = "num_shards is specified and currently requires shard_id as well"
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with pytest.raises(RuntimeError, match=error_msg_3):
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ds.STL10Dataset(DATA_DIR, "all", num_shards=10)
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error_msg_4 = "shard_id is specified but num_shards is not"
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with pytest.raises(RuntimeError, match=error_msg_4):
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ds.STL10Dataset(DATA_DIR, "all", shard_id=0)
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error_msg_5 = "Input shard_id is not within the required interval"
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with pytest.raises(ValueError, match=error_msg_5):
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ds.STL10Dataset(DATA_DIR, "all", num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.STL10Dataset(DATA_DIR, "all", num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.STL10Dataset(DATA_DIR, "all", num_shards=2, shard_id=5)
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error_msg_6 = "num_parallel_workers exceeds"
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with pytest.raises(ValueError, match=error_msg_6):
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ds.STL10Dataset(DATA_DIR, "all", shuffle=False, num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.STL10Dataset(DATA_DIR, "all", shuffle=False, num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.STL10Dataset(DATA_DIR, "all", shuffle=False, num_parallel_workers=-2)
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error_msg_7 = "Argument shard_id"
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with pytest.raises(TypeError, match=error_msg_7):
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ds.STL10Dataset(DATA_DIR, "all", num_shards=2, shard_id="0")
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def exception_func(item):
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raise Exception("Error occur!")
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error_msg_8 = "The corresponding data files"
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with pytest.raises(RuntimeError, match=error_msg_8):
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all_data = ds.STL10Dataset(DATA_DIR, "all")
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all_data = all_data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
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for _ in all_data.__iter__():
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pass
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with pytest.raises(RuntimeError, match=error_msg_8):
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all_data = ds.STL10Dataset(DATA_DIR, "all")
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all_data = all_data.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
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for _ in all_data.__iter__():
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pass
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error_msg_9 = "does not exist or permission denied!"
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with pytest.raises(ValueError, match=error_msg_9):
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all_data = ds.STL10Dataset(WRONG_DIR, "all")
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for _ in all_data.__iter__():
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pass
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def test_stl10_visualize(plot=False):
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"""
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Feature: test_stl10_visualize.
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Description: visualize STL10Dataset results.
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Expectation: get correct number of data and plot them.
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"""
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logger.info("Test STL10Dataset visualization")
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all_data = ds.STL10Dataset(DATA_DIR, "all", num_samples=2, shuffle=False)
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num_iter = 0
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image_list, label_list = [], []
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for item in all_data.create_dict_iterator(num_epochs=1, output_numpy=True):
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image = item["image"]
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label = item["label"]
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image_list.append(image)
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label_list.append("label {}".format(label))
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assert isinstance(image, np.ndarray)
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assert image.shape == (96, 96, 3)
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assert image.dtype == np.uint8
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assert label.dtype == np.int32
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num_iter += 1
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assert num_iter == 2
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if plot:
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visualize_dataset(image_list, label_list)
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def test_stl10_usage():
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"""
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Feature: test_stl10_usage.
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Description: validate STL10Dataset image readings.
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Expectation: get correct number of data.
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"""
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logger.info("Test STL10Dataset usage flag")
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def test_config(usage, path=None):
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path = DATA_DIR if path is None else path
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try:
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data = ds.STL10Dataset(path, usage=usage, shuffle=False)
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num_rows = 0
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for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_rows += 1
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except (ValueError, TypeError, RuntimeError) as e:
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return str(e)
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return num_rows
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assert test_config("train") == 1
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assert test_config("test") == 1
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assert test_config("unlabeled") == 1
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assert test_config("train+unlabeled") == 2
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assert test_config("all") == 3
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assert "Input usage is not within the valid set of ['train', 'test', 'unlabeled', 'train+unlabeled', 'all']."\
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in test_config("invalid")
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assert "Argument usage with value ['list'] is not of type [<class 'str'>]" in test_config(["list"])
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# change this directory to the folder that contains all STL10 files.
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all_files_path = None
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# the following tests on the entire datasets.
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if all_files_path is not None:
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assert test_config("train", all_files_path) == 1
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assert ds.STL10Dataset(all_files_path, usage="train").get_dataset_size() == 1
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if __name__ == '__main__':
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test_stl10_content_check()
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test_stl10_basic()
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test_stl10_sequential_sampler()
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test_stl10_exception()
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test_stl10_visualize(plot=True)
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test_stl10_usage()
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