forked from mindspore-Ecosystem/mindspore
358 lines
14 KiB
Python
358 lines
14 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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Test QMnistDataset operations
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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 as vision
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testQMnistData"
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def load_qmnist(path, usage, compat=True):
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"""
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load QMNIST data
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"""
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image_path = []
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label_path = []
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image_ext = "images-idx3-ubyte"
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label_ext = "labels-idx2-int"
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train_prefix = "qmnist-train"
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test_prefix = "qmnist-test"
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nist_prefix = "xnist"
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assert usage in ["train", "test", "nist", "all"]
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if usage == "train":
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image_path.append(os.path.realpath(os.path.join(path, train_prefix + "-" + image_ext)))
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label_path.append(os.path.realpath(os.path.join(path, train_prefix + "-" + label_ext)))
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elif usage == "test":
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image_path.append(os.path.realpath(os.path.join(path, test_prefix + "-" + image_ext)))
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label_path.append(os.path.realpath(os.path.join(path, test_prefix + "-" + label_ext)))
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elif usage == "nist":
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image_path.append(os.path.realpath(os.path.join(path, nist_prefix + "-" + image_ext)))
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label_path.append(os.path.realpath(os.path.join(path, nist_prefix + "-" + label_ext)))
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elif usage == "all":
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image_path.append(os.path.realpath(os.path.join(path, train_prefix + "-" + image_ext)))
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label_path.append(os.path.realpath(os.path.join(path, train_prefix + "-" + label_ext)))
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image_path.append(os.path.realpath(os.path.join(path, test_prefix + "-" + image_ext)))
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label_path.append(os.path.realpath(os.path.join(path, test_prefix + "-" + label_ext)))
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image_path.append(os.path.realpath(os.path.join(path, nist_prefix + "-" + image_ext)))
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label_path.append(os.path.realpath(os.path.join(path, nist_prefix + "-" + label_ext)))
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assert len(image_path) == len(label_path)
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images = []
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labels = []
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for i, _ in enumerate(image_path):
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with open(image_path[i], 'rb') as image_file:
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image_file.read(16)
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image = np.fromfile(image_file, dtype=np.uint8)
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image = image.reshape(-1, 28, 28, 1)
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images.append(image)
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with open(label_path[i], 'rb') as label_file:
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label_file.read(12)
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label = np.fromfile(label_file, dtype='>u4')
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label = label.reshape(-1, 8)
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labels.append(label)
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images = np.concatenate(images, 0)
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labels = np.concatenate(labels, 0)
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if compat:
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return images, labels[:, 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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Helper function to visualize the dataset samples
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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(images[i].squeeze(), cmap=plt.cm.gray)
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plt.title(labels[i])
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plt.show()
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def test_qmnist_content_check():
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"""
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Feature: QMnistDataset
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Description: Test QMnistDataset image readings with content check
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test QMnistDataset Op with content check")
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for usage in ["train", "test", "nist", "all"]:
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data1 = ds.QMnistDataset(DATA_DIR, usage, True, num_samples=10, shuffle=False)
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images, labels = load_qmnist(DATA_DIR, usage, True)
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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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image_list, label_list = [], []
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for i, data in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image_list.append(data["image"])
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label_list.append("label {}".format(data["label"]))
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np.testing.assert_array_equal(data["image"], images[i])
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np.testing.assert_array_equal(data["label"], labels[i])
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num_iter += 1
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assert num_iter == 10
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for usage in ["train", "test", "nist", "all"]:
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data1 = ds.QMnistDataset(DATA_DIR, usage, False, num_samples=10, shuffle=False)
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images, labels = load_qmnist(DATA_DIR, usage, False)
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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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image_list, label_list = [], []
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for i, data in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image_list.append(data["image"])
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label_list.append("label {}".format(data["label"]))
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np.testing.assert_array_equal(data["image"], images[i])
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np.testing.assert_array_equal(data["label"], labels[i])
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num_iter += 1
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assert num_iter == 10
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def test_qmnist_basic():
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"""
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Feature: QMnistDataset
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Description: Test QMnistDataset basic usage
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test QMnistDataset Op")
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# case 1: test loading whole dataset
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data1 = ds.QMnistDataset(DATA_DIR, "train", True)
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num_iter1 = 0
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for _ in data1.create_dict_iterator(num_epochs=1):
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num_iter1 += 1
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assert num_iter1 == 10
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# case 2: test num_samples
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data2 = ds.QMnistDataset(DATA_DIR, "train", True, num_samples=5)
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num_iter2 = 0
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for _ in data2.create_dict_iterator(num_epochs=1):
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num_iter2 += 1
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assert num_iter2 == 5
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# case 3: test repeat
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data3 = ds.QMnistDataset(DATA_DIR, "train", True)
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data3 = data3.repeat(5)
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num_iter3 = 0
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for _ in data3.create_dict_iterator(num_epochs=1):
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num_iter3 += 1
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assert num_iter3 == 50
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# case 4: test batch with drop_remainder=False
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data4 = ds.QMnistDataset(DATA_DIR, "train", True, num_samples=10)
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assert data4.get_dataset_size() == 10
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assert data4.get_batch_size() == 1
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data4 = data4.batch(batch_size=7) # drop_remainder is default to be False
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assert data4.get_dataset_size() == 2
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assert data4.get_batch_size() == 7
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num_iter4 = 0
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for _ in data4.create_dict_iterator(num_epochs=1):
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num_iter4 += 1
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assert num_iter4 == 2
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# case 5: test batch with drop_remainder=True
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data5 = ds.QMnistDataset(DATA_DIR, "train", True, num_samples=10)
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assert data5.get_dataset_size() == 10
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assert data5.get_batch_size() == 1
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data5 = data5.batch(batch_size=3, drop_remainder=True) # the rest of incomplete batch will be dropped
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assert data5.get_dataset_size() == 3
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assert data5.get_batch_size() == 3
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num_iter5 = 0
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for _ in data5.create_dict_iterator(num_epochs=1):
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num_iter5 += 1
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assert num_iter5 == 3
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# case 6: test get_col_names
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dataset = ds.QMnistDataset(DATA_DIR, "train", True, num_samples=10)
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assert dataset.get_col_names() == ["image", "label"]
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def test_qmnist_pk_sampler():
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"""
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Feature: QMnistDataset
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Description: Test QMnistDataset with PKSampler
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test QMnistDataset Op with PKSampler")
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golden = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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sampler = ds.PKSampler(10)
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data = ds.QMnistDataset(DATA_DIR, "nist", True, sampler=sampler)
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num_iter = 0
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label_list = []
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for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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label_list.append(item["label"])
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num_iter += 1
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np.testing.assert_array_equal(golden, label_list)
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assert num_iter == 10
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def test_qmnist_sequential_sampler():
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"""
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Feature: QMnistDataset
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Description: Test QMnistDataset with SequentialSampler
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test QMnistDataset Op with SequentialSampler")
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num_samples = 10
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sampler = ds.SequentialSampler(num_samples=num_samples)
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data1 = ds.QMnistDataset(DATA_DIR, "train", True, sampler=sampler)
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data2 = ds.QMnistDataset(DATA_DIR, "train", True, shuffle=False, num_samples=num_samples)
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label_list1, label_list2 = [], []
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num_iter = 0
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
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label_list1.append(item1["label"].asnumpy())
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label_list2.append(item2["label"].asnumpy())
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num_iter += 1
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np.testing.assert_array_equal(label_list1, label_list2)
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assert num_iter == num_samples
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@pytest.mark.skip(reason="random failures")
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def test_qmnist_exception():
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"""
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Feature: QMnistDataset
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Description: Test error cases for QMnistDataset
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Expectation: Correct error is thrown as expected
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"""
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logger.info("Test error cases for MnistDataset")
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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.QMnistDataset(DATA_DIR, "train", True, 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.QMnistDataset(DATA_DIR, "nist", True, sampler=ds.PKSampler(1), 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.QMnistDataset(DATA_DIR, "train", True, 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.QMnistDataset(DATA_DIR, "train", True, 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.QMnistDataset(DATA_DIR, "train", True, num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.QMnistDataset(DATA_DIR, "train", True, num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.QMnistDataset(DATA_DIR, "train", True, 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.QMnistDataset(DATA_DIR, "train", True, shuffle=False, num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.QMnistDataset(DATA_DIR, "train", True, shuffle=False, num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.QMnistDataset(DATA_DIR, "train", True, 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.QMnistDataset(DATA_DIR, "train", True, 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 file is"
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with pytest.raises(RuntimeError, match=error_msg_8):
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data = ds.QMnistDataset(DATA_DIR, "train", True)
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data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
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for _ in data.__iter__():
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pass
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with pytest.raises(RuntimeError, match=error_msg_8):
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data = ds.QMnistDataset(DATA_DIR, "train", True)
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data = data.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
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data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
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for _ in data.__iter__():
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pass
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with pytest.raises(RuntimeError, match=error_msg_8):
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data = ds.QMnistDataset(DATA_DIR, "train", True)
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data = data.map(operations=exception_func, input_columns=["label"], num_parallel_workers=1)
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for _ in data.__iter__():
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pass
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def test_qmnist_visualize(plot=False):
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"""
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Feature: QMnistDataset
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Description: Test QMnistDataset visualized results
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test QMnistDataset visualization")
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data1 = ds.QMnistDataset(DATA_DIR, "train", True, num_samples=10, shuffle=False)
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num_iter = 0
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image_list, label_list = [], []
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for item in data1.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 == (28, 28, 1)
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assert image.dtype == np.uint8
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assert label.dtype == np.uint32
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num_iter += 1
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assert num_iter == 10
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if plot:
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visualize_dataset(image_list, label_list)
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def test_qmnist_usage():
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"""
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Feature: QMnistDataset
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Description: Test QMnistDataset image readings with usage flag
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test QMnistDataset 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.QMnistDataset(path, usage=usage, compat=True, 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") == 10
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assert test_config("test") == 10
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assert test_config("nist") == 10
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assert test_config("all") == 30
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assert "usage is not within the valid set of ['train', 'test', 'test10k', 'test50k', 'nist', 'all']" in\
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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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if __name__ == '__main__':
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test_qmnist_content_check()
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test_qmnist_basic()
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test_qmnist_pk_sampler()
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test_qmnist_sequential_sampler()
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test_qmnist_exception()
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test_qmnist_visualize(plot=True)
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test_qmnist_usage()
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