mindspore2022/tests/ut/python/dataset/test_datasets_qmnist.py

# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================
"""
Test QMnistDataset operator
"""
import os

import matplotlib.pyplot as plt
import numpy as np
import pytest

import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as vision
from mindspore import log as logger

DATA_DIR = "../data/dataset/testQMnistData"


def load_qmnist(path, usage, compat=True):
    """
    load QMNIST data
    """
    image_path = []
    label_path = []
    image_ext = "images-idx3-ubyte"
    label_ext = "labels-idx2-int"
    train_prefix = "qmnist-train"
    test_prefix = "qmnist-test"
    nist_prefix = "xnist"
    assert usage in ["train", "test", "nist", "all"]
    if usage == "train":
        image_path.append(os.path.realpath(os.path.join(path, train_prefix + "-" + image_ext)))
        label_path.append(os.path.realpath(os.path.join(path, train_prefix + "-" + label_ext)))
    elif usage == "test":
        image_path.append(os.path.realpath(os.path.join(path, test_prefix + "-" + image_ext)))
        label_path.append(os.path.realpath(os.path.join(path, test_prefix + "-" + label_ext)))
    elif usage == "nist":
        image_path.append(os.path.realpath(os.path.join(path, nist_prefix + "-" + image_ext)))
        label_path.append(os.path.realpath(os.path.join(path, nist_prefix + "-" + label_ext)))
    elif usage == "all":
        image_path.append(os.path.realpath(os.path.join(path, train_prefix + "-" + image_ext)))
        label_path.append(os.path.realpath(os.path.join(path, train_prefix + "-" + label_ext)))
        image_path.append(os.path.realpath(os.path.join(path, test_prefix + "-" + image_ext)))
        label_path.append(os.path.realpath(os.path.join(path, test_prefix + "-" + label_ext)))
        image_path.append(os.path.realpath(os.path.join(path, nist_prefix + "-" + image_ext)))
        label_path.append(os.path.realpath(os.path.join(path, nist_prefix + "-" + label_ext)))

    assert len(image_path) == len(label_path)

    images = []
    labels = []
    for i, _ in enumerate(image_path):
        with open(image_path[i], 'rb') as image_file:
            image_file.read(16)
            image = np.fromfile(image_file, dtype=np.uint8)
            image = image.reshape(-1, 28, 28, 1)
            images.append(image)
        with open(label_path[i], 'rb') as label_file:
            label_file.read(12)
            label = np.fromfile(label_file, dtype='>u4')
            label = label.reshape(-1, 8)
            labels.append(label)

    images = np.concatenate(images, 0)
    labels = np.concatenate(labels, 0)
    if compat:
        return images, labels[:, 0]
    return images, labels


def visualize_dataset(images, labels):
    """
    Helper function to visualize the dataset samples
    """
    num_samples = len(images)
    for i in range(num_samples):
        plt.subplot(1, num_samples, i + 1)
        plt.imshow(images[i].squeeze(), cmap=plt.cm.gray)
        plt.title(labels[i])
    plt.show()


def test_qmnist_content_check():
    """
    Validate QMnistDataset image readings
    """
    logger.info("Test QMnistDataset Op with content check")
    for usage in ["train", "test", "nist", "all"]:
        data1 = ds.QMnistDataset(DATA_DIR, usage, True, num_samples=10, shuffle=False)
        images, labels = load_qmnist(DATA_DIR, usage, True)
        num_iter = 0
        # in this example, each dictionary has keys "image" and "label"
        image_list, label_list = [], []
        for i, data in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
            image_list.append(data["image"])
            label_list.append("label {}".format(data["label"]))
            np.testing.assert_array_equal(data["image"], images[i])
            np.testing.assert_array_equal(data["label"], labels[i])
            num_iter += 1
        assert num_iter == 10

    for usage in ["train", "test", "nist", "all"]:
        data1 = ds.QMnistDataset(DATA_DIR, usage, False, num_samples=10, shuffle=False)
        images, labels = load_qmnist(DATA_DIR, usage, False)
        num_iter = 0
        # in this example, each dictionary has keys "image" and "label"
        image_list, label_list = [], []
        for i, data in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
            image_list.append(data["image"])
            label_list.append("label {}".format(data["label"]))
            np.testing.assert_array_equal(data["image"], images[i])
            np.testing.assert_array_equal(data["label"], labels[i])
            num_iter += 1
        assert num_iter == 10


def test_qmnist_basic():
    """
    Validate QMnistDataset
    """
    logger.info("Test QMnistDataset Op")

    # case 1: test loading whole dataset
    data1 = ds.QMnistDataset(DATA_DIR, "train", True)
    num_iter1 = 0
    for _ in data1.create_dict_iterator(num_epochs=1):
        num_iter1 += 1
    assert num_iter1 == 10

    # case 2: test num_samples
    data2 = ds.QMnistDataset(DATA_DIR, "train", True, num_samples=5)
    num_iter2 = 0
    for _ in data2.create_dict_iterator(num_epochs=1):
        num_iter2 += 1
    assert num_iter2 == 5

    # case 3: test repeat
    data3 = ds.QMnistDataset(DATA_DIR, "train", True)
    data3 = data3.repeat(5)
    num_iter3 = 0
    for _ in data3.create_dict_iterator(num_epochs=1):
        num_iter3 += 1
    assert num_iter3 == 50

    # case 4: test batch with drop_remainder=False
    data4 = ds.QMnistDataset(DATA_DIR, "train", True, num_samples=10)
    assert data4.get_dataset_size() == 10
    assert data4.get_batch_size() == 1
    data4 = data4.batch(batch_size=7)  # drop_remainder is default to be False
    assert data4.get_dataset_size() == 2
    assert data4.get_batch_size() == 7
    num_iter4 = 0
    for _ in data4.create_dict_iterator(num_epochs=1):
        num_iter4 += 1
    assert num_iter4 == 2

    # case 5: test batch with drop_remainder=True
    data5 = ds.QMnistDataset(DATA_DIR, "train", True, num_samples=10)
    assert data5.get_dataset_size() == 10
    assert data5.get_batch_size() == 1
    data5 = data5.batch(batch_size=3, drop_remainder=True)  # the rest of incomplete batch will be dropped
    assert data5.get_dataset_size() == 3
    assert data5.get_batch_size() == 3
    num_iter5 = 0
    for _ in data5.create_dict_iterator(num_epochs=1):
        num_iter5 += 1
    assert num_iter5 == 3

    # case 6: test get_col_names
    dataset = ds.QMnistDataset(DATA_DIR, "train", True, num_samples=10)
    assert dataset.get_col_names() == ["image", "label"]


def test_qmnist_pk_sampler():
    """
    Test QMnistDataset with PKSampler
    """
    logger.info("Test QMnistDataset Op with PKSampler")
    golden = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    sampler = ds.PKSampler(10)
    data = ds.QMnistDataset(DATA_DIR, "nist", True, sampler=sampler)
    num_iter = 0
    label_list = []
    for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
        label_list.append(item["label"])
        num_iter += 1
    np.testing.assert_array_equal(golden, label_list)
    assert num_iter == 10


def test_qmnist_sequential_sampler():
    """
    Test QMnistDataset with SequentialSampler
    """
    logger.info("Test QMnistDataset Op with SequentialSampler")
    num_samples = 10
    sampler = ds.SequentialSampler(num_samples=num_samples)
    data1 = ds.QMnistDataset(DATA_DIR, "train", True, sampler=sampler)
    data2 = ds.QMnistDataset(DATA_DIR, "train", True, shuffle=False, num_samples=num_samples)
    label_list1, label_list2 = [], []
    num_iter = 0
    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
        label_list1.append(item1["label"].asnumpy())
        label_list2.append(item2["label"].asnumpy())
        num_iter += 1
    np.testing.assert_array_equal(label_list1, label_list2)
    assert num_iter == num_samples


def test_qmnist_exception():
    """
    Test error cases for QMnistDataset
    """
    logger.info("Test error cases for MnistDataset")
    error_msg_1 = "sampler and shuffle cannot be specified at the same time"
    with pytest.raises(RuntimeError, match=error_msg_1):
        ds.QMnistDataset(DATA_DIR, "train", True, shuffle=False, sampler=ds.PKSampler(3))

    error_msg_2 = "sampler and sharding cannot be specified at the same time"
    with pytest.raises(RuntimeError, match=error_msg_2):
        ds.QMnistDataset(DATA_DIR, "nist", True, sampler=ds.PKSampler(1), num_shards=2, shard_id=0)

    error_msg_3 = "num_shards is specified and currently requires shard_id as well"
    with pytest.raises(RuntimeError, match=error_msg_3):
        ds.QMnistDataset(DATA_DIR, "train", True, num_shards=10)

    error_msg_4 = "shard_id is specified but num_shards is not"
    with pytest.raises(RuntimeError, match=error_msg_4):
        ds.QMnistDataset(DATA_DIR, "train", True, shard_id=0)

    error_msg_5 = "Input shard_id is not within the required interval"
    with pytest.raises(ValueError, match=error_msg_5):
        ds.QMnistDataset(DATA_DIR, "train", True, num_shards=5, shard_id=-1)
    with pytest.raises(ValueError, match=error_msg_5):
        ds.QMnistDataset(DATA_DIR, "train", True, num_shards=5, shard_id=5)
    with pytest.raises(ValueError, match=error_msg_5):
        ds.QMnistDataset(DATA_DIR, "train", True, num_shards=2, shard_id=5)

    error_msg_6 = "num_parallel_workers exceeds"
    with pytest.raises(ValueError, match=error_msg_6):
        ds.QMnistDataset(DATA_DIR, "train", True, shuffle=False, num_parallel_workers=0)
    with pytest.raises(ValueError, match=error_msg_6):
        ds.QMnistDataset(DATA_DIR, "train", True, shuffle=False, num_parallel_workers=256)
    with pytest.raises(ValueError, match=error_msg_6):
        ds.QMnistDataset(DATA_DIR, "train", True, shuffle=False, num_parallel_workers=-2)

    error_msg_7 = "Argument shard_id"
    with pytest.raises(TypeError, match=error_msg_7):
        ds.QMnistDataset(DATA_DIR, "train", True, num_shards=2, shard_id="0")

    def exception_func(item):
        raise Exception("Error occur!")

    error_msg_8 = "The corresponding data files"
    with pytest.raises(RuntimeError, match=error_msg_8):
        data = ds.QMnistDataset(DATA_DIR, "train", True)
        data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
        for _ in data.__iter__():
            pass
    with pytest.raises(RuntimeError, match=error_msg_8):
        data = ds.QMnistDataset(DATA_DIR, "train", True)
        data = data.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
        data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
        for _ in data.__iter__():
            pass
    with pytest.raises(RuntimeError, match=error_msg_8):
        data = ds.QMnistDataset(DATA_DIR, "train", True)
        data = data.map(operations=exception_func, input_columns=["label"], num_parallel_workers=1)
        for _ in data.__iter__():
            pass


def test_qmnist_visualize(plot=False):
    """
    Visualize QMnistDataset results
    """
    logger.info("Test QMnistDataset visualization")

    data1 = ds.QMnistDataset(DATA_DIR, "train", True, num_samples=10, shuffle=False)
    num_iter = 0
    image_list, label_list = [], []
    for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
        image = item["image"]
        label = item["label"]
        image_list.append(image)
        label_list.append("label {}".format(label))
        assert isinstance(image, np.ndarray)
        assert image.shape == (28, 28, 1)
        assert image.dtype == np.uint8
        assert label.dtype == np.uint32
        num_iter += 1
    assert num_iter == 10
    if plot:
        visualize_dataset(image_list, label_list)


def test_qmnist_usage():
    """
    Validate QMnistDataset image readings
    """
    logger.info("Test QMnistDataset usage flag")

    def test_config(usage, path=None):
        path = DATA_DIR if path is None else path
        try:
            data = ds.QMnistDataset(path, usage=usage, compat=True, shuffle=False)
            num_rows = 0
            for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
                num_rows += 1
        except (ValueError, TypeError, RuntimeError) as e:
            return str(e)
        return num_rows

    assert test_config("train") == 10
    assert test_config("test") == 10
    assert test_config("nist") == 10
    assert test_config("all") == 30
    assert "usage is not within the valid set of ['train', 'test', 'test10k', 'test50k', 'nist', 'all']" in\
           test_config("invalid")
    assert "Argument usage with value ['list'] is not of type [<class 'str'>]" in test_config(["list"])


if __name__ == '__main__':
    test_qmnist_content_check()
    test_qmnist_basic()
    test_qmnist_pk_sampler()
    test_qmnist_sequential_sampler()
    test_qmnist_exception()
    test_qmnist_visualize(plot=True)
    test_qmnist_usage()