# 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 # limitations under the License. # ============================================================================== """ 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) image[image > 0] = 255 # Perform binarization to maintain consistency with our API 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 []" 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()