# Copyright 2020 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 Mnist dataset operators """ import os import pytest import numpy as np import matplotlib.pyplot as plt import mindspore.dataset as ds from mindspore import log as logger DATA_DIR = "../data/dataset/testMnistData" def load_mnist(path): """ load Mnist data """ labels_path = os.path.join(path, 't10k-labels-idx1-ubyte') images_path = os.path.join(path, 't10k-images-idx3-ubyte') with open(labels_path, 'rb') as lbpath: lbpath.read(8) labels = np.fromfile(lbpath, dtype=np.uint8) with open(images_path, 'rb') as imgpath: imgpath.read(16) images = np.fromfile(imgpath, dtype=np.uint8) images = images.reshape(-1, 28, 28, 1) images[images > 0] = 255 # Perform binarization to maintain consistency with our API 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_mnist_content_check(): """ Validate MnistDataset image readings """ logger.info("Test MnistDataset Op with content check") data1 = ds.MnistDataset(DATA_DIR, num_samples=100, shuffle=False) images, labels = load_mnist(DATA_DIR) 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)): 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 == 100 def test_mnist_basic(): """ Validate MnistDataset """ logger.info("Test MnistDataset Op") # case 1: test loading whole dataset data1 = ds.MnistDataset(DATA_DIR) num_iter1 = 0 for _ in data1.create_dict_iterator(num_epochs=1): num_iter1 += 1 assert num_iter1 == 10000 # case 2: test num_samples data2 = ds.MnistDataset(DATA_DIR, num_samples=500) num_iter2 = 0 for _ in data2.create_dict_iterator(num_epochs=1): num_iter2 += 1 assert num_iter2 == 500 # case 3: test repeat data3 = ds.MnistDataset(DATA_DIR, num_samples=200) data3 = data3.repeat(5) num_iter3 = 0 for _ in data3.create_dict_iterator(num_epochs=1): num_iter3 += 1 assert num_iter3 == 1000 # case 4: test batch with drop_remainder=False data4 = ds.MnistDataset(DATA_DIR, num_samples=100) assert data4.get_dataset_size() == 100 assert data4.get_batch_size() == 1 data4 = data4.batch(batch_size=7) # drop_remainder is default to be False assert data4.get_dataset_size() == 15 assert data4.get_batch_size() == 7 num_iter4 = 0 for _ in data4.create_dict_iterator(num_epochs=1): num_iter4 += 1 assert num_iter4 == 15 # case 5: test batch with drop_remainder=True data5 = ds.MnistDataset(DATA_DIR, num_samples=100) assert data5.get_dataset_size() == 100 assert data5.get_batch_size() == 1 data5 = data5.batch(batch_size=7, drop_remainder=True) # the rest of incomplete batch will be dropped assert data5.get_dataset_size() == 14 assert data5.get_batch_size() == 7 num_iter5 = 0 for _ in data5.create_dict_iterator(num_epochs=1): num_iter5 += 1 assert num_iter5 == 14 def test_mnist_pk_sampler(): """ Test MnistDataset with PKSampler """ logger.info("Test MnistDataset Op with PKSampler") golden = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9] sampler = ds.PKSampler(3) data = ds.MnistDataset(DATA_DIR, sampler=sampler) num_iter = 0 label_list = [] for item in data.create_dict_iterator(num_epochs=1): label_list.append(item["label"]) num_iter += 1 np.testing.assert_array_equal(golden, label_list) assert num_iter == 30 def test_mnist_sequential_sampler(): """ Test MnistDataset with SequentialSampler """ logger.info("Test MnistDataset Op with SequentialSampler") num_samples = 50 sampler = ds.SequentialSampler(num_samples=num_samples) data1 = ds.MnistDataset(DATA_DIR, sampler=sampler) data2 = ds.MnistDataset(DATA_DIR, 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"]) label_list2.append(item2["label"]) num_iter += 1 np.testing.assert_array_equal(label_list1, label_list2) assert num_iter == num_samples def test_mnist_exception(): """ Test error cases for MnistDataset """ 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.MnistDataset(DATA_DIR, 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.MnistDataset(DATA_DIR, sampler=ds.PKSampler(3), 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.MnistDataset(DATA_DIR, num_shards=10) error_msg_4 = "shard_id is specified but num_shards is not" with pytest.raises(RuntimeError, match=error_msg_4): ds.MnistDataset(DATA_DIR, shard_id=0) error_msg_5 = "Input shard_id is not within the required interval" with pytest.raises(ValueError, match=error_msg_5): ds.MnistDataset(DATA_DIR, num_shards=5, shard_id=-1) with pytest.raises(ValueError, match=error_msg_5): ds.MnistDataset(DATA_DIR, num_shards=5, shard_id=5) with pytest.raises(ValueError, match=error_msg_5): ds.MnistDataset(DATA_DIR, num_shards=2, shard_id=5) error_msg_6 = "num_parallel_workers exceeds" with pytest.raises(ValueError, match=error_msg_6): ds.MnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=0) with pytest.raises(ValueError, match=error_msg_6): ds.MnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=65) with pytest.raises(ValueError, match=error_msg_6): ds.MnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=-2) error_msg_7 = "Argument shard_id" with pytest.raises(TypeError, match=error_msg_7): ds.MnistDataset(DATA_DIR, num_shards=2, shard_id="0") def test_mnist_visualize(plot=False): """ Visualize MnistDataset results """ logger.info("Test MnistDataset visualization") data1 = ds.MnistDataset(DATA_DIR, num_samples=10, shuffle=False) num_iter = 0 image_list, label_list = [], [] for item in data1.create_dict_iterator(num_epochs=1): 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_mnist_usage(): """ Validate MnistDataset image readings """ logger.info("Test MnistDataset usage flag") def test_config(usage, mnist_path=None): mnist_path = DATA_DIR if mnist_path is None else mnist_path try: data = ds.MnistDataset(mnist_path, usage=usage, shuffle=False) num_rows = 0 for _ in data.create_dict_iterator(): num_rows += 1 except (ValueError, TypeError, RuntimeError) as e: return str(e) return num_rows assert test_config("test") == 10000 assert test_config("all") == 10000 assert " no valid data matching the dataset API MnistDataset" in test_config("train") assert "usage is not within the valid set of ['train', 'test', 'all']" in test_config("invalid") assert "Argument usage with value ['list'] is not of type (,)" in test_config(["list"]) # change this directory to the folder that contains all mnist files all_files_path = None # the following tests on the entire datasets if all_files_path is not None: assert test_config("train", all_files_path) == 60000 assert test_config("test", all_files_path) == 10000 assert test_config("all", all_files_path) == 70000 assert ds.MnistDataset(all_files_path, usage="train").get_dataset_size() == 60000 assert ds.MnistDataset(all_files_path, usage="test").get_dataset_size() == 10000 assert ds.MnistDataset(all_files_path, usage="all").get_dataset_size() == 70000 if __name__ == '__main__': test_mnist_content_check() test_mnist_basic() test_mnist_pk_sampler() test_mnist_sequential_sampler() test_mnist_exception() test_mnist_visualize(plot=True) test_mnist_usage()