adding Mnist python ut coverage

tests/ut/python/dataset/test_datasets_cifarop.py

@@ -87,6 +87,13 @@ def test_cifar10_basic():
     """
     logger.info("Test Cifar10Dataset Op")
 
+    # case 0: test loading the whole dataset
+    data0 = ds.Cifar10Dataset(DATA_DIR_10)
+    num_iter0 = 0
+    for _ in data0.create_dict_iterator():
+        num_iter0 += 1
+    assert num_iter0 == 10000
+
     # case 1: test num_samples
     data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
     num_iter1 = 0

tests/ut/python/dataset/test_datasets_mnist.py

+# 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()):
+        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_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_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_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_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_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():
+        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(), data2.create_dict_iterator()):
+        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():
+        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)
+
+
+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)

tests/ut/python/dataset/test_datasets_sharding.py

@@ -200,7 +200,7 @@ def test_cifar10_shardings(print_res=False):
             logger.info("labels of dataset: {}".format(res))
         return res
 
-    # 60000 rows in total. CIFAR reads everything in memory which would make each test case very slow
+    # 10000 rows in total. CIFAR reads everything in memory which would make each test case very slow
     # therefore, only 2 test cases for now.
     assert sharding_config(10000, 9999, 7, False, 1) == [9]
     assert sharding_config(10000, 0, 4, False, 3) == [0, 0, 0]