added test case to cifar_op

update cifar10 dataset
fixing missing error handling code in validator

mindspore/dataset/core/validator_helpers.py

@@ -271,6 +271,8 @@ def check_sampler_shuffle_shard_options(param_dict):
     if sampler is not None:
         if shuffle is not None:
             raise RuntimeError("sampler and shuffle cannot be specified at the same time.")
+        if num_shards is not None:
+            raise RuntimeError("sampler and sharding cannot be specified at the same time.")
 
     if num_shards is not None:
         check_pos_int32(num_shards)

tests/ut/data/dataset/testCifar100Data/datasetSchema.json

-{
-  "datasetType": "CIFAR100",
-  "numRows": 100,
-  "columns": {
-    "image": {
-      "type": "uint8",
-      "rank": 1,
-	  "t_impl": "cvmat"
-    },
-    "coarse_label" : {
-      "type": "uint32",
-      "rank": 1,
-      "t_impl": "flex"
-    },
-    "fine_label" : {
-      "type": "uint32",
-      "rank": 1,
-      "t_impl": "flex"
-    }
-  }
-}

tests/ut/data/dataset/testCifar100Data/datasetSchemaTestRepeat.json

-{
-  "datasetType": "CIFAR100",
-  "numRows": 33,
-  "columns": {
-    "image": {
-      "type": "uint8",
-      "rank": 1,
-	  "t_impl": "cvmat"
-    },
-    "coarse_label" : {
-      "type": "uint32",
-      "rank": 1,
-      "t_impl": "flex"
-    },
-    "fine_label" : {
-      "type": "uint32",
-      "rank": 1,
-      "t_impl": "flex"
-    }
-  }
-}

tests/ut/data/dataset/testCifar10Data/datasetDistributionAll.json

-{ 
-  "deviceNum" : 3,
-  "deviceId" : 1,
-  "shardConfig" : "ALL",
-  "shuffle" : "ON",
-  "seed" : 0,
-  "epoch" : 2
-}
-

tests/ut/data/dataset/testCifar10Data/datasetDistributionRandom.json

-{ 
-  "deviceNum" : 3,
-  "deviceId" : 1,
-  "shardConfig" : "RANDOM",
-  "shuffle" : "ON",
-  "seed" : 0,
-  "epoch" : 1
-}
-

tests/ut/data/dataset/testCifar10Data/datasetDistributionUnique.json

-{ 
-  "deviceNum" : 3,
-  "deviceId" : 1,
-  "shardConfig" : "UNIQUE",
-  "shuffle" : "ON",
-  "seed" : 0,
-  "epoch" : 3
-}
-

tests/ut/data/dataset/testCifar10Data/datasetSchema.json

-{
-  "datasetType": "CIFAR10",
-  "numRows": 60000,
-  "columns": {
-    "image": {
-      "type": "uint8",
-      "rank": 1,
-	  "t_impl": "cvmat"
-    },
-    "label" : {
-      "type": "uint32",
-      "rank": 1,
-      "t_impl": "flex"
-    }
-  }
-}

tests/ut/data/dataset/testCifar10Data/datasetSchemaTestRepeat.json

-{
-  "datasetType": "CIFAR10",
-  "numRows": 33,
-  "columns": {
-    "image": {
-      "type": "uint8",
-      "rank": 1,
-	  "t_impl": "cvmat"
-    },
-    "label" : {
-      "type": "uint32",
-      "rank": 1,
-      "t_impl": "flex"
-    }
-  }
-}

tests/ut/python/dataset/test_cifarop.py

-# Copyright 2019 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.
-# ==============================================================================
-import os
-
-import numpy as np
-
-import mindspore.dataset as ds
-from mindspore import log as logger
-
-# Data for CIFAR and MNIST are not part of build tree
-# They need to be downloaded directly
-# prep_data.py can be executed or code below
-# import sys
-# sys.path.insert(0,"../../data")
-# import prep_data
-# prep_data.download_all_for_test("../../data")
-DATA_DIR_10 = "../data/dataset/testCifar10Data"
-DATA_DIR_100 = "../data/dataset/testCifar100Data"
-
-
-def load_cifar(path):
-    raw = np.empty(0, dtype=np.uint8)
-    for file_name in os.listdir(path):
-        if file_name.endswith(".bin"):
-            with open(os.path.join(path, file_name), mode='rb') as file:
-                raw = np.append(raw, np.fromfile(file, dtype=np.uint8), axis=0)
-    raw = raw.reshape(-1, 3073)
-    labels = raw[:, 0]
-    images = raw[:, 1:]
-    images = images.reshape(-1, 3, 32, 32)
-    images = images.transpose(0, 2, 3, 1)
-    return images, labels
-
-
-def test_case_dataset_cifar10():
-    """
-    dataset parameter
-    """
-    logger.info("Test dataset parameter")
-    # apply dataset operations
-    data1 = ds.Cifar10Dataset(DATA_DIR_10, 100)
-
-    num_iter = 0
-    for _ in data1.create_dict_iterator():
-        # in this example, each dictionary has keys "image" and "label"
-        num_iter += 1
-    assert num_iter == 100
-
-
-def test_case_dataset_cifar100():
-    """
-    dataset parameter
-    """
-    logger.info("Test dataset parameter")
-    # apply dataset operations
-    data1 = ds.Cifar100Dataset(DATA_DIR_100, 100)
-
-    num_iter = 0
-    for _ in data1.create_dict_iterator():
-        # in this example, each dictionary has keys "image" and "label"
-        num_iter += 1
-    assert num_iter == 100
-
-
-def test_reading_cifar10():
-    """
-    Validate CIFAR10 image readings
-    """
-    data1 = ds.Cifar10Dataset(DATA_DIR_10, 100, shuffle=False)
-    images, labels = load_cifar(DATA_DIR_10)
-    for i, d in enumerate(data1.create_dict_iterator()):
-        np.testing.assert_array_equal(d["image"], images[i])
-        np.testing.assert_array_equal(d["label"], labels[i])
-
-
-if __name__ == '__main__':
-    test_case_dataset_cifar10()
-    test_case_dataset_cifar100()
-    test_reading_cifar10()

tests/ut/python/dataset/test_config.py

@@ -245,17 +245,17 @@ def test_deterministic_run_distribution():
 
     # First dataset
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
-    random_crop_op = c_vision.RandomHorizontalFlip(0.1)
+    random_horizontal_flip_op = c_vision.RandomHorizontalFlip(0.1)
     decode_op = c_vision.Decode()
     data1 = data1.map(input_columns=["image"], operations=decode_op)
-    data1 = data1.map(input_columns=["image"], operations=random_crop_op)
+    data1 = data1.map(input_columns=["image"], operations=random_horizontal_flip_op)
 
     # Second dataset
     data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
     data2 = data2.map(input_columns=["image"], operations=decode_op)
     # If seed is set up on constructor, so the two ops output deterministic sequence
-    random_crop_op2 = c_vision.RandomHorizontalFlip(0.1)
-    data2 = data2.map(input_columns=["image"], operations=random_crop_op2)
+    random_horizontal_flip_op2 = c_vision.RandomHorizontalFlip(0.1)
+    data2 = data2.map(input_columns=["image"], operations=random_horizontal_flip_op2)
 
     for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
         np.testing.assert_equal(item1["image"], item2["image"])

tests/ut/python/dataset/test_datasets_cifarop.py

+# Copyright 2019 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 Cifar10 and Cifar100 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_10 = "../data/dataset/testCifar10Data"
+DATA_DIR_100 = "../data/dataset/testCifar100Data"
+
+
+def load_cifar(path, kind="cifar10"):
+    """
+    load Cifar10/100 data
+    """
+    raw = np.empty(0, dtype=np.uint8)
+    for file_name in os.listdir(path):
+        if file_name.endswith(".bin"):
+            with open(os.path.join(path, file_name), mode='rb') as file:
+                raw = np.append(raw, np.fromfile(file, dtype=np.uint8), axis=0)
+    if kind == "cifar10":
+        raw = raw.reshape(-1, 3073)
+        labels = raw[:, 0]
+        images = raw[:, 1:]
+    elif kind == "cifar100":
+        raw = raw.reshape(-1, 3074)
+        labels = raw[:, :2]
+        images = raw[:, 2:]
+    else:
+        raise ValueError("Invalid parameter value")
+    images = images.reshape(-1, 3, 32, 32)
+    images = images.transpose(0, 2, 3, 1)
+    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])
+        plt.title(labels[i])
+    plt.show()
+
+
+### Testcases for Cifar10Dataset Op ###
+
+
+def test_cifar10_content_check():
+    """
+    Validate Cifar10Dataset image readings
+    """
+    logger.info("Test Cifar10Dataset Op with content check")
+    data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100, shuffle=False)
+    images, labels = load_cifar(DATA_DIR_10)
+    num_iter = 0
+    # in this example, each dictionary has keys "image" and "label"
+    for i, d in enumerate(data1.create_dict_iterator()):
+        np.testing.assert_array_equal(d["image"], images[i])
+        np.testing.assert_array_equal(d["label"], labels[i])
+        num_iter += 1
+    assert num_iter == 100
+
+
+def test_cifar10_basic():
+    """
+    Validate CIFAR10
+    """
+    logger.info("Test Cifar10Dataset Op")
+
+    # case 1: test num_samples
+    data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
+    num_iter1 = 0
+    for _ in data1.create_dict_iterator():
+        num_iter1 += 1
+    assert num_iter1 == 100
+
+    # case 2: test num_parallel_workers
+    data2 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=50, num_parallel_workers=1)
+    num_iter2 = 0
+    for _ in data2.create_dict_iterator():
+        num_iter2 += 1
+    assert num_iter2 == 50
+
+    # case 3: test repeat
+    data3 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
+    data3 = data3.repeat(3)
+    num_iter3 = 0
+    for _ in data3.create_dict_iterator():
+        num_iter3 += 1
+    assert num_iter3 == 300
+
+    # case 4: test batch with drop_remainder=False
+    data4 = ds.Cifar10Dataset(DATA_DIR_10, 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.Cifar10Dataset(DATA_DIR_10, 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_cifar10_pk_sampler():
+    """
+    Test Cifar10Dataset with PKSampler
+    """
+    logger.info("Test Cifar10Dataset 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.Cifar10Dataset(DATA_DIR_10, 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_cifar10_sequential_sampler():
+    """
+    Test Cifar10Dataset with SequentialSampler
+    """
+    logger.info("Test Cifar10Dataset Op with SequentialSampler")
+    num_samples = 30
+    sampler = ds.SequentialSampler(num_samples=num_samples)
+    data1 = ds.Cifar10Dataset(DATA_DIR_10, sampler=sampler)
+    data2 = ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, num_samples=num_samples)
+    num_iter = 0
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        np.testing.assert_equal(item1["label"], item2["label"])
+        num_iter += 1
+    assert num_iter == num_samples
+
+
+def test_cifar10_exception():
+    """
+    Test error cases for Cifar10Dataset
+    """
+    logger.info("Test error cases for Cifar10Dataset")
+    error_msg_1 = "sampler and shuffle cannot be specified at the same time"
+    with pytest.raises(RuntimeError, match=error_msg_1):
+        ds.Cifar10Dataset(DATA_DIR_10, 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.Cifar10Dataset(DATA_DIR_10, 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.Cifar10Dataset(DATA_DIR_10, num_shards=10)
+
+    error_msg_4 = "shard_id is specified but num_shards is not"
+    with pytest.raises(RuntimeError, match=error_msg_4):
+        ds.Cifar10Dataset(DATA_DIR_10, shard_id=0)
+
+    error_msg_5 = "Input shard_id is not within the required interval"
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.Cifar10Dataset(DATA_DIR_10, num_shards=2, shard_id=-1)
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.Cifar10Dataset(DATA_DIR_10, num_shards=2, shard_id=5)
+
+    error_msg_6 = "num_parallel_workers exceeds"
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, num_parallel_workers=0)
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, num_parallel_workers=88)
+
+
+def test_cifar10_visualize(plot=False):
+    """
+    Visualize Cifar10Dataset results
+    """
+    logger.info("Test Cifar10Dataset visualization")
+
+    data1 = ds.Cifar10Dataset(DATA_DIR_10, 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 == (32, 32, 3)
+        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)
+
+
+### Testcases for Cifar100Dataset Op ###
+
+def test_cifar100_content_check():
+    """
+    Validate Cifar100Dataset image readings
+    """
+    logger.info("Test Cifar100Dataset with content check")
+    data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100, shuffle=False)
+    images, labels = load_cifar(DATA_DIR_100, kind="cifar100")
+    num_iter = 0
+    # in this example, each dictionary has keys "image", "coarse_label" and "fine_image"
+    for i, d in enumerate(data1.create_dict_iterator()):
+        np.testing.assert_array_equal(d["image"], images[i])
+        np.testing.assert_array_equal(d["coarse_label"], labels[i][0])
+        np.testing.assert_array_equal(d["fine_label"], labels[i][1])
+        num_iter += 1
+    assert num_iter == 100
+
+
+def test_cifar100_basic():
+    """
+    Test Cifar100Dataset
+    """
+    logger.info("Test Cifar100Dataset")
+
+    # case 1: test num_samples
+    data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100)
+    num_iter1 = 0
+    for _ in data1.create_dict_iterator():
+        num_iter1 += 1
+    assert num_iter1 == 100
+
+    # case 2: test repeat
+    data1 = data1.repeat(2)
+    num_iter2 = 0
+    for _ in data1.create_dict_iterator():
+        num_iter2 += 1
+    assert num_iter2 == 200
+
+    # case 3: test num_parallel_workers
+    data2 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100, num_parallel_workers=1)
+    num_iter3 = 0
+    for _ in data2.create_dict_iterator():
+        num_iter3 += 1
+    assert num_iter3 == 100
+
+    # case 4: test batch with drop_remainder=False
+    data3 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100)
+    assert data3.get_dataset_size() == 100
+    assert data3.get_batch_size() == 1
+    data3 = data3.batch(batch_size=3)
+    assert data3.get_dataset_size() == 34
+    assert data3.get_batch_size() == 3
+    num_iter4 = 0
+    for _ in data3.create_dict_iterator():
+        num_iter4 += 1
+    assert num_iter4 == 34
+
+    # case 4: test batch with drop_remainder=True
+    data4 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100)
+    data4 = data4.batch(batch_size=3, drop_remainder=True)
+    assert data4.get_dataset_size() == 33
+    assert data4.get_batch_size() == 3
+    num_iter5 = 0
+    for _ in data4.create_dict_iterator():
+        num_iter5 += 1
+    assert num_iter5 == 33
+
+
+def test_cifar100_pk_sampler():
+    """
+    Test Cifar100Dataset with PKSampler
+    """
+    logger.info("Test Cifar100Dataset with PKSampler")
+    golden = [i for i in range(20)]
+    sampler = ds.PKSampler(1)
+    data = ds.Cifar100Dataset(DATA_DIR_100, sampler=sampler)
+    num_iter = 0
+    label_list = []
+    for item in data.create_dict_iterator():
+        label_list.append(item["coarse_label"])
+        num_iter += 1
+    np.testing.assert_array_equal(golden, label_list)
+    assert num_iter == 20
+
+
+def test_cifar100_exception():
+    """
+    Test error cases for Cifar100Dataset
+    """
+    logger.info("Test error cases for Cifar100Dataset")
+    error_msg_1 = "sampler and shuffle cannot be specified at the same time"
+    with pytest.raises(RuntimeError, match=error_msg_1):
+        ds.Cifar100Dataset(DATA_DIR_100, 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.Cifar100Dataset(DATA_DIR_100, 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.Cifar100Dataset(DATA_DIR_100, num_shards=10)
+
+    error_msg_4 = "shard_id is specified but num_shards is not"
+    with pytest.raises(RuntimeError, match=error_msg_4):
+        ds.Cifar100Dataset(DATA_DIR_100, shard_id=0)
+
+    error_msg_5 = "Input shard_id is not within the required interval"
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.Cifar100Dataset(DATA_DIR_100, num_shards=2, shard_id=-1)
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.Cifar10Dataset(DATA_DIR_100, num_shards=2, shard_id=5)
+
+    error_msg_6 = "num_parallel_workers exceeds"
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, num_parallel_workers=0)
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, num_parallel_workers=88)
+
+
+def test_cifar100_visualize(plot=False):
+    """
+    Visualize Cifar100Dataset results
+    """
+    logger.info("Test Cifar100Dataset visualization")
+
+    data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=10, shuffle=False)
+    num_iter = 0
+    image_list, label_list = [], []
+    for item in data1.create_dict_iterator():
+        image = item["image"]
+        coarse_label = item["coarse_label"]
+        fine_label = item["fine_label"]
+        image_list.append(image)
+        label_list.append("coarse_label {}\nfine_label {}".format(coarse_label, fine_label))
+        assert isinstance(image, np.ndarray)
+        assert image.shape == (32, 32, 3)
+        assert image.dtype == np.uint8
+        assert coarse_label.dtype == np.uint32
+        assert fine_label.dtype == np.uint32
+        num_iter += 1
+    assert num_iter == 10
+    if plot:
+        visualize_dataset(image_list, label_list)
+
+
+if __name__ == '__main__':
+    test_cifar10_content_check()
+    test_cifar10_basic()
+    test_cifar10_pk_sampler()
+    test_cifar10_sequential_sampler()
+    test_cifar10_exception()
+    test_cifar10_visualize(plot=False)
+
+    test_cifar100_content_check()
+    test_cifar100_basic()
+    test_cifar100_pk_sampler()
+    test_cifar100_exception()
+    test_cifar100_visualize(plot=False)