flat_map first commit

mindspore/dataset/engine/datasets.py

@@ -268,6 +268,50 @@ class Dataset:
         """
         return ShuffleDataset(self, buffer_size)
 
+    def flat_map(self, func):
+        """
+        Maps `func` to each row in dataset and flatten the result.
+
+        The specified `func` is a function that must take one 'Ndarray' as input
+        and return a 'Dataset'.
+
+        Args:
+            func (function): A function that must take one 'Ndarray' as an argument and
+                return a 'Dataset'.
+
+        Returns:
+            Dataset, applied by the function.
+
+        Examples:
+            >>> import mindspore.dataset as ds
+            >>> import mindspore.dataset.transforms.nlp.utils as nlp
+            >>> # declare a function which returns a Dataset object
+            >>> def flat_map_func(x):
+            >>>     data_dir = nlp.as_text(x[0])
+            >>>     d = ds.ImageFolderDatasetV2(data_dir)
+            >>>     return d
+            >>> # data is a Dataset object
+            >>> data = ds.TextFileDataset(DATA_FILE)
+            >>> data = data.flat_map(flat_map_func)
+
+        Raises:
+            TypeError: If `func` is not a function.
+            TypeError: If `func` doesn't return a Dataset.
+        """
+        dataset = None
+        if not hasattr(func, '__call__'):
+            raise TypeError("func must be a function.")
+
+        for row_data in self:
+            if dataset is None:
+                dataset = func(row_data)
+            else:
+                dataset += func(row_data)
+
+        if not isinstance(dataset, Dataset):
+            raise TypeError("flat_map must return a Dataset object.")
+        return dataset
+
     @check_map
     def map(self, input_columns=None, operations=None, output_columns=None, columns_order=None,
             num_parallel_workers=None, python_multiprocessing=False):

tests/ut/data/dataset/test_flat_map/image_index.txt

@@ -0,0 +1,2 @@
+../data/dataset/test_flat_map/images1.txt
+../data/dataset/test_flat_map/images2.txt

tests/ut/data/dataset/test_flat_map/images.txt

@@ -0,0 +1,3 @@
+../data/dataset/testPK/data
+../data/dataset/testImageNetData/train
+../data/dataset/testImageNetData2/train

tests/ut/data/dataset/test_flat_map/images1.txt

@@ -0,0 +1,3 @@
+../data/dataset/testPK/data
+../data/dataset/testImageNetData/train
+../data/dataset/testImageNetData2/train

tests/ut/data/dataset/test_flat_map/images2.txt

@@ -0,0 +1,3 @@
+../data/dataset/testPK/data
+../data/dataset/testImageNetData/train
+../data/dataset/testImageNetData2/train

tests/ut/python/dataset/test_flat_map.py

@@ -0,0 +1,72 @@
+# 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.
+# ==============================================================================
+import numpy as np
+import mindspore.dataset as ds
+
+DATA_FILE = "../data/dataset/test_flat_map/images1.txt"
+INDEX_FILE = "../data/dataset/test_flat_map/image_index.txt"
+
+
+def test_flat_map_1():
+    '''
+    DATA_FILE records the path of image folders, load the images from them.
+    '''
+    import mindspore.dataset.transforms.nlp.utils as nlp
+
+    def flat_map_func(x):
+        data_dir = nlp.as_text(x[0])
+        d = ds.ImageFolderDatasetV2(data_dir)
+        return d
+
+    data = ds.TextFileDataset(DATA_FILE)
+    data = data.flat_map(flat_map_func)
+
+    count = 0
+    for d in data:
+        assert isinstance(d[0], np.ndarray)
+        count += 1
+    assert count == 52
+
+
+def test_flat_map_2():
+    '''
+    Flatten 3D structure data
+    '''
+    import mindspore.dataset.transforms.nlp.utils as nlp
+
+    def flat_map_func_1(x):
+        data_dir = nlp.as_text(x[0])
+        d = ds.ImageFolderDatasetV2(data_dir)
+        return d
+
+    def flat_map_func_2(x):
+        text_file = nlp.as_text(x[0])
+        d = ds.TextFileDataset(text_file)
+        d = d.flat_map(flat_map_func_1)
+        return d
+
+    data = ds.TextFileDataset(INDEX_FILE)
+    data = data.flat_map(flat_map_func_2)
+
+    count = 0
+    for d in data:
+        assert isinstance(d[0], np.ndarray)
+        count += 1
+    assert count == 104
+
+
+if __name__ == "__main__":
+    test_flat_map_1()
+    test_flat_map_2()