Support creating Imageset from rdd of ndarray (intel-analytics#1507)

* support create imageset from rdd from python

* add unit tests

* fix style

python/dllib/src/bigdl/dllib/feature/common.py

@@ -229,6 +229,21 @@ def image_frame(cls, image_frame, memory_type="DRAM", bigdl_type="float"):
                                image_frame, memory_type)
         return cls(jvalue=jvalue)
 
+    @classmethod
+    def image_set(cls, imageset, memory_type="DRAM", bigdl_type="float"):
+        """
+        Create FeatureSet from ImageFrame.
+        :param image_frame: ImageFrame
+        :param memory_type: string, DRAM or PMEM
+                            If it's DRAM, will cache dataset into dynamic random-access memory
+                            If it's PMEM, will cache dataset into Intel Optane DC Persistent Memory
+        :param bigdl_type: numeric type
+        :return: A feature set
+        """
+        jvalue = callBigDlFunc(bigdl_type, "createFeatureSetFromImageFrame",
+                               imageset.to_image_frame(), memory_type)
+        return cls(jvalue=jvalue)
+
     @classmethod
     def rdd(cls, rdd, memory_type="DRAM", bigdl_type="float"):
         """

python/dllib/src/bigdl/dllib/feature/image/imageset.py

@@ -86,6 +86,21 @@ class should be put into the same class folder. So each image in the path is lab
     def from_image_frame(cls, image_frame, bigdl_type="float"):
         return ImageSet(jvalue=callBigDlFunc(bigdl_type, "imageFrameToImageSet", image_frame))
 
+    @classmethod
+    def from_rdds(cls, image_rdd, label_rdd=None, bigdl_type="float"):
+        """
+        Create a ImageSet from rdds of ndarray.
+
+        :param image_rdd: a rdd of ndarray, each ndarray should has dimension of 3 or 4 (3D images)
+        :param label_rdd: a rdd of ndarray
+        :return: a DistributedImageSet
+        """
+        image_rdd = image_rdd.map(lambda x: JTensor.from_ndarray(x))
+        if label_rdd is not None:
+            label_rdd = label_rdd.map(lambda x: JTensor.from_ndarray(x))
+        return ImageSet(jvalue=callBigDlFunc(bigdl_type, "createDistributedImageSet",
+                                             image_rdd, label_rdd), bigdl_type=bigdl_type)
+
     def transform(self, transformer):
         """
         transformImageSet