tf_image inputTensor default setter bug-fix

Author: phi-dbq

python/sparkdl/param/converters.py

@@ -92,14 +92,15 @@ def toTFHParams(value):
 
     @staticmethod
     def toTFTensorName(value):
-        """ Convert a value to a str or a :py:obj:`tf.Tensor` object, if possible. """
+        """ Convert a value to a :py:obj:`tf.Tensor` name, if possible. """
         if isinstance(value, tf.Tensor):
             return value.name
         try:
-            _check_is_tensor_name(value)
-            return TypeConverters.toString(value)
+            _maybe_tnsr_name = TypeConverters.toString(value)
+            _check_is_tensor_name(_maybe_tnsr_name)
+            return _maybe_tnsr_name
         except Exception as exc:
-            err_msg = "Could not convert [type {}] {} to tf.Tensor or str. {}"
+            err_msg = "Could not convert [type {}] {} to tf.Tensor name. {}"
             raise TypeError(err_msg.format(type(value), value, exc))
 
     @staticmethod
@@ -142,19 +143,19 @@ def toKerasOptimizer(value):
 
 def _check_is_tensor_name(_maybe_tnsr_name):
     """ Check if the input is a valid tensor name """
-    try:
-        assert isinstance(_maybe_tnsr_name, six.string_types), \
-            "must provide a strict tensor name as input, but got {}".format(type(_maybe_tnsr_name))
+    assert isinstance(_maybe_tnsr_name, six.string_types), \
+        "expect tensor name to be of string type, but got [type {}]".format(type(_maybe_tnsr_name))
 
-        # The check is taken from TensorFlow's NodeDef protocol buffer.
-        # https://github.com/tensorflow/tensorflow/blob/r1.3/tensorflow/core/framework/node_def.proto#L21-L25
+    # The check is taken from TensorFlow's NodeDef protocol buffer.
+    # https://github.com/tensorflow/tensorflow/blob/r1.3/tensorflow/core/framework/node_def.proto#L21-L25
+    try:
         _, src_idx = _maybe_tnsr_name.split(":")
         _ = int(src_idx)
-    except Exception as exc:
-        err_msg = "Can NOT convert [type {}] {} to tf.Tensor name: {}"
-        raise TypeError(err_msg.format(type(_maybe_tnsr_name), _maybe_tnsr_name, exc))
-    else:
-        return _maybe_tnsr_name
+    except Exception:
+        err_msg = "Tensor name must be of type <op_name>:<index>, but got {}"
+        raise TypeError(err_msg.format(_maybe_tnsr_name))
+
+    return _maybe_tnsr_name
 
 
 def _check_is_str(_maybe_col_name):

python/sparkdl/transformers/keras_applications.py

@@ -109,4 +109,3 @@ def _testKerasModel(self, include_top):
     "InceptionV3": InceptionV3Model,
     "Xception": XceptionModel
 }
-

python/sparkdl/transformers/tf_image.py

@@ -28,6 +28,12 @@
 import sparkdl.utils.jvmapi as JVMAPI
 import sparkdl.graph.utils as tfx
 
+__all__ = ['TFImageTransformer']
+
+IMAGE_INPUT_TENSOR_NAME = tfx.as_tensor_name(utils.IMAGE_INPUT_PLACEHOLDER_NAME)
+USER_GRAPH_NAMESPACE = 'given'
+NEW_OUTPUT_PREFIX = 'sdl_flattened'
+
 class TFImageTransformer(Transformer, HasInputCol, HasOutputCol, HasOutputMode):
     """
     Applies the Tensorflow graph to the image column in DataFrame.
@@ -47,9 +53,6 @@ class TFImageTransformer(Transformer, HasInputCol, HasOutputCol, HasOutputMode):
               since a new session is created inside this transformer.
     """
 
-    USER_GRAPH_NAMESPACE = 'given'
-    NEW_OUTPUT_PREFIX = 'sdl_flattened'
-
     graph = Param(Params._dummy(), "graph", "A TensorFlow computation graph",
                   typeConverter=SparkDLTypeConverters.toTFGraph)
     inputTensor = Param(Params._dummy(), "inputTensor",
@@ -61,28 +64,28 @@ class TFImageTransformer(Transformer, HasInputCol, HasOutputCol, HasOutputMode):
 
     @keyword_only
     def __init__(self, inputCol=None, outputCol=None, graph=None,
-                 inputTensor=utils.IMAGE_INPUT_PLACEHOLDER_NAME, outputTensor=None,
+                 inputTensor=IMAGE_INPUT_TENSOR_NAME, outputTensor=None,
                  outputMode="vector"):
         """
         __init__(self, inputCol=None, outputCol=None, graph=None,
-                 inputTensor=utils.IMAGE_INPUT_PLACEHOLDER_NAME, outputTensor=None,
+                 inputTensor=IMAGE_INPUT_TENSOR_NAME, outputTensor=None,
                  outputMode="vector")
         """
         super(TFImageTransformer, self).__init__()
-        self._setDefault(inputTensor=utils.IMAGE_INPUT_PLACEHOLDER_NAME)
-        self._setDefault(outputMode="vector")
         kwargs = self._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
     def setParams(self, inputCol=None, outputCol=None, graph=None,
-                  inputTensor=utils.IMAGE_INPUT_PLACEHOLDER_NAME, outputTensor=None,
+                  inputTensor=IMAGE_INPUT_TENSOR_NAME, outputTensor=None,
                   outputMode="vector"):
         """
         setParams(self, inputCol=None, outputCol=None, graph=None,
-                  inputTensor=utils.IMAGE_INPUT_PLACEHOLDER_NAME, outputTensor=None,
+                  inputTensor=IMAGE_INPUT_TENSOR_NAME, outputTensor=None,
                   outputMode="vector")
         """
+        self._setDefault(inputTensor=IMAGE_INPUT_TENSOR_NAME)
+        self._setDefault(outputMode="vector")
         kwargs = self._input_kwargs
         return self._set(**kwargs)
 
@@ -179,7 +182,7 @@ def _addReshapeLayers(self, tf_graph, dtype="uint8"):
             # Add on the original graph
             tf.import_graph_def(gdef, input_map={input_tensor_name: image_reshaped_expanded},
                                 return_elements=[self.getOutputTensor().name],
-                                name=self.USER_GRAPH_NAMESPACE)
+                                name=USER_GRAPH_NAMESPACE)
 
             # Flatten the output for tensorframes
             output_node = g.get_tensor_by_name(self._getOriginalOutputTensorName())
@@ -198,10 +201,10 @@ def _stripGraph(self, tf_graph):
         return g
 
     def _getOriginalOutputTensorName(self):
-        return self.USER_GRAPH_NAMESPACE + '/' + self.getOutputTensor().name
+        return USER_GRAPH_NAMESPACE + '/' + self.getOutputTensor().name
 
     def _getFinalOutputTensorName(self):
-        return self.NEW_OUTPUT_PREFIX + '_' + self.getOutputTensor().name
+        return NEW_OUTPUT_PREFIX + '_' + self.getOutputTensor().name
 
     def _getFinalOutputOpName(self):
         return tfx.as_op_name(self._getFinalOutputTensorName())