Update Cast handlers for version 9

The Cast operator was updated in ONNX opset version 9, causning frontend and backend conversion to fail. This PR should fix the issue: onnx#362
chinhuang007 · Feb 15, 2019 · c525605 · c525605
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diff --git a/doc/CLI.md b/doc/CLI.md
@@ -6,6 +6,13 @@ ONNX-Tensorflow Command Line Interface
 
 More information: `onnx-tf -h`
 ```
+
+WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
+For more information, please see:
+  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
+  * https://github.com/tensorflow/addons
+If you depend on functionality not listed there, please file an issue.
+
 usage: onnx-tf [-h] {convert,check,optimize}
 
 ONNX-Tensorflow Command Line Interface
@@ -36,6 +43,13 @@ optional arguments:
 
 More information: `onnx-tf convert -h`
 ```
+
+WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
+For more information, please see:
+  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
+  * https://github.com/tensorflow/addons
+If you depend on functionality not listed there, please file an issue.
+
 usage: onnx-tf [-h] --infile INFILE --outfile OUTFILE --convert_to {onnx,tf}
                [--graph GRAPH] [--device DEVICE] [--strict STRICT]
                [--output OUTPUT] [--opset OPSET]

diff --git a/doc/support_status.md b/doc/support_status.md
@@ -21,7 +21,7 @@ ______
 |Atanh|9|
 |AveragePool|1, 7|
 |BatchNormalization|1, 6, 7, 9|
-|Cast|1, 6|
+|Cast|1, 6, 9|
 |Ceil|1, 6|
 |Clip|1, 6|
 |Compress|9|
@@ -162,7 +162,7 @@ ______
 |AvgPool|1, 7|
 |BatchNorm|1, 6, 7, 9|
 |BiasAdd|1, 6, 7|
-|Cast|1, 6|
+|Cast|1, 6, 9|
 |Ceil|1, 6|
 |ConcatV2|1, 4|
 |Conv1D|1|

diff --git a/onnx_tf/handlers/backend/cast.py b/onnx_tf/handlers/backend/cast.py
@@ -20,3 +20,20 @@ def version_1(cls, node, **kwargs):
   @classmethod
   def version_6(cls, node, **kwargs):
     return [cls.make_tensor_from_onnx_node(node, **kwargs)]
+
+  @classmethod
+  def version_9(cls, node, **kwargs):
+    inp = kwargs["tensor_dict"][node.inputs[0]]
+    to_type = node.attrs.get("to")
+
+    if to_type == tf.string:
+      return [tf.as_string(inp)]
+
+    if inp.dtype == tf.string:
+      if to_type not in [tf.float32, tf.float64, tf.int32, tf.int64]:
+        raise RuntimeError(
+            "Cast string to type {} is not supported in Tensorflow.".format(
+                to_type))
+      return [tf.strings.to_number(inp, to_type)]
+
+    return [cls.make_tensor_from_onnx_node(node, **kwargs)]
diff --git a/onnx_tf/handlers/frontend/cast.py b/onnx_tf/handlers/frontend/cast.py
@@ -24,3 +24,9 @@ def version_6(cls, node, **kwargs):
     dst_t = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(
         tf.as_dtype(node.attr["DstT"]).as_numpy_dtype)]
     return cls.make_node_from_tf_node(node, [node.inputs[0]], to=dst_t)
+
+  @classmethod
+  def version_9(cls, node, **kwargs):
+    dst_t = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(
+        tf.as_dtype(node.attr["DstT"]).as_numpy_dtype)]
+    return cls.make_node_from_tf_node(node, [node.inputs[0]], to=dst_t)
diff --git a/onnx_tf/opset_version.py b/onnx_tf/opset_version.py
@@ -14,7 +14,7 @@
     'Atanh': [9],
     'AveragePool': [1, 7],
     'BatchNormalization': [1, 6, 7, 9],
-    'Cast': [1, 6],
+    'Cast': [1, 6, 9],
     'Ceil': [1, 6],
     'Clip': [1, 6],
     'Compress': [9],
@@ -152,7 +152,7 @@
     'Atanh': [9],
     'AveragePool': [1, 7],
     'BatchNormalization': [1, 6, 7, 9],
-    'Cast': [1, 6],
+    'Cast': [1, 6, 9],
     'Ceil': [1, 6],
     'Clip': [],
     'Compress': [],
@@ -288,7 +288,7 @@
     'AvgPool': [1, 7],
     'BatchNorm': [1, 6, 7, 9],
     'BiasAdd': [1, 6, 7],
-    'Cast': [1, 6],
+    'Cast': [1, 6, 9],
     'Ceil': [1, 6],
     'ConcatV2': [1, 4],
     'Conv1D': [1],

diff --git a/test/backend/test_node.py b/test/backend/test_node.py
@@ -193,13 +193,24 @@ def test_cast(self):
                     (TensorProto.DOUBLE,
                      tf.float64), (TensorProto.COMPLEX64,
                                    tf.complex64), (TensorProto.COMPLEX128,
-                                                   tf.complex128)]
+                                                   tf.complex128),
+                    (TensorProto.STRING, tf.string)]
     for ty, tf_type in test_cases:
       node_def = helper.make_node("Cast", ["input"], ["output"], to=ty)
       vector = [2, 3]
       output = run_node(node_def, [vector])
       np.testing.assert_equal(output["output"].dtype, tf_type)
 
+    test_cases2 = [(TensorProto.FLOAT, tf.float32),
+                   (TensorProto.INT32, tf.int32),
+                   (TensorProto.INT64, tf.int64),
+                   (TensorProto.DOUBLE, tf.float64)]
+    for ty, tf_type in test_cases2:
+      node_def = helper.make_node("Cast", ["input"], ["output"], to=ty)
+      vector = ['2', '3']
+      output = run_node(node_def, [vector])
+      np.testing.assert_equal(output["output"].dtype, tf_type)
+
   def test_ceil(self):
     node_def = helper.make_node("Ceil", ["X"], ["Y"])
     x = self._get_rnd([1000])