fix: Use 1 element tensor as scalar for Eltwise

tools/python/transform/onnx_converter.py

@@ -950,7 +950,8 @@ class OnnxConverter(base_converter.ConverterInterface):
                     node.inputs[0] not in self._consts:
                 const_name = node.inputs[1]
                 const_tensor = self._consts[const_name]
-                if len(const_tensor.dims) == 0:
+                dims = const_tensor.dims
+                if len(dims) == 0 or (len(dims) == 1 and dims[0] == 1):
                     value_arg = op.arg.add()
                     value_arg.name = MaceKeyword.mace_scalar_input_str
                     if const_tensor.data_type == mace_pb2.DT_INT32:
@@ -970,7 +971,8 @@ class OnnxConverter(base_converter.ConverterInterface):
                     node.inputs[1] not in self._consts:
                 const_name = node.inputs[0]
                 const_tensor = self._consts[const_name]
-                if len(const_tensor.dims) == 0:
+                dims = const_tensor.dims
+                if len(dims) == 0 or (len(dims) == 1 and dims[0] == 1):
                     value_arg = op.arg.add()
                     value_arg.name = MaceKeyword.mace_scalar_input_str
                     if const_tensor.data_type == mace_pb2.DT_INT32:

tools/python/transform/tensorflow_converter.py

@@ -587,33 +587,38 @@ class TensorflowConverter(base_converter.ConverterInterface):
                         EltwiseType.SUM, EltwiseType.PROD,
                         EltwiseType.MAX, EltwiseType.MIN]
 
-                if (len(tf_op.inputs) > 1 and
-                        len(self.infer_tensor_shape(tf_op.inputs[1])) == 0 and
-                        tf_op.inputs[1].op.type == TFOpType.Const.name):
-                    scalar = tf_op.inputs[1].eval().astype(np.float32)
-                    value_arg = op.arg.add()
-                    value_arg.name = MaceKeyword.mace_scalar_input_str
-                    value_arg.f = scalar
-                    self._skip_tensor.add(tf_op.inputs[1].name)
-                    value_index_arg = op.arg.add()
-                    value_index_arg.name = \
-                        MaceKeyword.mace_scalar_input_index_str
-                    value_index_arg.i = 1
-                    self._skip_tensor.add(tf_op.inputs[1].name)
-                    del op.input[1]
-                elif len(self.infer_tensor_shape(tf_op.inputs[0])) == 0 and \
-                        tf_op.inputs[0].op.type == TFOpType.Const.name and \
-                        is_commutative(type_arg.i):
-                    scalar = tf_op.inputs[0].eval().astype(np.float32)
-                    value_arg = op.arg.add()
-                    value_arg.name = MaceKeyword.mace_scalar_input_str
-                    value_arg.f = scalar
-                    value_index_arg = op.arg.add()
-                    value_index_arg.name = \
-                        MaceKeyword.mace_scalar_input_index_str
-                    value_index_arg.i = 0
-                    self._skip_tensor.add(tf_op.inputs[0].name)
-                    del op.input[0]
+                if len(tf_op.inputs) > 1:
+                    shape = self.infer_tensor_shape(tf_op.inputs[1])
+                    if (len(shape) == 0 or
+                            (len(shape) == 1 and shape[0] == 1)) and \
+                            tf_op.inputs[1].op.type == TFOpType.Const.name:
+                        scalar = tf_op.inputs[1].eval().astype(np.float32)
+                        value_arg = op.arg.add()
+                        value_arg.name = MaceKeyword.mace_scalar_input_str
+                        value_arg.f = scalar
+                        self._skip_tensor.add(tf_op.inputs[1].name)
+                        value_index_arg = op.arg.add()
+                        value_index_arg.name = \
+                            MaceKeyword.mace_scalar_input_index_str
+                        value_index_arg.i = 1
+                        self._skip_tensor.add(tf_op.inputs[1].name)
+                        del op.input[1]
+                else:
+                    shape = self.infer_tensor_shape(tf_op.inputs[0])
+                    if (len(shape) == 0 or
+                            (len(shape) == 1 and shape[0] == 1)) and \
+                            is_commutative(type_arg.i) and \
+                            tf_op.inputs[0].op.type == TFOpType.Const.name:
+                        scalar = tf_op.inputs[0].eval().astype(np.float32)
+                        value_arg = op.arg.add()
+                        value_arg.name = MaceKeyword.mace_scalar_input_str
+                        value_arg.f = scalar
+                        value_index_arg = op.arg.add()
+                        value_index_arg.name = \
+                            MaceKeyword.mace_scalar_input_index_str
+                        value_index_arg.i = 0
+                        self._skip_tensor.add(tf_op.inputs[0].name)
+                        del op.input[0]
             except tf.errors.InvalidArgumentError:
                 pass