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new f5cef48 ONNX export: Scalar, Reshape - Set appropriate tensor type
(#13067)
f5cef48 is described below
commit f5cef48161b5864ce49692b0e4b3bf4b452b7d72
Author: Vandana Kannan <[email protected]>
AuthorDate: Sun Nov 4 09:30:28 2018 -0800
ONNX export: Scalar, Reshape - Set appropriate tensor type (#13067)
np.array sets default dtype to float64 which is
not supported by ONNX. Setting these to appropriate type.
---
python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
index 11e75d9..fb2e697 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -843,7 +843,9 @@ def scalar_op_helper(node, op_name, **kwargs):
"""Helper function for scalar arithmetic operations"""
name, input_nodes, attrs = get_inputs(node, kwargs)
- scalar_value = [float(attrs.get("scalar", 1))]
+ input_type = kwargs["in_type"]
+ scalar_value = np.array([attrs.get("scalar", 1)],
+
dtype=onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[input_type])
initializer = kwargs["initializer"]
flag = True
@@ -864,17 +866,15 @@ def scalar_op_helper(node, op_name, **kwargs):
# else create a new tensor of the scalar value, add it in initializer
if flag is True:
- np_arr = np.array(scalar_value)
- data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np_arr.dtype]
- dims = np.shape(np_arr)
+ dims = np.shape(scalar_value)
scalar_op_name = "scalar_op" + str(kwargs["idx"])
- tensor_node = onnx.helper.make_tensor_value_info(scalar_op_name,
data_type, dims)
+ tensor_node = onnx.helper.make_tensor_value_info(scalar_op_name,
input_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=scalar_op_name,
- data_type=data_type,
+ data_type=input_type,
dims=dims,
vals=scalar_value,
raw=False,
@@ -1249,7 +1249,7 @@ def convert_reshape(node, **kwargs):
output_shape_list = convert_string_to_list(attrs["shape"])
initializer = kwargs["initializer"]
- output_shape_np = np.array(output_shape_list)
+ output_shape_np = np.array(output_shape_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[output_shape_np.dtype]
dims = np.shape(output_shape_np)
