KellenSunderland closed pull request #11893: WIP: Add deconvolutions to onnx
exporter
URL: https://github.com/apache/incubator-mxnet/pull/11893
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diff --git a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
index b2c93670bb4..c920595ae44 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -88,6 +88,37 @@ def parse_helper(attrs, attrs_name, alt_value=None):
raise AttributeError("Malformed %s dimensions: %s" % (attrs_name,
str(attrs_str)))
return alt_value
+
+def parse_padding(attrs):
+ tuple_re = re.compile('\([0-9|,| ]+\)')
+
+ def parse_helper(attrs_name, alt_value=None):
+ if attrs is None:
+ return alt_value
+ attrs_str = attrs.get(attrs_name)
+ if attrs_str is None:
+ return alt_value
+ attrs_match = tuple_re.search(attrs_str)
+ if attrs_match is not None:
+ if attrs_match.span() == (0, len(attrs_str)):
+ dims = eval(attrs_str)
+ return dims
+ else:
+ raise AttributeError("Malformed %s dimensions: %s" %
(attrs_name, str(attrs_str)))
+ return alt_value
+
+ symetric_pads = list(parse_helper("pad", [0, 0]))
+ result = []
+
+ # Each padding in MXNet is assumed to be symmetric in dim1, dim2 ...
+ # In ONNX we need to have a start_dim1, start_dim2, ..., end_dim1, end_dim2
+ for pad in symetric_pads:
+ result.append(pad)
+ for pad in symetric_pads:
+ result.append(pad)
+ return result
+
+
def transform_padding(pad_width):
"""Helper function to convert padding format for pad operator.
"""
@@ -160,6 +191,44 @@ def convert_weights_and_inputs(node, **kwargs):
return [tval_node]
+@mx_op.register("Deconvolution")
+def convert_deconvolution(node, **kwargs):
+ helper, _, _ = import_onnx_modules()
+ name = node["name"]
+ inputs = node["inputs"]
+
+ num_inputs = len(inputs)
+
+ proc_nodes = kwargs["proc_nodes"]
+ input_node = proc_nodes[kwargs["index_lookup"][inputs[0][0]]].name
+ weights_node = proc_nodes[kwargs["index_lookup"][inputs[1][0]]].name
+
+ if num_inputs > 2:
+ bias_node = proc_nodes[kwargs["index_lookup"][inputs[2][0]]].name
+
+ attrs = node.get("attrs")
+ num_filter = int(attrs["num_filter"])
+ kernel_dims = list(parse_helper(attrs, "kernel"))
+ stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
+ pad_dims = parse_padding(attrs)
+ num_group = int(attrs.get("num_group", 1))
+ input_nodes = [input_node, weights_node]
+ if num_inputs > 2:
+ input_nodes.append(bias_node)
+
+ deconv_node = helper.make_node(
+ "ConvTranspose",
+ inputs=input_nodes,
+ outputs=[name],
+ kernel_shape=kernel_dims,
+ strides=stride_dims,
+ pads=pad_dims,
+ group=num_group,
+ name=name
+ )
+
+ return deconv_node
+
@mx_op.register("Convolution")
def convert_convolution(node, **kwargs):
"""Map MXNet's convolution operator attributes to onnx's Conv operator
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