vandanavk commented on a change in pull request #12399: ONNX export: Add Crop,
Deconvolution and fix the default stride of Pooling to 1
URL: https://github.com/apache/incubator-mxnet/pull/12399#discussion_r245162200
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File path: python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
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@@ -219,6 +219,72 @@ def convert_convolution(node, **kwargs):
return [conv_node]
+@mx_op.register("Deconvolution")
+def convert_deconvolution(node, **kwargs):
+ """Map MXNet's deconvolution operator attributes to onnx's ConvTranspose
operator
+ and return the created node.
+ """
+ name, inputs, attrs = get_inputs(node, kwargs)
+
+ kernel_dims = list(parse_helper(attrs, "kernel"))
+ stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
+ pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
+ num_group = int(attrs.get("num_group", 1))
+ dilations = list(parse_helper(attrs, "dilate", [1, 1]))
+ adj_dims = list(parse_helper(attrs, "adj", [0, 0]))
+
+ pad_dims = pad_dims + pad_dims
+
+ deconv_node = onnx.helper.make_node(
+ "ConvTranspose",
+ inputs=inputs,
+ outputs=[name],
+ kernel_shape=kernel_dims,
+ strides=stride_dims,
+ dilations=dilations,
+ output_padding=adj_dims,
+ pads=pad_dims,
+ group=num_group,
+ name=name
+ )
+
+ return [deconv_node]
+
+
+@mx_op.register("Crop")
+def convert_crop(node, **kwargs):
+ """Map MXNet's crop operator attributes to onnx's Crop operator
+ and return the created node.
+ """
+ name, inputs, attrs = get_inputs(node, kwargs)
+
+ num_inputs = len(inputs)
+
+ proc_nodes = kwargs["proc_nodes"]
+ input_node = proc_nodes[kwargs["index_lookup"][inputs[0][0]]].name
+
+ x, y = list(parse_helper(attrs, "offset"))
+ h, w = list(parse_helper(attrs, "h_w", [0, 0]))
+ if num_inputs > 1:
+ h, w = kwargs["out_shape"][-2:]
+ border = [x, y, x + w, y + h]
+
+ crop_node = onnx.helper.make_node(
+ "Crop",
+ inputs=[input_node],
+ outputs=[name],
+ border=border,
+ scale=[1, 1],
+ name=name
+ )
+
+ logging.warning(
+ "Using an experimental ONNX operator: Crop. " \
+ "Its definition can change.")
+
+ return [crop_node]
+
Review comment:
@ptrendx export tests usually perform an import-export-import on an ONNX
model and then compare inference results.
if you are planning to add an ONNX import for crop, then adding the test
would be straightforward - just add a test_case in test_node.py.
if you are adding export alone for crop, then in test_node.py, create a new
test case list (same format as the existing maybe), add a new function
test_export() in class TestNode(unittest.TestCase), export to onnx format. not
sure how to test inference in this case though.
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