KellenSunderland commented on a change in pull request #13310: [MXNET-703]
Update to TensorRT 5, ONNX IR 3. Fix inference bugs.
URL: https://github.com/apache/incubator-mxnet/pull/13310#discussion_r236058541
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File path: src/operator/contrib/nnvm_to_onnx.cc
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@@ -382,31 +387,16 @@ void ConvertBatchNorm(NodeProto* node_proto, const
NodeAttrs& attrs,
AttributeProto* const spatial = node_proto->add_attribute();
spatial->set_name("spatial");
spatial->set_type(AttributeProto::INT);
- spatial->set_i(1);
-
- AttributeProto* const consumed = node_proto->add_attribute();
- consumed->set_name("consumed_inputs");
- consumed->set_type(AttributeProto::INTS);
-
- for (int i = 0; i < 5; i++) {
- int val = (i < 3) ? 0 : 1;
- consumed->add_ints(static_cast<int64>(val));
- }
+ // MXNet computes mean and variance per feature for batchnorm. Enabling
spatial mode
+ // (default in ONNX3) implies running batchnorm on all spatial features so
we need to explicitly
+ // disable this for MXNet's BatchNorm.
+ spatial->set_i(0);
Review comment:
Good call out, but the test that has been added will cover this. Batchnorms
are in resnet and the math would be way off if the default values didn't align.
One of the reasons I converted to ONNX 3 opset 8 was that it seemed to align
well with MXNet's defaults which means we were able to reduce code in more
places, but there were still a few customizations like this one required.
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