From a6f9170f109c3456f3adf2f8e882699ae9e90651 Mon Sep 17 00:00:00 2001
From: Dmitry Kurtaev <dmitry.kurtaev+github@gmail.com>
Date: Wed, 31 Oct 2018 18:24:05 +0300
Subject: [PATCH] Add ONNX's padding import
---
modules/dnn/src/onnx/onnx_importer.cpp | 36 +++++++++++++++++++++----
modules/dnn/test/test_onnx_importer.cpp | 5 ++++
2 files changed, 36 insertions(+), 5 deletions(-)
diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp
index 3c50566449..ac48d47b55 100644
--- a/modules/dnn/src/onnx/onnx_importer.cpp
+++ b/modules/dnn/src/onnx/onnx_importer.cpp
@@ -96,6 +96,8 @@ Mat getMatFromTensor(opencv_onnx::TensorProto& tensor_proto)
for (int i = 0; i < tensor_proto.dims_size(); i++) {
sizes.push_back(tensor_proto.dims(i));
}
+ if (sizes.empty())
+ sizes.assign(1, 1);
if (datatype == opencv_onnx::TensorProto_DataType_FLOAT) {
if (!tensor_proto.float_data().empty()) {
@@ -173,11 +175,31 @@ LayerParams ONNXImporter::getLayerParams(const opencv_onnx::NodeProto& node_prot
}
else if(attribute_name == "pads")
{
- CV_Assert(attribute_proto.ints_size() == 4);
- lp.set("pad_t", saturate_cast<int32_t>(attribute_proto.ints(0)));
- lp.set("pad_l", saturate_cast<int32_t>(attribute_proto.ints(1)));
- lp.set("pad_b", saturate_cast<int32_t>(attribute_proto.ints(2)));
- lp.set("pad_r", saturate_cast<int32_t>(attribute_proto.ints(3)));
+ if (node_proto.op_type() == "Pad")
+ {
+ // Padding layer.
+ // Paddings are in order begin0, begin1, .. beginN, end0, end1, ..., endN.
+ // We need to shuffle it to begin0, end0, begin1, end1, ...
+ CV_Assert(attribute_proto.ints_size() % 2 == 0);
+ const int dims = attribute_proto.ints_size() / 2;
+ std::vector<int32_t> paddings;
+ paddings.reserve(attribute_proto.ints_size());
+ for (int i = 0; i < dims; ++i)
+ {
+ paddings.push_back(attribute_proto.ints(i));
+ paddings.push_back(attribute_proto.ints(dims + i));
+ }
+ lp.set("paddings", DictValue::arrayInt(&paddings[0], paddings.size()));
+ }
+ else
+ {
+ // Convolution or pooling.
+ CV_Assert(attribute_proto.ints_size() == 4);
+ lp.set("pad_t", saturate_cast<int32_t>(attribute_proto.ints(0)));
+ lp.set("pad_l", saturate_cast<int32_t>(attribute_proto.ints(1)));
+ lp.set("pad_b", saturate_cast<int32_t>(attribute_proto.ints(2)));
+ lp.set("pad_r", saturate_cast<int32_t>(attribute_proto.ints(3)));
+ }
}
else if(attribute_name == "auto_pad")
{
@@ -543,6 +565,10 @@ void ONNXImporter::populateNet(Net dstNet)
replaceLayerParam(layerParams, "shape", "dim");
}
}
+ else if (layer_type == "Pad")
+ {
+ layerParams.type = "Padding";
+ }
else
{
for (int j = 0; j < node_proto.input_size(); j++) {
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
index 52e867a499..c7a48fe139 100644
--- a/modules/dnn/test/test_onnx_importer.cpp
+++ b/modules/dnn/test/test_onnx_importer.cpp
@@ -129,6 +129,11 @@ TEST_P(Test_ONNX_layers, Constant)
testONNXModels("constant");
}
+TEST_P(Test_ONNX_layers, Padding)
+{
+ testONNXModels("padding");
+}
+
TEST_P(Test_ONNX_layers, MultyInputs)
{
const String model = _tf("models/multy_inputs.onnx");
--
2.18.0