support asymmetric paddings for qconv

This commit is contained in:
fengyuentau 2022-05-16 19:01:37 +08:00
parent c8228e5789
commit ff88132620
2 changed files with 43 additions and 1 deletions

View File

@ -3230,14 +3230,54 @@ void ONNXImporter::parseQuantDequant(LayerParams& layerParams, const opencv_onnx
addLayer(layerParams, node_proto);
}
void ONNXImporter::parseQConv(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
void ONNXImporter::parseQConv(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
{
opencv_onnx::NodeProto node_proto = node_proto_;
int ninputs = node_proto.input_size();
CV_Assert(ninputs == 8 || ninputs == 9);
Mat inp_sc = getBlob(node_proto, 1);
Mat inp_zp = getBlob(node_proto, 2);
if (layerParams.has("pad"))
{
bool asymmetricPadding = false;
DictValue pads = layerParams.get("pad");
const int dims = pads.size() / 2;
for (int i = 0; i < dims; ++i)
{
if (pads.get<int>(i) != pads.get<int>(i + dims))
{
asymmetricPadding = true;
break;
}
}
if (asymmetricPadding && pads.size() == 4)
{
layerParams.erase("pad");
std::vector<int> paddings(4, 0);
for (int i = 0; i < dims; ++i)
{
paddings.push_back(pads.get<int>(i));
paddings.push_back(pads.get<int>(dims + i));
}
LayerParams padLp;
padLp.name = layerParams.name + "/pad";
padLp.type = "PaddingInt8";
padLp.set("paddings", DictValue::arrayInt(&paddings[0], paddings.size()));
padLp.set("depth", CV_8S);
padLp.set("value", inp_zp.at<int8_t>(0));
opencv_onnx::NodeProto proto;
proto.add_input(node_proto.input(0));
proto.add_output(padLp.name);
addLayer(padLp, proto);
node_proto.set_input(0, padLp.name);
}
}
Mat weights = getBlob(node_proto, 3);
int outCn = weights.size[0];
Mat w_scale = getBlob(node_proto, 4);

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@ -1752,6 +1752,8 @@ TEST_P(Test_ONNX_layers, Quantized_Convolution)
testONNXModels("quantized_conv_uint8_weights", npy, 0.004, 0.02);
testONNXModels("quantized_conv_int8_weights", npy, 0.03, 0.5);
testONNXModels("quantized_conv_per_channel_weights", npy, 0.06, 0.4);
testONNXModels("quantized_conv_asymmetric_pads_int8_weights");
}
TEST_P(Test_ONNX_layers, Quantized_MatMul)