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support asymmetric paddings for qconv
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c8228e5789
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@ -3230,14 +3230,54 @@ void ONNXImporter::parseQuantDequant(LayerParams& layerParams, const opencv_onnx
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addLayer(layerParams, node_proto);
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}
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void ONNXImporter::parseQConv(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
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void ONNXImporter::parseQConv(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
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{
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opencv_onnx::NodeProto node_proto = node_proto_;
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int ninputs = node_proto.input_size();
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CV_Assert(ninputs == 8 || ninputs == 9);
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Mat inp_sc = getBlob(node_proto, 1);
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Mat inp_zp = getBlob(node_proto, 2);
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if (layerParams.has("pad"))
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{
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bool asymmetricPadding = false;
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DictValue pads = layerParams.get("pad");
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const int dims = pads.size() / 2;
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for (int i = 0; i < dims; ++i)
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{
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if (pads.get<int>(i) != pads.get<int>(i + dims))
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{
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asymmetricPadding = true;
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break;
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}
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}
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if (asymmetricPadding && pads.size() == 4)
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{
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layerParams.erase("pad");
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std::vector<int> paddings(4, 0);
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for (int i = 0; i < dims; ++i)
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{
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paddings.push_back(pads.get<int>(i));
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paddings.push_back(pads.get<int>(dims + i));
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}
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LayerParams padLp;
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padLp.name = layerParams.name + "/pad";
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padLp.type = "PaddingInt8";
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padLp.set("paddings", DictValue::arrayInt(&paddings[0], paddings.size()));
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padLp.set("depth", CV_8S);
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padLp.set("value", inp_zp.at<int8_t>(0));
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opencv_onnx::NodeProto proto;
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proto.add_input(node_proto.input(0));
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proto.add_output(padLp.name);
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addLayer(padLp, proto);
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node_proto.set_input(0, padLp.name);
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}
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}
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Mat weights = getBlob(node_proto, 3);
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int outCn = weights.size[0];
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Mat w_scale = getBlob(node_proto, 4);
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@ -1752,6 +1752,8 @@ TEST_P(Test_ONNX_layers, Quantized_Convolution)
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testONNXModels("quantized_conv_uint8_weights", npy, 0.004, 0.02);
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testONNXModels("quantized_conv_int8_weights", npy, 0.03, 0.5);
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testONNXModels("quantized_conv_per_channel_weights", npy, 0.06, 0.4);
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testONNXModels("quantized_conv_asymmetric_pads_int8_weights");
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}
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TEST_P(Test_ONNX_layers, Quantized_MatMul)
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