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Make use of explicit output shape for the de-convolution layer.

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This is relevant to cases where the net is loaded from an ONNX model
containing ConvTranspose layers, which may include the output_shape
attribute.

c.f. https://github.com/onnx/onnx/blob/master/docs/Operators.md#ConvTranspose
This commit is contained in:
Kohei Yoshida 2019-02-28 14:16:27 -05:00
parent cedd78d526
commit 3bcff7f941
1 changed files with 31 additions and 0 deletions
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31
modules/dnn/src/onnx/onnx_importer.cpp
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@ -591,6 +591,37 @@ void ONNXImporter::populateNet(Net dstNet)
}
layerParams.set("num_output", layerParams.blobs[0].size[1] * layerParams.get<int>("group", 1));
layerParams.set("bias_term", node_proto.input_size() == 3);
if (layerParams.has("output_shape"))
{
const DictValue& outShape = layerParams.get("output_shape");
if (outShape.size() != 4)
CV_Error(Error::StsNotImplemented, "Output shape must have 4 elements.");
const int strideY = layerParams.get<int>("stride_h", 1);
const int strideX = layerParams.get<int>("stride_w", 1);
const int outH = outShape.getIntValue(2);
const int outW = outShape.getIntValue(3);
if (layerParams.get<String>("pad_mode") == "SAME")
{
layerParams.set("adj_w", (outW - 1) % strideX);
layerParams.set("adj_h", (outH - 1) % strideY);
}
else if (layerParams.get<String>("pad_mode") == "VALID")
{
if (!layerParams.has("kernel_h") || !layerParams.has("kernel_w"))
CV_Error(Error::StsNotImplemented,
"Required attributes 'kernel_h' and 'kernel_w' are not present.");
int kernelH = layerParams.get<int>("kernel_h");
int kernelW = layerParams.get<int>("kernel_w");
layerParams.set("adj_w", (outW - kernelW) % strideX);
layerParams.set("adj_h", (outH - kernelH) % strideY);
}
}
}
else if (layer_type == "Transpose")
{