bool ConvertNormal(Layer layer, ModelBuilder net)
{
if (layer.inputs.Length == 1)
{
return(true);
}
var shape = new TensorShape(layer.pool);
var permutations = shape.Get8DPermutationsForNCHWPermutationsAndShape(k_FromNCHWtoNHWC);
// Preserve symbolic shape by operating on int array instead of TensorShape, which would resolve unknown dimensions
layer.pool = TensorExtensions.Permute(layer.pool, permutations);
return(true);
}
void Rewrite(ref Model model)
{
IRShapeInferenceHelper.RankInference.ListTemporaryTensorRanks(model, out m_RanksByName);
var inputShapes = new Dictionary <string, TensorShape>();
foreach (var i in model.inputs)
{
if (!ModelAnalyzer.IsInputShapeAcceptablyKnowForShapeInference(i))
{
continue;
}
inputShapes.Add(i.name, new TensorShape(i.shape));
}
IRShapeInferenceHelper.ShapeInference.ListTemporaryTensorShapesNCHW(model, inputShapes, ref m_RanksByName, out m_ShapesByName);
var nhwc = model.ShallowCopy();
nhwc.layers.Clear();
nhwc.layout = "NHWC";
// TF2ONNX transpose pattern -> part of the model are in NHWC and not NCHW
// * identify those
// * transpose inputs to NCHW
// * remove layout transposes
// * convert axis/constants accordingly
LayoutTransposeRemovalHelper transposeRemoval = new LayoutTransposeRemovalHelper();
m_isModelExportedFromNHWC = transposeRemoval.InferAllLayersChannelOrder(model, out m_layersChannelOrder);
if (m_isModelExportedFromNHWC && !transposeRemoval.IsImporterLikelyNHWCLayout(model.ProducerName))
{
nhwc.Warnings.Add(new Model.ImporterWarning("model", "model detected as NCHW, but not natively in this layout, behavior might be erroneous"));
}
// remove layout change transposes
if (m_isModelExportedFromNHWC)
{
transposeRemoval.RemoveAllChannelLayoutTransposes(ref model, m_layersChannelOrder);
}
var modelBuilder = new ModelBuilder(nhwc);
for (int i = 0; i < nhwc.inputs.Count; i++)
{
Model.Input input = nhwc.inputs[i];
int[] shape = input.shape;
var tensorShape = new TensorShape(shape);
int[] rankPermutations = GetChannelsLastPermutationsFromRank(input.rank);
int[] permutations = tensorShape.Get8DPermutationsForNCHWPermutationsAndShape(rankPermutations);
// Preserve symbolic shape by operating on int array instead of TensorShape, which would resolve unknown dimensions
if (m_isModelExportedFromNHWC) // transpose input shape if importer preserved NHWC layout
{
if (m_layersChannelOrder[input.name] == LayoutTransposeRemovalHelper.ChannelsOrder.NCHW)
{
input.shape = TensorExtensions.Permute(shape, permutations);
}
else
{
var onnxShape = new List <int> {
shape[2], shape[5], shape[6], shape[7]
};
onnxShape.RemoveRange(input.rank, 4 - input.rank);
input.shape = IRShapeInferenceHelper.ShapeInference.BarracudaLayoutToTensorShapeLayout(onnxShape.ToArray());
}
}
else
{
input.shape = TensorExtensions.Permute(shape, permutations);
}
nhwc.inputs[i] = input;
}
// NCHW -> Barracuda NHWC rewriter (some layer need to insert aditional layers to be Barracuda compatible)
var rewriters = InstantiateRewriterNCHWToNHWC();
// NHWC -> Barracuda NHWC rewriter (axis and constant padding padding)
var rewritersNHWC = InstantiateRewriterNHWCToNHWC();
foreach (var l in model.layers)
{
// Some nodes output multiple layers (e.g. LSTM), so don't process or include those layers
if (nhwc.layers.Exists(alreadyOutputLayer => alreadyOutputLayer.name == l.name))
{
continue;
}
if (m_layersChannelOrder.TryGetValue(l.name, out LayoutTransposeRemovalHelper.ChannelsOrder layerChannelOrder))
{
if (m_isModelExportedFromNHWC && (layerChannelOrder == LayoutTransposeRemovalHelper.ChannelsOrder.NHWC))
{
if (!rewritersNHWC.TryGetValue(l.type, out Func <Layer, ModelBuilder, bool> rwNCHW) || rwNCHW(l, modelBuilder))
{
nhwc.layers.Add(l);
}
continue;
}
}
if (!rewriters.TryGetValue(l.type, out Func <Layer, ModelBuilder, bool> rw) || rw(l, modelBuilder))
{
// Either no re-write was needed or the layer was not replaced
nhwc.layers.Add(l);
}
}
// We need to correct constants to have broadcast work correctly
// ONNX: 1,64,32 + c:32
// Barracuda: 1,_32,64 + c:_,_,32,64 and not c:32,_,_,_
// X:5,7 + c: 6,9,5,7 = 6,9,5,7
// X: 5,_,_,7 + c: 6,5,7,9 = ???
CorrectConstantsForBroadCast(ref nhwc);
CorrectDynamicInputsForBroadCast(ref nhwc);
// for NHWC importers, perform slightly more aggressive output shape check
// => add transposes to match onnx layout
if (transposeRemoval.IsImporterLikelyNHWCLayout(model.ProducerName))
{
CorrectOutputLayoutToMatchNHWCLayout(ref nhwc);
}
model = nhwc;
}
