public static Model ValidateModel(Model model)
{
// validate, model contains no broken links
var brokenLinks = ModelAnalyzer.FindBrokenLinks(model);
if (brokenLinks.Length > 0)
{
D.LogWarning($"Model contains {brokenLinks.Length} broken links: {string.Join(",", brokenLinks)}");
}
// validate, all model outputs are unique
// https://stackoverflow.com/questions/18547354/c-sharp-linq-find-duplicates-in-list
var duplicateOutputs = model.outputs.GroupBy(x => x)
.Where(g => g.Count() > 1)
.Select(y => y.Key);
foreach (var o in duplicateOutputs)
{
D.LogWarning($"Output is specified more than once in the model: {o}");
}
// validate, model contains no unconnected layers
var unconnectedOutputs = ModelAnalyzer.FindUnconnectedOutputs(model);
foreach (var o in unconnectedOutputs)
{
D.LogWarning($"Layer is specified as output, but is missing in the model: {o}");
}
return(model);
}
public static Model PatchModel(Model model, string[] additionalOutputs, string[] trimOutputs = null)
{
bool trimModel = trimOutputs != null;
if (trimOutputs != null)
{
foreach (var o in trimOutputs.Except(model.outputs))
{
if (additionalOutputs == null || !additionalOutputs.Contains(o))
{
D.LogWarning($"Output specified in trimOutputs was not found in the model: {o}");
}
}
var newModel = model.ShallowCopy();
newModel.outputs = trimOutputs.Intersect(model.outputs).ToList();
model = newModel;
}
if (additionalOutputs != null)
{
foreach (var o in additionalOutputs.Except(model.layers.Select(l => l.name)))
{
D.LogWarning($"Layer specified in additionalOutputs was not found in the model: {o}");
}
// 'new' means that output name does not yet exist in model.outputs
// 'valid' means that output name matches one of the existing model.layer names
var newAndValidAdditionalOutputs =
additionalOutputs.Except(model.outputs).Intersect(model.layers.Select(l => l.name));
var newModel = model.ShallowCopy();
newModel.outputs.AddRange(newAndValidAdditionalOutputs);
model = newModel;
}
if (trimModel)
{
var newModel = model.ShallowCopy();
var upstream = ModelAnalyzer.FindUpstreamLayers(model, newModel.outputs.ToArray());
foreach (var l in model.layers)
{
if (!upstream.Contains(l))
{
newModel.layers.Remove(l);
}
}
model = newModel;
}
model = ModelOptimizer.RemoveNoop(model);
return(model);
}
public GenericWorker(Model model, IOps ops, IVars vars, bool verbose = false)
{
m_Model = model;
m_DefaultInputName = ModelAnalyzer.GetDefaultInputName(model);
m_DefaultOutputName = ModelAnalyzer.GetDefaultOutputName(model);
m_Ops = ops;
m_Vars = vars;
m_ModelCompiler = ops as IModelCompiler;
m_Verbose = verbose;
m_RequestResetAllocator = true;
}
public override void PrepareStorage(Model model, IOps ops, IDictionary <string, TensorShape> inputShapes)
{
base.PrepareStorage(model, ops, inputShapes);
ReleaseTemporary();
if (m_CachedModel != model)
{
m_LayersWithStorage = ModelAnalyzer.FindLayersThatRequireStorage(model);
}
m_CachedModel = model;
Assert.AreEqual(m_Temporary, null);
}
public override void PrepareStorage(Model model, IOps ops, IDictionary <string, TensorShape> inputShapes)
{
base.PrepareStorage(model, ops, inputShapes);
if (m_CachedModel != model)
{
// pre-allocate 2 buffers that can be cycled for temporaries
var allocator = m_TemporaryAllocator;
var maxShape = ModelAnalyzer.FindLargestNecessaryTensorShape(model, inputShapes);
var alloc1 = allocator.Alloc(maxShape);
var alloc2 = allocator.Alloc(maxShape);
alloc1 = ops.Prepare(alloc1);
alloc2 = ops.Prepare(alloc2);
allocator.Release(alloc1, false);
allocator.Release(alloc2, false);
}
m_CachedModel = model;
}
static public TensorShape GetShapeByName(this Model model, string name)
{
foreach (var i in model.inputs)
{
if (i.name == name)
{
return(new TensorShape(i.shape));
}
}
TensorShape shape;
if (ModelAnalyzer.TryGetOutputTensorShape(model, name, out shape))
{
return(shape);
}
foreach (var l in model.layers)
{
foreach (var ds in l.datasets)
{
if (ds.name == name)
{
return(ds.shape);
}
}
}
foreach (var mem in model.memories)
{
if (mem.input == name || mem.output == name)
{
return(mem.shape);
}
}
throw new System.Collections.Generic.KeyNotFoundException("Shape " + name + " not found!");
}
void OnEnable()
{
// TODO: investigate perf -- method takes 1s the first time you click on the model in the UI
var nnModel = target as NNModel;
if (nnModel == null)
{
return;
}
if (nnModel.modelData == null)
{
return;
}
m_Model = ModelLoader.Load(nnModel, verbose: false);
if (m_Model == null)
{
return;
}
m_Inputs = m_Model.inputs.Select(i => i.name).ToList();
m_InputsDesc = m_Model.inputs.Select(i => $"shape: ({String.Join(",", i.shape)})").ToList();
m_Outputs = m_Model.outputs.ToList();
bool allKnownShapes = true;
var inputShapes = new Dictionary <string, TensorShape>();
foreach (var i in m_Model.inputs)
{
allKnownShapes = allKnownShapes && !i.shape.Contains(-1) && !i.shape.Contains(0);
if (!allKnownShapes)
{
break;
}
inputShapes.Add(i.name, new TensorShape(i.shape));
}
if (allKnownShapes)
{
m_OutputsDesc = m_Model.outputs.Select(i => {
string output = "(-1,-1,-1,-1)";
try
{
TensorShape shape;
if (ModelAnalyzer.TryGetOutputTensorShape(m_Model, inputShapes, i, out shape))
{
output = shape.ToString();
}
}
catch (Exception e)
{
Debug.LogError($"Unexpected error while evaluating model output {i}. {e}");
}
return($"shape: {output}");
}).ToList();
}
else
{
m_OutputsDesc = m_Model.outputs.Select(i => "shape: (-1,-1,-1,-1)").ToList();
}
m_Memories = m_Model.memories.Select(i => i.input).ToList();
m_MemoriesDesc = m_Model.memories.Select(i => $"shape:{i.shape.ToString()} output:{i.output}").ToList();
var layers = m_Model.layers.Where(i => i.type != Layer.Type.Load);
var constants = m_Model.layers.Where(i => i.type == Layer.Type.Load);
m_Layers = layers.Select(i => i.type.ToString()).ToList();
m_LayersDesc = layers.Select(i => i.ToString()).ToList();
m_Constants = constants.Select(i => i.type.ToString()).ToList();
m_ConstantsDesc = constants.Select(i => i.ToString()).ToList();
m_NumEmbeddedWeights = layers.Sum(l => (long)l.weights.Length).ToString();
m_NumConstantWeights = constants.Sum(l => (long)l.weights.Length).ToString();
m_Warnings = m_Model.Warnings.Select(i => i.LayerName).ToList();
m_WarningsDesc = m_Model.Warnings.Select(i => i.Message).ToList();
}
public virtual void PrepareModel(Model model, IDictionary <string, TensorShape> inputShapes)
{
var modelHash = CalcModelWithInputsHashCode(model, inputShapes);
if (modelHash == m_CachedModelHash)
{
return;
}
m_CachedModelHash = modelHash;
m_CompiledLayers.Clear();
IDictionary <string, TensorShape> shapesByName;
ModelAnalyzer.ListTemporaryTensorShapes(model, inputShapes, out shapesByName);
foreach (var l in model.layers)
{
if (m_CompiledLayers.ContainsKey(l))
{
continue; // already compiled
}
if (l.inputs.Length == 0)
{
continue; // don't need to compile layers without inputs, so far all of them are CPU only
}
var X = shapesByName[l.inputs[0]];
var O = shapesByName[l.name];
ComputeKernel kernel = new ComputeKernel();
if (l.type == Layer.Type.Dense)
{
var itemSize = 4; // @TODO: itemSizeInBytes == 2 | float16
kernel = BestKernel(
ComputeKernelLibrary.Dense(X, l.datasets[0].shape, O, itemSize >> 2));
}
else if (
l.type == Layer.Type.Conv2D)
{
Assert.IsNotNull(l.stride);
Assert.IsNotNull(l.pad);
kernel = BestKernel(
ComputeKernelLibrary.Conv2D(X, l.datasets[0].shape, O, l.stride, l.pad));
}
else if (
l.type == Layer.Type.DepthwiseConv2D)
{
kernel = BestKernel(
ComputeKernelLibrary.DepthwiseConv2D(X, l.datasets[0].shape, O));
}
else if (
l.type == Layer.Type.Conv2DTrans)
{
kernel = BestKernel(
ComputeKernelLibrary.Conv2DTrans(X, l.datasets[0].shape, O));
}
else if (
l.type == Layer.Type.Upsample2D)
{
kernel = BestKernel(
ComputeKernelLibrary.Upsample2D(X, O));
}
else if (
l.type == Layer.Type.MaxPool2D ||
l.type == Layer.Type.AvgPool2D)
{
var kernelName = l.type.ToString();
Assert.IsNotNull(l.pool);
Assert.IsNotNull(l.stride);
Assert.IsNotNull(l.pad);
var pad = X.AdjustPadToPool(l.pool, l.stride, l.pad);
if (pad[0] == 0 && pad[1] == 0 && pad[2] == 0 && pad[3] == 0)
{
kernelName += "_NoPads";
}
kernel = BestKernel(
ComputeKernelLibrary.Pool2D(X, O, kernelName));
}
// @TODO: reimplement GlobalPools, currently require different kernels for each pyramid step
//else if (
// l.type == Layer.Type.GlobalMaxPool2D ||
// l.type == Layer.Type.GlobalAvgPool2D)
//{
// var kernelName = l.type.ToString();
// kernel = BestKernel(
// ComputeKernelLibrary.GlobalPool2D(X, O, kernelName));
//}
else if (
l.type == Layer.Type.ScaleBias)
{
kernel = BestKernel(
ComputeKernelLibrary.ScaleBias(X, O));
}
// @TODO: reimplement Normalization, which became a multi-kernel operation after optimizations
//else if (
// l.type == Layer.Type.Normalization)
//{
// kernel = BestKernel(
// ComputeKernelLibrary.Normalization(X, O));
//}
else if (
l.type == Layer.Type.Add ||
l.type == Layer.Type.Sub ||
l.type == Layer.Type.Mul ||
l.type == Layer.Type.Div ||
l.type == Layer.Type.Pow ||
l.type == Layer.Type.Min ||
l.type == Layer.Type.Max
// || l.type == Layer.Type.Mean @TODO: implement BroadcastMean
)
{
var kernelName = "Broadcast" + l.type;
kernel = BestKernel(
ComputeKernelLibrary.Broadcast(X, O, kernelName));
}
// @TODO: implement Concat, currently might require different kernel for each tensor
//else if (
// l.type == Layer.Type.Concat) {}
// Activations
else if (l.type == Layer.Type.Activation)
{
if (l.activation == Layer.Activation.Softmax)
{
kernel = BestKernel(
ComputeKernelLibrary.Softmax(X, O));
}
else if (l.activation == Layer.Activation.LogSoftmax)
{
kernel = BestKernel(
ComputeKernelLibrary.LogSoftmax(X, O));
}
else if (l.activation == Layer.Activation.PRelu)
{
kernel = BestKernel(
ComputeKernelLibrary.PRelu(X, O));
}
else if (l.activation != Layer.Activation.None)
{
var kernelName = l.activation.ToString();
kernel = BestKernel(
ComputeKernelLibrary.Activation(X, O, kernelName));
}
}
m_CompiledLayers.Add(l, new CompiledLayer {
kernel = kernel, shape = O
});
}
}
private Model ConvertOnnxModel(ModelProto onnxModel)
{
var model = new Model();
var modelBuilder = new ModelBuilder(model);
// Convert graph inputs & outputs
var initializersByName = onnxModel.Graph.Initializer.ToDictionary(i => i.Name, i => true);
foreach (ValueInfoProto i in onnxModel.Graph.Input)
{
// skip input tensors that have initializer data, they are constant tensors not global inputs
if (initializersByName.ContainsKey(i.Name))
{
continue;
}
if (m_OverrideGlobalInputs.ContainsKey(i.Name))
{
Const(i.Name, m_OverrideGlobalInputs[i.Name]);
continue;
}
modelBuilder.Input(i.Name, ONNXLayout.ConvertSymbolicShapeToBarracuda(i.Type.TensorType.Shape, onnxLayout: "NCHW"));
Output(i.Name, onnxShape: i.Type.TensorType.Shape.Dim.Select(d => d.DimValue).ToArray(), onnxLayout: "NCHW");
}
foreach (ValueInfoProto o in onnxModel.Graph.Output)
{
modelBuilder.Output(o.Name);
}
// TODO: process model (recurrent nodes) memories
// Read constants from initializer list
foreach (TensorProto initializer in onnxModel.Graph.Initializer)
{
Const(initializer.Name, new ONNXTensor(initializer));
}
// Convert graph nodes
foreach (NodeProto onnxNode in onnxModel.Graph.Node)
{
var node = new ONNXNodeWrapper(onnxNode, m_ModelTensors, model.Warnings);
var nodeId = node.Name;
var opType = node.OperatorType;
Output(node);
bool injectDummy = false;
if (m_NodeImporters.ContainsKey(opType))
{
try
{
if (node.AreAllInputsConst && !m_ShouldNotBeBaked.Contains(opType))
{
Profiler.BeginSample($"Bake {opType} {node.Name}");
var bakedTensor = BakeNodeIntoConstant(m_NodeImporters[opType], node);
Const(node.Name, bakedTensor);
var printTensor = bakedTensor.ToBarracuda("NCHW");
D.Log($"Baked node {nodeId} into constant of shape {printTensor.shape} and values: {printTensor.DataToString()}");
Profiler.EndSample();
}
else
{
Profiler.BeginSample($"Import {opType} {node.Name}");
m_NodeImporters[opType](modelBuilder, node);
Profiler.EndSample();
}
}
catch (Exception e)
{
// We support the layer but something went wrong while importing it
// We log the problem and insert an identity layer
string message = $"Unexpected error while parsing layer {nodeId} of type {opType}.\n{e.Message}\n\nJson: {onnxNode}\n{e.StackTrace}\n";
Warn(model, nodeId, message);
injectDummy = true;
}
}
else
{
//We don't support this type of layer
//We log the problem and insert an identity layer
string message = $"Unknown type encountered while parsing layer {nodeId} of type {opType}. We replace by an identity layer.";
Warn(model, nodeId, message);
injectDummy = true;
}
if (injectDummy)
{
var originalLayerHadInputs = (node.InputCount > 0);
if (originalLayerHadInputs)
{
modelBuilder.Identity(nodeId, node.Input0);
}
else // if errorneous layer had no inputs, inject dummy constant which does not require any inputs
{
modelBuilder.Const(nodeId, new Tensor());
}
}
m_ModelTensors.CompleteUninitializedFields(node);
}
// Convert constant tensors
int insertionIndex = 0;
foreach (var entry in constantTensors)
{
modelBuilder.Const(entry.Key, entry.Value.ToBarracuda(onnxLayout: "CONST"),
insertionIndex++);
}
// Model should not contain any broken links in the end
var unconnectedInputs = ModelAnalyzer.FindBrokenLinks(model);
Debug.Assert(unconnectedInputs.Length == 0);
if (unconnectedInputs.Length > 0)
{
var message = $"Broken links: {string.Join(", ", unconnectedInputs)}";
Warn(model, "", message);
}
// Parse meta data
var irVersion = onnxModel.IrVersion; // legacy
if (onnxModel.OpsetImport?.Count > 0)
{
irVersion = onnxModel.OpsetImport[0].Version;
}
model.ProducerName = $"{onnxModel.ProducerName} v{onnxModel.ProducerVersion}";
model.IrSource = "ONNX";
model.IrVersion = $"{irVersion}";
// strip :0 at the end of string name for TF import
if (patchRemoveTrailingTFExportCharacters)
{
model.inputs = model.inputs.Select(i => { i.name = i.name.EndsWith(":0") ? i.name.Remove(i.name.Length - 2) : i.name;
return(i); }).ToList();
model.outputs = model.outputs.Select(o => { o = o.EndsWith(":0") ? o.Remove(o.Length - 2) : o;
return(o); }).ToList();
model.memories = model.memories.Select(m => { m.input = m.input.EndsWith(":0") ? m.input.Remove(m.input.Length - 2) : m.input;
m.output = m.output.EndsWith(":0") ? m.output.Remove(m.output.Length - 2) : m.output;
return(m); }).ToList();
model.layers = model.layers.Select(l => { l.name = l.name.EndsWith(":0") ? l.name.Remove(l.name.Length - 2) : l.name;
for (int i = 0; i < l.datasets.Length; i++)
{
l.datasets[i].name = l.datasets[i].name.EndsWith(":0") ? l.datasets[i].name.Remove(l.datasets[i].name.Length - 2) : l.datasets[i].name;
}
for (int i = 0; i < l.inputs.Length; i++)
{
l.inputs[i] = l.inputs[i].EndsWith(":0") ? l.inputs[i].Remove(l.inputs[i].Length - 2) : l.inputs[i];
}
return(l); }).ToList();
}
return(model);
}
void OnEnable()
{
// TODO: investigate perf -- method takes 1s the first time you click on the model in the UI
var nnModel = target as NNModel;
if (nnModel == null)
{
return;
}
if (nnModel.modelData == null)
{
return;
}
m_Model = ModelLoader.Load(nnModel, verbose: false);
if (m_Model == null)
{
return;
}
m_Inputs = m_Model.inputs.Select(i => i.name).ToList();
m_InputsDesc = m_Model.inputs.Select(i => $"shape: ({String.Join(",", i.shape)})").ToList();
m_Outputs = m_Model.outputs.ToList();
bool allKnownShapes = true;
var inputShapes = new Dictionary <string, TensorShape>();
foreach (var i in m_Model.inputs)
{
allKnownShapes = allKnownShapes && !i.shape.Contains(-1) && !i.shape.Contains(0);
if (!allKnownShapes)
{
break;
}
inputShapes.Add(i.name, new TensorShape(i.shape));
}
if (allKnownShapes)
{
m_OutputsDesc = m_Model.outputs.Select(i => { TensorShape shape; bool sucess = ModelAnalyzer.TryGetOutputTensorShape(m_Model, inputShapes, i, out shape); return(sucess ? $"shape: {shape.ToString()}" : "shape: (-1,-1,-1,-1)"); }).ToList();
}
else
{
m_OutputsDesc = m_Model.outputs.Select(i => "shape: (-1,-1,-1,-1)").ToList();
}
m_Memories = m_Model.memories.Select(i => i.input).ToList();
m_MemoriesDesc = m_Model.memories.Select(i => $"shape:{i.shape.ToString()} output:{i.output}").ToList();
m_Layers = m_Model.layers.Select(i => i.type.ToString()).ToList();
m_LayersDesc = m_Model.layers.Select(i => i.ToString()).ToList();
m_NumWeights = m_Model.layers.Sum(l => (long)l.weights.Length).ToString();
m_Warnings = m_Model.Warnings.Select(i => i.LayerName).ToList();
m_WarningsDesc = m_Model.Warnings.Select(i => i.Message).ToList();
}
