/// <summary>
/// Get model tensor shape by name
/// </summary>
/// <param name="model">Model</param>
/// <param name="name">Tensor name</param>
/// <returns>Tensor shape</returns>
/// <exception cref="KeyNotFoundException"></exception>
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, skipWeights: true);
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.datasets.Sum(ds => (long)ds.length));
m_NumConstantWeights = constants.Sum(l => (long)l.datasets.Sum(ds => (long)ds.length));
// weights are not loaded for UI, recompute size
m_TotalWeightsSizeInBytes = 0;
for (var l = 0; l < m_Model.layers.Count; ++l)
{
for (var d = 0; d < m_Model.layers[l].datasets.Length; ++d)
{
m_TotalWeightsSizeInBytes += m_Model.layers[l].datasets[d].length;
}
}
m_Warnings = m_Model.Warnings.Select(i => i.LayerName).ToList();
m_WarningsDesc = m_Model.Warnings.Select(i => i.Message).ToList();
}