/// <summary>
/// Scripted importer callback
/// </summary>
/// <param name="ctx">Asset import context</param>
public override void OnImportAsset(AssetImportContext ctx)
{
var model = File.ReadAllBytes(ctx.assetPath);
// Analyze model and send analytics if enabled
var nnModel = ModelLoader.Load(ctx.assetPath, skipWeights: true);
BarracudaAnalytics.SendBarracudaImportEvent(null, nnModel);
var assetData = ScriptableObject.CreateInstance <NNModelData>();
assetData.Value = model;
assetData.name = "Data";
assetData.hideFlags = HideFlags.HideInHierarchy;
var asset = ScriptableObject.CreateInstance <NNModel>();
asset.modelData = assetData;
ctx.AddObjectToAsset("main obj", asset, LoadIconTexture());
ctx.AddObjectToAsset("model data", assetData);
ctx.SetMainObject(asset);
}
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();
}
