private List <OnnxVariableInfo> GetOnnxVariablesFromMetadata(IReadOnlyDictionary <string, NodeMetadata> nodeMetadata,
IDictionary <string, int[]> shapeDictionary,
Dictionary <string, DataViewType> typePool,
Dictionary <string, Func <NamedOnnxValue, object> > casterPool)
{
var onnxVariableInfos = new List <OnnxVariableInfo>();
foreach (var pair in nodeMetadata)
{
var name = pair.Key;
var meta = pair.Value;
var dataViewType = typePool[name];
var caster = casterPool?[name];
if (name.StartsWith("mlnet.") &&
(name.EndsWith(".unusedInput") || name.EndsWith(".unusedOutput")))
{
continue;
}
OnnxVariableInfo info = null;
if (shapeDictionary != null && shapeDictionary.ContainsKey(name))
{
if (!CheckOnnxShapeCompatibility(shapeDictionary[name].ToList(), meta.Dimensions.ToList()))
{
throw Contracts.ExceptParamValue(shapeDictionary[name], nameof(shapeDictionary),
"The specified shape " + string.Join(",", shapeDictionary[name]) +
" is not compatible with the shape " + string.Join(",", meta.Dimensions) +
" loaded from the ONNX model file. Only unknown dimension can replace or " +
"be replaced by another dimension.");
}
if (dataViewType is VectorDataViewType vectorType)
{
if (shapeDictionary[name].All(value => value > 0))
{
dataViewType = new VectorDataViewType(vectorType.ItemType, shapeDictionary[name]);
}
else
{
dataViewType = new VectorDataViewType(vectorType.ItemType);
}
}
info = new OnnxVariableInfo(name, shapeDictionary[name].ToList(), meta.ElementType, dataViewType, caster);
}
else
{
// No user-specified shape is found, so the shape loaded from ONNX model file is used.
info = new OnnxVariableInfo(name, meta.Dimensions.ToList(), meta.ElementType, dataViewType, caster);
}
onnxVariableInfos.Add(info);
}
return(onnxVariableInfos);
}
/// <summary>
/// Constructs OnnxModel object from file.
/// </summary>
/// <param name="modelFile">Model file path.</param>
/// <param name="gpuDeviceId">GPU device ID to execute on. Null for CPU.</param>
/// <param name="fallbackToCpu">If true, resumes CPU execution quitely upon GPU error.</param>
/// <param name="ownModelFile">If true, the <paramref name="modelFile"/> will be deleted when <see cref="OnnxModel"/> is
/// no longer needed.</param>
/// <param name="shapeDictionary"></param>
public OnnxModel(string modelFile, int?gpuDeviceId = null, bool fallbackToCpu = false,
bool ownModelFile = false, IDictionary <string, int[]> shapeDictionary = null)
{
ModelFile = modelFile;
// If we don't own the model file, _disposed should be false to prevent deleting user's file.
_ownModelFile = ownModelFile;
_disposed = false;
if (gpuDeviceId != null)
{
// The onnxruntime v1.0 currently does not support running on the GPU on all of ML.NET's supported platforms.
// This code path will be re-enabled when there is appropriate support in onnxruntime
throw new NotSupportedException("Running Onnx models on a GPU is temporarily not supported!");
}
else
{
_session = new InferenceSession(modelFile);
}
// Load ONNX model file and parse its input and output schema. The reason of doing so is that ONNXRuntime
// doesn't expose full type information via its C# APIs.
ModelFile = modelFile;
var model = new OnnxCSharpToProtoWrapper.ModelProto();
using (var modelStream = File.OpenRead(modelFile))
using (var codedStream = Google.Protobuf.CodedInputStream.CreateWithLimits(modelStream, Int32.MaxValue, 10))
model = OnnxCSharpToProtoWrapper.ModelProto.Parser.ParseFrom(codedStream);
// Parse actual input and output types stored in the loaded ONNX model to get their DataViewType's.
var inputTypePool = new Dictionary <string, DataViewType>();
foreach (var valueInfo in model.Graph.Input)
{
inputTypePool[valueInfo.Name] = OnnxTypeParser.GetDataViewType(valueInfo.Type);
}
var outputTypePool = new Dictionary <string, DataViewType>();
// Build casters which maps NamedOnnxValue to .NET objects.
var casterPool = new Dictionary <string, Func <NamedOnnxValue, object> >();
foreach (var valueInfo in model.Graph.Output)
{
outputTypePool[valueInfo.Name] = OnnxTypeParser.GetDataViewType(valueInfo.Type);
casterPool[valueInfo.Name] = OnnxTypeParser.GetDataViewValueCasterAndResultedType(valueInfo.Type, out Type actualType);
}
var onnxRuntimeInputInfos = new List <OnnxVariableInfo>();
// Collect input information for this ONNX model from ONNXRuntime's perspective.
foreach (var pair in _session.InputMetadata)
{
var name = pair.Key;
var meta = pair.Value;
var dataViewType = inputTypePool[name];
OnnxVariableInfo info = null;
if (shapeDictionary != null && shapeDictionary.ContainsKey(name))
{
// If user provides a shape of a specific tensor, the provided shape overwrites the corresponding one loaded from
// ONNX model file and the deduced DataViewVectorType.
if (!CheckOnnxShapeCompatibility(shapeDictionary[name].ToList(), meta.Dimensions.ToList()))
{
throw Contracts.ExceptParamValue(shapeDictionary[name], nameof(shapeDictionary),
"The specified shape " + string.Join(",", shapeDictionary[name]) +
" is not compatible with the shape " + string.Join(",", meta.Dimensions) +
" loaded from the ONNX model file. Only unknown dimension can replace or " +
"be replaced by another dimension.");
}
if (dataViewType is VectorDataViewType vectorType)
{
if (shapeDictionary[name].All(value => value > 0))
{
dataViewType = new VectorDataViewType(vectorType.ItemType, shapeDictionary[name]);
}
else
{
dataViewType = new VectorDataViewType(vectorType.ItemType);
}
}
info = new OnnxVariableInfo(name, shapeDictionary[name].ToList(), meta.ElementType, dataViewType, null);
}
else
{
// No user-specified shape is found, so the shape loaded from ONNX model file is used.
info = new OnnxVariableInfo(name, meta.Dimensions.ToList(), meta.ElementType, dataViewType, null);
}
onnxRuntimeInputInfos.Add(info);
}
var onnxRuntimeOutputInfos = new List <OnnxVariableInfo>();
// Collect output information for this ONNX model from ONNXRuntime's perspective.
foreach (var pair in _session.OutputMetadata)
{
var name = pair.Key;
var meta = pair.Value;
var dataViewType = outputTypePool[name];
var caster = casterPool[name];
OnnxVariableInfo info = null;
if (shapeDictionary != null && shapeDictionary.ContainsKey(name))
{
// If user provide a shape of a specific tensor, the provided shape overwrites the corresponding one loaded from
// ONNX model file.
if (!CheckOnnxShapeCompatibility(shapeDictionary[name].ToList(), meta.Dimensions.ToList()))
{
throw Contracts.ExceptParamValue(shapeDictionary[name], nameof(shapeDictionary),
"The specified shape " + string.Join(",", shapeDictionary[name]) +
" is not compatible with the shape " + string.Join(",", meta.Dimensions) +
" loaded from the ONNX model file. Only unknown dimension can replace or " +
"be replaced by another dimension.");
}
if (dataViewType is VectorDataViewType vectorType)
{
if (shapeDictionary[name].All(value => value > 0))
{
dataViewType = new VectorDataViewType(vectorType.ItemType, shapeDictionary[name]);
}
else
{
dataViewType = new VectorDataViewType(vectorType.ItemType);
}
}
info = new OnnxVariableInfo(name, shapeDictionary[name].ToList(), meta.ElementType, dataViewType, caster);
}
else
{
// No user-specified shape is found, so the shape loaded from ONNX model file is used.
info = new OnnxVariableInfo(name, meta.Dimensions.ToList(), meta.ElementType, dataViewType, caster);
}
onnxRuntimeOutputInfos.Add(info);
}
// Create a view to the used ONNX model from ONNXRuntime's perspective.
ModelInfo = new OnnxModelInfo(onnxRuntimeInputInfos, onnxRuntimeOutputInfos);
}
/// <summary>
/// Constructs OnnxModel object from file.
/// </summary>
/// <param name="modelFile">Model file path.</param>
/// <param name="gpuDeviceId">GPU device ID to execute on. Null for CPU.</param>
/// <param name="fallbackToCpu">If true, resumes CPU execution quitely upon GPU error.</param>
/// <param name="ownModelFile">If true, the <paramref name="modelFile"/> will be deleted when <see cref="OnnxModel"/> is
/// no longer needed.</param>
public OnnxModel(string modelFile, int?gpuDeviceId = null, bool fallbackToCpu = false, bool ownModelFile = false)
{
ModelFile = modelFile;
// If we don't own the model file, _disposed should be false to prevent deleting user's file.
_ownModelFile = ownModelFile;
_disposed = false;
if (gpuDeviceId != null)
{
try
{
_session = new InferenceSession(modelFile,
SessionOptions.MakeSessionOptionWithCudaProvider(gpuDeviceId.Value));
}
catch (OnnxRuntimeException)
{
if (fallbackToCpu)
{
_session = new InferenceSession(modelFile);
}
else
{
// if called from OnnxTranform, is caught and rethrown.
throw;
}
}
}
else
{
_session = new InferenceSession(modelFile);
}
// Load ONNX model file and parse its input and output schema. The reason of doing so is that ONNXRuntime
// doesn't expose full type information via its C# APIs.
ModelFile = modelFile;
var model = new OnnxCSharpToProtoWrapper.ModelProto();
using (var modelStream = File.OpenRead(modelFile))
model = OnnxCSharpToProtoWrapper.ModelProto.Parser.ParseFrom(modelStream);
// Parse actual input and output types stored in the loaded ONNX model to get their DataViewType's.
var inputTypePool = new Dictionary <string, DataViewType>();
foreach (var valueInfo in model.Graph.Input)
{
inputTypePool[valueInfo.Name] = OnnxTypeParser.GetDataViewType(valueInfo.Type);
}
var outputTypePool = new Dictionary <string, DataViewType>();
// Build casters which maps NamedOnnxValue to .NET objects.
var casterPool = new Dictionary <string, Func <NamedOnnxValue, object> >();
foreach (var valueInfo in model.Graph.Output)
{
outputTypePool[valueInfo.Name] = OnnxTypeParser.GetDataViewType(valueInfo.Type);
casterPool[valueInfo.Name] = OnnxTypeParser.GetDataViewValueCasterAndResultedType(valueInfo.Type, out Type actualType);
}
var onnxRuntimeInputInfos = new List <OnnxVariableInfo>();
foreach (var pair in _session.InputMetadata)
{
var name = pair.Key;
var meta = pair.Value;
var dataViewType = inputTypePool[name];
var info = new OnnxVariableInfo(name, meta.Dimensions.ToList(), meta.ElementType, dataViewType, null);
onnxRuntimeInputInfos.Add(info);
}
var onnxRuntimeOutputInfos = new List <OnnxVariableInfo>();
foreach (var pair in _session.OutputMetadata)
{
var name = pair.Key;
var meta = pair.Value;
var dataViewType = outputTypePool[name];
var caster = casterPool[name];
var info = new OnnxVariableInfo(name, meta.Dimensions.ToList(), meta.ElementType, dataViewType, caster);
onnxRuntimeOutputInfos.Add(info);
}
ModelInfo = new OnnxModelInfo(onnxRuntimeInputInfos, onnxRuntimeOutputInfos);
}
