/// <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 initializerTypePool = new Dictionary <string, DataViewType>();
foreach (var valueInfo in model.Graph.Initializer)
{
initializerTypePool[valueInfo.Name] = OnnxTypeParser.GetScalarDataViewType(valueInfo.DataType);
}
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 inputInfos = GetOnnxVariablesFromMetadata(_session.InputMetadata, shapeDictionary, inputTypePool, null);
var outputInfos = GetOnnxVariablesFromMetadata(_session.OutputMetadata, shapeDictionary, outputTypePool, casterPool);
var overrideableInitializers = GetOnnxVariablesFromMetadata(_session.OverridableInitializerMetadata, shapeDictionary, inputTypePool, null);
// Create a view to the used ONNX model from ONNXRuntime's perspective.
ModelInfo = new OnnxModelInfo(inputInfos, outputInfos, overrideableInitializers);
}
/// <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 quietly 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)
{
// If we don't own the model file, _disposed should be false to prevent deleting user's file.
_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 OnnxTransform, is caught and rethrown
throw;
}
}
}
else
{
_session = new InferenceSession(modelFile);
}
try
{
// 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.
var model = new OnnxCSharpToProtoWrapper.ModelProto();
// If we own the model file set the DeleteOnClose flag so it is always deleted.
if (ownModelFile)
{
ModelStream = new FileStream(modelFile, FileMode.Open, FileAccess.Read, FileShare.Read, 4096, FileOptions.DeleteOnClose);
}
else
{
ModelStream = new FileStream(modelFile, FileMode.Open, FileAccess.Read);
}
// The CodedInputStream auto closes the stream, and we need to make sure that our main stream stays open, so creating a new one here.
using (var modelStream = new FileStream(modelFile, FileMode.Open, FileAccess.Read, FileShare.Delete | FileShare.Read))
using (var codedStream = Google.Protobuf.CodedInputStream.CreateWithLimits(modelStream, Int32.MaxValue, 100))
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 initializerTypePool = new Dictionary <string, DataViewType>();
foreach (var valueInfo in model.Graph.Initializer)
{
initializerTypePool[valueInfo.Name] = OnnxTypeParser.GetScalarDataViewType(valueInfo.DataType);
}
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 inputInfos = GetOnnxVariablesFromMetadata(_session.InputMetadata, shapeDictionary, inputTypePool, null);
var outputInfos = GetOnnxVariablesFromMetadata(_session.OutputMetadata, shapeDictionary, outputTypePool, casterPool);
var overrideableInitializers = GetOnnxVariablesFromMetadata(_session.OverridableInitializerMetadata, shapeDictionary, inputTypePool, null);
// Create a view to the used ONNX model from ONNXRuntime's perspective.
ModelInfo = new OnnxModelInfo(inputInfos, outputInfos, overrideableInitializers);
Graph = model.Graph;
}
catch
{
_session.Dispose();
_session = null;
throw;
}
}
