public async Task TestResnetBatch()
{
using (Tensor imageTensor = ImageIO.ReadTensorFromImageFiles <float>(
new string[] { "surfers.jpg", "surfers.jpg" },
224,
224,
0,
1.0f / 255.0f))
using (Resnet resnet = new Resnet())
{
await resnet.Init();
Resnet.RecognitionResult[][] results = resnet.Recognize(imageTensor);
}
}
public async Task TestResnet()
{
using (Tensor imageTensor = ImageIO.ReadTensorFromImageFile <float>("surfers.jpg", 224, 224, 0, 1.0f / 255.0f))
using (Resnet resnet = new Resnet())
{
await resnet.Init();
MetaGraphDef metaGraphDef = MetaGraphDef.Parser.ParseFrom(resnet.MetaGraphDefBuffer.Data);
var signatureDef = metaGraphDef.SignatureDef["serving_default"];
var inputNode = signatureDef.Inputs;
var outputNode = signatureDef.Outputs;
HashSet <string> opNames = new HashSet <string>();
HashSet <string> couldBeInputs = new HashSet <string>();
HashSet <string> couldBeOutputs = new HashSet <string>();
foreach (Operation op in resnet.Graph)
{
String name = op.Name;
opNames.Add(name);
if (op.NumInputs == 0 && op.OpType.Equals("Placeholder"))
{
couldBeInputs.Add(op.Name);
AttrMetadata dtypeMeta = op.GetAttrMetadata("dtype");
AttrMetadata shapeMeta = op.GetAttrMetadata("shape");
DataType type = op.GetAttrType("dtype");
Int64[] shape = op.GetAttrShape("shape");
Buffer valueBuffer = op.GetAttrValueProto("shape");
Buffer shapeBuffer = op.GetAttrTensorShapeProto("shape");
Tensorflow.TensorShapeProto shapeProto =
Tensorflow.TensorShapeProto.Parser.ParseFrom(shapeBuffer.Data);
}
if (op.OpType.Equals("Const"))
{
AttrMetadata dtypeMeta = op.GetAttrMetadata("dtype");
AttrMetadata valueMeta = op.GetAttrMetadata("value");
using (Tensor valueTensor = op.GetAttrTensor("value"))
{
var dim = valueTensor.Dim;
}
}
if (op.OpType.Equals("Conv2D"))
{
AttrMetadata stridesMeta = op.GetAttrMetadata("strides");
AttrMetadata paddingMeta = op.GetAttrMetadata("padding");
AttrMetadata boolMeta = op.GetAttrMetadata("use_cudnn_on_gpu");
Int64[] strides = op.GetAttrIntList("strides");
bool useCudnn = op.GetAttrBool("use_cudnn_on_gpu");
String padding = op.GetAttrString("padding");
}
foreach (Output output in op.Outputs)
{
int[] shape = resnet.Graph.GetTensorShape(output);
if (output.NumConsumers == 0)
{
couldBeOutputs.Add(name);
}
}
Buffer buffer = resnet.Graph.GetOpDef(op.OpType);
Tensorflow.OpDef opDef = Tensorflow.OpDef.Parser.ParseFrom(buffer.Data);
}
using (Buffer versionDef = resnet.Graph.Versions())
{
int l = versionDef.Length;
}
Resnet.RecognitionResult[][] results = resnet.Recognize(imageTensor);
}
}
