public async Task Invokes_predict_async_call()
{
var expectedResultValues = new[] { 1f, 2f, 3f };
var outputTensorProto = new TensorProto {
Dtype = DataType.DtFloat
};
outputTensorProto.FloatVal.Add(expectedResultValues);
outputTensorProto.TensorShape = new TensorShapeProto();
outputTensorProto.TensorShape.Dim.Add(new TensorShapeProto.Types.Dim());
outputTensorProto.TensorShape.Dim[0].Size = 3;
var predictRequest = new PredictRequest();
var predictResponse = new PredictResponse();
predictResponse.Outputs.Add("output_alias", outputTensorProto);
var predictionServiceClientMock = new Mock <IPredictionServiceClient>();
predictionServiceClientMock.Setup(x => x.PredictAsync(predictRequest)).ReturnsAsync(predictResponse).Verifiable();
var scoringRequestMock = new Mock <IScoringRequest>();
scoringRequestMock.Setup(x => x.MakePredictRequest()).Returns(() => predictRequest);
var scoringClient = new ScoringClient(predictionServiceClientMock.Object);
var result = await scoringClient.ScoreAsync(scoringRequestMock.Object);
Assert.Equal(expectedResultValues, result);
scoringRequestMock.Verify(x => x.MakePredictRequest(), Times.Exactly(1));
predictionServiceClientMock.Verify(x => x.PredictAsync(predictRequest), Times.Exactly(1));
}
private static async Task <int> MainAsync(string[] args)
{
if (args.Length < 2)
{
Console.WriteLine($"Use as {errorMessage}");
return(1);
}
var host = args[0];
var image = args[1];
var serviceName = args[2];
string auth = null;
if (args.Length > 3)
{
auth = args[4];
Console.WriteLine(!string.IsNullOrEmpty(auth) ? "Using auth" : "Not using auth");
}
var useSSL = false;
if (args.Length > 4)
{
var useSslString = args[5];
var parsed = bool.TryParse(useSslString, out useSSL);
Console.WriteLine(useSSL ? "Using SSL" : "Not using SSL");
}
var client = new ScoringClient(host, useSSL ? 443 : 80, useSSL, auth, serviceName);
using (var content = File.OpenRead(image))
{
IScoringRequest request = new ImageRequest("Placeholder:0", content);
//Dimensions depend on model - client.ScoreAsync<float[,,,,]>(request); for ssd-vgg
var result = await client.ScoreAsync <float[, ]>(request);
for (int i = 0; i < result.GetLength(0); i++)
{
Console.WriteLine($"Batch {i}:");
var length = result.GetLength(1);
var results = new Dictionary <int, float>();
for (int j = 0; j < length; j++)
{
results.Add(j, result[i, j]);
}
foreach (var kvp in results.Where(x => x.Value > 0.001).OrderByDescending(x => x.Value).Take(5))
{
Console.WriteLine(
$" {GetLabel(kvp.Key)} {kvp.Value * 100}%");
}
}
}
return(0);
}
private static int Main(string[] args)
{
if (args.Length < 2)
{
Console.WriteLine($"Use as {errorMessage}");
return(1);
}
var host = args[0];
var image = args[1];
var useSSL = false;
if (args.Length > 2)
{
var useSslString = args[2];
var parsed = bool.TryParse(useSslString, out useSSL);
Console.WriteLine(parsed ? "Using SSL" : "Not using SSL");
}
string auth = null;
if (args.Length > 3 && useSSL)
{
auth = args[3];
Console.WriteLine(!string.IsNullOrEmpty(auth) ? "Using auth" : "Not using auth");
}
var client = new ScoringClient(host, useSSL?443:80, useSSL, auth);
using (var content = File.OpenRead(image))
{
IScoringRequest request = new ImageRequest(content);
var result = client.Score <float[, ]>(request);
for (int i = 0; i < result.GetLength(0); i++)
{
Console.WriteLine($"Batch {i}:");
var length = result.GetLength(1);
var results = new Dictionary <int, float>();
for (int j = 0; j < length; j++)
{
results.Add(j, result[i, j]);
}
foreach (var kvp in results.Where(x => x.Value > 0.001).OrderByDescending(x => x.Value).Take(5))
{
Console.WriteLine(
$" {GetLabel(kvp.Key)} {kvp.Value * 100}%");
}
}
}
return(0);
}
public async Task Retries_transient_exceptions(StatusCode transientStatusCode)
{
var exception = new RpcException(new Status(transientStatusCode, string.Empty));
var expectedResultValues = new[] { 1f, 2f, 3f };
var outputTensorProto = new TensorProto {
Dtype = DataType.DtFloat
};
outputTensorProto.FloatVal.Add(expectedResultValues);
outputTensorProto.TensorShape = new TensorShapeProto();
outputTensorProto.TensorShape.Dim.Add(new TensorShapeProto.Types.Dim());
outputTensorProto.TensorShape.Dim[0].Size = 3;
var predictRequest = new PredictRequest();
var predictResponse = new PredictResponse();
predictResponse.Outputs.Add("output_alias", outputTensorProto);
var predictionServiceClientMock = new Mock <IPredictionServiceClient>();
predictionServiceClientMock.Setup(x => x.PredictAsync(predictRequest)).Returns(async() =>
{
await Task.CompletedTask;
if (exception != null)
{
var x = exception;
exception = null;
throw x;
}
return(predictResponse);
}).Verifiable();
var scoringRequestMock = new Mock <IScoringRequest>();
scoringRequestMock.Setup(x => x.MakePredictRequest()).Returns(() => predictRequest);
var scoringClient = new ScoringClient(predictionServiceClientMock.Object);
var result = await scoringClient.ScoreAsync(scoringRequestMock.Object);
Assert.Equal(expectedResultValues, result);
scoringRequestMock.Verify(x => x.MakePredictRequest(), Times.Exactly(1));
predictionServiceClientMock.Verify(x => x.PredictAsync(predictRequest), Times.Exactly(2));
}
public async Task Rethrows_non_transient_exception(Exception exception)
{
var expectedResultValues = new[] { 1f, 2f, 3f };
var outputTensorProto = new TensorProto {
Dtype = DataType.DtFloat
};
outputTensorProto.FloatVal.Add(expectedResultValues);
outputTensorProto.TensorShape = new TensorShapeProto();
outputTensorProto.TensorShape.Dim.Add(new TensorShapeProto.Types.Dim());
outputTensorProto.TensorShape.Dim[0].Size = 3;
var predictRequest = new PredictRequest();
var predictResponse = new PredictResponse();
predictResponse.Outputs.Add("output_alias", outputTensorProto);
var predictionServiceClientMock = new Mock <IPredictionServiceClient>();
predictionServiceClientMock.Setup(x => x.PredictAsync(predictRequest)).Returns(async() =>
{
await Task.CompletedTask;
throw exception;
}).Verifiable();
var scoringRequestMock = new Mock <IScoringRequest>();
scoringRequestMock.Setup(x => x.MakePredictRequest()).Returns(() => predictRequest);
var scoringClient = new ScoringClient(predictionServiceClientMock.Object);
var actualException = await Assert.ThrowsAnyAsync <Exception>(async() => await scoringClient.ScoreAsync(scoringRequestMock.Object));
Assert.Same(exception, actualException);
scoringRequestMock.Verify(x => x.MakePredictRequest(), Times.Exactly(1));
predictionServiceClientMock.Verify(x => x.PredictAsync(predictRequest), Times.Exactly(1));
}
public async Task Retries_transient_exceptions_until_retries_exhausted(StatusCode transientStatusCode, int retryCount)
{
var exception = new RpcException(new Status(transientStatusCode, string.Empty));
var expectedResultValues = new[] { 1f, 2f, 3f };
var outputTensorProto = new TensorProto {
Dtype = DataType.DtFloat
};
outputTensorProto.FloatVal.Add(expectedResultValues);
outputTensorProto.TensorShape = new TensorShapeProto();
outputTensorProto.TensorShape.Dim.Add(new TensorShapeProto.Types.Dim());
outputTensorProto.TensorShape.Dim[0].Size = 3;
var predictRequest = new PredictRequest();
var predictResponse = new PredictResponse();
predictResponse.Outputs.Add("output_alias", outputTensorProto);
var predictionServiceClientMock = new Mock <IPredictionServiceClient>();
predictionServiceClientMock.Setup(x => x.PredictAsync(predictRequest)).ThrowsAsync(exception).Verifiable();
var scoringRequestMock = new Mock <IScoringRequest>();
scoringRequestMock.Setup(x => x.MakePredictRequest()).Returns(() => predictRequest);
var scoringClient = new ScoringClient(predictionServiceClientMock.Object);
var resultException = await Assert.ThrowsAsync <RpcException>(() => scoringClient.ScoreAsync(scoringRequestMock.Object, retryCount));
Assert.Equal(exception, resultException);
scoringRequestMock.Verify(x => x.MakePredictRequest(), Times.Exactly(1));
predictionServiceClientMock.Verify(x => x.PredictAsync(predictRequest), Times.Exactly(retryCount));
}
