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Merged
codemzs merged 21 commits into from
Oct 2, 2019
Merged

Tensor extensions #4260

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c962327
Add TensorTypeExtensions
KsenijaS Sep 19, 2019
c1da00b
Upgrade performance by replacing ToArray method
KsenijaS Sep 25, 2019
271947f
Fix failing tests
KsenijaS Sep 26, 2019
5bf10d4
Remove unused code
KsenijaS Sep 27, 2019
edb4963
Remove duplicate file TensorTypeExtensions
KsenijaS Sep 27, 2019
0b2c97e
Remove comented code
KsenijaS Sep 27, 2019
8014a1e
Add exceptions
KsenijaS Sep 27, 2019
a9c8119
Add copyright header
KsenijaS Sep 27, 2019
34bc241
Rename ToSpan to CopyTo
KsenijaS Sep 27, 2019
3c3ebd8
Use Utils.EnsureSize method
KsenijaS Sep 27, 2019
5029298
Use CopyTo method to copy the values to span
KsenijaS Sep 27, 2019
97f1109
Fix Destination too short error
KsenijaS Sep 30, 2019
b2f3c78
Call CopyTo inside ToArray method to ensure code reuse
KsenijaS Sep 30, 2019
00e18b8
No need to assign values span to dst
KsenijaS Sep 30, 2019
3b7f984
Use checked keyword around cast to len
KsenijaS Sep 30, 2019
5b1cee7
Set keepOld to false instead of true in Utils.EnsureSize
KsenijaS Sep 30, 2019
606dad6
Add assert to ensure that imageArray and featurizedImage are the same…
KsenijaS Oct 1, 2019
016079b
Cast tensor.size to int instead of long
KsenijaS Oct 1, 2019
fc5b6bc
Remove FetchData since it's no longer used
KsenijaS Oct 1, 2019
ecbfc65
Add checked keyword around cast
KsenijaS Oct 1, 2019
bc2fc7f
Merge https://github.com/dotnet/machinelearning into tensor_extensions
KsenijaS Oct 1, 2019
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16 changes: 10 additions & 6 deletions src/Microsoft.ML.Dnn/DnnRetrainTransform.cs
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Expand Up @@ -379,14 +379,18 @@ private void TrainCore(DnnRetrainEstimator.Options options, IDataView input, IDa
ITensorValueGetter[] srcTensorGetters,
Runner runner)
{
float loss = 0;
float metric = 0;
float loss = 0.0f;
float metric = 0.0f;
for (int i = 0; i < inputs.Length; i++)
runner.AddInput(inputs[i], srcTensorGetters[i].GetBufferedBatchTensor());

Tensor[] tensor = runner.Run();
loss = tensor.Length > 0 && tensor[0] != IntPtr.Zero ? (float)tensor[0].ToArray<float>()[0] : 0.0f;
metric = tensor.Length > 1 && tensor[1] != IntPtr.Zero ? (float)tensor[1].ToArray<float>()[0] : 0.0f;
if (tensor.Length > 0 && tensor[0] != IntPtr.Zero)
tensor[0].ToScalar<float>(ref loss);

if (tensor.Length > 1 && tensor[1] != IntPtr.Zero)
tensor[1].ToScalar<float>(ref metric);

return (loss, metric);
}

Expand Down Expand Up @@ -871,7 +875,7 @@ private Delegate MakeGetter<T>(DataViewRow input, int iinfo, ITensorValueGetter[
UpdateCacheIfNeeded(input.Position, srcTensorGetters, activeOutputColNames, outputCache);

var tensor = outputCache.Outputs[_parent._outputs[iinfo]];
dst = tensor.ToArray<T>()[0];
tensor.ToScalar<T>(ref dst);
};
return valuegetter;
}
Expand Down Expand Up @@ -903,7 +907,7 @@ private Delegate MakeGetter<T>(DataViewRow input, int iinfo, ITensorValueGetter[

var editor = VBufferEditor.Create(ref dst, (int)tensorSize);

DnnUtils.FetchData<T>(tensor.ToArray<T>(), editor.Values);
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tensor.ToSpan<T>(editor.Values);
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dst = editor.Commit();
};
return valuegetter;
Expand Down
27 changes: 18 additions & 9 deletions src/Microsoft.ML.Dnn/ImageClassificationTransform.cs
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Expand Up @@ -235,15 +235,17 @@ private void CacheFeaturizedImagesToDisk(IDataView input, string labelColumnName
ImageClassificationMetrics metrics = new ImageClassificationMetrics();
metrics.Bottleneck = new BottleneckMetrics();
metrics.Bottleneck.DatasetUsed = dataset;
float[] imageArray = null;
while (cursor.MoveNext())
{
labelGetter(ref label);
imagePathGetter(ref imagePath);
var imagePathStr = imagePath.ToString();
var imageTensor = imageProcessor.ProcessImage(imagePathStr);
runner.AddInput(imageTensor, 0);
var featurizedImage = runner.Run()[0]; // Reuse memory?
writer.WriteLine(label - 1 + "," + string.Join(",", featurizedImage.ToArray<float>()));
var featurizedImage = runner.Run()[0]; // Reuse memory
featurizedImage.ToArray<float>(ref imageArray);
writer.WriteLine(label - 1 + "," + string.Join(",", imageArray));
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featurizedImage.Dispose();
imageTensor.Dispose();
metrics.Bottleneck.Index++;
Expand Down Expand Up @@ -338,6 +340,8 @@ private void TrainAndEvaluateClassificationLayer(string trainBottleneckFilePath,

ImageClassificationMetrics metrics = new ImageClassificationMetrics();
metrics.Train = new TrainMetrics();
float accuracy = 0;
float crossentropy = 0;
for (int epoch = 0; epoch < epochs; epoch += 1)
{
metrics.Train.Accuracy = 0;
Expand Down Expand Up @@ -378,8 +382,10 @@ private void TrainAndEvaluateClassificationLayer(string trainBottleneckFilePath,
.AddInput(new Tensor(labelBatchPtr, labelTensorShape, TF_DataType.TF_INT64, labelBatchSizeInBytes), 1)
.Run();

metrics.Train.Accuracy += outputTensors[0].ToArray<float>()[0];
metrics.Train.CrossEntropy += outputTensors[1].ToArray<float>()[0];
outputTensors[0].ToScalar<float>(ref accuracy);
outputTensors[1].ToScalar<float>(ref crossentropy);
metrics.Train.Accuracy += accuracy;
metrics.Train.CrossEntropy += crossentropy;

outputTensors[0].Dispose();
outputTensors[1].Dispose();
Expand Down Expand Up @@ -429,7 +435,8 @@ private void TrainAndEvaluateClassificationLayer(string trainBottleneckFilePath,
.AddInput(new Tensor(labelBatchPtr, labelTensorShape, TF_DataType.TF_INT64, labelBatchSizeInBytes), 1)
.Run();

metrics.Train.Accuracy += outputTensors[0].ToArray<float>()[0];
outputTensors[0].ToScalar<float>(ref accuracy);
metrics.Train.Accuracy += accuracy;
metrics.Train.BatchProcessedCount += 1;
batchIndex = 0;

Expand Down Expand Up @@ -799,8 +806,10 @@ private class OutputCache
private ReadOnlyMemory<char> _imagePath;
private Runner _runner;
private ImageProcessor _imageProcessor;
public UInt32 PredictedLabel { get; set; }
public float[] ClassProbabilities { get; set; }
private long _predictedLabel;
public UInt32 PredictedLabel => (uint)_predictedLabel;
private float[] _classProbability;
public float[] ClassProbabilities => _classProbability;
private DataViewRow _inputRow;

public OutputCache(DataViewRow input, ImageClassificationTransformer transformer)
Expand All @@ -826,8 +835,8 @@ public void UpdateCacheIfNeeded()
_imagePathGetter(ref _imagePath);
var processedTensor = _imageProcessor.ProcessImage(_imagePath.ToString());
var outputTensor = _runner.AddInput(processedTensor, 0).Run();
ClassProbabilities = outputTensor[0].ToArray<float>();
PredictedLabel = (UInt32)outputTensor[1].ToArray<long>()[0];
outputTensor[0].ToArray<float>(ref _classProbability);
outputTensor[1].ToScalar<long>(ref _predictedLabel);
outputTensor[0].Dispose();
outputTensor[1].Dispose();
processedTensor.Dispose();
Expand Down
67 changes: 67 additions & 0 deletions src/Microsoft.ML.Dnn/TensorTypeExtensions.cs
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@@ -0,0 +1,67 @@
using System;
using NumSharp.Backends;
using NumSharp.Backends.Unmanaged;
using NumSharp.Utilities;
using Tensorflow;

namespace Microsoft.ML.Transforms
{
[BestFriend]
internal static class TensorTypeExtensions
{
public static void ToScalar<T>(this Tensor tensor, ref T dst) where T : unmanaged
{
if (typeof(T).as_dtype() == tensor.dtype)
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{
unsafe
{
dst = *(T*)tensor.buffer;
}
}
}

public static void ToSpan<T>(this Tensor tensor, Span<T> values) where T: unmanaged
{
if (typeof(T).as_dtype() == tensor.dtype)
{
unsafe
{
var len = (long)tensor.size;
fixed (T* dst = values)
{
var src = (T*)tensor.buffer;
len *= ((long)tensor.itemsize);
System.Buffer.MemoryCopy(src, dst, len, len);
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}
}
}
}

public static void ToArray<T>(this Tensor tensor, ref T[] array) where T : unmanaged
{
ulong arrayLen = 0;
if (array != null)
arrayLen = (ulong)array.Length;

if (array == null || arrayLen == 0 || arrayLen < tensor.size)
{
array = new T[tensor.size];
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arrayLen = tensor.size;
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}

if (typeof(T).as_dtype() == tensor.dtype)
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{
unsafe
{
var len = (long)tensor.size;
fixed (T* dst = array)
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{
var src = (T*)tensor.buffer;
len *= ((long)tensor.itemsize);
System.Buffer.MemoryCopy(src, dst, len, len);
}
}
}
}
}
}
6 changes: 2 additions & 4 deletions src/Microsoft.ML.TensorFlow/TensorflowTransform.cs
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Expand Up @@ -588,15 +588,13 @@ protected override Delegate MakeGetter(DataViewRow input, int iinfo, Func<int, b
private Delegate MakeGetter<T>(DataViewRow input, int iinfo, ITensorValueGetter[] srcTensorGetters, string[] activeOutputColNames, OutputCache outputCache) where T : unmanaged
{
Host.AssertValue(input);

if (_parent.OutputTypes[iinfo].IsStandardScalar())
{
ValueGetter<T> valuegetter = (ref T dst) =>
{
UpdateCacheIfNeeded(input.Position, srcTensorGetters, activeOutputColNames, outputCache);

var tensor = outputCache.Outputs[_parent.Outputs[iinfo]];
dst = tensor.ToArray<T>()[0];
tensor.ToScalar<T>(ref dst);
};
return valuegetter;
}
Expand Down Expand Up @@ -628,7 +626,7 @@ private Delegate MakeGetter<T>(DataViewRow input, int iinfo, ITensorValueGetter[

var editor = VBufferEditor.Create(ref dst, (int)tensorSize);

DnnUtils.FetchData<T>(tensor.ToArray<T>(), editor.Values);
tensor.ToSpan<T>(editor.Values);
dst = editor.Commit();
};
return valuegetter;
Expand Down