Merge pull request #119 from cbovar/Simplify_shape

Simplify shape

Author: cbovar

src/ConvNetSharp.Core.Tests/FullyConnLayerTests.cs

@@ -49,10 +49,10 @@ public void Forward()
             layer.Init(inputWidth, inputHeight, inputDepth);
 
             // Make sure filter shape had flatten input shape
-            Assert.AreEqual(1, layer.Filters.Shape.GetDimension(0));
-            Assert.AreEqual(1, layer.Filters.Shape.GetDimension(1));
-            Assert.AreEqual(8, layer.Filters.Shape.GetDimension(2));
-            Assert.AreEqual(2, layer.Filters.Shape.GetDimension(3));
+            Assert.AreEqual(1, layer.Filters.Shape.Dimensions[0]);
+            Assert.AreEqual(1, layer.Filters.Shape.Dimensions[1]);
+            Assert.AreEqual(8, layer.Filters.Shape.Dimensions[2]);
+            Assert.AreEqual(2, layer.Filters.Shape.Dimensions[3]);
 
             for (var i = 0; i < 8; i++)
             {

src/ConvNetSharp.Core.Tests/PoolLayerTests.cs

@@ -29,10 +29,10 @@ public void Forward()
             var input = BuilderInstance.Volume.From(data, new Shape(inputWidth, inputHeight, inputDepth, inputBatchSize));
             layer.DoForward(input);
 
-            Assert.AreEqual(2, layer.OutputActivation.Shape.GetDimension(0));
-            Assert.AreEqual(2, layer.OutputActivation.Shape.GetDimension(1));
-            Assert.AreEqual(4, layer.OutputActivation.Shape.GetDimension(2));
-            Assert.AreEqual(4, layer.OutputActivation.Shape.GetDimension(3));
+            Assert.AreEqual(2, layer.OutputActivation.Shape.Dimensions[0]);
+            Assert.AreEqual(2, layer.OutputActivation.Shape.Dimensions[1]);
+            Assert.AreEqual(4, layer.OutputActivation.Shape.Dimensions[2]);
+            Assert.AreEqual(4, layer.OutputActivation.Shape.Dimensions[3]);
 
             Assert.AreEqual(5.0, layer.OutputActivation.Get(0,0,0,0));
             Assert.AreEqual(21.0, layer.OutputActivation.Get(0, 0, 1, 0));

src/ConvNetSharp.Core.Tests/SoftMaxLayerTests.cs

@@ -30,10 +30,10 @@ public SoftmaxLayerTests()
         public void OutputIsNormalized()
         {
             var output = this.layer.DoForward(input, true);
-            Assert.AreEqual(1, output.Shape.GetDimension(0));
-            Assert.AreEqual(1, output.Shape.GetDimension(1));
-            Assert.AreEqual(4, output.Shape.GetDimension(2));
-            Assert.AreEqual(3, output.Shape.GetDimension(3));
+            Assert.AreEqual(1, output.Shape.Dimensions[0]);
+            Assert.AreEqual(1, output.Shape.Dimensions[1]);
+            Assert.AreEqual(4, output.Shape.Dimensions[2]);
+            Assert.AreEqual(3, output.Shape.Dimensions[3]);
 
             var values = output.ToArray();
             Assert.AreEqual(0.25, values[0]);

src/ConvNetSharp.Core/Fluent/FluentNet.cs

@@ -92,8 +92,8 @@ public int[] GetPrediction()
             }
 
             var activation = softmaxLayer.OutputActivation;
-            var N = activation.Shape.GetDimension(3);
-            var C = activation.Shape.GetDimension(2);
+            var N = activation.Shape.Dimensions[3];
+            var C = activation.Shape.Dimensions[2];
             var result = new int[N];
 
             for (var n = 0; n < N; n++)

src/ConvNetSharp.Core/Layers/FullyConnLayer.cs

@@ -63,8 +63,8 @@ public override void Backward(Volume<T> outputGradient)
             this.OutputActivationGradients = outputGradient;
 
             // compute gradient wrt weights and data
-            using (var reshapedInput = this.InputActivation.ReShape(1, 1, -1, this.InputActivation.Shape.GetDimension(3)))
-            using (var reshapedInputGradients = this.InputActivationGradients.ReShape(1, 1, -1, this.InputActivationGradients.Shape.GetDimension(3)))
+            using (var reshapedInput = this.InputActivation.ReShape(1, 1, -1, this.InputActivation.Shape.Dimensions[3]))
+            using (var reshapedInputGradients = this.InputActivationGradients.ReShape(1, 1, -1, this.InputActivationGradients.Shape.Dimensions[3]))
             {
                 reshapedInput.ConvolveGradient(
                     this.Filters, this.OutputActivationGradients,
@@ -77,7 +77,7 @@ public override void Backward(Volume<T> outputGradient)
 
         protected override Volume<T> Forward(Volume<T> input, bool isTraining = false)
         {
-            using (var reshapedInput = input.ReShape(1, 1, -1, input.Shape.GetDimension(3)))
+            using (var reshapedInput = input.ReShape(1, 1, -1, input.Shape.Dimensions[3]))
             {
                 reshapedInput.DoConvolution(this.Filters, 0, 1, this.OutputActivation);
                 this.OutputActivation.DoAdd(this.Bias, this.OutputActivation);

src/ConvNetSharp.Core/Layers/LayerBase.cs

@@ -67,7 +67,7 @@ public virtual Volume<T> DoForward(Volume<T> input, bool isTraining = false)
 
             this.InputActivation = input;
 
-            var outputShape = new Shape(this.OutputWidth, this.OutputHeight, this.OutputDepth, input.Shape.DimensionCount == 4 ?  input.Shape.GetDimension(3) : 1);
+            var outputShape = new Shape(this.OutputWidth, this.OutputHeight, this.OutputDepth, input.Shape.Dimensions[3]);
 
             if (this.OutputActivation == null ||
                 !this.OutputActivation.Shape.Equals(outputShape))

src/ConvNetSharp.Core/Layers/RegressionLayer.cs

@@ -40,7 +40,7 @@ public override void Backward(Volume<T> outputGradient)
         public override void Backward(Volume<T> y, out T loss)
         {
             var reshape = y.ReShape(new Shape(1, 1, -1, Shape.Keep));
-            var dy = this.InputActivationGradients.ReShape(this.OutputActivation.Shape.Dimensions.ToArray());
+            var dy = this.InputActivationGradients.ReShape(this.OutputActivation.Shape.Dimensions);
             reshape.DoSubtractFrom(this.OutputActivation, dy);
 
             if (this._result == null)
@@ -54,7 +54,7 @@ public override void Backward(Volume<T> y, out T loss)
             var half = (T)Convert.ChangeType(0.5, typeof(T));
             this._result.DoMultiply(this._result, half); // dy * dy * 0.5
             this._result.DoSum(this._sum); // sum over all batch
-            var batchSize = y.Shape.GetDimension(3);
+            var batchSize = y.Shape.Dimensions[3];
             loss = Ops<T>.Divide(this._sum.Get(0), Ops<T>.Cast(batchSize)); // average
         }
 

src/ConvNetSharp.Core/Layers/SoftMaxLayer.cs

@@ -26,17 +26,17 @@ public SoftmaxLayer(int classCount)
         public override void Backward(Volume<T> y, out T loss)
         {
             // input gradient = pi - yi
-            y.DoSubtractFrom(this.OutputActivation, this.InputActivationGradients.ReShape(this.OutputActivation.Shape.Dimensions.ToArray()));
+            y.DoSubtractFrom(this.OutputActivation, this.InputActivationGradients.ReShape(this.OutputActivation.Shape.Dimensions));
 
             //loss is the class negative log likelihood
             loss = Ops<T>.Zero;
-            for (var n = 0; n < y.Shape.GetDimension(3); n++)
+            for (var n = 0; n < y.Shape.Dimensions[3]; n++)
             {
-                for (var d = 0; d < y.Shape.GetDimension(2); d++)
+                for (var d = 0; d < y.Shape.Dimensions[2]; d++)
                 {
-                    for (var h = 0; h < y.Shape.GetDimension(1); h++)
+                    for (var h = 0; h < y.Shape.Dimensions[1]; h++)
                     {
-                        for (var w = 0; w < y.Shape.GetDimension(0); w++)
+                        for (var w = 0; w < y.Shape.Dimensions[0]; w++)
                         {
                             var expected = y.Get(w, h, d, n);
                             var actual = this.OutputActivation.Get(w, h, d, n);

src/ConvNetSharp.Core/Net.cs

@@ -68,8 +68,8 @@ public int[] GetPrediction()
             }
 
             var activation = softmaxLayer.OutputActivation;
-            var N = activation.Shape.GetDimension(3);
-            var C = activation.Shape.GetDimension(2);
+            var N = activation.Shape.Dimensions[3];
+            var C = activation.Shape.Dimensions[2];
             var result = new int[N];
 
             for (var n = 0; n < N; n++)

src/ConvNetSharp.Core/Training/TrainerBase.cs

@@ -27,15 +27,15 @@ protected virtual void Backward(Volume<T> y)
         {
             var chrono = Stopwatch.StartNew();
 
-            var batchSize = y.Shape.GetDimension(3);
+            var batchSize = y.Shape.Dimensions[3];
             this.Loss = Ops<T>.Divide(this.Net.Backward(y), Ops<T>.Cast(batchSize));
             this.BackwardTimeMs = chrono.Elapsed.TotalMilliseconds/batchSize;
         }
 
         private void Forward(Volume<T> x)
         {
             var chrono = Stopwatch.StartNew();
-            var batchSize = x.Shape.GetDimension(3);
+            var batchSize = x.Shape.Dimensions[3];
             this.Net.Forward(x, true); // also set the flag that lets the net know we're just training
             this.ForwardTimeMs = chrono.Elapsed.TotalMilliseconds/batchSize;
         }
@@ -46,7 +46,7 @@ public virtual void Train(Volume<T> x, Volume<T> y)
 
             Backward(y);
 
-            var batchSize = x.Shape.GetDimension(3);
+            var batchSize = x.Shape.Dimensions[3];
             var chrono = Stopwatch.StartNew();
             TrainImplem();
             this.UpdateWeightsTimeMs = chrono.Elapsed.TotalMilliseconds/batchSize;