Using @SuperBuilder for LayerConfigurations

Signed-off-by: brian <brian@brutex.de>

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/BaseLayerConfiguration.java

@@ -210,7 +210,6 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration
           C extends BaseLayerConfiguration, B extends BaseLayerConfigurationBuilder<C, B>>
       extends LayerConfigurationBuilder<C, B> {
 
-
     /**
      * Set weight initialization scheme to random sampling via the specified distribution.
      * Equivalent to: {@code .weightInit(new WeightInitDistribution(distribution))}
@@ -394,7 +393,7 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration
     public B constrainBias(LayerConstraint... constraints) {
       biasConstraints$value = Arrays.asList(constraints);
       biasConstraints$set = true;
-      return (B) this;
+      return self();
     }
 
     /**

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ConvolutionLayer.java

@@ -73,7 +73,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
    */
   @Builder.Default
   protected CNN2DFormat convFormat =
-          CNN2DFormat.NCHW; // default value for legacy serialization reasons
+      CNN2DFormat.NCHW; // default value for legacy serialization reasons
 
   /**
    * Kernel dilation. Default: {1, 1}, which is standard convolutions. Used for implementing dilated
@@ -110,6 +110,19 @@ public class ConvolutionLayer extends FeedForwardLayer {
   @Builder.Default @JsonIgnore @EqualsAndHashCode.Exclude
   private boolean defaultValueOverriden = false;
 
+  public static ConvolutionLayerBuilder<?, ?> builder(int... kernelSize) {
+    return innerBuilder().kernelSize(kernelSize);
+  }
+
+  public static ConvolutionLayerBuilder<?, ?> builder(int[] kernelSize, int[] stride) {
+    return innerBuilder().kernelSize(kernelSize).stride(stride);
+  }
+
+  public static ConvolutionLayerBuilder<?, ?> builder(
+      int[] kernelSize, int[] stride, int[] padding) {
+    return innerBuilder().kernelSize(kernelSize).stride(stride).padding(padding);
+  }
+
   public boolean hasBias() {
     return hasBias;
   }
@@ -201,16 +214,14 @@ public class ConvolutionLayer extends FeedForwardLayer {
     }
 
     if (convFormat == null || override)
-     this.convFormat = ((InputType.InputTypeConvolutional) inputType).getFormat();
+      this.convFormat = ((InputType.InputTypeConvolutional) inputType).getFormat();
   }
 
   @Override
   public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
     if (inputType == null) {
       throw new IllegalStateException(
-          "Invalid input for Convolution layer (layer name=\""
+          "Invalid input for Convolution layer (layer name=\"" + getName() + "\"): input is null");
-              + getName()
-              + "\"): input is null");
     }
 
     return InputTypeUtil.getPreProcessorForInputTypeCnnLayers(inputType, getName());
@@ -272,7 +283,6 @@ public class ConvolutionLayer extends FeedForwardLayer {
         .cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, cachedPerEx)
         .build();
   }
-
   /**
    * The "PREFER_FASTEST" mode will pick the fastest algorithm for the specified parameters from the
    * {@link FwdAlgo}, {@link BwdFilterAlgo}, and {@link BwdDataAlgo} lists, but they may be very
@@ -320,6 +330,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
     FFT_TILING,
     COUNT
   }
+
   /**
    * The backward data algorithm to use when {@link AlgoMode} is set to "USER_SPECIFIED".
    *
@@ -335,25 +346,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
     COUNT
   }
 
-  public static ConvolutionLayerBuilder<?, ?> builder(int... kernelSize) {
+  public abstract static class ConvolutionLayerBuilder<
-    return innerBuilder().kernelSize(kernelSize);
-  }
-
-  public static ConvolutionLayerBuilder<?, ?> builder(int[] kernelSize, int[] stride) {
-    return innerBuilder()
-            .kernelSize(kernelSize)
-            .stride(stride);
-  }
-
-  public static ConvolutionLayerBuilder<?, ?> builder(int[] kernelSize, int[] stride, int[] padding) {
-    return innerBuilder()
-            .kernelSize(kernelSize)
-            .stride(stride)
-            .padding(padding);
-  }
-
-
-  public static abstract class ConvolutionLayerBuilder<
           C extends ConvolutionLayer, B extends ConvolutionLayerBuilder<C, B>>
       extends FeedForwardLayerBuilder<C, B> {
 
@@ -438,7 +431,6 @@ public class ConvolutionLayer extends FeedForwardLayer {
 
       C l = this.initBuild();
       l.setType(LayerType.CONV);
-
       l.initializeConstraints();
       return l;
     }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/FeedForwardLayer.java

@@ -20,9 +20,11 @@
 
 package org.deeplearning4j.nn.conf.layers;
 
-import lombok.*;
+import lombok.EqualsAndHashCode;
+import lombok.Getter;
+import lombok.Setter;
+import lombok.ToString;
 import lombok.experimental.SuperBuilder;
-import net.brutex.ai.dnn.api.LayerType;
 import org.deeplearning4j.nn.conf.DataFormat;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
 import org.deeplearning4j.nn.conf.inputs.InputType;
@@ -41,6 +43,13 @@ public abstract class FeedForwardLayer extends BaseLayerConfiguration {
      */
     @Getter
     protected long nIn;
+
+    public void setNIn(int in) {
+        this.nIn = in;
+    }
+    public void setNIn(long in) {
+        this.nIn = in;
+    }
     /**
      * Number of inputs for the layer (usually the size of the last layer). <br> Note that for Convolutional layers,
      * this is the input channels, otherwise is the previous layer size.
@@ -49,6 +58,13 @@ public abstract class FeedForwardLayer extends BaseLayerConfiguration {
     @Getter
     protected long nOut;
     protected DataFormat timeDistributedFormat;
+
+    protected FeedForwardLayer(FeedForwardLayerBuilder<?, ?> b) {
+        super(b);
+        this.nIn = b.nIn;
+        this.nOut = b.nOut;
+        this.timeDistributedFormat = b.timeDistributedFormat;
+    }
 //
   //  { //Initializer block
       // setType(LayerType.FC);

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LayerConfiguration.java

@@ -325,141 +325,4 @@ public abstract class LayerConfiguration
     runInheritance(getNetConfiguration());
   }
 
-  public abstract static class LayerConfigurationBuilder<
-      C extends LayerConfiguration, B extends LayerConfigurationBuilder<C, B>> {
-    private String name;
-    private List<LayerConstraint> allParamConstraints;
-    private List<LayerConstraint> weightConstraints;
-    private List<LayerConstraint> biasConstraints;
-    private List<LayerConstraint> constraints;
-    private IWeightNoise weightNoise;
-    private LinkedHashSet<String> variables$value;
-    private boolean variables$set;
-    private IDropout dropOut;
-    private @NonNull LayerType type$value;
-    private boolean type$set;
-    private NeuralNetConfiguration netConfiguration;
-    private IActivation activation$value;
-    private boolean activation$set;
-
-    public B activation(Activation activation) {
-      this.activation$value = activation;
-      this.activation$set = true;
-      return self();
-    }
-
-    public B activation(IActivation activation) {
-      this.activation$value = activation;
-      this.activation$set = true;
-      return self();
-    }
-
-    public B dropOut(double d) {
-      this.dropOut = new Dropout(d);
-      return self();
-    }
-
-    public B dropOut(IDropout d) {
-      this.dropOut = d;
-      return self();
-    }
-
-    public B constrainBias(LayerConstraint constraint) {
-      return this.biasConstraints(List.of(constraint));
-    }
-
-    public B constrainWeights(LayerConstraint constraint) {
-      return this.weightConstraints(List.of(constraint));
-    }
-
-    public B name(String name) {
-      this.name = name;
-      return self();
-    }
-
-    public B allParamConstraints(List<LayerConstraint> allParamConstraints) {
-      this.allParamConstraints = allParamConstraints;
-      return self();
-    }
-
-    public B weightConstraints(List<LayerConstraint> weightConstraints) {
-      this.weightConstraints = weightConstraints;
-      return self();
-    }
-
-    public B biasConstraints(List<LayerConstraint> biasConstraints) {
-      this.biasConstraints = biasConstraints;
-      return self();
-    }
-
-    public B constraints(List<LayerConstraint> constraints) {
-      this.constraints = constraints;
-      return self();
-    }
-
-    public B weightNoise(IWeightNoise weightNoise) {
-      this.weightNoise = weightNoise;
-      return self();
-    }
-
-    public B variables(LinkedHashSet<String> variables) {
-      this.variables$value = variables;
-      this.variables$set = true;
-      return self();
-    }
-
-    public B type(@NonNull LayerType type) {
-      this.type$value = type;
-      this.type$set = true;
-      return self();
-    }
-
-    @JsonIgnore
-    public B netConfiguration(NeuralNetConfiguration netConfiguration) {
-      this.netConfiguration = netConfiguration;
-      return self();
-    }
-
-    protected abstract B self();
-
-    public abstract C build();
-
-    public String toString() {
-      return "LayerConfiguration.LayerConfigurationBuilder(name="
-          + this.name
-          + ", allParamConstraints="
-          + this.allParamConstraints
-          + ", weightConstraints="
-          + this.weightConstraints
-          + ", biasConstraints="
-          + this.biasConstraints
-          + ", constraints="
-          + this.constraints
-          + ", weightNoise="
-          + this.weightNoise
-          + ", variables$value="
-          + this.variables$value
-          + ", variables$set="
-          + this.variables$set
-          + ", dropOut="
-          + this.dropOut
-          + ", type$value="
-          + this.type$value
-          + ", type$set="
-          + this.type$set
-          + ", netConfiguration="
-          + this.netConfiguration
-          + ", activation$value="
-          + this.activation$value
-          + ", activation$set="
-          + this.activation$set
-          + ", variables$value="
-          + this.variables$value
-          + ", type$value="
-          + this.type$value
-          + ", activation$value="
-          + this.activation$value
-          + ")";
-    }
-  }
 }