InputPreProcessor InputType API change + CNN preprocessors getOutputType method

Author: AlexDBlack

deeplearning4j-core/src/main/java/org/deeplearning4j/nn/conf/InputPreProcessor.java

@@ -21,6 +21,7 @@
 
 import com.fasterxml.jackson.annotation.JsonSubTypes;
 import com.fasterxml.jackson.annotation.JsonTypeInfo;
+import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.preprocessor.*;
 import org.nd4j.linalg.api.ndarray.INDArray;
 
@@ -68,4 +69,12 @@ public interface InputPreProcessor extends Serializable, Cloneable {
     INDArray backprop(INDArray output, int miniBatchSize);
 
     InputPreProcessor clone();
+
+    /**
+     * For a given type of input to this preprocessor, what is the type of the output?
+     *
+     * @param inputType    Type of input for the preprocessor
+     * @return             Type of input after applying the preprocessor
+     */
+    InputType getOutputType(InputType inputType);
 }

deeplearning4j-core/src/main/java/org/deeplearning4j/nn/conf/preprocessor/CnnToFeedForwardPreProcessor.java

@@ -24,6 +24,7 @@
 import lombok.Data;
 
 import org.deeplearning4j.nn.conf.InputPreProcessor;
+import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.linalg.util.ArrayUtil;
@@ -113,4 +114,15 @@ public CnnToFeedForwardPreProcessor clone() {
             throw new RuntimeException(e);
         }
     }
+
+    @Override
+    public InputType getOutputType(InputType inputType) {
+        if(inputType == null || inputType.getType() != InputType.Type.CNN){
+            throw new IllegalStateException("Invalid input type: Expected input of type CNN, got " + inputType);
+        }
+
+        InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional)inputType;
+        int outSize = c.getDepth() * c.getHeight() * c.getWidth();
+        return InputType.feedForward(outSize);
+    }
 }

deeplearning4j-core/src/main/java/org/deeplearning4j/nn/conf/preprocessor/CnnToRnnPreProcessor.java

@@ -7,6 +7,7 @@
 import lombok.Getter;
 import lombok.Setter;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
+import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.util.ArrayUtil;
 
@@ -91,4 +92,15 @@ public INDArray backprop(INDArray output, int miniBatchSize) {
     public CnnToRnnPreProcessor clone() {
         return new CnnToRnnPreProcessor(inputHeight,inputWidth,numChannels);
     }
+
+    @Override
+    public InputType getOutputType(InputType inputType) {
+        if(inputType == null || inputType.getType() != InputType.Type.CNN){
+            throw new IllegalStateException("Invalid input type: Expected input of type CNN, got " + inputType);
+        }
+
+        InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional)inputType;
+        int outSize = c.getDepth() * c.getHeight() * c.getWidth();
+        return InputType.recurrent(outSize);
+    }
 }

deeplearning4j-core/src/main/java/org/deeplearning4j/nn/conf/preprocessor/FeedForwardToCnnPreProcessor.java

@@ -25,6 +25,7 @@
 import lombok.Getter;
 import lombok.Setter;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
+import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.linalg.util.ArrayUtil;
@@ -117,5 +118,20 @@ public FeedForwardToCnnPreProcessor clone() {
         }
     }
 
+    @Override
+    public InputType getOutputType(InputType inputType) {
+        if(inputType == null || inputType.getType() != InputType.Type.FF){
+            throw new IllegalStateException("Invalid input type: Expected input of type FeedForward, got " + inputType);
+        }
+
+        InputType.InputTypeFeedForward c = (InputType.InputTypeFeedForward)inputType;
+        int expSize = inputHeight * inputWidth * numChannels;
+        if(c.getSize() != expSize){
+            throw new IllegalStateException("Invalid input: expected FeedForward input of size " + expSize + " = (d=" + numChannels +
+            " * w=" + inputWidth + " * h=" + inputHeight + "), got " + inputType);
+        }
+
+        return InputType.convolutional(inputHeight, inputWidth, numChannels);
+    }
 
 }

deeplearning4j-core/src/main/java/org/deeplearning4j/nn/conf/preprocessor/RnnToCnnPreProcessor.java

@@ -6,6 +6,7 @@
 import lombok.Getter;
 import lombok.Setter;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
+import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.util.ArrayUtil;
 
@@ -82,4 +83,20 @@ public INDArray backprop(INDArray output, int miniBatchSize) {
     public RnnToCnnPreProcessor clone() {
         return new RnnToCnnPreProcessor(inputHeight, inputWidth, numChannels);
     }
+
+    @Override
+    public InputType getOutputType(InputType inputType) {
+        if(inputType == null || inputType.getType() != InputType.Type.RNN){
+            throw new IllegalStateException("Invalid input type: Expected input of type RNN, got " + inputType);
+        }
+
+        InputType.InputTypeRecurrent c = (InputType.InputTypeRecurrent)inputType;
+        int expSize = inputHeight * inputWidth * numChannels;
+        if(c.getSize() != expSize){
+            throw new IllegalStateException("Invalid input: expected RNN input of size " + expSize + " = (d=" + numChannels +
+                    " * w=" + inputWidth + " * h=" + inputHeight + "), got " + inputType);
+        }
+
+        return InputType.convolutional(inputHeight, inputWidth, numChannels);
+    }
 }