Fixes Keras import issue (8373) (#54)

* test

* fix

* rem prn

* add test

deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/preprocessors/ReshapePreprocessor.java

@@ -43,13 +43,14 @@ import static org.nd4j.linalg.util.ArrayUtil.prodLong;
 @Data
 @Slf4j
 @EqualsAndHashCode(callSuper = false)
-@JsonIgnoreProperties({"hasMiniBatchDimension", "miniBatchSize"})
+@JsonIgnoreProperties({"hasMiniBatchDimension", "miniBatchSize", "staticTargetShape"})
 public class ReshapePreprocessor extends BaseInputPreProcessor {
 
     private long[] inputShape;
     private long[] targetShape;
     private boolean hasMiniBatchDimension = false;
     private int miniBatchSize;
+    private long[] staticTargetShape;
 
     public ReshapePreprocessor(@JsonProperty("inputShape") long[] inputShape, @JsonProperty("targetShape") long[] targetShape) {
         this.inputShape = inputShape;
@@ -80,6 +81,16 @@ public class ReshapePreprocessor extends BaseInputPreProcessor {
     public INDArray preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr) {
         // the target shape read from a keras config does not have mini-batch size
         // included. We prepend it here dynamically.
+
+        long[] targetShape;
+        if (staticTargetShape != null){
+            targetShape = prependMiniBatchSize(staticTargetShape, miniBatchSize);
+            hasMiniBatchDimension = true;
+            this.miniBatchSize = miniBatchSize;
+        }
+        else{
+            targetShape = this.targetShape;
+        }
         if (!this.hasMiniBatchDimension) {
             targetShape = prependMiniBatchSize(targetShape, miniBatchSize);
             inputShape = prependMiniBatchSize(inputShape, miniBatchSize);
@@ -95,7 +106,7 @@ public class ReshapePreprocessor extends BaseInputPreProcessor {
             if(input.ordering() != 'c' || !Shape.hasDefaultStridesForShape(input)){
                 input = workspaceMgr.dup(ArrayType.ACTIVATIONS, input, 'c');
             }
-            return workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, input.reshape(this.targetShape));
+            return workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, input.reshape(targetShape));
         } else {
             throw new IllegalStateException("Input shape " + Arrays.toString(input.shape())
                     + " and output shape" + Arrays.toString(inputShape) + " do not match");
@@ -122,20 +133,27 @@ public class ReshapePreprocessor extends BaseInputPreProcessor {
     @Override
     public InputType getOutputType(InputType inputType) throws InvalidInputTypeException {
         val shape = hasMiniBatchDimension ? targetShape : prependMiniBatchSize(targetShape, 0);
+        InputType ret;
         switch (shape.length) {
             case 2:
-                return InputType.feedForward(shape[1]);
+                ret = InputType.feedForward(shape[1]);
+                break;
             case 3:
-                return InputType.recurrent(shape[2], shape[1]);
+                ret = InputType.recurrent(shape[2], shape[1]);
+                break;
             case 4:
                 if (inputShape.length == 1 || inputType.getType() == InputType.Type.RNN){
-                    return InputType.convolutional(shape[1], shape[2], shape[3]);
+                    ret = InputType.convolutional(shape[1], shape[2], shape[3]);
                 }else {
-                    return InputType.convolutional(shape[2], shape[3], shape[1]);
+                    ret = InputType.convolutional(shape[2], shape[3], shape[1]);
                 }
+                break;
             default:
                 throw new UnsupportedOperationException(
                         "Cannot infer input type for reshape array " + Arrays.toString(shape));
+
         }
+        this.staticTargetShape = ret.getShape();
+        return ret;
     }
 }

deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/Keras2ModelConfigurationTest.java

@@ -255,6 +255,17 @@ public class Keras2ModelConfigurationTest extends BaseDL4JTest {
         }
     }
 
+    @Test
+    public void ReshapeEmbeddingConcatTest() throws Exception{
+        try(InputStream is = Resources.asStream("/modelimport/keras/configs/keras2/reshape_embedding_concat.json")) {
+            ComputationGraphConfiguration config =
+                    new KerasModel().modelBuilder().modelJsonInputStream(is)
+                            .enforceTrainingConfig(false).buildModel().getComputationGraphConfiguration();
+            ComputationGraph model = new ComputationGraph(config);
+            model.init();
+            model.outputSingle(Nd4j.zeros(1, 1), Nd4j.zeros(1, 1), Nd4j.zeros(1, 1));
+        }
+    }
 
     private void runSequentialConfigTest(String path) throws Exception {
         try(InputStream is = Resources.asStream(path)) {