Fixes (#434)

* #8901 Avoid unnecessary warning in SameDiffLoss

Signed-off-by: Alex Black <blacka101@gmail.com>

* Improved error messages for conv2d layers - NCHW vs. NHWC

Signed-off-by: Alex Black <blacka101@gmail.com>

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/convolution/ConvDataFormatTests.java

@@ -18,11 +18,9 @@ package org.deeplearning4j.nn.layers.convolution;
 import lombok.*;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.TestUtils;
+import org.deeplearning4j.exception.DL4JInvalidInputException;
 import org.deeplearning4j.nn.api.MaskState;
-import org.deeplearning4j.nn.conf.CNN2DFormat;
+import org.deeplearning4j.nn.conf.*;
-import org.deeplearning4j.nn.conf.ConvolutionMode;
-import org.deeplearning4j.nn.conf.InputPreProcessor;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.*;
 import org.deeplearning4j.nn.conf.layers.CnnLossLayer;
@@ -35,6 +33,7 @@ import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.workspace.ArrayType;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
+import org.deeplearning4j.util.ConvolutionUtils;
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.junit.runners.Parameterized;
@@ -49,6 +48,7 @@ import java.util.List;
 import java.util.Map;
 
 import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
 
 @RunWith(Parameterized.class)
 public class ConvDataFormatTests extends BaseDL4JTest {
@@ -971,4 +971,58 @@ public class ConvDataFormatTests extends BaseDL4JTest {
             return null;
         }
     }
+
+
+    @Test
+    public void testWrongFormatIn(){
+
+        for(CNN2DFormat df : CNN2DFormat.values()){
+
+
+            for(int i=0; i<4; i++ ){
+
+                NeuralNetConfiguration.ListBuilder b = new NeuralNetConfiguration.Builder()
+                        .list();
+                switch (i){
+                    case 0:
+                        b.layer(new ConvolutionLayer.Builder().kernelSize(2,2).nIn(3).nOut(3).dataFormat(df).build());
+                        break;
+                    case 1:
+                        b.layer(new DepthwiseConvolution2D.Builder().kernelSize(2,2).nIn(3).nOut(3).dataFormat(df).build());
+                        break;
+                    case 2:
+                        b.layer(new Deconvolution2D.Builder().dataFormat(df).kernelSize(2,2).nIn(3).nOut(3).build());
+                        break;
+                    case 3:
+                        b.layer(new SeparableConvolution2D.Builder().dataFormat(df).kernelSize(2,2).nIn(3).nOut(3).build());
+                        break;
+                }
+
+                MultiLayerNetwork net = new MultiLayerNetwork(b.build());
+                net.init();
+
+                INDArray in;
+                INDArray wrongFormatIn;
+                if(df == CNN2DFormat.NCHW){
+                    in = Nd4j.create(DataType.FLOAT, 5, 3, 12, 12);
+                    wrongFormatIn = Nd4j.create(DataType.FLOAT, 5, 12, 12, 3);
+                } else {
+                    in = Nd4j.create(DataType.FLOAT, 5, 12, 12, 3);
+                    wrongFormatIn = Nd4j.create(DataType.FLOAT, 5, 3, 12, 12);
+                }
+
+                net.output(in);
+
+                try {
+                    net.output(wrongFormatIn);
+                } catch (DL4JInvalidInputException e){
+//                    e.printStackTrace();
+                    String msg = e.getMessage();
+                    assertTrue(msg, msg.contains(ConvolutionUtils.NCHW_NHWC_ERROR_MSG));
+                }
+            }
+        }
+
+
+    }
 }

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Deconvolution2D.java

@@ -63,6 +63,9 @@ public class Deconvolution2D extends ConvolutionLayer {
     protected Deconvolution2D(BaseConvBuilder<?> builder) {
         super(builder);
         initializeConstraints(builder);
+        if(builder instanceof Builder){
+            this.cnn2dDataFormat = ((Builder) builder).format;
+        }
     }
 
     public boolean hasBias() {
@@ -136,7 +139,7 @@ public class Deconvolution2D extends ConvolutionLayer {
 
         private CNN2DFormat format = CNN2DFormat.NCHW;
 
-        public Builder format(CNN2DFormat format){
+        public Builder dataFormat(CNN2DFormat format){
             this.format = format;
             return this;
         }

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/ConvolutionLayer.java

@@ -310,11 +310,21 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
             String layerName = conf.getLayer().getLayerName();
             if (layerName == null)
                 layerName = "(not named)";
-            throw new DL4JInvalidInputException("Cannot do forward pass in Convolution layer (layer name = " + layerName
+
+            String s = "Cannot do forward pass in Convolution layer (layer name = " + layerName
                     + ", layer index = " + index + "): input array channels does not match CNN layer configuration"
-                    + " (data input channels = " + input.size(dim) + ", " + layerConf().getCnn2dDataFormat().dimensionNames()
+                    + " (data format = " + format + ", data input channels = " + input.size(dim) + ", " + layerConf().getCnn2dDataFormat().dimensionNames()
                     + "=" + Arrays.toString(input.shape()) + "; expected" + " input channels = " + inDepth + ") "
-                    + layerId());
+                    + layerId();
+
+            int dimIfWrongFormat = format == CNN2DFormat.NHWC ? 1 : 3;
+            if(input.size(dimIfWrongFormat) == inDepth){
+                //User might have passed NCHW data to a NHWC net, or vice versa?
+                s += "\n" + ConvolutionUtils.NCHW_NHWC_ERROR_MSG;
+            }
+
+
+            throw new DL4JInvalidInputException(s);
         }
     }
 

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/Deconvolution2DLayer.java

@@ -190,12 +190,21 @@ public class Deconvolution2DLayer extends ConvolutionLayer {
             String layerName = conf.getLayer().getLayerName();
             if (layerName == null)
                 layerName = "(not named)";
-            throw new DL4JInvalidInputException("Cannot do forward pass in Deconvolution2D layer (layer name = " + layerName
+
+            String s = "Cannot do forward pass in Deconvolution2D layer (layer name = " + layerName
                     + ", layer index = " + index + "): input array channels does not match CNN layer configuration"
-                    + " (data input channels = " + input.size(cDim) + ", "
+                    + " (data format = " + format + ", data input channels = " + input.size(cDim) + ", "
                     + (nchw ? "[minibatch,inputDepth,height,width]" : "[minibatch,height,width,inputDepth]") + "="
                     + Arrays.toString(input.shape()) + "; expected" + " input channels = " + inDepth + ") "
-                    + layerId());
+                    + layerId();
+
+            int dimIfWrongFormat = format == CNN2DFormat.NHWC ? 1 : 3;
+            if(input.size(dimIfWrongFormat) == inDepth){
+                //User might have passed NCHW data to a NHWC net, or vice versa?
+                s += "\n" + ConvolutionUtils.NCHW_NHWC_ERROR_MSG;
+            }
+
+            throw new DL4JInvalidInputException(s);
         }
         int kH = (int) weights.size(2);
         int kW = (int) weights.size(3);

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/DepthwiseConvolution2DLayer.java

@@ -183,13 +183,21 @@ public class DepthwiseConvolution2DLayer extends ConvolutionLayer {
             String layerName = conf.getLayer().getLayerName();
             if (layerName == null)
                 layerName = "(not named)";
-            throw new DL4JInvalidInputException("Cannot do forward pass in DepthwiseConvolution2D layer " +
+
+            String s = "Cannot do forward pass in DepthwiseConvolution2D layer " +
                     "(layer name = " + layerName
                     + ", layer index = " + index + "): input array channels does not match CNN layer configuration"
-                    + " (data input channels = " + input.size(1) + ", "
+                    + " (data format = " + format + ", data input channels = " + input.size(1) + ", "
                     + (nchw ? "[minibatch,inputDepth,height,width]=" : "[minibatch,height,width,inputDepth]=")
                     + Arrays.toString(input.shape()) + "; expected" + " input channels = " + inDepth + ") "
-                    + layerId());
+                    + layerId();
+            int dimIfWrongFormat = format == CNN2DFormat.NHWC ? 1 : 3;
+            if(input.size(dimIfWrongFormat) == inDepth){
+                //User might have passed NCHW data to a NHWC net, or vice versa?
+                s += "\n" + ConvolutionUtils.NCHW_NHWC_ERROR_MSG;
+            }
+
+            throw new DL4JInvalidInputException(s);
         }
         int kH = (int) depthWiseWeights.size(0);
         int kW = (int) depthWiseWeights.size(1);

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/SeparableConvolution2DLayer.java

@@ -211,11 +211,20 @@ public class SeparableConvolution2DLayer extends ConvolutionLayer {
             String layerName = conf.getLayer().getLayerName();
             if (layerName == null)
                 layerName = "(not named)";
-            throw new DL4JInvalidInputException("Cannot do forward pass in SeparableConvolution2D layer (layer name = " + layerName
+
+            String s = "Cannot do forward pass in SeparableConvolution2D layer (layer name = " + layerName
                     + ", layer index = " + index + "): input array channels does not match CNN layer configuration"
-                    + " (data input channels = " + input.size(1) + ", [minibatch,inputDepth,height,width]="
+                    + " (data format = " + format + ", data input channels = " + input.size(1) + ", [minibatch,inputDepth,height,width]="
                     + Arrays.toString(input.shape()) + "; expected" + " input channels = " + inDepth + ") "
-                    + layerId());
+                    + layerId();
+
+            int dimIfWrongFormat = format == CNN2DFormat.NHWC ? 1 : 3;
+            if(input.size(dimIfWrongFormat) == inDepth){
+                //User might have passed NCHW data to a NHWC net, or vice versa?
+                s += "\n" + ConvolutionUtils.NCHW_NHWC_ERROR_MSG;
+            }
+
+            throw new DL4JInvalidInputException(s);
         }
         int kH = (int) depthWiseWeights.size(2);
         int kW = (int) depthWiseWeights.size(3);

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/ConvolutionUtils.java

@@ -48,6 +48,13 @@ import java.util.Arrays;
  */
 public class ConvolutionUtils {
 
+    public static final String NCHW_NHWC_ERROR_MSG = "Note: Convolution layers can be configured for either NCHW (channels first)" +
+            " or NHWC (channels last) format for input images and activations.\n" +
+            "Layers can be configured using .dataFormat(CNN2DFormat.NCHW/NHWC) when constructing the layer, or for the entire net using" +
+            " .setInputType(InputType.convolutional(height, width, depth, CNN2DForman.NCHW/NHWC)).\n" +
+            "ImageRecordReader and NativeImageLoader can also be configured to load image data in either NCHW or NHWC format which must match the network";
+
+
     private static final int[] ONES = new int[]{1, 1};
 
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/lossfunctions/SameDiffLoss.java

@@ -38,7 +38,7 @@ import java.util.Map;
  */
 public abstract class SameDiffLoss implements ILossFunction {
     protected transient SameDiff sd;
-    protected transient SDVariable scoreVariable;
+    protected transient SDVariable scorePerExampleVariable;
 
     protected SameDiffLoss() {
 
@@ -60,7 +60,8 @@ public abstract class SameDiffLoss implements ILossFunction {
         sd = SameDiff.create();
         SDVariable layerInput = sd.placeHolder("layerInput", dataType, -1);
         SDVariable labels = sd.placeHolder("labels", dataType, -1);
-        scoreVariable = this.defineLoss(sd, layerInput, labels);
+        scorePerExampleVariable = this.defineLoss(sd, layerInput, labels);
+        scorePerExampleVariable.markAsLoss();
         sd.createGradFunction("layerInput");
     }
 
@@ -112,7 +113,7 @@ public abstract class SameDiffLoss implements ILossFunction {
         m.put("labels", labels);
         m.put("layerInput", output);
 
-        INDArray scoreArr = sd.outputSingle(m,scoreVariable.name());
+        INDArray scoreArr = sd.outputSingle(m, scorePerExampleVariable.name());
 
         if (mask != null) {
             LossUtil.applyMask(scoreArr, mask);