Merge pull request deeplearning4j#4041 from deeplearning4j/mp_upsampl…

…ing2d_import

Upsampling2d model import + DCGAN import example

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/KerasModel.java

@@ -153,7 +153,7 @@ protected KerasModel(String modelJson, String modelYaml, Hdf5Archive weightsArch
         prepareLayers((List<Object>) layerLists.get((config.getModelFieldLayers())));
 
         /* Import training configuration. */
-        if (trainingJson != null)
+        if (trainingJson != null && enforceTrainingConfig)
             importTrainingConfiguration(trainingJson);
 
         /* Infer output types for each layer. */

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/KerasSequentialModel.java

@@ -118,7 +118,7 @@ public KerasSequentialModel(String modelJson, String modelYaml, Hdf5Archive weig
         }
 
         /* Import training configuration. */
-        if (trainingJson != null)
+        if (trainingJson != null && enforceTrainingConfig)
             importTrainingConfiguration(trainingJson);
 
         /* Infer output types for each layer. */

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/config/Keras1LayerConfiguration.java

@@ -76,7 +76,7 @@ public class Keras1LayerConfiguration extends KerasLayerConfiguration {
     /* Pooling / Upsampling layer properties */
     private final String LAYER_FIELD_POOL_1D_SIZE = "pool_length";
     private final String LAYER_FIELD_POOL_1D_STRIDES = "stride";
-    private final String LAYER_FIELD_UPSAMPLING_SIZE = "length";
+    private final String LAYER_FIELD_UPSAMPLING_1D_SIZE = "length";
 
     /* Keras convolution border modes. */
     private final String LAYER_FIELD_BORDER_MODE = "border_mode";

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/config/Keras2LayerConfiguration.java

@@ -74,7 +74,7 @@ public class Keras2LayerConfiguration extends KerasLayerConfiguration {
     /* Pooling / Upsampling layer properties */
     private final String LAYER_FIELD_POOL_1D_SIZE = "pool_size";
     private final String LAYER_FIELD_POOL_1D_STRIDES = "strides";
-    private final String LAYER_FIELD_UPSAMPLING_SIZE = "size";
+    private final String LAYER_FIELD_UPSAMPLING_1D_SIZE = "size";
 
     /* Keras convolution border modes. */
     private final String LAYER_FIELD_BORDER_MODE = "padding";

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/config/KerasLayerConfiguration.java

@@ -67,6 +67,7 @@ public class KerasLayerConfiguration {
     private final String LAYER_CLASS_NAME_CONVOLUTION_1D = ""; // 1: Convolution1D, 2: Conv1D
     private final String LAYER_CLASS_NAME_CONVOLUTION_2D = ""; // 1: Convolution2D, 2: Conv2D
     private final String LAYER_CLASS_NAME_LEAKY_RELU = "LeakyReLU";
+    private final String LAYER_CLASS_NAME_UPSAMPLING_2D = "UpSampling2D";
 
 
     /* Partially shared layer configurations. */
@@ -140,7 +141,9 @@ public class KerasLayerConfiguration {
     private final String LAYER_FIELD_POOL_STRIDES = "strides";
     private final String LAYER_FIELD_POOL_1D_SIZE = ""; // 1: pool_length, 2: pool_size
     private final String LAYER_FIELD_POOL_1D_STRIDES = ""; // 1: stride, 2: strides
-    private final String LAYER_FIELD_UPSAMPLING_SIZE = ""; // 1: length, 2: size
+    private final String LAYER_FIELD_UPSAMPLING_1D_SIZE = ""; // 1: length, 2: size
+    private final String LAYER_FIELD_UPSAMPLING_2D_SIZE = "size";
+
 
     /* Keras convolution border modes. */
     private final String LAYER_FIELD_BORDER_MODE = ""; // 1: border_mode, 2: padding

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/KerasLoss.java

@@ -69,10 +69,6 @@ public KerasLoss(String layerName, String inboundLayerName, String kerasLoss, bo
         this.layer = new LossLayer.Builder(loss).name(layerName).build();
     }
 
-//    private KerasLoss(Map<String, Object> layerConfig) {}
-//
-//    private KerasLoss(Map<String, Object> layerConfig, boolean enforceTrainingConfig) {}
-
     /**
      * Get DL4J LossLayer.
      *

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolutionUtils.java

@@ -106,6 +106,33 @@ public static int[] getDilationRate(Map<String, Object> layerConfig, int dimensi
 
     }
 
+    /**
+     * Get upsampling size from Keras layer configuration.
+     *
+     * @param layerConfig dictionary containing Keras layer configuration
+     * @return
+     * @throws InvalidKerasConfigurationException
+     */
+    public static int[] getUpsamplingSizeFromConfig(Map<String, Object> layerConfig, int dimension,
+                                                KerasLayerConfiguration conf)
+            throws InvalidKerasConfigurationException {
+        Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
+        int[] size;
+            if (innerConfig.containsKey(conf.getLAYER_FIELD_UPSAMPLING_2D_SIZE()) && dimension == 2) {
+                List<Integer> sizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_UPSAMPLING_2D_SIZE());
+                size = ArrayUtil.toArray(sizeList);
+            } else if (innerConfig.containsKey(conf.getLAYER_FIELD_UPSAMPLING_1D_SIZE()) && dimension == 1) {
+                int upsamplingSize1D = (int) innerConfig.get(conf.getLAYER_FIELD_UPSAMPLING_1D_SIZE());
+                size = new int[]{ upsamplingSize1D };
+            } else {
+                throw new InvalidKerasConfigurationException("Could not determine kernel size: no "
+                        + conf.getLAYER_FIELD_UPSAMPLING_1D_SIZE() + ", "
+                        + conf.getLAYER_FIELD_UPSAMPLING_2D_SIZE());
+            }
+        return size;
+    }
+
+
 
     /**
      * Get (convolution) kernel size from Keras layer configuration.

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasUpsampling2D.java

@@ -0,0 +1,98 @@
+/*-
+ *
+ *  * Copyright 2017 Skymind,Inc.
+ *  *
+ *  *    Licensed under the Apache License, Version 2.0 (the "License");
+ *  *    you may not use this file except in compliance with the License.
+ *  *    You may obtain a copy of the License at
+ *  *
+ *  *        http://www.apache.org/licenses/LICENSE-2.0
+ *  *
+ *  *    Unless required by applicable law or agreed to in writing, software
+ *  *    distributed under the License is distributed on an "AS IS" BASIS,
+ *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  *    See the License for the specific language governing permissions and
+ *  *    limitations under the License.
+ *
+ */
+package org.deeplearning4j.nn.modelimport.keras.layers.convolutional;
+
+import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.deeplearning4j.nn.conf.layers.Upsampling2D;
+import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
+import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
+import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
+
+import java.util.Map;
+
+
+/**
+ * Keras Upsampling2D layer support
+ *
+ * @author Max Pumperla
+ */
+public class KerasUpsampling2D extends KerasLayer {
+
+    /**
+     * Constructor from parsed Keras layer configuration dictionary.
+     *
+     * @param layerConfig dictionary containing Keras layer configuration.
+     * @throws InvalidKerasConfigurationException     Invalid Keras configuration exception
+     * @throws UnsupportedKerasConfigurationException Unsupported Keras configuration exception
+     */
+    public KerasUpsampling2D(Map<String, Object> layerConfig)
+            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
+        this(layerConfig, true);
+    }
+
+    /**
+     * Constructor from parsed Keras layer configuration dictionary.
+     *
+     * @param layerConfig           dictionary containing Keras layer configuration
+     * @param enforceTrainingConfig whether to enforce training-related configuration options
+     * @throws InvalidKerasConfigurationException     Invalid Keras configuration exception
+     * @throws UnsupportedKerasConfigurationException Invalid Keras configuration exception
+     */
+    public KerasUpsampling2D(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
+            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
+        super(layerConfig, enforceTrainingConfig);
+
+        int[] size = KerasConvolutionUtils.getUpsamplingSizeFromConfig(layerConfig, 2, conf);
+        if (size[0] != size[1])
+            throw new UnsupportedKerasConfigurationException("First and second size arguments have to be the same" +
+                    "got: " + size[0] + " and " + size[1]);
+
+        Upsampling2D.Builder builder = new Upsampling2D.Builder()
+                .name(this.layerName)
+                .dropOut(this.dropout)
+                .size(size[0]);
+
+        this.layer = builder.build();
+        this.vertex = null;
+    }
+
+    /**
+     * Get DL4J Upsampling2D layer.
+     *
+     * @return Upsampling2D layer
+     */
+    public Upsampling2D getUpsampling2DLayer() {
+        return (Upsampling2D) this.layer;
+    }
+
+    /**
+     * Get layer output type.
+     *
+     * @param inputType Array of InputTypes
+     * @return output type as InputType
+     * @throws InvalidKerasConfigurationException
+     */
+    @Override
+    public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException {
+        if (inputType.length > 1)
+            throw new InvalidKerasConfigurationException(
+                    "Keras Subsampling layer accepts only one input (received " + inputType.length + ")");
+        return this.getUpsampling2DLayer().getOutputType(-1, inputType[0]);
+    }
+
+}

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java

@@ -1,3 +1,20 @@
+/*-
+ *
+ *  * Copyright 2017 Skymind,Inc.
+ *  *
+ *  *    Licensed under the Apache License, Version 2.0 (the "License");
+ *  *    you may not use this file except in compliance with the License.
+ *  *    You may obtain a copy of the License at
+ *  *
+ *  *        http://www.apache.org/licenses/LICENSE-2.0
+ *  *
+ *  *    Unless required by applicable law or agreed to in writing, software
+ *  *    distributed under the License is distributed on an "AS IS" BASIS,
+ *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  *    See the License for the specific language governing permissions and
+ *  *    limitations under the License.
+ *
+ */
 package org.deeplearning4j.nn.modelimport.keras.layers.pooling;
 
 import lombok.Data;

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling1D.java

@@ -1,3 +1,20 @@
+/*-
+ *
+ *  * Copyright 2017 Skymind,Inc.
+ *  *
+ *  *    Licensed under the Apache License, Version 2.0 (the "License");
+ *  *    you may not use this file except in compliance with the License.
+ *  *    You may obtain a copy of the License at
+ *  *
+ *  *        http://www.apache.org/licenses/LICENSE-2.0
+ *  *
+ *  *    Unless required by applicable law or agreed to in writing, software
+ *  *    distributed under the License is distributed on an "AS IS" BASIS,
+ *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  *    See the License for the specific language governing permissions and
+ *  *    limitations under the License.
+ *
+ */
 package org.deeplearning4j.nn.modelimport.keras.layers.pooling;
 
 import lombok.extern.slf4j.Slf4j;

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling2D.java

@@ -1,3 +1,20 @@
+/*-
+ *
+ *  * Copyright 2017 Skymind,Inc.
+ *  *
+ *  *    Licensed under the Apache License, Version 2.0 (the "License");
+ *  *    you may not use this file except in compliance with the License.
+ *  *    You may obtain a copy of the License at
+ *  *
+ *  *        http://www.apache.org/licenses/LICENSE-2.0
+ *  *
+ *  *    Unless required by applicable law or agreed to in writing, software
+ *  *    distributed under the License is distributed on an "AS IS" BASIS,
+ *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  *    See the License for the specific language governing permissions and
+ *  *    limitations under the License.
+ *
+ */
 package org.deeplearning4j.nn.modelimport.keras.layers.pooling;
 
 import lombok.extern.slf4j.Slf4j;

deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasLayerUtils.java

@@ -212,6 +212,8 @@ public static KerasLayer getKerasLayerFromConfig(Map<String, Object> layerConfig
             layer = new KerasReshape(layerConfig, enforceTrainingConfig);
         } else if (layerClassName.equals(conf.getLAYER_CLASS_NAME_ZERO_PADDING_2D())) {
             layer = new KerasZeroPadding2D(layerConfig, enforceTrainingConfig);
+        } else if (layerClassName.equals(conf.getLAYER_CLASS_NAME_UPSAMPLING_2D())) {
+            layer = new KerasUpsampling2D(layerConfig, enforceTrainingConfig);
         } else {
             // check if user registered a custom config
             Class<? extends KerasLayer> customConfig = customLayers.get(layerClassName);

deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java

@@ -195,6 +195,30 @@ public void importCnnNoBiasTfKeras2() throws Exception {
         importEndModelTest(modelPath, inputsOutputPath, true, false);
     }
 
+    /**
+     * DCGAN import test
+     */
+    @Test
+    public void importDcganDiscriminator() throws Exception {
+        importModelH5Test("modelimport/keras/examples/mnist_dcgan/dcgan_discriminator_epoch_50.h5");
+    }
+    @Test
+    public void importDcganGenerator() throws Exception {
+        importModelH5Test("modelimport/keras/examples/mnist_dcgan/dcgan_generator_epoch_50.h5");
+    }
+
+
+    void importModelH5Test(String modelPath) throws Exception {
+        ClassPathResource modelResource =
+                new ClassPathResource(modelPath,
+                        KerasModelEndToEndTest.class.getClassLoader());
+        File modelFile = File.createTempFile(TEMP_MODEL_FILENAME, H5_EXTENSION);
+        Files.copy(modelResource.getInputStream(), modelFile.toPath(), StandardCopyOption.REPLACE_EXISTING);
+        MultiLayerNetwork model = new KerasModel().modelBuilder().modelHdf5Filename(modelFile.getAbsolutePath())
+                .enforceTrainingConfig(false).buildSequential().getMultiLayerNetwork();
+    }
+
+
     void importEndModelTest(String modelPath, String inputsOutputsPath, boolean tfOrdering, boolean checkPredictions) throws Exception {
         ClassPathResource modelResource =
                 new ClassPathResource(modelPath,

deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasUpsampling2DTest.java

@@ -0,0 +1,73 @@
+/*-
+ *
+ *  * Copyright 2017 Skymind,Inc.
+ *  *
+ *  *    Licensed under the Apache License, Version 2.0 (the "License");
+ *  *    you may not use this file except in compliance with the License.
+ *  *    You may obtain a copy of the License at
+ *  *
+ *  *        http://www.apache.org/licenses/LICENSE-2.0
+ *  *
+ *  *    Unless required by applicable law or agreed to in writing, software
+ *  *    distributed under the License is distributed on an "AS IS" BASIS,
+ *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  *    See the License for the specific language governing permissions and
+ *  *    limitations under the License.
+ *
+ */
+package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
+
+import org.deeplearning4j.nn.conf.layers.Upsampling2D;
+import org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer;
+import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
+import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration;
+import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration;
+import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasUpsampling2D;
+import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasZeroPadding1D;
+import org.junit.Test;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+import static org.junit.Assert.assertEquals;
+
+/**
+ * @author Max Pumperla
+ */
+public class KerasUpsampling2DTest {
+
+    private final String LAYER_NAME = "upsampling_2D_layer";
+    private int[] size = new int[] {2, 2};
+
+    private Integer keras1 = 1;
+    private Integer keras2 = 2;
+    private Keras1LayerConfiguration conf1 = new Keras1LayerConfiguration();
+    private Keras2LayerConfiguration conf2 = new Keras2LayerConfiguration();
+
+    @Test
+    public void testUpsampling2DLayer() throws Exception {
+        buildUpsampling2DLayer(conf1, keras1);
+        buildUpsampling2DLayer(conf2, keras2);
+    }
+
+
+    public void buildUpsampling2DLayer(KerasLayerConfiguration conf, Integer kerasVersion) throws Exception {
+        Map<String, Object> layerConfig = new HashMap<>();
+        layerConfig.put(conf.getLAYER_FIELD_CLASS_NAME(), conf.getLAYER_CLASS_NAME_MAX_POOLING_1D());
+        Map<String, Object> config = new HashMap<>();
+        List<Integer> sizeList = new ArrayList<>();
+        sizeList.add(size[0]);
+        sizeList.add(size[1]);
+        config.put(conf.getLAYER_FIELD_UPSAMPLING_2D_SIZE(), sizeList);
+        config.put(conf.getLAYER_FIELD_NAME(), LAYER_NAME);
+        layerConfig.put(conf.getLAYER_FIELD_CONFIG(), config);
+        layerConfig.put(conf.getLAYER_FIELD_KERAS_VERSION(), kerasVersion);
+
+        Upsampling2D layer = new KerasUpsampling2D(layerConfig).getUpsampling2DLayer();
+        assertEquals(LAYER_NAME, layer.getLayerName());
+        assertEquals(size[0], layer.getSize());
+    }
+
+}