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Keras cropping fixes

master
agibsonccc 2021-03-05 10:30:46 +09:00
parent 2ae9f58909
commit 87cf665e22
2 changed files with 16 additions and 13 deletions
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27
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolutionUtils.java
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@ -59,7 +59,7 @@ public class KerasConvolutionUtils {
List<Integer> stridesList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_CONVOLUTION_STRIDES()); List<Integer> stridesList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_CONVOLUTION_STRIDES());
strides = ArrayUtil.toArray(stridesList); strides = ArrayUtil.toArray(stridesList);
} else if (innerConfig.containsKey(conf.getLAYER_FIELD_SUBSAMPLE_LENGTH()) && dimension == 1) { } else if (innerConfig.containsKey(conf.getLAYER_FIELD_SUBSAMPLE_LENGTH()) && dimension == 1) {
/* 1D Convolutional layers. */ /* 1D Convolutional layers. */
if ((int) layerConfig.get("keras_version") == 2) { if ((int) layerConfig.get("keras_version") == 2) {
@SuppressWarnings("unchecked") @SuppressWarnings("unchecked")
List<Integer> stridesList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_SUBSAMPLE_LENGTH()); List<Integer> stridesList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_SUBSAMPLE_LENGTH());
@ -163,7 +163,7 @@ public class KerasConvolutionUtils {
* @throws InvalidKerasConfigurationException Invalid Keras configuration * @throws InvalidKerasConfigurationException Invalid Keras configuration
*/ */
static int[] getUpsamplingSizeFromConfig(Map<String, Object> layerConfig, int dimension, static int[] getUpsamplingSizeFromConfig(Map<String, Object> layerConfig, int dimension,
KerasLayerConfiguration conf) KerasLayerConfiguration conf)
throws InvalidKerasConfigurationException { throws InvalidKerasConfigurationException {
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf); Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
int[] size; int[] size;
@ -200,7 +200,7 @@ public class KerasConvolutionUtils {
if (kerasMajorVersion != 2) { if (kerasMajorVersion != 2) {
if (innerConfig.containsKey(conf.getLAYER_FIELD_NB_ROW()) && dimension == 2 if (innerConfig.containsKey(conf.getLAYER_FIELD_NB_ROW()) && dimension == 2
&& innerConfig.containsKey(conf.getLAYER_FIELD_NB_COL())) { && innerConfig.containsKey(conf.getLAYER_FIELD_NB_COL())) {
/* 2D Convolutional layers. */ /* 2D Convolutional layers. */
List<Integer> kernelSizeList = new ArrayList<>(); List<Integer> kernelSizeList = new ArrayList<>();
kernelSizeList.add((Integer) innerConfig.get(conf.getLAYER_FIELD_NB_ROW())); kernelSizeList.add((Integer) innerConfig.get(conf.getLAYER_FIELD_NB_ROW()));
kernelSizeList.add((Integer) innerConfig.get(conf.getLAYER_FIELD_NB_COL())); kernelSizeList.add((Integer) innerConfig.get(conf.getLAYER_FIELD_NB_COL()));
@ -208,23 +208,23 @@ public class KerasConvolutionUtils {
} else if (innerConfig.containsKey(conf.getLAYER_FIELD_3D_KERNEL_1()) && dimension == 3 } else if (innerConfig.containsKey(conf.getLAYER_FIELD_3D_KERNEL_1()) && dimension == 3
&& innerConfig.containsKey(conf.getLAYER_FIELD_3D_KERNEL_2()) && innerConfig.containsKey(conf.getLAYER_FIELD_3D_KERNEL_2())
&& innerConfig.containsKey(conf.getLAYER_FIELD_3D_KERNEL_3())) { && innerConfig.containsKey(conf.getLAYER_FIELD_3D_KERNEL_3())) {
/* 3D Convolutional layers. */ /* 3D Convolutional layers. */
List<Integer> kernelSizeList = new ArrayList<>(); List<Integer> kernelSizeList = new ArrayList<>();
kernelSizeList.add((Integer) innerConfig.get(conf.getLAYER_FIELD_3D_KERNEL_1())); kernelSizeList.add((Integer) innerConfig.get(conf.getLAYER_FIELD_3D_KERNEL_1()));
kernelSizeList.add((Integer) innerConfig.get(conf.getLAYER_FIELD_3D_KERNEL_2())); kernelSizeList.add((Integer) innerConfig.get(conf.getLAYER_FIELD_3D_KERNEL_2()));
kernelSizeList.add((Integer) innerConfig.get(conf.getLAYER_FIELD_3D_KERNEL_3())); kernelSizeList.add((Integer) innerConfig.get(conf.getLAYER_FIELD_3D_KERNEL_3()));
kernelSize = ArrayUtil.toArray(kernelSizeList); kernelSize = ArrayUtil.toArray(kernelSizeList);
} else if (innerConfig.containsKey(conf.getLAYER_FIELD_FILTER_LENGTH()) && dimension == 1) { } else if (innerConfig.containsKey(conf.getLAYER_FIELD_FILTER_LENGTH()) && dimension == 1) {
/* 1D Convolutional layers. */ /* 1D Convolutional layers. */
int filterLength = (int) innerConfig.get(conf.getLAYER_FIELD_FILTER_LENGTH()); int filterLength = (int) innerConfig.get(conf.getLAYER_FIELD_FILTER_LENGTH());
kernelSize = new int[]{filterLength}; kernelSize = new int[]{filterLength};
} else if (innerConfig.containsKey(conf.getLAYER_FIELD_POOL_SIZE()) && dimension >= 2) { } else if (innerConfig.containsKey(conf.getLAYER_FIELD_POOL_SIZE()) && dimension >= 2) {
/* 2D/3D Pooling layers. */ /* 2D/3D Pooling layers. */
@SuppressWarnings("unchecked") @SuppressWarnings("unchecked")
List<Integer> kernelSizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_POOL_SIZE()); List<Integer> kernelSizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_POOL_SIZE());
kernelSize = ArrayUtil.toArray(kernelSizeList); kernelSize = ArrayUtil.toArray(kernelSizeList);
} else if (innerConfig.containsKey(conf.getLAYER_FIELD_POOL_1D_SIZE()) && dimension == 1) { } else if (innerConfig.containsKey(conf.getLAYER_FIELD_POOL_1D_SIZE()) && dimension == 1) {
/* 1D Pooling layers. */ /* 1D Pooling layers. */
int poolSize1D = (int) innerConfig.get(conf.getLAYER_FIELD_POOL_1D_SIZE()); int poolSize1D = (int) innerConfig.get(conf.getLAYER_FIELD_POOL_1D_SIZE());
kernelSize = new int[]{poolSize1D}; kernelSize = new int[]{poolSize1D};
} else { } else {
@ -242,17 +242,17 @@ public class KerasConvolutionUtils {
List<Integer> kernelSizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_KERNEL_SIZE()); List<Integer> kernelSizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_KERNEL_SIZE());
kernelSize = ArrayUtil.toArray(kernelSizeList); kernelSize = ArrayUtil.toArray(kernelSizeList);
} else if (innerConfig.containsKey(conf.getLAYER_FIELD_FILTER_LENGTH()) && dimension == 1) { } else if (innerConfig.containsKey(conf.getLAYER_FIELD_FILTER_LENGTH()) && dimension == 1) {
/* 1D Convolutional layers. */ /* 1D Convolutional layers. */
@SuppressWarnings("unchecked") @SuppressWarnings("unchecked")
List<Integer> kernelSizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_FILTER_LENGTH()); List<Integer> kernelSizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_FILTER_LENGTH());
kernelSize = ArrayUtil.toArray(kernelSizeList); kernelSize = ArrayUtil.toArray(kernelSizeList);
} else if (innerConfig.containsKey(conf.getLAYER_FIELD_POOL_SIZE()) && dimension >= 2) { } else if (innerConfig.containsKey(conf.getLAYER_FIELD_POOL_SIZE()) && dimension >= 2) {
/* 2D Pooling layers. */ /* 2D Pooling layers. */
@SuppressWarnings("unchecked") @SuppressWarnings("unchecked")
List<Integer> kernelSizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_POOL_SIZE()); List<Integer> kernelSizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_POOL_SIZE());
kernelSize = ArrayUtil.toArray(kernelSizeList); kernelSize = ArrayUtil.toArray(kernelSizeList);
} else if (innerConfig.containsKey(conf.getLAYER_FIELD_POOL_1D_SIZE()) && dimension == 1) { } else if (innerConfig.containsKey(conf.getLAYER_FIELD_POOL_1D_SIZE()) && dimension == 1) {
/* 1D Pooling layers. */ /* 1D Pooling layers. */
@SuppressWarnings("unchecked") @SuppressWarnings("unchecked")
List<Integer> kernelSizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_POOL_1D_SIZE()); List<Integer> kernelSizeList = (List<Integer>) innerConfig.get(conf.getLAYER_FIELD_POOL_1D_SIZE());
kernelSize = ArrayUtil.toArray(kernelSizeList); kernelSize = ArrayUtil.toArray(kernelSizeList);
@ -364,16 +364,17 @@ public class KerasConvolutionUtils {
} }
if ((paddingNoCast.size() == dimension) && !isNested) { if ((paddingNoCast.size() == dimension) && !isNested) {
for (int i=0; i < dimension; i++) for (int i = 0; i < dimension; i++)
paddingList.add((int) paddingNoCast.get(i)); paddingList.add((int) paddingNoCast.get(i));
padding = ArrayUtil.toArray(paddingList); padding = ArrayUtil.toArray(paddingList);
} else if ((paddingNoCast.size() == dimension) && isNested) { } else if ((paddingNoCast.size() == dimension) && isNested) {
for (int j=0; j < dimension; j++) { for (int j = 0; j < dimension; j++) {
@SuppressWarnings("unchecked") @SuppressWarnings("unchecked")
List<Integer> item = (List<Integer>) paddingNoCast.get(0); List<Integer> item = (List<Integer>) paddingNoCast.get(j);
paddingList.add((item.get(0))); paddingList.add((item.get(0)));
paddingList.add((item.get(1))); paddingList.add((item.get(1)));
} }
padding = ArrayUtil.toArray(paddingList); padding = ArrayUtil.toArray(paddingList);
} else { } else {
throw new InvalidKerasConfigurationException("Found Keras ZeroPadding" + dimension throw new InvalidKerasConfigurationException("Found Keras ZeroPadding" + dimension

2
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasCropping2D.java
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@ -29,6 +29,8 @@ import org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer; import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.nd4j.common.util.ArrayUtil;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Map; import java.util.Map;