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Posted to commits@systemml.apache.org by ni...@apache.org on 2017/04/19 22:08:34 UTC
[3/3] incubator-systemml git commit: [SYSTEMML-692] Added initial
version of DML generator for Caffe
[SYSTEMML-692] Added initial version of DML generator for Caffe
This experimental interface is called Caffe2DML and doesnot affect other functionality.
- Updated the interface to match the Caffe specification as per
@bertholdreinwald 's suggestion.
- Added support for fine-tuning.
- Added support for explain, statistics and gpu.
Closes #422.
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/cc7993fc
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/cc7993fc
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/cc7993fc
Branch: refs/heads/master
Commit: cc7993fc87ccf7d404bc8802f9529aee7da5de5e
Parents: ad3e78a
Author: Niketan Pansare <np...@us.ibm.com>
Authored: Wed Apr 19 14:07:44 2017 -0800
Committer: Niketan Pansare <np...@us.ibm.com>
Committed: Wed Apr 19 15:07:43 2017 -0700
----------------------------------------------------------------------
docs/beginners-guide-caffe2dml.md | 124 ++
docs/devdocs/deep-learning.md | 84 ++
pom.xml | 47 +-
.../cp/AggregateUnaryCPInstruction.java | 2 +-
.../sysml/runtime/util/ConvolutionUtils.java | 12 +
.../udf/lib/Caffe2DMLVisualizeWrapper.java | 66 +
.../apache/sysml/utils/TensorboardLogger.java | 177 +++
src/main/proto/caffe/caffe.proto | 1424 ++++++++++++++++++
src/main/proto/tensorflow/event.proto | 102 ++
src/main/proto/tensorflow/summary.proto | 123 ++
src/main/python/setup.py | 4 +-
src/main/python/systemml/converters.py | 31 +-
src/main/python/systemml/mllearn/estimators.py | 168 ++-
.../org/apache/sysml/api/dl/Caffe2DML.scala | 510 +++++++
.../org/apache/sysml/api/dl/CaffeLayer.scala | 357 +++++
.../org/apache/sysml/api/dl/CaffeNetwork.scala | 180 +++
.../org/apache/sysml/api/dl/CaffeSolver.scala | 158 ++
.../org/apache/sysml/api/dl/DMLGenerator.scala | 311 ++++
.../scala/org/apache/sysml/api/dl/Utils.scala | 127 ++
.../sysml/api/ml/BaseSystemMLClassifier.scala | 38 +-
.../sysml/api/ml/BaseSystemMLRegressor.scala | 4 +
.../sysml/api/ml/LogisticRegression.scala | 2 +-
.../org/apache/sysml/api/ml/NaiveBayes.scala | 2 +-
.../scala/org/apache/sysml/api/ml/SVM.scala | 2 +-
24 files changed, 4036 insertions(+), 19 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/cc7993fc/docs/beginners-guide-caffe2dml.md
----------------------------------------------------------------------
diff --git a/docs/beginners-guide-caffe2dml.md b/docs/beginners-guide-caffe2dml.md
new file mode 100644
index 0000000..cfcc0cb
--- /dev/null
+++ b/docs/beginners-guide-caffe2dml.md
@@ -0,0 +1,124 @@
+---
+layout: global
+title: Beginner's Guide for Caffe2DML users
+description: Beginner's Guide for Caffe2DML users
+---
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you 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.
+{% endcomment %}
+-->
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+<br/>
+
+## Introduction
+
+Caffe2DML is an experimental API that converts an Caffe specification to DML.
+
+## Frequently asked questions
+
+- How to set batch size ?
+
+Batch size is set in `data_param` of the Data layer:
+
+ layer {
+ name: "mnist"
+ type: "Data"
+ top: "data"
+ top: "label"
+ data_param {
+ source: "mnist_train"
+ batch_size: 64
+ backend: LMDB
+ }
+ }
+
+- How to set maximum number of iterations for training ?
+
+Caffe allows you to set the maximum number of iterations in solver specification
+
+ # The maximum number of iterations
+ max_iter: 2000
+
+- How to set the size of the validation dataset ?
+
+The size of the validation dataset is determined by the parameters `test_iter` and the batch size. For example: If the batch size is 64 and
+`test_iter` is 10, then the validation size is 640. This setting generates following DML code internally:
+
+ num_images = nrow(y_full)
+ BATCH_SIZE = 64
+ num_validation = 10 * BATCH_SIZE
+ X = X_full[(num_validation+1):num_images,]; y = y_full[(num_validation+1):num_images,]
+ X_val = X_full[1:num_validation,]; y_val = y_full[1:num_validation,]
+ num_images = nrow(y)
+
+- How to monitor loss via command-line ?
+
+To monitor loss, please set following parameters in the solver specification
+
+ # Display training loss and accuracy every 100 iterations
+ display: 100
+ # Carry out validation every 500 training iterations and display validation loss and accuracy.
+ test_iter: 10
+ test_interval: 500
+
+ - How to pass a single jpeg image to Caffe2DML for prediction ?
+
+ from PIL import Image
+ import systemml as sml
+ from systemml.mllearn import Caffe2DML
+ img_shape = (3, 224, 224)
+ input_image = sml.convertImageToNumPyArr(Image.open(img_file_path), img_shape=img_shape)
+ resnet = Caffe2DML(sqlCtx, solver='ResNet_50_solver.proto', weights='ResNet_50_pretrained_weights', input_shape=img_shape)
+ resnet.predict(input_image)
+
+- How to prepare a directory of jpeg images for training with Caffe2DML ?
+
+The below example assumes that the input dataset has 2 labels `cat` and `dogs` and the filename has these labels as prefix.
+We iterate through the directory and convert each jpeg image into pyspark.ml.linalg.Vector using pyspark.
+These vectors are stored as DataFrame and randomized using Spark SQL's `orderBy(rand())` function.
+The DataFrame is then saved in parquet format to reduce the cost of preprocessing for repeated training.
+
+ from systemml.mllearn import Caffe2DML
+ from pyspark.sql import SQLContext
+ import numpy as np
+ import urllib, os, scipy.ndimage
+ from pyspark.ml.linalg import Vectors
+ from pyspark import StorageLevel
+ import systemml as sml
+ from pyspark.sql.functions import rand
+ # ImageNet specific parameters
+ img_shape = (3, 224, 224)
+ train_dir = '/home/biuser/dogs_vs_cats/train'
+ def getLabelFeatures(filename):
+ from PIL import Image
+ vec = Vectors.dense(sml.convertImageToNumPyArr(Image.open(os.path.join(train_dir, filename)), img_shape=img_shape)[0,:])
+ if filename.lower().startswith('cat'):
+ return (1, vec)
+ elif filename.lower().startswith('dog'):
+ return (2, vec)
+ else:
+ raise ValueError('Expected the filename to start with either cat or dog')
+
+ list_jpeg_files = os.listdir(train_dir)
+ # 10 files per partition
+ train_df = sc.parallelize(list_jpeg_files, int(len(list_jpeg_files)/10)).map(lambda filename : getLabelFeatures(filename)).toDF(['label', 'features']).orderBy(rand())
+ # Optional: but helps seperates conversion-related from training
+ # Alternatively, this dataframe can be passed directly to `caffe2dml_model.fit(train_df)`
+ train_df.write.parquet('kaggle-cats-dogs.parquet')
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/cc7993fc/docs/devdocs/deep-learning.md
----------------------------------------------------------------------
diff --git a/docs/devdocs/deep-learning.md b/docs/devdocs/deep-learning.md
index 1fb951a..329c6c8 100644
--- a/docs/devdocs/deep-learning.md
+++ b/docs/devdocs/deep-learning.md
@@ -139,3 +139,87 @@ updates for the image:
|-----------------|---------------------------------|-----------------|
| `w3*y1 + w1*y3` | `w4*y1 + w3*y2 + w2*y3 + w1*y4` | `w4*y2 + w2*y4` |
| `w3*y3` | `w4*y3 + w3*y4` | `w4*y4` |
+
+# Caffe2DML examples
+
+## Training using Caffe models on Lenet
+
+The below script also demonstrates how to save the trained model.
+
+```python
+# Download the MNIST dataset
+from mlxtend.data import mnist_data
+import numpy as np
+from sklearn.utils import shuffle
+X, y = mnist_data()
+X, y = shuffle(X, y)
+num_classes = np.unique(y).shape[0]
+img_shape = (1, 28, 28)
+
+# Split the data into training and test
+n_samples = len(X)
+X_train = X[:int(.9 * n_samples)]
+y_train = y[:int(.9 * n_samples)]
+X_test = X[int(.9 * n_samples):]
+y_test = y[int(.9 * n_samples):]
+
+# Download the Lenet network
+import urllib
+urllib.urlretrieve('https://raw.githubusercontent.com/niketanpansare/model_zoo/master/caffe/vision/lenet/mnist/lenet.proto', 'lenet.proto')
+urllib.urlretrieve('https://raw.githubusercontent.com/niketanpansare/model_zoo/master/caffe/vision/lenet/mnist/lenet_solver.proto', 'lenet_solver.proto')
+
+# Train Lenet On MNIST using scikit-learn like API
+from systemml.mllearn import Caffe2DML
+lenet = Caffe2DML(sqlCtx, solver='lenet_solver.proto').set(max_iter=500, debug=True).setStatistics(True)
+print('Lenet score: %f' % lenet.fit(X_train, y_train).score(X_test, y_test))
+
+# Save the trained model
+lenet.save('lenet_model')
+```
+
+## Load the trained model and retrain (i.e. finetuning)
+
+```python
+# Fine-tune the existing trained model
+new_lenet = Caffe2DML(sqlCtx, solver='lenet_solver.proto', weights='lenet_model').set(max_iter=500, debug=True)
+new_lenet.fit(X_train, y_train)
+new_lenet.save('lenet_model')
+```
+
+## Perform prediction using the above trained model
+
+```python
+# Use the new model for prediction
+predict_lenet = Caffe2DML(sqlCtx, solver='lenet_solver.proto', weights='lenet_model')
+print('Lenet score: %f' % predict_lenet.score(X_test, y_test))
+```
+
+Similarly, you can perform prediction using the pre-trained ResNet network
+
+```python
+from systemml.mllearn import Caffe2DML
+from pyspark.sql import SQLContext
+import numpy as np
+import urllib, os, scipy.ndimage
+from PIL import Image
+import systemml as sml
+
+# ImageNet specific parameters
+img_shape = (3, 224, 224)
+
+# Downloads a jpg image, resizes it to 224 and return as numpy array in N X CHW format
+url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/5/58/MountainLion.jpg/312px-MountainLion.jpg'
+outFile = 'test.jpg'
+urllib.urlretrieve(url, outFile)
+input_image = sml.convertImageToNumPyArr(Image.open(outFile), img_shape=img_shape)
+
+# Download the ResNet network
+import urllib
+urllib.urlretrieve('https://raw.githubusercontent.com/niketanpansare/model_zoo/master/caffe/vision/resnet/ilsvrc12/ResNet_50_network.proto', 'ResNet_50_network.proto')
+urllib.urlretrieve('https://raw.githubusercontent.com/niketanpansare/model_zoo/master/caffe/vision/resnet/ilsvrc12/ResNet_50_solver.proto', 'ResNet_50_solver.proto')
+
+# Assumes that you have cloned the model_zoo repository
+# git clone https://github.com/niketanpansare/model_zoo.git
+resnet = Caffe2DML(sqlCtx, solver='ResNet_50_solver.proto', weights='~/model_zoo/caffe/vision/resnet/ilsvrc12/ResNet_50_pretrained_weights').set(input_shape=img_shape)
+resnet.predict(input_image)
+```
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/cc7993fc/pom.xml
----------------------------------------------------------------------
diff --git a/pom.xml b/pom.xml
index eba7f57..d107f64 100644
--- a/pom.xml
+++ b/pom.xml
@@ -324,6 +324,46 @@
</execution>
</executions>
</plugin>
+
+ <plugin>
+ <groupId>com.github.os72</groupId>
+ <artifactId>protoc-jar-maven-plugin</artifactId>
+ <version>3.0.0-b2.1</version>
+ <executions>
+ <execution>
+ <id>caffe-sources</id>
+ <phase>generate-sources</phase>
+ <goals>
+ <goal>run</goal>
+ </goals>
+ <configuration>
+ <protocVersion>2.5.0</protocVersion> <!-- 2.4.1, 2.5.0, 2.6.1, 3.0.0 -->
+ <inputDirectories>
+ <include>src/main/proto/caffe</include>
+ </inputDirectories>
+ <outputDirectories>
+ <include>src/main/java</include>
+ </outputDirectories>
+ </configuration>
+ </execution>
+ <execution>
+ <id>tf-sources</id>
+ <phase>generate-sources</phase>
+ <goals>
+ <goal>run</goal>
+ </goals>
+ <configuration>
+ <protocVersion>3.0.0</protocVersion> <!-- 2.4.1, 2.5.0, 2.6.1, 3.0.0 -->
+ <inputDirectories>
+ <include>src/main/proto/tensorflow</include>
+ </inputDirectories>
+ <outputDirectories>
+ <include>src/main/java</include>
+ </outputDirectories>
+ </configuration>
+ </execution>
+ </executions>
+ </plugin>
<!-- Currently, all tests are integration tests. -->
<plugin>
@@ -1076,7 +1116,12 @@
<dependencies>
-
+ <dependency>
+ <groupId>com.google.protobuf</groupId>
+ <artifactId>protobuf-java</artifactId>
+ <version>3.2.0</version>
+ <scope>provided</scope>
+ </dependency>
<dependency>
<groupId>org.jcuda</groupId>
<artifactId>jcuda</artifactId>
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/cc7993fc/src/main/java/org/apache/sysml/runtime/instructions/cp/AggregateUnaryCPInstruction.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/sysml/runtime/instructions/cp/AggregateUnaryCPInstruction.java b/src/main/java/org/apache/sysml/runtime/instructions/cp/AggregateUnaryCPInstruction.java
index 8790a53..8dd372a 100644
--- a/src/main/java/org/apache/sysml/runtime/instructions/cp/AggregateUnaryCPInstruction.java
+++ b/src/main/java/org/apache/sysml/runtime/instructions/cp/AggregateUnaryCPInstruction.java
@@ -121,7 +121,7 @@ public class AggregateUnaryCPInstruction extends UnaryCPInstruction
rval = mc.getRows() * mc.getCols();
}
else {
- throw new DMLRuntimeException("Invalid meta data returned by '"+opcode+"': "+rval);
+ throw new DMLRuntimeException("Invalid meta data returned by '"+opcode+"': "+rval + ":" + instString);
}
}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/cc7993fc/src/main/java/org/apache/sysml/runtime/util/ConvolutionUtils.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/sysml/runtime/util/ConvolutionUtils.java b/src/main/java/org/apache/sysml/runtime/util/ConvolutionUtils.java
index 80b20cd..814cf22 100644
--- a/src/main/java/org/apache/sysml/runtime/util/ConvolutionUtils.java
+++ b/src/main/java/org/apache/sysml/runtime/util/ConvolutionUtils.java
@@ -22,6 +22,18 @@ package org.apache.sysml.runtime.util;
public class ConvolutionUtils {
+ public static String getConv2dOutputMap(String H, String R, String verticalStride, String heightPadding) {
+ long padX2 = -1;
+ try {
+ padX2 = Long.parseLong(heightPadding)*2;
+ return "" + getP(Long.parseLong(H), Long.parseLong(R), Long.parseLong(verticalStride), Long.parseLong(heightPadding));
+ } catch(Exception e) {
+ if(padX2 == -1) return "((" + H + " + 2*" + heightPadding + " - " + R + ") / " + verticalStride + "+ 1)";
+ else if(padX2 == 0) return "((" + H + " - " + R + ") / " + verticalStride + "+ 1)";
+ else return "((" + H + " + " + padX2 + " - " + R + ") / " + verticalStride + "+ 1)";
+ }
+ }
+
public static long getP(long H, long R, long verticalStride, long heightPadding) {
long ret = (H + 2 * heightPadding - R) / verticalStride + 1;
if(ret <= 0) {
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/cc7993fc/src/main/java/org/apache/sysml/udf/lib/Caffe2DMLVisualizeWrapper.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/sysml/udf/lib/Caffe2DMLVisualizeWrapper.java b/src/main/java/org/apache/sysml/udf/lib/Caffe2DMLVisualizeWrapper.java
new file mode 100644
index 0000000..15c867b
--- /dev/null
+++ b/src/main/java/org/apache/sysml/udf/lib/Caffe2DMLVisualizeWrapper.java
@@ -0,0 +1,66 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.apache.sysml.udf.lib;
+
+import org.apache.sysml.udf.FunctionParameter;
+import org.apache.sysml.udf.PackageFunction;
+import org.apache.sysml.udf.Scalar;
+import org.apache.sysml.udf.Scalar.ScalarValueType;
+import org.apache.sysml.utils.TensorboardLogger;
+
+public class Caffe2DMLVisualizeWrapper extends PackageFunction
+{
+ private static final long serialVersionUID = 1L;
+ private Scalar _ret;
+
+ @Override
+ public int getNumFunctionOutputs() {
+ return 1;
+ }
+
+ @Override
+ public FunctionParameter getFunctionOutput(int pos) {
+ if (pos == 0)
+ return _ret;
+
+ throw new RuntimeException(
+ "Invalid function output being requested");
+ }
+
+ @Override
+ public void execute() {
+ String layerName = ((Scalar) this.getFunctionInput(0)).getValue();
+ String varType = ((Scalar) this.getFunctionInput(1)).getValue();
+ String aggFn = ((Scalar) this.getFunctionInput(2)).getValue();
+ double x = Double.parseDouble(((Scalar) this.getFunctionInput(3)).getValue());
+ double y = Double.parseDouble(((Scalar) this.getFunctionInput(4)).getValue());
+ String logDir = ((Scalar) this.getFunctionInput(5)).getValue();
+
+ String key = null;
+ if(aggFn.equals("training_loss") || aggFn.equals("validation_loss") ||
+ aggFn.equals("training_accuracy") || aggFn.equals("validation_accuracy"))
+ key = aggFn;
+ else
+ key = aggFn + "_" + varType + "_" + layerName;
+ TensorboardLogger.writeScalar(logDir, key, (long)x, (float)y);
+ _ret = new Scalar(ScalarValueType.Double, String.valueOf(1));
+ }
+
+}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/cc7993fc/src/main/java/org/apache/sysml/utils/TensorboardLogger.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/sysml/utils/TensorboardLogger.java b/src/main/java/org/apache/sysml/utils/TensorboardLogger.java
new file mode 100644
index 0000000..245d757
--- /dev/null
+++ b/src/main/java/org/apache/sysml/utils/TensorboardLogger.java
@@ -0,0 +1,177 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.apache.sysml.utils;
+
+import java.io.File;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.util.zip.Checksum;
+import org.tensorflow.framework.Summary;
+import org.tensorflow.util.Event;
+
+import com.google.common.primitives.Ints;
+import com.google.common.primitives.Longs;
+
+public class TensorboardLogger {
+ private static Crc32c crc32 = new Crc32c();
+
+ /**
+ * Writes scalar of given value in tensorboard format
+ *
+ * @param logDir log directory of tensorboard
+ * @param tag scalar tag (for example: training_loss, validation_loss, ...)
+ * @param step usually the iteration number
+ * @param value value of the scalar
+ */
+ public static void writeScalar(String logDir, String tag, long step, float value) {
+ String filePath = logDir + File.separator + "tfevents.event_systemml_scalar";
+ try {
+ FileOutputStream outputStream = new FileOutputStream(filePath, true);
+ Event event = Event.newBuilder()
+ .setWallTime(System.currentTimeMillis() / 1e3)
+ .setStep(step)
+ .setSummary(Summary.newBuilder().addValue(
+ Summary.Value.newBuilder().setTag(tag).setSimpleValue(value)
+ ).build())
+ .build();
+ byte[] eventString = event.toByteArray();
+ byte[] header = reverse(Longs.toByteArray((long)eventString.length));
+ write(outputStream, header);
+ write(outputStream, eventString);
+ outputStream.close();
+ }
+ catch(IOException e) {
+ throw new RuntimeException("Error writing event in tensorboard directory:" + filePath, e);
+ }
+ }
+
+ private static void write(FileOutputStream outputStream, byte[] byteString) throws IOException {
+ outputStream.write(byteString);
+ outputStream.write(reverse(Ints.toByteArray((int)maskedCRC32(byteString))));
+ }
+
+ private static byte[] reverse(byte[] nums) {
+ byte[] reversed = new byte[nums.length];
+ for (int i=0; i<nums.length; i++) {
+ reversed[i] = nums[nums.length - 1 - i];
+ }
+ return reversed;
+ }
+
+ private static long maskedCRC32(byte[] data){
+ crc32.reset();
+ crc32.update(data, 0, data.length);
+ long x = u32(crc32.getValue());
+ return u32(((x >> 15) | u32(x << 17)) + 0xa282ead8);
+ }
+
+ private static long u32(long x){
+ return x & 0xffffffff;
+ }
+}
+
+class Crc32c implements Checksum {
+ private static final int[] crcTable = {
+ 0x00000000, 0xF26B8303, 0xE13B70F7, 0x1350F3F4,
+ 0xC79A971F, 0x35F1141C, 0x26A1E7E8, 0xD4CA64EB,
+ 0x8AD958CF, 0x78B2DBCC, 0x6BE22838, 0x9989AB3B,
+ 0x4D43CFD0, 0xBF284CD3, 0xAC78BF27, 0x5E133C24,
+ 0x105EC76F, 0xE235446C, 0xF165B798, 0x030E349B,
+ 0xD7C45070, 0x25AFD373, 0x36FF2087, 0xC494A384,
+ 0x9A879FA0, 0x68EC1CA3, 0x7BBCEF57, 0x89D76C54,
+ 0x5D1D08BF, 0xAF768BBC, 0xBC267848, 0x4E4DFB4B,
+ 0x20BD8EDE, 0xD2D60DDD, 0xC186FE29, 0x33ED7D2A,
+ 0xE72719C1, 0x154C9AC2, 0x061C6936, 0xF477EA35,
+ 0xAA64D611, 0x580F5512, 0x4B5FA6E6, 0xB93425E5,
+ 0x6DFE410E, 0x9F95C20D, 0x8CC531F9, 0x7EAEB2FA,
+ 0x30E349B1, 0xC288CAB2, 0xD1D83946, 0x23B3BA45,
+ 0xF779DEAE, 0x05125DAD, 0x1642AE59, 0xE4292D5A,
+ 0xBA3A117E, 0x4851927D, 0x5B016189, 0xA96AE28A,
+ 0x7DA08661, 0x8FCB0562, 0x9C9BF696, 0x6EF07595,
+ 0x417B1DBC, 0xB3109EBF, 0xA0406D4B, 0x522BEE48,
+ 0x86E18AA3, 0x748A09A0, 0x67DAFA54, 0x95B17957,
+ 0xCBA24573, 0x39C9C670, 0x2A993584, 0xD8F2B687,
+ 0x0C38D26C, 0xFE53516F, 0xED03A29B, 0x1F682198,
+ 0x5125DAD3, 0xA34E59D0, 0xB01EAA24, 0x42752927,
+ 0x96BF4DCC, 0x64D4CECF, 0x77843D3B, 0x85EFBE38,
+ 0xDBFC821C, 0x2997011F, 0x3AC7F2EB, 0xC8AC71E8,
+ 0x1C661503, 0xEE0D9600, 0xFD5D65F4, 0x0F36E6F7,
+ 0x61C69362, 0x93AD1061, 0x80FDE395, 0x72966096,
+ 0xA65C047D, 0x5437877E, 0x4767748A, 0xB50CF789,
+ 0xEB1FCBAD, 0x197448AE, 0x0A24BB5A, 0xF84F3859,
+ 0x2C855CB2, 0xDEEEDFB1, 0xCDBE2C45, 0x3FD5AF46,
+ 0x7198540D, 0x83F3D70E, 0x90A324FA, 0x62C8A7F9,
+ 0xB602C312, 0x44694011, 0x5739B3E5, 0xA55230E6,
+ 0xFB410CC2, 0x092A8FC1, 0x1A7A7C35, 0xE811FF36,
+ 0x3CDB9BDD, 0xCEB018DE, 0xDDE0EB2A, 0x2F8B6829,
+ 0x82F63B78, 0x709DB87B, 0x63CD4B8F, 0x91A6C88C,
+ 0x456CAC67, 0xB7072F64, 0xA457DC90, 0x563C5F93,
+ 0x082F63B7, 0xFA44E0B4, 0xE9141340, 0x1B7F9043,
+ 0xCFB5F4A8, 0x3DDE77AB, 0x2E8E845F, 0xDCE5075C,
+ 0x92A8FC17, 0x60C37F14, 0x73938CE0, 0x81F80FE3,
+ 0x55326B08, 0xA759E80B, 0xB4091BFF, 0x466298FC,
+ 0x1871A4D8, 0xEA1A27DB, 0xF94AD42F, 0x0B21572C,
+ 0xDFEB33C7, 0x2D80B0C4, 0x3ED04330, 0xCCBBC033,
+ 0xA24BB5A6, 0x502036A5, 0x4370C551, 0xB11B4652,
+ 0x65D122B9, 0x97BAA1BA, 0x84EA524E, 0x7681D14D,
+ 0x2892ED69, 0xDAF96E6A, 0xC9A99D9E, 0x3BC21E9D,
+ 0xEF087A76, 0x1D63F975, 0x0E330A81, 0xFC588982,
+ 0xB21572C9, 0x407EF1CA, 0x532E023E, 0xA145813D,
+ 0x758FE5D6, 0x87E466D5, 0x94B49521, 0x66DF1622,
+ 0x38CC2A06, 0xCAA7A905, 0xD9F75AF1, 0x2B9CD9F2,
+ 0xFF56BD19, 0x0D3D3E1A, 0x1E6DCDEE, 0xEC064EED,
+ 0xC38D26C4, 0x31E6A5C7, 0x22B65633, 0xD0DDD530,
+ 0x0417B1DB, 0xF67C32D8, 0xE52CC12C, 0x1747422F,
+ 0x49547E0B, 0xBB3FFD08, 0xA86F0EFC, 0x5A048DFF,
+ 0x8ECEE914, 0x7CA56A17, 0x6FF599E3, 0x9D9E1AE0,
+ 0xD3D3E1AB, 0x21B862A8, 0x32E8915C, 0xC083125F,
+ 0x144976B4, 0xE622F5B7, 0xF5720643, 0x07198540,
+ 0x590AB964, 0xAB613A67, 0xB831C993, 0x4A5A4A90,
+ 0x9E902E7B, 0x6CFBAD78, 0x7FAB5E8C, 0x8DC0DD8F,
+ 0xE330A81A, 0x115B2B19, 0x020BD8ED, 0xF0605BEE,
+ 0x24AA3F05, 0xD6C1BC06, 0xC5914FF2, 0x37FACCF1,
+ 0x69E9F0D5, 0x9B8273D6, 0x88D28022, 0x7AB90321,
+ 0xAE7367CA, 0x5C18E4C9, 0x4F48173D, 0xBD23943E,
+ 0xF36E6F75, 0x0105EC76, 0x12551F82, 0xE03E9C81,
+ 0x34F4F86A, 0xC69F7B69, 0xD5CF889D, 0x27A40B9E,
+ 0x79B737BA, 0x8BDCB4B9, 0x988C474D, 0x6AE7C44E,
+ 0xBE2DA0A5, 0x4C4623A6, 0x5F16D052, 0xAD7D5351,
+ };
+
+ private int crc = ~0;
+
+ public void update(byte[] buffer, int offset, int length) {
+ for (int i = offset; i < offset + length; i++) {
+ crc = crc32c(crc, buffer[i]);
+ }
+ }
+ public long getValue() {
+ return (crc ^ 0xFFFFFFFFL) & 0xFFFFFFFFL;
+ }
+ public void reset() {
+ crc = ~0;
+ }
+ private static int crc32c(int crc, int b) {
+ return crc >>> 8 ^ crcTable[(crc ^ b & 0xFF) & 0xFF];
+ }
+ public void update(int arg0) {
+ throw new RuntimeException("Not implemented");
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/cc7993fc/src/main/proto/caffe/caffe.proto
----------------------------------------------------------------------
diff --git a/src/main/proto/caffe/caffe.proto b/src/main/proto/caffe/caffe.proto
new file mode 100644
index 0000000..cf53e17
--- /dev/null
+++ b/src/main/proto/caffe/caffe.proto
@@ -0,0 +1,1424 @@
+//-------------------------------------------------------------
+//
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you 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.
+//
+//-------------------------------------------------------------
+
+syntax = "proto2";
+
+package caffe;
+
+// Specifies the shape (dimensions) of a Blob.
+message BlobShape {
+ repeated int64 dim = 1 [packed = true];
+}
+
+message BlobProto {
+ optional BlobShape shape = 7;
+ repeated float data = 5 [packed = true];
+ repeated float diff = 6 [packed = true];
+ repeated double double_data = 8 [packed = true];
+ repeated double double_diff = 9 [packed = true];
+
+ // 4D dimensions -- deprecated. Use "shape" instead.
+ optional int32 num = 1 [default = 0];
+ optional int32 channels = 2 [default = 0];
+ optional int32 height = 3 [default = 0];
+ optional int32 width = 4 [default = 0];
+}
+
+// The BlobProtoVector is simply a way to pass multiple blobproto instances
+// around.
+message BlobProtoVector {
+ repeated BlobProto blobs = 1;
+}
+
+message Datum {
+ optional int32 channels = 1;
+ optional int32 height = 2;
+ optional int32 width = 3;
+ // the actual image data, in bytes
+ optional bytes data = 4;
+ optional int32 label = 5;
+ // Optionally, the datum could also hold float data.
+ repeated float float_data = 6;
+ // If true data contains an encoded image that need to be decoded
+ optional bool encoded = 7 [default = false];
+}
+
+message FillerParameter {
+ // The filler type.
+ optional string type = 1 [default = 'constant'];
+ optional float value = 2 [default = 0]; // the value in constant filler
+ optional float min = 3 [default = 0]; // the min value in uniform filler
+ optional float max = 4 [default = 1]; // the max value in uniform filler
+ optional float mean = 5 [default = 0]; // the mean value in Gaussian filler
+ optional float std = 6 [default = 1]; // the std value in Gaussian filler
+ // The expected number of non-zero output weights for a given input in
+ // Gaussian filler -- the default -1 means don't perform sparsification.
+ optional int32 sparse = 7 [default = -1];
+ // Normalize the filler variance by fan_in, fan_out, or their average.
+ // Applies to 'xavier' and 'msra' fillers.
+ enum VarianceNorm {
+ FAN_IN = 0;
+ FAN_OUT = 1;
+ AVERAGE = 2;
+ }
+ optional VarianceNorm variance_norm = 8 [default = FAN_IN];
+}
+
+message NetParameter {
+ optional string name = 1; // consider giving the network a name
+ // DEPRECATED. See InputParameter. The input blobs to the network.
+ repeated string input = 3;
+ // DEPRECATED. See InputParameter. The shape of the input blobs.
+ repeated BlobShape input_shape = 8;
+
+ // 4D input dimensions -- deprecated. Use "input_shape" instead.
+ // If specified, for each input blob there should be four
+ // values specifying the num, channels, height and width of the input blob.
+ // Thus, there should be a total of (4 * #input) numbers.
+ repeated int32 input_dim = 4;
+
+ // Whether the network will force every layer to carry out backward operation.
+ // If set False, then whether to carry out backward is determined
+ // automatically according to the net structure and learning rates.
+ optional bool force_backward = 5 [default = false];
+ // The current "state" of the network, including the phase, level, and stage.
+ // Some layers may be included/excluded depending on this state and the states
+ // specified in the layers' include and exclude fields.
+ optional NetState state = 6;
+
+ // Print debugging information about results while running Net::Forward,
+ // Net::Backward, and Net::Update.
+ optional bool debug_info = 7 [default = false];
+
+ // The layers that make up the net. Each of their configurations, including
+ // connectivity and behavior, is specified as a LayerParameter.
+ repeated LayerParameter layer = 100; // ID 100 so layers are printed last.
+
+ // DEPRECATED: use 'layer' instead.
+ repeated V1LayerParameter layers = 2;
+}
+
+// NOTE
+// Update the next available ID when you add a new SolverParameter field.
+//
+// SolverParameter next available ID: 43 (last added: test_algo)
+message SolverParameter {
+ //////////////////////////////////////////////////////////////////////////////
+ // Specifying the train and test networks
+ //
+ // Exactly one train net must be specified using one of the following fields:
+ // train_net_param, train_net, net_param, net
+ // One or more test nets may be specified using any of the following fields:
+ // test_net_param, test_net, net_param, net
+ // If more than one test net field is specified (e.g., both net and
+ // test_net are specified), they will be evaluated in the field order given
+ // above: (1) test_net_param, (2) test_net, (3) net_param/net.
+ // A test_iter must be specified for each test_net.
+ // A test_level and/or a test_stage may also be specified for each test_net.
+ //////////////////////////////////////////////////////////////////////////////
+
+ // SystemML extension
+ optional string train_algo = 41 [default = "minibatch"];
+ optional string test_algo = 42 [default = "minibatch"];
+
+ // Proto filename for the train net, possibly combined with one or more
+ // test nets.
+ optional string net = 24;
+ // Inline train net param, possibly combined with one or more test nets.
+ optional NetParameter net_param = 25;
+
+ optional string train_net = 1; // Proto filename for the train net.
+ repeated string test_net = 2; // Proto filenames for the test nets.
+ optional NetParameter train_net_param = 21; // Inline train net params.
+ repeated NetParameter test_net_param = 22; // Inline test net params.
+
+ // The states for the train/test nets. Must be unspecified or
+ // specified once per net.
+ //
+ // By default, all states will have solver = true;
+ // train_state will have phase = TRAIN,
+ // and all test_state's will have phase = TEST.
+ // Other defaults are set according to the NetState defaults.
+ optional NetState train_state = 26;
+ repeated NetState test_state = 27;
+
+ // The number of iterations for each test net.
+ repeated int32 test_iter = 3;
+
+ // The number of iterations between two testing phases.
+ optional int32 test_interval = 4 [default = 0];
+ optional bool test_compute_loss = 19 [default = false];
+ // If true, run an initial test pass before the first iteration,
+ // ensuring memory availability and printing the starting value of the loss.
+ optional bool test_initialization = 32 [default = true];
+ optional float base_lr = 5; // The base learning rate
+ // the number of iterations between displaying info. If display = 0, no info
+ // will be displayed.
+ optional int32 display = 6;
+ // Display the loss averaged over the last average_loss iterations
+ optional int32 average_loss = 33 [default = 1];
+ optional int32 max_iter = 7; // the maximum number of iterations
+ // accumulate gradients over `iter_size` x `batch_size` instances
+ optional int32 iter_size = 36 [default = 1];
+
+ // The learning rate decay policy. The currently implemented learning rate
+ // policies are as follows:
+ // - fixed: always return base_lr.
+ // - step: return base_lr * gamma ^ (floor(iter / step))
+ // - exp: return base_lr * gamma ^ iter
+ // - inv: return base_lr * (1 + gamma * iter) ^ (- power)
+ // - multistep: similar to step but it allows non uniform steps defined by
+ // stepvalue
+ // - poly: the effective learning rate follows a polynomial decay, to be
+ // zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+ // - sigmoid: the effective learning rate follows a sigmod decay
+ // return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+ //
+ // where base_lr, max_iter, gamma, step, stepvalue and power are defined
+ // in the solver parameter protocol buffer, and iter is the current iteration.
+ optional string lr_policy = 8;
+ optional float gamma = 9; // The parameter to compute the learning rate.
+ optional float power = 10; // The parameter to compute the learning rate.
+ optional float momentum = 11; // The momentum value.
+ optional float weight_decay = 12; // The weight decay.
+ // regularization types supported: L1 and L2
+ // controlled by weight_decay
+ optional string regularization_type = 29 [default = "L2"];
+ // the stepsize for learning rate policy "step"
+ optional int32 stepsize = 13;
+ // the stepsize for learning rate policy "multistep"
+ repeated int32 stepvalue = 34;
+
+ // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,
+ // whenever their actual L2 norm is larger.
+ optional float clip_gradients = 35 [default = -1];
+
+ optional int32 snapshot = 14 [default = 0]; // The snapshot interval
+ optional string snapshot_prefix = 15; // The prefix for the snapshot.
+ // whether to snapshot diff in the results or not. Snapshotting diff will help
+ // debugging but the final protocol buffer size will be much larger.
+ optional bool snapshot_diff = 16 [default = false];
+ enum SnapshotFormat {
+ HDF5 = 0;
+ BINARYPROTO = 1;
+ }
+ optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];
+ // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
+ enum SolverMode {
+ CPU = 0;
+ GPU = 1;
+ }
+ optional SolverMode solver_mode = 17 [default = GPU];
+ // the device_id will that be used in GPU mode. Use device_id = 0 in default.
+ optional int32 device_id = 18 [default = 0];
+ // If non-negative, the seed with which the Solver will initialize the Caffe
+ // random number generator -- useful for reproducible results. Otherwise,
+ // (and by default) initialize using a seed derived from the system clock.
+ optional int64 random_seed = 20 [default = -1];
+
+ // type of the solver
+ optional string type = 40 [default = "SGD"];
+
+ // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
+ optional float delta = 31 [default = 1e-8];
+ // parameters for the Adam solver
+ optional float momentum2 = 39 [default = 0.999];
+
+ // RMSProp decay value
+ // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
+ optional float rms_decay = 38 [default = 0.99];
+
+ // If true, print information about the state of the net that may help with
+ // debugging learning problems.
+ optional bool debug_info = 23 [default = false];
+
+ // If false, don't save a snapshot after training finishes.
+ optional bool snapshot_after_train = 28 [default = true];
+
+ // DEPRECATED: old solver enum types, use string instead
+ enum SolverType {
+ SGD = 0;
+ NESTEROV = 1;
+ ADAGRAD = 2;
+ RMSPROP = 3;
+ ADADELTA = 4;
+ ADAM = 5;
+ }
+ // DEPRECATED: use type instead of solver_type
+ optional SolverType solver_type = 30 [default = SGD];
+}
+
+// A message that stores the solver snapshots
+message SolverState {
+ optional int32 iter = 1; // The current iteration
+ optional string learned_net = 2; // The file that stores the learned net.
+ repeated BlobProto history = 3; // The history for sgd solvers
+ optional int32 current_step = 4 [default = 0]; // The current step for learning rate
+}
+
+enum Phase {
+ TRAIN = 0;
+ TEST = 1;
+}
+
+message NetState {
+ optional Phase phase = 1 [default = TEST];
+ optional int32 level = 2 [default = 0];
+ repeated string stage = 3;
+}
+
+message NetStateRule {
+ // Set phase to require the NetState have a particular phase (TRAIN or TEST)
+ // to meet this rule.
+ optional Phase phase = 1;
+
+ // Set the minimum and/or maximum levels in which the layer should be used.
+ // Leave undefined to meet the rule regardless of level.
+ optional int32 min_level = 2;
+ optional int32 max_level = 3;
+
+ // Customizable sets of stages to include or exclude.
+ // The net must have ALL of the specified stages and NONE of the specified
+ // "not_stage"s to meet the rule.
+ // (Use multiple NetStateRules to specify conjunctions of stages.)
+ repeated string stage = 4;
+ repeated string not_stage = 5;
+}
+
+// Specifies training parameters (multipliers on global learning constants,
+// and the name and other settings used for weight sharing).
+message ParamSpec {
+ // The names of the parameter blobs -- useful for sharing parameters among
+ // layers, but never required otherwise. To share a parameter between two
+ // layers, give it a (non-empty) name.
+ optional string name = 1;
+
+ // Whether to require shared weights to have the same shape, or just the same
+ // count -- defaults to STRICT if unspecified.
+ optional DimCheckMode share_mode = 2;
+ enum DimCheckMode {
+ // STRICT (default) requires that num, channels, height, width each match.
+ STRICT = 0;
+ // PERMISSIVE requires only the count (num*channels*height*width) to match.
+ PERMISSIVE = 1;
+ }
+
+ // The multiplier on the global learning rate for this parameter.
+ optional float lr_mult = 3 [default = 1.0];
+
+ // The multiplier on the global weight decay for this parameter.
+ optional float decay_mult = 4 [default = 1.0];
+}
+
+// NOTE
+// Update the next available ID when you add a new LayerParameter field.
+//
+// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
+message LayerParameter {
+ optional string name = 1; // the layer name
+ optional string type = 2; // the layer type
+ repeated string bottom = 3; // the name of each bottom blob
+ repeated string top = 4; // the name of each top blob
+
+ // The train / test phase for computation.
+ optional Phase phase = 10;
+
+ // The amount of weight to assign each top blob in the objective.
+ // Each layer assigns a default value, usually of either 0 or 1,
+ // to each top blob.
+ repeated float loss_weight = 5;
+
+ // Specifies training parameters (multipliers on global learning constants,
+ // and the name and other settings used for weight sharing).
+ repeated ParamSpec param = 6;
+
+ // The blobs containing the numeric parameters of the layer.
+ repeated BlobProto blobs = 7;
+
+ // Specifies whether to backpropagate to each bottom. If unspecified,
+ // Caffe will automatically infer whether each input needs backpropagation
+ // to compute parameter gradients. If set to true for some inputs,
+ // backpropagation to those inputs is forced; if set false for some inputs,
+ // backpropagation to those inputs is skipped.
+ //
+ // The size must be either 0 or equal to the number of bottoms.
+ repeated bool propagate_down = 11;
+
+ // Rules controlling whether and when a layer is included in the network,
+ // based on the current NetState. You may specify a non-zero number of rules
+ // to include OR exclude, but not both. If no include or exclude rules are
+ // specified, the layer is always included. If the current NetState meets
+ // ANY (i.e., one or more) of the specified rules, the layer is
+ // included/excluded.
+ repeated NetStateRule include = 8;
+ repeated NetStateRule exclude = 9;
+
+ // Parameters for data pre-processing.
+ optional TransformationParameter transform_param = 100;
+
+ // Parameters shared by loss layers.
+ optional LossParameter loss_param = 101;
+
+ // Layer type-specific parameters.
+ //
+ // Note: certain layers may have more than one computational engine
+ // for their implementation. These layers include an Engine type and
+ // engine parameter for selecting the implementation.
+ // The default for the engine is set by the ENGINE switch at compile-time.
+ optional AccuracyParameter accuracy_param = 102;
+ optional ArgMaxParameter argmax_param = 103;
+ optional BatchNormParameter batch_norm_param = 139;
+ optional BiasParameter bias_param = 141;
+ optional ConcatParameter concat_param = 104;
+ optional ContrastiveLossParameter contrastive_loss_param = 105;
+ optional ConvolutionParameter convolution_param = 106;
+ optional CropParameter crop_param = 144;
+ optional DataParameter data_param = 107;
+ optional DropoutParameter dropout_param = 108;
+ optional DummyDataParameter dummy_data_param = 109;
+ optional EltwiseParameter eltwise_param = 110;
+ optional ELUParameter elu_param = 140;
+ optional EmbedParameter embed_param = 137;
+ optional ExpParameter exp_param = 111;
+ optional FlattenParameter flatten_param = 135;
+ optional HDF5DataParameter hdf5_data_param = 112;
+ optional HDF5OutputParameter hdf5_output_param = 113;
+ optional HingeLossParameter hinge_loss_param = 114;
+ optional ImageDataParameter image_data_param = 115;
+ optional InfogainLossParameter infogain_loss_param = 116;
+ optional InnerProductParameter inner_product_param = 117;
+ optional InputParameter input_param = 143;
+ optional LogParameter log_param = 134;
+ optional LRNParameter lrn_param = 118;
+ optional MemoryDataParameter memory_data_param = 119;
+ optional MVNParameter mvn_param = 120;
+ optional ParameterParameter parameter_param = 145;
+ optional PoolingParameter pooling_param = 121;
+ optional PowerParameter power_param = 122;
+ optional PReLUParameter prelu_param = 131;
+ optional PythonParameter python_param = 130;
+ optional RecurrentParameter recurrent_param = 146;
+ optional ReductionParameter reduction_param = 136;
+ optional ReLUParameter relu_param = 123;
+ optional ReshapeParameter reshape_param = 133;
+ optional ScaleParameter scale_param = 142;
+ optional SigmoidParameter sigmoid_param = 124;
+ optional SoftmaxParameter softmax_param = 125;
+ optional SPPParameter spp_param = 132;
+ optional SliceParameter slice_param = 126;
+ optional TanHParameter tanh_param = 127;
+ optional ThresholdParameter threshold_param = 128;
+ optional TileParameter tile_param = 138;
+ optional WindowDataParameter window_data_param = 129;
+}
+
+// Message that stores parameters used to apply transformation
+// to the data layer's data
+message TransformationParameter {
+ // For data pre-processing, we can do simple scaling and subtracting the
+ // data mean, if provided. Note that the mean subtraction is always carried
+ // out before scaling.
+ optional float scale = 1 [default = 1];
+ // Specify if we want to randomly mirror data.
+ optional bool mirror = 2 [default = false];
+ // Specify if we would like to randomly crop an image.
+ optional uint32 crop_size = 3 [default = 0];
+ // mean_file and mean_value cannot be specified at the same time
+ optional string mean_file = 4;
+ // if specified can be repeated once (would substract it from all the channels)
+ // or can be repeated the same number of times as channels
+ // (would subtract them from the corresponding channel)
+ repeated float mean_value = 5;
+ // Force the decoded image to have 3 color channels.
+ optional bool force_color = 6 [default = false];
+ // Force the decoded image to have 1 color channels.
+ optional bool force_gray = 7 [default = false];
+}
+
+// Message that stores parameters shared by loss layers
+message LossParameter {
+ // If specified, ignore instances with the given label.
+ optional int32 ignore_label = 1;
+ // How to normalize the loss for loss layers that aggregate across batches,
+ // spatial dimensions, or other dimensions. Currently only implemented in
+ // SoftmaxWithLoss layer.
+ enum NormalizationMode {
+ // Divide by the number of examples in the batch times spatial dimensions.
+ // Outputs that receive the ignore label will NOT be ignored in computing
+ // the normalization factor.
+ FULL = 0;
+ // Divide by the total number of output locations that do not take the
+ // ignore_label. If ignore_label is not set, this behaves like FULL.
+ VALID = 1;
+ // Divide by the batch size.
+ BATCH_SIZE = 2;
+ // Do not normalize the loss.
+ NONE = 3;
+ }
+ optional NormalizationMode normalization = 3 [default = VALID];
+ // Deprecated. Ignored if normalization is specified. If normalization
+ // is not specified, then setting this to false will be equivalent to
+ // normalization = BATCH_SIZE to be consistent with previous behavior.
+ optional bool normalize = 2;
+}
+
+// Messages that store parameters used by individual layer types follow, in
+// alphabetical order.
+
+message AccuracyParameter {
+ // When computing accuracy, count as correct by comparing the true label to
+ // the top k scoring classes. By default, only compare to the top scoring
+ // class (i.e. argmax).
+ optional uint32 top_k = 1 [default = 1];
+
+ // The "label" axis of the prediction blob, whose argmax corresponds to the
+ // predicted label -- may be negative to index from the end (e.g., -1 for the
+ // last axis). For example, if axis == 1 and the predictions are
+ // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
+ // labels with integer values in {0, 1, ..., C-1}.
+ optional int32 axis = 2 [default = 1];
+
+ // If specified, ignore instances with the given label.
+ optional int32 ignore_label = 3;
+}
+
+message ArgMaxParameter {
+ // If true produce pairs (argmax, maxval)
+ optional bool out_max_val = 1 [default = false];
+ optional uint32 top_k = 2 [default = 1];
+ // The axis along which to maximise -- may be negative to index from the
+ // end (e.g., -1 for the last axis).
+ // By default ArgMaxLayer maximizes over the flattened trailing dimensions
+ // for each index of the first / num dimension.
+ optional int32 axis = 3;
+}
+
+message ConcatParameter {
+ // The axis along which to concatenate -- may be negative to index from the
+ // end (e.g., -1 for the last axis). Other axes must have the
+ // same dimension for all the bottom blobs.
+ // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
+ optional int32 axis = 2 [default = 1];
+
+ // DEPRECATED: alias for "axis" -- does not support negative indexing.
+ optional uint32 concat_dim = 1 [default = 1];
+}
+
+message BatchNormParameter {
+ // If false, accumulate global mean/variance values via a moving average. If
+ // true, use those accumulated values instead of computing mean/variance
+ // across the batch.
+ optional bool use_global_stats = 1;
+ // How much does the moving average decay each iteration?
+ optional float moving_average_fraction = 2 [default = .999];
+ // Small value to add to the variance estimate so that we don't divide by
+ // zero.
+ optional float eps = 3 [default = 1e-5];
+}
+
+message BiasParameter {
+ // The first axis of bottom[0] (the first input Blob) along which to apply
+ // bottom[1] (the second input Blob). May be negative to index from the end
+ // (e.g., -1 for the last axis).
+ //
+ // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+ // top[0] will have the same shape, and bottom[1] may have any of the
+ // following shapes (for the given value of axis):
+ // (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+ // (axis == 1 == -3) 3; 3x40; 3x40x60
+ // (axis == 2 == -2) 40; 40x60
+ // (axis == 3 == -1) 60
+ // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+ // "axis") -- a scalar bias.
+ optional int32 axis = 1 [default = 1];
+
+ // (num_axes is ignored unless just one bottom is given and the bias is
+ // a learned parameter of the layer. Otherwise, num_axes is determined by the
+ // number of axes by the second bottom.)
+ // The number of axes of the input (bottom[0]) covered by the bias
+ // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+ // Set num_axes := 0, to add a zero-axis Blob: a scalar.
+ optional int32 num_axes = 2 [default = 1];
+
+ // (filler is ignored unless just one bottom is given and the bias is
+ // a learned parameter of the layer.)
+ // The initialization for the learned bias parameter.
+ // Default is the zero (0) initialization, resulting in the BiasLayer
+ // initially performing the identity operation.
+ optional FillerParameter filler = 3;
+}
+
+message ContrastiveLossParameter {
+ // margin for dissimilar pair
+ optional float margin = 1 [default = 1.0];
+ // The first implementation of this cost did not exactly match the cost of
+ // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
+ // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
+ // Hadsell paper. New models should probably use this version.
+ // legacy_version = true uses (margin - d^2). This is kept to support /
+ // reproduce existing models and results
+ optional bool legacy_version = 2 [default = false];
+}
+
+message ConvolutionParameter {
+ optional uint32 num_output = 1; // The number of outputs for the layer
+ optional bool bias_term = 2 [default = true]; // whether to have bias terms
+
+ // Pad, kernel size, and stride are all given as a single value for equal
+ // dimensions in all spatial dimensions, or once per spatial dimension.
+ repeated uint32 pad = 3; // The padding size; defaults to 0
+ repeated uint32 kernel_size = 4; // The kernel size
+ repeated uint32 stride = 6; // The stride; defaults to 1
+ // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
+ // holes. (Kernel dilation is sometimes referred to by its use in the
+ // algorithme � trous from Holschneider et al. 1987.)
+ repeated uint32 dilation = 18; // The dilation; defaults to 1
+
+ // For 2D convolution only, the *_h and *_w versions may also be used to
+ // specify both spatial dimensions.
+ optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
+ optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
+ optional uint32 kernel_h = 11; // The kernel height (2D only)
+ optional uint32 kernel_w = 12; // The kernel width (2D only)
+ optional uint32 stride_h = 13; // The stride height (2D only)
+ optional uint32 stride_w = 14; // The stride width (2D only)
+
+ optional uint32 group = 5 [default = 1]; // The group size for group conv
+
+ optional FillerParameter weight_filler = 7; // The filler for the weight
+ optional FillerParameter bias_filler = 8; // The filler for the bias
+ enum Engine {
+ DEFAULT = 0;
+ CAFFE = 1;
+ CUDNN = 2;
+ }
+ optional Engine engine = 15 [default = DEFAULT];
+
+ // The axis to interpret as "channels" when performing convolution.
+ // Preceding dimensions are treated as independent inputs;
+ // succeeding dimensions are treated as "spatial".
+ // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
+ // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
+ // groups g>1) filters across the spatial axes (H, W) of the input.
+ // With (N, C, D, H, W) inputs, and axis == 1, we perform
+ // N independent 3D convolutions, sliding (C/g)-channels
+ // filters across the spatial axes (D, H, W) of the input.
+ optional int32 axis = 16 [default = 1];
+
+ // Whether to force use of the general ND convolution, even if a specific
+ // implementation for blobs of the appropriate number of spatial dimensions
+ // is available. (Currently, there is only a 2D-specific convolution
+ // implementation; for input blobs with num_axes != 2, this option is
+ // ignored and the ND implementation will be used.)
+ optional bool force_nd_im2col = 17 [default = false];
+}
+
+message CropParameter {
+ // To crop, elements of the first bottom are selected to fit the dimensions
+ // of the second, reference bottom. The crop is configured by
+ // - the crop `axis` to pick the dimensions for cropping
+ // - the crop `offset` to set the shift for all/each dimension
+ // to align the cropped bottom with the reference bottom.
+ // All dimensions up to but excluding `axis` are preserved, while
+ // the dimensions including and trailing `axis` are cropped.
+ // If only one `offset` is set, then all dimensions are offset by this amount.
+ // Otherwise, the number of offsets must equal the number of cropped axes to
+ // shift the crop in each dimension accordingly.
+ // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
+ // and `axis` may be negative to index from the end (e.g., -1 for the last
+ // axis).
+ optional int32 axis = 1 [default = 2];
+ repeated uint32 offset = 2;
+}
+
+message DataParameter {
+ enum DB {
+ LEVELDB = 0;
+ LMDB = 1;
+ }
+ // Specify the data source.
+ optional string source = 1;
+ // Specify the batch size.
+ optional uint32 batch_size = 4;
+ // The rand_skip variable is for the data layer to skip a few data points
+ // to avoid all asynchronous sgd clients to start at the same point. The skip
+ // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+ // be larger than the number of keys in the database.
+ // DEPRECATED. Each solver accesses a different subset of the database.
+ optional uint32 rand_skip = 7 [default = 0];
+ optional DB backend = 8 [default = LEVELDB];
+ // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
+ // simple scaling and subtracting the data mean, if provided. Note that the
+ // mean subtraction is always carried out before scaling.
+ optional float scale = 2 [default = 1];
+ optional string mean_file = 3;
+ // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
+ // crop an image.
+ optional uint32 crop_size = 5 [default = 0];
+ // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
+ // data.
+ optional bool mirror = 6 [default = false];
+ // Force the encoded image to have 3 color channels
+ optional bool force_encoded_color = 9 [default = false];
+ // Prefetch queue (Number of batches to prefetch to host memory, increase if
+ // data access bandwidth varies).
+ optional uint32 prefetch = 10 [default = 4];
+}
+
+message DropoutParameter {
+ optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
+}
+
+// DummyDataLayer fills any number of arbitrarily shaped blobs with random
+// (or constant) data generated by "Fillers" (see "message FillerParameter").
+message DummyDataParameter {
+ // This layer produces N >= 1 top blobs. DummyDataParameter must specify 1 or N
+ // shape fields, and 0, 1 or N data_fillers.
+ //
+ // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
+ // If 1 data_filler is specified, it is applied to all top blobs. If N are
+ // specified, the ith is applied to the ith top blob.
+ repeated FillerParameter data_filler = 1;
+ repeated BlobShape shape = 6;
+
+ // 4D dimensions -- deprecated. Use "shape" instead.
+ repeated uint32 num = 2;
+ repeated uint32 channels = 3;
+ repeated uint32 height = 4;
+ repeated uint32 width = 5;
+}
+
+message EltwiseParameter {
+ enum EltwiseOp {
+ PROD = 0;
+ SUM = 1;
+ MAX = 2;
+ }
+ optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
+ repeated float coeff = 2; // blob-wise coefficient for SUM operation
+
+ // Whether to use an asymptotically slower (for >2 inputs) but stabler method
+ // of computing the gradient for the PROD operation. (No effect for SUM op.)
+ optional bool stable_prod_grad = 3 [default = true];
+}
+
+// Message that stores parameters used by ELULayer
+message ELUParameter {
+ // Described in:
+ // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
+ // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
+ optional float alpha = 1 [default = 1];
+}
+
+// Message that stores parameters used by EmbedLayer
+message EmbedParameter {
+ optional uint32 num_output = 1; // The number of outputs for the layer
+ // The input is given as integers to be interpreted as one-hot
+ // vector indices with dimension num_input. Hence num_input should be
+ // 1 greater than the maximum possible input value.
+ optional uint32 input_dim = 2;
+
+ optional bool bias_term = 3 [default = true]; // Whether to use a bias term
+ optional FillerParameter weight_filler = 4; // The filler for the weight
+ optional FillerParameter bias_filler = 5; // The filler for the bias
+
+}
+
+// Message that stores parameters used by ExpLayer
+message ExpParameter {
+ // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
+ // Or if base is set to the default (-1), base is set to e,
+ // so y = exp(shift + scale * x).
+ optional float base = 1 [default = -1.0];
+ optional float scale = 2 [default = 1.0];
+ optional float shift = 3 [default = 0.0];
+}
+
+/// Message that stores parameters used by FlattenLayer
+message FlattenParameter {
+ // The first axis to flatten: all preceding axes are retained in the output.
+ // May be negative to index from the end (e.g., -1 for the last axis).
+ optional int32 axis = 1 [default = 1];
+
+ // The last axis to flatten: all following axes are retained in the output.
+ // May be negative to index from the end (e.g., the default -1 for the last
+ // axis).
+ optional int32 end_axis = 2 [default = -1];
+}
+
+// Message that stores parameters used by HDF5DataLayer
+message HDF5DataParameter {
+ // Specify the data source.
+ optional string source = 1;
+ // Specify the batch size.
+ optional uint32 batch_size = 2;
+
+ // Specify whether to shuffle the data.
+ // If shuffle == true, the ordering of the HDF5 files is shuffled,
+ // and the ordering of data within any given HDF5 file is shuffled,
+ // but data between different files are not interleaved; all of a file's
+ // data are output (in a random order) before moving onto another file.
+ optional bool shuffle = 3 [default = false];
+}
+
+message HDF5OutputParameter {
+ optional string file_name = 1;
+}
+
+message HingeLossParameter {
+ enum Norm {
+ L1 = 1;
+ L2 = 2;
+ }
+ // Specify the Norm to use L1 or L2
+ optional Norm norm = 1 [default = L1];
+}
+
+message ImageDataParameter {
+ // Specify the data source.
+ optional string source = 1;
+ // Specify the batch size.
+ optional uint32 batch_size = 4 [default = 1];
+ // The rand_skip variable is for the data layer to skip a few data points
+ // to avoid all asynchronous sgd clients to start at the same point. The skip
+ // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+ // be larger than the number of keys in the database.
+ optional uint32 rand_skip = 7 [default = 0];
+ // Whether or not ImageLayer should shuffle the list of files at every epoch.
+ optional bool shuffle = 8 [default = false];
+ // It will also resize images if new_height or new_width are not zero.
+ optional uint32 new_height = 9 [default = 0];
+ optional uint32 new_width = 10 [default = 0];
+ // Specify if the images are color or gray
+ optional bool is_color = 11 [default = true];
+ // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
+ // simple scaling and subtracting the data mean, if provided. Note that the
+ // mean subtraction is always carried out before scaling.
+ optional float scale = 2 [default = 1];
+ optional string mean_file = 3;
+ // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
+ // crop an image.
+ optional uint32 crop_size = 5 [default = 0];
+ // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
+ // data.
+ optional bool mirror = 6 [default = false];
+ optional string root_folder = 12 [default = ""];
+}
+
+message InfogainLossParameter {
+ // Specify the infogain matrix source.
+ optional string source = 1;
+}
+
+message InnerProductParameter {
+ optional uint32 num_output = 1; // The number of outputs for the layer
+ optional bool bias_term = 2 [default = true]; // whether to have bias terms
+ optional FillerParameter weight_filler = 3; // The filler for the weight
+ optional FillerParameter bias_filler = 4; // The filler for the bias
+
+ // The first axis to be lumped into a single inner product computation;
+ // all preceding axes are retained in the output.
+ // May be negative to index from the end (e.g., -1 for the last axis).
+ optional int32 axis = 5 [default = 1];
+ // Specify whether to transpose the weight matrix or not.
+ // If transpose == true, any operations will be performed on the transpose
+ // of the weight matrix. The weight matrix itself is not going to be transposed
+ // but rather the transfer flag of operations will be toggled accordingly.
+ optional bool transpose = 6 [default = false];
+}
+
+message InputParameter {
+ // This layer produces N >= 1 top blob(s) to be assigned manually.
+ // Define N shapes to set a shape for each top.
+ // Define 1 shape to set the same shape for every top.
+ // Define no shape to defer to reshaping manually.
+ repeated BlobShape shape = 1;
+}
+
+// Message that stores parameters used by LogLayer
+message LogParameter {
+ // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
+ // Or if base is set to the default (-1), base is set to e,
+ // so y = ln(shift + scale * x) = log_e(shift + scale * x)
+ optional float base = 1 [default = -1.0];
+ optional float scale = 2 [default = 1.0];
+ optional float shift = 3 [default = 0.0];
+}
+
+// Message that stores parameters used by LRNLayer
+message LRNParameter {
+ optional uint32 local_size = 1 [default = 5];
+ optional float alpha = 2 [default = 1.];
+ optional float beta = 3 [default = 0.75];
+ enum NormRegion {
+ ACROSS_CHANNELS = 0;
+ WITHIN_CHANNEL = 1;
+ }
+ optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
+ optional float k = 5 [default = 1.];
+ enum Engine {
+ DEFAULT = 0;
+ CAFFE = 1;
+ CUDNN = 2;
+ }
+ optional Engine engine = 6 [default = DEFAULT];
+}
+
+message MemoryDataParameter {
+ optional uint32 batch_size = 1;
+ optional uint32 channels = 2;
+ optional uint32 height = 3;
+ optional uint32 width = 4;
+}
+
+message MVNParameter {
+ // This parameter can be set to false to normalize mean only
+ optional bool normalize_variance = 1 [default = true];
+
+ // This parameter can be set to true to perform DNN-like MVN
+ optional bool across_channels = 2 [default = false];
+
+ // Epsilon for not dividing by zero while normalizing variance
+ optional float eps = 3 [default = 1e-9];
+}
+
+message ParameterParameter {
+ optional BlobShape shape = 1;
+}
+
+message PoolingParameter {
+ enum PoolMethod {
+ MAX = 0;
+ AVE = 1;
+ STOCHASTIC = 2;
+ }
+ optional PoolMethod pool = 1 [default = MAX]; // The pooling method
+ // Pad, kernel size, and stride are all given as a single value for equal
+ // dimensions in height and width or as Y, X pairs.
+ optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
+ optional uint32 pad_h = 9 [default = 0]; // The padding height
+ optional uint32 pad_w = 10 [default = 0]; // The padding width
+ optional uint32 kernel_size = 2; // The kernel size (square)
+ optional uint32 kernel_h = 5; // The kernel height
+ optional uint32 kernel_w = 6; // The kernel width
+ optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
+ optional uint32 stride_h = 7; // The stride height
+ optional uint32 stride_w = 8; // The stride width
+ enum Engine {
+ DEFAULT = 0;
+ CAFFE = 1;
+ CUDNN = 2;
+ }
+ optional Engine engine = 11 [default = DEFAULT];
+ // If global_pooling then it will pool over the size of the bottom by doing
+ // kernel_h = bottom->height and kernel_w = bottom->width
+ optional bool global_pooling = 12 [default = false];
+}
+
+message PowerParameter {
+ // PowerLayer computes outputs y = (shift + scale * x) ^ power.
+ optional float power = 1 [default = 1.0];
+ optional float scale = 2 [default = 1.0];
+ optional float shift = 3 [default = 0.0];
+}
+
+message PythonParameter {
+ optional string module = 1;
+ optional string layer = 2;
+ // This value is set to the attribute `param_str` of the `PythonLayer` object
+ // in Python before calling the `setup()` method. This could be a number,
+ // string, dictionary in Python dict format, JSON, etc. You may parse this
+ // string in `setup` method and use it in `forward` and `backward`.
+ optional string param_str = 3 [default = ''];
+ // Whether this PythonLayer is shared among worker solvers during data parallelism.
+ // If true, each worker solver sequentially run forward from this layer.
+ // This value should be set true if you are using it as a data layer.
+ optional bool share_in_parallel = 4 [default = false];
+}
+
+// Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+ // The dimension of the output (and usually hidden state) representation --
+ // must be explicitly set to non-zero.
+ optional uint32 num_output = 1 [default = 0];
+
+ optional FillerParameter weight_filler = 2; // The filler for the weight
+ optional FillerParameter bias_filler = 3; // The filler for the bias
+
+ // Whether to enable displaying debug_info in the unrolled recurrent net.
+ optional bool debug_info = 4 [default = false];
+
+ // Whether to add as additional inputs (bottoms) the initial hidden state
+ // blobs, and add as additional outputs (tops) the final timestep hidden state
+ // blobs. The number of additional bottom/top blobs required depends on the
+ // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+ optional bool expose_hidden = 5 [default = false];
+}
+
+// Message that stores parameters used by ReductionLayer
+message ReductionParameter {
+ enum ReductionOp {
+ SUM = 1;
+ ASUM = 2;
+ SUMSQ = 3;
+ MEAN = 4;
+ }
+
+ optional ReductionOp operation = 1 [default = SUM]; // reduction operation
+
+ // The first axis to reduce to a scalar -- may be negative to index from the
+ // end (e.g., -1 for the last axis).
+ // (Currently, only reduction along ALL "tail" axes is supported; reduction
+ // of axis M through N, where N < num_axes - 1, is unsupported.)
+ // Suppose we have an n-axis bottom Blob with shape:
+ // (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
+ // If axis == m, the output Blob will have shape
+ // (d0, d1, d2, ..., d(m-1)),
+ // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
+ // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
+ // If axis == 0 (the default), the output Blob always has the empty shape
+ // (count 1), performing reduction across the entire input --
+ // often useful for creating new loss functions.
+ optional int32 axis = 2 [default = 0];
+
+ optional float coeff = 3 [default = 1.0]; // coefficient for output
+}
+
+// Message that stores parameters used by ReLULayer
+message ReLUParameter {
+ // Allow non-zero slope for negative inputs to speed up optimization
+ // Described in:
+ // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
+ // improve neural network acoustic models. In ICML Workshop on Deep Learning
+ // for Audio, Speech, and Language Processing.
+ optional float negative_slope = 1 [default = 0];
+ enum Engine {
+ DEFAULT = 0;
+ CAFFE = 1;
+ CUDNN = 2;
+ }
+ optional Engine engine = 2 [default = DEFAULT];
+}
+
+message ReshapeParameter {
+ // Specify the output dimensions. If some of the dimensions are set to 0,
+ // the corresponding dimension from the bottom layer is used (unchanged).
+ // Exactly one dimension may be set to -1, in which case its value is
+ // inferred from the count of the bottom blob and the remaining dimensions.
+ // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
+ //
+ // layer {
+ // type: "Reshape" bottom: "input" top: "output"
+ // reshape_param { ... }
+ // }
+ //
+ // If "input" is 2D with shape 2 x 8, then the following reshape_param
+ // specifications are all equivalent, producing a 3D blob "output" with shape
+ // 2 x 2 x 4:
+ //
+ // reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
+ // reshape_param { shape { dim: 0 dim: 2 dim: 4 } }
+ // reshape_param { shape { dim: 0 dim: 2 dim: -1 } }
+ // reshape_param { shape { dim: 0 dim:-1 dim: 4 } }
+ //
+ optional BlobShape shape = 1;
+
+ // axis and num_axes control the portion of the bottom blob's shape that are
+ // replaced by (included in) the reshape. By default (axis == 0 and
+ // num_axes == -1), the entire bottom blob shape is included in the reshape,
+ // and hence the shape field must specify the entire output shape.
+ //
+ // axis may be non-zero to retain some portion of the beginning of the input
+ // shape (and may be negative to index from the end; e.g., -1 to begin the
+ // reshape after the last axis, including nothing in the reshape,
+ // -2 to include only the last axis, etc.).
+ //
+ // For example, suppose "input" is a 2D blob with shape 2 x 8.
+ // Then the following ReshapeLayer specifications are all equivalent,
+ // producing a blob "output" with shape 2 x 2 x 4:
+ //
+ // reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
+ // reshape_param { shape { dim: 2 dim: 4 } axis: 1 }
+ // reshape_param { shape { dim: 2 dim: 4 } axis: -3 }
+ //
+ // num_axes specifies the extent of the reshape.
+ // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
+ // input axes in the range [axis, axis+num_axes].
+ // num_axes may also be -1, the default, to include all remaining axes
+ // (starting from axis).
+ //
+ // For example, suppose "input" is a 2D blob with shape 2 x 8.
+ // Then the following ReshapeLayer specifications are equivalent,
+ // producing a blob "output" with shape 1 x 2 x 8.
+ //
+ // reshape_param { shape { dim: 1 dim: 2 dim: 8 } }
+ // reshape_param { shape { dim: 1 dim: 2 } num_axes: 1 }
+ // reshape_param { shape { dim: 1 } num_axes: 0 }
+ //
+ // On the other hand, these would produce output blob shape 2 x 1 x 8:
+ //
+ // reshape_param { shape { dim: 2 dim: 1 dim: 8 } }
+ // reshape_param { shape { dim: 1 } axis: 1 num_axes: 0 }
+ //
+ optional int32 axis = 2 [default = 0];
+ optional int32 num_axes = 3 [default = -1];
+}
+
+message ScaleParameter {
+ // The first axis of bottom[0] (the first input Blob) along which to apply
+ // bottom[1] (the second input Blob). May be negative to index from the end
+ // (e.g., -1 for the last axis).
+ //
+ // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+ // top[0] will have the same shape, and bottom[1] may have any of the
+ // following shapes (for the given value of axis):
+ // (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+ // (axis == 1 == -3) 3; 3x40; 3x40x60
+ // (axis == 2 == -2) 40; 40x60
+ // (axis == 3 == -1) 60
+ // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+ // "axis") -- a scalar multiplier.
+ optional int32 axis = 1 [default = 1];
+
+ // (num_axes is ignored unless just one bottom is given and the scale is
+ // a learned parameter of the layer. Otherwise, num_axes is determined by the
+ // number of axes by the second bottom.)
+ // The number of axes of the input (bottom[0]) covered by the scale
+ // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+ // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
+ optional int32 num_axes = 2 [default = 1];
+
+ // (filler is ignored unless just one bottom is given and the scale is
+ // a learned parameter of the layer.)
+ // The initialization for the learned scale parameter.
+ // Default is the unit (1) initialization, resulting in the ScaleLayer
+ // initially performing the identity operation.
+ optional FillerParameter filler = 3;
+
+ // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
+ // may be more efficient). Initialized with bias_filler (defaults to 0).
+ optional bool bias_term = 4 [default = false];
+ optional FillerParameter bias_filler = 5;
+}
+
+message SigmoidParameter {
+ enum Engine {
+ DEFAULT = 0;
+ CAFFE = 1;
+ CUDNN = 2;
+ }
+ optional Engine engine = 1 [default = DEFAULT];
+}
+
+message SliceParameter {
+ // The axis along which to slice -- may be negative to index from the end
+ // (e.g., -1 for the last axis).
+ // By default, SliceLayer concatenates blobs along the "channels" axis (1).
+ optional int32 axis = 3 [default = 1];
+ repeated uint32 slice_point = 2;
+
+ // DEPRECATED: alias for "axis" -- does not support negative indexing.
+ optional uint32 slice_dim = 1 [default = 1];
+}
+
+// Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
+message SoftmaxParameter {
+ enum Engine {
+ DEFAULT = 0;
+ CAFFE = 1;
+ CUDNN = 2;
+ }
+ optional Engine engine = 1 [default = DEFAULT];
+
+ // The axis along which to perform the softmax -- may be negative to index
+ // from the end (e.g., -1 for the last axis).
+ // Any other axes will be evaluated as independent softmaxes.
+ optional int32 axis = 2 [default = 1];
+}
+
+message TanHParameter {
+ enum Engine {
+ DEFAULT = 0;
+ CAFFE = 1;
+ CUDNN = 2;
+ }
+ optional Engine engine = 1 [default = DEFAULT];
+}
+
+// Message that stores parameters used by TileLayer
+message TileParameter {
+ // The index of the axis to tile.
+ optional int32 axis = 1 [default = 1];
+
+ // The number of copies (tiles) of the blob to output.
+ optional int32 tiles = 2;
+}
+
+// Message that stores parameters used by ThresholdLayer
+message ThresholdParameter {
+ optional float threshold = 1 [default = 0]; // Strictly positive values
+}
+
+message WindowDataParameter {
+ // Specify the data source.
+ optional string source = 1;
+ // For data pre-processing, we can do simple scaling and subtracting the
+ // data mean, if provided. Note that the mean subtraction is always carried
+ // out before scaling.
+ optional float scale = 2 [default = 1];
+ optional string mean_file = 3;
+ // Specify the batch size.
+ optional uint32 batch_size = 4;
+ // Specify if we would like to randomly crop an image.
+ optional uint32 crop_size = 5 [default = 0];
+ // Specify if we want to randomly mirror data.
+ optional bool mirror = 6 [default = false];
+ // Foreground (object) overlap threshold
+ optional float fg_threshold = 7 [default = 0.5];
+ // Background (non-object) overlap threshold
+ optional float bg_threshold = 8 [default = 0.5];
+ // Fraction of batch that should be foreground objects
+ optional float fg_fraction = 9 [default = 0.25];
+ // Amount of contextual padding to add around a window
+ // (used only by the window_data_layer)
+ optional uint32 context_pad = 10 [default = 0];
+ // Mode for cropping out a detection window
+ // warp: cropped window is warped to a fixed size and aspect ratio
+ // square: the tightest square around the window is cropped
+ optional string crop_mode = 11 [default = "warp"];
+ // cache_images: will load all images in memory for faster access
+ optional bool cache_images = 12 [default = false];
+ // append root_folder to locate images
+ optional string root_folder = 13 [default = ""];
+}
+
+message SPPParameter {
+ enum PoolMethod {
+ MAX = 0;
+ AVE = 1;
+ STOCHASTIC = 2;
+ }
+ optional uint32 pyramid_height = 1;
+ optional PoolMethod pool = 2 [default = MAX]; // The pooling method
+ enum Engine {
+ DEFAULT = 0;
+ CAFFE = 1;
+ CUDNN = 2;
+ }
+ optional Engine engine = 6 [default = DEFAULT];
+}
+
+// DEPRECATED: use LayerParameter.
+message V1LayerParameter {
+ repeated string bottom = 2;
+ repeated string top = 3;
+ optional string name = 4;
+ repeated NetStateRule include = 32;
+ repeated NetStateRule exclude = 33;
+ enum LayerType {
+ NONE = 0;
+ ABSVAL = 35;
+ ACCURACY = 1;
+ ARGMAX = 30;
+ BNLL = 2;
+ CONCAT = 3;
+ CONTRASTIVE_LOSS = 37;
+ CONVOLUTION = 4;
+ DATA = 5;
+ DECONVOLUTION = 39;
+ DROPOUT = 6;
+ DUMMY_DATA = 32;
+ EUCLIDEAN_LOSS = 7;
+ ELTWISE = 25;
+ EXP = 38;
+ FLATTEN = 8;
+ HDF5_DATA = 9;
+ HDF5_OUTPUT = 10;
+ HINGE_LOSS = 28;
+ IM2COL = 11;
+ IMAGE_DATA = 12;
+ INFOGAIN_LOSS = 13;
+ INNER_PRODUCT = 14;
+ LRN = 15;
+ MEMORY_DATA = 29;
+ MULTINOMIAL_LOGISTIC_LOSS = 16;
+ MVN = 34;
+ POOLING = 17;
+ POWER = 26;
+ RELU = 18;
+ SIGMOID = 19;
+ SIGMOID_CROSS_ENTROPY_LOSS = 27;
+ SILENCE = 36;
+ SOFTMAX = 20;
+ SOFTMAX_LOSS = 21;
+ SPLIT = 22;
+ SLICE = 33;
+ TANH = 23;
+ WINDOW_DATA = 24;
+ THRESHOLD = 31;
+ }
+ optional LayerType type = 5;
+ repeated BlobProto blobs = 6;
+ repeated string param = 1001;
+ repeated DimCheckMode blob_share_mode = 1002;
+ enum DimCheckMode {
+ STRICT = 0;
+ PERMISSIVE = 1;
+ }
+ repeated float blobs_lr = 7;
+ repeated float weight_decay = 8;
+ repeated float loss_weight = 35;
+ optional AccuracyParameter accuracy_param = 27;
+ optional ArgMaxParameter argmax_param = 23;
+ optional ConcatParameter concat_param = 9;
+ optional ContrastiveLossParameter contrastive_loss_param = 40;
+ optional ConvolutionParameter convolution_param = 10;
+ optional DataParameter data_param = 11;
+ optional DropoutParameter dropout_param = 12;
+ optional DummyDataParameter dummy_data_param = 26;
+ optional EltwiseParameter eltwise_param = 24;
+ optional ExpParameter exp_param = 41;
+ optional HDF5DataParameter hdf5_data_param = 13;
+ optional HDF5OutputParameter hdf5_output_param = 14;
+ optional HingeLossParameter hinge_loss_param = 29;
+ optional ImageDataParameter image_data_param = 15;
+ optional InfogainLossParameter infogain_loss_param = 16;
+ optional InnerProductParameter inner_product_param = 17;
+ optional LRNParameter lrn_param = 18;
+ optional MemoryDataParameter memory_data_param = 22;
+ optional MVNParameter mvn_param = 34;
+ optional PoolingParameter pooling_param = 19;
+ optional PowerParameter power_param = 21;
+ optional ReLUParameter relu_param = 30;
+ optional SigmoidParameter sigmoid_param = 38;
+ optional SoftmaxParameter softmax_param = 39;
+ optional SliceParameter slice_param = 31;
+ optional TanHParameter tanh_param = 37;
+ optional ThresholdParameter threshold_param = 25;
+ optional WindowDataParameter window_data_param = 20;
+ optional TransformationParameter transform_param = 36;
+ optional LossParameter loss_param = 42;
+ optional V0LayerParameter layer = 1;
+}
+
+// DEPRECATED: V0LayerParameter is the old way of specifying layer parameters
+// in Caffe. We keep this message type around for legacy support.
+message V0LayerParameter {
+ optional string name = 1; // the layer name
+ optional string type = 2; // the string to specify the layer type
+
+ // Parameters to specify layers with inner products.
+ optional uint32 num_output = 3; // The number of outputs for the layer
+ optional bool biasterm = 4 [default = true]; // whether to have bias terms
+ optional FillerParameter weight_filler = 5; // The filler for the weight
+ optional FillerParameter bias_filler = 6; // The filler for the bias
+
+ optional uint32 pad = 7 [default = 0]; // The padding size
+ optional uint32 kernelsize = 8; // The kernel size
+ optional uint32 group = 9 [default = 1]; // The group size for group conv
+ optional uint32 stride = 10 [default = 1]; // The stride
+ enum PoolMethod {
+ MAX = 0;
+ AVE = 1;
+ STOCHASTIC = 2;
+ }
+ optional PoolMethod pool = 11 [default = MAX]; // The pooling method
+ optional float dropout_ratio = 12 [default = 0.5]; // dropout ratio
+
+ optional uint32 local_size = 13 [default = 5]; // for local response norm
+ optional float alpha = 14 [default = 1.]; // for local response norm
+ optional float beta = 15 [default = 0.75]; // for local response norm
+ optional float k = 22 [default = 1.];
+
+ // For data layers, specify the data source
+ optional string source = 16;
+ // For data pre-processing, we can do simple scaling and subtracting the
+ // data mean, if provided. Note that the mean subtraction is always carried
+ // out before scaling.
+ optional float scale = 17 [default = 1];
+ optional string meanfile = 18;
+ // For data layers, specify the batch size.
+ optional uint32 batchsize = 19;
+ // For data layers, specify if we would like to randomly crop an image.
+ optional uint32 cropsize = 20 [default = 0];
+ // For data layers, specify if we want to randomly mirror data.
+ optional bool mirror = 21 [default = false];
+
+ // The blobs containing the numeric parameters of the layer
+ repeated BlobProto blobs = 50;
+ // The ratio that is multiplied on the global learning rate. If you want to
+ // set the learning ratio for one blob, you need to set it for all blobs.
+ repeated float blobs_lr = 51;
+ // The weight decay that is multiplied on the global weight decay.
+ repeated float weight_decay = 52;
+
+ // The rand_skip variable is for the data layer to skip a few data points
+ // to avoid all asynchronous sgd clients to start at the same point. The skip
+ // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+ // be larger than the number of keys in the database.
+ optional uint32 rand_skip = 53 [default = 0];
+
+ // Fields related to detection (det_*)
+ // foreground (object) overlap threshold
+ optional float det_fg_threshold = 54 [default = 0.5];
+ // background (non-object) overlap threshold
+ optional float det_bg_threshold = 55 [default = 0.5];
+ // Fraction of batch that should be foreground objects
+ optional float det_fg_fraction = 56 [default = 0.25];
+
+ // optional bool OBSOLETE_can_clobber = 57 [default = true];
+
+ // Amount of contextual padding to add around a window
+ // (used only by the window_data_layer)
+ optional uint32 det_context_pad = 58 [default = 0];
+
+ // Mode for cropping out a detection window
+ // warp: cropped window is warped to a fixed size and aspect ratio
+ // square: the tightest square around the window is cropped
+ optional string det_crop_mode = 59 [default = "warp"];
+
+ // For ReshapeLayer, one needs to specify the new dimensions.
+ optional int32 new_num = 60 [default = 0];
+ optional int32 new_channels = 61 [default = 0];
+ optional int32 new_height = 62 [default = 0];
+ optional int32 new_width = 63 [default = 0];
+
+ // Whether or not ImageLayer should shuffle the list of files at every epoch.
+ // It will also resize images if new_height or new_width are not zero.
+ optional bool shuffle_images = 64 [default = false];
+
+ // For ConcatLayer, one needs to specify the dimension for concatenation, and
+ // the other dimensions must be the same for all the bottom blobs.
+ // By default it will concatenate blobs along the channels dimension.
+ optional uint32 concat_dim = 65 [default = 1];
+
+ optional HDF5OutputParameter hdf5_output_param = 1001;
+}
+
+message PReLUParameter {
+ // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
+ // Surpassing Human-Level Performance on ImageNet Classification, 2015.
+
+ // Initial value of a_i. Default is a_i=0.25 for all i.
+ optional FillerParameter filler = 1;
+ // Whether or not slope paramters are shared across channels.
+ optional bool channel_shared = 2 [default = false];
+}
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