[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 <[email protected]> Authored: Wed Apr 19 14:07:44 2017 -0800 Committer: Niketan Pansare <[email protected]> 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 à 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]; +} \ No newline at end of file
