org.bytedeco.javacpp.presets.caffe Maven / Gradle / Ivy
/*
* Copyright (C) 2015 Samuel Audet
*
* Licensed either under the Apache License, Version 2.0, or (at your option)
* under the terms of the GNU General Public License as published by
* the Free Software Foundation (subject to the "Classpath" exception),
* either version 2, or any later version (collectively, 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
* http://www.gnu.org/licenses/
* http://www.gnu.org/software/classpath/license.html
*
* or as provided in the LICENSE.txt file that accompanied this code.
* 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.bytedeco.javacpp.presets;
import org.bytedeco.javacpp.FunctionPointer;
import org.bytedeco.javacpp.Loader;
import org.bytedeco.javacpp.Pointer;
import org.bytedeco.javacpp.annotation.Cast;
import org.bytedeco.javacpp.annotation.Platform;
import org.bytedeco.javacpp.annotation.Properties;
import org.bytedeco.javacpp.tools.Info;
import org.bytedeco.javacpp.tools.InfoMap;
import org.bytedeco.javacpp.tools.InfoMapper;
/**
*
* @author Samuel Audet
*/
@Properties(inherit=opencv_highgui.class, target="org.bytedeco.javacpp.caffe", value={
@Platform(value={"linux", "macosx"}, define={"CPU_ONLY", "SHARED_PTR_NAMESPACE boost", "USE_LEVELDB", "USE_LMDB", "USE_OPENCV"}, include={"caffe/caffe.hpp", "caffe/util/device_alternate.hpp",
"caffe/common.hpp", "caffe/proto/caffe.pb.h", "caffe/util/blocking_queue.hpp", "caffe/data_reader.hpp", "caffe/util/math_functions.hpp", "caffe/syncedmem.hpp",
"caffe/blob.hpp", "caffe/data_transformer.hpp", "caffe/filler.hpp", "caffe/internal_thread.hpp", "caffe/util/hdf5.hpp", "caffe/data_layers.hpp", "caffe/layer_factory.hpp",
"caffe/layer.hpp", "caffe/loss_layers.hpp", "caffe/neuron_layers.hpp", "caffe/common_layers.hpp", "caffe/net.hpp", "caffe/parallel.hpp", "caffe/solver.hpp", "caffe/solver_factory.hpp",
"caffe/sgd_solvers.hpp", "caffe/vision_layers.hpp", "caffe/util/benchmark.hpp", "caffe/util/db.hpp", "caffe/util/db_leveldb.hpp", "caffe/util/db_lmdb.hpp",
"caffe/util/io.hpp", "caffe/util/rng.hpp", "caffe/util/im2col.hpp", "caffe/util/insert_splits.hpp", "caffe/util/mkl_alternate.hpp",
"caffe/util/upgrade_proto.hpp", /* "caffe/util/cudnn.hpp" */}, link="caffe", includepath={"/usr/local/cuda/include/",
"/System/Library/Frameworks/vecLib.framework/", "/System/Library/Frameworks/Accelerate.framework/"}, linkpath="/usr/local/cuda/lib/") })
public class caffe implements InfoMapper {
public void map(InfoMap infoMap) {
infoMap.put(new Info("NOT_IMPLEMENTED", "NO_GPU", "CUDA_POST_KERNEL_CHECK").cppTypes().annotations())
.put(new Info("CPU_ONLY", "GFLAGS_GFLAGS_H_", "SWIG").define())
.put(new Info("cublasHandle_t", "curandGenerator_t").cast().valueTypes("Pointer"))
.put(new Info("CBLAS_TRANSPOSE", "cublasStatus_t", "curandStatus_t", "hid_t").cast().valueTypes("int"))
.put(new Info("std::string").annotations("@StdString").valueTypes("BytePointer", "String").pointerTypes("@Cast({\"char*\", \"std::string*\"}) BytePointer"))
.put(new Info("std::vector").pointerTypes("StringVector").define())
.put(new Info("caffe::BlockingQueue").pointerTypes("DatumBlockingQueue"))
.put(new Info("google::protobuf::int8", "google::protobuf::uint8").cast().valueTypes("byte").pointerTypes("BytePointer", "ByteBuffer", "byte[]"))
.put(new Info("google::protobuf::int16", "google::protobuf::uint16").cast().valueTypes("short").pointerTypes("ShortPointer", "ShortBuffer", "short[]"))
.put(new Info("google::protobuf::int32", "google::protobuf::uint32").cast().valueTypes("int").pointerTypes("IntPointer", "IntBuffer", "int[]"))
.put(new Info("google::protobuf::int64", "google::protobuf::uint64").cast().valueTypes("long").pointerTypes("LongPointer", "LongBuffer", "long[]"))
.put(new Info("google::protobuf::Descriptor", "google::protobuf::EnumDescriptor", "google::protobuf::Message", "google::protobuf::Metadata",
"google::protobuf::UnknownFieldSet", "google::protobuf::io::CodedInputStream", "google::protobuf::io::CodedOutputStream",
"leveldb::Iterator", "leveldb::DB", "MDB_txn", "MDB_cursor", "MDB_dbi", "boost::mt19937").cast().pointerTypes("Pointer"))
.put(new Info("google::protobuf::RepeatedField", "google::protobuf::RepeatedPtrField").skip());
String[] functionTemplates = { "caffe_cpu_gemm", "caffe_cpu_gemv", "caffe_axpy", "caffe_cpu_axpby", "caffe_copy", "caffe_set", "caffe_add_scalar",
"caffe_scal", "caffe_sqr", "caffe_add", "caffe_sub", "caffe_mul", "caffe_div", "caffe_powx", "caffe_nextafter", "caffe_rng_uniform",
"caffe_rng_gaussian", "caffe_rng_bernoulli", "caffe_rng_bernoulli", "caffe_exp", "caffe_log", "caffe_abs", "caffe_cpu_dot", "caffe_cpu_strided_dot",
"caffe_cpu_hamming_distance", "caffe_cpu_asum", "caffe_sign", "caffe_cpu_scale", "caffe_gpu_gemm", "caffe_gpu_gemv", "caffe_gpu_axpy",
"caffe_gpu_axpby", "caffe_gpu_memcpy", "caffe_gpu_set", "caffe_gpu_memset", "caffe_gpu_add_scalar", "caffe_gpu_scal", "caffe_gpu_add",
"caffe_gpu_sub", "caffe_gpu_mul", "caffe_gpu_div", "caffe_gpu_abs", "caffe_gpu_exp", "caffe_gpu_log", "caffe_gpu_powx", "caffe_gpu_rng_uniform",
"caffe_gpu_rng_gaussian", "caffe_gpu_rng_bernoulli", "caffe_gpu_dot", "caffe_gpu_hamming_distance", "caffe_gpu_asum", "caffe_gpu_sign",
"caffe_gpu_sgnbit", "caffe_gpu_fabs", "caffe_gpu_scale", "hdf5_load_nd_dataset_helper", "hdf5_load_nd_dataset", "hdf5_save_nd_dataset",
"im2col_nd_cpu", "im2col_cpu", "col2im_nd_cpu", "col2im_cpu", "im2col_nd_gpu", "im2col_gpu", "col2im_nd_gpu", "col2im_gpu" };
for (String t : functionTemplates) {
infoMap.put(new Info("caffe::" + t + "").javaNames(t + "_float"))
.put(new Info("caffe::" + t + "").javaNames(t + "_double"));
}
String classTemplates[] = { "Blob", "DataTransformer", "Filler", "ConstantFiller", "UniformFiller", "GaussianFiller", "PositiveUnitballFiller", "XavierFiller", "MSRAFiller", "BilinearFiller",
"BaseDataLayer", "Batch", "BasePrefetchingDataLayer", "DataLayer", "DummyDataLayer", "HDF5DataLayer", "HDF5OutputLayer", "ImageDataLayer", "MemoryDataLayer",
"WindowDataLayer", "Layer", "LayerRegistry", "LayerRegisterer", "AccuracyLayer", "LossLayer", "ContrastiveLossLayer", "EuclideanLossLayer", "HingeLossLayer",
"InfogainLossLayer", "MultinomialLogisticLossLayer", "SigmoidCrossEntropyLossLayer", "SoftmaxWithLossLayer", "NeuronLayer", "AbsValLayer", "BNLLLayer",
"DropoutLayer", "ExpLayer", "PowerLayer", "ReLULayer", "SigmoidLayer", "TanHLayer", "ThresholdLayer", "PReLULayer", "PythonLayer", "ArgMaxLayer", "BatchNormLayer",
"BatchReindexLayer", "ConcatLayer", "EltwiseLayer", "EmbedLayer", "FilterLayer", "FlattenLayer", "InnerProductLayer", "MVNLayer", "ReshapeLayer", "ReductionLayer",
"SilenceLayer", "SoftmaxLayer", "SplitLayer", "SliceLayer", "TileLayer", "Net", "Solver", "WorkerSolver", "SolverRegistry", "SolverRegisterer", "SGDSolver", "NesterovSolver",
"AdaGradSolver", "RMSPropSolver", "AdaDeltaSolver", "AdamSolver", "BaseConvolutionLayer", "ConvolutionLayer", "DeconvolutionLayer", "Im2colLayer", "LRNLayer", "PoolingLayer",
/* "SPPLayer", "CuDNNReLULayer", "CuDNNSigmoidLayer", "CuDNNTanHLayer", "CuDNNSoftmaxLayer", "CuDNNConvolutionLayer", "CuDNNPoolingLayer" */ };
for (String t : classTemplates) {
boolean purify = t.equals("BaseDataLayer") || t.equals("LossLayer") || t.equals("NeuronLayer");
boolean virtualize = t.endsWith("Layer");
infoMap.put(new Info("caffe::" + t + "").pointerTypes("Float" + t).purify(purify).virtualize(virtualize))
.put(new Info("caffe::" + t + "").pointerTypes("Double" + t).purify(purify).virtualize(virtualize));
}
infoMap.put(new Info("caffe::BasePrefetchingDataLayer::InternalThreadEntry()",
"caffe::BasePrefetchingDataLayer::InternalThreadEntry()").skip())
.put(new Info("caffe::Batch::data_").javaText("@MemberGetter public native @ByRef FloatBlob data_();"))
.put(new Info("caffe::Batch::data_").javaText("@MemberGetter public native @ByRef DoubleBlob data_();"))
.put(new Info("caffe::Batch::label_").javaText("@MemberGetter public native @ByRef FloatBlob label_();"))
.put(new Info("caffe::Batch::label_").javaText("@MemberGetter public native @ByRef DoubleBlob label_();"))
.put(new Info("caffe::GetFiller").javaNames("GetFloatFiller"))
.put(new Info("caffe::GetFiller").javaNames("GetDoubleFiller"))
.put(new Info("caffe::GetSolver").javaNames("GetFloatSolver"))
.put(new Info("caffe::GetSolver").javaNames("GetDoubleSolver"))
.put(new Info("boost::shared_ptr >").annotations("@SharedPtr").pointerTypes("FloatBlob"))
.put(new Info("boost::shared_ptr >").annotations("@SharedPtr").pointerTypes("DoubleBlob"))
.put(new Info("std::vector > >").pointerTypes("FloatBlobSharedVector").define())
.put(new Info("std::vector > >").pointerTypes("DoubleBlobSharedVector").define())
.put(new Info("boost::shared_ptr >").annotations("@Cast({\"\", \"boost::shared_ptr >\"}) @SharedPtr").pointerTypes("FloatLayer"))
.put(new Info("boost::shared_ptr >").annotations("@Cast({\"\", \"boost::shared_ptr >\"}) @SharedPtr").pointerTypes("DoubleLayer"))
.put(new Info("std::vector > >").pointerTypes("FloatLayerSharedVector").define())
.put(new Info("std::vector > >").pointerTypes("DoubleLayerSharedVector").define())
.put(new Info("boost::shared_ptr >").annotations("@SharedPtr").pointerTypes("FloatNet"))
.put(new Info("boost::shared_ptr >").annotations("@SharedPtr").pointerTypes("DoubleNet"))
.put(new Info("std::vector > >").pointerTypes("FloatNetSharedVector").define())
.put(new Info("std::vector > >").pointerTypes("DoubleNetSharedVector").define())
.put(new Info("std::vector*>").pointerTypes("FloatBlobVector").define())
.put(new Info("std::vector*>").pointerTypes("DoubleBlobVector").define())
.put(new Info("std::vector*> >").pointerTypes("FloatBlobVectorVector").define())
.put(new Info("std::vector*> >").pointerTypes("DoubleBlobVectorVector").define())
.put(new Info("caffe::LayerRegistry::Creator").valueTypes("FloatLayerRegistry.Creator"))
.put(new Info("caffe::LayerRegistry::Creator").valueTypes("DoubleLayerRegistry.Creator"))
.put(new Info("std::map::Creator>").pointerTypes("FloatRegistry").define())
.put(new Info("std::map::Creator>").pointerTypes("DoubleRegistry").define())
.put(new Info("std::vector").pointerTypes("BoolVector").define())
.put(new Info("std::vector >").pointerTypes("BoolVectorVector").define())
.put(new Info("std::map").pointerTypes("StringIntMap").define())
.put(new Info("caffe::Net::layer_by_name").javaText(
"public FloatLayer layer_by_name(BytePointer layer_name) { return layer_by_name(FloatLayer.class, layer_name); }\n"
+ "public FloatLayer layer_by_name(String layer_name) { return layer_by_name(FloatLayer.class, layer_name); };\n"
+ "public native @Const @Cast({\"\", \"boost::shared_ptr >\"}) @SharedPtr @ByVal L layer_by_name(Class cls, @StdString BytePointer layer_name);\n"
+ "public native @Const @Cast({\"\", \"boost::shared_ptr >\"}) @SharedPtr @ByVal L layer_by_name(Class cls, @StdString String layer_name);\n"))
.put(new Info("caffe::Net::layer_by_name").javaText(
"public DoubleLayer layer_by_name(BytePointer layer_name) { return layer_by_name(DoubleLayer.class, layer_name); }\n"
+ "public DoubleLayer layer_by_name(String layer_name) { return layer_by_name(DoubleLayer.class, layer_name); };\n"
+ "public native @Const @Cast({\"\", \"boost::shared_ptr >\"}) @SharedPtr @ByVal L layer_by_name(Class cls, @StdString BytePointer layer_name);\n"
+ "public native @Const @Cast({\"\", \"boost::shared_ptr >\"}) @SharedPtr @ByVal L layer_by_name(Class cls, @StdString String layer_name);\n"))
.put(new Info("boost::function").pointerTypes("ActionCallback"));
}
public static class ActionCallback extends FunctionPointer {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
public ActionCallback(Pointer p) { super(p); }
protected ActionCallback() { allocate(); }
private native void allocate();
public native @Cast("caffe::SolverAction::Enum") int call();
}
}
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