Consolidate tools.

examples/cifar10/readme.md

@@ -42,7 +42,7 @@ Training the model is simple after you have written the network definition proto
     cd $CAFFE_ROOT/examples/cifar10
     ./train_quick.sh
 
-`train_quick.sh` is a simple script, so have a look inside. `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `train_net.bin`, with the solver protobuf text file as its argument.
+`train_quick.sh` is a simple script, so have a look inside. `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `caffe.bin` with the `train` action, and the solver protobuf text file as its argument.
 
 When you run the code, you will see a lot of messages flying by like this:
 

examples/cifar10/train_full.sh

@@ -2,15 +2,15 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+    --solver_proto_file=cifar10_full_solver.prototxt
 
 #reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver_lr1.prototxt \
-    cifar10_full_iter_60000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+    --solver_proto_file=cifar10_full_solver_lr1.prototxt \
+    --resume_point_file=cifar10_full_iter_60000.solverstate
 
 #reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver_lr2.prototxt \
-    cifar10_full_iter_65000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+    --solver_proto_file=cifar10_full_solver_lr2.prototxt \
+    --resume_point_file=cifar10_full_iter_65000.solverstate

examples/cifar10/train_quick.sh

@@ -2,7 +2,10 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_quick_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=cifar10_quick_solver.prototxt
 
 #reduce learning rate by fctor of 10 after 8 epochs
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_quick_solver_lr1.prototxt cifar10_quick_iter_4000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=cifar10_quick_solver_lr1.prototxt \
+  --resume_point_file=cifar10_quick_iter_4000.solverstate

examples/imagenet/resume_training.sh

@@ -2,7 +2,8 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    imagenet_solver.prototxt caffe_imagenet_train_10000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train\
+    --solver_proto_file=imagenet_solver.prototxt \
+    --resume_point_file=caffe_imagenet_train_10000.solverstate
 
 echo "Done."

examples/imagenet/train_alexnet.sh

@@ -2,6 +2,7 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin alexnet_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=alexnet_solver.prototxt
 
 echo "Done."

examples/imagenet/train_imagenet.sh

@@ -2,6 +2,7 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin imagenet_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=imagenet_solver.prototxt
 
 echo "Done."

examples/mnist/readme.md

@@ -212,7 +212,7 @@ Training the model is simple after you have written the network definition proto
     cd $CAFFE_ROOT/examples/mnist
     ./train_lenet.sh
 
-`train_lenet.sh` is a simple script, but here are a few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `train_net.bin`, with the solver protobuf text file as its argument.
+`train_lenet.sh` is a simple script, but here are a few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `caffe.bin` with action `train`, with the solver protobuf text file as its argument.
 
 When you run the code, you will see a lot of messages flying by like this:
 

examples/mnist/train_lenet.sh

@@ -2,4 +2,5 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin lenet_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=lenet_solver.prototxt

examples/mnist/train_lenet_consolidated.sh

@@ -2,4 +2,5 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin lenet_consolidated_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=lenet_consolidated_solver.prototxt

examples/mnist/train_mnist_autoencoder.sh

@@ -1,4 +1,5 @@
 #!/bin/bash
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin mnist_autoencoder_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=mnist_autoencoder_solver.prototxt

include/caffe/caffe.hpp

@@ -5,15 +5,15 @@
 #ifndef CAFFE_CAFFE_HPP_
 #define CAFFE_CAFFE_HPP_
 
-#include "caffe/common.hpp"
 #include "caffe/blob.hpp"
+#include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-#include "caffe/proto/caffe.pb.h"
-
 #endif  // CAFFE_CAFFE_HPP_

tools/caffe.cpp

@@ -0,0 +1,178 @@
+// Copyright 2014 BVLC and contributors.
+#include <gflags/gflags.h>
+#include <glog/logging.h>
+
+#include <cstring>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/caffe.hpp"
+
+using caffe::Blob;
+using caffe::Caffe;
+using caffe::Net;
+using caffe::Layer;
+using caffe::shared_ptr;
+using caffe::Timer;
+using caffe::vector;
+
+
+// Used in device query
+DEFINE_int32(device_id, 0,
+             "[devicequery,speedtest] The device id to use.");
+// Used in training
+DEFINE_string(solver_proto_file, "",
+              "[train] The protobuf containing the solver definition.");
+DEFINE_string(net_proto_file, "",
+              "[speedtest] The net proto file to use.");
+DEFINE_string(resume_point_file, "",
+              "[train] (optional) The snapshot from which to resume training.");
+DEFINE_string(pretrained_net_file, "",
+              "[train] (optional) A pretrained network to run finetune from. "
+              "Cannot be set simultaneously with resume_point_file.");
+DEFINE_int32(run_iterations, 50,
+             "[speedtest] The number of iterations to run.");
+DEFINE_bool(speedtest_with_gpu, false,
+            "[speedtest] Test the model with GPU.");
+
+// A simple registry for caffe commands.
+typedef int (*BrewFunction)();
+typedef std::map<caffe::string, BrewFunction> BrewMap;
+BrewMap g_brew_map;
+
+#define RegisterBrewFunction(func) \
+namespace { \
+class __Registerer_##func { \
+ public: /* NOLINT */ \
+  __Registerer_##func() { \
+    g_brew_map[#func] = &func; \
+  } \
+}; \
+__Registerer_##func g_registerer_##func; \
+}
+
+static BrewFunction GetBrewFunction(const caffe::string& name) {
+  if (g_brew_map.count(name)) {
+    return g_brew_map[name];
+  } else {
+    LOG(ERROR) << "Available caffe actions:";
+    for (BrewMap::iterator it = g_brew_map.begin();
+         it != g_brew_map.end(); ++it) {
+      LOG(ERROR) << "\t" << it->first;
+    }
+    LOG(FATAL) << "Unknown action: " << name;
+    return NULL;  // not reachable, just to suppress old compiler warnings.
+  }
+}
+
+// caffe actions that could be called in the form
+//     caffe.bin action
+// To do so, define actions as "int action()" functions, and register it with
+// RegisterBrewFunction(action);
+
+int devicequery() {
+  LOG(INFO) << "Querying device_id = " << FLAGS_device_id;
+  caffe::Caffe::SetDevice(FLAGS_device_id);
+  caffe::Caffe::DeviceQuery();
+  return 0;
+}
+RegisterBrewFunction(devicequery);
+
+int train() {
+  CHECK_GT(FLAGS_solver_proto_file.size(), 0);
+
+  caffe::SolverParameter solver_param;
+  caffe::ReadProtoFromTextFileOrDie(FLAGS_solver_proto_file, &solver_param);
+
+  LOG(INFO) << "Starting Optimization";
+  caffe::SGDSolver<float> solver(solver_param);
+  if (FLAGS_resume_point_file.size()) {
+    LOG(INFO) << "Resuming from " << FLAGS_resume_point_file;
+    solver.Solve(FLAGS_resume_point_file);
+  } else if (FLAGS_pretrained_net_file.size()) {
+    LOG(INFO) << "Finetuning from " << FLAGS_pretrained_net_file;
+    solver.net()->CopyTrainedLayersFrom(FLAGS_pretrained_net_file);
+    solver.Solve();
+  } else {
+    solver.Solve();
+  }
+  LOG(INFO) << "Optimization Done.";
+  return 0;
+}
+RegisterBrewFunction(train);
+
+int speedtest() {
+  // Set device id and mode
+  if (FLAGS_speedtest_with_gpu) {
+    LOG(INFO) << "Use GPU with device id " << FLAGS_device_id;
+    Caffe::SetDevice(FLAGS_device_id);
+    Caffe::set_mode(Caffe::GPU);
+  } else {
+    LOG(INFO) << "Use CPU.";
+    Caffe::set_mode(Caffe::CPU);
+  }
+  // Instantiate the caffe net.
+  Caffe::set_phase(Caffe::TRAIN);
+  Net<float> caffe_net(FLAGS_net_proto_file);
+
+  // Do a clean forward and backward pass, so that memory allocation are done
+  // and future iterations will be more stable.
+  LOG(INFO) << "Performing Forward";
+  // Note that for the speed benchmark, we will assume that the network does
+  // not take any input blobs.
+  float initial_loss;
+  caffe_net.Forward(vector<Blob<float>*>(), &initial_loss);
+  LOG(INFO) << "Initial loss: " << initial_loss;
+  LOG(INFO) << "Performing Backward";
+  caffe_net.Backward();
+
+  const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
+  vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
+  vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
+  const vector<vector<bool> >& bottom_need_backward =
+      caffe_net.bottom_need_backward();
+  LOG(INFO) << "*** Benchmark begins ***";
+  LOG(INFO) << "Testing for " << FLAGS_run_iterations << " iterations.";
+  Timer total_timer;
+  total_timer.Start();
+  Timer forward_timer;
+  forward_timer.Start();
+  Timer timer;
+  for (int i = 0; i < layers.size(); ++i) {
+    const caffe::string& layername = layers[i]->layer_param().name();
+    timer.Start();
+    for (int j = 0; j < FLAGS_run_iterations; ++j) {
+      layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
+    }
+    LOG(INFO) << layername << "\tforward: " << timer.MilliSeconds() <<
+        " milli seconds.";
+  }
+  LOG(INFO) << "Forward pass: " << forward_timer.MilliSeconds() <<
+      " milli seconds.";
+  Timer backward_timer;
+  backward_timer.Start();
+  for (int i = layers.size() - 1; i >= 0; --i) {
+    const caffe::string& layername = layers[i]->layer_param().name();
+    timer.Start();
+    for (int j = 0; j < FLAGS_run_iterations; ++j) {
+      layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
+                          &bottom_vecs[i]);
+    }
+    LOG(INFO) << layername << "\tbackward: "
+        << timer.MilliSeconds() << " milli seconds.";
+  }
+  LOG(INFO) << "Backward pass: " << backward_timer.MilliSeconds() <<
+      " milli seconds.";
+  LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() <<
+      " milli seconds.";
+  LOG(INFO) << "*** Benchmark ends ***";
+  return 0;
+}
+RegisterBrewFunction(speedtest);
+
+int main(int argc, char** argv) {
+  caffe::GlobalInit(&argc, &argv);
+  CHECK_EQ(argc, 2);
+  return GetBrewFunction(caffe::string(argv[1]))();
+}

tools/device_query.cpp

@@ -1,23 +1,9 @@
 // Copyright 2014 BVLC and contributors.
 
-
 #include "caffe/common.hpp"
-#include "caffe/net.hpp"
-
-
-using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
-  if (argc > 2) {
-    LOG(ERROR) << "device_query [device_id=0]";
-    return 1;
-  }
-  if (argc == 2) {
-    LOG(INFO) << "Querying device_id=" << argv[1];
-    Caffe::SetDevice(atoi(argv[1]));
-    Caffe::DeviceQuery();
-  } else {
-    Caffe::DeviceQuery();
-  }
+  LOG(FATAL) << "Deprecated. Use caffe.bin devicequery "
+                "[--device_id=0] instead.";
   return 0;
 }

tools/finetune_net.cpp

@@ -1,31 +1,9 @@
 // Copyright 2014 BVLC and contributors.
-//
-// This is a simple script that allows one to quickly finetune a network.
-// Usage:
-//    finetune_net solver_proto_file pretrained_net
-
-#include <string>
 
 #include "caffe/caffe.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
-
 int main(int argc, char** argv) {
-  ::google::InitGoogleLogging(argv[0]);
-  if (argc != 3) {
-    LOG(ERROR) << "Usage: finetune_net solver_proto_file pretrained_net";
-    return 1;
-  }
-
-  SolverParameter solver_param;
-  ReadProtoFromTextFileOrDie(argv[1], &solver_param);
-
-  LOG(INFO) << "Starting Optimization";
-  SGDSolver<float> solver(solver_param);
-  LOG(INFO) << "Loading from " << argv[2];
-  solver.net()->CopyTrainedLayersFrom(string(argv[2]));
-  solver.Solve();
-  LOG(INFO) << "Optimization Done.";
-
+  LOG(FATAL) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
+                "[--pretrained_net_file=...] instead.";
   return 0;
 }