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Merge pull request #789 from Yangqing/sweep
Consolidate tools.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,5 @@ | ||
| #!/bin/bash | ||
| TOOLS=../../build/tools | ||
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| 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 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,178 @@ | ||
| // Copyright 2014 BVLC and contributors. | ||
| #include <gflags/gflags.h> | ||
| #include <glog/logging.h> | ||
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| #include <cstring> | ||
| #include <map> | ||
| #include <string> | ||
| #include <vector> | ||
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| #include "caffe/caffe.hpp" | ||
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| using caffe::Blob; | ||
| using caffe::Caffe; | ||
| using caffe::Net; | ||
| using caffe::Layer; | ||
| using caffe::shared_ptr; | ||
| using caffe::Timer; | ||
| using caffe::vector; | ||
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| // 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."); | ||
|
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| // A simple registry for caffe commands. | ||
| typedef int (*BrewFunction)(); | ||
| typedef std::map<caffe::string, BrewFunction> BrewMap; | ||
| BrewMap g_brew_map; | ||
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| #define RegisterBrewFunction(func) \ | ||
| namespace { \ | ||
| class __Registerer_##func { \ | ||
| public: /* NOLINT */ \ | ||
| __Registerer_##func() { \ | ||
| g_brew_map[#func] = &func; \ | ||
| } \ | ||
| }; \ | ||
| __Registerer_##func g_registerer_##func; \ | ||
| } | ||
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| 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. | ||
| } | ||
| } | ||
|
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| // 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); | ||
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| int devicequery() { | ||
| LOG(INFO) << "Querying device_id = " << FLAGS_device_id; | ||
| caffe::Caffe::SetDevice(FLAGS_device_id); | ||
| caffe::Caffe::DeviceQuery(); | ||
| return 0; | ||
| } | ||
| RegisterBrewFunction(devicequery); | ||
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| int train() { | ||
| CHECK_GT(FLAGS_solver_proto_file.size(), 0); | ||
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| caffe::SolverParameter solver_param; | ||
| caffe::ReadProtoFromTextFileOrDie(FLAGS_solver_proto_file, &solver_param); | ||
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| 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); | ||
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| 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); | ||
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| // 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(); | ||
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| 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); | ||
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| int main(int argc, char** argv) { | ||
| caffe::GlobalInit(&argc, &argv); | ||
| CHECK_EQ(argc, 2); | ||
| return GetBrewFunction(caffe::string(argv[1]))(); | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,23 +1,9 @@ | ||
| // Copyright 2014 BVLC and contributors. | ||
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| #include "caffe/common.hpp" | ||
| #include "caffe/net.hpp" | ||
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| using namespace caffe; // NOLINT(build/namespaces) | ||
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| 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; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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 | ||
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| #include <string> | ||
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| #include "caffe/caffe.hpp" | ||
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| using namespace caffe; // NOLINT(build/namespaces) | ||
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| 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; | ||
| } | ||
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| SolverParameter solver_param; | ||
| ReadProtoFromTextFileOrDie(argv[1], &solver_param); | ||
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| 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."; | ||
|
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| LOG(FATAL) << "Deprecated. Use caffe.bin train --solver_proto_file=... " | ||
| "[--pretrained_net_file=...] instead."; | ||
| return 0; | ||
| } |
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