glog mkl_rt mkl_intel_thread leveldb snappy pthread
WARNINGS := -Wall
-COMMON_FLAGS := $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
+COMMON_FLAGS := -DNDEBUG $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
CXXFLAGS += -pthread -fPIC -O2 $(COMMON_FLAGS)
NVCCFLAGS := -Xcompiler -fPIC -O2 $(COMMON_FLAGS)
LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) \
--- /dev/null
+train_net: "data/lenet.prototxt"
+test_net: "data/lenet_test.prototxt"
+base_lr: 0.01
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+display: 100
+max_iter: 5000
+momentum: 0.9
+weight_decay: 0.0005
+test_iter: 100
+test_interval: 500
\ No newline at end of file
+++ /dev/null
-// Copyright 2013 Yangqing Jia
-// This example shows how to run a modified version of LeNet using Caffe.
-
-#include <cuda_runtime.h>
-#include <fcntl.h>
-#include <google/protobuf/text_format.h>
-
-#include <cstring>
-#include <iostream>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/net.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/util/io.hpp"
-#include "caffe/solver.hpp"
-
-using namespace caffe;
-
-int main(int argc, char** argv) {
- if (argc < 3) {
- std::cout << "Usage:" << std::endl;
- std::cout << "demo_mnist.bin train_file test_file [CPU/GPU]" << std::endl;
- return 0;
- }
- google::InitGoogleLogging(argv[0]);
- Caffe::DeviceQuery();
-
- if (argc == 4 && strcmp(argv[3], "GPU") == 0) {
- LOG(ERROR) << "Using GPU";
- Caffe::set_mode(Caffe::GPU);
- } else {
- LOG(ERROR) << "Using CPU";
- Caffe::set_mode(Caffe::CPU);
- }
-
- // Start training
- Caffe::set_phase(Caffe::TRAIN);
-
- NetParameter net_param;
- ReadProtoFromTextFile(argv[1],
- &net_param);
- vector<Blob<float>*> bottom_vec;
- Net<float> caffe_net(net_param, bottom_vec);
-
- // Run the network without training.
- LOG(ERROR) << "Performing Forward";
- caffe_net.Forward(bottom_vec);
- LOG(ERROR) << "Performing Backward";
- LOG(ERROR) << "Initial loss: " << caffe_net.Backward();
-
- SolverParameter solver_param;
- // Solver Parameters are hard-coded in this case, but you can write a
- // SolverParameter protocol buffer to specify all these values.
- solver_param.set_base_lr(0.01);
- solver_param.set_display(100);
- solver_param.set_max_iter(5000);
- solver_param.set_lr_policy("inv");
- solver_param.set_gamma(0.0001);
- solver_param.set_power(0.75);
- solver_param.set_momentum(0.9);
- solver_param.set_weight_decay(0.0005);
-
- LOG(ERROR) << "Starting Optimization";
- SGDSolver<float> solver(solver_param);
- solver.Solve(&caffe_net);
- LOG(ERROR) << "Optimization Done.";
-
- // Write the trained network to a NetParameter protobuf. If you are training
- // the model and saving it for later, this is what you want to serialize and
- // store.
- NetParameter trained_net_param;
- caffe_net.ToProto(&trained_net_param);
-
- // Now, let's starting doing testing.
- Caffe::set_phase(Caffe::TEST);
-
- // Using the testing data to test the accuracy.
- NetParameter test_net_param;
- ReadProtoFromTextFile(argv[2], &test_net_param);
- Net<float> caffe_test_net(test_net_param, bottom_vec);
- caffe_test_net.CopyTrainedLayersFrom(trained_net_param);
-
- double test_accuracy = 0;
- int batch_size = test_net_param.layers(0).layer().batchsize();
- for (int i = 0; i < 10000 / batch_size; ++i) {
- const vector<Blob<float>*>& result =
- caffe_test_net.Forward(bottom_vec);
- test_accuracy += result[0]->cpu_data()[0];
- }
- test_accuracy /= 10000 / batch_size;
- LOG(ERROR) << "Test accuracy:" << test_accuracy;
-
- return 0;
-}
using namespace caffe;
int main(int argc, char** argv) {
- if (argc < 3) {
- LOG(ERROR) << "Usage: train_net net_proto_file solver_proto_file "
- << "[resume_point_file]";
+ ::google::InitGoogleLogging(argv[0]);
+ if (argc < 2) {
+ LOG(ERROR) << "Usage: train_net solver_proto_file [resume_point_file]";
return 0;
}
- cudaSetDevice(0);
+ Caffe::SetDevice(0);
Caffe::set_mode(Caffe::GPU);
- Caffe::set_phase(Caffe::TRAIN);
-
- NetParameter net_param;
- ReadProtoFromTextFile(argv[1], &net_param);
- vector<Blob<float>*> bottom_vec;
- Net<float> caffe_net(net_param, bottom_vec);
SolverParameter solver_param;
- ReadProtoFromTextFile(argv[2], &solver_param);
+ ReadProtoFromTextFile(argv[1], &solver_param);
- LOG(ERROR) << "Starting Optimization";
+ LOG(INFO) << "Starting Optimization";
SGDSolver<float> solver(solver_param);
- if (argc == 4) {
- LOG(ERROR) << "Resuming from " << argv[3];
- solver.Solve(&caffe_net, argv[3]);
+ if (argc == 3) {
+ LOG(INFO) << "Resuming from " << argv[2];
+ solver.Solve(argv[2]);
} else {
- solver.Solve(&caffe_net);
+ solver.Solve();
}
- LOG(ERROR) << "Optimization Done.";
+ LOG(INFO) << "Optimization Done.";
return 0;
}
inline static void set_phase(Phase phase) { Get().phase_ = phase; }
// Sets the random seed of both MKL and curand
static void set_random_seed(const unsigned int seed);
+ // Sets the device. Since we have cublas and curand stuff, set device also
+ // requires us to reset those values.
+ static void SetDevice(const int device_id);
// Prints the current GPU status.
static void DeviceQuery();
template <typename Dtype>
class Solver {
public:
- explicit Solver(const SolverParameter& param)
- : param_(param) {}
+ explicit Solver(const SolverParameter& param);
// The main entry of the solver function. In default, iter will be zero. Pass
// in a non-zero iter number to resume training for a pre-trained net.
- void Solve(Net<Dtype>* net, char* state_file = NULL);
+ void Solve(const char* resume_file = NULL);
+ inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
virtual ~Solver() {}
protected:
// function that produces a SolverState protocol buffer that needs to be
// written to disk together with the learned net.
void Snapshot();
+ // The test routine
+ void Test();
virtual void SnapshotSolverState(SolverState* state) = 0;
// The Restore function implements how one should restore the solver to a
// previously snapshotted state. You should implement the RestoreSolverState()
// function that restores the state from a SolverState protocol buffer.
- void Restore(char* state_file);
+ void Restore(const char* resume_file);
virtual void RestoreSolverState(const SolverState& state) = 0;
SolverParameter param_;
int iter_;
Net<Dtype>* net_;
+ Net<Dtype>* test_net_;
DISABLE_COPY_AND_ASSIGN(Solver);
};
VSL_CHECK(vslNewStream(&(Get().vsl_stream_), VSL_BRNG_MT19937, seed));
}
+void Caffe::SetDevice(const int device_id) {
+ int current_device;
+ CUDA_CHECK(cudaGetDevice(¤t_device));
+ if (current_device == device_id) {
+ return;
+ }
+ if (Get().cublas_handle_) CUBLAS_CHECK(cublasDestroy(Get().cublas_handle_));
+ if (Get().curand_generator_) {
+ CURAND_CHECK(curandDestroyGenerator(Get().curand_generator_));
+ }
+ CUDA_CHECK(cudaSetDevice(device_id));
+ CUBLAS_CHECK(cublasCreate(&Get().cublas_handle_));
+ CURAND_CHECK(curandCreateGenerator(&Get().curand_generator_,
+ CURAND_RNG_PSEUDO_DEFAULT));
+ CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(Get().curand_generator_,
+ time(NULL)));
+}
+
void Caffe::DeviceQuery() {
cudaDeviceProp prop;
int device;
layer->iter_->Next();
if (!layer->iter_->Valid()) {
// We have reached the end. Restart from the first.
- LOG(INFO) << "Restarting data prefetching from start.";
+ DLOG(INFO) << "Restarting data prefetching from start.";
layer->iter_->SeekToFirst();
}
}
prefetch_data_->mutable_cpu_data();
prefetch_label_->mutable_cpu_data();
data_mean_.cpu_data();
- // LOG(INFO) << "Initializing prefetch";
+ DLOG(INFO) << "Initializing prefetch";
CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>,
reinterpret_cast<void*>(this))) << "Pthread execution failed.";
- // LOG(INFO) << "Prefetch initialized.";
+ DLOG(INFO) << "Prefetch initialized.";
}
template <typename Dtype>
// In the end, all remaining blobs are considered output blobs.
for (set<string>::iterator it = available_blobs.begin();
it != available_blobs.end(); ++it) {
- LOG(ERROR) << "This network produces output " << *it;
+ LOG(INFO) << "This network produces output " << *it;
net_output_blob_indices_.push_back(blob_name_to_idx[*it]);
net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());
}
++target_layer_id;
}
if (target_layer_id == layer_names_.size()) {
- LOG(INFO) << "Ignoring source layer " << source_layer_name;
+ DLOG(INFO) << "Ignoring source layer " << source_layer_name;
continue;
}
- LOG(INFO) << "Loading source layer " << source_layer_name;
+ DLOG(INFO) << "Loading source layer " << source_layer_name;
vector<shared_ptr<Blob<Dtype> > >& target_blobs =
layers_[target_layer_id]->blobs();
CHECK_EQ(target_blobs.size(), source_layer.blobs_size())
for (int i = 0; i < net_input_blob_indices_.size(); ++i) {
param->add_input(blob_names_[net_input_blob_indices_[i]]);
}
- LOG(INFO) << "Serializing " << layers_.size() << " layers";
+ DLOG(INFO) << "Serializing " << layers_.size() << " layers";
for (int i = 0; i < layers_.size(); ++i) {
LayerConnection* layer_connection = param->add_layers();
for (int j = 0; j < bottom_id_vecs_[i].size(); ++j) {
}
message SolverParameter {
- optional float base_lr = 1; // The base learning rate
+ optional string train_net = 1; // The proto file for the training net.
+ optional string test_net = 2; // The proto file for the testing net.
+ // The number of iterations for each testing phase.
+ optional int32 test_iter = 3 [ default = 0 ];
+ // The number of iterations between two testing phases.
+ optional int32 test_interval = 4 [ default = 0 ];
+ 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 = 2;
- optional int32 max_iter = 3; // the maximum number of iterations
- optional int32 snapshot = 4 [default = 0]; // The snapshot interval
- optional string lr_policy = 5; // The learning rate decay policy.
- optional float min_lr = 6 [default = 0]; // The mininum learning rate
- optional float max_lr = 7 [default = 1e10]; // The maximum learning rate
- optional float gamma = 8; // The parameter to compute the learning rate.
- optional float power = 9; // The parameter to compute the learning rate.
- optional float momentum = 10; // The momentum value.
- optional float weight_decay = 11; // The weight decay.
- optional int32 stepsize = 12; // the stepsize for learning rate policy "step"
-
- optional string snapshot_prefix = 13; // The prefix for the snapshot.
+ optional int32 display = 6;
+ optional int32 max_iter = 7; // the maximum number of iterations
+ optional string lr_policy = 8; // The learning rate decay policy.
+ 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.
+ optional int32 stepsize = 13; // the stepsize for learning rate policy "step"
+ 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 = 14 [ default = false];
+ optional bool snapshot_diff = 16 [ default = false];
}
// A message that stores the solver snapshots
namespace caffe {
template <typename Dtype>
-void Solver<Dtype>::Solve(Net<Dtype>* net, char* resume_file) {
- net_ = net;
+Solver<Dtype>::Solver(const SolverParameter& param)
+ : param_(param), net_(NULL), test_net_(NULL) {
+ // Scaffolding code
+ NetParameter train_net_param;
+ ReadProtoFromTextFile(param_.train_net(), &train_net_param);
+ // For the training network, there should be no input - so we simply create
+ // a dummy bottom_vec instance to initialize the networks.
+ vector<Blob<Dtype>*> bottom_vec;
+ LOG(INFO) << "Creating training net.";
+ net_ = new Net<Dtype>(train_net_param, bottom_vec);
+ if (param_.has_test_net()) {
+ LOG(INFO) << "Creating testing net.";
+ NetParameter test_net_param;
+ ReadProtoFromTextFile(param_.test_net(), &test_net_param);
+ test_net_ = new Net<Dtype>(test_net_param, bottom_vec);
+ CHECK_GT(param_.test_iter(), 0);
+ CHECK_GT(param_.test_interval(), 0);
+ }
+ LOG(INFO) << "Solver scaffolding done.";
+}
+
+
+template <typename Dtype>
+void Solver<Dtype>::Solve(const char* resume_file) {
+ Caffe::set_phase(Caffe::TRAIN);
LOG(INFO) << "Solving " << net_->name();
PreSolve();
Snapshot();
}
if (param_.display() && iter_ % param_.display() == 0) {
- LOG(ERROR) << "Iteration " << iter_ << ", loss = " << loss;
+ LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+ }
+ if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
+ // We need to set phase to test before running.
+ Caffe::set_phase(Caffe::TEST);
+ Test();
+ Caffe::set_phase(Caffe::TRAIN);
}
}
LOG(INFO) << "Optimization Done.";
template <typename Dtype>
+void Solver<Dtype>::Test() {
+ LOG(INFO) << "Testing net";
+ NetParameter net_param;
+ net_->ToProto(&net_param);
+ CHECK_NOTNULL(test_net_)->CopyTrainedLayersFrom(net_param);
+ vector<Dtype> test_score;
+ vector<Blob<Dtype>*> bottom_vec;
+ for (int i = 0; i < param_.test_iter(); ++i) {
+ const vector<Blob<Dtype>*>& result =
+ test_net_->Forward(bottom_vec);
+ if (i == 0) {
+ for (int j = 0; j < result.size(); ++j) {
+ const Dtype* result_vec = result[j]->cpu_data();
+ for (int k = 0; k < result[j]->count(); ++k) {
+ test_score.push_back(result_vec[k]);
+ }
+ }
+ } else {
+ int idx = 0;
+ for (int j = 0; j < result.size(); ++j) {
+ const Dtype* result_vec = result[j]->cpu_data();
+ for (int k = 0; k < result[j]->count(); ++k) {
+ test_score[idx++] += result_vec[k];
+ }
+ }
+ }
+ }
+ for (int i = 0; i < test_score.size(); ++i) {
+ LOG(INFO) << "Test score #" << i << ": "
+ << test_score[i] / param_.test_iter();
+ }
+}
+
+
+template <typename Dtype>
void Solver<Dtype>::Snapshot() {
NetParameter net_param;
// For intermediate results, we will also dump the gradient values.
}
template <typename Dtype>
-void Solver<Dtype>::Restore(char* state_file) {
+void Solver<Dtype>::Restore(const char* state_file) {
SolverState state;
NetParameter net_param;
ReadProtoFromBinaryFile(state_file, &state);
} else {
LOG(FATAL) << "Unknown learning rate policy: " << lr_policy;
}
- rate = min(max(rate, Dtype(this->param_.min_lr())),
- Dtype(this->param_.max_lr()));
return rate;
}
// get the learning rate
Dtype rate = GetLearningRate();
if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
- LOG(ERROR) << "Iteration " << this->iter_ << ", lr = " << rate;
+ LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
}
Dtype momentum = this->param_.momentum();
Dtype weight_decay = this->param_.weight_decay();
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