зеркало из https://github.com/microsoft/caffe.git
104 строки
3.1 KiB
C++
104 строки
3.1 KiB
C++
// Copyright 2013 Yangqing Jia
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#include <ctime>
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#include <string>
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#include <vector>
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#include "cuda_runtime.h"
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#include "fcntl.h"
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#include "google/protobuf/text_format.h"
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#include "caffe/blob.hpp"
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#include "caffe/common.hpp"
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#include "caffe/net.hpp"
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#include "caffe/filler.hpp"
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#include "caffe/proto/caffe.pb.h"
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#include "caffe/util/io.hpp"
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#include "caffe/solver.hpp"
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using boost::shared_ptr;
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using namespace caffe; // NOLINT(build/namespaces)
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int main(int argc, char** argv) {
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int total_iter = 50;
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if (argc < 2) {
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LOG(ERROR) << "net_speed_benchmark net_proto [iterations=50] [CPU/GPU] "
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<< "[Device_id=0]";
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return 0;
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}
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if (argc >=3) {
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total_iter = atoi(argv[2]);
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}
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LOG(ERROR) << "Testing for " << total_iter << "Iterations.";
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if (argc >= 4 && strcmp(argv[3], "GPU") == 0) {
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LOG(ERROR) << "Using GPU";
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uint device_id = 0;
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if (argc >= 5 && strcmp(argv[3], "GPU") == 0) {
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device_id = atoi(argv[4]);
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}
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LOG(ERROR) << "Using Device_id=" << device_id;
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Caffe::SetDevice(device_id);
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Caffe::set_mode(Caffe::GPU);
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} else {
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LOG(ERROR) << "Using CPU";
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Caffe::set_mode(Caffe::CPU);
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}
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Caffe::set_phase(Caffe::TRAIN);
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NetParameter net_param;
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ReadProtoFromTextFile(argv[1],
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&net_param);
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Net<float> caffe_net(net_param);
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// Run the network without training.
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LOG(ERROR) << "Performing Forward";
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// Note that for the speed benchmark, we will assume that the network does
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// not take any input blobs.
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caffe_net.Forward(vector<Blob<float>*>());
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LOG(ERROR) << "Performing Backward";
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LOG(ERROR) << "Initial loss: " << caffe_net.Backward();
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const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
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vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
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vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
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LOG(ERROR) << "*** Benchmark begins ***";
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clock_t forward_start = clock();
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for (int i = 0; i < layers.size(); ++i) {
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const string& layername = layers[i]->layer_param().name();
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clock_t start = clock();
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for (int j = 0; j < total_iter; ++j) {
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layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
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}
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LOG(ERROR) << layername << "\tforward: "
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<< static_cast<float>(clock() - start) / CLOCKS_PER_SEC
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<< " seconds.";
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}
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LOG(ERROR) << "Forward pass: "
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<< static_cast<float>(clock() - forward_start) / CLOCKS_PER_SEC
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<< " seconds.";
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clock_t backward_start = clock();
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for (int i = layers.size() - 1; i >= 0; --i) {
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const string& layername = layers[i]->layer_param().name();
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clock_t start = clock();
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for (int j = 0; j < total_iter; ++j) {
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layers[i]->Backward(top_vecs[i], true, &bottom_vecs[i]);
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}
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LOG(ERROR) << layername << "\tbackward: "
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<< static_cast<float>(clock() - start) / CLOCKS_PER_SEC
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<< " seconds.";
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}
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LOG(ERROR) << "Backward pass: "
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<< static_cast<float>(clock() - backward_start) / CLOCKS_PER_SEC
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<< " seconds.";
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LOG(ERROR) << "Total Time: "
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<< static_cast<float>(clock() - forward_start) / CLOCKS_PER_SEC
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<< " seconds.";
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LOG(ERROR) << "*** Benchmark ends ***";
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return 0;
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}
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