/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core/net_simple.h (2606B)
#ifndef CAFFE2_CORE_NET_SIMPLE_H_
#define CAFFE2_CORE_NET_SIMPLE_H_
#include
#include "c10/util/Registry.h"
#include "caffe2/core/common.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/net.h"
#include "caffe2/core/tensor.h"
#include "caffe2/core/workspace.h"
#include "caffe2/proto/caffe2_pb.h"
namespace caffe2 {
struct IndividualMetrics {
public:
explicit IndividualMetrics(const std::vector& operators)
: main_runs_(0), operators_(operators) {
const auto num_ops = operators_.size();
time_per_op.resize(num_ops, 0.0);
}
// run ops while collecting profiling results
void RunOpsWithProfiling();
// print out profiling results
void PrintOperatorProfilingResults();
const vector& GetTimePerOp() {
return time_per_op;
}
float setup_time{0.0};
float memory_alloc_time{0.0};
float memory_dealloc_time{0.0};
float output_dealloc_time{0.0};
private:
int main_runs_;
const std::vector& operators_;
vector time_per_op;
vector flops_per_op;
vector memory_bytes_read_per_op;
vector memory_bytes_written_per_op;
vector param_bytes_per_op;
CaffeMap num_ops_per_op_type_;
CaffeMap time_per_op_type;
CaffeMap flops_per_op_type;
CaffeMap memory_bytes_read_per_op_type;
CaffeMap memory_bytes_written_per_op_type;
CaffeMap param_bytes_per_op_type;
};
// This is the very basic structure you need to run a network - all it
// does is simply to run everything in sequence. If you want more fancy control
// such as a DAG-like execution, check out other better net implementations.
class TORCH_API SimpleNet : public NetBase {
public:
SimpleNet(const std::shared_ptr& net_def, Workspace* ws);
bool SupportsAsync() override {
return false;
}
vector TEST_Benchmark(
const int warmup_runs,
const int main_runs,
const bool run_individual) override;
/*
* This returns a list of pointers to objects stored in unique_ptrs.
* Used by Observers.
*
* Think carefully before using.
*/
vector GetOperators() const override {
vector op_list;
for (auto& op : operators_) {
op_list.push_back(op.get());
}
return op_list;
}
protected:
bool Run() override;
bool RunAsync() override;
vector> operators_;
C10_DISABLE_COPY_AND_ASSIGN(SimpleNet);
};
} // namespace caffe2
#endif // CAFFE2_CORE_NET_SIMPLE_H_