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/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
#include <string>
#include "caffe2/core/blob_serialization.h"
#include "caffe2/core/init.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/net.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/string_utils.h"
#include "c10/util/string_utils.h"
using std::map;
using std::shared_ptr;
using std::string;
using std::vector;
template <typename ContextType, typename TensorType>
void writeTextOutput(
TensorType* tensor,
const string& output_prefix,
const string& name,
int index,
int num_blobs) {
if (index >= num_blobs) {
return;
}
string filename = name;
std::replace(filename.begin(), filename.end(), '/', '_');
string output_name = output_prefix + "/" + filename + ".txt";
caffe2::TensorSerializer ser;
caffe2::BlobProto blob_proto;
ser.Serialize(
*tensor, output_name, blob_proto.mutable_tensor(), 0, tensor->numel());
blob_proto.set_name(output_name);
blob_proto.set_type("Tensor");
CAFFE_ENFORCE(blob_proto.has_tensor());
caffe2::TensorProto tensor_proto = blob_proto.tensor();
int dims_size = tensor_proto.dims_size();
long long elem_dim_size =
dims_size > 1 ? tensor_proto.dims(1) : tensor_proto.dims(0);
for (int i = 2; i < dims_size; i++) {
elem_dim_size *= tensor_proto.dims(i);
}
std::vector<std::string> lines;
std::string dims;
for (int i = 0; i < dims_size; i++) {
int dim = tensor_proto.dims(i);
if (i > 0) {
dims += ", ";
}
dims += c10::to_string(dim);
}
lines.push_back(dims);
std::stringstream line;
if (tensor_proto.data_type() == caffe2::TensorProto::FLOAT) {
auto start = tensor_proto.float_data().begin();
auto end = tensor_proto.float_data().end();
copy(start, end, std::ostream_iterator<float>(line, ","));
} else if (tensor_proto.data_type() == caffe2::TensorProto::INT32) {
auto start = tensor_proto.int32_data().begin();
auto end = tensor_proto.int32_data().end();
copy(start, end, std::ostream_iterator<int>(line, ","));
} else {
CAFFE_THROW("Unimplemented Blob type.");
}
// remove the last ,
string str = line.str();
str.pop_back();
lines.push_back(str);
// static casts are workaround for MSVC build
auto flags = static_cast<std::ios_base::openmode>(std::ios::out);
if (index != 0) {
flags |= static_cast<std::ios_base::openmode>(std::ios::app);
} else {
flags |= static_cast<std::ios_base::openmode>(std::ios::trunc);
}
std::ofstream output_file(output_name, flags);
std::ostream_iterator<std::string> output_iterator(output_file, "\n");
std::copy(lines.begin(), lines.end(), output_iterator);
}
void observerConfig();
bool backendCudaSet(const string&);
void setDeviceType(caffe2::NetDef*, caffe2::DeviceType&);
void setOperatorEngine(caffe2::NetDef*, const string&);
int loadInput(
shared_ptr<caffe2::Workspace> workspace,
const bool run_on_gpu,
map<string, caffe2::TensorProtos>& tensor_protos_map,
const string& input,
const string& input_file,
const string& input_dims,
const string& input_type);
void fillInputBlob(
shared_ptr<caffe2::Workspace> workspace,
map<string, caffe2::TensorProtos>& tensor_protos_map,
int iteration);
void writeOutput(
shared_ptr<caffe2::Workspace> workspace,
const bool run_on_gpu,
const string& output,
const string& output_folder,
const bool text_output,
const int index,
const int num_blobs);
void runNetwork(
shared_ptr<caffe2::Workspace> workspace,
caffe2::NetDef& net_def,
map<string, caffe2::TensorProtos>& tensor_protos_map,
const bool wipe_cache,
const bool run_individual,
const bool run_on_gpu,
const bool text_output,
const int warmup,
const int iter,
const int num_blobs,
const int sleep_before_run,
const int sleep_between_iteration,
const int sleep_between_net_and_operator,
const std::string& output,
const std::string& output_folder);
int benchmark(
int argc,
char* argv[],
const string& FLAGS_backend,
const string& FLAGS_init_net,
const string& FLAGS_input,
const string& FLAGS_input_dims,
const string& FLAGS_input_file,
const string& FLAGS_input_type,
int FLAGS_iter,
const string& FLAGS_net,
const string& FLAGS_output,
const string& FLAGS_output_folder,
bool FLAGS_run_individual,
int FLAGS_sleep_before_run,
int FLAGS_sleep_between_iteration,
int FLAGS_sleep_between_net_and_operator,
bool FLAGS_text_output,
int FLAGS_warmup,
bool FLAGS_wipe_cache);
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