Update Caffe2 benchmark file to write text output
Summary:
The Caffe2 benchmarking framework can now compare the output of a model with some golden output. In order to do that, and reduce the dependency of the benchmarking framework and caffe2, the output is dumped as text format without any schema.
The output is read in by the benchmarking framework and perform the comparison.
Closes https://github.com/caffe2/caffe2/pull/1301
Reviewed By: bwasti
Differential Revision: D5992836
Pulled By: sf-wind
fbshipit-source-id: f6b403103949f4b9880c8372bbdc36966475a387
diff --git a/caffe2/share/contrib/binaries/caffe2_benchmark/caffe2_benchmark.cc b/caffe2/share/contrib/binaries/caffe2_benchmark/caffe2_benchmark.cc
index 3ddd8e1..ddbeeb6 100644
--- a/caffe2/share/contrib/binaries/caffe2_benchmark/caffe2_benchmark.cc
+++ b/caffe2/share/contrib/binaries/caffe2_benchmark/caffe2_benchmark.cc
@@ -1,5 +1,8 @@
+#include <fstream>
+#include <iterator>
#include <string>
+#include "caffe2/core/blob_serialization.h"
#include "caffe2/core/init.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
@@ -52,12 +55,53 @@
run_individual,
false,
"Whether to benchmark individual operators.");
+CAFFE2_DEFINE_bool(
+ text_output,
+ false,
+ "Whether to write out output in text format for regression purpose.");
CAFFE2_DEFINE_int(warmup, 0, "The number of iterations to warm up.");
using std::string;
using std::unique_ptr;
using std::vector;
+static void writeTextOutput(
+ caffe2::TensorCPU* tensor,
+ const string& output_prefix,
+ const string& name) {
+ string output_name = output_prefix + "/" + name + ".txt";
+ caffe2::TensorSerializer<caffe2::CPUContext> ser;
+ caffe2::BlobProto blob_proto;
+ ser.Serialize(
+ *tensor, output_name, blob_proto.mutable_tensor(), 0, tensor->size());
+ blob_proto.set_name(output_name);
+ blob_proto.set_type("Tensor");
+ CAFFE_ENFORCE(blob_proto.has_tensor());
+ caffe2::TensorProto tensor_proto = blob_proto.tensor();
+ vector<float> data;
+ switch (tensor_proto.data_type()) {
+ case caffe2::TensorProto::FLOAT: {
+ std::copy(
+ tensor_proto.float_data().begin(),
+ tensor_proto.float_data().end(),
+ std::back_inserter(data));
+ break;
+ }
+ case caffe2::TensorProto::INT32: {
+ std::copy(
+ tensor_proto.int32_data().begin(),
+ tensor_proto.int32_data().end(),
+ std::back_inserter(data));
+ break;
+ }
+ default:
+ CAFFE_THROW("Unimplemented Blob type.");
+ }
+ std::ofstream output_file(output_name);
+ std::ostream_iterator<float> output_iterator(output_file, "\n");
+ std::copy(data.begin(), data.end(), output_iterator);
+}
+
int main(int argc, char** argv) {
caffe2::GlobalInit(&argc, &argv);
caffe2::ShowLogInfoToStderr();
@@ -150,9 +194,14 @@
workspace->HasBlob(name),
"You requested a non-existing blob: ",
name);
- string serialized = workspace->GetBlob(name)->Serialize(name);
- string output_filename = output_prefix + name;
- caffe2::WriteStringToFile(serialized, output_filename.c_str());
+ if (caffe2::FLAGS_text_output) {
+ auto blob = workspace->GetBlob(name)->GetMutable<caffe2::TensorCPU>();
+ writeTextOutput(blob, output_prefix, name);
+ } else {
+ string serialized = workspace->GetBlob(name)->Serialize(name);
+ string output_filename = output_prefix + name;
+ caffe2::WriteStringToFile(serialized, output_filename.c_str());
+ }
}
}
