From 47d7cb7b20a7fc4563423ae669c5237e20670206 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 23 Jan 2015 22:46:53 -0800
Subject: [PATCH] drop dump_network tool

Nets are better serialized as a single binaryproto or saved however
desired through the Python and MATLAB interfaces.
---
 tools/dump_network.cpp | 82 ------------------------------------------
 1 file changed, 82 deletions(-)
 delete mode 100644 tools/dump_network.cpp

diff --git a/tools/dump_network.cpp b/tools/dump_network.cpp
deleted file mode 100644
index 9cb996ef612..00000000000
--- a/tools/dump_network.cpp
+++ /dev/null
@@ -1,82 +0,0 @@
-// This program takes in a trained network and an input blob, and then dumps
-// all the intermediate blobs produced by the net to individual binary
-// files stored in protobuffer binary formats.
-// Usage:
-//    dump_network input_net_param trained_net_param
-//        input_blob output_prefix 0/1
-// if input_net_param is 'none', we will directly load the network from
-// trained_net_param. If the last argv is 1, we will do a forward-backward pass
-// before dumping everyting, and also dump the who network.
-
-#include <string>
-#include <vector>
-
-#include "fcntl.h"
-#include "google/protobuf/text_format.h"
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/solver.hpp"
-#include "caffe/util/io.hpp"
-
-using boost::shared_ptr;
-using caffe::Blob;
-using caffe::BlobProto;
-using caffe::Caffe;
-using caffe::Net;
-using caffe::NetParameter;
-
-int main(int argc, char** argv) {
-  Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TEST);
-
-  shared_ptr<Net<float> > caffe_net;
-  if (strcmp(argv[1], "none") == 0) {
-    // We directly load the net param from trained file
-    caffe_net.reset(new Net<float>(argv[2]));
-  } else {
-    caffe_net.reset(new Net<float>(argv[1]));
-  }
-  caffe_net->CopyTrainedLayersFrom(argv[2]);
-
-  std::vector<Blob<float>* > input_vec;
-  shared_ptr<Blob<float> > input_blob(new Blob<float>());
-  if (strcmp(argv[3], "none") != 0) {
-    BlobProto input_blob_proto;
-    ReadProtoFromBinaryFile(argv[3], &input_blob_proto);
-    input_blob->FromProto(input_blob_proto);
-    input_vec.push_back(input_blob.get());
-  }
-
-  std::string output_prefix(argv[4]);
-  // Run the network without training.
-  LOG(ERROR) << "Performing Forward";
-  caffe_net->Forward(input_vec);
-  if (argc > 5 && strcmp(argv[5], "1") == 0) {
-    LOG(ERROR) << "Performing Backward";
-    Caffe::set_phase(Caffe::TRAIN);
-    caffe_net->Backward();
-    // Dump the network
-    NetParameter output_net_param;
-    caffe_net->ToProto(&output_net_param, true);
-    WriteProtoToBinaryFile(output_net_param,
-        output_prefix + output_net_param.name());
-  }
-  // Now, let's dump all the layers
-
-  const std::vector<std::string>& blob_names = caffe_net->blob_names();
-  const std::vector<shared_ptr<Blob<float> > >& blobs = caffe_net->blobs();
-  for (int blobid = 0; blobid < caffe_net->blobs().size(); ++blobid) {
-    // Serialize blob
-    LOG(ERROR) << "Dumping " << blob_names[blobid];
-    BlobProto output_blob_proto;
-    blobs[blobid]->ToProto(&output_blob_proto);
-    WriteProtoToBinaryFile(output_blob_proto,
-        output_prefix + blob_names[blobid]);
-  }
-
-  return 0;
-}