From 0edff71a0117ae93cb0708f2d1eefa76debc542d Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Mon, 31 Mar 2014 14:37:58 -0700
Subject: Resolved merge conflicts

---
 matlab/caffe/matcaffe.cpp    | 22 +++++++++++++++----
 matlab/caffe/matcaffe_demo.m | 52 ++++++++++++++++++++++++++++++++++++++++----
 2 files changed, 66 insertions(+), 8 deletions(-)

(limited to 'matlab')

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 7dca6412..c3482eda 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 // Copyright 2014 BVLC and contributors.
 //
 // matcaffe.cpp provides a wrapper of the caffe::Net class as well as some
@@ -42,7 +43,9 @@ static int init_key = -2;
 // If you have multiple images, cat them with cat(4, ...)
 //
 // The actual forward function. It takes in a cell array of 4-D arrays as
-// input and outputs a cell array.
+
+// input and outputs a cell array. 
+
 static mxArray* do_forward(const mxArray* const bottom) {
   vector<Blob<float>*>& input_blobs = net_->input_blobs();
   CHECK_EQ(static_cast<unsigned int>(mxGetDimensions(bottom)[0]),
@@ -67,8 +70,11 @@ static mxArray* do_forward(const mxArray* const bottom) {
   const vector<Blob<float>*>& output_blobs = net_->ForwardPrefilled();
   mxArray* mx_out = mxCreateCellMatrix(output_blobs.size(), 1);
   for (unsigned int i = 0; i < output_blobs.size(); ++i) {
-    mxArray* mx_blob = mxCreateNumericMatrix(output_blobs[i]->count(),
-        1, mxSINGLE_CLASS, mxREAL);
+    // internally data is stored as (width, height, channels, num)
+    // where width is the fastest dimension
+    mwSize dims[4] = {output_blobs[i]->width(), output_blobs[i]->height(),
+      output_blobs[i]->channels(), output_blobs[i]->num()};
+    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
     mxSetCell(mx_out, i, mx_blob);
     float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
     switch (Caffe::mode()) {
@@ -227,6 +233,14 @@ static void init(MEX_ARGS) {
   }
 }
 
+static void end(MEX_ARGS){
+  if (net_) {
+    net_.reset();
+    init_key = -2;
+    LOG(INFO) << "Network deleted, now it needs to init before used again";
+  }
+
+}
 static void forward(MEX_ARGS) {
   if (nrhs != 1) {
     LOG(ERROR) << "Only given " << nrhs << " arguments";
@@ -265,7 +279,7 @@ static handler_registry handlers[] = {
   { "get_weights",        get_weights     },
   { "get_init_key",       get_init_key    },
   // The end.
-  { "END",                NULL            },
+  { "END",                end             },
 };
 
 
diff --git a/matlab/caffe/matcaffe_demo.m b/matlab/caffe/matcaffe_demo.m
index ff27f970..1faeb4be 100644
--- a/matlab/caffe/matcaffe_demo.m
+++ b/matlab/caffe/matcaffe_demo.m
@@ -19,8 +19,42 @@ function [scores, layers] = matcaffe_demo(im, use_gpu)
 %  im = imread('../../examples/images/cat.jpg');
 %  scores = matcaffe_demo(im, 1);
 %  [score, class] = max(scores);
+% Five things to be aware of:
+%   caffe uses row-major order
+%   matlab uses column-major order
+%   caffe uses BGR color channel order
+%   matlab uses RGB color channel order
+%   images need to have the data mean subtracted
+
+% Data coming in from matlab needs to be in the order 
+%   [width, height, channels, images]
+% where width is the fastest dimension.
+% Data coming out from matcaffe is 
+%  [height, width, channels, images]
+% Here is the rough matlab for putting image data into the correct
+% format:
+%   % convert from uint8 to single
+%   im = single(im);
+%   % reshape to a fixed size (e.g., 227x227)
+%   im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
+%   % permute from RGB to BGR and subtract the data mean (already in BGR)
+%   im = im(:,:,[3 2 1]) - data_mean;
+%   % flip width and height to make width the fastest dimension
+%   im = permute(im, [2 1 3]);
+
+% If you have multiple images, cat them with cat(4, ...)
+
+% The actual forward function. It takes in a cell array of 4-D arrays as
+% input and outputs a cell array. 
+
+% init caffe network (spews logging info)
 
 % init caffe network (spews logging info)
+if nargin < 1
+  % For demo purposes we will use the peppers image
+  im = imread('peppers.png');
+end
+
 if caffe('is_initialized') == 0
   model_def_file = '../../examples/imagenet/imagenet_deploy.prototxt';
   model_file = '../../examples/imagenet/caffe_reference_imagenet_model';
@@ -28,6 +62,10 @@ if caffe('is_initialized') == 0
     % NOTE: you'll have to get the pre-trained ILSVRC network
     error('You need a network model file');
   end
+  if ~exist(model_def_file,'file')
+    % NOTE: you'll have to get network definition
+    error('You need the network prototxt definition');
+  end
   caffe('init', model_def_file, model_file);
 end
 
@@ -42,21 +80,27 @@ end
 caffe('set_phase_test');
 
 % prepare oversampled input
+% input_data is Height x Width x Channel x Num
 tic;
 input_data = {prepare_image(im)};
 toc;
 
 % do forward pass to get scores
+% scores are now Width x Height x Channels x Num
 tic;
 scores = caffe('forward', input_data);
 toc;
 
-% average output scores
-scores = reshape(scores{1}, [1000 10]);
-scores = mean(scores, 2);
+% One can permute back Width and Height.
+scores = scores{1};
+size(scores)
+scores = squeeze(scores);
+scores = mean(squeeze,2);
 
 % you can also get network weights by calling
-layers = caffe('get_weights');
+if nargout == 2
+  weights = caffe('get_weights');
+end
 
 % ------------------------------------------------------------------------
 function images = prepare_image(im)
-- 
cgit v1.2.3