first pass at matlab wrapper (somewhat messy still)

2 files changed, 241 insertions, 0 deletions

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
new file mode 100644
index 00000000..1f11a2bc
--- /dev/null
+++ b/matlab/caffe/matcaffe.cpp
@@ -0,0 +1,182 @@
+// Copyright Ross Girshick and Yangqing Jia 2013
+//
+// matcaffe.cpp provides a wrapper of the caffe::Net class as well as some
+// caffe::Caffe functions so that one could easily call it from matlab.
+// Note that for matlab, we will simply use float as the data type.
+
+#include "mex.h"
+#include "caffe/caffe.hpp"
+
+#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
+
+using namespace caffe;
+
+// A simple wrapper over CaffeNet that runs the forward process.
+struct CaffeNet
+{
+  // The pointer to the internal caffe::Net instance
+	shared_ptr<Net<float> > net_;
+
+  CaffeNet() {}
+  
+  void init(string param_file, string pretrained_param_file) {
+    net_.reset(new Net<float>(param_file));
+    net_->CopyTrainedLayersFrom(pretrained_param_file);
+  }
+
+  virtual ~CaffeNet() {}
+
+  /*
+  inline void check_array_against_blob(
+      PyArrayObject* arr, Blob<float>* blob) {
+    CHECK(PyArray_FLAGS(arr) & NPY_ARRAY_C_CONTIGUOUS);
+    CHECK_EQ(PyArray_NDIM(arr), 4);
+    CHECK_EQ(PyArray_ITEMSIZE(arr), 4);
+    npy_intp* dims = PyArray_DIMS(arr);
+    CHECK_EQ(dims[0], blob->num());
+    CHECK_EQ(dims[1], blob->channels());
+    CHECK_EQ(dims[2], blob->height());
+    CHECK_EQ(dims[3], blob->width());
+  }
+  */
+
+  // Data needs to be [images, channels, height, width] where width is the fastest dimension
+  // 
+  // In matlab, reading an image gives [height, width, channels] where height is the fastest dimension
+  //  - want to have the order as [width, height, channels, images]
+  //    (channels in BGR order)
+  //  - 
+  //
+  // The matlab model is: 
+  //   - bottom is a cell array of 4D tensors in the correct format
+  //   - top is allocated in here as a cell array of outputs
+  //
+  // The actual forward function. It takes in a python list of numpy arrays as
+  // input and a python list of numpy arrays as output. The input and output
+  // should all have correct shapes, are single-precisionabcdnt- and c contiguous.
+  //
+  //
+  mxArray* Forward(const mxArray* const bottom) {
+    vector<Blob<float>*>& input_blobs = net_->input_blobs();
+    CHECK_EQ(static_cast<unsigned int>(mxGetDimensions(bottom)[0]), 
+        input_blobs.size());
+    for (unsigned int i = 0; i < input_blobs.size(); ++i) {
+      const mxArray* const elem = mxGetCell(bottom, i);
+      const float* const data_ptr = 
+          reinterpret_cast<const float* const>(mxGetPr(elem));
+      //check_array_against_blob(arr, input_blobs[i]);
+      switch (Caffe::mode()) {
+      case Caffe::CPU:
+        memcpy(input_blobs[i]->mutable_cpu_data(), data_ptr,
+            sizeof(float) * input_blobs[i]->count());
+        break;
+      case Caffe::GPU:
+        cudaMemcpy(input_blobs[i]->mutable_gpu_data(), data_ptr,
+            sizeof(float) * input_blobs[i]->count(), cudaMemcpyHostToDevice);
+        break;
+      default:
+        LOG(FATAL) << "Unknown Caffe mode.";
+      }  // switch (Caffe::mode())
+    }
+    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);
+      mxSetCell(mx_out, i, mx_blob);
+      float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
+      //check_array_against_blob(arr, output_blobs[i]);
+      switch (Caffe::mode()) {
+      case Caffe::CPU:
+        memcpy(data_ptr, output_blobs[i]->cpu_data(),
+            sizeof(float) * output_blobs[i]->count());
+        break;
+      case Caffe::GPU:
+        cudaMemcpy(data_ptr, output_blobs[i]->gpu_data(),
+            sizeof(float) * output_blobs[i]->count(), cudaMemcpyDeviceToHost);
+        break;
+      default:
+        LOG(FATAL) << "Unknown Caffe mode.";
+      }  // switch (Caffe::mode())
+    }
+
+    return mx_out;
+  }
+
+};
+
+// The caffe::Caffe utility functions.
+static void set_mode_cpu(MEX_ARGS) { 
+  Caffe::set_mode(Caffe::CPU); 
+}
+
+static void set_mode_gpu(MEX_ARGS) { 
+  Caffe::set_mode(Caffe::GPU); 
+}
+
+static void set_phase_train(MEX_ARGS) { 
+  Caffe::set_phase(Caffe::TRAIN); 
+}
+
+static void set_phase_test(MEX_ARGS) { 
+  Caffe::set_phase(Caffe::TEST); 
+}
+
+static void set_device(MEX_ARGS) { 
+  int device_id = static_cast<int>(mxGetScalar(prhs[0]));
+  Caffe::SetDevice(device_id); 
+}
+
+static CaffeNet net;
+
+static void net_init(MEX_ARGS) {
+  net.init("/home/rbg/working/caffe/examples/imagenet_deploy.prototxt", 
+           "/home/rbg/working/caffe/examples/alexnet_train_iter_470000");
+}
+
+static void net_forward(MEX_ARGS) {
+  plhs[0] = net.Forward(prhs[0]);
+}
+
+/** -----------------------------------------------------------------
+ ** Available commands.
+ **/
+struct handler_registry {
+  string cmd;
+  void (*func)(MEX_ARGS);
+};
+
+static handler_registry handlers[] = {
+  // Public API functions
+  { "forward",            net_forward     },
+  { "init",               net_init        },
+  { "set_mode_cpu",       set_mode_cpu    },
+  { "set_mode_gpu",       set_mode_gpu    },
+  { "set_phase_train",    set_phase_train },
+  { "set_phase_test",     set_phase_test  },
+  { "set_device",         set_device      },
+  // The end.
+  { "END",                NULL            },
+};
+
+
+/** -----------------------------------------------------------------
+ ** matlab entry point: caffe(api_command, arg1, arg2, ...)
+ **/
+void mexFunction(MEX_ARGS) {
+  // TODO: check args
+  { // Handle input command
+    char *cmd = mxArrayToString(prhs[0]);
+    //bool dispatched = false;
+    // Dispatch to cmd handler
+    for (int i = 0; handlers[i].func != NULL; i++) {
+      if (handlers[i].cmd.compare(cmd) == 0) {
+        handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
+        //dispatched = true;
+        break;
+      }
+    }
+    mxFree(cmd);
+    //checkM(dispatched, "Command not found!");
+  }
+}
diff --git a/matlab/caffe/matcaffe_demo.m b/matlab/caffe/matcaffe_demo.m
new file mode 100644
index 00000000..6b4ca2f7
--- /dev/null
+++ b/matlab/caffe/matcaffe_demo.m
@@ -0,0 +1,59 @@
+function res = matcaffe_demo(im, gpu)
+
+% load image net mean
+%  // In matlab, reading an image gives [height, width, channels] where height is the fastest dimension
+%  //  - want to have the order as [width, height, channels, images]
+%  //    (channels in BGR order)
+%  //  - 
+
+% 1: swap channel order to BGR
+% 2: extract 5 crops and their flips
+% 3: swap rows and columns and concat along 4th dim
+% 4: wrap in cell aray
+
+caffe('init');
+if gpu
+  caffe('set_mode_gpu');
+else
+  caffe('set_mode_cpu');
+end
+caffe('set_phase_test');
+tic;
+blob = {prepare_image(im)};
+toc;
+tic;
+res = caffe('forward', blob);
+toc;
+res = reshape(res{1}, [1000 10]);
+res = mean(res, 2);
+
+
+function images = prepare_image(im)
+d = load('ilsvrc_2012_mean');
+image_mean = d.image_mean;
+IMAGE_DIM = 256;
+CROPPED_DIM = 227;
+
+% resize to fixed input size
+im = single(im);
+im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
+% permute from RGB to BGR
+im = im(:,:,[3 2 1]) - image_mean;
+
+% oversample
+images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
+indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
+curr = 1;
+for i = indices
+  for j = indices
+    images(:, :, :, curr) = ...
+        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
+    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
+    curr = curr + 1;
+  end
+end
+center = floor(indices(2) / 2)+1;
+images(:,:,:,5) = ...
+    permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ...
+        [2 1 3]);
+images(:,:,:,10) = images(end:-1:1, :, :, curr);