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Commit ec6f3b84 authored by Jeff Donahue's avatar Jeff Donahue
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minor cleanup in rcnn-finetuning -- rcnn feature computation tested at 

this commit (in addition to all caffe unit tests passing)
parent dfec9471
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matlab/caffe/matcaffe.cpp
View file @ ec6f3b84
......@@ -25,7 +25,7 @@ static int init_key = -2;
// 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
// Data coming in from matlab needs to be in the order
// [width, height, channels, images]
// where width is the fastest dimension.
// Here is the rough matlab for putting image data into the correct
......@@ -131,7 +131,7 @@ static mxArray* do_get_weights() {
prev_layer_name = layer_names[i];
const mwSize dims[2] = {layer_blobs.size(), 1};
mx_layer_cells = mxCreateCellArray(2, dims);
mxSetField(mx_layers, mx_layer_index, "weights", mx_layer_cells);
mxSetField(mx_layers, mx_layer_index, "weights", mx_layer_cells);
mxSetField(mx_layers, mx_layer_index, "layer_names",
mxCreateString(layer_names[i].c_str()));
mx_layer_index++;
......@@ -142,18 +142,19 @@ static mxArray* do_get_weights() {
// where width is the fastest dimension
mwSize dims[4] = {layer_blobs[j]->width(), layer_blobs[j]->height(),
layer_blobs[j]->channels(), layer_blobs[j]->num()};
mxArray* mx_weights = mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
mxArray* mx_weights = mxCreateNumericArray(4, dims, mxSINGLE_CLASS,
mxREAL);
mxSetCell(mx_layer_cells, j, mx_weights);
float* weights_ptr = reinterpret_cast<float*>(mxGetPr(mx_weights));
// mexPrintf("layer: %s (%d) blob: %d %d: (%d, %d, %d) %d\n",
// layer_names[i].c_str(), i, j, layer_blobs[j]->num(),
// layer_blobs[j]->height(), layer_blobs[j]->width(),
// layer_blobs[j]->channels(), layer_blobs[j]->count());
// mexPrintf("layer: %s (%d) blob: %d %d: (%d, %d, %d) %d\n",
// layer_names[i].c_str(), i, j, layer_blobs[j]->num(),
// layer_blobs[j]->height(), layer_blobs[j]->width(),
// layer_blobs[j]->channels(), layer_blobs[j]->count());
switch (Caffe::mode()) {
case Caffe::CPU:
memcpy(weights_ptr, layer_blobs[j]->cpu_data(),
memcpy(weights_ptr, layer_blobs[j]->cpu_data(),
sizeof(float) * layer_blobs[j]->count());
break;
case Caffe::GPU:
......@@ -219,6 +220,7 @@ static void init(MEX_ARGS) {
mxFree(param_file);
mxFree(model_file);
// NOLINT_NEXT_LINE(runtime/threadsafe_fn)
init_key = rand();
if (nlhs == 1) {
plhs[0] = mxCreateDoubleScalar(init_key);
......
src/caffe/layers/window_data_layer.cpp
View file @ ec6f3b84
......@@ -5,19 +5,21 @@
#include <stdint.h>
#include <pthread.h>
#include <algorithm>
#include <string>
#include <vector>
#include <map>
#include <fstream>
#include <fstream> // NOLINT(readability/streams)
#include <utility>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "caffe/layer.hpp"
#include "caffe/util/io.hpp"
#include "caffe/vision_layers.hpp"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
using std::string;
using std::map;
using std::pair;
......@@ -30,7 +32,7 @@ namespace caffe {
template <typename Dtype>
void* WindowDataLayerPrefetch(void* layer_pointer) {
WindowDataLayer<Dtype>* layer =
WindowDataLayer<Dtype>* layer =
reinterpret_cast<WindowDataLayer<Dtype>*>(layer_pointer);
// At each iteration, sample N windows where N*p are foreground (object)
......@@ -56,7 +58,7 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
// zero out batch
memset(top_data, 0, sizeof(Dtype)*layer->prefetch_data_->count());
const int num_fg = static_cast<int>(static_cast<float>(batchsize)
const int num_fg = static_cast<int>(static_cast<float>(batchsize)
* fg_fraction);
const int num_samples[2] = { batchsize - num_fg, num_fg };
......@@ -65,23 +67,26 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
for (int is_fg = 0; is_fg < 2; ++is_fg) {
for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {
// sample a window
vector<float> window = (is_fg)
vector<float> window = (is_fg)
// NOLINT_NEXT_LINE(runtime/threadsafe_fn)
? layer->fg_windows_[rand() % layer->fg_windows_.size()]
// NOLINT_NEXT_LINE(runtime/threadsafe_fn)
: layer->bg_windows_[rand() % layer->bg_windows_.size()];
bool do_mirror = false;
// NOLINT_NEXT_LINE(runtime/threadsafe_fn)
if (mirror && rand() % 2) {
do_mirror = true;
}
// load the image containing the window
pair<std::string, vector<int> > image =
pair<std::string, vector<int> > image =
layer->image_database_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
cv::Mat cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
if (!cv_img.data) {
LOG(ERROR) << "Could not open or find file " << image.first;
return (void*)NULL;
return reinterpret_cast<void*>(NULL);
}
const int channels = cv_img.channels();
......@@ -94,7 +99,7 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
int pad_w = 0;
int pad_h = 0;
if (context_pad > 0 || use_square) {
// scale factor by which to expand the original region
// scale factor by which to expand the original region
// such that after warping the expanded region to cropsize x cropsize
// there's exactly context_pad amount of padding on each side
Dtype context_scale = static_cast<Dtype>(cropsize) /
......@@ -116,7 +121,7 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
x2 = static_cast<int>(round(center_x + half_width*context_scale));
y1 = static_cast<int>(round(center_y - half_height*context_scale));
y2 = static_cast<int>(round(center_y + half_height*context_scale));
// the expanded region may go outside of the image
// so we compute the clipped (expanded) region and keep track of
// the extent beyond the image
......@@ -139,17 +144,17 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
int clipped_height = y2-y1+1;
int clipped_width = x2-x1+1;
// scale factors that would be used to warp the unclipped
// scale factors that would be used to warp the unclipped
// expanded region
Dtype scale_x =
Dtype scale_x =
static_cast<Dtype>(cropsize)/static_cast<Dtype>(unclipped_width);
Dtype scale_y =
Dtype scale_y =
static_cast<Dtype>(cropsize)/static_cast<Dtype>(unclipped_height);
// size to warp the clipped expanded region to
cv_crop_size.width =
cv_crop_size.width =
static_cast<int>(round(static_cast<Dtype>(clipped_width)*scale_x));
cv_crop_size.height =
cv_crop_size.height =
static_cast<int>(round(static_cast<Dtype>(clipped_height)*scale_y));
pad_x1 = static_cast<int>(round(static_cast<Dtype>(pad_x1)*scale_x));
pad_x2 = static_cast<int>(round(static_cast<Dtype>(pad_x2)*scale_x));
......@@ -176,9 +181,9 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
cv::Rect roi(x1, y1, x2-x1+1, y2-y1+1);
cv::Mat cv_cropped_img = cv_img(roi);
cv::resize(cv_cropped_img, cv_cropped_img,
cv::resize(cv_cropped_img, cv_cropped_img,
cv_crop_size, 0, 0, cv::INTER_LINEAR);
// horizontal flip at random
if (do_mirror) {
cv::flip(cv_cropped_img, cv_cropped_img, 1);
......@@ -188,12 +193,13 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < cv_cropped_img.rows; ++h) {
for (int w = 0; w < cv_cropped_img.cols; ++w) {
Dtype pixel =
Dtype pixel =
static_cast<Dtype>(cv_cropped_img.at<cv::Vec3b>(h, w)[c]);
top_data[((itemid * channels + c) * cropsize + h + pad_h) * cropsize + w + pad_w]
top_data[((itemid * channels + c) * cropsize + h + pad_h)
* cropsize + w + pad_w]
= (pixel
- mean[(c * mean_height + h + mean_off + pad_h)
- mean[(c * mean_height + h + mean_off + pad_h)
* mean_width + w + mean_off + pad_w])
* scale;
}
......@@ -207,11 +213,12 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
// useful debugging code for dumping transformed windows to disk
string file_id;
std::stringstream ss;
// NOLINT_NEXT_LINE(runtime/threadsafe_fn)
ss << rand();
ss >> file_id;
std::ofstream inf((string("dump/") + file_id +
std::ofstream inf((string("dump/") + file_id +
string("_info.txt")).c_str(), std::ofstream::out);
inf << image.first << std::endl
inf << image.first << std::endl
<< window[WindowDataLayer<Dtype>::X1]+1 << std::endl
<< window[WindowDataLayer<Dtype>::Y1]+1 << std::endl
<< window[WindowDataLayer<Dtype>::X2]+1 << std::endl
......@@ -220,15 +227,15 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
<< top_label[itemid] << std::endl
<< is_fg << std::endl;
inf.close();
std::ofstream top_data_file((string("dump/") + file_id +
string("_data.txt")).c_str(),
std::ofstream top_data_file((string("dump/") + file_id +
string("_data.txt")).c_str(),
std::ofstream::out | std::ofstream::binary);
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < cropsize; ++h) {
for (int w = 0; w < cropsize; ++w) {
top_data_file.write(
reinterpret_cast<char*>(&top_data[((itemid * channels + c)
* cropsize + h) * cropsize + w]),
top_data_file.write(reinterpret_cast<char*>(
&top_data[((itemid * channels + c) * cropsize + h)
* cropsize + w]),
sizeof(Dtype));
}
}
......@@ -240,7 +247,7 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
}
}
return (void*)NULL;
return reinterpret_cast<void*>(NULL);
}
template <typename Dtype>
......@@ -251,9 +258,9 @@ WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
template <typename Dtype>
void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
// SetUp runs through the window_file and creates two structures
// that hold windows: one for foreground (object) windows and one
// for background (non-object) windows. We use an overlap threshold
// SetUp runs through the window_file and creates two structures
// that hold windows: one for foreground (object) windows and one
// for background (non-object) windows. We use an overlap threshold
// to decide which is which.
CHECK_EQ(bottom.size(), 0) << "Window data Layer takes no input blobs.";
......@@ -270,15 +277,15 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
// class_index overlap x1 y1 x2 y2
LOG(INFO) << "Window data layer:" << std::endl
<< " foreground (object) overlap threshold: "
<< " foreground (object) overlap threshold: "
<< this->layer_param_.det_fg_threshold() << std::endl
<< " background (non-object) overlap threshold: "
<< " background (non-object) overlap threshold: "
<< this->layer_param_.det_bg_threshold() << std::endl
<< " foreground sampling fraction: "
<< this->layer_param_.det_fg_fraction();
std::ifstream infile(this->layer_param_.source().c_str());
CHECK(infile.good()) << "Failed to open window file "
CHECK(infile.good()) << "Failed to open window file "
<< this->layer_param_.source() << std::endl;
map<int, int> label_hist;
......@@ -313,7 +320,7 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
window[WindowDataLayer::Y1] = y1;
window[WindowDataLayer::X2] = x2;
window[WindowDataLayer::Y2] = y2;
// add window to foreground list or background list
if (overlap >= this->layer_param_.det_fg_threshold()) {
int label = window[WindowDataLayer::LABEL];
......@@ -332,7 +339,7 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
if (image_index % 100 == 0) {
LOG(INFO) << "num: " << image_index << " "
<< image_path << " "
<< image_path << " "
<< image_size[0] << " "
<< image_size[1] << " "
<< image_size[2] << " "
......@@ -342,12 +349,13 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
LOG(INFO) << "Number of images: " << image_index+1;
for (map<int,int>::iterator it = label_hist.begin();
for (map<int, int>::iterator it = label_hist.begin();
it != label_hist.end(); ++it) {
LOG(INFO) << "class " << it->first << " has " << label_hist[it->first] << " samples";
LOG(INFO) << "class " << it->first << " has " << label_hist[it->first]
<< " samples";
}
LOG(INFO) << "Amount of context padding: "
LOG(INFO) << "Amount of context padding: "
<< this->layer_param_.det_context_pad();
LOG(INFO) << "Crop mode: " << this->layer_param_.det_crop_mode();
......
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