CNN Triplet Tranining

cmake/Modules/FindvecLib.cmake

@@ -23,7 +23,7 @@ find_package_handle_standard_args(vecLib DEFAULT_MSG vecLib_INCLUDE_DIR)
 
 if(VECLIB_FOUND)
   if(vecLib_INCLUDE_DIR MATCHES "^/System/Library/Frameworks/vecLib.framework.*")
-    set(vecLib_LINKER_LIBS -lcblas "-framework vecLib")
+    set(vecLib_LINKER_LIBS -lcblas "-framework Accelerate")
     message(STATUS "Found standalone vecLib.framework")
   else()
     set(vecLib_LINKER_LIBS -lcblas "-framework Accelerate")

examples/triplet/convert_triplet_data.cpp

@@ -0,0 +1,236 @@
+// Usage:
+// convert_3d_data input_image_file input_label_file output_db_file
+// Codes are disigned for binary files including data and label. You can modify
+// the condition if information for arranging training data is not the same with
+// category and pose of object.
+#include <fstream>  // NOLINT(readability/streams)
+#include <string>
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/math_functions.hpp"
+#include "glog/logging.h"
+#include "google/protobuf/text_format.h"
+#ifdef USE_LEVELDB
+#include "leveldb/db.h"
+#include "math.h"
+#include "stdint.h"
+
+uint32_t swap_endian(uint32_t val) {
+    val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF);
+    return (val << 16) | (val >> 16);
+}
+
+void read_image(std::ifstream* image_file, std::ifstream* label_file,
+        uint32_t index, uint32_t rows, uint32_t cols,
+        char* pixels, char* label_temp, signed char* label, int rgb_use) {
+  if (rgb_use == 0) {
+    image_file->seekg(index * rows * cols + 16);
+    image_file->read(pixels, rows * cols);
+    label_file->seekg(index * 4 + 8);  // 4 = 1 catory label+3 coordinate label
+    label_file->read(label_temp, 4);
+    for (int i = 0; i < 4; i++)
+      *(label+i) = (signed char)*(label_temp+i);
+  } else {
+    image_file->seekg(3 * index * rows * cols + 16);
+    image_file->read(pixels, 3 * rows * cols);
+    label_file->seekg(index * 4 + 8);  // 4 = 1 catory label+3 coordinate label
+    label_file->read(label_temp, 4);
+    for (int i = 0; i < 4; i++)
+      *(label+i) = (signed char)*(label_temp+i);
+  }
+}
+
+void convert_dataset(const char* image_filename, const char* label_filename,
+        const char* db_filename,
+                     const char* class_number, const char* rgb_use) {
+  int rgb_use1 = atoi(rgb_use);
+  int class_num = atoi(class_number);
+  // Open files
+  std::ifstream image_file(image_filename, std::ios::in | std::ios::binary);
+  std::ifstream label_file(label_filename, std::ios::in | std::ios::binary);
+  CHECK(image_file) << "Unable to open file " << image_filename;
+  CHECK(label_file) << "Unable to open file " << label_filename;
+  // Read the magic and the meta data
+  uint32_t magic;
+  uint32_t num_items;
+  uint32_t num_labels;
+  uint32_t rows;
+  uint32_t cols;
+
+  image_file.read(reinterpret_cast<char*>(&magic), 4);
+  magic = swap_endian(magic);
+  CHECK_EQ(magic, 2051) << "Incorrect image file magic.";
+  label_file.read(reinterpret_cast<char*>(&magic), 4);
+  magic = swap_endian(magic);
+  CHECK_EQ(magic, 2050) << "Incorrect label file magic.";
+  image_file.read(reinterpret_cast<char*>(&num_items), 4);
+  num_items = swap_endian(num_items);
+  label_file.read(reinterpret_cast<char*>(&num_labels), 4);
+  num_labels = swap_endian(num_labels);
+  CHECK_EQ(num_items, num_labels);
+  image_file.read(reinterpret_cast<char*>(&rows), 4);
+  rows = swap_endian(rows);
+  image_file.read(reinterpret_cast<char*>(&cols), 4);
+  cols = swap_endian(cols);
+
+  // Open leveldb
+  leveldb::DB* db;
+  leveldb::Options options;
+  options.create_if_missing = true;
+  options.error_if_exists = true;
+  leveldb::Status status = leveldb::DB::Open(
+      options, db_filename, &db);
+  CHECK(status.ok()) << "Failed to open leveldb " << db_filename
+      << ". Is it already existing?";
+
+  char* label_temp = new char[4];  // label for unsigned char*
+  signed char* label_i = new signed char[4];  // label for triplet
+  signed char* label_j = new signed char[4];
+  signed char* label_k = new signed char[4];
+  signed char* label_l = new signed char[4];  // label for pair wise
+  signed char* label_m = new signed char[4];
+  int db_size;
+  if (rgb_use1 == 0)
+    db_size = rows * cols;
+  else
+    db_size = 3 * rows * cols;
+  char* pixels1 = new char[db_size];
+  char* pixels2 = new char[db_size];
+  char* pixels3 = new char[db_size];
+  char* pixels4 = new char[db_size];
+  char* pixels5 = new char[db_size];
+  const int kMaxKeyLength = 10;
+  char key[kMaxKeyLength];
+  std::string value;
+  caffe::Datum datum;
+  if (rgb_use1 == 0)
+    datum.set_channels(1);
+  else
+    datum.set_channels(3);
+  datum.set_height(rows);
+  datum.set_width(cols);
+  LOG(INFO) << "A total of " << num_items << " items.";
+  LOG(INFO) << "Rows: " << rows << " Cols: " << cols;
+  int counter = 0;
+  // This codes selecting 1 positive sample and 3 negative samples for a triplet
+  // set. We randomly select data and decide whether concatenating data set to
+  // DB file according to labels.
+  for (unsigned int times = 0; times < 10; ++times) {
+    // iteration in the samples of all class
+    for (unsigned int itemid = 0; itemid < num_items/class_num; ++itemid) {
+      // iteration in the samples in one class
+      for (unsigned int class_ind = 0; class_ind < class_num; ++class_ind) {
+      // use reference sample one by one at each iteration
+      int i = itemid % num_items + class_ind*num_items/class_num;
+      int j = caffe::caffe_rng_rand() % num_items;  // pick triplet groups
+      int k = caffe::caffe_rng_rand() % num_items;
+      int l = caffe::caffe_rng_rand() % num_items;  // pick pair wise groups
+      int m = caffe::caffe_rng_rand() % num_items;
+      read_image(&image_file, &label_file, i, rows, cols,  // read triplet
+        pixels1, label_temp, label_i, rgb_use1);
+      read_image(&image_file, &label_file, j, rows, cols,
+        pixels2, label_temp, label_j, rgb_use1);
+      read_image(&image_file, &label_file, k, rows, cols,
+        pixels3, label_temp, label_k, rgb_use1);
+      read_image(&image_file, &label_file, l, rows, cols,  // read pair wise
+        pixels4, label_temp, label_l, rgb_use1);
+      read_image(&image_file, &label_file, m, rows, cols,
+        pixels5, label_temp, label_m, rgb_use1);
+
+      bool pair_pass = false;
+      bool triplet1_pass = false;
+      bool triplet2_pass = false;
+      bool triplet3_class_same = false;
+      bool triplet3_pass = false;
+
+      int ij_diff_x = static_cast<int>(*(label_i+1)-*(label_j+1));
+      int ij_diff_y = static_cast<int>(*(label_i+2)-*(label_j+2));
+      int ij_diff_z = static_cast<int>(*(label_i+3)-*(label_j+3));
+      int im_diff_x = static_cast<int>(*(label_i+1)-*(label_m+1));
+      int im_diff_y = static_cast<int>(*(label_i+2)-*(label_m+2));
+      int im_diff_z = static_cast<int>(*(label_i+3)-*(label_m+3));
+
+      int ij_x = ij_diff_x*ij_diff_x;
+      int ij_y = ij_diff_y*ij_diff_y;
+      int ij_z = ij_diff_z*ij_diff_z;
+      int im_x = im_diff_x*im_diff_x;
+      int im_y = im_diff_y*im_diff_y;
+      int im_z = im_diff_z*im_diff_z;
+
+      float dist_ij = std::sqrt(ij_x + ij_y + ij_z);
+      float dist_im = std::sqrt(im_x + im_y + im_z);
+      // Arrange training data according to conditionals including category
+      // and pose of synthetic data, dist_* could be ignored if you
+      // only concentrate on category.
+      if (*label_i == *label_j && dist_ij < 100/3 && dist_ij != 0)
+        pair_pass = true;
+      if (pair_pass && (*label_i  != *label_k))
+        triplet1_pass = true;
+      if (pair_pass && (*label_i  != *label_l))
+        triplet2_pass = true;
+      if (pair_pass && (*label_i  == *label_m))
+        triplet3_class_same = true;
+      if (triplet3_class_same && dist_im > 100/3)
+        triplet3_pass = true;
+      if (pair_pass && triplet1_pass && triplet2_pass && triplet3_pass) {
+        datum.set_data(pixels1, db_size);  // set data
+        datum.set_label(static_cast<int>(*label_i));
+        datum.SerializeToString(&value);
+        snprintf(key, kMaxKeyLength, "%08d", counter);
+        db->Put(leveldb::WriteOptions(), std::string(key), value);
+        counter++;
+        datum.set_data(pixels2, db_size);  // set data
+        datum.set_label(static_cast<int>(*label_j));
+        datum.SerializeToString(&value);
+        snprintf(key, kMaxKeyLength, "%08d", counter);
+        db->Put(leveldb::WriteOptions(), std::string(key), value);
+        counter++;
+        datum.set_data(pixels3, db_size);  // set data
+        datum.set_label(static_cast<int>(*label_k));
+        datum.SerializeToString(&value);
+        snprintf(key, kMaxKeyLength, "%08d", counter);
+        db->Put(leveldb::WriteOptions(), std::string(key), value);
+        counter++;
+        datum.set_data(pixels4, db_size);  // set data
+        datum.set_label(static_cast<int>(*label_l));
+        datum.SerializeToString(&value);
+        snprintf(key, kMaxKeyLength, "%08d", counter);
+        db->Put(leveldb::WriteOptions(), std::string(key), value);
+        counter++;
+        datum.set_data(pixels5, db_size);  // set data
+        datum.set_label(static_cast<int>(*label_m));
+        datum.SerializeToString(&value);
+        snprintf(key, kMaxKeyLength, "%08d", counter);
+        db->Put(leveldb::WriteOptions(), std::string(key), value);
+        counter++;
+      } else {
+        class_ind--;
+      }
+      }  // iteration in the samples of all class
+    }  // iteration in the samples in one class
+  }  // iteration in times
+  delete db;
+  delete pixels1;
+  delete pixels2;
+  delete pixels3;
+  delete pixels4;
+  delete pixels5;
+}
+
+int main(int argc, char** argv) {
+  if (argc != 6) {
+    printf("This script converts the dataset to the leveldb format used\n"
+           "by caffe to train a triplet network.\n"
+           "Usage:\n"
+           "    convert_3d_data input_image_file input_label_file "
+           "output_db_file class_number rgb_use \n");
+  } else {
+    google::InitGoogleLogging(argv[0]);
+    convert_dataset(argv[1], argv[2], argv[3], argv[4], argv[5]);
+  }
+  return 0;
+}
+#else
+int main(int argc, char** argv) {
+    LOG(FATAL) << "This example requires LevelDB; compile with USE_LEVELDB.";
+}
+#endif  // USE_LEVELDB

examples/triplet/create_3d_triplet.sh

@@ -0,0 +1,27 @@
+#!/usr/bin/env sh
+# This script converts the mnist data into leveldb format.
+
+EXAMPLES=./build/examples/triplet
+DATA=./data/linemod
+
+echo "Creating leveldb..."
+
+# this script taking data which consist of 6 categories to leveldb format for
+# tripplet training.
+
+rm -rf ./examples/triplet/3d_triplet_train_leveldb
+rm -rf ./examples/triplet/3d_triplet_test_leveldb
+
+$EXAMPLES/convert_3d_triplet_data.bin \
+    $DATA/binary_image_train \
+    $DATA/binary_label_train \
+    ./examples/triplet/3d_triplet_train_leveldb \
+    6 \
+    0
+$EXAMPLES/convert_3d_triplet_data.bin \
+    $DATA/binary_image_test \
+    $DATA/binary_label_test \
+    ./examples/triplet/3d_triplet_test_leveldb \
+    6 \
+    0
+echo "Done."

examples/triplet/multipie_triplet.prototxt

@@ -0,0 +1,110 @@
+name: "multipie_triplet"
+input: "data"
+input_dim: 1
+input_dim: 1
+input_dim: 75
+input_dim: 65
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 16
+    kernel_size: 8
+    stride: 1
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "pool1"
+  top: "pool1"
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 7
+    kernel_size: 5
+    stride: 1
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "pool2"
+  top: "pool2"
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool2"
+  top: "ip1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 256
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "feat"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "feat"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 150
+  }
+}