diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index b5de038fbf5..d1bc4e35af6 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -226,8 +226,6 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new LRNFixedLayer<Dtype>(param);
   case LayerParameter_LayerType_MEMORY_DATA:
     return new MemoryDataLayer<Dtype>(param);
-  case LayerParameter_LayerType_MULTILANE_LABEL:
-    return new MultilaneLabelLayer<Dtype>(param);
   case LayerParameter_LayerType_MVN:
     return new MVNLayer<Dtype>(param);
   case LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS:
@@ -254,8 +252,6 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new SplitLayer<Dtype>(param);
   case LayerParameter_LayerType_TANH:
     return GetTanHLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_VIDEO_DATA:
-    return new VideoDataLayer<Dtype>(param);
   case LayerParameter_LayerType_WINDOW_DATA:
     return new WindowDataLayer<Dtype>(param);
   case LayerParameter_LayerType_L1_LOSS:
diff --git a/src/caffe/layers/multilane_label_layer.cpp b/src/caffe/layers/multilane_label_layer.cpp
deleted file mode 100644
index 199e943cd27..00000000000
--- a/src/caffe/layers/multilane_label_layer.cpp
+++ /dev/null
@@ -1,213 +0,0 @@
-#include <fstream>  // NOLINT(readability/streams)
-#include <iostream>  // NOLINT(readability/streams)
-#include <string>
-#include <utility>
-#include <vector>
-#include <stdlib.h>
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/util/rng.hpp"
-#include <boost/algorithm/string.hpp>
-#include <boost/python.hpp>
-#include <boost/numpy.hpp>
-#include <numpy/arrayobject.h>
-
-
-namespace py = boost::python; // create namespace variable for boost::python
-namespace np = boost::numpy; // create namespace variable for boost::python
-namespace caffe {
-
-//np::ndarray ReadLabelBatch(const string& filename, std::vector<size_t> &frame_ids, std::vector<size_t> &trans,
-//    const int height, const int width) {
-//};
-template <typename Dtype>
-MultilaneLabelLayer<Dtype>::~MultilaneLabelLayer<Dtype>() {
-  this->JoinPrefetchThread();
-}
-
-
-template <typename Dtype>
-void MultilaneLabelLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  const int new_height = this->layer_param_.multilane_label_param().new_height();
-  const int new_width  = this->layer_param_.multilane_label_param().new_width();
-  CHECK((new_height == 0 && new_width == 0) ||
-      (new_height > 0 && new_width > 0)) << "Current implementation requires "
-      "new_height and new_width to be set at the same time.";
-  // Read the file with filenames and labels
-  const string& source = this->layer_param_.multilane_label_param().source();
-  LOG(INFO) << "Opening schedule file " << source;
-  std::ifstream infile(source.c_str());
-
-
-
-  string batch_string;
-
-  string filename;
-  //while (infile >> batch_string) {
-  while (getline (infile, batch_string)) {
-    if(!infile)
-    {
-      if(infile.eof())
-      {
-        LOG(INFO) << "Reached EOF of schedule file.";
-        break;
-      }
-      else
-        LOG(FATAL)<< "Error while reading schedule file. Possibly corrupted.";
-    }
-    std::vector<string> batch_fields;
-    // first split a line into fields with delimiter ",". Fields should be [filename, frame_ids, transform_ids]
-    boost::split(batch_fields, batch_string, boost::is_any_of(","),
-                 boost::token_compress_on);
-    if(batch_fields.size()!=3)
-      LOG(FATAL) << "Each line must have 3 fields separated by comma, "
-                 <<batch_fields.size()<<" found instead";
-    // store filename
-    filename = batch_fields[0];
-    // store frame ids
-    std::vector<string> frame_ids_str;
-    std::vector<int> frame_ids;
-    boost::split(frame_ids_str, batch_fields[1], boost::is_any_of(" "),
-                 boost::token_compress_on);
-    for (int f=0; f<frame_ids_str.size(); ++f) {
-      frame_ids.push_back((int)atoi(frame_ids_str[f].c_str()));
-    }
-
-    // store persp transform ids
-    std::vector<string> trans_ids_str;
-    std::vector<int> trans_ids;
-    boost::split(trans_ids_str, batch_fields[2], boost::is_any_of(" "),
-                 boost::token_compress_on);
-    for (int f=0; f<trans_ids_str.size(); ++f) {
-      trans_ids.push_back((int)atoi(trans_ids_str[f].c_str()));
-    }
-
-    lines_.push_back(std::make_pair(filename, std::make_pair(frame_ids, trans_ids)));
-  }
-
-  if (this->layer_param_.multilane_label_param().shuffle()) {
-    // randomly shuffle data
-    LOG(INFO) << "Shuffling batches";
-    const unsigned int prefetch_rng_seed = caffe_rng_rand();
-    prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
-    ShuffleBatches();
-  }
-  LOG(INFO) << "A total of " << lines_.size() << " batches.";
-
-  lines_id_ = 0;
-  // Check if we would need to randomly skip a few data points
-  if (this->layer_param_.multilane_label_param().rand_skip()) {
-    unsigned int skip = caffe_rng_rand() %
-        this->layer_param_.multilane_label_param().rand_skip();
-    LOG(INFO) << "Skipping first " << skip << " data points.";
-    CHECK_GT(lines_.size(), skip) << "Not enough points to skip";
-    lines_id_ = skip;
-  }
-  const int crop_size = this->layer_param_.transform_param().crop_size();
-  const int batch_size = this->layer_param_.multilane_label_param().batch_size();
-  if (crop_size > 0) {
-    (*top)[0]->Reshape(batch_size, 80, crop_size, crop_size);
-    this->prefetch_data_.Reshape(batch_size, 80, crop_size,
-                                 crop_size);
-  } else {
-    (*top)[0]->Reshape(batch_size, 80, 15,20);
-    this->prefetch_data_.Reshape(batch_size, 80, 15,20);
-  }
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
-  // label
-  //(*top)[1]->Reshape(batch_size, 1, 1, 1);
-  this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
-  // datum size
-  this->datum_channels_ = 80;
-  this->datum_height_ = 15;
-  this->datum_width_ = 20;
-  this->datum_size_ = 80*15*20;
-}
-
-template <typename Dtype>
-void MultilaneLabelLayer<Dtype>::ShuffleBatches() {
-  caffe::rng_t* prefetch_rng =
-      static_cast<caffe::rng_t*>(prefetch_rng_->generator());
-  shuffle(lines_.begin(), lines_.end(), prefetch_rng);
-}
-
-
-
-// This function is used to create a thread that prefetches the data.
-template <typename Dtype>
-void MultilaneLabelLayer<Dtype>::InternalThreadEntry() {
-  CHECK(this->prefetch_data_.count());
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
-  //Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
-  MultilaneLabelParameter multilane_label_param = this->layer_param_.multilane_label_param();
-  const int batch_size = multilane_label_param.batch_size();
-  const int new_height = multilane_label_param.new_height();
-  const int new_width = multilane_label_param.new_width();
-
-  // datum scales
-  const int lines_size = lines_.size();
-  string filename = lines_[lines_id_].first;
-  std::vector<int> frameIds = lines_[lines_id_].second.first;
-  std::vector<int> trans = lines_[lines_id_].second.second;
-  if (batch_size!=frameIds.size() || batch_size!=trans.size())
-    LOG(ERROR)<<"Frame count mismatch!";
-  LOG(INFO)<<"reading label batch "<<filename;
-
-  try{
-    Py_Initialize();
-    PyRun_SimpleString("import sys");
-    PyRun_SimpleString("sys.path.append(\"/afs/cs.stanford.edu/u/twangcat/scratch/caffenet/src/caffe/py_lane_label_reader/\")");
-    np::initialize();
-  }catch(py::error_already_set const &){
-    PyErr_Print();
-    LOG(FATAL) <<"boost python and numpy initialization failed.";
-  }
-  py::object arr_handle;
-  // get a label blob from python helper class
-  try{
-    // initialize python helper class
-    py::object module = py::import("multilane_label_reader");
-    py::object readerClass = module.attr("MultilaneLabelReader");
-    py::object reader = readerClass();
-    // initialize arguments to python helper class function
-    np::dtype dt = np::dtype::get_builtin<int>();
-    py::tuple shape = py::make_tuple(batch_size) ;
-    py::tuple stride = py::make_tuple(4) ;
-    py::object own1;
-    py::object own2;
-    int* framePtr = const_cast<int *>(&frameIds[0]);
-    int* transPtr = const_cast<int *>(&trans[0]);
-    np::ndarray frameArr = np::from_data(framePtr,dt,shape,stride,own1);
-    np::ndarray transArr = np::from_data(transPtr,dt,shape,stride,own2);
-    // call function
-    arr_handle = reader.attr("runLabelling")(filename, frameArr, transArr);
-  }catch(py::error_already_set const &){
-    PyErr_Print();
-    LOG(FATAL) <<"numpy label reading function failed!";
-  }
-  np::ndarray pArray = np::from_object(arr_handle);
-
-  int array_size = py::extract<int>(pArray.attr("size"));
-  LOG(INFO)<<"numpy array size = "<<array_size;
-  float* pArrayPtr = (float*)(pArray.get_data());
-  std::copy(pArrayPtr, pArrayPtr+array_size, top_data);
-  // go to the next iter
-  lines_id_++;
-  if (lines_id_ >= lines_size) {
-    // We have reached the end. Restart from the first.
-    DLOG(INFO) << "Restarting data prefetching from start.";
-    lines_id_ = 0;
-    if (this->layer_param_.image_data_param().shuffle()) {
-      ShuffleBatches();
-    }
-  }
-}
-
-INSTANTIATE_CLASS(MultilaneLabelLayer);
-
-}  // namespace caffe
diff --git a/src/caffe/layers/video_data_layer.cpp b/src/caffe/layers/video_data_layer.cpp
deleted file mode 100644
index 1563cbebdfd..00000000000
--- a/src/caffe/layers/video_data_layer.cpp
+++ /dev/null
@@ -1,289 +0,0 @@
-#include <fstream>  // NOLINT(readability/streams)
-#include <iostream>  // NOLINT(readability/streams)
-#include <string>
-#include <utility>
-#include <vector>
-#include <stdlib.h>
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/util/rng.hpp"
-#include <boost/algorithm/string.hpp>
-#include <boost/python.hpp>
-#include <boost/numpy.hpp>
-namespace py = boost::python; // create namespace variable for boost::python  
-namespace np = boost::numpy; // create namespace variable for boost::python  
-namespace caffe {
-
-template <typename Dtype>
-VideoDataLayer<Dtype>::~VideoDataLayer<Dtype>() {
-  this->JoinPrefetchThread();
-}
-
-template <typename Dtype>
-bool VideoDataLayer<Dtype>:: ReadVideoFrameToDatum(const string& filename, size_t id, size_t persp,
-    const int height, const int width, Datum* datum) {
-  int cam_num = (int)(filename.at(filename.length()-5) - '0');
-  cam_num = cam_num>2?2:cam_num; // 3rd cam is for testing only. So using cam2 distortions as dummy
-  //int numPersp = mTransforms.size()/2;
-  cv::Mat cv_img, cv_img_origin;
-  bool set_ok = this->cap->set(CV_CAP_PROP_POS_FRAMES, id );
-  if(!set_ok) {
-    LOG(ERROR)<<"Failed to set video frame"; 
-    return false;
-  }
-  bool read_ok = this->cap->read(cv_img_origin);
-  if(!read_ok) {
-    LOG(ERROR)<<"Failed to read video frame";
-    return false;
-  }
-  // resize image if necessary
-  if (height > 0 && width > 0) {
-    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
-  } else {
-    cv_img = cv_img_origin;
-  }
-  // apply perspective transform
-  cv::Mat warpMatrix = this->mTransforms[persp];
-  cv::warpPerspective(cv_img, cv_img, warpMatrix, cv_img.size(), cv::INTER_LINEAR, cv::BORDER_REPLICATE);
-  // copy data to datum  
-  int num_channels = 3;
-  datum->set_channels(num_channels);
-  datum->set_height(cv_img.rows);
-  datum->set_width(cv_img.cols);
-  datum->set_label(0); // dummy label for now.
-  datum->clear_data();
-  datum->clear_float_data();
-  string* datum_string = datum->mutable_data();
-  for (int c = 0; c < num_channels; ++c) {
-    for (int h = 0; h < cv_img.rows; ++h) {
-      for (int w = 0; w < cv_img.cols; ++w) {
-        datum_string->push_back(
-          static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
-      }
-    }
-  }
-  return true;
-}
-
-
-template <typename Dtype>
-void VideoDataLayer<Dtype>::setPerspective() {
-
-  /*try{
-    Py_Initialize();
-    PyRun_SimpleString("import sys");
-    PyRun_SimpleString("sys.path.append(\"/afs/cs.stanford.edu/u/twangcat/scratch/caffenet/src/caffe/py_lane_label_reader/\")");
-    np::initialize();
-  }catch(py::error_already_set const &){
-    PyErr_Print();
-    LOG(FATAL) <<"boost python and numpy initialization failed.";
-  }
-  py::object list;
-  try{
-  // initialize python helper class
-       py::object module = py::import("perspective_reader");
-       py::object readerClass = module.attr("PerspectiveReader");
-       py::object reader = readerClass();
-       list = reader.attr("read")();
-     }catch(py::error_already_set const &){                                                                          
-    PyErr_Print();                                                                                                
-    LOG(FATAL) <<"numpy perspective reading function failed!";                                                          
-  }  
-  int num_persps = py::len(list);
-  for(int i=0;i<num_persps;++i)
-  {
-    //py::object arr_handle = list[i];
-    //np::ndarray pArray = np::from_object(arr_handle);
-    
-    //float* persp_data = (float*)(pArray.get_data());
-    this->mTransforms.push_back(cv::Mat::eye(3, 3, CV_32F, persp_data));
-  }
-*/
-  float persp_data[] = {2.00000000e+00,   4.85722573e-16,  -1.98951966e-13, 1.55172414e-01,   2.20689655e+00,  -9.93103448e+01, 2.35813973e-18,   1.46367293e-18,   2.00000000e+00};
-  // essential to do a clone here otherwise data is not copied properly. not sure why
-  this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone());
-  persp_data = {2.00000000e+00,  -2.60991180e-16,  -1.35814320e-13, -1.55172414e-01,   2.20689655e+00,  -7.89095352e-14, -1.39242621e-19,  -7.96792478e-19,   2.00000000e+00};
-  this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone());
-  persp_data = {2.22608696e+00,   3.01449275e-01,  -1.44695652e+02, -1.74860126e-15,   2.00000000e+00,  -9.94759830e-14, 2.00577402e-18,  -4.60785923e-18,   2.00000000e+00};
-  this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone());
-  persp_data = {2.22608696e+00,  -2.08695652e-01,  -4.81606942e-14, -1.21601236e-15,   2.06956522e+00,  -2.68140544e-14, 1.30767163e-18,   1.44927536e-04,   2.00000000e+00};
-  this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone());
-  persp_data = {1.76207983e+00,   2.42673669e-16,  -1.35389096e-13, -1.65464295e-01,   2.18780567e+00,  -8.38443898e-14, -6.37686030e-04,   3.91261819e-04,   2.00000000e+00};
-  this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone());
-  persp_data = {2.15126050e+00,  -2.48222366e-01,   1.11700065e+01, -7.49400542e-16,   2.46153846e+00,  -1.10769231e+02, 2.33103467e-18,   8.13151629e-19,   2.00000000e+00};
-  this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone());
-  persp_data = {1.,  0.,  0., 0.,  1.,  0., 0.,  0.,  1.};
-  this->mTransforms.push_back(cv::Mat(3, 3, CV_32F, persp_data).clone());
-}
-
-
-
-template <typename Dtype>
-void VideoDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  const int new_height = this->layer_param_.video_data_param().new_height();
-  const int new_width  = this->layer_param_.video_data_param().new_width();
-  CHECK((new_height == 0 && new_width == 0) ||
-      (new_height > 0 && new_width > 0)) << "Current implementation requires "
-      "new_height and new_width to be set at the same time.";
-  // Read the file with filenames and labels
-  const string& source = this->layer_param_.video_data_param().source();
-  setPerspective();
-
-
-  LOG(INFO) << "Opening schedule file " << source;
-  std::ifstream infile(source.c_str());
-  string batch_string;
-  string filename;
-  //while (infile >> batch_string) {
-  while (getline (infile, batch_string)) {
-    if(!infile)
-    {
-      if(infile.eof())
-      {
-        LOG(INFO) << "Reached EOF of schedule file.";
-        break;
-      }
-      else
-        LOG(FATAL)<< "Error while reading schedule file. Possibly corrupted.";
-    }
-    std::vector<string> batch_fields;
-    // first split a line into fields with delimiter ",". Fields should be [filename, frame_ids, transform_ids]
-    boost::split(batch_fields, batch_string, boost::is_any_of(","), 
-                 boost::token_compress_on);
-    if(batch_fields.size()!=3)
-      LOG(FATAL) << "Each line must have 3 fields separated by comma, "
-                 <<batch_fields.size()<<" found instead";
-    // store filename
-    filename = batch_fields[0];
-    // store frame ids
-    std::vector<string> frame_ids_str;
-    std::vector<size_t> frame_ids;
-    boost::split(frame_ids_str, batch_fields[1], boost::is_any_of(" "), 
-                 boost::token_compress_on);
-    for (int f=0; f<frame_ids_str.size(); ++f) {
-      frame_ids.push_back((size_t)atoi(frame_ids_str[f].c_str()));
-    }
-
-    // store persp transform ids
-    std::vector<string> trans_ids_str;
-    std::vector<size_t> trans_ids;
-    boost::split(trans_ids_str, batch_fields[2], boost::is_any_of(" "), 
-                 boost::token_compress_on);
-    for (int f=0; f<trans_ids_str.size(); ++f) {
-      trans_ids.push_back((size_t)atoi(trans_ids_str[f].c_str()));
-    }
-       
-    lines_.push_back(std::make_pair(filename, std::make_pair(frame_ids, trans_ids)));
-  }
-
-  if (this->layer_param_.video_data_param().shuffle()) {
-    // randomly shuffle data
-    LOG(INFO) << "Shuffling batches";
-    const unsigned int prefetch_rng_seed = caffe_rng_rand();
-    prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
-    ShuffleBatches();
-  }
-  LOG(INFO) << "A total of " << lines_.size() << " batches.";
-
-  lines_id_ = 0;
-  // Check if we would need to randomly skip a few data points
-  if (this->layer_param_.video_data_param().rand_skip()) {
-    unsigned int skip = caffe_rng_rand() %
-        this->layer_param_.video_data_param().rand_skip();
-    LOG(INFO) << "Skipping first " << skip << " data points.";
-    CHECK_GT(lines_.size(), skip) << "Not enough points to skip";
-    lines_id_ = skip;
-  }
-  // Read a data batch, and use it to initialize the top blob.
-  this->cap = new cv::VideoCapture(lines_[lines_id_].first);
-  Datum datum;
-  CHECK(ReadVideoFrameToDatum(lines_[lines_id_].first, lines_[lines_id_].second.first[0],
-                         lines_[lines_id_].second.second[0], new_height, new_width, &datum)); 
-  this->cap->release();
-  // image
-  const int crop_size = this->layer_param_.transform_param().crop_size();
-  const int batch_size = this->layer_param_.video_data_param().batch_size();
-  if (crop_size > 0) {
-    (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
-    this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size,
-                                 crop_size);
-  } else {
-    (*top)[0]->Reshape(batch_size, datum.channels(), datum.height(),
-                       datum.width());
-    this->prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(),
-        datum.width());
-  }
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
-  // label
-  //(*top)[1]->Reshape(batch_size, 1, 1, 1);
-  this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
-  // datum size
-  this->datum_channels_ = datum.channels();
-  this->datum_height_ = datum.height();
-  this->datum_width_ = datum.width();
-  this->datum_size_ = datum.channels() * datum.height() * datum.width();
-}
-
-template <typename Dtype>
-void VideoDataLayer<Dtype>::ShuffleBatches() {
-  caffe::rng_t* prefetch_rng =
-      static_cast<caffe::rng_t*>(prefetch_rng_->generator());
-  shuffle(lines_.begin(), lines_.end(), prefetch_rng);
-}
-
-
-
-// This function is used to create a thread that prefetches the data.
-template <typename Dtype>
-void VideoDataLayer<Dtype>::InternalThreadEntry() {
-  Datum datum;
-  CHECK(this->prefetch_data_.count());
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
-  //Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
-  VideoDataParameter video_data_param = this->layer_param_.video_data_param();
-  const int batch_size = video_data_param.batch_size();
-  const int new_height = video_data_param.new_height();
-  const int new_width = video_data_param.new_width();
-
-  // datum scales
-  const int lines_size = lines_.size();
-  string filename = lines_[lines_id_].first;
-  std::vector<size_t> frameIds = lines_[lines_id_].second.first;
-  std::vector<size_t> trans = lines_[lines_id_].second.second;
-  if (batch_size!=frameIds.size() || batch_size!=trans.size())
-    LOG(ERROR)<<"Frame count mismatch!";
-  LOG(INFO)<<"reading video file "<<filename;
-  this->cap = new cv::VideoCapture(filename);
-  for (int item_id = 0; item_id < batch_size; ++item_id) {
-    // get a blob
-    //CHECK_GT(lines_size, lines_id_);
-    if (!ReadVideoFrameToDatum(filename, frameIds[item_id], trans[item_id],
-          new_height, new_width, &datum)) {
-      LOG(ERROR)<< "Error reading frame from video!";
-      continue;
-    }
-
-    // Apply transformations (mirror, crop...) to the data
-    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
-    // go to the next iter
-    lines_id_++;
-    if (lines_id_ >= lines_size) {
-      // We have reached the end. Restart from the first.
-      DLOG(INFO) << "Restarting data prefetching from start.";
-      lines_id_ = 0;
-      if (this->layer_param_.image_data_param().shuffle()) {
-        ShuffleBatches();
-      }
-    }
-  }
-  this->cap->release();
-}
-
-INSTANTIATE_CLASS(VideoDataLayer);
-
-}  // namespace caffe