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added multilane label reading python modules, interfaced with C++
using boost numpy
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Tao Wang
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Nov 29, 2014
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| #!/bin/bash | ||
| export PKG_CONFIG_PATH=/deep/group/driving_data/twangcat/env_deep6/opencv/lib/pkgconfig/ | ||
| export LD_LIBRARY_PATH=/deep/group/driving_data/twangcat/env_deep6/opencv/lib/:$LD_LIBRARY_PATH | ||
| export PATH=/deep/group/driving_data/twangcat/env_deep6/opencv/include/:$PATH | ||
| export CPLUS_INCLUDE_PATH=/deep/group/driving_data/twangcat/env_deep6/opencv/include/:$CPLUS_INCLUDE_PATH | ||
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| #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> | ||
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| namespace py = boost::python; // create namespace variable for boost::python | ||
| namespace np = boost::numpy; // create namespace variable for boost::python | ||
| namespace caffe { | ||
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| //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(); | ||
| } | ||
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| 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()); | ||
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| string batch_string; | ||
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| 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())); | ||
| } | ||
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| // 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())); | ||
| } | ||
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| lines_.push_back(std::make_pair(filename, std::make_pair(frame_ids, trans_ids))); | ||
| } | ||
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| 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."; | ||
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| 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; | ||
| } | ||
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| 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); | ||
| } | ||
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| // 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(); | ||
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| // 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; | ||
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| 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); | ||
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| 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(); | ||
| } | ||
| } | ||
| } | ||
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| INSTANTIATE_CLASS(MultilaneLabelLayer); | ||
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| } // namespace caffe |
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