caffe代码阅读8: Data_layers的实现细节（各个数据读取层的实现细节） 2016.3.25-28
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一、Data_layers.hpp文件的作用简介
Data_layers.hpp在目前caffe的master分支中已经不能存在了，分散到各个文件中去了。
而之前是存在于cafferoot\include\caffe中。现在已经变成了各个类的名称的头文件了。这里做个提醒
首先给出这个文件中所包含的几个与数据读取有关的类。
分别为：
BaseDataLayer
数据层的基类，继承自通用的类Layer
Batch
Batch实际上就是一个data_和label_类标
BasePrefetchingDataLayer
是预取层的基类，继承自BaseDataLayer和InternalThread，包含能够读取一批数据的能力
DataLayer
DataLayer才是主角，继承自BasePrefetchingDataLayer
使用DataReader来进行数据共享，从而实现并行化
DummyDataLayer
该类是继承自Layer,通过Filler产生数据
HDF5DataLayer
从HDF5中读取，继承自Layer
HDF5OutputLayer
将数据写入到HDF5文件，继承自Layer
ImageDataLayer
从图像文件中读取数据，这个应该比较常用，继承自BasePrefetchingDataLayer
MemoryDataLayer
从内存中读取数据，这里指已经从数据文件或者图像文件中读取到了数据，然后输入到该层，继承自BaseDataLayer
WindowDataLayer
从图像文件的窗口获取数据，需要指定窗口数据文件，继承自BasePrefetchingDataLayer
二、Data_layers文件的的详细介绍
上述类虽然在同一个头文件中进行的定义，但是却都是在不同的cpp文件进行的实现。
下面给出类的实现文件
BaseDataLayer和BasePrefetchingDataLayer
对应于：
base_data_layer.cpp
base_data_layer.cu
DataLayer
对应于：
data_layer.cpp
DummyDataLayer
对应于：
dummy_data_layer.cpp
HDF5DataLayer
HDF5OutputLayer
对应于：
hdf5_data_layer.cpp
hdf5_data_layer.cu
以及
hdf5_output_layer.cpp
hdf5_output_layer.cu
ImageDataLayer
对应于：
image_data_layer.cpp
MemoryDataLayer
对应于：
memory_data_layer.cpp
WindowDataLayer
对应于
window_data_layer.cpp
接下来对这些类进行详细阐述：
（1）BaseDataLayer的类定义以及实现如下：
/**
* @brief Provides base for data layers that feed blobs to the Net.
*
* TODO(dox): thorough documentation for Forward and proto params.
* 数据层的基类
*/
template <typename Dtype>
class BaseDataLayer : public Layer<Dtype> {
public:
// 显式构造函数
explicit BaseDataLayer(const LayerParameter& param);
// LayerSetUp: implements common data layer setup functionality, and calls
// DataLayerSetUp to do special data layer setup for individual layer types.
// This method may not be overridden except by the BasePrefetchingDataLayer.
// 该函数只能被BasePrefetchingDataLayer层进行重载
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
// Data layers should be shared by multiple solvers in parallel
// 数据是否需要给多个并行solver进行共享
virtual inline bool ShareInParallel() const { return true; }
// 数据层的初始化
virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {}
// 数据层是没有输入的(即bottoms)，所以reshape只是形式
// Data layers have no bottoms, so reshaping is trivial.
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {}
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
protected:
// 对输入的数据进行变换的参数，这其中包括是否需要mirror，是否需要crop
// 是否需要减去meanfile，是否需要scale
TransformationParameter transform_param_;
// 实际执行数据变换类的指针(一个Transform函数加上参数即可完成对数据的变换，参数是数据哈)
shared_ptr<DataTransformer<Dtype> > data_transformer_;
bool output_labels_;
};
具体的实现：
// 构造函数就是初始化数据变换参数
template <typename Dtype>
BaseDataLayer<Dtype>::BaseDataLayer(const LayerParameter& param)
: Layer<Dtype>(param),
transform_param_(param.transform_param()) {
}
// 初始化的时候根据top的大小来确定，如果是1表明只输出数据，而不输出类标
template <typename Dtype>
void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
if (top.size() == 1) {
output_labels_ = false;
} else {
output_labels_ = true;
}
// 初始化一个DataTransformer实例，便于对数据进行预处理
data_transformer_.reset(
new DataTransformer<Dtype>(transform_param_, this->phase_));
// 初始化种子
data_transformer_->InitRand();
// The subclasses should setup the size of bottom and top
// 执行数据层的初始化
DataLayerSetUp(bottom, top);
}（2）BasePrefetchingDataLayer类的定义以及实现如下：
BasePrefetchingDataLayer类的定义如下：
// BasePrefetchingDataLayer层是继承于BaseDataLayer的
// 是预取层的基类
template <typename Dtype>
class BasePrefetchingDataLayer :
public BaseDataLayer<Dtype>, public InternalThread {
public:
explicit BasePrefetchingDataLayer(const LayerParameter& param);
// LayerSetUp: implements common data layer setup functionality, and calls
// DataLayerSetUp to do special data layer setup for individual layer types.
// This method may not be overridden.
void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
// Prefetches batches (asynchronously if to GPU memory)
static const int PREFETCH_COUNT = 3;
protected:
virtual void InternalThreadEntry();
// 多了load_batch函数，该函数是纯虚函数，继承该函数的类都需要实现的
virtual void load_batch(Batch<Dtype>* batch) = 0;
// 还有prefetch数组,prefetch_free_,prefetch_full_
Batch<Dtype> prefetch_[PREFETCH_COUNT];
BlockingQueue<Batch<Dtype>*> prefetch_free_;
BlockingQueue<Batch<Dtype>*> prefetch_full_;
Blob<Dtype> transformed_data_;
};
BasePrefetchingDataLayer类的具体实现如下：
// 构造函数，初始化预取的队列,free和full
template <typename Dtype>
BasePrefetchingDataLayer<Dtype>::BasePrefetchingDataLayer(
const LayerParameter& param)
: BaseDataLayer<Dtype>(param),
prefetch_free_(), prefetch_full_() {
for (int i = 0; i < PREFETCH_COUNT; ++i) {
prefetch_free_.push(&prefetch_[i]);
}
}
// 进行层的初始化
template <typename Dtype>
void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
// 首先执行基类BaseDataLayer的层初始化
BaseDataLayer<Dtype>::LayerSetUp(bottom, top);
// Before starting the prefetch thread, we make cpu_data and gpu_data
// calls so that the prefetch thread does not accidentally make simultaneous
// cudaMalloc calls when the main thread is running. In some GPUs this
// seems to cause failures if we do not so.
// 在开启预取线程的时候，需要让cpu数据和gpu数据分配空间
// 这样才能够避免在某些GPU上出现问题
// 首先是CPU
for (int i = 0; i < PREFETCH_COUNT; ++i) {
prefetch_[i].data_.mutable_cpu_data();
if (this->output_labels_) {
prefetch_[i].label_.mutable_cpu_data();
}
}
#ifndef CPU_ONLY
// 然后是GPU
if (Caffe::mode() == Caffe::GPU) {
for (int i = 0; i < PREFETCH_COUNT; ++i) {
prefetch_[i].data_.mutable_gpu_data();
if (this->output_labels_) {
prefetch_[i].label_.mutable_gpu_data();
}
}
}
#endif
DLOG(INFO) << "Initializing prefetch";
// 初始化随机数种子
this->data_transformer_->InitRand();
// 开启线程
StartInternalThread();
DLOG(INFO) << "Prefetch initialized.";
}
// 在StartInternalThread开启线程后就会执行下面自己定义的函数
// 这个就是自己定义的函数，让线程去执行的
template <typename Dtype>
void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
#ifndef CPU_ONLY
cudaStream_t stream;
if (Caffe::mode() == Caffe::GPU) {
// 创建非阻塞流
CUDA_CHECK(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
}
#endif
try {
while (!must_stop()) {
// 弹出一个batch
Batch<Dtype>* batch = prefetch_free_.pop();
// 装载batch
load_batch(batch);
#ifndef CPU_ONLY
if (Caffe::mode() == Caffe::GPU) {
// 如果GPU模式开始，则推送到GPU
batch->data_.data().get()->async_gpu_push(stream);
// 检查是否成功
CUDA_CHECK(cudaStreamSynchronize(stream));
}
#endif
// 将装好的batch压入full队列
prefetch_full_.push(batch);
}
} catch (boost::thread_interrupted&) {
// Interrupted exception is expected on shutdown
}
#ifndef CPU_ONLY
if (Caffe::mode() == Caffe::GPU) {
// 销毁流
CUDA_CHECK(cudaStreamDestroy(stream));
}
#endif
}
template <typename Dtype>
void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
// 传递的时候是从full队列中弹出一个数据
Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
// Reshape to loaded data.
// 根据batch的形状改变数据形状
top[0]->ReshapeLike(batch->data_);
// Copy the data
// 将batch数据复制到top[0]
caffe_copy(batch->data_.count(), batch->data_.cpu_data(),
top[0]->mutable_cpu_data());
DLOG(INFO) << "Prefetch copied";
if (this->output_labels_) {
// 输出类标的话
// Reshape to loaded labels.
// 根据batch中类标的形状改变top[1]的形状
top[1]->ReshapeLike(batch->label_);
// Copy the labels.
// 复制类标到top[1]
caffe_copy(batch->label_.count(), batch->label_.cpu_data(),
top[1]->mutable_cpu_data());
}
// 将该batch压入free队列
prefetch_free_.push(batch);
}
// 如果没有GPU的话则在BasePrefetchingDataLayer类中生成一个Forward函数
// 该函数并不前传，而是直接报错
#ifdef CPU_ONLY
STUB_GPU_FORWARD(BasePrefetchingDataLayer, Forward);
#endif
// 初始化层
INSTANTIATE_CLASS(BaseDataLayer);
INSTANTIATE_CLASS(BasePrefetchingDataLayer);（3）DataLayer类的定义以及实现如下：
数据层的主要功能是：
首先给出类的定义
// DataLayer才是主角，继承自BasePrefetchingDataLayer
template <typename Dtype>
class DataLayer : public BasePrefetchingDataLayer<Dtype> {
public:
explicit DataLayer(const LayerParameter& param);
virtual ~DataLayer();
virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
// DataLayer uses DataReader instead for sharing for parallelism
// 多了下面几个
virtual inline bool ShareInParallel() const { return false; }
virtual inline const char* type() const { return "Data"; }
virtual inline int ExactNumBottomBlobs() const { return 0; }
virtual inline int MinTopBlobs() const { return 1; }
virtual inline int MaxTopBlobs() const { return 2; }
protected:
virtual void load_batch(Batch<Dtype>* batch);
DataReader reader_;
};
具体的实现如下：
#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
#endif // USE_OPENCV
#include <stdint.h>
#include <string>
#include <vector>
#include "caffe/common.hpp"
#include "caffe/data_layers.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/benchmark.hpp"
#include "caffe/util/io.hpp"
namespace caffe {
// 初始化DataReader，层参数
template <typename Dtype>
DataLayer<Dtype>::DataLayer(const LayerParameter& param)
: BasePrefetchingDataLayer<Dtype>(param),
reader_(param) {
}
// 析构函数停止内部线程
template <typename Dtype>
DataLayer<Dtype>::~DataLayer() {
this->StopInternalThread();
}
// 数据层的初始化
template <typename Dtype>
void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
// 从层参数中读取batch_size
const int batch_size = this->layer_param_.data_param().batch_size();
// Read a data point, and use it to initialize the top blob.
// 从reader_中获取一个数据
Datum& datum = *(reader_.full().peek());
// Use data_transformer to infer the expected blob shape from datum.
// 用数据来推断blob的形状存放到top_shape
vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
this->transformed_data_.Reshape(top_shape);
// Reshape top[0] and prefetch_data according to the batch_size.
// 既然获取了数据的形状(channel,height,width)，那么这里再设置一下batch_size
// top_shape[0]=batch_size
// top_shape[1]=channel
// top_shape[2]=height
// top_shape[3]=width
top_shape[0] = batch_size;
// 根据形状设置top[0]的形状
top[0]->Reshape(top_shape);
// 设置预取数据的形状
for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
this->prefetch_[i].data_.Reshape(top_shape);
}
LOG(INFO) << "output data size: " << top[0]->num() << ","
<< top[0]->channels() << "," << top[0]->height() << ","
<< top[0]->width();
// label
// 如果输出类标的话则把top[1]的形状也弄一下
if (this->output_labels_) {
vector<int> label_shape(1, batch_size);
top[1]->Reshape(label_shape);
for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
this->prefetch_[i].label_.Reshape(label_shape);
}
}
}
// This function is called on prefetch thread
// 这个函数是在自己定义的线程执行函数内部执行的
template<typename Dtype>
void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
CPUTimer batch_timer;
batch_timer.Start();
double read_time = 0;
double trans_time = 0;
CPUTimer timer;
CHECK(batch->data_.count());
CHECK(this->transformed_data_.count());
// Reshape according to the first datum of each batch
// on single input batches allows for inputs of varying dimension.
// 意思是像以下这种做法这样的话，每个batch的数据的维度可以不一样
// 从参数文件获取batch_size
const int batch_size = this->layer_param_.data_param().batch_size();
// 获取第一个数据
Datum& datum = *(reader_.full().peek());
// Use data_transformer to infer the expected blob shape from datum.
// 使用第一个数据推断blob的形状
vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
this->transformed_data_.Reshape(top_shape);
// Reshape batch according to the batch_size.
top_shape[0] = batch_size;
batch->data_.Reshape(top_shape);
// top_data存数据
Dtype* top_data = batch->data_.mutable_cpu_data();
Dtype* top_label = NULL; // suppress warnings about uninitialized variables
// top_label存类标
if (this->output_labels_) {
top_label = batch->label_.mutable_cpu_data();
}
// 对这批数据进行处理
for (int item_id = 0; item_id < batch_size; ++item_id) {
timer.Start();
// get a datum
Datum& datum = *(reader_.full().pop("Waiting for data"));
read_time += timer.MicroSeconds();
timer.Start();
// Apply data transformations (mirror, scale, crop...)
// 对于给定批的数据获取offset，这里调用的是给定batchid，然后获取offset
int offset = batch->data_.offset(item_id);
this->transformed_data_.set_cpu_data(top_data + offset);
this->data_transformer_->Transform(datum, &(this->transformed_data_));
// Copy label.
// 复制类标
if (this->output_labels_) {
top_label[item_id] = datum.label();
}
// 数据传输时间
trans_time += timer.MicroSeconds();
// 将数据指针压到free队列
reader_.free().push(const_cast<Datum*>(&datum));
}
timer.Stop();
batch_timer.Stop();
DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
DLOG(INFO) << " Read time: " << read_time / 1000 << " ms.";
DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
}
INSTANTIATE_CLASS(DataLayer);
REGISTER_LAYER_CLASS(Data);
} // namespace caffe
（4）DummyDataLayer类的定义与实现介绍：
Dummy数据层的主要功能就是根据所给定的Filler产生数据，然后前向传
首先给出定义
/**
* @brief Provides data to the Net generated by a Filler.
*
* TODO(dox): thorough documentation for Forward and proto params.
* 该类是继承自Layer,通过Filler产生数据
*/
template <typename Dtype>
class DummyDataLayer : public Layer<Dtype> {
public:
explicit DummyDataLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
// Data layers should be shared by multiple solvers in parallel
virtual inline bool ShareInParallel() const { return true; }
// Data layers have no bottoms, so reshaping is trivial.
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {}
virtual inline const char* type() const { return "DummyData"; }
virtual inline int ExactNumBottomBlobs() const { return 0; }
virtual inline int MinTopBlobs() const { return 1; }
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
vector<shared_ptr<Filler<Dtype> > > fillers_;
vector<bool> refill_;
};
接下来给出详细的定义：
首先给出FillerParameter的定义，里面指定了值的类型，值是啥，最小是啥，最大是啥，平均值、方差是啥、是否稀疏、以及将扇入个数还是扇出个数还是所有的加起来求均值作为分母
message FillerParameter {
// The filler type.
optional string type = 1 [default = 'constant'];
optional float value = 2 [default = 0]; // the value in constant filler
optional float min = 3 [default = 0]; // the min value in uniform filler
optional float max = 4 [default = 1]; // the max value in uniform filler
optional float mean = 5 [default = 0]; // the mean value in Gaussian filler
optional float std = 6 [default = 1]; // the std value in Gaussian filler
// The expected number of non-zero output weights for a given input in
// Gaussian filler -- the default -1 means don't perform sparsification.
optional int32 sparse = 7 [default = -1];
// Normalize the filler variance by fan_in, fan_out, or their average.
// Applies to 'xavier' and 'msra' fillers.
enum VarianceNorm {
FAN_IN = 0;
FAN_OUT = 1;
AVERAGE = 2;
}
optional VarianceNorm variance_norm = 8 [default = FAN_IN];
}再看看该类的参数
</pre><pre name="code" class="plain">// DummyDataLayer fills any number of arbitrarily shaped blobs with random
// (or constant) data generated by "Fillers" (see "message FillerParameter").
message DummyDataParameter {
// This layer produces N >= 1 top blobs. DummyDataParameter must specify 1 or N
// shape fields, and 0, 1 or N data_fillers.
//
// If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
// If 1 data_filler is specified, it is applied to all top blobs. If N are
// specified, the ith is applied to the ith top blob.
repeated FillerParameter data_filler = 1;
repeated BlobShape shape = 6;
// 4D dimensions -- deprecated. Use "shape" instead.
repeated uint32 num = 2;
repeated uint32 channels = 3;
repeated uint32 height = 4;
repeated uint32 width = 5;
}
接下来给出具体的实现
#include <vector>
#include "caffe/filler.hpp"
#include "caffe/layer.hpp"
#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
// 输出有几个
const int num_top = top.size();
// 获取该层的参数
const DummyDataParameter& param = this->layer_param_.dummy_data_param();
// 有几个filler
const int num_data_filler = param.data_filler_size();
// 检查filler的个数，要么为0、1、或者等于输出的个数
CHECK(num_data_filler == 0 || num_data_filler == 1 ||
num_data_filler == num_top)
<< "Number of data fillers must be 0, 1 or equal to the number of tops: "
<< num_top << "; you specified " << num_data_filler << " data fillers.";
// 判断是否全部为0
const bool legacy_dims = param.num_size() || param.channels_size() ||
param.height_size() || param.width_size();
// 下面就是检查参数是不是满足要求，1或者0或者等于num_top
if (legacy_dims) {// 如果不是全部为0
CHECK_EQ(0, param.shape_size())
<< "Both shape and legacy fields were specified";
// Using deprecated 4D output dim specifiers.
CHECK(param.num_size() == 1 || param.num_size() == num_top)
<< "Must specify 'num' once, or once per top blob "
<< "(" << num_top << "); specified " << param.num_size() << ".";
CHECK(param.channels_size() == 1 || param.channels_size() == num_top)
<< "Must specify 'channels' once, or once per top blob "
<< "(" << num_top << "); specified " << param.channels_size() << ".";
CHECK(param.height_size() == 1 || param.height_size() == num_top)
<< "Must specify 'height' once, or once per top blob "
<< "(" << num_top << "); specified " << param.height_size() << ".";
CHECK(param.width_size() == 1 || param.width_size() == num_top)
<< "Must specify 'width' once, or once per top blob "
<< "(" << num_top << "); specified " << param.width_size() << ".";
} else {
CHECK(param.shape_size() == 1 || param.shape_size() == num_top)
<< "Must specify 'shape' once, or once per top blob "
<< "(" << num_top << "); specified " << param.shape_size() << ".";
}
// refill_[i] tells Forward i whether or not to actually refill top Blob i.
// If refill_[i] is false, Forward does nothing for Blob i. We use this to
// avoid wastefully refilling "constant" Blobs in every forward pass.
// We first fill refill_ in with the INVERSE of its final values.
// The first time we run Forward from the LayerSetUp method, we'll fill only
// Blobs for which refill_ is normally false. These Blobs will never be
// filled again.
// refill_表明是不是需要填充Blob，如果refill_[i]=false，那么就不会Blob i做任何事
//
refill_.clear();
fillers_.clear();
// 要么是0，要么是1
if (num_data_filler <= 1) {
// 定义了生成数据的参数
// 比如均值、方差等，详细请看其定义
FillerParameter filler_param;
if (num_data_filler == 0) {
// 如果没有指定，那么就是常数值填充
filler_param.set_type("constant");
filler_param.set_value(0);
} else {
// 否则复制filler到filler_param
filler_param.CopyFrom(param.data_filler(0));
}
// Refill on each iteration iff not using a constant filler,
// but use the inverse of this rule for the first run.
// 如果
refill_.resize(1);
refill_[0] = (strcmp(filler_param.type().c_str(), "constant") == 0);
fillers_.resize(1);
// 实例化填充器
fillers_[0].reset(GetFiller<Dtype>(filler_param));
} else {// 如果等于=num_top
refill_.resize(num_top);
fillers_.resize(num_top);
for (int i = 0; i < num_top; ++i) {
fillers_[i].reset(GetFiller<Dtype>(param.data_filler(i)));
// Refill on each iteration iff not using a constant filler,
// but use the inverse of this rule for the first run.
refill_[i] =
(strcmp(param.data_filler(i).type().c_str(), "constant") == 0);
}
}
// 改变形状
for (int i = 0; i < num_top; ++i) {
if (legacy_dims) {
const int num = (param.num_size() == 1) ? param.num(0) : param.num(i);
const int channels =
(param.channels_size() == 1) ? param.channels(0) : param.channels(i);
const int height =
(param.height_size() == 1) ? param.height(0) : param.height(i);
const int width =
(param.width_size() == 1) ? param.width(0) : param.width(i);
top[i]->Reshape(num, channels, height, width);
} else {
const int shape_index = (param.shape_size() == 1) ? 0 : i;
top[i]->Reshape(param.shape(shape_index));
}
}
// Run Forward once, with refill_ inverted, to fill the constant Blobs.
// 执行forward_cpu
this->Forward(bottom, top);
// Invert the inverted refill_ values to refill the desired (non-constant)
// Blobs in every usual forward pass.
for (int i = 0; i < refill_.size(); ++i) {
refill_[i] = !refill_[i];
}
}
// Forward里调用了该函数
template <typename Dtype>
void DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
// 调用fillers_来进行錐ill
for (int i = 0; i < top.size(); ++i) {
const int filler_id = (fillers_.size() > 1) ? i : 0;
if (refill_[filler_id]) {
fillers_[filler_id]->Fill(top[i]);
}
}
}
// 初始化类
// 注册类
INSTANTIATE_CLASS(DummyDataLayer);
REGISTER_LAYER_CLASS(DummyData);
} // namespace caffe（5）HDF5DataLayer类的定义以及实现如下：
HDF5数据层的主要功能是从给定的HDF5文件列表读取数据，然后设置top，即向前传播的数据。
首先给出类的定义：
template <typename Dtype>
class HDF5DataLayer : public Layer<Dtype> {
public:
explicit HDF5DataLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual ~HDF5DataLayer();
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
// Data layers should be shared by multiple solvers in parallel
virtual inline bool ShareInParallel() const { return true; }
// Data layers have no bottoms, so reshaping is trivial.
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {}
virtual inline const char* type() const { return "HDF5Data"; }
virtual inline int ExactNumBottomBlobs() const { return 0; }
virtual inline int MinTopBlobs() const { return 1; }
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
// 从HDF5文件读取数据
virtual void LoadHDF5FileData(const char* filename);
std::vector<std::string> hdf_filenames_;
unsigned int num_files_;
unsigned int current_file_;
hsize_t current_row_;
std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
// 存放的是数据的索引,可以对索引进行shuffle
std::vector<unsigned int> data_permutation_;
// 存放的是文件名字的索引，可以对索引进行shuffle
std::vector<unsigned int> file_permutation_;
};
接下来给出类的具体实现：
给出实现之前先给出HDF5的操作
头文件：
#ifndef CAFFE_UTIL_HDF5_H_
#define CAFFE_UTIL_HDF5_H_
#include <string>
#include "hdf5.h"
#include "hdf5_hl.h"
#include "caffe/blob.hpp"
namespace caffe {
// 获取HDF5文件的信息以及数据的维度
template <typename Dtype>
void hdf5_load_nd_dataset_helper(
hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
Blob<Dtype>* blob);
// float类型的获取数据维度和信息的包裹函数
template <typename Dtype>
void hdf5_load_nd_dataset(
hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
Blob<Dtype>* blob);
// double类型的获取数据维度和信息的包裹函数
template <typename Dtype>
void hdf5_save_nd_dataset(
const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob,
bool write_diff = false);
// 读取int和存储int，读取字符串和存储字符串到文件
int hdf5_load_int(hid_t loc_id, const string& dataset_name);
void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i);
string hdf5_load_string(hid_t loc_id, const string& dataset_name);
void hdf5_save_string(hid_t loc_id, const string& dataset_name,
const string& s);
// 获取链接数
int hdf5_get_num_links(hid_t loc_id);
// 根据名字找到索引
string hdf5_get_name_by_idx(hid_t loc_id, int idx);
} // namespace caffe
#endif // CAFFE_UTIL_HDF5_H_
cpp文件：
#include "caffe/util/hdf5.hpp"
#include <string>
#include <vector>
namespace caffe {
// Verifies format of data stored in HDF5 file and reshapes blob accordingly.
// 获取HDF5文件的信息以及数据的维度
template <typename Dtype>
void hdf5_load_nd_dataset_helper(
hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
Blob<Dtype>* blob) {
// Verify that the dataset exists.
// 检查是否存在
CHECK(H5LTfind_dataset(file_id, dataset_name_))
<< "Failed to find HDF5 dataset " << dataset_name_;
// Verify that the number of dimensions is in the accepted range.
herr_t status;
int ndims;
// 获取数据维度
status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims);
CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_;
CHECK_GE(ndims, min_dim);
CHECK_LE(ndims, max_dim);
// Verify that the data format is what we expect: float or double.
std::vector<hsize_t> dims(ndims);
H5T_class_t class_;
// 获取数据信息
status = H5LTget_dataset_info(
file_id, dataset_name_, dims.data(), &class_, NULL);
CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
switch (class_) {
case H5T_FLOAT:
LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_FLOAT";
break;
case H5T_INTEGER:
LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_INTEGER";
break;
case H5T_TIME:
LOG(FATAL) << "Unsupported datatype class: H5T_TIME";
case H5T_STRING:
LOG(FATAL) << "Unsupported datatype class: H5T_STRING";
case H5T_BITFIELD:
LOG(FATAL) << "Unsupported datatype class: H5T_BITFIELD";
case H5T_OPAQUE:
LOG(FATAL) << "Unsupported datatype class: H5T_OPAQUE";
case H5T_COMPOUND:
LOG(FATAL) << "Unsupported datatype class: H5T_COMPOUND";
case H5T_REFERENCE:
LOG(FATAL) << "Unsupported datatype class: H5T_REFERENCE";
case H5T_ENUM:
LOG(FATAL) << "Unsupported datatype class: H5T_ENUM";
case H5T_VLEN:
LOG(FATAL) << "Unsupported datatype class: H5T_VLEN";
case H5T_ARRAY:
LOG(FATAL) << "Unsupported datatype class: H5T_ARRAY";
default:
LOG(FATAL) << "Datatype class unknown";
}
// 设置blob的维度
vector<int> blob_dims(dims.size());
for (int i = 0; i < dims.size(); ++i) {
blob_dims[i] = dims[i];
}
blob->Reshape(blob_dims);
}
// float类型的获取数据维度和信息的包裹函数
template <>
void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
int min_dim, int max_dim, Blob<float>* blob) {
hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
herr_t status = H5LTread_dataset_float(
file_id, dataset_name_, blob->mutable_cpu_data());
CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
}
// double类型的获取数据维度和信息的包裹函数
template <>
void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
int min_dim, int max_dim, Blob<double>* blob) {
hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
herr_t status = H5LTread_dataset_double(
file_id, dataset_name_, blob->mutable_cpu_data());
CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;
}
// 存放float类型到hdf5文件
template <>
void hdf5_save_nd_dataset<float>(
const hid_t file_id, const string& dataset_name, const Blob<float>& blob,
bool write_diff) {
// blob信息放到dims
int num_axes = blob.num_axes();
hsize_t *dims = new hsize_t[num_axes];
for (int i = 0; i < num_axes; ++i) {
dims[i] = blob.shape(i);
}
// 获取数据指针
const float* data;
if (write_diff) {
data = blob.cpu_diff();
} else {
data = blob.cpu_data();
}
// 存放数据到hdf5
herr_t status = H5LTmake_dataset_float(
file_id, dataset_name.c_str(), num_axes, dims, data);
CHECK_GE(status, 0) << "Failed to make float dataset " << dataset_name;
delete[] dims;
}
// 存放double类型到hdf5文件
template <>
void hdf5_save_nd_dataset<double>(
hid_t file_id, const string& dataset_name, const Blob<double>& blob,
bool write_diff) {
int num_axes = blob.num_axes();
hsize_t *dims = new hsize_t[num_axes];
for (int i = 0; i < num_axes; ++i) {
dims[i] = blob.shape(i);
}
const double* data;
if (write_diff) {
data = blob.cpu_diff();
} else {
data = blob.cpu_data();
}
herr_t status = H5LTmake_dataset_double(
file_id, dataset_name.c_str(), num_axes, dims, data);
CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name;
delete[] dims;
}
// 读取string到字符串
string hdf5_load_string(hid_t loc_id, const string& dataset_name) {
// Get size of dataset
size_t size;
H5T_class_t class_;
herr_t status = H5LTget_dataset_info(loc_id, dataset_name.c_str(), NULL, &class_, &size);
CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name;
char *buf = new char[size];
status = H5LTread_dataset_string(loc_id, dataset_name.c_str(), buf);
CHECK_GE(status, 0)
<< "Failed to load int dataset with name " << dataset_name;
string val(buf);
delete[] buf;
return val;
}
// 保存string到字符串
void hdf5_save_string(hid_t loc_id, const string& dataset_name,
const string& s) {
herr_t status = H5LTmake_dataset_string(loc_id, dataset_name.c_str(), s.c_str());
CHECK_GE(status, 0)
<< "Failed to save string dataset with name " << dataset_name;
}
// 载入int类型
int hdf5_load_int(hid_t loc_id, const string& dataset_name) {
int val;
herr_t status = H5LTread_dataset_int(loc_id, dataset_name.c_str(), &val);
CHECK_GE(status, 0)
<< "Failed to load int dataset with name " << dataset_name;
return val;
}
// 存储int类型
void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i) {
hsize_t one = 1;
herr_t status = H5LTmake_dataset_int(loc_id, dataset_name.c_str(), 1, &one, &i);
CHECK_GE(status, 0)
<< "Failed to save int dataset with name " << dataset_name;
}
// 获取链接数
int hdf5_get_num_links(hid_t loc_id) {
H5G_info_t info;
herr_t status = H5Gget_info(loc_id, &info);
CHECK_GE(status, 0) << "Error while counting HDF5 links.";
return info.nlinks;
}
// 通过名字找到索引
string hdf5_get_name_by_idx(hid_t loc_id, int idx) {
ssize_t str_size = H5Lget_name_by_idx(
loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, NULL, 0, H5P_DEFAULT);
CHECK_GE(str_size, 0) << "Error retrieving HDF5 dataset at index " << idx;
char *c_str = new char[str_size+1];
ssize_t status = H5Lget_name_by_idx(
loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, c_str, str_size+1,
H5P_DEFAULT);
CHECK_GE(status, 0) << "Error retrieving HDF5 dataset at index " << idx;
string result(c_str);
delete[] c_str;
return result;
}
} // namespace caffe
给出具体实现：
/*
TODO:
- load file in a separate thread ("prefetch")
- can be smarter about the memcpy call instead of doing it row-by-row
:: use util functions caffe_copy, and Blob->offset()
:: don't forget to update hdf5_daa_layer.cu accordingly
- add ability to shuffle filenames if flag is set
*/
#include <fstream> // NOLINT(readability/streams)
#include <string>
#include <vector>
#include "hdf5.h"
#include "hdf5_hl.h"
#include "stdint.h"
#include "caffe/data_layers.hpp"
#include "caffe/layer.hpp"
#include "caffe/util/hdf5.hpp"
namespace caffe {
template <typename Dtype>
HDF5DataLayer<Dtype>::~HDF5DataLayer<Dtype>() { }
// Load data and label from HDF5 filename into the class property blobs.
// 读取HDF5文件数据到hdf_blobs
template <typename Dtype>
void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
DLOG(INFO) << "Loading HDF5 file: " << filename;
// 打开文件
hid_t file_id = H5Fopen(filename, H5F_ACC_RDONLY, H5P_DEFAULT);
if (file_id < 0) {
LOG(FATAL) << "Failed opening HDF5 file: " << filename;
}
int top_size = this->layer_param_.top_size();
hdf_blobs_.resize(top_size);
const int MIN_DATA_DIM = 1;
const int MAX_DATA_DIM = INT_MAX;
for (int i = 0; i < top_size; ++i) {
hdf_blobs_[i] = shared_ptr<Blob<Dtype> >(new Blob<Dtype>());
// message LayerParameter {
// optional string name = 1; // the layer name
// optional string type = 2; // the layer type
// repeated string bottom = 3; // the name of each bottom blob
// repeated string top = 4; // the name of each top blob
hdf5_load_nd_dataset(file_id, this->layer_param_.top(i).c_str(),
MIN_DATA_DIM, MAX_DATA_DIM, hdf_blobs_[i].get());
}
herr_t status = H5Fclose(file_id);
CHECK_GE(status, 0) << "Failed to close HDF5 file: " << filename;
// MinTopBlobs==1 guarantees at least one top blob
CHECK_GE(hdf_blobs_[0]->num_axes(), 1) << "Input must have at least 1 axis.";
const int num = hdf_blobs_[0]->shape(0);
for (int i = 1; i < top_size; ++i) {
CHECK_EQ(hdf_blobs_[i]->shape(0), num);
}
// Default to identity permutation.
data_permutation_.clear();
data_permutation_.resize(hdf_blobs_[0]->shape(0));
for (int i = 0; i < hdf_blobs_[0]->shape(0); i++)
data_permutation_[i] = i;
// Shuffle if needed.
// 将数据索引映射表进行shuffle
if (this->layer_param_.hdf5_data_param().shuffle()) {
std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0)
<< " rows (shuffled)";
} else {
DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0) << " rows";
}
}
// 主要的功能就是读取HDF5文件，并且设置top blob的形状
template <typename Dtype>
void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
// Refuse transformation parameters since HDF5 is totally generic.
CHECK(!this->layer_param_.has_transform_param()) <<
this->type() << " does not transform data.";
// Read the source to parse the filenames.
// 读取HDF列表文件
const string& source = this->layer_param_.hdf5_data_param().source();
LOG(INFO) << "Loading list of HDF5 filenames from: " << source;
hdf_filenames_.clear();
std::ifstream source_file(source.c_str());
if (source_file.is_open()) {
std::string line;
while (source_file >> line) {
hdf_filenames_.push_back(line);
}
} else {
LOG(FATAL) << "Failed to open source file: " << source;
}
source_file.close();
num_files_ = hdf_filenames_.size();
current_file_ = 0;
LOG(INFO) << "Number of HDF5 files: " << num_files_;
CHECK_GE(num_files_, 1) << "Must have at least 1 HDF5 filename listed in "
<< source;
file_permutation_.clear();
file_permutation_.resize(num_files_);
// 文件名字是否shuffle
// Default to identity permutation.
for (int i = 0; i < num_files_; i++) {
file_permutation_[i] = i;
}
// Shuffle if needed.
if (this->layer_param_.hdf5_data_param().shuffle()) {
std::random_shuffle(file_permutation_.begin(), file_permutation_.end());
}
// Load the first HDF5 file and initialize the line counter.
// 从给定的文件名列表中的第一个文件名读取数据到hdf_blobs
LoadHDF5FileData(hdf_filenames_[file_permutation_[current_file_]].c_str());
// 设置行指针
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