转载请注明出处,楼燚(yì)航的blog,http://home.cnblogs.com/louyihang-loves-baiyan/
因为利用Pyhon来做数据的预处理比较方便,因此在data_layer选择上,采用了MemoryDataLayer,可以比较方便的直接用Python 根据set_input_array进行feed数据,然后再调用solver进行step就可以了。说一下我碰到的问题,当时检查了一下感觉没有哪里出错,但是报
Segmentation Fault(Core Abort)
感觉好囧,最怕这个了。一般说段错误都是内存错误,比如数组越界,无效的指针,引用被释放的资源等等。经过一步步debug之后发现问题出现在
solver.net.set_input_arrays
solver在将数据传送到网络低端的时候报错。那么接下来找到python
目录下的caffe\python\caffe\_caffe.cpp
文件,这个文件是基于boost python的,用来将C++的接口导出,供python调用。进一步我们找到相关函数
void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
bp::object labels_obj) {
// check that this network has an input MemoryDataLayer
shared_ptr<MemoryDataLayer<Dtype> > md_layer =
boost::dynamic_pointer_cast<MemoryDataLayer<Dtype> >(net->layers()[0]);
if (!md_layer) {
throw std::runtime_error("set_input_arrays may only be called if the"
" first layer is a MemoryDataLayer");
}
// check that we were passed appropriately-sized contiguous memory
PyArrayObject* data_arr =
reinterpret_cast<PyArrayObject*>(data_obj.ptr());
PyArrayObject* labels_arr =
reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
CheckContiguousArray(data_arr, "data array", md_layer->channels(),
md_layer->height(), md_layer->width());
CheckContiguousArray(labels_arr, "labels array", 1, 1, 1);
if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
throw std::runtime_error("data and labels must have the same first"
" dimension");
}
if (PyArray_DIMS(data_arr)[0] % md_layer->batch_size() != 0) {
throw std::runtime_error("first dimensions of input arrays must be a"
" multiple of batch size");
}
md_layer->Reset(static_cast<Dtype*>(PyArray_DATA(data_arr)),
static_cast<Dtype*>(PyArray_DATA(labels_arr)),
PyArray_DIMS(data_arr)[0]);
}
问题就出在了最后的一个语句
md_layer->Reset(static_cast<Dtype*>(PyArray_DATA(data_arr)),
static_cast<Dtype*>(PyArray_DATA(labels_arr)),
PyArray_DIMS(data_arr)[0]);
当执行reset MemoryDataLayer的Reset函数时出错。于此同时在github上也发现了同样的问题,https://github.com/BVLC/caffe/issues/2334也是因为Python MemoryDataLayer引发的段错误。他说到,在里面把传入的data和label做要给深拷贝就可以解决,估计是运行时数据已经被释放了,只传了指针引发了段错误。
解决方案:
找到caffe\src\layers\memory_data_layer.cpp
打开,给Reset函数添加相应的深拷贝代码
template <typename Dtype>
void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
CHECK(data);
CHECK(labels);
CHECK_EQ(n % batch_size_, 0) << "n must be a multiple of batch size";
// Warn with transformation parameters since a memory array is meant to
// be generic and no transformations are done with Reset().
if (this->layer_param_.has_transform_param()) {
LOG(WARNING) << this->type() << " does not transform array data on Reset()";
}
// data_ = data; 将这里注释掉,
// labels_ = labels;
//以下部分是进行深拷贝
if(data_)
delete []data_;
if(labels_)
delete [] labels_;
data_ = new Dtype[n*size_];
labels_ = new Dtype[n * num_tasks_];
memcpy(data_, data, sizeof(Dtype)*n*size_);
memcpy(labels_, labels, sizeof(Dtype) * n * num_tasks_);
n_ = n;
pos_ = 0;
}
Ok进行修改之后,回到Caffe的根目录,执行make all
,make test
,``make runtest,
make pycaffe`。重新编译完成之后,重新运行就好了,继续开始训练。