[caffe]:检查失败:检查失败:hdf_blobs_[i]->shape(0) == num (200 vs. 6000) [英] [caffe]: check fails: Check failed: hdf_blobs_[i]->shape(0) == num (200 vs. 6000)

问题描述

我将火车和标签数据作为 data.mat.(我有 200 个训练数据和 6000 个特征，标签是 (-1, +1) 保存在 data.mat 中).

我正在尝试在 hdf5 中转换我的数据并使用以下方法运行 Caffe:

加载data.mathdf5write('my_data.h5', '/new_train_x', single( reshape(new_train_x,[200, 6000, 1, 1]) ) );hdf5write('my_data.h5', '/label_train', single( reshape(label_train,[200, 1, 1, 1]) ), 'WriteMode', 'append' );

而我的 layer.prototxt(只是数据层)是:

层{类型:HDF5Data"名称:数据"top: "new_train_x" # 注意:与 HDF5 中的名称相同顶部:label_train"#hdf5_data_param {来源:/path/to/list/file.txt"批量大小:20}包括{阶段:火车}}

但是，我有一个错误:(检查失败:hdf_blobs_[i]->shape(0) == num (200 vs. 6000))

<块引用>

I1222 17:02:48.915861 3941 layer_factory.hpp:76] 创建层数据I1222 17:02:48.915871 3941 net.cpp:110] 创建层数据I1222 17:02:48.915877 3941 net.cpp:433] 数据 ->new_train_xI1222 17:02:48.915890 3941 net.cpp:433] 数据 ->标签火车I1222 17:02:48.915900 3941 hdf5_data_layer.cpp:81] 正在加载 HDF5 文件名列表:file.txtI1222 17:02:48.915923 3941 hdf5_data_layer.cpp:95] HDF5 文件数:1F1222 17:02:48.993865 3941 hdf5_data_layer.cpp:55] 检查失败:hdf_blobs_[i]->shape(0) == num (200 vs. 6000)*** 检查失败堆栈跟踪:***@ 0x7fd2e6608ddd google::LogMessage::Fail()@ 0x7fd2e660ac90 google::LogMessage::SendToLog()@ 0x7fd2e66089a2 google::LogMessage::Flush()@ 0x7fd2e660b6ae google::LogMessageFatal::~LogMessageFatal()@ 0x7fd2e69f9eda caffe::HDF5DataLayer<>::LoadHDF5FileData()@ 0x7fd2e69f901f caffe::HDF5DataLayer<>::LayerSetUp()@ 0x7fd2e6a48030 caffe::Net<>::Init()@ 0x7fd2e6a49278 caffe::Net<>::Net()@ 0x7fd2e6a9157a caffe::Solver<>::InitTrainNet()@ 0x7fd2e6a928b1 caffe::Solver<>::Init()@ 0x7fd2e6a92c19 caffe::Solver<>::Solver()@ 0x41222d caffe::GetSolver<>()@ 0x408ed9 火车()@ 0x406741 主@ 0x7fd2e533ca40(未知)@ 0x406f69 _start中止(核心转储)

非常感谢！！！！任何建议将不胜感激！

解决方案

问题

看来确实是数组中元素的顺序有冲突:matlab从第一维到最后一维排列元素(类似fortran)，而caffe和hdf5是从最后一维到第一维存储数组:
假设我们有形状为 nxcxhxw 的 X 那么X 的第二个元素"；在 matlab 中是 X[2,1,1,1] 但在 C 中是 X[0,0,0,1](基于 1 和基于 0 的索引不根本不会让生活更轻松).
因此，当您在 Matlab 中保存 size=[200, 6000, 1, 1] 数组时，hdf5 和 caffe 实际看到的是 shape=[6000,200] 数组.

使用 h5ls 命令行工具可以帮助您发现问题.
在matlab中你保存了

<代码>>>hdf5write('my_data.h5', '/new_train_x',单(重塑(new_train_x，[200, 6000, 1, 1]))；>>hdf5write('my_data.h5', '/label_train',单(重塑(label_train，[200, 1, 1, 1]))，'WriteMode', '追加' );

现在您可以使用 h5ls(在 Linux 终端中)检查生成的 my_data.h5:

user@host:~/$ h5ls ./my_data.h5label_train 数据集{200}new_train_x 数据集 {6000, 200}

如您所见，数组是向后"写的.

解决方案

在从 Matlab 导出数据时考虑到这种冲突，您应该 置换:

加载data.mathdf5write('my_data.h5', '/new_train_x',单(置换(重塑(new_train_x，[200, 6000, 1, 1])，[4:-1:1]))；hdf5write('my_data.h5', '/label_train',单(置换(重塑(标签火车，[200，1，1，1])，[4:-1:1]))，'WriteMode', '追加' );

使用 h5ls 检查结果 my_data.h5 现在结果为:

user@host:~/$ h5ls ./my_data.h5label_train 数据集 {200, 1, 1, 1}new_train_x 数据集 {200, 6000, 1, 1}

这是您最初的预期.

I have the train and label data as data.mat. (I have 200 training data with 6000 features and labels are (-1, +1) that have saved in data.mat).

I am trying to convert my data in hdf5 and run Caffe using:

load data.mat
hdf5write('my_data.h5', '/new_train_x', single( reshape(new_train_x,[200, 6000, 1, 1]) ) );
hdf5write('my_data.h5', '/label_train', single( reshape(label_train,[200, 1, 1, 1]) ), 'WriteMode', 'append' );

And my layer.prototxt (just data layer) is:

layer {
  type: "HDF5Data"
  name: "data"
  top: "new_train_x"     # note: same name as in HDF5
  top: "label_train"     # 
  hdf5_data_param {
    source: "/path/to/list/file.txt"
    batch_size: 20
  }
  include { phase: TRAIN }
}

but, i have an error: ( Check failed: hdf_blobs_[i]->shape(0) == num (200 vs. 6000))

I1222 17:02:48.915861  3941 layer_factory.hpp:76] Creating layer data
I1222 17:02:48.915871  3941 net.cpp:110] Creating Layer data
I1222 17:02:48.915877  3941 net.cpp:433] data -> new_train_x
I1222 17:02:48.915890  3941 net.cpp:433] data -> label_train
I1222 17:02:48.915900  3941 hdf5_data_layer.cpp:81] Loading list of HDF5 filenames from: file.txt
I1222 17:02:48.915923  3941 hdf5_data_layer.cpp:95] Number of HDF5 files: 1
F1222 17:02:48.993865  3941 hdf5_data_layer.cpp:55] Check failed: hdf_blobs_[i]->shape(0) == num (200 vs. 6000) 
*** Check failure stack trace: ***
    @     0x7fd2e6608ddd  google::LogMessage::Fail()
    @     0x7fd2e660ac90  google::LogMessage::SendToLog()
    @     0x7fd2e66089a2  google::LogMessage::Flush()
    @     0x7fd2e660b6ae  google::LogMessageFatal::~LogMessageFatal()
    @     0x7fd2e69f9eda  caffe::HDF5DataLayer<>::LoadHDF5FileData()
    @     0x7fd2e69f901f  caffe::HDF5DataLayer<>::LayerSetUp()
    @     0x7fd2e6a48030  caffe::Net<>::Init()
    @     0x7fd2e6a49278  caffe::Net<>::Net()
    @     0x7fd2e6a9157a  caffe::Solver<>::InitTrainNet()
    @     0x7fd2e6a928b1  caffe::Solver<>::Init()
    @     0x7fd2e6a92c19  caffe::Solver<>::Solver()
    @           0x41222d  caffe::GetSolver<>()
    @           0x408ed9  train()
    @           0x406741  main
    @     0x7fd2e533ca40  (unknown)
    @           0x406f69  _start
Aborted (core dumped)

Many thanks!!!! Any advice would be appreciated!

解决方案

The problem

It seems like there is indeed a conflict with the order of elements in arrays: matlab arranges the elements from the first dimension to the last (like fortran), while caffe and hdf5 stores the arrays from last dimension to first:
Suppose we have X of shape nxcxhxw then the "second element of X" is X[2,1,1,1] in matlab but X[0,0,0,1] in C (1-based vs 0-based indexing doesn't make life easier at all).
Therefore, when you save an array of size=[200, 6000, 1, 1] in Matlab, what hdf5 and caffe are actually seeing is as array of shape=[6000,200].

Using the h5ls command line tool can help you spot the problem.
In matlab you saved

>> hdf5write('my_data.h5', '/new_train_x', 
  single( reshape(new_train_x,[200, 6000, 1, 1]) );
>> hdf5write('my_data.h5', '/label_train', 
  single( reshape(label_train,[200, 1, 1, 1]) ),
  'WriteMode', 'append' );

Now you can inspect the resulting my_data.h5 using h5ls (in Linux terminal):

user@host:~/$ h5ls ./my_data.h5
  label_train              Dataset {200}
  new_train_x              Dataset {6000, 200}

As you can see, the arrays are written "backwards".

Solution

Taking this conflict into account when exporting data from Matlab, you should permute:

load data.mat
hdf5write('my_data.h5', '/new_train_x', 
  single( permute(reshape(new_train_x,[200, 6000, 1, 1]),[4:-1:1] ) );
hdf5write('my_data.h5', '/label_train', 
  single( permute(reshape(label_train,[200, 1, 1, 1]), [4:-1:1] ) ),
  'WriteMode', 'append' );

Inspect the resulting my_data.h5 using h5ls now results with:

user@host:~/$ h5ls ./my_data.h5
  label_train              Dataset {200, 1, 1, 1}
  new_train_x              Dataset {200, 6000, 1, 1}

Which is what you expected in the first place.

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