MC-CNN 双目测距代码复现

最新推荐文章于 2024-12-23 15:07:37 发布

原创最新推荐文章于 2024-12-23 15:07:37 发布 · 3.2k 阅读

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博主在复现下面的代码时，遇到了一系列的问题，在Google在徘徊了好久才解决，先大概列出几个问题以及解决方法。

https://github.com/jzbontar/mc-cnn

问题一：

运行下面代码时出错：

./main.lua kitti fast -a predict -net_fname net/net_kitti_fast_-a_train_all.t7 -left samples/input/kittiL.png -right samples/input/kittiR.png -disp_max 70

kitti fast -a predict -net_fname net/net_kitti_fast_-a_train_all.t7 -left samples/input/kittiL.png -right samples/input/kittiR.png -disp_max 70
Found Environment variable CUDNN_PATH = /usr/local/cuda/lib64/libcudnn.so.5luajit: /home/june/torch/install/share/lua/5.1/torch/File.lua:259: read error: read 0 blocks instead of 1 at /home/june/torch/pkg/torch/lib/TH/THDiskFile.c:352
stack traceback:
[C]: in function 'readInt'
/home/june/torch/install/share/lua/5.1/torch/File.lua:259: in function 'readObject'
/home/june/torch/install/share/lua/5.1/torch/File.lua:409: in function 'load'
./main.lua:898: in main chunk
[C]: at 0x00405d50

是因为现在net与运行的不符，名字与代码里面的不对应，仔细分别或重新下载后即可解决。

问题二：

运行下面代码时出错：

./main.lua kitti slow -a train_tr

luajit: ./main.lua:378: inconsistent tensor size, expected tensor [389 x 1 x 350 x 1242] and src [] to have the same number of elements, but got 169098300 and 0 elements respectively at /home/rohan140290/torch/pkg/torch/lib/TH/generic/THTensorCopy.c:86
stack traceback:
[C]: in function 'reshape'
./main.lua:378: in function 'fromfile'
./main.lua:428: in main chunk
[C]: at 0x00405d50

问题是在main.lua里面，只要改一些代码即可：（在main.lua的378行）

if type == 'float32' then
--x = torch.FloatTensor(torch.FloatStorage(fname))

  s=1
  for i = 1,#dim do	 
 s = s * dim[i]
  end

  x = torch.FloatTensor(torch.FloatStorage(s))
  torch.DiskFile(fname,'r'):binary():readFloat(x:storage())

elseif type == 'int32' then
print('inside --------------------- 1 ')
--x = torch.IntTensor(torch.IntStorage(fname))

  s=1
  for i = 1,#dim do	 
 s = s * dim[i]
  end

  x = torch.IntTensor(torch.IntStorage(s))
  torch.DiskFile(fname,'r'):binary():readInt(x:storage())

elseif type == 'int64' then
print('inside --------------------- 3 ')
--x = torch.LongTensor(torch.LongStorage(fname))

  s=1
  for i = 1,#dim do	 
 s = s * dim[i]
  end

  x = torch.LongTensor(torch.LongStorage(s))
  torch.DiskFile(fname,'r'):binary():readLong(x:storage())