casywang / cuda-convnet Goto Github PK

It seems like Layer with weights class lacks a destructor. I wonder why it is 
so? Won't it cause memory leaks (for example, biases are never deleted)?

Since the CUDA 5 I have had no success in compiling the cuda convnet code. 
Are there any plans to make it compatible with CUDA 5?

fprop(NVMatrixV&) does not remember forward activities, so backprop will fail. 
Currently backprop relies on forward activities being provided by layers below.

What steps will reproduce the problem?
1. compiler error,
2.
3.

What is the expected output? What do you see instead?
when i compile usr ./build.sh will broke see this error

obj/x86_64/release/src/util.cu.o: In function `pyDictGetMatrix(_object*, char 
const*)':
tmpxft_0000033f_00000000-3_util.cudafe1.cpp:(.text+0x27a): undefined reference 
to `Matrix::Matrix(PyArrayObject const*)'
obj/x86_64/release/src/util.cu.o: In function `getMatrixV(_object*)':
tmpxft_0000033f_00000000-3_util.cudafe1.cpp:(.text+0x3fe): undefined reference 
to `Matrix::Matrix(PyArrayObject const*)'

What version of the product are you using? On what operating system?
centos 6.3
gcc 4.4.6-4
cuda 5.0

Please provide any additional information below.

What steps will reproduce the problem?
1. use a big image like 512 x 512
2. put lots of filters (like 64)
3. have lots of color channels (again 64?)

What is the expected output? What do you see instead?
I expect a big filtered image, but instead it crashes. 

The blocks are defined such that blocks.y > (2^16) so CUDA refuses to launch 
the kernel.

I'm not sure I understand how to set the number of modules when doing a normal 
convolution, but it seems that an outer loop is required. The trouble with an 
outer loop is that the data is arranged in such a way that it is impossible to 
apply just a fraction of the  filters, or to process just some of each image. 
The data arrangement makes it natural to process just some of the image 
channels... but the color channels don't come into the blocking structure.

Basically... can I use this kernel to perform big convolutions?

Hi Alex,

Could you confirm that this slightly strange-looking line from filter_act.cu is 
correct?

assert(paddingStart <= 0 && paddingStart + (numModules-1)*moduleStride + 
filterSize >= imgSize);

In particular, why are you multiplying numModules (which the square of 
numModulesX) by the stride and adding filterSize (which is not the square, but 
the side-length).

If you're sure, I trust you, but if you could additionally lend some intuition 
for why I'd appreciate it.

There is a comment in the code but I still don't get it:
// These routines don't handle the case when only part of the image is visited 
in the convolution

Thanks,
- James

Add support for local non-convolutional layers.

The network training is fine without adding any contrast normalization layer 
(all types), but ones add the contrast normalization layers, after several 
iterations the net gets nan values.  I tried different values of the size, 
scale and pow values, and tried to place the layer before and after pooling 
layer.