caffe conv kernel for theano. tests work, but needs integration and some...

- changed conv to corr as suggested by Fred

Only create the handle if we specify a device to gpu_init().

[CRASH,DOC,ENH] Mixed

mostly useless.

Including support for 'full' convolutions. It uses the existing pad functionality of the c/cuda code. If mode == valid, I make pad = filter_size -1. This is passed to the C code, and everything else gets taken care of automatically.

The theano-nose for test_full does not pass, however, the test function test_gemm()  passes. @nouiz: do you know why?

Fix compile on cuda < 5.0.

[CRASH] Be more resistent to NumPy bad strides for shapes of 1.

[TEST fix] Fix debugmode checking in python3

Merge commit 'refs/pullreqs/origin/pr/2' into conv_gemm after Fred's commit which said valid finally works.

- added a test that does a check on variety of shapes and sizes of image and kernel
- removed flip form local_conv_gemm in cuda/opt.py is the code never reached there for me
- added flip in the kernel in the test code itself

[CRASH] Conv2d none

Look what I did on like 117 in file theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py. I rotated the kernel by 180 before convolution, and this now gives the same result as GpuConvMM. So, I think the cuda/c part is completely fine and the corrent arguments are being passed to the cublas function.

Hi Fred, I tried it out, but for me, it doesnt find conv() in package cuda_ndarray gpuval = cuda_ndarray.conv(img, kern, mode, subsample). So, made the changes in the test_conv_cuda_ndarray _test_dummy().

I see that the cpu version is computed using py_conv(), which in turn calls scipy.signal.convolve2d. How can the result 'gpuval' now be the same as scipy.signal.convolve2d instead of the scipy.signal.correlate?

Also, this still passes tests for all image, kernel, channel and batch sizes: https://github.com/stencilman/Theano-1/blob/fb66035292ef070b86466bf61c9c42b8faaa0a1c/theano/sandbox/cuda/tests/test_conv_gemm.py

Fix optimization error