test_conv_cuda_ndarray.py 文件源码

def py_conv_scipy(img, kern, mode, subsample):
    assert img.shape[1] == kern.shape[1]
    if mode == 'valid':
        outshp = (img.shape[0], kern.shape[0],
                  img.shape[2] - kern.shape[2] + 1,
                  img.shape[3] - kern.shape[3] + 1)
    else:
        outshp = (img.shape[0], kern.shape[0],
                  img.shape[2] + kern.shape[2] - 1,
                  img.shape[3] + kern.shape[3] - 1)
    out = numpy.zeros(outshp, dtype='float32')
    for b in xrange(out.shape[0]):
        for k in xrange(out.shape[1]):
            for s in xrange(img.shape[1]):
                # convolve2d or correlate
                out[b, k, :, :] += convolve2d(img[b, s, :, :],
                                              kern[k, s, :, :],
                                              mode)
    return out[:, :, ::subsample[0], ::subsample[1]]