def _oper_gpu(cls, x, in_shape, out_shape, karnel, stride, padding):
N = x.shape[0]
pool_desc = cu.PoolingDescriptor(karnel, padding, stride, pool_mode=1)
y = GPUValue(shape=tuple([
N,
] + list(out_shape)))
with cu.cudnn_handler() as handle:
cu.cuPoolingForward(handle, pool_desc, get_gpu(x), y)
ret = cls._create_node(y)
ret.attrs._pool_desc = pool_desc
ret.attrs._kernel = karnel
ret.attrs._stride = stride
ret.attrs._padding = padding
ret.attrs._x = x
return ret
def _oper_gpu(cls, x, karnel, stride, padding):
pool_desc = cu.PoolingNDescriptor(karnel, padding, stride, pool_mode=1)
output_shape = [x.shape[0], x.shape[1]]
for i in range(len(x.shape[2:])):
output_shape.append(
(x.shape[i + 2] + padding[i] * 2 - karnel[i]) // stride[i] + 1)
y = GPUValue(shape=tuple(output_shape))
with cu.cudnn_handler() as handle:
cu.cuPoolingForward(handle, pool_desc, get_gpu(x), get_gpu(y))
ret = cls._create_node(y)
ret.attrs._pool_desc = pool_desc
ret.attrs._kernel = karnel
ret.attrs._stride = stride
ret.attrs._padding = padding
ret.attrs._x = x
return ret