Exemplo n.º 1
0
def local_gpua_pool_dnn_grad_stride(fgraph, op, ctx_name, inputs, outputs):
    if not dnn_available(ctx_name):
        return
    if not op.ignore_border:
        return
    inp, out, out_grad, ws, stride, pad = inputs
    nd = op.ndim
    if nd not in (2, 3):
        return
    inp = gpu_contiguous(as_gpuarray_variable(inp, ctx_name))
    out = gpu_contiguous(as_gpuarray_variable(out, ctx_name))
    out_grad = gpu_contiguous(as_gpuarray_variable(out_grad, ctx_name))
    mode = op.mode

    # the GPU ops expect exactly 2 non-pooling dimensions
    if inp.ndim == nd + 2:
        return GpuDnnPoolGrad(mode=mode)(inp, out, out_grad, ws, stride, pad)
    else:
        # reshape to 4D or 5D with 2 non-pooling dimensions
        inp_padded = pad_dims(inp, 2, nd)
        out_padded = pad_dims(out, 2, nd)
        out_grad_padded = pad_dims(out_grad, 2, nd)
        ret_padded = GpuDnnPoolGrad(mode=mode)(inp_padded, out_padded,
                                               out_grad_padded, ws, stride,
                                               pad)
        return unpad_dims(ret_padded, inp, 2, nd)
Exemplo n.º 2
0
def local_gpua_avg_pool_dnn_grad_stride(fgraph, op, ctx_name, inputs, outputs):
    if not dnn_available(ctx_name):
        return
    if not op.ignore_border:
        return
    inp, out_grad, ws, stride, pad = inputs
    nd = op.ndim
    if nd not in (2, 3):
        return
    inp = gpu_contiguous(as_gpuarray_variable(inp, ctx_name))
    out_grad = gpu_contiguous(as_gpuarray_variable(out_grad, ctx_name))
    mode = op.mode

    # the GPU ops expect exactly 2 non-pooling dimensions
    if inp.ndim == nd + 2:
        # We reuse out_grad because cuDNN does not use the value of the `out`
        # argument but still checks its shape for average pooling. This
        # has been observed in v2 and v3 as far as I know.
        return GpuDnnPoolGrad(mode=mode)(inp, out_grad, out_grad, ws, stride,
                                         pad)
    else:
        # reshape to 4D or 5D with 2 non-pooling dimensions
        inp_padded = pad_dims(inp, 2, nd)
        out_grad_padded = pad_dims(out_grad, 2, nd)
        ret_padded = GpuDnnPoolGrad(mode=mode)(inp_padded, out_grad_padded,
                                               out_grad_padded, ws, stride,
                                               pad)
        return unpad_dims(ret_padded, inp, 2, nd)
Exemplo n.º 3
0
def local_gpua_pool_dnn_alternative(fgraph, op, ctx_name, inputs, outputs):
    if not dnn_available(ctx_name):
        return
    if not op.ignore_border:
        return
    img, ws, stride, pad = inputs
    nd = op.ndim
    if nd not in (2, 3):
        return
    img = gpu_contiguous(as_gpuarray_variable(img, ctx_name))
    mode = op.mode
    # dnn_pool expects exactly 2 non-pooling dimensions
    if img.ndim == nd + 2:
        return dnn_pool(img, ws, stride=stride, pad=pad, mode=mode)
    else:
        # reshape to 4D or 5D with 2 non-pooling dimensions
        img_padded = pad_dims(img, 2, nd)
        ret_padded = dnn_pool(img_padded,
                              ws,
                              stride=stride,
                              pad=pad,
                              mode=mode)
        return unpad_dims(ret_padded, img, 2, nd)