from torch.autograd import Function
from torch.nn.modules.utils import _pair

from .. import roi_align_cuda


class RoIAlignFunction(Function):

    @staticmethod
    def forward(ctx, features, rois, out_size, spatial_scale, sample_num=0):
        out_h, out_w = _pair(out_size)
        assert isinstance(out_h, int) and isinstance(out_w, int)
        ctx.spatial_scale = spatial_scale
        ctx.sample_num = sample_num
        ctx.save_for_backward(rois)
        ctx.feature_size = features.size()

        batch_size, num_channels, data_height, data_width = features.size()
        num_rois = rois.size(0)

        output = features.new_zeros(num_rois, num_channels, out_h, out_w)
        if features.is_cuda:
            roi_align_cuda.forward(features, rois, out_h, out_w, spatial_scale,
                                   sample_num, output)
        else:
            raise NotImplementedError

        return output

    @staticmethod
    def backward(ctx, grad_output):
        feature_size = ctx.feature_size
        spatial_scale = ctx.spatial_scale
        sample_num = ctx.sample_num
        rois = ctx.saved_tensors[0]
        assert (feature_size is not None and grad_output.is_cuda)

        batch_size, num_channels, data_height, data_width = feature_size
        out_w = grad_output.size(3)
        out_h = grad_output.size(2)

        grad_input = grad_rois = None
        if ctx.needs_input_grad[0]:
            grad_input = rois.new_zeros(batch_size, num_channels, data_height,
                                        data_width)
            roi_align_cuda.backward(grad_output.contiguous(), rois, out_h,
                                    out_w, spatial_scale, sample_num,
                                    grad_input)

        return grad_input, grad_rois, None, None, None


roi_align = RoIAlignFunction.apply