from __future__ import division

import torch
import torch.nn as nn

from mmdet import ops


class SingleLevelRoI(nn.Module):
    """Extract RoI features from a single level feature map. Each RoI is
    mapped to a level according to its scale."""

    def __init__(self,
                 roi_layer,
                 out_channels,
                 featmap_strides,
                 finest_scale=56):
        super(SingleLevelRoI, self).__init__()
        self.roi_layers = self.build_roi_layers(roi_layer, featmap_strides)
        self.out_channels = out_channels
        self.featmap_strides = featmap_strides
        self.finest_scale = finest_scale

    @property
    def num_inputs(self):
        return len(self.featmap_strides)

    def init_weights(self):
        pass

    def build_roi_layers(self, layer_cfg, featmap_strides):
        cfg = layer_cfg.copy()
        layer_type = cfg.pop('type')
        assert hasattr(ops, layer_type)
        layer_cls = getattr(ops, layer_type)
        roi_layers = nn.ModuleList(
            [layer_cls(spatial_scale=1 / s, **cfg) for s in featmap_strides])
        return roi_layers

    def map_roi_levels(self, rois, num_levels):
        """Map rois to corresponding feature levels (0-based) by scales.

        scale < finest_scale: level 0
        finest_scale <= scale < finest_scale * 2: level 1
        finest_scale * 2 <= scale < finest_scale * 4: level 2
        scale >= finest_scale * 4: level 3
        """
        scale = torch.sqrt(
            (rois[:, 3] - rois[:, 1] + 1) * (rois[:, 4] - rois[:, 2] + 1))
        target_lvls = torch.floor(torch.log2(scale / self.finest_scale + 1e-6))
        target_lvls = target_lvls.clamp(min=0, max=num_levels - 1).long()
        return target_lvls

    def forward(self, feats, rois):
        """Extract roi features with the roi layer. If multiple feature levels
        are used, then rois are mapped to corresponding levels according to
        their scales.
        """
        if len(feats) == 1:
            return self.roi_layers[0](feats[0], rois)

        out_size = self.roi_layers[0].out_size
        num_levels = len(feats)
        target_lvls = self.map_roi_levels(rois, num_levels)
        roi_feats = torch.cuda.FloatTensor(rois.size()[0], self.out_channels,
                                           out_size, out_size).fill_(0)
        for i in range(num_levels):
            inds = target_lvls == i
            if inds.any():
                rois_ = rois[inds, :]
                roi_feats_t = self.roi_layers[i](feats[i], rois_)
                roi_feats[inds] += roi_feats_t
        return roi_feats