def _PyramidRoI_Feat(self, feat_maps, rois, im_info):
        ''' roi pool on pyramid feature maps'''
        # do roi pooling based on predicted rois
        img_area = im_info[0][0] * im_info[0][1]
        h = rois.data[:, 4] - rois.data[:, 2] + 1
        w = rois.data[:, 3] - rois.data[:, 1] + 1
        roi_level = torch.log(torch.sqrt(h * w) / 224.0)
        roi_level = torch.round(roi_level + 4)
        roi_level[roi_level < 2] = 2
        roi_level[roi_level > 5] = 5
        # roi_level.fill_(5)
        if cfg.POOLING_MODE == 'crop':
            # pdb.set_trace()
            # pooled_feat_anchor = _crop_pool_layer(base_feat, rois.view(-1, 5))
            # NOTE: need to add pyrmaid
            grid_xy = _affine_grid_gen(rois, base_feat.size()[2:], self.grid_size)
            grid_yx = torch.stack([grid_xy.data[:,:,:,1], grid_xy.data[:,:,:,0]], 3).contiguous()
            roi_pool_feat = self.RCNN_roi_crop(base_feat, Variable(grid_yx).detach())
            if cfg.CROP_RESIZE_WITH_MAX_POOL:
                roi_pool_feat = F.max_pool2d(roi_pool_feat, 2, 2)

        elif cfg.POOLING_MODE == 'align':
            roi_pool_feats = []
            box_to_levels = []
            for i, l in enumerate(range(2, 6)):
                if (roi_level == l).sum() == 0:
                    continue
                idx_l = (roi_level == l).nonzero().squeeze()
                box_to_levels.append(idx_l)
                scale = feat_maps[i].size(2) / im_info[0][0]
                feat = self.RCNN_roi_align(feat_maps[i], rois[idx_l], scale)
                roi_pool_feats.append(feat)
            roi_pool_feat = torch.cat(roi_pool_feats, 0)
            box_to_level = torch.cat(box_to_levels, 0)
            idx_sorted, order = torch.sort(box_to_level)
            roi_pool_feat = roi_pool_feat[order]

        elif cfg.POOLING_MODE == 'pool':
            roi_pool_feats = []
            box_to_levels = []
            for i, l in enumerate(range(2, 6)):
                if (roi_level == l).sum() == 0:
                    continue
                idx_l = (roi_level == l).nonzero().squeeze()
                box_to_levels.append(idx_l)
                scale = feat_maps[i].size(2) / im_info[0][0]
                feat = self.RCNN_roi_pool(feat_maps[i], rois[idx_l], scale)
                roi_pool_feats.append(feat)
            roi_pool_feat = torch.cat(roi_pool_feats, 0)
            box_to_level = torch.cat(box_to_levels, 0)
            idx_sorted, order = torch.sort(box_to_level)
            roi_pool_feat = roi_pool_feat[order]
            
        return roi_pool_feat
 roi pool on pyramid feature maps
lib/model/fpn/fpn.py