SingleRoIExtractor類定義在\mmdet\models\roi_heads\roi_extractors\single_level_roi_extractor.py中,其作用是對ROI特征層進行特征提取,繼承自BaseRoIExtractor類.
import torch from mmcv.runner import force_fp32 from mmdet.models.builder import ROI_EXTRACTORS from .base_roi_extractor import BaseRoIExtractor @ROI_EXTRACTORS.register_module() class SingleRoIExtractor(BaseRoIExtractor): """Extract RoI features from a single level feature map. If there are multiple input feature levels, each RoI is mapped to a level according to its scale. The mapping rule is proposed in `FPN <https://arxiv.org/abs/1612.03144>`_. Args: roi_layer (dict): Specify RoI layer type and arguments. out_channels (int): Output channels of RoI layers. featmap_strides (int): Strides of input feature maps. finest_scale (int): Scale threshold of mapping to level 0. Default: 56. """ def __init__(self, roi_layer, out_channels, featmap_strides, finest_scale=56): super(SingleRoIExtractor, self).__init__(roi_layer, out_channels, featmap_strides) self.finest_scale = finest_scale def map_roi_levels(self, rois, num_levels): """Map rois to corresponding feature levels by scales. - scale < finest_scale * 2: level 0 - finest_scale * 2 <= scale < finest_scale * 4: level 1 - finest_scale * 4 <= scale < finest_scale * 8: level 2 - scale >= finest_scale * 8: level 3 Args: rois (Tensor): Input RoIs, shape (k, 5). num_levels (int): Total level number. Returns: Tensor: Level index (0-based) of each RoI, shape (k, ) """ scale = torch.sqrt( (rois[:, 3] - rois[:, 1]) * (rois[:, 4] - rois[:, 2])) 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 @force_fp32(apply_to=('feats', ), out_fp16=True) def forward(self, feats, rois, roi_scale_factor=None): """Forward function.""" out_size = self.roi_layers[0].output_size num_levels = len(feats) roi_feats = feats[0].new_zeros( rois.size(0), self.out_channels, *out_size) # TODO: remove this when parrots supports if torch.__version__ == 'parrots': roi_feats.requires_grad = True if num_levels == 1: if len(rois) == 0: return roi_feats return self.roi_layers[0](feats[0], rois) target_lvls = self.map_roi_levels(rois, num_levels) if roi_scale_factor is not None: rois = self.roi_rescale(rois, roi_scale_factor) 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 else: roi_feats += sum( x.view(-1)[0] for x in self.parameters()) * 0. + feats[i].sum() * 0. return roi_feats
主要的函數有:
(1) __init__():初始化函數,設置finest_scale的值,作為分配樣本到FPN哪一層的依據;
(2)map_roi_levels():分配樣本到FPN的某一層,根據論文的公式計算;
(3)forward():前向傳播,有feats,rois等幾個參數,feats即FPN的幾個特征層,rois即ROI的坐標.
SingleRoIExtractor類的函數較少,很多功能是通過調用BaseRoIExtractor類的方法來實現,BaseRoIExtractor類定義在\mmdet\models\roi_heads\roi_extractors\base_roi_extractor.py中:
from abc import ABCMeta, abstractmethod import torch import torch.nn as nn from mmcv import ops class BaseRoIExtractor(nn.Module, metaclass=ABCMeta): """Base class for RoI extractor. Args: roi_layer (dict): Specify RoI layer type and arguments. out_channels (int): Output channels of RoI layers. featmap_strides (int): Strides of input feature maps. """ def __init__(self, roi_layer, out_channels, featmap_strides): super(BaseRoIExtractor, self).__init__() self.roi_layers = self.build_roi_layers(roi_layer, featmap_strides) self.out_channels = out_channels self.featmap_strides = featmap_strides self.fp16_enabled = False @property def num_inputs(self): """int: Number of input feature maps.""" return len(self.featmap_strides) def init_weights(self): pass def build_roi_layers(self, layer_cfg, featmap_strides): """Build RoI operator to extract feature from each level feature map. Args: layer_cfg (dict): Dictionary to construct and config RoI layer operation. Options are modules under ``mmcv/ops`` such as ``RoIAlign``. featmap_strides (int): The stride of input feature map w.r.t to the original image size, which would be used to scale RoI coordinate (original image coordinate system) to feature coordinate system. Returns: nn.ModuleList: The RoI extractor modules for each level feature map. """ 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 roi_rescale(self, rois, scale_factor): """Scale RoI coordinates by scale factor. Args: rois (torch.Tensor): RoI (Region of Interest), shape (n, 5) scale_factor (float): Scale factor that RoI will be multiplied by. Returns: torch.Tensor: Scaled RoI. """ cx = (rois[:, 1] + rois[:, 3]) * 0.5 cy = (rois[:, 2] + rois[:, 4]) * 0.5 w = rois[:, 3] - rois[:, 1] h = rois[:, 4] - rois[:, 2] new_w = w * scale_factor new_h = h * scale_factor x1 = cx - new_w * 0.5 x2 = cx + new_w * 0.5 y1 = cy - new_h * 0.5 y2 = cy + new_h * 0.5 new_rois = torch.stack((rois[:, 0], x1, y1, x2, y2), dim=-1) return new_rois @abstractmethod def forward(self, feats, rois, roi_scale_factor=None): pass
主要的函數有:
(1) __init__():初始化函數,有roi_layer, out_channels, featmap_strides等幾個參數,通過build_roi_layers()函數構造ROI層;
(2) build_roi_layers():構造ROI層,有layer_cfg,featmap_strides等幾個參數,通過nn.ModuleList()構造出幾個ROI層;
(3) roi_rescale():ROI的縮放;
(4) forward():抽象方法,由子類實現.