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bbox_head.py
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bbox_head.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from mmdet.core import (auto_fp16, bbox_target, delta2bbox, force_fp32,
multiclass_nms)
from ..builder import build_loss
from ..losses import accuracy
from ..registry import HEADS
@HEADS.register_module
class BBoxHead(nn.Module):
"""Simplest RoI head, with only two fc layers for classification and
regression respectively"""
def __init__(self,
with_avg_pool=False,
with_cls=True,
with_reg=True,
roi_feat_size=7,
in_channels=256,
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=1.0)):
super(BBoxHead, self).__init__()
assert with_cls or with_reg
self.with_avg_pool = with_avg_pool
self.with_cls = with_cls
self.with_reg = with_reg
self.roi_feat_size = _pair(roi_feat_size)
self.roi_feat_area = self.roi_feat_size[0] * self.roi_feat_size[1]
self.in_channels = in_channels
self.num_classes = num_classes
self.target_means = target_means
self.target_stds = target_stds
self.reg_class_agnostic = reg_class_agnostic
self.fp16_enabled = False
self.loss_cls = build_loss(loss_cls)
self.loss_bbox = build_loss(loss_bbox)
in_channels = self.in_channels
if self.with_avg_pool:
self.avg_pool = nn.AvgPool2d(self.roi_feat_size)
else:
in_channels *= self.roi_feat_area
if self.with_cls:
self.fc_cls = nn.Linear(in_channels, num_classes)
if self.with_reg:
out_dim_reg = 4 if reg_class_agnostic else 4 * num_classes
self.fc_reg = nn.Linear(in_channels, out_dim_reg)
self.debug_imgs = None
def init_weights(self):
if self.with_cls:
nn.init.normal_(self.fc_cls.weight, 0, 0.01)
nn.init.constant_(self.fc_cls.bias, 0)
if self.with_reg:
nn.init.normal_(self.fc_reg.weight, 0, 0.001)
nn.init.constant_(self.fc_reg.bias, 0)
@auto_fp16()
def forward(self, x):
if self.with_avg_pool:
x = self.avg_pool(x)
x = x.view(x.size(0), -1)
cls_score = self.fc_cls(x) if self.with_cls else None
bbox_pred = self.fc_reg(x) if self.with_reg else None
return cls_score, bbox_pred
def get_target(self, sampling_results, gt_bboxes, gt_labels,
rcnn_train_cfg):
pos_proposals = [res.pos_bboxes for res in sampling_results]
neg_proposals = [res.neg_bboxes for res in sampling_results]
pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results]
pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
reg_classes = 1 if self.reg_class_agnostic else self.num_classes
cls_reg_targets = bbox_target(
pos_proposals,
neg_proposals,
pos_gt_bboxes,
pos_gt_labels,
rcnn_train_cfg,
reg_classes,
target_means=self.target_means,
target_stds=self.target_stds)
return cls_reg_targets
@force_fp32(apply_to=('cls_score', 'bbox_pred'))
def loss(self,
cls_score,
bbox_pred,
labels,
label_weights,
bbox_targets,
bbox_weights,
reduction_override=None):
losses = dict()
if cls_score is not None:
avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
if cls_score.numel() > 0:
losses['loss_cls'] = self.loss_cls(
cls_score,
labels,
label_weights,
avg_factor=avg_factor,
reduction_override=reduction_override)
losses['acc'] = accuracy(cls_score, labels)
if bbox_pred is not None:
pos_inds = labels > 0
if self.reg_class_agnostic:
pos_bbox_pred = bbox_pred.view(bbox_pred.size(0), 4)[pos_inds]
else:
pos_bbox_pred = bbox_pred.view(bbox_pred.size(0), -1,
4)[pos_inds, labels[pos_inds]]
if pos_inds.numel() > 0:
losses['loss_bbox'] = self.loss_bbox(
pos_bbox_pred,
bbox_targets[pos_inds],
bbox_weights[pos_inds],
avg_factor=bbox_targets.size(0),
reduction_override=reduction_override)
return losses
@force_fp32(apply_to=('cls_score', 'bbox_pred'))
def get_det_bboxes(self,
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=False,
cfg=None):
if isinstance(cls_score, list):
cls_score = sum(cls_score) / float(len(cls_score))
scores = F.softmax(cls_score, dim=1) if cls_score is not None else None
if bbox_pred is not None:
bboxes = delta2bbox(rois[:, 1:], bbox_pred, self.target_means,
self.target_stds, img_shape)
else:
bboxes = rois[:, 1:].clone()
if img_shape is not None:
bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1)
bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1)
if rescale:
if isinstance(scale_factor, float):
bboxes /= scale_factor
else:
bboxes /= torch.from_numpy(scale_factor).to(bboxes.device)
if cfg is None:
return bboxes, scores
else:
det_bboxes, det_labels = multiclass_nms(bboxes, scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
return det_bboxes, det_labels
@force_fp32(apply_to=('bbox_preds', ))
def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
"""Refine bboxes during training.
Args:
rois (Tensor): Shape (n*bs, 5), where n is image number per GPU,
and bs is the sampled RoIs per image.
labels (Tensor): Shape (n*bs, ).
bbox_preds (Tensor): Shape (n*bs, 4) or (n*bs, 4*#class).
pos_is_gts (list[Tensor]): Flags indicating if each positive bbox
is a gt bbox.
img_metas (list[dict]): Meta info of each image.
Returns:
list[Tensor]: Refined bboxes of each image in a mini-batch.
"""
bboxes_list = []
for i in range(len(img_metas)):
inds = torch.nonzero(rois[:, 0] == i).squeeze()
num_rois = inds.numel()
bboxes_ = rois[inds, 1:]
label_ = labels[inds]
bbox_pred_ = bbox_preds[inds]
img_meta_ = img_metas[i]
pos_is_gts_ = pos_is_gts[i]
bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_,
img_meta_)
# filter gt bboxes
pos_keep = 1 - pos_is_gts_
keep_inds = pos_is_gts_.new_ones(num_rois)
keep_inds[:len(pos_is_gts_)] = pos_keep
bboxes_list.append(bboxes[keep_inds])
return bboxes_list
@force_fp32(apply_to=('bbox_pred', ))
def regress_by_class(self, rois, label, bbox_pred, img_meta):
"""Regress the bbox for the predicted class. Used in Cascade R-CNN.
Args:
rois (Tensor): shape (n, 4) or (n, 5)
label (Tensor): shape (n, )
bbox_pred (Tensor): shape (n, 4*(#class+1)) or (n, 4)
img_meta (dict): Image meta info.
Returns:
Tensor: Regressed bboxes, the same shape as input rois.
"""
assert rois.size(1) == 4 or rois.size(1) == 5
if not self.reg_class_agnostic:
label = label * 4
inds = torch.stack((label, label + 1, label + 2, label + 3), 1)
bbox_pred = torch.gather(bbox_pred, 1, inds)
assert bbox_pred.size(1) == 4
if rois.size(1) == 4:
new_rois = delta2bbox(rois, bbox_pred, self.target_means,
self.target_stds, img_meta['img_shape'])
else:
bboxes = delta2bbox(rois[:, 1:], bbox_pred, self.target_means,
self.target_stds, img_meta['img_shape'])
new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
return new_rois