Spaces:
Runtime error
Runtime error
File size: 6,654 Bytes
51f6859 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmdet.core import bbox2roi
from mmdet.models.losses import SmoothL1Loss
from ..builder import HEADS
from .standard_roi_head import StandardRoIHead
EPS = 1e-15
@HEADS.register_module()
class DynamicRoIHead(StandardRoIHead):
"""RoI head for `Dynamic R-CNN <https://arxiv.org/abs/2004.06002>`_."""
def __init__(self, **kwargs):
super(DynamicRoIHead, self).__init__(**kwargs)
assert isinstance(self.bbox_head.loss_bbox, SmoothL1Loss)
# the IoU history of the past `update_iter_interval` iterations
self.iou_history = []
# the beta history of the past `update_iter_interval` iterations
self.beta_history = []
def forward_train(self,
x,
img_metas,
proposal_list,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None):
"""Forward function for training.
Args:
x (list[Tensor]): list of multi-level img features.
img_metas (list[dict]): list of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmdet/datasets/pipelines/formatting.py:Collect`.
proposals (list[Tensors]): list of region proposals.
gt_bboxes (list[Tensor]): each item are the truth boxes for each
image in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
gt_masks (None | Tensor) : true segmentation masks for each box
used if the architecture supports a segmentation task.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
# assign gts and sample proposals
if self.with_bbox or self.with_mask:
num_imgs = len(img_metas)
if gt_bboxes_ignore is None:
gt_bboxes_ignore = [None for _ in range(num_imgs)]
sampling_results = []
cur_iou = []
for i in range(num_imgs):
assign_result = self.bbox_assigner.assign(
proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
gt_labels[i])
sampling_result = self.bbox_sampler.sample(
assign_result,
proposal_list[i],
gt_bboxes[i],
gt_labels[i],
feats=[lvl_feat[i][None] for lvl_feat in x])
# record the `iou_topk`-th largest IoU in an image
iou_topk = min(self.train_cfg.dynamic_rcnn.iou_topk,
len(assign_result.max_overlaps))
ious, _ = torch.topk(assign_result.max_overlaps, iou_topk)
cur_iou.append(ious[-1].item())
sampling_results.append(sampling_result)
# average the current IoUs over images
cur_iou = np.mean(cur_iou)
self.iou_history.append(cur_iou)
losses = dict()
# bbox head forward and loss
if self.with_bbox:
bbox_results = self._bbox_forward_train(x, sampling_results,
gt_bboxes, gt_labels,
img_metas)
losses.update(bbox_results['loss_bbox'])
# mask head forward and loss
if self.with_mask:
mask_results = self._mask_forward_train(x, sampling_results,
bbox_results['bbox_feats'],
gt_masks, img_metas)
losses.update(mask_results['loss_mask'])
# update IoU threshold and SmoothL1 beta
update_iter_interval = self.train_cfg.dynamic_rcnn.update_iter_interval
if len(self.iou_history) % update_iter_interval == 0:
new_iou_thr, new_beta = self.update_hyperparameters()
return losses
def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
img_metas):
num_imgs = len(img_metas)
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_results = self._bbox_forward(x, rois)
bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
gt_labels, self.train_cfg)
# record the `beta_topk`-th smallest target
# `bbox_targets[2]` and `bbox_targets[3]` stand for bbox_targets
# and bbox_weights, respectively
pos_inds = bbox_targets[3][:, 0].nonzero().squeeze(1)
num_pos = len(pos_inds)
cur_target = bbox_targets[2][pos_inds, :2].abs().mean(dim=1)
beta_topk = min(self.train_cfg.dynamic_rcnn.beta_topk * num_imgs,
num_pos)
cur_target = torch.kthvalue(cur_target, beta_topk)[0].item()
self.beta_history.append(cur_target)
loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
bbox_results['bbox_pred'], rois,
*bbox_targets)
bbox_results.update(loss_bbox=loss_bbox)
return bbox_results
def update_hyperparameters(self):
"""Update hyperparameters like IoU thresholds for assigner and beta for
SmoothL1 loss based on the training statistics.
Returns:
tuple[float]: the updated ``iou_thr`` and ``beta``.
"""
new_iou_thr = max(self.train_cfg.dynamic_rcnn.initial_iou,
np.mean(self.iou_history))
self.iou_history = []
self.bbox_assigner.pos_iou_thr = new_iou_thr
self.bbox_assigner.neg_iou_thr = new_iou_thr
self.bbox_assigner.min_pos_iou = new_iou_thr
if (np.median(self.beta_history) < EPS):
# avoid 0 or too small value for new_beta
new_beta = self.bbox_head.loss_bbox.beta
else:
new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta,
np.median(self.beta_history))
self.beta_history = []
self.bbox_head.loss_bbox.beta = new_beta
return new_iou_thr, new_beta
|