Source code for mmdet.models.roi_heads.sparse_roi_head
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh
from mmdet.core.bbox.samplers import PseudoSampler
from ..builder import HEADS
from .cascade_roi_head import CascadeRoIHead
[docs]@HEADS.register_module()
class SparseRoIHead(CascadeRoIHead):
r"""The RoIHead for `Sparse R-CNN: End-to-End Object Detection with
Learnable Proposals <https://arxiv.org/abs/2011.12450>`_
and `Instances as Queries <http://arxiv.org/abs/2105.01928>`_
Args:
num_stages (int): Number of stage whole iterative process.
Defaults to 6.
stage_loss_weights (Tuple[float]): The loss
weight of each stage. By default all stages have
the same weight 1.
bbox_roi_extractor (dict): Config of box roi extractor.
mask_roi_extractor (dict): Config of mask roi extractor.
bbox_head (dict): Config of box head.
mask_head (dict): Config of mask head.
train_cfg (dict, optional): Configuration information in train stage.
Defaults to None.
test_cfg (dict, optional): Configuration information in test stage.
Defaults to None.
pretrained (str, optional): model pretrained path. Default: None
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
"""
def __init__(self,
num_stages=6,
stage_loss_weights=(1, 1, 1, 1, 1, 1),
proposal_feature_channel=256,
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_roi_extractor=None,
bbox_head=dict(
type='DIIHead',
num_classes=80,
num_fcs=2,
num_heads=8,
num_cls_fcs=1,
num_reg_fcs=3,
feedforward_channels=2048,
hidden_channels=256,
dropout=0.0,
roi_feat_size=7,
ffn_act_cfg=dict(type='ReLU', inplace=True)),
mask_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None,
init_cfg=None):
assert bbox_roi_extractor is not None
assert bbox_head is not None
assert len(stage_loss_weights) == num_stages
self.num_stages = num_stages
self.stage_loss_weights = stage_loss_weights
self.proposal_feature_channel = proposal_feature_channel
super(SparseRoIHead, self).__init__(
num_stages,
stage_loss_weights,
bbox_roi_extractor=bbox_roi_extractor,
mask_roi_extractor=mask_roi_extractor,
bbox_head=bbox_head,
mask_head=mask_head,
train_cfg=train_cfg,
test_cfg=test_cfg,
pretrained=pretrained,
init_cfg=init_cfg)
# train_cfg would be None when run the test.py
if train_cfg is not None:
for stage in range(num_stages):
assert isinstance(self.bbox_sampler[stage], PseudoSampler), \
'Sparse R-CNN and QueryInst only support `PseudoSampler`'
def _bbox_forward(self, stage, x, rois, object_feats, img_metas):
"""Box head forward function used in both training and testing. Returns
all regression, classification results and a intermediate feature.
Args:
stage (int): The index of current stage in
iterative process.
x (List[Tensor]): List of FPN features
rois (Tensor): Rois in total batch. With shape (num_proposal, 5).
the last dimension 5 represents (img_index, x1, y1, x2, y2).
object_feats (Tensor): The object feature extracted from
the previous stage.
img_metas (dict): meta information of images.
Returns:
dict[str, Tensor]: a dictionary of bbox head outputs,
Containing the following results:
- cls_score (Tensor): The score of each class, has
shape (batch_size, num_proposals, num_classes)
when use focal loss or
(batch_size, num_proposals, num_classes+1)
otherwise.
- decode_bbox_pred (Tensor): The regression results
with shape (batch_size, num_proposal, 4).
The last dimension 4 represents
[tl_x, tl_y, br_x, br_y].
- object_feats (Tensor): The object feature extracted
from current stage
- detach_cls_score_list (list[Tensor]): The detached
classification results, length is batch_size, and
each tensor has shape (num_proposal, num_classes).
- detach_proposal_list (list[tensor]): The detached
regression results, length is batch_size, and each
tensor has shape (num_proposal, 4). The last
dimension 4 represents [tl_x, tl_y, br_x, br_y].
"""
num_imgs = len(img_metas)
bbox_roi_extractor = self.bbox_roi_extractor[stage]
bbox_head = self.bbox_head[stage]
bbox_feats = bbox_roi_extractor(x[:bbox_roi_extractor.num_inputs],
rois)
cls_score, bbox_pred, object_feats, attn_feats = bbox_head(
bbox_feats, object_feats)
proposal_list = self.bbox_head[stage].refine_bboxes(
rois,
rois.new_zeros(len(rois)), # dummy arg
bbox_pred.view(-1, bbox_pred.size(-1)),
[rois.new_zeros(object_feats.size(1)) for _ in range(num_imgs)],
img_metas)
bbox_results = dict(
cls_score=cls_score,
decode_bbox_pred=torch.cat(proposal_list),
object_feats=object_feats,
attn_feats=attn_feats,
# detach then use it in label assign
detach_cls_score_list=[
cls_score[i].detach() for i in range(num_imgs)
],
detach_proposal_list=[item.detach() for item in proposal_list])
return bbox_results
def _mask_forward(self, stage, x, rois, attn_feats):
"""Mask head forward function used in both training and testing."""
mask_roi_extractor = self.mask_roi_extractor[stage]
mask_head = self.mask_head[stage]
mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs],
rois)
# do not support caffe_c4 model anymore
mask_pred = mask_head(mask_feats, attn_feats)
mask_results = dict(mask_pred=mask_pred)
return mask_results
def _mask_forward_train(self, stage, x, attn_feats, sampling_results,
gt_masks, rcnn_train_cfg):
"""Run forward function and calculate loss for mask head in
training."""
pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
attn_feats = torch.cat([
feats[res.pos_inds]
for (feats, res) in zip(attn_feats, sampling_results)
])
mask_results = self._mask_forward(stage, x, pos_rois, attn_feats)
mask_targets = self.mask_head[stage].get_targets(
sampling_results, gt_masks, rcnn_train_cfg)
pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
loss_mask = self.mask_head[stage].loss(mask_results['mask_pred'],
mask_targets, pos_labels)
mask_results.update(loss_mask)
return mask_results
[docs] def forward_train(self,
x,
proposal_boxes,
proposal_features,
img_metas,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
imgs_whwh=None,
gt_masks=None):
"""Forward function in training stage.
Args:
x (list[Tensor]): list of multi-level img features.
proposals (Tensor): Decoded proposal bboxes, has shape
(batch_size, num_proposals, 4)
proposal_features (Tensor): Expanded proposal
features, has shape
(batch_size, num_proposals, proposal_feature_channel)
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`.
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) 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.
imgs_whwh (Tensor): Tensor with shape (batch_size, 4),
the dimension means
[img_width,img_height, img_width, img_height].
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 of all stage.
"""
num_imgs = len(img_metas)
num_proposals = proposal_boxes.size(1)
imgs_whwh = imgs_whwh.repeat(1, num_proposals, 1)
all_stage_bbox_results = []
proposal_list = [proposal_boxes[i] for i in range(len(proposal_boxes))]
object_feats = proposal_features
all_stage_loss = {}
for stage in range(self.num_stages):
rois = bbox2roi(proposal_list)
bbox_results = self._bbox_forward(stage, x, rois, object_feats,
img_metas)
all_stage_bbox_results.append(bbox_results)
if gt_bboxes_ignore is None:
# TODO support ignore
gt_bboxes_ignore = [None for _ in range(num_imgs)]
sampling_results = []
cls_pred_list = bbox_results['detach_cls_score_list']
proposal_list = bbox_results['detach_proposal_list']
for i in range(num_imgs):
normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(proposal_list[i] /
imgs_whwh[i])
assign_result = self.bbox_assigner[stage].assign(
normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i],
gt_labels[i], img_metas[i])
sampling_result = self.bbox_sampler[stage].sample(
assign_result, proposal_list[i], gt_bboxes[i])
sampling_results.append(sampling_result)
bbox_targets = self.bbox_head[stage].get_targets(
sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage],
True)
cls_score = bbox_results['cls_score']
decode_bbox_pred = bbox_results['decode_bbox_pred']
single_stage_loss = self.bbox_head[stage].loss(
cls_score.view(-1, cls_score.size(-1)),
decode_bbox_pred.view(-1, 4),
*bbox_targets,
imgs_whwh=imgs_whwh)
if self.with_mask:
mask_results = self._mask_forward_train(
stage, x, bbox_results['attn_feats'], sampling_results,
gt_masks, self.train_cfg[stage])
single_stage_loss['loss_mask'] = mask_results['loss_mask']
for key, value in single_stage_loss.items():
all_stage_loss[f'stage{stage}_{key}'] = value * \
self.stage_loss_weights[stage]
object_feats = bbox_results['object_feats']
return all_stage_loss
[docs] def simple_test(self,
x,
proposal_boxes,
proposal_features,
img_metas,
imgs_whwh,
rescale=False):
"""Test without augmentation.
Args:
x (list[Tensor]): list of multi-level img features.
proposal_boxes (Tensor): Decoded proposal bboxes, has shape
(batch_size, num_proposals, 4)
proposal_features (Tensor): Expanded proposal
features, has shape
(batch_size, num_proposals, proposal_feature_channel)
img_metas (dict): meta information of images.
imgs_whwh (Tensor): Tensor with shape (batch_size, 4),
the dimension means
[img_width,img_height, img_width, img_height].
rescale (bool): If True, return boxes in original image
space. Defaults to False.
Returns:
list[list[np.ndarray]] or list[tuple]: When no mask branch,
it is bbox results of each image and classes with type
`list[list[np.ndarray]]`. The outer list
corresponds to each image. The inner list
corresponds to each class. When the model has a mask branch,
it is a list[tuple] that contains bbox results and mask results.
The outer list corresponds to each image, and first element
of tuple is bbox results, second element is mask results.
"""
assert self.with_bbox, 'Bbox head must be implemented.'
# Decode initial proposals
num_imgs = len(img_metas)
proposal_list = [proposal_boxes[i] for i in range(num_imgs)]
ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
object_feats = proposal_features
if all([proposal.shape[0] == 0 for proposal in proposal_list]):
# There is no proposal in the whole batch
bbox_results = [[
np.zeros((0, 5), dtype=np.float32)
for i in range(self.bbox_head[-1].num_classes)
]] * num_imgs
return bbox_results
for stage in range(self.num_stages):
rois = bbox2roi(proposal_list)
bbox_results = self._bbox_forward(stage, x, rois, object_feats,
img_metas)
object_feats = bbox_results['object_feats']
cls_score = bbox_results['cls_score']
proposal_list = bbox_results['detach_proposal_list']
if self.with_mask:
rois = bbox2roi(proposal_list)
mask_results = self._mask_forward(stage, x, rois,
bbox_results['attn_feats'])
mask_results['mask_pred'] = mask_results['mask_pred'].reshape(
num_imgs, -1, *mask_results['mask_pred'].size()[1:])
num_classes = self.bbox_head[-1].num_classes
det_bboxes = []
det_labels = []
if self.bbox_head[-1].loss_cls.use_sigmoid:
cls_score = cls_score.sigmoid()
else:
cls_score = cls_score.softmax(-1)[..., :-1]
for img_id in range(num_imgs):
cls_score_per_img = cls_score[img_id]
scores_per_img, topk_indices = cls_score_per_img.flatten(
0, 1).topk(
self.test_cfg.max_per_img, sorted=False)
labels_per_img = topk_indices % num_classes
bbox_pred_per_img = proposal_list[img_id][topk_indices //
num_classes]
if rescale:
scale_factor = img_metas[img_id]['scale_factor']
bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor)
det_bboxes.append(
torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1))
det_labels.append(labels_per_img)
bbox_results = [
bbox2result(det_bboxes[i], det_labels[i], num_classes)
for i in range(num_imgs)
]
if self.with_mask:
if rescale and not isinstance(scale_factors[0], float):
scale_factors = [
torch.from_numpy(scale_factor).to(det_bboxes[0].device)
for scale_factor in scale_factors
]
_bboxes = [
det_bboxes[i][:, :4] *
scale_factors[i] if rescale else det_bboxes[i][:, :4]
for i in range(len(det_bboxes))
]
segm_results = []
mask_pred = mask_results['mask_pred']
for img_id in range(num_imgs):
mask_pred_per_img = mask_pred[img_id].flatten(0,
1)[topk_indices]
mask_pred_per_img = mask_pred_per_img[:, None, ...].repeat(
1, num_classes, 1, 1)
segm_result = self.mask_head[-1].get_seg_masks(
mask_pred_per_img, _bboxes[img_id], det_labels[img_id],
self.test_cfg, ori_shapes[img_id], scale_factors[img_id],
rescale)
segm_results.append(segm_result)
if self.with_mask:
results = list(zip(bbox_results, segm_results))
else:
results = bbox_results
return results
[docs] def aug_test(self, features, proposal_list, img_metas, rescale=False):
raise NotImplementedError(
'Sparse R-CNN and QueryInst does not support `aug_test`')
[docs] def forward_dummy(self, x, proposal_boxes, proposal_features, img_metas):
"""Dummy forward function when do the flops computing."""
all_stage_bbox_results = []
proposal_list = [proposal_boxes[i] for i in range(len(proposal_boxes))]
object_feats = proposal_features
if self.with_bbox:
for stage in range(self.num_stages):
rois = bbox2roi(proposal_list)
bbox_results = self._bbox_forward(stage, x, rois, object_feats,
img_metas)
all_stage_bbox_results.append((bbox_results, ))
proposal_list = bbox_results['detach_proposal_list']
object_feats = bbox_results['object_feats']
if self.with_mask:
rois = bbox2roi(proposal_list)
mask_results = self._mask_forward(
stage, x, rois, bbox_results['attn_feats'])
all_stage_bbox_results[-1] += (mask_results, )
return all_stage_bbox_results