import numpy as np
import torch
import torch.nn as nn
from mmcv.runner import ModuleList
from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner,
build_sampler, merge_aug_bboxes, merge_aug_masks,
multiclass_nms)
from ..builder import HEADS, build_head, build_roi_extractor
from .base_roi_head import BaseRoIHead
from .test_mixins import BBoxTestMixin, MaskTestMixin
[docs]@HEADS.register_module()
class CascadeRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin):
"""Cascade roi head including one bbox head and one mask head.
https://arxiv.org/abs/1712.00726
"""
def __init__(self,
num_stages,
stage_loss_weights,
bbox_roi_extractor=None,
bbox_head=None,
mask_roi_extractor=None,
mask_head=None,
shared_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 shared_head is None, \
'Shared head is not supported in Cascade RCNN anymore'
self.num_stages = num_stages
self.stage_loss_weights = stage_loss_weights
super(CascadeRoIHead, self).__init__(
bbox_roi_extractor=bbox_roi_extractor,
bbox_head=bbox_head,
mask_roi_extractor=mask_roi_extractor,
mask_head=mask_head,
shared_head=shared_head,
train_cfg=train_cfg,
test_cfg=test_cfg,
pretrained=pretrained,
init_cfg=init_cfg)
[docs] def init_bbox_head(self, bbox_roi_extractor, bbox_head):
"""Initialize box head and box roi extractor.
Args:
bbox_roi_extractor (dict): Config of box roi extractor.
bbox_head (dict): Config of box in box head.
"""
self.bbox_roi_extractor = ModuleList()
self.bbox_head = ModuleList()
if not isinstance(bbox_roi_extractor, list):
bbox_roi_extractor = [
bbox_roi_extractor for _ in range(self.num_stages)
]
if not isinstance(bbox_head, list):
bbox_head = [bbox_head for _ in range(self.num_stages)]
assert len(bbox_roi_extractor) == len(bbox_head) == self.num_stages
for roi_extractor, head in zip(bbox_roi_extractor, bbox_head):
self.bbox_roi_extractor.append(build_roi_extractor(roi_extractor))
self.bbox_head.append(build_head(head))
[docs] def init_mask_head(self, mask_roi_extractor, mask_head):
"""Initialize mask head and mask roi extractor.
Args:
mask_roi_extractor (dict): Config of mask roi extractor.
mask_head (dict): Config of mask in mask head.
"""
self.mask_head = nn.ModuleList()
if not isinstance(mask_head, list):
mask_head = [mask_head for _ in range(self.num_stages)]
assert len(mask_head) == self.num_stages
for head in mask_head:
self.mask_head.append(build_head(head))
if mask_roi_extractor is not None:
self.share_roi_extractor = False
self.mask_roi_extractor = ModuleList()
if not isinstance(mask_roi_extractor, list):
mask_roi_extractor = [
mask_roi_extractor for _ in range(self.num_stages)
]
assert len(mask_roi_extractor) == self.num_stages
for roi_extractor in mask_roi_extractor:
self.mask_roi_extractor.append(
build_roi_extractor(roi_extractor))
else:
self.share_roi_extractor = True
self.mask_roi_extractor = self.bbox_roi_extractor
[docs] def init_assigner_sampler(self):
"""Initialize assigner and sampler for each stage."""
self.bbox_assigner = []
self.bbox_sampler = []
if self.train_cfg is not None:
for idx, rcnn_train_cfg in enumerate(self.train_cfg):
self.bbox_assigner.append(
build_assigner(rcnn_train_cfg.assigner))
self.current_stage = idx
self.bbox_sampler.append(
build_sampler(rcnn_train_cfg.sampler, context=self))
[docs] def forward_dummy(self, x, proposals):
"""Dummy forward function."""
# bbox head
outs = ()
rois = bbox2roi([proposals])
if self.with_bbox:
for i in range(self.num_stages):
bbox_results = self._bbox_forward(i, x, rois)
outs = outs + (bbox_results['cls_score'],
bbox_results['bbox_pred'])
# mask heads
if self.with_mask:
mask_rois = rois[:100]
for i in range(self.num_stages):
mask_results = self._mask_forward(i, x, mask_rois)
outs = outs + (mask_results['mask_pred'], )
return outs
def _bbox_forward(self, stage, x, rois):
"""Box head forward function used in both training and testing."""
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)
# do not support caffe_c4 model anymore
cls_score, bbox_pred = bbox_head(bbox_feats)
bbox_results = dict(
cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
return bbox_results
def _bbox_forward_train(self, stage, x, sampling_results, gt_bboxes,
gt_labels, rcnn_train_cfg):
"""Run forward function and calculate loss for box head in training."""
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_results = self._bbox_forward(stage, x, rois)
bbox_targets = self.bbox_head[stage].get_targets(
sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg)
loss_bbox = self.bbox_head[stage].loss(bbox_results['cls_score'],
bbox_results['bbox_pred'], rois,
*bbox_targets)
bbox_results.update(
loss_bbox=loss_bbox, rois=rois, bbox_targets=bbox_targets)
return bbox_results
def _mask_forward(self, stage, x, rois):
"""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)
mask_results = dict(mask_pred=mask_pred)
return mask_results
def _mask_forward_train(self,
stage,
x,
sampling_results,
gt_masks,
rcnn_train_cfg,
bbox_feats=None):
"""Run forward function and calculate loss for mask head in
training."""
pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
mask_results = self._mask_forward(stage, x, pos_rois)
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=loss_mask)
return mask_results
[docs] def forward_train(self,
x,
img_metas,
proposal_list,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None):
"""
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]): 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.
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
"""
losses = dict()
for i in range(self.num_stages):
self.current_stage = i
rcnn_train_cfg = self.train_cfg[i]
lw = self.stage_loss_weights[i]
# assign gts and sample proposals
sampling_results = []
if self.with_bbox or self.with_mask:
bbox_assigner = self.bbox_assigner[i]
bbox_sampler = self.bbox_sampler[i]
num_imgs = len(img_metas)
if gt_bboxes_ignore is None:
gt_bboxes_ignore = [None for _ in range(num_imgs)]
for j in range(num_imgs):
assign_result = bbox_assigner.assign(
proposal_list[j], gt_bboxes[j], gt_bboxes_ignore[j],
gt_labels[j])
sampling_result = bbox_sampler.sample(
assign_result,
proposal_list[j],
gt_bboxes[j],
gt_labels[j],
feats=[lvl_feat[j][None] for lvl_feat in x])
sampling_results.append(sampling_result)
# bbox head forward and loss
bbox_results = self._bbox_forward_train(i, x, sampling_results,
gt_bboxes, gt_labels,
rcnn_train_cfg)
for name, value in bbox_results['loss_bbox'].items():
losses[f's{i}.{name}'] = (
value * lw if 'loss' in name else value)
# mask head forward and loss
if self.with_mask:
mask_results = self._mask_forward_train(
i, x, sampling_results, gt_masks, rcnn_train_cfg,
bbox_results['bbox_feats'])
for name, value in mask_results['loss_mask'].items():
losses[f's{i}.{name}'] = (
value * lw if 'loss' in name else value)
# refine bboxes
if i < self.num_stages - 1:
pos_is_gts = [res.pos_is_gt for res in sampling_results]
# bbox_targets is a tuple
roi_labels = bbox_results['bbox_targets'][0]
with torch.no_grad():
cls_score = bbox_results['cls_score']
if self.bbox_head[i].custom_activation:
cls_score = self.bbox_head[i].loss_cls.get_activation(
cls_score)
roi_labels = torch.where(
roi_labels == self.bbox_head[i].num_classes,
cls_score[:, :-1].argmax(1), roi_labels)
proposal_list = self.bbox_head[i].refine_bboxes(
bbox_results['rois'], roi_labels,
bbox_results['bbox_pred'], pos_is_gts, img_metas)
return losses
[docs] def simple_test(self, x, proposal_list, img_metas, rescale=False):
"""Test without augmentation."""
assert self.with_bbox, 'Bbox head must be implemented.'
num_imgs = len(proposal_list)
img_shapes = tuple(meta['img_shape'] for meta in img_metas)
ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
# "ms" in variable names means multi-stage
ms_bbox_result = {}
ms_segm_result = {}
ms_scores = []
rcnn_test_cfg = self.test_cfg
rois = bbox2roi(proposal_list)
if rois.shape[0] == 0:
# There is no proposal in the whole batch
bbox_results = [[
np.zeros((0, 5), dtype=np.float32)
for _ in range(self.bbox_head[-1].num_classes)
]] * num_imgs
if self.with_mask:
mask_classes = self.mask_head[-1].num_classes
segm_results = [[[] for _ in range(mask_classes)]
for _ in range(num_imgs)]
results = list(zip(bbox_results, segm_results))
else:
results = bbox_results
return results
for i in range(self.num_stages):
bbox_results = self._bbox_forward(i, x, rois)
# split batch bbox prediction back to each image
cls_score = bbox_results['cls_score']
bbox_pred = bbox_results['bbox_pred']
num_proposals_per_img = tuple(
len(proposals) for proposals in proposal_list)
rois = rois.split(num_proposals_per_img, 0)
cls_score = cls_score.split(num_proposals_per_img, 0)
if isinstance(bbox_pred, torch.Tensor):
bbox_pred = bbox_pred.split(num_proposals_per_img, 0)
else:
bbox_pred = self.bbox_head[i].bbox_pred_split(
bbox_pred, num_proposals_per_img)
ms_scores.append(cls_score)
if i < self.num_stages - 1:
if self.bbox_head[i].custom_activation:
cls_score = [
self.bbox_head[i].loss_cls.get_activation(s)
for s in cls_score
]
refine_rois_list = []
for j in range(num_imgs):
if rois[j].shape[0] > 0:
bbox_label = cls_score[j][:, :-1].argmax(dim=1)
refined_rois = self.bbox_head[i].regress_by_class(
rois[j], bbox_label[j], bbox_pred[j], img_metas[j])
refine_rois_list.append(refined_rois)
rois = torch.cat(refine_rois_list)
# average scores of each image by stages
cls_score = [
sum([score[i] for score in ms_scores]) / float(len(ms_scores))
for i in range(num_imgs)
]
# apply bbox post-processing to each image individually
det_bboxes = []
det_labels = []
for i in range(num_imgs):
det_bbox, det_label = self.bbox_head[-1].get_bboxes(
rois[i],
cls_score[i],
bbox_pred[i],
img_shapes[i],
scale_factors[i],
rescale=rescale,
cfg=rcnn_test_cfg)
det_bboxes.append(det_bbox)
det_labels.append(det_label)
bbox_results = [
bbox2result(det_bboxes[i], det_labels[i],
self.bbox_head[-1].num_classes)
for i in range(num_imgs)
]
ms_bbox_result['ensemble'] = bbox_results
if self.with_mask:
if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
mask_classes = self.mask_head[-1].num_classes
segm_results = [[[] for _ in range(mask_classes)]
for _ in range(num_imgs)]
else:
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))
]
mask_rois = bbox2roi(_bboxes)
num_mask_rois_per_img = tuple(
_bbox.size(0) for _bbox in _bboxes)
aug_masks = []
for i in range(self.num_stages):
mask_results = self._mask_forward(i, x, mask_rois)
mask_pred = mask_results['mask_pred']
# split batch mask prediction back to each image
mask_pred = mask_pred.split(num_mask_rois_per_img, 0)
aug_masks.append([
m.sigmoid().cpu().detach().numpy() for m in mask_pred
])
# apply mask post-processing to each image individually
segm_results = []
for i in range(num_imgs):
if det_bboxes[i].shape[0] == 0:
segm_results.append(
[[]
for _ in range(self.mask_head[-1].num_classes)])
else:
aug_mask = [mask[i] for mask in aug_masks]
merged_masks = merge_aug_masks(
aug_mask, [[img_metas[i]]] * self.num_stages,
rcnn_test_cfg)
segm_result = self.mask_head[-1].get_seg_masks(
merged_masks, _bboxes[i], det_labels[i],
rcnn_test_cfg, ori_shapes[i], scale_factors[i],
rescale)
segm_results.append(segm_result)
ms_segm_result['ensemble'] = segm_results
if self.with_mask:
results = list(
zip(ms_bbox_result['ensemble'], ms_segm_result['ensemble']))
else:
results = ms_bbox_result['ensemble']
return results
[docs] def aug_test(self, features, proposal_list, img_metas, rescale=False):
"""Test with augmentations.
If rescale is False, then returned bboxes and masks will fit the scale
of imgs[0].
"""
rcnn_test_cfg = self.test_cfg
aug_bboxes = []
aug_scores = []
for x, img_meta in zip(features, img_metas):
# only one image in the batch
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
flip_direction = img_meta[0]['flip_direction']
proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
scale_factor, flip, flip_direction)
# "ms" in variable names means multi-stage
ms_scores = []
rois = bbox2roi([proposals])
if rois.shape[0] == 0:
# There is no proposal in the single image
aug_bboxes.append(rois.new_zeros(0, 4))
aug_scores.append(rois.new_zeros(0, 1))
continue
for i in range(self.num_stages):
bbox_results = self._bbox_forward(i, x, rois)
ms_scores.append(bbox_results['cls_score'])
if i < self.num_stages - 1:
cls_score = bbox_results['cls_score']
if self.bbox_head[i].custom_activation:
cls_score = self.bbox_head[i].loss_cls.get_activation(
cls_score)
bbox_label = cls_score[:, :-1].argmax(dim=1)
rois = self.bbox_head[i].regress_by_class(
rois, bbox_label, bbox_results['bbox_pred'],
img_meta[0])
cls_score = sum(ms_scores) / float(len(ms_scores))
bboxes, scores = self.bbox_head[-1].get_bboxes(
rois,
cls_score,
bbox_results['bbox_pred'],
img_shape,
scale_factor,
rescale=False,
cfg=None)
aug_bboxes.append(bboxes)
aug_scores.append(scores)
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms,
rcnn_test_cfg.max_per_img)
bbox_result = bbox2result(det_bboxes, det_labels,
self.bbox_head[-1].num_classes)
if self.with_mask:
if det_bboxes.shape[0] == 0:
segm_result = [[]
for _ in range(self.mask_head[-1].num_classes)]
else:
aug_masks = []
aug_img_metas = []
for x, img_meta in zip(features, img_metas):
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
flip_direction = img_meta[0]['flip_direction']
_bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
scale_factor, flip, flip_direction)
mask_rois = bbox2roi([_bboxes])
for i in range(self.num_stages):
mask_results = self._mask_forward(i, x, mask_rois)
aug_masks.append(
mask_results['mask_pred'].sigmoid().cpu().numpy())
aug_img_metas.append(img_meta)
merged_masks = merge_aug_masks(aug_masks, aug_img_metas,
self.test_cfg)
ori_shape = img_metas[0][0]['ori_shape']
dummy_scale_factor = np.ones(4)
segm_result = self.mask_head[-1].get_seg_masks(
merged_masks,
det_bboxes,
det_labels,
rcnn_test_cfg,
ori_shape,
scale_factor=dummy_scale_factor,
rescale=False)
return [(bbox_result, segm_result)]
else:
return [bbox_result]
def onnx_export(self, x, proposals, img_metas):
assert self.with_bbox, 'Bbox head must be implemented.'
assert proposals.shape[0] == 1, 'Only support one input image ' \
'while in exporting to ONNX'
# remove the scores
rois = proposals[..., :-1]
batch_size = rois.shape[0]
num_proposals_per_img = rois.shape[1]
# Eliminate the batch dimension
rois = rois.view(-1, 4)
# add dummy batch index
rois = torch.cat([rois.new_zeros(rois.shape[0], 1), rois], dim=-1)
max_shape = img_metas[0]['img_shape_for_onnx']
ms_scores = []
rcnn_test_cfg = self.test_cfg
for i in range(self.num_stages):
bbox_results = self._bbox_forward(i, x, rois)
cls_score = bbox_results['cls_score']
bbox_pred = bbox_results['bbox_pred']
# Recover the batch dimension
rois = rois.reshape(batch_size, num_proposals_per_img,
rois.size(-1))
cls_score = cls_score.reshape(batch_size, num_proposals_per_img,
cls_score.size(-1))
bbox_pred = bbox_pred.reshape(batch_size, num_proposals_per_img, 4)
ms_scores.append(cls_score)
if i < self.num_stages - 1:
assert self.bbox_head[i].reg_class_agnostic
new_rois = self.bbox_head[i].bbox_coder.decode(
rois[..., 1:], bbox_pred, max_shape=max_shape)
rois = new_rois.reshape(-1, new_rois.shape[-1])
# add dummy batch index
rois = torch.cat([rois.new_zeros(rois.shape[0], 1), rois],
dim=-1)
cls_score = sum(ms_scores) / float(len(ms_scores))
bbox_pred = bbox_pred.reshape(batch_size, num_proposals_per_img, 4)
rois = rois.reshape(batch_size, num_proposals_per_img, -1)
det_bboxes, det_labels = self.bbox_head[-1].onnx_export(
rois, cls_score, bbox_pred, max_shape, cfg=rcnn_test_cfg)
if not self.with_mask:
return det_bboxes, det_labels
else:
batch_index = torch.arange(
det_bboxes.size(0),
device=det_bboxes.device).float().view(-1, 1, 1).expand(
det_bboxes.size(0), det_bboxes.size(1), 1)
rois = det_bboxes[..., :4]
mask_rois = torch.cat([batch_index, rois], dim=-1)
mask_rois = mask_rois.view(-1, 5)
aug_masks = []
for i in range(self.num_stages):
mask_results = self._mask_forward(i, x, mask_rois)
mask_pred = mask_results['mask_pred']
aug_masks.append(mask_pred)
max_shape = img_metas[0]['img_shape_for_onnx']
# calculate the mean of masks from several stage
mask_pred = sum(aug_masks) / len(aug_masks)
segm_results = self.mask_head[-1].onnx_export(
mask_pred, rois.reshape(-1, 4), det_labels.reshape(-1),
self.test_cfg, max_shape)
segm_results = segm_results.reshape(batch_size,
det_bboxes.shape[1],
max_shape[0], max_shape[1])
return det_bboxes, det_labels, segm_results