Spaces:
Runtime error
Runtime error
File size: 17,440 Bytes
f549064 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from mmdet.registry import MODELS, TASK_UTILS
from mmdet.structures import DetDataSample, SampleList
from mmdet.structures.bbox import bbox2roi
from mmdet.utils import ConfigType, InstanceList
from ..task_modules.samplers import SamplingResult
from ..utils import empty_instances, unpack_gt_instances
from .base_roi_head import BaseRoIHead
@MODELS.register_module()
class StandardRoIHead(BaseRoIHead):
"""Simplest base roi head including one bbox head and one mask head."""
def init_assigner_sampler(self) -> None:
"""Initialize assigner and sampler."""
self.bbox_assigner = None
self.bbox_sampler = None
if self.train_cfg:
self.bbox_assigner = TASK_UTILS.build(self.train_cfg.assigner)
self.bbox_sampler = TASK_UTILS.build(
self.train_cfg.sampler, default_args=dict(context=self))
def init_bbox_head(self, bbox_roi_extractor: ConfigType,
bbox_head: ConfigType) -> None:
"""Initialize box head and box roi extractor.
Args:
bbox_roi_extractor (dict or ConfigDict): Config of box
roi extractor.
bbox_head (dict or ConfigDict): Config of box in box head.
"""
self.bbox_roi_extractor = MODELS.build(bbox_roi_extractor)
self.bbox_head = MODELS.build(bbox_head)
def init_mask_head(self, mask_roi_extractor: ConfigType,
mask_head: ConfigType) -> None:
"""Initialize mask head and mask roi extractor.
Args:
mask_roi_extractor (dict or ConfigDict): Config of mask roi
extractor.
mask_head (dict or ConfigDict): Config of mask in mask head.
"""
if mask_roi_extractor is not None:
self.mask_roi_extractor = MODELS.build(mask_roi_extractor)
self.share_roi_extractor = False
else:
self.share_roi_extractor = True
self.mask_roi_extractor = self.bbox_roi_extractor
self.mask_head = MODELS.build(mask_head)
# TODO: Need to refactor later
def forward(self,
x: Tuple[Tensor],
rpn_results_list: InstanceList,
batch_data_samples: SampleList = None) -> tuple:
"""Network forward process. Usually includes backbone, neck and head
forward without any post-processing.
Args:
x (List[Tensor]): Multi-level features that may have different
resolutions.
rpn_results_list (list[:obj:`InstanceData`]): List of region
proposals.
batch_data_samples (list[:obj:`DetDataSample`]): Each item contains
the meta information of each image and corresponding
annotations.
Returns
tuple: A tuple of features from ``bbox_head`` and ``mask_head``
forward.
"""
results = ()
proposals = [rpn_results.bboxes for rpn_results in rpn_results_list]
rois = bbox2roi(proposals)
# bbox head
if self.with_bbox:
bbox_results = self._bbox_forward(x, rois)
results = results + (bbox_results['cls_score'],
bbox_results['bbox_pred'])
# mask head
if self.with_mask:
mask_rois = rois[:100]
mask_results = self._mask_forward(x, mask_rois)
results = results + (mask_results['mask_preds'], )
return results
def loss(self, x: Tuple[Tensor], rpn_results_list: InstanceList,
batch_data_samples: List[DetDataSample]) -> dict:
"""Perform forward propagation and loss calculation of the detection
roi on the features of the upstream network.
Args:
x (tuple[Tensor]): List of multi-level img features.
rpn_results_list (list[:obj:`InstanceData`]): List of region
proposals.
batch_data_samples (list[:obj:`DetDataSample`]): The batch
data samples. It usually includes information such
as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
Returns:
dict[str, Tensor]: A dictionary of loss components
"""
assert len(rpn_results_list) == len(batch_data_samples)
outputs = unpack_gt_instances(batch_data_samples)
batch_gt_instances, batch_gt_instances_ignore, _ = outputs
# assign gts and sample proposals
num_imgs = len(batch_data_samples)
sampling_results = []
for i in range(num_imgs):
# rename rpn_results.bboxes to rpn_results.priors
rpn_results = rpn_results_list[i]
rpn_results.priors = rpn_results.pop('bboxes')
assign_result = self.bbox_assigner.assign(
rpn_results, batch_gt_instances[i],
batch_gt_instances_ignore[i])
sampling_result = self.bbox_sampler.sample(
assign_result,
rpn_results,
batch_gt_instances[i],
feats=[lvl_feat[i][None] for lvl_feat in x])
sampling_results.append(sampling_result)
losses = dict()
# bbox head loss
if self.with_bbox:
bbox_results = self.bbox_loss(x, sampling_results)
losses.update(bbox_results['loss_bbox'])
# mask head forward and loss
if self.with_mask:
mask_results = self.mask_loss(x, sampling_results,
bbox_results['bbox_feats'],
batch_gt_instances)
losses.update(mask_results['loss_mask'])
return losses
def _bbox_forward(self, x: Tuple[Tensor], rois: Tensor) -> dict:
"""Box head forward function used in both training and testing.
Args:
x (tuple[Tensor]): List of multi-level img features.
rois (Tensor): RoIs with the shape (n, 5) where the first
column indicates batch id of each RoI.
Returns:
dict[str, Tensor]: Usually returns a dictionary with keys:
- `cls_score` (Tensor): Classification scores.
- `bbox_pred` (Tensor): Box energies / deltas.
- `bbox_feats` (Tensor): Extract bbox RoI features.
"""
# TODO: a more flexible way to decide which feature maps to use
bbox_feats = self.bbox_roi_extractor(
x[:self.bbox_roi_extractor.num_inputs], rois)
if self.with_shared_head:
bbox_feats = self.shared_head(bbox_feats)
cls_score, bbox_pred = self.bbox_head(bbox_feats)
bbox_results = dict(
cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
return bbox_results
def bbox_loss(self, x: Tuple[Tensor],
sampling_results: List[SamplingResult]) -> dict:
"""Perform forward propagation and loss calculation of the bbox head on
the features of the upstream network.
Args:
x (tuple[Tensor]): List of multi-level img features.
sampling_results (list["obj:`SamplingResult`]): Sampling results.
Returns:
dict[str, Tensor]: Usually returns a dictionary with keys:
- `cls_score` (Tensor): Classification scores.
- `bbox_pred` (Tensor): Box energies / deltas.
- `bbox_feats` (Tensor): Extract bbox RoI features.
- `loss_bbox` (dict): A dictionary of bbox loss components.
"""
rois = bbox2roi([res.priors for res in sampling_results])
bbox_results = self._bbox_forward(x, rois)
bbox_loss_and_target = self.bbox_head.loss_and_target(
cls_score=bbox_results['cls_score'],
bbox_pred=bbox_results['bbox_pred'],
rois=rois,
sampling_results=sampling_results,
rcnn_train_cfg=self.train_cfg)
bbox_results.update(loss_bbox=bbox_loss_and_target['loss_bbox'])
return bbox_results
def mask_loss(self, x: Tuple[Tensor],
sampling_results: List[SamplingResult], bbox_feats: Tensor,
batch_gt_instances: InstanceList) -> dict:
"""Perform forward propagation and loss calculation of the mask head on
the features of the upstream network.
Args:
x (tuple[Tensor]): Tuple of multi-level img features.
sampling_results (list["obj:`SamplingResult`]): Sampling results.
bbox_feats (Tensor): Extract bbox RoI features.
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes``, ``labels``, and
``masks`` attributes.
Returns:
dict: Usually returns a dictionary with keys:
- `mask_preds` (Tensor): Mask prediction.
- `mask_feats` (Tensor): Extract mask RoI features.
- `mask_targets` (Tensor): Mask target of each positive\
proposals in the image.
- `loss_mask` (dict): A dictionary of mask loss components.
"""
if not self.share_roi_extractor:
pos_rois = bbox2roi([res.pos_priors for res in sampling_results])
mask_results = self._mask_forward(x, pos_rois)
else:
pos_inds = []
device = bbox_feats.device
for res in sampling_results:
pos_inds.append(
torch.ones(
res.pos_priors.shape[0],
device=device,
dtype=torch.uint8))
pos_inds.append(
torch.zeros(
res.neg_priors.shape[0],
device=device,
dtype=torch.uint8))
pos_inds = torch.cat(pos_inds)
mask_results = self._mask_forward(
x, pos_inds=pos_inds, bbox_feats=bbox_feats)
mask_loss_and_target = self.mask_head.loss_and_target(
mask_preds=mask_results['mask_preds'],
sampling_results=sampling_results,
batch_gt_instances=batch_gt_instances,
rcnn_train_cfg=self.train_cfg)
mask_results.update(loss_mask=mask_loss_and_target['loss_mask'])
return mask_results
def _mask_forward(self,
x: Tuple[Tensor],
rois: Tensor = None,
pos_inds: Optional[Tensor] = None,
bbox_feats: Optional[Tensor] = None) -> dict:
"""Mask head forward function used in both training and testing.
Args:
x (tuple[Tensor]): Tuple of multi-level img features.
rois (Tensor): RoIs with the shape (n, 5) where the first
column indicates batch id of each RoI.
pos_inds (Tensor, optional): Indices of positive samples.
Defaults to None.
bbox_feats (Tensor): Extract bbox RoI features. Defaults to None.
Returns:
dict[str, Tensor]: Usually returns a dictionary with keys:
- `mask_preds` (Tensor): Mask prediction.
- `mask_feats` (Tensor): Extract mask RoI features.
"""
assert ((rois is not None) ^
(pos_inds is not None and bbox_feats is not None))
if rois is not None:
mask_feats = self.mask_roi_extractor(
x[:self.mask_roi_extractor.num_inputs], rois)
if self.with_shared_head:
mask_feats = self.shared_head(mask_feats)
else:
assert bbox_feats is not None
mask_feats = bbox_feats[pos_inds]
mask_preds = self.mask_head(mask_feats)
mask_results = dict(mask_preds=mask_preds, mask_feats=mask_feats)
return mask_results
def predict_bbox(self,
x: Tuple[Tensor],
batch_img_metas: List[dict],
rpn_results_list: InstanceList,
rcnn_test_cfg: ConfigType,
rescale: bool = False) -> InstanceList:
"""Perform forward propagation of the bbox head and predict detection
results on the features of the upstream network.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
batch_img_metas (list[dict]): List of image information.
rpn_results_list (list[:obj:`InstanceData`]): List of region
proposals.
rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
Returns:
list[:obj:`InstanceData`]: Detection results of each image
after the post process.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
"""
proposals = [res.bboxes for res in rpn_results_list]
rois = bbox2roi(proposals)
if rois.shape[0] == 0:
return empty_instances(
batch_img_metas,
rois.device,
task_type='bbox',
box_type=self.bbox_head.predict_box_type,
num_classes=self.bbox_head.num_classes,
score_per_cls=rcnn_test_cfg is None)
bbox_results = self._bbox_forward(x, rois)
# split batch bbox prediction back to each image
cls_scores = bbox_results['cls_score']
bbox_preds = bbox_results['bbox_pred']
num_proposals_per_img = tuple(len(p) for p in proposals)
rois = rois.split(num_proposals_per_img, 0)
cls_scores = cls_scores.split(num_proposals_per_img, 0)
# some detector with_reg is False, bbox_preds will be None
if bbox_preds is not None:
# TODO move this to a sabl_roi_head
# the bbox prediction of some detectors like SABL is not Tensor
if isinstance(bbox_preds, torch.Tensor):
bbox_preds = bbox_preds.split(num_proposals_per_img, 0)
else:
bbox_preds = self.bbox_head.bbox_pred_split(
bbox_preds, num_proposals_per_img)
else:
bbox_preds = (None, ) * len(proposals)
result_list = self.bbox_head.predict_by_feat(
rois=rois,
cls_scores=cls_scores,
bbox_preds=bbox_preds,
batch_img_metas=batch_img_metas,
rcnn_test_cfg=rcnn_test_cfg,
rescale=rescale)
return result_list
def predict_mask(self,
x: Tuple[Tensor],
batch_img_metas: List[dict],
results_list: InstanceList,
rescale: bool = False) -> InstanceList:
"""Perform forward propagation of the mask head and predict detection
results on the features of the upstream network.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
batch_img_metas (list[dict]): List of image information.
results_list (list[:obj:`InstanceData`]): Detection results of
each image.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
Returns:
list[:obj:`InstanceData`]: Detection results of each image
after the post process.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- masks (Tensor): Has a shape (num_instances, H, W).
"""
# don't need to consider aug_test.
bboxes = [res.bboxes for res in results_list]
mask_rois = bbox2roi(bboxes)
if mask_rois.shape[0] == 0:
results_list = empty_instances(
batch_img_metas,
mask_rois.device,
task_type='mask',
instance_results=results_list,
mask_thr_binary=self.test_cfg.mask_thr_binary)
return results_list
mask_results = self._mask_forward(x, mask_rois)
mask_preds = mask_results['mask_preds']
# split batch mask prediction back to each image
num_mask_rois_per_img = [len(res) for res in results_list]
mask_preds = mask_preds.split(num_mask_rois_per_img, 0)
# TODO: Handle the case where rescale is false
results_list = self.mask_head.predict_by_feat(
mask_preds=mask_preds,
results_list=results_list,
batch_img_metas=batch_img_metas,
rcnn_test_cfg=self.test_cfg,
rescale=rescale)
return results_list
|