Spaces:
Build error
Build error
File size: 15,155 Bytes
cdfecf8 |
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 |
import logging
import sys
import torch
from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes,
merge_aug_masks, multiclass_nms)
logger = logging.getLogger(__name__)
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import completed
class BBoxTestMixin(object):
if sys.version_info >= (3, 7):
async def async_test_bboxes(self,
x,
img_metas,
proposals,
rcnn_test_cfg,
rescale=False,
bbox_semaphore=None,
global_lock=None):
"""Asynchronized test for box head without augmentation."""
rois = bbox2roi(proposals)
roi_feats = self.bbox_roi_extractor(
x[:len(self.bbox_roi_extractor.featmap_strides)], rois)
if self.with_shared_head:
roi_feats = self.shared_head(roi_feats)
sleep_interval = rcnn_test_cfg.get('async_sleep_interval', 0.017)
async with completed(
__name__, 'bbox_head_forward',
sleep_interval=sleep_interval):
cls_score, bbox_pred = self.bbox_head(roi_feats)
img_shape = img_metas[0]['img_shape']
scale_factor = img_metas[0]['scale_factor']
det_bboxes, det_labels = self.bbox_head.get_bboxes(
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=rescale,
cfg=rcnn_test_cfg)
return det_bboxes, det_labels
def simple_test_bboxes(self,
x,
img_metas,
proposals,
rcnn_test_cfg,
rescale=False):
"""Test only det bboxes without augmentation.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
img_metas (list[dict]): Image meta info.
proposals (Tensor or List[Tensor]): Region proposals.
rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN.
rescale (bool): If True, return boxes in original image space.
Default: False.
Returns:
tuple[list[Tensor], list[Tensor]]: The first list contains
the boxes of the corresponding image in a batch, each
tensor has the shape (num_boxes, 5) and last dimension
5 represent (tl_x, tl_y, br_x, br_y, score). Each Tensor
in the second list is the labels with shape (num_boxes, ).
The length of both lists should be equal to batch_size.
"""
# get origin input shape to support onnx dynamic input shape
if torch.onnx.is_in_onnx_export():
assert len(
img_metas
) == 1, 'Only support one input image while in exporting to ONNX'
img_shapes = img_metas[0]['img_shape_for_onnx']
else:
img_shapes = tuple(meta['img_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
# The length of proposals of different batches may be different.
# In order to form a batch, a padding operation is required.
if isinstance(proposals, list):
# padding to form a batch
max_size = max([proposal.size(0) for proposal in proposals])
for i, proposal in enumerate(proposals):
supplement = proposal.new_full(
(max_size - proposal.size(0), proposal.size(1)), 0)
proposals[i] = torch.cat((supplement, proposal), dim=0)
rois = torch.stack(proposals, dim=0)
else:
rois = proposals
batch_index = torch.arange(
rois.size(0), device=rois.device).float().view(-1, 1, 1).expand(
rois.size(0), rois.size(1), 1)
rois = torch.cat([batch_index, rois[..., :4]], dim=-1)
batch_size = rois.shape[0]
num_proposals_per_img = rois.shape[1]
# Eliminate the batch dimension
rois = rois.view(-1, 5)
bbox_results = self._bbox_forward(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, -1)
cls_score = cls_score.reshape(batch_size, num_proposals_per_img, -1)
if not torch.onnx.is_in_onnx_export():
# remove padding
supplement_mask = rois[..., -1] == 0
cls_score[supplement_mask, :] = 0
# bbox_pred would be None in some detector when with_reg is False,
# e.g. Grid R-CNN.
if bbox_pred is not None:
# the bbox prediction of some detectors like SABL is not Tensor
if isinstance(bbox_pred, torch.Tensor):
bbox_pred = bbox_pred.reshape(batch_size,
num_proposals_per_img, -1)
if not torch.onnx.is_in_onnx_export():
bbox_pred[supplement_mask, :] = 0
else:
# TODO: Looking forward to a better way
# For SABL
bbox_preds = self.bbox_head.bbox_pred_split(
bbox_pred, num_proposals_per_img)
# apply bbox post-processing to each image individually
det_bboxes = []
det_labels = []
for i in range(len(proposals)):
# remove padding
supplement_mask = proposals[i][..., -1] == 0
for bbox in bbox_preds[i]:
bbox[supplement_mask] = 0
det_bbox, det_label = self.bbox_head.get_bboxes(
rois[i],
cls_score[i],
bbox_preds[i],
img_shapes[i],
scale_factors[i],
rescale=rescale,
cfg=rcnn_test_cfg)
det_bboxes.append(det_bbox)
det_labels.append(det_label)
return det_bboxes, det_labels
else:
bbox_pred = None
return self.bbox_head.get_bboxes(
rois,
cls_score,
bbox_pred,
img_shapes,
scale_factors,
rescale=rescale,
cfg=rcnn_test_cfg)
def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
"""Test det bboxes with test time augmentation."""
aug_bboxes = []
aug_scores = []
for x, img_meta in zip(feats, 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']
# TODO more flexible
proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
scale_factor, flip, flip_direction)
rois = bbox2roi([proposals])
bbox_results = self._bbox_forward(x, rois)
bboxes, scores = self.bbox_head.get_bboxes(
rois,
bbox_results['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)
return det_bboxes, det_labels
class MaskTestMixin(object):
if sys.version_info >= (3, 7):
async def async_test_mask(self,
x,
img_metas,
det_bboxes,
det_labels,
rescale=False,
mask_test_cfg=None):
"""Asynchronized test for mask head without augmentation."""
# image shape of the first image in the batch (only one)
ori_shape = img_metas[0]['ori_shape']
scale_factor = img_metas[0]['scale_factor']
if det_bboxes.shape[0] == 0:
segm_result = [[] for _ in range(self.mask_head.num_classes)]
else:
if rescale and not isinstance(scale_factor,
(float, torch.Tensor)):
scale_factor = det_bboxes.new_tensor(scale_factor)
_bboxes = (
det_bboxes[:, :4] *
scale_factor if rescale else det_bboxes)
mask_rois = bbox2roi([_bboxes])
mask_feats = self.mask_roi_extractor(
x[:len(self.mask_roi_extractor.featmap_strides)],
mask_rois)
if self.with_shared_head:
mask_feats = self.shared_head(mask_feats)
if mask_test_cfg and mask_test_cfg.get('async_sleep_interval'):
sleep_interval = mask_test_cfg['async_sleep_interval']
else:
sleep_interval = 0.035
async with completed(
__name__,
'mask_head_forward',
sleep_interval=sleep_interval):
mask_pred = self.mask_head(mask_feats)
segm_result = self.mask_head.get_seg_masks(
mask_pred, _bboxes, det_labels, self.test_cfg, ori_shape,
scale_factor, rescale)
return segm_result
def simple_test_mask(self,
x,
img_metas,
det_bboxes,
det_labels,
rescale=False):
"""Simple test for mask head without augmentation."""
# image shapes of images in the batch
ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
# The length of proposals of different batches may be different.
# In order to form a batch, a padding operation is required.
if isinstance(det_bboxes, list):
# padding to form a batch
max_size = max([bboxes.size(0) for bboxes in det_bboxes])
for i, (bbox, label) in enumerate(zip(det_bboxes, det_labels)):
supplement_bbox = bbox.new_full(
(max_size - bbox.size(0), bbox.size(1)), 0)
supplement_label = label.new_full((max_size - label.size(0), ),
0)
det_bboxes[i] = torch.cat((supplement_bbox, bbox), dim=0)
det_labels[i] = torch.cat((supplement_label, label), dim=0)
det_bboxes = torch.stack(det_bboxes, dim=0)
det_labels = torch.stack(det_labels, dim=0)
batch_size = det_bboxes.size(0)
num_proposals_per_img = det_bboxes.shape[1]
# if det_bboxes is rescaled to the original image size, we need to
# rescale it back to the testing scale to obtain RoIs.
det_bboxes = det_bboxes[..., :4]
if rescale:
if not isinstance(scale_factors[0], float):
scale_factors = det_bboxes.new_tensor(scale_factors)
det_bboxes = det_bboxes * scale_factors.unsqueeze(1)
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)
mask_rois = torch.cat([batch_index, det_bboxes], dim=-1)
mask_rois = mask_rois.view(-1, 5)
mask_results = self._mask_forward(x, mask_rois)
mask_pred = mask_results['mask_pred']
# Recover the batch dimension
mask_preds = mask_pred.reshape(batch_size, num_proposals_per_img,
*mask_pred.shape[1:])
# apply mask post-processing to each image individually
segm_results = []
for i in range(batch_size):
mask_pred = mask_preds[i]
det_bbox = det_bboxes[i]
det_label = det_labels[i]
# remove padding
supplement_mask = det_bbox[..., -1] != 0
mask_pred = mask_pred[supplement_mask]
det_bbox = det_bbox[supplement_mask]
det_label = det_label[supplement_mask]
if det_label.shape[0] == 0:
segm_results.append([[]
for _ in range(self.mask_head.num_classes)
])
else:
segm_result = self.mask_head.get_seg_masks(
mask_pred, det_bbox, det_label, self.test_cfg,
ori_shapes[i], scale_factors[i], rescale)
segm_results.append(segm_result)
return segm_results
def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
"""Test for mask head with test time augmentation."""
if det_bboxes.shape[0] == 0:
segm_result = [[] for _ in range(self.mask_head.num_classes)]
else:
aug_masks = []
for x, img_meta in zip(feats, 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])
mask_results = self._mask_forward(x, mask_rois)
# convert to numpy array to save memory
aug_masks.append(
mask_results['mask_pred'].sigmoid().cpu().numpy())
merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)
ori_shape = img_metas[0][0]['ori_shape']
segm_result = self.mask_head.get_seg_masks(
merged_masks,
det_bboxes,
det_labels,
self.test_cfg,
ori_shape,
scale_factor=1.0,
rescale=False)
return segm_result
|