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April 8, 2018 17:26
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RegionLoss Profile
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| Timer unit: 1e-06 s | |
| Pystone time: 0.026254 s | |
| File: /home/joncrall/code/netharn/netharn/models/yolo2/light_region_loss.py | |
| Function: _build_targets_tensor at line 382 | |
| Line # Hits Time Per Hit % Time Line Contents | |
| ============================================================== | |
| 382 @profiler.profile | |
| 383 def _build_targets_tensor(self, pred_boxes, pred_confs, ground_truth, nH, nW, seen=0): | |
| 384 """ | |
| 385 Compare prediction boxes and ground truths, convert ground truths to network output tensors | |
| 386 | |
| 387 Example: | |
| 388 >>> from netharn.models.yolo2.light_yolo import Yolo | |
| 389 >>> from netharn.models.yolo2.light_region_loss import RegionLoss | |
| 390 >>> torch.random.manual_seed(0) | |
| 391 >>> network = Yolo(num_classes=2, conf_thresh=4e-2) | |
| 392 >>> self = RegionLoss(num_classes=network.num_classes, anchors=network.anchors) | |
| 393 >>> Win, Hin = 96, 96 | |
| 394 >>> nW, nH = 3, 3 | |
| 395 >>> # true boxes for each item in the batch | |
| 396 >>> # each box encodes class, center, width, and height | |
| 397 >>> # coordinates are normalized in the range 0 to 1 | |
| 398 >>> # items in each batch are padded with dummy boxes with class_id=-1 | |
| 399 >>> ground_truth = torch.FloatTensor([ | |
| 400 >>> # boxes for batch item 0 (it has no objects, note the pad!) | |
| 401 >>> [[-1, 0, 0, 0, 0], | |
| 402 >>> [-1, 0, 0, 0, 0], | |
| 403 >>> [-1, 0, 0, 0, 0]], | |
| 404 >>> # boxes for batch item 1 | |
| 405 >>> [[0, 0.50, 0.50, 1.00, 1.00], | |
| 406 >>> [1, 0.34, 0.32, 0.12, 0.32], | |
| 407 >>> [1, 0.32, 0.42, 0.22, 0.12]], | |
| 408 >>> ]) | |
| 409 >>> pred_boxes = torch.rand(90, 4) | |
| 410 >>> pred_confs = torch.rand(90) | |
| 411 >>> seen = 0 | |
| 412 | |
| 413 """ | |
| 414 # Parameters | |
| 415 1 10.0 10.0 0.0 nB = ground_truth.size(0) | |
| 416 1 8.0 8.0 0.0 nT = ground_truth.size(1) | |
| 417 1 8.0 8.0 0.0 nA = self.num_anchors | |
| 418 1 8.0 8.0 0.0 nAnchors = nA * nH * nW | |
| 419 1 8.0 8.0 0.0 nPixels = nH * nW | |
| 420 | |
| 421 1 8.0 8.0 0.0 seen = seen + nB | |
| 422 | |
| 423 # Tensors | |
| 424 1 36.0 36.0 0.1 conf_mask = torch.ones(nB, nA, nPixels) * self.noobject_scale | |
| 425 1 19.0 19.0 0.1 coord_mask = torch.zeros(nB, nA, 1, nPixels) | |
| 426 1 64.0 64.0 0.2 cls_mask = torch.zeros(nB, nA, nPixels).byte() | |
| 427 1 24.0 24.0 0.1 tcoord = torch.zeros(nB, nA, 4, nPixels) | |
| 428 1 18.0 18.0 0.1 tconf = torch.zeros(nB, nA, nPixels) | |
| 429 1 17.0 17.0 0.1 tcls = torch.zeros(nB, nA, nPixels) | |
| 430 | |
| 431 1 9.0 9.0 0.0 if seen < 12800: | |
| 432 1 16.0 16.0 0.1 coord_mask.fill_(1) | |
| 433 1 24.0 24.0 0.1 tcoord[:, :, 0].fill_(0.5) | |
| 434 1 19.0 19.0 0.1 tcoord[:, :, 1].fill_(0.5) | |
| 435 | |
| 436 1 8.0 8.0 0.0 pred_cxywh = pred_boxes | |
| 437 1 243.0 243.0 0.9 pred_tlbr = util.Boxes(pred_cxywh.data.cpu().numpy(), 'cxywh').toformat('tlbr').data | |
| 438 | |
| 439 1 23.0 23.0 0.1 gt_class = ground_truth[..., 0].data.cpu().numpy() | |
| 440 1 91.0 91.0 0.3 gt_cxywh = util.Boxes(ground_truth[..., 1:5].numpy().astype(np.float32), 'cxywh').scale([nW, nH]) | |
| 441 | |
| 442 1 72.0 72.0 0.3 gt_tlbr = gt_cxywh.to_tlbr().data | |
| 443 | |
| 444 1 19.0 19.0 0.1 rel_gt_cxywh = gt_cxywh.copy() | |
| 445 1 16.0 16.0 0.1 rel_gt_cxywh.data.T[0:2] = 0 | |
| 446 | |
| 447 1 70.0 70.0 0.3 rel_gt_tlbr = rel_gt_cxywh.toformat('tlbr').data | |
| 448 | |
| 449 1 19.0 19.0 0.1 gt_isvalid = (gt_class >= 0) | |
| 450 | |
| 451 1 8.0 8.0 0.0 batch_assigns = [] | |
| 452 9 70.0 7.8 0.3 for bx in range(nB): | |
| 453 # Get the actual groundtruth boxes for this batch item | |
| 454 8 64.0 8.0 0.2 flags = gt_isvalid[bx] | |
| 455 8 191.0 23.9 0.7 if not np.any(flags): | |
| 456 1 6.0 6.0 0.0 batch_assigns.append((None, None)) | |
| 457 1 5.0 5.0 0.0 continue | |
| 458 | |
| 459 # Create gt anchor assignments | |
| 460 7 135.0 19.3 0.5 batch_rel_gt_tlbr = rel_gt_tlbr[bx][flags] | |
| 461 7 56.0 8.0 0.2 anchor_ious = util.box_ious(self.rel_anchors_tlbr, | |
| 462 7 46.0 6.6 0.2 batch_rel_gt_tlbr, bias=0, | |
| 463 7 172.0 24.6 0.7 mode=self.iou_mode) | |
| 464 7 126.0 18.0 0.5 best_ns = np.argmax(anchor_ious, axis=0) | |
| 465 7 136.0 19.4 0.5 best_anchor_ious = anchor_ious.max(axis=0) | |
| 466 7 50.0 7.1 0.2 batch_assigns.append((best_ns, best_anchor_ious)) | |
| 467 | |
| 468 # Setting confidence mask | |
| 469 7 64.0 9.1 0.2 cur_pred_tlbr = pred_tlbr[bx * nAnchors:(bx + 1) * nAnchors] | |
| 470 7 108.0 15.4 0.4 cur_gt_tlbr = gt_tlbr[bx][flags] | |
| 471 | |
| 472 7 50.0 7.1 0.2 ious = util.box_ious(cur_pred_tlbr, cur_gt_tlbr, bias=0, | |
| 473 7 533.0 76.1 2.0 mode=self.iou_mode) | |
| 474 7 421.0 60.1 1.6 cur_ious = torch.FloatTensor(ious.max(-1)) | |
| 475 7 452.0 64.6 1.7 conf_mask[bx].view(-1)[cur_ious > self.thresh] = 0 | |
| 476 | |
| 477 # Loop over ground_truths and construct tensors | |
| 478 9 50.0 5.6 0.2 for bx in range(nB): | |
| 479 8 45.0 5.6 0.2 best_ns, best_anchor_ious = batch_assigns[bx] | |
| 480 8 53.0 6.6 0.2 flags = gt_isvalid[bx] | |
| 481 8 158.0 19.8 0.6 if not np.any(flags): | |
| 482 1 5.0 5.0 0.0 continue | |
| 483 75 441.0 5.9 1.7 for t in range(nT): | |
| 484 74 451.0 6.1 1.7 if not flags[t]: | |
| 485 6 30.0 5.0 0.1 break | |
| 486 68 672.0 9.9 2.6 gx, gy, gw, gh = gt_cxywh.data[bx][t] | |
| 487 68 516.0 7.6 2.0 gi = min(nW - 1, max(0, int(gx))) | |
| 488 68 438.0 6.4 1.7 gj = min(nH - 1, max(0, int(gy))) | |
| 489 | |
| 490 68 395.0 5.8 1.5 best_n = best_ns[t] | |
| 491 | |
| 492 68 432.0 6.4 1.6 gt_box_ = gt_tlbr[bx][t] | |
| 493 68 568.0 8.4 2.2 pred_box_ = pred_tlbr[bx * nAnchors + best_n * nPixels + gj * nW + gi] | |
| 494 | |
| 495 68 463.0 6.8 1.8 iou = float(util.box_ious(gt_box_[None, :], pred_box_[None, :], | |
| 496 68 1390.0 20.4 5.3 bias=0, mode=self.iou_mode)[0, 0]) | |
| 497 | |
| 498 68 472.0 6.9 1.8 best_anchor = self.anchors[best_n] | |
| 499 68 541.0 8.0 2.1 best_aw, best_ah = best_anchor | |
| 500 | |
| 501 68 1655.0 24.3 6.3 coord_mask[bx, best_n, 0, gj * nW + gi] = 1 | |
| 502 68 1470.0 21.6 5.6 cls_mask[bx, best_n, gj * nW + gi] = 1 | |
| 503 68 1344.0 19.8 5.1 conf_mask[bx, best_n, gj * nW + gi] = self.object_scale | |
| 504 | |
| 505 68 2211.0 32.5 8.4 tcoord[bx, best_n, 0, gj * nW + gi] = gx - gi | |
| 506 68 2083.0 30.6 7.9 tcoord[bx, best_n, 1, gj * nW + gi] = gy - gj | |
| 507 68 2102.0 30.9 8.0 tcoord[bx, best_n, 2, gj * nW + gi] = math.log(gw / best_aw) | |
| 508 68 2061.0 30.3 7.9 tcoord[bx, best_n, 3, gj * nW + gi] = math.log(gh / best_ah) | |
| 509 68 1277.0 18.8 4.9 tconf[bx, best_n, gj * nW + gi] = iou | |
| 510 68 1376.0 20.2 5.2 tcls[bx, best_n, gj * nW + gi] = ground_truth[bx, t, 0] | |
| 511 | |
| 512 1 5.0 5.0 0.0 return coord_mask, conf_mask, cls_mask, tcoord, tconf, tcls | |
| Pystone time: 0.062961 s | |
| File: /home/joncrall/code/netharn/netharn/models/yolo2/light_region_loss.py | |
| Function: forward at line 284 | |
| Line # Hits Time Per Hit % Time Line Contents | |
| ============================================================== | |
| 284 @profiler.profile | |
| 285 def forward(self, output, target, seen=0): | |
| 286 """ Compute Region loss. | |
| 287 | |
| 288 Args: | |
| 289 output (torch.autograd.Variable): Output from the network | |
| 290 target (brambox.boxes.annotations.Annotation or torch.Tensor): Brambox annotations or tensor containing the annotation targets (see :class:`lightnet.data.BramboxToTensor`) | |
| 291 seen (int): if specified, overrides the `seen` attribute read from `self.net` (default None) | |
| 292 | |
| 293 Note: | |
| 294 If target is a tensor, the shape should be [B, T, 5], where B is | |
| 295 the batch size, T is the maximum number of boxes in an item, and | |
| 296 the final dimension should correspond to [class_idx, center_x, | |
| 297 center_y, width, height]. Items with fewer than T boxes should be | |
| 298 padded with dummy boxes with class_idx=-1. | |
| 299 """ | |
| 300 # Parameters | |
| 301 1 14.0 14.0 0.0 nB = output.data.size(0) | |
| 302 1 8.0 8.0 0.0 nA = self.num_anchors | |
| 303 1 6.0 6.0 0.0 nC = self.num_classes | |
| 304 1 9.0 9.0 0.0 nH = output.data.size(2) | |
| 305 1 8.0 8.0 0.0 nW = output.data.size(3) | |
| 306 1 10.0 10.0 0.0 cuda = output.is_cuda | |
| 307 1 9.0 9.0 0.0 if isinstance(target, Variable): | |
| 308 1 7.0 7.0 0.0 target = target.data | |
| 309 | |
| 310 # Get x,y,w,h,conf,cls | |
| 311 1 39.0 39.0 0.1 output = output.view(nB, nA, -1, nH * nW) | |
| 312 1 116.0 116.0 0.2 coord = torch.zeros_like(output[:, :, :4]) | |
| 313 1 343.0 343.0 0.5 coord[:, :, 0:2] = output[:, :, 0:2].sigmoid() # tx,ty | |
| 314 1 47.0 47.0 0.1 coord[:, :, 2:4] = output[:, :, 2:4] # tw,th | |
| 315 1 158.0 158.0 0.3 conf = output[:, :, 4].sigmoid() | |
| 316 1 7.0 7.0 0.0 if nC > 1: | |
| 317 1 90.0 90.0 0.1 cls = output[:, :, 5:].contiguous().view(nB * nA, nC, nH * nW).transpose(1, 2).contiguous().view(-1, nC) | |
| 318 | |
| 319 # Create prediction boxes | |
| 320 1 157.0 157.0 0.2 pred_boxes, lin_x, lin_y = self._init_pred_boxes(cuda, nB, nA, nH, nW) | |
| 321 | |
| 322 1 97.0 97.0 0.2 pred_boxes[:, 0] = (coord[:, :, 0].data + lin_x).view(-1) | |
| 323 1 58.0 58.0 0.1 pred_boxes[:, 1] = (coord[:, :, 1].data + lin_y).view(-1) | |
| 324 1 211.0 211.0 0.3 pred_boxes[:, 2] = (coord[:, :, 2].data.exp() * self.anchor_w).view(-1) | |
| 325 1 175.0 175.0 0.3 pred_boxes[:, 3] = (coord[:, :, 3].data.exp() * self.anchor_h).view(-1) | |
| 326 1 14.0 14.0 0.0 pred_boxes = pred_boxes.cpu() | |
| 327 | |
| 328 # Create predicted confs | |
| 329 1 13.0 13.0 0.0 pred_confs = torch.FloatTensor(nB * nA * nH * nW) | |
| 330 1 17.0 17.0 0.0 pred_confs = conf.data.view(-1).cpu() | |
| 331 | |
| 332 # Get target values | |
| 333 1 7.0 7.0 0.0 coord_mask, conf_mask, cls_mask, tcoord, tconf, tcls = self.build_targets( | |
| 334 1 36113.0 36113.0 57.4 pred_boxes, pred_confs, target, nH, nW, seen=seen) | |
| 335 1 23.0 23.0 0.0 coord_mask = coord_mask.expand_as(tcoord) | |
| 336 1 4.0 4.0 0.0 if nC > 1: | |
| 337 1 75.0 75.0 0.1 tcls = tcls.view(-1)[cls_mask.view(-1)].long() | |
| 338 1 74.0 74.0 0.1 cls_mask = cls_mask.view(-1, 1).repeat(1, nC) | |
| 339 | |
| 340 1 4.0 4.0 0.0 if cuda: | |
| 341 tcoord = tcoord.cuda() | |
| 342 tconf = tconf.cuda() | |
| 343 coord_mask = coord_mask.cuda() | |
| 344 conf_mask = conf_mask.cuda() | |
| 345 if nC > 1: | |
| 346 tcls = tcls.cuda() | |
| 347 cls_mask = cls_mask.cuda() | |
| 348 | |
| 349 1 9.0 9.0 0.0 tcoord = Variable(tcoord, requires_grad=False) | |
| 350 1 6.0 6.0 0.0 tconf = Variable(tconf, requires_grad=False) | |
| 351 1 7.0 7.0 0.0 coord_mask = Variable(coord_mask, requires_grad=False) | |
| 352 1 23556.0 23556.0 37.4 conf_mask = Variable(conf_mask.sqrt(), requires_grad=False) | |
| 353 1 10.0 10.0 0.0 if nC > 1: | |
| 354 1 13.0 13.0 0.0 tcls = Variable(tcls, requires_grad=False) | |
| 355 1 12.0 12.0 0.0 cls_mask = Variable(cls_mask, requires_grad=False) | |
| 356 1 161.0 161.0 0.3 cls = cls[cls_mask].view(-1, nC) | |
| 357 | |
| 358 # Compute losses | |
| 359 1 391.0 391.0 0.6 loss_coord = self.coord_scale * self.mse(coord * coord_mask, tcoord * coord_mask) / nB | |
| 360 1 134.0 134.0 0.2 loss_conf = self.mse(conf * conf_mask, tconf * conf_mask) / nB | |
| 361 1 10.0 10.0 0.0 if nC > 1: | |
| 362 1 529.0 529.0 0.8 loss_cls = self.class_scale * 2 * nn.CrossEntropyLoss(size_average=False)(cls, tcls) / nB | |
| 363 1 51.0 51.0 0.1 loss_tot = loss_coord + loss_conf + loss_cls | |
| 364 1 45.0 45.0 0.1 self.loss_cls = float(loss_cls.data.cpu().numpy()) | |
| 365 else: | |
| 366 self.loss_cls = 0 | |
| 367 loss_tot = loss_coord + loss_conf | |
| 368 | |
| 369 1 36.0 36.0 0.1 self.loss_tot = float(loss_tot.data.cpu().numpy()) | |
| 370 1 34.0 34.0 0.1 self.loss_coord = float(loss_coord.data.cpu().numpy()) | |
| 371 1 34.0 34.0 0.1 self.loss_conf = float(loss_conf.data.cpu().numpy()) | |
| 372 | |
| 373 1 10.0 10.0 0.0 return loss_tot | |
| Pystone time: 0.128014 s | |
| File: /home/joncrall/code/lightnet/lightnet/network/loss.py | |
| Function: __build_targets_tensor at line 141 | |
| Line # Hits Time Per Hit % Time Line Contents | |
| ============================================================== | |
| 141 @profiler.profile | |
| 142 def __build_targets_tensor(self, pred_boxes, pred_confs, ground_truth, nH, nW): | |
| 143 """ Compare prediction boxes and ground truths, convert ground truths to network output tensors """ | |
| 144 # Parameters | |
| 145 1 3.0 3.0 0.0 nB = ground_truth.size(0) | |
| 146 1 3.0 3.0 0.0 nT = ground_truth.size(1) | |
| 147 1 2.0 2.0 0.0 nA = self.num_anchors | |
| 148 1 2.0 2.0 0.0 nAnchors = nA*nH*nW | |
| 149 1 2.0 2.0 0.0 nPixels = nH*nW | |
| 150 1 3.0 3.0 0.0 seen = self.net.seen + nB | |
| 151 | |
| 152 # Tensors | |
| 153 1 25.0 25.0 0.0 conf_mask = torch.ones(nB, nA, nH*nW) * self.noobject_scale | |
| 154 1 12.0 12.0 0.0 coord_mask = torch.zeros(nB, nA, 1, nH*nW) | |
| 155 1 30.0 30.0 0.0 cls_mask = torch.zeros(nB, nA, nH*nW).byte() | |
| 156 1 16.0 16.0 0.0 tcoord = torch.zeros(nB, nA, 4, nH*nW) | |
| 157 1 11.0 11.0 0.0 tconf = torch.zeros(nB, nA, nH*nW) | |
| 158 1 10.0 10.0 0.0 tcls = torch.zeros(nB, nA, nH*nW) | |
| 159 | |
| 160 1 2.0 2.0 0.0 if seen < 12800: | |
| 161 1 9.0 9.0 0.0 coord_mask.fill_(1) | |
| 162 1 3.0 3.0 0.0 if self.anchor_step == 4: | |
| 163 tcoord[:,:,0] = torch.Tensor(self.anchors[2::self.anchor_step]).view(1,nA,1,1).repeat(nB,1,1,nH*nW) | |
| 164 tcoord[:,:,1] = torch.Tensor(self.anchors[3::self.anchor_step]).view(1,nA,1,1).repeat(nB,1,1,nH*nW) | |
| 165 else: | |
| 166 1 16.0 16.0 0.0 tcoord[:,:,0].fill_(0.5) | |
| 167 1 13.0 13.0 0.0 tcoord[:,:,1].fill_(0.5) | |
| 168 | |
| 169 # Setting confidence mask | |
| 170 9 17.0 1.9 0.0 for b in range(nB): | |
| 171 8 42.0 5.2 0.0 cur_pred_boxes = pred_boxes[b*nAnchors:(b+1)*nAnchors] | |
| 172 8 36.0 4.5 0.0 cur_pred_confs = pred_confs[b*nAnchors:(b+1)*nAnchors] | |
| 173 8 67.0 8.4 0.1 cur_ious = torch.zeros(nAnchors) | |
| 174 76 141.0 1.9 0.1 for t in range(nT): | |
| 175 75 1003.0 13.4 0.8 if ground_truth[b][t][0] < 0: | |
| 176 7 11.0 1.6 0.0 break | |
| 177 68 836.0 12.3 0.7 gx = ground_truth[b][t][1] * nW | |
| 178 68 766.0 11.3 0.6 gy = ground_truth[b][t][2] * nH | |
| 179 68 778.0 11.4 0.6 gw = ground_truth[b][t][3] * nW | |
| 180 68 738.0 10.9 0.6 gh = ground_truth[b][t][4] * nH | |
| 181 68 1632.0 24.0 1.3 cur_gt_boxes = torch.FloatTensor([gx,gy,gw,gh]).repeat(nAnchors,1) | |
| 182 68 22055.0 324.3 17.2 cur_ious = torch.max(cur_ious, bbox_multi_ious(cur_pred_boxes, cur_gt_boxes)) | |
| 183 8 265.0 33.1 0.2 conf_mask[b].view(-1)[cur_ious > self.thresh] = 0 | |
| 184 | |
| 185 # Loop over ground_truths and construct tensors | |
| 186 9 11.0 1.2 0.0 for b in range(nB): | |
| 187 76 114.0 1.5 0.1 for t in range(nT): | |
| 188 75 785.0 10.5 0.6 if ground_truth[b][t][0] < 0: | |
| 189 7 10.0 1.4 0.0 break | |
| 190 68 135.0 2.0 0.1 best_iou = 0.0 | |
| 191 68 80.0 1.2 0.1 best_n = -1 | |
| 192 68 72.0 1.1 0.1 min_dist = 10000 | |
| 193 68 748.0 11.0 0.6 gx = ground_truth[b][t][1] * nW | |
| 194 68 681.0 10.0 0.5 gy = ground_truth[b][t][2] * nH | |
| 195 68 687.0 10.1 0.5 gw = ground_truth[b][t][3] * nW | |
| 196 68 679.0 10.0 0.5 gh = ground_truth[b][t][4] * nH | |
| 197 68 304.0 4.5 0.2 gi = min(nW-1, max(0, int(gx))) | |
| 198 68 196.0 2.9 0.2 gj = min(nH-1, max(0, int(gy))) | |
| 199 68 210.0 3.1 0.2 gt_box = [0, 0, gw, gh] | |
| 200 408 533.0 1.3 0.4 for n in range(nA): | |
| 201 340 714.0 2.1 0.6 aw = self.anchors[self.anchor_step*n] | |
| 202 340 481.0 1.4 0.4 ah = self.anchors[self.anchor_step*n+1] | |
| 203 340 496.0 1.5 0.4 anchor_box = [0, 0, aw, ah] | |
| 204 340 68238.0 200.7 53.3 iou = bbox_iou(anchor_box, gt_box) | |
| 205 340 657.0 1.9 0.5 if self.anchor_step == 4: | |
| 206 ax = self.anchors[self.anchor_step*n+2] | |
| 207 ay = self.anchors[self.anchor_step*n+3] | |
| 208 dist = pow(((gi+ax) - gx), 2) + pow(((gj+ay) - gy), 2) | |
| 209 340 2078.0 6.1 1.6 if iou > best_iou: | |
| 210 124 188.0 1.5 0.1 best_iou = iou | |
| 211 124 157.0 1.3 0.1 best_n = n | |
| 212 216 326.0 1.5 0.3 elif self.anchor_step==4 and iou == best_iou and dist < min_dist: | |
| 213 best_iou = iou | |
| 214 best_n = n | |
| 215 min_dist = dist | |
| 216 | |
| 217 68 94.0 1.4 0.1 gt_box = [gx, gy, gw, gh] | |
| 218 68 370.0 5.4 0.3 pred_box = pred_boxes[b*nAnchors+best_n*nPixels+gj*nW+gi] | |
| 219 68 266.0 3.9 0.2 pred_conf = pred_confs[b*nAnchors+best_n*nPixels+gj*nW+gi] | |
| 220 68 10066.0 148.0 7.9 iou = bbox_iou(gt_box, pred_box) | |
| 221 | |
| 222 68 1254.0 18.4 1.0 coord_mask[b][best_n][0][gj*nW+gi] = 1 | |
| 223 68 1051.0 15.5 0.8 cls_mask[b][best_n][gj*nW+gi] = 1 | |
| 224 68 928.0 13.6 0.7 conf_mask[b][best_n][gj*nW+gi] = self.object_scale | |
| 225 68 1140.0 16.8 0.9 tcoord[b][best_n][0][gj*nW+gi] = gx - gi | |
| 226 68 1070.0 15.7 0.8 tcoord[b][best_n][1][gj*nW+gi] = gy - gj | |
| 227 68 1598.0 23.5 1.2 tcoord[b][best_n][2][gj*nW+gi] = math.log(gw/self.anchors[self.anchor_step*best_n]) | |
| 228 68 1510.0 22.2 1.2 tcoord[b][best_n][3][gj*nW+gi] = math.log(gh/self.anchors[self.anchor_step*best_n+1]) | |
| 229 68 655.0 9.6 0.5 tconf[b][best_n][gj*nW+gi] = iou | |
| 230 68 882.0 13.0 0.7 tcls[b][best_n][gj*nW+gi] = ground_truth[b][t][0] | |
| 231 | |
| 232 1 1.0 1.0 0.0 return coord_mask, conf_mask, cls_mask, tcoord, tconf, tcls | |
| Pystone time: 0.138044 s | |
| File: /home/joncrall/code/lightnet/lightnet/network/loss.py | |
| Function: __call__ at line 39 | |
| Line # Hits Time Per Hit % Time Line Contents | |
| ============================================================== | |
| 39 @profiler.profile | |
| 40 def __call__(self, output, target): | |
| 41 """ Compute Region loss. | |
| 42 | |
| 43 Args: | |
| 44 output (torch.autograd.Variable): Output from the network | |
| 45 target (brambox.boxes.annotations.Annotation or torch.Tensor): Brambox annotations or tensor containing the annotation targets (see :class:`lightnet.data.BramboxToTensor`) | |
| 46 | |
| 47 Note: | |
| 48 If target is a tensor, the shape should be [B, T, 5], where B is | |
| 49 the batch size, T is the maximum number of boxes in an item, and | |
| 50 the final dimension should correspond to [class_idx, center_x, | |
| 51 center_y, width, height]. Items with fewer than T boxes should be | |
| 52 padded with dummy boxes with class_idx=-1. | |
| 53 """ | |
| 54 # Parameters | |
| 55 1 8.0 8.0 0.0 nB = output.data.size(0) | |
| 56 1 3.0 3.0 0.0 nA = self.num_anchors | |
| 57 1 3.0 3.0 0.0 nC = self.num_classes | |
| 58 1 4.0 4.0 0.0 nH = output.data.size(2) | |
| 59 1 4.0 4.0 0.0 nW = output.data.size(3) | |
| 60 1 4.0 4.0 0.0 cuda = output.is_cuda | |
| 61 1 4.0 4.0 0.0 if isinstance(target, Variable): | |
| 62 1 3.0 3.0 0.0 target = target.data | |
| 63 | |
| 64 # Get x,y,w,h,conf,cls | |
| 65 1 20.0 20.0 0.0 output = output.view(nB, nA, -1, nH*nW) | |
| 66 1 45.0 45.0 0.0 coord = torch.zeros_like(output[:,:,:4]) | |
| 67 1 468.0 468.0 0.3 coord[:,:,:2] = output[:,:,:2].sigmoid() # tx,ty | |
| 68 1 66.0 66.0 0.0 coord[:,:,2:4] = output[:,:,2:4] # tw,th | |
| 69 1 220.0 220.0 0.2 conf = output[:,:,4].sigmoid() | |
| 70 1 3.0 3.0 0.0 if nC > 1: | |
| 71 1 99.0 99.0 0.1 cls = output[:,:,5:].contiguous().view(nB*nA, nC, nH*nW).transpose(1,2).contiguous().view(-1, nC) | |
| 72 | |
| 73 # Create prediction boxes | |
| 74 1 13.0 13.0 0.0 pred_boxes = torch.FloatTensor(nB*nA*nH*nW, 4) | |
| 75 1 44.0 44.0 0.0 lin_x = torch.linspace(0, nW-1, nW).repeat(nH,1).view(nH*nW) | |
| 76 1 52.0 52.0 0.0 lin_y = torch.linspace(0, nH-1, nH).repeat(nW,1).t().contiguous().view(nH*nW) | |
| 77 1 41.0 41.0 0.0 anchor_w = torch.Tensor(self.anchors[::self.anchor_step]).view(nA, 1) | |
| 78 1 28.0 28.0 0.0 anchor_h = torch.Tensor(self.anchors[1::self.anchor_step]).view(nA, 1) | |
| 79 1 3.0 3.0 0.0 if cuda: | |
| 80 pred_boxes = pred_boxes.cuda() | |
| 81 lin_x = lin_x.cuda() | |
| 82 lin_y = lin_y.cuda() | |
| 83 anchor_w = anchor_w.cuda() | |
| 84 anchor_h = anchor_h.cuda() | |
| 85 | |
| 86 1 85.0 85.0 0.1 pred_boxes[:,0] = (coord[:,:,0].data + lin_x).view(-1) | |
| 87 1 92.0 92.0 0.1 pred_boxes[:,1] = (coord[:,:,1].data + lin_y).view(-1) | |
| 88 1 208.0 208.0 0.2 pred_boxes[:,2] = (coord[:,:,2].data.exp() * anchor_w).view(-1) | |
| 89 1 188.0 188.0 0.1 pred_boxes[:,3] = (coord[:,:,3].data.exp() * anchor_h).view(-1) | |
| 90 1 5.0 5.0 0.0 pred_boxes = pred_boxes.cpu() | |
| 91 | |
| 92 # Create predicted confs | |
| 93 1 9.0 9.0 0.0 pred_confs = torch.FloatTensor(nB*nA*nH*nW) | |
| 94 1 12.0 12.0 0.0 pred_confs = conf.data.view(-1).cpu() | |
| 95 | |
| 96 # Get target values | |
| 97 1 135520.0 135520.0 98.2 coord_mask,conf_mask,cls_mask,tcoord,tconf,tcls = self.build_targets(pred_boxes,pred_confs,target,nH,nW) | |
| 98 1 9.0 9.0 0.0 coord_mask = coord_mask.expand_as(tcoord) | |
| 99 1 2.0 2.0 0.0 if nC > 1: | |
| 100 1 53.0 53.0 0.0 tcls = tcls.view(-1)[cls_mask.view(-1)].long() | |
| 101 1 67.0 67.0 0.0 cls_mask = cls_mask.view(-1, 1).repeat(1, nC) | |
| 102 | |
| 103 1 1.0 1.0 0.0 if cuda: | |
| 104 tcoord = tcoord.cuda() | |
| 105 tconf = tconf.cuda() | |
| 106 coord_mask = coord_mask.cuda() | |
| 107 conf_mask = conf_mask.cuda() | |
| 108 if nC > 1: | |
| 109 tcls = tcls.cuda() | |
| 110 cls_mask = cls_mask.cuda() | |
| 111 | |
| 112 1 5.0 5.0 0.0 tcoord = Variable(tcoord, requires_grad=False) | |
| 113 1 3.0 3.0 0.0 tconf = Variable(tconf, requires_grad=False) | |
| 114 1 3.0 3.0 0.0 coord_mask = Variable(coord_mask, requires_grad=False) | |
| 115 1 15.0 15.0 0.0 conf_mask = Variable(conf_mask.sqrt(), requires_grad=False) | |
| 116 1 2.0 2.0 0.0 if nC > 1: | |
| 117 1 3.0 3.0 0.0 tcls = Variable(tcls, requires_grad=False) | |
| 118 1 2.0 2.0 0.0 cls_mask = Variable(cls_mask, requires_grad=False) | |
| 119 1 63.0 63.0 0.0 cls = cls[cls_mask].view(-1, nC) | |
| 120 | |
| 121 # Compute losses | |
| 122 1 71.0 71.0 0.1 mse = nn.MSELoss(size_average=False) | |
| 123 1 192.0 192.0 0.1 self.loss_coord = self.coord_scale * mse(coord*coord_mask, tcoord*coord_mask) / nB | |
| 124 1 67.0 67.0 0.0 self.loss_conf = mse(conf*conf_mask, tconf*conf_mask) / nB | |
| 125 1 2.0 2.0 0.0 if nC > 1: | |
| 126 1 209.0 209.0 0.2 self.loss_cls = self.class_scale * 2 * nn.CrossEntropyLoss(size_average=False)(cls, tcls) / nB | |
| 127 1 19.0 19.0 0.0 self.loss_tot = self.loss_coord + self.loss_conf + self.loss_cls | |
| 128 else: | |
| 129 self.loss_cls = None | |
| 130 self.loss_tot = self.loss_coord + self.loss_conf | |
| 131 | |
| 132 1 2.0 2.0 0.0 return self.loss_tot | |
| Pystone time: 16.0487 s | |
| File: /home/joncrall/code/netharn/netharn/models/yolo2/light_region_loss.py | |
| Function: profile_loss_speed at line 61 | |
| Line # Hits Time Per Hit % Time Line Contents | |
| ============================================================== | |
| 61 @profiler.profile | |
| 62 def profile_loss_speed(): | |
| 63 """ | |
| 64 python ~/code/netharn/netharn/models/yolo2/light_region_loss.py profile_loss_speed --profile | |
| 65 | |
| 66 Example: | |
| 67 >>> profile_loss_speed() | |
| 68 """ | |
| 69 1 660.0 660.0 0.0 from netharn.models.yolo2.light_yolo import Yolo | |
| 70 1 573.0 573.0 0.0 import netharn.models.yolo2.light_region_loss | |
| 71 1 9.0 9.0 0.0 import lightnet.network | |
| 72 1 8.0 8.0 0.0 import netharn as nh | |
| 73 | |
| 74 1 23.0 23.0 0.0 rng = util.ensure_rng(0) | |
| 75 1 942.0 942.0 0.0 torch.random.manual_seed(0) | |
| 76 1 438761.0 438761.0 2.7 network = Yolo(num_classes=2, conf_thresh=4e-2) | |
| 77 | |
| 78 1 10.0 10.0 0.0 self1 = netharn.models.yolo2.light_region_loss.RegionLoss( | |
| 79 1 561.0 561.0 0.0 num_classes=network.num_classes, anchors=network.anchors) | |
| 80 1 21.0 21.0 0.0 self2 = lightnet.network.RegionLoss(network=network) | |
| 81 | |
| 82 1 8.0 8.0 0.0 bsize = 8 | |
| 83 # Make a random semi-realistic set of groundtruth items | |
| 84 1 51.0 51.0 0.0 n_targets = [rng.randint(0, 20) for _ in range(bsize)] | |
| 85 1 9.0 9.0 0.0 target_list = [torch.FloatTensor( | |
| 86 np.hstack([rng.randint(0, network.num_classes, nT)[:, None], | |
| 87 util.Boxes.random(nT, scale=1.0, rng=rng).data])) | |
| 88 1 502.0 502.0 0.0 for nT in n_targets] | |
| 89 1 318.0 318.0 0.0 target = nh.data.collate.padded_collate(target_list) | |
| 90 | |
| 91 1 8.0 8.0 0.0 Win, Hin = 416, 416 | |
| 92 1 335648.0 335648.0 2.1 im_data = torch.randn(len(target), 3, Hin, Win) | |
| 93 1 15068571.0 15068571.0 93.9 output = network.forward(im_data) | |
| 94 | |
| 95 1 63498.0 63498.0 0.4 loss1 = float(self1(output, target)) | |
| 96 1 138240.0 138240.0 0.9 loss2 = float(self2(output, target)) | |
| 97 1 154.0 154.0 0.0 print('loss1 = {!r}'.format(loss1)) | |
| 98 1 102.0 102.0 0.0 print('loss2 = {!r}'.format(loss2)) | |
| -1.00 seconds - None :None:None | |
| 0.03 seconds - RegionLoss._build_targets_tensor:/home/joncrall/code/netharn/netharn/models/yolo2/light_region_loss.py:382 | |
| 0.06 seconds - RegionLoss.forward :/home/joncrall/code/netharn/netharn/models/yolo2/light_region_loss.py:284 | |
| 0.13 seconds - __build_targets_tensor :/home/joncrall/code/lightnet/lightnet/network/loss.py:141 | |
| 0.14 seconds - __call__ :/home/joncrall/code/lightnet/lightnet/network/loss.py:39 | |
| 16.05 seconds - profile_loss_speed :/home/joncrall/code/netharn/netharn/models/yolo2/light_region_loss.py:61 |
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