maskrcnn_benchmark训练过程
->训练命令：
python tools/train_net.py --config-file "configs/e2e_mask_rcnn_R_50_FPN_1x.yaml" SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025 SOLVER.MAX_ITER 720000 SOLVER.STEPS "(480000, 640000)" TEST.IMS_PER_BATCH 1
->调用train_net.py，在train()函数中建立模型，优化器，dataloader,checkpointerd等，进入trainer.py核心训练代码：
def do_train( model, data_loader, optimizer, scheduler, checkpointer, device, checkpoint_period, arguments, ): logger = logging.getLogger("maskrcnn_benchmark.trainer") logger.info("Start training") meters = MetricLogger(delimiter=" ") max_iter = len(data_loader) start_iter = arguments["iteration"] model.train() start_training_time = time.time() end = time.time() for iteration, (images, targets, _) in enumerate(data_loader, start_iter): data_time = time.time() - end arguments["iteration"] = iteration scheduler.step() images = images.to(device) targets = [target.to(device) for target in targets] loss_dict = model(images, targets) ipdb.set_trace() losses = sum(loss for loss in loss_dict.values()) # reduce losses over all GPUs for logging purposes loss_dict_reduced = reduce_loss_dict(loss_dict) losses_reduced = sum(loss for loss in loss_dict_reduced.values()) meters.update(loss=losses_reduced, **loss_dict_reduced) optimizer.zero_grad() losses.backward() optimizer.step() batch_time = time.time() - end end = time.time() meters.update(time=batch_time, data=data_time) eta_seconds = meters.time.global_avg * (max_iter - iteration) eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) if iteration % 20 == 0 or iteration == (max_iter - 1): logger.info( meters.delimiter.join( [ "eta: {eta}", "iter: {iter}", "{meters}", "lr: {lr:.6f}", "max mem: {memory:.0f}", ] ).format( eta=eta_string, iter=iteration, meters=str(meters), lr=optimizer.param_groups[0]["lr"], memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0, ) ) if iteration % checkpoint_period == 0 and iteration > 0: checkpointer.save("model_{:07d}".format(iteration), **arguments) checkpointer.save("model_{:07d}".format(iteration), **arguments) total_training_time = time.time() - start_training_time total_time_str = str(datetime.timedelta(seconds=total_training_time)) logger.info( "Total training time: {} ({:.4f} s / it)".format( total_time_str, total_training_time / (max_iter) ) )
->输出一次迭代，变量过程，target为batch=2的gt图像：
ipdb> loss_dict {'loss_box_reg': tensor(0.1005, device='cuda:0', grad_fn=<DivBackward0>), 'loss_rpn_box_reg': tensor(0.0486, device='cuda:0', grad_fn=<DivBackward0>), 'loss_objectness': tensor(0.0165, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>), 'loss_classifier': tensor(0.2494, device='cuda:0', grad_fn=<NllLossBackward>), 'loss_mask': tensor(0.2332, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>)} ipdb> images <maskrcnn_benchmark.structures.image_list.ImageList object at 0x7f9cb9190668> ipdb> targets [BoxList(num_boxes=3, image_width=1066, image_height=800, mode=xyxy), BoxList(num_boxes=17, image_width=1199, image_height=800, mode=xyxy)]
进入model内部进行：
->在generalized_rcnn.py中经过backbone网络提取特征feature:features = self.backbone(images.tensors)
ipdb> features[0].size() torch.Size([2, 256, 200, 336]) ipdb> features[1].size() torch.Size([2, 256, 100, 168]) ipdb> features[2].size() torch.Size([2, 256, 50, 84]) ipdb> features[3].size() torch.Size([2, 256, 25, 42]) ipdb> features[4].size() torch.Size([2, 256, 13, 21])
RNP网络
->proposals, proposal_losses = self.rpn(images, features, targets)
def forward(self, images, features, targets=None): """ Arguments: images (ImageList): images for which we want to compute the predictions features (list[Tensor]): features computed from the images that are used for computing the predictions. Each tensor in the list correspond to different feature levels targets (list[BoxList): ground-truth boxes present in the image (optional) Returns: boxes (list[BoxList]): the predicted boxes from the RPN, one BoxList per image. losses (dict[Tensor]): the losses for the model during training. During testing, it is an empty dict. """ objectness, rpn_box_regression = self.head(features) anchors = self.anchor_generator(images, features) if self.training: return self._forward_train(anchors, objectness, rpn_box_regression, targets) else: return self._forward_test(anchors, objectness, rpn_box_regression)
def _forward_train(self, anchors, objectness, rpn_box_regression, targets):
if self.cfg.MODEL.RPN_ONLY:
# When training an RPN-only model, the loss is determined by the
# predicted objectness and rpn_box_regression values and there is
# no need to transform the anchors into predicted boxes; this is an
# optimization that avoids the unnecessary transformation.
boxes = anchors
else:
# For end-to-end models, anchors must be transformed into boxes and
# sampled into a training batch.
with torch.no_grad():
boxes = self.box_selector_train(
anchors, objectness, rpn_box_regression, targets
)
loss_objectness, loss_rpn_box_reg = self.loss_evaluator(
anchors, objectness, rpn_box_regression, targets
)
losses = {
"loss_objectness": loss_objectness,
"loss_rpn_box_reg": loss_rpn_box_reg,
}
return boxes, losses
->首先所有feature通过rpn_head网络（3×3+1×1分类与回归）得到结果;然后和生成的anchor进行算loss
->objectness, rpn_box_regression = self.head(features)返回5个stage下回归和分类的结果，每个等级3个anchor
ipdb> objectness[0].size() torch.Size([2, 3, 200, 336]) =200*336*3=201600 ipdb> objectness[1].size() torch.Size([2, 3, 100, 168]) ipdb> objectness[2].size() torch.Size([2, 3, 50, 84]) ipdb> objectness[3].size() torch.Size([2, 3, 25, 42]) ipdb> objectness[4].size() torch.Size([2, 3, 13, 21]) ipdb> objectness[5].size() *** IndexError: list index out of range ipdb> rpn_box_regression[0].size() torch.Size([2, 12, 200, 336]) ipdb> rpn_box_regression[4].size() torch.Size([2, 12, 13, 21])
-> anchors = self.anchor_generator(images, features)生成anchor
ipdb> anchors[1][0] BoxList(num_boxes=201600, image_width=1204, image_height=800, mode=xyxy) ipdb> anchors[1][1] BoxList(num_boxes=50400, image_width=1204, image_height=800, mode=xyxy) ipdb> anchors[0][1] BoxList(num_boxes=50400, image_width=1333, image_height=794, mode=xyxy) ipdb> anchors[1][2] BoxList(num_boxes=12600, image_width=1204, image_height=800, mode=xyxy) ipdb> anchors[1][3] BoxList(num_boxes=3150, image_width=1204, image_height=800, mode=xyxy) ipdb> anchors[1][4] BoxList(num_boxes=819, image_width=1204, image_height=800, mode=xyxy)
->boxes = self.box_selector_train(anchors, objectness, rpn_box_regression, targets）选择boxes去训练fast rcnn,这一步不需要梯度更新
ipdb> boxes
[BoxList(num_boxes=316, image_width=1333, image_height=794, mode=xyxy), BoxList(num_boxes=1696, image_width=1204, image_height=800, mode=xyxy)]
-> loss_objectness, loss_rpn_box_reg = self.loss_evaluator(anchors, objectness, rpn_box_regression, targets) 算loss时候选择正负1：1的anchor进行训练rpn网络
->这里选择512个样本训练;_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256；两张图像
ipdb> sampled_pos_inds tensor([ 16477, 16480, 16483, 16486, 17485, 17488, 17491, 17494, 18493, 18496, 18499, 18502, 19501, 19504, 19507, 19510, 217452, 217453, 217455, 217456, 217458, 217459, 217960, 268151, 529150, 534017, 534020, 534143, 534146, 534586, 534607, 534712, 534733, 534838, 534859, 535356, 535359, 535362, 535365, 535368, 536602, 536652, 536655, 536658, 536661, 536664, 536667, 536670, 536715, 536718, 536721, 536724, 536727, 536730, 536733, 536778, 536781, 536784, 536787, 536790, 536793, 536796, 536841, 536844, 536847, 536850, 536853, 536856, 536859], device='cuda:0') ipdb> sampled_neg_inds tensor([ 3045, 4275, 5323, 6555, 7538, 8406, 8469, 9761, 11316, 11684, 12319, 13195, 13354, 15405, 20431, 25105, 26405, 26786, 27324, 30698, 33503, 38168, 39244, 40064, 40535, 41046, 41162, 41203, 41864, 43170, 44060, 44416, 44905, 45161, 47299, 48043, 49890, 49900, 50992, 51248, 52082, 52236, 52371, 52568, 54079, 54207, 55251, 56973, 57135, 58376, 59816, 61509, 62473, 62942, 64722, 65548, 66681, 67925, 68650, 71368, 72610, 73268, 74727, 75655, 77795, 78937, 79115, 80101, 80808, 81001, 83846, 87064, 89891, 91207, 92579, 92771, 93113, 94118, 94526, 94586, 95822, 96850, 97256, 97303, 97500, 98194, 98338, 101724, 102082, 103835, 103947, 104678, 105168, 105630, 106132, 108751, 108933, 109684, 110552, 111373, 111965, 114691, 114736, 115213, 115468, 120710, 121785, 123138, 126383, 126957, 128197, 128282, 129449, 130472, 132269, 133131, 133384, 135197, 135926, 136468, 137306, 137620, 138671, 141848, 142643, 145618, 147402, 148283, 148353, 149313, 150389, 150528, 151949, 154413, 156156, 157155, 158716, 160001, 160227, 160428, 160496, 160920, 161023, 162605, 163131, 166371, 166561, 167200, 171280, 174531, 175690, 175957, 175996, 179025, 179766, 180781, 182893, 182980, 183152, 183159, 183531, 183785, 184531, 185565, 186520, 187194, 187772, 188100, 191068, 191289, 191419, 192022, 193388, 194892, 196902, 204682, 206878, 207981, 208066, 208366, 210761, 210862, 211624, 213567, 213627, 214601, 214651, 214770, 215032, 216806, 218299, 220127, 220221, 221133, 222489, 223512, 224844, 225115, 225225, 225337, 228044, 228580, 228691, 229787, 231390, 231405, 231666, 233068, 233379, 233416, 234464, 236145, 238078, 239161, 239633, 240260, 240492, 241033, 241702, 241758, 242546, 243372, 244102, 248078, 248632, 255377, 256325, 257079, 258010, 259857, 260872, 261896, 271659, 274495, 275822, 276450, 276728, 278865, 279179, 279338, 279735, 280208, 280216, 282300, 283240, 283717, 285074, 285157, 287528, 287804, 288191, 289901, 290179, 294877, 296999, 298420, 301631, 301890, 303575, 304982, 305983, 305992, 307922, 312438, 313507, 314289, 316348, 318599, 319751, 321304, 321735, 321748, 326308, 326315, 327131, 327290, 327671, 328439, 332674, 333130, 333144, 334633, 336337, 337399, 340980, 341619, 347289, 347364, 347579, 353057, 353309, 354001, 355039, 355271, 355597, 356617, 359064, 359068, 360402, 362098, 362652, 363356, 363741, 364744, 365997, 370109, 370949, 372977, 373248, 373992, 374786, 375293, 376785, 377661, 377761, 378991, 379663, 380167, 380817, 382269, 383560, 387387, 388389, 389665, 389862, 390138, 391941, 394183, 399113, 400423, 402411, 404907, 405436, 406457, 407348, 408005, 408356, 409728, 411376, 411571, 412210, 412426, 415363, 415453, 415601, 418159, 418174, 418928, 419064, 419394, 419783, 421039, 421405, 423287, 426369, 429895, 430293, 431338, 432330, 432745, 433529, 433699, 433738, 435389, 437567, 438410, 439164, 440481, 442532, 445424, 446074, 446146, 446550, 447703, 449683, 450601, 451138, 452505, 455922, 457464, 460557, 461150, 461431, 462641, 463544, 471945, 472032, 473327, 474938, 475450, 477505, 477917, 478033, 479038, 480127, 481613, 482384, 484433, 484542, 484556, 484588, 487380, 490897, 492173, 493279, 493464, 494139, 498077, 498172, 498426, 499201, 500289, 500739, 503145, 506227, 506661, 509266, 509355, 509382, 509556, 510331, 510346, 511426, 511604, 512428, 512560, 513306, 514096, 515320, 516682, 516949, 517815, 517984, 524421, 525174, 525384, 525697, 526692, 527047, 527576, 532272, 535005, 535582], device='cuda:0') ipdb> sampled_pos_inds.size() torch.Size([69]) ipdb> sampled_neg_inds.size() torch.Size([443])
-> 调用rpn/loss.py: class RPNLossComputation(object):
def __call__(self, anchors, objectness, box_regression, targets): """ Arguments: anchors (list[BoxList]) objectness (list[Tensor]) box_regression (list[Tensor]) targets (list[BoxList]) Returns: objectness_loss (Tensor) box_loss (Tensor """ anchors = [cat_boxlist(anchors_per_image) for anchors_per_image in anchors] labels, regression_targets = self.prepare_targets(anchors, targets) sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels) sampled_pos_inds = torch.nonzero(torch.cat(sampled_pos_inds, dim=0)).squeeze(1) sampled_neg_inds = torch.nonzero(torch.cat(sampled_neg_inds, dim=0)).squeeze(1) sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0) objectness_flattened = [] box_regression_flattened = [] # for each feature level, permute the outputs to make them be in the # same format as the labels. Note that the labels are computed for # all feature levels concatenated, so we keep the same representation # for the objectness and the box_regression for objectness_per_level, box_regression_per_level in zip( objectness, box_regression ): N, A, H, W = objectness_per_level.shape objectness_per_level = objectness_per_level.permute(0, 2, 3, 1).reshape( N, -1 ) box_regression_per_level = box_regression_per_level.view(N, -1, 4, H, W) box_regression_per_level = box_regression_per_level.permute(0, 3, 4, 1, 2) box_regression_per_level = box_regression_per_level.reshape(N, -1, 4) objectness_flattened.append(objectness_per_level) box_regression_flattened.append(box_regression_per_level) # concatenate on the first dimension (representing the feature levels), to # take into account the way the labels were generated (with all feature maps # being concatenated as well) objectness = cat(objectness_flattened, dim=1).reshape(-1) box_regression = cat(box_regression_flattened, dim=1).reshape(-1, 4) labels = torch.cat(labels, dim=0) regression_targets = torch.cat(regression_targets, dim=0) box_loss = smooth_l1_loss( box_regression[sampled_pos_inds], regression_targets[sampled_pos_inds], beta=1.0 / 9, size_average=False, ) / (sampled_inds.numel()) objectness_loss = F.binary_cross_entropy_with_logits( objectness[sampled_inds], labels[sampled_inds] ) return objectness_loss, box_loss
->变量打印：最后只使用选中的sampled_inds进行rpn的loss计算：
ipdb> objectness tensor([-1.7661, 1.3304, -3.6243, ..., 0.0558, 1.1206, 0.6639], device='cuda:0', grad_fn=<AsStridedBackward>) ipdb> objectness.shape torch.Size([537138]) ipdb> labels tensor([-1., -1., -1., ..., -1., -1., -1.], device='cuda:0') ipdb> labels.shape torch.Size([537138]) ipdb> box_regression tensor([[-0.1721, -0.2121, 0.1083, -0.5830], [-0.1728, -0.0665, -0.6760, -0.8508], [-0.0958, -0.0096, -0.1450, 0.2591], ..., [-0.0041, 0.0209, 0.2075, -0.0639], [ 0.0016, 0.0539, -0.1746, -0.1428], [ 0.0038, -0.0308, -0.0916, 0.0726]], device='cuda:0', grad_fn=<AsStridedBackward>) ipdb> box_regression.shape torch.Size([537138, 4]) ipdb> regression_targets tensor([[10.3858, 12.5126, 1.8582, 3.0168], [15.5788, 9.3845, 2.2637, 2.7292], [20.7717, 6.2563, 2.5514, 2.3237], ..., [-1.0482, -1.0875, -1.2006, -0.7158], [-1.4904, -0.7816, -0.8487, -1.0460], [-2.1197, -0.5558, -0.4964, -1.3870]], device='cuda:0') ipdb> regression_targets.shape torch.Size([537138, 4])
-> 最后rpn网络返回：
ipdb> loss_objectness tensor(0.0268, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>) ipdb> loss_rpn_box_reg tensor(0.0690, device='cuda:0', grad_fn=<DivBackward0>) ipdb> boxes [BoxList(num_boxes=316, image_width=1333, image_height=794, mode=xyxy), BoxList(num_boxes=1696, image_width=1204, image_height=800, mode=xyxy)]
Fast RCNN+Mask
->generalized_rcnn.py文件: x, result, detector_losses = self.roi_heads(features, proposals, targets)
->重新换的图像rpn网络输出信息：
ipdb> proposals [BoxList(num_boxes=571, image_width=1201, image_height=800, mode=xyxy), BoxList(num_boxes=1468, image_width=1199, image_height=800, mode=xyxy)] ipdb> proposal_losses {'loss_objectness': tensor(0.0656, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>), 'loss_rpn_box_reg': tensor(0.2036, device='cuda:0', grad_fn=<DivBackward0>)}
->roi_heads.py分box和mask两部分：
->这里用FPN网络，所以在box和mask进行特征抽取（进行roipool）的时候,进行每个层级上的pool操作，这里还可以进行特征抽取时参数共享;
-> 所以输入mask分支的mask_features是原始的backbone网络的features,只不过在上面去box分支出来的detections区域进行loss计算;
def forward(self, features, proposals, targets=None): losses = {} # TODO rename x to roi_box_features, if it doesn't increase memory consumption x, detections, loss_box = self.box(features, proposals, targets) losses.update(loss_box) if self.cfg.MODEL.MASK_ON: mask_features = features # optimization: during training, if we share the feature extractor between # the box and the mask heads, then we can reuse the features already computed if ( self.training and self.cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR ): mask_features = x # During training, self.box() will return the unaltered proposals as "detections" # this makes the API consistent during training and testing x, detections, loss_mask = self.mask(mask_features, detections, targets) losses.update(loss_mask) return x, detections, losses
->x, detections, loss_box = self.box(features, proposals, targets) fast rcnn的分类与回归部分：
->x = self.feature_extractor(features, proposals)这里的特征提取分roipooling和抽取成roipool_feature,可以和mask分支共享，然后再分（分类+回归，mask）两个loss分支;
def forward(self, features, proposals, targets=None): """ Arguments: features (list[Tensor]): feature-maps from possibly several levels proposals (list[BoxList]): proposal boxes targets (list[BoxList], optional): the ground-truth targets. Returns: x (Tensor): the result of the feature extractor proposals (list[BoxList]): during training, the subsampled proposals are returned. During testing, the predicted boxlists are returned losses (dict[Tensor]): During training, returns the losses for the head. During testing, returns an empty dict. """ if self.training: # Faster R-CNN subsamples during training the proposals with a fixed # positive / negative ratio with torch.no_grad(): proposals = self.loss_evaluator.subsample(proposals, targets) # extract features that will be fed to the final classifier. The # feature_extractor generally corresponds to the pooler + heads x = self.feature_extractor(features, proposals) # final classifier that converts the features into predictions class_logits, box_regression = self.predictor(x) if not self.training: result = self.post_processor((class_logits, box_regression), proposals) return x, result, {} loss_classifier, loss_box_reg = self.loss_evaluator( [class_logits], [box_regression] ) return ( x, proposals, dict(loss_classifier=loss_classifier, loss_box_reg=loss_box_reg), )
->训练的时候每张图选择512个box训练，输出([1024, 81])类别; ([1024, 324])回归坐标81×4=324;
ipdb> x.shape torch.Size([1024, 1024]) ipdb> proposals [BoxList(num_boxes=512, image_width=1201, image_height=800, mode=xyxy), BoxList(num_boxes=512, image_width=1199, image_height=800, mode=xyxy)] ipdb> class_logits.shape torch.Size([1024, 81]) ipdb> box_regression tensor([[ 1.2481e-02, -1.5032e-02, 2.6849e-03, ..., 2.6986e-03, 1.4723e-01, -5.2207e-01], [-5.7448e-03, -7.5938e-03, -2.6571e-03, ..., 1.3588e-01, -3.1587e-01, 6.2171e-01], [-6.6426e-03, -3.4121e-03, -9.5814e-04, ..., -4.7817e-01, -2.8117e-03, 1.6653e-01], ..., [-1.1446e-02, -4.6574e-03, -8.0981e-04, ..., -5.0460e-01, 6.2465e-01, -4.1426e-01], [ 6.0940e-05, -1.2032e-02, -5.0753e-03, ..., 1.0396e+00, -1.9913e-01, -1.2819e+00], [-4.9718e-03, -6.6546e-03, -2.5202e-03, ..., 3.9986e-02, -6.0675e-02, -1.1396e-01]], device='cuda:0', grad_fn=<AddmmBackward>) ipdb> box_regression.shape torch.Size([1024, 324]) ipdb> loss_classifier tensor(0.3894, device='cuda:0', grad_fn=<NllLossBackward>) ipdb> loss_box_reg tensor(0.1674, device='cuda:0', grad_fn=<DivBackward0>)
->整体x, detections, loss_box = self.box(features, proposals, targets)输出，x为box和mask分支的特征;选择512个box计算loss并传入mask分支
ipdb> x.shape torch.Size([1024, 1024]) ipdb> proposals [BoxList(num_boxes=571, image_width=1201, image_height=800, mode=xyxy), BoxList(num_boxes=1468, image_width=1199, image_height=800, mode=xyxy)] ipdb> detections [BoxList(num_boxes=512, image_width=1201, image_height=800, mode=xyxy), BoxList(num_boxes=512, image_width=1199, image_height=800, mode=xyxy)] ipdb> loss_box {'loss_box_reg': tensor(0.1674, device='cuda:0', grad_fn=<DivBackward0>), 'loss_classifier': tensor(0.3894, device='cuda:0', grad_fn=<NllLossBackward>)}
->x, detections, loss_mask = self.mask(mask_features, detections, targets) mask分支：
-> 仅利用检测出来的proposals中有目标的positive_inds;
def forward(self, features, proposals, targets=None): """ Arguments: features (list[Tensor]): feature-maps from possibly several levels proposals (list[BoxList]): proposal boxes targets (list[BoxList], optional): the ground-truth targets. Returns: x (Tensor): the result of the feature extractor proposals (list[BoxList]): during training, the original proposals are returned. During testing, the predicted boxlists are returned with the `mask` field set losses (dict[Tensor]): During training, returns the losses for the head. During testing, returns an empty dict. """ if self.training: # during training, only focus on positive boxes all_proposals = proposals proposals, positive_inds = keep_only_positive_boxes(proposals) if self.training and self.cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR: x = features x = x[torch.cat(positive_inds, dim=0)] else: x = self.feature_extractor(features, proposals) mask_logits = self.predictor(x) if not self.training: result = self.post_processor(mask_logits, proposals) return x, result, {} loss_mask = self.loss_evaluator(proposals, mask_logits, targets) return x, all_proposals, dict(loss_mask=loss_mask)
-> 变量结果：只把正例进行loss计算，变少很多; 然后pool后的特征维度([171, 256, 14, 14])（由于选的box只有43+128=171）
->训练时，真正有用的返回就是loss_mask;测试的时候返回的是经过后处理的result;
ipdb> all_proposals [BoxList(num_boxes=512, image_width=1201, image_height=800, mode=xyxy), BoxList(num_boxes=512, image_width=1199, image_height=800, mode=xyxy)] ipdb> proposals [BoxList(num_boxes=43, image_width=1201, image_height=800, mode=xyxy), BoxList(num_boxes=128, image_width=1199, image_height=800, mode=xyxy)] ipdb> positive_inds.shape *** AttributeError: 'list' object has no attribute 'shape' ipdb> positive_inds[0].shape torch.Size([512]) ipdb> x.shape torch.Size([171, 256, 14, 14]) ipdb> mask_logits.shape torch.Size([171, 81, 28, 28]) ipdb> targets[0] BoxList(num_boxes=4, image_width=1201, image_height=800, mode=xyxy) ipdb> targets[1] BoxList(num_boxes=35, image_width=1199, image_height=800, mode=xyxy) ipdb> loss_mask tensor(0.3287, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>)
-> 至此真个训练loss完成; 进行迭代...
总结：
1. 在模型中已经很好的区分训练和测试部分，处理后返回的结果也不一样；
2. 后续对一些数据结构，数据细节处理在看看！