定义的模型继承自Base3DDetector,Base3DDetector继承自BaseDetector,BaseDetector继承自BaseModel,BaseModel中有train_step,val_step,而_train_loop是build_train_loop(train_cfg)的返回值 train_step定义如下: def train_step(self, data: Union[dict, tuple, list], optim_wrapper: OptimWrapper) -> Dict[str, ...
(通过查看代码,train_step是定义在mmdet的BaseDetector类的,mmdet3d用BaseDetector3D继承它,然后各类模型又继承我mmdet3d中的这个基类。另外,train_step其实是只执行forward,得到loss,在里面其实是执行了这样一行:losses = self(**data),而不执行optimizer的bp和update) """The iteration step during training. This ...
训练过程将由runner管理,提供了hook机制,拓展性强,最后将调用model.val_step和model.train_step,其中val_step和train_step差别就在于with torch.no_grad()。注意这里model是经过了DataParallel包装的。 Runner(实现在mmcv中)主要是用来管理模型训练时的生命周期,负责 OpenMMLab 中所有框架的训练过程调度 Runner(实现在mm...
train_pipeline = [dict(type='LoadPointsFromFile',coord_type='DEPTH',shift_height=False,use_color=True,load_dim=6,use_dim=[0, 1, 2, 3, 4, 5]),dict(type='LoadAnnotations3D',with_bbox_3d=False,with_label_3d=False,with_mask_3d=False,with_seg_3d=True),dict(type='PointSegClassMappi...
# bbox 1 will be assigned as the best target for bbox A in step 3.# However, if GT bbox 2's gt_argmax_overlaps = A, bbox A's# assigned_gt_inds will be overwritten to be bbox B.# This might be the reason that it is not used in ROI Heads.foriinrange(num_gts):ifgt_...
python tools/train.py \ ${CONFIG_FILE} \ --auto-scale-lr \ [optional arguments] 1. 2. 3. 4. 执行命令之后,会根据机器的GPU数量和训练的批次大小对学习率进行自动缩放,缩放方式详见线性扩展规则,比如:在 4 块 GPU 并且每张 GPU 上有 2 张图片的情况下 lr=0.01,那么在 16 块 GPU 并且每张 GPU...
在上述代码中,我们首先检查GPU是否可用,然后将模型和数据转移到GPU上。我们还定义了优化器和损失函数,并使用GPU进行训练。注意,我们在每个迭代周期中都需要将数据转移到GPU上,并在优化器的step()方法中更新参数。 最后,当你完成训练后,记得将模型转移到CPU上进行推断或保存模型。你可以使用以下代码将模型转移到CPU上...
net.train() optimizer.zero_grad() X,y = X(device),y(device) y_hat = net(X) loss = criterion(y_hat, y) loss.backward() optimizer.step() with torch.no_grad(): y = y.long() train_l_sum += loss.float() train_acc_sum += (torch.sum((torch.argmax(y_hat, dim=1) == y...
train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='MultiStepLR', begin=0, end=max_epochs,
(backend_model)# process input imageinput_shape=get_input_shape(deploy_cfg)model_inputs,_=task_processor.create_input(image,input_shape)# do model inferencewithtorch.no_grad():result=model.test_step(model_inputs)# visualize resultstask_processor.visualize(image=image,model=model,result=result[0...