scheduler.get_last_lr()输出大小

时间: 2024-01-15 22:05:12 浏览: 22
`scheduler.get_last_lr()`方法的返回值是一个列表，包含了当前优化器中每个参数组的学习率。如果优化器只有一个参数组，则返回一个长度为1的列表，列表中的元素是一个标量，表示当前学习率的大小。如果优化器有多个参数组，则返回一个长度为参数组数的列表，每个元素是一个标量，表示对应参数组的学习率大小。因此，返回列表的长度取决于优化器中参数组的数量。
相关问题

scheduler.get_last_lr()

`scheduler.get_last_lr()`是在使用PyTorch进行模型训练时，用于获取当前优化器的学习率的方法。学习率是控制模型参数更新步长的超参数，它的大小直接影响着模型的收敛速度和性能。在训练过程中，我们通常会使用学习率调度器来自动调整学习率，以便更好地控制模型的训练过程。在调度器更新学习率之后，我们可以使用`scheduler.get_last_lr()`方法获取当前优化器的学习率，并将其记录下来，以便后续分析和可视化。

torch.optim.lr_scheduler.get_last_lr

`torch.optim.lr_scheduler.get_last_lr()` 是 PyTorch 中的一个函数，它用于获取最近一次优化器的学习率。具体来说，它返回一个列表，其中包含每个分组的学习率，这些分组是通过 `torch.optim.Optimizer` 的 `param_groups` 参数指定的。如果您的优化器只有一个参数组，则该函数将返回一个包含一个学习率的列表。请注意，此函数仅在 PyTorch 版本大于等于 1.4.0 时才可用。您可以在 PyTorch 官方文档中找到更多关于此函数的信息。

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解释代码：def lr_range_test(model, train_loader, optimizer, criterion, init_lr, final_lr, epochs): lr_values = [] loss_values = [] lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: epoch) for epoch in range(epochs): for inputs, targets in train_loader: optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() lr_values.append(lr_scheduler.get_last_lr()[0]) loss_values.append(loss.item()) lr_scheduler.step() return lr_values, loss_values

6. 将当前学习率和损失值加入到对应的列表中：lr_values.append(lr_scheduler.get_last_lr()[0]) 和 loss_values.append(loss.item()) 7. 调整学习率：lr_scheduler.step() 最终，函数返回了记录了每个学习率...

def train(self): self.loss.step() epoch = self.scheduler.last_epoch + 1 learn_rate = self.scheduler.get_last_lr()[0] self.ckp.write_log( '[Epoch {}]\tLearning rate: {:.2e}'.format(epoch, Decimal(learn_rate)) ) self.loss.start_log() self.model.train() timer_data, timer_model = utils.timer(), utils.timer() # timer_model.tic() for batch, (lr, hr, file_names) in enumerate(self.loader_train): lr, hr = self.prepare([lr, hr]) timer_data.hold() timer_model.tic() self.optimizer.zero_grad() sr = self.model(lr) loss = self.loss(sr, hr) if loss.item() < self.args.skip_threshold * self.error_last: loss.backward() self.optimizer.step() else: print('Skip this batch {}! (Loss: {})'.format( batch + 1, loss.item() )) timer_model.hold() if (batch + 1) % self.args.print_every == 0: self.ckp.write_log('[{}/{}]\t{}\t{:.1f}+{:.1f}s'.format( (batch + 1) * self.args.batch_size, len(self.loader_train.dataset), self.loss.display_loss(batch), timer_model.release(), timer_data.release())) timer_data.tic() self.scheduler.step() self.loss.end_log(len(self.loader_train)) self.error_last = self.loss.log[-1, -1]

这段代码是在训练模型，其中self.loss.step()是更新损失函数，self.scheduler.last_epoch是获取当前epoch数，self.scheduler.get_last_lr()[0]是获取当前学习率。self.ckp.write_log()是将当前epoch数和学习率写入...

def get_lr(self): if self.last_epoch > self.total_epoch: if self.after_scheduler: if not self.finished: self.after_scheduler.base_lrs = [base_lr * self.multiplier for base_lr in self.base_lrs] self.finished = True return self.after_scheduler.get_lr() return [base_lr * self.multiplier for base_lr in self.base_lrs] if self.multiplier == 1.0: return [base_lr * (float(self.last_epoch) / self.total_epoch) for base_lr in self.base_lrs] else: return [base_lr * ((self.multiplier - 1.) * self.last_epoch / self.total_epoch + 1.) for base_lr in self.base_lrs]

这是 GradualWarmupScheduler 类中的一个 get_lr 方法，该方法用于计算当前轮次（last_epoch）下的学习率。具体来说，该方法首先判断当前轮次是否超过总的训练轮数（total_epoch），如果超过了，就表示开始使用 ...

余弦调度器net = net_fn() trainer = torch.optim.SGD(net.parameters(), lr=0.5) scheduler = lr_scheduler.MultiStepLR(trainer, milestones=[15, 30], gamma=0.5) def get_lr(trainer, scheduler): lr = scheduler.get_last_lr()[0] trainer.step() scheduler.step() return lr d2l.plot(torch.arange(num_epochs), [get_lr(trainer, scheduler) for t in range(num_epochs)])是什么意思

这段代码是一个 PyTorch 中的余弦调度器示例，用于在训练深度神经...接着定义了一个函数 get_lr，用于获取当前的学习率并更新优化器和调度器的状态。最后，使用 d2l.plot 函数绘制了学习率在训练过程中的变化情况。

AttributeError: module 'torch.optim.lr_scheduler' has no attribute 'get_last_l

它指出了一个名为“torch.optim.lr_scheduler”的模块中缺少名为“get_last_l”的属性。这可能是因为您正在使用的版本不支持该属性或该属性的名称已更改。您可以检查您的代码和依赖项，了解该属性是否存在，或者尝试...

train with base lr in the first 100 epochs # and half the lr in the last 100 epochs To train with a base learning rate for the first 100 epochs and half the learning rate for the last 100 epochs, you can use a learning rate scheduler in PyTorch. Here's an example of how you can modify the training loop in your code: import torch import torch.nn as nn import torch.optim as optim from torch.optim.lr_scheduler import MultiStepLR # Define your model, criterion, and optimizer model = YourModel() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01) # Define the number of epochs and the milestone epochs num_epochs = 200 milestones = [100] # Create a learning rate scheduler scheduler = MultiStepLR(optimizer, milestones=milestones, gamma=0.5) # Train the model for epoch in range(num_epochs): # Train with base lr for the first 100 epochs, and half the lr for the last 100 epochs if epoch >= milestones[0]: scheduler.step() for inputs, labels in train_loader: # Forward pass outputs = model(inputs) loss = criterion(outputs, labels) # Backward pass and optimization optimizer.zero_grad() loss.backward() optimizer.step() # Perform validation or testing after each epoch with torch.no_grad(): # Validation or testing code # Print training information print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}, LR: {scheduler.get_last_lr()[0]}") # Save the model or perform other operations after training In this code snippet, we create a MultiStepLR scheduler and specify the milestones as [100] and gamma as 0.5. The learning rate is halved at the specified milestone epochs. Inside the training loop, we check if the current epoch is greater than or equal to the milestone epoch, and if so, we call scheduler.step() to update the learning rate. Remember to adjust the num_epochs and other hyperparameters according to your specific requirements. 翻译成中文

print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}, LR: {scheduler.get_last_lr()[0]}") # 保存模型或进行其他操作 在这段代码中，我们创建了一个MultiStepLR调度器，并将里程碑轮数设为[100]，...

将代码转化为paddlepaddle框架可以使用的代码:class CosineAnnealingWarmbootingLR: # cawb learning rate scheduler: given the warm booting steps, calculate the learning rate automatically def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale # Initialize epochs and base learning rates for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter # cos warm boot policy iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale *= self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None

class CosineAnnealingWarmbootingLR(paddle.optimizer.lr.CosineAnnealingDecay): def __init__(self, T_max, T_warmup, eta_min=0, last_epoch=-1, verbose=False): super(CosineAnnealingWarmbootingLR, self)....

给以下代码写注释，要求每行写一句：class CosineAnnealingWarmbootingLR: # cawb learning rate scheduler: given the warm booting steps, calculate the learning rate automatically def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale # Initialize epochs and base learning rates for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter # cos warm boot policy iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale *= self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None

定义一个名为get_lr的函数。 if self.T_cur == self.T_max: 如果当前T值等于T_max，则将T_cur重置为0，并将T_max乘以T_mult。 self.T_cur = 0 self.T_max *= self.T_mult return self.eta_min + (1 + math....

在paddle框架中实现下面的所有代码:class CosineAnnealingWarmbootingLR: # cawb learning rate scheduler: given the warm booting steps, calculate the learning rate automatically def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale # Initialize epochs and base learning rates for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter # cos warm boot policy iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale *= self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None

super(CosineAnnealingWarmbootingLR, self).__init__(last_epoch) def get_lr(self): if self.last_epoch < self.T_warmup: return self.eta_min + (self.base_lr - self.eta_min) * self.last_epoch / self.T...

用伪代码书写以下代码 r_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) model.Unfreeze_backbone() epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size if epoch_step == 0 or epoch_step_val == 0: raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。") if distributed: batch_size = batch_size // ngpus_per_node gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=train_sampler) gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=val_sampler) UnFreeze_flag = True if distributed: train_sampler.set_epoch(epoch) set_optimizer_lr(optimizer, lr_scheduler_func, epoch) fit_one_epoch(model_train, model, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank) if local_rank == 0: loss_history.writer.close()

r_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) model.Unfreeze_backbone() epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size if ...

lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) model.Unfreeze_backbone() epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size if epoch_step == 0 or epoch_step_val == 0: raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。") if distributed: batch_size = batch_size // ngpus_per_node gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=train_sampler) gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=val_sampler) UnFreeze_flag = True if distributed: train_sampler.set_epoch(epoch) set_optimizer_lr(optimizer, lr_scheduler_func, epoch) fit_one_epoch(model_train, model, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank) if local_rank == 0: loss_history.writer.close() 转为伪代码

lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) model.Unfreeze_backbone() epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size ...

Traceback (most recent call last): File "C:/Users/vipuser/Desktop/yolov4-pytorch-master/train.py", line 456, in <module> lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) File "C:\Users\vipuser\Desktop\yolov4-pytorch-master\nets\yolo_training.py", line 452, in get_lr_scheduler func = partial(yolox_warm_cos_lr ,lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter) NameError: name 'partial' is not defined

这个错误是由于Python无法找到partial函数的定义而引起的。这个函数属于functools模块，因此你需要在你的代码中添加以下导入语句： python from functools import partial 然后再次运行你的代码，就...

Let's use 2 GPUs! /home/csim/anaconda3/envs/pointcept/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:131: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate warnings.warn("Detected call of lr_scheduler.step() before optimizer.step(). " Traceback (most recent call last): File "/home/csim/ZSTMSP/MPRNet-main/Deraining/train.py", line 91, in <module> train_dataset = get_training_data(train_dir, {'patch_size':opt.TRAINING.TRAIN_PS}) File "/home/csim/ZSTMSP/MPRNet-main/Deraining/data_RGB.py", line 5, in get_training_data assert os.path.exists(rgb_dir) AssertionError

这段信息中包含了两个部分： 1. 建议使用两个 GPU 进行训练，具体的实现方法可能需要修改代码，具体实现可以参考 PyTorch 的官方文档。 2. 该异常信息与之前的一样，是在 data_RGB.py 文件的第 5 行发生的 ...

cosine annealing scheduler代码

super(CosineAnnealingLRWithRestarts, self).__init__(optimizer, T_max, eta_min, last_epoch) def get_lr(self): if self.last_epoch == self.T_max_restart: self.last_epoch = 0 self.T_max_restart = ...

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