if self.label_smoother is not None and "labels" in inputs: labels = inputs.pop("labels") else: labels = None outputs = model(**inputs) # Save past state if it exists # TODO: this needs to be fixed and made cleaner later. if self.args.past_index >= 0: self._past = outputs[self.args.past_index] if labels is not None: if unwrap_model(model)._get_name() in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values(): loss = self.label_smoother(outputs, labels, shift_labels=True) else: loss = self.label_smoother(outputs, labels) else: if isinstance(outputs, dict) and "loss" not in outputs: raise ValueError( "The model did not return a loss from the inputs, only the following keys: " f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}." )详细解释一个这个los
时间: 2024-02-10 12:07:39 浏览: 36
这段代码是在训练模型时计算损失函数的部分。首先判断是否有标签(labels)输入,若有则从输入中弹出标签并保存到labels变量中,否则labels为None。然后通过调用模型(model)的forward方法(model(**inputs))获取模型的输出(outputs)。如果模型有过去状态(past state),则将其保存到self._past中。接下来,如果labels不为None,则使用标签平滑器(label smoother)计算损失(loss)。标签平滑器的作用是在训练过程中减少过拟合现象。如果labels为None,则判断模型的输出是否包含损失值,若不包含则抛出ValueError异常。
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def compute_loss(self, model, inputs, return_outputs=False): """ How the loss is computed by Trainer. By default, all models return the loss in the first element. Subclass and override for custom behavior. """ if self.label_smoother is not None and "labels" in inputs: labels = inputs.pop("labels") else: labels = None outputs = model(**inputs) # Save past state if it exists # TODO: this needs to be fixed and made cleaner later. if self.args.past_index >= 0: self._past = outputs[self.args.past_index] if labels is not None: if unwrap_model(model)._get_name() in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values(): loss = self.label_smoother(outputs, labels, shift_labels=True) else: loss = self.label_smoother(outputs, labels) else: if isinstance(outputs, dict) and "loss" not in outputs: raise ValueError( "The model did not return a loss from the inputs, only the following keys: " f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}." ) # We don't use .loss here since the model may return tuples instead of ModelOutput. loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0] return (loss, outputs) if return_outputs else loss详细解释一下上面的代码
这段代码是在Trainer类中的compute_loss方法中实现的。该方法用于计算模型的损失,其默认行为是从模型的输出中获取第一个元素作为损失值。在这段代码中,首先判断是否使用了标签平滑器(label_smoother),如果使用了则将标签从输入中弹出,否则将标签置为None。接着,调用模型的forward方法获取输出,如果模型具有过去的状态,则将其保存在Trainer对象中。如果标签不为None,则使用标签平滑器计算损失值。最后,返回损失值和输出,或者仅返回损失值,具体取决于return_outputs参数的值。如果模型返回的是一个字典,并且其中不包含"loss"键,则会抛出ValueError异常。
机器人velocity_smoother 代码
以下是一个简单的机器人速度平滑器的 Python 代码示例:
```
class VelocitySmoother:
def __init__(self, alpha):
self.alpha = alpha
self.prev_velocity = None
def smooth_velocity(self, current_velocity):
if self.prev_velocity is None:
self.prev_velocity = current_velocity
smoothed_velocity = self.alpha * current_velocity + (1 - self.alpha) * self.prev_velocity
self.prev_velocity = smoothed_velocity
return smoothed_velocity
```
在上面的代码中,`VelocitySmoother` 类接受一个 `alpha` 参数,表示平滑器的平滑程度。`prev_velocity` 变量存储了上一个时间步的速度值,在第一次调用时初始化为当前速度值。`smooth_velocity` 方法接受当前的速度值 `current_velocity`,并使用指数平滑算法计算出平滑后的速度值 `smoothed_velocity`。最后,将 `prev_velocity` 更新为 `smoothed_velocity` 以便下一次调用时使用。
你可以根据实际需求调整 `alpha` 参数的值,以达到最佳的平滑效果。同时,你还需要将 `smooth_velocity` 方法集成到机器人的控制逻辑中,以确保机器人的运动速度信号始终保持平滑。