iters = math.log(1 - P) / math.log(1 - pow(total_inlier / n, 3))

这段代码计算了迭代次数 `iters`，其中使用了数学库 `math` 中的对数函数 `log` 和幂函数 `pow`。

具体解释如下：

- `P` 是预设的概率，表示在 RANSAC 迭代过程中至少有一个迭代中所有采样点都是内点的概率。常见的取值为 0.99，表示我们希望的是至少有 99% 的概率找到正确的模型。
- `total_inlier` 是总的内点数量。
- `n` 是用于拟合模型所需的最小样本集的大小。

这段代码使用了 RANSAC 算法的迭代次数估计公式，根据预设的概率 `P`、总内点数量 `total_inlier` 和最小样本集大小 `n` 来计算迭代次数。该公式基于统计学原理，用于估计需要多少次迭代才能以给定的概率找到合适的模型。

需要注意的是，该代码片段只是计算迭代次数，具体的 RANSAC 算法实现可能还需要其他步骤。

相关问题

代码解释并给每行代码添加注释:class CosineAnnealingWarmbootingLR: 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 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 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

这是一个名为 CosineAnnealingWarmbootingLR 的类，用于实现余弦退火学习率调整。以下是每行代码的注释：

class CosineAnnealingWarmbootingLR:
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):
# 初始化函数，接受一些参数
self.warmup_iters = batchs * warmup_epoch # 热身迭代次数
self.optimizer = optimizer # 优化器
self.eta_min = eta_min # 最小学习率
self.iters = -1 # 当前迭代次数
self.iters_batch = -1 # 当前批次迭代次数
self.base_lr = [group['lr'] for group in optimizer.param_groups] # 初始学习率
self.step_scale = step_scale # 步长缩放因子
steps.sort() # 步长列表排序
self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] # 步长列表
self.gap = 0 # 步长间隔
self.last_epoch = 0 # 上一个 epoch
self.lf = lf # 学习率函数
self.epoch_scale = epoch_scale # epoch 缩放因子

for group in optimizer.param_groups:
group.setdefault('initial_lr', group['lr']) # 设置默认初始学习率

def step(self, external_iter=None):
# 学习率调整函数
self.iters = 1 # 当前迭代次数
if external_iter is not None:
self.iters = external_iter

iters = self.iters - self.warmup_iters # 当前迭代次数减去热身迭代次数
last_epoch = self.last_epoch # 上一个 epoch
scale = 1.0 # 缩放因子
for i in range(len(self.steps)-1):
if (iters <= self.steps[i+1]):
self.gap = self.steps[i+1] - self.steps[i] # 步长间隔
iters = iters - self.steps[i] # 当前迭代次数减去当前步长
last_epoch = self.steps[i] # 上一个 epoch
if i != len(self.steps)-2:
self.gap *= self.epoch_scale # 如果不是最后一个步长，乘以 epoch 缩放因子
break
scale *= self.step_scale # 缩放因子乘以步长缩放因子

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) # 计算学习率
else:
for group, lr in zip(self.optimizer.param_groups, self.base_lr):
group['lr'] = scale * lr * self.lf(iters, self.gap) # 使用学习率函数计算学习率

self.last_epoch = last_epoch # 更新上一个 epoch
return self.optimizer.param_groups[0]['lr'] # 返回学习率

def step_batch(self):
# 批次学习率调整函数
self.iters_batch = 1 # 当前批次迭代次数
if self.iters_batch < self.warmup_iters:
rate = self.iters_batch / self.warmup_iters # 计算学习率缩放因子
for group, lr in zip(self.optimizer.param_groups, self.base_lr):
group['lr'] = lr * rate # 缩放学习率
return self.optimizer.param_groups[0]['lr'] # 返回学习率
else:
return None # 如果已经完成热身，返回 None

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

这是一个类的定义，类名为CosineAnnealingWarmbootingLR。

def __init__(self, optimizer, T_max, T_mult=1, eta_min=0, last_epoch=-1):

这是类的初始化函数，接受optimizer、T_max、T_mult、eta_min和last_epoch五个参数。

self.optimizer = optimizer
self.T_max = T_max
self.T_mult = T_mult
self.eta_min = eta_min
self.last_epoch = last_epoch

将传入的参数分别赋值给类的属性。

self.T_cur = float(self.last_epoch)

初始化当前的T值为last_epoch。

def get_lr(self):

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.cos(math.pi * self.T_cur / self.T_max)) / 2 * (self.optimizer.param_groups[0]['lr'] - self.eta_min)

如果当前T值不等于T_max，则根据公式计算当前的学习率，并返回。