add autodl
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263
AutoDL-Projects/xautodl/procedures/optimizers.py
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263
AutoDL-Projects/xautodl/procedures/optimizers.py
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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#####################################################
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import math, torch
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import torch.nn as nn
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from bisect import bisect_right
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from torch.optim import Optimizer
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class _LRScheduler(object):
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def __init__(self, optimizer, warmup_epochs, epochs):
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if not isinstance(optimizer, Optimizer):
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raise TypeError("{:} is not an Optimizer".format(type(optimizer).__name__))
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self.optimizer = optimizer
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for group in optimizer.param_groups:
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group.setdefault("initial_lr", group["lr"])
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self.base_lrs = list(
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map(lambda group: group["initial_lr"], optimizer.param_groups)
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)
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self.max_epochs = epochs
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self.warmup_epochs = warmup_epochs
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self.current_epoch = 0
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self.current_iter = 0
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def extra_repr(self):
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return ""
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def __repr__(self):
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return "{name}(warmup={warmup_epochs}, max-epoch={max_epochs}, current::epoch={current_epoch}, iter={current_iter:.2f}".format(
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name=self.__class__.__name__, **self.__dict__
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) + ", {:})".format(
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self.extra_repr()
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)
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def state_dict(self):
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return {
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key: value for key, value in self.__dict__.items() if key != "optimizer"
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}
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def load_state_dict(self, state_dict):
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self.__dict__.update(state_dict)
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def get_lr(self):
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raise NotImplementedError
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def get_min_info(self):
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lrs = self.get_lr()
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return "#LR=[{:.6f}~{:.6f}] epoch={:03d}, iter={:4.2f}#".format(
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min(lrs), max(lrs), self.current_epoch, self.current_iter
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)
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def get_min_lr(self):
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return min(self.get_lr())
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def update(self, cur_epoch, cur_iter):
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if cur_epoch is not None:
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assert (
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isinstance(cur_epoch, int) and cur_epoch >= 0
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), "invalid cur-epoch : {:}".format(cur_epoch)
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self.current_epoch = cur_epoch
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if cur_iter is not None:
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assert (
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isinstance(cur_iter, float) and cur_iter >= 0
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), "invalid cur-iter : {:}".format(cur_iter)
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self.current_iter = cur_iter
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for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
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param_group["lr"] = lr
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class CosineAnnealingLR(_LRScheduler):
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def __init__(self, optimizer, warmup_epochs, epochs, T_max, eta_min):
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self.T_max = T_max
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self.eta_min = eta_min
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super(CosineAnnealingLR, self).__init__(optimizer, warmup_epochs, epochs)
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def extra_repr(self):
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return "type={:}, T-max={:}, eta-min={:}".format(
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"cosine", self.T_max, self.eta_min
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)
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def get_lr(self):
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lrs = []
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for base_lr in self.base_lrs:
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if (
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self.current_epoch >= self.warmup_epochs
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and self.current_epoch < self.max_epochs
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):
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last_epoch = self.current_epoch - self.warmup_epochs
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# if last_epoch < self.T_max:
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# if last_epoch < self.max_epochs:
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lr = (
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self.eta_min
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+ (base_lr - self.eta_min)
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* (1 + math.cos(math.pi * last_epoch / self.T_max))
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/ 2
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)
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# else:
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# lr = self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * (self.T_max-1.0) / self.T_max)) / 2
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elif self.current_epoch >= self.max_epochs:
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lr = self.eta_min
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else:
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lr = (
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self.current_epoch / self.warmup_epochs
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+ self.current_iter / self.warmup_epochs
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) * base_lr
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lrs.append(lr)
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return lrs
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class MultiStepLR(_LRScheduler):
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def __init__(self, optimizer, warmup_epochs, epochs, milestones, gammas):
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assert len(milestones) == len(gammas), "invalid {:} vs {:}".format(
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len(milestones), len(gammas)
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)
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self.milestones = milestones
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self.gammas = gammas
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super(MultiStepLR, self).__init__(optimizer, warmup_epochs, epochs)
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def extra_repr(self):
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return "type={:}, milestones={:}, gammas={:}, base-lrs={:}".format(
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"multistep", self.milestones, self.gammas, self.base_lrs
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)
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def get_lr(self):
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lrs = []
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for base_lr in self.base_lrs:
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if self.current_epoch >= self.warmup_epochs:
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last_epoch = self.current_epoch - self.warmup_epochs
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idx = bisect_right(self.milestones, last_epoch)
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lr = base_lr
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for x in self.gammas[:idx]:
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lr *= x
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else:
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lr = (
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self.current_epoch / self.warmup_epochs
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+ self.current_iter / self.warmup_epochs
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) * base_lr
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lrs.append(lr)
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return lrs
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class ExponentialLR(_LRScheduler):
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def __init__(self, optimizer, warmup_epochs, epochs, gamma):
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self.gamma = gamma
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super(ExponentialLR, self).__init__(optimizer, warmup_epochs, epochs)
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def extra_repr(self):
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return "type={:}, gamma={:}, base-lrs={:}".format(
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"exponential", self.gamma, self.base_lrs
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)
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def get_lr(self):
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lrs = []
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for base_lr in self.base_lrs:
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if self.current_epoch >= self.warmup_epochs:
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last_epoch = self.current_epoch - self.warmup_epochs
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assert last_epoch >= 0, "invalid last_epoch : {:}".format(last_epoch)
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lr = base_lr * (self.gamma**last_epoch)
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else:
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lr = (
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self.current_epoch / self.warmup_epochs
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+ self.current_iter / self.warmup_epochs
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) * base_lr
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lrs.append(lr)
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return lrs
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class LinearLR(_LRScheduler):
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def __init__(self, optimizer, warmup_epochs, epochs, max_LR, min_LR):
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self.max_LR = max_LR
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self.min_LR = min_LR
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super(LinearLR, self).__init__(optimizer, warmup_epochs, epochs)
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def extra_repr(self):
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return "type={:}, max_LR={:}, min_LR={:}, base-lrs={:}".format(
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"LinearLR", self.max_LR, self.min_LR, self.base_lrs
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)
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def get_lr(self):
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lrs = []
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for base_lr in self.base_lrs:
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if self.current_epoch >= self.warmup_epochs:
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last_epoch = self.current_epoch - self.warmup_epochs
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assert last_epoch >= 0, "invalid last_epoch : {:}".format(last_epoch)
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ratio = (
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(self.max_LR - self.min_LR)
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* last_epoch
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/ self.max_epochs
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/ self.max_LR
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)
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lr = base_lr * (1 - ratio)
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else:
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lr = (
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self.current_epoch / self.warmup_epochs
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+ self.current_iter / self.warmup_epochs
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) * base_lr
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lrs.append(lr)
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return lrs
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class CrossEntropyLabelSmooth(nn.Module):
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def __init__(self, num_classes, epsilon):
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super(CrossEntropyLabelSmooth, self).__init__()
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self.num_classes = num_classes
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self.epsilon = epsilon
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self.logsoftmax = nn.LogSoftmax(dim=1)
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def forward(self, inputs, targets):
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log_probs = self.logsoftmax(inputs)
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targets = torch.zeros_like(log_probs).scatter_(1, targets.unsqueeze(1), 1)
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targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
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loss = (-targets * log_probs).mean(0).sum()
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return loss
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def get_optim_scheduler(parameters, config):
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assert (
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hasattr(config, "optim")
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and hasattr(config, "scheduler")
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and hasattr(config, "criterion")
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), "config must have optim / scheduler / criterion keys instead of {:}".format(
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config
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)
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if config.optim == "SGD":
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optim = torch.optim.SGD(
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parameters,
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config.LR,
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momentum=config.momentum,
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weight_decay=config.decay,
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nesterov=config.nesterov,
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)
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elif config.optim == "RMSprop":
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optim = torch.optim.RMSprop(
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parameters, config.LR, momentum=config.momentum, weight_decay=config.decay
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)
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else:
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raise ValueError("invalid optim : {:}".format(config.optim))
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if config.scheduler == "cos":
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T_max = getattr(config, "T_max", config.epochs)
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scheduler = CosineAnnealingLR(
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optim, config.warmup, config.epochs, T_max, config.eta_min
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)
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elif config.scheduler == "multistep":
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scheduler = MultiStepLR(
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optim, config.warmup, config.epochs, config.milestones, config.gammas
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)
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elif config.scheduler == "exponential":
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scheduler = ExponentialLR(optim, config.warmup, config.epochs, config.gamma)
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elif config.scheduler == "linear":
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scheduler = LinearLR(
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optim, config.warmup, config.epochs, config.LR, config.LR_min
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)
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else:
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raise ValueError("invalid scheduler : {:}".format(config.scheduler))
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if config.criterion == "Softmax":
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criterion = torch.nn.CrossEntropyLoss()
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elif config.criterion == "SmoothSoftmax":
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criterion = CrossEntropyLabelSmooth(config.class_num, config.label_smooth)
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else:
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raise ValueError("invalid criterion : {:}".format(config.criterion))
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return optim, scheduler, criterion
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