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import math
from contextlib import _GeneratorContextManager, nullcontext
from typing import Callable, Any, Tuple, Union
from functools import partial
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch.optim.lr_scheduler import LambdaLR
from tqdm.auto import tqdm
from training.functional import TrainingCallbacks
from training.util import AverageMeter
def noop(*args, **kwards):
pass
def noop_ctx(*args, **kwards):
return nullcontext()
class LRFinder():
def __init__(
self,
accelerator,
optimizer,
train_dataloader,
val_dataloader,
loss_fn: Union[Callable[[int, Any], Tuple[Any, Any, int]], Callable[[int, Any, bool], Tuple[Any, Any, int]]],
callbacks: TrainingCallbacks = TrainingCallbacks()
):
self.accelerator = accelerator
self.model = callbacks.on_model()
self.optimizer = optimizer
self.train_dataloader = train_dataloader
self.val_dataloader = val_dataloader
self.loss_fn = loss_fn
self.callbacks = callbacks
# self.model_state = copy.deepcopy(model.state_dict())
# self.optimizer_state = copy.deepcopy(optimizer.state_dict())
def run(
self,
end_lr,
skip_start: int = 10,
skip_end: int = 5,
num_epochs: int = 100,
num_train_batches: int = math.inf,
num_val_batches: int = math.inf,
smooth_f: float = 0.05,
):
best_loss = None
best_acc = None
lrs = []
losses = []
accs = []
lr_scheduler = get_exponential_schedule(
self.optimizer,
end_lr,
num_epochs * min(num_train_batches, len(self.train_dataloader))
)
steps = min(num_train_batches, len(self.train_dataloader))
steps += min(num_val_batches, len(self.val_dataloader))
steps *= num_epochs
progress_bar = tqdm(
range(steps),
disable=not self.accelerator.is_local_main_process,
dynamic_ncols=True
)
progress_bar.set_description("Epoch X / Y")
self.callbacks.on_prepare()
on_train = self.callbacks.on_train
on_before_optimize = self.callbacks.on_before_optimize
on_after_optimize = self.callbacks.on_after_optimize
on_eval = self.callbacks.on_eval
for epoch in range(num_epochs):
progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}")
avg_loss = AverageMeter()
avg_acc = AverageMeter()
self.model.train()
with on_train(epoch):
for step, batch in enumerate(self.train_dataloader):
if step >= num_train_batches:
break
with self.accelerator.accumulate(self.model):
loss, acc, bsz = self.loss_fn(step, batch)
self.accelerator.backward(loss)
on_before_optimize(epoch)
self.optimizer.step()
lr_scheduler.step()
self.optimizer.zero_grad(set_to_none=True)
if self.accelerator.sync_gradients:
on_after_optimize(lr_scheduler.get_last_lr()[0])
progress_bar.update(1)
self.model.eval()
with torch.inference_mode():
with on_eval():
for step, batch in enumerate(self.val_dataloader):
if step >= num_val_batches:
break
loss, acc, bsz = self.loss_fn(step, batch, True)
avg_loss.update(loss.detach_(), bsz)
avg_acc.update(acc.detach_(), bsz)
progress_bar.update(1)
loss = avg_loss.avg.item()
acc = avg_acc.avg.item()
if epoch == 0:
best_loss = loss
best_acc = acc
else:
if smooth_f > 0:
loss = smooth_f * loss + (1 - smooth_f) * losses[-1]
acc = smooth_f * acc + (1 - smooth_f) * accs[-1]
if loss < best_loss:
best_loss = loss
if acc > best_acc:
best_acc = acc
lr = lr_scheduler.get_last_lr()[0]
lrs.append(lr)
losses.append(loss)
accs.append(acc)
self.accelerator.log({
"loss": loss,
"acc": acc,
"lr": lr,
}, step=epoch)
progress_bar.set_postfix({
"loss": loss,
"loss/best": best_loss,
"acc": acc,
"acc/best": best_acc,
"lr": lr,
})
# self.model.load_state_dict(self.model_state)
# self.optimizer.load_state_dict(self.optimizer_state)
if skip_end == 0:
lrs = lrs[skip_start:]
losses = losses[skip_start:]
accs = accs[skip_start:]
else:
lrs = lrs[skip_start:-skip_end]
losses = losses[skip_start:-skip_end]
accs = accs[skip_start:-skip_end]
fig, ax_loss = plt.subplots()
ax_acc = ax_loss.twinx()
ax_loss.plot(lrs, losses, color='red')
ax_loss.set_xscale("log")
ax_loss.set_xlabel(f"Learning rate")
ax_loss.set_ylabel("Loss")
ax_acc.plot(lrs, accs, color='blue')
ax_acc.set_xscale("log")
ax_acc.set_ylabel("Accuracy")
print("LR suggestion: steepest gradient")
min_grad_idx = None
try:
min_grad_idx = np.gradient(np.array(losses)).argmin()
except ValueError:
print(
"Failed to compute the gradients, there might not be enough points."
)
try:
max_val_idx = np.array(accs).argmax()
except ValueError:
print(
"Failed to compute the gradients, there might not be enough points."
)
if min_grad_idx is not None:
print("Suggested LR (loss): {:.2E}".format(lrs[min_grad_idx]))
ax_loss.scatter(
lrs[min_grad_idx],
losses[min_grad_idx],
s=75,
marker="o",
color="red",
zorder=3,
label="steepest gradient",
)
ax_loss.legend()
if max_val_idx is not None:
print("Suggested LR (acc): {:.2E}".format(lrs[max_val_idx]))
ax_acc.scatter(
lrs[max_val_idx],
accs[max_val_idx],
s=75,
marker="o",
color="blue",
zorder=3,
label="maximum",
)
ax_acc.legend()
def get_exponential_schedule(optimizer, end_lr: float, num_epochs: int, last_epoch: int = -1):
def lr_lambda(base_lr: float, current_epoch: int):
return (end_lr / base_lr) ** (current_epoch / num_epochs)
lr_lambdas = [partial(lr_lambda, group["lr"]) for group in optimizer.param_groups]
return LambdaLR(optimizer, lr_lambdas, last_epoch)
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