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- # Copyright (c) Meta Platforms, Inc. and affiliates.
-
- # All rights reserved.
-
- # This source code is licensed under the license found in the
- # LICENSE file in the root directory of this source tree.
-
-
- import math
- from typing import Iterable, Optional
- import torch
- from timm.data import Mixup
- from timm.utils import accuracy, ModelEma
-
- import utils
-
- def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
- data_loader: Iterable, optimizer: torch.optim.Optimizer,
- device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
- model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None, log_writer=None,
- wandb_logger=None, start_steps=None, lr_schedule_values=None, wd_schedule_values=None,
- num_training_steps_per_epoch=None, update_freq=None, use_amp=False):
- model.train(True)
- metric_logger = utils.MetricLogger(delimiter=" ")
- metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
- metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
- header = 'Epoch: [{}]'.format(epoch)
- print_freq = 10
-
- optimizer.zero_grad()
-
- for data_iter_step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
- step = data_iter_step // update_freq
- if step >= num_training_steps_per_epoch:
- continue
- it = start_steps + step # global training iteration
- # Update LR & WD for the first acc
- if lr_schedule_values is not None or wd_schedule_values is not None and data_iter_step % update_freq == 0:
- for i, param_group in enumerate(optimizer.param_groups):
- if lr_schedule_values is not None:
- param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"]
- if wd_schedule_values is not None and param_group["weight_decay"] > 0:
- param_group["weight_decay"] = wd_schedule_values[it]
-
- samples = samples.to(device, non_blocking=True)
- targets = targets.to(device, non_blocking=True)
-
- if mixup_fn is not None:
- samples, targets = mixup_fn(samples, targets)
-
- if use_amp:
- with torch.cuda.amp.autocast():
- output = model(samples)
- loss = criterion(output, targets)
- else: # full precision
- output = model(samples)
- loss = criterion(output, targets)
-
- loss_value = loss.item()
-
- if not math.isfinite(loss_value): # this could trigger if using AMP
- print("Loss is {}, stopping training".format(loss_value))
- assert math.isfinite(loss_value)
-
- if use_amp:
- # this attribute is added by timm on one optimizer (adahessian)
- is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
- loss /= update_freq
- grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm,
- parameters=model.parameters(), create_graph=is_second_order,
- update_grad=(data_iter_step + 1) % update_freq == 0)
- if (data_iter_step + 1) % update_freq == 0:
- optimizer.zero_grad()
- if model_ema is not None:
- model_ema.update(model)
- else: # full precision
- loss /= update_freq
- loss.backward()
- if (data_iter_step + 1) % update_freq == 0:
- optimizer.step()
- optimizer.zero_grad()
- if model_ema is not None:
- model_ema.update(model)
-
- torch.cuda.synchronize()
-
- if mixup_fn is None:
- class_acc = (output.max(-1)[-1] == targets).float().mean()
- else:
- class_acc = None
- metric_logger.update(loss=loss_value)
- metric_logger.update(class_acc=class_acc)
- min_lr = 10.
- max_lr = 0.
- for group in optimizer.param_groups:
- min_lr = min(min_lr, group["lr"])
- max_lr = max(max_lr, group["lr"])
-
- metric_logger.update(lr=max_lr)
- metric_logger.update(min_lr=min_lr)
- weight_decay_value = None
- for group in optimizer.param_groups:
- if group["weight_decay"] > 0:
- weight_decay_value = group["weight_decay"]
- metric_logger.update(weight_decay=weight_decay_value)
- if use_amp:
- metric_logger.update(grad_norm=grad_norm)
-
- if log_writer is not None:
- log_writer.update(loss=loss_value, head="loss")
- log_writer.update(class_acc=class_acc, head="loss")
- log_writer.update(lr=max_lr, head="opt")
- log_writer.update(min_lr=min_lr, head="opt")
- log_writer.update(weight_decay=weight_decay_value, head="opt")
- if use_amp:
- log_writer.update(grad_norm=grad_norm, head="opt")
- log_writer.set_step()
-
- if wandb_logger:
- wandb_logger._wandb.log({
- 'Rank-0 Batch Wise/train_loss': loss_value,
- 'Rank-0 Batch Wise/train_max_lr': max_lr,
- 'Rank-0 Batch Wise/train_min_lr': min_lr
- }, commit=False)
- if class_acc:
- wandb_logger._wandb.log({'Rank-0 Batch Wise/train_class_acc': class_acc}, commit=False)
- if use_amp:
- wandb_logger._wandb.log({'Rank-0 Batch Wise/train_grad_norm': grad_norm}, commit=False)
- wandb_logger._wandb.log({'Rank-0 Batch Wise/global_train_step': it})
-
-
- # gather the stats from all processes
- metric_logger.synchronize_between_processes()
- print("Averaged stats:", metric_logger)
- return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
-
- @torch.no_grad()
- def evaluate(data_loader, model, device, use_amp=False):
- criterion = torch.nn.CrossEntropyLoss()
-
- metric_logger = utils.MetricLogger(delimiter=" ")
- header = 'Test:'
-
- # switch to evaluation mode
- model.eval()
- for batch in metric_logger.log_every(data_loader, 10, header):
- images = batch[0]
- target = batch[-1]
-
- images = images.to(device, non_blocking=True)
- target = target.to(device, non_blocking=True)
-
- # compute output
- if use_amp:
- with torch.cuda.amp.autocast():
- output = model(images)
- loss = criterion(output, target)
- else:
- output = model(images)
- loss = criterion(output, target)
-
- acc1, acc5 = accuracy(output, target, topk=(1, 5))
-
- batch_size = images.shape[0]
- metric_logger.update(loss=loss.item())
- metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
- metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
- # gather the stats from all processes
- metric_logger.synchronize_between_processes()
- print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
- .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
-
- return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
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