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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.
- # --------------------------------------------------------
- # References:
- # DeiT: https://github.com/facebookresearch/deit
- # BEiT: https://github.com/microsoft/unilm/tree/master/beit
- # --------------------------------------------------------
-
- import math
- import sys
- from typing import Iterable, Optional
-
- import torch
-
- from timm.data import Mixup
- from timm.utils import accuracy
-
- import util.misc as misc
- import util.lr_sched as lr_sched
-
-
- 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,
- mixup_fn: Optional[Mixup] = None, log_writer=None,
- args=None):
- model.train(True)
- metric_logger = misc.MetricLogger(delimiter=" ")
- metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
- header = 'Epoch: [{}]'.format(epoch)
- print_freq = 20
-
- accum_iter = args.accum_iter
-
- optimizer.zero_grad()
-
- if log_writer is not None:
- print('log_dir: {}'.format(log_writer.log_dir))
-
- for data_iter_step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
-
- # we use a per iteration (instead of per epoch) lr scheduler
- if data_iter_step % accum_iter == 0:
- lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
-
- 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)
-
- with torch.cuda.amp.autocast():
- outputs = model(samples)
- loss = criterion(outputs, targets)
-
- loss_value = loss.item()
-
- if not math.isfinite(loss_value):
- print("Loss is {}, stopping training".format(loss_value))
- sys.exit(1)
-
- loss /= accum_iter
- loss_scaler(loss, optimizer, clip_grad=max_norm,
- parameters=model.parameters(), create_graph=False,
- update_grad=(data_iter_step + 1) % accum_iter == 0)
- if (data_iter_step + 1) % accum_iter == 0:
- optimizer.zero_grad()
-
- torch.cuda.synchronize()
-
- metric_logger.update(loss=loss_value)
- 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)
-
- loss_value_reduce = misc.all_reduce_mean(loss_value)
- if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
- """ We use epoch_1000x as the x-axis in tensorboard.
- This calibrates different curves when batch size changes.
- """
- epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
- log_writer.add_scalar('loss', loss_value_reduce, epoch_1000x)
- log_writer.add_scalar('lr', max_lr, epoch_1000x)
-
- # 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):
- criterion = torch.nn.CrossEntropyLoss()
-
- metric_logger = misc.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
- with torch.cuda.amp.autocast():
- 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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