130 lines
4.7 KiB
Python
130 lines
4.7 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# DeiT: https://github.com/facebookresearch/deit
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# BEiT: https://github.com/microsoft/unilm/tree/master/beit
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# --------------------------------------------------------
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import math
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import sys
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from typing import Iterable, Optional
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import torch
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from timm.data import Mixup
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from timm.utils import accuracy
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import util.misc as misc
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import util.lr_sched as lr_sched
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def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
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data_loader: Iterable, optimizer: torch.optim.Optimizer,
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device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
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mixup_fn: Optional[Mixup] = None, log_writer=None,
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args=None):
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model.train(True)
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metric_logger = misc.MetricLogger(delimiter=" ")
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metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
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header = 'Epoch: [{}]'.format(epoch)
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print_freq = 20
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accum_iter = args.accum_iter
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optimizer.zero_grad()
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if log_writer is not None:
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print('log_dir: {}'.format(log_writer.log_dir))
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for data_iter_step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
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# we use a per iteration (instead of per epoch) lr scheduler
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if data_iter_step % accum_iter == 0:
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lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
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samples = samples.to(device, non_blocking=True)
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targets = targets.to(device, non_blocking=True)
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if mixup_fn is not None:
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samples, targets = mixup_fn(samples, targets)
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with torch.cuda.amp.autocast():
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outputs = model(samples)
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loss = criterion(outputs, targets)
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loss_value = loss.item()
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if not math.isfinite(loss_value):
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print("Loss is {}, stopping training".format(loss_value))
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sys.exit(1)
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loss /= accum_iter
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loss_scaler(loss, optimizer, clip_grad=max_norm,
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parameters=model.parameters(), create_graph=False,
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update_grad=(data_iter_step + 1) % accum_iter == 0)
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if (data_iter_step + 1) % accum_iter == 0:
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optimizer.zero_grad()
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torch.cuda.synchronize()
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metric_logger.update(loss=loss_value)
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min_lr = 10.
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max_lr = 0.
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for group in optimizer.param_groups:
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min_lr = min(min_lr, group["lr"])
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max_lr = max(max_lr, group["lr"])
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metric_logger.update(lr=max_lr)
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loss_value_reduce = misc.all_reduce_mean(loss_value)
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if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
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""" We use epoch_1000x as the x-axis in tensorboard.
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This calibrates different curves when batch size changes.
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"""
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epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
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log_writer.add_scalar('loss', loss_value_reduce, epoch_1000x)
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log_writer.add_scalar('lr', max_lr, epoch_1000x)
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# gather the stats from all processes
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metric_logger.synchronize_between_processes()
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print("Averaged stats:", metric_logger)
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return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
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@torch.no_grad()
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def evaluate(data_loader, model, device):
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criterion = torch.nn.CrossEntropyLoss()
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metric_logger = misc.MetricLogger(delimiter=" ")
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header = 'Test:'
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# switch to evaluation mode
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model.eval()
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for batch in metric_logger.log_every(data_loader, 10, header):
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images = batch[0]
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target = batch[-1]
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images = images.to(device, non_blocking=True)
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target = target.to(device, non_blocking=True)
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# compute output
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with torch.cuda.amp.autocast():
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output = model(images)
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loss = criterion(output, target)
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acc1, acc5 = accuracy(output, target, topk=(1, 5))
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batch_size = images.shape[0]
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metric_logger.update(loss=loss.item())
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metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
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metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
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# gather the stats from all processes
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metric_logger.synchronize_between_processes()
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print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
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.format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
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return {k: meter.global_avg for k, meter in metric_logger.meters.items()} |