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- # --------------------------------------------------------
- # BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
- # Github source: https://github.com/microsoft/unilm/tree/master/beit
- # Copyright (c) 2021 Microsoft
- # Licensed under The MIT License [see LICENSE for details]
- # By Hangbo Bao
- # Based on timm, DINO and DeiT code bases
- # https://github.com/rwightman/pytorch-image-models/tree/master/timm
- # https://github.com/facebookresearch/deit/
- # https://github.com/facebookresearch/dino
- # --------------------------------------------------------'
- import math
- import sys
- from typing import Iterable, Optional
-
- import torch
-
- from timm.data import Mixup
- from timm.utils import accuracy, ModelEma
-
- import utils
-
-
- def train_class_batch(model, samples, target, criterion):
- outputs = model(samples)
- loss = criterion(outputs, target)
- return loss, outputs
-
-
- def get_loss_scale_for_deepspeed(model):
- optimizer = model.optimizer
- return optimizer.loss_scale if hasattr(optimizer, "loss_scale") else optimizer.cur_scale
-
-
- 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,
- start_steps=None, lr_schedule_values=None, wd_schedule_values=None,
- num_training_steps_per_epoch=None, update_freq=None):
- 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
-
- if loss_scaler is None:
- model.zero_grad()
- model.micro_steps = 0
- else:
- 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 loss_scaler is None:
- samples = samples.half()
- loss, output = train_class_batch(
- model, samples, targets, criterion)
- else:
- with torch.cuda.amp.autocast():
- loss, output = train_class_batch(
- model, samples, targets, criterion)
-
- loss_value = loss.item()
-
- if not math.isfinite(loss_value):
- print("Loss is {}, stopping training".format(loss_value))
- sys.exit(1)
-
- if loss_scaler is None:
- loss /= update_freq
- model.backward(loss)
- model.step()
-
- if (data_iter_step + 1) % update_freq == 0:
- # model.zero_grad()
- # Deepspeed will call step() & model.zero_grad() automatic
- if model_ema is not None:
- model_ema.update(model)
- grad_norm = None
- loss_scale_value = get_loss_scale_for_deepspeed(model)
- else:
- # 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)
- loss_scale_value = loss_scaler.state_dict()["scale"]
-
- 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)
- metric_logger.update(loss_scale=loss_scale_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)
- 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)
- 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(loss_scale=loss_scale_value, head="opt")
- 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")
- log_writer.update(grad_norm=grad_norm, head="opt")
-
- log_writer.set_step()
-
- # 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 = 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
- 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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