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- # Copyright (c) 2015-present, Facebook, Inc.
- # All rights reserved.
- """
- Train and eval functions used in main.py
- """
- import math
- import sys
- from typing import Iterable, Optional
-
- import torch
-
- from timm.data import Mixup
- from timm.utils import accuracy, ModelEma
- import kornia as K
-
- from losses import DistillationLoss
- import utils
- import torch.nn as nn
- import torch.nn.functional as F
-
- def clamp(X, lower_limit, upper_limit):
- return torch.max(torch.min(X, upper_limit), lower_limit)
-
- def PGDAttack(x, y, model, attack_epsilon, attack_alpha, lower_limit, loss_fn, upper_limit, max_iters, random_init):
- model.eval()
-
- delta = torch.zeros_like(x).cuda()
- if random_init:
- for iiiii in range(len(attack_epsilon)):
- delta[:, iiiii, :, :].uniform_(-attack_epsilon[iiiii][0][0].item(), attack_epsilon[iiiii][0][0].item())
-
- adv_imgs = clamp(x+delta, lower_limit, upper_limit)
- max_iters = int(max_iters)
- adv_imgs.requires_grad = True
-
- with torch.enable_grad():
- for _iter in range(max_iters):
-
- outputs = model(adv_imgs)
-
- loss = loss_fn(outputs, y)
-
- grads = torch.autograd.grad(loss, adv_imgs, grad_outputs=None,
- only_inputs=True)[0]
-
- adv_imgs.data += attack_alpha * torch.sign(grads.data)
-
- adv_imgs = clamp(adv_imgs, x-attack_epsilon, x+attack_epsilon)
-
- adv_imgs = clamp(adv_imgs, lower_limit, upper_limit)
-
- return adv_imgs.detach()
-
- def patch_level_aug(input1, patch_transform, upper_limit, lower_limit):
- bs, channle_size, H, W = input1.shape
- patches = input1.unfold(2, 16, 16).unfold(3, 16, 16).permute(0,2,3,1,4,5).contiguous().reshape(-1, channle_size,16,16)
- patches = patch_transform(patches)
-
- patches = patches.reshape(bs, -1, channle_size,16,16).permute(0,2,3,4,1).contiguous().reshape(bs, channle_size*16*16, -1)
- output_images = F.fold(patches, (H,W), 16, stride=16)
- output_images = clamp(output_images, lower_limit, upper_limit)
- return output_images
-
-
- def train_one_epoch(args, model: torch.nn.Module, criterion: DistillationLoss,
- 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,
- set_training_mode=True):
- model.train(set_training_mode)
- metric_logger = utils.MetricLogger(delimiter=" ")
- metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
- header = 'Epoch: [{}]'.format(epoch)
- print_freq = 10
-
- std_imagenet = torch.tensor((0.229, 0.224, 0.225)).view(3,1,1).to(device)
- mu_imagenet = torch.tensor((0.485, 0.456, 0.406)).view(3,1,1).to(device)
- upper_limit = ((1 - mu_imagenet)/ std_imagenet)
- lower_limit = ((0 - mu_imagenet)/ std_imagenet)
-
- for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
-
- 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 args.use_patch_aug:
- patch_transform = nn.Sequential(
- K.augmentation.RandomResizedCrop(size=(16,16), scale=(0.85,1.0), ratio=(1.0,1.0), p=0.1),
- K.augmentation.RandomGaussianNoise(mean=0., std=0.01, p=0.1),
- K.augmentation.RandomHorizontalFlip(p=0.1)
- )
- aug_samples = patch_level_aug(samples, patch_transform, upper_limit, lower_limit)
-
- is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
-
- with torch.cuda.amp.autocast():
- if args.use_patch_aug:
- outputs2 = model(aug_samples)
- loss = criterion(aug_samples, outputs2, targets)
- loss_scaler._scaler.scale(loss).backward(create_graph=is_second_order)
- outputs = model(samples)
- loss = criterion(samples, outputs, targets)
- else:
- outputs = model(samples)
- loss = criterion(samples, outputs, targets)
-
- loss_value = loss.item()
-
- if not math.isfinite(loss_value):
- print("Loss is {}, stopping training".format(loss_value))
- sys.exit(1)
-
- optimizer.zero_grad()
-
- # this attribute is added by timm on one optimizer (adahessian)
- loss_scaler(loss, optimizer, clip_grad=max_norm,
- parameters=model.parameters(), create_graph=is_second_order)
-
- torch.cuda.synchronize()
- if model_ema is not None:
- model_ema.update(model)
-
- metric_logger.update(loss=loss_value)
- metric_logger.update(lr=optimizer.param_groups[0]["lr"])
- # 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, mask=None, adv=None):
- criterion = torch.nn.CrossEntropyLoss()
-
- metric_logger = utils.MetricLogger(delimiter=" ")
- header = 'Test:'
-
- # switch to evaluation mode
- model.eval()
-
- for images, target in metric_logger.log_every(data_loader, 10, header):
- images = images.to(device, non_blocking=True)
- target = target.to(device, non_blocking=True)
-
- if adv == 'FGSM':
- std_imagenet = torch.tensor((0.229, 0.224, 0.225)).view(3,1,1).cuda()
- mu_imagenet = torch.tensor((0.485, 0.456, 0.406)).view(3,1,1).cuda()
- attack_epsilon = (1 / 255.) / std_imagenet
- attack_alpha = (1 / 255.) / std_imagenet
- upper_limit = ((1 - mu_imagenet)/ std_imagenet)
- lower_limit = ((0 - mu_imagenet)/ std_imagenet)
- adv_input = PGDAttack(images, target, model, attack_epsilon, attack_alpha, lower_limit, criterion, upper_limit, max_iters=1, random_init=False)
- elif adv == "PGD":
- std_imagenet = torch.tensor((0.229, 0.224, 0.225)).view(3,1,1).cuda()
- mu_imagenet = torch.tensor((0.485, 0.456, 0.406)).view(3,1,1).cuda()
- attack_epsilon = (1 / 255.) / std_imagenet
- attack_alpha = (0.5 / 255.) / std_imagenet
- upper_limit = ((1 - mu_imagenet)/ std_imagenet)
- lower_limit = ((0 - mu_imagenet)/ std_imagenet)
- adv_input = PGDAttack(images, target, model, attack_epsilon, attack_alpha, lower_limit, criterion, upper_limit, max_iters=5, random_init=True)
-
- # compute output
- with torch.cuda.amp.autocast():
- if adv:
- output = model(adv_input)
- else:
- output = model(images)
- loss = criterion(output, target)
-
- if mask is None:
- acc1, acc5 = accuracy(output, target, topk=(1, 5))
- else:
- acc1, acc5 = accuracy(output[:,mask], 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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