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adversarial_training_logit.py
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adversarial_training_logit.py
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# Code adapted from: https://github.com/thu-ml/adversarial_training_imagenet
# @article{liu2023comprehensive,
# title={A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking},
# author={Liu, Chang and Dong, Yinpeng and Xiang, Wenzhao and Yang, Xiao and Su, Hang and Zhu, Jun and Chen, Yuefeng and He, Yuan and Xue, Hui and Zheng, Shibao},
# journal={arXiv preprint arXiv:2302.14301},
# year={2023}
# }
import warnings
warnings.filterwarnings("ignore")
import argparse
import time
import yaml
import os
import logging
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as NativeDDP
# timm functions
from timm.models import resume_checkpoint, load_checkpoint, model_parameters
from timm.scheduler import create_scheduler
from timm.optim import create_optimizer_v2, optimizer_kwargs
from timm.utils import ModelEmaV2, distribute_bn, AverageMeter, reduce_tensor, dispatch_clip_grad, accuracy, get_outdir, CheckpointSaver, update_summary
# in functions
from utils import distributed_init, random_seed, create_logger, formatted_array_str
from model.model import build_model
from model.loss import build_loss, resolve_amp, build_loss_scaler
from data.dataset import build_dataset
from adv.adv_utils import adv_generator
from eval_y0_gradients_single_image import get_dataloader as get_dataloader_for_visualization
from eval_y0_gradients_single_image import abs_normalize
import torchvision
from pathlib import Path
# gradient teachers
from gradient_teachers import ContourEnergy
def get_args_parser():
parser = argparse.ArgumentParser('Robust training script', add_help=False)
parser.add_argument('--configs', default='', type=str)
#* distributed setting
parser.add_argument('--distributed', default=True)
parser.add_argument('--local-rank', default=-1, type=int)
parser.add_argument('--device-id', type=int, default=0)
parser.add_argument('--rank', default=-1, type=int, help='rank')
parser.add_argument('--world-size', default=1, type=int, help='number of distributed processes')
parser.add_argument('--dist-backend', default='nccl', help='backend used to set up distributed training')
parser.add_argument('--dist-url', default='env://', help='url used to set up distributed training')
#* amp parameters
parser.add_argument('--apex-amp', action='store_true', default=False,
help='Use NVIDIA Apex AMP mixed precision')
parser.add_argument('--native-amp', action='store_true', default=False,
help='Use Native Torch AMP mixed precision')
parser.add_argument('--amp_version', default='', help='amp version')
#* model parameters
parser.add_argument('--model', default='resnet50', type=str, metavar='MODEL', help='Name of model to train')
parser.add_argument('--replace_relu_with_gelu', default=False, help='if use GELU')
parser.add_argument('--replace_relu_with_silu', default=False, help='if use SiLU')
parser.add_argument('--relu_not_inplace', default=False, help='if switch to not inplace ReLU')
parser.add_argument('--num-classes', default=1000, type=int, help='number of classes')
parser.add_argument('--create_model_pretrained', default=True, help='Create model pretrained')
parser.add_argument('--resume', default='', help='resume from checkpoint')
parser.add_argument('--pretrain', default='', help='pretrain from checkpoint')
parser.add_argument('--gp', default=None, type=str, metavar='POOL',
help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None. (opt)')
parser.add_argument('--channels-last', action='store_true', default=False,
help='Use channels_last memory layout (opt)')
#* Batch norm parameters
parser.add_argument('--bn-momentum', type=float, default=None, help='BatchNorm momentum override (if not None)')
parser.add_argument('--bn-eps', type=float, default=None, help='BatchNorm epsilon override (if not None)')
parser.add_argument('--sync-bn', action='store_true', default=False, help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
parser.add_argument('--dist-bn', type=str, default='reduce', help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
parser.add_argument('--split-bn', action='store_true', default=False,
help='Enable separate BN layers per augmentation split.')
#* Optimizer parameters
parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
help='Optimizer (default: "adamw"')
parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
help='Optimizer Epsilon (default: 1e-8)')
parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=2e-5,
help='weight decay (default: 0.0001)')
parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--clip-mode', type=str, default='norm',
help='Gradient clipping mode. One of ("norm", "value", "agc")')
parser.add_argument('--layer-decay', type=float, default=None,
help='layer-wise learning rate decay (default: None)')
#* Learning rate schedule parameters
parser.add_argument('--epochs', default=150, type=int)
parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
help='LR scheduler (default: "cosine"')
parser.add_argument('--lrb', type=float, default=0.1, metavar='LR',
help='base learning rate (default: 5e-4)')
parser.add_argument('--lr', type=float, default=None, help='actual learning rate')
parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
help='learning rate noise on/off epoch percentages')
parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
help='learning rate noise limit percent (default: 0.67)')
parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
help='learning rate noise std-dev (default: 1.0)')
parser.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
help='learning rate cycle len multiplier (default: 1.0)')
parser.add_argument('--lr-cycle-decay', type=float, default=0.5, metavar='MULT',
help='amount to decay each learning rate cycle (default: 0.5)')
parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
help='learning rate cycle limit, cycles enabled if > 1')
parser.add_argument('--lr-k-decay', type=float, default=1.0,
help='learning rate k-decay for cosine/poly (default: 1.0)')
parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
help='warmup learning rate (default: 0.0001)')
parser.add_argument('--min-lr', type=float, default=1e-6, metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
parser.add_argument('--epoch-repeats', type=float, default=0., metavar='N',
help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).')
parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
help='epoch interval to decay LR')
parser.add_argument('--warmup-epochs', type=int, default=3, metavar='N',
help='epochs to warmup LR, if scheduler supports')
parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
help='patience epochs for Plateau LR scheduler (default: 10')
parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
help='LR decay rate (default: 0.1)')
#* dataset parameters
parser.add_argument('--batch-size', default=64, type=int) # batch size per gpu
parser.add_argument('--grad-accum', default=1, type=int) # gradient accumulation
parser.add_argument('--train-dir', default='', type=str, help='train dataset path')
parser.add_argument('--eval-dir', default='', type=str, help='validation dataset path')
parser.add_argument('--input-size', default=224, type=int, help='images input size')
parser.add_argument('--crop-pct', default=0.875, type=float,
metavar='N', help='Input image center crop percent (for validation only)')
parser.add_argument('--interpolation', type=str, default='bicubic',
help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
parser.add_argument('--mean', type=float, nargs='+', default=(0.485, 0.456, 0.406), metavar='MEAN',
help='Override mean pixel value of dataset')
parser.add_argument('--std', type=float, nargs='+', default=(0.229, 0.224, 0.225), metavar='STD',
help='Override std deviation of of dataset')
#* Augmentation & regularization parameters
parser.add_argument('--no-aug', action='store_true', default=False,
help='Disable all training augmentation, override other train aug args')
parser.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
help='Random resize scale (default: 0.08 1.0)')
parser.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO',
help='Random resize aspect ratio (default: 0.75 1.33)')
parser.add_argument('--hflip', type=float, default=0.5,
help='Horizontal flip training aug probability')
parser.add_argument('--vflip', type=float, default=0.,
help='Vertical flip training aug probability')
parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
help='Color jitter factor (default: 0.4)')
parser.add_argument('--aa', type=str, default=None, metavar='NAME',
help='Use AutoAugment policy. "v0" or "original". (default: None)'),
parser.add_argument('--aug-repeats', type=float, default=0,
help='Number of augmentation repetitions (distributed training only) (default: 0)')
parser.add_argument('--aug-splits', type=int, default=0,
help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
parser.add_argument('--jsd-loss', action='store_true', default=False,
help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
parser.add_argument('--bce-loss', action='store_true', default=False,
help='Enable BCE loss w/ Mixup/CutMix use.')
parser.add_argument('--bce-target-thresh', type=float, default=None,
help='Threshold for binarizing softened BCE targets (default: None, disabled)')
# random erase
parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
help='Random erase prob (default: 0.)')
parser.add_argument('--remode', type=str, default='pixel',
help='Random erase mode (default: "pixel")')
parser.add_argument('--recount', type=int, default=1,
help='Random erase count (default: 1)')
parser.add_argument('--resplit', action='store_true', default=False,
help='Do not random erase first (clean) augmentation split')
parser.add_argument('--mixup', type=float, default=0.0,
help='mixup alpha, mixup enabled if > 0. (default: 0.)')
parser.add_argument('--cutmix', type=float, default=0.0,
help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')
parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
parser.add_argument('--mixup-prob', type=float, default=1.0,
help='Probability of performing mixup or cutmix when either/both is enabled')
parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
help='Probability of switching to cutmix when both mixup and cutmix enabled')
parser.add_argument('--mixup-mode', type=str, default='batch',
help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
parser.add_argument('--smoothing', type=float, default=0.1,
help='Label smoothing (default: 0.1)')
parser.add_argument('--train-interpolation', type=str, default='random',
help='Training interpolation (random, bilinear, bicubic default: "random")')
# drop connection
parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
help='Dropout rate (default: 0.)')
parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
parser.add_argument('--drop-path', type=float, default=None, metavar='PCT',
help='Drop path rate (default: None)')
parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
help='Drop block rate (default: None)')
#* ema
parser.add_argument('--model-ema', action='store_true', default=False,
help='Enable tracking moving average of model weights')
parser.add_argument('--model-ema-force-cpu', action='store_true', default=False,
help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
parser.add_argument('--model-ema-decay', type=float, default=0.9998,
help='decay factor for model weights moving average (default: 0.9998)')
# misc
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--log-interval', type=int, default=50, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
help='how many batches to wait before writing recovery checkpoint')
parser.add_argument('--max-history', type=int, default=5, help='how many recovery checkpoints')
parser.add_argument('--num-workers', type=int, default=4, metavar='N',
help='how many training processes to use (default: 4)')
parser.add_argument('--output-dir', default='',
help='path where to save, empty for no saving')
parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
help='Best metric (default: "top1")')
parser.add_argument('--pin-mem', action='store_true',
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
# advtrain
parser.add_argument('--advtrain', default=False, help='if use advtrain')
parser.add_argument('--attack-criterion', type=str, default='regular', choices=['regular', 'smooth', 'mixup'], help='default args for: adversarial training')
parser.add_argument('--attack-eps', type=float, default=4.0/255, help='attack epsilon.')
parser.add_argument('--attack-step', type=float, default=8.0/255/3, help='attack epsilon.')
parser.add_argument('--attack-it', type=int, default=3, help='attack iteration')
# advprop
parser.add_argument('--advprop', default=False, help='if use advprop')
# contourtrain
parser.add_argument('--contourtrain', default=False, help='if use contourtrain')
parser.add_argument('--alpha', type=float, nargs='+', default=(1.0, 0.09, 10.0), help='Alpha ramp (start, step per epoch, max)')
parser.add_argument('--sigma', type=float, default=1.0, help='Guassian filter sigma')
parser.add_argument('--smooth', default=False, help='If use soft index distribution')
parser.add_argument('--temp', type=float, default=1.0, help='RelaxedOneHotCategorical temp')
parser.add_argument('--saliency', default=False, help='Perform absolute value of maximum channel')
return parser
def main(args, args_text):
# distributed settings and logger
if "WORLD_SIZE" in os.environ:
args.world_size=int(os.environ["WORLD_SIZE"])
if "LOCAL_RANK" in os.environ:
args.local_rank=int(os.environ["LOCAL_RANK"])
args.distributed=True #args.world_size>1
distributed_init(args)
args.output_dir = f'{args.output_dir}/{os.environ["HYDRA_NOW"]}'
if args.rank == 0 and not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
_logger = create_logger(args.output_dir, dist_rank=args.rank, name='main_train', default_level=logging.INFO)
# fix the seed for reproducibility
random_seed(args.seed, args.rank)
torch.backends.cudnn.deterministic=False
torch.backends.cudnn.benchmark = True
# setup augmentation batch splits for contrastive loss or split bn
num_aug_splits = 0
if args.aug_splits > 0:
assert args.aug_splits > 1, 'A split of 1 makes no sense'
num_aug_splits = args.aug_splits
# resolve amp
resolve_amp(args, _logger)
# build model
model = build_model(args, _logger, num_aug_splits)
# Replace activations
def replace_layers(model, old, new):
for n, module in model.named_children():
if len(list(module.children())) > 0:
## compound module, go inside it
replace_layers(module, old, new)
if isinstance(module, old):
## simple module
setattr(model, n, new)
if args.replace_relu_with_gelu:
replace_layers(model, nn.ReLU, nn.GELU())
if args.replace_relu_with_silu:
replace_layers(model, nn.ReLU, nn.SiLU())
if args.relu_not_inplace:
replace_layers(model, nn.ReLU, nn.ReLU(inplace=False))
# create optimizer
optimizer=None
if args.lr is None:
args.lr=args.lrb * args.batch_size * args.world_size * args.grad_accum / 512
optimizer = create_optimizer_v2(model, **optimizer_kwargs(cfg=args))
# build loss scaler
amp_autocast, loss_scaler = build_loss_scaler(args, _logger)
# resume from a checkpoint
resume_epoch = None
if args.resume:
resume_epoch = resume_checkpoint(
model, args.resume,
optimizer=optimizer,
loss_scaler=loss_scaler,
log_info=args.rank == 0)
if args.pretrain:
_ = resume_checkpoint(
model, args.pretrain,
optimizer=None,
loss_scaler=None,
log_info=args.rank == 0)
# setup ema
model_ema = None
if args.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
model_ema = ModelEmaV2(
model, decay=args.model_ema_decay, device='cpu' if args.model_ema_force_cpu else None)
if args.resume:
load_checkpoint(model_ema.module, args.resume, use_ema=True)
if args.pretrain:
load_checkpoint(model_ema.module, args.pretrain, use_ema=True)
# setup distributed training
if args.distributed:
if args.amp_version == 'apex':
# Apex DDP preferred unless native amp is activated
from apex.parallel import DistributedDataParallel as ApexDDP
_logger.info("Using NVIDIA APEX DistributedDataParallel.")
model = ApexDDP(model, delay_allreduce=True)
else:
_logger.info("Using native Torch DistributedDataParallel.")
model = NativeDDP(model, device_ids=[args.device_id])
# NOTE: EMA model does not need to be wrapped by DDP
# setup learning rate schedule and starting epoch
lr_scheduler, num_epochs = create_scheduler(args, optimizer)
start_epoch = 0
if resume_epoch is not None:
start_epoch = resume_epoch
if lr_scheduler is not None and start_epoch > 0:
lr_scheduler.step(start_epoch)
_logger.info('Scheduled epochs: {}'.format(num_epochs))
# create the train and eval dataloaders
if 'SLURM_PROCID' in os.environ:
cmd = os.popen('modulecmd python load "/home/gridsan/groups/datasets/ImageNet/modulefile"')
cmd.read()
cmd.close()
#_logger.info(f'Imagenet path {os.environ["IMAGENET_PATH"]}')
args.train_dir = '/run/user/61863/imagenet' + '/normal/train'
args.eval_dir = '/run/user/61863/imagenet' + '/normal/val'
loader_train, loader_eval, mixup_fn = build_dataset(args, num_aug_splits)
# setup loss function
train_loss_fn, validate_loss_fn = build_loss(args, mixup_fn, num_aug_splits)
# setup gradient teacher
gradient_teacher = None
if args.contourtrain:
gradient_teacher = ContourEnergy(args.sigma, 3).to('cuda')
# saver
eval_metric = args.eval_metric
saver = None
best_metric = None
best_epoch = None
output_dir = None
if args.rank == 0:
output_dir = get_outdir(args.output_dir)
decreasing=True if (eval_metric=='loss' or eval_metric=='advloss') else False
saver = CheckpointSaver(
model=model, optimizer=optimizer, args=args, model_ema=model_ema, amp_scaler=loss_scaler,
checkpoint_dir=output_dir, recovery_dir=output_dir, decreasing=decreasing, max_history=args.max_history)
with open(os.path.join(output_dir, 'args.yaml'), 'w') as f:
f.write(args_text)
# Visualize
#visualize_gradients(args, model, _logger, 'train', 0, 0)
#visualize_gradients(args, model, _logger, 'val', 0, 0)
# start training
_logger.info(f"Start training for {args.epochs} epochs")
for epoch in range(start_epoch, args.epochs):
if hasattr(loader_train, 'sampler'):
loader_train.sampler.set_epoch(epoch)
# one epoch training
train_metrics = train_one_epoch(
epoch, model, loader_train, optimizer, train_loss_fn, args,
gradient_teacher=gradient_teacher,
lr_scheduler=lr_scheduler, saver=saver, amp_autocast=amp_autocast,
loss_scaler=loss_scaler, model_ema=model_ema, mixup_fn=mixup_fn, _logger=_logger)
# distributed bn sync
if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
_logger.info("Distributing BatchNorm running means and vars")
distribute_bn(model, args.world_size, args.dist_bn == 'reduce')
# calculate evaluation metric
eval_metrics = validate(model, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast, _logger=_logger)
# visualize gradients
#visualize_gradients(args, model, _logger, 'val', epoch, 0)
model.eval()
# model ema update
if model_ema is not None and not args.model_ema_force_cpu:
if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce')
ema_eval_metrics = validate(model_ema.module, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast, log_suffix=' (EMA)', _logger=_logger)
eval_metrics = ema_eval_metrics
# visualize gradients
#visualize_gradients(args, model_ema.module, _logger, 'val', epoch, 0, ema='ema')
model_ema.module.eval()
# lr_scheduler update
if lr_scheduler is not None:
# step LR for next epoch
lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])
# output summary.csv
if output_dir is not None:
update_summary(
epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'),
write_header=best_metric is None)
# save checkpoint, print best metric
if saver is not None:
best_metric, best_epoch = saver.save_checkpoint(epoch, eval_metrics[eval_metric])
torch.distributed.barrier()
if best_metric is not None:
_logger.info('*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch))
def train_one_epoch(
epoch, model, loader, optimizer, loss_fn, args,
gradient_teacher=None,
lr_scheduler=None, saver=None, amp_autocast=None,
loss_scaler=None, model_ema=None, mixup_fn=None, _logger=None):
# mixup setting
if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
if mixup_fn is not None:
mixup_fn.mixup_enabled = False
# statistical variables
second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
batch_time_m = AverageMeter()
data_time_m = AverageMeter()
losses_m = AverageMeter()
num_epochs = args.epochs + args.cooldown_epochs
model.train()
end = time.time()
last_idx = len(loader) - 1
num_updates = epoch * len(loader)
att_step = args.attack_step * min(epoch, 5)/5
att_eps=args.attack_eps
att_it=args.attack_it
alpha=0
optimizer.zero_grad()
for batch_idx, (input, target) in enumerate(loader):
last_batch = batch_idx == last_idx
# processing input and target
input, target = input.cuda(non_blocking=True), target.cuda(non_blocking=True)
if mixup_fn is not None:
input, target = mixup_fn(input, target)
if args.channels_last:
input=input.contiguous(memory_format=torch.channels_last)
data_time_m.update(time.time() - end)
# generate adv input
if args.advtrain:
input_advtrain = adv_generator(args, input, target, model, att_eps, att_it, att_step, random_start=False, attack_criterion=args.attack_criterion)
# generate advprop input
if args.advprop:
model.apply(lambda m: setattr(m, 'bn_mode', 'adv'))
input_advprop = adv_generator(args, input, target, model, 1/255, 1, 1/255, random_start=True, attack_criterion=args.attack_criterion, use_best=False)
# forward
with amp_autocast():
if args.advprop:
outputs = model(input_advprop)
adv_loss = loss_fn(outputs, target)
model.apply(lambda m: setattr(m, 'bn_mode', 'clean'))
outputs = model(input)
loss = loss_fn(outputs, target) + adv_loss
elif args.advtrain:
output = model(input_advtrain)
loss = loss_fn(output, target)
elif args.contourtrain:
input.requires_grad_(True)
output = model(input)
# Calculate gradient
if not args.smooth:
class_sample = torch.distributions.one_hot_categorical.OneHotCategorical(logits=output/args.temp, validate_args=False).sample()
else:
class_sample = torch.distributions.relaxed_categorical.RelaxedOneHotCategorical(torch.tensor([args.temp]).to(output.device), logits=output).rsample()
v = (output * class_sample).sum(-1) # (output * target).sum(-1)
gradient = torch.autograd.grad(v.sum(), input, create_graph=True, retain_graph=True)[0]
if args.saliency:
gradient = gradient.abs().max(1, keepdim=True).values
# template = torch.autograd.grad(output[torch.arange(output.size(0)), target].sum(), input, create_graph=True, retain_graph=True)[0]
n_student = gradient.square().mean()**0.5
gradient = gradient / n_student
# Calculate teacher gradient
with torch.no_grad():
ref_gradient = gradient_teacher(input)
if args.saliency:
ref_gradient = ref_gradient[:, 0:1, :, :]
n_teacher = ref_gradient.square().mean()**0.5
ref_gradient = ref_gradient / n_teacher
# Calculate loss
alpha = min(args.alpha[0] + (epoch + (batch_idx // args.grad_accum) / (len(loader) / args.grad_accum)) * args.alpha[1], args.alpha[2])
loss_class = loss_fn(output, target)
loss_gradient = torch.nn.functional.mse_loss(gradient, ref_gradient)
loss_v = torch.stack([loss_class, loss_gradient], dim=0)
loss = loss_class + alpha * loss_gradient
else:
output = model(input)
loss = loss_fn(output, target)
if not args.distributed:
losses_m.update(loss.item(), input.size(0))
else:
torch.cuda.synchronize()
reduced_loss = reduce_tensor(loss_v.data, args.world_size)
losses_m.update(reduced_loss.detach().cpu().numpy(), input.size(0))
if loss_scaler is not None:
loss_scaler(
loss, optimizer,
clip_grad=args.clip_grad, clip_mode=args.clip_mode,
parameters=model_parameters(model, exclude_head='agc' in args.clip_mode),
create_graph=second_order)
else:
loss.backward(create_graph=second_order)
if (batch_idx + 1) % args.grad_accum == 0:
if args.clip_grad is not None:
dispatch_clip_grad(
model_parameters(model, exclude_head='agc' in args.clip_mode),
value=args.clip_grad, mode=args.clip_mode)
optimizer.step()
optimizer.zero_grad()
if model_ema is not None:
if (batch_idx + 1) % args.grad_accum == 0:
model_ema.update(model)
if (batch_idx + 1) % args.grad_accum == 0:
torch.cuda.synchronize()
num_updates += 1
batch_time_m.update(time.time() - end)
# Visualize gradients
#visualize_gradients(args, model, _logger, 'train', epoch, (batch_idx // args.grad_accum))
# model.train()
#if args.model_ema:
# visualize_gradients(args, model_ema.module, _logger, 'train', epoch, (batch_idx // args.grad_accum), ema='ema')
if last_batch or (batch_idx // args.grad_accum) % args.log_interval == 0:
lrl = [param_group['lr'] for param_group in optimizer.param_groups]
lr = sum(lrl) / len(lrl)
# if args.distributed:
# reduced_loss = reduce_tensor(loss_v.data, args.world_size)
# losses_m.update(reduced_loss.detach().cpu().numpy(), input.size(0))
_logger.info(
'Train: [{}/{}] [{:>4d}/{} ({:>3.0f}%)] '
'Loss: {loss_val} ({loss_avg}) '
'Time: {batch_time.val:.3f}s, {rate:>7.2f}/s '
'({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s) '
'LR: {lr:.3e} '
'LossWeights: {loss_weights} '
'Data: {data_time.val:.3f} ({data_time.avg:.3f})'.format(
epoch, num_epochs,
batch_idx // args.grad_accum, len(loader) // args.grad_accum,
100. * batch_idx / last_idx,
loss_val=formatted_array_str(losses_m.val, '#.4g'),
loss_avg=formatted_array_str(losses_m.avg, '#.3g'),
batch_time=batch_time_m,
rate=input.size(0) * args.world_size / batch_time_m.val,
rate_avg=input.size(0) * args.world_size / batch_time_m.avg,
lr=lr,
loss_weights=formatted_array_str([alpha], '#.4g'),
data_time=data_time_m))
# # save checkpoint
# if saver is not None and args.recovery_interval and (
# last_batch or (batch_idx + 1) % args.recovery_interval == 0):
# saver.save_recovery(epoch, batch_idx=batch_idx)
# update lr scheduler
if lr_scheduler is not None:
lr_scheduler.step_update(num_updates=num_updates/(len(loader)//args.grad_accum), metric=losses_m.avg)
end = time.time()
# end for
if hasattr(optimizer, 'sync_lookahead'):
optimizer.sync_lookahead()
return OrderedDict([('loss', losses_m.avg)])
def validate(model, loader, loss_fn, args, amp_autocast=None, log_suffix='', _logger=None):
batch_time_m = AverageMeter()
losses_m = AverageMeter()
top1_m = AverageMeter()
top5_m = AverageMeter()
adv_losses_m = AverageMeter()
adv_top1_m = AverageMeter()
adv_top5_m = AverageMeter()
model.eval()
end = time.time()
last_idx = len(loader) - 1
for batch_idx, (input, target) in enumerate(loader):
# read eval input
last_batch = batch_idx == last_idx
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
if args.channels_last:
input = input.contiguous(memory_format=torch.channels_last)
# normal eval process
with torch.no_grad():
with amp_autocast():
output = model(input)
if isinstance(output, (tuple, list)):
output = output[0]
loss = loss_fn(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
if args.distributed:
reduced_loss = reduce_tensor(loss.data, args.world_size)
acc1 = reduce_tensor(acc1, args.world_size)
acc5 = reduce_tensor(acc5, args.world_size)
else:
reduced_loss = loss.data
torch.cuda.synchronize()
# record normal results
losses_m.update(reduced_loss.item(), input.size(0))
top1_m.update(acc1.item(), output.size(0))
top5_m.update(acc5.item(), output.size(0))
# adv eval process
if True:
adv_input=adv_generator(args, input, target, model, 4/255, 10, 1/255, random_start=True, use_best=False, attack_criterion='regular')
with torch.no_grad():
with amp_autocast():
adv_output = model(adv_input)
if isinstance(adv_output, (tuple, list)):
adv_output = adv_output[0]
adv_loss = loss_fn(adv_output, target)
adv_acc1, adv_acc5 = accuracy(adv_output, target, topk=(1, 5))
if args.distributed:
adv_reduced_loss = reduce_tensor(adv_loss.data, args.world_size)
adv_acc1 = reduce_tensor(adv_acc1, args.world_size)
adv_acc5 = reduce_tensor(adv_acc5, args.world_size)
else:
adv_reduced_loss = adv_loss.data
torch.cuda.synchronize()
# record adv results
adv_losses_m.update(adv_reduced_loss.item(), adv_input.size(0))
adv_top1_m.update(adv_acc1.item(), adv_output.size(0))
adv_top5_m.update(adv_acc5.item(), adv_output.size(0))
batch_time_m.update(time.time() - end)
end = time.time()
if last_batch or batch_idx % args.log_interval == 0:
log_name = 'Test' + log_suffix
_logger.info(
'{0}: [{1:>4d}/{2}] '
'Time: {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f}) '
'Acc@1: {top1.val:>7.4f} ({top1.avg:>7.4f}) '
'Acc@5: {top5.val:>7.4f} ({top5.avg:>7.4f}) '
'AdvLoss: {adv_loss.val:>7.4f} ({adv_loss.avg:>6.4f}) '
'AdvAcc@1: {adv_top1.val:>7.4f} ({adv_top1.avg:>7.4f}) '
'AdvAcc@5: {adv_top5.val:>7.4f} ({adv_top5.avg:>7.4f})'.format(
log_name, batch_idx, last_idx, batch_time=batch_time_m,
loss=losses_m, top1=top1_m, top5=top5_m,
adv_loss=adv_losses_m, adv_top1=adv_top1_m, adv_top5=adv_top5_m))
metrics = OrderedDict([('loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg), ('advloss', adv_losses_m.avg), ('advtop1', adv_top1_m.avg), ('advtop5', adv_top5_m.avg)])
return metrics
def visualize_gradients(args, model, _logger, meta, epoch, iter, ema=''):
meta_file='val_grads' if 'val' == meta else 'train_grads'
imagenet_path=args.eval_dir if 'val' == meta else args.train_dir
dataloader_eval, dataset_eval = get_dataloader_for_visualization(args, root=imagenet_path, meta_file=meta_file, batch_size=1)
# print('length of dataloader', len(dataloader_eval), 'length of dataset', len(dataset_eval))
# assert False
# epr_loss_fn = reg_losses.EnergyConcentrationRatio(q=0.5).cuda()
std_tensor=torch.Tensor(args.std).cuda(non_blocking=True)[None, :, None, None]
mean_tensor=torch.Tensor(args.mean).cuda(non_blocking=True)[None, :, None, None]
mode = model.training
model.eval()
for idx, (input, target) in enumerate(dataloader_eval):
input = input.cuda()
target = target.cuda()
input = (input-mean_tensor)/std_tensor
input.requires_grad_(True)
output = model(input)
loss = torch.nn.functional.cross_entropy(output, target)
# Backward
loss_gradient = torch.autograd.grad(loss, input, create_graph=False, retain_graph=True)[0]
# logit_gradient = torch.autograd.grad(output[torch.arange(output.size(0)), target], input, create_graph=False, retain_graph=True)[0]
# Grayscale
#loss_gradient = loss_gradient.mean(-3, keepdim=True)
#logit_gradient = logit_gradient.mean(-3, keepdim=True)
# Print reg losses
# epr_loss = epr_loss_fn(loss_gradient, input)
# meta='val' if 'val' in meta_file else 'train'
# _logger.info(f'EPR loss {meta} idx={idx} {epr_loss:.4f}')
# Prepare images
# _, N, H, W = input.shape
# loss_gradient_rgb = loss_gradient.view(N, 1, H, W).expand(-1, N, -1, -1)
# logit_gradient_rgb = logit_gradient.view(N, 1, H, W).expand(-1, N, -1, -1)
# images = torch.concat([input, loss_gradient, loss_gradient_rgb, logit_gradient, logit_gradient_rgb])
images = torch.concat([input, loss_gradient])
images = abs_normalize(images, q=0.01)
if ema == '':
ema = 'normal'
if args.rank == 0:
os.makedirs(Path(args.output_dir) / 'gradient_images' / f'{ema}' / f'{meta}' / f'{idx}', exist_ok=True)
torchvision.utils.save_image(images, Path(args.output_dir) / 'gradient_images' / f'{ema}' / f'{meta}' / f'{idx}' / f'lossgrad_epoch{epoch}_{iter:04}.png', nrow=2, normalize=True, scale_each=True)
if mode:
model.train()
if __name__ == '__main__':
parser = argparse.ArgumentParser('Robust training script', parents=[get_args_parser()])
args = parser.parse_args()
opt = vars(args)
if args.configs:
opt.update(yaml.load(open(args.configs), Loader=yaml.FullLoader))
args = argparse.Namespace(**opt)
args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
main(args, args_text)