# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # Partly revised by YZ @UCL&Moorfields # -------------------------------------------------------- import argparse import datetime import json import numpy as np import os import time from pathlib import Path import torch import torch.backends.cudnn as cudnn from torch.utils.tensorboard import SummaryWriter import timm assert timm.__version__ == "0.3.2" # version check from timm.models.layers import trunc_normal_ from timm.data.mixup import Mixup from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy import util.lr_decay as lrd import util.misc as misc from util.datasets import build_dataset from util.pos_embed import interpolate_pos_embed from util.misc import NativeScalerWithGradNormCount as NativeScaler import models_vit from engine_finetune import train_one_epoch, evaluate def get_args_parser(): parser = argparse.ArgumentParser('MAE fine-tuning for image classification', add_help=False) parser.add_argument('--batch_size', default=64, type=int, help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus') parser.add_argument('--epochs', default=50, type=int) parser.add_argument('--accum_iter', default=1, type=int, help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)') # Model parameters parser.add_argument('--model', default='vit_large_patch16', type=str, metavar='MODEL', help='Name of model to train') parser.add_argument('--input_size', default=224, type=int, help='images input size') parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT', help='Drop path rate (default: 0.1)') # Optimizer parameters parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM', help='Clip gradient norm (default: None, no clipping)') parser.add_argument('--weight_decay', type=float, default=0.05, help='weight decay (default: 0.05)') parser.add_argument('--lr', type=float, default=None, metavar='LR', help='learning rate (absolute lr)') parser.add_argument('--blr', type=float, default=1e-3, metavar='LR', help='base learning rate: absolute_lr = base_lr * total_batch_size / 256') parser.add_argument('--layer_decay', type=float, default=0.75, help='layer-wise lr decay from ELECTRA/BEiT') parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR', help='lower lr bound for cyclic schedulers that hit 0') parser.add_argument('--warmup_epochs', type=int, default=10, metavar='N', help='epochs to warmup LR') # Augmentation parameters parser.add_argument('--color_jitter', type=float, default=None, metavar='PCT', help='Color jitter factor (enabled only when not using Auto/RandAug)') parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME', help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)'), parser.add_argument('--smoothing', type=float, default=0.1, help='Label smoothing (default: 0.1)') # * Random Erase params parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT', help='Random erase prob (default: 0.25)') 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') # * Mixup params parser.add_argument('--mixup', type=float, default=0, help='mixup alpha, mixup enabled if > 0.') parser.add_argument('--cutmix', type=float, default=0, help='cutmix alpha, cutmix enabled if > 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"') # * Finetuning params parser.add_argument('--finetune', default='',type=str, help='finetune from checkpoint') parser.add_argument('--task', default='',type=str, help='finetune from checkpoint') parser.add_argument('--global_pool', action='store_true') parser.set_defaults(global_pool=True) parser.add_argument('--cls_token', action='store_false', dest='global_pool', help='Use class token instead of global pool for classification') # Dataset parameters parser.add_argument('--data_path', default='/home/jupyter/Mor_DR_data/data/data/IDRID/Disease_Grading/', type=str, help='dataset path') parser.add_argument('--nb_classes', default=1000, type=int, help='number of the classification types') parser.add_argument('--output_dir', default='./output_dir', help='path where to save, empty for no saving') parser.add_argument('--log_dir', default='./output_dir', help='path where to tensorboard log') parser.add_argument('--device', default='cuda', help='device to use for training / testing') parser.add_argument('--seed', default=0, type=int) parser.add_argument('--resume', default='', help='resume from checkpoint') parser.add_argument('--start_epoch', default=0, type=int, metavar='N', help='start epoch') parser.add_argument('--eval', action='store_true', help='Perform evaluation only') parser.add_argument('--dist_eval', action='store_true', default=False, help='Enabling distributed evaluation (recommended during training for faster monitor') parser.add_argument('--num_workers', default=10, type=int) parser.add_argument('--pin_mem', action='store_true', help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.') parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem') parser.set_defaults(pin_mem=True) # distributed training parameters parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') parser.add_argument('--local_rank', default=-1, type=int) parser.add_argument('--dist_on_itp', action='store_true') parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') return parser def main(args): misc.init_distributed_mode(args) print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) print("{}".format(args).replace(', ', ',\n')) device = torch.device(args.device) # fix the seed for reproducibility seed = args.seed + misc.get_rank() torch.manual_seed(seed) np.random.seed(seed) cudnn.benchmark = True dataset_train = build_dataset(is_train='train', args=args) dataset_val = build_dataset(is_train='val', args=args) dataset_test = build_dataset(is_train='test', args=args) if True: # args.distributed: num_tasks = misc.get_world_size() global_rank = misc.get_rank() sampler_train = torch.utils.data.DistributedSampler( dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True ) print("Sampler_train = %s" % str(sampler_train)) if args.dist_eval: if len(dataset_val) % num_tasks != 0: print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. ' 'This will slightly alter validation results as extra duplicate entries are added to achieve ' 'equal num of samples per-process.') sampler_val = torch.utils.data.DistributedSampler( dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=True) # shuffle=True to reduce monitor bias else: sampler_val = torch.utils.data.SequentialSampler(dataset_val) if args.dist_eval: if len(dataset_test) % num_tasks != 0: print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. ' 'This will slightly alter validation results as extra duplicate entries are added to achieve ' 'equal num of samples per-process.') sampler_test = torch.utils.data.DistributedSampler( dataset_test, num_replicas=num_tasks, rank=global_rank, shuffle=True) # shuffle=True to reduce monitor bias else: sampler_test = torch.utils.data.SequentialSampler(dataset_test) if global_rank == 0 and args.log_dir is not None and not args.eval: os.makedirs(args.log_dir, exist_ok=True) log_writer = SummaryWriter(log_dir=args.log_dir+args.task) else: log_writer = None data_loader_train = torch.utils.data.DataLoader( dataset_train, sampler=sampler_train, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=True, ) data_loader_val = torch.utils.data.DataLoader( dataset_val, sampler=sampler_val, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=False ) data_loader_test = torch.utils.data.DataLoader( dataset_test, sampler=sampler_test, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=False ) mixup_fn = None mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None if mixup_active: print("Mixup is activated!") mixup_fn = Mixup( mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax, prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode, label_smoothing=args.smoothing, num_classes=args.nb_classes) model = models_vit.__dict__[args.model]( num_classes=args.nb_classes, drop_path_rate=args.drop_path, global_pool=args.global_pool, ) if args.finetune and not args.eval: checkpoint = torch.load(args.finetune, map_location='cpu') print("Load pre-trained checkpoint from: %s" % args.finetune) checkpoint_model = checkpoint['model'] state_dict = model.state_dict() for k in ['head.weight', 'head.bias']: if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape: print(f"Removing key {k} from pretrained checkpoint") del checkpoint_model[k] # interpolate position embedding interpolate_pos_embed(model, checkpoint_model) # load pre-trained model msg = model.load_state_dict(checkpoint_model, strict=False) print(msg) if args.global_pool: assert set(msg.missing_keys) == {'head.weight', 'head.bias', 'fc_norm.weight', 'fc_norm.bias'} else: assert set(msg.missing_keys) == {'head.weight', 'head.bias'} # manually initialize fc layer trunc_normal_(model.head.weight, std=2e-5) model.to(device) model_without_ddp = model n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) print("Model = %s" % str(model_without_ddp)) print('number of params (M): %.2f' % (n_parameters / 1.e6)) eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() if args.lr is None: # only base_lr is specified args.lr = args.blr * eff_batch_size / 256 print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) print("actual lr: %.2e" % args.lr) print("accumulate grad iterations: %d" % args.accum_iter) print("effective batch size: %d" % eff_batch_size) if args.distributed: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) model_without_ddp = model.module # build optimizer with layer-wise lr decay (lrd) param_groups = lrd.param_groups_lrd(model_without_ddp, args.weight_decay, no_weight_decay_list=model_without_ddp.no_weight_decay(), layer_decay=args.layer_decay ) optimizer = torch.optim.AdamW(param_groups, lr=args.lr) loss_scaler = NativeScaler() if mixup_fn is not None: # smoothing is handled with mixup label transform criterion = SoftTargetCrossEntropy() elif args.smoothing > 0.: criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing) else: criterion = torch.nn.CrossEntropyLoss() print("criterion = %s" % str(criterion)) misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler) if args.eval: test_stats,auc_roc = evaluate(data_loader_test, model, device, args.task, epoch=0, mode='test',num_class=args.nb_classes) exit(0) print(f"Start training for {args.epochs} epochs") start_time = time.time() max_accuracy = 0.0 max_auc = 0.0 for epoch in range(args.start_epoch, args.epochs): if args.distributed: data_loader_train.sampler.set_epoch(epoch) train_stats = train_one_epoch( model, criterion, data_loader_train, optimizer, device, epoch, loss_scaler, args.clip_grad, mixup_fn, log_writer=log_writer, args=args ) val_stats,val_auc_roc = evaluate(data_loader_val, model, device,args.task,epoch, mode='val',num_class=args.nb_classes) if max_auc