20240603

engine_pretrain.py

@@ -0,0 +1,82 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# DeiT: https://github.com/facebookresearch/deit
+# BEiT: https://github.com/microsoft/unilm/tree/master/beit
+# --------------------------------------------------------
+import math
+import sys
+from typing import Iterable
+
+import torch
+
+import util.misc as misc
+import util.lr_sched as lr_sched
+
+
+def train_one_epoch(model: torch.nn.Module,
+                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
+                    device: torch.device, epoch: int, loss_scaler,
+                    log_writer=None,
+                    args=None):
+    model.train(True)
+    metric_logger = misc.MetricLogger(delimiter="  ")
+    metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
+    header = 'Epoch: [{}]'.format(epoch)
+    print_freq = 20
+
+    accum_iter = args.accum_iter
+
+    optimizer.zero_grad()
+
+    if log_writer is not None:
+        print('log_dir: {}'.format(log_writer.log_dir))
+
+    for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
+
+        # we use a per iteration (instead of per epoch) lr scheduler
+        if data_iter_step % accum_iter == 0:
+            lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
+
+        samples = samples.to(device, non_blocking=True)
+
+        with torch.cuda.amp.autocast():
+            loss, _, _ = model(samples, mask_ratio=args.mask_ratio)
+
+        loss_value = loss.item()
+
+        if not math.isfinite(loss_value):
+            print("Loss is {}, stopping training".format(loss_value))
+            sys.exit(1)
+
+        loss /= accum_iter
+        loss_scaler(loss, optimizer, parameters=model.parameters(),
+                    update_grad=(data_iter_step + 1) % accum_iter == 0)
+        if (data_iter_step + 1) % accum_iter == 0:
+            optimizer.zero_grad()
+
+        torch.cuda.synchronize()
+
+        metric_logger.update(loss=loss_value)
+
+        lr = optimizer.param_groups[0]["lr"]
+        metric_logger.update(lr=lr)
+
+        loss_value_reduce = misc.all_reduce_mean(loss_value)
+        if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
+            """ We use epoch_1000x as the x-axis in tensorboard.
+            This calibrates different curves when batch size changes.
+            """
+            epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
+            log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x)
+            log_writer.add_scalar('lr', lr, epoch_1000x)
+
+
+    # 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()}

main_finetune.py

@@ -342,11 +342,6 @@ def main(args):
                     args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                     loss_scaler=loss_scaler, epoch=epoch)
         
-
-        if epoch==(args.epochs-1):
-            test_stats,auc_roc = evaluate(data_loader_test, model, device,args.task,epoch, mode='test',num_class=args.nb_classes)
-
-        
         if log_writer is not None:
             log_writer.add_scalar('perf/val_acc1', val_stats['acc1'], epoch)
             log_writer.add_scalar('perf/val_auc', val_auc_roc, epoch)
@@ -366,7 +361,9 @@ def main(args):
     total_time = time.time() - start_time
     total_time_str = str(datetime.timedelta(seconds=int(total_time)))
     print('Training time {}'.format(total_time_str))
-
+    state_dict_best = torch.load(args.task+'checkpoint-best.pth', map_location='cpu')
+    model_without_ddp.load_state_dict(state_dict_best['model'])
+    test_stats,auc_roc = evaluate(data_loader_test, model_without_ddp, device,args.task,epoch=0, mode='test',num_class=args.nb_classes)
 
 if __name__ == '__main__':
     args = get_args_parser()

main_pretrain.py

@@ -0,0 +1,221 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# DeiT: https://github.com/facebookresearch/deit
+# BEiT: https://github.com/microsoft/unilm/tree/master/beit
+# --------------------------------------------------------
+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 torchvision.transforms as transforms
+import torchvision.datasets as datasets
+
+import timm
+
+assert timm.__version__ == "0.3.2"  # version check
+import timm.optim.optim_factory as optim_factory
+
+import util.misc as misc
+from util.misc import NativeScalerWithGradNormCount as NativeScaler
+
+import models_mae
+
+from engine_pretrain import train_one_epoch
+
+
+def get_args_parser():
+    parser = argparse.ArgumentParser('MAE pre-training', 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=400, 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='mae_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('--mask_ratio', default=0.75, type=float,
+                        help='Masking ratio (percentage of removed patches).')
+
+    parser.add_argument('--norm_pix_loss', action='store_true',
+                        help='Use (per-patch) normalized pixels as targets for computing loss')
+    parser.set_defaults(norm_pix_loss=False)
+
+    # Optimizer parameters
+    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('--min_lr', type=float, default=0., metavar='LR',
+                        help='lower lr bound for cyclic schedulers that hit 0')
+
+    parser.add_argument('--warmup_epochs', type=int, default=40, metavar='N',
+                        help='epochs to warmup LR')
+
+    # Dataset parameters
+    parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
+                        help='dataset path')
+
+    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('--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
+
+    # simple augmentation
+    transform_train = transforms.Compose([
+            transforms.RandomResizedCrop(args.input_size, scale=(0.2, 1.0), interpolation=3),  # 3 is bicubic
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+    dataset_train = datasets.ImageFolder(os.path.join(args.data_path, 'train'), transform=transform_train)
+    print(dataset_train)
+
+    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))
+    else:
+        sampler_train = torch.utils.data.RandomSampler(dataset_train)
+
+    if global_rank == 0 and args.log_dir is not None:
+        os.makedirs(args.log_dir, exist_ok=True)
+        log_writer = SummaryWriter(log_dir=args.log_dir)
+    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,
+    )
+    
+    # define the model
+    model = models_mae.__dict__[args.model](norm_pix_loss=args.norm_pix_loss)
+
+    model.to(device)
+
+    model_without_ddp = model
+    print("Model = %s" % str(model_without_ddp))
+
+    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], find_unused_parameters=True)
+        model_without_ddp = model.module
+    
+    # following timm: set wd as 0 for bias and norm layers
+    param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay)
+    optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95))
+    print(optimizer)
+    loss_scaler = NativeScaler()
+
+    misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)
+
+    print(f"Start training for {args.epochs} epochs")
+    start_time = time.time()
+    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, data_loader_train,
+            optimizer, device, epoch, loss_scaler,
+            log_writer=log_writer,
+            args=args
+        )
+        if args.output_dir and (epoch % 50 == 0 or epoch + 1 == args.epochs):
+            misc.save_model_pretrain(
+                args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
+                loss_scaler=loss_scaler, epoch=epoch)
+
+        log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
+                        'epoch': epoch,}
+
+        if args.output_dir and misc.is_main_process():
+            if log_writer is not None:
+                log_writer.flush()
+            with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
+                f.write(json.dumps(log_stats) + "\n")
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print('Training time {}'.format(total_time_str))
+
+
+if __name__ == '__main__':
+    args = get_args_parser()
+    args = args.parse_args()
+    if args.output_dir:
+        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
+    main(args)

util/misc.py

@@ -306,6 +306,28 @@ def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler):
         model.save_checkpoint(save_dir=args.task, tag="checkpoint-best", client_state=client_state)
 
 
+def save_model_pretrain(args, epoch, model, model_without_ddp, optimizer, loss_scaler):
+    output_dir = Path(args.output_dir)
+    epoch_name = str(epoch)
+    if loss_scaler is not None:
+        print(model_without_ddp.state_dict().keys())
+        checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)]
+        for checkpoint_path in checkpoint_paths:
+            to_save = {
+                'model': model_without_ddp.state_dict(),
+                'optimizer': optimizer.state_dict(),
+                'epoch': epoch,
+                'scaler': loss_scaler.state_dict(),
+                'args': args,
+            }
+
+            save_on_master(to_save, checkpoint_path)
+    else:
+        client_state = {'epoch': epoch}
+        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)
+        
+        
+
 def load_model(args, model_without_ddp, optimizer, loss_scaler):
     if args.resume:
         if args.resume.startswith('https'):