evaluate.py

# 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.


from pathlib import Path
import argparse
import json
import os
import random
import signal
import sys
import time
import urllib

from torch import nn, optim
from torchvision import datasets, transforms
import torch

import resnet


def get_arguments():
    parser = argparse.ArgumentParser(
        description="Evaluate a pretrained model on ImageNet"
    )

    # Data
    parser.add_argument("--data-dir", type=Path, help="path to dataset")
    parser.add_argument(
        "--train-percent",
        default=100,
        type=int,
        choices=(100, 10, 1),
        help="size of traing set in percent",
    )

    # Checkpoint
    parser.add_argument("--pretrained", type=Path, help="path to pretrained model")
    parser.add_argument(
        "--exp-dir",
        default="./checkpoint/lincls/",
        type=Path,
        metavar="DIR",
        help="path to checkpoint directory",
    )
    parser.add_argument(
        "--print-freq", default=100, type=int, metavar="N", help="print frequency"
    )

    # Model
    parser.add_argument("--arch", type=str, default="resnet50")

    # Optim
    parser.add_argument(
        "--epochs",
        default=100,
        type=int,
        metavar="N",
        help="number of total epochs to run",
    )
    parser.add_argument(
        "--batch-size", default=256, type=int, metavar="N", help="mini-batch size"
    )
    parser.add_argument(
        "--lr-backbone",
        default=0.0,
        type=float,
        metavar="LR",
        help="backbone base learning rate",
    )
    parser.add_argument(
        "--lr-head",
        default=0.3,
        type=float,
        metavar="LR",
        help="classifier base learning rate",
    )
    parser.add_argument(
        "--weight-decay", default=1e-6, type=float, metavar="W", help="weight decay"
    )
    parser.add_argument(
        "--weights",
        default="freeze",
        type=str,
        choices=("finetune", "freeze"),
        help="finetune or freeze resnet weights",
    )

    # Running
    parser.add_argument(
        "--workers",
        default=8,
        type=int,
        metavar="N",
        help="number of data loader workers",
    )

    return parser


def main():
    parser = get_arguments()
    args = parser.parse_args()
    if args.train_percent in {1, 10}:
        args.train_files = urllib.request.urlopen(
            f"https://raw.githubusercontent.com/google-research/simclr/master/imagenet_subsets/{args.train_percent}percent.txt"
        ).readlines()
    args.ngpus_per_node = torch.cuda.device_count()
    if "SLURM_JOB_ID" in os.environ:
        signal.signal(signal.SIGUSR1, handle_sigusr1)
        signal.signal(signal.SIGTERM, handle_sigterm)
    # single-node distributed training
    args.rank = 0
    args.dist_url = f"tcp://localhost:{random.randrange(49152, 65535)}"
    args.world_size = args.ngpus_per_node
    torch.multiprocessing.spawn(main_worker, (args,), args.ngpus_per_node)


def main_worker(gpu, args):
    args.rank += gpu
    torch.distributed.init_process_group(
        backend="nccl",
        init_method=args.dist_url,
        world_size=args.world_size,
        rank=args.rank,
    )

    if args.rank == 0:
        args.exp_dir.mkdir(parents=True, exist_ok=True)
        stats_file = open(args.exp_dir / "stats.txt", "a", buffering=1)
        print(" ".join(sys.argv))
        print(" ".join(sys.argv), file=stats_file)

    torch.cuda.set_device(gpu)
    torch.backends.cudnn.benchmark = True

    backbone, embedding = resnet.__dict__[args.arch](zero_init_residual=True)
    state_dict = torch.load(args.pretrained, map_location="cpu")
    if "model" in state_dict:
        state_dict = state_dict["model"]
        state_dict = {
            key.replace("module.backbone.", ""): value
            for (key, value) in state_dict.items()
        }
    backbone.load_state_dict(state_dict, strict=False)

    head = nn.Linear(embedding, 1000)
    head.weight.data.normal_(mean=0.0, std=0.01)
    head.bias.data.zero_()
    model = nn.Sequential(backbone, head)
    model.cuda(gpu)

    if args.weights == "freeze":
        backbone.requires_grad_(False)
        head.requires_grad_(True)
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpu])

    criterion = nn.CrossEntropyLoss().cuda(gpu)

    param_groups = [dict(params=head.parameters(), lr=args.lr_head)]
    if args.weights == "finetune":
        param_groups.append(dict(params=backbone.parameters(), lr=args.lr_backbone))
    optimizer = optim.SGD(param_groups, 0, momentum=0.9, weight_decay=args.weight_decay)
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs)

    # automatically resume from checkpoint if it exists
    if (args.exp_dir / "checkpoint.pth").is_file():
        ckpt = torch.load(args.exp_dir / "checkpoint.pth", map_location="cpu")
        start_epoch = ckpt["epoch"]
        best_acc = ckpt["best_acc"]
        model.load_state_dict(ckpt["model"])
        optimizer.load_state_dict(ckpt["optimizer"])
        scheduler.load_state_dict(ckpt["scheduler"])
    else:
        start_epoch = 0
        best_acc = argparse.Namespace(top1=0, top5=0)

    # Data loading code
    traindir = args.data_dir / "train"
    valdir = args.data_dir / "val"
    normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
    )

    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose(
            [
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ]
        ),
    )
    val_dataset = datasets.ImageFolder(
        valdir,
        transforms.Compose(
            [
                transforms.Resize(256),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                normalize,
            ]
        ),
    )

    if args.train_percent in {1, 10}:
        train_dataset.samples = []
        for fname in args.train_files:
            fname = fname.decode().strip()
            cls = fname.split("_")[0]
            train_dataset.samples.append(
                (traindir / cls / fname, train_dataset.class_to_idx[cls])
            )

    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    kwargs = dict(
        batch_size=args.batch_size // args.world_size,
        num_workers=args.workers,
        pin_memory=True,
    )
    train_loader = torch.utils.data.DataLoader(
        train_dataset, sampler=train_sampler, **kwargs
    )
    val_loader = torch.utils.data.DataLoader(val_dataset, **kwargs)

    start_time = time.time()
    for epoch in range(start_epoch, args.epochs):
        # train
        if args.weights == "finetune":
            model.train()
        elif args.weights == "freeze":
            model.eval()
        else:
            assert False
        train_sampler.set_epoch(epoch)
        for step, (images, target) in enumerate(
            train_loader, start=epoch * len(train_loader)
        ):
            output = model(images.cuda(gpu, non_blocking=True))
            loss = criterion(output, target.cuda(gpu, non_blocking=True))
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            if step % args.print_freq == 0:
                torch.distributed.reduce(loss.div_(args.world_size), 0)
                if args.rank == 0:
                    pg = optimizer.param_groups
                    lr_head = pg[0]["lr"]
                    lr_backbone = pg[1]["lr"] if len(pg) == 2 else 0
                    stats = dict(
                        epoch=epoch,
                        step=step,
                        lr_backbone=lr_backbone,
                        lr_head=lr_head,
                        loss=loss.item(),
                        time=int(time.time() - start_time),
                    )
                    print(json.dumps(stats))
                    print(json.dumps(stats), file=stats_file)

        # evaluate
        model.eval()
        if args.rank == 0:
            top1 = AverageMeter("Acc@1")
            top5 = AverageMeter("Acc@5")
            with torch.no_grad():
                for images, target in val_loader:
                    output = model(images.cuda(gpu, non_blocking=True))
                    acc1, acc5 = accuracy(
                        output, target.cuda(gpu, non_blocking=True), topk=(1, 5)
                    )
                    top1.update(acc1[0].item(), images.size(0))
                    top5.update(acc5[0].item(), images.size(0))
            best_acc.top1 = max(best_acc.top1, top1.avg)
            best_acc.top5 = max(best_acc.top5, top5.avg)
            stats = dict(
                epoch=epoch,
                acc1=top1.avg,
                acc5=top5.avg,
                best_acc1=best_acc.top1,
                best_acc5=best_acc.top5,
            )
            print(json.dumps(stats))
            print(json.dumps(stats), file=stats_file)

        scheduler.step()
        if args.rank == 0:
            state = dict(
                epoch=epoch + 1,
                best_acc=best_acc,
                model=model.state_dict(),
                optimizer=optimizer.state_dict(),
                scheduler=scheduler.state_dict(),
            )
            torch.save(state, args.exp_dir / "checkpoint.pth")


def handle_sigusr1(signum, frame):
    os.system(f'scontrol requeue {os.getenv("SLURM_JOB_ID")}')
    exit()


def handle_sigterm(signum, frame):
    pass


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self, name, fmt=":f"):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
        return fmtstr.format(**self.__dict__)


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


if __name__ == "__main__":
    main()