eval_linear.py

import argparse
import json
import math
import os
from pathlib import Path

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from omegaconf import OmegaConf
from torch.utils.tensorboard import SummaryWriter

import data
from utils import dist as dist
import optimizers
import utils


def main(cfg):
    dist.init_distributed_mode(cfg) if not dist.is_enabled() else None
    cudnn.benchmark = True

    print("git:\n  {}\n".format(utils.get_sha()))
    print(OmegaConf.to_yaml(cfg))

    if cfg.dataset == "CIFAR10":
        import resnet_cifar as models
    else:
        import resnet_imagenet as models

    # prepare data
    if cfg.dataset == "CIFAR10":
        mean, std = data.CIFAR10_DEFAULT_MEAN, data.CIFAR10_DEFAULT_STD
    else:
        mean, std = data.IMAGENET_DEFAULT_MEAN, data.IMAGENET_DEFAULT_STD
    val_transform = data.make_classification_val_transform(
        resize_size=cfg.resize_size,
        crop_size=cfg.crop_size,
        mean=mean,
        std=std,
    )
    val_data, cfg.num_labels = data.make_dataset(cfg.data_path, cfg.dataset, False, val_transform)

    sampler = torch.utils.data.SequentialSampler(val_data)
    cfg.batch_size_per_gpu = cfg.batch_size // dist.get_world_size()
    val_loader = torch.utils.data.DataLoader(
        val_data,
        sampler=sampler,
        batch_size=cfg.batch_size_per_gpu,
        num_workers=cfg.num_workers,
        pin_memory=True,
        drop_last=False
    )

    train_transform = data.make_classification_train_transform(
        crop_size=cfg.crop_size,
        mean=mean,
        std=std,
    )
    train_data, _ = data.make_dataset(cfg.data_path, cfg.dataset, True, train_transform)

    sampler = torch.utils.data.distributed.DistributedSampler(train_data)
    train_loader = torch.utils.data.DataLoader(
        train_data,
        sampler=sampler,
        batch_size=cfg.batch_size_per_gpu,
        num_workers=cfg.num_workers,
        pin_memory=True,
    )
    print(f"Data loaded with {len(train_data)} train and {len(val_data)} val images.")

    # create model
    print("=> creating model '{}'".format(cfg.arch))
    model = models.__dict__[cfg.arch](
        num_classes=cfg.num_labels
    ).cuda()

    # freeze all layers but the last fc
    for name, param in model.named_parameters():
        if name not in ['fc.weight', 'fc.bias']:
            param.requires_grad = False
    # init the fc layer
    model.fc.weight.data.normal_(mean=0.0, std=0.01)
    model.fc.bias.data.zero_()
    # optimize only the linear classifier
    parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
    assert len(parameters) == 2  # fc.weight, fc.bias

    # load from pre-trained, before DistributedDataParallel constructor
    utils.load_pretrained_weights(model, cfg.pretrained, cfg.ckp_key)
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[cfg.gpu])

    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()

    # infer learning rate before changing batch size
    init_lr = cfg.lr * cfg.batch_size / 256
    if cfg.lars:
        optimizer = optimizers.LARS(parameters, init_lr,
                                    momentum=cfg.momentum,
                                    weight_decay=cfg.weight_decay)
    else:
        optimizer = torch.optim.SGD(parameters, init_lr,
                                    momentum=cfg.momentum,
                                    weight_decay=cfg.weight_decay)

    log_dir = os.path.join(cfg.output_dir, "tensorboard")
    board = SummaryWriter(log_dir) if dist.is_main_process() else None

    # Optionally resume from a checkpoint
    to_restore = {"epoch": 0, "best_acc": 0.}
    utils.restart_from_checkpoint(
        os.path.join(cfg.output_dir, "checkpoint.pth.tar"),
        run_variables=to_restore,
        model=model,
        optimizer=optimizer,
    )
    start_epoch = to_restore["epoch"]
    best_acc = to_restore["best_acc"]

    for epoch in range(start_epoch, cfg.epochs):
        sampler.set_epoch(epoch)
        adjust_learning_rate(optimizer, init_lr, epoch, cfg)

        # train for one epoch
        train_stats = train(train_loader, model, criterion, optimizer, epoch, cfg, board)
        log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, 'epoch': epoch}

        # evaluate on validation set
        if epoch % cfg.val_freq == 0 or epoch == cfg.epochs - 1:
            test_stats = validate(val_loader, model, criterion)
            print(f"Accuracy at epoch {epoch} of the network on the {len(val_data)} test images: "
                  f"{test_stats['acc1']:.1f}%")
            # remember best acc@1 and save checkpoint
            best_acc = max(test_stats["acc1"], best_acc)
            log_stats = {**{k: v for k, v in log_stats.items()},
                         **{f'test_{k}': v for k, v in test_stats.items()}}

            if board:
                board.add_scalar(tag="acc1", scalar_value=test_stats["acc1"], global_step=epoch)
                board.add_scalar(tag="acc5", scalar_value=test_stats["acc5"], global_step=epoch)
                board.add_scalar(tag="best-acc", scalar_value=best_acc, global_step=epoch)

        if dist.is_main_process():
            with (Path(cfg.output_dir) / "eval.log").open("a") as f:
                f.write(json.dumps(log_stats) + "\n")
            save_dict = {
                "epoch": epoch + 1,
                "model": model.state_dict(),
                "optimizer": optimizer.state_dict(),
                "best_acc": best_acc,
            }
            path = os.path.join(cfg.output_dir, "checkpoint.pth.tar")
            torch.save(save_dict, path)

    print("Training of the supervised linear classifier on frozen features completed.\n"
          "Top-1 test accuracy: {acc:.1f}".format(acc=best_acc))


def train(loader, model, criterion, optimizer, epoch, cfg, board):
    metric_logger = utils.MetricLogger(delimiter=" ")
    header = 'Epoch: [{}/{}]'.format(epoch, cfg.epochs)
    model.eval()
    for it, (images, targets) in enumerate(metric_logger.log_every(loader, 10, header)):
        it = len(loader) * epoch + it

        images = images.cuda(non_blocking=True)
        targets = targets.cuda(non_blocking=True)

        # compute output
        output = model(images)
        loss = criterion(output, targets)

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # measure accuracy and record loss
        acc1, acc5 = utils.accuracy(output, targets, topk=(1, 5))

        # logging
        torch.cuda.synchronize()
        metric_logger.update(loss=loss.item())
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
        metric_logger.update(acc1=acc1[0])
        metric_logger.update(acc5=acc5[0])

        if dist.is_main_process() and it % cfg.logger_freq:
            board.add_scalar(tag="eval acc1", scalar_value=acc1, global_step=it)
            board.add_scalar(tag="eval loss", scalar_value=loss.item(), global_step=it)

    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger)
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}


def validate(loader, model, criterion):
    # switch to evaluate mode
    model.eval()

    metric_logger = utils.MetricLogger(delimiter=" ")
    header = 'Test:'
    with torch.no_grad():
        for i, (images, target) in enumerate(metric_logger.log_every(loader, 10, header)):
            images = images.cuda(non_blocking=True)
            target = target.cuda(non_blocking=True)

            # compute output
            output = model(images)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
            # logging
            torch.cuda.synchronize()
            batch_size = images.size(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)

        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()}


def adjust_learning_rate(optimizer, init_lr, epoch, args):
    """Decay the learning rate based on schedule"""
    cur_lr = init_lr * 0.5 * (1. + math.cos(math.pi * epoch / args.epochs))
    for param_group in optimizer.param_groups:
        param_group['lr'] = cur_lr


def get_args_parser():
    p = argparse.ArgumentParser(description='PyTorch Eval-Linear ImageNet', add_help=False)
    p.add_argument('--dataset', default="ImageNet", type=str,
                   help='Specify dataset. (default: ImageNet)')
    p.add_argument('--data_path', type=str,
                   help='(root) path to dataset')
    p.add_argument('-a', '--arch', type=str,
                   help="Name of architecture to train (default: resnet50)")
    p.add_argument('--epochs', type=int,
                   help='number of total epochs to run (default: 90)')
    p.add_argument('-b', '--batch-size', type=int,
                   help='total-batch-size (default: 4096)')
    p.add_argument('--lr', type=float,
                   help='initial (base) learning rate (default: 0.1)')
    p.add_argument('--momentum', type=float,
                   help='momentum (default: 0.9)')
    p.add_argument('--wd', '--weight_decay', type=float, dest='weight_decay',
                   help='weight decay (default: 0.)')
    p.add_argument('--resize_size', type=int,
                   help="Resize size of images before center-crop (default: 256)")
    p.add_argument('--crop_size', type=int,
                   help="Size of center-crop (default: 224)")
    p.add_argument('--lars', default=True, type=utils.bool_flag,
                   help="Whether or not to use LARS optimizer (default: True)")

    # additional configs:
    p.add_argument('--pretrained', default="checkpoint.pth", type=str,
                   help="path to simsiam pretrained checkpoint (default: checkpoint.pth)")
    p.add_argument('--output_dir', default=".", type=str,
                   help='Path to save logs and checkpoints (default: .)')
    p.add_argument('--ckp_key', default="model", type=str,
                   help='Checkpoint key (default: model)')
    p.add_argument('--val_freq', default=1, type=int,
                   help="Validate model every x epochs (default: 1)")
    p.add_argument('--logger_freq', default=50, type=int,
                   help="Log progress every x iterations to tensorboard (default: 50)")
    p.add_argument('--dist-url', default="env://", type=str,
                   help="url used to set up distributed training (default: env://)")
    p.add_argument('--dist-backend', default="nccl", type=str,
                   help="distributed backend (default: nccl)")
    p.add_argument('--num_workers', default=8, type=int,
                   help="number of data loading workers (default: 8)")

    return p


if __name__ == '__main__':
    parser = get_args_parser()
    args = parser.parse_args()
    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    main(args)