train_ssl.py

import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models

import condssl.loader
import condssl.builder

import sys
from network.inception_v4 import InceptionV4
from dataset.dataloader import TCGA_CPTAC_Dataset


class TwoCropsTransform:
    """Take two random crops of one image as the query and key."""

    def __init__(self, base_transform):
        self.base_transform = base_transform

    def __call__(self, x):
        q = self.base_transform(x)
        k = self.base_transform(x)
        return [q, k]


def collate_fn_moco(batch):
    q_list = []
    k_list = []
    for imgs, indices in batch:
        for img in imgs:
            q_list.append(img[0].unsqueeze(0))
            k_list.append(img[1].unsqueeze(0))
    return torch.cat(q_list, dim=0), torch.cat(k_list, dim=0)


def train(train_loader, model, criterion, optimizer, epoch, args):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()

    end = time.time()
    for i, (images) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)
        if args.gpu is not None:
            images[0] = images[0].cuda(args.gpu, non_blocking=True)
            images[1] = images[1].cuda(args.gpu, non_blocking=True)
        # compute output
        output, target = model(im_q=images[0].cuda(), im_k=images[1].cuda())
        loss = criterion(output, target)

        # acc1/acc5 are (K+1)-way contrast classifier accuracy
        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images[0].size(0))
        top1.update(acc1[0], images[0].size(0))
        top5.update(acc5[0], images[0].size(0))

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

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            progress.display(i)

def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')


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__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'


def adjust_learning_rate(optimizer, epoch, args):
    """Decay the learning rate based on schedule"""
    lr = args.lr
    if args.cos:  # cosine lr schedule
        lr *= 0.5 * (1. + math.cos(math.pi * epoch / args.epochs))
    else:  # stepwise lr schedule
        for milestone in args.schedule:
            lr *= 0.1 if epoch >= milestone else 1.
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


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


    
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
# parser.add_argument('data', metavar='DIR',
#                     help='path to dataset')

parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
                    help='number of data loading workers (default: 32)')
parser.add_argument('--epochs', default=200, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=1, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.03, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--schedule', default=[120, 160], nargs='*', type=int,
                    help='learning rate schedule (when to drop lr by 10x)')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum of SGD solver')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('--world-size', default=-1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')
parser.add_argument('--multiprocessing-distributed', action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')
parser.add_argument("--local_rank", type=int, default=0)

# moco specific configs:
parser.add_argument('--moco-dim', default=128, type=int,
                    help='feature dimension (default: 128)')
parser.add_argument('--moco-k', default=65536, type=int,
                    help='queue size; number of negative keys (default: 65536)')
parser.add_argument('--moco-m', default=0.999, type=float,
                    help='moco momentum of updating key encoder (default: 0.999)')
parser.add_argument('--moco-t', default=0.07, type=float,
                    help='softmax temperature (default: 0.07)')

# options for moco v2
parser.add_argument('--mlp', action='store_true',
                    help='use mlp head')
parser.add_argument('--aug-plus', action='store_true',
                    help='use moco v2 data augmentation')
parser.add_argument('--cos', action='store_true',
                    help='use cosine lr schedule')

parser.add_argument('--partition_name', default='train_Lung', type=str)
parser.add_argument('--data_dir', default='./data/', type=str,
                    help='path to output directory')
parser.add_argument('--split_dir', default='./split/', type=str,
                    help='path to output directory')
parser.add_argument('--out_dir', default='./models/', type=str,
                    help='path to output directory')
parser.add_argument('--batch_slide_num', default=4, type=int)
parser.add_argument('--condition', default=True, type=bool)

args = parser.parse_args()

print(args.out_dir)

if args.seed is not None:
    random.seed(args.seed)
    torch.manual_seed(args.seed)
    cudnn.deterministic = True
    warnings.warn('You have chosen to seed training. '
                  'This will turn on the CUDNN deterministic setting, '
                  'which can slow down your training considerably! '
                  'You may see unexpected behavior when restarting '
                  'from checkpoints.')

if args.gpu is not None:
    warnings.warn('You have chosen a specific GPU. This will completely '
                  'disable data parallelism.')

if args.dist_url == "env://" and args.world_size == -1:
    args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
ngpus_per_node = torch.cuda.device_count()


print("=> creating model '{}'".format(args.arch))

encoder = InceptionV4

model = condssl.builder.MoCo(
    encoder, args.moco_dim, args.moco_k, args.moco_m, args.moco_t, args.mlp, condition=args.condition)

model = model.cuda()
torch.distributed.init_process_group('nccl')

model = torch.nn.parallel.DistributedDataParallel(model)

print('Model builder Done.')
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

augmentation = [
        transforms.RandomResizedCrop(299, scale=(0.2, 1.)),
        transforms.RandomApply([
            transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)  # not strengthened
        ], p=0.8),
        transforms.RandomGrayscale(p=0.2),
        transforms.RandomApply([moco.loader.GaussianBlur([.1, 2.])], p=0.5),
        transforms.RandomHorizontalFlip(),
        transforms.RandomVerticalFlip(),
        transforms.ToTensor()
        # normalize
    ]

print('Create dataset')


train_dataset = TCGA_CPTAC_Dataset(cptac_dir=args.data_dir + "/CPTAC/tiles/",
                          tcga_dir=args.data_dir + "/TCGA/tiles/",
                          split_dir=arg.split_dir,
                          transform=TwoCropsTransform(transforms.Compose(augmentation)), 
                          batch_slide_num=args.batch_slide_num)


print("Dataset Created ...")
print(args.batch_slide_num)

if args.distributed:
    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
    train_sampler = None
train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler, drop_last=True, collate_fn=collate_fn_moco)


for epoch in range(args.start_epoch, args.epochs):
    if args.distributed:
        train_sampler.set_epoch(epoch)
    adjust_learning_rate(optimizer, epoch, args)

    # train for one epoch
    train(train_loader, model, criterion, optimizer, epoch, args)

    if not args.multiprocessing_distributed or (args.multiprocessing_distributed
            and args.rank % ngpus_per_node == 0):
        if (epoch + 1) % 50 == 0:
            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'optimizer' : optimizer.state_dict(),
            }, is_best=False, filename='{}/checkpoint_{:04d}.pth.tar'.format(args.out_dir, epoch))