ours_stage1.py

import argparse
import logging
import math
import os
import random
import shutil
import time
from collections import OrderedDict
from copy import deepcopy

import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from torchvision import transforms
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from skimage.filters import threshold_otsu
from dataset.cifar100 import DATASET_GETTERS
from utils import AverageMeter, accuracy

logger = logging.getLogger(__name__)
best_acc = 0
best_acc_val = 0


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


def set_seed(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)


def get_cosine_schedule_with_warmup(optimizer,
                                    num_warmup_steps,
                                    num_training_steps,
                                    num_cycles=7./16.,
                                    last_epoch=-1):
    def _lr_lambda(current_step):
        if current_step < num_warmup_steps:
            return float(current_step) / float(max(1, num_warmup_steps))
        no_progress = float(current_step - num_warmup_steps) / \
            float(max(1, num_training_steps - num_warmup_steps))
        return max(0., math.cos(math.pi * num_cycles * no_progress))

    return LambdaLR(optimizer, _lr_lambda, last_epoch)


def main():
    parser = argparse.ArgumentParser(description='PyTorch T2T Stage1 Training')
    
    parser.add_argument('--n_labels_per_cls', type=int, default=100)
    parser.add_argument('--n_val_per_class', type=int, default=50)

    parser.add_argument('--n_unlabels', type=int, default=20000)
    parser.add_argument('--tot_class', type=int, default=50)
    parser.add_argument('--ratio', type=float, default=6)  # 6 // 3
    
    parser.add_argument('--gpu-id', default='0', type=int,
                        help='id(s) for CUDA_VISIBLE_DEVICES')
    parser.add_argument('--num-workers', type=int, default=4,
                        help='number of workers')
    parser.add_argument('--dataset', default='cifar100', type=str,
                        choices=['cifar100'],
                        help='dataset name')



    parser.add_argument("--expand-labels", action="store_true",
                        help="expand labels to fit eval steps")
    parser.add_argument('--arch', default='wideresnet', type=str,
                        choices=['wideresnet'],
                        help='dataset name')
    parser.add_argument('--total-steps', default=2**20, type=int,
                        help='number of total steps to run')
    parser.add_argument('--eval-step', default=1024, type=int,
                        help='number of eval steps to run')
    parser.add_argument('--start-epoch', default=0, type=int,
                        help='manual epoch number (useful on restarts)')
    parser.add_argument('--batch-size', default=64, type=int,
                        help='train batchsize')
    parser.add_argument('--lr', '--learning-rate', default=0.03, type=float,
                        help='initial learning rate')
    parser.add_argument('--warmup', default=0, type=float,
                        help='warmup epochs (unlabeled data based)')
    parser.add_argument('--wdecay', default=5e-4, type=float,
                        help='weight decay')
    parser.add_argument('--nesterov', action='store_true', default=True,
                        help='use nesterov momentum')
    parser.add_argument('--mu', default=7, type=int,
                        help='coefficient of unlabeled batch size')
    parser.add_argument('--lambda-u', default=1, type=float,
                        help='coefficient of unlabeled loss')
    parser.add_argument('--T', default=1, type=float,
                        help='pseudo label temperature')
    parser.add_argument('--threshold', default=0.95, type=float,
                        help='pseudo label threshold')
    parser.add_argument('--out', default='result',
                        help='directory to output the result')
    parser.add_argument('--resume', default='', type=str,
                        help='path to latest checkpoint (default: none)')
    parser.add_argument('--seed', default=None, type=int,
                        help="random seed")
    parser.add_argument("--amp", action="store_true",
                        help="use 16-bit (mixed) precision through NVIDIA apex AMP")
    parser.add_argument("--opt_level", type=str, default="O1",
                        help="apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                        "See details at https://nvidia.github.io/apex/amp.html")
    parser.add_argument("--local_rank", type=int, default=-1,
                        help="For distributed training: local_rank")
    parser.add_argument('--no-progress', action='store_true',
                        help="don't use progress bar")
    parser.add_argument('--ood-dataset', type=str, default='TIN', 
                        choices=['TIN', 'LSUN', 'Gaussian', 'Uniform'],
                        help='choose one dataset as ood data source')

    args = parser.parse_args()
    args.ratio = args.ratio/10


    if args.seed is not None:
        set_seed(args)

    os.makedirs(args.out, exist_ok=True)
    args.writer = SummaryWriter(args.out)

    if torch.cuda.is_available():
        args.device = 'cuda'
    else:
        args.device = 'cpu'

    if args.dataset == 'cifar100':
        args.num_classes = 100
        if args.arch == 'wideresnet':
            args.model_depth = 28
            args.model_width = 2

    elif args.dataset == 'cifar10':
        args.num_classes = 10
        if args.arch == 'wideresnet':
            args.model_depth = 28
            args.model_width = 2   
    
    # logger
    local_time = time.localtime(time.time())
    date_format_localtime = time.strftime('%Y-%m-%d %H:%M:%S', local_time)
    log_file = date_format_localtime + ".log"
    log_dir = args.out
    if not os.path.isdir(log_dir):
        os.makedirs(log_dir)
    logging.basicConfig(
        filename=log_dir + log_file,
        filemode='a',
        format="%(asctime)s - %(levelname)s - %(name)s -  %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)

    if args.resume:
        logger.info("load model from" + args.resume)
    logger.info("out: " + args.out)
    
    

    args.epochs = math.ceil(args.total_steps / args.eval_step)

    def create_model(args):
        if args.arch == 'wideresnet':
            import models.wideresnet_stage1 as models
            model = models.build_wideresnet(depth=args.model_depth,
                                            widen_factor=args.model_width,
                                            dropout=0,
                                            num_classes=args.tot_class)
        logger.info("Total params: {:.2f}M".format(
            sum(p.numel() for p in model.parameters())/1e6))

        rotnet_head = torch.nn.Linear(64*args.model_width, 4)

        from models import CrossModalMatchingHead

        cmm_head = CrossModalMatchingHead(args.tot_class , 64*args.model_width)

        return model, rotnet_head, cmm_head

    labeled_dataset, unlabeled_dataset, val_dataset, test_dataset = DATASET_GETTERS[args.dataset](args, './data')

    # unlabeled_dataset contains all training data
    # however, it use randaugment as data transformation
    # so we copy it and use simple data transformation for rotnet dataloader
    udst_rotnet = deepcopy(unlabeled_dataset)
    udst_rotnet.transform = labeled_dataset.transform

    udst_rotnet_loader = DataLoader(
        udst_rotnet,
        batch_size=args.batch_size,
        shuffle=True,
        num_workers=args.num_workers,
        drop_last=True)

    labeled_trainloader = DataLoader(
        labeled_dataset,
        batch_size=args.batch_size,
        shuffle=True,
        num_workers=args.num_workers,
        drop_last=True)

    val_loader = DataLoader(
        val_dataset,
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=args.num_workers)

    test_loader = DataLoader(
        test_dataset,
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=args.num_workers)
    
    model, rotnet_head, cmm_head = create_model(args)
    model, rotnet_head, cmm_head = model.to(args.device), rotnet_head.to(args.device), cmm_head.to(args.device)

    train_stage1(args, labeled_trainloader, udst_rotnet_loader, val_loader, test_loader,
                 model, rotnet_head, cmm_head)


def train_stage1(args, labeled_trainloader, unlabeled_trainloader, val_loader, test_loader,
                 model, rotnet_head, cmm_head):
    """
    In this stage, we train the model with three losses:
    1. Lx:  cross-entropy loss for labeled data (ref to L_ce in origin paper)
    2. Lmx: cross-modal matching loss for labeled data (ref to L_cm^l in origin paper)
    3. Lr:  rotation recognition loss for all training data (ref to L_rot in origin paper)
    """

    global best_acc, best_acc_val
    val_accs = []
    test_accs = []
    end = time.time()

    grouped_parameters = [
        {'params': model.parameters()},
        {'params': rotnet_head.parameters()},
        {'params': cmm_head.parameters()}
    ]

    optimizer = optim.SGD(grouped_parameters, lr=0.1, momentum=0.9, weight_decay=5e-4, nesterov=True)
    scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[30, 60, 80, 100], gamma=0.2)

    labeled_iter = iter(labeled_trainloader)
    unlabeled_iter = iter(unlabeled_trainloader)

    for epoch in range(args.start_epoch, args.epochs):
        batch_time = AverageMeter()
        data_time = AverageMeter()
        losses = AverageMeter()
        losses_x = AverageMeter()
        losses_mx = AverageMeter()
        losses_r = AverageMeter()
        losses_info = AverageMeter()
        
        model.train()
        rotnet_head.train()
        cmm_head.train()

        if not args.no_progress:
            p_bar = tqdm(range(args.eval_step))
            
        m = 10
        for batch_idx in range(args.eval_step):
            try:
                inputs_x, targets_x, index_x = labeled_iter.next()
            except:
                labeled_iter = iter(labeled_trainloader)
                inputs_x, targets_x, index_x = labeled_iter.next()

            try:
                inputs_u_w, gt_u, index_u = unlabeled_iter.next()
            except:
                unlabeled_iter = iter(unlabeled_trainloader)
                inputs_u_w, gt_u, index_u = unlabeled_iter.next()

            # rotate unlabeled data with 0, 90, 180, 270 degrees
            inputs_r = torch.cat(
                [torch.rot90(inputs_u_w, i, [2, 3]) for i in range(4)], dim=0)
            targets_r = torch.cat(
                [torch.empty(inputs_u_w.size(0)).fill_(i).long() for i in range(4)], dim=0).to(args.device)

            data_time.update(time.time() - end)
            
            batch_size = inputs_x.shape[0]
            inputs_x, targets_x = inputs_x.to(args.device), targets_x.to(args.device)
            logits_x, feats_x, std = model(inputs_x, output_feats=True)
            
#             Linfo = -0.5 * (1 + 2 * std.log() - feats_x.pow(2) - std.pow(2)).sum(dim=-1).mean().div(math.log(2))

            # Cross Entropy Loss for Labeled Data
            Lx = F.cross_entropy(logits_x, targets_x, reduction='mean')

            # Cross Modal Matching Training: 1 positve pair + 2 negative pair for each labeled data
            # [--pos--, --hard_neg--, --easy_neg--]
            matching_gt = torch.zeros(3 * batch_size).to(args.device)
            matching_gt[:batch_size] = 1
            y_onehot = torch.zeros((3 * batch_size, args.tot_class)).float().to(args.device)
            y = torch.zeros(3 * batch_size).long().to(args.device)
            y[:batch_size] = targets_x
            with torch.no_grad():
                prob_sorted_index = torch.argsort(logits_x, descending=True)
                for i in range(batch_size):
                    if prob_sorted_index[i, 0] == targets_x[i]:
                        y[1 * batch_size + i] = prob_sorted_index[i, 1]
                        y[2 * batch_size + i] = int(np.random.choice(prob_sorted_index[i, 2:].cpu(), 1))
                    else:
                        y[1 * batch_size + i] = prob_sorted_index[i, 0]
                        choice = int(np.random.choice(prob_sorted_index[i, 1:].cpu(), 1))
                        while choice == targets_x[i]:
                            choice = int(np.random.choice(prob_sorted_index[i, 1:].cpu(), 1))
                        y[2 * batch_size + i] = choice
            y_onehot.scatter_(1, y.view(-1, 1), 1)
            matching_score_x = cmm_head(feats_x.repeat(3, 1), y_onehot)
            Lmx = F.binary_cross_entropy_with_logits(matching_score_x.view(-1), matching_gt)

            # Cross Entropy Loss for Rotation Recognition
            inputs_r = inputs_r.to(args.device)
            _, feats_r , _ = model(inputs_r, output_feats=True)
            Lr = F.cross_entropy(rotnet_head(feats_r), targets_r, reduction='mean')

            loss = Lx + Lmx  + Lr  # + 1e-3 * Linfo
            
            optimizer.zero_grad()
            loss.backward()

            losses.update(loss.item())
            losses_x.update(Lx.item())
            losses_mx.update(Lmx.item())
            losses_r.update(Lr.item())
#             losses_info.update(Linfo.item())

            optimizer.step()

            batch_time.update(time.time() - end)
            end = time.time()

            if not args.no_progress:
                p_bar.set_description("Train Epoch: {epoch}/{epochs:4}. Iter: {batch:4}/{iter:4}. LR: {lr:.4f}. Data: {data:.3f}s. Batch: {bt:.3f}s. "
                                      "Loss: {loss:.4f}. Loss_x: {loss_x:.4f}. Loss_mx: {loss_mx:.4f}. Loss_info: {loss_info:.4f}. Loss_r: {loss_r:.4f}.".format(
                    epoch=epoch + 1,
                    epochs=args.epochs,
                    batch=batch_idx + 1,
                    iter=args.eval_step,
                    lr=scheduler.get_last_lr()[0],
                    data=data_time.avg,
                    bt=batch_time.avg,
                    loss=losses.avg,
                    loss_x=losses_x.avg,
                    loss_mx=losses_mx.avg,
                    loss_r=losses_r.avg,
                    loss_info = losses_info.avg,
                ))
                p_bar.update()

        scheduler.step()

        if not args.no_progress:
            p_bar.close()

        test_model = model

        val_loss, val_acc = test(args, val_loader, test_model, epoch)
        test_loss, test_acc = test(args, test_loader, test_model, epoch)

        args.writer.add_scalar('train/1.train_loss', losses.avg, epoch)
        args.writer.add_scalar('train/2.train_loss_x', losses_x.avg, epoch)
        args.writer.add_scalar('train/3.train_loss_mx', losses_mx.avg, epoch)
        args.writer.add_scalar('train/4.train_loss_r', losses_r.avg, epoch)
        args.writer.add_scalar('train/6.train_loss_info', losses_info.avg , epoch)
        args.writer.add_scalar('test/1.test_acc', test_acc, epoch)
        args.writer.add_scalar('test/2.test_loss', test_loss, epoch)
        args.writer.add_scalar('val/1.val_acc', val_acc, epoch)
        args.writer.add_scalar('val/2.val_loss', val_loss, epoch)

        best_acc_val = max(val_acc, best_acc_val)

        is_best = test_acc > best_acc
        best_acc = max(test_acc, best_acc)

        model_to_save = model.module if hasattr(model, "module") else model
        save_checkpoint({
            'epoch': epoch + 1,
            'model_state_dict': model_to_save.state_dict(),
            'rotnet_state_dict': rotnet_head.state_dict(),
            'cmm_state_dict': cmm_head.state_dict(),
            'acc': test_acc,
            'best_acc': best_acc,
            'optimizer': optimizer.state_dict(),
            'scheduler': scheduler.state_dict(),
        }, is_best, args.out)

        test_accs.append(test_acc)
        val_accs.append(val_acc)
        logger.info('Best top-1 acc(test): {:.2f} | acc(val): {:.2f}'.format(best_acc, best_acc_val))
        logger.info('Mean top-1 acc(test): {:.2f} | acc(val): {:.2f}\n'.format(
            np.mean(test_accs[-20:]), np.mean(val_accs[-20:])))


def test(args, test_loader, model, epoch):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()
    end = time.time()

    if not args.no_progress:
        test_loader = tqdm(test_loader)

    with torch.no_grad():
        for batch_idx, (inputs, targets, indexs) in enumerate(test_loader):
            data_time.update(time.time() - end)
            model.eval()

            inputs = inputs.to(args.device)
            targets = targets.to(args.device)
            outputs = model(inputs)
            loss = F.cross_entropy(outputs, targets)

            prec1, prec5 = accuracy(outputs, targets, topk=(1, 5))
            losses.update(loss.item(), inputs.shape[0])
            top1.update(prec1.item(), inputs.shape[0])
            top5.update(prec5.item(), inputs.shape[0])
            batch_time.update(time.time() - end)
            end = time.time()
            if not args.no_progress:
                test_loader.set_description("Test Iter: {batch:4}/{iter:4}. Data: {data:.3f}s. Batch: {bt:.3f}s. Loss: {loss:.4f}. top1: {top1:.2f}. top5: {top5:.2f}. ".format(
                    batch=batch_idx + 1,
                    iter=len(test_loader),
                    data=data_time.avg,
                    bt=batch_time.avg,
                    loss=losses.avg,
                    top1=top1.avg,
                    top5=top5.avg,
                ))
        if not args.no_progress:
            test_loader.close()

    logger.info("top-1 acc: {:.2f}".format(top1.avg))
    logger.info("top-5 acc: {:.2f}".format(top5.avg))
    return losses.avg, top1.avg


if __name__ == '__main__':
    main()