pretrain_moco_mask.py

import argparse
import math
import os
import random
import shutil
import time
import numpy as np
from tqdm import tqdm
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.nn.functional as F
import moco
import torch.distributed as dist
from utils import *
from torch.utils.tensorboard import SummaryWriter
from dataset import get_pretraining_set_base


parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
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=256, 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=0.001, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--schedule', default=[100, 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=50, 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)')

# Distributed
parser.add_argument('--local_rank', default=-1, type=int,
                    help='node rank for distributed training')

parser.add_argument('--seed', default=42, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--checkpoint-path', default='./checkpoints', type=str)
parser.add_argument('--stream', default='joint', type=str)
parser.add_argument('--exp-descri', default='', type=str)
parser.add_argument('--skeleton-representation', type=str,
                    help='input skeleton-representation  for self supervised training (image-based or graph-based or seq-based)')
parser.add_argument('--pre-dataset', default='ntu60', type=str,
                    help='which dataset to use for self supervised training (ntu60 or ntu120)')
parser.add_argument('--protocol', default='cross_subject', type=str,
                    help='traiining protocol cross_view/cross_subject/cross_setup')

# contrast specific configs:
parser.add_argument('--contrast-dim', default=128, type=int,
                    help='feature dimension (default: 128)')
parser.add_argument('--contrast-k', default=32768, type=int,
                    help='queue size; number of negative keys (default: 16384)')
parser.add_argument('--contrast-m', default=0.999, type=float,
                    help='contrast momentum of updating key encoder (default: 0.999)')
parser.add_argument('--contrast-t', default=0.07, type=float,
                    help='softmax temperature (default: 0.07)')
parser.add_argument('--teacher-t', default=0.05, type=float,
                    help='softmax temperature (default: 0.05)')
parser.add_argument('--student-t', default=0.1, type=float,
                    help='softmax temperature (default: 0.1)')
parser.add_argument('--cmd-weight', default=1.0, type=float,
                    help='weight of sim loss (default: 1.0)')
parser.add_argument('--topk', default=1024, type=int,
                    help='number of contrastive context')
parser.add_argument('--save-fre', default=100, type=int,
                    help='save frequency')
parser.add_argument('--mlp', action='store_true',
                    help='use mlp head')
parser.add_argument('--use-motion-feeder', action='store_true',
                    help='use motion feeder to generate motion data first and then shuffle temporally')
parser.add_argument('--use-bone-feeder', action='store_true',
                    help='use bone feeder to generate bone data first and then joint corruption')
parser.add_argument('--cos', action='store_true',
                    help='use cosine lr schedule')

def init_seeds(seed):
    random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.
    cudnn.deterministic = True
    cudnn.benchmark = True

def main():
    args = parser.parse_args()

    if args.local_rank != -1:
        dist.init_process_group(backend='nccl')
        torch.cuda.set_device(args.local_rank)
    else:
        torch.cuda.set_device(0)
    # Simply call main_worker function
    main_worker(args)

def main_worker(args):
    if args.local_rank != -1:
        init_seeds(args.seed + args.local_rank)
    else:
        init_seeds(args.seed)
    
    # pretraining dataset and protocol
    from options import options_pretraining as options 
    if args.pre_dataset == 'ntu60' and args.protocol == 'cross_view':
        opts = options.opts_ntu_60_cross_view()
    elif args.pre_dataset == 'ntu60' and args.protocol == 'cross_subject':
        opts = options.opts_ntu_60_cross_subject()
    elif args.pre_dataset == 'ntu120' and args.protocol == 'cross_setup':
        opts = options.opts_ntu_120_cross_setup()
    elif args.pre_dataset == 'ntu120' and args.protocol == 'cross_subject':
        opts = options.opts_ntu_120_cross_subject()
    elif args.pre_dataset == 'pku_v2' and args.protocol == 'cross_view':
        opts = options.opts_pku_v2_cross_view()
    elif args.pre_dataset == 'pku_v2' and args.protocol == 'cross_subject':
        opts = options.opts_pku_v2_cross_subject()
    elif args.pre_dataset == 'pku_v1' and args.protocol == 'cross_subject':
        opts = options.opts_pku_v1_cross_subject()
    else:
        print('Not Implemented Error!')

    opts.train_feeder_args['input_representation'] = args.skeleton_representation

    # create model
    print("=> creating model")
    
    
    model = moco.builder_mask_recons.MoCo(args.skeleton_representation, opts.bi_gru_model_args,
                                  args.contrast_dim, args.contrast_k, args.contrast_m, args.contrast_t,
                                  args.teacher_t, args.student_t, args.cmd_weight, args.topk, args.mlp)
    

    print("options",opts.train_feeder_args)
    print(model)

    model.cuda()
    if args.local_rank != -1:
        model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
        model = nn.parallel.distributed.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=True)
        print('Distributed data parallel model used')
    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()
    
    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                            momentum=args.momentum,
                            weight_decay=args.weight_decay)

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            # Map model to be loaded to specified single gpu.
            checkpoint = torch.load(args.resume, map_location='cpu')
            args.start_epoch = checkpoint['epoch']
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    ## Data loading code
    train_dataset = get_pretraining_set_base(opts)

    if args.local_rank != -1:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    else:
        train_sampler = None

    def worker_init_fn(worker_id):
        return np.random.seed(torch.initial_seed()%(2**31) + worker_id)  # for single gpu
    
    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers,
        worker_init_fn=worker_init_fn, pin_memory=True, sampler=train_sampler, drop_last=True)

    writer = SummaryWriter(args.checkpoint_path)
    log_txt = os.path.join(args.checkpoint_path,'pretrain_log.txt')
    f = open(log_txt,"a+")
    f.write(str(vars(args)))
    f.write('\n')
    f.close()
    for epoch in range(args.start_epoch, args.epochs):
        if args.local_rank != -1:
            train_sampler.set_epoch(epoch)

        adjust_learning_rate(optimizer, epoch, args)

        # train for one epoch
        loss_joint, loss_motion, loss_bone, loss_sim, top1_joint, top1_motion, top1_bone = train(train_loader, model, criterion, optimizer, epoch, args, f=log_txt)
        
        if args.local_rank in [-1, 0]:
            writer.add_scalar('loss_joint', loss_joint.avg, global_step=epoch)
            writer.add_scalar('loss_motion', loss_motion.avg, global_step=epoch)
            writer.add_scalar('loss_bone', loss_bone.avg, global_step=epoch)
            writer.add_scalar('loss_sim', loss_sim.avg, global_step=epoch)
            writer.add_scalar('top1_joint',top1_joint.avg, global_step=epoch)
            writer.add_scalar('top1_motion',top1_motion.avg, global_step=epoch)
            writer.add_scalar('top1_bone',top1_bone.avg, global_step=epoch)

            if epoch % args.save_fre == 0 or epoch==args.epochs-1:
                    save_checkpoint({
                        'epoch': epoch + 1,
                        'state_dict': model.state_dict(),
                        'optimizer' : optimizer.state_dict(),
                    }, is_best=False, filename=args.checkpoint_path+'/checkpoint_{:04d}.pth.tar'.format(epoch))


def train(train_loader, model, criterion, optimizer, epoch, args, f=None):
    mask_gen = Jointmask3(mask_ratio=0.6,person_num=2,joint_num=25,channel_num=3)
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':6.3f')
    losses_base = AverageMeter('Loss Basic', ':6.3f')
    losses_mix = AverageMeter('Loss Mix', ':6.3f')
    losses_mp = AverageMeter('Loss Mask&Predict', ':6.3f')
    losses_rec = AverageMeter('Loss Reconstruction', ':6.3f')
    losses_sim = AverageMeter('Loss Sim', ':6.3f')
    top1_joint = AverageMeter('Acc Basic@1', ':6.2f')
    top1_motion = AverageMeter('Acc Mix@1', ':6.2f')
    top1_bone = AverageMeter('Acc Predict@1', ':6.2f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, losses_base, losses_mix, losses_mp,losses_rec, losses_sim, top1_joint, top1_motion, top1_bone],
        prefix="Epoch: [{}] Lr_rate [{}]".format(epoch,optimizer.param_groups[0]['lr']))
    # switch to train mode
    model.train()

    end = time.time()
    def view_gen(data):
        if args.stream == 'joint':
            pass
        elif args.stream == 'motion':
            if args.use_motion_feeder:
                pass
            else:
                motion = torch.zeros_like(data)
                motion[:, :, :-1, :, :] = data[:, :, 1:, :, :] - data[:, :, :-1, :, :]
                data = motion
        elif args.stream == 'bone':
            if args.use_bone_feeder:
                pass
            else:
                Bone = [(1, 2), (2, 21), (3, 21), (4, 3), (5, 21), (6, 5), (7, 6), (8, 7), (9, 21),
                        (10, 9), (11, 10), (12, 11), (13, 1), (14, 13), (15, 14), (16, 15), (17, 1),
                        (18, 17), (19, 18), (20, 19), (21, 21), (22, 23), (23, 8), (24, 25), (25, 12)]

                bone = torch.zeros_like(data)

                for v1, v2 in Bone:
                    bone[:, :, :, v1 - 1, :] = data[:, :, :, v1 - 1, :] - data[:, :, :, v2 - 1, :]

                data = bone
        else:
            raise ValueError
        return data
    for i, (input_v1, input_v2, input_v3,label) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        inputs= [input_v1,input_v2,input_v3]
        inputs[0] = view_gen(inputs[0]).float().cuda(non_blocking=True)
        inputs[1] = view_gen(inputs[1]).float().cuda(non_blocking=True)
        inputs[2] = view_gen(inputs[2]).float().cuda(non_blocking=True)
        label = label.long().cuda(non_blocking=True)

        if args.exp_descri =='PCM3_wprompt':
            # for joint view
            m = mask_gen(inputs[0]).cuda() #NTMVC
            
            output1, output2, output3, output4, target, recons_data, loss_sim = model.forward_pcm3_wprompt(inputs[0], inputs[1], inputs[2], local_rank=args.local_rank,mask=m)
            
            batch_size = output1.size(0)
            # compute loss
            loss_base = criterion(output1, target)
            loss_mix = criterion(output2, target)
            mask_region = (1.0-m).permute(0,4,1,3,2)#NCTVM
            loss_rec = ((recons_data-inputs[2])*mask_region)**2
            loss_rec = loss_rec.sum()/(mask_region.sum())
            loss_mp = criterion(output4, target) + criterion(output3, target) 

            loss = loss_base + loss_mix + loss_sim + loss_rec*40 + loss_mp
        elif args.exp_descri =='PCM3_woprompt':
            # for training stability of motion bone views 
            m = mask_gen(inputs[0]).cuda() #NTMVC
            
            output1, output2, output3, output4, target, recons_data, loss_sim = model.forward_pcm3_woprompt(inputs[0], inputs[1], inputs[2], local_rank=args.local_rank,mask=m)
            
            batch_size = output1.size(0)
            # compute loss
            loss_base = criterion(output1, target)
            loss_mix = criterion(output2, target)
            mask_region = (1.0-m).permute(0,4,1,3,2)#NCTVM
            loss_rec = ((recons_data-inputs[2])*mask_region)**2
            loss_rec = loss_rec.sum()/(mask_region.sum())
            loss_mp = criterion(output3, target) + criterion(output4, target) 

            loss = loss_base + loss_mix + loss_rec * 40 + loss_mp


        losses.update(loss.item(), batch_size)
        losses_base.update(loss_base.item(), batch_size)
        losses_mix.update(loss_mix.item(), batch_size)
        losses_mp.update(loss_mp.item(), batch_size)
        losses_rec.update(loss_rec.item(), batch_size)
        losses_sim.update(loss_sim.item(), batch_size)

        # measure accuracy of model m1 and m2 individually
        # acc1/acc5 are (K+1)-way contrast classifier accuracy
        # measure accuracy and record loss
        acc1_joint, _ = accuracy(output1, target, topk=(1, 5))
        acc1_motion, _ = accuracy(output2, target, topk=(1, 5))
        acc1_bone, _ = accuracy(output3, target, topk=(1, 5))
        top1_joint.update(acc1_joint[0], batch_size)
        top1_motion.update(acc1_motion[0], batch_size)
        top1_bone.update(acc1_bone[0], batch_size)

        #print("input output size",output.size(),images[0].size(),half_size)

        # 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 and args.local_rank in [-1, 0]:
            progress.display(i,f)

    return losses_base, losses_mix, losses_mp, losses_sim, top1_joint, top1_motion, top1_bone


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,f=None):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        if f != None:
            with open(f,'a+') as ff: 
                #print('1')
                ff.write('\t'.join(entries))
                ff.write('\n')
        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].contiguous().view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

if __name__ == '__main__':
    main()