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train.py
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train.py
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#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its 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.
# This codebase is modified based on moco implementations: https://github.com/facebookresearch/moco
import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
from functools import partial
import wandb
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 torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as torchvision_models
from torch.utils.tensorboard import SummaryWriter
import sogclr.builder
import sogclr.loader
import sogclr.optimizer
import sogclr.cifar # cifar
import torch_optimizer
# ignore all warnings
import warnings
warnings.filterwarnings("ignore")
torchvision_model_names = sorted(name for name in torchvision_models.__dict__
if name.islower() and not name.startswith("__")
and callable(torchvision_models.__dict__[name]))
model_names = torchvision_model_names
parser = argparse.ArgumentParser(description='SogCLR Pre-Training')
parser.add_argument('--data', metavar='DIR', default='./data/',
help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
choices=model_names,
help='model architecture: '
' | '.join(model_names)
' (default: resnet50)')
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
help='number of data loading workers (default: 32)')
parser.add_argument('--epochs', default=400, 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=64, type=int,
metavar='N',
help='mini-batch size (default: 64), this is the total '
'batch size of all GPUs on all nodes when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=1.0, type=float,
metavar='LR', help='initial (base) learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
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('--seed', default=None, type=int,
help='seed for initializing training. ')
parser.add_argument('--gpu', default=0, type=int,
help='GPU id to use.')
# moco specific configs:
parser.add_argument('--dim', default=128, type=int,
help='feature dimension (default: 128)')
parser.add_argument('--mlp-dim', default=2048, type=int,
help='hidden dimension in MLPs (default: 2048)')
parser.add_argument('--t', default=0.3, type=float,
help='softmax temperature (default: 0.3)')
parser.add_argument('--num_proj_layers', default=2, type=int,
help='number of non-linear projection heads')
# vit specific configs:
parser.add_argument('--stop-grad-conv1', action='store_true',
help='stop-grad after first conv, or patch embedding')
# other upgrades
parser.add_argument('--optimizer', default='lars', type=str,
choices=['lars', 'adamw', 'adafactor'],
help='optimizer used (default: lars)')
parser.add_argument('--warmup-epochs', default=10, type=int, metavar='N',
help='number of warmup epochs')
parser.add_argument('--crop-min', default=0.08, type=float,
help='minimum scale for random cropping (default: 0.08)')
# dataset
parser.add_argument('--data_name', default='cifar10', type=str)
parser.add_argument('--save_dir', default='./saved_models/', type=str)
# sogclr
parser.add_argument('--loss_type', default='dcl', type=str,
choices=['dcl', 'cl'],
help='learing rate scaling (default: linear)')
parser.add_argument('--gamma', default=0.9, type=float,
help='for updating u')
parser.add_argument('--learning-rate-scaling', default='linear', type=str,
choices=['sqrt', 'linear'],
help='learing rate scaling (default: linear)')
# others
parser.add_argument('--wandb', default=0, type=int,
help='if log to wandb')
parser.add_argument('--accloss', default=1, type=int,
help='accumulate loss')
parser.add_argument('--acclosstype', default='max', type=str)
def main():
args = parser.parse_args()
if args.wandb == 1:
wandb.init(project="csce689", config=args)
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.')
ngpus_per_node = torch.cuda.device_count()
main_worker(args.gpu, ngpus_per_node, args)
def main_worker(gpu, ngpus_per_node, args):
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
else:
args.gpu = 0
print("Use GPU: {} for training".format(args.gpu))
# data sizes
if args.data_name == 'cifar10' or args.data_name == 'cifar100' :
data_size = 50000
else:
data_size = 1000000
print ('pretraining on %s'%args.data_name)
# create model
print("=> creating model '{}'".format(args.arch))
model = sogclr.builder.SimCLR_ResNet(
partial(torchvision_models.__dict__[args.arch], zero_init_residual=True),
args.dim, args.mlp_dim, args.t, cifar_head=('cifar' in args.data_name), loss_type=args.loss_type, N=data_size, num_proj_layers=args.num_proj_layers)
# infer learning rate before changing batch size
if args.learning_rate_scaling == 'linear':
# linear scaling
args.lr = args.lr * args.batch_size / 256
else:
# sqrt scaling
args.lr = args.lr * math.sqrt(args.batch_size)
print ('initial learning rate:', args.lr)
if not torch.cuda.is_available():
print('using CPU, this will be slow')
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
# optimizers
if args.optimizer == 'lars':
optimizer = sogclr.optimizer.LARS(model.parameters(), args.lr,
weight_decay=args.weight_decay,
momentum=args.momentum)
elif args.optimizer == 'adamw':
optimizer = torch.optim.AdamW(model.parameters(), args.lr,
weight_decay=args.weight_decay)
elif args.optimizer == 'adafactor':
optimizer = torch_optimizer.Adafactor(model.parameters(), args.lr,
weight_decay=args.weight_decay)
scaler = torch.cuda.amp.GradScaler()
# log_dir
save_root_path = args.save_dir
global_batch_size = args.batch_size
method_name = {'dcl': 'sogclr', 'cl': 'simclr'}[args.loss_type]
logdir = '20221013_%s_%s_%s-%s-%s_bz_%s_E%s_WR%s_lr_%.3f_%s_wd_%s_t_%s_g_%s_%s'%(args.data_name, args.arch, method_name, args.dim, args.mlp_dim, global_batch_size, args.epochs, args.warmup_epochs, args.lr, args.learning_rate_scaling, args.weight_decay, args.t, args.gamma, args.optimizer )
summary_writer = SummaryWriter(log_dir=os.path.join(save_root_path, logdir))
print (logdir)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint = torch.load(args.resume)
else:
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scaler.load_state_dict(checkpoint['scaler'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
mean = {'cifar10': [0.4914, 0.4822, 0.4465],
'cifar100': [0.4914, 0.4822, 0.4465]
}[args.data_name]
std = {'cifar10': [0.2470, 0.2435, 0.2616],
'cifar100': [0.2470, 0.2435, 0.2616]
}[args.data_name]
image_size = {'cifar10':32, 'cifar100':32}[args.data_name]
normalize = transforms.Normalize(mean=mean, std=std)
# simclr augmentations
augmentation1 = [
transforms.RandomGrayscale(p=0.2),
transforms.RandomResizedCrop(image_size, scale=(args.crop_min, 1.)),
transforms.RandomApply([
transforms.ColorJitter(0.4, 0.4, 0.2, 0.1), # not strengthened
# transforms.RandomRotation([-8, 8]),
# transforms.ElasticTransform(alpha=250.0),
# transforms.RandomPerspective(distortion_scale = 0.6, p=1.0),
], p=0.8),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
]
if args.data_name == 'cifar10':
DATA_ROOT = args.data
train_dataset = sogclr.cifar.CIFAR10(root=DATA_ROOT, train=True, download=True,
transform=sogclr.loader.TwoCropsTransform(transforms.Compose(augmentation1),
transforms.Compose(augmentation1)))
elif args.data_name == 'cifar100':
DATA_ROOT = args.data
train_dataset = sogclr.cifar.CIFAR100(root=DATA_ROOT, train=True, download=True,
transform=sogclr.loader.TwoCropsTransform(transforms.Compose(augmentation1),
transforms.Compose(augmentation1)))
else:
raise ValueError
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=False, sampler=train_sampler, drop_last=True)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train(train_loader, model, optimizer, scaler, summary_writer, epoch, args)
if epoch % 10 == 0 or args.epochs - epoch < 3:
save_checkpoint({
'epoch': epoch 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer' : optimizer.state_dict(),
'scaler': scaler.state_dict(),
}, is_best=False, filename=os.path.join(save_root_path, logdir, 'checkpoint_d.pth.tar' % epoch) )
summary_writer.close()
def train(train_loader, model, optimizer, scaler, summary_writer, epoch, args):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
learning_rates = AverageMeter('LR', ':.4e')
losses = AverageMeter('Loss', ':.4e')
progress = ProgressMeter(
len(train_loader),
[batch_time, data_time, learning_rates, losses],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
iters_per_epoch = len(train_loader)
loss_list = []
for i, (images, _, index) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
# adjust learning rate and momentum coefficient per iteration
lr = adjust_learning_rate(optimizer, epoch i / iters_per_epoch, args)
learning_rates.update(lr)
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
with torch.cuda.amp.autocast(True):
loss = model(images[0], images[1], index, args.gamma, i, epoch)
losses.update(loss.item(), images[0].size(0))
summary_writer.add_scalar("loss", loss.item(), epoch * iters_per_epoch i)
# loss *= 1./ args.accloss
if args.acclosstype != "None":
loss_list.append(loss)
# compute gradient and do SGD step
if i % args.accloss == 0:
if args.acclosstype == "max":
loss = max(loss_list)
elif args.acclosstype == "min":
loss = min(loss_list)
else:
pass
optimizer.zero_grad()
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
loss_list = []
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0 and loss.item() <= 1:
progress.display(i)
wandb.log({
"batch_time_avg": batch_time.avg,
"data_time_avg": data_time.avg,
"loss": loss.item(),
"learning_rate": lr,
"epoch": epoch,
})
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):
"""Decays the learning rate with half-cycle cosine after warmup"""
if epoch < args.warmup_epochs:
lr = args.lr * epoch / args.warmup_epochs
else:
lr = args.lr * 0.5 * (1. math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
if __name__ == '__main__':
main()