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main_crosscl.py
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main_crosscl.py
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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 crosscl.loader
import crosscl.builder
import crosscl.transforms
import crosscl.datasets
import crosscl.models
model_names = sorted(name for name in crosscl.models.__dict__
if name.islower() and not name.startswith("__")
and callable(crosscl.models.__dict__[name]))
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR',
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=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=0.03, 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=100, type=int,
metavar='N', help='print frequency (default: 100)')
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')
# 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('--fpn-bn', action='store_true',
help='Use bn layers in fpn output')
parser.add_argument('--mst', action='store_true',
help='use multi scale training')
parser.add_argument('--img-size', default=224, type=int)
parser.add_argument('--dataset', default='imagenet', type=str)
def main():
args = parser.parse_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.')
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()
if args.multiprocessing_distributed:
# Since we have ngpus_per_node processes per node, the total world_size
# needs to be adjusted accordingly
args.world_size = ngpus_per_node * args.world_size
# Use torch.multiprocessing.spawn to launch distributed processes: the
# main_worker process function
mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
else:
# Simply call main_worker function
main_worker(args.gpu, ngpus_per_node, args)
def main_worker(gpu, ngpus_per_node, args):
args.gpu = gpu
# suppress printing if not master
if args.multiprocessing_distributed and args.gpu != 0:
def print_pass(*args):
pass
builtins.print = print_pass
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
# create model
print("=> creating model '{}'".format(args.arch))
model = crosscl.builder.CrossCL(
crosscl.models.__dict__[args.arch],
args.moco_dim, args.moco_k, args.moco_m, args.moco_t, args.mlp, fpn_bn=args.fpn_bn)
print(model)
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
# comment out the following line for debugging
raise NotImplementedError("Only DistributedDataParallel is supported.")
else:
# AllGather implementation (batch shuffle, queue update, etc.) in
# this code only supports DistributedDataParallel.
raise NotImplementedError("Only DistributedDataParallel is supported.")
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss(ignore_index=-1).cuda(args.gpu)
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))
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'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
if args.dataset == 'imagenet':
traindir = os.path.join(args.data, 'train')
else:
traindir = args.data
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
if args.aug_plus:
# MoCo v2's aug: similar to SimCLR https://arxiv.org/abs/2002.05709
base_augmentation = None
flip_augmentation = crosscl.transforms.RandomHorizontalFlip()
crop_augmentation = crosscl.transforms.RandomResizedCrop(args.img_size, scale=(0.2, 1.))
ending_augmentation = transforms.Compose([
transforms.RandomApply([crosscl.loader.GaussianBlur([.1, 2.])], p=0.5),
transforms.RandomApply([
transforms.ColorJitter(0.4, 0.4, 0.4, 0.1) # not strengthened
], p=0.8),
transforms.RandomGrayscale(p=0.2),
transforms.ToTensor(),
normalize
])
else:
raise NotImplementedError
if args.dataset == 'imagenet' or args.dataset == 'coco':
train_dataset = crosscl.datasets.ImageFolder(
traindir,
args.img_size,
transform=base_augmentation,
crop_transform=crop_augmentation,
flip_transform=flip_augmentation,
ending_transform=ending_augmentation,
dataset=args.dataset
)
else:
raise NotImplementedError
print(len(train_dataset))
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)
mst_epoch = int(0.01 * args.epochs)
for epoch in range(args.start_epoch, args.epochs):
if epoch % mst_epoch == 0 and args.mst:
img_size = torch.Tensor([-1]).cuda()
if dist.get_rank() == 0:
img_size = torch.Tensor([random.choice([224, 384, 480])]).cuda()
print('MST img_size is:', img_size)
f = open('train.log', 'a')
f.write('MST img_size is:' + str(img_size) + '\n')
f.close()
torch.distributed.broadcast(img_size, src=0)
img_size = int(img_size)
if args.aug_plus:
crop_augmentation = crosscl.transforms.RandomResizedCrop(img_size, scale=(0.2, 1.))
else:
raise NotImplementedError
train_dataset = crosscl.datasets.ImageFolder(
traindir,
img_size,
transform=base_augmentation,
crop_transform=crop_augmentation,
flip_transform=flip_augmentation,
ending_transform=ending_augmentation
)
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)
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) % 100 == 0:
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, is_best=False, filename='CrossCL_{:04d}.pth'.format(epoch + 1))
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, is_best=False, filename='CrossCL_last.pth')
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')
losses_d = AverageMeter('LossD', ':.4e')
losses_d_c3 = AverageMeter('LossD_C3', ':.4e')
losses_m = AverageMeter('LossM', ':.4e')
losses_c4_to_c3 = AverageMeter('LossC4ToC3', ':.4e')
losses_c3_to_c4 = AverageMeter('LossC3ToC4', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top1_d = AverageMeter('AccD@1', ':6.2f')
top1_d_c3 = AverageMeter('AccD_C3@1', ':6.2f')
top1_c4_to_c3 = AverageMeter('Acc_C4ToC3', ':6.2f')
top1_c3_to_c4 = AverageMeter('Acc_C3ToC4', ':6.2f')
progress = ProgressMeter(
len(train_loader),
[batch_time, data_time, losses, losses_m, losses_d, losses_d_c3, losses_c4_to_c3, losses_c3_to_c4,
top1, top1_d, top1_d_c3, top1_c4_to_c3, top1_c3_to_c4],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
for i, (images, anchors) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
anchors_q, anchors_k, anchors_labels = anchors[0].float(), anchors[1].float(), anchors[2].long()
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)
anchors_q = anchors_q.cuda(args.gpu, non_blocking=True)
anchors_k = anchors_k.cuda(args.gpu, non_blocking=True)
anchors_labels = anchors_labels.cuda(args.gpu, non_blocking=True)
anchors_labels = anchors_labels.view(-1)
# compute output
(output, target), \
output_d, output_d_c3,\
output_d_c4_to_c3, output_d_c3_to_c4 = model(im_q=images[0], im_k=images[1], ac_q=anchors_q, ac_k=anchors_k)
loss_moco = criterion(output, target)
loss_det = criterion(output_d, anchors_labels)
loss_det_c3 = criterion(output_d_c3, anchors_labels)
loss_cross_c4_to_c3 = criterion(output_d_c4_to_c3, anchors_labels)
loss_cross_c3_to_c4 = criterion(output_d_c3_to_c4, anchors_labels)
loss = loss_moco + loss_det + loss_det_c3 + loss_cross_c4_to_c3 + loss_cross_c3_to_c4
# acc1/acc5 are (K+1)-way contrast classifier accuracy
# measure accuracy and record loss
losses.update(loss.item(), images[0].size(0))
losses_m.update(loss_moco.item(), images[0].size(0))
losses_d.update(loss_det.item(), images[0].size(0))
losses_d_c3.update(loss_det_c3.item(), images[0].size(0))
losses_c4_to_c3.update(loss_cross_c4_to_c3.item(), images[0].size(0))
losses_c3_to_c4.update(loss_cross_c3_to_c4.item(), images[0].size(0))
acc1 = accuracy(output, target, topk=(1,))
top1.update(acc1[0].item(), images[0].size(0))
valid_ac_ids = torch.where(anchors_labels != -1)[0]
if valid_ac_ids.shape[0] != 0:
output_d = output_d[valid_ac_ids]
anchors_labels = anchors_labels[valid_ac_ids]
acc1 = accuracy(output_d, anchors_labels, topk=(1,))
top1_d.update(acc1[0].item(), images[0].size(0))
output_d_c3 = output_d_c3[valid_ac_ids]
acc1_c3 = accuracy(output_d_c3, anchors_labels, topk=(1,))
top1_d_c3.update(acc1_c3[0].item(), images[0].size(0))
output_d_c4_to_c3 = output_d_c4_to_c3[valid_ac_ids]
acc1_c4_to_c3 = accuracy(output_d_c4_to_c3, anchors_labels, topk=(1,))
top1_c4_to_c3.update(acc1_c4_to_c3[0].item(), images[0].size(0))
output_d_c3_to_c4 = output_d_c3_to_c4[valid_ac_ids]
acc1_c3_to_c4 = accuracy(output_d_c3_to_c4, anchors_labels, topk=(1,))
top1_c3_to_c4.update(acc1_c3_to_c4[0].item(), 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:
log = progress.display(i)
print(log)
if dist.get_rank() == 0:
f = open('train.log', 'a')
f.write(log + '\n')
f.close()
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]
return '\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].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
if __name__ == '__main__':
main()