/
vicreg.py
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/
vicreg.py
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import argparse
import json
import os
import sys
import time
from copy import deepcopy
import torch
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from src.data.transform import TwoCropsTransform
import src.data.dataset as datasets
from src.models.knn import KNNEvaluator
from src.models.misc import adjust_learning_rate, save_checkpoint
from src.torch_utils.misc import InfiniteSampler
from src.models.extractor import ProjExtractor
import src.models.backbone as models
from src.models.generator import SynDataGenerator
from src.utils.logger import set_logger
from src.utils.meters import AverageMeter, ProgressMeter
from src.torch_utils.misc import strip_ddp
from src.engine import main, setup_env, get_transform, module_to_gpu
# add dnnlib and torch_utils to PYTHONPATH
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path)
add_path("./src")
logger = set_logger(__name__, to_console=False)
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
dataset_names = sorted(name for name in datasets.__dict__
if not name.startswith("__")
and callable(datasets.__dict__[name]))
parser = argparse.ArgumentParser(description='PyTorch CIFAR-10 Training with StyleGAN2-ada')
parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument('--dataset-name', default='CIFAR10', choices=dataset_names)
parser.add_argument('--output-dir', metavar='OUTPUT_DIR', default="output",
help='path to logging output')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet18)')
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
help='number of data loading workers (default: 32)')
parser.add_argument('--epochs', default=800, 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=512, type=int,
metavar='N',
help='mini-batch size (default: 512), 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 (base) learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum of SGD solver')
parser.add_argument('--wd', '--weight-decay', default=5e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)',
dest='weight_decay')
parser.add_argument('--save-freq', default=20, type=int,
metavar='SAVE_FREQ', help='save frequency (default: 20)') # TODO: consider to change it
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='auto', 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')
# vicreg specific configs:
parser.add_argument('-i', '--input-scale', default=32, type=int, metavar='N',
help='The input scale which the representation learning'
'network is working on')
parser.add_argument('--dim', default=2048, type=int,
help='feature dimension (default: 2048)')
parser.add_argument('--num-proj-layers', default=2, type=int,
help='number of projection layer (default: 2)')
parser.add_argument('--sim_loss_weight', default=25.0, type=float)
parser.add_argument('--var_loss_weight', default=25.0, type=float)
parser.add_argument('--cov_loss_weight', default=1.0, type=float)
# StyleGAN-ada specific configs:
parser.add_argument('--syn_ratio', type=float, help='If specified, train network with real data as well', default=0.)
parser.add_argument('--gpath', type=str, help='path to stylegan-ada trained pickle')
# knn specific configs
parser.add_argument('--disable-knn', action='store_true', help='')
parser.add_argument('--knn-k', default=200, type=int, help='k in kNN monitor')
parser.add_argument('--knn-t', default=0.1, type=float, help='softmax temperature in kNN monitor')
#----------------------------------------------------------------------------
def main_worker(gpu, ngpus_per_node, args):
# setup
init_lr = setup_env(gpu, ngpus_per_node, args)
args.syn_batch_size = int(args.batch_size * args.syn_ratio)
args.real_batch_size = args.batch_size - args.syn_batch_size
args.logger.info(f"synthetic batch size: {args.syn_batch_size}, real batch size: {args.real_batch_size}")
# create synthetic data generator
syn_transform = get_transform(args.input_scale, real_image=False, cifar=args.dataset_name in ["CIFAR10", "CIFAR100"])
args.logger.info("=> creating stylegan2-ada")
if args.syn_batch_size > 0:
assert os.path.isfile(args.gpath), f"File {args.gpath} does not exist."
G = SynDataGenerator(args.gpath, regex="").eval().requires_grad_(False).cuda(args.gpu)
else:
G = None
# create real data iterator
train_dataset = datasets.__dict__[args.dataset_name](
args.data, 'train', TwoCropsTransform(
get_transform(args.input_scale,
cifar=args.dataset_name in ["CIFAR10", "CIFAR100"])
)
)
train_sampler = InfiniteSampler(train_dataset, rank=args.rank, num_replicas=args.world_size)
if args.real_batch_size > 0:
train_iterator = iter(torch.utils.data.DataLoader(
train_dataset, batch_size=args.real_batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True, sampler=train_sampler, drop_last=False,
))
else:
train_iterator = None
iters_per_epoch = len(train_dataset) // (args.batch_size * ngpus_per_node)
# create model
args.logger.info("=> creating model '{}'".format(args.arch))
model = ProjExtractor(
models.__dict__[args.arch], args.dim,
num_proj_layers=args.num_proj_layers,
)
# wrap ddp
if args.distributed:
# Apply SyncBN
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
# 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:
model.cuda(args.gpu)
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)
else:
model.cuda(args.gpu)
args.logger.info(f"Model: \n{str(model)}\n") # print model
args.logger.info(f"Generator: \n{str(G)}\n") # print G
# define loss function (criterion) and optimizer
optimizer = torch.optim.SGD(
model.parameters(), init_lr,
momentum=args.momentum, weight_decay=args.weight_decay
)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
args.logger.info("=> 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.
checkpoint = torch.load(args.resume, map_location=f"cuda:{args.gpu}")
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
args.logger.info("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
args.logger.info("=> no checkpoint found at '{}'".format(args.resume))
# knn evaluator
if not args.disable_knn:
knn_evaluator = KNNEvaluator(args.dataset_name, args.data, args.input_scale, args.workers, args.knn_k, args.knn_t)
for epoch in range(args.start_epoch, args.epochs):
model.train().requires_grad_(True)
adjust_learning_rate(optimizer, init_lr, epoch, args.epochs)
# train for one epoch
train(
G, model, optimizer, syn_transform, epoch,
iters_per_epoch, args, train_iterator
)
if ((not args.multiprocessing_distributed) or (args.multiprocessing_distributed
and args.rank % ngpus_per_node == 0)) and (epoch + 1) % args.save_freq == 0:
# evaluate
model.eval()
with torch.no_grad():
backbone = deepcopy(strip_ddp(model).backbone).eval().requires_grad_(False)
knn_acc = knn_evaluator.evaluate(backbone)
del backbone
args.logger.info("\t".join([f"Epoch: [{epoch:d}]", f"KNN Acc: {knn_acc:6.3f}%"]))
args.tb_writer.add_scalar('Val/KNN_Acc', knn_acc, iters_per_epoch * (epoch + 1))
# record
jsonl_line = json.dumps(dict(knn_acc=knn_acc, epoch=epoch, timestamp=time.time()))
with open(os.path.join(args.run_dir, f'metric-knn-acc.jsonl'), 'at') as f:
f.write(jsonl_line + '\n')
# save
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer' : optimizer.state_dict(),
}, is_best=False,
filename=os.path.join(args.run_dir, 'checkpoint_epoch{:04d}.pth.tar'.format(epoch))
)
#----------------------------------------------------------------------------
def train(
G,
model,
optimizer,
transform,
epoch,
iters_per_epoch,
args,
real_data_iterator,
):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4f')
losses_sim = AverageMeter('Loss_sim', ':.4f')
losses_var = AverageMeter('Loss_var', ':.4f')
losses_cov = AverageMeter('Loss_cov', ':.4f')
progress = ProgressMeter(
iters_per_epoch,
[batch_time, data_time, losses],
prefix="Epoch: [{}]".format(epoch)
)
# generator always fixed
if G is not None:
G.eval().requires_grad_(False)
# switch to train mode
model.train()
two_crops_transform = TwoCropsTransform(transform)
end = time.time()
for i in range(iters_per_epoch):
# measure data loading time
data_time.update(time.time() - end)
# synthesize images
images = [[], []]
if args.syn_batch_size > 0:
assert G is not None
z = torch.randn([args.syn_batch_size, G.z_dim]).cuda(args.gpu, non_blocking=True) # latent codes
syn_images, _ = G(z, None) # NCHW, float32, dynamic range [-1, +1]
syn_images = syn_images.clamp(min=-1., max=1.)
syn_images = two_crops_transform(syn_images)
syn_images = [x.contiguous() for x in syn_images]
images[0].append(syn_images[0])
images[1].append(syn_images[1])
# load real images
if args.real_batch_size > 0:
assert real_data_iterator is not None
real_images, _ = next(real_data_iterator)
images[0].append(real_images[0].cuda(args.gpu, non_blocking=True))
images[1].append(real_images[1].cuda(args.gpu, non_blocking=True))
# cat two images
images[0] = torch.cat(images[0], dim=0)
images[1] = torch.cat(images[1], dim=0)
# compute output and loss
z1 = model(images[0])
z2 = model(images[1])
# compute loss
sim_loss = F.mse_loss(z1, z2)
var_loss_1 = variance_loss(z1)
var_loss_2 = variance_loss(z2)
cov_loss_1 = covariance_loss(z1)
cov_loss_2 = covariance_loss(z2)
#
loss = args.sim_loss_weight * sim_loss + \
args.var_loss_weight * (var_loss_1 + var_loss_2) + \
args.cov_loss_weight * (cov_loss_1 + cov_loss_2)
losses.update(loss.item(), args.batch_size)
losses_sim.update(sim_loss.item(), args.batch_size)
losses_var.update((var_loss_1 + var_loss_2).item(), args.batch_size)
losses_cov.update((cov_loss_1 + cov_loss_2).item(), args.batch_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:
args.logger.info(progress.display(i))
args.tb_writer.add_scalar('Train/Loss', loss.item(), iters_per_epoch * epoch + i)
args.tb_writer.add_scalar('Train/Loss/invariance', sim_loss.item(), iters_per_epoch * epoch + i)
args.tb_writer.add_scalar('Train/Loss/variance_1', var_loss_1.item(), iters_per_epoch * epoch + i)
args.tb_writer.add_scalar('Train/Loss/variance_2', var_loss_2.item(), iters_per_epoch * epoch + i)
args.tb_writer.add_scalar('Train/Loss/covariance_1', cov_loss_1.item(), iters_per_epoch * epoch + i)
args.tb_writer.add_scalar('Train/Loss/covariance_2', cov_loss_2.item(), iters_per_epoch * epoch + i)
#----------------------------------------------------------------------------
def variance_loss(z: torch.Tensor) -> torch.Tensor:
"""Computes variance loss given batch of projected features z.
Args:
z (torch.Tensor): NxD Tensor containing projected features from view 1.
Returns:
torch.Tensor: variance regularization loss.
"""
eps = 1e-4
std_z = torch.sqrt(z.var(dim=0) + eps)
std_loss = torch.mean(F.relu(1 - std_z))
return std_loss
#----------------------------------------------------------------------------
def covariance_loss(z: torch.Tensor) -> torch.Tensor:
"""Computes covariance loss given batch of projected features z.
Args:
z1 (torch.Tensor): NxD Tensor containing projected features from view 1.
Returns:
torch.Tensor: covariance regularization loss.
"""
N, D = z.size()
z = z - z.mean(dim=0)
cov_z = (z.T @ z) / (N - 1)
diag = torch.eye(D, device=z.device)
cov_loss = cov_z[~diag.bool()].pow_(2).sum() / D
return cov_loss
#----------------------------------------------------------------------------
if __name__ == '__main__':
args = parser.parse_args()
main(args, main_worker)
#----------------------------------------------------------------------------