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pretrain.py
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pretrain.py
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import os
import time
import logging
import warnings
import numpy
import torch
import torch.nn as nn
import torch.multiprocessing as mp
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.distributed as dist
from models.MAT import netrunc
from datasets.dataset import DeepfakeDataset
from AGDA import AGDA
import cv2
from utils import dist_average,ACC
#from torch.utils.tensorboard import SummaryWriter
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
# GPU settings
assert torch.cuda.is_available()
#torch.autograd.set_detect_anomaly(True)
def load_state(net,ckpt):
sd=net.state_dict()
nd={}
goodmatch=True
for i in ckpt:
if i in sd and sd[i].shape==ckpt[i].shape:
nd[i]=ckpt[i]
#print(i)
else:
print('fail to load %s'%i)
goodmatch=False
net.load_state_dict(nd,strict=False)
return goodmatch
def main_worker(local_rank,world_size,rank_offset,config):
rank=local_rank+rank_offset
if rank==0:
logging.basicConfig(
filename=os.path.join('runs', config.name,'train.log'),
filemode='w',
format='%(asctime)s: %(levelname)s: [%(filename)s:%(lineno)d]: %(message)s',
level=logging.INFO)
warnings.filterwarnings("ignore")
dist.init_process_group(backend='nccl', init_method='file://%s/.pytorch_distribute'%os.getcwd(),world_size=world_size, rank=rank)
if rank==0:
try:
os.remove('.pytorch_distribute')
except:
pass
numpy.random.seed(1234567)
torch.manual_seed(1234567)
torch.cuda.manual_seed(1234567)
torch.cuda.set_device(local_rank)
train_dataset = DeepfakeDataset(phase='train',**config.train_dataset)
validate_dataset=DeepfakeDataset(phase='val',**config.val_dataset)
train_sampler=torch.utils.data.distributed.DistributedSampler(train_dataset)
validate_sampler=torch.utils.data.distributed.DistributedSampler(validate_dataset)
train_loader=torch.utils.data.DataLoader(train_dataset, batch_size=config.batch_size,sampler=train_sampler,pin_memory=True,num_workers=config.workers)
validate_loader=torch.utils.data.DataLoader(validate_dataset, batch_size=config.batch_size,sampler=validate_sampler,pin_memory=True,num_workers=config.workers)
logs = {}
start_epoch = 0
net = netrunc(config.net,config.feature_layer,config.num_classes,config.dropout_rate,config.pretrained)
net=nn.SyncBatchNorm.convert_sync_batchnorm(net).to(local_rank)
net = nn.parallel.DistributedDataParallel(net,device_ids=[local_rank],output_device=local_rank,find_unused_parameters=True)
optimizer = torch.optim.AdamW(net.parameters(), lr=config.learning_rate, betas=config.adam_betas, weight_decay=config.weight_decay)
scheduler=torch.optim.lr_scheduler.StepLR(optimizer, step_size=config.scheduler_step, gamma=config.scheduler_gamma)
if config.ckpt:
loc = 'cuda:{}'.format(local_rank)
checkpoint = torch.load(config.ckpt, map_location=loc)
logs = checkpoint['logs']
start_epoch = int(logs['epoch'])+1
if load_state(net.module,checkpoint['state_dict']) and config.resume_optim:
optimizer.load_state_dict(checkpoint['optimizer_state'])
try:
scheduler.load_state_dict(checkpoint['scheduler_state'])
except:
pass
for epoch in range(start_epoch, config.epochs):
logs['epoch'] = epoch
train_sampler.set_epoch(epoch)
train_sampler.dataset.next_epoch()
run(logs=logs,data_loader=train_loader,net=net,optimizer=optimizer,local_rank=local_rank,config=config,phase='train')
run(logs=logs,data_loader=validate_loader,net=net,optimizer=optimizer,local_rank=local_rank,config=config,phase='valid')
scheduler.step()
if local_rank==0:
torch.save({
'logs': logs,
'state_dict': net.module.state_dict(),
'optimizer_state': optimizer.state_dict(),
'scheduler_state':scheduler.state_dict()}, 'checkpoints/'+config.name+'/ckpt_%s.pth'%epoch)
dist.barrier()
def run(logs,data_loader,net,optimizer,local_rank,config,phase='train'):
if local_rank==0:
print('start ',phase)
recorder={}
record_list=['loss','acc']
for i in record_list:
recorder[i]=dist_average(local_rank)
# begin training
start_time = time.time()
if phase=='train':
net.train()
else: net.eval()
for i, (X, y) in enumerate(data_loader):
loss_pack={}
X = X.to(local_rank,non_blocking=True)
y = y.to(local_rank,non_blocking=True)
with torch.set_grad_enabled(phase=='train'):
logit=net(X)
batch_loss = F.cross_entropy(logit,y)
if phase=='train':
batch_loss.backward()
optimizer.step()
optimizer.zero_grad()
with torch.no_grad():
loss_pack['loss']=batch_loss
loss_pack['acc']=ACC(logit,y)
for i in record_list:
recorder[i].step(loss_pack[i])
# end of this epoch
batch_info=[]
for i in record_list:
mesg=recorder[i].get()
logs[i]=mesg
batch_info.append('{}:{:.4f}'.format(i,mesg))
end_time = time.time()
# write log for this epoch
if local_rank==0:
logging.info('{}: {}, Time {:3.2f}'.format(phase,' '.join(batch_info), end_time - start_time))
def distributed_train(config,world_size=0,num_gpus=0,rank_offset=0):
if not num_gpus:
num_gpus = torch.cuda.device_count()
if not world_size:
world_size=num_gpus
mp.spawn(main_worker, nprocs=num_gpus, args=(world_size,rank_offset,config))
if __name__=="__main__":
from config import train_config
for feature in range(2,5):
feature_layer='b%s'%feature
name='EFB4_ALL_c23_trunc_%s'%feature_layer
Config=train_config(name,['ff-all-c23','efficientnet-b4'],attention_layer='',feature_layer=feature_layer,epochs=20,batch_size=10,augment='augment1')
Config.mkdirs()
distributed_train(Config)