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multi_gpu_rgbtrain.py
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multi_gpu_rgbtrain.py
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import argparse
import os
import random
import sys
import time
import torch
from torch.utils.data import DataLoader
from datasets.provider.randomTnsData import RandomTnsData, RandomTnsPair
from evluate.lossfunc import NTGLoss
from model.cnn_registration_model import CNNRegistration
from tnf_transform.img_process import NormalizeImageDict
from tnf_transform.transformation import AffineGridGen
from util import utils, torch_util
from util.torch_util import save_checkpoint, init_seeds
from util.train_test_fn import train, test
from util.utils import is_main_process
from visualization.train_visual import VisdomHelper
try: # Mixed precision training https://github.com/NVIDIA/apex
from apex import amp
except:
mixed_precision = False # not installed
# 加载已经保存的模型
def load_checkpoint(model_without_ddp,optimizer,lr_scheduler,checkpoint_path,local_rank):
if os.path.exists(checkpoint_path):
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage.cuda(local_rank))
model_without_ddp.load_state_dict(checkpoint['state_dict'])
minium_loss = checkpoint['minium_loss']
model_loss = checkpoint['model_loss']
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
epoch = checkpoint['epoch']
print(epoch, "minium loss:", minium_loss, "model loss:", model_loss)
else:
print('checkpoint file not found')
minium_loss = sys.maxsize
epoch = 0
return minium_loss,epoch
def main(args):
# checkpoint_path = "/home/zale/project/registration_cnn_ntg/trained_weight/voc2011_multi_gpu/checkpoint_voc2011_multi_gpu_paper_NTG_resnet101.pth.tar"
# checkpoint_path = "/home/zale/project/registration_cnn_ntg/trained_weight/coco2017_multi_gpu/checkpoint_coco2017_multi_gpu_paper30_NTG_resnet101.pth.tar"
#args.training_image_path = '/home/zale/datasets/vocdata/VOC_train_2011/VOCdevkit/VOC2011/JPEGImages'
# args.training_image_path = '/media/disk2/zale/datasets/coco2017/train2017'
checkpoint_path = "/home/zlk/project/registration_cnn_ntg/trained_weight/voc2011_multi_gpu/checkpoint_voc2011_multi_gpu_three_channel_paper_origin_NTG_resnet101.pth.tar"
args.training_image_path = '/home/zlk/datasets/vocdata/VOC_train_2011/VOCdevkit/VOC2011/JPEGImages'
random_seed = 10021
init_seeds(random_seed + random.randint(0, 10000))
mixed_precision = True
utils.init_distributed_mode(args)
print(args)
#device,local_rank = torch_util.select_device(multi_process =True,apex=mixed_precision)
device = torch.device(args.device)
use_cuda =True
# Data loading code
print("Loading data")
RandomTnsDataset = RandomTnsData(args.training_image_path, cache_images=False, paper_affine_generator=True,
transform=NormalizeImageDict(["image"]))
# train_dataloader = DataLoader(RandomTnsDataset, batch_size=args.batch_size, shuffle=True, num_workers=4,
# pin_memory=True)
print("Creating data loaders")
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(RandomTnsDataset)
# test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test)
else:
train_sampler = torch.utils.data.RandomSampler(RandomTnsDataset)
# test_sampler = torch.utils.data.SequentialSampler(dataset_test)
# train_batch_sampler = torch.utils.data.BatchSampler(
# train_sampler, args.batch_size, drop_last=True)
data_loader = DataLoader(RandomTnsDataset,sampler =train_sampler, num_workers=4,
shuffle=(train_sampler is None),pin_memory=False,batch_size=args.batch_size)
# data_loader_test = torch.utils.data.DataLoader(
# dataset_test, batch_size=1,
# sampler=test_sampler, num_workers=args.workers,
# collate_fn=utils.collate_fn)
print("Creating model")
model = CNNRegistration(use_cuda=use_cuda,single_channel=False)
model.to(device)
# 优化器 和scheduler
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adam(params, lr=args.lr)
# 学习率小于1e-6 ntg损失下降很慢,所以最小设置为1e-6
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
T_max=args.lr_max_iter,
eta_min=1e-6)
# if mixed_precision:
# model,optimizer = amp.initialize(model,optimizer,opt_level='O1',verbosity=0)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
model_without_ddp = model.module
minium_loss, saved_epoch = load_checkpoint(model_without_ddp,
optimizer, lr_scheduler,checkpoint_path, args.rank)
vis_env = "multi_gpu_rgb_train_paper_30"
loss = NTGLoss()
pair_generator = RandomTnsPair(use_cuda=use_cuda)
gridGen = AffineGridGen()
vis = VisdomHelper(env_name=vis_env)
print('Starting training...')
start_time = time.time()
draw_test_loss = False
log_interval = 20
for epoch in range(saved_epoch, args.num_epochs):
start_time = time.time()
if args.distributed:
train_sampler.set_epoch(epoch)
train_loss = train(epoch, model, loss, optimizer, data_loader, pair_generator, gridGen, vis,
use_cuda=use_cuda, log_interval=log_interval,lr_scheduler = lr_scheduler,rank=args.rank)
if draw_test_loss:
#test_loss = test(model,loss,test_dataloader,pair_generator,gridGen,use_cuda=use_cuda)
#vis.drawBothLoss(epoch,train_loss,test_loss,'loss_table')
pass
else:
vis.drawLoss(epoch,train_loss)
end_time = time.time()
print("epoch:", str(end_time - start_time),'秒')
is_best = train_loss < minium_loss
minium_loss = min(train_loss, minium_loss)
state_dict = model_without_ddp.state_dict()
if is_main_process():
save_checkpoint({
'epoch': epoch + 1,
'args': args,
# 'state_dict': model.state_dict(),
'state_dict': state_dict,
'minium_loss': minium_loss,
'model_loss': train_loss,
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
}, is_best, checkpoint_path)
if __name__ == '__main__':
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,3'
parser = argparse.ArgumentParser(description='Multispectral Image Registration PyTorch implementation')
# Paths
parser.add_argument('--training-dataset', type=str, default='pascal', help='dataset to use for training')
# parser.add_argument('--training-image-path', type=str, default='/home/zlk/datasets/vocdata/VOCdevkit/VOC2012/JPEGImages', help='path to folder containing training images')
parser.add_argument('--training-image-path', type=str, default='/home/zlk/datasets/coco2014/train2014',
help='path to folder containing training images')
parser.add_argument('--trained-models-path', type=str, default='training_models',
help='path to trained models ')
parser.add_argument('--trained-models-fn', type=str, default='checkpoint_adam', help='trained model filename')
# Optimization parameters
parser.add_argument('--lr', type=float, default=0.0001, help='learning rate')
parser.add_argument('--lr_max_iter', type=int, default=10000,
help='Number of steps between lr starting value and 1e-6 '
'(lr default min) when choosing lr_scheduler')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-7, type=float,
metavar='W', help='weight decay (default: 1e-4)',
dest='weight_decay')
parser.add_argument('--num-epochs', type=int, default=10000, help='number of training epochs')
parser.add_argument('--batch-size', type=int, default=164, help='training batch size')
parser.add_argument('--seed', type=int, default=1, help='Pseudo-RNG seed')
parser.add_argument('--local_rank', type=int, default=0, help='local rank')
# Model parameters
parser.add_argument('--geometric-model', type=str, default='affine',
help='geometric model to be regressed at output: affine or tps')
parser.add_argument('--feature-extraction-cnn', type=str, default='resnet101',
help='Feature extraction architecture: vgg/resnet101')
parser.add_argument('--device', default='cuda', help='device')
# Synthetic dataset parameters
parser.add_argument('--random-sample', type=bool, nargs='?', const=True, default=True,
help='sample random transformations')
# distributed training parameters
parser.add_argument('--world-size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--dist-url', default='env://', help='url used to set up distributed training')
args = parser.parse_args()
main(args)