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import gc
import torch
import torchvision
from torch import nn
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torch.optim import Adam, SGD
from Diffusion.diffuser import DeformDDPM
from Diffusion.networks import get_net, STN
from torchvision.transforms import Lambda
import Diffusion.losses as losses
import random
import glob
import numpy as np
import utils
from Dataloader.dataloader0 import get_dataloader
from Dataloader.dataLoader import *
from Dataloader.dataloader_utils import thresh_img
import yaml
import argparse
####################
import torch.multiprocessing as mp
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as dist
# from torch.distributed import init_process_group
###############
def ddp_setup(rank, world_size):
"""
Args:
rank: Unique identifier of each process
world_size: Total number of processes
"""
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "12355"
dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
torch.cuda.set_device(rank)
use_distributed = True
# use_distributed = False
EPS = 1e-5
parser = argparse.ArgumentParser()
# config_file_path = 'Config/config_cmr.yaml'
parser.add_argument(
"--config",
"-C",
help="Path for the config file",
type=str,
# default="Config/config_cmr.yaml",
# default="Config/config_lct.yaml",
default="Config/config_all.yaml",
required=False,
)
args = parser.parse_args()
#=======================================================================================================================
def main_train(rank=0,world_size=1):
if use_distributed:
ddp_setup(rank,world_size)
gpu_id = rank
# Load the YAML file into a dictionary
with open(args.config, 'r') as file:
hyp_parameters = yaml.safe_load(file)
print(hyp_parameters)
# epoch_per_save=10
epoch_per_save=hyp_parameters['epoch_per_save']
data_name=hyp_parameters['data_name']
net_name = hyp_parameters['net_name']
Net=get_net(net_name)
suffix_pth=f'_{data_name}_{net_name}.pth'
model_save_path = os.path.join('Models',f'{data_name}_{net_name}/')
model_dir=model_save_path
transformer=utils.get_transformer(img_sz=hyp_parameters["ndims"]*[hyp_parameters['img_size']])
# Data_Loader=get_dataloader(data_name=hyp_parameters['data_name'], mode='train')
# tsfm = torchvision.transforms.Compose([
# torchvision.transforms.ToTensor(),
# ])
# dataset = Data_Loader(target_res = [hyp_parameters["img_size"]]*hyp_parameters["ndims"], transforms=None, noise_scale=hyp_parameters['noise_scale'])
# train_loader = DataLoader(
# dataset,
# batch_size=hyp_parameters['batchsize'],
# # shuffle=False,
# shuffle=True,
# drop_last=True,
# )
dataset = OminiDataset_v1(transform=None)
train_loader = DataLoader(
dataset,
batch_size=hyp_parameters['batchsize'],
shuffle=True,
drop_last=True,
)
Deformddpm = DeformDDPM(
network=Net(
n_steps=hyp_parameters["timesteps"],
ndims=hyp_parameters["ndims"],
num_input_chn = hyp_parameters["num_input_chn"],
res = hyp_parameters['img_size']
),
n_steps=hyp_parameters["timesteps"],
image_chw=[1] + [hyp_parameters["img_size"]]*hyp_parameters["ndims"],
device=hyp_parameters["device"],
batch_size=hyp_parameters["batchsize"],
img_pad_mode=hyp_parameters["img_pad_mode"],
v_scale=hyp_parameters["v_scale"],
)
ddf_stn = STN(
img_sz=hyp_parameters["img_size"],
ndims=hyp_parameters["ndims"],
# padding_mode="zeros",
padding_mode=hyp_parameters["padding_mode"],
device=hyp_parameters["device"],
)
if use_distributed:
Deformddpm.to(rank)
Deformddpm = DDP(Deformddpm, device_ids=[rank])
ddf_stn.to(rank)
else:
Deformddpm.to(hyp_parameters["device"])
ddf_stn.to(hyp_parameters["device"])
# ddf_stn = DDP(ddf_stn, device_ids=[rank])
# mse = nn.MSELoss()
loss_reg = losses.Grad(penalty=['l1', 'negdetj'], ndims=hyp_parameters["ndims"])
loss_dist = losses.MRSE(img_sz=hyp_parameters["img_size"])
# loss_ang = losses.MRSE(img_sz=hyp_parameters["img_size"])
loss_ang = losses.NCC(img_sz=hyp_parameters["img_size"])
optimizer = Adam(Deformddpm.parameters(), lr=hyp_parameters["lr"])
# hyp_parameters["lr"]=0.00000001
# # optimizer = SGD(Deformddpm.parameters(), lr=hyp_parameters["lr"], momentum=0.95)
# optimizer = SGD(Deformddpm.parameters(), lr=hyp_parameters["lr"], momentum=0.9)
# # LR scheduler ----- YHM
# scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, hyp_parameters["lr"], hyp_parameters["lr"]*10, step_size_up=500, step_size_down=500, mode='triangular', gamma=1.0, scale_fn=None, scale_mode='cycle', cycle_momentum=True, base_momentum=0.8, max_momentum=0.9, last_epoch=-1)
# Deformddpm.network.load_state_dict(torch.load('/home/data/jzheng/Adaptive_Motion_Generator-master/models/1000.pth'))
# check for existing models
if not os.path.exists(model_dir):
os.makedirs(model_dir, exist_ok=True)
model_files = glob.glob(os.path.join(model_dir, "*.pth"))
model_files.sort()
if model_files:
if gpu_id == 0:
print(model_files)
initial_epoch, Deformddpm, optimizer = ddp_load_dict(gpu_id, Deformddpm, optimizer, model_files[-1])
else:
initial_epoch = 0
if gpu_id == 0:
print('len_train_data: ',len(dataset))
for epoch in range(initial_epoch,hyp_parameters["epoch"]):
epoch_loss_tot = 0.0
epoch_loss_gen_d = 0.0
epoch_loss_gen_a = 0.0
epoch_loss_reg = 0.0
# Set model inside to train model
Deformddpm.train()
for step, batch in enumerate(train_loader):
# for step, batch in enumerate(train_loader_omni):
# x0, _ = batch
x0 = batch # for omni dataset
x0 = x0.to(hyp_parameters["device"]).type(torch.float32)
n = x0.size()[0] # batch_size -> n
x0 = x0.to(hyp_parameters["device"])
blind_mask = utils.get_random_deformed_mask(x0.shape[2:],apply_possibility=0.6).to(hyp_parameters["device"])
# random deformation + rotation
if hyp_parameters["ndims"]>2:
if np.random.uniform(0,1)<0.6:
x0 = utils.random_resample(x0, deform_scale=0)
x0 = transformer(x0)
if hyp_parameters['noise_scale']>0:
x0 = thresh_img(x0, [0, 2*hyp_parameters['noise_scale']])
x0 = x0 * (np.random.normal(1, hyp_parameters['noise_scale'] * 1)) + np.random.normal(0, hyp_parameters['noise_scale'] * 1)
# Picking some noise for each of the images in the batch, a timestep and the respective alpha_bars
t = torch.randint(0, hyp_parameters["timesteps"], (n,)).to(
hyp_parameters["device"]
) # pick up a seq of rand number from 0 to 'timestep'
pre_dvf_I,dvf_I = Deformddpm(img_org=x0, t=t, mask=blind_mask) # forward diffusion process
loss_tot=0
loss_ddf = loss_reg(pre_dvf_I)
trm_pred = ddf_stn(pre_dvf_I, dvf_I)
loss_gen_d = loss_dist(pred=trm_pred,inv_lab=dvf_I,ddf_stn=None,mask=blind_mask)
loss_gen_a = loss_ang(pred=trm_pred,inv_lab=dvf_I,ddf_stn=None,mask=blind_mask)
loss_tot += 1. * loss_gen_d + 1. * loss_gen_a
loss_tot += 1.0 * loss_ddf
optimizer.zero_grad()
loss_tot.backward()
optimizer.step()
epoch_loss_tot += loss_tot.item() * len(x0) / len(train_loader.dataset)
epoch_loss_gen_d += loss_gen_d.item() * len(x0) / len(train_loader.dataset)
epoch_loss_gen_a += loss_gen_a.item() * len(x0) / len(train_loader.dataset)
epoch_loss_reg += loss_ddf.item() * len(x0) / len(train_loader.dataset)
# print('step:',step,':', loss_tot.item(),'=',loss_gen_a.item(),'+', loss_gen_d.item(),'+',loss_ddf.item())
# break # FOR TESTING
if gpu_id == 0:
print(epoch,':', epoch_loss_tot,'=',epoch_loss_gen_a,'+', epoch_loss_gen_d,'+',epoch_loss_reg, ' (ang+dist+regul)')
# # LR schedular step ----- YHM
# scheduler.step()
if 0 == epoch % epoch_per_save:
save_dir=model_save_path + str(epoch).rjust(6, '0') + suffix_pth
os.makedirs(os.path.dirname(model_save_path), exist_ok=True)
# break # FOR TESTING
if not use_distributed:
print(f"saved in {save_dir}")
# torch.save(Deformddpm.state_dict(), save_dir)
torch.save({
'model_state_dict': Deformddpm.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'epoch': epoch
}, save_dir)
elif gpu_id == 0:
print(f"saved in {save_dir}")
# torch.save(Deformddpm.module.state_dict(), save_dir)
torch.save({
'model_state_dict': Deformddpm.module.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'epoch': epoch
}, save_dir)
def ddp_load_dict(gpu_id, Deformddpm, optimizer, model_file,use_distributed=True):
if gpu_id == 0:
# if 0:
utils.print_memory_usage("Before Loading Model")
if 1:
gc.collect()
torch.cuda.empty_cache()
# Deformddpm.network.load_state_dict(torch.load(latest_model_file))
# Deformddpm.load_state_dict(torch.load(latest_model_file), strict=False)
checkpoint = torch.load(model_file)
# checkpoint = torch.load(latest_model_file, map_location=f"cuda:{rank}")
if use_distributed:
Deformddpm.module.load_state_dict(checkpoint['model_state_dict'])
else:
Deformddpm.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
utils.print_memory_usage("After Loading Checkpoint on GPU")
if use_distributed:
# Broadcast model weights from rank 0 to all other GPUs
dist.barrier()
for param in Deformddpm.parameters():
dist.broadcast(param.data, src=0) # Synchronize model across ranks
dist.barrier()
for param_group in optimizer.param_groups:
for param in param_group['params']:
if param.grad is not None:
dist.broadcast(param.grad, src=0) # Sync optimizer gradients
# initial_epoch = checkpoint['epoch'] + 1
# get the epoch number from the filename and add 1 to set as initial_epoch
initial_epoch = int(os.path.basename(model_file).split('.')[0][:6]) + 1
return initial_epoch, Deformddpm, optimizer
if __name__ == "__main__":
if use_distributed:
world_size = torch.cuda.device_count()
print(f"Distributed GPU number = {world_size}")
mp.spawn(main_train,args = (world_size,),nprocs = world_size)
else:
main_train(0,1) |