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Trainer.py
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import torch
import tqdm
from torch.optim.lr_scheduler import LambdaLR
from torchvision.utils import make_grid
from core.infra import Infra
from core.logger import LogTracker
import copy
import wandb
from mri_utils import FFT_Wrapper, FFT_NN_Wrapper
class EMA():
def __init__(self, beta=0.9999):
super().__init__()
self.beta = beta
def update_model_average(self, ma_model, current_model):
for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
if current_params.requires_grad:
old_weight, up_weight = ma_params.data, current_params.data
ma_params.data = self.update_average(old_weight, up_weight)
def update_average(self, old, new):
if old is None:
return new
return old * self.beta + (1 - self.beta) * new
class Trainer(Infra):
def __init__(self, network, diffusion, optimizers, sigma_0,
base_change, model_wrapper, Lambda, gsure, ema_scheduler=None, **kwargs):
''' must to init BaseModel with kwargs '''
super(Trainer, self).__init__(**kwargs)
''' networks, dataloder, optimizers, losses, etc. '''
self.netG = network
self.diffusion = diffusion
self.sigma_0 = sigma_0
self.base_change = base_change
self.Lambda = Lambda
self.gsure = gsure
if model_wrapper:
print("Activating model FFT NN wrapper")
self.netG = FFT_NN_Wrapper(self.netG)
if ema_scheduler is not None:
self.ema_scheduler = ema_scheduler
self.netG_EMA = copy.deepcopy(self.netG)
self.EMA = EMA(beta=self.ema_scheduler['ema_decay'])
else:
self.ema_scheduler = None
''' networks can be a list, and must convert by self.set_device function if using multiple GPU. '''
self.netG = self.set_device(self.netG, distributed=self.opt['distributed'])
if self.ema_scheduler is not None:
self.netG_EMA = self.set_device(self.netG_EMA, distributed=self.opt['distributed'])
self.load_networks()
self.optG = torch.optim.Adam(self.netG.parameters(), **optimizers[0])
self.optimizers.append(self.optG)
# self.schedulers.append(LambdaLR(self.optG, lr_lambda=lambda epoch: max(epoch / 10, 0.1) if epoch <= 10 else 1))
self.resume_training()
''' can rewrite in inherited class for more informations logging '''
self.train_metrics = LogTracker(*["mse"], phase='train')
self.val_metrics = LogTracker(*[m.__name__ for m in self.metrics], phase='val')
def set_input(self, data):
''' must use set_device in tensor '''
with torch.no_grad():
if isinstance(data, dict):
img = data["image"]
mask = data["mask"]
else:
img = data
mask = torch.ones_like(data)
self.image = self.set_device(img)
self.mask = self.set_device(mask.long())
self.class_label = self.set_device(torch.tensor([0]))
self.batch_size = img.shape[0]
def get_current_visuals(self, phase='train'):
dict = {
'gt_image': (self.gt_image.detach()[:].float().cpu()+1)/2,
}
if phase != 'train':
dict.update({
'output': (self.output.detach()[:].float().cpu()+1)/2
})
return dict
def train_step(self):
self.netG.train()
self.train_metrics.reset()
self.optG.zero_grad()
for train_data in tqdm.tqdm(self.phase_loader):
self.optG.zero_grad()
self.set_input(train_data)
if self.gsure:
loss = self.diffusion.training_losses_gsure(model=self.netG, x_start=self.image, mask=self.mask,
sigma_0=self.sigma_0, Lambda=self.Lambda)
else:
loss = self.diffusion.training_losses(model=self.netG, x_start=self.image)
loss.backward()
self.iter += 1
self.optG.step()
self.writer.set_iter(self.epoch, self.iter, phase='train')
self.train_metrics.update("mse", loss.item())
if self.iter % self.opt['train']['log_iter'] == 0:
for key, value in self.train_metrics.result().items():
self.logger.info('{:5s}: {}\t'.format(str(key), value))
self.writer.add_scalar(key, value)
if self.wandb_run is not None and self.wandb_run:
if self.schedulers:
base = {"batch": self.iter, "epoch": self.epoch, "learning-rate": self.schedulers[0].get_last_lr()[0]}
else:
base = {"batch": self.iter, "epoch": self.epoch}
base.update(self.train_metrics.result())
self.wandb_run.log(base)
self.train_metrics.reset()
if self.ema_scheduler is not None:
if self.iter > self.ema_scheduler['ema_start'] and self.iter % self.ema_scheduler['ema_iter'] == 0:
self.EMA.update_model_average(self.netG_EMA, self.netG)
for scheduler in self.schedulers:
scheduler.step()
return self.train_metrics.result()
def val_step(self):
self.netG.eval()
self.val_metrics.reset()
with torch.no_grad():
for i, val_data in tqdm.tqdm(enumerate(self.val_loader)):
self.set_input(val_data)
clean_image, noisy_image = self.diffusion.clean_image(model=self.netG, x_start=self.image,
with_P=self.with_P)
self.writer.set_iter(self.epoch, self.iter, phase='val')
for met in self.metrics:
key = met.__name__
value = met(self.image, clean_image)
self.val_metrics.update(key, value)
self.writer.add_scalar(key, value)
if self.wandb_run is not None and self.wandb_run:
if self.base_change is not None:
self.image, noisy_image, clean_image = self.base_change(self.image), self.base_change(noisy_image), self.base_change(clean_image)
base = {"epoch": self.epoch}
base.update(self.val_metrics.result())
im = make_grid((0.5 + 0.5 * torch.cat([self.image, noisy_image, clean_image], dim=0)).clamp(0, 1), nrow=self.batch_size, padding=0)
images = wandb.Image(im, caption="Top: Output, Bottom: Input")
base.update({"images": images})
self.wandb_run.log(base)
return self.val_metrics.result()
def load_networks(self):
""" save pretrained model and training state, which only do on GPU 0. """
if self.opt['distributed']:
netG_label = self.netG.module.__class__.__name__
else:
netG_label = self.netG.__class__.__name__
self.load_network(network=self.netG, network_label=netG_label, strict=True)
if self.ema_scheduler is not None:
self.load_network(network=self.netG_EMA, network_label=netG_label+'_ema', strict=True)
def save_everything(self):
""" load pretrained model and training state. """
if self.opt['distributed']:
netG_label = self.netG.module.__class__.__name__
else:
netG_label = self.netG.__class__.__name__
self.save_network(network=self.netG, network_label=netG_label)
if self.ema_scheduler is not None:
self.save_network(network=self.netG_EMA, network_label=netG_label+'_ema')
self.save_training_state()