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pretrained.py
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import logging
from functools import partial
import os
import cv2
import torch
import torch.optim as optim
import tqdm
import yaml
from joblib import cpu_count
from torch.utils.data import DataLoader
import random
from dataset import PairedDataset
from metric_counter import MetricCounter
from models.losses import get_loss
from models.models import get_model
from models.networks import get_nets
import numpy as np
from torch.optim.lr_scheduler import StepLR, CosineAnnealingLR
cv2.setNumThreads(0)
class Trainer:
def __init__(self, config, train: DataLoader, val: DataLoader):
self.config = config
self.train_dataset = train
self.val_dataset = val
self.metric_counter = MetricCounter(config['experiment_desc'])
def train(self):
self._init_params()
start_epoch = 0
if os.path.exists('last_BANet_pretrained.pth'):
print('load_pretrained')
training_state = (torch.load('last_BANet_pretrained.pth'))
start_epoch = training_state['epoch']
new_weight = self.netG.state_dict()
new_weight.update(training_state['model_state'])
self.netG.load_state_dict(new_weight)
new_optimizer = self.optimizer_G.state_dict()
new_optimizer.update(training_state['optimizer_state'])
self.optimizer_G.load_state_dict(new_optimizer)
new_scheduler = self.scheduler_G.state_dict()
new_scheduler.update(training_state['scheduler_state'])
self.scheduler_G.load_state_dict(new_scheduler)
for epoch in range(start_epoch, config['num_epochs']):
self._run_epoch(epoch)
if epoch % 30 == 0 or epoch == (config['num_epochs']-1):
self._validate(epoch)
self.scheduler_G.step()
scheduler_state = self.scheduler_G.state_dict()
training_state = {'epoch': epoch, 'model_state': self.netG.state_dict(),
'scheduler_state': scheduler_state, 'optimizer_state': self.optimizer_G.state_dict()}
if self.metric_counter.update_best_model():
torch.save(training_state['model_state'], 'best_{}.pth'.format(self.config['experiment_desc']))
if epoch == (config['num_epochs']-1):
torch.save(training_state['model_state'], 'final_{}.pth'.format(self.config['experiment_desc']))
torch.save(training_state, 'last_{}.pth'.format(self.config['experiment_desc']))
logging.debug("Experiment Name: %s, Epoch: %d, Loss: %s" % (
self.config['experiment_desc'], epoch, self.metric_counter.loss_message()))
def _run_epoch(self, epoch):
self.metric_counter.clear()
for param_group in self.optimizer_G.param_groups:
lr = param_group['lr']
epoch_size = config.get('train_batches_per_epoch') or len(self.train_dataset)
tq = tqdm.tqdm(self.train_dataset, total=epoch_size)
tq.set_description('Epoch {}, lr {}'.format(epoch, lr))
i = 0
for data in tq:
inputs, targets = self.model.get_input(data)
outputs = self.netG(inputs).clamp(-0.5, 0.5)
self.optimizer_G.zero_grad()
loss_G = self.criterionG(outputs, targets)
loss_G.backward()
self.optimizer_G.step()
self.metric_counter.add_losses(loss_G.item())
curr_psnr, curr_ssim, img_for_vis = self.model.get_images_and_metrics(inputs, outputs, targets)
self.metric_counter.add_metrics(curr_psnr, curr_ssim)
tq.set_postfix(loss=self.metric_counter.loss_message())
if not i:
self.metric_counter.add_image(img_for_vis, tag='train')
i += 1
if i > epoch_size:
break
tq.close()
self.metric_counter.write_to_tensorboard(epoch)
def _validate(self, epoch):
self.metric_counter.clear()
epoch_size = config.get('val_batches_per_epoch') or len(self.val_dataset)
tq = tqdm.tqdm(self.val_dataset, total=epoch_size)
tq.set_description('Validation')
i = 0
for data in tq:
with torch.no_grad():
inputs, targets = self.model.get_input(data)
outputs = self.netG(inputs).clamp(-0.5, 0.5)
loss_G = self.criterionG(outputs, targets)
self.metric_counter.add_losses(loss_G.item())
curr_psnr, curr_ssim, img_for_vis = self.model.get_images_and_metrics(inputs, outputs, targets)
self.metric_counter.add_metrics(curr_psnr, curr_ssim)
if not i:
self.metric_counter.add_image(img_for_vis, tag='val')
i += 1
if i > epoch_size:
break
tq.close()
self.metric_counter.write_to_tensorboard(epoch, validation=True)
def _get_optim(self, params):
if self.config['optimizer']['name'] == 'adam':
optimizer = optim.Adam(params, lr=self.config['optimizer']['lr'])
else:
raise ValueError("Optimizer [%s] not recognized." % self.config['optimizer']['name'])
return optimizer
def _get_scheduler(self, optimizer):
if self.config['scheduler']['name'] == 'cosine':
scheduler = CosineAnnealingLR(optimizer, T_max=self.config['num_epochs'], eta_min=self.config['scheduler']['min_lr'])
else:
raise ValueError("Scheduler [%s] not recognized." % self.config['scheduler']['name'])
return scheduler
def _init_params(self):
self.criterionG = get_loss(self.config['model'])
self.netG = get_nets(self.config['model'])
self.netG.cuda()
self.model = get_model(self.config['model'])
self.optimizer_G = self._get_optim(filter(lambda p: p.requires_grad, self.netG.parameters()))
self.scheduler_G = self._get_scheduler(self.optimizer_G)
if __name__ == '__main__':
with open('config/config_pretrained.yaml', 'r') as f:
config = yaml.safe_load(f)
# setup
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
# set random seed
seed = 666
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
batch_size = config.pop('batch_size')
get_dataloader = partial(DataLoader, batch_size=batch_size, num_workers=cpu_count(), shuffle=True, drop_last=False)
datasets = map(config.pop, ('train', 'val'))
datasets = map(PairedDataset.from_config, datasets)
train, val = map(get_dataloader, datasets)
trainer = Trainer(config, train=train, val=val)
trainer.train()