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import os
import math
from decimal import Decimal
import utility
import torch
import torch.nn.utils as utils
from tqdm import tqdm
class Trainer():
def __init__(self, args, loader, my_model, my_loss, ckp, sub_model):
self.args = args
self.scale = args.scale
self.ckp = ckp
self.loader_train = loader.loader_train
self.loader_test = loader.loader_test
self.model = my_model
self.sub_sspcab = sub_model
self.loss = my_loss
self.optimizer = utility.make_optimizer(args, self.model)
if self.args.load != '':
self.optimizer.load(ckp.dir, epoch=len(ckp.log))
self.error_last = 1e8
def train(self):
self.loss.step()
epoch = self.optimizer.get_last_epoch() + 1
lr = self.optimizer.get_lr()
self.ckp.write_log(
'[Epoch {}]\tLearning rate: {:.2e}'.format(epoch, Decimal(lr))
)
self.loss.start_log()
self.model.train()
timer_data, timer_model = utility.timer(), utility.timer()
# TEMP
self.loader_train.dataset.set_scale(0)
for batch, (lr, hr, _,) in enumerate(self.loader_train):
lr, hr = self.prepare(lr, hr)
timer_data.hold()
timer_model.tic()
self.optimizer.zero_grad()
sub_sr = self.sub_sspcab(lr)
sub_loss = self.loss(sub_sr, lr)
sr = self.model(lr, 0)
loss = self.loss(sr, hr) + 0.1*sub_loss
loss.backward()
if self.args.gclip > 0:
utils.clip_grad_value_(
self.model.parameters(),
self.args.gclip
)
self.optimizer.step()
timer_model.hold()
if (batch + 1) % self.args.print_every == 0:
self.ckp.write_log('[{}/{}]\t{}\t{:.1f}+{:.1f}s'.format(
(batch + 1) * self.args.batch_size,
len(self.loader_train.dataset),
self.loss.display_loss(batch),
timer_model.release(),
timer_data.release()))
timer_data.tic()
self.loss.end_log(len(self.loader_train))
self.error_last = self.loss.log[-1, -1]
self.optimizer.schedule()
def test(self):
torch.set_grad_enabled(False)
epoch = self.optimizer.get_last_epoch()
self.ckp.write_log('\nEvaluation:')
self.ckp.add_log(
torch.zeros(1, len(self.loader_test), len(self.scale))
)
self.model.eval()
timer_test = utility.timer()
if self.args.save_results: self.ckp.begin_background()
for idx_data, d in enumerate(self.loader_test):
for idx_scale, scale in enumerate(self.scale):
d.dataset.set_scale(idx_scale)
for lr, hr, filename in tqdm(d, ncols=80):
lr, hr = self.prepare(lr, hr)
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
self.ckp.log[-1, idx_data, idx_scale] += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range, dataset=d
)
if self.args.save_gt:
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(d, filename[0], save_list, scale)
self.ckp.log[-1, idx_data, idx_scale] /= len(d)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
d.dataset.name,
scale,
self.ckp.log[-1, idx_data, idx_scale],
best[0][idx_data, idx_scale],
best[1][idx_data, idx_scale] + 1
)
)
self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
self.ckp.write_log('Saving...')
if self.args.save_results:
self.ckp.end_background()
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))
self.ckp.write_log(
'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
torch.set_grad_enabled(True)
def prepare(self, *args):
if self.args.cpu:
device = torch.device('cpu')
else:
#if torch.backends.mps.is_available():
# device = torch.device('mps')
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
def _prepare(tensor):
if self.args.precision == 'half': tensor = tensor.half()
return tensor.to(device)
return [_prepare(a) for a in args]
def terminate(self):
if self.args.test_only:
self.test()
return True
else:
epoch = self.optimizer.get_last_epoch() + 1
return epoch >= self.args.epochs