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trainer.py
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trainer.py
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import os
import math
from decimal import Decimal
import utility
import torch
import torch.nn as nn
from torch.autograd import Variable
from tqdm import tqdm
import scipy.io as sio
from data import common
import numpy as np
# import model
class Trainer():
def __init__(self, args, loader, my_model, my_loss, ckp):
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.loss = my_loss
self.optimizer = utility.make_optimizer(args, self.model)
self.scheduler = utility.make_scheduler(args, self.optimizer)
if self.args.load != '.':
self.optimizer.load_state_dict(
torch.load(os.path.join(ckp.dir, 'optimizer.pt'))
)
for _ in range(len(ckp.log)): self.scheduler.step()
self.error_last = 1e5
def train(self):
self.scheduler.step()
self.loss.step()
epoch = self.scheduler.last_epoch + 1
lr = self.scheduler.get_lr()[0]
self.ckp.write_log(
'[Epoch {}]\tLearning rate: {:.2e}'.format(epoch, Decimal(lr))
)
self.loss.start_log()
self.model.train()
# self.model_NLEst.train()
# self.model_KMEst.train()
timer_data, timer_model = utility.timer(), utility.timer()
for batch, (lr, hr, _) in enumerate(self.loader_train):
lr, hr = self.prepare([lr, hr])
# print(scale_factor[0,0,0,0])
timer_data.hold()
timer_model.tic()
# _, _, hei, wid = hr.data.size()
self.optimizer.zero_grad()
idx_scale = 0
sr = self.model(lr, idx_scale)
loss = self.loss(sr, hr)
if loss.item() < self.args.skip_threshold * self.error_last:
loss.backward()
self.optimizer.step()
else:
print('Skip this batch {}! (Loss: {})'.format(
batch + 1, loss.item()
))
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]
def test(self):
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('\nEvaluation:')
# kernel_test = sio.loadmat('data/Compared_kernels_JPEG_noise_x234.mat')
scale_list = self.scale #[2,3,4,8]
self.ckp.add_log(torch.zeros(1, len(scale_list)))
self.model.eval()
no_eval = 0
# self.model_NLEst.eval()
# self.model_KMEst.eval()
timer_test = utility.timer()
with torch.no_grad():
for idx_scale, scale in enumerate(scale_list):
eval_acc = 0
self.loader_test.dataset.set_scale(idx_scale)
tqdm_test = tqdm(self.loader_test, ncols=120)
for idx_img, (lr, hr, filename) in enumerate(tqdm_test):
filename = filename[0]
# sz = lr.size()
# scale_tensor = torch.ones([1, 1, sz[2], sz[3]]).float() * (scale / 80.0)
if not no_eval:
lr, hr = self.prepare([lr, hr])
else:
lr = self.prepare([lr])[0]
#sz = lr.size()
#scale_tensor = torch.ones([1, 1, sz[2], sz[3]]).float() * (2.0 / 80)
# print(lr.size())
# hr_ = torch.squeeze(hr_)
# hr_ = hr_.numpy()
# lr = hr
sr = self.model(lr, idx_scale)
sr = utility.quantize(sr, self.args.rgb_range)
save_list = [sr]
eval_acc += utility.calc_psnr(
sr, hr, scale, self.args.rgb_range,
benchmark=self.loader_test.dataset.benchmark
)
save_list.extend([lr, hr])
# # if not no_eval:
# # eval_acc += utility.calc_psnr(
# # sr, hr, scale, self.args.rgb_range,
# # benchmark=self.loader_test.dataset.benchmark
# # )
# # save_list.extend([lr, hr])
#
if self.args.save_results:
self.ckp.save_results(filename, save_list, idx_img, scale)
self.ckp.log[-1, idx_scale] = eval_acc / len(self.loader_test)
best = self.ckp.log.max(0)
self.ckp.write_log(
'[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
self.args.data_test,
scale,
self.ckp.log[-1, idx_scale],
best[0][idx_scale],
best[1][idx_scale] + 1
)
)
self.ckp.write_log(
'Total time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
)
if not self.args.test_only:
self.ckp.save(self, epoch, is_best=(best[1][0] + 1 == epoch))
def prepare(self, l, volatile=False):
device = torch.device('cpu' if self.args.cpu else 'cuda')
def _prepare(tensor):
if self.args.precision == 'half': tensor = tensor.half()
return tensor.to(device)
return [_prepare(_l) for _l in l]
def terminate(self):
if self.args.test_only:
self.test()
return True
else:
epoch = self.scheduler.last_epoch + 1
return epoch >= self.args.epochs