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trainer.py
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import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
import math
import time
from decimal import Decimal
import numpy as np
import imageio
import utility
import torch
import torch.nn.utils as utils
from tqdm import tqdm
from data import data_utils
class Trainer():
def __init__(self, args, loader, my_model, my_loss, ckp):
super(Trainer, self).__init__()
self.args = args
self.scale = args.scale
self.gt_size = args.gt_size
self.batch_size = args.batch_size
self.ckp = ckp
self.model = my_model
self.num_frames_samples = args.num_frames_samples
self.train_loader = loader.loader_train
self.valid_loader = loader.loader_valid
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()
print(self.train_loader.n_samples)
# TEMP
for batch, (LR_lst, HR_lst, MV_up_lst, Mask_up_lst, _) in enumerate(self.train_loader):
print(f"Batch {batch} - LR: {LR_lst[0].shape}, HR: {HR_lst[0].shape}, MV_up: {MV_up_lst[0].shape}, Mask_up: {Mask_up_lst[0].shape}")
self.optimizer.zero_grad()
b, c, h, w = HR_lst[0].size()
zero_tensor = torch.zeros(b, c, h, w, dtype=torch.float32)
lr0, zero_tensor, hr0 = self.prepare(LR_lst[0], zero_tensor, HR_lst[0])
sr_pre, lstm_state = self.model((lr0, zero_tensor, None))
lstm_state = utility.repackage_hidden(lstm_state)
loss = self.loss(sr_pre, hr0, needTem=False)
print(f"Initial loss for batch {batch}: {loss.item()}")
for i in range(1, self.num_frames_samples):
sr_pre = sr_pre.detach()
sr_pre.requires_grad = False
lr, hr, mv_up, mask_up = self.prepare(LR_lst[i], HR_lst[i], MV_up_lst[i], Mask_up_lst[i])
timer_data.hold()
timer_model.tic()
sr_pre_warped = data_utils.warp(sr_pre, mv_up)
sr_cur, lstm_state = self.model((lr, sr_pre_warped, lstm_state))
lstm_state = utility.repackage_hidden(lstm_state)
loss += self.loss(sr_cur, hr, sr_pre_warped, mask_up, needTem=True)
sr_pre = sr_cur
loss.backward()
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,
self.train_loader.n_samples,
self.loss.display_loss(batch),
timer_model.release(),
timer_data.release()))
timer_data.tic()
self.loss.end_log(len(self.train_loader))
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, 3)
)
self.model.eval()
timer_test = utility.timer()
run_model_time = 0
flag = 0
if self.args.save_results: self.ckp.begin_background()
pre_sr = torch.zeros(1, 3, self.gt_size[0], self.gt_size[1],
dtype=torch.float32).cuda()
lstm_state = None
for index, (LR_lst, HR_lst, MV_up_lst, Mask_up_lst, filename) in tqdm(enumerate(self.valid_loader)):
lr, hr, mv_up, mask_up = self.prepare(LR_lst[0], HR_lst[0], MV_up_lst[0], Mask_up_lst[0])
if index == 0:
pre_sr, lstm_state = self.model((lr, pre_sr, lstm_state))
lstm_state = utility.repackage_hidden(lstm_state)
continue
t1 = time.time()
sr_pre_warped = data_utils.warp(pre_sr, mv_up)
cur_sr, lstm_state = self.model((lr, sr_pre_warped, lstm_state))
lstm_state = utility.repackage_hidden(lstm_state)
t2 = time.time()
run_model_time += (t2 - t1)
if self.args.sr_content == "View":
sr = utility.quantize_img(cur_sr)
sr_last = utility.quantize_img(pre_sr)
if flag < 2:
try:
print(f"Saving SR image to ./check/sr_{flag}.png")
data_utils.save2Exr(np.array(sr[0, :3, :, :].permute(1, 2, 0).detach().cpu()) * 255,
f"./check/sr_{flag}.png")
except Exception as e:
print(f"Error saving SR image: {e}")
try:
print(f"Saving GT image to ./check/gt_{flag}.png")
data_utils.save2Exr(np.array(hr[0, :3, :, :].permute(1, 2, 0).detach().cpu()) * 255,
f"./check/gt_{flag}.png")
except Exception as e:
print(f"Error saving GT image: {e}")
flag += 1
else:
sr = utility.quantize(cur_sr)
sr_last = utility.quantize(pre_sr)
if flag < 2:
try:
print(f"Saving SR image to ./check/sr_{flag}.exr")
data_utils.save2Exr(np.array(sr[0, :3, :, :].permute(1, 2, 0).detach().cpu()),
f"./check/sr_{flag}.exr")
except Exception as e:
print(f"Error saving SR image: {e}")
try:
print(f"Saving GT image to ./check/gt_{flag}.exr")
data_utils.save2Exr(np.array(hr[0, :3, :, :].permute(1, 2, 0).detach().cpu()),
f"./check/gt_{flag}.exr")
except Exception as e:
print(f"Error saving GT image: {e}")
flag += 1
pre_sr = cur_sr
save_list = [sr]
assert sr is not torch.nan, "sr is nan!"
val_ssim = 1.0 - utility.calc_ssim(sr, hr).cpu()
warped_sr = data_utils.warp(sr_last, mv_up)
val_tempory = utility.calc_tempory(warped_sr, sr, mask_up).cpu()
self.ckp.log[-1, 0] += val_ssim
self.ckp.log[-1, 1] += val_tempory
self.ckp.log[-1, 2] += val_tempory + val_ssim
if self.args.save_gt:
save_list.extend([lr, hr])
if self.args.save_results:
self.ckp.save_results(self.valid_loader, filename[0], save_list, self.scale)
self.ckp.log[-1] /= (len(self.valid_loader) - 1)
best = self.ckp.log.min(0)
self.ckp.write_log(
'[{} x{}]\tSSIM: {:.6f}, Tempory: {:.6f}, Total :{:.6f} (Best: {:.6f} @epoch {})'.format(
self.valid_loader.dataset.name,
self.scale,
self.ckp.log[-1][0],
self.ckp.log[-1][1],
self.ckp.log[-1][2],
best[0][2],
best[1][2] + 1
)
)
self.ckp.write_log('Run model time {:.5f}s\n'.format(run_model_time))
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[0][2] is not torch.nan and best[1][2] + 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):
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(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