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train_P.py
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import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.optim as optim
import os
from tensorboardX import SummaryWriter
from config import config as _config
from dataset import dataset
from validation import P_validation
from models import PerPix_SFTMD as Generator
from models import Predictor
from utils import multiple_downsample, kernel_collage
# proj_directory = '/project'
# data_directory = '/dataset'
def train(config, epoch_from=0):
dataParallel = True
kernel_size = config['model']['kernel_size']
print('process before training...')
train_dataset = dataset(config['path']['dataset']['train'], patch_size=config['train']['patch size'],
scale=config['model']['scale'], kernel_size=kernel_size)
train_data = DataLoader(
dataset=train_dataset, batch_size=config['train']['batch size'],
shuffle=True, num_workers=16
)
valid_dataset = dataset(config['path']['dataset']['valid'], patch_size=config['train']['patch size'],
scale=config['model']['scale'], is_train=False)
valid_data = DataLoader(dataset=valid_dataset, batch_size=config['valid']['batch size'], num_workers=4)
# training details - epochs & iterations
iterations_per_epoch = len(train_dataset) // config['train']['batch size'] + 1
n_epoch = config['train']['iterations_P'] // iterations_per_epoch + 1
print('epochs scheduled: %d , iterations per epoch %d...' % (n_epoch, iterations_per_epoch))
# define main SR network as generator
generator = Generator(scale=config['model']['scale'], code_len=config['model']['code_len'])
save_path_G = config['path']['ckpt']
generator.load_state_dict(torch.load(save_path_G))
for param in generator.parameters():
param.requires_grad = False
predictor = Predictor(config['model']['code_len']).cuda()
save_path_P = save_path_G[:-4] + '_Predictor.pth'
# optimizer
learning_rate = config['train']['lr_P']
P_optimizer = optim.Adam(predictor.parameters(), lr=learning_rate)
save_path_Opt = save_path_P[:-4] + '_Opt.pth'
lr_scheduler = optim.lr_scheduler.StepLR(P_optimizer, config['train']['decay']['every'],
config['train']['decay']['by'])
lr_scheduler.last_epoch = epoch_from * iterations_per_epoch
# if training from scratch, remove all validation images and logs
if epoch_from == 0:
if os.path.exists(config['path']['validation']):
_old = os.listdir(config['path']['validation'])
for f in _old:
if os.path.isfile(os.path.join(config['path']['validation'], f)):
os.remove(os.path.join(config['path']['validation'], f))
if os.path.exists(config['path']['logs']):
_old = os.listdir(config['path']['logs'])
for f in _old:
if os.path.isfile(os.path.join(config['path']['logs'], f)):
os.remove(os.path.join(config['path']['logs'], f))
# if training not from scratch, load weights
else:
if os.path.exists(save_path_P):
print('reading predictor checkpoints...')
predictor.load_state_dict(torch.load(save_path_P))
print('reading optimizer checkpoints...')
P_optimizer.load_state_dict(torch.load(save_path_Opt))
else:
raise FileNotFoundError('Pretrained weight not found.')
if not os.path.exists(config['path']['validation']):
os.makedirs(config['path']['validation'])
if not os.path.exists(os.path.dirname(config['path']['ckpt'])):
os.makedirs(os.path.dirname(config['path']['ckpt']))
if not os.path.exists(config['path']['logs']):
os.makedirs(config['path']['logs'])
writer = SummaryWriter(config['path']['logs'])
# loss functions
loss = nn.L1Loss().cuda()
if dataParallel:
generator = nn.DataParallel(generator)
predictor = nn.DataParallel(predictor)
generator = generator.cuda()
predictor = predictor.cuda()
# validation
valid = P_validation(generator, predictor, valid_data, writer, config['path']['validation'])
# training
print('start training...')
for epoch in range(epoch_from, n_epoch):
generator = generator.eval()
predictor = predictor.train()
epoch_loss = 0
for i, data in enumerate(train_data):
# lr, gt, gt_k_map, _ = data
# lr = lr.cuda()
# gt_k_map = gt_k_map.cuda()
hr, gt, kernels, k_code, _ = data
hr = hr.cuda()
kernels = kernels.cuda()
k_code = k_code.cuda()
kernels = kernels.view(-1, 1, 1, kernel_size, kernel_size)
k_code = k_code.view(-1, config['model']['code_len'])
# downsample via kernel collage
lr = multiple_downsample(hr, kernels, config['model']['scale'])
lr, gt_k_map = kernel_collage(lr, k_code)
# if direct loss on kernel map
# pred_k_map = predictor(lr)
# p_loss = loss(pred_k_map, gt_k_map)
#
# # back propagation
# P_optimizer.zero_grad()
# p_loss.backward()
# P_optimizer.step()
# epoch_loss += p_loss.item()
#
# if reconstruction with GT HR image
# gt = gt.cuda()
# pred_k_map = predictor(lr)
# sr = generator(lr, pred_k_map)
# p_loss = loss(sr, gt)
#
# # back propagation
# P_optimizer.zero_grad()
# p_loss.backward()
# P_optimizer.step()
# epoch_loss += p_loss.item()
#
# with our proposed indirect reconstruction loss
with torch.no_grad():
gt = generator(lr, gt_k_map)
pred_k_map = predictor(lr)
sr = generator(lr, pred_k_map)
p_loss = loss(sr, gt)
# back propagation
P_optimizer.zero_grad()
p_loss.backward()
P_optimizer.step()
lr_scheduler.step()
epoch_loss += p_loss.item()
print('Training loss at {:d} : {:.8f}'.format(epoch, epoch_loss))
# validation after epoch
if (epoch + 1) % config['valid']['every'] == 0:
is_best = valid.run(epoch + 1)
# save validation image
valid.save(tag='latest')
if is_best:
if dataParallel:
torch.save(predictor.module.state_dict(), save_path_P)
else:
torch.save(predictor.state_dict(), save_path_P)
torch.save(P_optimizer.state_dict(), save_path_Opt)
# training process finished.
# final validation and save checkpoints
is_best = valid.run(n_epoch)
valid.save(tag='final')
writer.close()
if is_best:
if dataParallel:
torch.save(predictor.module.state_dict(), save_path_P)
else:
torch.save(predictor.state_dict(), save_path_P)
torch.save(P_optimizer.state_dict(), save_path_Opt)
print('training finished.')
if __name__ == '__main__':
train(_config, epoch_from=0)