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main_train_urrdb_gan.py
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main_train_urrdb_gan.py
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import os.path
import math
import argparse
import time
import random
import numpy as np
from collections import OrderedDict
import logging
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
import torch
import warnings
from utils import utils_logger
from utils import utils_image as util
from utils import utils_option as option
from utils.utils_dist import get_dist_info, init_dist
from data.select_dataset import define_Dataset
from models.select_model import define_Model
from torch.utils.tensorboard import SummaryWriter
warnings.filterwarnings(action='ignore')
# options/train_urrdb_psnr.json
def main(option_path='options/train_rrdb_gan.yaml'): # options/train_urrdb_psnr.yaml
'''
# ----------------------------------------
# Step--1 (prepare opt)
# ----------------------------------------
'''
parser = argparse.ArgumentParser()
parser.add_argument('--opt', type=str, default=option_path, help='Path to option JSON file.')
parser.add_argument('--launcher', default='pytorch', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--dist', default=False)
parser.add_argument('--amp', default=True)
parser.add_argument('--resume', default= False)
opt = option.parse(parser.parse_args().opt, is_train=True)
opt['dist'] = parser.parse_args().dist
opt['amp'] = parser.parse_args().amp
opt['resume'] = parser.parse_args().resume
tb_writer = SummaryWriter(opt['path']['task'])
# FP32 -> FP16
scaler = torch.cuda.amp.GradScaler()
# ----------------------------------------
# distributed settings
# ----------------------------------------
if opt['dist']:
init_dist('pytorch')
opt['rank'], opt['world_size'] = get_dist_info()
if opt['rank'] == 0:
util.mkdirs((path for key, path in opt['path'].items() if 'pretrained' not in key))
# ----------------------------------------
# update opt
# ----------------------------------------
# -->-->-->-->-->-->-->-->-->-->-->-->-->-
if opt['resume'] == True:
init_iter_G, init_path_G = option.find_last_checkpoint(opt['path']['models'], net_type='G')
init_iter_optimizerG, init_path_optimizerG = option.find_last_checkpoint(opt['path']['models'], net_type='optimizerG')
else:
init_iter_G = 0
init_path_G = None
init_iter_optimizerG = 0
init_path_optimizerG = None
opt['path']['pretrained_netG'] = init_path_G
opt['path']['pretrained_optimizerG'] = init_path_optimizerG
current_step = max(init_iter_G, init_iter_optimizerG)
# opt['path']['pretrained_netG'] = ''
# current_step = 0
border = opt['scale']
# --<--<--<--<--<--<--<--<--<--<--<--<--<-
# ----------------------------------------
# save opt to a '../option.json' file
# ----------------------------------------
if opt['rank'] == 0:
option.save(opt)
# ----------------------------------------
# return None for missing key
# ----------------------------------------
opt = option.dict_to_nonedict(opt)
# ----------------------------------------
# configure logger
# ----------------------------------------
if opt['rank'] == 0:
logger_name = 'train'
utils_logger.logger_info(logger_name, os.path.join(opt['path']['log'], logger_name + '.log'))
logger = logging.getLogger(logger_name)
logger.info(option.dict2str(opt))
logger.info(f"Tensorboard Start with 'tensorboard --logdir {opt['path']['task']}', view at http://localhost:6006/")
# ----------------------------------------
# seed
# ----------------------------------------
seed = opt['train']['manual_seed']
if seed is None:
seed = random.randint(1, 10000)
print('Random seed: {}'.format(seed))
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
'''
# ----------------------------------------
# Step--2 (creat dataloader)
# ----------------------------------------
'''
# ----------------------------------------
# 1) create_dataset
# 2) creat_dataloader for train and test
# ----------------------------------------
for phase, dataset_opt in opt['datasets'].items():
if phase == 'train':
train_set = define_Dataset(dataset_opt)
train_size = int(math.ceil(len(train_set) / dataset_opt['dataloader_batch_size']))
if opt['rank'] == 0:
logger.info('Number of train images: {:,d}, iters: {:,d}'.format(len(train_set), train_size))
if opt['dist']:
train_sampler = DistributedSampler(train_set, shuffle=dataset_opt['dataloader_shuffle'], drop_last=True,
seed=seed)
train_loader = DataLoader(train_set,
batch_size=dataset_opt['dataloader_batch_size'] // opt['num_gpu'],
shuffle=False,
num_workers=dataset_opt['dataloader_num_workers'] // opt['num_gpu'],
drop_last=True,
pin_memory=True,
sampler=train_sampler)
else:
train_loader = DataLoader(train_set,
batch_size=dataset_opt['dataloader_batch_size'],
shuffle=dataset_opt['dataloader_shuffle'],
num_workers=dataset_opt['dataloader_num_workers'],
drop_last=True,
pin_memory=True)
elif phase == 'test':
test_set = define_Dataset(dataset_opt)
test_loader = DataLoader(test_set, batch_size=1,
shuffle=False, num_workers=1,
drop_last=False, pin_memory=True)
else:
raise NotImplementedError("Phase [%s] is not recognized." % phase)
'''
# ----------------------------------------
# Step--3 (initialize model)
# ----------------------------------------
'''
model = define_Model(opt, scaler)
model.init_train()
if opt['rank'] == 0:
# logger.info(model.info_network())
# logger.info(model.info_params())
pass
'''
# ----------------------------------------
# Step--4 (main training)
# ----------------------------------------
'''
best_psnr = [0.0]
for epoch in range(1000000): # keep running
for i, train_data in enumerate(train_loader):
current_step += 1
# -------------------------------
# 1) update learning rate
# -------------------------------
model.update_learning_rate(current_step)
# -------------------------------
# 2) feed patch pairs
# -------------------------------
model.feed_data(train_data)
# -------------------------------
# 3) optimize parameters
# -------------------------------
model.optimize_parameters(current_step)
# -------------------------------
# 4) training information
# -------------------------------
if current_step % opt['train']['checkpoint_print'] == 0 and opt['rank'] == 0:
logs = model.current_log() # such as loss
message = '<epoch:{:3d}, iter:{:8,d}, lr:{:.3e}> '.format(epoch, current_step,
model.current_learning_rate())
for k, v in logs.items(): # merge log information into message
message += '{:s}: {:.3e} '.format(k, v)
logger.info(message)
# -------------------------------
# 5) testing
# -------------------------------
if current_step % opt['train']['checkpoint_test'] == 0 and opt['rank'] == 0:
avg_psnr = 0.0
avg_ssim = 0.0
idx = 0
for test_data in test_loader:
idx += 1
image_name_ext = os.path.basename(test_data['L_path'][0])
img_name, ext = os.path.splitext(image_name_ext)
img_dir = os.path.join(opt['path']['images'], img_name)
util.mkdir(img_dir)
model.is_train = False
model.feed_data(test_data)
model.test()
visuals = model.current_visuals()
E_img = util.tensor2uint(visuals['E'])
H_img = util.tensor2uint(visuals['H'])
# -----------------------
# save estimated image E
# -----------------------
save_img_path = os.path.join(img_dir, '{:s}_{:d}.png'.format(img_name, current_step))
util.imsave(E_img, save_img_path)
# -----------------------
# calculate PSNR and SSIM
# -----------------------
current_psnr = util.calculate_psnr(E_img, H_img, border=border)
current_ssim = util.calculate_ssim(E_img, H_img, border=border)
logger.info('{:->4d}--> {:>10s} | {:<4.2f}dB | {:.4f}'.format(idx, image_name_ext, current_psnr, current_ssim))
avg_psnr += current_psnr
avg_ssim += current_ssim
model.is_train = True
avg_psnr = avg_psnr / idx
if avg_psnr >= best_psnr[-1]:
best_psnr.append(avg_psnr)
best_psnr.sort(reverse=True)
# print(best_psnr)
if len(best_psnr) >= 11:
best_psnr.pop()
# write tensorboard
tags = ['train/G_loss', 'train/F_loss', 'PSNR', 'lr']
for x, tag in zip(list(logs.values()) + [avg_psnr + model.current_learning_rate()], tags):
tb_writer.add_scalar(tag, x, current_step)
# testing log
logger.info(
'<epoch:{:3d}, iter:{:8,d}, Average PSNR : {:<.2f}dB, Average SSIM : {:.4f}\n'.format(epoch, current_step, avg_psnr, avg_ssim))
# average 10 psnr
logger.info('{}_average PSNR is {:<.2f}dB\n'.format(len(best_psnr), sum(best_psnr)/len(best_psnr)))
# -------------------------------
# 6) save model
# -------------------------------
if current_step % opt['train']['checkpoint_save'] == 0 and opt['rank'] == 0:
logger.info('Saving the model.')
model.save(current_step)
if best_psnr[0] == avg_psnr:
best_current_step = current_step
logger.info('save best PSNR model : {:<.2f}dB | {:}_G.pth\n'.format(best_psnr[0], best_current_step))
else:
logger.info('best PSNR : {:<.2f}dB\n'.format(best_psnr[0]))
if __name__ == '__main__':
main()