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main_train_srsc_rsflow_multi.py
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main_train_srsc_rsflow_multi.py
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import os.path
import math
import argparse
import random
import numpy as np
import logging
from mindspore.dataset import GeneratorDataset as DataLoader
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 models.select_model import define_Model
from data.dataset_rsgopro_self import RSGOPRO as D
import mindspore as ms
import mindspore.dataset as ds
ds.config.set_enable_autotune(True)
def main(json_path='options/train_srsc_rsflow_multi_psnr.json'):
'''
# ----------------------------------------
# Step--1 (prepare opt)
# ----------------------------------------
'''
parser = argparse.ArgumentParser()
parser.add_argument('--opt', type=str, default=json_path, help='Path to option JSON file.')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--dist', default=False)
opt = option.parse(parser.parse_args().opt, is_train=True)
opt['dist'] = parser.parse_args().dist
# ----------------------------------------
# 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
# ----------------------------------------
# -->-->-->-->-->-->-->-->-->-->-->-->-->-
init_iter_G, init_path_G = option.find_last_checkpoint(opt['path']['models'], net_type='G')
init_iter_E, init_path_E = option.find_last_checkpoint(opt['path']['models'], net_type='E')
# opt['path']['pretrained_netG'] = init_path_G
# opt['path']['pretrained_netE'] = init_path_E
init_iter_optimizerG, init_path_optimizerG = option.find_last_checkpoint(opt['path']['models'], net_type='optimizerG')
opt['path']['pretrained_optimizerG'] = init_path_optimizerG
current_step = max(init_iter_G, init_iter_E, init_iter_optimizerG)
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))
# ----------------------------------------
# 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)
ms.set_seed(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)
path = os.path.join(dataset_opt['data_root'], 'train')
# flow = True
# if not 'EPE' in para.loss:
flow = False
train_set = D(path, dataset_opt['future_frames'], dataset_opt['past_frames'], dataset_opt['frames'], dataset_opt['patch_size'], dataset_opt['centralize'],
dataset_opt['normalize'], flow, False)
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_loader = DataLoader(train_set,
shuffle=False,
num_parallel_workers=dataset_opt['dataloader_num_workers']//opt['num_gpu'],column_names=["rs_imgs", "gs_imgs", "fl_imgs", "prior_imgs", "time_rsc", "all_time_rsc", "out_paths", "input_path"]
,sampler=None).batch(dataset_opt['dataloader_batch_size']//opt['num_gpu'],drop_remainder=True)
else:
train_loader = DataLoader(train_set,
shuffle=dataset_opt['dataloader_shuffle'],
num_parallel_workers=dataset_opt['dataloader_num_workers'],column_names=["rs_imgs", "gs_imgs", "fl_imgs", "prior_imgs", "time_rsc", "all_time_rsc", "out_paths", "input_path"]).batch(dataset_opt['dataloader_batch_size'],drop_remainder=True)
elif phase == 'test':
# test_set = define_Dataset(dataset_opt)
path = os.path.join(dataset_opt['data_root'], 'test') #'valid') #
# flow = True
# if not 'EPE' in para.loss:
flow = False
test_set = D(path, dataset_opt['future_frames'], dataset_opt['past_frames'], dataset_opt['frames'], None, dataset_opt['centralize'],
dataset_opt['normalize'], flow, False, True)
test_loader = DataLoader(test_set,shuffle=False, num_parallel_workers=1,
column_names=["rs_imgs", "gs_imgs", "fl_imgs", "prior_imgs", "time_rsc", "all_time_rsc",
"out_paths", "input_path"]).batch(batch_size=1, drop_remainder=False)# test_set = define_Dataset(dataset_opt)
else:
raise NotImplementedError("Phase [%s] is not recognized." % phase)
'''
# ----------------------------------------
# Step--3 (initialize model)
# ----------------------------------------
'''
model = define_Model(opt)
model.init_train()
# if opt['rank'] == 0:
# logger.info(model.info_network())
# logger.info(model.info_params())
'''
# ----------------------------------------
# Step--4 (main training)
# ----------------------------------------
'''
for epoch in range(1000000): # keep running
if opt['dist']:
train_sampler.set_epoch(epoch)
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) save model
# -------------------------------
if current_step % opt['train']['checkpoint_save'] == 0 and opt['rank'] == 0:
logger.info('Saving the model.')
model.save(current_step)
# -------------------------------
# 6) testing
# -------------------------------
if current_step % opt['train']['checkpoint_test'] == 0 and opt['rank'] == 0:
avg_psnr = 0.0
idx = 0
for test_data in test_loader:
idx += 1
# print(test_data[5][0])
image_name_ext = os.path.basename(test_data[6][0][0]) #test_data[5][0][0]
img_name, ext = os.path.splitext(image_name_ext)
img_dir = os.path.join(opt['path']['images'], img_name)
util.mkdir(img_dir)
#rs_imgs, gs_imgs, fl_imgs, prior_imgs, is_b2t, out_paths, input_path
# print(test_data[3].shape)
model.feed_data(test_data)
model.test()
visuals = model.current_visuals()
E_img = util.tensor2uint_list(visuals['E'])
H_img = util.tensor2uint_list(visuals['H'])
# print(len(E_img), len(H_img))
# -----------------------
# save estimated image E
# -----------------------
# save_img_path = os.path.join(img_dir, 'E_{:d}.png'.format( current_step))
# util.imsave(E_img, save_img_path)
current_psnr = 0
for save_idx in range(len(E_img)):
save_img_path = os.path.join(img_dir, 'E{:d}_{:d}.png'.format(save_idx,current_step))
util.imsave(E_img[save_idx], save_img_path)
current_psnr += util.calculate_psnr(E_img[save_idx], H_img[save_idx], border=border)
# -----------------------
# calculate PSNR
# -----------------------
# for j in range(len(E_img)):
# print(E_img[j].shape, H_img[j].shape)
# if j == 0:
current_psnr = current_psnr / len(E_img)
# else:
# current_psnr = current_psnr + util.calculate_psnr(E_img[j], H_img[j], border=border)
# current_psnr = current_psnr / len(E_img)
logger.info('{:->4d}--> {:>10s} | {:<4.2f}dB'.format(idx, image_name_ext, current_psnr))
avg_psnr += current_psnr
avg_psnr = avg_psnr / idx
# testing log
logger.info('<epoch:{:3d}, iter:{:8,d}, Average PSNR : {:<.2f}dB\n'.format(epoch, current_step, avg_psnr))
if __name__ == '__main__':
main()