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main_test_srsc_rsflow_multi_distillv2_real.py
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main_test_srsc_rsflow_multi_distillv2_real.py
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import os.path
import math
import argparse
import logging
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_real import RSGOPRO as D
import mindspore as ms
import mindspore.dataset as ds
from mindspore.dataset import GeneratorDataset as DataLoader
ds.config.set_enable_autotune(True)
'''
# --------------------------------------------
# training code for MSRResNet
# --------------------------------------------
# Kai Zhang (cskaizhang@gmail.com)
# github: https://github.com/cszn/KAIR
# --------------------------------------------
# https://github.com/xinntao/BasicSR
# --------------------------------------------
'''
def main(json_path='options/test_srsc_rsflow_multi_distillv2_real.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('--launcher', default='pytorch', help='job launcher')
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 = 'test_real_110000' # 35000
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)
# 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 == 'test':
# test_set = define_Dataset(dataset_opt)
path = os.path.join(dataset_opt['data_root']) #, 'test'
# 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)
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)
# ----------------------------------------
'''
# avg_psnr = 0.0
idx = 0
for test_data in test_loader:
idx += 1
# print(test_data[5][0])
video_name = test_data[7][0][0].split('/')[-2]
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']['inference_results'], video_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'])
# current_psnr = 0
for save_idx in range(len(E_img)):
# image_name_ext = os.path.basename(test_data[7][save_idx][0])
# img_name, ext = os.path.splitext(image_name_ext)
save_img_path = os.path.join(img_dir, '{:s}_{:02d}.png'.format(img_name, save_idx)) #'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}'.format(idx, image_name_ext))
# avg_psnr += current_psnr
# avg_psnr = avg_psnr / idx
#
# # testing log
# logger.info('Average PSNR : {:<.2f}dB\n'.format(avg_psnr))
if __name__ == '__main__':
main()