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train_esrgan_WV.py
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import os.path
import sys
import math
import argparse
import time
import random
import numpy as np
from collections import OrderedDict
import logging
import matplotlib
matplotlib.use("Agg")
import torch
import matplotlib.pyplot as plt
import options.options as option
from utils import util
from data import create_dataloader, create_dataset
from models import create_model
### Imports luis
import earthpy as et
import geopandas as gpd
import earthpy.plot as ep
import cv2
import os
def main():
idxx= 0
# options
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, required=True, help='Path to option JSON file.')
opt = option.parse(parser.parse_args().opt, is_train=True)
opt = option.dict_to_nonedict(opt) # Convert to NoneDict, which return None for missing key.
ratio = opt["scale"]
print("DATASET items", opt['datasets'].items)
PreUp = opt["datasets"]["train"]["PreUP"]
LR_down = opt["datasets"]["train"]["LR_down"]
standa = opt["datasets"]["train"]["stand"]
if PreUp == True:
ratio=5
# train from scratch OR resume training
if opt['path']['resume_state']: # resuming training
resume_state = torch.load(opt['path']['resume_state'])
else: # training from scratch
resume_state = None
util.mkdir_and_rename(opt['path']['experiments_root']) # rename old folder if exists
util.mkdirs((path for key, path in opt['path'].items() if not key == 'experiments_root'
and 'pretrain_model' not in key and 'resume' not in key))
# config loggers. Before it, the log will not work
util.setup_logger(None, opt['path']['log'], 'train', level=logging.INFO, screen=True)
util.setup_logger('val', opt['path']['log'], 'val', level=logging.INFO)
logger = logging.getLogger('base')
if resume_state:
logger.info('Resuming training from epoch: {}, iter: {}.'.format(
resume_state['epoch'], resume_state['iter']))
option.check_resume(opt) # check resume options
logger.info(option.dict2str(opt))
# tensorboard logger
if opt['use_tb_logger'] and 'debug' not in opt['name']:
from tensorboardX import SummaryWriter
tb_logger_train = SummaryWriter(log_dir='/mnt/gpid07/users/luis.salgueiro/git/mnt/BasicSR_2020/tb_logger/' + opt['name'] + "/train")
tb_logger_val = SummaryWriter(log_dir='//mnt/gpid07/users/luis.salgueiro/git/mnt/BasicSR_2020/tb_logger/' + opt['name'] + "/val" )
# random seed
seed = opt['train']['manual_seed']
if seed is None:
seed = 100 #random.randint(1, 10000)
logger.info('Random seed: {}'.format(seed))
util.set_random_seed(seed)
torch.backends.cudnn.benckmark = True
# torch.backends.cudnn.deterministic = True
# #########################################
# ######## DATA LOADER ####################
# #########################################
# create train and val dataloader
for phase, dataset_opt in opt['datasets'].items():
if phase == 'train':
print("Entro DATASET train......")
train_set = create_dataset(dataset_opt)
print("CREO DATASET train_set ", train_set)
train_size = int(math.ceil(len(train_set) / dataset_opt['batch_size']))
logger.info('Number of train images: {:,d}, iters: {:,d}'.format(
len(train_set), train_size))
total_iters = int(opt['train']['niter'])
total_epochs = int(math.ceil(total_iters / train_size))
logger.info('Total epochs needed: {:d} for iters {:,d}'.format(
total_epochs, total_iters))
train_loader = create_dataloader(train_set, dataset_opt)
print("CREO train loader: ", train_loader)
elif phase == 'val':
print("Entro en phase VAL....")
val_set = create_dataset(dataset_opt)
val_loader = create_dataloader(val_set, dataset_opt)
logger.info('Number of val images in [{:s}]: {:d}'.format(dataset_opt['name'],
len(val_set)))
else:
raise NotImplementedError('Phase [{:s}] is not recognized.'.format(phase))
assert train_loader is not None
assert val_loader is not None
# create model
model = create_model(opt)
# resume training
if resume_state:
print("RESUMING state")
start_epoch = resume_state['epoch']
current_step = resume_state['iter']
model.resume_training(resume_state) # handle optimizers and schedulers
else:
current_step = 0
start_epoch = 0
print("PASO..... INIT ")
# #########################################
# ######### training ################
# #########################################
# ii=0
logger.info('Start training from epoch: {:d}, iter: {:d}'.format(start_epoch, current_step))
for epoch in range(start_epoch, total_epochs):
for _, train_data in enumerate(train_loader):
current_step += 1
if current_step > total_iters:
break
# update learning rate
model.update_learning_rate()
# training
model.feed_data(train_data)
model.optimize_parameters(current_step)
# log train
if current_step % opt['logger']['print_freq'] == 0:
logs = model.get_current_log()
message = '<epoch:{:3d}, iter:{:8,d}, lr:{:.3e}> '.format(
epoch, current_step, model.get_current_learning_rate())
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v)
# tensorboard logger
if opt['use_tb_logger'] and 'debug' not in opt['name']:
if "test" in k:
tb_logger_val.add_scalar(k, v, current_step)
else:
tb_logger_train.add_scalar(k, v, current_step)
logger.info(message)
if current_step % opt['train']['val_freq'] == 0:
avg_psnr_sr, avg_psnr_lr, avg_psnr_dif = 0.0, 0.0, 0.0
avg_ssim_lr, avg_ssim_sr, avg_ssim_dif = 0.0, 0.0, 0.0
avg_ergas_lr, avg_ergas_sr, avg_ergas_dif = 0.0, 0.0, 0.0
idx = 0
freqpass =0.0
if idxx==0:
best_psnr, best_ssim, best_ergas = 0.0, 0.0, 0.0
idxx=1
for _, val_data in enumerate(val_loader):
idx += 1
img_name = os.path.splitext(os.path.basename(val_data['LR_path'][0]))[0]
img_dir = os.path.join(opt['path']['val_images'], img_name)
# print("*****Img nameVaL: ", img_name)
model.feed_data(val_data)
model.test()
visuals = model.get_current_visuals()
freqpass=0
if standa == True:
# print("UTIL_STANDA")
sr_img = util.tensor2imgStand(visuals['SR'], MeanVal = val_data["LR_mean"], StdVal = val_data["LR_std"]) # uint16
gt_img = util.tensor2imgStand(visuals['HR'], MeanVal = val_data["HR_mean"], StdVal = val_data["HR_std"]) # uint16
lr_img = util.tensor2imgStand(visuals['LR'], MeanVal = val_data["LR_mean"], StdVal = val_data["LR_std"]) # uint16
else:
# print("******UTIL_NORM**********")
sr_img = util.tensor2imgNorm(visuals['SR'],out_type=np.uint8, min_max=(0, 1),
MinVal=val_data["LR_min"], MaxVal=val_data["LR_max"], freqpass=val_data["lowpass_lr"] ) # uint16
gt_img = util.tensor2imgNorm(visuals['HR'],out_type=np.uint8, min_max=(0, 1),
MinVal=val_data["HR_min"], MaxVal=val_data["HR_max"], freqpass=val_data["lowpass_hr"] ) # uint16
lr_img = util.tensor2imgNorm(visuals['LR'], out_type=np.uint8, min_max=(0, 1),
MinVal=val_data["LR_min"], MaxVal=val_data["LR_max"],
freqpass=val_data["lowpass_lr"]) # uint16
if LR_down==True or PreUp==False:
dim2 = (gt_img.shape[1], gt_img.shape[1])
lr_img = cv2.resize(np.transpose(lr_img,(1,2,0)), dim2, interpolation=cv2.INTER_NEAREST)
lr_img = np.transpose(lr_img,(2,0,1))
# Save SR images for reference
if idx < 10:
util.mkdir(img_dir)
save_img_path = os.path.join(img_dir, '{:s}_{:d}'.format(img_name, current_step))
# print("SAVING CROPS PREUP", PreUp )
util.save_imgCROP(lr_img,gt_img,sr_img , save_img_path, ratio, PreUp=PreUp)
avg_psnr_sr += util.calculate_psnr2(sr_img, gt_img)
avg_psnr_lr += util.calculate_psnr2(lr_img, gt_img)
avg_ssim_lr += util.calculate_ssim2(lr_img, gt_img)
avg_ssim_sr += util.calculate_ssim2(sr_img, gt_img)
avg_ergas_lr += util.calculate_ergas(lr_img, gt_img, pixratio=ratio)
avg_ergas_sr += util.calculate_ergas(sr_img, gt_img, pixratio=ratio)
avg_psnr_sr = avg_psnr_sr / idx
avg_psnr_lr = avg_psnr_lr / idx
avg_psnr_dif = avg_psnr_lr - avg_psnr_sr
avg_ssim_lr = avg_ssim_lr / idx
avg_ssim_sr = avg_ssim_sr / idx
avg_ssim_dif = avg_ssim_lr - avg_ssim_sr
avg_ergas_lr = avg_ergas_lr / idx
avg_ergas_sr = avg_ergas_sr / idx
avg_ergas_dif = avg_ergas_lr - avg_ergas_sr
# log VALIDATION
logger.info('# Validation # PSNR: {:.4e}'.format(avg_psnr_sr))
logger.info('# Validation # SSIM: {:.4e}'.format(avg_ssim_sr))
logger.info('# Validation # ERGAS: {:.4e}'.format(avg_ergas_sr))
logger_val = logging.getLogger('val') # validation logger
logger_val.info('<epoch:{:3d}, iter:{:8,d}> psnr_SR: {:.4e}'.format(
epoch, current_step, avg_psnr_sr))
logger_val.info('<epoch:{:3d}, iter:{:8,d}> psnr_LR: {:.4e}'.format(
epoch, current_step, avg_psnr_lr))
logger_val.info('<epoch:{:3d}, iter:{:8,d}> psnr_DIF: {:.4e}'.format(
epoch, current_step, avg_psnr_dif))
logger_val.info('<epoch:{:3d}, iter:{:8,d}> ssim_LR: {:.4e}'.format(
epoch, current_step, avg_ssim_lr))
logger_val.info('<epoch:{:3d}, iter:{:8,d}> ssim_SR: {:.4e}'.format(
epoch, current_step, avg_ssim_sr))
logger_val.info('<epoch:{:3d}, iter:{:8,d}> ssim_DIF: {:.4e}'.format(
epoch, current_step, avg_ssim_dif))
logger_val.info('<epoch:{:3d}, iter:{:8,d}> ergas_LR: {:.4e}'.format(
epoch, current_step, avg_ergas_lr))
logger_val.info('<epoch:{:3d}, iter:{:8,d}> ergas_SR: {:.4e}'.format(
epoch, current_step, avg_ergas_sr))
logger_val.info('<epoch:{:3d}, iter:{:8,d}> ergas_DIF: {:.4e}'.format(
epoch, current_step, avg_ergas_dif))
# tensorboarqd logger
# if opt['use_tb_logger'] and 'debug' not in opt['name']:
# tb_logger_val.add_scalar('dif_PSNR', avg_psnr_dif, current_step)
# # tb_logger.add_scalar('psnr', avg_psnr, current_step)
# tb_logger_val.add_scalar('dif_SSIM', avg_ssim_dif, current_step)
# tb_logger_val.add_scalar('dif_ERGAS', avg_ergas_dif, current_step)
# tb_logger_val.add_scalar('psnr_LR', avg_psnr_lr, current_step)
# # tb_logger.add_scalar('psnr', avg_psnr, current_step)
# tb_logger_val.add_scalar('ssim_LR', avg_ssim_lr, current_step)
# tb_logger_val.add_scalar('ERGAS_LR', avg_ergas_lr, current_step)
# tb_logger_val.add_scalar('psnr_SR', avg_psnr_sr, current_step)
# # tb_logger.add_scalar('psnr', avg_psnr, current_step)
# tb_logger_val.add_scalar('ssim_SR', avg_ssim_sr, current_step)
# tb_logger_val.add_scalar('ERGAS_SR', avg_ergas_sr, current_step)
# fig1= ep.plot_rgb(sr_img, rgb=[2, 1, 0], stretch=True)
# tb_logger_val.add_figure("SR_plt", fig1, current_step,close=True)
# fig2 = ep.plot_rgb(gt_img, rgb=[2, 1, 0], stretch=True)
# tb_logger_val.add_figure("GT_plt", fig2, current_step, close=True)
# fig3 = ep.plot_rgb(lr_img, rgb=[2, 1, 0], stretch=True)
# tb_logger_val.add_figure("LR_plt", fig3, current_step, close=True)
# # print("TERMINO GUARDAR IMG TB")
if current_step % opt['logger']['save_checkpoint_freq'] == 0:
logger.info('Saving models and training states.')
print("Current step: ", current_step)
model.save(current_step)
model.save_training_state(epoch, current_step)
logger.info('Saving the final model.')
model.save('latest')
logger.info('End of training.')
if __name__ == '__main__':
main()