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train.py
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import time
import torch
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
import os
from data.unpaired_dataset import ValidationSet
from torch.utils.data import Dataset, DataLoader
from metrics.metric import Normalization, calculate_ssim
from torchmetrics.functional import structural_similarity_index_measure as ssim
if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
dataset2 = create_dataset(opt)
dataset_size = len(dataset) # get the number of images in the dataset.
model = create_model(opt) # create a model given opt.model and other options
print('The number of training images = %d' % dataset_size)
visualizer = Visualizer(opt) # create a visualizer that display/save images and plots
opt.visualizer = visualizer
total_iters = 0 # the total number of training iterations
optimize_time = 0.1
###
if opt.if_validation ==True: # track validation result during training
print(f'Creating Validation Dataloder based on set:{opt.validation_dict_path}')
vali_data = ValidationSet(opt)
validation_dataloader = DataLoader(vali_data,batch_size=opt.validation_batch,shuffle=False,drop_last=False) ##validation
print(f'Validation set has been created')
###
times = []
best_ssim = opt.best_SSIM ## best ssim value for validation, need to modify when continue training ###
for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1): # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
epoch_start_time = time.time() # timer for entire epoch
iter_data_time = time.time() # timer for data loading per iteration
epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch
visualizer.reset() # reset the visualizer: make sure it saves the results to HTML at least once every epoch
dataset.set_epoch(epoch)
for i, (data,data2) in enumerate(zip(dataset,dataset2)): # inner loop within one epoch
iter_start_time = time.time() # timer for computation per iteration
if total_iters % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
batch_size = data["A"].size(0)
total_iters += batch_size ### how many images has been processed ###
epoch_iter += batch_size
if len(opt.gpu_ids) > 0:
torch.cuda.synchronize()
optimize_start_time = time.time()
if epoch == opt.epoch_count and i == 0: ### intialize the network start from opt.epoch_count ###
model.data_dependent_initialize(data,data2)
model.setup(opt) # regular setup: load and print networks; create schedulers
model.parallelize()
model.set_input(data,data2) # unpack data from dataset and apply preprocessing
model.optimize_parameters() # calculate loss functions, get gradients, update network weights
if len(opt.gpu_ids) > 0:
torch.cuda.synchronize()
optimize_time = (time.time() - optimize_start_time) / batch_size * 0.005 + 0.995 * optimize_time
if total_iters % opt.display_freq == 0: # display images on visdom and save images to a HTML file
save_result = total_iters % opt.update_html_freq == 0
model.compute_visuals()
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk
losses = model.get_current_losses()
visualizer.print_current_losses(epoch, epoch_iter, losses, optimize_time, t_data)
if opt.display_id is None or opt.display_id > 0:
visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
if total_iters % opt.save_latest_freq == 0: # cache our latest model every <save_latest_freq> iterations
print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
print(opt.name) # it's useful to occasionally show the experiment name on console
save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
model.save_networks(save_suffix)
iter_data_time = time.time()
if epoch % opt.save_epoch_freq == 0: # cache our model every <save_epoch_freq> epochs
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
model.save_networks('latest')
model.save_networks(epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))
model.update_learning_rate() # update learning rates at the end of every epoch.
### calculate the psnr,ssim over validation set att the end of every epoch
if opt.if_validation ==True and opt.validation_phase == True:
model.eval() ## set to eval state
ssim_value = 0 ## ssim value for each validation epoch ###
opt.phase = 'test' # modify the phase for validation
with torch.no_grad():
for i,data in enumerate(validation_dataloader): #data is a dict contain 2 tensor(b,c,h,w) and 2 image list
model.set_input(data)
model.forward() ## call forward function to get related interval value
fake_target = getattr(model,f'fake_1')
true_target = model.real_B
ssim_value += ssim(fake_target,true_target,data_range=(-1.0,1.0))
ssim_best = ssim_value / len(validation_dataloader)
print(f'Epochs:{epoch}: average validation SSIM:{ssim_best}')
if ssim_best > best_ssim:
best_ssim = ssim_best
model.save_networks(f'ssim_best')
print(f'Saving best Epochs:{epoch}: best SSIM:{best_ssim}')
model.train() # set back to training
opt.phase = 'train' # set phase back to training