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trainVal4dVel.py
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trainVal4dVel.py
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"""General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model').
It first creates model, dataset, and visualizer given the option.
It then does standard network training. During the training, it also visualize/save the images, print/save the loss plot, and save models.
The script supports continue/resume training. Use '--continue_train' to resume your previous training.
Example:
Train a CycleGAN model:
python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
Train a pix2pix model:
python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
See options/base_options.py and options/train_options.py for more training options.
See training and test tips at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md
See frequently asked questions at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md
"""
import time
from typing import OrderedDict
from options.train_options import TrainOptions
from data import create_dataset
from data import create_dataset2
from models import create_model
from util.visualizer import Visualizer
import numpy as np
#import ray
if __name__ == '__main__':
#ray.init()
print('run till here 0')
opt = TrainOptions().parse() # get training options
print('run till here 1')
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
dataset2 = create_dataset2(opt) #create dataset for validation
dataset_size = len(dataset) # get the number of images in the dataset.
print("shape of dataset :", np.shape(dataset2))
print("shape of dataset :", np.shape(dataset))
dataset2_size = len(dataset2) #get the number of images in validation dataset
print('The number of training images = %d' % dataset_size)
print('The number of validation images = %d' % dataset2_size)
model = create_model(opt) # create a model given opt.model and other options
model.setup(opt) # regular setup: load and print networks; create schedulers
visualizer = Visualizer(opt) # create a visualizer that display/save images and plots
total_iters = 0 # the total number of training iterations
losses1 = OrderedDict()
lstart = 50000
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
model.eval() #For going to validation
Validationloss = 0.0
for k, data2 in enumerate(dataset2):
model.set_input(data2)
model.test()
model.compute_loss_only()
Validationloss = Validationloss + model.loss_V_MSE.item()
#model.update_epoch(epoch)
model.train()
model.update_learning_rate() # update learning rates in the beginning of every epoch.
Modelloss = 0.0
Dataloss = 0.0
Model1loss = 0.0
#KLloss = 0.0
for i, data in enumerate(dataset): # inner loop within one epoch
##print("i: " + str(i))
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
total_iters += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data) # unpack data from dataset and apply preprocessing
####model.optimize_parameters(epoch,i,lstart) # calculate loss functions, get gradients, update network weights
model.optimize_parameters(epoch)
#model.test()
#if (i==190):
# visuals = model.get_current_visuals()
# print(visuals['real_B'])
#print("model losses out of loop")
#print(model.loss_M_MSE.item())
# if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk
# losses = model.get_current_losses()
# t_comp = (time.time() - iter_start_time) / opt.batch_size
# visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
# print("---epoch----")
# print(epoch)
# print("--losses---")
# print(losses)
# if 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))
# save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
# model.save_networks(save_suffix)
#if (i==259):
# print(data['A'])
# print(data['C'])
# model.print_values()
#print(model.fake_B)
#np.save('./datasets/testO/A.npy',data['A'].numpy())
#np.save('./datasets/testO/B.npy',data['B'].numpy())
iter_data_time = time.time()
Modelloss = Modelloss + model.loss_M_MSE.item()
Dataloss = Dataloss + model.loss_D_MSE
if (epoch > lstart):
Model1loss = Model1loss + model.loss_M1_MSE.item()
else:
Model1loss = Model1loss + model.loss_M1_MSE
#KLloss = KLloss + model.loss_K_MSE.item()
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)
if epoch % 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 epoch % opt.display_freq == 0: #plot losses
losses1['Modelloss'] = Modelloss/(i+1)
losses1['Dataloss'] = Dataloss/(i+1)
losses1['Validationloss'] = Validationloss/(k+1)
losses1['Model1loss'] = Model1loss/(i+1)
#losses1['KL divergence'] = KLloss/i
#print(losses1)
#losses2 = model.get_current_losses()
#print(losses2)
visualizer.plot_current_losses(epoch, 0, losses1)
print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))