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train.py
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train.py
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import time
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
import os
from util import html
from util.visualizer import save_images
from util.metrics import AverageMeter
import copy
import numpy as np
import torch
import random
def seed_torch(seed=2019):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
# set seeds
# seed_torch(2019)
ifSaveImage = False
def make_val_opt(opt):
val_opt = copy.deepcopy(opt)
val_opt.preprocess = '' #
# hard-code some parameters for test
val_opt.num_threads = 0 # test code only supports num_threads = 1
val_opt.batch_size = 4 # test code only supports batch_size = 1
val_opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
val_opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
val_opt.angle = 0
val_opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file.
val_opt.phase = 'val'
val_opt.split = opt.val_split # function in jsonDataset and ListDataset
val_opt.isTrain = False
val_opt.aspect_ratio = 1
val_opt.results_dir = './results/'
val_opt.dataroot = opt.val_dataroot
val_opt.dataset_mode = opt.val_dataset_mode
val_opt.dataset_type = opt.val_dataset_type
val_opt.json_name = opt.val_json_name
val_opt.eval = True
val_opt.num_test = 2000
return val_opt
def print_current_acc(log_name, epoch, score):
"""print current acc on console; also save the losses to the disk
Parameters:
"""
message = '(epoch: %d) ' % epoch
for k, v in score.items():
message += '%s: %.3f ' % (k, v)
print(message) # print the message
with open(log_name, "a") as log_file:
log_file.write('%s\n' % message) # save the message
def val(opt, model):
opt = make_val_opt(opt)
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
# model = create_model(opt) # create a model given opt.model and other options
# model.setup(opt) # regular setup: load and print networks; create schedulers
web_dir = os.path.join(opt.checkpoints_dir, opt.name, '%s_%s' % (opt.phase, opt.epoch)) # define the website directory
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
model.eval()
# create a logging file to store training losses
log_name = os.path.join(opt.checkpoints_dir, opt.name, 'val_log.txt')
with open(log_name, "a") as log_file:
now = time.strftime("%c")
log_file.write('================ val acc (%s) ================\n' % now)
running_metrics = AverageMeter()
for i, data in enumerate(dataset):
if i >= opt.num_test: # only apply our model to opt.num_test images.
break
model.set_input(data) # unpack data from data loader
score = model.test(val=True) # run inference
running_metrics.update(score)
visuals = model.get_current_visuals() # get image results
img_path = model.get_image_paths() # get image paths
if i % 5 == 0: # save images to an HTML file
print('processing (%04d)-th image... %s' % (i, img_path))
if ifSaveImage:
save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
score = running_metrics.get_scores()
print_current_acc(log_name, epoch, score)
if opt.display_id > 0:
visualizer.plot_current_acc(epoch, float(epoch_iter) / dataset_size, score)
webpage.save() # save the HTML
return score[metric_name]
metric_name = 'F1_1'
if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
dataset_size = len(dataset) # get the number of images in the dataset.
print('The number of training images = %d' % dataset_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
miou_best = 0
n_epoch_bad = 0
epoch_best = 0
time_metric = AverageMeter()
time_log_name = os.path.join(opt.checkpoints_dir, opt.name, 'time_log.txt')
with open(time_log_name, "a") as log_file:
now = time.strftime("%c")
log_file.write('================ training time (%s) ================\n' % now)
for epoch in range(opt.epoch_count, opt.niter + opt.niter_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
model.train()
# miou_current = val(opt, model)
for i, data in enumerate(dataset): # 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
visualizer.reset()
total_iters += opt.batch_size
epoch_iter += opt.batch_size
n_epoch = opt.niter + opt.niter_decay
model.set_input(data) # unpack data from dataset and apply preprocessing
model.optimize_parameters() # calculate loss functions, get gradients, update network weights
if ifSaveImage:
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()
t_comp = (time.time() - iter_start_time) / opt.batch_size
visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
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)
iter_data_time = time.time()
t_epoch = time.time()-epoch_start_time
time_metric.update(t_epoch)
print_current_acc(time_log_name, epoch,{"current_t_epoch": t_epoch})
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')
miou_current = val(opt, model)
if miou_current > miou_best:
miou_best = miou_current
epoch_best = epoch
model.save_networks(str(epoch_best)+"_"+metric_name+'_'+'%0.5f'% miou_best)
print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
model.update_learning_rate() # update learning rates at the end of every epoch.
time_ave = time_metric.average()
print_current_acc(time_log_name, epoch, {"ave_t_epoch": time_ave})