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_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 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 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})