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