import numpy as np import os import sys import ntpath import time from . import util, html from subprocess import Popen, PIPE import torch if sys.version_info[0] == 2: VisdomExceptionBase = Exception else: VisdomExceptionBase = ConnectionError def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256): """Save images to the disk. Parameters: webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details) visuals (OrderedDict) -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs image_path (str) -- the string is used to create image paths aspect_ratio (float) -- the aspect ratio of saved images width (int) -- the images will be resized to width x width This function will save images stored in 'visuals' to the HTML file specified by 'webpage'. """ image_dir = webpage.get_image_dir() short_path = ntpath.basename(image_path[0]) name = os.path.splitext(short_path)[0] webpage.add_header(name) ims, txts, links = [], [], [] for label, im_data in visuals.items(): im = util.tensor2im(im_data) image_name = '%s_%s.png' % (name, label) save_path = os.path.join(image_dir, image_name) util.save_image(im, save_path, aspect_ratio=aspect_ratio) ims.append(image_name) txts.append(label) links.append(image_name) webpage.add_images(ims, txts, links, width=width) class Visualizer(): """This class includes several functions that can display/save images and print/save logging information. It uses a Python library 'visdom' for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with images. """ def __init__(self, opt): """Initialize the Visualizer class Parameters: opt -- stores all the experiment flags; needs to be a subclass of BaseOptions Step 1: Cache the training/test options Step 2: connect to a visdom server Step 3: create an HTML object for saveing HTML filters Step 4: create a logging file to store training losses """ self.opt = opt # cache the option self.display_id = opt.display_id self.use_html = opt.isTrain and not opt.no_html self.win_size = opt.display_winsize self.name = opt.name self.port = opt.display_port self.saved = False if self.use_html: # create an HTML object at /web/; images will be saved under /web/images/ self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') self.img_dir = os.path.join(self.web_dir, 'images') print('create web directory %s...' % self.web_dir) util.mkdirs([self.web_dir, self.img_dir]) # create a logging file to store training losses self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') with open(self.log_name, "a") as log_file: now = time.strftime("%c") log_file.write('================ Training Loss (%s) ================\n' % now) def reset(self): """Reset the self.saved status""" self.saved = False def create_visdom_connections(self): """If the program could not connect to Visdom server, this function will start a new server at port < self.port > """ cmd = sys.executable + ' -m visdom.server -p %d &>/dev/null &' % self.port print('\n\nCould not connect to Visdom server. \n Trying to start a server....') print('Command: %s' % cmd) Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE) def display_current_results(self, visuals, epoch, save_result): """Display current results on visdom; save current results to an HTML file. Parameters: visuals (OrderedDict) - - dictionary of images to display or save epoch (int) - - the current epoch save_result (bool) - - if save the current results to an HTML file """ if self.use_html and (save_result or not self.saved): # save images to an HTML file if they haven't been saved. self.saved = True # save images to the disk for label, image in visuals.items(): image_numpy = util.tensor2im(image) img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label)) util.save_image(image_numpy, img_path) # update website webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=1) for n in range(epoch, 0, -1): webpage.add_header('epoch [%d]' % n) ims, txts, links = [], [], [] for label, image_numpy in visuals.items(): # image_numpy = util.tensor2im(image) img_path = 'epoch%.3d_%s.png' % (n, label) ims.append(img_path) txts.append(label) links.append(img_path) webpage.add_images(ims, txts, links, width=self.win_size) webpage.save() # def plot_current_losses(self, epoch, counter_ratio, losses): # """display the current losses on visdom display: dictionary of error labels and values # # Parameters: # epoch (int) -- current epoch # counter_ratio (float) -- progress (percentage) in the current epoch, between 0 to 1 # losses (OrderedDict) -- training losses stored in the format of (name, float) pairs # """ # if not hasattr(self, 'plot_data'): # self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} # self.plot_data['X'].append(epoch + counter_ratio) # self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']]) # try: # self.vis.line( # X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1), # Y=np.array(self.plot_data['Y']), # opts={ # 'title': self.name + ' loss over time', # 'legend': self.plot_data['legend'], # 'xlabel': 'epoch', # 'ylabel': 'loss'}, # win=self.display_id) # except VisdomExceptionBase: # self.create_visdom_connections() # losses: same format as |losses| of plot_current_losses def print_current_losses(self, epoch, iters, losses, t_comp, t_data): """print current losses on console; also save the losses to the disk Parameters: epoch (int) -- current epoch iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch) losses (OrderedDict) -- training losses stored in the format of (name, float) pairs t_comp (float) -- computational time per data point (normalized by batch_size) t_data (float) -- data loading time per data point (normalized by batch_size) """ message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data) for k, v in losses.items(): message += '%s: %.3f ' % (k, v) print(message) # print the message with open(self.log_name, "a") as log_file: log_file.write('%s\n' % message) # save the message