annotator/leres/pix2pix/util/visualizer.py

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 <checkpoints_dir>/web/; images will be saved under <checkpoints_dir>/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