From 0658fb672e4d3f79f8aaaf53bcea3c2210afaf23 Mon Sep 17 00:00:00 2001 From: AshuKV <48694961+AshuKV@users.noreply.github.com> Date: Fri, 12 Jul 2019 11:08:18 +0530 Subject: [PATCH] Create visualizer11.py --- util/visualizer11.py | 232 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 232 insertions(+) create mode 100644 util/visualizer11.py diff --git a/util/visualizer11.py b/util/visualizer11.py new file mode 100644 index 00000000000..1eba1838781 --- /dev/null +++ b/util/visualizer11.py @@ -0,0 +1,232 @@ +import numpy as np +import os +import sys +import ntpath +import time +from . import util, html +from subprocess import Popen, PIPE +#from scipy.misc import imresize + +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) + h, w, _ = im.shape + if aspect_ratio > 1.0: +# #im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic') + im = np.array(Image.fromarray(im).resize((h, int(w * aspect_ratio)))) + if aspect_ratio < 1.0: + #im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic') + im = np.array(Image.fromarray(im).resize((int(h / aspect_ratio), w))) + util.save_image(im, save_path) + + 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.display_id > 0: # connect to a visdom server given and + import visdom + self.ncols = opt.display_ncols + self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env) + if not self.vis.check_connection(): + self.create_visdom_connections() + + 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.display_id > 0: # show images in the browser using visdom + ncols = self.ncols + if ncols > 0: # show all the images in one visdom panel + ncols = min(ncols, len(visuals)) + h, w = next(iter(visuals.values())).shape[:2] + table_css = """""" % (w, h) # create a table css + # create a table of images. + title = self.name + label_html = '' + label_html_row = '' + images = [] + idx = 0 + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + label_html_row += '%s' % label + images.append(image_numpy.transpose([2, 0, 1])) + idx += 1 + if idx % ncols == 0: + label_html += '%s' % label_html_row + label_html_row = '' + white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255 + while idx % ncols != 0: + images.append(white_image) + label_html_row += '' + idx += 1 + if label_html_row != '': + label_html += '%s' % label_html_row + try: + self.vis.images(images, nrow=ncols, win=self.display_id + 1, + padding=2, opts=dict(title=title + ' images')) + label_html = '%s
' % label_html + self.vis.text(table_css + label_html, win=self.display_id + 2, + opts=dict(title=title + ' labels')) + except VisdomExceptionBase: + self.create_visdom_connections() + + else: # show each image in a separate visdom panel; + idx = 1 + try: + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label), + win=self.display_id + idx) + idx += 1 + except VisdomExceptionBase: + self.create_visdom_connections() + + 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 228 lines (200 sloc) 10.5 KB +import numpy as np +import os +import sys.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