"""This script defines the visualizer for Deep3DFaceRecon_pytorch """ import os import ntpath import time from . import util, html from torch.utils.tensorboard import SummaryWriter 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' % (label, name) os.makedirs(os.path.join(image_dir, label), exist_ok=True) 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 tensprboardX 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: create a tensorboard writer 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.use_html = opt.isTrain and not opt.no_html self.writer = SummaryWriter(os.path.join(opt.checkpoints_dir, 'logs', opt.name)) self.win_size = opt.display_winsize self.name = opt.name 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 display_current_results(self, visuals, total_iters, epoch, save_result): """Display current results on tensorboad; save current results to an HTML file. Parameters: visuals (OrderedDict) - - dictionary of images to display or save total_iters (int) -- total iterations epoch (int) - - the current epoch save_result (bool) - - if save the current results to an HTML file """ for label, image in visuals.items(): self.writer.add_image(label, util.tensor2im(image), total_iters, dataformats='HWC') 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=0) 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, total_iters, losses): # G_loss_collection = {} # D_loss_collection = {} # for name, value in losses.items(): # if 'G' in name or 'NCE' in name or 'idt' in name: # G_loss_collection[name] = value # else: # D_loss_collection[name] = value # self.writer.add_scalars('G_collec', G_loss_collection, total_iters) # self.writer.add_scalars('D_collec', D_loss_collection, total_iters) for name, value in losses.items(): self.writer.add_scalar(name, value, total_iters) # 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 class MyVisualizer: 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: create a tensorboard writer 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 optio self.name = opt.name self.img_folder = opt.img_folder self.img_dir = os.path.join(opt.checkpoints_dir, opt.name, 'results') if opt.phase != 'test': self.writer = SummaryWriter(os.path.join(opt.checkpoints_dir, opt.name, 'logs')) # 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 display_current_results(self, visuals, total_iters, epoch, dataset='train', save_results=False, count=0, name=None, add_image=True): """Display current results on tensorboad; save current results to an HTML file. Parameters: visuals (OrderedDict) - - dictionary of images to display or save total_iters (int) -- total iterations epoch (int) - - the current epoch dataset (str) - - 'train' or 'val' or 'test' """ # if (not add_image) and (not save_results): return for label, image in visuals.items(): for i in range(image.shape[0]): image_numpy = util.tensor2im(image[i]) if add_image: self.writer.add_image(label + '%s_%02d'%(dataset, i + count), image_numpy, total_iters, dataformats='HWC') if save_results: #save_path = os.path.join(self.img_dir, dataset, 'epoch_%s_%06d'%(epoch, total_iters)) save_path = os.path.join(self.img_folder, 'epoch_%s_%06d'%(epoch, total_iters)) #print(self.img_folder) if not os.path.isdir(save_path): os.makedirs(save_path) if name is not None: img_path = os.path.join(save_path, '%s.png' % name) else: img_path = os.path.join(save_path, '%s_%03d.png' % (label, i + count)) util.save_image(image_numpy, img_path) def plot_current_losses(self, total_iters, losses, dataset='train'): for name, value in losses.items(): self.writer.add_scalar(name + '/%s'%dataset, value, total_iters) # losses: same format as |losses| of plot_current_losses def print_current_losses(self, epoch, iters, losses, t_comp, t_data, dataset='train'): """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 = '(dataset: %s, epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % ( dataset, 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