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| """This script defines the visualizer for Deep3DFaceRecon_pytorch | |
| """ | |
| import numpy as np | |
| import os | |
| import sys | |
| import ntpath | |
| import time | |
| from . import util, html | |
| from subprocess import Popen, PIPE | |
| 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 saving 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 <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 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 saving HTML filters | |
| Step 4: create a logging file to store training losses | |
| """ | |
| self.opt = opt # cache the option | |
| self.name = opt.name | |
| 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)) | |
| 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 | |