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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 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 <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 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_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
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