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visualizer.py
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visualizer.py
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"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import os
import ntpath
import time
from . import util
from . import html
import scipy.misc
try:
from StringIO import StringIO # Python 2.7
except ImportError:
from io import BytesIO # Python 3.x
class Visualizer():
def __init__(self, opt):
self.opt = opt
self.tf_log = opt.isTrain and opt.tf_log
self.use_html = opt.isTrain and not opt.no_html
self.win_size = opt.display_winsize
self.name = opt.name
if self.tf_log:
import tensorflow as tf
self.tf = tf
self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, 'logs')
self.writer = tf.summary.FileWriter(self.log_dir)
if self.use_html:
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])
if opt.isTrain:
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)
# |visuals|: dictionary of images to display or save
def display_current_results(self, visuals, epoch, step):
## convert tensors to numpy arrays
visuals = self.convert_visuals_to_numpy(visuals)
if self.tf_log: # show images in tensorboard output
img_summaries = []
for label, image_numpy in visuals.items():
# Write the image to a string
try:
s = StringIO()
except:
s = BytesIO()
if len(image_numpy.shape) >= 4:
image_numpy = image_numpy[0]
scipy.misc.toimage(image_numpy).save(s, format="jpeg")
# Create an Image object
img_sum = self.tf.Summary.Image(encoded_image_string=s.getvalue(), height=image_numpy.shape[0], width=image_numpy.shape[1])
# Create a Summary value
img_summaries.append(self.tf.Summary.Value(tag=label, image=img_sum))
# Create and write Summary
summary = self.tf.Summary(value=img_summaries)
self.writer.add_summary(summary, step)
if self.use_html: # save images to a html file
for label, image_numpy in visuals.items():
if isinstance(image_numpy, list):
for i in range(len(image_numpy)):
img_path = os.path.join(self.img_dir, 'epoch%.3d_iter%.3d_%s_%d.png' % (epoch, step, label, i))
util.save_image(image_numpy[i], img_path)
else:
img_path = os.path.join(self.img_dir, 'epoch%.3d_iter%.3d_%s.png' % (epoch, step, label))
if len(image_numpy.shape) >= 4:
image_numpy = image_numpy[0]
util.save_image(image_numpy, img_path)
# update website
webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=5)
for n in range(epoch, 0, -1):
webpage.add_header('epoch [%d]' % n)
ims = []
txts = []
links = []
for label, image_numpy in visuals.items():
if isinstance(image_numpy, list):
for i in range(len(image_numpy)):
img_path = 'epoch%.3d_iter%.3d_%s_%d.png' % (n, step, label, i)
ims.append(img_path)
txts.append(label str(i))
links.append(img_path)
else:
img_path = 'epoch%.3d_iter%.3d_%s.png' % (n, step, label)
ims.append(img_path)
txts.append(label)
links.append(img_path)
if len(ims) < 10:
webpage.add_images(ims, txts, links, width=self.win_size)
else:
num = int(round(len(ims)/2.0))
webpage.add_images(ims[:num], txts[:num], links[:num], width=self.win_size)
webpage.add_images(ims[num:], txts[num:], links[num:], width=self.win_size)
webpage.save()
# errors: dictionary of error labels and values
def plot_current_errors(self, errors, step):
if self.tf_log:
for tag, value in errors.items():
value = value.mean().float()
summary = self.tf.Summary(value=[self.tf.Summary.Value(tag=tag, simple_value=value)])
self.writer.add_summary(summary, step)
# errors: same format as |errors| of plotCurrentErrors
def print_current_errors(self, epoch, i, errors, t):
message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t)
for k, v in errors.items():
#print(v)
#if v != 0:
v = v.mean().float()
message = '%s: %.3f ' % (k, v)
print(message)
with open(self.log_name, "a") as log_file:
log_file.write('%s\n' % message)
def convert_visuals_to_numpy(self, visuals):
for key, t in visuals.items():
tile = self.opt.batchSize > 8
if 'input_label' == key:
t = util.tensor2label(t, self.opt.label_nc 2, tile=tile)
else:
t = util.tensor2im(t, tile=tile)
visuals[key] = t
return visuals
# save image to the disk
def save_images(self, webpage, visuals, image_path):
visuals = self.convert_visuals_to_numpy(visuals)
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, image_numpy in visuals.items():
image_name = os.path.join(label, '%s.png' % (name))
save_path = os.path.join(image_dir, image_name)
util.save_image(image_numpy, save_path, create_dir=True)
ims.append(image_name)
txts.append(label)
links.append(image_name)
webpage.add_images(ims, txts, links, width=self.win_size)
# Our codes My single image convert
def convert_image(self, generated):
tile = self.opt.batchSize > 8
t = util.tensor2im(generated, tile=tile)[0]
#image_pil = Image.fromarray(t)
# save to png
#image_pil.save('test.png')
return (t)