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import os
import numpy as np
import torch
import math
from PIL import Image
# import matplotlib.pyplot as plt
class Visualizer(object):
"""docstring for Visualizer"""
def __init__(self):
super(Visualizer, self).__init__()
def initialize(self, opt):
self.opt = opt
# self.vis_saved_dir = os.path.join(self.opt.ckpt_dir, 'vis_pics')
# if not os.path.isdir(self.vis_saved_dir):
# os.makedirs(self.vis_saved_dir)
# plt.switch_backend('agg')
self.display_id = self.opt.visdom_display_id
if self.display_id > 0:
import visdom
self.ncols = 8
self.vis = visdom.Visdom(server="http://localhost", port=self.opt.visdom_port, env=self.opt.visdom_env)
def throw_visdom_connection_error(self):
print('\n\nno visdom server.')
exit(1)
def print_losses_info(self, info_dict):
msg = '[{}][Epoch: {:0>3}/{:0>3}; Images: {:0>4}/{:0>4}; Time: {:.3f}s/Batch({}); LR: {:.7f}] '.format(
self.opt.name, info_dict['epoch'], info_dict['epoch_len'],
info_dict['epoch_steps'], info_dict['epoch_steps_len'],
info_dict['step_time'], self.opt.batch_size, info_dict['cur_lr'])
for k, v in info_dict['losses'].items():
msg += '| {}: {:.4f} '.format(k, v)
msg += '|'
print(msg)
with open(info_dict['log_path'], 'a+') as f:
f.write(msg + '\n')
def display_current_losses(self, epoch, counter_ratio, losses_dict):
if not hasattr(self, 'plot_data'):
self.plot_data = {'X': [], 'Y': [], 'legend': list(losses_dict.keys())}
self.plot_data['X'].append(epoch + counter_ratio)
self.plot_data['Y'].append([losses_dict[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.opt.name + ' loss over time',
'legend':self.plot_data['legend'],
'xlabel':'epoch',
'ylabel':'loss'},
win=self.display_id)
except ConnectionError:
self.throw_visdom_connection_error()
def display_online_results(self, visuals, epoch):
win_id = self.display_id + 24
images = []
labels = []
for label, image in visuals.items():
if 'mask' in label: # or 'focus' in label:
image = (image - 0.5) / 0.5 # convert map from [0, 1] to [-1, 1]
image_numpy = self.tensor2im(image)
images.append(image_numpy.transpose([2, 0, 1]))
labels.append(label)
try:
title = ' || '.join(labels)
self.vis.images(images, nrow=self.ncols, win=win_id,
padding=5, opts=dict(title=title))
except ConnectionError:
self.throw_visdom_connection_error()
# utils
def tensor2im(self, input_image, imtype=np.uint8):
if isinstance(input_image, torch.Tensor):
image_tensor = input_image.data
else:
return input_image
image_numpy = image_tensor[0].cpu().float().numpy()
im = self.numpy2im(image_numpy, imtype).resize((80, 80), Image.ANTIALIAS)
return np.array(im)
def numpy2im(self, image_numpy, imtype=np.uint8):
if image_numpy.shape[0] == 1:
image_numpy = np.tile(image_numpy, (3, 1, 1))
# input should be [0, 1]
#image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) / 2. + 0.5) * 255.0
# print(image_numpy.shape)
image_numpy = image_numpy.astype(imtype)
im = Image.fromarray(image_numpy)
# im = Image.fromarray(image_numpy).resize((64, 64), Image.ANTIALIAS)
return im # np.array(im)
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