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utils.py
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utils.py
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import os
import numpy as np
import errno
import torchvision.utils as vutils
from tensorboardX import SummaryWriter
from IPython import display
from matplotlib import pyplot as plt
import torch
from torch.autograd.variable import Variable
'''
TensorBoard Data will be stored in './runs' path
'''
class Logger:
def __init__(self, model_name, data_name):
self.model_name = model_name
self.data_name = data_name
self.comment = '{}_{}'.format(model_name, data_name)
self.data_subdir = '{}/{}'.format(model_name, data_name)
# TensorBoard
self.writer = SummaryWriter(comment=self.comment)
def log(self, d_error, g_error, epoch, n_batch, num_batches):
var_class = Variable
if type(d_error)==var_class:
d_error = d_error.data.cpu().numpy()
if type(g_error)==var_class:
g_error = g_error.data.cpu().numpy()
step = Logger._step(epoch, n_batch, num_batches)
self.writer.add_scalar(
'{}/D_error'.format(self.comment), d_error, step)
self.writer.add_scalar(
'{}/G_error'.format(self.comment), g_error, step)
def log_images(self, images, num_images, epoch, n_batch, num_batches, format='NCHW', normalize=True):
'''
input images are expected in format (NCHW)
'''
if type(images) == np.ndarray:
images = torch.from_numpy(images)
if format=='NHWC':
images = images.transpose(1,3)
step = Logger._step(epoch, n_batch, num_batches)
img_name = '{}/images{}'.format(self.comment, '')
# Make horizontal grid from image tensor
horizontal_grid = vutils.make_grid(
images, normalize=normalize, scale_each=True)
# Make vertical grid from image tensor
nrows = int(np.sqrt(num_images))
grid = vutils.make_grid(
images, nrow=nrows, normalize=True, scale_each=True)
# Add horizontal images to tensorboard
self.writer.add_image(img_name, horizontal_grid, step)
# Save plots
self.save_torch_images(horizontal_grid, grid, epoch, n_batch)
def save_torch_images(self, horizontal_grid, grid, epoch, n_batch, plot_horizontal=True):
out_dir = './data/images/{}'.format(self.data_subdir)
Logger._make_dir(out_dir)
# Plot and save horizontal
fig = plt.figure(figsize=(16, 16))
plt.imshow(np.moveaxis(horizontal_grid.numpy(), 0, -1))
plt.axis('off')
if plot_horizontal:
display.display(plt.gcf())
self._save_images(fig, epoch, n_batch, 'hori')
plt.close()
# Save squared
fig = plt.figure()
plt.imshow(np.moveaxis(grid.numpy(), 0, -1))
plt.axis('off')
self._save_images(fig, epoch, n_batch)
plt.close()
def _save_images(self, fig, epoch, n_batch, comment=''):
out_dir = './data/images/{}'.format(self.data_subdir)
Logger._make_dir(out_dir)
fig.savefig('{}/{}_epoch_{}_batch_{}.png'.format(out_dir,
comment, epoch, n_batch))
def display_status(self, epoch, num_epochs, n_batch, num_batches, d_error, g_error, d_pred_real, d_pred_fake):
var_class = Variable
if type(d_error)==var_class:
d_error = d_error.data.cpu().numpy()[0]
if type(g_error)==var_class:
g_error = g_error.data.cpu().numpy()[0]
if type(d_pred_real)==var_class:
d_pred_real = d_pred_real.data
if type(d_pred_fake)==var_class:
d_pred_fake = d_pred_fake.data
print('Epoch: [{}/{}], Batch Num: [{}/{}]'.format(
epoch,num_epochs, n_batch, num_batches)
)
print('Discriminator Loss: {:.4f}, Generator Loss: {:.4f}'.format(d_error, g_error))
print('D(x): {:.4f}, D(G(z)): {:.4f}'.format(d_pred_real.mean(), d_pred_fake.mean()))
def save_models(self, generator, discriminator, epoch):
out_dir = './data/models/{}'.format(self.data_subdir)
Logger._make_dir(out_dir)
torch.save(generator.state_dict(),
'{}/G_epoch_{}'.format(out_dir, epoch))
torch.save(discriminator.state_dict(),
'{}/D_epoch_{}'.format(out_dir, epoch))
def close(self):
self.writer.close()
# Private Functionality
@staticmethod
def _step(epoch, n_batch, num_batches):
return epoch * num_batches + n_batch
@staticmethod
def _make_dir(directory):
try:
os.makedirs(directory)
except OSError as e:
if e.errno != errno.EEXIST:
raise