def y_real(self, xreal, y):
# Run discriminator on real images
yreal = self.discriminate(xreal, y)
# Save images for later
self._state_hooks['xreal'] = xreal
self._state_hooks['real_images'] = format_images(xreal)
return yreal
def save_images(self, source=False, real=False):
# Generate images
path = os.path.join(self.trainer.save_directory, 'generated_images')
os.makedirs(path, exist_ok=True) # create directory if necessary
self.image_count += 1
if(source):
images = [Variable(self.y)]
image_paths = [os.path.join(path, 'source.png'.format(self.image_count))]
elif(real):
images = [Variable(self.xreal)]
image_paths = [os.path.join(path, 'real_target.png'.format(self.image_count))]
else:
images = self.generate()
image_paths = [os.path.join(path, 'test_'+str(i)+'/target_generated{:08d}.png'.format(self.image_count)) for i in range(len(images))]
for i in range(len(images)):
image = images[i]
# Reshape, scale, and cast the data so it can be saved
grid = format_images(image).squeeze(0).permute(1, 2, 0)
if grid.size(2) == 1:
grid = grid.squeeze(2)
array = grid.data.cpu().numpy() * 255.
array = array.astype(np.uint8)
# Save the image
Image.fromarray(array).save(image_paths[i])
def generate(self, y):
# Generate fake images from input
xfake = self.generator(y)
# Save images for later
self._state_hooks['xfake'] = xfake
self._state_hooks['y'] = y
self._state_hooks['generated_images'] = format_images(xfake) # log the generated images
return xfake
def save_images(self):
# Generate images
path = os.path.join(self.trainer.save_directory, 'generated_images')
os.makedirs(path, exist_ok=True) # create directory if necessary
image_path = os.path.join(path, '{:08d}.png'.format(self.image_count))
self.image_count += 1
generated = self.generate()
# Reshape, scale, and cast the data so it can be saved
grid = format_images(generated).squeeze(0).permute(1, 2, 0)
if grid.size(2) == 1:
grid = grid.squeeze(2)
array = grid.data.cpu().numpy() * 255.
array = array.astype(np.uint8)
# Save the image
Image.fromarray(array).save(image_path)
