def preprocess_example(self, example, mode, hparams):
image = example["inputs"]
if hasattr(hparams, "resize_method"):
method = getattr(tf.image.ResizeMethod, hparams.resize_method)
else: # default
method = tf.image.ResizeMethod.BICUBIC
scaled_images = image_utils.make_multiscale(
image,
hparams.resolutions,
resize_method=method,
num_channels=self.num_channels)
highest_res = hparams.resolutions[-1]
# Pack tuple of scaled images into one tensor. We do this by enforcing the
# columns to match for every resolution.
# TODO(avaswani, trandustin): We should create tuples because this will not
# work if height*width of low res < width of high res
example["inputs"] = tf.concat([
tf.reshape(scaled_image,
[res**2 // highest_res, highest_res, self.num_channels])
for scaled_image, res in zip(scaled_images, hparams.resolutions)
],
axis=0)
return example
def preprocess_example(self, example, mode, hparams):
image = example["inputs"]
# Get resize method. Include a default if not specified, or if it's not in
# TensorFlow's collection of pre-implemented resize methods.
resize_method = getattr(hparams, "resize_method", "BICUBIC")
resize_method = getattr(tf.image.ResizeMethod, resize_method,
resize_method)
if resize_method == "DILATED":
scaled_images = image_utils.make_multiscale_dilated(
image, hparams.resolutions, num_channels=self.num_channels)
else:
scaled_images = image_utils.make_multiscale(
image,
hparams.resolutions,
resize_method=resize_method,
num_channels=self.num_channels)
# Pack tuple of scaled images into one tensor. We do this by enforcing the
# columns to match for every resolution.
# TODO(avaswani, trandustin): We should create tuples because this will not
# work if height*width of low res < width of high res
highest_res = hparams.resolutions[-1]
example["inputs"] = tf.concat([
tf.reshape(scaled_image,
[res**2 // highest_res, highest_res, self.num_channels])
for scaled_image, res in zip(scaled_images, hparams.resolutions)
],
axis=0)
return example
def preprocess_example(self, example, mode, hparams):
image = example["inputs"]
if hasattr(hparams, "resize_method"):
method = getattr(tf.image.ResizeMethod, hparams.resize_method)
else: # default
method = tf.image.ResizeMethod.BICUBIC
# Remove boundaries in CelebA images. Remove 40 pixels each side
# vertically and 20 pixels each side horizontally.
image = tf.image.crop_to_bounding_box(image, 40, 20, 218 - 80,
178 - 40)
scaled_images = image_utils.make_multiscale(
image,
hparams.resolutions,
resize_method=method,
num_channels=self.num_channels)
# Pack tuple of scaled images into one tensor. We do this by enforcing the
# columns to match for every resolution.
highest_res = hparams.resolutions[-1]
example["inputs"] = image
example["targets"] = tf.concat([
tf.reshape(scaled_image,
[res**2 // highest_res, highest_res, self.num_channels])
for scaled_image, res in zip(scaled_images, hparams.resolutions)
],
axis=0)
return example
def preprocess_example(self, example, mode, hparams):
image = example["inputs"]
# Get resize method. Include a default if not specified, or if it's not in
# TensorFlow's collection of pre-implemented resize methods.
resize_method = getattr(hparams, "resize_method", "BICUBIC")
resize_method = getattr(tf.image.ResizeMethod, resize_method, resize_method)
highest_res = hparams.resolutions[-1]
if resize_method == "DILATED":
# Resize image so that dilated subsampling is properly divisible.
scaled_image = image_utils.resize_by_area(image, highest_res)
scaled_images = image_utils.make_multiscale_dilated(
scaled_image, hparams.resolutions, num_channels=self.num_channels)
else:
scaled_images = image_utils.make_multiscale(
image, hparams.resolutions,
resize_method=resize_method, num_channels=self.num_channels)
# Pack tuple of scaled images into one tensor. We do this by enforcing the
# columns to match for every resolution.
example["inputs"] = tf.concat([
tf.reshape(scaled_image,
[res**2 // highest_res, highest_res, self.num_channels])
for scaled_image, res in zip(scaled_images, hparams.resolutions)],
axis=0)
return example
def preprocess_example(self, example, mode, hparams):
image = example["inputs"]
# Get resize method. Include a default if not specified, or if it's not in
# TensorFlow's collection of pre-implemented resize methods.
resize_method = getattr(hparams, "resize_method", "BICUBIC")
resize_method = getattr(tf.image.ResizeMethod, resize_method, resize_method)
# Remove boundaries in CelebA images. Remove 40 pixels each side
# vertically and 20 pixels each side horizontally.
image = tf.image.crop_to_bounding_box(image, 40, 20, 218 - 80, 178 - 40)
highest_res = hparams.resolutions[-1]
if resize_method == "DILATED":
# Resize image so that dilated subsampling is properly divisible.
scaled_image = image_utils.resize_by_area(image, highest_res)
scaled_images = image_utils.make_multiscale_dilated(
scaled_image, hparams.resolutions, num_channels=self.num_channels)
else:
scaled_images = image_utils.make_multiscale(
image, hparams.resolutions,
resize_method=resize_method, num_channels=self.num_channels)
# Pack tuple of scaled images into one tensor. We do this by enforcing the
# columns to match for every resolution.
example["inputs"] = image
example["targets"] = tf.concat([
tf.reshape(scaled_image,
[res**2 // highest_res, highest_res, self.num_channels])
for scaled_image, res in zip(scaled_images, hparams.resolutions)],
axis=0)
return example
def preprocess_example(self, example, mode, hparams):
image = example["inputs"]
# Get resize method. Include a default if not specified, or if it's not in
# TensorFlow's collection of pre-implemented resize methods.
resize_method = getattr(hparams, "resize_method", "BICUBIC")
resize_method = getattr(tf.image.ResizeMethod, resize_method, resize_method)
if resize_method == "DILATED":
scaled_images = image_utils.make_multiscale_dilated(
image, hparams.resolutions, num_channels=self.num_channels)
else:
scaled_images = image_utils.make_multiscale(
image, hparams.resolutions,
resize_method=resize_method, num_channels=self.num_channels)
# Pack tuple of scaled images into one tensor. We do this by enforcing the
# columns to match for every resolution.
# TODO(avaswani, trandustin): We should create tuples because this will not
# work if height*width of low res < width of high res
highest_res = hparams.resolutions[-1]
example["inputs"] = tf.concat([
tf.reshape(scaled_image,
[res**2 // highest_res, highest_res, self.num_channels])
for scaled_image, res in zip(scaled_images, hparams.resolutions)],
axis=0)
return example
def testMakeMultiscaleDivisible(self):
image = tf.random_normal([256, 256, 3])
resolutions = [8, 16, 64, 256]
scaled_images = image_utils.make_multiscale(image, resolutions)
self.assertEqual(scaled_images[0].shape, (8, 8, 3))
self.assertEqual(scaled_images[1].shape, (16, 16, 3))
self.assertEqual(scaled_images[2].shape, (64, 64, 3))
self.assertEqual(scaled_images[3].shape, (256, 256, 3))
def testMakeMultiscaleDivisible(self): image = tf.random_normal([256, 256, 3]) resolutions = [8, 16, 64, 256] scaled_images = image_utils.make_multiscale(image, resolutions) self.assertEqual(scaled_images[0].shape, (8, 8, 3)) self.assertEqual(scaled_images[1].shape, (16, 16, 3)) self.assertEqual(scaled_images[2].shape, (64, 64, 3)) self.assertEqual(scaled_images[3].shape, (256, 256, 3))
def preprocess_example(self, example, mode, hparams):
image = example["inputs"]
scaled_images = image_utils.make_multiscale(
image, hparams.resolutions, num_channels=self.num_channels)
# Pack tuple of scaled images into one tensor. We do this by enforcing the
# columns to match for every resolution.
highest_res = hparams.resolutions[-1]
example["inputs"] = tf.concat([
tf.reshape(scaled_image,
[res**2 // highest_res, highest_res, self.num_channels])
for scaled_image, res in zip(scaled_images, hparams.resolutions)],
axis=0)
return example
def testMakeMultiscaleLarger(self):
image = tf.random_normal([256, 256, 3])
resolutions = [257]
scaled_images = image_utils.make_multiscale(image, resolutions)
self.assertEqual(scaled_images[0].shape, (257, 257, 3))
def testMakeMultiscaleLarger(self): image = tf.random_normal([256, 256, 3]) resolutions = [257] scaled_images = image_utils.make_multiscale(image, resolutions) self.assertEqual(scaled_images[0].shape, (257, 257, 3))
