def preprocess_example(self, example, unused_mode, unused_hparams):
inputs = example["inputs"]
# For Img2Img resize input and output images as desired.
example["inputs"] = image_utils.resize_by_area(inputs, 8)
example["targets"] = image_utils.resize_by_area(inputs, 32)
return example
def preprocess_example(self, example, unused_mode, unused_hparams):
inputs = example["inputs"]
# For Img2Img resize input and output images as desired.
example["inputs"] = image_utils.resize_by_area(inputs, 8)
example["targets"] = image_utils.resize_by_area(inputs, 32)
return example
def preprocess_example(self, example, unused_mode, unused_hparams):
image = example["inputs"]
# 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)
image_8 = image_utils.resize_by_area(image, 8)
image_64 = image_utils.resize_by_area(image, 64)
example["inputs"] = image_8
example["targets"] = image_64
return example
def preprocess_example(self, example, unused_mode, unused_hparams):
image = example["inputs"]
# 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)
image_8 = image_utils.resize_by_area(image, 8)
image_64 = image_utils.resize_by_area(image, 64)
example["inputs"] = image_8
example["targets"] = image_64
return example
def preprocess_example(self, example, mode, unused_hparams):
image = example["inputs"]
image = image_utils.resize_by_area(image, 8)
if not self._was_reversed:
image = tf.image.per_image_standardization(image)
example["inputs"] = image
return example
def preprocess_example(self, example, mode, hparams):
# Crop to target shape instead of down-sampling target, leaving target
# of maximum available resolution.
target_shape = (self.output_dim, self.output_dim, self.num_channels)
example["targets"] = tf.random_crop(example["targets"], target_shape)
example["inputs"] = image_utils.resize_by_area(example["targets"],
self.input_dim)
if self.inpaint_fraction is not None and self.inpaint_fraction > 0:
mask = random_square_mask((self.input_dim,
self.input_dim,
self.num_channels),
self.inpaint_fraction)
example["inputs"] = tf.multiply(
tf.convert_to_tensor(mask, dtype=tf.int64),
example["inputs"])
if self.input_dim is None:
raise ValueError("Cannot train in-painting for examples with "
"only targets (i.e. input_dim is None, "
"implying there are only targets to be "
"generated).")
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, unused_hparams):
image = example["inputs"]
image = image_utils.resize_by_area(image, 8)
if not self._was_reversed:
image = tf.image.per_image_standardization(image)
example["inputs"] = image
return example
def preprocess_example(self, example, mode, hparams):
# Crop to target shape instead of down-sampling target, leaving target
# of maximum available resolution.
target_shape = (self.output_dim, self.output_dim, self.num_channels)
example["targets"] = tf.random_crop(example["targets"], target_shape)
example["inputs"] = image_utils.resize_by_area(example["targets"],
self.input_dim)
if self.inpaint_fraction is not None and self.inpaint_fraction > 0:
mask = random_square_mask((self.input_dim,
self.input_dim,
self.num_channels),
self.inpaint_fraction)
example["inputs"] = tf.multiply(
tf.convert_to_tensor(mask, dtype=tf.int64),
example["inputs"])
if self.input_dim is None:
raise ValueError("Cannot train in-painting for examples with "
"only targets (i.e. input_dim is None, "
"implying there are only targets to be "
"generated).")
return example
def preprocess_example(self,
example,
unused_mode=None,
unused_hparams=None):
print('process', example)
image = example["inputs"]
# 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)
image_8 = image_utils.resize_by_area(image, 8)
image_32 = image_utils.resize_by_area(image, 32)
example["inputs"] = image_8
example["targets"] = image_32
print(example['inputs'])
print(example['targets'])
return example
def preprocess_example(self, example, mode, unused_hparams):
example["inputs"] = image_utils.resize_by_area(example["inputs"],
self._FLICKR_IMAGE_SIZE)
return example
def preprocess_example(self, example, mode, unused_hparams):
example["inputs"] = image_utils.resize_by_area(
example["inputs"], self._MSCOCO_IMAGE_SIZE)
return example
