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