Python preprocessing.preprocess_image() Examples
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code examples of preprocessing.preprocess_image().
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Example #1
Source File: imagenet_input.py From tpu_models with Apache License 2.0 | 6 votes |
def build_image_serving_input_fn(image_size): """Builds a serving input fn for raw images.""" def _image_serving_input_fn(): """Serving input fn for raw images.""" def _preprocess_image(image_bytes): """Preprocess a single raw image.""" image = preprocessing.preprocess_image( image_bytes=image_bytes, is_training=False, image_size=image_size) return image image_bytes_list = tf.placeholder( shape=[None], dtype=tf.string, ) images = tf.map_fn( _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32) return tf.estimator.export.ServingInputReceiver( images, {'image_bytes': image_bytes_list}) return _image_serving_input_fn
Example #2
Source File: imagenet_input.py From tpu_models with Apache License 2.0 | 6 votes |
def __init__(self, is_training, use_bfloat16, num_cores=8, image_size=224, transpose_input=False, include_background_label=False, autoaugment_name=None): self.image_preprocessing_fn = preprocessing.preprocess_image self.is_training = is_training self.use_bfloat16 = use_bfloat16 self.num_cores = num_cores self.transpose_input = transpose_input self.image_size = image_size self.include_background_label = include_background_label self.autoaugment_name = autoaugment_name
Example #3
Source File: imagenet_input.py From tpu_models with Apache License 2.0 | 6 votes |
def build_image_serving_input_fn(image_size): """Builds a serving input fn for raw images.""" def _image_serving_input_fn(): """Serving input fn for raw images.""" def _preprocess_image(image_bytes): """Preprocess a single raw image.""" image = preprocessing.preprocess_image( image_bytes=image_bytes, is_training=False, image_size=image_size) return image image_bytes_list = tf.placeholder( shape=[None], dtype=tf.string, ) images = tf.map_fn( _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32) return tf.estimator.export.ServingInputReceiver( images, {'image_bytes': image_bytes_list}) return _image_serving_input_fn
Example #4
Source File: eval_ckpt_main.py From EfficientNet-PyTorch with Apache License 2.0 | 6 votes |
def build_dataset(self, filenames, labels, is_training): """Build input dataset.""" filenames = tf.constant(filenames) labels = tf.constant(labels) dataset = tf.data.Dataset.from_tensor_slices((filenames, labels)) def _parse_function(filename, label): image_string = tf.read_file(filename) image_decoded = preprocessing.preprocess_image( image_string, is_training, self.image_size) image = tf.cast(image_decoded, tf.float32) return image, label dataset = dataset.map(_parse_function) dataset = dataset.batch(self.batch_size) iterator = dataset.make_one_shot_iterator() images, labels = iterator.get_next() return images, labels
Example #5
Source File: eval_ckpt_main.py From efficientnet-pytorch with MIT License | 6 votes |
def build_dataset(self, filenames, labels, is_training): """Build input dataset.""" filenames = tf.constant(filenames) labels = tf.constant(labels) dataset = tf.data.Dataset.from_tensor_slices((filenames, labels)) def _parse_function(filename, label): image_string = tf.read_file(filename) image_decoded = preprocessing.preprocess_image( image_string, is_training, image_size=self.image_size) image = tf.cast(image_decoded, tf.float32) return image, label dataset = dataset.map(_parse_function) dataset = dataset.batch(self.batch_size) iterator = dataset.make_one_shot_iterator() images, labels = iterator.get_next() return images, labels
Example #6
Source File: imagenet_input.py From single-path-nas with Apache License 2.0 | 6 votes |
def image_serving_input_fn(): """Serving input fn for raw images.""" def _preprocess_image(image_bytes): """Preprocess a single raw image.""" image = preprocessing.preprocess_image( image_bytes=image_bytes, is_training=False) return image image_bytes_list = tf.placeholder( shape=[None], dtype=tf.string, ) images = tf.map_fn( _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32) return tf.estimator.export.ServingInputReceiver( images, {'image_bytes': image_bytes_list})
Example #7
Source File: imagenet_input.py From single-path-nas with Apache License 2.0 | 6 votes |
def build_image_serving_input_fn(image_size): """Builds a serving input fn for raw images.""" def _image_serving_input_fn(): """Serving input fn for raw images.""" def _preprocess_image(image_bytes): """Preprocess a single raw image.""" image = preprocessing.preprocess_image( image_bytes=image_bytes, is_training=False, image_size=image_size) return image image_bytes_list = tf.placeholder( shape=[None], dtype=tf.string, ) images = tf.map_fn( _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32) return tf.estimator.export.ServingInputReceiver( images, {'image_bytes': image_bytes_list}) return _image_serving_input_fn
Example #8
Source File: imagenet_input.py From single-path-nas with Apache License 2.0 | 6 votes |
def build_image_serving_input_fn(image_size): """Builds a serving input fn for raw images.""" def _image_serving_input_fn(): """Serving input fn for raw images.""" def _preprocess_image(image_bytes): """Preprocess a single raw image.""" image = preprocessing.preprocess_image( image_bytes=image_bytes, is_training=False, image_size=image_size) return image image_bytes_list = tf.placeholder( shape=[None], dtype=tf.string, ) images = tf.map_fn( _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32) return tf.estimator.export.ServingInputReceiver( images, {'image_bytes': image_bytes_list}) return _image_serving_input_fn
Example #9
Source File: imagenet_input.py From tpu_models with Apache License 2.0 | 5 votes |
def __init__(self, is_training, use_bfloat16, num_cores=8, image_size=224, transpose_input=False): self.image_preprocessing_fn = preprocessing.preprocess_image self.is_training = is_training self.use_bfloat16 = use_bfloat16 self.num_cores = num_cores self.transpose_input = transpose_input self.image_size = image_size
Example #10
Source File: imagenet_input.py From single-path-nas with Apache License 2.0 | 5 votes |
def __init__(self, is_training, use_bfloat16, num_cores=8, image_size=224, transpose_input=False): self.image_preprocessing_fn = preprocessing.preprocess_image self.is_training = is_training self.use_bfloat16 = use_bfloat16 self.num_cores = num_cores self.transpose_input = transpose_input self.image_size = image_size
Example #11
Source File: imagenet_input.py From single-path-nas with Apache License 2.0 | 5 votes |
def __init__(self, is_training, use_bfloat16, num_cores=8, image_size=224, transpose_input=False): self.image_preprocessing_fn = preprocessing.preprocess_image self.is_training = is_training self.use_bfloat16 = use_bfloat16 self.num_cores = num_cores self.transpose_input = transpose_input self.image_size = image_size