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# Eric: tfds is not used since we are using local dataset
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#import tensorflow_datasets as tfds
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import math
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import os.path
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import glob
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from image_processing import parse_example_proto, image_preprocessing
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"""
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This model expects training input in the following format:
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batches of images
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float32 tensor of shape [batch, height, width, channels]
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each value ranging between -1.0 and 1.0
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The width and height of the image should match the architecture in use
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e.g. 128x128, 256x256, 512x512, all with three channels
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"""
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# --------------------------------------------------------------------------
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# Input functions for each training mode
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# --------------------------------------------------------------------------
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def train_input_fn(params):
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return factory_input_fn(params, is_training=True)
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def eval_input_fn(params):
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return factory_input_fn(params, is_training=False)
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def predict_input_fn(params):
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count = params['num_labels'] * params['num_labels']
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if params['use_inception_score']:
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count = max(count, params['inception_score_sample_size'])
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# Since we drop_remainder, we need to round up to nearest batch size
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count = math.ceil(count / params['batch_size']) * params['batch_size']
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# How many times to repeat the eye
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num_tiles = math.ceil(count / params['num_labels'])
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# Which labels to generate
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label_data = np.eye(params['num_labels'], dtype=np.float32)
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dataset = tf.data.Dataset.from_tensor_slices(label_data)
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dataset = dataset.repeat(num_tiles)
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dataset = dataset.take(count)
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dataset = dataset.batch(params['batch_size'], drop_remainder=True)
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return dataset
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# --------------------------------------------------------------------------
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# Train/eval data sources
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# --------------------------------------------------------------------------
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def tfr_input_fn(params, is_training):
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path = params['train_input_path'] if is_training else params['eval_input_path']
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matching_files = tf.gfile.Glob(path)
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dataset = tf.data.TFRecordDataset(matching_files)
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if params['take_examples'] is not None:
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dataset = dataset.take(params['take_examples'])
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dataset = dataset.map(lambda record: parse_tfrecord(params, record))
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dataset = dataset.shuffle(params['batch_size']*20)
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dataset = dataset.repeat()
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dataset = dataset.batch(params['batch_size'], drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE)
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return dataset
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def tfds_input_fn(params, dataset, is_training=True):
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dataset = tfds.load(
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name=dataset,
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split=tfds.Split.TRAIN if is_training else tfds.Split.TEST,
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data_dir=params['data_dir'])
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if params['take_examples'] is not None:
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dataset = dataset.take(params['take_examples'])
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dataset = dataset.shuffle(params['batch_size']*20)
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dataset = dataset.repeat()
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dataset = dataset.batch(params['batch_size'], drop_remainder=True)
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dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
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def map_fn(features):
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image = tf.cast(features["image"], tf.float32) / 127.5 - 1
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if "label" in features:
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label = tf.one_hot(features["label"], params['num_labels'], dtype=tf.float32)
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else:
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label = tf.zeros([params['batch_size'], 1])
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