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March 2, 2019 16:42
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TensorFlow 1.x Estimator input pipeline function to read images organised in their class folders
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def input_fn(file_pattern, labels, | |
image_size=(224,224), | |
shuffle=False, | |
batch_size=64, | |
num_epochs=None, | |
buffer_size=4096, | |
prefetch_buffer_size=None): | |
table = tf.contrib.lookup.index_table_from_tensor(mapping=tf.constant(labels)) | |
num_classes = len(labels) | |
def _map_func(filename): | |
label = tf.string_split([filename], delimiter=os.sep).values[-2] | |
image = tf.image.decode_jpeg(tf.read_file(filename), channels=3) | |
image = tf.image.convert_image_dtype(image, dtype=tf.float32) | |
image = tf.image.resize_images(image, size=image_size) | |
return (image, tf.one_hot(table.lookup(label), num_classes)) | |
dataset = tf.data.Dataset.list_files(file_pattern, shuffle=shuffle) | |
if num_epochs is not None and shuffle: | |
dataset = dataset.apply( | |
tf.contrib.data.shuffle_and_repeat(buffer_size, num_epochs)) | |
elif shuffle: | |
dataset = dataset.shuffle(buffer_size) | |
elif num_epochs is not None: | |
dataset = dataset.repeat(num_epochs) | |
dataset = dataset.apply( | |
tf.contrib.data.map_and_batch(map_func=_map_func, | |
batch_size=batch_size, | |
num_parallel_calls=os.cpu_count())) | |
dataset = dataset.prefetch(buffer_size=prefetch_buffer_size) | |
return dataset |
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