DeepLabV3ImageSegmenterPreprocessor layer

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DeepLabV3ImageSegmenterPreprocessor class

keras_hub.models.DeepLabV3ImageSegmenterPreprocessor(
    image_converter=None, resize_output_mask=False, **kwargs
)

Base class for image segmentation preprocessing layers.

ImageSegmenterPreprocessor wraps a keras_hub.layers.ImageConverter to create a preprocessing layer for image segmentation tasks. It is intended to be paired with a keras_hub.models.ImageSegmenter task.

All ImageSegmenterPreprocessor instances take three inputs: x, y, and sample_weight.

• x: The first input, should always be included. It can be an image or a batch of images.
• y: (Optional) Usually the segmentation mask(s), if resize_output_mask is set to True this will be resized to input image shape else will be passed through unaltered.
• sample_weight: (Optional) Will be passed through unaltered.
• resize_output_mask bool: If set to True the output mask will be resized to the same size as the input image. Defaults to False.

The layer will output either x, an (x, y) tuple if labels were provided, or an (x, y, sample_weight) tuple if labels and sample weight were provided. x will be the input images after all model preprocessing has been applied.

All ImageSegmenterPreprocessor tasks include a from_preset() constructor which can be used to load a pre-trained config. You can call the from_preset() constructor directly on this base class, in which case the correct class for your model will be automatically instantiated.

Examples.

preprocessor = keras_hub.models.ImageSegmenterPreprocessor.from_preset(
    "deeplabv3_resnet50",
)

# Resize a single image for the model.
x = np.ones((512, 512, 3))
x = preprocessor(x)

# Resize an image and its mask.
x, y = np.ones((512, 512, 3)), np.zeros((512, 512, 1))
x, y = preprocessor(x, y)

# Resize a batch of images and masks.
x, y = [np.ones((512, 512, 3)), np.zeros((512, 512, 3))],
       [np.ones((512, 512, 1)), np.zeros((512, 512, 1))]
x, y = preprocessor(x, y)

# Use a [`tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset).
ds = tf.data.Dataset.from_tensor_slices((x, y)).batch(2)
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

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from_preset method

DeepLabV3ImageSegmenterPreprocessor.from_preset(
    preset, config_file="preprocessor.json", **kwargs
)

Instantiate a keras_hub.models.Preprocessor from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as one of:

1. a built-in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './bert_base_en'

For any Preprocessor subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

As there are usually multiple preprocessing classes for a given model, this method should be called on a specific subclass like keras_hub.models.BertTextClassifierPreprocessor.from_preset().

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.

Examples

# Load a preprocessor for Gemma generation.
preprocessor = keras_hub.models.GemmaCausalLMPreprocessor.from_preset(
    "gemma_2b_en",
)

# Load a preprocessor for Bert classification.
preprocessor = keras_hub.models.BertTextClassifierPreprocessor.from_preset(
    "bert_base_en",
)

image_converter property

keras_hub.models.DeepLabV3ImageSegmenterPreprocessor.image_converter

The image converter used to preprocess image data.