mirror of
https://github.com/google/earthengine-api.git
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289 lines
12 KiB
Python
289 lines
12 KiB
Python
"""A wrapper for Models."""
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from ee import _arg_types
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from ee import apifunction
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from ee import computedobject
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from ee import deprecation
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from ee import featurecollection
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from ee import image as ee_image
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class Model(computedobject.ComputedObject):
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"""An object to represent an Earth Engine Model.
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Example:
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model = ee.Model.fromVertexAi(
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endpoint='endpoint-name',
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inputTileSize=[8, 8],
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outputBands={
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'probability': {'type': ee.PixelType.float(), 'dimensions': 1}
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},
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)
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Please visit one of the following links for more info:
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- https://developers.google.com/earth-engine/guides/machine-learning
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- https://developers.google.com/earth-engine/guides/tensorflow-vertex
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"""
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_initialized: bool = False
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def __init__(self, model: computedobject.ComputedObject):
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"""Creates a Model wrapper.
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Args:
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model: A Model to cast.
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"""
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self.initialize()
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if isinstance(model, computedobject.ComputedObject):
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# There is no server-side constructor for ee.Model. Pass the object as-is
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# to the server in case it is intended to be a Model cast.
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super().__init__(model.func, model.args, model.varName)
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return
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raise TypeError('Model constructor can only cast to Model.')
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@classmethod
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def initialize(cls) -> None:
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"""Imports API functions to this class."""
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if not cls._initialized:
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apifunction.ApiFunction.importApi(cls, cls.name(), cls.name())
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cls._initialized = True
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@classmethod
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def reset(cls) -> None:
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"""Removes imported API functions from this class."""
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apifunction.ApiFunction.clearApi(cls)
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cls._initialized = False
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@staticmethod
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def name() -> str:
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return 'Model'
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@staticmethod
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@deprecation.Deprecated('Migrate to Vertex AI')
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def fromAiPlatformPredictor(
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# pylint: disable=invalid-name
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projectName: _arg_types.Any | None = None,
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projectId: _arg_types.String | None = None,
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modelName: _arg_types.String | None = None,
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# pylint: enable=invalid-name
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version: _arg_types.String | None = None,
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region: _arg_types.String | None = None,
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# pylint: disable=invalid-name
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inputProperties: _arg_types.List | None = None,
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inputTypeOverride: _arg_types.Dictionary | None = None,
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inputShapes: _arg_types.Dictionary | None = None,
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# pylint: enable=invalid-name
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proj: _arg_types.Projection | None = None,
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# pylint: disable=invalid-name
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fixInputProj: _arg_types.Bool | None = None,
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inputTileSize: _arg_types.List | None = None,
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inputOverlapSize: _arg_types.List | None = None,
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outputTileSize: _arg_types.List | None = None,
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outputBands: _arg_types.Dictionary | None = None,
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outputProperties: _arg_types.Dictionary | None = None,
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outputMultiplier: _arg_types.Number | None = None,
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# pylint: enable=invalid-name
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) -> 'Model':
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"""Returns an ee.Model from a description of an AI Platform prediction model.
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DEPRECATED: Cloud AI Platform is deprecated migrate to Vertex AI:
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https://developers.google.com/earth-engine/guides/ee-vertex-migrate
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(See https://cloud.google.com/ml-engine/).
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Args:
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projectName: The Google Cloud project that owns the model. Deprecated: use
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"projectId" instead.
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projectId: The ID of the Google Cloud project that owns the model.
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modelName: The name of the model.
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version: The model version. Defaults to the AI Platform default model
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version.
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region: The model deployment region. Defaults to "us-central1".
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inputProperties: Properties passed with each prediction instance. Image
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predictions are tiled, so these properties will be replicated into each
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image tile instance. Defaults to no properties.
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inputTypeOverride: Types to which model inputs will be coerced if
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specified. Both Image bands and Image/Feature properties are valid.
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inputShapes: The fixed shape of input array bands. For each array band not
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specified, the fixed array shape will be automatically deduced from a
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non-masked pixel.
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proj: The input projection at which to sample all bands. Defaults to the
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default projection of an image's first band.
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fixInputProj: If true, pixels will be sampled in a fixed projection
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specified by 'proj'. The output projection is used otherwise. Defaults
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to false.
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inputTileSize: Rectangular dimensions of pixel tiles passed in to
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prediction instances. Required for image predictions.
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inputOverlapSize: Amount of adjacent-tile overlap in X/Y along each edge
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of pixel tiles passed in to prediction instances. Defaults to [0, 0].
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outputTileSize: Rectangular dimensions of pixel tiles returned from AI
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Platform. Defaults to the value in 'inputTileSize'.
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outputBands: A map from output band names to a dictionary of output band
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info. Valid band info fields are 'type' and 'dimensions'. 'type' should
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be a ee.PixelType describing the output band, and 'dimensions' is an
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optional integer with the number of dimensions in that band e.g.,
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"outputBands: {'p': {'type': ee.PixelType.int8(), 'dimensions': 1}}".
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Required for image predictions.
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outputProperties: A map from output property names to a dictionary of
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output property info. Valid property info fields are 'type' and
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'dimensions'. 'type' should be a ee.PixelType describing the output
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property, and 'dimensions' is an optional integer with the number of
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dimensions for that property if it is an array e.g., "outputBands: {'p':
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{'type': ee.PixelType.int8(), 'dimensions': 1}}". Required for
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predictions from FeatureCollections.
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outputMultiplier: An approximation to the increase in data volume for the
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model outputs over the model inputs. If specified this must be >= 1.
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This is only needed if the model produces more data than it consumes,
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e.g., a model that takes 5 bands and produces 10 outputs per pixel.
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Returns:
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An ee.Model.
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"""
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return apifunction.ApiFunction.call_(
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'Model.fromAiPlatformPredictor',
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projectName,
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projectId,
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modelName,
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version,
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region,
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inputProperties,
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inputTypeOverride,
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inputShapes,
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proj,
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fixInputProj,
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inputTileSize,
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inputOverlapSize,
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outputTileSize,
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outputBands,
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outputProperties,
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outputMultiplier,
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)
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@staticmethod
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def fromVertexAi(
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endpoint: _arg_types.String,
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# pylint: disable=invalid-name
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inputProperties: _arg_types.List | None = None,
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inputTypeOverride: _arg_types.Dictionary | None = None,
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inputShapes: _arg_types.Dictionary | None = None,
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# pylint: enable=invalid-name
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proj: _arg_types.Projection | None = None,
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# pylint: disable=invalid-name
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fixInputProj: _arg_types.Bool | None = None,
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inputTileSize: _arg_types.List | None = None,
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inputOverlapSize: _arg_types.List | None = None,
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outputTileSize: _arg_types.List | None = None,
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outputBands: _arg_types.Dictionary | None = None,
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outputProperties: _arg_types.Dictionary | None = None,
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outputMultiplier: _arg_types.Number | None = None,
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maxPayloadBytes: _arg_types.Integer | None = None,
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payloadFormat: _arg_types.String | None = None,
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# pylint: enable=invalid-name
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) -> 'Model':
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"""Returns an ee.Model from a description of a Vertex AI model endpoint.
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See https://cloud.google.com/vertex-ai.
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Warning: This method is in public preview and may undergo breaking changes.
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Args:
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endpoint: The endpoint name for predictions.
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inputProperties: Properties passed with each prediction instance. Image
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predictions are tiled, so these properties will be replicated into each
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image tile instance. Defaults to no properties.
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inputTypeOverride: Types to which model inputs will be coerced if
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specified. Both Image bands and Image/Feature properties are valid.
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inputShapes: The fixed shape of input array bands. For each array band not
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specified, the fixed array shape will be automatically deduced from a
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non-masked pixel.
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proj: The input projection at which to sample all bands. Defaults to the
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default projection of an image's first band.
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fixInputProj: If true, pixels will be sampled in a fixed projection
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specified by 'proj'. The output projection is used otherwise. Defaults
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to false.
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inputTileSize: Rectangular dimensions of pixel tiles passed in to
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prediction instances. Required for image predictions.
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inputOverlapSize: Amount of adjacent-tile overlap in X/Y along each edge
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of pixel tiles passed in to prediction instances. Defaults to [0, 0].
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outputTileSize: Rectangular dimensions of pixel tiles returned from AI
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Platform. Defaults to the value in 'inputTileSize'.
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outputBands: A map from output band names to a dictionary of output band
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info. Valid band info fields are 'type' and 'dimensions'. 'type' should
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be a ee.PixelType describing the output band, and 'dimensions' is an
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optional integer with the number of dimensions in that band e.g.,
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"outputBands: {'p': {'type': ee.PixelType.int8(), 'dimensions': 1}}".
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Required for image predictions.
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outputProperties: A map from output property names to a dictionary of
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output property info. Valid property info fields are 'type' and
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'dimensions'. 'type' should be a ee.PixelType describing the output
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property, and 'dimensions' is an optional integer with the number of
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dimensions for that property if it is an array e.g., "outputBands: {'p':
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{'type': ee.PixelType.int8(), 'dimensions': 1}}". Required for
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predictions from FeatureCollections.
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outputMultiplier: An approximation to the increase in data volume for the
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model outputs over the model inputs. If specified this must be >= 1.
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This is only needed if the model produces more data than it consumes,
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e.g., a model that takes 5 bands and produces 10 outputs per pixel.
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maxPayloadBytes: The prediction payload size limit in bytes. Defaults to
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1.5MB (1500000 bytes.)
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payloadFormat: The payload format of entries in prediction requests and
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responses. One of: ['SERIALIZED_TF_TENSORS, 'RAW_JSON', 'ND_ARRAYS',
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'GRPC_TF_TENSORS', 'GRPC_SERIALIZED_TF_TENSORS', 'GRPC_TF_EXAMPLES'].
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Defaults to 'SERIALIZED_TF_TENSORS'.
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Returns:
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An ee.Model.
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"""
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return apifunction.ApiFunction.call_(
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'Model.fromVertexAi',
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endpoint,
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inputProperties,
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inputTypeOverride,
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inputShapes,
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proj,
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fixInputProj,
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inputTileSize,
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inputOverlapSize,
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outputTileSize,
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outputBands,
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outputProperties,
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outputMultiplier,
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maxPayloadBytes,
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payloadFormat,
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)
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def predictImage(self, image: _arg_types.Image) -> ee_image.Image:
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"""Returns an image with predictions from pixel tiles of an image.
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The predictions are merged as bands with the input image.
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The model will receive 0s in place of masked pixels. The masks of predicted
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output bands are the minimum of the masks of the inputs.
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Args:
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image: The input image.
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"""
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return apifunction.ApiFunction.call_(
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self.name() + '.predictImage', self, image
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)
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def predictProperties(
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self, collection: _arg_types.FeatureCollection
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) -> featurecollection.FeatureCollection:
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"""Returns a feature collection with predictions for each feature.
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Predicted properties are merged with the properties of the input feature.
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Args:
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collection: The input collection.
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"""
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return apifunction.ApiFunction.call_(
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self.name() + '.predictProperties', self, collection
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)
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