wehub-resource-sync
110 lines
2.7 KiB
Python
110 lines
2.7 KiB
Python
import warnings
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tf = None
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def _import_tf():
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"""Tries to import tensorflow."""
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global tf
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if tf is None:
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import tensorflow as tf
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def _get_session(session):
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"""Common utility to get the session for the tensorflow-based explainer.
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Parameters
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----------
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explainer : Explainer
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One of the tensorflow-based explainers.
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session : tf.compat.v1.Session
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An optional existing session.
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"""
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_import_tf()
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# if we are not given a session find a default session
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if session is None:
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try:
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session = tf.compat.v1.keras.backend.get_session()
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except Exception:
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session = tf.keras.backend.get_session()
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return tf.get_default_session() if session is None else session
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def _get_graph(explainer):
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"""Common utility to get the graph for the tensorflow-based explainer.
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Parameters
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----------
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explainer : Explainer
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One of the tensorflow-based explainers.
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"""
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_import_tf()
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if not tf.executing_eagerly():
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return explainer.session.graph
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else:
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from tensorflow.python.keras import backend
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graph = backend.get_graph()
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return graph
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def _get_model_inputs(model):
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"""Common utility to determine the model inputs.
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Parameters
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----------
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model : Tensorflow Keras model or tuple
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The tensorflow model or tuple.
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"""
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_import_tf()
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if (
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str(type(model)).endswith("keras.engine.sequential.Sequential'>")
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or str(type(model)).endswith("keras.models.Sequential'>")
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or str(type(model)).endswith("keras.engine.training.Model'>")
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or isinstance(model, tf.keras.Model)
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):
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return model.inputs
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if str(type(model)).endswith("tuple'>"):
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return model[0]
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emsg = f"{type(model)} is not currently a supported model type!"
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raise ValueError(emsg)
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def _get_model_output(model):
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"""Common utility to determine the model output.
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Parameters
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----------
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model : Tensorflow Keras model or tuple
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The tensorflow model or tuple.
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"""
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_import_tf()
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if (
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str(type(model)).endswith("keras.engine.sequential.Sequential'>")
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or str(type(model)).endswith("keras.models.Sequential'>")
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or str(type(model)).endswith("keras.engine.training.Model'>")
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or isinstance(model, tf.keras.Model)
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):
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if len(model.layers[-1]._inbound_nodes) == 0:
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if len(model.outputs) > 1:
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warnings.warn("Only one model output supported.")
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return model.outputs[0]
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else:
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return model.layers[-1].output
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if str(type(model)).endswith("tuple'>"):
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return model[1]
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emsg = f"{type(model)} is not currently a supported model type!"
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raise ValueError(emsg)
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