# /usr/bin/env python3.5
# -*- mode: python -*-
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""" utilities for tf graph related operations """
import os
import tensorflow as tf
from tensorflow.keras.models import load_model, save_model
def op_not_in_loop_control_flow_context(graph: tf.Graph, input_op: tf.Operation) -> bool:
"""
checks if the op is not in loop control flow context or not
:param graph: tf.Graph is the active graph
:param input_op: op as tf.Operation
:return: True if op is not in a loop control flow context, False otherwise.
"""
# pylint: disable=protected-access
active_ctxt = graph._get_control_flow_context()
input_ctxt = input_op._get_control_flow_context()
if not input_ctxt or input_ctxt is active_ctxt:
# input_op isn't in 'a' loop control flow context or
# input_op is in the same context as op.
return True
return False
def updated_graph_flow_context_to_loop_context(graph: tf.Graph, preceeding_tensor: tf.Tensor):
"""
updates graph flow context to loop context
:param graph: TensorFlow Graph (tf.Graph)
:param preceeding_tensor: TF tensor that feeds into the op which needs modification
:return: old graph context object
"""
# pylint: disable=protected-access
old_graph_context = graph._get_control_flow_context()
graph._set_control_flow_context(preceeding_tensor.op._get_control_flow_context())
return old_graph_context
def set_graph_flow_context(graph: tf.Graph, active_context):
"""
sets graph context to active context provided
:param graph: TensorFlow Graph (tf.Graph)
:param active_context: context object to be set as current graph's context
:return:
"""
# pylint: disable=protected-access
graph._set_control_flow_context(active_context)
[docs]def update_keras_bn_ops_trainable_flag(model: tf.keras.Model, trainable: bool, load_save_path: str) -> tf.keras.Model:
"""
helper method to update Keras BN ops trainable state in a given keras model.
:param model: Keras model to be updated with BN ops trainable flag
:param trainable: bool flag to indicate trainable to be set to true or false
:param load_save_path: temp folder to perform load/save, cleans up file created
:return: updated keras model
"""
if not os.path.exists(load_save_path):
os.mkdir(load_save_path)
output_file_with_path = os.path.join(load_save_path, 't.h5')
# update BN ops trainable flag
for layer in model.layers:
if isinstance(layer, tf.keras.layers.BatchNormalization):
layer.trainable = trainable
save_model(model, output_file_with_path)
tf.compat.v1.keras.backend.clear_session()
model = load_model(output_file_with_path)
# clean up file after use
if os.path.exists(output_file_with_path):
os.remove(output_file_with_path)
# return updated keras model
return model