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| # Copyright 2019 The TensorFlow Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """Utility functions for manipulating Keras models.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import tensorflow.compat.v1 as tf | |
| def extract_submodel(model, inputs, outputs, name=None): | |
| """Extracts a section of a Keras model into a new model. | |
| This method walks an existing model from the specified outputs back to the | |
| specified inputs in order to construct a new model containing only a portion | |
| of the old model, while sharing the layers and weights with the original | |
| model. | |
| WARNING: This method does not work for submodels containing layers that have | |
| been used multiple times in the original model, or in other models beyond | |
| the original model. (E.g. does not work for submodels that contain layers that | |
| use shared weights). This also means that multiple overlapping submodels | |
| cannot be extracted from the same model. | |
| It also relies on recursion and will hit python's recursion limit for large | |
| submodels. | |
| Args: | |
| model: The existing Keras model this method extracts a submodel from. | |
| inputs: The layer inputs in the existing model that start the submodel | |
| outputs: The layer outputs in the existing model that should be output by | |
| the submodel | |
| name: The name for the extracted model | |
| Returns: | |
| The extracted submodel specified by the given inputs and outputs | |
| """ | |
| output_to_layer = {} | |
| output_to_layer_input = {} | |
| for layer in model.layers: | |
| layer_output = layer.output | |
| layer_inputs = layer.input | |
| output_to_layer[layer_output.ref()] = layer | |
| output_to_layer_input[layer_output.ref()] = layer_inputs | |
| model_inputs_dict = {} | |
| memoized_results = {} | |
| # Relies on recursion, very low limit in python | |
| def _recurse_in_model(tensor): | |
| """Walk the existing model recursively to copy a submodel.""" | |
| if tensor.ref() in memoized_results: | |
| return memoized_results[tensor.ref()] | |
| if (tensor.ref() == inputs.ref()) or ( | |
| isinstance(inputs, list) and tensor in inputs): | |
| if tensor.ref() not in model_inputs_dict: | |
| model_inputs_dict[tensor.ref()] = tf.keras.layers.Input(tensor=tensor) | |
| out = model_inputs_dict[tensor.ref()] | |
| else: | |
| cur_inputs = output_to_layer_input[tensor.ref()] | |
| cur_layer = output_to_layer[tensor.ref()] | |
| if isinstance(cur_inputs, list): | |
| out = cur_layer([_recurse_in_model(inp) for inp in cur_inputs]) | |
| else: | |
| out = cur_layer(_recurse_in_model(cur_inputs)) | |
| memoized_results[tensor.ref()] = out | |
| return out | |
| if isinstance(outputs, list): | |
| model_outputs = [_recurse_in_model(tensor) for tensor in outputs] | |
| else: | |
| model_outputs = _recurse_in_model(outputs) | |
| if isinstance(inputs, list): | |
| model_inputs = [model_inputs_dict[tensor.ref()] for tensor in inputs] | |
| else: | |
| model_inputs = model_inputs_dict[inputs.ref()] | |
| return tf.keras.Model(inputs=model_inputs, outputs=model_outputs, name=name) | |