#!/usr/bin/env python
# ******************************************************************************
# Copyright 2023 Brainchip Holdings Ltd.
#
# 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
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# ******************************************************************************
"""
Helper that replaces lambdas with their equivalent Keras layer.
"""
__all__ = ["replace_lambda"]
from copy import deepcopy
from keras.layers import Activation, TFOpLambda
from .transforms_utils import get_layers_by_type, get_layer_index, inbound_node_generator
def _update_inbound_nodes(layer_config, target):
""" Update a Lambda layer inbound node towards their Layer equivalent.
Args:
layer_config (dict): config of the lambda layer
target (str): class name of the target layer
Returns:
str: name of the lambda operation
"""
name = None
if 'inbound_nodes' not in layer_config:
# A layer from a sequence model does not have 'inbound_nodes'.
# So, nothing to do in this case
return name
for inbound_node in inbound_node_generator(layer_config):
if isinstance(inbound_node, dict):
inbound_node = inbound_node.values()
connection_info = inbound_node[0]
# "connection_info[-1]" holds the lambda config, that is the op name and other lambda
# specific parameters. Code below will retrieve meaningful parameters that will be used to
# define the config of the new layer and will then be dropped.
if target == 'Reshape':
# Set the 'target_shape' parameter from the 'shape' attribute
layer_config['config']['target_shape'] = connection_info[-1].get('shape')
elif target == 'Permute':
# Set the 'dims' parameter from the 'perm' attribute
perm = connection_info[-1].get('perm')
# Permute 'dims' must start at 1 but transpose 'perm' starts at 0
layer_config['config']['dims'] = [p + 1 for p in perm]
elif target == "Add":
# Get the second inbound currently defined as {'y': ['name', 0, 0]}
other_inbound = connection_info[-1].get('y')
# Modify it to fit the ['name', 0, 0, {}] convention
other_inbound.append({})
# Add the updated inbound into the inbound_node list and set it in layer_config
inbound_node.append(other_inbound)
layer_config['inbound_nodes'] = [inbound_node]
# Retrieve the lambda name as it will be used to set the name for the layer
name = connection_info[-1].get('name', None)
# Drop the lambda config
connection_info[-1] = {}
return name
[docs]def replace_lambda(model):
""" Replaces lambda layers from a model with their equivalent Keras layer.
This transform handles the following replacements:
- Lambda(relu) or Activation('relu') → ReLU,
- Lambda(transpose) → Permute,
- Lambda(reshape) → Reshape,
- Lambda(add) → Add,
- Lambda('gelu') → Activation('gelu'),
- Lambda('silu') → Activation('silu').
Args:
model (keras.Model): the model of interest
Returns:
keras.Model: the original model or a new one with lambda replaced.
"""
# Map function names to Keras layers
lambda_to_layer = {
'nn.relu': 'ReLU',
'math.add': 'Add',
'__operators__.add': 'Add',
'reshape': 'Reshape',
'transpose': 'Permute',
'compat.v1.transpose': 'Permute',
'nn.silu': 'Activation',
'nn.gelu': 'Activation',
}
# Get all Activations and TFOpLambda layers present in the model
lambdas = get_layers_by_type(model, (Activation, TFOpLambda))
# When there are no valid candidates, return the original model
if not lambdas:
return model
# Copy configuration before applying modifications
config = deepcopy(model.get_config())
for layer in lambdas:
layer_index = get_layer_index(config['layers'], layer.name)
layer_config = config['layers'][layer_index]
# Replace 'relu' Activations layers with ReLU layers
if (layer_config['class_name'] == 'Activation'
and layer_config['config']['activation'] == 'relu'):
# Drop the 'activation' parameter and update 'class_name'
layer_config['config'].pop('activation')
layer_config['class_name'] = 'ReLU'
# Replace TFOpLambda layers
elif layer_config['class_name'] == 'TFOpLambda':
# Retrieve the function used in the config and get the equivalent Keras layer name
target = lambda_to_layer.get(layer_config['config']['function'], None)
if target:
# Drop the 'function' parameter and update 'class_name'
op_name = layer_config['config'].pop('function').replace('nn.', '')
layer_config['class_name'] = target
# If target is 'Activation', put op_name in 'activation' parameter
if target == "Activation":
layer_config['config']['activation'] = op_name
# Update the inbound part of the config: the last element of the inbound list of
# lambda layers will contain the lambda op parameters that are used to set the
# config for the new layer.
new_name = _update_inbound_nodes(layer_config, target)
# If layer name was updated, use the new name everywhere in the config
if new_name:
# Serialize the dict into a string
str_config = str(config)
# Replace name using 'old_name' for an exact match
str_config = str_config.replace(f"'{layer_config['name']}'", f"'{new_name}'")
# Deserialize the updated string into a dict
config = eval(str_config)
# Reconstruct model from the config
updated_model = model.from_config(config)
# Restore model weights
updated_model.set_weights(model.get_weights())
return updated_model