#!/usr/bin/env python
# ******************************************************************************
# Copyright 2020 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
# limitations under the License.
# ******************************************************************************
"""
This model is an adaptation of the `mobilenet_imagenet` model for edge
applications. It is based on MobileNetBC with top layers replaced by a quantized
spike extractor and a classification layer.
"""
from keras import Model
from keras.layers import Reshape, Activation
from cnn2snn import load_quantized_model, quantize_layer
from ..layer_blocks import separable_conv_block, dense_block
from ..utils import fetch_file
BASE_WEIGHT_PATH = 'http://data.brainchip.com/models/mobilenet_edge/'
[docs]def mobilenet_edge_imagenet(base_model, classes):
"""Instantiates a MobileNet-edge architecture.
Args:
base_model (str/keras.Model): a mobilenet_imagenet quantized model.
classes (int): the number of classes for the edge classifier.
Returns:
keras.Model: a Keras Model instance.
"""
if isinstance(base_model, str):
base_model = load_quantized_model(base_model)
try:
# Identify the last separable, which is the base model classifier
base_classifier = base_model.get_layer("separable_14")
# remember the classifier weight bitwidth
wq = base_classifier.quantizer.bitwidth
except Exception as e:
raise ValueError("The base model is not a quantized \
Mobilenet/Imagenet model") from e
# Recreate a model with all layers up to the classifier
x = base_classifier.input
# Add the new end layer with kernel_size (3, 3) instead of (1,1) for
# hardware compatibility reasons
x = separable_conv_block(x,
filters=2048,
kernel_size=(3, 3),
padding='same',
use_bias=False,
add_batchnorm=True,
name='spike_generator',
add_activation=True)
# Then add the Akida edge learning layer that will be dropped after
x = dense_block(x,
classes,
name="classification_layer",
add_activation=False,
add_batchnorm=False,
use_bias=False)
act_function = 'softmax' if classes > 1 else 'sigmoid'
x = Activation(act_function, name=f'act_{act_function}')(x)
x = Reshape((classes,), name='reshape_2')(x)
# Create model
model = Model(inputs=base_model.input,
outputs=x,
name=f"{base_model.name}_edge")
# Quantize edge layers
model = quantize_layer(model, 'spike_generator', wq)
model = quantize_layer(model, 'spike_generator/relu', 1)
# NOTE: quantization set to 2 here, to be as close as
# possible to the Akida native layer that will replace this one,
# with binary weights.
model = quantize_layer(model, 'classification_layer', 2)
return model
[docs]def mobilenet_edge_imagenet_pretrained():
""" Helper method to retrieve a `mobilenet_edge_imagenet` model that was
trained on ImageNet dataset.
Returns:
keras.Model: a Keras Model instance.
"""
model_name = 'mobilenet_imagenet_224_alpha_50_edge_iq8_wq4_aq4.h5'
file_hash = '29ade25ce1907a4dcaa8454a4aff93f3cdc739455ab878156e39e39f12304cbd'
model_path = fetch_file(BASE_WEIGHT_PATH + model_name,
fname=model_name,
file_hash=file_hash,
cache_subdir='models')
return load_quantized_model(model_path)