#!/usr/bin/env python
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# Copyright 2022 Brainchip Holdings Ltd.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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"""
Akida U-Net model definition for semantic segmentation.
"""
__all__ = ["akida_unet_portrait128", "akida_unet_portrait128_pretrained"]
from keras import Model, regularizers
from keras.layers import Dropout, Activation
from cnn2snn import set_akida_version, AkidaVersion
from ..layer_blocks import sepconv_transpose_block, conv_block
from ..imagenet.model_akidanet import akidanet_imagenet
from ..utils import fetch_file
from ..model_io import load_model, get_model_path
[docs]def akida_unet_portrait128(input_shape=(128, 128, 3),
alpha=0.5,
input_scaling=(128, -1)):
"""Instantiates an Akida U-Net architecture.
It is composed of an AkidaNet-ImageNet encoder followed by a succession of Conv2DTranspose
layers for the decoder part.
It does not contain any skip connection (concatenation) between the encoder and the decoder
branches.
Args:
input_shape (tuple, optional): input shape tuple. Defaults to (128, 128, 3).
alpha (float, optional): controls the width (number of filters) of the model. Defaults to
0.5.
input_scaling (tuple, optional): scale factor and offset to apply to inputs. Defaults to
(128, -1). Note that following Akida convention, the scale factor is a number used as a
divisor.
Returns:
keras.Model: a Keras Model instance.
"""
# This model is only available for akida 2.0
with set_akida_version(AkidaVersion.v2):
# Define weight regularization, will apply to pointwise weights of sepconv transposed layers
weight_regularizer = regularizers.l2(4e-5)
# Create an AkidaNet network without top layers
encoder = akidanet_imagenet(input_shape=input_shape,
alpha=alpha,
include_top=False,
input_scaling=input_scaling)
# Add the decoder layers
x = encoder.layers[-1].output
x = sepconv_transpose_block(x,
filters=int(512 * alpha),
kernel_size=(3, 3),
strides=2,
padding='same',
kernel_initializer='he_normal',
add_batchnorm=True,
relu_activation='ReLU7.5',
name='sepconv_t_0',
pointwise_regularizer=weight_regularizer)
x = Dropout(0.5)(x)
x = sepconv_transpose_block(x,
filters=int(256 * alpha),
kernel_size=(3, 3),
strides=2,
padding='same',
kernel_initializer='he_normal',
add_batchnorm=True,
relu_activation='ReLU7.5',
name='sepconv_t_1',
pointwise_regularizer=weight_regularizer)
x = Dropout(0.5)(x)
x = sepconv_transpose_block(x,
filters=int(128 * alpha),
kernel_size=(3, 3),
strides=2,
padding='same',
kernel_initializer='he_normal',
add_batchnorm=True,
relu_activation='ReLU7.5',
name='sepconv_t_2',
pointwise_regularizer=weight_regularizer)
x = Dropout(0.5)(x)
x = sepconv_transpose_block(x,
filters=int(64 * alpha),
kernel_size=(3, 3),
strides=2,
padding='same',
kernel_initializer='he_normal',
add_batchnorm=True,
relu_activation='ReLU7.5',
name='sepconv_t_3',
pointwise_regularizer=weight_regularizer)
x = Dropout(0.5)(x)
x = sepconv_transpose_block(x,
filters=int(32 * alpha),
kernel_size=(3, 3),
strides=2,
padding='same',
kernel_initializer='he_normal',
add_batchnorm=True,
relu_activation='ReLU7.5',
name='sepconv_t_4',
pointwise_regularizer=weight_regularizer)
x = Dropout(0.5)(x)
x = conv_block(x, filters=1, kernel_size=(1, 1), relu_activation=False, name='head')
x = Activation('sigmoid', name="sigmoid_act")(x)
# Build the whole model: encoder followed by decoder
return Model(inputs=encoder.input, outputs=x, name='akida_unet')
[docs]def akida_unet_portrait128_pretrained(quantized=True):
"""
Helper method to retrieve an `akida_unet` model that was trained on portrait128 dataset.
Args:
quantized (bool, optional): a boolean indicating whether the model should be loaded
quantized or not. Defaults to True.
Returns:
keras.Model: a Keras Model instance.
"""
if quantized:
model_name_v2 = 'akida_unet_portrait128_i8_w8_a8.h5'
file_hash_v2 = 'cf5b2eff3b272e3bf3070b36f9e6ca903ac2694b976557053498a72643899a59'
else:
model_name_v2 = 'akida_unet_portrait128.h5'
file_hash_v2 = '2274a375c02c104a5078b11f64d6986e1ca45c0da7ef5eacce8b2c71e1c35037'
model_path, model_name, file_hash = get_model_path("akida_unet", model_name_v2=model_name_v2,
file_hash_v2=file_hash_v2)
model_path = fetch_file(model_path, fname=model_name, file_hash=file_hash,
cache_subdir='models')
return load_model(model_path)