Source code for akida_models.portrait128.model_akida_unet

#!/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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#    http://www.apache.org/licenses/LICENSE-2.0
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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 = '5b1590d1c572e842c9be2c405a0b7af169ced7a4af07e175b0842e734761cb4b' else: model_name_v2 = 'akida_unet_portrait128.h5' file_hash_v2 = '22bd8c31fd9548479b36bfe6403d7dfa38d9bc7127999c60d96aec742f2309d7' 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)