Source code for akida_models.utk_face.model_vgg

#!/usr/bin/env python
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# Copyright 2020 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.
# You may obtain a copy of the License at
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#    http://www.apache.org/licenses/LICENSE-2.0
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"""
VGG model definition for UTKFace regression.
"""

__all__ = ["vgg_utk_face", "vgg_utk_face_pretrained"]

from keras import Model
from keras.layers import Dropout, Input, Rescaling

from ..layer_blocks import conv_block, dense_block
from ..utils import fetch_file, get_params_by_version
from ..model_io import load_model, get_model_path


[docs]def vgg_utk_face(input_shape=(32, 32, 3), input_scaling=(127, -1)): """Instantiates a VGG-like model for the regression example on age estimation using UTKFace dataset. Note: input preprocessing is included as part of the model (as a Rescaling layer). This model expects inputs to be float tensors of pixels with values in the [0, 255] range. Args: input_shape (tuple, optional): input shape tuple of the model. Defaults to (32, 32, 3). input_scaling (tuple, optional): scale factor and offset to apply to inputs. Defaults to (127, -1). Note that following Akida convention, the scale factor is an integer used as a divisor. Returns: keras.Model: a Keras model for VGG/UTKFace """ img_input = Input(shape=input_shape, name="input") if input_scaling is None: x = img_input else: scale, offset = input_scaling x = Rescaling(1. / scale, offset, name="rescaling")(img_input) # Model version management _, post_relu_gap, relu_activation = get_params_by_version() x = conv_block(x, filters=32, kernel_size=(3, 3), name='conv_0', use_bias=False, relu_activation=relu_activation, add_batchnorm=True) x = conv_block(x, filters=32, kernel_size=(3, 3), name='conv_1', padding='same', pooling='max', pool_size=2, use_bias=False, relu_activation=relu_activation, add_batchnorm=True) x = Dropout(0.3, name="dropout_3")(x) x = conv_block(x, filters=64, kernel_size=(3, 3), padding='same', name='conv_2', use_bias=False, relu_activation=relu_activation, add_batchnorm=True) x = conv_block(x, filters=64, kernel_size=(3, 3), padding='same', name='conv_3', pooling='max', pool_size=2, use_bias=False, relu_activation=relu_activation, add_batchnorm=True) x = Dropout(0.3, name="dropout_4")(x) x = conv_block(x, filters=84, kernel_size=(3, 3), padding='same', name='conv_4', use_bias=False, relu_activation=relu_activation, pooling='global_avg', post_relu_gap=post_relu_gap, add_batchnorm=True) x = Dropout(0.3, name="dropout_5")(x) x = dense_block(x, units=64, name='dense_1', use_bias=False, relu_activation=relu_activation, add_batchnorm=True) x = dense_block(x, units=1, name='dense_2', relu_activation=False) return Model(img_input, x, name='vgg_utk_face')
[docs]def vgg_utk_face_pretrained(quantized=True): """ Helper method to retrieve a `vgg_utk_face` model that was trained on UTK Face 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_v1 = 'vgg_utk_face_iq8_wq2_aq2.h5' file_hash_v1 = 'e341d2d5e4655846ddc7aceff0d4e324cbfbcca16f3cfefc65e7b0863e4a23a3' model_name_v2 = 'vgg_utk_face_i8_w4_a4.h5' file_hash_v2 = '06036da929d1606ed38ec7c76cf80f8e784cebcd3e5340dfcd7f25705f3c0bc2' else: model_name_v1 = 'vgg_utk_face.h5' file_hash_v1 = '3f9084c452ef006a16f10f2d124804b6db267f7a5149b735a9c383a9b00a4922' model_name_v2 = 'vgg_utk_face.h5' file_hash_v2 = 'f1fc29e4ab0c1615902d0dd48afc6bad1f3bbf4b1a6a2e3642e5c2ee37208121' model_path, model_name, file_hash = get_model_path("vgg", model_name_v1, file_hash_v1, model_name_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)