Source code for akida_models.imagenet.model_mobilenet

#!/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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"""
MobileNet model definition for ImageNet classification.

MobileNet V1 is a general architecture and can be used for multiple use cases.

This specific version includes parameter options to generate a mobilenet version
compatible for Akida with:
    - overall architecture compatible with Akida (conv stride 2 replaced with
     max pool),
    - options to quantize weights and activations,
    - different initialization options.
"""

import warnings

from keras import Model, regularizers
from keras.utils.data_utils import get_file
from keras.layers import (Input, Reshape, Dropout, Activation, Rescaling)

from cnn2snn import load_quantized_model, quantize

from ..layer_blocks import conv_block, separable_conv_block

BASE_WEIGHT_PATH = 'http://data.brainchip.com/models/mobilenet/'


def _obtain_input_shape(input_shape, default_size, min_size, include_top):
    """Internal utility to compute/validate a model's input shape.

    Args:
        input_shape: either None (will return the default model input shape),
            or a user-provided shape to be validated.
        default_size: default input width/height for the model.
        min_size: minimum input width/height accepted by the model.
        include_top: whether the model is expected to
            be linked to a classifier via a Flatten layer.

    Returns:
        tuple of integers: input shape (may include None entries).

    Raises:
        ValueError: in case of invalid argument values.
    """
    if input_shape and len(input_shape) == 3:
        if input_shape[-1] not in {1, 3}:
            warnings.warn('This model usually expects 1 or 3 input channels. '
                          'However, it was passed an input_shape with ' +
                          str(input_shape[-1]) + ' input channels.')
        default_shape = (default_size, default_size, input_shape[-1])
    else:
        default_shape = (default_size, default_size, 3)

    if input_shape:
        if input_shape is not None:
            if len(input_shape) != 3:
                raise ValueError(
                    '`input_shape` must be a tuple of three integers.')
            if ((input_shape[0] is not None and input_shape[0] < min_size) or
                (input_shape[1] is not None and input_shape[1] < min_size)):
                raise ValueError('Input size must be at least ' +
                                 str(min_size) + 'x' + str(min_size) +
                                 '; got `input_shape=' + str(input_shape) + '`')
    else:
        if include_top:
            input_shape = default_shape
        else:
            input_shape = (None, None, 3)
    if include_top:
        if None in input_shape:
            raise ValueError('If `include_top` is True, '
                             'you should specify a static `input_shape`. '
                             'Got `input_shape=' + str(input_shape) + '`')
    return input_shape


[docs]def mobilenet_imagenet(input_shape=None, alpha=1.0, dropout=1e-3, include_top=True, pooling=None, classes=1000, use_stride2=False, weight_quantization=0, activ_quantization=0, input_weight_quantization=None, input_scaling=(128, -1)): """Instantiates the MobileNet architecture. Args: input_shape (tuple): optional shape tuple. alpha (float): controls the width of the model. * If `alpha` < 1.0, proportionally decreases the number of filters in each layer. * If `alpha` > 1.0, proportionally increases the number of filters in each layer. * If `alpha` = 1, default number of filters from the paper are used at each layer. dropout (float): dropout rate include_top (bool): whether to include the fully-connected layer at the top of the model. pooling (str): Optional pooling mode for feature extraction when `include_top` is `False`. * `None` means that the output of the model will be the 4D tensor output of the last convolutional block. * `avg` means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. classes (int): optional number of classes to classify images into, only to be specified if `include_top` is True. use_stride2 (bool): optional, replace max pooling operations by stride 2 convolutions in layers separable 2, 4, 6 and 12. weight_quantization (int): sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer. * '0' implements floating point 32-bit weights. * '2' through '8' implements n-bit weights where n is from 2-8 bits. activ_quantization: sets all activations in the model to have a particular activation quantization bitwidth. * '0' implements floating point 32-bit activations. * '2' through '8' implements n-bit weights where n is from 2-8 bits. input_weight_quantization: sets weight quantization in the first layer. Defaults to weight_quantization value. * '0' implements floating point 32-bit weights. * '2' through '8' implements n-bit weights where n is from 2-8 bits. 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 an integer used as a divider. Returns: keras.Model: a Keras model for MobileNet/ImageNet. Raises: ValueError: in case of invalid input shape. """ # check if overrides have been provided and override if input_weight_quantization is None: input_weight_quantization = weight_quantization # Define weight regularization, will apply to the first convolutional layer # and to all pointwise weights of separable convolutional layers. weight_regularizer = regularizers.l2(4e-5) # Define stride 2 or max pooling if use_stride2: sep_conv_pooling = None strides = 2 else: sep_conv_pooling = 'max' strides = 1 # Determine proper input shape and default size. if input_shape is None: default_size = 224 else: rows = input_shape[0] cols = input_shape[1] if rows == cols and rows in [128, 160, 192, 224]: default_size = rows else: default_size = 224 input_shape = _obtain_input_shape(input_shape, default_size=default_size, min_size=32, include_top=include_top) rows = input_shape[0] cols = input_shape[1] img_input = Input(shape=input_shape) if input_scaling is None: x = img_input else: scale, offset = input_scaling x = Rescaling(1. / scale, offset)(img_input) x = conv_block(x, filters=int(32 * alpha), name='conv_0', kernel_size=(3, 3), padding='same', use_bias=False, strides=2, add_batchnorm=True, add_activation=True, kernel_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(64 * alpha), name='separable_1', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(128 * alpha), name='separable_2', kernel_size=(3, 3), padding='same', pooling=sep_conv_pooling, strides=strides, use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(128 * alpha), name='separable_3', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(256 * alpha), name='separable_4', kernel_size=(3, 3), padding='same', pooling=sep_conv_pooling, strides=strides, use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(256 * alpha), name='separable_5', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(512 * alpha), name='separable_6', kernel_size=(3, 3), padding='same', pooling=sep_conv_pooling, strides=strides, use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(512 * alpha), name='separable_7', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(512 * alpha), name='separable_8', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(512 * alpha), name='separable_9', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(512 * alpha), name='separable_10', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(512 * alpha), name='separable_11', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) x = separable_conv_block(x, filters=int(1024 * alpha), name='separable_12', kernel_size=(3, 3), padding='same', pooling=sep_conv_pooling, strides=strides, use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) # Last separable layer with global pooling layer_pooling = 'global_avg' if include_top or pooling == 'avg' else None x = separable_conv_block(x, filters=int(1024 * alpha), name='separable_13', kernel_size=(3, 3), padding='same', pooling=layer_pooling, use_bias=False, add_batchnorm=True, add_activation=True, pointwise_regularizer=weight_regularizer) if include_top: shape = (1, 1, int(1024 * alpha)) x = Reshape(shape, name='reshape_1')(x) x = Dropout(dropout, name='dropout')(x) x = separable_conv_block(x, filters=classes, name='separable_14', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=False, add_activation=False, pointwise_regularizer=weight_regularizer) 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(img_input, x, name='mobilenet_%0.2f_%s_%s' % (alpha, rows, classes)) if ((weight_quantization != 0) or (activ_quantization != 0) or (input_weight_quantization != 0)): return quantize(model, weight_quantization, activ_quantization, input_weight_quantization) return model
[docs]def mobilenet_imagenet_pretrained(alpha=1.0): """ Helper method to retrieve a `mobilenet_imagenet` model that was trained on ImageNet dataset. Args: alpha (float): width of the model. Returns: keras.Model: a Keras Model instance. """ if alpha == 1.0: model_name = 'mobilenet_imagenet_224_iq8_wq4_aq4.h5' file_hash = '7d509ed9156af1b8739537d4b16b64fc6ad7320e94638ec8e554c483f6464aaf' elif alpha == 0.5: model_name = 'mobilenet_imagenet_224_alpha_50_iq8_wq4_aq4.h5' file_hash = 'daa6cb88c0422b8218c6aec97925a790aa08c3d52c14d70aec16ddd73a42fd2b' elif alpha == 0.25: model_name = 'mobilenet_imagenet_224_alpha_25_iq8_wq4_aq4.h5' file_hash = 'f90f6730ab02279883e0ae372b4d853b01fcf7fe354f5103685c74f119654492' else: raise ValueError( f"Requested model with alpha={alpha} is not available.") model_path = get_file(fname=model_name, origin=BASE_WEIGHT_PATH + model_name, file_hash=file_hash, cache_subdir='models') return load_quantized_model(model_path)
[docs]def mobilenet_cats_vs_dogs_pretrained(): """ Helper method to retrieve a `mobilenet_imagenet` model that was trained on Cats vs.Dogs dataset. Returns: keras.Model: a Keras Model instance. """ model_name = 'mobilenet_cats_vs_dogs_iq8_wq4_aq4.h5' file_hash = 'd65eb369844d6fa4ce5cee85c41ef3a662620d1a05b838df94c8d173b8f92b2c' model_path = get_file(fname=model_name, origin=BASE_WEIGHT_PATH + model_name, file_hash=file_hash, cache_subdir='models') return load_quantized_model(model_path)
[docs]def mobilenet_imagenette_pretrained(alpha=1.0): """ Helper method to retrieve a `mobilenet_imagenet` model that was trained on Imagenette dataset. Args: alpha (float): width of the model. Returns: keras.Model: a Keras Model instance. """ if alpha == 1.0: model_name = 'mobilenet_imagenette_224_iq8_wq4_aq4.h5' file_hash = '9e5f7294c2462fadee74499ac463c734acff571796ea1313e4e3eb6f93a4b090' elif alpha == 0.5: model_name = 'mobilenet_imagenette_224_alpha_50_iq8_wq4_aq4.h5' file_hash = '0fcf9a1bd6a82042bf27b70fd337f38f2557fdbe3eb33ced76f6ce2c8bb6a710' elif alpha == 0.25: model_name = 'mobilenet_imagenette_224_alpha_25_iq8_wq4_aq4.h5' file_hash = 'b0dbd0d87b7e496afdd62400760fe1cccecebb18a4b2b8db1edb6eb65a31beb8' else: raise ValueError( f"Requested model with alpha={alpha} is not available.") model_path = get_file(fname=model_name, origin=BASE_WEIGHT_PATH + model_name, file_hash=file_hash, cache_subdir='models') return load_quantized_model(model_path)
[docs]def mobilenet_faceidentification_pretrained(): """ Helper method to retrieve a `mobilenet_imagenet` model that was trained on CASIA Webface dataset and that performs face identification. Returns: keras.Model: a Keras Model instance. """ model_name = 'mobilenet_faceidentification_iq8_wq4_aq4.h5' file_hash = 'd12f1dbd40267b483056728252eb56f578403071dcd472e39143319d6c84096c' model_path = get_file(fname=model_name, origin=BASE_WEIGHT_PATH + model_name, file_hash=file_hash, cache_subdir='models') return load_quantized_model(model_path)
[docs]def mobilenet_faceverification_pretrained(): """ Helper method to retrieve a `mobilenet_imagenet` model that was trained on CASIA Webface dataset and optimized with ArcFace that can perform face verification on LFW. Returns: keras.Model: a Keras Model instance. """ model_name = 'mobilenet_faceverification_iq8_wq4_aq4.h5' file_hash = 'ee54055f6baa38339c9f45632a0ef37c8b617fe81b9e68b709845b5d1a711919' model_path = get_file(fname=model_name, origin=BASE_WEIGHT_PATH + model_name, file_hash=file_hash, cache_subdir='models') return load_quantized_model(model_path)