#!/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
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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.
"""
__all__ = ["mobilenet_imagenet", "mobilenet_imagenet_pretrained"]
from keras import Model, regularizers
from keras.layers import Input, Dropout, Rescaling
from .imagenet_utils import obtain_input_shape
from ..layer_blocks import conv_block, separable_conv_block, dense_block
from ..utils import fetch_file, get_params_by_version
from ..model_io import load_model, get_model_path
[docs]def mobilenet_imagenet(input_shape=None,
alpha=1.0,
dropout=1e-3,
include_top=True,
pooling=None,
classes=1000,
use_stride2=True,
input_scaling=(128, -1)):
"""Instantiates the MobileNet architecture.
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): shape tuple. Defaults to None.
alpha (float, optional): controls the width of the model.
Defaults to 1.0.
* 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, optional): dropout rate. Defaults to 1e-3.
include_top (bool, optional): whether to include the fully-connected
layer at the top of the model. Defaults to True.
pooling (str, optional): optional pooling mode for feature extraction
when `include_top` is `False`.
Defaults to None.
* `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): optional number of classes to classify images
into, only to be specified if `include_top` is `True`. Defaults to 1000.
use_stride2 (bool, optional): replace max pooling operations by stride 2
convolutions in layers separable 2, 4, 6 and 12. Defaults to True.
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 divisor.
Returns:
keras.Model: a Keras model for MobileNet/ImageNet.
Raises:
ValueError: in case of invalid input shape.
"""
# Model version management
fused, post_relu_gap, relu_activation = get_params_by_version(relu_v2='ReLU7.5')
# 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, name="input")
if input_scaling is None:
x = img_input
else:
scale, offset = input_scaling
x = Rescaling(1. / scale, offset, name="rescaling")(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,
relu_activation=relu_activation,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
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,
relu_activation=relu_activation,
fused=fused,
post_relu_gap=post_relu_gap,
pointwise_regularizer=weight_regularizer)
if include_top:
x = Dropout(dropout, name='dropout')(x)
x = dense_block(x,
units=classes,
name='classifier',
use_bias=False,
add_batchnorm=False,
relu_activation=False,
kernel_regularizer=weight_regularizer)
# Create model.
return Model(img_input, x, name='mobilenet_%0.2f_%s_%s' % (alpha, rows, classes))
[docs]def mobilenet_imagenet_pretrained(alpha=1.0, quantized=True):
"""
Helper method to retrieve a `mobilenet_imagenet` model that was trained on
ImageNet dataset.
Args:
alpha (float): width of the model.
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 alpha == 1.0:
if quantized:
model_name_v1 = 'mobilenet_imagenet_224_iq8_wq4_aq4.h5'
file_hash_v1 = '4e63ea329b3f2f773a0b9d6fef4e449639fda89d17e48bb7132b6ad86585c867'
model_name_v2 = 'mobilenet_imagenet_224_alpha_1_i8_w4_a4.h5'
file_hash_v2 = 'da329c8b6836c141a7b836875dbcabd36d8efd5b90d9719f50e2d4facea4868a'
else:
model_name_v1 = 'mobilenet_imagenet_224.h5'
file_hash_v1 = 'fb674f627337995f4aa372e4a270445457573fc7aae61e28c05f94da14cf2980'
model_name_v2 = 'mobilenet_imagenet_224_alpha_1.h5'
file_hash_v2 = '4305ca6e03bffaf4e39a840fb2c6108101136f572ae784bebc3bd9406a6ecd0c'
elif alpha == 0.5:
if quantized:
model_name_v1 = 'mobilenet_imagenet_224_alpha_50_iq8_wq4_aq4.h5'
file_hash_v1 = 'af9a31774467de96acceeed46cee14d7864fa2cc247601beff5521e7a5e6da99'
model_name_v2 = 'mobilenet_imagenet_224_alpha_0.5_i8_w4_a4.h5'
file_hash_v2 = 'c7277a7850c777a39c90023a7c509696bc7a2cb61590d3c88841f1ee3bc14003'
else:
model_name_v1 = 'mobilenet_imagenet_224_alpha_50.h5'
file_hash_v1 = '1bffadb48f3fd194cdeecf4e02d91d0b3a87df103620c645f1c00f5e5cfbca9a'
model_name_v2 = 'mobilenet_imagenet_224_alpha_0.5.h5'
file_hash_v2 = '1ded4e456b59b4286329b2e8694d1d3adce9745a73a59cc4670dc0eae60ff2bc'
elif alpha == 0.25:
if quantized:
model_name_v1 = 'mobilenet_imagenet_224_alpha_25_iq8_wq4_aq4.h5'
file_hash_v1 = '35ad51e662ed04b68978865ccb1c16bf024fa013146488b2a9c18c80d1efab29'
model_name_v2 = 'mobilenet_imagenet_224_alpha_0.25_i8_w4_a4.h5'
file_hash_v2 = '29eb66a023fa808735e66f3716e3a8d6429a5d9621e6ed875de4fc8104682a14'
else:
model_name_v1 = 'mobilenet_imagenet_224_alpha_25.h5'
file_hash_v1 = '33c49bb4ec868558a2a9687be19a7e51756ddb9f00a032535069dcd5a727e0dd'
model_name_v2 = 'mobilenet_imagenet_224_alpha_0.25.h5'
file_hash_v2 = '5f70598352ddeb7bf2c19126d7009f569fc9cf6d52124334c4ccebe9030f3c39'
else:
raise ValueError(
f"Requested model with alpha={alpha} is not available.")
model_path, model_name, file_hash = get_model_path("mobilenet", 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)