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747451d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | # /*---------------------------------------------------------------------------------------------
# * Copyright (c) 2016 Refikcanmalli
# * Copyright (c) 2022 STMicroelectronics.
# * All rights reserved.
# * This software is licensed under terms that can be found in the LICENSE file in
# * the root directory of this software component.
# * If no LICENSE file comes with this software, it is provided AS-IS.
# *--------------------------------------------------------------------------------------------*/
import keras
from keras import layers
from typing import Tuple
def _fire_module(x: keras.layers.Layer, fire_id: int, squeeze: int = 16, expand: int = 64) -> keras.layers.Layer:
"""
Fire module for the SqueezeNet model.
Implements expand layer, which has a mix of 1x1 and 3x3 filters,
by using two conv layers concatenated in the channel dimension.
Args:
- x: input tensor.
- fire_id: integer, the id of the fire module.
- squeeze: integer, the number of filters in the squeeze layer.
- expand: integer, the number of filters in the expand layer.
Returns:
- keras.layers.Layer object, the output tensor.
"""
# Define some constants
sq1x1 = "squeeze1x1"
exp1x1 = "expand1x1"
exp3x3 = "expand3x3"
relu = "relu_"
# Define the id of the fire module
s_id = 'fire' + str(fire_id) + '_'
# Get the channel axis
if keras.backend.image_data_format() == 'channels_first':
channel_axis = 1
else:
channel_axis = 3
# Squeeze layer
x = layers.Convolution2D(squeeze, (1, 1), padding='valid', name=s_id + sq1x1)(x)
x = layers.Activation('relu', name=s_id + relu + sq1x1)(x)
# Expand layer
left = layers.Convolution2D(expand, (1, 1), padding='valid', name=s_id + exp1x1)(x)
left = layers.Activation('relu', name=s_id + relu + exp1x1)(left)
right = layers.Convolution2D(expand, (3, 3), padding='same', name=s_id + exp3x3)(x)
right = layers.Activation('relu', name=s_id + relu + exp3x3)(right)
x = layers.concatenate([left, right], axis=channel_axis, name=s_id + 'concat')
return x
def get_squeezenetv11(num_classes: int = None, input_shape: Tuple[int, int, int] = None,
dropout: float = None, pretrained: bool = False, **kwargs) -> keras.Model:
"""
Returns a SqueezeNet model with the specified number of output classes.
Args:
- num_classes: integer, the number of output classes.
- input_shape: tuple of integers, the shape of the input tensor (height, width, channels).
- dropout: float between 0 and 1, the dropout rate to apply.
Returns:
- keras.Model object, the SqueezeNet model.
"""
if pretrained:
print("WARNING: No pretrained weights are found for 'squeezenetv11' model. Random weights are used instead.")
# Define the input tensor
input_image = layers.Input(input_shape)
# First convolutional layer
x = layers.Convolution2D(64, (3, 3), strides=(2, 2), padding='valid', name='conv1')(input_image)
x = layers.Activation('relu', name='relu_conv1')(x)
x = layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool1')(x)
# Fire modules
x = _fire_module(x, fire_id=2, squeeze=16, expand=64)
x = _fire_module(x, fire_id=3, squeeze=16, expand=64)
x = layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool3')(x)
x = _fire_module(x, fire_id=4, squeeze=32, expand=128)
x = _fire_module(x, fire_id=5, squeeze=32, expand=128)
x = layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool5')(x)
x = _fire_module(x, fire_id=6, squeeze=48, expand=192)
x = _fire_module(x, fire_id=7, squeeze=48, expand=192)
x = _fire_module(x, fire_id=8, squeeze=64, expand=256)
x = _fire_module(x, fire_id=9, squeeze=64, expand=256)
# Dropout layer
if dropout:
x = layers.Dropout(dropout, name='drop9')(x)
# Final convolutional layer
x = layers.Convolution2D(num_classes, (1, 1), padding='valid', name='conv10')(x)
x = layers.Activation('relu', name='relu_conv10')(x)
# Global average pooling layer
x = layers.GlobalAveragePooling2D()(x)
# Softmax activation layer
output = layers.Activation('softmax', name='loss')(x)
# Create the model
model = keras.Model(input_image, output, name='squeezenetv11')
return model
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