MobileNetV2详细总结以及代码讲解

模型介绍

特点：1.相比于MobileNetV1，先进行了1x1的卷积进行升维，目的在于获得更多特征，然后用3x3的空间卷积，最后再用1x1降维。核心思想是升维再降维，参数量更少。
2.为了避免Relu对特征的破坏，在在3x3网络结构前利用1x1卷积升维，在3x3网络结构后，再利用1x1卷积降维后，不再进行Relu6层，直接进行残差网络的加法。
PS：Relu对于负的输入，输出全为零；而本来特征就已经被“压缩”，再经过Relu的话，又要“损失”一部分特征，因此这里不采用Relu。

模型网络

t：1x1升维扩张的倍数。
c：输出通道数。
n：进行多少次。
s：步长。

如下图是bottleneck的结构，这里我们需要注意的是，步长维1和2的情况是不同的。stride=2时，不采用shortcut。因为维度变化，无法相加。

代码实现+图片预测

我们先将代码块分开说明。

首先，我们来看一下MoblieNet的更新点吧。

从上面我们知道：
1. (1,1)的pointwise卷积，提高通道数，结果Relu
2. (3,3)的depthwise卷积，提特征，结果Relu
3. (1,1)的pointwise卷积，单纯降维，不Relu
4.输入与输出的shortcut
所以在步长(2,2)的时候，要注意使用correct_pad填充特征图，
避免卷积出小数宽高特征图的情况。

这就是第一步啦，1x1扩张，relu6。

    if block_id:
        # Expand
        x = layers.Conv2D(expansion * in_channels,
                          kernel_size=1,
                          padding='same',
                          use_bias=False,
                          activation=None,
                          name=prefix + 'expand')(x)
        x = layers.BatchNormalization(axis=channel_axis,
                                      epsilon=1e-3,
                                      momentum=0.999,
                                      name=prefix + 'expand_BN')(x)
        x = layers.ReLU(6., name=prefix + 'expand_relu')(x)
    else:
        prefix = 'expanded_conv_'

在这里我们进行填充0的操作，为的是在3x3卷积后，长宽缩短到1/2，correct_pad是计算padding。

if stride == 2:
        x = ZeroPadding2D(padding=correct_pad(x, 3),
                          name=prefix + 'pad')(x)

然后就是可分离卷积了。

 # part2 可分离卷积
    x = DepthwiseConv2D(kernel_size=3,
                        strides=stride,
                        activation=None,
                        use_bias=False,
                        padding='same' if stride == 1 else 'valid',
                        name=prefix + 'depthwise')(x)
    x = BatchNormalization(epsilon=1e-3,
                           momentum=0.999,
                           name=prefix + 'depthwise_BN')(x)

    x = Activation(relu6, name=prefix + 'depthwise_relu')(x)

最后就是进行降维，这里不用relu。当stride=1以及输入输出通道数相同时，进行shortcut。

    # part3压缩特征，而且不使用relu函数，保证特征不被破坏
    x = Conv2D(pointwise_filters,
               kernel_size=1,
               padding='same',
               use_bias=False,
               activation=None,
               name=prefix + 'project')(x)

    x = BatchNormalization(epsilon=1e-3,
                           momentum=0.999,
                           name=prefix + 'project_BN')(x)

    if in_channels == pointwise_filters and stride == 1:
        return Add(name=prefix + 'add')([inputs, x])
    return x

下面是代码整合

#-------------------------------------------------------------#
#   MobileNetV2的网络部分
#-------------------------------------------------------------#
import math
import numpy as np
import tensorflow as tf
from tensorflow.keras import backend
from keras import backend as K
from keras.preprocessing import image
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.layers import Conv2D, Add, ZeroPadding2D, GlobalAveragePooling2D, Dropout, Dense
from keras.layers import MaxPooling2D,Activation,DepthwiseConv2D,Input,GlobalMaxPooling2D
from keras.applications import imagenet_utils
from keras.applications.imagenet_utils import decode_predictions
from keras.utils.data_utils import get_file


# TODO Change path to v1.1
BASE_WEIGHT_PATH = ('https://github.com/JonathanCMitchell/mobilenet_v2_keras/'
                    'releases/download/v1.1/')

# relu6！
def relu6(x):
    return K.relu(x, max_value=6)
    
# 用于计算padding的大小
def correct_pad(inputs, kernel_size):
    img_dim = 1
    input_size = backend.int_shape(inputs)[img_dim:(img_dim + 2)]

    if isinstance(kernel_size, int):
        kernel_size = (kernel_size, kernel_size)

    if input_size[0] is None:
        adjust = (1, 1)
    else:
        adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2)

    correct = (kernel_size[0] // 2, kernel_size[1] // 2)

    return ((correct[0] - adjust[0], correct[0]),
            (correct[1] - adjust[1], correct[1]))

# 使其结果可以被8整除，因为使用到了膨胀系数α
def _make_divisible(v, divisor, min_value=None):
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


def MobileNetV2(input_shape=[224,224,3],
                alpha=1.0,
                include_top=True,
                weights='imagenet',
                classes=1000):

    rows = input_shape[0]

    img_input = Input(shape=input_shape)

    # stem部分
    # 224,224,3 -> 112,112,32
    first_block_filters = _make_divisible(32 * alpha, 8)
    x = ZeroPadding2D(padding=correct_pad(img_input, 3),
                             name='Conv1_pad')(img_input)
    x = Conv2D(first_block_filters,
                      kernel_size=3,
                      strides=(2, 2),
                      padding='valid',
                      use_bias=False,
                      name='Conv1')(x)
    x = BatchNormalization(epsilon=1e-3,
                                  momentum=0.999,
                                  name='bn_Conv1')(x)
    x = Activation(relu6, name='Conv1_relu')(x)

    # 112,112,32 -> 112,112,16
    x = _inverted_res_block(x, filters=16, alpha=alpha, stride=1,
                            expansion=1, block_id=0)

    # 112,112,16 -> 56,56,24
    x = _inverted_res_block(x, filters=24, alpha=alpha, stride=2,
                            expansion=6, block_id=1)
    x = _inverted_res_block(x, filters=24, alpha=alpha, stride=1,
                            expansion=6, block_id=2)

    # 56,56,24 -> 28,28,32
    x = _inverted_res_block(x, filters=32, alpha=alpha, stride=2,
                            expansion=6, block_id=3)
    x = _inverted_res_block(x, filters=32, alpha=alpha, stride=1,
                            expansion=6, block_id=4)
    x = _inverted_res_block(x, filters=32, alpha=alpha, stride=1,
                            expansion=6, block_id=5)

    # 28,28,32 -> 14,14,64
    x = _inverted_res_block(x, filters=64, alpha=alpha, stride=2,
                            expansion=6, block_id=6)
    x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1,
                            expansion=6, block_id=7)
    x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1,
                            expansion=6, block_id=8)
    x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1,
                            expansion=6, block_id=9)

    # 14,14,64 -> 14,14,96
    x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1,
                            expansion=6, block_id=10)
    x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1,
                            expansion=6, block_id=11)
    x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1,
                            expansion=6, block_id=12)
    # 14,14,96 -> 7,7,160
    x = _inverted_res_block(x, filters=160, alpha=alpha, stride=2,
                            expansion=6, block_id=13)
    x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1,
                            expansion=6, block_id=14)
    x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1,
                            expansion=6, block_id=15)

    # 7,7,160 -> 7,7,320
    x = _inverted_res_block(x, filters=320, alpha=alpha, stride=1,
                            expansion=6, block_id=16)

    if alpha > 1.0:
        last_block_filters = _make_divisible(1280 * alpha, 8)
    else:
        last_block_filters = 1280

    # 7,7,320 -> 7,7,1280
    x = Conv2D(last_block_filters,
                      kernel_size=1,
                      use_bias=False,
                      name='Conv_1')(x)
    x = BatchNormalization(epsilon=1e-3,
                                  momentum=0.999,
                                  name='Conv_1_bn')(x)
    x = Activation(relu6, name='out_relu')(x)


    x = GlobalAveragePooling2D()(x)
    x = Dense(classes, activation='softmax',
                        use_bias=True, name='Logits')(x)

    inputs = img_input

    model = Model(inputs, x, name='mobilenetv2_%0.2f_%s' % (alpha, rows))

    # Load weights.
    if weights == 'imagenet':
        if include_top:
            model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
                          str(alpha) + '_' + str(rows) + '.h5')
            weight_path = BASE_WEIGHT_PATH + model_name
            weights_path = get_file(
                model_name, weight_path, cache_subdir='models')
        else:
            model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
                          str(alpha) + '_' + str(rows) + '_no_top' + '.h5')
            weight_path = BASE_WEIGHT_PATH + model_name
            weights_path = get_file(
                model_name, weight_path, cache_subdir='models')
        model.load_weights(weights_path)
    elif weights is not None:
        model.load_weights(weights)

    return model


def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
    in_channels = backend.int_shape(inputs)[-1]
    pointwise_conv_filters = int(filters * alpha)
    pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
    x = inputs
    prefix = 'block_{}_'.format(block_id)

    # part1 数据扩张
    if block_id:
        # Expand
        x = Conv2D(expansion * in_channels,
                          kernel_size=1,
                          padding='same',
                          use_bias=False,
                          activation=None,
                          name=prefix + 'expand')(x)
        x = BatchNormalization(epsilon=1e-3,
                                      momentum=0.999,
                                      name=prefix + 'expand_BN')(x)
        x = Activation(relu6, name=prefix + 'expand_relu')(x)
    else:
        prefix = 'expanded_conv_'

    if stride == 2:
        x = ZeroPadding2D(padding=correct_pad(x, 3),
                                 name=prefix + 'pad')(x)
    
    # part2 可分离卷积
    x = DepthwiseConv2D(kernel_size=3,
                               strides=stride,
                               activation=None,
                               use_bias=False,
                               padding='same' if stride == 1 else 'valid',
                               name=prefix + 'depthwise')(x)
    x = BatchNormalization(epsilon=1e-3,
                                  momentum=0.999,
                                  name=prefix + 'depthwise_BN')(x)

    x = Activation(relu6, name=prefix + 'depthwise_relu')(x)

    # part3压缩特征，而且不使用relu函数，保证特征不被破坏
    x = Conv2D(pointwise_filters,
                      kernel_size=1,
                      padding='same',
                      use_bias=False,
                      activation=None,
                      name=prefix + 'project')(x)

    x = BatchNormalization(epsilon=1e-3,
                                  momentum=0.999,
                                  name=prefix + 'project_BN')(x)

    if in_channels == pointwise_filters and stride == 1:
        return Add(name=prefix + 'add')([inputs, x])
    return x
def preprocess_input(x):
    x /= 255.
    x -= 0.5
    x *= 2.
    return x

if __name__ == '__main__':
    model = MobileNetV2(input_shape=(224, 224, 3))
    model.summary()
    img_path = 'elephant.jpg'
    img = image.load_img(img_path, target_size=(224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    print('Input image shape:', x.shape)

    preds = model.predict(x)
    print(np.argmax(preds))
    print('Predicted:', decode_predictions(preds, 1))