Keras版MobileNetv2模型是否带口罩二分类问题训练

首先数据集准备：

队医时候戴口罩这种二分类问题，我们采用keras框架下 MobileNetv2模型进行分类训练。

"""MobileNet v2 models for Keras.
# Reference
- [Inverted Residuals and Linear Bottlenecks Mobile Networks for
   Classification, Detection and Segmentation]
   (https://arxiv.org/abs/1801.04381)
"""


from keras.models import Model
from keras.layers import Input, Conv2D, GlobalAveragePooling2D, Dropout
from keras.layers import Activation, BatchNormalization, Add, Reshape, DepthwiseConv2D
from keras.utils.vis_utils import plot_model
from keras.optimizers import Nadam
from keras import backend as K


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)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


def relu6(x):
    """Relu 6
    """
    return K.relu(x, max_value=6.0)
def _conv_block(inputs, filters, kernel, strides):
    """Convolution Block
    This function defines a 2D convolution operation with BN and relu6.
    # Arguments
        inputs: Tensor, input tensor of conv layer.
        filters: Integer, the dimensionality of the output space.
        kernel: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
        strides: An integer or tuple/list of 2 integers,
            specifying the strides of the convolution along the width and height.
            Can be a single integer to specify the same value for
            all spatial dimensions.
    # Returns
        Output tensor.
    """

    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1

    x = Conv2D(filters, kernel, padding='same', strides=strides)(inputs)
    x = BatchNormalization(axis=channel_axis)(x)
    return Activation(relu6)(x)


def _bottleneck(inputs, filters, kernel, t, alpha, s, r=False):
    """Bottleneck
    This function defines a basic bottleneck structure.
    # Arguments
        inputs: Tensor, input tensor of conv layer.
        filters: Integer, the dimensionality of the output space.
        kernel: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
        t: Integer, expansion factor.
            t is always applied to the input size.
        s: An integer or tuple/list of 2 integers,specifying the strides
            of the convolution along the width and height.Can be a single
            integer to specify the same value for all spatial dimensions.
        alpha: Integer, width multiplier.
        r: Boolean, Whether to use the residuals.
    # Returns
        Output tensor.
    """

    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
    # Depth
    tchannel = K.int_shape(inputs)[channel_axis] * t
    # Width
    cchannel = int(filters * alpha)

    x = _conv_block(inputs, tchannel, (1, 1), (1, 1))

    x = DepthwiseConv2D(kernel, strides=(s, s), depth_multiplier=1, padding='same')(x)
    x = BatchNormalization(axis=channel_axis)(x)
    x = Activation(relu6)(x)

    x = Conv2D(cchannel, (1, 1), strides=(1, 1), padding='same')(x)
    x = BatchNormalization(axis=channel_axis)(x)

    if r:
        x = Add()([x, inputs])

    return x


def _inverted_residual_block(inputs, filters, kernel, t, alpha, strides, n):
    """Inverted Residual Block
    This function defines a sequence of 1 or more identical layers.
    # Arguments
        inputs: Tensor, input tensor of conv layer.
        filters: Integer, the dimensionality of the output space.
        kernel: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
        t: Integer, expansion factor.
            t is always applied to the input size.
        alpha: Integer, width multiplier.
        s: An integer or tuple/list of 2 integers,specifying the strides
            of the convolution along the width and height.Can be a single
            integer to specify the same value for all spatial dimensions.
        n: Integer, layer repeat times.
    # Returns
        Output tensor.
    """

    x = _bottleneck(inputs, filters, kernel, t, alpha, strides)

    for i in range(1, n):
        x = _bottleneck(x, filters, kernel, t, alpha, 1, True)

    return x


def MobileNetv2(input_shape, k, alpha=1.0):
    """MobileNetv2
    This function defines a MobileNetv2 architectures.
    # Arguments
        input_shape: An integer or tuple/list of 3 integers, shape
            of input tensor.
        k: Integer, number of classes.
        alpha: Integer, width multiplier, better in [0.35, 0.50, 0.75, 1.0, 1.3, 1.4].
    # Returns
        MobileNetv2 model.
    """
    inputs = Input(shape=input_shape)

    first_filters = _make_divisible(32 * alpha, 8)
    x = _conv_block(inputs, first_filters, (3, 3), strides=(2, 2))

    x = _inverted_residual_block(x, 16, (3, 3), t=1, alpha=alpha, strides=1, n=1)
    x = _inverted_residual_block(x, 24, (3, 3), t=6, alpha=alpha, strides=2, n=2)
    x = _inverted_residual_block(x, 32, (3, 3), t=6, alpha=alpha, strides=2, n=3)
    x = _inverted_residual_block(x, 64, (3, 3), t=6, alpha=alpha, strides=2, n=4)
    x = _inverted_residual_block(x, 96, (3, 3), t=6, alpha=alpha, strides=1, n=3)
    x = _inverted_residual_block(x, 160, (3, 3), t=6, alpha=alpha, strides=2, n=3)
    x = _inverted_residual_block(x, 320, (3, 3), t=6, alpha=alpha, strides=1, n=1)

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

    x = _conv_block(x, last_filters, (1, 1), strides=(1, 1))
    x = GlobalAveragePooling2D()(x)
    x = Reshape((1, 1, last_filters))(x)
    x = Dropout(0.3, name='Dropout')(x)
    x = Conv2D(k, (1, 1), padding='same')(x)

    x = Activation('sigmoid', name='sigmoid')(x)
    output = Reshape((k,))(x)

    model = Model(inputs, output)
    # plot_model(model, to_file='images/MobileNetv2.png', show_shapes=True)
    model.compile(optimizer = Nadam(lr = 2e-4), loss = "binary_crossentropy", metrics = ['accuracy'])
    return model


if __name__ == '__main__':
    model = MobileNetv2((224, 224, 3), 100, 1.0)
    print(model.summary())

 

from model import *
from keras.callbacks import TensorBoard,ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
import numpy as np 
import cv2
#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
batch_size=8
tensorboard = TensorBoard(log_dir='./log')

data_gen_args = dict(rotation_range=0.2,
                    width_shift_range=0.05,
                    height_shift_range=0.05,
                    shear_range=0.05,
                    zoom_range=0.05,
                    horizontal_flip=True,
                    fill_mode='nearest')

image_datagen = ImageDataGenerator(**data_gen_args)
image_generator = image_datagen.flow_from_directory(r'data/train', batch_size=batch_size,class_mode='binary')


model = MobileNetv2((256, 256, 3), 1, 1.0)
# model=unet_resnet_101()
model.summary()
model_checkpoint = ModelCheckpoint('MobileNetv2.hdf5', monitor='loss',verbose=1, save_best_only=True)
model.fit_generator(image_generator,steps_per_epoch=400,epochs=130,callbacks=[model_checkpoint,tensorboard])

# testGene = testGenerator(r"data/membrane/test")
# model = unet_resnet_101()
# model.load_weights(r"unet_membrane_resnet101.hdf5")
# results = model.predict_generator(testGene,30,verbose=1)
# saveResult(r"data/predice",results)

目前正在训练：