首先數據集準備：
隊醫時候戴口罩這種二分類問題，我們採用keras框架下 MobileNetv2模型進行分類訓練。
"""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)
目前正在訓練：
