import numpy as np import tensorflow.keras.backend as K from tensorflow.keras.layers import (Activation, Concatenate, Conv2D, Flatten, Input, InputSpec, Layer, Reshape) from tensorflow.keras.models import Model from nets.vgg import VGG16 class Normalize(Layer): def __init__(self, scale, **kwargs): self.axis = 3 self.scale = scale super(Normalize, self).__init__(**kwargs) def build(self, input_shape): self.input_spec = [InputSpec(shape=input_shape)] shape = (input_shape[self.axis],) init_gamma = self.scale * np.ones(shape) self.gamma = K.variable(init_gamma, name='{}_gamma'.format(self.name)) def call(self, x, mask=None): output = K.l2_normalize(x, self.axis) output *= self.gamma return output def SSD300(input_shape, num_classes=21): #---------------------------------# # 典型的输入大小为[300,300,3] #---------------------------------# input_tensor = Input(shape=input_shape) # net变量里面包含了整个SSD的结构，通过层名可以找到对应的特征层 net = VGG16(input_tensor) #-----------------------将提取到的主干特征进行处理---------------------------# # 对conv4_3的通道进行l2标准化处理 # 38,38,512 net['conv4_3_norm'] = Normalize(20, name='conv4_3_norm')(net['conv4_3']) num_priors = 4 # 预测框的处理 # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整 net['conv4_3_norm_mbox_loc'] = Conv2D(num_priors * 4, kernel_size=(3,3), padding='same', name='conv4_3_norm_mbox_loc')(net['conv4_3_norm']) net['conv4_3_norm_mbox_loc_flat'] = Flatten(name='conv4_3_norm_mbox_loc_flat')(net['conv4_3_norm_mbox_loc']) # num_priors表示每个网格点先验框的数量，num_classes是所分的类 net['conv4_3_norm_mbox_conf'] = Conv2D(num_priors * num_classes, kernel_size=(3,3), padding='same',name='conv4_3_norm_mbox_conf')(net['conv4_3_norm']) net['conv4_3_norm_mbox_conf_flat'] = Flatten(name='conv4_3_norm_mbox_conf_flat')(net['conv4_3_norm_mbox_conf']) # 对fc7层进行处理 # 19,19,1024 num_priors = 6 # 预测框的处理 # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整 net['fc7_mbox_loc'] = Conv2D(num_priors * 4, kernel_size=(3,3),padding='same',name='fc7_mbox_loc')(net['fc7']) net['fc7_mbox_loc_flat'] = Flatten(name='fc7_mbox_loc_flat')(net['fc7_mbox_loc']) # num_priors表示每个网格点先验框的数量，num_classes是所分的类 net['fc7_mbox_conf'] = Conv2D(num_priors * num_classes, kernel_size=(3,3),padding='same',name='fc7_mbox_conf')(net['fc7']) net['fc7_mbox_conf_flat'] = Flatten(name='fc7_mbox_conf_flat')(net['fc7_mbox_conf']) # 对conv6_2进行处理 # 10,10,512 num_priors = 6 # 预测框的处理 # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整 net['conv6_2_mbox_loc'] = Conv2D(num_priors * 4, kernel_size=(3,3), padding='same',name='conv6_2_mbox_loc')(net['conv6_2']) net['conv6_2_mbox_loc_flat'] = Flatten(name='conv6_2_mbox_loc_flat')(net['conv6_2_mbox_loc']) # num_priors表示每个网格点先验框的数量，num_classes是所分的类 net['conv6_2_mbox_conf'] = Conv2D(num_priors * num_classes, kernel_size=(3,3), padding='same',name='conv6_2_mbox_conf')(net['conv6_2']) net['conv6_2_mbox_conf_flat'] = Flatten(name='conv6_2_mbox_conf_flat')(net['conv6_2_mbox_conf']) # 对conv7_2进行处理 # 5,5,256 num_priors = 6 # 预测框的处理 # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整 net['conv7_2_mbox_loc'] = Conv2D(num_priors * 4, kernel_size=(3,3), padding='same',name='conv7_2_mbox_loc')(net['conv7_2']) net['conv7_2_mbox_loc_flat'] = Flatten(name='conv7_2_mbox_loc_flat')(net['conv7_2_mbox_loc']) # num_priors表示每个网格点先验框的数量，num_classes是所分的类 net['conv7_2_mbox_conf'] = Conv2D(num_priors * num_classes, kernel_size=(3,3), padding='same',name='conv7_2_mbox_conf')(net['conv7_2']) net['conv7_2_mbox_conf_flat'] = Flatten(name='conv7_2_mbox_conf_flat')(net['conv7_2_mbox_conf']) # 对conv8_2进行处理 # 3,3,256 num_priors = 4 # 预测框的处理 # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整 net['conv8_2_mbox_loc'] = Conv2D(num_priors * 4, kernel_size=(3,3), padding='same',name='conv8_2_mbox_loc')(net['conv8_2']) net['conv8_2_mbox_loc_flat'] = Flatten(name='conv8_2_mbox_loc_flat')(net['conv8_2_mbox_loc']) # num_priors表示每个网格点先验框的数量，num_classes是所分的类 net['conv8_2_mbox_conf'] = Conv2D(num_priors * num_classes, kernel_size=(3,3), padding='same',name='conv8_2_mbox_conf')(net['conv8_2']) net['conv8_2_mbox_conf_flat'] = Flatten(name='conv8_2_mbox_conf_flat')(net['conv8_2_mbox_conf']) # 对conv9_2进行处理 # 1,1,256 num_priors = 4 # 预测框的处理 # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整 net['conv9_2_mbox_loc'] = Conv2D(num_priors * 4, kernel_size=(3,3), padding='same',name='conv9_2_mbox_loc')(net['conv9_2']) net['conv9_2_mbox_loc_flat'] = Flatten(name='conv9_2_mbox_loc_flat')(net['conv9_2_mbox_loc']) # num_priors表示每个网格点先验框的数量，num_classes是所分的类 net['conv9_2_mbox_conf'] = Conv2D(num_priors * num_classes, kernel_size=(3,3), padding='same',name='conv9_2_mbox_conf')(net['conv9_2']) net['conv9_2_mbox_conf_flat'] = Flatten(name='conv9_2_mbox_conf_flat')(net['conv9_2_mbox_conf']) # 将所有结果进行堆叠 net['mbox_loc'] = Concatenate(axis=1, name='mbox_loc')([net['conv4_3_norm_mbox_loc_flat'], net['fc7_mbox_loc_flat'], net['conv6_2_mbox_loc_flat'], net['conv7_2_mbox_loc_flat'], net['conv8_2_mbox_loc_flat'], net['conv9_2_mbox_loc_flat']]) net['mbox_conf'] = Concatenate(axis=1, name='mbox_conf')([net['conv4_3_norm_mbox_conf_flat'], net['fc7_mbox_conf_flat'], net['conv6_2_mbox_conf_flat'], net['conv7_2_mbox_conf_flat'], net['conv8_2_mbox_conf_flat'], net['conv9_2_mbox_conf_flat']]) # 8732,4 net['mbox_loc'] = Reshape((-1, 4), name='mbox_loc_final')(net['mbox_loc']) # 8732,21 net['mbox_conf'] = Reshape((-1, num_classes), name='mbox_conf_logits')(net['mbox_conf']) net['mbox_conf'] = Activation('softmax', name='mbox_conf_final')(net['mbox_conf']) # 8732,25 net['predictions'] = Concatenate(axis =-1, name='predictions')([net['mbox_loc'], net['mbox_conf']]) model = Model(net['input'], net['predictions']) return model