Model.py 文件源码

def group_layer(self, group_num, filters, name, kernel_regularizer_l2):
        def f(input):
            if group_num == 1:
                tower = Conv2D(filters, (1, 1), name=name + '_conv2d_0_1', padding='same',
                               kernel_initializer=IdentityConv())(input)
                tower = Conv2D(filters, (3, 3), name=name + '_conv2d_0_2', padding='same',
                               kernel_initializer=IdentityConv(),
                               kernel_regularizer=regularizers.l2(kernel_regularizer_l2))(tower)
                tower = PReLU()(tower)
                return tower
            else:
                group_output = []
                for i in range(group_num):
                    filter_num = filters / group_num
                    # if filters = 201, group_num = 4, make sure last group filters num = 51
                    if i == group_num - 1:  # last group
                        filter_num = filters - i * (filters / group_num)

                    tower = Conv2D(filter_num, (1, 1), name=name + '_conv2d_' + str(i) + '_1', padding='same',
                                   kernel_initializer=GroupIdentityConv(i, group_num))(input)
                    tower = Conv2D(filter_num, (3, 3), name=name + '_conv2d_' + str(i) + '_2', padding='same',
                                   kernel_initializer=IdentityConv(),
                                   kernel_regularizer=regularizers.l2(kernel_regularizer_l2))(tower)
                    tower = PReLU()(tower)
                    group_output.append(tower)

                if K.image_data_format() == 'channels_first':
                    axis = 1
                elif K.image_data_format() == 'channels_last':
                    axis = 3
                output = Concatenate(axis=axis)(group_output)

                return output

        return f