DarknetTiny模型结构

darknet 是yolov3 里用的模型，如下是是其模型结构

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun  7 13:40:34 2021

@author: ledi
"""

import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import (
    Add,
    Concatenate,
    Conv2D,
    Input,
    Lambda,
    LeakyReLU,
    MaxPool2D,
    UpSampling2D,
    ZeroPadding2D,
    BatchNormalization,
)
from tensorflow.keras.regularizers import l2
from tensorflow.keras.losses import (
    binary_crossentropy,
    sparse_categorical_crossentropy
)

def DarknetConv(x, filters, size, strides=1, batch_norm=True):
    if strides == 1:
        padding = 'same'
    else:
        x = ZeroPadding2D(((1, 0), (1, 0)))(x)  # top left half-padding
        padding = 'valid'
    x = Conv2D(filters=filters, kernel_size=size,
               strides=strides, padding=padding,
               use_bias=not batch_norm, kernel_regularizer=l2(0.0005))(x)
    if batch_norm:
        x = BatchNormalization()(x)
        x = LeakyReLU(alpha=0.1)(x)
    return x


def DarknetResidual(x, filters):
    prev = x
    x = DarknetConv(x, filters // 2, 1)
    x = DarknetConv(x, filters, 3)
    x = Add()([prev, x])
    return x


def DarknetBlock(x, filters, blocks):
    x = DarknetConv(x, filters, 3, strides=2)
    for _ in range(blocks):
        x = DarknetResidual(x, filters)
    return x


def Darknet(name=None):
    x = inputs = Input([None, None, 3])
    x = DarknetConv(x, 32, 3)
    x = DarknetBlock(x, 64, 1)
    x = DarknetBlock(x, 128, 2)  # skip connection
    x = x_36 = DarknetBlock(x, 256, 8)  # skip connection
    x = x_61 = DarknetBlock(x, 512, 8)
    x = DarknetBlock(x, 1024, 4)
    return tf.keras.Model(inputs, (x_36, x_61, x), name=name)




def DarknetTiny(name=None):
    x = inputs = Input([None, None, 3])
    x = DarknetConv(x, 16, 3)
    x = MaxPool2D(2, 2, 'same')(x)
    x = DarknetConv(x, 32, 3)
    x = MaxPool2D(2, 2, 'same')(x)
    x = DarknetConv(x, 64, 3)
    x = MaxPool2D(2, 2, 'same')(x)
    x = DarknetConv(x, 128, 3)
    x = MaxPool2D(2, 2, 'same')(x)
    x = x_8 = DarknetConv(x, 256, 3)  # skip connection
    x = MaxPool2D(2, 2, 'same')(x)
    x = DarknetConv(x, 512, 3)
    x = MaxPool2D(2, 1, 'same')(x)
    x = DarknetConv(x, 1024, 3)
    return tf.keras.Model(inputs, (x_8, x), name=name)


model=DarknetTiny(name=None)



from keras.utils import plot_model
plot_model(model, to_file='darknet.png')