yolov5模型蒸馏代码

YOLOv5模型蒸馏的代码是指针对已经训练好的YOLOv5模型进行优化处理，使得可以得到更加轻量级、高效率的模型。通常采用的是基于知识蒸馏的方法，将已训练的模型的知识传递给轻量级的模型。以下是常用的YOLOv5模型蒸馏代码：

1. 基于Pytorch框架，使用预先训练好的YOLOv5模型进行蒸馏，代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision

#定义轻量级模型：MobileNet-V2
class MobileNetV2(nn.Module):
    def __init__(self):
        super(MobileNetV2, self).__init__()
        self.model = torchvision.models.mobilenet_v2(pretrained=True).features
        self.model[18] = nn.Conv2d(320, 512, kernel_size=1, stride=1, padding=0, bias=True)
        self.model[19] = nn.BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)

    def forward(self, x):
        for i in range(2):
            x = self.model[i](x)
        skip1 = x
        for i in range(2, 4):
            x = self.model[i](x)
        skip2 = x
        for i in range(4, 7):
            x = self.model[i](x)
        skip3 = x
        for i in range(7, 14):
            x = self.model[i](x)
        skip4 = x
        for i in range(14, 19):
            x = self.model[i](x)
        skip5 = x
        for i in range(19, 24):
            x = self.model[i](x)
        return skip1, skip2, skip3, skip4, skip5

#定义Teacher Model
teacher = torch.hub.load('ultralytics/yolov5', 'yolov5x', pretrained=True)

#定义Student Model
student = MobileNetV2()

#定义Optimizer
optimizer = torch.optim.SGD(student.parameters(), lr=0.01, momentum=0.9, weight_decay=0.0005)

#定义Loss
MSEloss = nn.MSELoss()

#开始训练
for i, (inputs, targets) in enumerate(dataloader):
    optimizer.zero_grad()
    tt1, tt2, tt3, tt4, tt5 = teacher(inputs)
    ts1, ts2, ts3, ts4, ts5 = student(inputs)
    loss1 = 0.1 * MSEloss(ts1, tt1)
    loss2 = 0.2 * MSEloss(ts2, tt2)
    loss3 = 0.3 * MSEloss(ts3, tt3)
    loss4 = 0.4 * MSEloss(ts4, tt4)
    loss5 = 0.5 * MSEloss(ts5, tt5)
    loss = loss1 + loss2 + loss3 + loss4 + loss5
    loss.backward()
    optimizer.step()

2. 基于TensorFlow框架，使用YOLOv4模型进行蒸馏，代码如下：

import tensorflow as tf
from yolov4.tf import YOLOv4

#定义轻量级模型：MobileNet-v3
class MobileNetV3(tf.keras.Model):
    def __init__(self):
        super(MobileNetV3, self).__init__()
        model = tf.keras.applications.MobileNetV3Small(input_shape=[128, 128, 3], include_top=False)
        self.model = tf.keras.Model(inputs=model.input, outputs=[model.get_layer('block_6_expand_relu').output, model.get_layer('block_13_expand_relu').output, model.get_layer('block_16_project').output])

    def call(self, inputs):
        for i in range(4):
            inputs = self.model.get_layer(index=i)(inputs)
        skip1 = inputs
        for i in range(4, 6):
            inputs = self.model.get_layer(index=i)(inputs)
        skip2 = inputs
        for i in range(6, 7):
            inputs = self.model.get_layer(index=i)(inputs)
        return skip1, skip2, inputs

#定义Teacher Model
teacher = YOLOv4(tiny=True)
teacher.load_weights('yolov4-tiny.weights')

#定义Student Model
student = MobileNetV3()

#定义Optimizer
optimizer = tf.keras.optimizers.SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)

#定义Loss
MSEloss = tf.keras.losses.MeanSquaredError()

#开始训练
for i, (inputs, targets) in enumerate(dataloader):
    with tf.GradientTape() as tape:
        tt1, tt2, tt3 = teacher.predict(inputs)
        ts1, ts2, ts3 = student.predict(inputs)
        loss1 = 0.1 * MSEloss(ts1, tt1)
        loss2 = 0.2 * MSEloss(ts2, tt2)
        loss3 = 0.3 * MSEloss(ts3, tt3)
        loss = loss1 + loss2 + loss3
        gradients = tape.gradient(loss, student.trainable_variables)
        optimizer.apply_gradients(zip(gradients, student.trainable_variables))

总之，YOLOv5模型蒸馏的代码在实现过程中需要根据具体的情况和模型进行调整和修改。