MindSpore中如何进行模型压缩和量化

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import mindspore.nn as nn
import mindspore.ops.operations as P
conv = nn.Conv2d(3, 64, kernel_size=3, padding=1)
pruned_conv = nn.prune(conv, nn.dependence.Dependency(6, P.Mul()) > 0)

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import mindspore.nn as nn
import mindspore.ops.operations as P
fc = nn.Dense(1024, 10)
pruned_fc = nn.prune(fc, nn.dependence.Dependency(6, P.ReLU()) < 0.5)

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import mindspore.nn as nn
import mindspore.context as context
from mindspore.train import Model
from mindspore.train.callback import LossMonitor
from mindspore import Tensor
import numpy as np
# Define your network
network = MyNetwork()
# Define your data and optimizer
# Set context and quantization aware training
context.set_context(mode=context.GRAPH_MODE)
network = nn.QuantizationAwareTrainingWrapper(network)
# Train your model
model = Model(network, optimizer, loss_fn, metrics=None)
model.train(..., callbacks=[LossMonitor()])

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import mindspore.nn as nn
import mindspore.export as export
# Define your network
network = MyNetwork()
# Load your trained parameters
network.set_train(False)
network.load_parameters("my_model.ckpt")
# Quantize the parameters
quantized_network = export.quantize(network)

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import mindspore.nn as nn
import mindspore.quant as quant
# Define your network
network = MyNetwork()
# Apply GLU quantization to the network
network = quant.GLUQuantization(network)

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import mindspore.nn as nn
import mindspore.quant as quant
# Define your network
network = MyNetwork()
# Apply quantization-aware training
network = quant.QAT(network, quant_delay=0)
# Apply channel pruning
network = quant.Pruning(network, "channel", 0.5)

pythonCopy code
import mindspore as ms
import mindspore.context as context
from mindspore import nn, Tensor
from mindspore.train.model import Model
from mindspore.train.callback import LossMonitor
import numpy as np
# 设置上下文
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
# 定义数据集和网络
...
# 进行数据预处理和准备
# 定义网络模型
network = MyNetwork()
# 定义损失函数和优化器
criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
optimizer = nn.SGD(network.trainable_params(), learning_rate=0.01)
# 定义训练数据集和测试数据集
train_dataset = ...
test_dataset = ...
# 定义模型
model = Model(network, optimizer, criterion)
# 使用量化感知训练进行训练，训练过程中模拟量化操作
quantizer = ms.nn.QuantizationAwareTraining(bn_fold=True, per_channel=True, symmetric=True)
# 量化感知训练封装
network = quantizer.quantize(network)
# 开始训练
model.train(epoch=10, train_dataset=train_dataset, callbacks=[LossMonitor()])
# 保存量化后的模型
model.save_checkpoint("quantized_model.ckpt")

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import paho.mqtt.client as mqtt
# 连接到MQTT代理
def on_connect(client, userdata, flags, rc):
    print("Connected with result code " + str(rc))
    # 订阅主题
    client.subscribe("iot/devices/device_id")  # 你可以自定义一个主题以适应你的应用
# 接收到MQTT消息时的回调函数
def on_message(client, userdata, msg):
    print(msg.topic + " " + str(msg.payload))
# 发布消息到MQTT代理
def publish_message(client, topic, message):
    client.publish(topic, message)
# 创建MQTT客户端对象
client = mqtt.Client()
# 设置连接和消息回调函数
client.on_connect = on_connect
client.on_message = on_message
# 使用用户名和密码连接到MQTT代理
client.username_pw_set("username", "password")  # 你需要提供用户名和密码来连接到你的MQTT代理
# 连接到MQTT代理（这里的地址需要根据你的具体情况来调整）
client.connect("mqtt.example.com", 1883, 60)
# 循环监听MQTT消息
client.loop_start()
# 主线程中执行其他操作
# ...
# 发布消息到指定主题
publish_message(client, "iot/devices/device_id", "Hello, IoT!")
# 停止MQTT客户端循环
client.loop_stop()
client.disconnect()