python mindspore框架搭建cnn分类afhq图像数据集

下面是使用MindSpore框架搭建CNN分类AFHQ图像数据集的基本步骤：

1. 导入必要的模块和库

import os
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
import mindspore.nn as nn
from mindspore import context
from mindspore import Tensor, Model
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train.serialization import load_checkpoint, load_param_into_net

2. 定义数据集

data_dir = '/path/to/afhq/train'  # AFHQ数据集路径

# 定义数据集，使用MindSpore的Cifar10Dataset类
dataset = ds.ImageFolderDataset(data_dir, num_parallel_workers=8, shuffle=True)

# 定义数据增强操作
trans = [
    CV.Resize((256, 256)),
    CV.RandomCrop((224, 224)),
    CV.RandomHorizontalFlip(prob=0.5),
    CV.RandomVerticalFlip(prob=0.5),
    CV.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
    CV.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
                 std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
]

# 对数据集进行增强操作
dataset = dataset.map(input_columns="image", num_parallel_workers=8, operations=trans)

3. 定义CNN网络模型

class CNN(nn.Cell):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.conv5 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()
        self.fc1 = nn.Dense(512 * 14 * 14, 4096)
        self.fc2 = nn.Dense(4096, 4096)
        self.fc3 = nn.Dense(4096, 3)
        self.relu = nn.ReLU()

    def construct(self, x):
        x = self.relu(self.conv1(x))
        x = self.pool(x)
        x = self.relu(self.conv2(x))
        x = self.pool(x)
        x = self.relu(self.conv3(x))
        x = self.pool(x)
        x = self.relu(self.conv4(x))
        x = self.pool(x)
        x = self.relu(self.conv5(x))
        x = self.pool(x)
        x = self.flatten(x)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

4. 定义损失函数和优化器

net = CNN()  # 实例化网络模型
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')  # 定义损失函数
opt = nn.Momentum(net.trainable_params(), learning_rate=0.01, momentum=0.9)  # 定义优化器

5. 进行训练

context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})  # 实例化模型

# 定义回调函数
config_ck = CheckpointConfig(save_checkpoint_steps=1000, keep_checkpoint_max=10)
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_cnn", config=config_ck)
loss_cb = LossMonitor()

# 开始训练
model.train(epoch=10, train_dataset=dataset, callbacks=[ckpoint_cb, loss_cb])

以上就是使用MindSpore框架搭建CNN分类AFHQ图像数据集的基本步骤。需要注意的是，代码中的路径、超参数等需要根据实际情况进行修改。