华为mindspore框架下实现afhq数据集分类 python代码

以下是一个使用华为MindSpore框架实现AFHQ数据集分类的Python代码示例：

import os
from mindspore import context, nn, Model, load_checkpoint, load_param_into_net, Tensor
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.common import set_seed
from mindspore.dataset.transforms import py_transforms
from mindspore.dataset.vision import Inter
from mindspore.dataset.vision import Normalize
from mindspore.dataset import Dataset
from mindspore.ops import operations as P

set_seed(1)

context.set_context(mode=context.GRAPH_MODE, device_target="CPU")

# 数据路径和文件名
data_path = "/path/to/afhq_dataset"
train_file = os.path.join(data_path, "train.txt")
val_file = os.path.join(data_path, "val.txt")

# 数据处理
train_transforms = py_transforms.Compose([
    py_transforms.RandomCrop((256, 256)),
    py_transforms.RandomHorizontalFlip(),
    py_transforms.Resize((224, 224), interpolation=Inter.LINEAR),
    Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

val_transforms = py_transforms.Compose([
    py_transforms.Resize((224, 224), interpolation=Inter.LINEAR),
    Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# 数据集加载
train_dataset = Dataset.from_file(train_file)
train_dataset = train_dataset.map(operations=train_transforms, input_columns=["image"], num_parallel_workers=8)
train_dataset = train_dataset.batch(32, drop_remainder=True)
train_dataset = train_dataset.repeat(50)

val_dataset = Dataset.from_file(val_file)
val_dataset = val_dataset.map(operations=val_transforms, input_columns=["image"], num_parallel_workers=8)
val_dataset = val_dataset.batch(32, drop_remainder=True)

# 网络模型
class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()

        self.conv1 = nn.Conv2d(3, 64, 3, pad_mode="pad", padding=1)
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(64, 128, 3, pad_mode="pad", padding=1)
        self.conv3 = nn.Conv2d(128, 256, 3, pad_mode="pad", padding=1)
        self.conv4 = nn.Conv2d(256, 256, 3, pad_mode="pad", padding=1)
        self.conv5 = nn.Conv2d(256, 512, 3, pad_mode="pad", padding=1)
        self.conv6 = nn.Conv2d(512, 512, 3, pad_mode="pad", padding=1)
        self.avg_pool2d = nn.AvgPool2d(kernel_size=7, stride=1)
        self.flatten = nn.Flatten()
        self.fc = nn.Dense(512, 3)

    def construct(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = self.conv3(x)
        x = self.relu(x)
        x = self.conv4(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = self.conv5(x)
        x = self.relu(x)
        x = self.conv6(x)
        x = self.relu(x)
        x = self.avg_pool2d(x)
        x = self.flatten(x)
        x = self.fc(x)
        return x

net = Net()

# 定义损失函数和优化器
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
opt = nn.Adam(params=net.trainable_params(), learning_rate=0.0001)

# 训练模型
model = Model(net, loss_fn=loss, optimizer=opt, metrics={"acc"})
model.train(50, train_dataset, callbacks=[LossMonitor(50), ModelCheckpoint(prefix="checkpoint", directory="./")])

# 加载最佳模型并在验证集上测试
best_model = os.path.join("./", "checkpoint_0050.ckpt")
load_checkpoint(best_model, net=net)
load_param_into_net(net, load_checkpoint(best_model))
model = Model(net, loss_fn=loss, metrics={"acc"})
output = model.eval(val_dataset)
print("Accuracy:", output["acc"])

在这个示例中，我们使用了一个简单的卷积神经网络模型，并使用Adam优化器进行训练。在训练过程中，我们使用了损失函数和准确率作为指标，并将训练过程中的损失值和准确率打印出来。在训练完成后，我们加载了最佳模型并在验证集上进行测试，输出了模型的准确率。