python在华为mindspore框架下搭建VGG模型实现afhq图像数据集分类

华为MindSpore框架是一款新兴的深度学习框架，可以用Python语言进行编程。在该框架下搭建VGG模型实现afhq图像数据集分类，可以按照以下步骤进行：

1. 导入相关的库和模块：

import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import Tensor
from mindspore.common.initializer import TruncatedNormal
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.model import Model
from mindspore.nn.metrics import Accuracy
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
import numpy as np

2. 定义VGG网络模型：

class VGG(nn.Cell):
    def __init__(self, num_classes=3):
        super(VGG, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1, pad_mode='pad', has_bias=True)
        self.relu = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1, pad_mode='pad', has_bias=True)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1, pad_mode='pad', has_bias=True)
        self.conv4 = nn.Conv2d(256, 256, kernel_size=3, padding=1, pad_mode='pad', has_bias=True)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv5 = nn.Conv2d(256, 512, kernel_size=3, padding=1, pad_mode='pad', has_bias=True)
        self.conv6 = nn.Conv2d(512, 512, kernel_size=3, padding=1, pad_mode='pad', has_bias=True)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv7 = nn.Conv2d(512, 512, kernel_size=3, padding=1, pad_mode='pad', has_bias=True)
        self.conv8 = nn.Conv2d(512, 512, kernel_size=3, padding=1, pad_mode='pad', has_bias=True)
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()
        self.fc1 = nn.Dense(7 * 7 * 512, 4096, weight_init=TruncatedNormal(0.02))
        self.fc2 = nn.Dense(4096, 4096, weight_init=TruncatedNormal(0.02))
        self.fc3 = nn.Dense(4096, num_classes, weight_init=TruncatedNormal(0.02))

    def construct(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = self.relu(x)
        x = self.conv4(x)
        x = self.relu(x)
        x = self.pool3(x)
        x = self.conv5(x)
        x = self.relu(x)
        x = self.conv6(x)
        x = self.relu(x)
        x = self.pool4(x)
        x = self.conv7(x)
        x = self.relu(x)
        x = self.conv8(x)
        x = self.relu(x)
        x = self.pool5(x)
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.relu(x)
        x = self.fc3(x)
        return x

3. 定义训练数据集和测试数据集的transform：

train_transform = [
    CV.Resize((256, 256)),
    CV.RandomCrop((224, 224)),
    CV.RandomHorizontalFlip(prob=0.5),
    CV.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
    CV.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]
test_transform = [
    CV.Resize((256, 256)),
    CV.CenterCrop((224, 224)),
    CV.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]

4. 加载数据集：

train_path = 'afhq/train'
test_path = 'afhq/val'
train_data = ds.ImageFolderDatasetV2(train_path, num_parallel_workers=8, shuffle=True)
test_data = ds.ImageFolderDatasetV2(test_path, num_parallel_workers=8, shuffle=True)
train_data = train_data.map(input_columns="image", operations=C.Compose(train_transform))
test_data = test_data.map(input_columns="image", operations=C.Compose(test_transform))
train_data = train_data.batch(batch_size=32, drop_remainder=True)
test_data = test_data.batch(batch_size=32, drop_remainder=True)

5. 定义损失函数和优化器：

net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
net_opt = nn.Momentum(net_trainable_params, learning_rate=0.001, momentum=0.9, weight_decay=1e-5)

6. 训练模型：

device_target = "GPU"
model = Model(VGG(), net_loss, net_opt, metrics={"Accuracy": Accuracy()})
model.train(2, train_data, callbacks=[LossMonitor(32)], dataset_sink_mode=True)

7. 测试模型：

model.eval(test_data, dataset_sink_mode=True)

以上就是在华为MindSpore框架下搭建VGG模型实现afhq图像数据集分类的完整步骤。