mindspore框架下搭建vgg模型实现afhq图像数据集分类 Python实现代码

好的，下面是在MindSpore框架下搭建VGG模型实现AFHQ图像数据集分类的Python实现代码：

import os
import numpy as np
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
import mindspore.ops.operations as P
import mindspore.common.dtype as mstype
from mindspore import Model, Tensor
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train.serialization import load_checkpoint, load_param_into_net

def create_dataset(dataset_path, batch_size=32, resize=(224, 224)):
    data_dir = dataset_path
    train_dir = os.path.join(data_dir, 'train')
    data_set = ds.ImageFolderDatasetV2(train_dir, num_parallel_workers=8, shuffle=True)
    resize_height, resize_width = resize
    transforms = [
        CV.Resize((resize_height, resize_width)),
        CV.Rescale(1.0 / 255.0, 0.0),
        CV.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]
    data_set = data_set.map(input_columns="image", operations=transforms)
    data_set = data_set.batch(batch_size, drop_remainder=True)
    return data_set

class VGG16(nn.Cell):
    def __init__(self, num_classes=3):
        super(VGG16, self).__init__()
        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, pad_mode='same', has_bias=True)
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()
        self.fc1 = nn.Dense(7 * 7 * 512, 4096, activation='relu')
        self.fc2 = nn.Dense(4096, 4096, activation='relu')
        self.fc3 = nn.Dense(4096, num_classes)
        self.relu = nn.ReLU()

    def construct(self, x):
        x = self.relu(self.conv1_1(x))
        x = self.relu(self.conv1_2(x))
        x = self.pool1(x)
        x = self.relu(self.conv2_1(x))
        x = self.relu(self.conv2_2(x))
        x = self.pool2(x)
        x = self.relu(self.conv3_1(x))
        x = self.relu(self.conv3_2(x))
        x = self.relu(self.conv3_3(x))
        x = self.pool3(x)
        x = self.relu(self.conv4_1(x))
        x = self.relu(self.conv4_2(x))
        x = self.relu(self.conv4_3(x))
        x = self.pool4(x)
        x = self.relu(self.conv5_1(x))
        x = self.relu(self.conv5_2(x))
        x = self.relu(self.conv5_3(x))
        x = self.pool5(x)
        x = self.flatten(x)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

if __name__ == '__main__':
    dataset_path = '/path/to/afhq/dataset'
    batch_size = 32
    resize = (224, 224)
    epoch_size = 100
    lr = 0.01
    momentum = 0.9
    weight_decay = 0.0001
    num_classes = 3
    ckpt_path = './vgg16.ckpt'
    if not os.path.exists(ckpt_path):
        os.makedirs(ckpt_path)
    ds_train = create_dataset(dataset_path, batch_size=batch_size, resize=resize)
    network = VGG16(num_classes=num_classes)
    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    opt = nn.Momentum(network.trainable_params(), lr, momentum, weight_decay)
    model = Model(network, loss, opt, metrics={'acc'})
    config_ck = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=10)
    ckpoint_cb = ModelCheckpoint(prefix='checkpoint_vgg16', directory=ckpt_path, config=config_ck)
    model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=True)

其中，`create_dataset`函数用于创建数据集，`VGG16`类用于定义VGG16模型，`main`函数中定义了超参数和训练流程，并使用`create_dataset`函数创建数据集，定义模型、损失函数和优化器，最后进行训练并保存模型。请根据实际需要修改数据集路径、超参数和模型保存路径等参数。