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

下面是MindSpore框架下搭建VGG模型实现AFHQ图像数据集分类以及预测的Python实现代码：

1. 安装MindSpore

在命令行输入以下命令进行安装：

pip install mindspore

2. 导入相关库和数据集

import os
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
from mindspore import nn, Model, context
from mindspore.train.callback import LossMonitor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.common.initializer import TruncatedNormal

data_dir = 'afhq/train'
batch_size = 32

3. 数据集处理

train_data = ds.ImageFolderDatasetV2(data_dir, num_parallel_workers=8, shuffle=True)
train_data = train_data.map(input_columns="image", num_parallel_workers=8, operations=[
    CV.Resize((224, 224)),
    CV.RandomCrop((224, 224)),
    CV.RandomHorizontalFlip(),
    CV.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    C.HWC2CHW()
])
train_data = train_data.batch(batch_size, drop_remainder=True)

4. 定义VGG模型

class VGG16(nn.Cell):
    def __init__(self, num_classes=3):
        super(VGG16, self).__init__()
        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.maxpool5 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Dense(25088, 4096, weight_init=TruncatedNormal(std=0.02))
        self.fc2 = nn.Dense(4096, 4096, weight_init=TruncatedNormal(std=0.02))
        self.fc3 = nn.Dense(4096, num_classes, weight_init=TruncatedNormal(std=0.02))
        self.relu = nn.ReLU()
        self.flatten = nn.Flatten()

    def construct(self, x):
        x = self.relu(self.conv1_1(x))
        x = self.relu(self.conv1_2(x))
        x = self.maxpool1(x)
        x = self.relu(self.conv2_1(x))
        x = self.relu(self.conv2_2(x))
        x = self.maxpool2(x)
        x = self.relu(self.conv3_1(x))
        x = self.relu(self.conv3_2(x))
        x = self.relu(self.conv3_3(x))
        x = self.maxpool3(x)
        x = self.relu(self.conv4_1(x))
        x = self.relu(self.conv4_2(x))
        x = self.relu(self.conv4_3(x))
        x = self.maxpool4(x)
        x = self.relu(self.conv5_1(x))
        x = self.relu(self.conv5_2(x))
        x = self.relu(self.conv5_3(x))
        x = self.maxpool5(x)
        x = self.flatten(x)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

5. 训练模型

context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
net = VGG16()
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
opt = nn.Momentum(net.trainable_params(), learning_rate=0.01, momentum=0.9)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})

model.train(10, train_data, callbacks=[LossMonitor()], dataset_sink_mode=True)

6. 加载模型并进行预测

test_data_dir = 'afhq/val'
test_data = ds.ImageFolderDatasetV2(test_data_dir, num_parallel_workers=8, shuffle=False)
test_data = test_data.map(input_columns="image", num_parallel_workers=8, operations=[
    CV.Resize((224, 224)),
    CV.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    C.HWC2CHW()
])
test_data = test_data.batch(batch_size, drop_remainder=True)

model = Model(net)
param_dict = load_checkpoint("vgg.ckpt")
load_param_into_net(net, param_dict)

predictions = []
for data in test_data.create_dict_iterator():
    prediction = model.predict(data['image'])
    predictions.extend(prediction.asnumpy())

print(predictions)