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"""
目的：输入一张验证图片，定义之前训练时的网络结构，加载训练好的模型，验证模型是否准确预测输入图片的类别即可

"""

import torch
import torchvision
from PIL import Image
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Linear, Flatten

image_path = r'./dataset/train/ants/0013035.jpg'

image = Image.open(image_path)
print(image)

image = image.convert('RGB')  # 因为png格式是四个通道（还拥有一个透明通道），调用这个语句是只保留三个通道，如果图片只有三个通道，调用这个语句也不会改变什么，但是这条语句能让我们适应各种图片格式

#开始改变照片尺寸，resize
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),
                                            torchvision.transforms.ToTensor()])  # compose是将一系列的变换放在一起执行

image = transform(image)
print(image.shape)

#定义网络结构
class Zkl(nn.Module):
    def __init__(self):
        super(Zkl, self).__init__()
        # self.conv1 = Conv2d(3,32,5,padding=2)
        # self.maxpool1 = MaxPool2d(2)
        # self.conv2 = Conv2d(32,32,5,padding=2)
        # self.maxpool2 = MaxPool2d(2)
        # self.conv3 = Conv2d(32,64,5,padding=2)
        # self.maxpool3 = MaxPool2d(2)
        # self.flatten = Flatten()
        # self.linear1 = Linear(1024,64)
        # self.linear2 = Linear(64,10)

        self.modle1 = nn.Sequential(
            Conv2d(3, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64,10)
        )

    def forward(self,x):
        # x = self.conv1(x)
        # x = self.maxpool1(x)
        # x = self.conv2(x)
        # x = self.maxpool2(x)
        # x = self.conv3(x)
        # x = self.maxpool3(x)
        # x = self.flatten(x)
        # x = self.linear1(x)
        # x = self.linear2(x)
        x = self.modle1(x)
        return x

#加载保存的模型
model = torch.load('modle_train_9.pth')
print(model)
image = torch.reshape(image,(1,3,32,32))
#使加载的模型规定为测试模型
model.eval()
#
with torch.no_grad(): # 没有梯度优化，节约性能
    output = model(image)
print(output)
print(output.argmax(1))