import torch
From torch.utils.data import Dataset, DataLoader
import numpy as np
import matplotlib
import os
import cv2
from PIL import Image
import torchvision.transforms as transforms
import torch.optim as optim
from torch.autograd import VARiable
import torch.nn as nn
import torch.nn.functional as F
from Test.CNN import Net
import json
from Test.train_data import Mydataset,pad_image

# 构建神经网络
class Net(nn.Module):#定义网络模块
    def __inIT__(self):
        suPEr(Net, self).__init__()
        # 卷积，该图片有3层，6个特征，长宽均为5*5的像素点，每隔1步跳一下
        self.conv1 = nn.Conv2d(3, 6, 5)
        #//(conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))
        self.pool = nn.Maxpool2d(2, 2)#最大池化
        #//(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
        self.conv2 = nn.Conv2d(6, 16, 5)#卷积
        #//(conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
        self.fc1 = nn.Linear(16*77*77, 120)#全连接层，图片的维度为16，
        #(fc1): Linear(in_features=94864, out_features=120, bias=True)
        self.fc2 = nn.Linear(120, 84)#全连接层,输入120个特征输出84个特征
        self.fc3 = nn.Linear(84, 7)#全连接层，输入84个特征输出7个特征
 
   def forward(self, x):
        PRint("x.Shape1: ", x.shape)
        x = self.pool(F.relu(self.conv1(x)))
        print("x.shape2: ", x.shape)
        x = self.pool(F.relu(self.conv2(x)))
        print("x.shape3: ", x.shape)
        x = x.view(-1, 16*77*77)
        print("x.shape4: ", x.shape)
        x = F.relu(self.fc1(x))
        print("x.shape5: ", x.shape)
        x = F.relu(self.fc2(x))
        print("x.shape6: ", x.shape)
        x = self.fc3(x)
        print("x.shape7: ", x.shape)
        return x

img_path = "TestData/test_data/1/Apple2 (1).jpg" #使用相对路径
image = Image.open(img_path).convert('RGB')
image_pad = pad_image(image, (320, 320))
input = transform(image_pad).to(device).unsqueeze(0)
output = F.softmax(net(input), 1)
_, predicted = torch.max(output, 1)
score = float(output[0][predicted]*100)
print(class_map[predicted], " ", str(score)+" %")
plt.imshow(image_pad) # 显示图片