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Devnet for pre.py

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+import torch
+import torchvision
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms,datasets
+from torchvision.transforms import ToTensor
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import matplotlib.pyplot as plt
+from PIL import Image
+
+
+device=torch.device("cpu")
+
+cropbox=(0,0,316,316)
+batch_size = 1
+
+class CNN(nn.Module):
+	def __init__(self):
+		super().__init__()
+		self.features=nn.Sequential(
+			nn.Conv2d(3,6, kernel_size=5),
+			nn.ReLU(inplace=True),
+			nn.MaxPool2d(kernel_size=2,stride=2),
+			nn.Conv2d(6,12, kernel_size=5),
+			nn.ReLU(inplace=True),
+			nn.MaxPool2d(kernel_size=2,stride=2),
+			nn.Conv2d(12,24,kernel_size=5,stride=1),
+			nn.ReLU(inplace=True),
+			nn.Conv2d(24,24,kernel_size=5,stride=1),
+			nn.ReLU(inplace=True),
+			nn.Conv2d(24,48,kernel_size=5,stride=1),
+			nn.ReLU(inplace=True),
+			nn.MaxPool2d(kernel_size=2,stride=2),
+		)
+         
+        # 请把之前 Network1 的 self.fc1 复制到这里：
+        # self.fc1 = nn.Linear(in_features=XXX, out_features=120)
+        
+		self.classifier=nn.Sequential(
+			nn.Linear(32*32*48,2048),
+			nn.ReLU(inplace=True),
+			#nn.Dropout(0.5),
+			nn.Linear(2048,1024),
+			nn.ReLU(inplace=True),
+			#nn.Dropout(0.5),
+			nn.Linear(1024,1),
+		)
+        
+        
+	def forward(self, t):   
+		t=self.features(t)
+        # 特别注意：最后一个卷积层的输出在流入全连接层之前，要对激活值的维度进行变换，变换后的张量
+        # 是二维张量，其中 dim0 对应该批次中的不同样本，dim1 对应每个样本流向全连接层的输入激活向量。
+        # 请完成如下代码。
+
+        # dim0：一个批次的样本数
+        # dim1：每一个样本的输入激活向量长度
+        
+        # t = t.reshape(XXX,XXX)
+		t = t.reshape(batch_size,32*32*48)
+        
+		t = self.classifier(t)
+        
+		return t
+
+const_avg=3.6156537532806396
+confidence_margin = 5.0
+
+if __name__ == '__main__':
+	network=CNN()
+	network = torch.load("Dev_netv1.0_CNN.pth") # PATH same as above
+	network.eval()
+	print("模型已导入完成。")
+	path=input("请输入待识别图片地址：")
+	img = Image.open(path)
+	img_2=img.crop(cropbox)
+	img_ten=transforms.ToTensor()(np.array(img_2))
+	pred=network(img_ten).item()
+	if pred<=const_avg:
+		print("该图片正常。预测输出：{:3f}".format(pred))
+	elif pred>=confidence_margin:
+		print("该图片异常。预测输出：{:3f}".format(pred))
+	else:
+		output=(pred-const_avg)/(confidence_margin-const_avg)
+		print("无法完全确定该图片是否正常。参考值：{:.3f}。参考值靠近0说明正常可能性更大，靠近1说明异常可能性更大。\n预测输出：{:3f}".format(output,pred))