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import torch
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import torchvision
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from torch.utils.data import Dataset, DataLoader
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from torchvision import transforms,datasets
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from torchvision.transforms import ToTensor
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import numpy as np
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import matplotlib.pyplot as plt
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from PIL import Image
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device=torch.device("cpu")
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cropbox=(0,0,316,316)
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learning_rate = 1e-3
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batch_size = 50
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epochs = 12
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def readimg(path):
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txt=open(path+"\\data.txt",'r')
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lines=txt.readlines()
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txt.close()
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imgs=[]
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labels=[]
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for line in lines:
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line = line.rstrip()
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word = line.split(' ')
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img = Image.open(path+'\\'+word[0])
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img_2=img.crop(cropbox)
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img_ten=transforms.ToTensor()(np.array(img_2))
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imgs.append(img_ten)
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if word[1]=='0':
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labels.append(torch.zeros(1))
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else:
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labels.append(torch.ones(1))
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return imgs,labels
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class CustomedDataSet(Dataset):
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def __init__(self, train=True, train_x = None, train_y = None, test_x = None, test_y = None, val = False, transform = None):
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self.train = train
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self.val = val
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self.transform = transform
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if self.train:
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self.dataset=train_x
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self.labels=train_y
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elif val:
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self.dataset=test_x
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self.labels=test_y
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else:
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self.dataset= test_x
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def __getitem__(self, index):
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if self.train:
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return torch.Tensor(self.dataset[index]).to(device), torch.Tensor(self.labels[index]).to(device)
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elif self.val:
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return torch.Tensor(self.dataset[index]).to(device), torch.Tensor(self.labels[index]).to(device)
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else:
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return torch.Tensor(self.dataset[index]).to(device)
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def __len__(self):
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return len(self.dataset)
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class CNN(nn.Module):
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def __init__(self):
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super().__init__()
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self.features=nn.Sequential(
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nn.Conv2d(3,6, kernel_size=5),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2,stride=2),
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nn.Conv2d(6,12, kernel_size=5),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2,stride=2),
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nn.Conv2d(12,24,kernel_size=5,stride=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(24,24,kernel_size=5,stride=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(24,48,kernel_size=5,stride=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2,stride=2),
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)
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# 请把之前 Network1 的 self.fc1 复制到这里：
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# self.fc1 = nn.Linear(in_features=XXX, out_features=120)
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self.classifier=nn.Sequential(
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nn.Linear(32*32*48,2048),
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nn.ReLU(inplace=True),
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#nn.Dropout(0.5),
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nn.Linear(2048,1024),
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nn.ReLU(inplace=True),
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#nn.Dropout(0.5),
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nn.Linear(1024,1),
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)
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def forward(self, t):
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t=self.features(t)
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# 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
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# 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。
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# 请完成如下代码。
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# dim0：一个批次的样本数
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# dim1：每一个样本的输入激活向量长度
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# t = t.reshape(XXX,XXX)
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t = t.reshape(batch_size,32*32*48)
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t = self.classifier(t)
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return t
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class DeviationLoss(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, y_pred, y_true):
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confidence_margin = 5.
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#ref = torch.normal(mean=0., std=torch.full([5000], 1.)).cuda()
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#dev = (y_pred - torch.mean(ref)) / torch.std(ref)
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dev = y_pred
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inlier_loss = torch.abs(dev)
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#outlier_loss = torch.abs((confidence_margin - dev).clamp_(min=0.))
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outlier_loss = torch.abs((confidence_margin - torch.abs(dev)))
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dev_loss = (1 - y_true) * inlier_loss + y_true * outlier_loss
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#print("y_pred:",y_pred.shape,'y_true:',y_true.shape,'mean_loss:',torch.mean(dev_loss).item())
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return torch.mean(dev_loss*(1+10*y_true))
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def pred_to_label(preds):
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a = torch.abs(preds)
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# 保证和a相同的维度大小
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zero = torch.zeros_like(a)
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one = torch.ones_like(a)
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# a中大于5的用one(1)替换,否则a替换,即不变
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a = torch.where(a >= 5.0, one, zero)
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# a中小于5的用zero(0)替换,否则a替换,即不变
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return a
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def get_tp(preds,labels):
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a=pred_to_label(preds)
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two=2.0*torch.ones_like(labels)
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b=torch.where(labels==0.0,two,labels)
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return a.eq(b).sum().item()
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def get_tn(preds,labels):
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a=pred_to_label(preds)
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two=2.0*torch.ones_like(labels)
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b=torch.where(labels==1.0,two,labels)
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return a.eq(b).sum().item()
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def get_fp(preds,labels):
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a=pred_to_label(preds)
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two=2.0*torch.ones_like(labels)
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one=torch.ones_like(labels)
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b=torch.where(labels==0.0,one,two)
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return a.eq(b).sum().item()
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def get_fn(preds,labels):
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a=pred_to_label(preds)
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two=2.0*torch.ones_like(labels)
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zero=torch.zeros_like(labels)
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b=torch.where(labels==1.0,zero,two)
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return a.eq(b).sum().item()
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if __name__ == '__main__':
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network=CNN()
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#network = torchvision.models.alexnet(pretrained=False)
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new_train_x,new_train_y=readimg("train")
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train_set = CustomedDataSet(train_x = new_train_x, train_y = new_train_y)
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new_val_x,new_val_y=readimg('val')
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val_set = CustomedDataSet(test_x = new_val_x, test_y = new_val_y, train=False, val = True)
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train_loader = DataLoader(dataset=train_set,batch_size=batch_size,shuffle=True,drop_last=True)
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val_loader = DataLoader(dataset=val_set,batch_size=batch_size,shuffle=False,drop_last=True)
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optimizer = torch.optim.SGD(network.parameters(), lr=learning_rate)
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loss_func=DeviationLoss()
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#loss_func=nn.BCEWithLogitsLoss()
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for epoch in range(epochs): # 训练周期
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total_loss = 0
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total_tp = 0
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total_tn = 0
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total_fp = 0
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total_fn = 0
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count = 0
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for batch in train_loader: # get a batch from the dataloader
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# 读取样本数据，完成正向传播，计算损失
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##################### please finish the code ########################
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# images, labels = XXX
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# preds = XXX
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# loss = XXX
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images, labels = batch
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preds = network(images)
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loss = loss_func(preds, labels)
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################################ end ################################
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# 下面这行非常重要，它使得优化器 (optimizer) 将权重的偏导重新归零；
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# 如果不归零，那么在反向传播时，计算出来的偏导会累加在原先的偏导上，
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# 造成错误。
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optimizer.zero_grad()
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# 完成反向传播，更新参数
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##################### please finish the code ########################
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# 反向传播 （一行代码）
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loss.backward()
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# 更新参数 （一行代码）
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optimizer.step()
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################################ end ################################
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total_loss += loss.item()
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count += 1
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total_tp += get_tp(preds,labels)
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total_tn += get_tn(preds,labels)
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total_fp += get_fp(preds,labels)
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total_fn += get_fn(preds,labels)
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Precision_ratio=total_tp/(total_tp+total_fp+1e-5)
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Recall_ratio=total_tp/(total_tp+total_fn+1e-5)
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F_Measure=2*(Precision_ratio*Recall_ratio)/(Precision_ratio+Recall_ratio+1e-5)
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print("epoch:", epoch,
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"\ncorrect times:", (total_tp+total_tn), f"training accuracy:", "%.3f" %((total_tp+total_tn)/(total_tp+total_tn+total_fp+total_fn+1e-5)*100), "%",
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"\ntrue positive times:", (total_tp), f"Precision ratio:", "%.3f" %(Precision_ratio*100), "%",
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f"Recall ratio:", "%.3f" %(Recall_ratio*100), "%",
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f"F-Measure:", "%.3f" %(F_Measure*100), "%",
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"\naverage_loss:", "%.3f" %(total_loss/count))
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preds = network(images)
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pred_list=(pred_to_label(preds)-0.5*labels).tolist()
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Preds=preds.tolist()
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for i in range(len(Preds)):
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print("({:.5f},{})".format(Preds[i][0],pred_list[i][0]),end='')
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print('\n')
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total_loss = 0
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total_tp = 0
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total_tn = 0
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total_fp = 0
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total_fn = 0
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count = 0
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for batch in val_loader:
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images, labels = batch
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preds = network(images)
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loss = loss_func(preds, labels)
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total_loss += loss.item()
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count += 1
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total_tp += get_tp(preds,labels)
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total_tn += get_tn(preds,labels)
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total_fp += get_fp(preds,labels)
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total_fn += get_fn(preds,labels)
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print("\nOn val:",
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"\ncorrect times:", (total_tp+total_tn), f"training accuracy:", "%.3f" %((total_tp+total_tn)/(total_tp+total_tn+total_fp+total_fn+1e-5)*100), "%",
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"\ntrue positive times:", (total_tp), f"Precision ratio:", "%.3f" %(total_tp/(total_tp+total_fp+1e-5)*100), "%",
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f"Recall ratio:", "%.3f" %(total_tp/(total_tp+total_fn+1e-5)*100), "%",
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f"F-Measure:", "%.3f" %(F_Measure*100), "%",
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"\naverage_loss:", "%.3f" %(total_loss/count))
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preds=network(images)
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print("output avg:",torch.mean(preds).item(),"output std:",torch.std(preds).item())
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torch.save(network, "Dev_netv1.0_CNN.pth") # PATH variable should be stated
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