深度学习(七十三)pytorch学习笔记

女爷i 2022-05-29 13:54 372阅读 0赞

先吐槽一下，深度学习发展速度真的很快，深度学习框架也在逐步迭代，真的是苦了俺这搞深度学习程序员。本人从三年前开始学深度学习开始，这些深度学习框架也是一个换过一个，从keras、theano、caffe、darknet、tensorflow，最后到现在要开始使用pytorch。

**一、变量、求导torch.autograd模块**

默认的variable定义的时候，requires\_grad是false，变量是不可导的，如果设置为true，表示变量可导。

#coding=utf-8
    #requires_grad默认为false
    # 如果调用backward的时候，所有的变量都是不可导的，那么最后会报出没有可到变量的错误
    import torch
    from torch import  autograd
    input=torch.FloatTensor([1,2,3])
    input_v=autograd.Variable(input,requires_grad=True)
    loss=torch.mean(input_v)
    
    print loss.requires_grad
    loss.backward()
    print input_v
    print input_v.grad

**二、数据层及其变换**

from PIL import Image
    import torchvision
    import matplotlib.pyplot as plt
    import numpy as np
    #数据变换
    data_transform_train = torchvision.transforms.Compose([
        torchvision.transforms.RandomRotation(30),
        torchvision.transforms.RandomCrop((32,32)),
        torchvision.transforms.RandomHorizontalFlip(),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
        ])
    data_transform_eval=torchvision.transforms.Compose([
        torchvision.transforms.CenterCrop((32,32)),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
        ])
    #对于自定义的数据，要重载下面三个函数getitem、len、init
    class mydata(Dataset):
        def __init__(self,label_file,image_root,is_train=True):
            self.imagepaths=[]
            self.labels=[]
            self.is_train=is_train
            if is_train:
                self.transforms=data_transform_train
            else:
                self.transforms=data_transform_eval
    
            with open(label_file,'r') as f:
                for line in f.readlines():#读取label文件
                    self.imagepaths.append(os.path.join(image_root,line.split()[0]))
                    self.labels.append(int(line.split()[1]))
        def __getitem__(self, item):
            x=Image.open(self.imagepaths[item]).resize((35,35))
    
            y=self.labels[item]
            if self.is_train:
                return [self.transforms(x),self.transforms(x)], y
            else:
                return self.transforms(x),y
        def __len__(self):
            return len(self.imagepaths)
    
    def make_weights_for_balanced_classes(labels, nclasses):
        count = {}
        for item in labels:
            if count.has_key(item):
                count[item] += 1
            else:
                count[item]=1
        weight_per_class ={}
        N = len(labels)
        for key,value in count.items():
            weight_per_class[key] = N/float(value)
        weight = [0] * len(labels)
        for idx, val in enumerate(labels):
            weight[idx] = weight_per_class[val]
        return weight
    
    
    train_data=mydata('data/train.txt','./',is_train=True)
    weights = make_weights_for_balanced_classes(train_data.labels, 3)
    weights = torch.DoubleTensor(weights)
    sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights))
    train_dataloader_student=DataLoader(train_data,batch_size=6,sampler=sampler)
    for x,y in train_dataloader_student:
        for xi in x:
            print y
            npimg = torchvision.utils.make_grid(xi).numpy()#可视化显示
            plt.imshow(np.transpose(npimg, (1, 2, 0)))
            plt.show()

**三、网络架构**

from moving_avarage_layer import conv2d_moving
    import torch
    from torch import  autograd,nn
    from torch.utils.data import DataLoader, Dataset
    from data_layer import mydata,make_weights_for_balanced_classes
    import torchvision
    import numpy as np
    import matplotlib.pyplot as plt
    import torch.nn.functional as function
    import os
    import time
    
    
    class MobileNet(nn.Module):
        def __init__(self):
            super(MobileNet, self).__init__()
    
            def conv_bn(inp, oup, stride):
                return nn.Sequential(
                    nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
                    nn.BatchNorm2d(oup),
                    nn.ReLU(inplace=True)
                )
    
            def conv_dw(inp, oup, stride):
                return nn.Sequential(
                    nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
                    nn.BatchNorm2d(inp),
                    nn.ReLU(inplace=True),
    
                    nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
                    nn.BatchNorm2d(oup),
                    nn.ReLU(inplace=True),
                )
    
            self.model = nn.Sequential(
                conv_bn(  3,  32, 2),
                conv_dw( 32,  64, 1),
                conv_dw( 64, 96, 2),
                conv_dw(96, 96, 1),
                conv_dw(96, 128, 2),
                conv_dw(128, 128, 1),
                conv_dw(128, 256, 2),
                conv_dw(256, 256, 1),
                conv_dw(256, 512, 1),
                nn.AvgPool2d(2),
            )
            self.fc = nn.Linear(512, 4)
    
        def forward(self, x):
            x = self.model(x)
            #print x.shape
            x = x.view(-1, 512)
            x = self.fc(x)
            return x

**四、优化求解**

def update_ema_variables(model, ema_model,alpha):
        for ema_param, param in zip(ema_model.parameters(), model.parameters()):
            ema_param.data.mul_(alpha).add_(1 - alpha, param.data)
    def softmax_mse_loss(input_logits, target_logits):
        assert input_logits.size() == target_logits.size()
        input_softmax = function.softmax(input_logits, dim=1)
        target_softmax = function.softmax(target_logits, dim=1)
        num_classes = input_logits.size()[1]
        return function.mse_loss(input_softmax, target_softmax, size_average=False) / num_classes
    
    torch.backends.cudnn.enabled = False
    torch.manual_seed(7)
    
    net_student=MobileNet().cuda()
    net_teacher=MobileNet().cuda()
    for param in net_teacher.parameters():
                    param.detach_()
    if os.path.isfile('teacher.pt'):
        net_student.load_state_dict(torch.load('teacher.pt'))
    
    
    
    
    
    train_data=mydata('data/race/train.txt','./',is_train=True)
    min_batch_size=32
    weights = make_weights_for_balanced_classes(train_data.labels, 5)
    weights = torch.DoubleTensor(weights)
    sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights))
    train_dataloader=DataLoader(train_data,batch_size=min_batch_size,sampler=sampler,num_workers=8)
    
    
    valid_data=mydata('data/race/val.txt','./',is_train=False)
    valid_dataloader=DataLoader(valid_data,batch_size=min_batch_size,shuffle=True,num_workers=8)
    
    
    
    
    classify_loss_function = torch.nn.CrossEntropyLoss(size_average=False,ignore_index=-1).cuda()
    optimizer = torch.optim.SGD(net_student.parameters(),lr = 0.001, momentum=0.9)
    
    
    
    
    globals_step=0
    for epoch in range(10000):
        globals_classify_loss=0
        globals_consistency_loss = 0
        net_student.train()
        start=time.time()
        end=0
        for index,(x,y) in enumerate(train_dataloader):
            optimizer.zero_grad()  #
    
            x_student=autograd.Variable(x[0]).cuda()
            y=autograd.Variable(y).cuda()
            predict_student=net_student(x_student)
    
            classify_loss=classify_loss_function(predict_student,y)/min_batch_size
            sum_loss = classify_loss
    
            x_teacher= autograd.Variable(x[1],volatile=True).cuda()
            predict_teacher = net_teacher(x_teacher)
            ema_logit = autograd.Variable(predict_teacher.detach().data, requires_grad=False)
            consistency_loss =softmax_mse_loss(predict_student,ema_logit)/min_batch_size
            consistency_weight=1
            sum_loss+=consistency_weight*consistency_loss
            globals_consistency_loss += consistency_loss.data[0]
    
            sum_loss.backward()
            optimizer.step()
            alpha = min(1 - 1 / (globals_step + 1), 0.99)
            update_ema_variables(net_student, net_teacher, alpha)
    
    
    
            globals_classify_loss +=classify_loss.data[0]
            globals_step += 1
    
    
        if epoch%5!=0:
            continue
    
        net_student.eval()
        correct = 0
        total = 0
        for images, labels in valid_dataloader:
            valid_input=autograd.Variable(images,volatile=True).cuda()
            outputs = net_student(valid_input)
            #print outputs.shape
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted.cpu() == labels).sum()
        print "epoch:%d"%epoch,"time:%d"%(time.time()-start),'accuracy %d' % (
            100 * correct / total),"consistency loss:%f"%globals_consistency_loss,'classify loss%f:'%globals_classify_loss
        torch.save(net_student.state_dict(),'teacher.pt')