pytorch 自定义网络结构 +自定义数据加载

àì夳堔傛蜴生んèń 2021-10-31 16:26 430阅读 0赞

自定义网络结构 
    import os
    os.environ['CUDA_VISIBLE_DEVICES'] = '3'
    os.system('echo $CUDA_VISIBLE_DEVICES')
     
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.utils.data as data
    import torch.optim as optim
    from torch.autograd import Variable
    import numpy as np
    from Encoding import load_feature
     
    class TransientModel(nn.Module):
        def __init__(self):
            super(TransientModel,self).__init__()
            self.conv1 = nn.Conv2d(16, 8, kernel_size=1)
            self.conv2 = nn.Conv2d(8, 4, kernel_size=1)
            self.conv3 = nn.Conv2d(4, 2, kernel_size=1)
            self.conv4 = nn.Conv2d(2, 1, kernel_size=1)
        def forward(self, x):
            x = F.relu(self.conv1(x))
            x = F.relu(self.conv2(x))
            x = F.relu(self.conv3(x))
            x = F.relu(self.conv4(x))
            return x
     
    class MyLoss(nn.Module):
        def __init__(self):
            super(MyLoss, self).__init__()
            print '1'
        def forward(self, pred, truth):
            truth = torch.mean(truth,1)
            truth = truth.view(-1,2048)
            pred  = pred.view(-1,2048)
            return  torch.mean(torch.mean((pred-truth)**2,1),0)
     
    class MyTrainData(data.Dataset):
        def __init__(self):
            self.video_path = '/data/FrameFeature/Penn/'
            self.video_file = '/data/FrameFeature/Penn_train.txt'
            fp = open(self.video_file,'r')
            lines = fp.readlines()
            fp.close()
            self.video_name = []
            for line in lines:
                self.video_name.append(line.strip().split(' ')[0])
        def __len__(self):
            return len(self.video_name)
        def __getitem__(self, index):
            data = load_feature(os.path.join(self.video_path,self.video_name[index]))
            data = np.expand_dims(data,2)
            return data
     
    def train(model, train_loader, myloss, optimizer, epoch):
        model.train()
        for batch_idx, train_data in enumerate(train_loader):
            train_data = Variable(train_data).cuda()
            optimizer.zero_grad()
            output = model(train_data)
            loss = myloss(output, train_data)
            loss.backward()
            optimizer.step()
            if batch_idx%100 == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tloss: {:.6f}'.format(
                    epoch, batch_idx*len(train_data), len(train_loader.dataset),
                    100.*batch_idx/len(train_loader), loss.data.cpu().numpy()[0]))
     
    def main():
        model = TransientModel().cuda()
        myloss= MyLoss()
     
        train_data = MyTrainData()
        train_loader = data.DataLoader(train_data,batch_size=1,shuffle=True,num_workers=1)
     
        optimizer = optim.SGD(model.parameters(),lr=0.001)
     
        for epoch in range(10):
            train(model, train_loader, myloss, optimizer, epoch)
     
    if __name__=='__main__':
        main()