sample_times.append的append是什么

`append()` 是 Python 内置的列表(list)对象的方法，用于在列表末尾添加新的元素。在这个代码中，我们通过 `sample_times.append(elapsed_time)` 把 `elapsed_time` 这个采样时间添加到了 `sample_times` 这个列表的末尾。也就是说，每次采样完成后，我们就把采样所用的时间 `elapsed_time` 添加到列表 `sample_times` 的末尾，以便后续的处理和分析。

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if __name__ == '__main__': # -------------Adjustable global parameters---------- n=512 # pixel number m=10 # number of time phases angle = 5 # #sample points = 360/angle on the boundary numOfAngles = int(180/angle) numOfContourPts = int(360/angle) labelID = 1 # 勾画的RS文件中第几个轮廓为GTV # path of the input data folder = 'E:\\MedData\\4DCT-202305\\' #patient = '0007921948' # 缺少时间信息 patient = '0000726380' # 病人的编号 # 呼吸曲线数据文件 vxpPath = folder+patient+'\\0000726380\\0000726380_20230420_143723.vxp' # Save the generated figures to the latex file path figPath = "D:\\HUNNU\\Research\\DMD\\4D-CT\\latex-DMD插值\\modify202305\\figure\\" # -------------Auto generated global parameters---------- # 每个dicom文件包含多少横截面 name = os.listdir(folder+patient+'\\0') cuts = [] for i in range(len(name)): if 'CT' in name[i][0:2]: cuts.append(i+1) cuts = np.array(cuts) # phase name times = np.linspace(0,90,10) # image pixel coordinate nums = np.linspace(0,n-1,n) x,y = np.meshgrid(nums,nums) # 输出dicom头文件信息 filename = folder+patient+'\\0\\CT.{}'.format(patient)+'.Image 1.dcm' print('CT dicom file information:') info = loadFileInformation(filename) # 像素之间的间距，包括列间距和行间距，单位mm SliceThickness = info['SliceThickness'] # Z轴的扫描分辨率，单位mm pixelSpace = info['pixelSpace'] # 一个像素所占的实际体积 pixelVol = float(pixelSpace[0])*float(pixelSpace[0])*float(SliceThickness) print('sliceThickness=',SliceThickness,' pixelSpace=',pixelSpace)

这段代码是一个 Python 脚本中的主函数部分。代码中定义了一些可调整的全局参数，例如像素数目、时间相位数目、采样点角度等。然后根据给定的病人信息和文件路径，读取 DICOM 文件并获取一些头文件信息，例如像素间距、扫描分辨率等。最后打印输出这些信息。

需要注意的是，这段代码中存在一些依赖的库和函数，例如 `os`、`np`、`loadFileInformation` 等。在运行代码之前需要确保这些依赖已经被正确安装和导入。

For Gradient Descent, we calculated the gradients for each sample individually. To improve the convergence behavior and to speed up the training process, we now consider batches of gradients. This stabilizes the convergence and prevents the parameters from being push in one direction and directly afterwards being pulled in the opposite direction. Therefore, we can use the gradient descent implementation and just swap the data loader to use the batched variant. The bad thing is that we need to iterate now multiple times over the dataset.帮我补全下面的代码。 net = ... epochs = 10 optimizer = GD(net.parameters(), 0.02) losses, states = ... fig = plot_losses(losses) conf_pltly() init_notebook_mode(connected=False) iplot(fig)

Sure! Here's the completed code:

net = ... # define your neural network architecture
epochs = 10
optimizer = GD(net.parameters(), 0.02) # define the optimizer

# define your batch size and create a data loader that batches the data
batch_size = 32
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

losses, states = [], []
for epoch in range(epochs):
    running_loss = 0.0
    for i, batch in enumerate(data_loader):
        optimizer.zero_grad() # zero the parameter gradients
        
        # forward + backward + optimize
        outputs = net(batch['input'])
        loss = loss_fn(outputs, batch['target'])
        loss.backward()
        optimizer.step()

        # track the loss and state of the network
        running_loss += loss.item()
        losses.append(running_loss / ((i+1) * batch_size))
        states.append(copy.deepcopy(net.state_dict()))

# plot the losses over training
fig = plot_losses(losses)
conf_pltly()
init_notebook_mode(connected=False)
iplot(fig)

Note that in this code, we've added a data loader that batches the data into sets of `batch_size` samples. We then iterate over this data loader, computing the gradients and updating the parameters for each batch. We also track the loss and network state over time, and use these to plot the loss curve at the end of training.