“无类型” Pytorch 模型测试部分中的对象不可迭代？

Question

我正在使用 Pytorch 模型研究高光谱图像。当我想测试模型时，出现“NoneType objectis not iterable”错误。图像上有一个滑动窗口，我将其添加到测试部分。在 for 循环中，批处理值变为 None，我不知道如何修复它。我附上下面的工作代码。感谢您的帮助。

def test(net, img, hyperparams):

net.eval()
patch_size = hyperparams["patch_size"]
center_pixel = hyperparams["center_pixel"]
batch_size, device = hyperparams["batch_size"], hyperparams["device"]
n_classes = hyperparams["n_classes"]

kwargs = {
    "step": hyperparams["test_stride"],
    "window_size": (patch_size, patch_size),
}
probs = np.zeros(img.shape[:2] + (n_classes,))

iterations = count_sliding_window(img, **kwargs) // batch_size
for batch in tqdm(
    grouper(batch_size, sliding_window(img, **kwargs)),
    total=(iterations),
    desc="Inference on the image",
):
    with torch.no_grad():
        if patch_size == 1:
            data = [b[0][0, 0] for b in batch]
            data = np.copy(data)
            data = torch.from_numpy(data)
        else:
            data = [b[0] for b in batch]
            data = np.copy(data)
            data = data.transpose(0, 3, 1, 2)
            data = torch.from_numpy(data)
            data = data.unsqueeze(1)

        indices = [b[1:] for b in batch]
        data = data.to(device)
        output = net(data)
        if isinstance(output, tuple):
            output = output[0]
        output = output.to("cpu")

        if patch_size == 1 or center_pixel:
            output = output.numpy()
        else:
            output = np.transpose(output.numpy(), (0, 2, 3, 1))
        for (x, y, w, h), out in zip(indices, output):
            if center_pixel:
                probs[x + w // 2, y + h // 2] += out
            else:
                probs[x : x + w, y : y + h] += out
return probs

probabilities = test(model, img, hyperparams)

然后我得到这个错误：

Inference on the image:   0%|          | 0/210 [00:00<?, ?it/s]
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-55-cabf8b6fa06d> in <module>()
----> 1 probabilities = test(model, img, hyperparams)
      2 prediction = np.argmax(probabilities, axis=-1)

<ipython-input-54-b248a53c93ce> in test(net, img, hyperparams)
        23         with torch.no_grad():
        24             if patch_size == 1:
   ---> 25                 data = [b[0][0, 0] for b in batch]
        26                 data = np.copy(data)
        27                 data = torch.from_numpy(data)

Answer 1

看起来是批次的内容。找到它并不容易，但这里有一些关于如何更接近错误的建议：

1. Python 3.10 有更好的错误消息（显示错误在行中的位置！）

2. 尝试通过批次进行 enumerate()，然后找到缺陷索引

3. 使用 try/expect （只是临时的）来中断此 ValueError 并打印出来并检查整个批次

4. 如果还不错，则该特定批次为“none”，您可以跳过它：

   data = [b[0][0, 0] for b in batch]

   变成

   data = [b[0][0, 0] for b in batch if b]