RuntimeError：只能计算浮点类型的平均值。取而代之的是字节。对于平均值 += images_data.mean(2).sum(0)

Posted 2023-02-16

【中文标题】RuntimeError：只能计算浮点类型的平均值。取而代之的是字节。对于平均值 += images_data.mean(2).sum(0)

【英文标题】：RuntimeError: Can only calculate the mean of floating types. Got Byte instead. for mean += images_data.mean(2).sum(0)

【发布时间】：2021-01-29 03:40:51

【问题描述】：

我有以下代码：

# Device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)

# split the dataset into validation and test sets
len_valid_set = int(0.1*len(dataset))
len_train_set = len(dataset) - len_valid_set

print("The length of Train set is ".format(len_train_set))
print("The length of Test set is ".format(len_valid_set))

train_dataset , valid_dataset,  = torch.utils.data.random_split(dataset , [len_train_set, len_valid_set])

# shuffle and batch the datasets
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=8, shuffle=True, num_workers=4)
test_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=8, shuffle=True, num_workers=4)

print("LOADERS",
    len(dataloader),
    len(train_loader),
    len(test_loader))

Train set的长度为720

测试集长度为80

装载机 267 90 10

mean = 0.0
std = 0.0
nb_samples = 0.0
for data in train_loader:
    images, landmarks = data["image"], data["landmarks"]
    batch_samples = images.size(0)

    images_data = images.view(batch_samples, images.size(1), -1)
    mean += images_data.mean(2).sum(0)
    std += images_data.std(2).sum(0)
    nb_samples += batch_samples

mean /= nb_samples
std /= nb_samples

我得到这个错误：

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-23-9e47ddfeff5e> in <module>
      7 
      8     images_data = images.view(batch_samples, images.size(1), -1)
----> 9     mean += images_data.mean(2).sum(0)
     10     std += images_data.std(2).sum(0)
     11     nb_samples += batch_samples

RuntimeError: Can only calculate the mean of floating types. Got Byte instead.

固定代码取自 https://***.com/a/64349380/2414957 它适用于 dataloader 但不适用于 train_loader

还有，这些是结果

print(type(images_data))
print(images_data)

我们有：

<class 'torch.Tensor'>
tensor([[[74, 74, 74,  ..., 63, 63, 63],
         [73, 73, 73,  ..., 61, 61, 61],
         [75, 75, 75,  ..., 61, 61, 61],
         ...,
         [74, 74, 74,  ..., 38, 38, 38],
         [75, 75, 75,  ..., 39, 39, 39],
         [72, 72, 72,  ..., 38, 38, 38]],

        [[75, 75, 75,  ..., 65, 65, 65],
         [75, 75, 75,  ..., 62, 62, 62],
         [75, 75, 75,  ..., 63, 63, 63],
         ...,
         [71, 71, 71,  ..., 39, 39, 39],
         [74, 74, 74,  ..., 38, 38, 38],
         [73, 73, 73,  ..., 37, 37, 37]],

        [[72, 72, 72,  ..., 62, 62, 62],
         [74, 74, 74,  ..., 63, 63, 63],
         [75, 75, 75,  ..., 61, 61, 61],
         ...,
         [74, 74, 74,  ..., 38, 38, 38],
         [74, 74, 74,  ..., 39, 39, 39],
         [73, 73, 73,  ..., 37, 37, 37]],

        ...,

        [[75, 75, 75,  ..., 63, 63, 63],
         [73, 73, 73,  ..., 63, 63, 63],
         [74, 74, 74,  ..., 62, 62, 62],
         ...,
         [74, 74, 74,  ..., 38, 38, 38],
         [73, 73, 73,  ..., 39, 39, 39],
         [73, 73, 73,  ..., 37, 37, 37]],

        [[73, 73, 73,  ..., 62, 62, 62],
         [75, 75, 75,  ..., 62, 62, 62],
         [74, 74, 74,  ..., 63, 63, 63],
         ...,
         [73, 73, 73,  ..., 39, 39, 39],
         [74, 74, 74,  ..., 38, 38, 38],
         [74, 74, 74,  ..., 38, 38, 38]],

        [[74, 74, 74,  ..., 62, 62, 62],
         [74, 74, 74,  ..., 63, 63, 63],
         [74, 74, 74,  ..., 62, 62, 62],
         ...,
         [74, 74, 74,  ..., 38, 38, 38],
         [73, 73, 73,  ..., 38, 38, 38],
         [72, 72, 72,  ..., 36, 36, 36]]], dtype=torch.uint8)

当我尝试时

images_data = images_data.float()
mean += images_data.mean(2).sum(0)

我没有像我预期的那样得到 3 个均值和 3 个标准值的张量，但得到了一个非常大的张量（每个 torch.Size([600])）

【参考方案1】：

正如错误所说，您的 images_data 是一个 ByteTensor，即具有 dtype uint8。 Torch 拒绝计算整数的平均值。您可以使用以下命令将数据转换为float：

(images_data * 1.0).mean(2)

或者

torch.Tensor.float(images_data).mean(2)

【讨论】：

它的工作原理与我使用的 float 类似，并为我提供了一个 600 个值的张量，而不是 3 个元素的张量