英文:
RuntimeError: expected scalar type Double but found Float in Pytorch code
问题
def encoder_block(inp, max_pool, in_channels):
conv = torch.nn.Conv2d(in_channels=in_channels, out_channels=64, kernel_size=3, padding='same')(inp.double())
relu = torch.nn.ReLU()(conv)
conv = torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding='same')(relu)
relu = torch.nn.ReLU()(conv)
if max_pool:
return torch.nn.MaxPool2d(2, 2)(relu)
return relu
test_load = nib.load(fpath).get_fdata()
test_numpy = test_load[:, :, 0].reshape(1, 1, 256, 256).astype(np.double)
tens = torch.DoubleTensor(test_numpy)
out = encoder_block(tens, True, 1)
这段代码应该从本地存储中获取一个NIfTI文件，将其转换为NumPy数组，然后在2D图像上执行一些卷积操作，仅用于基本测试。错误发生在第一个Conv2d 操作上，错误信息是 RuntimeError: expected scalar type Double but found Float。我不确定如何将数据转换为浮点数。
英文:
def encoder_block(inp, max_pool, in_channels):
conv = torch.nn.Conv2d(in_channels=in_channels, out_channels=64, kernel_size=3, padding='same')(inp.double())
relu = torch.nn.ReLU()(conv)
conv = torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding='same')(relu)
relu = torch.nn.ReLU()(conv)
if max_pool:
return torch.nn.MaxPool2d(2,2)(relu)
return relu
test_load = nib.load(fpath).get_fdata()
test_numpy = test_load[:,:,0].reshape(1,1,256,256).astype(np.double)
tens = torch.DoubleTensor(test_numpy)
out = encoder_block(tens, True, 1)
This code should take a nifti file from my local storage, convert it to a numpy array then perform some convolutions on the 2d image as a basic test just for now.
The error happens on the first conv2d and says RuntimeError: expected scalar type Double but found Float. I'm not sure what else I can do to convert my data to float.
答案1
得分: 1
这只是一个转换问题。PyTorch 默认使用 torch.float32，但您明确创建了一个 torch.float64 类型的张量。因此，您还需要使用关键字参数 dtype 来告诉卷积层正确的数据类型。代码的工作示例如下：
def encoder_block(inp, max_pool, in_channels):
conv = torch.nn.Conv2d(in_channels=in_channels,
out_channels=64,
kernel_size=3,
padding='same',
dtype=torch.float64)(inp)
relu = torch.nn.ReLU()(conv)
conv = torch.nn.Conv2d(in_channels=64,
out_channels=64,
kernel_size=3,
padding='same',
dtype=torch.float64)(relu)
relu = torch.nn.ReLU()(conv)
if max_pool:
return torch.nn.MaxPool2d(2,2)(relu)
return relu
test_load = nib.load(fpath).get_fdata()
# tested with the next line
# test_load = np.random.rand(256, 256, 1)
test_numpy = test_load[:,:,0].reshape(1,1,256,256)
tens = torch.DoubleTensor(test_numpy)
out = encoder_block(tens, True, 1)
英文:
This is simply a conversion problem. Pytorch uses torch.float32 by default but you explicitly create a tensor of type torch.float64. So you also have to tell the convolutional layers the correct dtype using the keyword argument dtype. A working example of the code looks like:
def encoder_block(inp, max_pool, in_channels):
conv = torch.nn.Conv2d(in_channels=in_channels,
out_channels=64,
kernel_size=3,
padding='same',
dtype=torch.float64)(inp)
relu = torch.nn.ReLU()(conv)
conv = torch.nn.Conv2d(in_channels=64,
out_channels=64,
kernel_size=3,
padding='same',
dtype=torch.float64)(relu)
relu = torch.nn.ReLU()(conv)
if max_pool:
return torch.nn.MaxPool2d(2,2)(relu)
return relu
test_load = nib.load(fpath).get_fdata()
# tested with the next line
# test_load = np.random.rand(256, 256, 1)
test_numpy = test_load[:,:,0].reshape(1,1,256,256)
tens = torch.DoubleTensor(test_numpy)
out = encoder_block(tens, True, 1)
答案2
得分: 1
get_fdata() 接受一个 dtype 参数。
test_load = nib.load(fpath).get_fdata(dtype=np.float32)
英文:
> I'm not sure what else I can do to convert my data to float.
get_fdata() takes a dtype parameter.
test_load = nib.load(fpath).get_fdata(dtype=np.float32)
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