is_contiguous()
时间: 2024-04-06 08:31:48 浏览: 18
is_contiguous()是一个Python中的函数,用于判断一个数组是否是连续的。在NumPy中,一个数组是连续的,如果它的元素在内存中是连续存储的。如果数组是连续的,那么它的内存布局可以最大化地利用缓存,从而提高访问速度。如果一个数组不是连续的,那么它的内存布局可能会导致访问速度变慢。因此,is_contiguous()函数可以用来检查一个数组是否是连续的,以便在需要时进行优化操作。
相关问题
class GatherOperation(Function): @staticmethod def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor: """ :param ctx: :param features: (B, C, N) :param idx: (B, npoint) index tensor of the features to gather :return: output: (B, C, npoint) """ assert features.is_contiguous() assert idx.is_contiguous() B, npoint = idx.size() _, C, N = features.size() output = torch.cuda.FloatTensor(B, C, npoint) pointnet2.gather_points_wrapper(B, C, N, npoint, features, idx, output) ctx.for_backwards = (idx, C, N) return output @staticmethod def backward(ctx, grad_out): idx, C, N = ctx.for_backwards B, npoint = idx.size() grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_()) grad_out_data = grad_out.data.contiguous() pointnet2.gather_points_grad_wrapper(B, C, N, npoint, grad_out_data, idx, grad_features.data) return grad_features, None gather_operation = GatherOperation.apply
这是一个 PyTorch 自定义函数 GatherOperation,用于实现从给定张量中根据索引提取特定元素的功能。具体地,输入 features 是形状为 (B, C, N) 的张量,表示 B 个样本中每个样本的 N 个特征向量,每个向量有 C 个元素。输入 idx 是形状为 (B, npoint) 的整数张量,表示要从 features 中提取的元素的索引。输出是形状为 (B, C, npoint) 的张量,表示提取的结果。
在 forward 函数中,首先对输入的 features 和 idx 进行了连续性检查,然后调用了一个 C++ 实现的 gather_points_wrapper 函数,将提取结果存储在 output 变量中,并将 idx、C、N 存储在 ctx 中以备反向传播使用。在 backward 函数中,首先从 ctx 中恢复 idx、C、N,然后创建一个和 features 相同形状的 grad_features 变量,调用 gather_points_grad_wrapper 函数计算 grad_features,并返回。
这个 GatherOperation 函数在 PointNet++ 中被广泛使用,用于实现点云分类和语义分割等任务中的采样和汇聚操作。
tensor.contiguous()
The `contiguous()` method in PyTorch is used to return a tensor with same data but a different memory layout. When a tensor is created, it may not be stored contiguously in memory. This means that the elements of the tensor might not be stored in a contiguous block of memory. If a tensor is not contiguous, it can cause performance issues when performing certain operations.
The `contiguous()` method returns a new tensor with the same data but stored contiguously in memory. This new tensor has the same values as the original tensor, but its memory layout is different.
For example, consider the following code:
```
import torch
x = torch.randn(3, 4)
print(x)
print(x.is_contiguous())
y = x.t()
print(y)
print(y.is_contiguous())
z = y.contiguous()
print(z)
print(z.is_contiguous())
```
The output of this code will be:
```
tensor([[-0.4677, 0.7443, -2.3223, 0.2032],
[-0.8475, 0.1588, -0.8358, -0.3137],
[-0.8270, -0.1226, -0.7252, -1.2620]])
True
tensor([[-0.4677, -0.8475, -0.8270],
[ 0.7443, 0.1588, -0.1226],
[-2.3223, -0.8358, -0.7252],
[ 0.2032, -0.3137, -1.2620]])
False
tensor([[-0.4677, -0.8475, -0.8270],
[ 0.7443, 0.1588, -0.1226],
[-2.3223, -0.8358, -0.7252],
[ 0.2032, -0.3137, -1.2620]])
True
```
Here, we first create a tensor `x` of shape (3, 4) and check if it is contiguous using the `is_contiguous()` method. The output is `True`.
We then transpose `x` to create a new tensor `y` using the `t()` method. We check if `y` is contiguous using `is_contiguous()`. The output is `False`, indicating that `y` is not contiguous.
To make `y` contiguous, we use the `contiguous()` method to create a new tensor `z`. We check if `z` is contiguous using `is_contiguous()`. The output is `True`, indicating that `z` is contiguous.
In summary, the `contiguous()` method is used to ensure that a tensor is stored contiguously in memory, which can improve performance for certain operations.
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