torch.append
时间: 2024-04-28 14:16:34 浏览: 19
很抱歉,目前没有torch.append这个函数。或许你是想了解PyTorch中的torch.cat函数,它用于将多个张量沿着指定的维度进行拼接。下面是对torch.cat函数的介绍:
torch.cat函数可以将多个张量按照指定的维度进行拼接。它接受一个张量列表作为输入,并返回拼接后的张量。拼接的维度由参数dim指定。
以下是torch.cat函数的使用示例:
```python
import torch
# 创建两个张量
x = torch.tensor([[1, 2], [3, 4]])
y = torch.tensor([[5, 6]])
# 沿着行维度拼接两个张量
result = torch.cat((x, y), dim=0)
print(result)
# 输出:
# tensor([[1, 2],
# [3, 4],
# [5, 6]])
# 沿着列维度拼接两个张量
result = torch.cat((x, y.T), dim=1)
print(result)
# 输出:
# tensor([[1, 2, 5],
# [3, 4, 6]])
```
相关问题
def decode_outputs(self, outputs, dtype): grids = [] strides = [] for (hsize, wsize), stride in zip(self.hw, self.strides): yv, xv = torch.meshgrid([torch.arange(hsize), torch.arange(wsize)]) grid = torch.stack((xv, yv), 2).view(1, -1, 2) grids.append(grid) shape = grid.shape[:2] strides.append(torch.full((*shape, 1), stride)) grids = torch.cat(grids, dim=1).type(dtype) strides = torch.cat(strides, dim=1).type(dtype) outputs[..., :2] = (outputs[..., :2] + grids) * strides outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides return outputs 在GPU环境进行速度优化 并提供代码示例
以下是一个使用CUDA并行计算的示例代码:
```python
import torch
class Decoder:
def __init__(self, hw, strides):
self.hw = hw
self.strides = strides
def decode_outputs(self, outputs, dtype):
grids = []
strides = []
for (hsize, wsize), stride in zip(self.hw, self.strides):
yv, xv = torch.meshgrid([torch.arange(hsize), torch.arange(wsize)])
grid = torch.stack((xv, yv), 2).view(1, -1, 2)
grids.append(grid)
shape = grid.shape[:2]
strides.append(torch.full((*shape, 1), stride))
grids = torch.cat(grids, dim=1).type(dtype).cuda()
strides = torch.cat(strides, dim=1).type(dtype).cuda()
outputs = outputs.cuda()
outputs[..., :2] = (outputs[..., :2] + grids) * strides
outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides
return outputs.cpu()
```
这段代码与之前的代码基本相同,只是在计算 grids 和 strides 时,将其转换为 CUDA 张量,并使用 GPU 进行计算。同时将模型输出 outputs 也转换为 CUDA 张量,并在计算后将其转换回 CPU 张量。这样可以在 GPU 环境下采用并行思维进行速度优化,提高代码的执行效率。
def decode_outputs(self, outputs, dtype): grids = [] strides = [] for (hsize, wsize), stride in zip(self.hw, self.strides): yv, xv = torch.meshgrid([torch.arange(hsize, dtype=dtype), torch.arange(wsize, dtype=dtype)]) grid = torch.stack((xv, yv), dim=2).view(1, -1, 2) grids.append(grid) shape = grid.shape[:2] strides.append(torch.full((*shape, 1), stride, dtype=dtype)) grids = torch.cat(grids, dim=1) strides = torch.cat(strides, dim=1) outputs[..., :2].add_(grids).mul_(strides) outputs[..., 2:4].exp_().mul_(strides) return outputs通过张量列表的形式替换for循环速度优化并提供代码
def decode_outputs(self, outputs, dtype):
hw = self.hw
strides = self.strides
grids = [torch.stack((torch.meshgrid([torch.arange(hsize, dtype=dtype), torch.arange(wsize, dtype=dtype)])), dim=2).view(1, -1, 2) for (hsize, wsize) in hw]
grids = torch.cat(grids, dim=1)
strides = torch.cat([torch.full((*grid.shape[:2], 1), stride, dtype=dtype) for stride, grid in zip(strides, grids)], dim=1)
outputs[..., :2] = (outputs[..., :2] + grids) * strides
outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides
return outputs