grids = [] grids.append(np.linspace(0, 1, s)) grids.append(np.linspace(0, 1, s)) grid = np.vstack([xx.ravel() for xx in np.meshgrid(*grids)]).T grid = grid.reshape(1,s,s,2) grid = torch.tensor(grid, dtype=torch.float)
时间: 2023-12-02 19:02:37 浏览: 112
这段代码的主要作用是生成一个网格,其中grids是一个列表,包含两个np.linspace的返回值,这两个返回值分别表示在0到1之间等间隔地取s个数,也就是生成了一个s行2列的矩阵。接着,使用np.meshgrid函数生成一个网格点矩阵,网格矩阵的每一行都是一对网格点坐标。然后使用ravel函数将二维网格矩阵变成一维数组,再使用vstack函数将多个一维数组按垂直方向堆叠起来形成一个二维数组。最后,使用reshape函数将二维数组变成一个四维数组,第一维为1,后三维分别为s、s和2。最后,将四维数组转换成torch tensor类型。
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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
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