x[torch.arange(x.shape[0]), text.argmax(dim=-1)]

这是一个 PyTorch 的张量操作，它的作用是从一个二维张量 `x` 中按行取出每行最大值所对应的下标。具体来说，`torch.argmax(dim=-1)` 表示在张量的最后一维上求最大值，返回一个一维张量，包含每行的最大值所在的列索引。`torch.arange(x.shape[0])` 表示生成一个从0到`x.shape[0]-1`的一维张量，包含每行的行索引。两个张量进行索引操作后，返回一个包含每行最大值的向量。

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, 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

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：

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。