outputs.gather
时间: 2023-09-08 22:02:38 浏览: 48
outputs.gather是一个用于多GPU并行计算中的方法。在多GPU训练中,模型通常会在每个GPU上并行计算不同的输入数据,得到各自的输出。outputs.gather的作用就是将不同GPU上的输出进行收集和合并,以得到最终的输出结果。
具体来说,outputs.gather接收一个outputs的列表作为输入,这个列表中的每个元素是一个tensor,代表一个GPU上的输出。函数会将这些tensor按照指定的方式进行合并,得到一个合并后的tensor作为输出。合并方式可以是按维度拼接(concatenate)或按维度堆叠(stack)。
举个例子,假设我们有两个GPU,在每个GPU上进行了相同的计算,得到了两个不同的输出tensor:output1和output2。如果我们希望将这两个tensor按照第0维进行拼接,就可以使用outputs.gather([output1, output2], dim=0)这样的方法。这样,函数会将两个tensor在第0维上拼接,并返回一个合并后的tensor作为最终输出。
outputs.gather的使用可以使得多GPU计算变得更加方便和高效。通过合并不同GPU上的输出,我们可以得到整个模型在整个训练集上的输出结果,以进行下一步的操作,比如计算损失函数或进行精度评估。
总结起来,outputs.gather是一个用于多GPU并行计算中的方法,用于将不同GPU上的输出进行收集和合并,得到最终的输出结果。使用这个方法,我们可以方便地将多个GPU上的计算结果整合在一起,以便进行后续的操作。
相关问题
分析这个代码class OhemCrossEntropy(nn.Module): def __init__(self, ignore_label=-1, thres=0.7, min_kept=100000, weight=None): super(OhemCrossEntropy, self).__init__() self.thresh = thres self.min_kept = max(1, min_kept) self.ignore_label = ignore_label self.criterion = nn.CrossEntropyLoss( weight=weight, ignore_index=ignore_label, reduction='none' ) def _ce_forward(self, score, target): ph, pw = score.size(2), score.size(3) h, w = target.size(1), target.size(2) if ph != h or pw != w: score = F.interpolate(input=score, size=( h, w), mode='bilinear', align_corners=config.MODEL.ALIGN_CORNERS) loss = self.criterion(score, target) return loss def _ohem_forward(self, score, target, **kwargs): ph, pw = score.size(2), score.size(3) h, w = target.size(1), target.size(2) if ph != h or pw != w: score = F.interpolate(input=score, size=( h, w), mode='bilinear', align_corners=config.MODEL.ALIGN_CORNERS) pred = F.softmax(score, dim=1) pixel_losses = self.criterion(score, target).contiguous().view(-1) mask = target.contiguous().view(-1) != self.ignore_label tmp_target = target.clone() tmp_target[tmp_target == self.ignore_label] = 0 pred = pred.gather(1, tmp_target.unsqueeze(1)) pred, ind = pred.contiguous().view(-1,)[mask].contiguous().sort() min_value = pred[min(self.min_kept, pred.numel() - 1)] threshold = max(min_value, self.thresh) pixel_losses = pixel_losses[mask][ind] pixel_losses = pixel_losses[pred < threshold] return pixel_losses.mean() def forward(self, score, target): if config.MODEL.NUM_OUTPUTS == 1: score = [score] weights = config.LOSS.BALANCE_WEIGHTS assert len(weights) == len(score) functions = [self._ce_forward] * \ (len(weights) - 1) + [self._ohem_forward] return sum([ w * func(x, target) for (w, x, func) in zip(weights, score, functions) ])
这是一个实现了OHEM(Online Hard Example Mining)的交叉熵损失函数,用于解决深度学习中存在难样本或噪声样本导致训练效果不好的问题。其中,thresh表示像素的softmax预测概率阈值,小于该阈值的像素被认为是难样本;min_kept表示每张图中保留的难样本最小数量;ignore_label表示忽略的标签;weight表示权重。_ce_forward函数实现了普通的交叉熵损失的计算;_ohem_forward函数实现了OHEM的计算。在forward函数中,对于有多个输出的模型,采用了权重平衡的方式进行计算。
matlab pipeline
Matlab pipeline refers to the process of creating a workflow or series of steps in Matlab to achieve a particular task or goal. This can involve loading and manipulating data, performing calculations or analysis, and generating visualizations or other outputs.
Here are some general steps that might be involved in a Matlab pipeline:
1. Define the problem or task to be solved.
2. Gather and prepare data as necessary.
3. Load data into Matlab.
4. Preprocess data as needed, such as cleaning or filtering.
5. Perform calculations or analysis using Matlab functions or custom code.
6. Visualize results using built-in Matlab tools or custom scripts.
7. Export or save results as needed.
The specific steps and tools used in a Matlab pipeline will depend on the particular task or problem being addressed. However, by breaking down the process into smaller steps and organizing them into a pipeline, it becomes easier to manage and troubleshoot the workflow.