scatter(dim, index, src, reduce=None) 先忽略 Reduce,最后再解释。先从最简单的开始。我们有一个 (2,4) 形状的张量,里面填充了 1: 粉红色的符号表示张量结构 并且我们传入相应的参数并得到输出: 注意index张量结构 现在我们增加index张量的第二个值,并比较输出: 观察数字 6 在output张量中的移动情况 好的,...
make_fallback(aten.cholesky_inverse)make_fallback(aten.cholesky_solve)make_fallback(aten._fft_r2c)make_fallback(aten.histogram.bin_ct)make_fallback(aten._histogramdd_bin_edges.default)make_fallback(aten._histogramdd_from_bin_cts.default)make_fallback(aten.index_reduce) 也就是说,要知道Lower...
Tensor.scatter_(dim, index, src, reduce=None) → Tensor 输入: dim: 进行scatter的维度 index:依据的index source:源数据 reduce:如何进行填充,默认值为None,亦即直接覆盖 功能:将source的数据填到self中,如何填的,往下看。 官方文档里给了个例子,比较抽象。 这里以二维矩阵, dim = 0 为例 以src[1][1...
torch.index_reduce: lambda input, dim, index, source, reduce, include_input=True: -1, torch.isfinite: lambda tensor: -1, torch.isin: lambda e, te, assume_unique=False, invert=False: -1, torch.isinf: lambda tensor: -1, torch.isreal: lambda tensor: -1, torch.isposinf: lambda ...
y = self.forward(x, T, self.QW_biases[index], train=train_phase)#scale w^hatself.target_modules[index].data = y.mul(alpha[index].data) 开发者ID:aliyun,项目名称:alibabacloud-quantization-networks,代码行数:17,代码来源:anybit.py
class torch.nn.NLLLoss(weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean') 1. 计算公式:loss(input, class) = -input[class] 公式理解:input = [-0.1187, 0.2110, 0.7463],target = [1],那么 loss = -0.2110。
2. ignore_index: 其中BCE 系列没有此参数,此参数用于指定忽略某些类别的 loss; 3. size_average: 该参数指定 loss 是否在一个 batch 内平均,即是否除以 N,目前此参数已经被弃用; 4. reduce: 目前此参数已经被弃用; 5. reduction: 此参数在新版本中是为了取代 ”size_average“ 和 "reduce" 参数的; ...
input (Tensor) – 输入张量 dim (int) – the dimension to reduce out (Tensor, 可选的) – 结果张量例子:>>> a = torch.randn(4, 4) >>> a -1.2738 -0.3058 0.1230 -1.9615 0.8771 -0.5430 -0.9233 0.9879 1.4107 0.0317 -0.6823 0.2255 -1.3854 0.4953 -0.2160 0.2435 [torch.FloatTensor of size...
arch_reduce_parameters) s0 = s1 = self.stem(inputs) for i, cell in enumerate(self.cells): if cell.reduction: hardwts, index = reduce_hardwts, reduce_index else : hardwts, index = normal_hardwts, normal_index s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index) out = self...
ReduceOp.SUM) ious = hist.diag() / (hist.sum(dim=0) + hist.sum(dim=1) - hist.diag()) miou = ious.mean() return miou.item() Example #14Source File: utils.py From ray with Apache License 2.0 5 votes def update(self, a, b): n = self.num_classes if self.mat is None: ...