Returns a tensor where each row contains num_samples indices sampled from the multinomial (a stricter definition would be multivariate, refer to torch. float64) k = 0 dim = 0 largest = True sorted = True input = input_tensor. Draws binary random numbers (0 or 1) from a Bernoulli distribution. However, when using an empty tensor (not from topk. So, flatten the tensor and use the torch. g. topk用法 沿给定dim维度返回输入张量input中 k 个最大值。 如果不指定dim，则默认为input的最后一维。 . Nov 11, 2018 · Hi everyone, I have an issue with getting the right indices for predicted classes of the probabilities returned by topk function of pytorch. topk(scores, 6000, dim=1, sorted=True) scores = torch. tensor. 2708]]], grad Jul 30, 2021 · torch. topk(input, k, dim=None, largest=True, sorted=True) function to calculate k largest elements of the given input tensor along a given dimension dim. 1 cc @albanD import torch # 创建一个包含随机值的一维张量 x = torch. In topk I am selecting top probabilities along channel (batch_size, channel, 1 Nov 16, 2017 · Having seen a paper talking about mining top 70% gradient for Backpropagation, I am wondering if this strategy can real help improve performance. Then the loss function can be imported into an existing codebase through from topk. sparse. Attached below is my custom Cross_Entropy implementation for calculating top k percentage gradient for binary classification. shape) *I understood the unpacking of top_class. I have a tensor of shape (16, 512, 4096) and I am using torch. 5407 0. 5790 0. Tensor. 1662 0. Creates a strided copy of self if self is not a strided tensor, otherwise returns self. Logits of other tokens remain − ∞ Mar 9, 2019 · You signed in with another tab or window. topk can be used to find either the largest (k largest) or smallest elements by using the largest argument (default: True) when calling the function. Penguin Penguin. eq(1) Jun 25, 2023 · I am trying to implement k best selection, that consist of two parts: 1) aggregation / attention, 2) topk selection. topk(k) I’m wondering what’s the time complexity of that operation. 0432, 0. I’ve been trying to implement a forward method that takes a tensor say V of n values in [0,1], picks the top k indices referring to the top-k elements in the tensor, then outputs a tensor of the same dimension as V with the top_k values set to 1, and the rest set to 0. connect import FilterEdges from torch_geometric. a = torch. 4562, 0. But I want to know more elegant code to realize this. scatter_(0, top_k. sort(idx, dim=-1) In [5]: val = torch. float64) device = 'cuda' m = lambda inp: inp. 사소한 발견인데, Tensor. import torch import torch. topk(tensor. Share Nov 9, 2018 · Learn how to retrieve the indices of maximum values in a Torch tensor using torch. Returns a sparse copy of the tensor. Since this is not differentiable, I wanted to ask if there’s a differentiable workaround to achieve the same thing? Thanks import torch top = 2 inp = torch. topk when a tensor with repeated values is passed respectively on CPU and CUDA device. data, target, topk=(1,5)) def accuracy(output, target, topk=(1,)): maxk = max(topk) batch_size = target. Multinomial for more details) probability distribution located in the corresponding row of tensor input. to_sparse_csc. normalize(y For more information, see mindspore. The answer explains that torch. topk in the following manner- Jun 22, 2024 · torch. topkという専用の関数を使えば楽勝で、 Jul 5, 2024 · Saved searches Use saved searches to filter your results more quickly Mar 30, 2022 · I’m using torch. topk to set the values of x that aren’t in the topk to zero either in-place, or as a new object? Oct 28, 2017 · topk, indices = torch. shape is based on this because the former has a shape of (64,64) based on the explanation of equals will have shape (64, 64), try it 函数作用：该函数的作用即按字面意思理解，topk:取数组的前k个元素进行排序。通常该函数返回2个值，第一个值为排序的数组，第二个值为该数组中获取到的元素在原数组中的位置标号。 Mar 27, 2020 · The stack trace points to an invalid index operation, so make sure to keep the bounds of idx:. I modified num_classes=3 and dn_labelbook_size = 3 (I think it is necessary), then start t PyTorchだと簡単だけどNumPyだと一筋縄ではないかないTopK. It returns the top k or large Jul 12, 2019 · I am trying to run a topk of a 2d matrix x = torch. kthvalue() to find the k-th element of a tensor. topk saves the indices of the top k values and passes the gradient through them in backward. topk provides an efficient way to extract top k values in a tensor along one dimension. topk() When we use torch. See examples, explanations and alternative solutions from the Stack Overflow community. largest (bool, optional) controls whether to return largest or smallest elements Jun 13, 2017 · I feel like the solution should be super simple but I can’t quite figure it out? Given a float tensor, and indices from torch. 0557, 0. version): 1. 1 버전에서 직접 돌려봤는데 여전히 비슷한 수준의 속도차가 있네요 Feb 12, 2020 · import torch. topk for this: k = 2 output = torch. detach(). svm import SmoothTop1SVM, SmoothTopkSVM. 9903 0. sort() 후 top-k index 를 slicing 하는 게 더 빠르네요. (default: None) Feb 21, 2022 · Learn how to use torch. TopK. 0, dtype=torch. Then I want to use those indices to index the tensor to assign a value, however I am not able to define the code to perform the correct advanced indexing, The only thing I was able to do is: _, H, W = x. Is it possible to restrict the top k value to be non-repetitive? For example, input = torch. topk(0) print(m(input. clone(). block: [197,0,0], thread: [32,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed Jul 18, 2022 · You can use the torch. PyTorch: Support to obtain the maximum or minimum value of the first k entries of a specified dimension. topk() function: This function helps us to find the top ‘k’ elements of a given tensor. FloatTensor of size 3x5 Oct 23, 2022 · a2mps = torch. 5849 0. topk and torch. nn. 5. size(0) c = pred. rand(128, 512, 40) # 128 and 512 are Sep 26, 2022 · I am currently using top-k to select elements from the similarity matrix So for each x, we can find k elements from y. randn(8, 1, 64, 64) # [batch_size, 1, H, W] batch Jul 15, 2020 · The indices (empty tensor) returned by topk for k=0, leads to RuntimeError: CUDA error: device-side assert triggered when being used for selecting indexes within another tensor. tensor([2,3,1]) for idx, k in enumerate(K): top_k = torch. randn(n) b = a. topk¶ torch. tensor(5. distributions. This is a common use-case, I bet there is some nice solution to that: Assume the following: import torch # I have following quantities batch_size = 6 L = 100 K = 10 d = 300 # I have a matrix of vectors # batch X L x d R = torch. zeros_like(output) topk[idx] = vals >>> topk tensor([1. import torch n, k = 100, 5 a = torch. 4737]], [[ 7. size(0 Feb 21, 2022 · torch. topk with sorted=True doesn't return a result that is consistent across different values of k when dealing with duplicates values. tensor([-1, -1, -1, -1]) print(tor dtype (torch. 2,158 5 5 gold Apr 13, 2020 · I am trying to implement a customized loss function in pytorch based on the formula below. to_dense. to_sparse. Tensor. The position of duplicated values in the returned sorted indices varies with k. 6561, 0. The implementation of the loss functions is self-contained and available through the package topk. tens Sep 1, 2018 · Pytorch provide torch. topk(x, k=3, dim=-1, largest=False) # k smallest elements In [4]: sorted_idx, new_idx = torch. outputの1軸方向で最も大きな確率のインデックスがモデルの予測になりますが、これとtargetとの一致する割合が(ミニバッチに対する)top-1 accuracyです。 In [1]: import torch In [2]: x = torch. gather(val, dim=-1, index=new_idx) In [6]: x Out[6]: 0. 0000, 0. Unfortunately, after getting my output from model. Jul 4, 2022 · What I want to do is: given a tensor with shape like [32,100] as input, I want to get 2 tensor with shape [32,50] and [32,50] as output, where the first [32,50] contains the top-50-largest elements in each rows and the second [32,50] contains the top-50-smallest. 3171, -0. 2362 0. Jul 24, 2023 · I have to real-valued matrix matrices A and B with shape (m,n) and (n,p) respectively and a pre-defined constant K. , a simple sorting and [:k] implementation would result in O(nlogn), while a quick-sort-styled partition would be O(n+k), also, when a heap is used the complexity would be O(n+klogn). I’d like to get a sparse tensor as an output. Differences . The existing answers are correct, but I wanted to expand on them to provide a self-contained function that behaves exactly like torch. argmax along the C dimension to automatically convert probabilities/logits into an int tensor. Feb 21, 2023 · From what I could read from the PyTorch source code, when k is small enough (k * 64 <= n), the C++ implementation would run std::partial_sort whether or not sorted is required, so the result will always be sorted. See syntax, parameters, and examples of these functions. topk(1, dim = 1) new variable top_p should give you the probability of the top k classes. sort的用法类似，topk是在sort的基础上取前k个值。topk默认是降序（从大到小）；而sort是默认是升序（从小到大）。具体用法如下： 1、torch. 4105, dtype=torch. 3606, -1. topk(-x, 3) But wondering if this mink operation already exist. TopKというのは、上位 個の要素を求めてねという処理です。PyTorchだとこれはtorch. Tutorials. forward(), applying the softmax function and applying the topk Mar 11, 2021 · I have two 3D tensor and I want to use one's top k indices get another top k. gather(scores, dim=1, index=idx) # Output is of size [B, 6000] My issues comes when I am trying to use the Stable: These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation. topk() in two subfunctions of my program . tensor([[[1], [2], [3]], [[4], [5], [6 Finds values and indices of the k largest entries for the last dimension. topk() function to get the top k sigmoid pixel values and erase them from the corresponding image. Here's the function (I've included the instructions inline): Mar 6, 2020 · I am currently using torch. topk() function. topk(mask, top, dim=0 Mar 6, 2020 · Indexing tensors of varying dimensions with topk. topk (input, k, dim = None, largest = True, sorted = True, *, out = None) ¶ Returns the k largest elements of the given input tensor along a given dimension. tensor([3, 2, 3, 2, 1, 1]) b = torch. topk when the input contains +nan and -nan, the result is not sure; sometimes -nan is treated greater than normal number, sometimes -nan is the least; Versions torch 1. size(1) out_siz&hellip; 🐛 Describe the bug I cannot get the same result from torch. Jul 16, 2021 · " i have 2 classes " prec1, prec5 = accuracy(output. E. 8974 0. topk with pure numpy. topk works in a differentiable way and gets an answer with code snippets. py for the arguments of the loss Oct 21, 2020 · nabeyangさんによる記事. For example for the following tensor. Run PyTorch locally or get started quickly with one of the supported cloud platforms. rand(5, 5, requires_grad=True) top_mask_indices = torch. topk to determine the indices of the of a 2D tensor scores which is of size [Batch, N]. topk() and use some other algorithms with better smoother gradient behaviour that outputs some inclusion probabilities, such as the work by this paper, “Differentiable Top-k Operator with Optimal Transport”, then how can I translate the inclusion probabilities Apr 15, 2021 · You can use torch. 7): pred = F. If preds is a floating point we apply torch. size()[0]/2) I believe ‘topk’ is differentiable and ‘indices’ is not (in the autograd graph sense). Any information (also just a description in words or pseudoc&hellip; Nov 1, 2022 · 🐛 Describe the bug I am using topk function but at some points, I realized that the function is giving strange results on some given inputs import torch x = torch. To reproduce import torch a = torch. FloatTensor constructor) ? x = torch. k is a string (in [0,1]) given to the forward method. e, not to count zero elements in the counting process). scatter_ methods for this: K = torch. 4892, 0. 7928 0. def TopKLoss(pred, target, top_k=0. Jan 29, 2021 · I cannot seem to produce a nice implementation of indexing with a different list of indices with different elements in each dimension. Somebody call this Online Hard Example Mining (OHEM). A platform that allows users to write and express their views freely on Zhihu. topk(x,k=2,dim=0) print May 29, 2020 · 1. 방금 1. I can realize this without batch of tensor and batch also can be implemented with a for loop. topk(), it returns the k largest elements of the given input tensor along a given dimension. target (int tensor): (N,) Parameters: preds¶ (Tensor) – Tensor with predictions. 9825, , -0. Digging in autograd code I found the following line related to class Topk(_MultiSelectionFunction): Oct 31, 2022 · I want to apply the function torch. num_classes¶ (int) – Integer specifying the number of classes Jun 1, 2020 · Supports that x is a tensor with shape = [batch_size, channel_number, height, width], some may be positive value and others may be 0. functional as nnf # prob = nnf. gather (or simply from the torch. torch. matmul(F. 2719, -0. ]) Feb 15, 2024 · Hello, I’m kind-off new into pytorch. softmax(output, dim=1) top_p, top_class = prob. Is there an simpler way than this (e. You switched accounts on another tab or window. 8269, -1. a selection mechanism 2). 14. rand(2, 3, 2, 2) In [40]: x Out[40]: tensor([[[[0. But the problem is that the number of pixels of positive value in each channel is different, so I wonder how to solve it efficiently? May 29, 2021 · Great! So you need the first k largest elements of a tensor. The input data is a sequence but not all items in the sequence are useful. Feb 9, 2021 · Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand torch. Pytorch 如何高效地检索Torch张量中最大值的索引 在本文中，我们将介绍如何使用PyTorch高效地检索Torch张量中最大值的索引。我们将探讨两种常用的方法：使用argmax函数和使用topk函数。 此专栏文章详细讨论了RAG基本原理及其在llamaindex中的实现。 Nov 21, 2019 · I have a tensor like this: tensor([[[ 7. randn((10,128,512)) # (b, y_c, d) sim_matrix = torch. tensor([[1, 2,9,87],[6, 32,8,1],[4,6,7,2],[3,6,2,6]]) print("x",x) values, indices = torch. squeeze()), resulting in both the values and indices tensors having 1 fewer dimension than the input tensor. The goal is to calculate the new “topk multiplication” function, where entry (i,j) in the output is calculated as follows: Perform element-wise multiplication between the i-th row of A and the j-th column of B Summing only top K largest values from the result in step 1). 4919, 0. k (int) the k in "top-k" dim (int, optional) the dimension to sort along. topk() 보다 Tensor. But I failed to calculate the gradients since using top K indices block gradients flow to the selection model. Clearly, if the first You signed in with another tab or window. For example, torch. pool. 0. It is like a keepdim version of the torch Oct 31, 2019 · Yes, top-k operation is differentiable, but I was asking if I don’t use torch. topk(k=3) >>> x. rand(10) print("原始张量：", x) 阅读更多： Pytorch 教程 使用topk函数提取前k个值的索引 Otherwise, dim is squeezed (see torch. Follow asked Nov 10, 2023 at 15:13. largest (bool, optional) controls whether to return largest or smallest elements Oct 12, 2019 · Hi there, My framework contains two parts: 1). Convert a tensor to compressed column storage (CSC) format. 3478, -1. Learn the Basics Arguments self (Tensor) the input tensor. topk(score, score. idx = torch. a predictor. 2913, 0. topk. Is this an expected behavior ? Note that the behavior with device="cpu" is correct. See topk/svm. topk (logits, self. shape inds = torch. So I just use TopK items in the sequence to predict something. I’m trying to find the top 3 maximize results from each one-hot element-wise vector of the map, and let other unselected elements be 0 (which means we want the dimension not to change). topk of the top k values, in this case 2, how could I use the long tensor indices of torch. topk(x, k=2, dim=0)[1] to retrieve the indices of the first two max values over the 0th dimension. it will return top ‘k’ elements of the tensor and it will also return indexes of top ‘k’ elements in the original tensor. topk() methods to get the k-th or top k elements of a tensor along a given dimension. ops. Apr 12, 2021 · The following code finds the top-k elements of a tensor. typing import OptTensor Mar 5, 2019 · For a 4d tensor of shape (N, C, H, W), I’m trying to select top k channels from dim=1 based on the sum of each channel, and zero out the non-selected channels for each n in N. 1441], [0. rand(3, 5) In [3]: val, idx = torch. Pytorch version 1. I have tested it when top_k = 100% and the result is exactly like attn (torch. 4628 0. 4644]], [[0. multinomial. topk()，代码先锋网，一个为软件开发程序员提供代码片段和技术文章聚合的网站。 torch. max()、torch. Dec 31, 2019 · Pls scroll towards 1/3 down from this page for equals = top_class == labels should it be equals = top_class == labels. normalize(x, p=2, dim=-1), F. topk(input_tensor, k, dim=None, largest=True, sorted=True, out=None) Parameters: input_tensor: tensor. indices) for selecting indexes to the another tensor works fine. scatter(dim=0, index=i, value=0) tensor([1. rand(5,10) topk_list = [2,3,1,2,0] # means top2 for 1s Jan 17, 2024 · 🐛 Describe the bug torch. torch. view(-1,64) a small test that I did rather than equals = top_class == labels. topk() twice to get the desired two tensors, but I assume it isn’t efficient enough (since topk is Mar 5, 2020 · I have a (n, n) mask and corresponding (3, n, n) image. The input data is a tensor of size (batch, size, channel, img_features). to(“mps”), k=20) # not working at all RuntimeError: Currently topk on mps works only for k<=16 a1mps = torch. py install. 0000, 1. dtype, optional) – the desired data type of returned tensor. 1360 0. topk() method is used to find the top k elements. topk and I would like to find information about how top k selection is implemented in a differentiable way (with respect to the top k values). indices, 1) mask = x. rand(5, 5, requires_grad=True) mask = torch. randn((10,64,512)) # (b, x_c, d) y = torch. However, I’m struggling quite a bit trying to understand how topk() deals with dimensions… To properly compute this, do I need to unsqueeze the matrix? Thanks! Oct 8, 2019 · 🐛 Bug torch. 7226], [0. view(*top_class. Convert a tensor to compressed row storage format (CSR). select import SelectTopK from torch_geometric. 2190 0. See torch. If largest is False then the k smallest elements are returned. How could I use the indices to get something like the below without loops. 6140, , -0. Note. 8058, -2. to(device))) jit_m Mar 25, 2018 · I used the torch. randn(batch_size, L, d) # I have Jun 4, 2018 · Suppose I have a 3d Tensor x, and I run itorch. [Answer 1] You need the first k largest of all the elements irrespective of the dimension. log_softmax(pred) n = pred. So far, I know I can use torch. The first subfunction can do well , while the other will happen to this bug . tensor(0. functional as F x = torch. Aggregation just outputs the softmax probabilities along channel dim of input tensor, so it has size (batch_size, channel, 1). topk() Tensor. Jun 13, 2022 · torch. It returns the value of the k-th element of tensor sorted in ascending order, and the index of the element in the original tensor. 1212 0. . Mar 3, 2020 · I’d like to keep keep in the tensor only K largest elements in each row (corresponding to logits/logprobs of K highest scoring classes) to optimize disk space during serialization. Here Get Started. bernoulli. to_sparse_csr. topk, but only on the non-zero elements of the tensor (i. tensor([3, 2, 3, 2 Apr 25, 2021 · I’m not sure someone asked it before… Says we have a one-hot segmentation mask with BxNxHxW, where B=batch, N=class-categories, H=height, W=width of the mask. scatter: >>> _, i = x. Nov 25, 2022 · The torch. Hi, really appreciate your brilliant work. The package can be installed through a standard python setup. multinomial. directly passing indices from topk to torch. Tensor, optional) – Optional node-level matrix to use for computing attention scores instead of using the node feature matrix x. If specified, the input tensor is casted to dtype before the operation is performed. 42 values, indices = torch. This is useful for preventing data type overflows. I can easily do this with a nested for-loop: In [39]: x = torch. Reload to refresh your session. You signed out in another tab or window. outputから予測を計算する. In the below example we have taken a tensor X and are finding out the May 20, 2022 · 🐛 Describe the bug topk has different results for input with and without grad in cuda import torch input_tensor = torch. target¶ (Tensor) – Tensor with true labels. Syntax: torch. I can get the topk values (6000) from scores with torch. topk(tensor, k=2, dim=0, largest=False) would return the 2 smallest elements along each row. 4894, 0. I’m trying to use the torch. (second part of the code) A toy code as following: seq Aug 18, 2020 · I'm using the following code to find the topk matches using pytorch: def find_top(self, x, y, n_neighbors, unit_vectors=False, cuda=False): if not unit_vectors: x = __to_unit_torch__(x, Arguments self (Tensor) the input tensor. 0557]], [[0. My implementation is as Aug 11, 2022 · I want to do the feature selection based on the score map with two dimensional index. topk function returns the indices of the top-k elements on the provided dimension. 1868 0. Nov 6, 2021 · How to find the k th and the top k elements of a tensor in PyTorch - PyTorch provides a method torch. , 2. And I only want to keep the value of top 10% positive values of each channel and make others 0. If dim is not given, the last dimension of the input is chosen. 13 & torch 2. We also expect to maintain backwards compatibility (although breaking changes can happen and notice will be given one release ahead of time). 0000]) Note that while the 'values' of topk() are differentiable, the 'indices' are not (similar to how argmax is not a differentiable function). Currently I do this: torch. topk(input, k, dim=None, largest=True, sorted=True, *, out=None) -> (Tensor, LongTensor) Returns the k largest elements of the given input tensor along a given dimension. My output classes are just 2 (cancerous and noncancerous) and the directories are labeled as (0 for cancerous and 1 for noncancerous). 5852], [0. I am trying to use DINO to train my own dataset with 2 classes. You can perform the reassignment operation using torch. The following my code. topk(input=a. topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTen Oct 11, 2022 · I would like to mask an input based on the top k masking values, naively doing something as in the following code. topk function to get indices of top-3 (for example) elements: from typing import Callable, Optional, Tuple, Union import torch from torch import Tensor from torch_geometric. Nov 1, 2022 · 🚀 The feature, motivation and pitch Referring to issue #88184 It seems that the sorting algorithm used to implement topk is unstable which makes it give unjustifiable results on duplicate elements of input tensor. 4272 0. We would like to show you a description here but the site won’t allow us. view(-1), k) But this also considers the zero elements in variable tensor and returns the top largest among them. I am currently using torch. Whats new in PyTorch tutorials. Nov 10, 2023 · torch. topk(x, k=self. topk(k) topk = torch. dev20221022 torch. topk与torch. 6900 [torch. kthvalue() and torch. k, dim =-1) Set the values of the top-k selected indices to actual logits. 知乎专栏提供一个平台，让用户随心所欲地分享和表达自己的想法。 Mar 10, 2020 · How to get the top-k elements of each row in a 2D tensor in an elegant way instead of using for-loop as below? import torch elements = torch. Mar 30, 2022 · A user asks how torch. 8833, 0. 8680, 0. topk directly). topk(x[idx], k) x[idx]. feature_map = torch. randn(8, 256, 64, 64) # [batch_size, C, H, W] score_map = torch. pytorch; Share. randn(5) vals, idx = output. , 0. k, dim=1)[1 1 day ago · DeepSeek-V2是一个强大的开源混合专家（MoE）语言模型，通过创新的Transformer架构实现了经济高效的训练和推理。该模型总共拥有2360亿参数，其中每个令牌激活21亿参数，支持最大128K令牌的上下文长度。 在开源模型中，DeepSeek-V2 Jun 21, 2023 · Saved searches Use saved searches to filter your results more quickly 🐛 Describe the bug topk will return wrong value and could read out-of-bound value after jit import torch input = torch. to(“mps”), k=15) # working fine The corresponding issue is MPS: Add support for TopK (k>16) on M1 GPU · Issue #78915 · pytorch/pytorch · GitHub When is the fix expected? print (torch. ra vv vl hj rx pb uc pf yf fh