pytorch

Filter out np.nan values from pytorch 1d tensor

由 ﹥＞﹥吖頭↗ 提交于 2021-02-10 05:15:13
问题 I have a 1d tensor looking kinda like this: import numpy as np import torch my_list = [0, 1, 2, np.nan, np.nan, 4] tensor = torch.Tensor(my_list) How do i filter out the nan-values, so it becomes a tensor of size 4? 回答1: You can use torch.isnan my_list = [0, 1, 2, np.nan, np.nan, 4] tensor = torch.Tensor(my_list) tensor[~torch.isnan(tensor)] tensor([0., 1., 2., 4.]) 来源： https://stackoverflow.com/questions/61503138/filter-out-np-nan-values-from-pytorch-1d-tensor

Filter out np.nan values from pytorch 1d tensor

由 北城以北 提交于 2021-02-10 05:14:45
问题 I have a 1d tensor looking kinda like this: import numpy as np import torch my_list = [0, 1, 2, np.nan, np.nan, 4] tensor = torch.Tensor(my_list) How do i filter out the nan-values, so it becomes a tensor of size 4? 回答1: You can use torch.isnan my_list = [0, 1, 2, np.nan, np.nan, 4] tensor = torch.Tensor(my_list) tensor[~torch.isnan(tensor)] tensor([0., 1., 2., 4.]) 来源： https://stackoverflow.com/questions/61503138/filter-out-np-nan-values-from-pytorch-1d-tensor

calculate Entropy for each class of the test set to measure uncertainty on pytorch

由 无人久伴 提交于 2021-02-10 05:11:13
问题 I am trying to calculate Entropy of each class of the dataset for an image classification task to measure uncertainty on pytorch,using the MC Dropout method and the solution proposed in this link Measuring uncertainty using MC Dropout on pytorch First,I have calculated the mean of each class per batch across different forward passes (class_mean_batch) and then for all the testloader (classes_mean) and then did some transformations to get (total_mean) to use it for calculating Entropy as shown

Add a index selected numpy array to another numpy array with overlapping indices

由 寵の児 提交于 2021-02-10 03:44:05
问题 I have two numpy arrays image and warped_image and indices arrays ix,iy . I need to add image to warped_image such that image[i,j] is added to warped_image[iy[i,j],ix[i,j]] . The below code works if the pairs (iy[i,j], ix[i,j]) are unique for all i,j . But when they are not unique i.e. when 2 elements from image need to be added to the same element in warped_image , only one of them gets added. How can I add both elements from image to the same element in warped_image ? Note that, I don't

Add a index selected numpy array to another numpy array with overlapping indices

由 耗尽温柔 提交于 2021-02-10 03:42:43
问题 I have two numpy arrays image and warped_image and indices arrays ix,iy . I need to add image to warped_image such that image[i,j] is added to warped_image[iy[i,j],ix[i,j]] . The below code works if the pairs (iy[i,j], ix[i,j]) are unique for all i,j . But when they are not unique i.e. when 2 elements from image need to be added to the same element in warped_image , only one of them gets added. How can I add both elements from image to the same element in warped_image ? Note that, I don't

Upsampling in Semantic Segmentation

由 生来就可爱ヽ(ⅴ＜●) 提交于 2021-02-09 11:14:29
问题 I am trying to implement a paper on Semantic Segmentation and I am confused about how to Upsample the prediction map produced by my segmentation network to match the input image size. For example, I am using a variant of Resnet101 as the segmentation network (as used by the paper). With this network structure, an input of size 321x321 (again used in the paper) produces a final prediction map of size 41x41xC (C is the number of classes). Because I have to make pixel-level predictions, I need

Upsampling in Semantic Segmentation

由 懵懂的女人 提交于 2021-02-09 11:13:47
问题 I am trying to implement a paper on Semantic Segmentation and I am confused about how to Upsample the prediction map produced by my segmentation network to match the input image size. For example, I am using a variant of Resnet101 as the segmentation network (as used by the paper). With this network structure, an input of size 321x321 (again used in the paper) produces a final prediction map of size 41x41xC (C is the number of classes). Because I have to make pixel-level predictions, I need

How to use torchvision.transforms for data augmentation of segmentation task in Pytorch?

由 北战南征 提交于 2021-02-09 00:50:49
问题 I am a little bit confused about the data augmentation performed in PyTorch. Because we are dealing with segmentation tasks, we need data and mask for the same data augmentation, but some of them are random, such as random rotation. Keras provides a random seed guarantee that data and mask do the same operation, as shown in the following code: data_gen_args = dict(featurewise_center=True, featurewise_std_normalization=True, rotation_range=25, horizontal_flip=True, vertical_flip=True) image

[NLP-CNN] Convolutional Neural Networks for Sentence Classification -2014-EMNLP

由 蹲街弑〆低调 提交于 2021-02-08 14:52:08
1. Overview 本文将CNN用于句子分类任务 (1) 使用静态vector + CNN即可取得很好的效果；=> 这表明预训练的vector是universal的特征提取器，可以被用于多种分类任务中。 (2) 根据特定任务进行fine-tuning 的vector + CNN 取得了更好的效果。 (3) 改进模型架构，使得可以使用 task-specific 和 static 的vector。 (4) 在7项任务中的4项取得了SOTA的效果。 思考：卷积神经网络的核心思想是捕获局部特征。在图像领域，由于图像本身具有局部相关性，因此，CNN是一个较为适用的特征提取器。在NLP中，可以将一段文本n-gram看做一个有相近特征的片段——窗口，因而希望通过CNN来捕获这个滑动窗口内的局部特征。卷积神经网络的优势在于可以对这样的n-gram特征进行组合和筛选，获取不同的抽象层次的语义信息。 2. Model 对于该模型，主要注意三点： 1. 如何应用的CNN，即在文本中如何使用CNN 2. 如何将static和fine-tuned vector结合在一个架构中 3. 正则化的策略 本文的思路是比较简单的。 2.1 CNN的应用 <1> feature map 的获取 word vector 是k维，sentence length = n (padded)

No module named 'torch' or 'torch.C'

由 喜夏-厌秋 提交于 2021-02-08 12:41:11
问题 Would appreciate an explanation like I'm 5 simply because I have checked all relevant answers and none have helped. I have installed Python. I have installed Pycharm. I have installed Anaconda. I have installed Microsoft Visual Studio. I have not installed the CUDA toolkit. In Anaconda, I used the commands mentioned on Pytorch.org (06/05/18) conda install pytorch -c pytorch pip3 install torchvision Both have downloaded and installed properly, and I can find them in my Users/Anaconda3/pkgs
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