My Task: I’m trying to calculate the pair-wise distance between every two samples in two big tensors (for k-Nearest-Neighbours), That is – given tensor test with shape (b1,c,h,w) and tensor train with shape (b2,c,h,w), I need || test[i]-train[j] || for every i,j. (where both test[i] and train[j] have shape (c,h,w), as those are sampes in the batch). The Problem both
Tag: torch
TorchServe: How to convert bytes output to tensors
I have a model that is served using TorchServe. I’m communicating with the TorchServe server using gRPC. The final postprocess method of the custom handler defined returns a list which is converted into bytes for transfer over the network. The post process method The main issue is at the client where converting the received bytes from TorchServe to a torch
Receiving Infinity Infinity in LineString
I am try get linestring so I can measure the distance and time. Here in this linestring I am getting nan distance and time. Also, pleased to hear any of your suggestion on my code or logic. Thanks data: Code: Output: Answer Probably, previous pyproj EPSG was switching the opposite interpretation. So I change the EPSG code with below:
Numpy: Optimal way to count indexs occurrence in an array
I have an array indexs. It’s very long (>10k), and each int value is rather small (<100). e.g. Now I want to count occurrence of each index value (e.g. 0 for 3 times, 1 for 2 times…), and get counts as np.array([3, 2, 1, 1, 1]). I have tested 4 methods as follows: UPDATE: _test4 is @Ch3steR’s sol: Run for
How to know torch version that installed locally in your device
I want to check torch version in my device using Jupyter Notebook. I’m used this but it didn’t work and Jupyter notebook raised an error as below Is there any command to check torch version using Jupyter notebook? Answer I have tried to install new Pytorch version. But, it didn’t work and then I deleted the Pytorch files manually suggested
How to convert a PyTorch sparse_coo_tensor into a PyTorch dense tensor?
I create a sparse_coo tensor in PyTorch: Now I want to convert a PyTorch sparse tensor into a PyTorch dense tensor. Which function can be used? Answer you can use to_dense as suggested in this example : And by the way, the documentation is here
PyTorch: Dataloader for time series task
I have a Pandas dataframe with n rows and k columns loaded into memory. I would like to get batches for a forecasting task where the first training example of a batch should have shape (q, k) with q referring to the number of rows from the original dataframe (e.g. 0:128). The next example should be (128:256, k) and so
What’s the reason of the error ValueError: Expected more than 1 value per channel?
reference fast.ai github repository of fast.ai (as the code elevates the library which is built on top of PyTorch) Please scroll the discussion a bit I am running the following code, and get an error while trying to pass the data to the predict_array function The code is failing when i am trying to use it to predict directly on