I am trying to install a github repo named multiperson for PyTorch 1.6 and I get the following error. How can I make it work for PyTorch 1.6? I have: Answer You would need to change all AT_CHECK in neural mesh renderer to TORCH_CHECK
Tag: pytorch
ML model not loading full batch
I tried to build a machine learning model using CIFAR 10 dataset, but I am encountering a bug that my model stops training past i = 78 (looped 78 times, see code for more). Sorry, I had to post the entire code because I cannot spot the mistake I made. Moreover, since I could not make it work, I tried
Python Error RuntimeError: expected scalar type Long but found Double
Firstly, I am fairly new to python/ML in general. I am attempting to utilize the model depicted at stackabuse over my own data set. Everything flows smoothly until I get ready to run the epochs. In debugging I see that it is failing on CrossEntropyLoss function and I get the error expected long found double. The data set it appears
How to save trained images without a burden on network
I am training a convolutional neural network in pytorch and want to save trained images. I append each trained image in a data loader loop to save all the trained images into numpy file (train_pred in …
Reshape 5-dimensional tiled image to 3-dimensional normal image
I’m creating a program that takes use of an RGB image that is tiled of the shape (n, n, 3, l, l). n is the number of each tile on each side of the image, and l is the length of each tile in pixels. I am trying to reshape this into a (3, l * n, l * n)
Initiaized class member in pytorch module
When declaring a model in pytorch, having a model class member variable declared and initialized mysteriously prevents it from being populated in the constructor. Is this expected? If so, why? Testing code below, with example models with a component member variable. The initialization value of the component (e.g. None, a number or a Tensor) does not change the behaviour. Answer
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 – RuntimeError: [enforce fail at inline_container.cc:209] . file not found: archive/data.pkl
Problem I’m trying to load a file using PyTorch, but the error states archive/data.pkl does not exist. Code Output Hypothesis I’m guessing this has something to do with pickle, from the docs: This save/load process uses the most intuitive syntax and involves the least amount of code. Saving a model in this way will save the entire module using Python’s
Specifying number of cells in LSTM layer in PyTorch
I don’t fully understand the LSTM layer in PyTorch. When I instantiate an LSTM layer how can I specify the number of LSTM cells inside the layer? My first thought was that it was the “num_layers” argument, if we assume that LSTM cells are connected vertically. But if that is the case how can we implement stacked LSTM with for
can anyone explain what “out = self(images)” do in below code
I am not able to understand, if prediction is calculated in forward method, then why there is need “out = self(images)” and what it will do. I am bit confuse about this code. model = MnistModel() Answer In Python, self refers to the instance that you have created from a class (similar to this in Java and C++). An instance