If I have a model: And then I’m defining my inputs, optimizer (with lr=0.1), scheduler (with base_lr=1e-3), and training: The optimizer seems to take the learning rate of the scheduler Does the learning rate scheduler overwrite the optimizer? How does it connect to it? Trying to understand the relation between them (i.e how they interact, etc.) Answer TL;DR: The LR
Tag: pytorch
Pytorch getting RuntimeError: Found dtype Double but expected Float
I am trying to implement a neural net in PyTorch but it doesn’t seem to work. The problem seems to be in the training loop. I’ve spend several hours into this but can’t get it right. Please help, thanks. I haven’t added the data preprocessing parts. (tensor([ 5., 5., 8., 14.], dtype=torch.float64), tensor(-0.3403, dtype=torch.float64)) Error: Answer You need the data
RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same – PyTorch
I’m trying to push both my mode and data, images and labels, to run on the GPU by doing: Followed by: I’m explicitly pushing my model and data to device however I am met by the error: I feel like I’m doing the right thing by pushing both model and data to GPU but I can’t figure out why it’s
How to get class and bounding box coordinates from YOLOv5 predictions?
I am trying to perform inference on my custom YOLOv5 model. The official documentation uses the default detect.py script for inference. I have written my own python script but I cannot access the predicted class and the bounding box coordinates from the output of the model. Here is my code: Answer This will give you labels, coordinates, and thresholds for
TypeError: img should be PIL Image. Got – PyTorch
I’m trying to prepare some image data for my neural to classify. As part of the image preprocessing step, I’m applying the HOG filter in my dataset class as such: After this, I define my train and validation transformation as such: and finally, I create the loaders as such: However, when I attempt to preview the training image with test_img[1]
PyTorch – AssertionError: Size mismatch between tensors
I am trying to adapt a Pytorch script that was created for linear regression. It was originally written to take in a set of random values(created with np.random) as features and targets. I have now created a dataframe of actual data for analysis: The df looks like this: …and I am currently extracting just two columns(X1 and X2) as my
How to add two separate layers on the top of one layer using pytorch?
I want to add two separate layers on the top of one layer (or a pre-trained model) Is that possible for me to do using Pytorch? Answer Yes, when defining your model’s forward function, you can specify how the inputs should be passed through the layers. For example: Where forward is a member of MyNet: Training The model should be
torchvision.io cannot find reference read_image() in __init.py__
I am trying to import read_image from torchvision.io and when i hover the error it says torchvision.io cannot find reference ‘read_image’ in ‘__init.py__’ I am following this example https://pytorch.org/tutorials/beginner/basics/data_tutorial.html from torchvision.io import read_image ImportError: cannot import name ‘read_image’ from ‘torchvision.io’ (C:UsersX.condaenvsPytorch37libsite-packagestorchvisionio__init__.py) This is the error I’m getting. Answer You need to upgrade the version of your pytorch
Confusion when displaying an image from matplotlib.pyplot to tensorflow
I have this error: TypeError: Invalid shape (28, 28, 1) for image data Here is my code: I know that the pytorch does processing via this way: C x H x W, and that matplotlib does it this way: H x W x C, yet when I change it to matplotlib’s way, it gives me an error here. Am I
Trouble implementing “concurrent” softmax function from paper (PyTorch)
I am trying to implement the so called ‘concurrent’ softmax function given in the paper “Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels”. Below is the definition of the concurrent softmax: NOTE: I have left the (1-rij) term out for the time being because I don’t think it applies to my problem given that my training dataset has a