How to resolve inconsistent prediction results in Pytorch networks?

When using PyTorch for network predictions, inconsistencies in results may occur. Here are some possible reasons for this issue and ways to resolve it:

1. Random Seed: In PyTorch, the random seed can impact the initialization of network weights and the order of data batches. To ensure consistency in results, it is recommended to set the same random seed in both training and testing code.

import torch
torch.manual_seed(0)

1. Set the deterministic mode to True in the cuDNN backend for PyTorch.

import torch
torch.backends.cudnn.deterministic = True

1. Switch to evaluation mode.

model.eval()

1. Data preprocessing: Before making network predictions, it is necessary to perform the same preprocessing operations on the input data as during training, such as normalization, scaling, and cropping. Ensuring consistent preprocessing can improve the consistency of the results.
2. Model loading: Make sure to load the same model weights file when testing if a pre-trained model is used.

By using the above method, the issue of inconsistent PyTorch network predictions can be resolved.