Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
The PyTorch doc Reproducibility has very detailed instructions on how to make your model training reproducible. Basically, you need the following code.
torch.manual_seed(args.seed)
np.random.seed(args.seed …It is common to customize a pretrained model by delete the output layer or replace it to the output layer that suits your use case. There are several ways to achieve this in PyTorch.
Define an identity layer.
Replace the fully connected layer with an Identity Layer (using ResNet18 as an example).
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
PyTorch uses pickle to serialize and deserialize objects.
The PyTorch convention is
to use the file extension .pt or .pth for saving model (or its parameters)
and use the file extension …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
https://github.com/dmlc/dgl
There are multiple ways to convert a Tensor to a numpy array in PyTorch.
First,
you can call the method Tensor.numpy.
my_tensor.numpy()
Second,
you can use the function numpy.array.
import numpy as np
np.array(my_tensor)
It is suggested that you use the function numpy.array
to convert a Tensor to a numpy array.
The reason is that numpy.array is more generic.
You can also use it to convert other objects (e.g., PIL.Image)
to numpy arrays
while those objects might not have a method named numpy