Implementations of recent Deep Learning tricks in Computer Vision, easily paired up with your favorite framework and model zoo.
Holocrons were information-storage datacron devices used by both the Jedi Order and the Sith that contained ancient lessons or valuable information in holographic form.
Source: Wookieepedia
This package was developed using minimal dependencies (pytorch, torchvision). You can install it using the following commands:
git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/
Using the models module, you can easily load torch modules or full models:
from holocron.models.resnets import TridentBlock
# Load pretrained Resnet
model = TridentBlock(64, 16, branches=3)
model.eval()
Then, let's generate a random feature maps
import torch
# Get random inputs
x1 = torch.rand(1, 64, 256, 256)
x2 = torch.rand(1, 64, 256, 256)
x3 = torch.rand(1, 64, 256, 256)
Now we can move them to GPU and forward them
# Move inputs and model to GPU
if torch.cuda.is_available():
model = model.cuda()
x1, x2, x3 = x1.cuda(), x2.cuda(), x3.cuda()
# Forward
with torch.no_grad():
output = model([x1, x2, x3])
Regarding issues, use the following format for the title:
[Topic] Your Issue name
Example:
[models resnet] Add spectral normalization option