yyfyan / Holocron

PyTorch implementations of recent Computer Vision tricks

Holocron

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

Installation

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/

Usage

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])

Submitting a request / Reporting an issue

Regarding issues, use the following format for the title:

[Topic] Your Issue name

Example:

[models resnet] Add spectral normalization option