NaMAZU: Pretty Usefull Library
Project description
NaMAZU
Lightning API
They are all written in PyTorch and following best practice to be used with pytorch lightning. They are all GPU enabled controlled by Lightning API.
import pytorch_lightning as pl
from NaMAZU import KNN
class YourLitModule(pl.LightningModule):
def __init__(self,*args, **kwargs):
...
self.encoder = SomeEncoder()
self.head_classifier = KNN(
n_neighbors=5,
distance_measure="cosine",
training_data=some_known_data
)
self.estimator = GMM(5, 10)
def training_step(self, batch):
x, t = batch
y = self.encoder(x)
neighbors = self.head_classifier(y)
probability = self.estimator(y)
Statistical Model
- KNN: Available with euqlidean, manhattan, cosine and mahalanobis distance.
- NBC: GPU enabled naive bayes classifier.
- GMM: Gaussian Mixture probabability estimator. Of course GPU enabled.
Deep Learning
- LitU2Net: LightningModule U2Net. Trainable and ready for prediction.
- AniNet: LightningModule image classifier pretrained for japanese animations.
- PredictionAssistant: Coming soon.
Functional API
You can use below functions via
import NaMAZU.functional as F
F.change_frame_rates_in("./test_data.mp4",fps=5)
image_control
- npy_to_img
- img_to_npy
- split_image
- compose_two_png
- apply_to_all
- change_frame_rates_in
- save_all_frames
file_control
- rename_file
- collect_file_pathes_by_ext
- zip_files
Coming
- st_integration. Usuful snipets and fast deoployment of LitModule to streamlit. (clf_template)
TODO
Debug AniNet
- Video Recognition Model
- Feature Learning
- Few-shot Learning
- Audio-Visual Multimodal
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