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Documenation

Aloception

Aloception is a set of packages for computer vision built on top of popular deep learning libraries: pytorch and pytorch lightning.

Aloscene

Aloscene extend the use of tensors with Augmented Tensors designed to facilitate the use of computer vision data (such as frames, 2d boxes, 3d boxes, optical flow, disparity, camera parameters…).

frame = aloscene.Frame("/path/to/image.jpg")
frame = frame.to("cpu")
frame.get_view().render()

Alodataset

Alodataset implement ready-to-use datasets for computer vision with the help of aloscene and augmented tensors to make it easier to transform and display your vision data.

coco_dataset = alodataset.CocoDetectionDataset(sample=True)
for frame in coco_dataset.stream_loader():
    frame.get_view().render()

Alonet

Alonet integrates several promising computer vision architectures. You can use it for research purposes or to finetune and deploy your model using TensorRT. Alonet is mainly built on top of lightning with the help of aloscene and alodataset.

Training

# Init the training pipeline
detr = alonet.detr.LitDetr()
# Init the data module
coco_loader = alonet.detr.CocoDetection2Detr()
# Run the training using the two components
detr.run_train(data_loader=coco_loader, project="detr", expe_name="test_experiment")

Inference

# Load model
model = alonet.detr.DetrR50(num_classes=91, weights="detr-r50").eval()

# Open and normalized frame
frame = aloscene.Frame("/path/to/image.jpg").norm_resnet()

# Run inference
pred_boxes = model.inference(model([frame]))

# Add and display the predicted boxes
frame.append_boxes2d(pred_boxes[0], "pred_boxes")
frame.get_view().render()

Note

One can use aloscene independently than the two other packages to handle computer vision data, or to improve its training pipelines with augmented tensors.

Install

Aloception’s packages are built on top of multiple libraries. Most of them are listed in the requirements.txt

pip install -r requirements.txt

Once the others packages are installed, you still need to install pytorch based on your hardware and environment configuration. Please, ref to the pytorch website
_ for this install.

Getting started

Tutorials

Alonet

Models

Model name Link alonet location Learn more
detr-r50 https://arxiv.org/abs/2005.12872 alonet.detr.DetrR50 Detr
deformable-detr https://arxiv.org/abs/2010.04159 alonet.deformable_detr.DeformableDETR Deformable detr
RAFT https://arxiv.org/abs/2003.12039 alonet.raft.RAFT RAFT

Detr

Here is a simple example to get started with Detr and aloception. To learn more about Detr, you can checkout the Tutorials or the detr README.

# Load model
model = alonet.detr.DetrR50(num_classes=91, weights="detr-r50").eval()

# Open and normalized frame
frame = aloscene.Frame("/path/to/image.jpg").norm_resnet()

# Run inference
pred_boxes = model.inference(model([frame]))

# Add and display the predicted boxes
frame.append_boxes2d(pred_boxes[0], "pred_boxes")
frame.get_view().render()

Deformable Detr

Here is a simple example to get started with Deformable Detr and aloception. To learn more about Deformable, you can checkout the Tutorials or the deformable detr README.

# Loading Deformable model
model = alonet.deformable_detr.DeformableDetrR50(num_classes=91, weights="deformable-detr-r50").eval()

# Open, normalize frame and send frame on the device
frame = aloscene.Frame("/home/thibault/Desktop/yoga.jpg").norm_resnet().to(torch.device("cuda"))

# Run inference
pred_boxes = model.inference(model([frame]))

# Add and display the predicted boxes
frame.append_boxes2d(pred_boxes[0], "pred_boxes")
frame.get_view().render()

RAFT

Here is a simple example to get started with RAFT and aloception. To learn more about RAFT, you can checkout the raft README.

# Use the left frame from the  Sintel Flow dataset and normalize the frame for the RAFT Model
frame = alodataset.SintelFlowDataset(sample=True).getitem(0)["left"].norm_minmax_sym()

# Load the model using the sintel weights
raft = alonet.raft.RAFT(weights="raft-sintel")

# Compute optical flow
padder = alonet.raft.utils.Padder()
flow = raft.inference(raft(padder.pad(frame[0:1]), padder.pad(frame[1:2])))

# Render the flow along with the first frame
flow[0].get_view().render()

Alodataset

Here is a list of all the datasets you can use on Aloception. If you’re dataset is not in the list but is important for computer vision. Please let us know using the issues or feel free to contribute.

Datasets

Dataset name alodataset location To try
CocoDetection alodataset.CocoDetectionDataset python alodataset/coco_detection_dataset.py
CrowdHuman alodataset.CrowdHumanDataset python alodataset/crowd_human_dataset.py
Waymo alodataset.WaymoDataset python alodataset/waymo_dataset.py
ChairsSDHom alodataset.ChairsSDHomDataset python alodataset/chairssdhom_dataset.py
FlyingThings3DSubset alodataset.FlyingThings3DSubsetDataset python alodataset/flyingthings3D_subset_dataset.py
FlyingChairs2 alodataset.FlyingChairs2Dataset python alodataset/flying_chairs2_dataset.py
SintelDisparityDataset alodataset.SintelDisparityDataset python alodataset/sintel_disparity_dataset.py
SintelFlowDataset alodataset.SintelFlowDataset python alodataset/sintel_flow_dataset.py
MOT17 alodataset.Mot17 python alodataset/mot17.py

Unit tests

python -m pytest

Licence

Shield: CC BY-NC-SA 4.0

This work is licensed under a .

CC BY-NC-SA 4.0

GitHub

GitHub - Visual-Behavior/aloception: Aloception is a set of package for computer vision: aloscene, alodataset, alonet.
Aloception is a set of package for computer vision: aloscene, alodataset, alonet. - GitHub - Visual-Behavior/aloception: Aloception is a set of package for computer vision: aloscene, alodataset, al...