https://github.com/akarazniewicz/smd
Simple mmdetection CPU inference
https://github.com/akarazniewicz/smd
computer-vision deeplearning faster-rcnn machine-learning mask-rcnn object-detection pytorch retinanet
Last synced: 4 months ago
JSON representation
Simple mmdetection CPU inference
- Host: GitHub
- URL: https://github.com/akarazniewicz/smd
- Owner: akarazniewicz
- License: apache-2.0
- Created: 2019-10-18T12:30:05.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-03-26T08:05:48.000Z (over 4 years ago)
- Last Synced: 2023-02-28T18:51:21.251Z (over 2 years ago)
- Topics: computer-vision, deeplearning, faster-rcnn, machine-learning, mask-rcnn, object-detection, pytorch, retinanet
- Language: Python
- Homepage:
- Size: 3.5 MB
- Stars: 28
- Watchers: 2
- Forks: 6
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
## Simple mmdetection
Overall goal of this project is to implement most popular, inference only, CPU friendly object detection models from [mmdetection framework](https://github.com/open-mmlab/mmdetection) for research and production use. Currently mmdetection does not support CPU only inference mode ([see here](https://github.com/open-mmlab/mmdetection/issues/1274)), however in real life, production models are rarely deployed on GPU enabled environments. SMD solves this problem.
### Goals
* Create foundation for better understanding and research of CPU-only DNN performance
* All mmdetection pretrained weights can be directly used with SMD ([mmedtecection model ZOO](https://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md)).
* SMD limits number of dependencies to: torch, torchvision, PIL and numpy
* Wherever possible mmdetection specific code is replaced with torch and torchvision alternatives (transforms, nms etc.)
### Non-goals
* By design this code has no training capabilities at all. Training specific code is either removed or reduced to the bare minimum. For training, finetuning or transfer learning use [mmdetection](https://github.com/open-mmlab/mmdetection) you can then just use trained model wit smd for CPU only inference.
### Implemented architectures
- [ ] TorchScript support (current priority)
- [x] RetinaNet with FPN and ResNet 50 backbone
- [x] RetinaNet with FPN and ResNet 101 backbone
- [x] Faster R-CNN with FPN and ResNet 50 backbone
- [x] Faster R-CNN with FPN and ResNet 101 backbone
- [x] Mask R-CNN with FPN and ResNet 50 backbone
- [x] Mask R-CNN with FPN and ResNet 101 backbone
- [ ] RetinaNet with FPN and ResNet 50 with deformable convolutions backbone
- [ ] RetinaNet with FPN and ResNet 101 with deformable convolutions backbone
- [ ] SSD 300
- [ ] SSD 512
- [ ] FoveaBox
### Installing
`pip install -r requirements.txt`
### Sample code
[See demo jupyter notebook complete example](demo/demo.jpnby)
```python
from models.detectors import create_detector
import torch
import torchvision
import cv2
import numpy as np
from matplotlib import pyplot as plt
# download pretrained mmdetection model from model zoo
torch.utils.model_zoo.load_url(
'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_1x_20181129-f016f384.pth',
model_dir='.')
# create RetinaNet with ResNet 101 backbone, and pretrained COCO weights
# Note: COCO has 80 classes plus one background class. You can use Your own model. Just set You number of classes and feed
# pretrained checkpoint.
retina = create_detector('retinanet_r101_fpn', number_of_classes=81, pretrained='retinanet_r101_fpn_1x_20181129-f016f384.pth')
# with pytorch 1.3, model can be easily quantized (better CPU performance, smaller footprint).
retina = torch.quantization.quantize_dynamic(retina, dtype=torch.qint8)
# inference result is exactly the same like in mmdetection
with torch.no_grad():
result = retina.detect('demo.jpg')
res = []
# Look for cars in COCO dataset, with threshold 0.3
for r in result[2]:
if r[-1] >= .3:
res.append(r[:-1].astype(dtype=np.int).tolist())
if len(res) > 0:
im = cv2.imread('demo.jpg')
for r in res:
cv2.rectangle(im, (r[0], r[1]), (r[2], r[3]), (0, 255, 255), 3)
cv2.putText(im, "Car", (r[0]-3, r[1]-3), cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 255), 3)
im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
plt.figure(figsize=(20,11))
plt.axis("off")
plt.imshow(im)
```
