Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix docstring for jit scripting #223

Merged
merged 2 commits into from
Nov 2, 2021
Merged

Fix docstring for jit scripting #223

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
ea6e608
Fix docstring for jit scripting
zhiqwang Nov 2, 2021
72b7ddc
Minor fix to the model path
zhiqwang Nov 2, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 9 additions & 5 deletions deployment/libtorch/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -37,10 +37,14 @@ The LibTorch inference for `yolort`, both GPU and CPU are supported.

Unlike [ultralytics's](https://github.com/ultralytics/yolov5/blob/8ee9fd1/export.py) `torch.jit.trace` mechanism, We're using `torch.jit.script` to trace the YOLOv5 models which containing the whole pre-processing (especially with the [`letterbox`](https://github.com/ultralytics/yolov5/blob/8ee9fd1/utils/augmentations.py#L85-L115) ops) and post-processing (especially with the `nms` ops) procedures, as such you don't need to rewrite manually the C++ codes of pre-processing and post-processing.

```bash
git clone https://github.com/zhiqwang/yolov5-rt-stack.git
cd yolov5-rt-stack
python -m test.tracing.trace_model
```python
from yolort.models import yolov5n

model = yolov5n(pretrained=True)
model.eval()

traced_model = torch.jit.script(model)
traced_model.save("yolov5n.torchscript.pt")
```

1. Then compile the source code.
Expand All @@ -56,7 +60,7 @@ The LibTorch inference for `yolort`, both GPU and CPU are supported.

```bash
./yolo_inference [--input_source ../../../test/assets/zidane.jpg]
[--checkpoint ../../../test/tracing/yolov5s.torchscript.pt]
[--checkpoint ../yolov5n.torchscript.pt]
[--labelmap ../../../notebooks/assets/coco.names]
[--gpu] # GPU switch, which is optional, and set False as default
```