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

Jump to bottom

pnnx readme remove duplicate space #4428

Merged
merged 1 commit into from
Dec 18, 2022
Merged

pnnx readme remove duplicate space #4428

Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
2 changes: 1 addition & 1 deletion tools/pnnx/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@ Before PNNX, we had the following methods:
2. export to ONNX, and convert onnx to inference-framework specific format, and deploy with TensorRT/OpenVINO/ncnn/etc.
3. export to TorchScript, and deploy with libtorch

As far as we know, ONNX has the ability to express the PyTorch model and it is an open standard. People usually use ONNX as an intermediate representation between PyTorch and the inference platform. However, ONNX still has the following fatal problems, which makes the birth of PNNX necessary:
As far as we know, ONNX has the ability to express the PyTorch model and it is an open standard. People usually use ONNX as an intermediate representation between PyTorch and the inference platform. However, ONNX still has the following fatal problems, which makes the birth of PNNX necessary:

1. ONNX does not have a human-readable and editable file representation, making it difficult for users to easily modify the computation graph or add custom operators.
2. The operator definition of ONNX is not completely in accordance with PyTorch. When exporting some PyTorch operators, glue operators are often added passively by ONNX, which makes the computation graph inconsistent with PyTorch and may impact the inference efficiency.
Expand Down