Introduce yolort.runtime Interface

This release improves support of ONNXRuntime by introducing the yolort.runtime interface and adding a C++ interface example. It also improves support for loading the checkpoint trained with ultralytics/yolov5.

Highlights

Intruduce the yolort.runtime Interface

We introduce the yolort.runtime interface to accelerate the application on the Python frontend. For example, you can define the runtime and do inferencing with ONNXRuntime as follows

from yolort.runtime import PredictorORT

detector = PredictorORT("best.onnx")
img_path = "bus.jpg"
scores, class_ids, boxes = detector.run_on_image(img_path)

ONNXRuntime C++ Inference Example

We adopt the dynamic shape mechanism when exporting the ONNX model, and within this, we can embed both pre-processing (letterbox) and post-processing (nms) into the model graph, which simplifies the deployment strategy. And @itsnine have provided an example on how to use it in here.

Load Checkpoint from Official YOLOv5

We now provide an interface for loading the checkpoint weights trained from the official yolov5 directly.

from yolort.models import YOLOv5

# 'yolov5s.pt' is downloaded from https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt
ckpt_path_from_ultralytics = "yolov5s.pt"
model = YOLOv5.load_from_yolov5(ckpt_path_from_ultralytics, score_thresh=0.25)

model.eval()
img_path = "test/assets/bus.jpg"
predictions = model.predict(img_path)

Backwards Incompatible Changes

• Move common utils to yolort.v5 (#167)
• Rename the YOLOModule to YOLOv5 (#179)
• Move graph visualization tools to yolort.relaying (#165)

New Features

• [PROTOTYPE] Add ncnn deployment example (#145)
• [PROTOTYPE] Train models from scratch (#115, #116, #124, #125, #128, #143)
• Support loading checkpoint from official yolov5 (#167, #168, #169, #179)
• Add ONNXRuntime C++ inference example (#171, #172, #174, #186), thanks @itsnine and @xiguadong
• Introduce yolort.runtime interface (#176, #178)
• Add pre-commit.ci hooks (#182)
• Add CLI tool for exporting ONNX models (#175), thanks @runrunrun1994
• Add CLI tool for evaluating mAP metrics (#148, #150)

Improvement

• Add alignment with ultralytics yolov5 tutorial (#117, #118, #121), thanks @xiguadong
• Refactor graph visualization tools (#126, #127, #165), thanks @xiguadong
• Update TorchVision C++ interfaces to 0.9.0+ (#136, #138), thanks @xiguadong
• Replace cxxopts with cmdline (#137)
• Update dependencies (#161, #162, #163, #164, #181)
• Simplify the unit-test (#149, #151, #152)
• Support autoenvolved anchors from ultralytics (#120), thanks @Tomakko
• Workaround on YOLOTransform to support fixed size input (#109)
• Unify onnx and JIT resize implementations (#105)

Bugfixes

• Fix exception (#183)
• Fix dtype in AnchorGenerator (#111, #113, #166), thanks @dkloving
• Made targets argument in YOLOTransform truly optional (#108), thanks @dkloving

Documents

• Update CONTRIBUTING.md (#185)
• Update badges and ONNXRuntime usage (#184)
• New issue templates (#173, #180)
• Fix docstring (#110, #158)
• Added more descriptive error logging (#140), thanks @mattpopovich
• Add yolort logo (#106)