Support upstream yolov5 v6.0 models

This release mainly supports upstream YOLOv5 v6.0 models.

Highlights

We set the model to r6.0 by default, and rest of the usage interface is the same as before.

from yolort.models import yolov5n6

# Load model
model = yolov5n6(upstream_version="r6.0", pretrained=True, score_thresh=0.45)
model.eval()

# Perform inference on an image file
predictions = model.predict("bus.jpg")
# Perform inference on a list of image files
predictions = model.predict(["bus.jpg", "zidane.jpg"])

New Features

• Fully support upstream YOLOv5 v6.0 models (#194, #195, #196, #199, #204, #206, #213, #215) Thanks @deepage
• Support PyTorch 1.10 (#207)
• Support skipping preprocess when exporting ONNX (#193)

Backwards Incompatible Changes

• Set main as the base branch (#200)
• Rename _yolov5_darknet_pan to build_model (#198)

Improvement

• Use torch.jit.trace in unit-test for easier use in downstream (#205) Thanks @nihui
• Update model graph visualization images (#212)
• [pre-commit.ci] pre-commit autoupdate (#191, #197)

Bugfixes

• Fix non-renderable images on PyPI (#209, #210)
• Fix the anchor configuration mechanism (#201, #202)
• Small fixes to the docstrings (#208)