Programatically retrieving anchors

[PeterSenyszyn]

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Question

Hi there,

I'm trying to find the anchors and strides for a custom YOLOv5 model. I found this thread from a year ago: #2585 and referenced this code:

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)  # hub model
# -- or --
model = torch.load('yolov5s.pt')['model']  # local model

# Anchors
m = model.model[-1]  # Detect() layer
print(m.anchor_grid.squeeze())  # print anchors

but this does not work on v6.0 or v6.1. With the previous code I get:
TypeError: 'Model' object is not subscriptable

Is there a new way to programatically get anchors then strides? Thanks.

Additional

No response

[github-actions]

👋 Hello @PeterSenyszyn, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@PeterSenyszyn YOLOv5 🚀 anchors are saved as Detect() layer attributes on model creation, and updated as necessary by AutoAnchor before training starts. Their exact location is here:

yolov5/models/yolo.py

Line 45 in f17c86b

self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)

You can examine the anchors of any trained YOLOv5 model like this:

Input

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)  # official model
model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/best.pt',  autoshape=False)  # custom model

# Anchors
m = model.model.model[-1]  # Detect() layer
print(m.anchors * m.stride.view(-1, 1, 1))  # print anchors

Output

YOLOv5 🚀 2021-11-22 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
#           x     y
tensor([[[ 10.,  13.],
         [ 16.,  30.],
         [ 33.,  23.]],  # P3/8-small

        [[ 30.,  61.],
         [ 62.,  45.],
         [ 59., 119.]],  # P4/16-medium

        [[116.,  90.],
         [156., 198.],
         [373., 326.]]], dtype=torch.float16)  # P5/32-large

Example

Good luck 🍀 and let us know if you have any other questions!