RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'

zinuok commented 1 year ago

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Question

Hello, I'm trying to use YOLOv5n with CPU.

I wanted to speed up model inference even more, so I applied the --half flag. As a result, the following error occurred. Is half-precision not supported in CPU mode?

Fusing layers... 
YOLOv5n summary: 213 layers, 1867405 parameters, 0 gradients
Traceback (most recent call last):
  File "/home/zinuok/ros/cps_new_ws/src/yolov5_ros/scripts/ros_yolo.py", line 261, in <module>
    run(**vars(opt))
  File "/home/zinuok/anaconda3/envs/yolo5/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/zinuok/ros/cps_new_ws/src/yolov5_ros/scripts/ros_yolo.py", line 140, in run
    pred = model(im, augment=augment, visualize=False)
  File "/home/zinuok/anaconda3/envs/yolo5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/zinuok/ros/cps_new_ws/src/yolov5_ros/scripts/models/common.py", line 515, in forward
    y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
  File "/home/zinuok/anaconda3/envs/yolo5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/zinuok/ros/cps_new_ws/src/yolov5_ros/scripts/models/yolo.py", line 209, in forward
    return self._forward_once(x, profile, visualize)  # single-scale inference, train
  File "/home/zinuok/ros/cps_new_ws/src/yolov5_ros/scripts/models/yolo.py", line 121, in _forward_once
    x = m(x)  # run
  File "/home/zinuok/anaconda3/envs/yolo5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/zinuok/ros/cps_new_ws/src/yolov5_ros/scripts/models/common.py", line 60, in forward_fuse
    return self.act(self.conv(x))
  File "/home/zinuok/anaconda3/envs/yolo5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/zinuok/anaconda3/envs/yolo5/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 447, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/home/zinuok/anaconda3/envs/yolo5/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 444, in _conv_forward
    self.padding, self.dilation, self.groups)
RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'

Additional

No response

github-actions[bot] commented 1 year ago

👋 Hello @zinuok, 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.

If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

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Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

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Google Cloud Deep Learning VM. See GCP Quickstart Guide
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Docker Image. See Docker Quickstart Guide

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glenn-jocher commented 1 year ago

@zinuok torch ops do not run on CPU at FP16. This has nothing to do with YOLOv5.

glenn-jocher commented 1 year ago

👋 Hello! Thanks for asking about inference speed issues. PyTorch Hub speeds will vary by hardware, software, model, inference settings, etc. Our default example in Colab with a V100 looks like this:

YOLOv5 🚀 can be run on CPU (i.e. --device cpu, slow) or GPU if available (i.e. --device 0, faster). You can determine your inference device by viewing the YOLOv5 console output:

detect.py inference

python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/

YOLOv5 PyTorch Hub inference

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# Images
dir = 'https://ultralytics.com/images/'
imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')]  # batch of images

# Inference
results = model(imgs)
results.print()  # or .show(), .save()
# Speed: 631.5ms pre-process, 19.2ms inference, 1.6ms NMS per image at shape (2, 3, 640, 640)

Increase Speeds

If you would like to increase your inference speed some options are:

Use batched inference with YOLOv5 PyTorch Hub
Reduce --img-size, i.e. 1280 -> 640 -> 320
Reduce model size, i.e. YOLOv5x -> YOLOv5l -> YOLOv5m -> YOLOv5s -> YOLOv5n
Use half precision FP16 inference with python detect.py --half and python val.py --half
Use a faster GPUs, i.e.: P100 -> V100 -> A100
Export to ONNX or OpenVINO for up to 3x CPU speedup (CPU Benchmarks)
Export to TensorRT for up to 5x GPU speedup (GPU Benchmarks)
Use a free GPU backends with up to 16GB of CUDA memory:

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

github-actions[bot] commented 1 year ago

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv5 🚀 resources:

Wiki – https://github.com/ultralytics/yolov5/wiki
Tutorials – https://docs.ultralytics.com/yolov5
Docs – https://docs.ultralytics.com

Access additional Ultralytics ⚡ resources:

Ultralytics HUB – https://ultralytics.com/hub
Vision API – https://ultralytics.com/yolov5
About Us – https://ultralytics.com/about
Join Our Team – https://ultralytics.com/work
Contact Us – https://ultralytics.com/contact

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!

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