RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'

[zinuok]

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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]

👋 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.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

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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

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

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[glenn-jocher]

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

[glenn-jocher]

👋 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]

👋 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!

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