conv2d() received an invalid combination of arguments

[niusme]

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Question

environment

windows10
python3.8

question

I used the trained model to detect. The following code throws an error

import pathlib

import torch
from PIL import Image
import numpy as np
from pathlib import Path

pathlib.PosixPath = pathlib.WindowsPath
model = torch.load(r'D:\py\yolo\yolov5\mymodel\testbest.pt', map_location=torch.device('cpu'))['model'].float()
model.eval()


results = model(r'D:\py\code\dnfm-yolo-tutorial\naima\28.png')  


results.print()  
results.show()   

the error

Traceback (most recent call last):
  File "D:/py/PyCharm 2024.1.6/plugins/python/helpers/pydev/pydevd.py", line 1551, in _exec
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "D:\py\PyCharm 2024.1.6\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "D:\py\yolo\yolov5\test.py", line 13, in <module>
    results = model(r'D:\py\code\dnfm-yolo-tutorial\naima\28.png')  
  File "D:\py\yolo\yolov5\venv\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "D:\py\yolo\yolov5\venv\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "D:\py\yolo\yolov5\models\yolo.py", line 267, in forward
    return self._forward_once(x, profile, visualize)  # single-scale inference, train
  File "D:\py\yolo\yolov5\models\yolo.py", line 167, in _forward_once
    x = m(x)  # run
  File "D:\py\yolo\yolov5\venv\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "D:\py\yolo\yolov5\venv\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "D:\py\yolo\yolov5\models\common.py", line 86, in forward
    return self.act(self.bn(self.conv(x)))
  File "D:\py\yolo\yolov5\venv\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "D:\py\yolo\yolov5\venv\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "D:\py\yolo\yolov5\venv\lib\site-packages\torch\nn\modules\conv.py", line 458, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "D:\py\yolo\yolov5\venv\lib\site-packages\torch\nn\modules\conv.py", line 454, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
TypeError: conv2d() received an invalid combination of arguments - got (str, Parameter, NoneType, tuple, tuple, tuple, int), but expected one of:
 * (Tensor input, Tensor weight, Tensor bias = None, tuple of ints stride = 1, tuple of ints padding = 0, tuple of ints dilation = 1, int groups = 1)
      didn't match because some of the arguments have invalid types: (!str!, !Parameter!, !NoneType!, !tuple of (int, int)!, !tuple of (int, int)!, !tuple of (int, int)!, !int!)
 * (Tensor input, Tensor weight, Tensor bias = None, tuple of ints stride = 1, str padding = "valid", tuple of ints dilation = 1, int groups = 1)
      didn't match because some of the arguments have invalid types: (!str!, !Parameter!, !NoneType!, !tuple of (int, int)!, !tuple of (int, int)!, !tuple of (int, int)!, !int!)

Additional

No response

[UltralyticsAssistant]

👋 Hello @niusme, thank you for your interest in YOLOv5 🚀!

It looks like you're encountering an issue during inference with a trained model. This may be related to how the input is being passed to the model. For better assistance, could you please provide a minimum reproducible example including the following details?

• The full code with all modifications made to the original YOLOv5 repository, if any
• Steps to reproduce the error
• A description of the exact YOLOv5 version or commit hash being used
• Information about your environment (e.g., Python version, PyTorch version, and whether you're running on CPU/GPU)

If this is related to custom training, ensure that your workflow aligns with best practices for data preparation, training, and inference, including correctly preparing the inputs for the model.

Here are some tips to troubleshoot while we investigate further:

1. Double-check the input being passed to the model (e.g., path to the image). Ensure it is of the correct type and format.
2. Ensure your dependencies like PyTorch are up-to-date with the correct versions required for YOLOv5.
3. Test the model with a small example to isolate where the issue might be occurring.

This is an automated response to assist you efficiently. An Ultralytics engineer will review your issue and provide further assistance soon 😊. Thank you for your patience!

[pderrenger]

@niusme the issue arises because the model() function in the provided code attempts to pass a string (the file path of the image) when it expects a Tensor as input. YOLOv5 models do not directly process file paths; an image or its corresponding data needs to be loaded into a tensor first.

Here’s how you can resolve it:

Replace the results = model(...) line with the following:

from PIL import Image
import torchvision.transforms as transforms

# Load image and preprocess
image_path = r'D:\py\code\dnfm-yolo-tutorial\naima\28.png'
image = Image.open(image_path).convert('RGB')
transform = transforms.ToTensor()
image_tensor = transform(image).unsqueeze(0)  # Add batch dimension

# Pass tensor to the model for inference
results = model(image_tensor)

If the problem persists, ensure your testbest.pt model is correctly trained and compatible with YOLOv5. Always make sure you're using the latest version of the YOLOv5 repository and PyTorch library for compatibility. You can refer to the model inference documentation for further guidance. Let us know if you encounter additional issues!

[BrunoKreiner]

@niusme the issue arises because the model() function in the provided code attempts to pass a string (the file path of the image) when it expects a Tensor as input. YOLOv5 models do not directly process file paths; an image or its corresponding data needs to be loaded into a tensor first.

Here’s how you can resolve it:

Replace the results = model(...) line with the following:

from PIL import Image
import torchvision.transforms as transforms

# Load image and preprocess
image_path = r'D:\py\code\dnfm-yolo-tutorial\naima\28.png'
image = Image.open(image_path).convert('RGB')
transform = transforms.ToTensor()
image_tensor = transform(image).unsqueeze(0)  # Add batch dimension

# Pass tensor to the model for inference
results = model(image_tensor)

If the problem persists, ensure your testbest.pt model is correctly trained and compatible with YOLOv5. Always make sure you're using the latest version of the YOLOv5 repository and PyTorch library for compatibility. You can refer to the model inference documentation for further guidance. Let us know if you encounter additional issues!

the image needs to be transformed to 640x640 first right? so replace this
transform = transforms.ToTensor()
with
transform = transforms.Compose([transforms.Resize((640, 640)), transforms.ToTensor()])