Given groups=1, weight of size [64, 3, 7, 7], expected input[64, 1, 28, 28] to have 3 channels, but got 1 channels instead
时间: 2023-09-28 20:06:27 浏览: 53
This error typically occurs when there is a mismatch between the number of input channels expected by a model and the actual number of channels in the input data.
In your case, you are trying to pass an input tensor with shape [64, 1, 28, 28] to a model that expects inputs with 3 channels. The first dimension represents the batch size, the second dimension represents the number of channels, and the last two dimensions represent the height and width of the input image.
To resolve this error, you need to ensure that the input tensor has the correct number of channels. In this case, you can try using a grayscale image with 3 channels (RGB) instead of a single-channel (grayscale) image.
If you are working with grayscale images and want to convert them to RGB, you can use the `torchvision.transforms` module in PyTorch. Specifically, you can use the `transforms.Grayscale` transform followed by the `transforms.ToTensor` transform to convert the image to a 3-channel tensor. Here's an example:
```python
import torch
import torchvision.transforms as transforms
# Assuming your input tensor is `input_tensor`
# Convert grayscale image to RGB
transform = transforms.Compose([
transforms.Grayscale(num_output_channels=3),
transforms.ToTensor()
])
input_tensor_rgb = transform(input_tensor)
```
After converting the grayscale image to RGB, you can pass `input_tensor_rgb` to your model without encountering the channel mismatch error.