# Dimensions of an input image

**URL:** <https://discuss.pytorch.org/t/dimensions-of-an-input-image/19439>\
**Category:** vision\
**Created:** [June 9, 2018, 1:20pm UTC](https://discuss.pytorch.org/t/dimensions-of-an-input-image/19439 "2018-06-09T13:20:37Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [June 9, 2018, 7:33pm UTC](https://discuss.pytorch.org/t/dimensions-of-an-input-image/19439/2 "2018-06-09T19:33:46Z")

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Your explanation is right in general.  
Just some minor issues:  
In PyTorch, images are represented as `[channels, height, width]`, so a color image would be `[3, 256, 256]`.  
During the training you will get batches of images, so your shape in the forward method will get an additional batch dimension at dim0: `[batch_size, channels, height, width]`.

The `.view()` operation gives you a new view on the tensor without copying any data.  
This means `view` is cheap to call and you don’t think too much about potential performance issues.  
In your example, `x` is reshaped into a `tensor` of shape `16*5*5` in dim1 and all remaining values in dim0. That’s what the `-1` stands for.  
In the case of a 256 -channel image, the `view` operation would resume the same, since the spatial dimensions of the image are the same and the channels are defined by the number of kernels of `conv2`.

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