Based on your model architecture, you should provide a 4-dimensional input as [batch_size, channels, height, width].
Both adaptive pooling layers will create an output of shape [batch_size, channels, 1, 1].
These outputs will be concatenated in their channel dimension and flattened, yielding an activation of [batch_size, channels*2].
The following nn.BatchNorm1d layer will normalize these channels and assume a sequence length of 1.
Given that its number of channels is defined as inp*2, your model should work with an input of shape [batch_size, 1920, h, w], where batch_size>=2 and h, w >= 1, e.g.:
[2, 1920, 2, 2].
Your output however seems to show that you are passing an input of [8, 1920].
Could you explain your use case and the architecture a bit, as I’m not sure to understand it completely?