You can transform the numpy array to a PyTorch tensor via tensor = torch.from_numpy(array) and would need to permute it afterwards, as PyTorch layers expect the input in the channels-first memory layout via: tensor = tensor.permute(0, 3, 1, 2).
If you are dealing with a multi-class classification, your target should have the shape [batch_size] without any additional dimension, so you could use target = torch.from_numpy(target_array).squeeze(1) to remove dim1.
Once this is done, you can directly pass the input data to the model or use a TensorDataset or a custom Dataset as described here 2.
I would generally recommend to take a look at the tutorials 1 for a general introduction.