It depends on what you mean by feed images that are 1024*1024. If you are using a pretrained model without further finetuning, you likely want to simply resize the images to something around 256x256 and take a 224x224 center crop. If you decide not to resize or crop, the model should “work” in that there should not be any shape mismatches due to the use of average pooling with input images at 1024x1024, but the model accuracy will likely not be great due to the potential scale mismatch. Choosing the right resolution is actually a tricky problem (e.g., as studied in [1906.06423] Fixing the train-test resolution discrepancy).
Finally, you might need to consider at at 1024x1024, the model will likely use ~(1024/224)**2 or ~9 times the memory and computation due to the quadratic scaling of convolution activations.