First layer weights for transfer learning with new input tensor in keras.applications models?
The first layers are convolution and pooling ones:


For the convolutional layers, the only weights are the kernels and the biases, and they have fixed size (e.g. 3x3x3, 5x5x3) and do not depend on the input tensor shape.
The pooling layers do not have weights at all.


That's why you can reuse the weights independently from the input tensor shape.

With dense layers (i.e. the final layers), you need shapes to match, so you cannot reuse them if they do not.