2635 enhance UNet doc for the typical use case

monai/networks/nets/unet.py

@@ -64,18 +64,21 @@ def __init__(
         Note: The acceptable spatial size of input data depends on the parameters of the network,
             to set appropriate spatial size, please check the tutorial for more details:
             https://github.com/Project-MONAI/tutorials/blob/master/modules/UNet_input_size_constrains.ipynb.
-            Typically, applying `resize`, `pad` or `crop` transforms can help adjust the spatial size of input data.
+            Typically, when using a stride of 2 in down / up sampling, the output dimensions are either half of the
+            input when downsampling, or twice when upsampling. In this case with N numbers of layers in the network,
+            the inputs must have spatial dimensions that are all multiples of 2^N.
+            Usually, applying `resize`, `pad` or `crop` transforms can help adjust the spatial size of input data.
 
         """
         super().__init__()
 
         if len(channels) < 2:
             raise ValueError("the length of `channels` should be no less than 2.")
-        delta = len(strides) - len(channels)
-        if delta < -1:
+        delta = len(strides) - (len(channels) - 1)
+        if delta < 0:
             raise ValueError("the length of `strides` should equal to `len(channels) - 1`.")
-        if delta >= 0:
-            warnings.warn(f"`len(strides) >= len(channels)`, the last {delta + 1} values of strides will not be used.")
+        if delta > 0:
+            warnings.warn(f"`len(strides) > len(channels) - 1`, the last {delta} values of strides will not be used.")
         if isinstance(kernel_size, Sequence):
             if len(kernel_size) != dimensions:
                 raise ValueError("the length of `kernel_size` should equal to `dimensions`.")