Fix (nn): removed unused caching in quant adaptive avgpool2d (#911)

src/brevitas/nn/quant_avg_pool.py

@@ -93,14 +93,10 @@ def __init__(
             output_size: Union[int, Tuple[int, int]],
             trunc_quant: Optional[AccQuantType] = RoundTo8bit,
             return_quant_tensor: bool = True,
-            cache_kernel_size_stride: bool = True,
             **kwargs):
         AdaptiveAvgPool2d.__init__(self, output_size=output_size)
         QuantLayerMixin.__init__(self, return_quant_tensor)
         TruncMixin.__init__(self, trunc_quant=trunc_quant, **kwargs)
-        self.cache_kernel_size_stride = cache_kernel_size_stride
-        self._cached_kernel_size = None
-        self._cached_kernel_stride = None
 
     @property
     def channelwise_separable(self) -> bool:
@@ -114,15 +110,8 @@ def requires_export_handler(self):
     def padding(self):
         return 0
 
-    @property
-    def kernel_size(self):
-        return self._cached_kernel_size
-
-    @property
-    def stride(self):
-        return self._cached_kernel_stride
-
-    def compute_kernel_size_stride(self, input_shape, output_shape):
+    @staticmethod
+    def compute_kernel_size_stride(input_shape, output_shape):
         kernel_size_list = []
         stride_list = []
         for inp, out in zip(input_shape, output_shape):
@@ -141,10 +130,6 @@ def forward(self, input: Union[Tensor, QuantTensor]):
             self._set_global_is_quant_layer(False)
             return out
 
-        if self.cache_kernel_size_stride:
-            self._cached_kernel_size = k_size
-            self._cached_kernel_stride = stride
-
         if isinstance(x, QuantTensor):
             y = x.set(value=super(TruncAdaptiveAvgPool2d, self).forward(x.value))
             k_size, stride = self.compute_kernel_size_stride(x.value.shape[2:], y.value.shape[2:])