Remove max tensor functions from coverage.

Add TODO for future PR.

model_compression_toolkit/core/common/graph/memory_graph/max_cut_astar.py

@@ -154,6 +154,9 @@ def solve(self, estimate_factor: float, iter_limit: int = 500) -> Tuple[List[Bas
             cut_route = routes[next_cut]
 
             if next_cut == self.target_cut:
+                # TODO maxcut: Why do we filter the cuts (cut_route) but not the max cut size (cut_sost).
+                #              This is a mismatch between max_cut and max(cuts).
+                #              Also, unfiltered cut_route seems perfect, including input and output tensor sizes of current op.
                 return self._remove_dummys_from_path(cut_route[0].op_order), cut_cost,\
                        list(set([self._remove_dummys_from_cut(self.clean_memory_for_next_step(c)) for c in cut_route]))
 

model_compression_toolkit/core/common/mixed_precision/resource_utilization_tools/resource_utilization_data.py

@@ -179,6 +179,7 @@ def compute_activation_output_maxcut_sizes(graph: Graph) -> Tuple[np.ndarray, np
     return activation_outputs_bytes, activation_outputs
 
 
+# pragma: no cover
 def compute_activation_output_sizes(graph: Graph) -> Tuple[np.ndarray, np.ndarray]:
     """
     Computes an array of the respective output tensor size and an array of the output tensor size in bytes for

model_compression_toolkit/core/common/mixed_precision/resource_utilization_tools/ru_methods.py

@@ -169,6 +169,7 @@ def activation_maxcut_size_utilization(mp_cfg: List[int],
     return np.array(activation_cut_memory)
 
 
+# pragma: no cover
 def activation_output_size_utilization(mp_cfg: List[int],
                                        graph: Graph,
                                        fw_info: FrameworkInfo,

tests/pytorch_tests/model_tests/feature_models/mixed_precision_activation_test.py

@@ -112,6 +112,7 @@ def compare(self, quantized_models, float_model, input_x=None, quantization_info
 class MixedPrecisionActivationSearch4BitFunctional(MixedPrecisionActivationBaseTest):
     def __init__(self, unit_test):
         super().__init__(unit_test)
+        # TODO maxcut: verify expected_config change is reasonable (was [1, 4, 4, 1])
         self.expected_config = [2, 5, 5, 1]
 
     def get_resource_utilization(self):
@@ -127,6 +128,7 @@ def compare(self, quantized_models, float_model, input_x=None, quantization_info
 class MixedPrecisionActivationMultipleInputs(MixedPrecisionActivationBaseTest):
     def __init__(self, unit_test):
         super().__init__(unit_test)
+        # TODO maxcut: verify expected_config change is reasonable (was all zeros)
         self.expected_config = [0, 0, 0, 0, 0, 0, 1, 0, 1]  # expected config for this test.
         self.num_calibration_iter = 3
         self.val_batch_size = 2