fix comments

sony · Oct 22, 2024 · 89837b0 · 89837b0
1 parent 1e1e223
commit 89837b0
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Showing 2 changed files with 2 additions and 2 deletions.
diff --git a/..._compression_toolkit/core/pytorch/hessian/activation_hessian_scores_calculator_pytorch.py b/..._compression_toolkit/core/pytorch/hessian/activation_hessian_scores_calculator_pytorch.py
@@ -117,7 +117,7 @@ def _compute_per_tensor(self, output, target_activation_tensors):
                                       for _ in range(len(target_activation_tensors))]
         prev_mean_results = None
         for j in tqdm(range(self.num_iterations_for_approximation), "Hessian random iterations"):  # Approximation iterations
-            # Getting a random vector with normal distribution
+            # Getting a random vector
             v = self._generate_random_vectors_batch(output.shape, output.device)
             f_v = torch.sum(v * output)
             for i, ipt_tensor in enumerate(target_activation_tensors):  # Per Interest point activation tensor

diff --git a/model_compression_toolkit/core/pytorch/hessian/weights_hessian_scores_calculator_pytorch.py b/model_compression_toolkit/core/pytorch/hessian/weights_hessian_scores_calculator_pytorch.py
@@ -86,7 +86,7 @@ def compute(self) -> List[np.ndarray]:
 
         prev_mean_results = None
         for j in tqdm(range(self.num_iterations_for_approximation)):
-            # Getting a random vector with normal distribution and the same shape as the model output
+            # Getting a random vector with the same shape as the model output
             v = self._generate_random_vectors_batch(output_tensor.shape, device=device)
             f_v = torch.mean(torch.sum(v * output_tensor, dim=-1))
             for i, ipt_node in enumerate(self.hessian_request.target_nodes):  # Per Interest point weights tensor