ndarray

Author: AlexanderDokuchaev

.github/workflows/weekly.yml

@@ -14,7 +14,7 @@ jobs:
       matrix:
         include:
           - python_version: "3.9"
-            override_requirements: "numpy==1.20.0"
+            override_requirements: "numpy==1.24.0"
           - python_version: "3.11"
           - python_version: "3.12"
     uses: ./.github/workflows/call_precommit.yml

nncf/common/tensor_statistics/collectors.py

@@ -15,6 +15,7 @@
 from typing import Any, Deque, Dict, List, Optional, Tuple, Union, cast
 
 import numpy as np
+from numpy.typing import NDArray
 
 from nncf.common.tensor import NNCFTensor
 from nncf.common.tensor import TensorType
@@ -317,7 +318,7 @@ class MedianMADStatisticCollector(OfflineTensorStatisticCollector):
     Collector estimates median and median absolute deviation (MAD).
     """
 
-    def _prepare_statistics(self) -> Tuple[np.ndarray[Any], np.ndarray[Any]]:
+    def _prepare_statistics(self) -> Tuple[NDArray[Any], NDArray[Any]]:
         per_channel_history = get_per_channel_history(
             self._samples, cast(List[int], self._reduction_shape), discard_zeros=True
         )

nncf/common/tensor_statistics/reduction.py

@@ -12,6 +12,7 @@
 from typing import Any, Deque, List, Tuple
 
 import numpy as np
+from numpy.typing import NDArray
 
 
 def get_channel_count_and_dim_idx(scale_shape: List[int]) -> Tuple[int, int]:
@@ -24,7 +25,7 @@ def get_channel_count_and_dim_idx(scale_shape: List[int]) -> Tuple[int, int]:
     return channel_count, channel_dim_idx
 
 
-def split_into_channels(input_: np.ndarray[Any], scale_shape: List[int]) -> List[np.ndarray[Any]]:
+def split_into_channels(input_: NDArray[Any], scale_shape: List[int]) -> List[NDArray[Any]]:
     channel_count, channel_dim_idx = get_channel_count_and_dim_idx(scale_shape)
     channel_first_tensor = np.moveaxis(input_, channel_dim_idx, 0)
     if channel_count == 1:
@@ -60,7 +61,7 @@ def get_per_channel_history(
     return per_channel_history
 
 
-def np_percentile_reduce_like(input_: np.ndarray[Any], ref_tensor_shape: Tuple[int], q: float) -> np.ndarray[Any]:
+def np_percentile_reduce_like(input_: NDArray[Any], ref_tensor_shape: Tuple[int], q: float) -> NDArray[Any]:
     numel = np.prod(ref_tensor_shape)
     if numel == 1:
         return np.array([np.percentile(input_, q)])