MLIA-507 Upgrade Vela version

Upgrade Vela version from 3.3.0 to 3.4.0.

- Adapt code to use new typing notation by replacing `numpy.array` with `numpy.ndarray` where necessary.

Change-Id: I035e9564d448652aa09a52d79c71ef09663ea776

2 files changed, 7 insertions, 7 deletions

diff --git a/src/mlia/nn/tensorflow/optimizations/pruning.py b/src/mlia/nn/tensorflow/optimizations/pruning.py
index f629ba1..f1e2976 100644
--- a/src/mlia/nn/tensorflow/optimizations/pruning.py
+++ b/src/mlia/nn/tensorflow/optimizations/pruning.py
@@ -29,8 +29,8 @@ class PruningConfiguration(OptimizerConfiguration):
 
     optimization_target: float
     layers_to_optimize: Optional[List[str]] = None
-    x_train: Optional[np.array] = None
-    y_train: Optional[np.array] = None
+    x_train: Optional[np.ndarray] = None
+    y_train: Optional[np.ndarray] = None
     batch_size: int = 1
     num_epochs: int = 1
 
@@ -73,7 +73,7 @@ class Pruner(Optimizer):
         """Return string representation of the optimization config."""
         return str(self.optimizer_configuration)
 
-    def _mock_train_data(self) -> Tuple[np.array, np.array]:
+    def _mock_train_data(self) -> Tuple[np.ndarray, np.ndarray]:
         # get rid of the batch_size dimension in input and output shape
         input_shape = tuple(x for x in self.model.input_shape if x is not None)
         output_shape = tuple(x for x in self.model.output_shape if x is not None)
diff --git a/src/mlia/nn/tensorflow/tflite_metrics.py b/src/mlia/nn/tensorflow/tflite_metrics.py
index b29fab3..9befb2f 100644
--- a/src/mlia/nn/tensorflow/tflite_metrics.py
+++ b/src/mlia/nn/tensorflow/tflite_metrics.py
@@ -31,13 +31,13 @@ DEFAULT_IGNORE_LIST = [
 ]
 
 
-def calculate_num_unique_weights(weights: np.array) -> int:
+def calculate_num_unique_weights(weights: np.ndarray) -> int:
     """Calculate the number of unique weights in the given weights."""
     num_unique_weights = len(np.unique(weights))
     return num_unique_weights
 
 
-def calculate_num_unique_weights_per_axis(weights: np.array, axis: int) -> List[int]:
+def calculate_num_unique_weights_per_axis(weights: np.ndarray, axis: int) -> List[int]:
     """Calculate unique weights per quantization axis."""
     # Make quantized dimension the first dimension
     weights_trans = np.swapaxes(weights, 0, axis)
@@ -57,7 +57,7 @@ class SparsityAccumulator:
         self.total_non_zero_weights: int = 0
         self.total_weights: int = 0
 
-    def __call__(self, weights: np.array) -> None:
+    def __call__(self, weights: np.ndarray) -> None:
         """Update the accumulator with the given weights."""
         non_zero_weights = np.count_nonzero(weights)
         self.total_non_zero_weights += non_zero_weights
@@ -69,7 +69,7 @@ class SparsityAccumulator:
 
 
 def calculate_sparsity(
-    weights: np.array, accumulator: Optional[SparsityAccumulator] = None
+    weights: np.ndarray, accumulator: Optional[SparsityAccumulator] = None
 ) -> float:
     """
     Calculate the sparsity for the given weights.