From 625c280433fef3c9d1b64f58eab930ba0f89cd82 Mon Sep 17 00:00:00 2001
From: Raul Farkas <raul.farkas@arm.com>
Date: Mon, 11 Jul 2022 20:09:48 +0100
Subject: 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
---
 setup.cfg                                       | 2 +-
 src/mlia/nn/tensorflow/optimizations/pruning.py | 6 +++---
 src/mlia/nn/tensorflow/tflite_metrics.py        | 8 ++++----
 tests/mlia/test_nn_tensorflow_tflite_metrics.py | 2 +-
 tests/mlia/utils/common.py                      | 2 +-
 5 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/setup.cfg b/setup.cfg
index b42ce9a..dbed6f7 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -31,7 +31,7 @@ packages = find:
 install_requires =
     tensorflow~=2.7.1
     tensorflow-model-optimization~=0.7.2
-    ethos-u-vela~=3.3.0
+    ethos-u-vela~=3.4.0
     requests
     rich
     sh
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.
diff --git a/tests/mlia/test_nn_tensorflow_tflite_metrics.py b/tests/mlia/test_nn_tensorflow_tflite_metrics.py
index 805f7d1..cf7aaeb 100644
--- a/tests/mlia/test_nn_tensorflow_tflite_metrics.py
+++ b/tests/mlia/test_nn_tensorflow_tflite_metrics.py
@@ -31,7 +31,7 @@ def _dummy_keras_model() -> tf.keras.Model:
 
 
 def _sparse_binary_keras_model() -> tf.keras.Model:
-    def get_sparse_weights(shape: List[int]) -> np.array:
+    def get_sparse_weights(shape: List[int]) -> np.ndarray:
         weights = np.zeros(shape)
         with np.nditer(weights, op_flags=["writeonly"]) as weight_iterator:
             for idx, value in enumerate(weight_iterator):
diff --git a/tests/mlia/utils/common.py b/tests/mlia/utils/common.py
index 4313cde..932343e 100644
--- a/tests/mlia/utils/common.py
+++ b/tests/mlia/utils/common.py
@@ -7,7 +7,7 @@ import numpy as np
 import tensorflow as tf
 
 
-def get_dataset() -> Tuple[np.array, np.array]:
+def get_dataset() -> Tuple[np.ndarray, np.ndarray]:
     """Return sample dataset."""
     mnist = tf.keras.datasets.mnist
     (x_train, y_train), _ = mnist.load_data()
-- 
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