Refactoring Tensor

Adding context to Tensor
Adding "active area" to Tensor

Forward declaring TensorImpl in Tensor instead of the another way around

CPU storage is unsigned char instead of void*

todo: memory location for device different than CPU
todo: a Tensor can share a TensorImpl with another Tensor
TensorImpl = storage
Tensor = view on storage

aidge_backend_cpu/unit_tests/test_tensor.py

@@ -2,7 +2,7 @@ import unittest
 import aidge_core
 import aidge_backend_cpu
 import numpy as np
-
+from functools import reduce
 
 class test_tensor(unittest.TestCase):
     """Test tensor binding
@@ -24,6 +24,7 @@ class test_tensor(unittest.TestCase):
             self.assertTrue(i_t == i_n)
         for i,j in zip(t.dims(), np_array.shape):
             self.assertEqual(i,j)
+            
     def test_tensor_int_to_numpy(self):
         np_array = np.arange(9).reshape(1,1,3,3)
         # Numpy -> Tensor
@@ -46,6 +47,18 @@ class test_tensor(unittest.TestCase):
         for i,j in zip(t.dims(), np_array.shape):
             self.assertEqual(i,j)
 
+    def test_getcoord_getidx(self):
+        dims = [2,2,2]
+        size = reduce((lambda x, y: x*y), dims)
+
+        np_array = np.arange(size).reshape(dims)
+
+        t = aidge_core.Tensor(np_array)
+        for i in range(size):
+            coord = t.get_coord(i)
+            idx = t.get_idx(coord)
+            self.assertEqual(idx, i)
+
     def test_get_set(self):
         dims = [2,2,2]