[Python] Add unittest for Scheduler and Tensor.

aidge_backend_cpu/unit_tests/test_scheduler.py

+import unittest
+import aidge_core
+import aidge_backend_cpu
+import numpy as np
+
+
+class test_scheduler(unittest.TestCase):
+    """Test tensor binding
+    """
+    def setUp(self):
+        pass
+    def tearDown(self):
+        pass
+
+    def test_relu_forward(self):
+        values = np.arange(6) - 3
+
+        input_node = aidge_core.Producer(aidge_core.Tensor(values), "Input")
+        relu = aidge_core.ReLU()
+
+        gv = aidge_core.GraphView()
+        gv.add(relu)
+        gv.add(input_node)
+
+        gv.set_datatype(aidge_core.DataType.Int32)
+        gv.set_backend("cpu")
+
+        input_node.add_child(relu)
+
+        scheduler = aidge_core.SequentialScheduler(gv)
+
+        scheduler.forward()
+
+        out_tensor = relu.get_operator().output(0)
+        expected_out = [0,0,0,0,1,2]
+        for i in range(len(expected_out)):
+            self.assertEqual(expected_out[i], out_tensor[i])
+
+
+
+if __name__ == '__main__':
+    unittest.main()

aidge_backend_cpu/unit_tests/test_tensor.py

+import unittest
+import aidge_core
+import aidge_backend_cpu
+import numpy as np
+
+
+class test_tensor(unittest.TestCase):
+    """Test tensor binding
+    """
+    def setUp(self):
+        pass
+    def tearDown(self):
+        pass
+
+    def test_getavailable_backends(self):
+        self.assertTrue("cpu" in aidge_core.Tensor.get_available_backends())
+
+    def test_numpy_int_to_tensor(self):
+        np_array = np.arange(9).reshape(1,1,3,3)
+        # Numpy -> Tensor
+        t = aidge_core.Tensor(np_array)
+        self.assertEqual(t.dtype(), aidge_core.DataType.Int32)
+        for i_t, i_n in zip(t, np_array.flatten()):
+            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
+        t = aidge_core.Tensor(np_array)
+        # Tensor -> Numpy
+        nnarray = np.array(t)
+        for i_nn, i_n in zip(nnarray.flatten(), np_array.flatten()):
+            self.assertTrue(i_nn == i_n)
+        for i,j in zip(t.dims(), nnarray.shape):
+            self.assertEqual(i,j)
+
+    def test_numpy_float_to_tensor(self):
+        t = aidge_core.Tensor()
+        np_array = np.random.rand(1, 1, 3, 3).astype(np.float32)
+        # Numpy -> Tensor
+        t = aidge_core.Tensor(np_array)
+        self.assertEqual(t.dtype(), aidge_core.DataType.Float32)
+        for i_t, i_n in zip(t, np_array.flatten()):
+            self.assertTrue(i_t == i_n) # TODO : May need to change this to a difference
+        for i,j in zip(t.dims(), np_array.shape):
+            self.assertEqual(i,j)
+if __name__ == '__main__':
+    unittest.main()