Fix TensorList constructor from list of tensors. (NVIDIA#3626)

Fixed issues (raising exceptions), when constructing TensorList from an empty list,
different backend tensors or different dtype tensors.

Signed-off-by: ksztenderski <ksztenderski@nvidia.com>

dali/python/backend_impl.cc

@@ -596,11 +596,34 @@ std::unique_ptr<Tensor<Backend> > TensorListGetItemImpl(TensorList<Backend> &t,
 template <typename Backend>
 std::shared_ptr<TensorList<Backend>> TensorListFromListOfTensors(py::list &list_of_tensors,
                                                                  string &layout) {
+  if (list_of_tensors.empty()) {
+    throw std::runtime_error("Cannot create TensorList from an empty list.");
+  }
+
   auto tl = std::make_shared<TensorList<Backend>>(list_of_tensors.size());
   TensorVector<Backend> tv(list_of_tensors.size());
+  int expected_type = -2;
+
   for (size_t i = 0; i < list_of_tensors.size(); ++i) {
-    auto &t = list_of_tensors[i].cast<Tensor<Backend>&>();
-    tv[i].ShareData(t);
+    try {
+      auto &t = list_of_tensors[i].cast<Tensor<Backend> &>();
+      DALIDataType cur_type = t.type();
+
+      if (expected_type == -2) {
+        expected_type = t.type();
+      } else if (expected_type != cur_type) {
+        throw py::type_error(make_string(
+            "Tensors cannot have different data types. Tensor at position ", i, " has type '",
+            cur_type, "' expected to have type '", DALIDataType(expected_type), "'."));
+      }
+
+      tv[i].ShareData(t);
+    } catch (const py::type_error &) {
+      throw;
+    } catch (const std::runtime_error &) {
+      throw py::type_error(make_string("Object at position ", i, " cannot be converted to Tensor",
+                                       std::is_same<Backend, GPUBackend>::value ? "GPU." : "CPU."));
+    }
   }
 
   cudaStream_t stream = 0;

dali/test/python/test_backend_impl.py

@@ -180,3 +180,29 @@ def test_tl_from_list_of_tensors_different_shapes():
         for arr, tensor_cpu, tensor_gpu in zip(np_arrays, tl_cpu, tl_gpu):
             np.testing.assert_array_equal(arr, tensor_cpu)
             np.testing.assert_array_equal(arr, tensor_gpu.as_cpu())
+
+
+def test_tl_from_list_of_tensors_empty():
+    with assert_raises(RuntimeError, glob='Cannot create TensorList from an empty list.'):
+        TensorListCPU([])
+    with assert_raises(RuntimeError, glob='Cannot create TensorList from an empty list.'):
+        TensorListGPU([])
+
+
+def test_tl_from_list_of_tensors_different_backends():
+    t1 = TensorCPU(np.zeros((1)))
+    t2 = TensorCPU(np.zeros((1)))._as_gpu()
+    with assert_raises(TypeError, glob='Object at position 1 cannot be converted to TensorCPU'):
+        TensorListCPU([t1, t2])
+    with assert_raises(TypeError, glob='Object at position 1 cannot be converted to TensorGPU'):
+        TensorListGPU([t2, t1])
+
+
+def test_tl_from_list_of_tensors_different_dtypes():
+    np_types = [np.float32, np.float16, np.int16, np.int8, np.uint16, np.uint8]
+    for dtypes in np.random.choice(np_types, size=(3, 2), replace=False):
+        t1 = TensorCPU(np.zeros((1), dtype=dtypes[0]))
+        t2 = TensorCPU(np.zeros((1), dtype=dtypes[1]))
+        with assert_raises(TypeError, glob=f"Tensors cannot have different data types. Tensor at position "
+                           "1 has type '*' expected to have type '*'."):
+            TensorListCPU([t1, t2])