[TFLite] added scalar axis value handling in reduce (apache#6970)

Axis value in reduce can now be specified as scalar

python/tvm/relay/frontend/tflite.py

@@ -1638,7 +1638,8 @@ def _convert_reduce(self, relay_op, op):
         in_expr = self.get_expr(input_tensor.tensor_idx)
 
         # axis
-        axis = tuple(self.get_tensor_value(input_tensors[1]))
+        axis_value = self.get_tensor_value(input_tensors[1])
+        axis = tuple(axis_value) if len(axis_value.shape) > 0 else tuple((axis_value.item(),))
 
         # Options - keep_dims (bool)
         assert op.BuiltinOptionsType() == BuiltinOptions.ReducerOptions

tests/python/frontend/tflite/test_forward.py

@@ -2216,18 +2216,22 @@ def _test_forward_reduce(testop, dtype="float32"):
     if dtype == "bool":
         data0 = [np.random.choice(a=[False, True], size=(16, 16, 16, 16)).astype(dtype), None]
         data1 = [
+            np.random.choice(a=[False, True], size=(16, 16, 16, 16)).astype(dtype),
+            np.array(1, dtype=np.int32),
+        ]
+        data2 = [
             np.random.choice(a=[False, True], size=(16, 16, 16, 16)).astype(dtype),
             np.array([1, 2], dtype=np.int32),
         ]
     else:
         data0 = [np.random.rand(16, 16, 16, 16).astype(dtype), None]
-        data1 = [np.random.rand(16, 16, 16, 16).astype(dtype), np.array([1, 2], dtype=np.int32)]
-    testop(data0)
-    testop(data0, keep_dims=False)
-    testop(data0, keep_dims=True)
-    testop(data1)
-    testop(data1, keep_dims=False)
-    testop(data1, keep_dims=True)
+        data1 = [np.random.rand(16, 16, 16, 16).astype(dtype), np.array(1, dtype=np.int32)]
+        data2 = [np.random.rand(16, 16, 16, 16).astype(dtype), np.array([1, 2], dtype=np.int32)]
+
+    for data in [data0, data1, data2]:
+        testop(data)
+        testop(data, keep_dims=False)
+        testop(data, keep_dims=True)
 
 
 def _test_forward_reduce_quantized(testop):