[Relay][Frontend][TFLite] Add parser support for logical operators

* Add parser support for logical_and, logical_or
* Add boolean dtype as a valid tensor type
* BOOLEAN dtype is supported only from tf 1.15
  so logical ops work only in that and newer versions
* Logical_not is ommited since tflite can't convert it -->
  throws errors for addv2

python/tvm/relay/frontend/tflite.py

@@ -117,6 +117,8 @@ def __init__(self, model, subgraph, exp_tab):
             'PRELU': self.convert_prelu,
             'TRANSPOSE_CONV': self.convert_transpose_conv,
             'SQUARED_DIFFERENCE': self.convert_squared_difference,
+            'LOGICAL_AND': self.convert_logical_and,
+            'LOGICAL_OR': self.convert_logical_or,
         }
 
     def check_unsupported_ops(self):
@@ -222,6 +224,9 @@ def get_tensor_value(self, tensor_wrapper):
         if tensor_wrapper.tensor.Type() == TensorType.INT64:
             return np.frombuffer(tensor_wrapper.buffer.DataAsNumpy(), dtype=np.int64).reshape(
                 tensor_wrapper.tensor.ShapeAsNumpy())
+        if tensor_wrapper.tensor.Type() == TensorType.BOOL:
+            return np.frombuffer(tensor_wrapper.buffer.DataAsNumpy(), dtype=np.bool_).reshape(
+                tensor_wrapper.tensor.ShapeAsNumpy())
         raise NotImplementedError("Tensor type {} is currently not supported"
                                   .format(str(tensor_wrapper.tensor.Type())))
 
@@ -240,6 +245,8 @@ def get_tensor_type_str(self, tensor_type):
             return "int32"
         if tensor_type == TensorType.INT64:
             return "int64"
+        if tensor_type == TensorType.BOOL:
+            return "bool"
         raise NotImplementedError("Tensor type {} is currently not supported"
                                   .format(str(tensor_type)))
 
@@ -792,6 +799,33 @@ def convert_not_equal(self, op):
                 'TFlite quantized NOT_EQUAL operator is not supported yet.')
         return self._convert_elemwise(_op.not_equal, op)
 
+    def _convert_logical_binary(self, relay_op, op):
+        """Generic method to convert logical binary ops"""
+        try:
+            from tflite.Operator import Operator
+        except ImportError:
+            raise ImportError("The tflite package must be installed")
+
+        assert isinstance(op, Operator)
+        input_tensors = self.get_input_tensors(op)
+        assert len(input_tensors) == 2, "input tensors length should be 2"
+
+        lhs_tensor = input_tensors[0]
+        lhs_expr = self.get_expr(lhs_tensor.tensor_idx)
+        rhs_tensor = input_tensors[1]
+        rhs_expr = self.get_expr(rhs_tensor.tensor_idx)
+        out = relay_op(lhs_expr, rhs_expr)
+
+        return out
+
+    def convert_logical_and(self, op):
+        """Convert tflite LOGICAL_AND"""
+        return self._convert_logical_binary(_op.logical_and, op)
+
+    def convert_logical_or(self, op):
+        """Convert tflite LOGICAL_OR"""
+        return self._convert_logical_binary(_op.logical_or, op)
+
     def convert_zeros_like(self, op):
         """Convert TFLite ZEROS LIKE"""
         try:

tests/python/frontend/tflite/test_forward.py

@@ -955,6 +955,32 @@ def test_all_elemwise():
     _test_forward_elemwise(_test_equal)
     _test_forward_elemwise(_test_not_equal)
 
+#######################################################################
+# Logical operators
+# -----------------
+
+def _test_logical_binary(logical_bin_op, data):
+
+    with tf.Graph().as_default():
+        in_data = [array_ops.placeholder(shape=data[0].shape, dtype='bool', name='in_0'),
+                   array_ops.placeholder(shape=data[1].shape, dtype='bool', name='in_1')]
+        out = logical_bin_op(in_data[0], in_data[1], name='out')
+        compare_tflite_with_tvm(data, ['in_0:0', 'in_1:0'], in_data, [out])
+
+def _test_forward_logical_and(data):
+    """ One iteration of logical and """
+    return _test_logical_binary(math_ops.logical_and, data)
+
+def _test_forward_logical_or(data):
+    """ One iteration of logical or """
+    return _test_logical_binary(math_ops.logical_or, data)
+
+def test_all_logical():
+    data = [np.random.choice(a=[False, True], size=(2, 3, 4)).astype('bool'),
+            np.random.choice(a=[False, True], size=(2, 3, 4)).astype('bool')]
+    _test_forward_logical_and(data)
+    _test_forward_logical_or(data)
+
 #######################################################################
 # Zeros like
 # --------
@@ -1519,6 +1545,9 @@ def test_forward_mediapipe_hand_landmark():
     # Reduce
     test_all_reduce()
 
+    # Logical
+    test_all_logical()
+
     # End to End
     test_forward_mobilenet_v1()
     test_forward_mobilenet_v2()