[Frontend][TFLite] Add parser support for 'square' operator (apache#4915

)

* [Frontend][TFLite] Add parser support for square operator

* Add parser implementation
* Add relevant tests
* Note: 'square' is an unary elemwise operator but it's added separately
  in the parser since there is no Relay 'square' op
  and instead we have to use 'multiply'

* Change relay operation from 'multiply' to 'power'

* Remove a redundant line as requested

python/tvm/relay/frontend/tflite.py

@@ -121,7 +121,8 @@ def __init__(self, model, subgraph, exp_tab):
             'SQUARED_DIFFERENCE': self.convert_squared_difference,
             'LOGICAL_AND': self.convert_logical_and,
             'LOGICAL_OR': self.convert_logical_or,
-            'DETECTION_POSTPROCESS': self.convert_detection_postprocess
+            'DETECTION_POSTPROCESS': self.convert_detection_postprocess,
+            'SQUARE': self.convert_square,
         }
 
     def check_unsupported_ops(self):
@@ -636,6 +637,32 @@ def convert_neg(self, op):
                 'TFlite quantized NEG operator is not supported yet.')
         return self._convert_unary_elemwise(_op.negative, op)
 
+    def convert_square(self, op):
+        """Convert TFLite SQUARE"""
+        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) == 1, "input tensors length should be 1"
+        input_tensor = input_tensors[0]
+        in_expr = self.get_expr(input_tensor.tensor_idx)
+
+        output_tensors = self.get_output_tensors(op)
+        assert len(output_tensors) == 1, "output tensors length should be 1"
+        output_tensor = output_tensors[0]
+
+        if self.is_quantized(op):
+            raise tvm.error.OpNotImplemented(
+                'TFlite quantized SQUARE operator is not supported yet.')
+
+        exp_type = self.get_tensor_type_str(output_tensor.tensor.Type())
+        out = _op.power(in_expr, relay.const(2, exp_type))
+
+        return out
+
     def _convert_elemwise(self, relay_op, op):
         """Generic method to Convert TFLite elemwise"""
         try:

tests/python/frontend/tflite/test_forward.py

@@ -742,6 +742,12 @@ def _test_neg(data):
     """ One iteration of neg """
     return _test_unary_elemwise(math_ops.neg, data)
 #######################################################################
+# Square
+# ------
+
+def _test_square(data):
+    """ One iteration of square """
+    return _test_unary_elemwise(math_ops.square, data)
 
 def _test_forward_unary_elemwise(test_op):
     # functions that need positive input
@@ -759,6 +765,7 @@ def test_all_unary_elemwise():
     _test_forward_unary_elemwise(_test_sqrt)
     _test_forward_unary_elemwise(_test_rsqrt)
     _test_forward_unary_elemwise(_test_neg)
+    _test_forward_unary_elemwise(_test_square)
     # ceil and cos come with TFLite 1.14.0.post1 fbs schema
     if package_version.parse(tf.VERSION) >= package_version.parse('1.14.0'):
         _test_forward_unary_elemwise(_test_ceil)