[GitHub] [incubator-tvm] siju-samuel commented on a change in pull request #4978: [TFLITE]Activation functions support

[GitBox <gi...@apache.org>]

siju-samuel commented on a change in pull request #4978: [TFLITE]Activation functions support
URL: https://github.com/apache/incubator-tvm/pull/4978#discussion_r386862168
 
 

 ##########
 File path: python/tvm/relay/frontend/tflite.py
 ##########
 @@ -454,6 +456,45 @@ def convert_l2_normalization(self, op):
 
         return out
 
+    def convert_lrn(self, op):
+        """Convert TFLite LOCAL_RESPONSE_NORMALIZATION """
+        try:
+            from tflite.Operator import Operator
+            from tflite.BuiltinOptions import BuiltinOptions
+            from tflite.LocalResponseNormalizationOptions import LocalResponseNormalizationOptions
+        except ImportError:
+            raise ImportError("The tflite package must be installed")
+
+        assert isinstance(op, Operator)
+        if self.is_quantized(op):
+            raise tvm.error.OpNotImplemented(
+                'TFlite quantized LRN operator is not supported yet.')
+
+        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"
+
+        assert op.BuiltinOptionsType() == BuiltinOptions.LocalResponseNormalizationOptions
+        op_options = op.BuiltinOptions()
+        lrn_options = LocalResponseNormalizationOptions()
+        lrn_options.Init(op_options.Bytes, op_options.Pos)
+        radius = lrn_options.Radius()
+        bias = lrn_options.Bias()
+        alpha = lrn_options.Alpha()
+        beta = lrn_options.Beta()
+        size = (radius * 2) + 1
+        alpha = alpha * size
+
+        # TFLite supports lrn only over the last dim
+        input_tensor_rank = len(input_tensor.tensor.ShapeAsNumpy())
+        axis = input_tensor_rank - 1
 
 Review comment:
   Yes, same, nhwc format. axis=3 

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