[GitHub] [incubator-tvm] anijain2305 commented on a change in pull request #5848: [TFLite] QNN support for TFLite 2.1.0 quantized models

[GitBox]

anijain2305 commented on a change in pull request #5848:
URL: https://github.com/apache/incubator-tvm/pull/5848#discussion_r445827227



##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -2566,17 +2613,27 @@ def convert_quantize(self, op):
         input_tensors = self.get_input_tensors(op)
         assert len(input_tensors) == 1, "input tensors length should be 1"
         input_tensor = input_tensors[0]
+        input_tensor_type_str = 
self.get_tensor_type_str(input_tensor.tensor.Type())
         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]
+        output_tensor_type_str = 
self.get_tensor_type_str(output_tensor.tensor.Type())
 
         # The output must be quantized
         assert output_tensor.qnn_params
-        # Quantize the input
-        out = self.quantize(in_expr, output_tensor)
 
+        # TFLite Quantize op can also act as Requantize op
+        if input_tensor_type_str == "float32":
+            out = self.quantize(in_expr, output_tensor)
+        else:
+            out = _qnn.op.requantize(in_expr,
+                                     
input_scale=input_tensor.qnn_params['scale'],
+                                     
input_zero_point=input_tensor.qnn_params['zero_point'],
+                                     
output_scale=output_tensor.qnn_params['scale'],
+                                     
output_zero_point=output_tensor.qnn_params['zero_point'],
+                                     out_dtype=output_tensor_type_str)
         return out

Review comment:
       
![image](https://user-images.githubusercontent.com/13822661/85793474-ebec7980-b6e9-11ea-8e53-fa3121ba50c0.png)
   
   Above test case added to force both types of quantize nodes




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