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

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anijain2305 commented on a change in pull request #5848:
URL: https://github.com/apache/incubator-tvm/pull/5848#discussion_r448586450



##########
File path: python/tvm/relay/frontend/tflite.py
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@@ -263,21 +305,29 @@ def get_tensor_value(self, tensor_wrapper):
         except ImportError:
             raise ImportError("The tflite package must be installed")
 
+        # Read the data from the buffer. Also extract the shape.
+        # The shape is used later to reshape the data.
+        data = tensor_wrapper.buffer.DataAsNumpy()
+        shape = tensor_wrapper.tensor.ShapeAsNumpy()
+
+        # When TFLite buffer is of size 1 (scalar), then TFLite tensor shape is set to 0.
+        # Therefore, we set the shape to 1 for numpy reshape to work.  Set shape to 1 if the data is
+        # a scalar type
+        if data.size == 1 and isinstance(shape, int) and shape == 0:
+            shape = (1,)
+
+        if tensor_wrapper.tensor.Type() == TensorType.INT8:

Review comment:
       For the first comment, thanks, let me take a look.
   
   For the second suggestion for has_same_qnn_params, I think we do not need that. For all the ops where we have to check params are same, they have scalar scale and zero point. This is because per-axis quantization is limited to weights, and thus limited to conv2d and dense op where we do not need this check.
   
   




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