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