d-smirnov commented on a change in pull request #6127:
URL: https://github.com/apache/incubator-tvm/pull/6127#discussion_r460439414
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -1850,7 +1850,7 @@ def _test_quantize_dequantize(data):
# First TFLite quantize op converts float32 tensor to int8 tensor - Qnn
quantize.
# Second TFLite quantize op converts int8 tensor to int8 tensor - Qnn
requantize.
data_in = tf.keras.layers.Input(shape=data.shape[1:])
- relu = tf.keras.layers.ReLU()(data_in)
+ relu = tf.keras.layers.ReLU()(data)
Review comment:
The idea is to use data as constant parameter for quantize operation
which will be inserted on quantisation step
##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -2726,7 +2726,13 @@ def convert_quantize(self, 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)
+
+ if self.has_expr(input_tensor.tensor_idx):
+ in_expr = self.get_expr(input_tensor.tensor_idx)
+ else:
+ in_value = self.get_tensor_value(input_tensor)
Review comment:
Deepspeech itself is here
https://github.com/mozilla/DeepSpeech/releases/tag/v0.7.4
However the model which was used was a quantised version fro internal model
zoo. I am not sure that I can share it fully
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