siju-samuel commented on a change in pull request #5848:
URL: https://github.com/apache/incubator-tvm/pull/5848#discussion_r447554160
##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -651,12 +701,43 @@ def convert_shape(self, op):
def convert_relu(self, op):
"""Convert TFLite ReLU"""
+ try:
+ from tflite.ActivationFunctionType import ActivationFunctionType
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
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)
- out = _op.nn.relu(in_expr)
+
+ output_tensors = self.get_output_tensors(op)
+ assert len(output_tensors) == 1, "output tensors length should be 1"
+ output_tensor = output_tensors[0]
+
+ if input_tensor.qnn_params:
+ # Quantize a float value to an quantized integer value
+ scale_val =
get_scalar_from_constant(input_tensor.qnn_params['scale'])
+ zero_point_val =
get_scalar_from_constant(input_tensor.qnn_params['zero_point'])
+
+ output_tensor_type_str =
self.get_tensor_type_str(output_tensor.tensor.Type())
+ out = self.convert_qnn_fused_activation_function(\
+ expr=in_expr,
+ fused_activation_fn=ActivationFunctionType.RELU,
+ scale=scale_val,
+ zero_point=zero_point_val,
+ dtype=output_tensor_type_str)
+ else:
+ out = _op.clip(in_expr, a_min=0, a_max=6)
Review comment:
This should be relu, not clip
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -1686,38 +1812,33 @@ def test_forward_squeeze():
def _test_quantize_dequantize(data):
""" One iteration of quantize and dequantize """
- # Define a dummy model
+ # Keras model to force TFLite converter to insert 2 TFLite quantize ops.
+ # First TFLite quantize op converts float32 tensor to int8 tensor - Qnn
quantize.
+ # Second TLite quantize op converts int8 tensor to int8 tensor - Qnn
requantize.
Review comment:
TLite -> TFLite
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