wyc-ruiker commented on a change in pull request #4805: [Frontend][TFlite] Add
parser support for relu6, leaky_relu, relu_n1_to_1, log_softmax
URL: https://github.com/apache/incubator-tvm/pull/4805#discussion_r374724376
##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -463,6 +484,114 @@ def convert_relu(self, op):
return out
+ def convert_relu6(self, op):
+ """Convert TFLite ReLU6"""
+ try:
+ from tflite.Operator import Operator
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
+ assert isinstance(op, Operator)
+ 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)
+
+ 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:
+ in_expr = self.dequantize(in_expr, input_tensor)
+ out = _op.clip(in_expr, a_min=0, a_max=6)
+ if output_tensor.qnn_params:
+ out = self.quantize(out, output_tensor)
+
+ return out
+
+ def convert_leaky_relu(self, op):
+ """Convert TFLite LEAKY_RELU"""
+ try:
+ from tflite.Operator import Operator
+ from tflite.BuiltinOptions import BuiltinOptions
+ from tflite.LeakyReluOptions import LeakyReluOptions
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
+ assert isinstance(op, Operator)
+ 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)
+
+ assert op.BuiltinOptionsType() == BuiltinOptions.LeakyReluOptions
+ op_options = op.BuiltinOptions()
+ leaky_relu_options = LeakyReluOptions()
+ leaky_relu_options.Init(op_options.Bytes, op_options.Pos)
+ alpha_tensor = leaky_relu_options.Alpha()
+
+ 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:
+ in_expr = self.dequantize(in_expr, input_tensor)
+ out = _op.nn.leaky_relu(in_expr, alpha_tensor)
+ if output_tensor.qnn_params:
+ out = self.quantize(out, output_tensor)
+
+ return out
+
+ def convert_relu_n1_to_1(self, op):
+ """Convert TFLite RELU_N1_TO_1"""
Review comment:
Not important, I think it should be `"""Convert TFLite ReLU_n1_to_1"""` to
align with `"""One iteration of ReLU_n1_to_1"""`.
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