dhruvaray commented on a change in pull request #5329:
URL: https://github.com/apache/incubator-tvm/pull/5329#discussion_r422081874
##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -579,6 +582,63 @@ def convert_tanh(self, op):
return out
+ def convert_range(self, op):
+ """Convert TFLite Range"""
+ try:
+ from tflite.Operator import Operator
+ from tflite.TensorType import TensorType
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
+ if self.is_quantized(op):
+ raise tvm.error.OpNotImplemented(
+ 'TFlite quantized RANGE operator is not supported yet.')
+
+ assert isinstance(op, Operator)
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) == 3, "input tensors length should be 3"
+
+ start, limit, delta = input_tensors[0], input_tensors[1],
input_tensors[2]
+ expressions = []
+
+ for t in [start, limit, delta]:
+ if self.has_expr(t.tensor_idx):
+ expressions.append(self.get_expr(t.tensor_idx))
+ else:
+ tensor_type = self.get_tensor_type_str(t.tensor.Type())
+ tensor_value = self.get_tensor_value(t)
+ expressions.append(self.exp_tab.new_const(tensor_value,
dtype=tensor_type))
+
+ #out type inference
+ if delta.tensor.Type() == TensorType.FLOAT32:
+ out_type = self.get_tensor_type_str(delta.tensor.Type())
+ else:
+ out_type = self.get_tensor_type_str(start.tensor.Type())
+
+ #put type here form op
+ out = _op.arange(expressions[0], expressions[1], expressions[2],
out_type)
+
+ return out
+
+ def convert_shape(self, op):
+ """Convert TFLite Shape"""
+ try:
+ from tflite.Operator import Operator
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
+ if self.is_quantized(op):
+ raise tvm.error.OpNotImplemented(
+ 'TFlite quantized SHAPE operator is not supported yet.')
+
Review comment:
removed these checks...
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