MLBEDSW-2528: MLCE-219: Custom operator pass through
- Fixed custom operator pass through
- Added error printing functions for operators and tensor
- Minor cleanup of custom exception handling
Signed-off-by: Tim Hall <tim.hall@arm.com>
Change-Id: Idf295df1e4c544381dc480244d880c32fb285e38
diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py
index 5667aff..9d312e5 100644
--- a/ethosu/vela/tflite_reader.py
+++ b/ethosu/vela/tflite_reader.py
@@ -20,6 +20,7 @@
import numpy as np
from .errors import InputFileError
+from .errors import TensorError
from .nn_graph import Graph
from .nn_graph import Subgraph
from .operation import Operation
@@ -69,14 +70,16 @@
self.tensors.append(self.parse_tensor(subgraph.Tensors(idx)))
for idx in range(subgraph.OperatorsLength()):
- self.parse_operator(subgraph.Operators(idx))
+ self.parse_operator(idx, subgraph.Operators(idx))
- self.outputs = [self.tensors[idx] for idx in subgraph.OutputsAsNumpy()]
- self.inputs = [self.tensors[idx] for idx in subgraph.InputsAsNumpy()]
+ self.outputs = self.get_tensors_from_indices_remove_duplicates(subgraph.OutputsAsNumpy(), "output")
+ self.inputs = self.get_tensors_from_indices_remove_duplicates(subgraph.InputsAsNumpy(), "input")
# Fix up tensors without operations. Generate either Placeholder or Constant ops
for tens in self.inputs:
- assert not tens.ops
+ if tens.ops != []:
+ TensorError(tens, "This subgraph input tensor has unexpected driving operators.")
+
op = Operation("Placeholder", tens.name)
op.outputs = [tens]
tens.ops = [op]
@@ -87,6 +90,21 @@
op.outputs = [tens]
tens.ops = [op]
+ def get_tensors_from_indices_remove_duplicates(self, indices, warning_str):
+ tensors = []
+ for idx in indices:
+ tensor = self.tensors[idx]
+ if tensor not in tensors:
+ tensors.append(tensor)
+ else:
+ print(
+ "Warning: Subgraph {0} tensor ({1}) with idx = {2} already seen. Removing the duplicate.".format(
+ warning_str, tensor, idx
+ )
+ )
+
+ return tensors
+
def parse_tensor(self, tens_data):
np_shape = tens_data.ShapeAsNumpy()
shape = list(np_shape) if type(np_shape) is np.ndarray else []
@@ -121,7 +139,7 @@
tens.values = tens.quantization.dequantize(tens.quant_values)
return tens
- def parse_operator(self, op_data):
+ def parse_operator(self, op_index, op_data):
op_type, opt_serializer = self.graph.operator_codes[op_data.OpcodeIndex()]
inputs = [self.tensors[idx] for idx in op_data.InputsAsNumpy()]
outputs = [self.tensors[idx] for idx in op_data.OutputsAsNumpy()]
@@ -129,6 +147,7 @@
if len(outputs):
name = outputs[0].name
op = Operation(op_type, name)
+ op.op_index = op_index
op.inputs = inputs
op.outputs = outputs
for out in op.outputs:
@@ -143,7 +162,7 @@
inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0))
if opt_serializer is not None:
- op.attrs = opt_serializer.deserialize(op_data.BuiltinOptions(), op_data.CustomOptionsAsNumpy())
+ op.attrs = opt_serializer.deserialize(op_data)
if "stride_w" in op.attrs:
op.attrs["strides"] = (1, op.attrs["stride_h"], op.attrs["stride_w"], 1)