deepakbabel23 commented on a change in pull request #5617:
URL: https://github.com/apache/incubator-tvm/pull/5617#discussion_r431623053
##########
File path: python/tvm/relay/frontend/tensorflow.py
##########
@@ -2896,15 +2903,29 @@ def _parse_import_prerequisites(self, graph):
"""
missing_operators = set()
for node in graph.node:
+ try:
+ from tensorflow.python.framework import op_def_registry
+ except ImportError as e:
+ raise ImportError(
+ "Unable to import tensorflow which is required
{}".format(e))
+ getOpDef = op_def_registry._registered_ops.get if
hasattr(op_def_registry,\
+ "_registered_ops") else op_def_registry.get
+ op_def = getOpDef(node.op)
if node.op == "Placeholder" or node.op == 'PlaceholderWithDefault':
pass
elif node.op == "Const":
pass
+ elif node.op in ["PartitionedCall", "StatefulPartitionedCall"]:
+ pass
else:
if any([node.op in t for t in [_identity_list, _convert_map,
_convert_map_rnn,
_control_flow_nodes]]):
pass
+ elif op_def is not None and op_def.is_stateful:
+ raise Exception("Found a stateful operator in this graph
{}. "\
Review comment:
@srkreddy1238 , Sure that's a good idea. Then i can create a list of
stateful_partitioned_ops(eg. StatefulPartitioned,PartitionedOp) just like
_freezed_graph_pruned_op_list to provide extended info to the user. Does it
make sense?
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