From 227305419075d3a57b245e189de3d51274c8100f Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Mon, 4 Nov 2019 15:29:58 +0000
Subject: [PATCH] [Transform] another go at fixing readable tensor names
switched to giving random names first because the old names can
still contain enumerated names
---
src/finn/transformation/general.py | 21 ++++++++++++++-------
1 file changed, 14 insertions(+), 7 deletions(-)
diff --git a/src/finn/transformation/general.py b/src/finn/transformation/general.py
index f233a1321..f9e00a274 100644
--- a/src/finn/transformation/general.py
+++ b/src/finn/transformation/general.py
@@ -1,3 +1,7 @@
+import random
+import string
+
+
def give_unique_node_names(model):
"""Give unique names to each node in the graph using enumeration."""
optype_count = {}
@@ -10,13 +14,16 @@ def give_unique_node_names(model):
return (model, False)
-def give_enumerated_tensor_names(model):
- """Give enumerated tensor names to all internal tensors."""
- tensor_ind = 0
+def give_random_tensor_names(model):
+ """Give random tensor names to all tensors."""
+
+ def randomStringDigits(stringLength=6):
+ lettersAndDigits = string.ascii_letters + string.digits
+ return "".join(random.choice(lettersAndDigits) for i in range(stringLength))
+
names = model.get_all_tensor_names()
for name in names:
- model.rename_tensor(name, "%d" % tensor_ind)
- tensor_ind += 1
+ model.rename_tensor(name, randomStringDigits())
# return model_was_changed = False as single iteration is always enough
return (model, False)
@@ -25,8 +32,8 @@ def give_readable_tensor_names(model):
"""Give more human-readable names to all internal tensors. It's recommended
to apply give_unique_node_names prior to this transform."""
# to ensure we can use rename_tensor safely (without renaming existing
- # tensors) we start by giving enumerated names to all tensors
- model = model.transform_single(give_enumerated_tensor_names)
+ # tensors) we start by giving random names to all tensors
+ model = model.transform_single(give_random_tensor_names)
graph = model.graph
for n in graph.node:
out_num = 0
--
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