From 15e8facada13bc747477d9a521e628fbe2a5ff67 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Mon, 6 Jul 2020 11:25:39 +0100
Subject: [PATCH] [Transform] introduce RemoveUnusedInitAndValueInfo
---
src/finn/transformation/general.py | 57 +++++++++++++++++++++---------
1 file changed, 41 insertions(+), 16 deletions(-)
diff --git a/src/finn/transformation/general.py b/src/finn/transformation/general.py
index 488391740..9a238ecf1 100644
--- a/src/finn/transformation/general.py
+++ b/src/finn/transformation/general.py
@@ -31,6 +31,32 @@ from finn.transformation import Transformation
from toposort import toposort_flatten
+class RemoveUnusedInitAndValueInfo(Transformation):
+ "Remove any unused initializers and value_info in the graph."
+
+ def apply(self, model):
+ graph_modified = False
+ onnx_graph = model.model.graph
+ # build a set of tensors that we actually use in the graph nodes
+ used_tensors = set()
+ for node in model.graph.node:
+ for i in node.input:
+ used_tensors.add(i)
+ for o in node.output:
+ used_tensors.add(o)
+ # remove initializers and value_info not in the used set
+ for init in onnx_graph.initializer:
+ if init.name not in used_tensors:
+ onnx_graph.initializer.remove(init)
+ graph_modified = True
+ for vi in onnx_graph.value_info:
+ if vi.name not in used_tensors:
+ onnx_graph.value_info.remove(vi)
+ graph_modified = True
+
+ return (model, graph_modified)
+
+
class GiveUniqueNodeNames(Transformation):
"""Give unique names to each node in the graph using enumeration."""
@@ -121,24 +147,23 @@ class GiveUniqueParameterTensors(Transformation):
class SortGraph(Transformation):
""" Returns the model with its node list sorted topologically.
- Any ONNX graph to be executed must have a topologically sorted node list, as dictated
- by the ONNX standard.
+ Any ONNX graph to be executed must have a topologically sorted node list,
+ as dictated by the ONNX standard.
"""
-
+
# Notes on SortGraph performance:
- # benchmark in tests/transformation/test_sort_graph.py
- #
- # The algorithm doesn't move initializers so its performance should only depend on
- # the number of nodes
- #
- # Relative order of magnitudes for time per step:
- # - Gather graph structure: base
- # - Sort nodes: 0.1 of base
- # - Remove and insert in order : 0.001 of base
- #
- # Notes:
- # Remove nodes and insert them in order:
- # Probably this is faster than copying initializers and more robust in general
+ # benchmark in tests/transformation/test_sort_graph.py
+ # The algorithm doesn't move initializers so its performance should only depend on
+ # the number of nodes
+ #
+ # Relative order of magnitudes for time per step:
+ # - Gather graph structure: base
+ # - Sort nodes: 0.1 of base
+ # - Remove and insert in order : 0.001 of base
+ #
+ # Notes:
+ # Remove nodes and insert them in order:
+ # Probably this is faster than copying initializers and more robust in general
def apply(self, model):
# Gather graph structure
--
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