From b0941313b762c3fd90a8f59d9a62e4cc47ad7462 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Fri, 1 Nov 2019 15:01:47 +0000
Subject: [PATCH] [MultiThres] support applying same threshold to all channels
---
src/finn/core/multithreshold.py | 10 ++++++----
1 file changed, 6 insertions(+), 4 deletions(-)
diff --git a/src/finn/core/multithreshold.py b/src/finn/core/multithreshold.py
index 23b5cca5a..009259c57 100755
--- a/src/finn/core/multithreshold.py
+++ b/src/finn/core/multithreshold.py
@@ -17,11 +17,13 @@ def execute(v, thresholds):
# W : Width of the input images
#
# the thresholds are expected to be in the shape (C, B)
- # C : Number of channels (must be the same value as C in input tensor)
+ # C : Number of channels (must be the same value as C in input tensor or 1
+ # if all channels use the same threshold value)
# B : Desired activation steps => i.e. for 4-bit activation, B=7 (2^(n)-1 and n=4)
- # assert if channel sizes do not match
- assert v.shape[1] == thresholds.shape[0]
+ # assert threshold shape
+ is_global_threshold = thresholds.shape[0] == 1
+ assert (v.shape[1] == thresholds.shape[0]) or is_global_threshold
# save the required shape sizes for the loops (N, C and B)
num_batch = v.shape[0]
@@ -40,7 +42,7 @@ def execute(v, thresholds):
# iterate over thresholds channel-wise
for t in range(num_channel):
- channel_thresh = thresholds[t]
+ channel_thresh = thresholds[0] if is_global_threshold else thresholds[t]
# iterate over batches
for b in range(num_batch):
--
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