From 39764a98f3c8791119901e7c61395d632b14a58d Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Tue, 29 Oct 2019 16:25:40 +0000
Subject: [PATCH] [Exec} Added missing comments
---
src/finn/core/multi_thresholding.py | 13 ++++++++++---
1 file changed, 10 insertions(+), 3 deletions(-)
diff --git a/src/finn/core/multi_thresholding.py b/src/finn/core/multi_thresholding.py
index 0e125895a..23b5cca5a 100755
--- a/src/finn/core/multi_thresholding.py
+++ b/src/finn/core/multi_thresholding.py
@@ -23,6 +23,7 @@ def execute(v, thresholds):
# assert if channel sizes do not match
assert v.shape[1] == thresholds.shape[0]
+ # save the required shape sizes for the loops (N, C and B)
num_batch = v.shape[0]
num_channel = v.shape[1]
@@ -31,6 +32,7 @@ def execute(v, thresholds):
# reshape inputs to enable channel-wise reading
vr = v.reshape((v.shape[0], v.shape[1], -1))
+ # save the new shape size of the images
num_img_elem = vr.shape[2]
# initiate output tensor
@@ -39,11 +41,16 @@ def execute(v, thresholds):
# iterate over thresholds channel-wise
for t in range(num_channel):
channel_thresh = thresholds[t]
+
+ # iterate over batches
for b in range(num_batch):
+
+ # iterate over image elements on which the thresholds should be applied
for elem in range(num_img_elem):
- print(vr[b][t][elem])
+
+ # iterate over the different thresholds that correspond to one channel
for a in range(num_act):
- print(channel_thresh[a])
+ # apply successive thresholding to every element of one channel
ret[b][t][elem] += compare(vr[b][t][elem], channel_thresh[a])
- print(ret)
+
return ret.reshape(v.shape)
--
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