From 95398b3b446dd6129c86f7b891ecf5f779a89787 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Thu, 17 Nov 2016 12:19:32 +0100
Subject: [PATCH] Fixed issue with feature normalization
---
.../datashuffler/TripletWithFastSelectionDisk.py | 16 +++++-----------
1 file changed, 5 insertions(+), 11 deletions(-)
diff --git a/bob/learn/tensorflow/datashuffler/TripletWithFastSelectionDisk.py b/bob/learn/tensorflow/datashuffler/TripletWithFastSelectionDisk.py
index e126e9b6..12759d91 100644
--- a/bob/learn/tensorflow/datashuffler/TripletWithFastSelectionDisk.py
+++ b/bob/learn/tensorflow/datashuffler/TripletWithFastSelectionDisk.py
@@ -106,13 +106,9 @@ class TripletWithFastSelectionDisk(Triplet, Disk, OnLineSampling):
for i in range(self.shape[0]):
file_name_a, file_name_p, file_name_n = self.get_one_triplet(self.data, self.labels)
- sample_a[i, ...] = self.load_from_file(str(file_name_a))
- sample_p[i, ...] = self.load_from_file(str(file_name_p))
- sample_n[i, ...] = self.load_from_file(str(file_name_n))
-
- sample_a = self.normalize_sample(sample_a)
- sample_p = self.normalize_sample(sample_p)
- sample_n = self.normalize_sample(sample_n)
+ sample_a[i, ...] = self.normalize_sample(self.load_from_file(str(file_name_a)))
+ sample_p[i, ...] = self.normalize_sample(self.load_from_file(str(file_name_p)))
+ sample_n[i, ...] = self.normalize_sample(self.load_from_file(str(file_name_n)))
return [sample_a, sample_p, sample_n]
@@ -180,9 +176,8 @@ class TripletWithFastSelectionDisk(Triplet, Disk, OnLineSampling):
indexes = numpy.where(self.labels == l)[0]
numpy.random.shuffle(indexes)
file_name = self.data[indexes[0], ...]
- samples_p[i, ...] = self.load_from_file(str(file_name))
+ samples_p[i, ...] = self.normalize_sample(self.load_from_file(str(file_name)))
- samples_p = self.normalize_sample(samples_p)
embedding_p = self.project(samples_p)
# Computing the distances
@@ -212,8 +207,7 @@ class TripletWithFastSelectionDisk(Triplet, Disk, OnLineSampling):
samples_n = numpy.zeros(shape=self.shape, dtype='float32')
for i in range(shape[0]):
file_name = self.data[indexes[i], ...]
- temp_samples_n[i, ...] = self.load_from_file(str(file_name))
- temp_samples_n = self.normalize_sample(temp_samples_n)
+ temp_samples_n[i, ...] = self.normalize_sample(self.load_from_file(str(file_name)))
# Computing all the embeddings
embedding_temp_n = self.project(temp_samples_n)
--
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