From 04f4192a081fdc2ef7f9de37994c63a6cae602c3 Mon Sep 17 00:00:00 2001
From: lucas_miranda <lucasmiranda42@gmail.com>
Date: Tue, 18 May 2021 19:26:03 +0200
Subject: [PATCH] Replaced for loop with vectorised mapping on ClusterOverlap
regularization layer
---
deepof/model_utils.py | 14 +++++++++-----
1 file changed, 9 insertions(+), 5 deletions(-)
diff --git a/deepof/model_utils.py b/deepof/model_utils.py
index b5763c28..37356da8 100644
--- a/deepof/model_utils.py
+++ b/deepof/model_utils.py
@@ -39,7 +39,7 @@ def compute_shannon_entropy(tensor):
@tf.function
def get_k_nearest_neighbors(tensor, k, index):
"""Retrieve indices of the k nearest neighbors in tensor to the vector with the specified index"""
- query = tensor[index]
+ query = tf.slice(tensor, index, 1)
distances = tf.norm(tensor - query, axis=1)
max_distance = tf.sort(distances)[k]
neighbourhood_mask = distances < max_distance
@@ -583,7 +583,7 @@ class ClusterOverlap(Layer):
config.update({"samples": self.samples})
return config
- #@tf.function
+ @tf.function
def call(self, inputs, **kwargs):
"""Updates Layer's call method"""
@@ -594,8 +594,12 @@ class ClusterOverlap(Layer):
# Iterate over samples and compute purity across neighbourhood
self.samples = tf.reduce_min([self.samples, tf.shape(encodings)[0]])
- random_idxs = range(encodings.shape[0])
- random_idxs = np.random.choice(random_idxs, self.samples)
+ random_idxs = tf.range(tf.shape(encodings)[0])
+ random_idxs = tf.random.categorical(
+ tf.expand_dims(random_idxs / tf.reduce_sum(random_idxs), 0),
+ self.samples,
+ dtype=tf.dtypes.int32,
+ )
@tf.function
def get_local_neighbourhood_entropy(index):
@@ -604,7 +608,7 @@ class ClusterOverlap(Layer):
)
purity_vector = tf.map_fn(
- get_local_neighbourhood_entropy, tf.constant(random_idxs), dtype=tf.dtypes.float32
+ get_local_neighbourhood_entropy, random_idxs, dtype=tf.dtypes.float32
)
### CANDIDATE FOR REMOVAL. EXPLORE HOW USEFUL THIS REALLY IS ###
--
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