From fcc104240637ecdcdcedf4652833985cda2b93f3 Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Wed, 17 Apr 2019 16:22:50 +0200
Subject: [PATCH] Fix the euclidean function so that its gradients don't become
nan. Also moves the bytes_to_human function
---
bob/learn/tensorflow/utils/util.py | 64 ++++++++++++++++++++++++++----
1 file changed, 56 insertions(+), 8 deletions(-)
diff --git a/bob/learn/tensorflow/utils/util.py b/bob/learn/tensorflow/utils/util.py
index a2fd7ebe..a9890cdc 100644
--- a/bob/learn/tensorflow/utils/util.py
+++ b/bob/learn/tensorflow/utils/util.py
@@ -6,6 +6,22 @@
import numpy
import tensorflow as tf
from tensorflow.python.client import device_lib
+from tensorflow.python.framework import function
+import tensorflow.keras.backend as K
+
+
+def keras_channels_index():
+ return -3 if K.image_data_format() == 'channels_first' else -1
+
+
+@function.Defun(tf.float32, tf.float32)
+def norm_grad(x, dy):
+ return tf.expand_dims(dy, -1) * (x / (tf.expand_dims(tf.norm(x, ord=2, axis=-1), -1) + 1.0e-19))
+
+
+@function.Defun(tf.float32, grad_func=norm_grad)
+def norm(x):
+ return tf.norm(x, ord=2, axis=-1)
def compute_euclidean_distance(x, y):
@@ -14,7 +30,8 @@ def compute_euclidean_distance(x, y):
"""
with tf.name_scope('euclidean_distance'):
- d = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(x, y)), 1))
+ # d = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(x, y)), 1))
+ d = norm(tf.subtract(x, y))
return d
@@ -167,10 +184,10 @@ def debug_embbeding(image, architecture, embbeding_dim=2, feature_layer="fc3"):
return embeddings
-def cdist(A):
+def pdist(A):
"""
Compute a pairwise euclidean distance in the same fashion
- as in scipy.spation.distance.cdist
+ as in scipy.spation.distance.pdist
"""
with tf.variable_scope('Pairwisedistance'):
ones_1 = tf.reshape(
@@ -199,7 +216,7 @@ def predict_using_tensors(embedding, labels, num=None):
# sample)
inf = tf.cast(tf.ones_like(labels), tf.float32) * numpy.inf
- distances = cdist(embedding)
+ distances = pdist(embedding)
distances = tf.matrix_set_diag(distances, inf)
indexes = tf.argmin(distances, axis=1)
return [labels[i] for i in tf.unstack(indexes, num=num)]
@@ -208,7 +225,7 @@ def predict_using_tensors(embedding, labels, num=None):
def compute_embedding_accuracy_tensors(embedding, labels, num=None):
"""
Compute the accuracy in a closed-set
-
+
**Parameters**
embeddings: `tf.Tensor`
@@ -232,7 +249,7 @@ def compute_embedding_accuracy_tensors(embedding, labels, num=None):
def compute_embedding_accuracy(embedding, labels):
"""
Compute the accuracy in a closed-set
-
+
**Parameters**
embeddings: :any:`numpy.array`
@@ -242,9 +259,9 @@ def compute_embedding_accuracy(embedding, labels):
Correspondent labels
"""
- from scipy.spatial.distance import cdist
+ from scipy.spatial.distance import pdist, squareform
- distances = cdist(embedding, embedding)
+ distances = squareform(pdist(embedding))
n_samples = embedding.shape[0]
@@ -344,3 +361,34 @@ def to_channels_first(image):
to_skimage = to_matplotlib = to_channels_last
to_bob = to_channels_first
+
+
+def bytes2human(n, format='%(value).1f %(symbol)s', symbols='customary'):
+ """Convert n bytes into a human readable string based on format.
+ From: https://code.activestate.com/recipes/578019-bytes-to-human-human-to-
+ bytes-converter/
+ Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>
+ License: MIT
+ symbols can be either "customary", "customary_ext", "iec" or "iec_ext",
+ see: http://goo.gl/kTQMs
+ """
+ SYMBOLS = {
+ 'customary': ('B', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y'),
+ 'customary_ext': ('byte', 'kilo', 'mega', 'giga', 'tera', 'peta',
+ 'exa', 'zetta', 'iotta'),
+ 'iec': ('Bi', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi', 'Yi'),
+ 'iec_ext': ('byte', 'kibi', 'mebi', 'gibi', 'tebi', 'pebi', 'exbi',
+ 'zebi', 'yobi'),
+ }
+ n = int(n)
+ if n < 0:
+ raise ValueError("n < 0")
+ symbols = SYMBOLS[symbols]
+ prefix = {}
+ for i, s in enumerate(symbols[1:]):
+ prefix[s] = 1 << (i + 1) * 10
+ for symbol in reversed(symbols[1:]):
+ if n >= prefix[symbol]:
+ value = float(n) / prefix[symbol]
+ return format % locals()
+ return format % dict(symbol=symbols[0], value=n)
--
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