Commit b066530f authored by Amir MOHAMMADI's avatar Amir MOHAMMADI

code clean-up

parent 55f2356e
......@@ -73,7 +73,8 @@ def append_image_augmentation(image, gray_scale=False,
if output_shape is not None:
assert len(output_shape) == 2
image = tf.image.resize_image_with_crop_or_pad(image, output_shape[0], output_shape[1])
image = tf.image.resize_image_with_crop_or_pad(
image, output_shape[0], output_shape[1])
if random_flip:
image = tf.image.random_flip_left_right(image)
......@@ -136,15 +137,18 @@ def triplets_random_generator(input_data, input_labels):
input_labels = numpy.array(input_labels)
total_samples = input_data.shape[0]
indexes_per_labels = arrange_indexes_by_label(input_labels, possible_labels)
indexes_per_labels = arrange_indexes_by_label(
input_labels, possible_labels)
# searching for random triplets
offset_class = 0
for i in range(total_samples):
anchor_sample = input_data[indexes_per_labels[possible_labels[offset_class]][numpy.random.randint(len(indexes_per_labels[possible_labels[offset_class]]))], ...]
anchor_sample = input_data[indexes_per_labels[possible_labels[offset_class]][numpy.random.randint(
len(indexes_per_labels[possible_labels[offset_class]]))], ...]
positive_sample = input_data[indexes_per_labels[possible_labels[offset_class]][numpy.random.randint(len(indexes_per_labels[possible_labels[offset_class]]))], ...]
positive_sample = input_data[indexes_per_labels[possible_labels[offset_class]][numpy.random.randint(
len(indexes_per_labels[possible_labels[offset_class]]))], ...]
# Changing the class
offset_class += 1
......@@ -152,10 +156,11 @@ def triplets_random_generator(input_data, input_labels):
if offset_class == len(possible_labels):
offset_class = 0
negative_sample = input_data[indexes_per_labels[possible_labels[offset_class]][numpy.random.randint(len(indexes_per_labels[possible_labels[offset_class]]))], ...]
negative_sample = input_data[indexes_per_labels[possible_labels[offset_class]][numpy.random.randint(
len(indexes_per_labels[possible_labels[offset_class]]))], ...]
append(str(anchor_sample), str(positive_sample), str(negative_sample))
#yield anchor, positive, negative
# yield anchor, positive, negative
return anchor, positive, negative
......@@ -191,13 +196,16 @@ def siamease_pairs_generator(input_data, input_labels):
# Filtering the samples by label and shuffling all the indexes
#indexes_per_labels = dict()
#for l in possible_labels:
# for l in possible_labels:
# indexes_per_labels[l] = numpy.where(input_labels == l)[0]
# numpy.random.shuffle(indexes_per_labels[l])
indexes_per_labels = arrange_indexes_by_label(input_labels, possible_labels)
indexes_per_labels = arrange_indexes_by_label(
input_labels, possible_labels)
left_possible_indexes = numpy.random.choice(possible_labels, total_samples, replace=True)
right_possible_indexes = numpy.random.choice(possible_labels, total_samples, replace=True)
left_possible_indexes = numpy.random.choice(
possible_labels, total_samples, replace=True)
right_possible_indexes = numpy.random.choice(
possible_labels, total_samples, replace=True)
genuine = True
for i in range(total_samples):
......@@ -207,10 +215,12 @@ def siamease_pairs_generator(input_data, input_labels):
class_index = left_possible_indexes[i]
# Now selecting the samples for the pair
left = input_data[indexes_per_labels[class_index][numpy.random.randint(len(indexes_per_labels[class_index]))]]
right = input_data[indexes_per_labels[class_index][numpy.random.randint(len(indexes_per_labels[class_index]))]]
left = input_data[indexes_per_labels[class_index][numpy.random.randint(
len(indexes_per_labels[class_index]))]]
right = input_data[indexes_per_labels[class_index][numpy.random.randint(
len(indexes_per_labels[class_index]))]]
append(left, right, 0)
#yield left, right, 0
# yield left, right, 0
else:
# Selecting the 2 classes
class_index = list()
......@@ -219,7 +229,7 @@ def siamease_pairs_generator(input_data, input_labels):
# Finding the right pair
j = i
# TODO: Lame solution. Fix this
while j < total_samples: # Here is an unidiretinal search for the negative pair
while j < total_samples: # Here is an unidiretinal search for the negative pair
if left_possible_indexes[i] != right_possible_indexes[j]:
class_index.append(right_possible_indexes[j])
break
......@@ -227,11 +237,12 @@ def siamease_pairs_generator(input_data, input_labels):
if j < total_samples:
# Now selecting the samples for the pair
left = input_data[indexes_per_labels[class_index[0]][numpy.random.randint(len(indexes_per_labels[class_index[0]]))]]
right = input_data[indexes_per_labels[class_index[1]][numpy.random.randint(len(indexes_per_labels[class_index[1]]))]]
left = input_data[indexes_per_labels[class_index[0]][numpy.random.randint(
len(indexes_per_labels[class_index[0]]))]]
right = input_data[indexes_per_labels[class_index[1]][numpy.random.randint(
len(indexes_per_labels[class_index[1]]))]]
append(left, right, 1)
genuine = not genuine
return left_data, right_data, labels
......@@ -296,3 +307,30 @@ def tf_repeat(tensor, repeats):
tiled_tensor = tf.tile(expanded_tensor, multiples=multiples)
repeated_tesnor = tf.reshape(tiled_tensor, tf.shape(tensor) * repeats)
return repeated_tesnor
def all_patches(image, label, key, size):
"""Extracts all patches of an image
Parameters
----------
image
The image should be channels_last format and already batched.
label
The label for the image
key
The key for the image
size : (int, int)
The height and width of the blocks.
Returns
-------
(blocks, label, key)
The non-overlapping blocks of size from image and labels and keys are
repeated.
"""
blocks, n_blocks = blocks_tensorflow(image, size)
# duplicate label and key as n_blocks
label = tf_repeat(label, [n_blocks])
key = tf_repeat(key, [n_blocks])
return blocks, label, key
This diff is collapsed.
......@@ -3,12 +3,14 @@
# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
import tensorflow as tf
slim = tf.contrib.slim
import tensorflow.contrib.slim as slim
def append_logits(graph, n_classes, reuse=False, l2_regularizer=0.001, weights_std=0.1):
return slim.fully_connected(graph, n_classes, activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=weights_std),
weights_regularizer=slim.l2_regularizer(l2_regularizer),
scope='Logits', reuse=reuse)
def append_logits(graph, n_classes, reuse=False, l2_regularizer=0.001,
weights_std=0.1):
return slim.fully_connected(
graph, n_classes, activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(
stddev=weights_std),
weights_regularizer=slim.l2_regularizer(l2_regularizer),
scope='Logits', reuse=reuse)
Markdown is supported
0%
or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment