def test_seed(self):
# make random input
input_batch = tf.random.uniform((16, 50, 50, 3), maxval=255)
input_batch = tf.cast(input_batch, tf.uint8)
# make random labels
input_labels = tf.random.uniform((16, ),
minval=0,
maxval=2,
dtype=tf.int32)
input_proba = tf.one_hot(input_labels, 20, dtype=tf.float32)
# generate output batches
output_batch1, output_proba1 = augment(input_batch,
input_proba,
mixup=0.75,
seed=123)
output_batch2, output_proba2 = augment(input_batch,
input_proba,
mixup=0.75,
seed=234)
output_batch3, output_proba3 = augment(input_batch,
input_proba,
mixup=0.75,
seed=123)
# compare
self.assertNotAllEqual(output_batch1, output_batch2)
self.assertNotAllEqual(output_proba1, output_proba2)
self.assertAllEqual(output_batch1, output_batch3)
self.assertAllEqual(output_proba1, output_proba3)
def test_yes_mixup(self):
# make random labels
input_labels = tf.random.uniform((50, ),
minval=0,
maxval=2,
dtype=tf.int32)
# make images from labels
input_batch = tf.cast(255 * input_labels, tf.uint8)
input_batch = tf.tile(tf.reshape(input_batch, (-1, 1, 1, 1)),
(1, 5, 5, 3))
# transform labels to one-hot
input_proba = tf.one_hot(input_labels, 2, dtype=tf.float32)
# apply mixup
output_batch, output_proba = augment(input_batch,
input_proba,
rotation=0,
flip_vertically=True,
flip_horizontally=True,
hue=0,
saturation=0,
brightness=0,
gamma_corr=0,
cutout=0,
mixup=0.9)
# check that probabilities sum up to 1
self.assertAllClose(output_proba[:, 0] + output_proba[:, 1],
tf.ones((50)))
# compare probabilities to center pixel values
self.assertAllClose(output_proba[:, 1], output_batch[:, 3, 3, 0])
def test_in_model(self):
# create input layers for images and labels
x_in = tf.keras.layers.Input(shape=(224, 224, 3), dtype=tf.uint8)
y_in = tf.keras.layers.Input(shape=(1000), dtype=tf.float32)
# add augmentation layer
x_out, y_out = Augment(training_only=False, mixup=0.75,
seed=111)([x_in, y_in])
# build a model
model = tf.keras.models.Model(inputs=[x_in, y_in],
outputs=[x_out, y_out])
# make random input
input_images = tf.random.uniform((5, 224, 224, 3), maxval=255)
input_images = tf.cast(input_images, tf.uint8)
input_prob = tf.random.uniform((5, ), maxval=2, dtype=tf.int32)
input_prob = tf.one_hot(input_prob, 1000)
# run prediction
output_images_test, output_prob_test = model.predict(
[input_images, input_prob])
# generate reference
output_images_ref, output_prob_ref = augment(x=input_images,
y=input_prob,
mixup=0.75,
seed=111)
# compare
self.assertAllEqual(output_images_test, output_images_ref)
self.assertAllEqual(output_prob_test, output_prob_ref)
def test_uint8_vs_float32(self):
# make random input
input_batch = tf.random.uniform((64, 32, 32, 3), maxval=255)
input_batch = tf.cast(input_batch, tf.uint8)
# apply identity transformation
output_batch_ref = augment(input_batch, output_type=tf.uint8, seed=96)
output_batch_float = augment(input_batch,
output_type=tf.float32,
seed=96)
# check output types
self.assertTrue(output_batch_ref.dtype == tf.uint8)
self.assertTrue(output_batch_float.dtype == tf.float32)
# cast back to uint8 and compare: expected same output
output_batch_test = tf.cast(
255 * tf.clip_by_value(output_batch_float, 0, 1), tf.uint8)
self.assertAllEqual(output_batch_ref, output_batch_test)
def test_no_mixup(self):
# make random input
input_batch = tf.random.uniform((8, 8, 8, 3), maxval=255)
input_batch = tf.cast(input_batch, tf.uint8)
input_labels = tf.random.uniform((8, 1000))
# apply random transformation
_, output_labels = augment(input_batch, input_labels)
# compare labels: expected same
self.assertAllEqual(input_labels, output_labels)
def test_identity(self):
# make random input
input_batch = tf.random.uniform((5, 23, 45, 3), maxval=255)
input_batch = tf.cast(input_batch, tf.uint8)
# apply identity transformation
output_batch = augment(input_batch,
output_type=tf.uint8,
**BYPASS_PARAMS)
# compare: expected same output
self.assertAllEqual(output_batch, input_batch)
def test_color_inversion(self):
# make random input
input_batch = tf.zeros((5, 23, 45, 3), tf.uint8)
# apply color inversion only
params = BYPASS_PARAMS.copy()
params['color_inversion'] = True
output_batch = augment(input_batch, output_type=tf.uint8, **params)
# compare colors
comp = numpy.logical_xor(input_batch == output_batch,
input_batch == 255 - output_batch)
self.assertTrue(numpy.all(comp))
def test_center_pixel(self):
# make random grayscale input
input_batch = tf.random.uniform((32, 1, 1, 1), maxval=255)
input_batch = tf.cast(input_batch, tf.uint8)
input_batch = tf.tile(input_batch, (1, 16, 16, 3))
# apply transformations keeping the center pixel color unchanged
output_batch = augment(input_batch,
output_type=tf.uint8,
rotation=90,
flip_vertically=True,
flip_horizontally=True,
hue=0,
saturation=0,
brightness=0,
gamma_corr=0,
cutout=0,
mixup=0)
# compare center pixel colors: expected same
self.assertAllEqual(output_batch[:, 8, 8, :], input_batch[:, 8, 8, :])
def test_in_model_without_labels(self):
# create input layer
x_in = tf.keras.layers.Input(shape=(224, 224, 3), dtype=tf.uint8)
# add augmentation layer
x_out = Augment(training_only=False, seed=111)(x_in)
# build a model
model = tf.keras.models.Model(inputs=x_in, outputs=x_out)
# make random input
input_images = tf.random.uniform((5, 224, 224, 3), maxval=255)
input_images = tf.cast(input_images, tf.uint8)
# run prediction
output_images = model.predict(input_images)
# generate reference
output_images_ref = augment(x=input_images, seed=111)
# compare
self.assertAllEqual(output_images, output_images_ref)
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '10'
import matplotlib.pyplot as plt
import numpy
import tensorflow as tf
import tensorflow_datasets as tfds
import timeit
from fast_augment import center_crop, augment
# benchmark on a synthetic input
batch = tf.cast(tf.random.uniform((128, 224, 224, 3), maxval=255), tf.uint8)
labels = tf.zeros((batch.shape[0], 1000))
number = 1000
time = timeit.timeit(lambda: augment(batch, labels), number=number)
print('Sampling %d batches of %s size in %0.3f s' %
(number, batch.shape, time))
# render an example on a real data
print('Getting tf_flowers dataset...')
data, info = tfds.load('tf_flowers', split='train', with_info=True)
class_names = info.features['label'].names
# make batches
data = data.map(lambda x: [
center_crop(x['image'], size=(224, 224)), # image
tf.one_hot(x['label'], 37) # probabilities
])
# apply augmentation
def test_specific_output_size(self):
input_batch = tf.zeros((7, 55, 66, 3), dtype=tf.uint8)
width, height = 88, 77
output_batch = augment(input_batch, output_size=[width, height])
self.assertEqual(output_batch.shape, (7, height, width, 3))
def test_default_output_size(self):
input_batch = tf.zeros((5, 123, 234, 3), dtype=tf.uint8)
output_batch = augment(input_batch)
self.assertEqual(output_batch.shape, input_batch.shape)
