def get_params():
args = get_args(image_cols = ['Image'])
input_params = InputParameters(args)
training_params = TrainingParameters(args)
image_generation_params = ImageGenerationParameters(args)
transformation_params = ImageDataTransformation.Parameters(samplewise_mean = True)
return input_params, training_params, image_generation_params, transformation_params
def test_transform_with_featurewise_mean_fit_not_called(self):
#Arrange
images = TestImageDataTransformation.get_featurewise_mean_examples()
#Transformation object
parameters = ImageDataTransformation.Parameters(featurewise_mean = True)
transformation = ImageDataTransformation(parameters = parameters)
with self.assertRaises(ValueError):
transformation.transform(images)
def test_transform_affine(self):
#Rotation transformation
parameters = ImageDataTransformation.Parameters(rotation_range = 20)
self.transform_affine(parameters)
#Shear transformation
parameters = ImageDataTransformation.Parameters(shear_range = 10)
self.transform_affine(parameters)
#Zoom transformation
parameters = ImageDataTransformation.Parameters(zoom_range = 0.2)
self.transform_affine(parameters)
#Width translation
parameters = ImageDataTransformation.Parameters(width_shift_range = 0.2)
self.transform_affine(parameters)
#Height translation
parameters = ImageDataTransformation.Parameters(height_shift_range = 0.2)
self.transform_affine(parameters)
def transform_featurewise_std_normalization(self, featurewise_std_normalization, images, result):
#Transformation object
parameters = ImageDataTransformation.Parameters(featurewise_std_normalization = featurewise_std_normalization)
transformation = ImageDataTransformation(parameters = parameters)
#Fit and perform transformation
transformation.fit(images)
transformed_images = transformation.transform(images)
transformed_std = transformed_images.std(axis = 0)
#Assert
self.assertTrue(np.allclose(transformed_std, result))
def transform_horizontal_flip(self, horizontal_flip, images, results):
#Transformation object
parameters = ImageDataTransformation.Parameters(horizontal_flip = horizontal_flip, horizontal_flip_prob = 1.0)
transformation = ImageDataTransformation(parameters = parameters)
#Transform
transformed_images = transformation.transform(images)
#Assert
self.assertTrue(
np.array_equal(transformed_images, results),
"transformed_images: {} != expected: {}".format(transformed_images, results))
def transform_samplewise_mean(self, samplewise_mean, images, results):
#Transformation object
parameters = ImageDataTransformation.Parameters(samplewise_mean = samplewise_mean)
transformation = ImageDataTransformation(parameters = parameters)
#Transform
transformed_images = transformation.transform(images)
#Assert
self.assertTrue(
np.array_equal(transformed_images, results),
"transformed_images: {} != expected: {}".format(transformed_images, results))
def get_augmentation_executor():
#Augmentation instances
augmentation_instances = [
#Horizontal flip
ImageAugmentation.Instance(ImageDataTransformation.Parameters(
horizontal_flip=True, horizontal_flip_prob=1.0),
num_output_images=1),
#Rotation
ImageAugmentation.Instance(
ImageDataTransformation.Parameters(rotation_range=20),
num_output_images=5),
#Zoom
ImageAugmentation.Instance(
ImageDataTransformation.Parameters(zoom_range=0.25),
num_output_images=5),
#Shear
ImageAugmentation.Instance(
ImageDataTransformation.Parameters(shear_range=15),
num_output_images=5),
#Width shift
ImageAugmentation.Instance(
ImageDataTransformation.Parameters(width_shift_range=.25),
num_output_images=5),
#Height shift
ImageAugmentation.Instance(
ImageDataTransformation.Parameters(height_shift_range=.20),
num_output_images=5)
]
executor = ImageAugmentation(augmentation_instances)
return executor
def transform_featurewise_mean(self, featurewise_mean, images, result):
#Transformation object
parameters = ImageDataTransformation.Parameters(featurewise_mean = featurewise_mean)
transformation = ImageDataTransformation(parameters = parameters)
#Fit and perform transformation
transformation.fit(images)
transformed_images = transformation.transform(images)
sum_image = transformed_images.sum(axis = 0)
#Assert
self.assertTrue(
np.array_equal(sum_image, result),
"Sum images: {} expected: {}".format(sum_image, result))
def transform_samplewise_std_normalization(self, samplewise_std_normalization, images):
#Transformation object
parameters = ImageDataTransformation.Parameters(samplewise_std_normalization = samplewise_std_normalization)
transformation = ImageDataTransformation(parameters = parameters)
#Transform
transformed_images = transformation.transform(images)
#Compute standard deviation
standard_deviations = np.std(transformed_images, axis = (1, 2, 3))
#Assert
if samplewise_std_normalization:
self.assertAlmostEqual(
np.sum(standard_deviations),
2.,
places = 2,
msg = "standard_deviations: {} != expected: 2.".format(standard_deviations))
else:
self.assertNotEqual(np.sum(standard_deviations), 2.)
def parse_args():
parser = ArgumentParser(description = 'It trains a siamese network for whale identification.')
parser.add_argument(
'-m', '--model_name',
required = True,
help = 'It specifies the name of the model to train.')
parser.add_argument(
'-d', '--dataset_location',
required = True, type = Path,
help = 'It specifies the input dataset location.')
parser.add_argument(
'--image_cols',
required = True, nargs = '+',
help = 'It specifies the names of the image column in the dataframe.')
parser.add_argument(
'--image_transform_cols',
nargs = '+',
help = 'It specifies the names of the image column in the dataframe that are to be transformed.')
parser.add_argument(
'--label_col',
required = True,
help = 'It specifies the names of the label column.')
parser.add_argument(
'-b', '--batch_size',
default = 128, type = int,
help = 'It specifies the training batch size.')
parser.add_argument(
'-c', '--image_cache_size',
default = 512, type = int,
help = 'It specifies the image cache size.')
parser.add_argument(
'-s', '--validation_split',
type = float,
help = 'It specifies the validation split on the training dataset. It must be a float between 0 and 1')
parser.add_argument(
'-r', '--learning_rate',
type = float,
help = 'It specifies the learning rate of the optimization algorithm. It must be a float between 0 and 1')
parser.add_argument(
'-t', '--transformations',
nargs = '+', default = [],
type = kv_str_to_tuple,
help = 'It specifies transformation parameters. Options: {}'
.format(ImageDataTransformation.Parameters().__dict__.keys()))
parser.add_argument(
'-x', '--num_fit_images',
default = 1000, type = int,
help = 'It specifies the number of images to send to fit()')
parser.add_argument(
'--epoch_id',
default = 0, type = int,
help = 'It specifies the start epoch id.')
parser.add_argument(
'--batch_id',
default = 0, type = int,
help = 'It specifies the start batch id.')
parser.add_argument(
'-e', '--number_of_epochs',
type = int, default = 1,
help = 'It specifies the number of epochs to train per input set.')
parser.add_argument(
'--input_shape',
default = [224, 224, 3],
type = int, nargs = 3,
help = 'It specifies the shape of the image input.')
parser.add_argument(
'--number_prediction_steps', default = 2,
type = int,
help = 'It specifies the number of prediction steps to evaluate trained model.')
parser.add_argument(
'--checkpoint_batch_interval', default = 1,
type = int,
help = 'It specifies the number of batches after which to take a checkpoint.')
parser.add_argument(
'--training_method', default = TrainingMethod.TRAIN_ON_BATCH,
type = TrainingMethod, choices = list(TrainingMethod),
help = 'It specifies the training method to use')
parser.add_argument(
'-p', '--dropbox_parameters',
nargs = 2,
help = 'It specifies dropbox parameters required to upload the checkpoints.')
parser.add_argument(
'-l', '--log_to_console',
action = 'store_true', default = False,
help = 'It enables logging to console')
args = parser.parse_args()
return args
#Unittest
import unittest as ut
from unittest.mock import MagicMock
#Constants
from common import constants
from common import ut_constants
#Image augmentation
from operation.transform import ImageDataTransformation
from operation.augmentation import ImageAugmentation
#Data processing
import numpy as np
hflip_tranformation_params = ImageDataTransformation.Parameters(
horizontal_flip=True)
hflip_num_output_images = 1
shear_transformation_params = ImageDataTransformation.Parameters(
shear_range=10)
shear_num_output_images = 3
class TestImageAugmentationInstance(ut.TestCase):
def test_init_invalid_params(self):
with self.assertRaises(ValueError):
_ = ImageAugmentation.Instance(None, 1)
def test_init(self):
#Arrange
augmentation_instance = ImageAugmentation.Instance(