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vectorized_solarization.py
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import time
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from tensorflow import keras
from keras_cv.layers import BaseImageAugmentationLayer
from keras_cv.layers import Solarization
from keras_cv.utils import preprocessing
class OldSolarization(BaseImageAugmentationLayer):
def __init__(
self,
value_range,
addition_factor=0.0,
threshold_factor=0.0,
seed=None,
**kwargs,
):
super().__init__(seed=seed, **kwargs)
self.seed = seed
self.addition_factor = preprocessing.parse_factor(
addition_factor,
max_value=255,
seed=seed,
param_name="addition_factor",
)
self.threshold_factor = preprocessing.parse_factor(
threshold_factor,
max_value=255,
seed=seed,
param_name="threshold_factor",
)
self.value_range = value_range
def get_random_transformation(self, **kwargs):
return (
self.addition_factor(dtype=self.compute_dtype),
self.threshold_factor(dtype=self.compute_dtype),
)
def augment_image(self, image, transformation=None, **kwargs):
(addition, threshold) = transformation
image = preprocessing.transform_value_range(
image,
original_range=self.value_range,
target_range=(0, 255),
dtype=self.compute_dtype,
)
result = image + addition
result = tf.clip_by_value(result, 0, 255)
result = tf.where(result < threshold, result, 255 - result)
result = preprocessing.transform_value_range(
result,
original_range=(0, 255),
target_range=self.value_range,
dtype=self.compute_dtype,
)
return result
def augment_bounding_boxes(self, bounding_boxes, **kwargs):
return bounding_boxes
def augment_label(self, label, transformation=None, **kwargs):
return label
def augment_segmentation_mask(
self, segmentation_mask, transformation, **kwargs
):
return segmentation_mask
def get_config(self):
config = {
"threshold_factor": self.threshold_factor,
"addition_factor": self.addition_factor,
"value_range": self.value_range,
"seed": self.seed,
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config):
if isinstance(config["threshold_factor"], dict):
config["threshold_factor"] = keras.utils.deserialize_keras_object(
config["threshold_factor"]
)
if isinstance(config["addition_factor"], dict):
config["addition_factor"] = keras.utils.deserialize_keras_object(
config["addition_factor"]
)
return cls(**config)
class SolarizationTest(tf.test.TestCase):
def test_consistency_with_old_implementation(self):
images = tf.random.uniform(shape=(16, 32, 32, 3))
output = Solarization(
value_range=(0, 1),
threshold_factor=(200, 200),
addition_factor=(100, 100),
)(images)
old_output = OldSolarization(
value_range=(0, 1),
threshold_factor=(200, 200),
addition_factor=(100, 100),
)(images)
self.assertAllClose(old_output, output)
if __name__ == "__main__":
# Run benchmark
(x_train, _), _ = keras.datasets.cifar10.load_data()
x_train = x_train.astype(np.float32)
num_images = [1000, 2000, 5000, 10000]
results = {}
aug_candidates = [Solarization, OldSolarization]
aug_args = {"value_range": (0, 255), "threshold_factor": 0.5}
for aug in aug_candidates:
# Eager Mode
c = aug.__name__
layer = aug(**aug_args)
runtimes = []
print(f"Timing {c}")
for n_images in num_images:
# warmup
layer(x_train[:n_images])
t0 = time.time()
r1 = layer(x_train[:n_images])
t1 = time.time()
runtimes.append(t1 - t0)
print(f"Runtime for {c}, n_images={n_images}: {t1-t0}")
results[c] = runtimes
# Graph Mode
c = aug.__name__ + " Graph Mode"
layer = aug(**aug_args)
@tf.function()
def apply_aug(inputs):
return layer(inputs)
runtimes = []
print(f"Timing {c}")
for n_images in num_images:
# warmup
apply_aug(x_train[:n_images])
t0 = time.time()
r1 = apply_aug(x_train[:n_images])
t1 = time.time()
runtimes.append(t1 - t0)
print(f"Runtime for {c}, n_images={n_images}: {t1-t0}")
results[c] = runtimes
# XLA Mode
c = aug.__name__ + " XLA Mode"
layer = aug(**aug_args)
@tf.function(jit_compile=True)
def apply_aug(inputs):
return layer(inputs)
runtimes = []
print(f"Timing {c}")
for n_images in num_images:
# warmup
apply_aug(x_train[:n_images])
t0 = time.time()
r1 = apply_aug(x_train[:n_images])
t1 = time.time()
runtimes.append(t1 - t0)
print(f"Runtime for {c}, n_images={n_images}: {t1-t0}")
results[c] = runtimes
plt.figure()
for key in results:
plt.plot(num_images, results[key], label=key)
plt.xlabel("Number images")
plt.ylabel("Runtime (seconds)")
plt.legend()
plt.savefig("comparison.png")
# So we can actually see more relevant margins
del results[aug_candidates[1].__name__]
plt.figure()
for key in results:
plt.plot(num_images, results[key], label=key)
plt.xlabel("Number images")
plt.ylabel("Runtime (seconds)")
plt.legend()
plt.savefig("comparison_no_old_eager.png")
# Run unit tests
tf.test.main()