Using Albumentations with Tensorflow¶
Author: Ayushman Buragohain
Python
!pip install -q -U albumentations
!echo "$(pip freeze | grep albumentations) is successfully installed"
albumentations==1.4.0 is successfully installed
[Recommended] Update the version of tensorflow_datasets if you want to use it¶
- We'll we using an example from
tensorflow_datasets.
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Run the example¶
Python
# necessary imports
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import tensorflow_datasets as tfds
from functools import partial
import albumentations as A
AUTOTUNE = tf.data.experimental.AUTOTUNE
'4.9.4'
Python
# load in the tf_flowers dataset
data, info= tfds.load(name="tf_flowers", split="train", as_supervised=True, with_info=True)
data
<_PrefetchDataset element_spec=(TensorSpec(shape=(None, None, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>
tfds.core.DatasetInfo(
name='tf_flowers',
full_name='tf_flowers/3.0.1',
description="""
A large set of images of flowers
""",
homepage='https://www.tensorflow.org/tutorials/load_data/images',
data_dir='/Users/vladimiriglovikov/tensorflow_datasets/tf_flowers/3.0.1',
file_format=tfrecord,
download_size=218.21 MiB,
dataset_size=221.83 MiB,
features=FeaturesDict({
'image': Image(shape=(None, None, 3), dtype=uint8),
'label': ClassLabel(shape=(), dtype=int64, num_classes=5),
}),
supervised_keys=('image', 'label'),
disable_shuffling=False,
splits={
'train': <SplitInfo num_examples=3670, num_shards=2>,
},
citation="""@ONLINE {tfflowers,
author = "The TensorFlow Team",
title = "Flowers",
month = "jan",
year = "2019",
url = "http://download.tensorflow.org/example_images/flower_photos.tgz" }""",
)
An Example Pipeline Using tf.image¶
Process Data¶
Python
def process_image(image, label, img_size):
# cast and normalize image
image = tf.image.convert_image_dtype(image, tf.float32)
# apply simple augmentations
image = tf.image.random_flip_left_right(image)
image = tf.image.resize(image,[img_size, img_size])
return image, label
ds_tf = data.map(partial(process_image, img_size=120), num_parallel_calls=AUTOTUNE).batch(30).prefetch(AUTOTUNE)
ds_tf
<_PrefetchDataset element_spec=(TensorSpec(shape=(None, 120, 120, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>
View images from the dataset¶
Python
def view_image(ds):
image, label = next(iter(ds)) # extract 1 batch from the dataset
image = image.numpy()
label = label.numpy()
fig = plt.figure(figsize=(22, 22))
for i in range(20):
ax = fig.add_subplot(4, 5, i+1, xticks=[], yticks=[])
ax.imshow(image[i])
ax.set_title(f"Label: {label[i]}")
2024-02-18 14:57:07.678378: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
Using tf.image is very efficient to create a pipeline but the disadvantage is that with tf.image we can only apply limited amounts of augmentations to our input data. One way to solve is issue is to use tf.keras ImageDataGenerator class but albumentations is faster.
An Example Pipeline using albumentations¶
To integrate albumentations into our tensorflow pipeline we can create two functions :
- Pipeline to apply augmentation. - a function that calls the above function and pass in our data through the pipeline. We can then wrap our 2nd Function under tf.numpy_function .
italicized text## Create Pipeline to Process data
Python
# Instantiate augments
# we can apply as many augments we want and adjust the values accordingly
# here I have chosen the augments and their arguments at random
transforms = A.Compose([
A.Rotate(limit=40),
A.RandomBrightnessContrast(brightness_limit=0.1, contrast_limit=0.1, p=0.5),
A.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),
A.HorizontalFlip(),
])
Python
def aug_fn(image, img_size):
data = {"image":image}
aug_data = transforms(**data)
aug_img = aug_data["image"]
aug_img = tf.cast(aug_img/255.0, tf.float32)
return tf.image.resize(aug_img, size=[img_size, img_size])
Python
def process_data(image, label, img_size):
aug_img = tf.numpy_function(func=aug_fn, inp=[image, img_size], Tout=tf.float32)
return aug_img, label
Python
# create dataset
ds_alb = data.map(partial(process_data, img_size=120),
num_parallel_calls=AUTOTUNE).prefetch(AUTOTUNE)
ds_alb
<_PrefetchDataset element_spec=(TensorSpec(shape=<unknown>, dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>
Restoring dataset shapes.¶
The datasets loses its shape after applying a tf.numpy_function, so this is necessary for the sequential model and when inheriting from the model class.
Python
def set_shapes(img, label, img_shape=(120,120,3)):
img.set_shape(img_shape)
label.set_shape([])
return img, label
Python
ds_alb = ds_alb.map(set_shapes, num_parallel_calls=AUTOTUNE).batch(32).prefetch(AUTOTUNE)
ds_alb
<_PrefetchDataset element_spec=(TensorSpec(shape=(None, 120, 120, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>
View images from the dataset¶
2024-02-18 14:57:09.800432: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
We can then pass in this dataset to out model and call fit on our model
Note:¶
Some API's of tensorflow.keras.Model might not work, if you dont map the dataset with the set_shapes function.
What works without setting shapes :¶
Python
from tensorflow.keras import models, layers
from tensorflow import keras
# Running the Model in eager mode using Sequential API
def create_model(input_shape):
return models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(5, activation='softmax')])
model = create_model((120,120,3))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy', run_eagerly=True)
model.fit(ds_alb, epochs=2)
Epoch 1/2
115/115 [==============================] - 22s 190ms/step - loss: 1.3435 - accuracy: 0.4322
Epoch 2/2
115/115 [==============================] - 21s 180ms/step - loss: 1.1221 - accuracy: 0.5529
<keras.src.callbacks.History at 0x2d9f3b130>
Python
# Functional API
input = keras.Input(shape=(120, 120, 3))
x = keras.layers.Conv2D(32, (3, 3), activation="relu")(input)
x = keras.layers.MaxPooling2D((2, 2))(x)
x = keras.layers.Conv2D(64, (3, 3), activation='relu')(x)
x = keras.layers.MaxPooling2D((2, 2))(x)
x = keras.layers.Conv2D(64, (3, 3), activation='relu')(x)
x = keras.layers.Flatten()(x)
x = keras.layers.Dense(64, activation='relu')(x)
x = keras.layers.Dense(5, activation='softmax')(x)
model = keras.Model(inputs=input, outputs=x)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')
model.fit(ds_alb, epochs=2)
Epoch 1/2
115/115 [==============================] - 12s 102ms/step - loss: 1.4318 - accuracy: 0.3807
Epoch 2/2
115/115 [==============================] - 12s 104ms/step - loss: 1.1839 - accuracy: 0.5183
<keras.src.callbacks.History at 0x2d9ee2d40>
Python
# Transfer Learning [freeze base model layers]: Sequential API
base_model = keras.applications.ResNet50(include_top=False, input_shape=(120, 120, 3), weights="imagenet")
base_model.trainable = False
model = keras.models.Sequential([
base_model,
keras.layers.Conv2D(32, (1, 1), activation="relu"),
keras.layers.Dropout(0.2),
keras.layers.Flatten(),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(5, activation='softmax'),
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')
model.fit(ds_alb, epochs=2)
Epoch 1/2
115/115 [==============================] - 30s 239ms/step - loss: 1.5254 - accuracy: 0.3188
Epoch 2/2
115/115 [==============================] - 29s 254ms/step - loss: 1.4476 - accuracy: 0.3768
<keras.src.callbacks.History at 0x2e53a5ed0>
Python
# Transfer Learning [unfreeze all layers]: Sequential API
base_model = keras.applications.ResNet50(include_top=False, input_shape=(120, 120, 3), weights="imagenet")
base_model.trainable = True
model = keras.models.Sequential([
base_model,
keras.layers.Conv2D(32, (1, 1), activation="relu"),
keras.layers.Flatten(),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(5, activation='softmax'),
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')
model.fit(ds_alb, epochs=2)
Epoch 1/2
115/115 [==============================] - 133s 1s/step - loss: 1.1360 - accuracy: 0.5924
Epoch 2/2
115/115 [==============================] - 143s 1s/step - loss: 0.7753 - accuracy: 0.7278
<keras.src.callbacks.History at 0x2e5be11e0>
Python
# Transfer Learning [freeze all layers of feature extractor]: Functional API
base_model = keras.applications.ResNet50(include_top=False, input_shape=(120, 120, 3), weights="imagenet")
base_model.trainable = False
input = keras.Input(shape=(120, 120, 3))
x = base_model(input, training=False)
x = keras.layers.Conv2D(32, (1, 1), activation="relu")(x)
x = keras.layers.Dropout(0.2)(x)
x = keras.layers.Flatten()(x)
x = keras.layers.Dense(64, activation='relu')(x)
x = keras.layers.Dense(5, activation='softmax')(x)
model = keras.Model(inputs=input, outputs=x)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')
model.fit(ds_alb, epochs=2)
Epoch 1/2
115/115 [==============================] - 37s 289ms/step - loss: 1.5292 - accuracy: 0.3240
Epoch 2/2
115/115 [==============================] - 29s 251ms/step - loss: 1.4471 - accuracy: 0.3779
<keras.src.callbacks.History at 0x2de238820>
Python
# Transfer Learning [freeze all layers of feature extractor]: Subclass API
base_model = keras.applications.ResNet50(include_top=False, input_shape=(120, 120, 3), weights="imagenet")
base_model.trainable = False
class MyModel(keras.Model):
def __init__(self, base_model):
super(MyModel, self).__init__()
self.base = base_model
self.layer_1 = keras.layers.Flatten()
self.layer_2 = keras.layers.Dense(64, activation='relu')
self.layer_3 = keras.layers.Dense(5, activation='softmax')
@tf.function
def call(self, xb):
x = self.base(xb)
x = self.layer_1(x)
x = self.layer_2(x)
return self.layer_3(x)
model = MyModel(base_model=base_model)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')
model.fit(ds_alb, epochs=2)
Epoch 1/2
115/115 [==============================] - 34s 266ms/step - loss: 1.6205 - accuracy: 0.2736
Epoch 2/2
115/115 [==============================] - 31s 266ms/step - loss: 1.5317 - accuracy: 0.3025
<keras.src.callbacks.History at 0x2de884220>
Python
# Transfer Learning using [unfreeze all layers of feature extractor]: Subclass API
base_model = keras.applications.ResNet50(include_top=False, input_shape=(120, 120, 3), weights="imagenet")
base_model.trainable = True
class MyModel(keras.Model):
def __init__(self, base_model):
super(MyModel, self).__init__()
self.base = base_model
self.layer_1 = keras.layers.Flatten()
self.layer_2 = keras.layers.Dense(64, activation='relu')
self.layer_3 = keras.layers.Dense(5, activation='softmax')
@tf.function
def call(self, xb):
x = self.base(xb)
x = self.layer_1(x)
x = self.layer_2(x)
return self.layer_3(x)
model = MyModel(base_model=base_model)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')
model.fit(ds_alb, epochs=2)
Epoch 1/2
115/115 [==============================] - 137s 1s/step - loss: 1.7294 - accuracy: 0.5883
Epoch 2/2
115/115 [==============================] - 139s 1s/step - loss: 1.6056 - accuracy: 0.5390
<keras.src.callbacks.History at 0x2e09d9c00>
What works only if you set the shapes of the dataset :¶
Python
# Using Sequential API without transfer learning & Eager Execution
def create_model(input_shape):
return models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(5, activation='softmax')])
model = create_model((120,120,3))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')
model.fit(ds_alb, epochs=2)
Epoch 1/2
115/115 [==============================] - 12s 103ms/step - loss: 1.3967 - accuracy: 0.3831
Epoch 2/2
115/115 [==============================] - 12s 100ms/step - loss: 1.1674 - accuracy: 0.5272
<keras.src.callbacks.History at 0x3dc67a4a0>
Python
# Using Subclass API without transfer learning & Eager Execution
class MyModel(keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = keras.layers.Conv2D(32, (3, 3), activation='relu')
self.pool1 = keras.layers.MaxPooling2D((2, 2))
self.conv2 = keras.layers.Conv2D(64, (3, 3), activation='relu')
self.pool2 = keras.layers.MaxPooling2D((2, 2))
self.conv3 = keras.layers.Conv2D(64, (3, 3), activation='relu')
self.flat = keras.layers.Flatten()
self.dense1 = keras.layers.Dense(64, activation='relu')
self.dense2 = keras.layers.Dense(5, activation='softmax')
def call(self, xb):
x = self.conv1(xb)
x = self.pool1(x)
x = self.conv2(x)
x = self.pool2(x)
x = self.conv3(x)
x = self.flat(x)
x = self.dense1(x)
x = self.dense2(x)
return x
model = MyModel()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')
model.fit(ds_alb, epochs=2)
Epoch 1/2
115/115 [==============================] - 12s 102ms/step - loss: 1.4053 - accuracy: 0.3921
Epoch 2/2
115/115 [==============================] - 13s 111ms/step - loss: 1.1591 - accuracy: 0.5360
<keras.src.callbacks.History at 0x3dc67a320>