semantic_segmentation_deeplab_v3_plus.py

"""
Title: Semantic Segmentation with KerasCV
Author: [Divyashree Sreepathihalli](https://github.com/divyashreepathihalli), [Ian Stenbit](https://github.com/ianstenbit)
Date created: 2023/08/22
Last modified: 2023/08/24
Description: Train and use DeepLabv3+ segmentation model with KerasCV.
Accelerator: GPU
"""

"""
![](https://storage.googleapis.com/keras-nlp/getting_started_guide/prof_keras_intermediate.png)

## Background
Semantic segmentation is a type of computer vision task that involves assigning a
class label such as person, bike, or background to each individual pixel of an
image, effectively dividing the image into regions that correspond to different
fobject classes or categories.

![](https://miro.medium.com/v2/resize:fit:4800/format:webp/1*z6ch-2BliDGLIHpOPFY_Sw.png)



KerasCV offers the DeepLabv3+ model developed by Google for semantic
segmentation. This guide demonstrates how to finetune and use DeepLabv3+ model for
image semantic segmentaion with KerasCV. Its architecture that combines atrous convolutions,
contextual information aggregation, and powerful backbones to achieve accurate and
detailed semantic segmentation. The DeepLabv3+ model has been shown to achieve
state-of-the-art results on a variety of image segmentation benchmarks.

### References
[Encoder-Decoder with Atrous Separable Convolution for Semantic Image
Segmentation](https://arxiv.org/abs/1802.02611)<br>
[Rethinking Atrous Convolution for Semantic Image
Segmentation](https://arxiv.org/abs/1706.05587)
"""

"""
## Setup and Imports

Let's install the dependencies and import the necessary modules.

To run this tutorial, you will need to install the following packages:

* `keras-cv`
* `keras-core`
"""

"""shell
pip install -q --upgrade keras-cv
pip install -q --upgrade keras # Upgrade to Keras 3.
"""

"""
After installing `keras-core` and `keras-cv`, set the backend for `keras-core`.
This guide can be run with any backend (Tensorflow, JAX, PyTorch).

```
import os

os.environ["KERAS_BACKEND"] = "jax"
```
"""

import keras
from keras import ops

import keras_cv
import numpy as np

from keras_cv.datasets.pascal_voc.segmentation import load as load_voc

"""
## Perform semantic segmentation with a pretrained DeepLabv3+ model

The highest level API in the KerasCV semantic segmentation API is the `keras_cv.models`
API. This API includes fully pretrained semantic segmentation models, such as
`keras_cv.models.DeepLabV3Plus`.

Let's get started by constructing a DeepLabv3+ pretrained on the pascalvoc dataset.
"""

model = keras_cv.models.DeepLabV3Plus.from_preset(
    "deeplab_v3_plus_resnet50_pascalvoc",
    num_classes=21,
    input_shape=[512, 512, 3],
)

"""
Let us visualize the results of this pretrained model
"""

filepath = keras.utils.get_file(origin="https://i.imgur.com/gCNcJJI.jpg")
image = keras.utils.load_img(filepath)

resize = keras_cv.layers.Resizing(height=512, width=512)
image = resize(image)
image = keras.ops.expand_dims(np.array(image), axis=0)
preds = ops.expand_dims(ops.argmax(model(image), axis=-1), axis=-1)
keras_cv.visualization.plot_segmentation_mask_gallery(
    image,
    value_range=(0, 255),
    num_classes=1,
    y_true=None,
    y_pred=preds,
    scale=3,
    rows=1,
    cols=1,
)

"""
## Train a custom semantic segmentation model
In this guide, we'll assemble a full training pipeline for a KerasCV DeepLabV3 semantic
segmentation model. This includes data loading, augmentation, training, metric
evaluation, and inference!
"""

"""
## Download the data

We download
[Pascal VOC dataset](https://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz)
with KerasCV datasets and split them into train dataset `train_ds` and `eval_ds`.
"""

train_ds = load_voc(split="sbd_train")
eval_ds = load_voc(split="sbd_eval")

"""
## Preprocess the data

The `preprocess_tfds_inputs` utility function preprocesses the inputs to a dictionary of
`images` and `segmentation_masks`. The images and segmentation masks are resized to
512x512. The resulting dataset is then batched into groups of 4 image and segmentation
mask pairs.

A batch of this preprocessed input training data can be visualized using the
`keras_cv.visualization.plot_segmentation_mask_gallery` function. This function takes a
batch of images and segmentation masks as input and displays them in a grid.
"""


def preprocess_tfds_inputs(inputs):
    def unpackage_tfds_inputs(tfds_inputs):
        return {
            "images": tfds_inputs["image"],
            "segmentation_masks": tfds_inputs["class_segmentation"],
        }

    outputs = inputs.map(unpackage_tfds_inputs)
    outputs = outputs.map(keras_cv.layers.Resizing(height=512, width=512))
    outputs = outputs.batch(4, drop_remainder=True)
    return outputs


train_ds = preprocess_tfds_inputs(train_ds)
batch = train_ds.take(1).get_single_element()
keras_cv.visualization.plot_segmentation_mask_gallery(
    batch["images"],
    value_range=(0, 255),
    num_classes=21,  # The number of classes for the oxford iiit pet dataset. The VOC dataset also includes 1 class for the background.
    y_true=batch["segmentation_masks"],
    scale=3,
    rows=2,
    cols=2,
)

"""
The preprocessing is applied to the evaluation dataset `eval_ds`.
"""

eval_ds = preprocess_tfds_inputs(eval_ds)

"""
## Data Augmentation

KerasCV provides a variety of image augmentation options. In this example, we will use
the `RandomFlip` augmentation to augment the training dataset. The `RandomFlip`
augmentation randomly flips the images in the training dataset horizontally or
vertically. This can help to improve the model's robustness to changes in the orientation
of the objects in the images.
"""

train_ds = train_ds.map(keras_cv.layers.RandomFlip())
batch = train_ds.take(1).get_single_element()

keras_cv.visualization.plot_segmentation_mask_gallery(
    batch["images"],
    value_range=(0, 255),
    num_classes=21,
    y_true=batch["segmentation_masks"],
    scale=3,
    rows=2,
    cols=2,
)

"""
## Model Configuration

Please feel free to modify the configurations for model training and note how the
training results changes. This is an great exercise to get a better understanding of the
training pipeline.

The learning rate schedule is used by the optimizer to calculate the learning rate for
each epoch. The optimizer then uses the learning rate to update the weights of the model.
In this case, the learning rate schedule uses a cosine decay function. A cosine decay
function starts high and then decreases over time, eventually reaching zero. The
cardinality of the VOC dataset is 2124 with a batch size of 4. The dataset cardinality
is important for learning rate decay because it determines how many steps the model
will train for. The initial learning rate is proportional to 0.007 and the decay
steps are 2124. This means that the learning rate will start at `INITIAL_LR` and then
decrease to zero over 2124 steps.
![png](/img/guides/semantic_segmentation_deeplab_v3_plus/learning_rate_schedule.png)
"""

BATCH_SIZE = 4
INITIAL_LR = 0.007 * BATCH_SIZE / 16
EPOCHS = 1
NUM_CLASSES = 21
learning_rate = keras.optimizers.schedules.CosineDecay(
    INITIAL_LR,
    decay_steps=EPOCHS * 2124,
)

"""
We instantiate a DeepLabV3+ model with a ResNet50 backbone pretrained on ImageNet classification:
`resnet50_v2_imagenet` pre-trained weights will be used as the backbone feature
extractor for the DeepLabV3Plus model. The `num_classes` parameter specifies the number of
classes that the model will be trained to segment.
"""

model = keras_cv.models.DeepLabV3Plus.from_preset(
    "resnet50_v2_imagenet", num_classes=NUM_CLASSES
)


"""
## Compile the model

The model.compile() function sets up the training process for the model. It defines the
- optimization algorithm - Stochastic Gradient Descent (SGD)
- the loss function - categorical cross-entropy
- the evaluation metrics - Mean IoU and categorical accuracy

Semantic segmentation evaluation metrics:

Mean Intersection over Union (MeanIoU):
MeanIoU measures how well a semantic segmentation model accurately identifies
and delineates different objects or regions in an image. It calculates the
overlap between predicted and actual object boundaries, providing a score
between 0 and 1, where 1 represents a perfect match.

Categorical Accuracy:
Categorical Accuracy measures the proportion of correctly classified pixels in
an image. It gives a simple percentage indicating how accurately the model
predicts the categories of pixels in the entire image.

In essence, MeanIoU emphasizes the accuracy of identifying specific object
boundaries, while Categorical Accuracy gives a broad overview of overall
pixel-level correctness.
"""

model.compile(
    optimizer=keras.optimizers.SGD(
        learning_rate=learning_rate, weight_decay=0.0001, momentum=0.9, clipnorm=10.0
    ),
    loss=keras.losses.CategoricalCrossentropy(from_logits=False),
    metrics=[
        keras.metrics.MeanIoU(
            num_classes=NUM_CLASSES, sparse_y_true=False, sparse_y_pred=False
        ),
        keras.metrics.CategoricalAccuracy(),
    ],
)

model.summary()

"""
The utility function `dict_to_tuple` effectively transforms the dictionaries of training
and validation datasets into tuples of images and one-hot encoded segmentation masks,
which is used during training and evaluation of the DeepLabv3+ model.
"""


def dict_to_tuple(x):
    import tensorflow as tf

    return x["images"], tf.one_hot(
        tf.cast(tf.squeeze(x["segmentation_masks"], axis=-1), "int32"), 21
    )


train_ds = train_ds.map(dict_to_tuple)
eval_ds = eval_ds.map(dict_to_tuple)

model.fit(train_ds, validation_data=eval_ds, epochs=EPOCHS)

"""
## Predictions with trained model
Now that the model training of DeepLabv3+ has completed, let's test it by making
predications
on a few sample images.
"""

test_ds = load_voc(split="sbd_eval")
test_ds = preprocess_tfds_inputs(test_ds)

images, masks = next(iter(train_ds.take(1)))
images = ops.convert_to_tensor(images)
masks = ops.convert_to_tensor(masks)
preds = ops.expand_dims(ops.argmax(model(images), axis=-1), axis=-1)
masks = ops.expand_dims(ops.argmax(masks, axis=-1), axis=-1)

keras_cv.visualization.plot_segmentation_mask_gallery(
    images,
    value_range=(0, 255),
    num_classes=21,
    y_true=masks,
    y_pred=preds,
    scale=3,
    rows=1,
    cols=4,
)


"""
Here are some additional tips for using the KerasCV DeepLabv3+ model:

- The model can be trained on a variety of datasets, including the COCO dataset, the
PASCAL VOC dataset, and the Cityscapes dataset.
- The model can be fine-tuned on a custom dataset to improve its performance on a
specific task.
- The model can be used to perform real-time inference on images.
- Also, try out KerasCV's SegFormer model `keras_cv.models.segmentation.SegFormer`. The
SegFormer model is a newer model that has been shown to achieve state-of-the-art results
on a variety of image segmentation benchmarks. It is based on the Swin Transformer
architecture, and it is more efficient and accurate than previous image segmentation
models.
"""