# TF Vision Example Project

This is a minimal example project to demonstrate how to use TF Model Garden's
building blocks to implement a new vision project from scratch.

Below we use classification as an example. We will walk you through the process
of creating a new projects leveraging existing components, such as tasks, data
loaders, models, etc. You will get better understanding of these components by
going through the process. You can also refer to the docstring of corresponding
components to get more information.

## Create Model

In
[example_model.py](example_model.py),
we show how to create a new model. The `ExampleModel` is a subclass of
`tf.keras.Model` that defines necessary parameters. Here, you need to have
`input_specs` to specify the input shape and dimensions, and build layers within
constructor:

```python
class ExampleModel(tf.keras.Model):
  def __init__(
    self,
    num_classes: int,
    input_specs: tf.keras.layers.InputSpec = tf.keras.layers.InputSpec(
        shape=[None, None, None, 3]),
    **kwargs):
    # Build layers.
```

Given the `ExampleModel`, you can define a function that takes a model config as
input and return an `ExampleModel` instance, similar as
[build_example_model](example_model.py#L80).
As a simple example, we define a single model. However, you can split the model
implementation to individual components, such as backbones, decoders, heads, as
what we do
[here](https://github.com/tensorflow/models/blob/master/official/vision/beta/modeling).
And then in `build_example_model` function, you can hook up these components
together to obtain your full model.

## Create Dataloader

A dataloader reads, decodes and parses the input data. We have created various
[dataloaders](https://github.com/tensorflow/models/blob/master/official/vision/beta/dataloaders)
to handle standard input formats for classification, detection and segmentation.
If you have non-standard or complex data, you may want to create your own
dataloader. It contains a `Decoder` and a `Parser`.

-   The
    [Decoder](example_input.py#L33)
    decodes a TF Example record and returns a dictionary of decoded tensors:

    ```python
    class Decoder(decoder.Decoder):
          """A tf.Example decoder for classification task."""
          def __init__(self):
            """Initializes the decoder.

            The constructor defines the mapping between the field name and the value
            from an input tf.Example. For example, we define two fields for image bytes
            and labels. There is no limit on the number of fields to decode.
            """
            self._keys_to_features = {
                'image/encoded':
                    tf.io.FixedLenFeature((), tf.string, default_value=''),
                'image/class/label':
                    tf.io.FixedLenFeature((), tf.int64, default_value=-1)
            }
    ```

-   The
    [Parser](example_input.py#L68)
    parses the decoded tensors and performs pre-processing to the input data,
    such as image decoding, augmentation and resizing, etc. It should have
    `_parse_train_data` and `_parse_eval_data` functions, in which the processed
    images and labels are returned.

## Create Config

Next you will define configs for your project. All configs are defined as
`dataclass` objects, and can have default parameter values.

First, you will define your
[`ExampleDataConfig`](example_config.py#L27).
It inherits from `config_definitions.DataConfig` that already defines a few
common fields, like `input_path`, `file_type`, `global_batch_size`, etc. You can
add more fields in your own config as needed.

You can then define you model config
[`ExampleModel`](example_config.py#L39)
that inherits from `hyperparams.Config`. Expose your own model parameters here.

You can then define your `Loss` and `Evaluation` configs.

Next, you will put all the above configs into an
[`ExampleTask`](example_config.py#L56)
config. Here you list the configs for your data, model, loss, and evaluation,
etc.

Finally, you can define a
[`tf_vision_example_experiment`](example_config.py#L66),
which creates a template for your experiments and fills with default parameters.
These default parameter values can be overridden by a YAML file, like
[example_config_tpu.yaml](example_config_tpu.yaml).
Also, make sure you give a unique name to your experiment template by the
decorator:

```python
@exp_factory.register_config_factory('tf_vision_example_experiment')
def tf_vision_example_experiment() -> cfg.ExperimentConfig:
  """Definition of a full example experiment."""
  # Create and return experiment template.
```

## Create Task

A task is a class that encapsules the logic of loading data, building models,
performing one-step training and validation, etc. It connects all components
together and is called by the base
[Trainer](https://github.com/tensorflow/models/blob/master/official/core/base_trainer.py).

You can create your own task by inheriting from base
[Task](https://github.com/tensorflow/models/blob/master/official/core/base_task.py),
or from one of the
[tasks](https://github.com/tensorflow/models/blob/master/official/vision/beta/tasks/)
we already defined, if most of the operations can be reused. An `ExampleTask`
inheriting from
[ImageClassificationTask](https://github.com/tensorflow/models/blob/master/official/vision/beta/tasks/image_classification.py#L32)
can be found
[here](example_task.py).
We will go through each important components in the task in the following.

-   `build_model`: you can instantiate a model you have defined above. It is
    also good practice to run forward pass with a dummy input to ensure layers
    within the model are properly initialized.

-   `build_inputs`: here you can instantiate a Decoder object and a Parser
    object. They are used to create an `InputReader` that will generate a
    `tf.data.Dataset` object.

-   `build_losses`: it takes groundtruth labels and model outputs as input, and
    computes the loss. It will be called in `train_step` and `validation_step`.
    You can also define different losses for training and validation, for
    example, `build_train_losses` and `build_validation_losses`. Just make sure
    they are called by the corresponding functions properly.

-   `build_metrics`: here you can define your own metrics. It should return a
    list of `tf.keras.metrics.Metric` objects. You can create your own metric
    class by subclassing `tf.keras.metrics.Metric`.

-   `train_step` and `validation_step`: they perform one-step training and
    validation. They take one batch of training/validation data, run forward
    pass, gather losses and update metrics. They assume the data format is
    consistency with that from the `Parser` output. `train_step` also contains
    backward pass to update model weights.

## Import registry

To use your custom dataloaders, models, tasks, etc., you will need to register
them properly. The recommended way is to have a single file with all relevant
files imported, for example,
[registry_imports.py](registry_imports.py).
You can see in this file we import all our custom components:

```python
# pylint: disable=unused-import
from official.common import registry_imports
from official.vision.beta.projects.example import example_config
from official.vision.beta.projects.example import example_input
from official.vision.beta.projects.example import example_model
from official.vision.beta.projects.example import example_task
```

## Training

You can create your own trainer by branching from our core
[trainer](https://github.com/tensorflow/models/blob/master/official/vision/beta/train.py).
Just make sure you import the registry like this:

```python
from official.vision.beta.projects.example import registry_imports  # pylint: disable=unused-import
```

You can run training locally for testing purpose:

```bash
# Assume you are under official/vision/beta/projects.
python3 example/train.py \
  --experiment=tf_vision_example_experiment \
  --config_file=${PWD}/example/example_config_local.yaml \
  --mode=train \
  --model_dir=/tmp/tfvision_test/
```

It can also run on Google Cloud using Cloud TPU.
[Here](https://cloud.google.com/tpu/docs/how-to) is the instruction of using
Cloud TPU and here is a more detailed
[tutorial](https://cloud.google.com/tpu/docs/tutorials/resnet-rs-2.x) of
training a ResNet-RS model. Following the instructions to set up Cloud TPU and
launch training by:

```bash
EXP_TYPE=tf_vision_example_experiment  # This should match the registered name of your experiment template.
EXP_NAME=exp_001  # You can give any name to the experiment.
TPU_NAME=experiment01
# Now launch the experiment.
python3 example/train.py \
  --experiment=$EXP_TYPE \
  --mode=train \
  --tpu=$TPU_NAME \
  --model_dir=/tmp/tfvision_test/
  --config_file=third_party/tensorflow_models/official/vision/beta/projects/example/example_config_tpu.yaml
```
