Walkthrough#

This guide provides a full walkthrough on how to use the CLI of CompressAI-Vision and configure the different parts.

Configuring paths#

To evaluate a pipeline and the performances of a codec, we must tell CompressAI-Vision where to find the datasets, and where to output logs of the different runs. Override the following paths specified in cfgs/paths/default.yaml:

cfgs/paths/default.yaml#

paths:
  _common_root: "./logs"
  _run_root:     "${._common_root}/runs"

It is recommended to use paths that are somewhere outside the source code directory.

For the test data, you can referer to the directory data which contains the information to retrieve the source content and label for the considered datasets.

The paths to the root of the generated folder structure is then configured using dataset.config.root, default config for datasets is as follows: :

cfgs/dataset/default.yaml#

type: 'DefaultDataset'
config:
  root: '/data/dataset'
  imgs_folder: 'valid'
  annotation_file: "annotations/sample.json"
  seqinfo: "seqinfo.ini"
  dataset_name: "sample_dataset"
  ext: "png"

Eval#

First, activate the previously installed virtual environment.

To evaluate your pipeline, run .. code-block:: bash

# Train using conf/example.yaml, and override criterion.lmbda. compressai-vision-eval –config-name=”eval_example” ++dataset.config.root=”/data/datasets/fcm_testdata/SFU_HW_Obj/Traffic_2560x1600_30_val” dataset.config.dataset_name=”Traffic_2560x1600_30_val”

Output directory structure#

Logs are written to the directory specified by "${paths._common_root}". By default, this has the following directory structure:

${paths._common_root} directory structure#

${paths._common_root}/
  runs/
    ...
    e4e6d4d5e5c59c69f3bd7be2/     # Aim run hash.
      checkpoints/
        runner.last.pth
      configs/
        config.yaml               # Final YAML configuration for reproducibility.
      engine/
      src/
        compressai.patch          # Auto generated git diff patch for reproducibility.
        compressai_trainer.patch  # Auto generated git diff patch for reproducibility.
      tensorboard/

Each experiment run is saved in a directory named by its run hash inside the runs/ directory. This directory includes the respective model checkpoints/weights, and various configurations and diffs for better reproducibility.

The default directory structure may be reconfigured by modifying conf/paths/default.yaml.

Viewing the experiment dashboard in Aim#

This section demonstrates how to start up the Aim UI for experiment tracking. Aim allows users to compare parameters, view metrics, and visualize results.

Navigate to Aim repository#

Aim logs all experiments to a single directory containing an .aim repository. By default, this is located in ./logs/aim/main. Before running the aim commands shown later, navigate to that directory:

cd "./logs/aim/main"

Local-only#

If the directory containing the .aim directory is directly accessible from the local machine, navigate to that directory and run:

aim up --host="localhost" --port=43800

Then, open a web browser and navigate to http://localhost:43800/.

Remote host (private)#

If the directory containing the .aim directory is on a remote host that is on an accessible private LAN, then on the remote host, navigate to that directory and run:

aim up --host="0.0.0.0" --port=43800

Then, open up a web browser on the local machine and navigate to http://REMOTE_SERVER:PORT or http://USERNAME@REMOTE_SERVER:PORT. The Aim UI should now be accessible.

Note

Anyone with access to the remote host may also be able to access the Aim UI without SSH authentication. If this is not desired, see below.

Remote host (public)#

If the directory containing the .aim directory is on a remote host that is publically accessible, then on the remote host, navigate to that directory and run:

aim up --host="localhost" --port=43800

The above restricts incoming connections to those originating from the remote host itself. Then, establish local port forwarding over ssh to bind the local localhost:43800 to the remote localhost:43800:

ssh -L "localhost:43800:localhost:43800" USERNAME@REMOTE_SERVER

Finally, open up a web browser on the local machine and navigate to http://localhost:43800/.

Note

Other user accounts on the remote host may also be able to bind to remote localhost:43800. If this is not desired, please configure the firewall on the remote host appropriately.

Defining a custom model#

Ensure that the model class will be imported at runtime by adding the following to compressai/models/__init__.py:

compressai/models/__init__.py#

from .custom import MyCustomModel

Then, create a file at compressai/models/custom.py, and define and register a model as follows:

compressai/models/custom.py#

from compressai.registry import register_model
from .base import CompressionModel

@register_model("my_custom_model")
class MyCustomModel(CompressionModel):
    def __init__(self, N, M):
        ...

Then, copy conf/example.yaml into conf/my_custom_model.yaml and customize the YAML configuration to use the custom model:

conf/my_custom_model.yaml#

model:
  name: "my_custom_model"

hp:
  N: 128
  M: 192

Defining a custom Runner training loop#

We provide the following pre-made runners:

BaseRunner (base compression class)
ImageCompressionRunner
VideoCompressionRunner (future release)

Begin by creating a file at compressai_trainer/runners/custom.py and then add the following line to compressai_trainer/runners/__init__.py:

compressai_trainer/runners/__init__.py#

from .custom import CustomImageCompressionRunner

Create conf/runners/CustomImageCompressionRunner.yaml with:

conf/runners/CustomImageCompressionRunner.yaml#

type: "CustomImageCompressionRunner"

# Additional arguments for CustomImageCompressionRunner.__init__ here:
# some_custom_argument: "value"

Then, in compressai_trainer/runners/custom.py, create a Runner by inheriting from BaseRunner or Runner:

compressai_trainer/runners/custom.py#

from compressai.registry import register_runner
from .base import BaseRunner

@register_runner("CustomImageCompressionRunner")
class CustomImageCompressionRunner(BaseRunner):
    ...

The following functions are called during the training loop:

Runner training loop call order.#

on_experiment_start   # Once, at the beginning.
  on_epoch_start      # Beginning of an epoch.
    on_loader_start   # For each loader (train / valid / infer).
      on_batch_start  # Before each batch.
        handle_batch  # For each image batch.
      on_batch_end
    on_loader_end
  on_epoch_end
on_experiment_end

The training loop is effectively equivalent to:

Runner training loop pseudo-code.#

on_experiment_start()

for epoch in range(1, num_epochs):
    on_epoch_start()

    for loader in ["train", "valid", "infer"]:
        on_loader_start()

        for batch in loader:
            on_batch_start()
            handle_batch(batch)
            on_batch_end()

        on_loader_end()

    on_epoch_end()

on_experiment_end()

Please see the Catalyst documentation for more information. Also consider using our provided runners as a template.

Using YAML configuration#

We use Hydra for our configuration framework. The section below covers some basics, but for more details, please see the Hydra documentation.

Basics#

conf/example.yaml contains an example configuration for training the bmshj2018-factorized model.

In the defaults list, one may import configurations from other YAML files:

conf/example.yaml#

defaults:
  # Imports conf/runner/ImageCompressionRunner.yaml into "runner:" dict.
  - runner: ImageCompressionRunner

  # Similarly, import into "paths:", "env:", "engine:", etc dicts.
  - paths: default
  - env: default
  - engine: default
  - criterion: RateDistortionLoss
  - optimizer: net_aux
  - scheduler: ReduceLROnPlateau
  - misc: default

  # Imports vimeo90k/train into "dataset.train:" dict, etc.
  - dataset@dataset.train: vimeo90k/train
  - dataset@dataset.valid: vimeo90k/valid
  - dataset@dataset.infer: kodak/infer

  # Imports current YAML's configuration, defined below.
  - _self_

One may also define or override configuration within the YAML file itself:

conf/example.yaml#

# Create configuration for current experiment, model, and hyperparameters.
exp:
  name: "example_experiment"
model:
  name: "bmshj2018-factorized"
hp:
  N: 128
  M: 192

# Override dataset.train.loader.batch size.
dataset:
  train:
    loader:
      batch_size: 8

# Alternatively, one can also override the above via a command line argument:
# compressai-train [...] ++dataset.train.loader.batch_size=8

Creating your own config#

Copy conf/example.yaml into conf/custom-config.yaml:

conf/custom-config.yaml#

defaults:
  - paths: default
  - env: default
  - engine: default
  - runner: ImageCompressionRunner
  - criterion: RateDistortionLoss
  - optimizer: net_aux
  - scheduler: ReduceLROnPlateau
  - dataset/vimeo90k/train@dataset
  - dataset/vimeo90k/valid@dataset
  - dataset/kodak/infer@dataset
  - misc: default
  - _self_

exp:
  name: "example_experiment"

model:
  name: "bmshj2018-factorized"

hp:
  # Qualities 1-5
  N: 128
  M: 192
  # Qualities 6-8
  # N: 192
  # M: 320

Modify it as desired. Then, train using:

compressai-train --config-name="custom-config"

Specify and override configuration via command line (CLI) arguments#

For example, this overrides criterion.lmbda:

compressai-train --config-name="example" ++criterion.lmbda=0.035

The above is equivalent to the following YAML configuration:

criterion:
  lmbda: 0.035

Please see the Hydra documentation on overriding for more information.

Resuming training#

Loading model checkpoints/weights#

Note

This starts a fresh run in the experiment tracker with a new run hash. To log to an existing run, see Continuing a previous run.

To load a checkpoint containing model/optimizer/scheduler/etc state, override paths.checkpoint:

++paths.checkpoint="/path/to/checkpoints/runner.last.pth"

To load only the model state_dict (i.e. weights), and not other training state, override paths.model_checkpoint instead:

++paths.model_checkpoint="/path/to/checkpoints/runner.last.pth"

Continuing a previous run#

To continue an existing run that was paused/cancelled during training, load the config corresponding to the run hash:

RUNS_ROOT="${paths.runs_root}"  # Example: "./logs/runs"
RUN_HASH="${env.aim.run_hash}"  # Example: "e4e6d4d5e5c59c69f3bd7be2"

--config-path="${RUNS_ROOT}/${RUN_HASH}/configs"
--config-name="config"
++paths.checkpoint='${paths.checkpoints}/runner.last.pth'

Additional loggers#

By default, CompressAI Trainer logs experiments to both Aim and Tensorboard. Additional loggers can be enabled as shown below.

CSV Logger#

Store CSV logs inside the current run directory via:

compressai-train \
  --config-name="example" \
  ++engine.loggers.csv.logdir='${paths._run_root}/csv'

MLflow Logger#

Connect CompressAI Trainer to an MLflow experiment tracking server:

compressai-train \
  --config-name="example" \
  ++exp.name="example_experiment" \
  ++engine.loggers.mlflow.run="example_run" \
  ++engine.loggers.mlflow.tracking_uri=http://localhost:5000 \
  ++engine.loggers.mlflow.registry_uri=http://localhost:5000

Tips#

Single-GPU and Multi-GPU training#

By default, CompressAI Trainer will use all available GPUs. To restrict to certain GPU devices, set the visible device IDs using:

export CUDA_VISIBLE_DEVICES="0,1"  # Restricts to GPU 0 and GPU 1.

Quick sanity check#

To quickly check that your code is working, run a few batches of train/validation/inference using the following CLI argument:

++engine.check=True

To avoid filling up the "${paths.runs_root}" directory with unnecessary checkpoints, we recommend adding the following variable to ~/.bashrc:

TRAIN_CHECK="++engine.check=True ++exp.name=check ++paths.runs_root=$USER/tmp_runs"

Example usage:

compressai-train --config-name="example" $TRAIN_CHECK

Walkthrough

Contents

Walkthrough#

Configuring paths#

Eval#

Output directory structure#

Viewing the experiment dashboard in Aim#

Navigate to Aim repository#

Local-only#

Remote host (private)#

Remote host (public)#

Defining a custom model#

Defining a custom Runner training loop#

Using YAML configuration#

Basics#

Creating your own config#

Specify and override configuration via command line (CLI) arguments#

Resuming training#

Loading model checkpoints/weights#

Continuing a previous run#

Additional loggers#

CSV Logger#

MLflow Logger#

Tips#

Single-GPU and Multi-GPU training#

Quick sanity check#