Add docs on Model, litpose

.readthedocs.yaml

@@ -9,7 +9,7 @@ version: 2
 build:
   os: ubuntu-20.04
   tools:
-    python: "3.8"
+    python: "3.10"
     # You can also specify other tool versions:
     # nodejs: "19"
     # rust: "1.64"

docs/_static/lightningpose.css

@@ -0,0 +1,16 @@
+/*
+    Hacks to fix sphinx-rtd-theme styles leaking past dark theme
+    Related issue: https://github.com/MrDogeBro/sphinx_rtd_dark_mode/issues/43
+*/
+html.writer-html4[data-theme="dark"] .rst-content dl:not(.docutils) .descclassname,
+html.writer-html4 .rst-content dl:not(.docutils) .descname,
+html.writer-html4 .rst-content dl:not(.docutils) .sig-name,
+html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descclassname,html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .descname,
+html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.citation):not(.glossary):not(.simple) .sig-name {
+    color: rgb(0, 125, 206);
+}
+html[data-theme="dark"].writer-html4 .rst-content dl:not(.docutils) dl:not(.field-list) > dt, html[data-theme="dark"].writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not( .glossary ):not(.simple) dl:not(.field-list) > dt {
+    background-color: #0f0f0f !important;
+    color: #959595 !important;
+    border-color: #2b2b2b !important;
+}

docs/conf.py

@@ -54,14 +54,14 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 html_theme = 'sphinx_rtd_theme'
-html_theme_options = {"logo": {"text": "Lightning Pose Docs - Home"}}
 html_logo = "images/LightningPose_logo_light.png"
 html_favicon = "images/favicon.ico"
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-# html_static_path = ['_static']
+html_static_path = ['_static']
+html_css_files = ['lightningpose.css']
 
 autosummary_generate = True
 
@@ -70,12 +70,7 @@
 
 
 # If you want to document __init__() functions for python classes
-# https://stackoverflow.com/a/5599712
-def skip(app, what, name, obj, skip, options):
-    if name == "__init__":
-        return False
-    return skip
-
-
-def setup(app):
-    app.connect("autodoc-skip-member", skip)
+# https://stackoverflow.com/a/61732050/1925967
+autodoc_default_options = {
+    'special-members': '__init__',
+}

docs/requirements.txt

@@ -12,7 +12,8 @@ kaleido
 kornia
 lightning
 matplotlib
-moviepy
+moviepy<2.0.0
+numpy<2.0.0
 opencv-python
 pandas
 pillow

docs/source/api.rst

@@ -1,5 +1,77 @@
+.. _lightning_pose_api:
+
+##################
 Lightning Pose API
-==================
+##################
+
+
+Train function
+==============
+
+.. autofunction:: lightning_pose.train.train
+
+To train a model using ``config.yaml`` and output to ``outputs/doc_model``:
+    .. code-block:: python
+
+        import os
+        from lightning_pose.train import train
+        from omegaconf import OmegaConf
+
+        cfg = OmegaConf.load("config.yaml")
+        os.chdir("outputs/doc_model")
+        train(cfg)
+
+To override settings before training:
+    .. code-block:: python
+
+        cfg = OmegaConf.load("config.yaml")
+        overrides = {
+            "training": {
+                "min_epochs": 5,
+                "max_epochs": 5
+            }
+        }
+        cfg = OmegaConf.merge(cfg, overrides)
+        train(cfg)
+
+Training returns a Model object, which is described next.
+
+Model class
+===========
+
+The ``Model`` class provides an easy-to-use interface to a lightning-pose
+model. It supports running inference and accessing model metadata.
+The set of supported Model operations will expand as we continue development.
+
+You create a model object using `Model.from_dir`:
+
+.. code-block:: python
+
+    from lightning_pose.model import Model
+
+    model = Model.from_dir("outputs/doc_model")
+
+Then, to predict on new data:
+
+.. code-block:: python
+
+    model.predict_on_video_file("path/to/video.mp4")
+
+or:
+
+.. code-block:: python
+
+    model.predict_on_label_csv("path/to/csv_file.csv")
+
+API Reference:
+
+.. autoclass:: lightning_pose.model.Model
+    :members:
+    :exclude-members: __init__, PredictionResult, predict_on_label_csv_internal
+
+
+Lightning Pose Internal API
+===========================
 
 * :ref:`metrics and callbacks modules <lp_modules>`
 * :ref:`data package <lp_modules_data>`

docs/source/developer_guide/add_a_loss.rst

@@ -198,7 +198,7 @@ from the ``data`` field) you can add those key-value pairs to the constructor in
 
 Step 4: update ``compute_metrics`` (optional)
 ---------------------------------------------
-The base training script ``scripts/train_hydra.py`` will automatically compute a set of metrics on
+Lightning pose will automatically compute a set of metrics on
 all labeled data and unlabeled videos upon training completion.
 To add your new metric to this operation, you must update
 :meth:`~lightning_pose.utils.scripts.compute_metrics`.

docs/source/developer_guide/add_a_model.rst

@@ -182,10 +182,6 @@ The field ``model.model_type`` is used to specify your model - the current suppo
 "regression", "heatmap", and "heatmap_mhcrnn".
 Add your new model name to this list.
 
-The basic training script can be found at ``scripts/train_hydra.py``.
-You do not need to update anything in this script to accommodate your new model, but this script
-uses several helper functions that we will update next.
-
 Step 2: update ``get_dataset``
 ------------------------------
 The first helper function you need to update is
@@ -217,8 +213,8 @@ Finally, there is helper function :meth:`~lightning_pose.utils.predictions.get_m
 is used to seamlessly load model parameters from checkpoint files.
 Again, there are various ``if/else`` statements where your model should be incorporated.
 
-Step 6: optional and miscellaneous additons
--------------------------------------------
+Step 6: optional and miscellaneous additions
+--------------------------------------------
 
 If you find yourself needing to write a new DALI dataloader to support your model training, you might also need to update the :class:`~lightning_pose.utils.predictions.PredictionHandler` class.
 

docs/source/user_guide/config_file.rst

@@ -95,7 +95,8 @@ See the :ref:`FAQs <faq_oom>` for more information on memory management.
   If the value is a float between 0 and 1 then it is interpreted as the fraction of total train frames.
   If the value is an integer greater than 1 then it is interpreted as the number of total train frames.
 
-.. _config_num_gpus:
+    .. _config_num_gpus:
+
 * ``training.num_gpus`` (*int, default: 1*): the number of GPUs for
   :ref:`multi-GPU training <multi_gpu_training>`
 
@@ -245,15 +246,14 @@ Evaluation
 
 The following parameters are used for general evaluation.
 
-* ``eval.predict_vids_after_training`` (*bool, default: true*): if true, after training (when using
-  scripts/train_hydra.py) run inference with the best model on all videos located in
-  ``eval.test_videos_directory`` (see below)
+* ``eval.predict_vids_after_training`` (*bool, default: true*): if true, after training run
+  inference on all videos located in ``eval.test_videos_directory`` (see below)
 
 * ``eval.test_videos_directory`` (*str, default: null*): absolute path to a video directory
-  containing videos for prediction; used in scripts/train_hydra.py and scripts/predict_new_vids.py
+  containing videos for post-training prediction.
 
 * ``eval.save_vids_after_training`` (*bool, default: false*): save out an mp4 file with predictions
-  overlaid after running inference; used in scripts/train_hydra.py and scripts/predict_new_vids.py
+  overlaid after running post-training prediction.
 
 * ``eval.colormap`` (*str, default: cool*): colormap options for labeled videos; options include
   sequential colormaps (viridis, plasma, magma, inferno, cool, etc) and diverging colormaps (RdBu,
@@ -262,11 +262,10 @@ The following parameters are used for general evaluation.
 * ``eval.confidence_thresh_for_vid`` (*float, default: 0.9*): predictions with confidence below this
   value will not be plotted in the labeled videos
 
-* ``eval.hydra_paths`` (*list, default: []*): absolute paths to hydra output folders for use with
-  scripts/predict_new_vids.py (see :ref:`inference <inference>` docs) and
-  scripts/create_fiftyone_dataset.py (see :ref:`FiftyOne <fiftyone>` docs)
-
 * ``eval.fiftyone.dataset_name`` (*str, default: test*): name of the FiftyOne dataset
 
 * ``eval.fiftyone.model_display_names`` (*list, default: [test_model]*): shorthand name for each of
   the models specified in ``hydra_paths``
+
+* ``eval.hydra_paths`` (*list, default: []*): absolute paths to model directories, only for use with
+  scripts/create_fiftyone_dataset.py (see :ref:`FiftyOne <fiftyone>` docs).

docs/source/user_guide/evaluation.streamlit.rst

@@ -13,13 +13,10 @@ Run the following command from inside the ``lightning-pose/lightning_pose/apps``
 
 .. code-block:: console
 
-    streamlit run labeled_frame_diagnostics.py -- --model_dir <ABOLUTE_PATH_TO_HYDRA_OUTPUTS_DIRECTORY>
+    streamlit run labeled_frame_diagnostics.py -- --model_dir <ABSOLUTE_PATH_TO_OUTPUT_DIRECTORY>
 
-The only argument needed is ``--model_dir``, which tells the app where to find models and their predictions. ``<ABOLUTE_PATH_TO_HYDRA_OUTPUTS_DIRECTORY>`` should contain hydra subfolders of the type ``YYYY-MM-DD/HH-MM-SS``.
-
-.. note:
-
-    The lightning-pose output folder for a single model is typically ``/path/to/lightning-pose/outputs/YYYY-MM-DD/HH-MM-SS``, where the last folder contains prediction csv files.
+The only argument needed is ``--model_dir``, which tells the app where to find model directories.
+It should contain model directories of the type ``YYYY-MM-DD/HH-MM-SS``.
 
 The app shows:
 

docs/source/user_guide/inference.rst

@@ -4,60 +4,93 @@
 Inference
 #########
 
-Once you have trained a model you'll likely want to run inference on new videos.
+Since version 1.7.0, installing lightning-pose also installs ``litpose``,
+a command-line tool built on top of the :ref:`lightning_pose_api`.
+The command ``litpose predict`` is used to run model inference on new data.
 
-Similar to training, there are several tools for running inference:
+Inference on new videos
+=======================
 
-#. A set of high-level functions used for processing videos and creating labeled clips. You can combine these to create your own custom inference script. This is required if you used the :ref:`pip package <pip_package>` installation method.
-#. An example inference script provided in the :ref:`conda from source <conda_from_source>` installation method. This demonstrates how to combine the high-level functions.
+The model_dir argument is the path to the model outputted by ``litpose train``.
+
+To predict on one or more video files:
+
+.. code-block:: shell
+
+    litpose predict <model_dir> <video_file1> <video_file2> ...
+
+To predict on a folder of video files:
+
+.. code-block:: shell
+
+    litpose predict <model_dir> <video_files_dir>
+
+The ``litpose predict`` command saves frame-by-frame predictions and confidences as a CSV file,
+unsupervised losses in CSV file per loss type. By default it also generates videos annotated with 
+predictions, a feature which can be disabled using the ``--skip_viz`` flag.
+
+For the full list of options:
+
+.. code-block:: shell
+
+    litpose predict --help
 
 .. note::
 
-    The steps below assume the :ref:`conda from source <conda_from_source>` installation method.
-    If you did not use this installation method, see the
-    `example inference script <https://github.com/danbider/lightning-pose/blob/main/scripts/predict_new_vids.py>`_.
-    You can also see how video inference is handled in the
-    `example train script <https://github.com/danbider/lightning-pose/blob/main/scripts/train_hydra.py>`_.
+  Videos *must* be mp4 files that use the h.264 codec; see more information in the
+  :ref:`FAQs<faq_video_formats>`.
 
-Inference with example data
-===========================
 
-To run inference with a model trained on the example dataset, run the following command from
-inside the ``lightning-pose`` directory
-(make sure you have activated your conda environment):
+Inference on new images
+=======================
 
-.. code-block:: console
+Lightning pose also supports inference on images, as well 
+as computing pixel error against new labeled images. This is useful
+for evaluating a model on out-of-distribution data to see how well the
+model generalizes.
 
-    python scripts/predict_new_vids.py eval.hydra_paths=["YYYY-MM-DD/HH-MM-SS/"]
+Currently it's required to create a CSV file similar to
+the one used for training labeled frames. Once you have a CSV file,
+run: 
 
-This overwrites the config field ``eval.hydra_paths``, which is a list that contains the relative
-paths of the model folders you want to run inference with
-(you will need to replace "YYYY-MM-DD/HH-MM-SS/" with the timestamp of your own model).
+.. code-block:: shell
 
-Inference with your data
-========================
+    litpose predict <model_dir> <csv_file>
 
-In order to use this script more generally, you need to update several config fields:
+Output location
+===============
 
-#. ``eval.hydra_paths``: path to models to use for prediction
-#. ``eval.test_videos_directory``: path to a `directory` containing videos to run inference on
-#. ``eval.save_vids_after_training``: if ``true``, the script will also save a copy of the full video with model predictions overlaid.
+Video predictions are saved to:
 
-The results will be stored in the model directory.
+.. code-block::
 
-As with training, you either directly edit your config file and run:
+    <model_dir>/
+    └── video_preds/
+        ├── <video_filename>.csv              (predictions)
+        ├── <video_filename>_<metric>.csv     (losses)
+        └── labeled_videos/
+            └── <video_filename>_labeled.mp4
 
-.. code-block:: console
+Image predictions are saved to:
 
-    python scripts/predict_new_vids.py --config-path=<PATH/TO/YOUR/CONFIGS/DIR> --config-name=<CONFIG_NAME.yaml>
+.. code-block::
 
-or override these arguments in the command line:
+    <model_dir>/
+    └── image_preds/
+        └── <image_dirname | csv_filename | timestamp>/
+            ├── predictions.csv
+            ├── predictions_<metric>.csv      (losses)
+            └── <image_filename>_labeled.png
 
-.. code-block:: console
+Inference on sample dataset
+===========================
 
-    python scripts/predict_new_vids.py --config-path=<PATH/TO/YOUR/CONFIGS/DIR> --config-name=<CONFIG_NAME.yaml> eval.hydra_paths=["YYYY-MM-DD/HH-MM-SS/"] eval.test_videos_directory=/absolute/path/to/videos
+The lightning pose repo includes a sample dataset (see :ref:`training-on-sample-dataset`).
+The sample video file is located in the git repo at ``data/mirror-mouse-example/videos``.
+Thus, to run inference on a model trained on the sample dataset,
+run from the ``lightning-pose`` directory
+(make sure you have activated your conda environment):
 
-.. note::
+.. code-block:: shell
 
-  Videos *must* be mp4 files that use the h.264 codec; see more information in the
-  :ref:`FAQs<faq_video_formats>`.
+    litpose predict <model_dir> data/mirror-mouse-example/videos