Comet HPO

docs/source/guides/hyperparameter_optimization.rst

@@ -3,12 +3,34 @@
 Hyperparameter Optimization
 ===========================
 
-The default configuration for the models will not always work on a new dataset. Additionally, to increase performance, learning rate, optimizers, activation functions, etc. need to be tuned/selected. To make it easier to run such broad experiments that isolate the right combination of hyperparameters, Anomalib supports hyperparameter optimization using weights and biases.
+The default configuration for the models will not always work on a new dataset. Additionally, to increase performance, learning rate, optimizers, activation functions, etc. need to be tuned/selected. To make it easier to run such broad experiments that isolate the right combination of hyperparameters, Anomalib supports hyperparameter optimization using Comet or weights and biases.
 
 YAML file
 **********
 
-A sample configuration file for hyperparameter optimization is provided at ``tools/hpo/sweep.yaml`` and is reproduced below:
+A Sample configuration files for hyperparameter optimization with Comet is provided at ``tools/hpo/config/comet_sweep.yaml`` and reproduced below:
+
+.. code-block:: yaml
+
+  algorithm: "bayes"
+  spec:
+    maxCombo: 10
+    metric: "image_F1Score"
+    objective: "maximize"
+  parameters:
+    dataset:
+      category: capsule
+      image_size:
+        type: discrete
+        values: [128, 256]
+    model:
+      backbone:
+        type: categorical
+        values: ["resnet18", "wide_resnet50_2"]
+
+The maxCombo defines the total number of experiments to run. The algorithm is the optimization method to be used. The metric is the metric to be used to evaluate the performance of the model. The parameters are the hyperparameters to be optimized. For details on other possible configurations with Comet's Optimizer  , refer to the `Comet's <https://www.comet.com/docs/v2/api-and-sdk/python-sdk/introduction-optimizer/>`_ documentation.
+
+A sample configuration file for hyperparameter optimization with Weights and Bias is provided at ``tools/hpo/config/wandb_sweep.yaml`` and is reproduced below:
 
 .. code-block:: yaml
 
@@ -26,33 +48,40 @@ A sample configuration file for hyperparameter optimization is provided at ``too
       backbone:
         values: [resnet18, wide_resnet50_2]
 
-The observation budget defines the total number of experiments to run. The method is the optimization method to be used. The metric is the metric to be used to evaluate the performance of the model. The parameters are the hyperparameters to be optimized. For details on methods other than ``bayes`` and parameter values apart from list, refer the `Weights and Biases <https://docs.wandb.ai/guides/sweeps/quickstart>`_ documentation. Everything under the ``parameters`` key overrides the default values defined in the model configuration. Currently, only the dataset and model parameters are overridden for the HPO search.
+The observation budget defines the total number of experiments to run. The method is the optimization method to be used. The metric is the metric to be used to evaluate the performance of the model. The parameters are the hyperparameters to be optimized. For details on methods other than ``bayes`` and parameter values apart from list, refer the `Weights and Biases <https://docs.wandb.ai/guides/sweeps/quickstart>`_ documentation.
+
+Everything under the ``parameters`` key (in both configuration formats) overrides the default values defined in the model configuration. In these examples, only the dataset and model parameters are overridden for the HPO search.
 
 Running HPO
 ************
 
 .. note::
 
-  You will need to have logged into a wandb account to use HPO search and view the results.
+  You will need to have logged into a Comet or wandb account to use HPO search and view the results.
 
 To run the hyperparameter optimization, use the following command:
 
 .. code-block:: bash
 
   python tools/hpo/sweep.py --model padim \
     --model_config ./path_to_config.yaml \
-    --sweep_config tools/hpo/sweep.yaml
+    --sweep_config tools/hpo/config/comet_sweep.yaml
 
 
 In case ``model_config`` is not provided, the script looks at the default config location for that model.
 
 .. code-block:: bash
 
-  python tools/hpo/sweep.py --sweep_config tools/hpo/sweep.yaml
+  python tools/hpo/sweep.py --sweep_config tools/hpo/config/comet_sweep.yaml
 
 Sample Output
 **************
 
+.. figure:: ../images/logging/comet_sweep.png
+  :alt: Sample configuration of a Comet sweep
+
+  Sample Comet sweep on Padim
+
 
 .. figure:: ../images/logging/wandb_sweep.png
   :alt: Sample configuration of a wandb sweep

docs/source/guides/logging.rst

@@ -51,7 +51,7 @@ Anomalib allows you to save predictions to the file system by setting ``log_imag
 
   Logging images to Comet,TensorBoard and wandb won't work if you don't have ``logger: [comet, tensorboard, wandb]`` set as well. This ensures that the respective logger is passed to the trainer object.
 
-.. figure:: ../images/logging/comet_media.jpg
+.. figure:: ../images/logging/comet_media.png
   :alt: comet dashboard showing logged images
 
   Comet Images in TensorBoard Dashboard
@@ -84,7 +84,7 @@ Anomalib makes it easier to log your model graph to Comet, TensorBoard or Weight
     logger: [comet, tensorboard]
     log_graph: true
 
-.. figure:: ../images/logging/comet_graph.jpg
+.. figure:: ../images/logging/comet_graph.png
   :alt: comet dashboard showing model graph
 
   Model Graph in Comet Dashboard

tools/hpo/configs/comet_sweep.yaml

@@ -0,0 +1,15 @@
+algorithm: "bayes"
+spec:
+  maxCombo: 10
+  metric: "image_F1Score"
+  objective: "maximize"
+parameters:
+  dataset:
+    category: capsule
+    image_size:
+      type: discrete
+      values: [128, 256]
+  model:
+    backbone:
+      type: categorical
+      values: ["resnet18", "wide_resnet50_2"]

tools/hpo/sweep.py

@@ -1,4 +1,4 @@
-"""Run wandb sweep."""
+"""Run hpo sweep."""
 
 # Copyright (C) 2022 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
@@ -8,9 +8,10 @@
 from typing import Union
 
 import pytorch_lightning as pl
+from comet_ml import Optimizer
 from omegaconf import DictConfig, ListConfig, OmegaConf
 from pytorch_lightning import seed_everything
-from pytorch_lightning.loggers import WandbLogger
+from pytorch_lightning.loggers import CometLogger, WandbLogger
 from utils import flatten_hpo_params
 
 import wandb
@@ -71,6 +72,51 @@ def sweep(self):
         trainer.fit(model, datamodule=datamodule)
 
 
+class CometSweep:
+    """comet sweep.
+
+    Args:
+        config (DictConfig): Original model configuration.
+        sweep_config (DictConfig): Sweep configuration.
+    """
+
+    def __init__(self, config: Union[DictConfig, ListConfig], sweep_config: Union[DictConfig, ListConfig]) -> None:
+        self.config = config
+        self.sweep_config = sweep_config
+
+    def run(self):
+        """Run the sweep."""
+        flattened_hpo_params = flatten_hpo_params(self.sweep_config.parameters)
+        self.sweep_config.parameters = flattened_hpo_params
+
+        # comet's Optmizer cannot takes dict as an input, not DictConfig
+        std_dict = OmegaConf.to_object(self.sweep_config)
+
+        opt = Optimizer(std_dict)
+
+        project_name = f"{self.config.model.name}_{self.config.dataset.name}"
+
+        for exp in opt.get_experiments(project_name=project_name):
+            comet_logger = CometLogger()
+
+            # allow pytorch-lightning to use the experiment from optimizer
+            comet_logger._experiment = exp  # pylint: disable=W0212
+            run_params = exp.params
+            for param in run_params.keys():
+                set_in_nested_config(self.config, param.split("."), run_params[param])
+            config = update_input_size_config(self.config)
+
+            model = get_model(config)
+            datamodule = get_datamodule(config)
+            callbacks = get_sweep_callbacks(config)
+
+            # Disable saving checkpoints as all checkpoints from the sweep will get uploaded
+            config.trainer.checkpoint_callback = False
+
+            trainer = pl.Trainer(**config.trainer, logger=comet_logger, callbacks=callbacks)
+            trainer.fit(model, datamodule=datamodule)
+
+
 def get_args():
     """Gets parameters from commandline."""
     parser = ArgumentParser()
@@ -89,5 +135,10 @@ def get_args():
     if model_config.project.seed != 0:
         seed_everything(model_config.project.seed)
 
-    sweep = WandbSweep(model_config, hpo_config)
+    # check hpo config structure to see whether it adheres to comet or wandb format
+    sweep: Union[CometSweep, WandbSweep]
+    if "spec" in hpo_config.keys():
+        sweep = CometSweep(model_config, hpo_config)
+    else:
+        sweep = WandbSweep(model_config, hpo_config)
     sweep.run()