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Logging Presets

The logging_presets module contains ready-to-use hooks for logging data, validating and saving your models, and early stoppage during training. It requires the record-keeper and tensorboard packages, which can be installed with pip:

pip install record-keeper tensorboard

Here's how you can use it in conjunction with a trainer and tester:

import pytorch_metric_learning.utils.logging_presets as LP
log_folder, tensorboard_folder = "example_logs", "example_tensorboard"
record_keeper, _, _ = LP.get_record_keeper(log_folder, tensorboard_folder)
hooks = LP.get_hook_container(record_keeper)
dataset_dict = {"val": val_dataset}
model_folder = "example_saved_models"

# Create the tester
tester = testers.GlobalEmbeddingSpaceTester(end_of_testing_hook=hooks.end_of_testing_hook)
end_of_epoch_hook = hooks.end_of_epoch_hook(tester, dataset_dict, model_folder)
trainer = trainers.MetricLossOnly(models,

With the provided hooks, data from both the training and validation stages will be saved in csv, sqlite, and tensorboard format, and models and optimizers will be saved in the specified model folder. See the example notebooks for complete examples. Read the next section to learn more about the provided hooks.


This class contains ready-to-use hooks to be used by trainers and testers.

import pytorch_metric_learning.utils.logging_presets as LP


  • record_keeper: A record-keeper object. Install: pip install record-keeper tensorboard.
  • record_group_name_prefix: A string which will be prepended to all record names and tensorboard tags.
  • primary_metric: A string that specifies the accuracy metric which will be used to determine the best checkpoint. Must be one of:
    • mean_average_precision_at_r
    • r_precision
    • precision_at_1
    • NMI
  • validation_split_name: Optional. Default value is "val". The name of your validation set in dataset_dict.
  • save_models: Optional. Models will be saved if this is True.
  • log_freq: Data will be logged every log_freq iterations.

Important functions:

  • end_of_iteration_hook: This function records data about models, optimizers, and loss and mining functions. You can pass this function directly into a trainer object.
  • end_of_epoch_hook: This function runs validation and saves models. This function returns the actual hook, i.e. you must pass in the following arguments to obtain the hook.
    • tester: A tester object.
    • dataset_dict: A dictionary mapping from split names to PyTorch datasets. For example: {"train": train_dataset, "val": val_dataset}
    • model_folder: A string which is the folder path where models, optimizers etc. will be saved.
    • test_interval: Optional. Default value is 1. Validation will be run every test_interval epochs.
    • patience: Optional. Default value is None. If not None, training will end early if epoch - best_epoch > patience.
    • splits_to_eval: Optional. See splits_to_eval.
    • test_collate_fn: Optional. Default value is None. This is the collate function used by the dataloader during testing.
  • end_of_testing_hook: This function records accuracy metrics. You can pass this function directly into a tester object.

Useful methods:

Getting loss history:

# Get a dictionary mapping from loss names to lists
loss_histories = hooks.get_loss_history() 

# You can also specify which loss histories you want
# It will still return a dictionary. In this case, the dictionary will contain only "total_loss"
loss_histories = hooks.get_loss_history(loss_names=["total_loss"])

Getting accuracy history

# The first argument is the tester object. The second is the split name.
# Get a dictionary containing the keys "epoch" and the primary metric
# The values are lists
acc_histories = hooks.get_accuracy_history(tester, "val")

# Get all accuracy histories
acc_histories = hooks.get_accuracy_history(tester, "val", return_all_metrics=True)

# Get a specific set of accuracy histories
acc_histories = hooks.get_accuracy_history(tester, "val", metrics=["AMI", "NMI"])