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Model Checkpoints

The first question that arises is: what is a checkpoint?

From the Pytorch Lightning documentation:

When a model is training, the performance changes as it sees more data. It is a best practice to save the state of a model throughout the training process. This gives you a version of the model, a checkpoint, at each key point during the development of the model. Once training has been completed, use the checkpoint corresponding to the best performance you found during the training process.

Checkpoints also enable your training to resume where it was in case the training process is interrupted.

Checkpoints Saving: Which, When, and Where?

From the previous subsection, we understand that checkpoints saved at different times during training have different purposes. That's why in SG, multiple checkpoints are saved throughout training:

Checkpoint Filename When is it saved?
ckpt_best.pth Each time we reach a new best metric_to_watch when performing validation.
ckpt_latest.pth At the end of every epoch, constantly overriding.
average_model.pth At the end of training - composed of 10 best models according to metric_to_watch and will only be save when the training_param average_best_models=True.
ckpt_epoch_{EPOCH_INDEX}.pth At the end of a fixed epoch number EPOCH_INDEX if it is specified through save_ckpt_epoch_list training_param

Where are the checkpoint files saved?

The checkpoint files will be saved at <ckpt_root_dir>/<experiment_name>/<run_dir>.

  • ckpt_root_dir and experiment_name can be set by the user when instantiating the Trainer.
    Trainer(ckpt_root_dir='path/to/ckpt_root_dir', experiment_name="my_experiment")
    
  • run_dir is unique and automatically generated each time you start a new training, with trainer.train(...)

When working with a cloned version of SG, one can leave out the ckpt_root_dir arg, and checkpoints will be saved under the super_gradients/checkpoints directory.

Checkpoint Structure

Checkpoints in SG are instances of state_dict. They hold additional information about the model's training besides the model weights.

The checkpoint keys:

  • net: The network's state_dict (state_dict).
  • acc: The network's achieved metric value on the validation set (metric_to_watch in training_params (float).
  • epoch: The last epoch performed.
  • optimizer_state_dict: The state_dict of the optimizer (state_dict).
  • scaler_state_dict: Optional - only present when training with mixed_precision=True. The state_dict of Trainer.scaler.
  • ema_net: Optional - only present when training with ema=True. The EMA model's state_dict. Note that average_model.pth lacks this entry even if ema=True since the average model's snapshots are of the EMA network already (i.e., the "net" entry is already an average of the EMA snapshots).
  • torch_scheduler_state_dict: Optional, will only be present when using a torch native lr scheduler (see LRScheduling)

Remote Checkpoint Saving with SG Loggers

SG supports remote checkpoint saving using 3rd party tools (for example, Weights & Biases). To do so, specify save_checkpoints_remote=True inside sg_logger_params training_param. See our documentation on Third-party experiment monitoring.

Loading Checkpoints

When discussing checkpoint loading in SG, we must separate it into two use cases: loading weights and resuming training. While the former requires the model's state_dict alone, SG checkpoint loading methods introduce more functionality than PyTorch's vanilla load_state_dict(), especially for SG-trained checkpoints.

Loading Model Weights from a Checkpoint

Loading model weights can be done right after model initialization, using models.get(...), or by explicitly calling load_checkpoint_to_model on the torch.nn.Module instance.

Suppose we have launched a training experiment with a similar structure to the one below:

from super_gradients.training import Trainer
...
...
from super_gradients.training import models
from super_gradients.common.object_names import Models

trainer = Trainer("my_resnet18_training_experiment", ckpt_root_dir="/path/to/my_checkpoints_folder")
train_dataloader = ...
valid_dataloader = ...
model = models.get(model_name=Models.RESNET18, num_classes=10)

train_params = {
    ...
    "loss": "LabelSmoothingCrossEntropyLoss",

    "save_ckpt_epoch_list": [10,15]
    ...
}
trainer.train(model=model, training_params=train_params, train_loader=train_dataloader, valid_loader=valid_dataloader)

Then at the end of the training, our ckpt_root_dir contents will look similar to the following:

<ckpt_root_dir>
│
├── <experiment_name>
│   │
│   ├─── <run_dir>
│   │     ├─ ckpt_best.pth                   # Best performance during validation
│   │     ├─ ckpt_latest.pth                 # End of the most recent epoch
│   │     ├─ average_model.pth               # Averaged over specified epochs
│   │     ├─ ckpt_epoch_*.pth                # Checkpoints from specific epochs (like epoch 10, 15, etc.)
│   │     ├─ events.out.tfevents.*           # Tensorflow run artifacts
│   │     └─ log_<timestamp>.txt             # Trainer logs of the specific run
│   │
│   └─── <other_run_dir>
│        └─ ...
│
└─── <other_experiment_name>
    │
    ├─── <run_dir>
    │     └─ ...
    │
    └─── <another_run_dir>
          └─ ...

Suppose we wish to load the weights from ckpt_best.pth. We can simply pass its path to the checkpoint_path argument in models.get(...):

from super_gradients.training import models
from super_gradients.common.object_names import Models

model = models.get(model_name=Models.RESNET18, num_classes=10, checkpoint_path="/path/to/my_checkpoints_folder/my_resnet18_training_experiment/RUN_20230802_131052_651906/ckpt_best.pth")

Important: when loading SG-trained checkpoints using models.get(...), if the network was trained with EMA, the EMA weights will be the ones loaded.

If we already possess an instance of our model, we can also directly use load_checkpoint_to_model:

from super_gradients.training import models
from super_gradients.common.object_names import Models
from super_gradients.training.utils.checkpoint_utils import load_checkpoint_to_model

model = models.get(model_name=Models.RESNET18, num_classes=10)
load_checkpoint_to_model(net=model, ckpt_local_path="/path/to/my_checkpoints_folder/my_resnet18_training_experiment/RUN_20230802_131052_651906/ckpt_best.pth")

Extending the Functionality of PyTorch's strict Parameter in load_state_dict()

When not familiar with PyTorch's strict parameter in load_state_dict(), please see PyTorch's docs on this matter first.

The equivalent arguments for PyTorch's strict parameter in load_state_dict() in models.get() and load_checkpoint_to_model are strict and strict_load respectively, and expect SG's StrictLoad enum type.

Let's have a look at its possible values:

class StrictLoad(Enum):
    """
    Wrapper for adding more functionality to torch's strict_load parameter in load_state_dict().
    Attributes:
        OFF              - Native torch "strict_load = off" behavior. See nn.Module.load_state_dict() documentation for more details.
        ON               - Native torch "strict_load = on" behavior. See nn.Module.load_state_dict() documentation for more details.
        NO_KEY_MATCHING  - Allows the usage of SuperGradient's adapt_checkpoint function, which loads a checkpoint by matching each
                           layer's shapes (and bypasses the strict matching of the names of each layer (i.e., disregards the state_dict key matching)).
    """

    OFF = False
    ON = True
    NO_KEY_MATCHING = "no_key_matching"

In other words, we added another loading mode option- no_key_matching. This option exploits the fact that the state_dicts are OrderedDicts, and comes in handy when the underlying network's structure remains the same, but the state_dicts keys do not match the ones inside the models state_dict. Let's demonstrate the different strict modes with a simple example:

import torch

class ModelA(torch.nn.Module):
    def __init__(self):
        super(ModelA, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 6, 5)
        self.conv2 = torch.nn.Conv2d(6, 16, 5)

class ModelB(torch.nn.Module):
    def __init__(self):
        super(ModelB, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 6, 5)
        self.CONV2 = torch.nn.Sequential([torch.nn.Conv2d(6, 16, 5)])

Notice the above networks have identical weight structures but will have different keys in their state_dicts. This is why loading a checkpoint from either one to the other, using strict=True, will fail and crash. Using strict=False will not crash and successfully load the first layer's weights only. Using SG's no_key_matching will successfully load a checkpoint from either one to the other.

Loading Pretrained Weights from the Model Zoo

Using models.get(...), you can load any of our pre-trained models in 3 lines of code:

from super_gradients.training import models
from super_gradients.common.object_names import Models

model = models.get(Models.YOLOX_S, pretrained_weights="coco")

The pretrained_weights argument specifies the dataset on which the pre-trained weights were trained. Here is the complete list of pre-trained weights.

Loading Checkpoints: Training with Configuration Files

Prerequisites: Training with Configuration Files

Recall the SGs recipes library structure:

The super_gradients/recipes include the following subdirectories

  • arch_params - containing configuration files for instantiating different models
  • checkpoint_params - containing configuration files that define the loaded and saved checkpoints parameters for the training
  • conversion_params - containing configuration files for the model conversion scripts (for deployment)
  • dataset_params - containing configuration files for instantiating different datasets and dataloaders
  • training_hyperparams - containing configuration files holding hyper-parameters for specific recipes

And now, let's take a look at the default parameters in checkpoint_params:

load_backbone: False # whether to load only the backbone part of the checkpoint
checkpoint_path: # checkpoint path that is located in super_gradients/checkpoints
strict_load: True # key matching strictness for loading checkpoint's weights
pretrained_weights: # a string describing the dataset of the pre-trained weights (for example, "imagenent").

And note the above parameters are used to start the training with different weights (fine-tuning etc.) - they are passed to model.get() in the underlying flow of Trainer.train_from_config(...):

@classmethod
def train_from_config(cls, cfg: Union[DictConfig, dict]) -> Tuple[nn.Module, Tuple]:
    ...

    # BUILD NETWORK
    model = models.get(
        ...
        strict_load=cfg.checkpoint_params.strict_load,
        pretrained_weights=cfg.checkpoint_params.pretrained_weights,
        checkpoint_path=cfg.checkpoint_params.checkpoint_path,
        load_backbone=cfg.checkpoint_params.load_backbone,
    )

    # INSTANTIATE DATA LOADERS

    train_dataloader = ...
    val_dataloader = ...

    ...

    # TRAIN
    res = trainer.train(...)
... 

Resuming Training

Resuming training in SG is a comprehensive process, controlled by three primary parameters that allow flexibility in continuing or branching off from specific training checkpoints. These parameters are used within training_params: resume, run_id, and resume_path.

resume: False  # Option to continue training from the latest checkpoint.
run_id:        # ID to resume from a specific run within the same experiment.
resume_path:   # Direct path to a specific checkpoint file (.pth) to resume training.

...

1. Resuming the Latest Run

By setting resume=True, SuperGradients will resume training from the last checkpoint within the same experiment.

Example:

# Continues from the latest run in the cifar_experiment.
python -m super_gradients.train_from_recipe --config-name=cifar10_resnet experiment_name=cifar_experiment training_hyperparams.resume=True

2. Resuming a Specific Run

Using run_id, you can resume training from a specific run within the same experiment, identified by the run ID.

Example:

# Continues from a specific run identified by the ID within cifar_experiment.
python -m super_gradients.train_from_recipe --config-name=cifar10_resnet experiment_name=cifar_experiment run_id=RUN_20230802_131052_651906

3. Branching off from a specific checkpoint

By specifying a resume_path, SuperGradients will create a new run directory, allowing training to resume from that specific checkpoint, and subsequently save the new checkpoints in this new directory.

Example:

# Branches from a specific checkpoint, creating a new run.
python -m super_gradients.train_from_recipe --config-name=cifar10_resnet experiment_name=cifar_experiment training_hyperparams.resume_path=/path/to/checkpoint.pth

4. Resuming with original recipe

Resuming is parameter dependant - you cannot resume the training of a model if there is a mismatch between the model architecture defined in your recipe, and the one in your checkpoint.

Therefore, if you trained a model a while ago, and that in the meantime you changed the model architecture definition, then you won't be able to resume its training, loading the model would simply raise an exception.

To avoid this issue, SuperGradients provides an option to resume a training based on the recipe that was originally used to train the model.

Trainer.resume_experiment(ckpt_root_dir=..., experiment_name=..., run_id=...)
  • run_id is optional. You can use it to chose which run you want to resume. By default, it will resume the latest run of your experiment.

Note that Trainer.resume_experiment can only resume training that were launched with Trainer.train_from_config.

See usage in our resume_experiment_example.

Resuming Training from SG Logger's Remote Storage (WandB only)

SG supports saving checkpoints throughout the training process in the remote storage defined by SG Logger (more info about this object and it's role during training in SG at Third-party experiment monitoring.) Suppose we run an experiment with a WandB SG logger, then our training_hyperparams should hold:

sg_logger: wandb_sg_logger, # Weights&Biases Logger, see class super_gradients.common.sg_loggers.wandb_sg_logger.WandBSGLogger for details
sg_logger_params:             # Params that will be passes to __init__ of the logger super_gradients.common.sg_loggers.wandb_sg_logger.WandBSGLogger
  project_name: project_name, # W&B project name
  save_checkpoints_remote: True,
  save_tensorboard_remote: True,
  save_logs_remote: True,
  entity: <YOUR-ENTITY-NAME>,         # username or team name where you're sending runs
  api_server: <OPTIONAL-WANDB-URL>    # Optional: In case your experiment tracking is not hosted at wandb servers

The save_checkpoints_remote flag is set which will result in saving checkpoints in WandB throughout training. Now, in case the training was interrupted, we can resume it from the checkpoint located in the WandB run storage by setting 2 training hyperparameters: 1. Set resume_from_remote_sg_logger:

resume_from_remote_sg_logger: True
2. Pass run_id through wandb_id to sg_logger_params:
sg_logger: wandb_sg_logger, # Weights&Biases Logger, see class super_gradients.common.sg_loggers.wandb_sg_logger.WandBSGLogger for details
sg_logger_params:             # Params that will be passes to __init__ of the logger super_gradients.common.sg_loggers.wandb_sg_logger.WandBSGLogger
  wandb_id: <YOUR_RUN_ID>
  project_name: project_name, # W&B project name
  save_checkpoints_remote: True,
  save_tensorboard_remote: True,
  save_logs_remote: True,
  entity: <YOUR-ENTITY-NAME>,         # username or team name where you're sending runs
  api_server: <OPTIONAL-WANDB-URL>    # Optional: In case your experiment tracking is not hosted at wandb servers

And that's it! Once you re-launch your training, ckpt_latest.pth (by default) will be downloaded to the checkpoints directory, and the training will resume from it just as if it was locally stored.

Evaluating Checkpoints

Analogically to the previous section, we often want to evaluate a checkpoint seamlessly without being familiar with the training configuration. For this reason, SG introduces two methods: Trainer.evaluate_checkpoint(...) and Trainer.evaluate_recipe(...) and play similar roles to the two previous ways of resuming experiments suggested in the last section:

Trainer.evaluate_checkpoint is used to evaluate a checkpoint resulting from one of your previous experiments, using the same parameters (dataset, valid_metrics,...) as used during the training of the experiment. Trainer.evaluate_recipe is used to evaluate a checkpoint from SGs pre-trained model zoo or to evaluate a checkpoint with different parameters. See both usages and documentation in the corresponding scripts evaluate_checkpoint and evaluate_recipe.