Loading a Pretrained Model Checkpoint#

This guide demonstrates how to load pretrained model checkpoints into a PyTorch model using the provided utility functions: load_latest and load_best_val. These functions help streamline the process of restoring a model’s state from previously saved checkpoints, which is useful for resuming training, performing inference, or evaluating model performance after training.

Overview#

The provided script defines two functions:

load_latest(search_path, model): - Searches for the latest (most recently modified) checkpoint that matches the specified search pattern. - Loads the state dictionary into the given PyTorch model. - Useful when you want to resume training from the most recent checkpoint.
load_best_val(logs_save_path, model_save_path, model, low_is_best=True): - Reads TensorBoard logs to find the checkpoint with the best validation score. - Loads the corresponding state dictionary into the provided model. - Ideal for inference or fine-tuning from the best-performing model state according to validation metrics.

Prerequisites#

A trained model with saved checkpoints.
TensorBoard event files for determining the best validation score (if using load_best_val).
PyTorch and associated libraries (torch, numpy, glob, tensorboard) installed.

Make sure you have:

pip install torch numpy tensorboard tensorboardX upath

Additionally, ensure that:

The checkpoint files are saved in the format expected (e.g., .pth files).
The logs_save_path directory contains TensorBoard event files for validation scores.

Function Definitions#

load_latest(search_path, model)

Parameters:

search_path (str): A file pattern or directory path (e.g., 'checkpoints/model_*.pth') to search for checkpoint files.
model (torch.nn.Module): The model instance to load the state dictionary into.

Behavior:

Finds all checkpoint files matching search_path.
Sorts them by modification time (descending) to get the latest file.
Loads the state dictionary into model with strict=False (allowing mismatched keys if any).

Example:

import torch
from my_model import MyModel
from load_pretrained import load_latest

# Initialize your model
model = MyModel()

# Load the latest checkpoint
load_latest("checkpoints/*.pth", model)
# Now 'model' contains weights from the most recently saved checkpoint.

load_best_val(logs_save_path, model_save_path, model, low_is_best=True)

Parameters:

logs_save_path (str): The directory containing TensorBoard event files with validation metrics.
model_save_path (str): A format string for the model checkpoints (e.g., ‘checkpoints/model_{epoch}.pth’).
model (torch.nn.Module): The model to load the best validation checkpoint into.
low_is_best (bool): If True, the lowest validation score is considered best. If False, the highest score is best.

Behavior:

Loads the TensorBoard events from logs_save_path.
Extracts validation scores and determines the epoch with the best validation performance.
Constructs the checkpoint path using model_save_path and best epoch.
Loads that checkpoint into model.

Example:

import torch
from my_model import MyModel
from load_pretrained import load_best_val

model = MyModel()

# Suppose 'logs' directory has TensorBoard event files and 'checkpoints/model_{epoch}.pth' are your saved checkpoints.
# If lower validation score is better (e.g., for a loss metric), keep low_is_best=True.
load_best_val("logs", "checkpoints/model_{epoch}.pth", model, low_is_best=True)
# 'model' now contains weights from the epoch with the best validation score.

Tutorial: Step-by-Step#

Training and Saving Checkpoints: During training, save your model checkpoints regularly, for example:

torch.save(model.state_dict(), f"checkpoints/model_{epoch}.pth")

Also, log validation metrics to TensorBoard so that the load_best_val function can analyze them:

from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter("logs")
# After computing validation_loss at the end of each epoch:
writer.add_scalar("validation", validation_loss, epoch)

Find the Latest Checkpoint: If you need to resume training from your most recent checkpoint, do:

model = MyModel()
load_latest("checkpoints/model_*.pth", model)
# Continue training from the loaded state

Find the Best Validation Checkpoint: For deployment or testing, you might want the model that performed best on validation:

model = MyModel()
load_best_val("logs", "checkpoints/model_{epoch}.pth", model, low_is_best=True)
# Model now contains the best weights based on validation metrics.

Run Inference or Fine-Tuning: With the loaded model, you can now run inference on test data or fine-tune further:
```
model.eval()
# inference code here
```

Notes#

The provided model_save_path should contain a placeholder for the epoch (e.g., “{epoch}”), allowing the function to construct the exact checkpoint filename for the best epoch.
If no checkpoints are found for load_latest, it won’t modify your model.
If TensorBoard logs don’t contain a validation tag, load_best_val will fail to find a best epoch.
If there’s a mismatch in model architecture and checkpoint keys, strict=False allows partial loading, but ensure that keys align where possible.

Troubleshooting#

No Checkpoints Found: Ensure the search_path or model_save_path pattern is correct.
No Validation Events: Verify that the validation scalar is logged to TensorBoard.
Key Mismatch in Checkpoints: The model definition must match the architecture of the checkpoint. If keys differ, consider updating the model or checkpoint keys or allow partial loading.

Conclusion#

By using load_latest and load_best_val, you can effortlessly restore model states, resume training, or select the optimal model for inference. These utilities integrate seamlessly into the training workflow, making it easier to manage long-running experiments and experiment with different model states.

Loading a Pretrained Model Checkpoint

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