The DetectronCheckpointer class is a utility class provided by the Python MaskRCNN_Benchmark library that simplifies saving and loading PyTorch models. It allows the user to save a model's weights, optimizer state, and other training-related variables as a checkpoint file. Later, the user can load the same checkpoint file to restore the model's weights and resume training from where it ended.
Example Code:
from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
# Initialize Checkpointer Class checkpointer = DetectronCheckpointer(cfg, model, optimizer=None, scheduler=None)
# Save Model Checkpoint checkpointer.save("model_checkpoint.pth")
# Load Model Checkpoint checkpointer.load("model_checkpoint.pth")
In the above code, first, we import the necessary DetectronCheckpointer class from the maskrcnn_benchmark.utils.checkpoint module. Then, We initialize the DetectronCheckpointer class by passing the model, optimizer, scheduler objects, and the configuration file. After initialization, we can save the checkpoint by calling its save() method and recover the checkpoint by calling its load() method.
The DetectronCheckpointer class is part of the MaskRCNN_Benchmark package, a PyTorch based framework for object detection and segmentation tasks.
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