graph LR
Application_Entry_Point["Application Entry Point"]
Data_Loader["Data Loader"]
Model_Definition["Model Definition"]
Training_Orchestrator["Training Orchestrator"]
Application_Entry_Point -- "initiates" --> Data_Loader
Application_Entry_Point -- "instantiates" --> Model_Definition
Application_Entry_Point -- "delegates training execution to" --> Training_Orchestrator
Data_Loader -- "provides data to" --> Training_Orchestrator
Model_Definition -- "provides predictions to" --> Training_Orchestrator
Training_Orchestrator -- "feeds data through" --> Model_Definition
Training_Orchestrator -- "requests data from" --> Data_Loader
click Application_Entry_Point href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/CBAM.PyTorch/Application_Entry_Point.md" "Details"
The CBAM.PyTorch project is structured around a clear separation of concerns for training a deep learning model. The Application Entry Point (train.py) orchestrates the entire process, initializing the Data Loader to prepare the dataset and instantiating the Model Definition which encapsulates the neural network architecture. The Application Entry Point then delegates the core training execution to the Training Orchestrator. The Data Loader provides processed data to the Training Orchestrator, which in turn feeds this data through the Model Definition for forward passes. The Model Definition then provides predictions back to the Training Orchestrator for loss calculation and backpropagation. This architecture ensures a modular and efficient training pipeline.
Application Entry Point [Expand]
The main script (train.py) that initializes and coordinates the training process. It sets up the data pipeline, instantiates the model, and delegates the training loop to the trainer component.
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Responsible for loading and preprocessing the dataset (data_loader.ImageNet_datasets.ImageNetData). It provides the training and validation data to the trainer in an iterable format.
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Encapsulates the neural network architecture, specifically the model.resnet_cbam.ResNet. This component defines the forward pass and the structure of the model, including the CBAM module.
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Manages the core training loop (trainer.trainer), including iterating over epochs, performing forward and backward passes, optimizing model parameters, and logging metrics. It receives data from the Data Loader and interacts with the Model Definition.
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