graph LR
ReFL_Data_Orchestrator["ReFL Data Orchestrator"]
Text_Tokenization_Utility["Text Tokenization Utility"]
Ranked_Pair_Dataset_Constructor["Ranked Pair Dataset Constructor"]
Raw_Data_Acquisition["Raw Data Acquisition"]
Data_Transformation_Pipeline["Data Transformation Pipeline"]
Tokenizer_Initialization["Tokenizer Initialization"]
ReFL_Data_Orchestrator -- "delegates text processing to" --> Text_Tokenization_Utility
Ranked_Pair_Dataset_Constructor -- "calls" --> Raw_Data_Acquisition
Ranked_Pair_Dataset_Constructor -- "calls" --> Data_Transformation_Pipeline
Ranked_Pair_Dataset_Constructor -- "calls" --> Tokenizer_Initialization
The ImageReward project's ReFL subsystem focuses on preparing and processing data for training. The ReFL Data Orchestrator (represented by the main function in ImageReward.ReFL) acts as the central control, initiating the data pipeline. A key dependency for text processing is the Text Tokenization Utility (utilizing transformers.CLIPTokenizer), which converts raw text into a machine-readable format. The Ranked Pair Dataset Constructor is crucial for building the training dataset, relying on Raw Data Acquisition to obtain initial data, the Data Transformation Pipeline for necessary preprocessing, and Tokenizer Initialization to ensure text data is correctly prepared for model input. This structured flow ensures efficient and accurate data preparation for the ReFL training module.
Serves as the high-level coordinator for data preprocessing specifically for the ReFL training module. It initiates and manages the overall flow of preparing ReFL-specific training data.
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A specialized utility component dedicated to converting raw text captions into tokenized formats. This is a fundamental text preprocessing step, ensuring text data is machine-readable for models.
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The core component responsible for initializing and orchestrating the creation of the rank_pair_dataset. It encapsulates the logic for building a dataset of ranked image-text pairs, which is crucial for training.
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Handles the process of obtaining raw data, either by loading it from storage or generating it, to form the basis of the ranked image-text pairs. This component is the entry point for raw data into the system.
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Applies essential data transformations, such as image resizing, normalization, and text formatting, to ensure data consistency and prepare it into a format directly consumable by machine learning models.
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Initializes the specific tokenizer instance required for processing the text components within the rank_pair_dataset. This ensures that text data is correctly prepared and aligned with the model's input requirements.
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