graph LR
build_dataset["build_dataset"]
build_transform["build_transform"]
McLoader["McLoader"]
image_list["image_list"]
data_prefetcher["data_prefetcher"]
align_resize_call["align_resize_call"]
build_dataset -- "utilizes" --> image_list
image_list -- "delegates to" --> McLoader
build_dataset -- "utilizes" --> build_transform
build_transform -- "integrates" --> align_resize_call
data_prefetcher -- "wraps" --> build_dataset
The Data Pipeline subsystem encompasses modules responsible for loading, transforming, and preparing image data for deep learning models.
The primary orchestrator for creating a complete dataset object, integrating raw data sources with the necessary transformation pipelines. It defines how data samples are structured and accessed.
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Responsible for defining and applying a sequence of image transformations (e.g., resizing, normalization, augmentation, cropping) that prepare raw images for model input.
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A specialized, high-performance loader designed for retrieving raw image data, likely from a memory cache or high-speed storage, optimizing I/O operations.
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Manages references to image files (e.g., paths, IDs) and coordinates their loading, often by delegating to McLoader. It acts as an intermediary between the dataset and the raw data source.
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Optimizes data loading by prefetching batches to the GPU, ensuring that the model's training or inference process is not bottlenecked by data I/O.
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A specific transformation component that applies image alignment and resizing operations, which are particularly crucial for preparing data for segmentation models.
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