51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
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from typing import Tuple
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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class MMP_Dataset(torch.utils.data.Dataset):
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def __init__(
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self,
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path_to_data: str,
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image_size: int,
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anchor_grid: np.ndarray,
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min_iou: float,
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is_test: bool,
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):
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"""
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@param anchor_grid: The anchor grid to be used for every image
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@param min_iou: The minimum IoU that is required for an overlap for the label grid.
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@param is_test: Whether this is the test set (True) or the validation/training set (False)
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"""
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raise NotImplementedError()
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def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, int]:
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"""
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@return: 3-tuple of image tensor, label grid, and image (file-)number
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"""
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raise NotImplementedError()
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def __len__(self) -> int:
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raise NotImplementedError()
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def get_dataloader(
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path_to_data: str,
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image_size: int,
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batch_size: int,
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num_workers: int,
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anchor_grid: np.ndarray,
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is_test: bool,
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) -> DataLoader:
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raise NotImplementedError()
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def calculate_max_coverage(loader: DataLoader, min_iou: float) -> float:
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"""
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@param loader: A DataLoader object, generated with the get_dataloader function.
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@param min_iou: Minimum IoU overlap that is required to count a ground truth box as covered.
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@return: Ratio of how mamy ground truth boxes are covered by a label grid box. Must be a value between 0 and 1.
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"""
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raise NotImplementedError()
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