64 lines
2.4 KiB
Python
64 lines
2.4 KiB
Python
import os
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from loguru import logger
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from PIL import Image
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from torch.utils.data import Dataset
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class CounterAnimal(Dataset):
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"""Dataset to load the CounterAnimal dataset with ImageNet labels."""
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def __init__(self, base_path, mode, transform=None, target_transform=None, train=False):
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"""Create the dataset.
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Args:
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base_path (str): path to the base folder (the one where the class folders are in)
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mode (str): mode/variant of the dataset (common/counter)
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transform: Image augmentation
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target_transform: label augmentation
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train: train or test set. Train set is not supported
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"""
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super().__init__()
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self.base = base_path
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assert mode in ["counter", "common"], f"Supported modes are counter and common, but got '{mode}'"
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assert not train, "CounterAnimal only consists of test data, not training data."
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self.transform = transform
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self.target_transform = target_transform
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self.index = []
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for class_folder in os.listdir(self.base):
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if not os.path.isdir(os.path.join(self.base, class_folder)):
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continue
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# print(f"looking in folder {class_folder}")
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class_idx = int(class_folder.split(" ")[0])
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for variant_folder in os.listdir(os.path.join(self.base, class_folder)):
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# print(f"\tlooking in variant {variant_folder}")
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if not variant_folder.startswith(mode):
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# print("\t\tskip")
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continue
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_folder = os.path.join(self.base, class_folder, variant_folder)
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# print(f"\t\tadding {len(os.listdir(_folder))} files to index")
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for file in os.listdir(_folder):
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if file.lower().split(".")[-1] in ["jpg", "jpeg", "png"]:
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self.index.append((os.path.join(_folder, file), class_idx))
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# print(f"loaded {len(self.index)} images into the index: {self.index[:5]}...")
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assert len(self.index) > 0, "did not find any images :("
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def __len__(self):
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return len(self.index)
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def __getitem__(self, idx):
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path, label = self.index[idx]
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img = Image.open(path).convert("RGB")
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if self.transform:
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img = self.transform(img)
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if self.target_transform:
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label = self.target_transform(label)
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return img, label
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