rpotter6298
58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
from torch.utils.data import Dataset
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from PIL import Image
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import numpy as np
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import torch
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class ClinicalDataset(Dataset):
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"""Generic dataset wrapping a DataBundle-like instance.
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Returns (img_tensor, meta_tensor, label)."""
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def __init__(
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self,
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clinical_data,
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img_transform,
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meta_transform=None,
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image_preprocessor=None,
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geometry_provider=None,
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geometry_dim: int = 0,
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):
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self.clinical = clinical_data
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self.transform_image = img_transform
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self.meta_transform = meta_transform or (lambda x: x)
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self.image_preprocessor = image_preprocessor
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self.geometry_provider = geometry_provider
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self.geometry_dim = geometry_dim if geometry_provider is not None else 0
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def __len__(self):
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return len(self.clinical.df)
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def __getitem__(self, idx: int):
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row = self.clinical.df.iloc[idx]
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# load & transform image
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img_path = self.clinical.get_image_path(row)
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orig_img = Image.open(img_path).convert("RGB")
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img = orig_img
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if self.image_preprocessor is not None:
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img = self.image_preprocessor(img, img_path)
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img_t = self.transform_image(img)
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# encode & transform metadata
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meta = self.clinical.encode_metadata(row)
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meta_t = self.meta_transform(meta)
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# label
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label = self.clinical.get_label(row)
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if self.geometry_dim > 0:
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features = None
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if self.geometry_provider is not None and hasattr(self.geometry_provider, "geometry_features"):
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features = self.geometry_provider.geometry_features(orig_img, img_path)
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if features is None:
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geom_vec = torch.zeros(self.geometry_dim, dtype=torch.float32)
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else:
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features = np.asarray(features, dtype=np.float32)
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if features.shape[0] != self.geometry_dim:
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geom_vec = torch.zeros(self.geometry_dim, dtype=torch.float32)
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else:
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geom_vec = torch.from_numpy(features)
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return img_t, meta_t, geom_vec, label
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return img_t, meta_t, label
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