rpotter6298
116 lines
3.8 KiB
Python
116 lines
3.8 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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image_cache: "dict | None" = None,
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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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self.image_cache = image_cache
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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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cache_key = str(img_path)
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if self.image_cache is not None and cache_key in self.image_cache:
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orig_img = Image.fromarray(self.image_cache[cache_key])
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else:
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orig_img = Image.open(img_path).convert("RGB")
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if self.image_cache is not None:
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self.image_cache[cache_key] = np.asarray(orig_img, dtype=np.uint8)
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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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# ---------------------------------------------------------------------------
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# _ClinicalView — shim used by V2HyperTower._run_fold
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# ---------------------------------------------------------------------------
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from .data_bundle import DataBundle # noqa: E402
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class _ClinicalView:
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"""Minimal shim so ClinicalDataset can iterate an epoch-specific DataFrame
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while still delegating encoding/paths/labels to the DataBundle object."""
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def __init__(self, base: DataBundle, df):
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self.base = base
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self.df = df
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@property
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def image_dir(self):
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return self.base.image_dir
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@property
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def clinical_dir(self):
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return self.base.clinical_dir
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@property
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def id_cols(self):
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return ("Patient ID", "eyeID")
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@property
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def label_col(self):
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return self.base.label_col
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@property
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def filename_template(self):
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return getattr(self.base, "filename_template", "RET{pid:03d}{eye}.jpg")
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@property
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def dim(self):
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return self.base.feature_dim
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def encode_metadata(self, row):
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vec = self.base.vectorize_row(row)
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return torch.as_tensor(vec, dtype=torch.float32)
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def get_image_path(self, row):
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return self.base.get_image_path(row)
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def get_label(self, row):
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return int(row[self.base.label_col])
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