REG/dataset.py

import os
import json

import torch
from torch.utils.data import Dataset
import numpy as np

from PIL import Image
import PIL.Image
try:
    import pyspng
except ImportError:
    pyspng = None


class CustomDataset(Dataset):
    """
    data_dir 下 VAE latent：imagenet_256_vae/
    无预处理语义时：VAE 统计量/配对文件在 vae-sd/（与原 REG 一致）。
    有 semantic_features_dir 时：与主仓库 dataset 一致，从该目录 dataset.json 索引，
    按特征文件名推断 imagenet_256_vae 中对应 npy。
    """

    def __init__(self, data_dir, semantic_features_dir=None):
        PIL.Image.init()
        supported_ext = PIL.Image.EXTENSION.keys() | {'.npy'}

        self.images_dir = os.path.join(data_dir, 'imagenet_256_vae')

        if semantic_features_dir is None:
            potential_semantic_dir = os.path.join(
                data_dir, 'imagenet_256_features', 'dinov2-vit-b_tmp', 'gpu0'
            )
            if os.path.exists(potential_semantic_dir):
                self.semantic_features_dir = potential_semantic_dir
                self.use_preprocessed_semantic = True
                print(f"Found preprocessed semantic features at: {self.semantic_features_dir}")
            else:
                self.semantic_features_dir = None
                self.use_preprocessed_semantic = False
        else:
            self.semantic_features_dir = semantic_features_dir
            self.use_preprocessed_semantic = True
            print(f"Using preprocessed semantic features from: {self.semantic_features_dir}")

        if self.use_preprocessed_semantic:
            label_fname = os.path.join(self.semantic_features_dir, 'dataset.json')
            if not os.path.exists(label_fname):
                raise FileNotFoundError(f"Label file not found: {label_fname}")

            print(f"Using {label_fname}.")
            with open(label_fname, 'rb') as f:
                data = json.load(f)
            labels_list = data.get('labels', None)
            if labels_list is None:
                raise ValueError(f"'labels' field is missing in {label_fname}")

            semantic_fnames = []
            labels = []
            for entry in labels_list:
                if entry is None:
                    continue
                fname, lab = entry
                semantic_fnames.append(fname)
                labels.append(0 if lab is None else lab)

            self.semantic_fnames = semantic_fnames
            self.labels = np.array(labels, dtype=np.int64)
            self.num_samples = len(self.semantic_fnames)
            print(f"Loaded {self.num_samples} semantic entries from dataset.json")
        else:
            self.features_dir = os.path.join(data_dir, 'vae-sd')

            self._image_fnames = {
                os.path.relpath(os.path.join(root, fname), start=self.images_dir)
                for root, _dirs, files in os.walk(self.images_dir) for fname in files
            }
            self.image_fnames = sorted(
                fname for fname in self._image_fnames if self._file_ext(fname) in supported_ext
            )
            self._feature_fnames = {
                os.path.relpath(os.path.join(root, fname), start=self.features_dir)
                for root, _dirs, files in os.walk(self.features_dir) for fname in files
            }
            self.feature_fnames = sorted(
                fname for fname in self._feature_fnames if self._file_ext(fname) in supported_ext
            )

            fname = os.path.join(self.features_dir, 'dataset.json')
            if os.path.exists(fname):
                print(f"Using {fname}.")
            else:
                raise FileNotFoundError("Neither of the specified files exists.")

            with open(fname, 'rb') as f:
                labels = json.load(f)['labels']
            labels = dict(labels)
            labels = [labels[fname.replace('\\', '/')] for fname in self.feature_fnames]
            labels = np.array(labels)
            self.labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim])

    def _file_ext(self, fname):
        return os.path.splitext(fname)[1].lower()

    def __len__(self):
        if self.use_preprocessed_semantic:
            return self.num_samples
        assert len(self.image_fnames) == len(self.feature_fnames), \
            "Number of feature files and label files should be same"
        return len(self.feature_fnames)

    def __getitem__(self, idx):
        if self.use_preprocessed_semantic:
            semantic_fname = self.semantic_fnames[idx]
            basename = os.path.basename(semantic_fname)
            idx_str = basename.split('-')[-1].split('.')[0]
            subdir = idx_str[:5]
            vae_relpath = os.path.join(subdir, f"img-mean-std-{idx_str}.npy")
            vae_path = os.path.join(self.images_dir, vae_relpath)

            with open(vae_path, 'rb') as f:
                image = np.load(f)

            semantic_path = os.path.join(self.semantic_features_dir, semantic_fname)
            semantic_features = np.load(semantic_path)

            return (
                torch.from_numpy(image).float(),
                torch.from_numpy(image).float(),
                torch.from_numpy(semantic_features).float(),
                torch.tensor(self.labels[idx]),
            )

        image_fname = self.image_fnames[idx]
        feature_fname = self.feature_fnames[idx]
        image_ext = self._file_ext(image_fname)
        with open(os.path.join(self.images_dir, image_fname), 'rb') as f:
            if image_ext == '.npy':
                image = np.load(f)
                image = image.reshape(-1, *image.shape[-2:])
            elif image_ext == '.png' and pyspng is not None:
                image = pyspng.load(f.read())
                image = image.reshape(*image.shape[:2], -1).transpose(2, 0, 1)
            else:
                image = np.array(PIL.Image.open(f))
                image = image.reshape(*image.shape[:2], -1).transpose(2, 0, 1)

        features = np.load(os.path.join(self.features_dir, feature_fname))
        return torch.from_numpy(image), torch.from_numpy(features), torch.tensor(self.labels[idx])