| """Lightweight eval data utilities. LPIPS lives in disc/lpips.py.""" | |
| import numpy as np | |
| import torch | |
| from torch.utils.data import Dataset | |
| class ImgArrDataset(Dataset): | |
| """Wrapper for torch-fidelity FID calculation — expects [B, H, W, C] uint8 arrays.""" | |
| def __init__(self, arr): | |
| self.arr = arr | |
| def __len__(self): | |
| return len(self.arr) | |
| def __getitem__(self, idx): | |
| return torch.from_numpy(self.arr[idx]).permute(2, 0, 1) | |
| def to_torch_tensor(np_array: np.ndarray) -> torch.Tensor: | |
| """Convert (B, H, W, C) NumPy array to (B, C, H, W) float32 tensor in [0, 1].""" | |
| tensor = torch.from_numpy(np_array).permute(0, 3, 1, 2).float() | |
| if tensor.max() > 1.0: | |
| tensor = tensor / 255.0 | |
| return tensor | |