Delete Transformer-Explainability/dataset

Transformer-Explainability/dataset/expl_hdf5.py

@@ -1,52 +0,0 @@
-import os
-
-import h5py
-import torch
-from torch.utils.data import Dataset
-
-
-class ImagenetResults(Dataset):
-    def __init__(self, path):
-        super(ImagenetResults, self).__init__()
-
-        self.path = os.path.join(path, "results.hdf5")
-        self.data = None
-
-        print("Reading dataset length...")
-        with h5py.File(self.path, "r") as f:
-            # tmp = h5py.File(self.path , 'r')
-            self.data_length = len(f["/image"])
-
-    def __len__(self):
-        return self.data_length
-
-    def __getitem__(self, item):
-        if self.data is None:
-            self.data = h5py.File(self.path, "r")
-
-        image = torch.tensor(self.data["image"][item])
-        vis = torch.tensor(self.data["vis"][item])
-        target = torch.tensor(self.data["target"][item]).long()
-
-        return image, vis, target
-
-
-if __name__ == "__main__":
-    import imageio
-    import numpy as np
-    from utils import render
-
-    ds = ImagenetResults("../visualizations/fullgrad")
-    sample_loader = torch.utils.data.DataLoader(ds, batch_size=5, shuffle=False)
-
-    iterator = iter(sample_loader)
-    image, vis, target = next(iterator)
-
-    maps = (
-        render.hm_to_rgb(vis[0].data.cpu().numpy(), scaling=3, sigma=1, cmap="seismic")
-        * 255
-    ).astype(np.uint8)
-
-    # imageio.imsave('../delete_hm.jpg', maps)
-
-    print(len(ds))