Delete duplicate/old file: \/data_loader.py

///data_loader.py

@@ -1,98 +0,0 @@
-#!/usr/bin/python
-# A simple data loader that imports the train and test mat files
-# from the `filename` and converts them to torch.tesnors()
-# to be loaded for training and testing DLoc network
-# `features_wo_offset`: targets for the consistency decoder
-# `features_w_offset` : inputs for the network/encoder
-# `labels_gaussian_2d`: targets for the location decoder
-import torch
-import h5py
-import scipy.io
-import numpy as np
-
-def load_data(filename):
-    print('Loading '+filename)
-    arrays = {}
-    f = h5py.File(filename,'r')
-    features_wo_offset = torch.tensor(np.transpose(np.array(f.get('features_wo_offset'), dtype=np.float32)), dtype=torch.float32)
-    features_w_offset = torch.tensor(np.transpose(np.array(f.get('features_w_offset'), dtype=np.float32)), dtype=torch.float32)
-    labels_gaussian_2d = torch.tensor(np.transpose(np.array(f.get('labels_gaussian_2d'), dtype=np.float32)), dtype=torch.float32)
-        
-    return features_wo_offset,features_w_offset, labels_gaussian_2d
-
-
-class LazyMultiHDF5Dataset(torch.utils.data.Dataset):
-    """Reads samples on demand from multiple HDF5 .mat files.
-    Uses ~0 RAM regardless of dataset size. Each __getitem__ does 3 small
-    HDF5 slice reads (~700 KB total per sample).
-
-    Returns the same tuple ordering as the original TensorDataset:
-        (features_wo_offset, features_w_offset, labels_gaussian_2d)
-    """
-
-    def __init__(self, file_paths):
-        self.file_paths = file_paths
-        self._handles = {}           # lazy-opened per worker/process
-        self.cum_sizes = []
-        self.var_names = {}          # per file: (w_name, wo_name, lbl_name)
-
-        total = 0
-        for fp in file_paths:
-            with h5py.File(fp, 'r') as f:
-                keys = list(f.keys())
-                w_name  = 'features_w_offset'  if 'features_w_offset'  in keys else 'features_with_offset'
-                wo_name = 'features_wo_offset' if 'features_wo_offset' in keys else 'features_without_offset'
-                lbl_name = 'labels_gaussian_2d'
-                self.var_names[fp] = (w_name, wo_name, lbl_name)
-                # HDF5 stores MATLAB dims reversed: last dim = N
-                n = f[w_name].shape[-1]
-                total += n
-                self.cum_sizes.append(total)
-                print(f'  Indexed {fp}: {n} samples')
-
-        self.total_len = total
-        print(f'  Total: {self.total_len} samples (lazy, ~0 MB RAM)')
-
-    def __len__(self):
-        return self.total_len
-
-    def _get_handle(self, fp):
-        if fp not in self._handles:
-            self._handles[fp] = h5py.File(fp, 'r')
-        return self._handles[fp]
-
-    def __getitem__(self, idx):
-        # Find which file this idx belongs to
-        file_idx = 0
-        for i, cs in enumerate(self.cum_sizes):
-            if idx < cs:
-                file_idx = i
-                break
-
-        offset = self.cum_sizes[file_idx - 1] if file_idx > 0 else 0
-        local_idx = idx - offset
-
-        fp = self.file_paths[file_idx]
-        f = self._get_handle(fp)
-        w_name, wo_name, lbl_name = self.var_names[fp]
-
-        # HDF5 dims are reversed vs MATLAB:
-        #   features: MATLAB [N,4,161,361] -> HDF5 [361,161,4,N]
-        #   labels:   MATLAB [N,161,361]   -> HDF5 [361,161,N]
-        feat_w  = np.array(f[w_name][:, :, :, local_idx],  dtype=np.float32).transpose()  # -> [4,161,361]
-        feat_wo = np.array(f[wo_name][:, :, :, local_idx], dtype=np.float32).transpose()  # -> [4,161,361]
-        label   = np.array(f[lbl_name][:, :, local_idx],   dtype=np.float32).transpose()  # -> [161,361]
-        label   = label[np.newaxis, :, :]   # -> [1,161,361]
-
-        return (
-            torch.from_numpy(feat_wo),
-            torch.from_numpy(feat_w),
-            torch.from_numpy(label)
-        )
-
-    def __del__(self):
-        for f in self._handles.values():
-            try:
-                f.close()
-            except Exception:
-                pass