Add more algorithms

others/GDAS/lib/utils/save_meta.py

@@ -0,0 +1,53 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import torch
+import os, sys
+import os.path as osp
+import numpy as np
+
+def tensor2np(x):
+  if isinstance(x, np.ndarray): return x
+  if x.is_cuda: x = x.cpu()
+  return x.numpy()
+
+class Save_Meta():
+
+  def __init__(self):
+    self.reset()
+
+  def __repr__(self):
+    return ('{name}'.format(name=self.__class__.__name__)+'(number of data = {})'.format(len(self)))
+
+  def reset(self):
+    self.predictions = []
+    self.groundtruth = []
+    
+  def __len__(self):
+    return len(self.predictions)
+
+  def append(self, _pred, _ground):
+    _pred, _ground = tensor2np(_pred), tensor2np(_ground)
+    assert _ground.shape[0] == _pred.shape[0] and len(_pred.shape) == 2 and len(_ground.shape) == 1, 'The shapes are wrong : {} & {}'.format(_pred.shape, _ground.shape)
+    self.predictions.append(_pred)
+    self.groundtruth.append(_ground)
+
+  def save(self, save_dir, filename, test=True):
+    meta = {'predictions': self.predictions, 
+            'groundtruth': self.groundtruth}
+    filename = osp.join(save_dir, filename)
+    torch.save(meta, filename)
+    if test:
+      predictions = np.concatenate(self.predictions)
+      groundtruth = np.concatenate(self.groundtruth)
+      predictions = np.argmax(predictions, axis=1)
+      accuracy = np.sum(groundtruth==predictions) * 100.0 / predictions.size
+    else:
+      accuracy = None
+    print ('save save_meta into {} with accuracy = {}'.format(filename, accuracy))
+
+  def load(self, filename):
+    assert os.path.isfile(filename), '{} is not a file'.format(filename)
+    checkpoint       = torch.load(filename)
+    self.predictions = checkpoint['predictions']
+    self.groundtruth = checkpoint['groundtruth']