init

lib/datasets/TieredImageNet.py

@@ -0,0 +1,84 @@
+from __future__ import print_function
+import numpy as np
+from PIL import Image
+import pickle as pkl
+import os, cv2, csv, glob
+import torch
+import torch.utils.data as data
+
+
+class TieredImageNet(data.Dataset):
+
+  def __init__(self, root_dir, split, transform=None):
+    self.split = split
+    self.root_dir = root_dir
+    self.transform = transform
+    splits = split.split('-')
+
+    images, labels, last = [], [], 0
+    for split in splits:
+      labels_name = '{:}/{:}_labels.pkl'.format(self.root_dir, split)
+      images_name = '{:}/{:}_images.npz'.format(self.root_dir, split)
+      # decompress images if npz not exits
+      if not os.path.exists(images_name):
+        png_pkl = images_name[:-4] + '_png.pkl'
+        if os.path.exists(png_pkl):
+          decompress(images_name, png_pkl)
+        else:
+          raise ValueError('png_pkl {:} not exits'.format( png_pkl ))
+      assert os.path.exists(images_name) and os.path.exists(labels_name), '{:} & {:}'.format(images_name, labels_name)
+      print ("Prepare {:} done".format(images_name))
+      try:
+        with open(labels_name) as f:
+          data = pkl.load(f)
+          label_specific = data["label_specific"]
+      except:
+        with open(labels_name, 'rb') as f:
+          data = pkl.load(f, encoding='bytes')
+          label_specific = data[b'label_specific']
+      with np.load(images_name, mmap_mode="r", encoding='latin1') as data:
+        image_data = data["images"]
+      images.append( image_data )
+      label_specific = label_specific + last
+      labels.append( label_specific )
+      last = np.max(label_specific) + 1
+      print ("Load {:} done, with image shape = {:}, label shape = {:}, [{:} ~ {:}]".format(images_name, image_data.shape, label_specific.shape, np.min(label_specific), np.max(label_specific)))
+    images, labels = np.concatenate(images), np.concatenate(labels)
+
+    self.images = images
+    self.labels = labels
+    self.n_classes = int( np.max(labels) + 1 )
+    self.dict_index_label = {}
+    for cls in range(self.n_classes):
+      idxs = np.where(labels==cls)[0]
+      self.dict_index_label[cls] = idxs
+    self.length = len(labels)
+    print ("There are {:} images, {:} labels [{:} ~ {:}]".format(images.shape, labels.shape, np.min(labels), np.max(labels)))
+  
+
+  def __repr__(self):
+    return ('{name}(length={length}, classes={n_classes})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  def __len__(self):
+    return self.length
+
+  def __getitem__(self, index):
+    assert index >= 0 and index < self.length, 'invalid index = {:}'.format(index)
+    image = self.images[index].copy()
+    label = int(self.labels[index])
+    image = Image.fromarray(image[:,:,::-1].astype('uint8'), 'RGB')
+    if self.transform is not None:
+      image = self.transform( image )
+    return image, label
+
+
+
+
+def decompress(path, output):
+  with open(output, 'rb') as f:
+    array = pkl.load(f, encoding='bytes')
+  images = np.zeros([len(array), 84, 84, 3], dtype=np.uint8)
+  for ii, item in enumerate(array):
+    im = cv2.imdecode(item, 1)
+    images[ii] = im
+  np.savez(path, images=images)