download cifar to datasets dir

.gitignore

@@ -17,3 +17,4 @@ recognize*
 .idea
 disassemblers/applegpu
 *.prof
+datasets/cifar-10-python.tar.gz

datasets/__init__.py

@@ -1,10 +1,9 @@
 import os
-import io
 import numpy as np
 import gzip
 import tarfile
 import pickle
-from extra.utils import fetch
+from extra.utils import download_file
 
 def fetch_mnist():
   parse = lambda file: np.frombuffer(gzip.open(file).read(), dtype=np.uint8).copy()
@@ -17,7 +16,9 @@ def fetch_mnist():
 def fetch_cifar(train=True):
   cifar10_mean = np.array([0.4913997551666284, 0.48215855929893703, 0.4465309133731618], dtype=np.float32).reshape(1,3,1,1)
   cifar10_std = np.array([0.24703225141799082, 0.24348516474564, 0.26158783926049628], dtype=np.float32).reshape(1,3,1,1)
-  tt = tarfile.open(fileobj=io.BytesIO(fetch('https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz')), mode='r:gz')
+  fn = os.path.dirname(__file__)+"/cifar-10-python.tar.gz"
+  download_file('https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz', fn, True)
+  tt = tarfile.open(fn, mode='r:gz')
   if train:
     db = [pickle.load(tt.extractfile(f'cifar-10-batches-py/data_batch_{i}'), encoding="bytes") for i in range(1,6)]
   else: