used download, checking and extraction logic consistent with other da…

…tasets

tonic/datasets/s_mnist.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 from tonic.dataset import Dataset
-from tonic.download_utils import download_and_extract_archive
+from tonic.download_utils import check_integrity, download_and_extract_archive
 from tonic.io import make_structured_array
 
 
@@ -25,9 +25,6 @@ class SMNIST(Dataset):
         duplicate (bool): If True, emits two spikes per threshold crossing
         num_neurons (integer): How many neurons to use to encode thresholds(must be odd)
         dt (float): Duration(in microseconds) of each timestep
-        download (bool): Choose to download data or verify existing files. If True
-                        and a file with the same name and correct hash is already
-                        in the directory, download is automatically skipped.
         transform (callable, optional): A callable of transforms to apply to the data.
         target_transform (callable, optional): A callable of transforms to apply to the targets/labels.
         transforms (callable, optional): A callable of transforms that is applied to both data and
@@ -43,6 +40,12 @@ class SMNIST(Dataset):
     train_labels_file = "train-labels-idx1-ubyte"
     test_images_file = "t10k-images-idx3-ubyte"
     test_labels_file = "t10k-labels-idx1-ubyte"
+
+    train_images_md5 = "f68b3c2dcbeaaa9fbdd348bbdeb94873"
+    train_labels_md5 = "d53e105ee54ea40749a09fcbcd1e9432"
+    test_images_md5 = "9fb629c4189551a2d022fa330f9573f3"
+    test_labels_md5 = "ec29112dd5afa0611ce80d1b7f02629c"
+
     dtype = np.dtype([("t", int), ("x", int), ("p", int)])
     ordering = dtype.names
 
@@ -66,7 +69,6 @@ def __init__(
         duplicate=True,
         num_neurons=99,
         dt=1000.0,
-        download=True,
         transform=None,
         target_transform=None,
     ):
@@ -82,10 +84,18 @@ def __init__(
         if (num_neurons % 2) == 0:
             raise Exception("Number of neurons must be odd")
 
-        self.images_file = self.train_images_file if train else self.test_images_file
-        self.labels_file = self.train_labels_file if train else self.test_labels_file
-
-        if download:
+        if train:
+            self.images_file = self.train_images_file
+            self.labels_file = self.train_labels_file
+            self.images_md5 = self.train_images_md5
+            self.labels_md5 = self.train_labels_md5
+        else:
+            self.images_file = self.test_images_file
+            self.labels_file = self.test_labels_file
+            self.images_md5 = self.test_images_md5
+            self.labels_md5 = self.test_labels_md5
+
+        if not self._check_exists():
             self.download()
 
         # Open images file
@@ -179,7 +189,32 @@ def __len__(self):
         return self.image_data.shape[0]
 
     def download(self):
-        for f in [self.images_file, self.labels_file]:
+        for (f, m) in [(self.images_file, self.images_md5),
+                       (self.labels_file, self.labels_md5)]:
             download_and_extract_archive(
-                self.base_url + f + ".gz", self.location_on_system, filename=f + ".gz"
+                self.base_url + f + ".gz", self.location_on_system, 
+                filename=f + ".gz", md5=m
             )
+
+    def _are_labels_present(self) -> bool:
+        """Check if the label file is present on disk.
+
+        No hashing.
+        """
+        return check_integrity(os.path.join(self.location_on_system,
+                                            self.labels_file))
+
+    def _are_images_present(self) -> bool:
+        """Check if the images file is present on disk.
+
+        No hashing.
+        """
+        return check_integrity(os.path.join(self.location_on_system,
+                                            self.images_file))
+
+
+    def _check_exists(self):
+        return (
+            self._are_labels_present()
+            and self._are_images_present()
+        )