Support streaming

data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:008da37ffe6fd498247a6d4d4a29d0aba4fa694018087bd9816dcad4ef525265
+size 27512552

fashion_mnist_corrupted.py

@@ -3,7 +3,7 @@
 This module contains the huggingface dataset adaptation of
 the Corrupted Fashion-Mnist Data Set.
 Find the full code at `https://github.com/testingautomated-usi/fashion-mnist-c`."""
-import struct
+import os.path
 
 import datasets
 import numpy as np
@@ -38,11 +38,9 @@ if CONFIG.version == datasets.Version("1.0.0"):
 else:
     raise ValueError("Unsupported version.")
 
-_URL = (
-    f"https://raw.githubusercontent.com/testingautomated-usi/fashion-mnist-c/{tag}/generated/npy/"
-)
-
-_URLS = {
+# Downloaded from: f"https://raw.githubusercontent.com/testingautomated-usi/fashion-mnist-c/{tag}/generated/npy/
+_URL = "data.zip"
+_FILENAMES = {
     "train_images": "fmnist-c-train.npy",
     "train_labels": "fmnist-c-train-labels.npy",
     "test_images": "fmnist-c-test.npy",
@@ -86,10 +84,10 @@ class FashionMnistCorrupted(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        urls_to_download = {
-            key: _URL + fname for key, fname in _URLS.items()
+        data_dir = dl_manager.download_and_extract(_URL)
+        downloaded_files = {
+            key: os.path.join(data_dir, fname) for key, fname in _FILENAMES.items()
         }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
 
         return [
             datasets.SplitGenerator(
@@ -117,8 +115,10 @@ class FashionMnistCorrupted(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, filepath, split):
         """This function returns the examples in the raw form."""
         # Images
-        images = np.load(filepath[0])
-        labels = np.load(filepath[1])
+        with open(filepath[0], "rb") as f:
+            images = np.load(f)
+        with open(filepath[1], "rb") as f:
+            labels = np.load(f)
 
         if images.shape[0] != labels.shape[0]:
             raise ValueError(