wip

fashion_mnist_label_drift.py

@@ -17,11 +17,7 @@
 
 
 import csv
-import json
-import os
-
 import datasets
-from datasets.tasks import TextClassification
 
 
 
@@ -132,6 +128,7 @@ class FashionMNISTLabelDrift(datasets.GeneratorBasedBuilder):
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
         extracted_paths = dl_manager.download_and_extract(_URLS)
+        print(extracted_paths)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split("training"),
@@ -176,21 +173,21 @@ class FashionMNISTLabelDrift(datasets.GeneratorBasedBuilder):
                     "ner_tags":ner_tags_list,
                 }
 
-    def _generate_examples(self, filepath, split):
-        """This function returns the examples in the raw form."""
-        # Images
-        with open(filepath[0], "rb") as f:
-            # First 16 bytes contain some metadata
-            _ = f.read(4)
-            size = struct.unpack(">I", f.read(4))[0]
-            _ = f.read(8)
-            images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
-
-        # Labels
-        with open(filepath[1], "rb") as f:
-            # First 8 bytes contain some metadata
-            _ = f.read(8)
-            labels = np.frombuffer(f.read(), dtype=np.uint8)
-
-        for idx in range(size):
-            yield idx, {"image": images[idx], "label": int(labels[idx])}
+    #def _generate_examples(self, filepath, split):
+    #    """This function returns the examples in the raw form."""
+    #    # Images
+    #    with open(filepath[0], "rb") as f:
+    #        # First 16 bytes contain some metadata
+    #        _ = f.read(4)
+    #        size = struct.unpack(">I", f.read(4))[0]
+    #        _ = f.read(8)
+    #        images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
+
+    #    # Labels
+    #    with open(filepath[1], "rb") as f:
+    #        # First 8 bytes contain some metadata
+    #        _ = f.read(8)
+    #        labels = np.frombuffer(f.read(), dtype=np.uint8)
+
+    #    for idx in range(size):
+    #        yield idx, {"image": images[idx], "label": int(labels[idx])}