Update Superimposed-Masked-Dataset.py

Superimposed-Masked-Dataset.py

@@ -1,13 +1,26 @@
-# This script was modified from the imagenet-1k HF dataset repo: https://huggingface.co/datasets/imagenet-1k
+# coding=utf-8
+# Copyright 2022 the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script was modified from the imagenet-1k HF dataset repo
 
 import os
-import numpy as np
 
 import datasets
 from datasets.tasks import ImageClassification
 
 from .classes import IMAGENET2012_CLASSES
-from io import BytesIO
 
 
 _CITATION = """\
@@ -28,11 +41,6 @@ _DATA_URL = {
     ]
 }
 
-_MASK_DATA_URL = {
-    "smd_masks": [
-        f"https://huggingface.co/datasets/ariellee/Superimposed-Masked-Dataset/resolve/main/SMD_masks.tar.gz"
-    ]
-}
 
 class SMD(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.0.0")
@@ -47,7 +55,6 @@ class SMD(datasets.GeneratorBasedBuilder):
                 {
                     "image": datasets.Image(),
                     "label": datasets.ClassLabel(names=list(IMAGENET2012_CLASSES.values())),
-                    "segmentation": datasets.Sequence(datasets.Array2D(shape=(None, None), dtype="float32"))
                 }
             ),
             homepage=_HOMEPAGE,
@@ -57,40 +64,26 @@ class SMD(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        archives = dl_manager.download_and_extract(_DATA_URL)
-        mask_archives = dl_manager.download_and_extract(_MASK_DATA_URL)
-    
+        archives = dl_manager.download(_DATA_URL)
+
         return [
             datasets.SplitGenerator(
-                name="SMD",
+                name="SMD", # "SMD (occluded IN-1K val set)"
                 gen_kwargs={
-                    "archives": archives["smd"],
-                    "mask_archives": mask_archives["smd_masks"],
+                    "archives": [dl_manager.iter_archive(archive) for archive in archives["smd"]],
                 },
             ),
         ]
-    
-    def _generate_examples(self, archives, mask_archives):
+
+
+    def _generate_examples(self, archives):
         """Yields examples."""
         idx = 0
-        mask_files = {}
-        for mask_archive in mask_archives:
-            for path, file in dl_manager.iter_archive(mask_archive):
-                if path.endswith(".npy"):
-                    mask_files[path] = np.load(BytesIO(file.read()))
-        
         for archive in archives:
-            for path, file in dl_manager.iter_archive(archive):
+            for path, file in archive:
                 if path.endswith(".png"):
                     synset_id = os.path.basename(os.path.dirname(path))
                     label = IMAGENET2012_CLASSES[synset_id]
-        
-                    mask_file_path = path.replace("_occluded.png", "_mask.npy")
-                    segmentation_mask = mask_files.get(mask_file_path)
-                    ex = {
-                        "image": {"path": path, "bytes": file.read()}, 
-                        "label": label, 
-                        "segmentation": segmentation_mask.tolist()  # Convert numpy array to list
-                    }
+                    ex = {"image": {"path": path, "bytes": file.read()}, "label": label}
                     yield idx, ex
-                    idx += 1
+                    idx += 1