Update Superimposed-Masked-Dataset.py

Superimposed-Masked-Dataset.py

@@ -1,21 +1,7 @@
-# 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
+# This script was modified from the imagenet-1k HF dataset repo: https://huggingface.co/datasets/imagenet-1k
 
 import os
+import numpy as np
 
 import datasets
 from datasets.tasks import ImageClassification
@@ -41,6 +27,11 @@ _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")
@@ -55,6 +46,7 @@ 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,
@@ -65,25 +57,35 @@ class SMD(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
         archives = dl_manager.download(_DATA_URL)
+        mask_archives = dl_manager.download(_MASK_DATA_URL)
 
         return [
             datasets.SplitGenerator(
-                name="SMD", # "SMD (occluded IN-1K val set)"
+                name="SMD",
                 gen_kwargs={
                     "archives": [dl_manager.iter_archive(archive) for archive in archives["smd"]],
+                    "mask_archives": [dl_manager.iter_archive(archive) for archive in mask_archives["smd_masks"]],
                 },
             ),
         ]
 
-
-    def _generate_examples(self, archives):
+    def _generate_examples(self, archives, mask_archives):
         """Yields examples."""
         idx = 0
-        for archive in archives:
+        for archive, mask_archive in zip(archives, mask_archives):
+            mask_files = {path: np.load(file) for path, file in mask_archive if path.endswith(".npy")}
             for path, file in archive:
                 if path.endswith(".png"):
                     synset_id = os.path.basename(os.path.dirname(path))
                     label = IMAGENET2012_CLASSES[synset_id]
-                    ex = {"image": {"path": path, "bytes": file.read()}, "label": label}
-                    yield idx, ex
-                    idx += 1
+                    
+                    mask_file_path = path.replace(".png", "_mask.npy")
+                    segmentation_mask = mask_files.get(mask_file_path, None)
+                    if segmentation_mask is not None:
+                        ex = {
+                            "image": {"path": path, "bytes": file.read()}, 
+                            "label": label, 
+                            "segmentation": segmentation_mask.tolist()  # Convert numpy array to list
+                        }
+                        yield idx, ex
+                        idx += 1