Update renovation.py

renovation.py

@@ -1,11 +1,12 @@
 import os
 import glob
 import random
-import zipfile
 
 import datasets
 from datasets.tasks import ImageClassification
-
+from datasets import load_dataset
+import os
+from huggingface_hub import login
 _HOMEPAGE = "https://github.com/your-github/renovation"
 
 _CITATION = """\
@@ -25,17 +26,16 @@ Data was collected by the your research lab.
 """
 
 _URLS = {
- "Not Applicable": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Not Applicable.zip",
+ "Not Applicable": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Not Applicable.zip",
  "Very Poor": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Very Poor.zip",
- "Poor": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Poor.zip",
- "Fair": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Fair.zip",
- "Good": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Good.zip",
- "Excellent": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Excellent.zip",
- "Exceptional": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Exceptional.zip"
+ "Poor": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Poor.zip",
+ "Fair": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Fair.zip",
+ "Good": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Good.zip",
+ "Excellent": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Excellent.zip",
+ "Exceptional": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/Exceptional.zip"
 }
 
 _NAMES = ["Not Applicable", "Very Poor", "Poor", "Fair", "Good", "Excellent", "Exceptional"]
-
 class Renovations(datasets.GeneratorBasedBuilder):
  """Renovations house images dataset."""
 
@@ -56,52 +56,49 @@ class Renovations(datasets.GeneratorBasedBuilder):
  )
 
  def _split_generators(self, dl_manager):
- data_dir = dl_manager.download_and_extract(_URLS)
+ data_files = dl_manager.download_and_extract(_URLS)
  return [
  datasets.SplitGenerator(
  name=datasets.Split.TRAIN,
  gen_kwargs={
- "data_dir": data_dir,
+ "data_files": data_files,
  "split": "train",
  },
  ),
  datasets.SplitGenerator(
  name=datasets.Split.VALIDATION,
  gen_kwargs={
- "data_dir": data_dir,
+ "data_files": data_files,
  "split": "val",
  },
  ),
  datasets.SplitGenerator(
  name=datasets.Split.TEST,
  gen_kwargs={
- "data_dir": data_dir,
+ "data_files": data_files,
  "split": "test",
  },
  ),
  ]
 
-
- def _generate_examples(self, data_dir, split):
+ def _generate_examples(self, data_files, split):
  all_files_and_labels = []
- for label in _NAMES:
- folder_path = os.path.join(data_dir, label)
- files = glob.glob(os.path.join(folder_path, "*.jpeg"))
- for file in files:
- all_files_and_labels.append((file, label))
- 
+ for label, path in data_files.items():
+ files = glob.glob(path + '/*.jpeg', recursive=True)
+ all_files_and_labels.extend((file, label) for file in files)
+
  random.seed(43) # ensure reproducibility
  random.shuffle(all_files_and_labels)
- 
+
  num_files = len(all_files_and_labels)
  train_data = all_files_and_labels[:int(num_files*0.9)]
  val_test_data = all_files_and_labels[int(num_files*0.9):] # This will be used for both val and test
- 
+
  if split == "train":
  data_to_use = train_data
  else: # "val" or "test" split
  data_to_use = val_test_data
- 
+
  for idx, (file, label) in enumerate(data_to_use):
  yield idx, {
  "image_file_path": file,
@@ -110,4 +107,3 @@ class Renovations(datasets.GeneratorBasedBuilder):
  }
 
 
-