Update README.md

README.md

@@ -8,4 +8,47 @@ task_categories:
 
 <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6018554e68258223ca22136f/6og2VldKOfp0Ci31h-r_w.mp4"></video>
 
-This dataset is used to train a transporter network for real-world pick/place within the RAD lab at the University of Edinburgh. The dataset is in TFDS format and was collected using [moveit2_data_collector](https://github.com/peterdavidfagan/moveit2_data_collector). In its current state the dataset is being tested as we are proving out this overall pipeline, keep monitoring this dataset and related repos for documentation updates.
+This dataset is used to train a transporter network for real-world pick/place within the RAD lab at the University of Edinburgh. The dataset is in TFDS format and was collected using [moveit2_data_collector](https://github.com/peterdavidfagan/moveit2_data_collector). In its current state the dataset is being tested as we are proving out this overall pipeline, keep monitoring this dataset and related repos for documentation updates.
+
+# Download
+An example of downloading and loading the dataset is given below, as larger datasets are uploaded this example script will change:
+
+```python
+import os
+import tarfile
+
+import tensorflow_datasets as tfds
+from huggingface_hub import hf_hub_download
+
+DATA_DIR="/home/robot"
+FILENAME="data.tar.xz"
+EXTRACTED_FILENAME="data"
+FILEPATH=os.path.join(DATA_DIR, FILENAME)
+EXTRACTED_FILEPATH=os.path.join(DATA_DIR, EXTRACTED_FILENAME)
+
+# download data from huggingface
+hf_hub_download(
+        repo_id="peterdavidfagan/transporter_networks", 
+        repo_type="dataset",
+        filename=FILENAME,
+        local_dir=DATA_DIR,
+        )
+
+# uncompress file
+with tarfile.open(FILEPATH, 'r:xz') as tar:
+    tar.extractall(path=DATA_DIR)
+#os.rm(FILEPATH)
+
+# load with tfds
+ds = tfds.builder_from_directory(EXTRACTED_FILEPATH).as_dataset()['train']
+
+# basic inspection of data
+print(ds.element_spec)
+for eps in ds:
+    print(eps["extrinsics"])
+    for step in eps["steps"]:
+        print(step["is_first"])
+        print(step["is_last"])
+        print(step["is_terminal"])
+        print(step["action"])
+```