loading of multiple shards (#5)

- feat: dataloader with multiple shards. (592761d9480e93d5df79d8b3313c7ab6803756ca)
- chore: change to relative paths. (a918863adf9c4f13aaa06d6daf6f11e158bdad69)
- chore: add readme generated from dataset-cli test. (0e7dfb0bff5480c59af5366131dc4f6b0bd71b08)

README.md

@@ -1,3 +1,18 @@
 ---
 license: apache-2.0
----
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: depth_map
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 20212097551
+    num_examples: 47584
+  - name: validation
+    num_bytes: 240785762
+    num_examples: 654
+  download_size: 35151124480
+  dataset_size: 20452883313
+---

nyu_depth_v2.py

@@ -14,7 +14,7 @@
 """NYU-Depth V2."""
 
 
-import os
+import io
 
 import datasets
 import h5py
@@ -44,9 +44,8 @@ _HOMEPAGE = "https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html"
 _LICENSE = "Apace 2.0 License"
 
 _URLS = {
-    "depth_estimation": {
-        "train/val": "http://datasets.lids.mit.edu/fastdepth/data/nyudepthv2.tar.gz",
-    }
+    "train": [f"data/train-{i:06d}.tar" for i in range(12)],
+    "val": [f"data/val-{i:06d}.tar" for i in range(2)],
 }
 
 _IMG_EXTENSIONS = [".h5"]
@@ -57,16 +56,6 @@ class NYUDepthV2(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("1.0.0")
 
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="depth_estimation",
-            version=VERSION,
-            description="The depth estimation variant.",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "depth_estimation"
-
     def _info(self):
         features = datasets.Features(
             {"image": datasets.Image(), "depth_map": datasets.Image()}
@@ -83,52 +72,42 @@ class NYUDepthV2(datasets.GeneratorBasedBuilder):
         # Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L21-L23
         return any(filename.endswith(extension) for extension in _IMG_EXTENSIONS)
 
-    def _get_file_paths(self, dir):
-        # Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L31-L44
-        file_paths = []
-        dir = os.path.expanduser(dir)
-
-        for target in sorted(os.listdir(dir)):
-            d = os.path.join(dir, target)
-            if not os.path.isdir(d):
-                continue
-            for root, _, fnames in sorted(os.walk(d)):
-                for fname in sorted(fnames):
-                    if self._is_image_file(fname):
-                        path = os.path.join(root, fname)
-                        file_paths.append(path)
-
-        return file_paths
-
-    def _h5_loader(self, path):
+    def _h5_loader(self, bytes_stream):
         # Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L8-L13
-        h5f = h5py.File(path, "r")
+        f = io.BytesIO(bytes_stream)
+        h5f = h5py.File(f, "r")
         rgb = np.array(h5f["rgb"])
         rgb = np.transpose(rgb, (1, 2, 0))
         depth = np.array(h5f["depth"])
         return rgb, depth
 
     def _split_generators(self, dl_manager):
-        urls = _URLS[self.config.name]
-        base_path = dl_manager.download_and_extract(urls)["train/val"]
-
-        train_data_files = self._get_file_paths(
-            os.path.join(base_path, "nyudepthv2", "train")
-        )
-        val_data_files = self._get_file_paths(os.path.join(base_path, "nyudepthv2", "val"))
+        archives = dl_manager.download(_URLS)
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"filepaths": train_data_files},
+                gen_kwargs={
+                    "archives": [
+                        dl_manager.iter_archive(archive) for archive in archives["train"]
+                    ]
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepaths": val_data_files},
+                gen_kwargs={
+                    "archives": [
+                        dl_manager.iter_archive(archive) for archive in archives["val"]
+                    ]
+                },
             ),
         ]
 
-    def _generate_examples(self, filepaths):
-        for idx, filepath in enumerate(filepaths):
-            image, depth = self._h5_loader(filepath)
-            yield idx, {"image": image, "depth_map": depth}
+    def _generate_examples(self, archives):
+        idx = 0
+        for archive in archives:
+            for path, file in archive:
+                if self._is_image_file(path):
+                    image, depth = self._h5_loader(file.read())
+                    yield idx, {"image": image, "depth_map": depth}
+                    idx += 1