Create gtea.py

gtea.py

@@ -0,0 +1,117 @@
+import os
+import datasets
+
+import numpy as np
+
+_DESCRIPTION = """\
+GTEA is composed of 50 recorded videos of 25 participants making two different mixed salads. The videos are captured by a camera with a top-down view onto the work-surface. The participants are provided with recipe steps which are randomly sampled from a statistical recipe model.
+"""
+
+_CITATION = """\
+@inproceedings{stein2013combining,
+title={Combining embedded accelerometers with computer vision for recognizing food preparation activities},
+author={Stein, Sebastian and McKenna, Stephen J},
+booktitle={Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing},
+pages={729--738},
+year={2013}
+}
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = "xxx"
+
+_URLS = {"full": "https://huggingface.co/datasets/dinggd/gtea/resolve/main/gtea.zip"}
+
+
+class GTEA(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="split1", version=VERSION, description="Cross Validation Split1"
+        ),
+        datasets.BuilderConfig(
+            name="split2", version=VERSION, description="Cross Validation Split2"
+        ),
+        datasets.BuilderConfig(
+            name="split3", version=VERSION, description="Cross Validation Split3"
+        ),
+        datasets.BuilderConfig(
+            name="split4", version=VERSION, description="Cross Validation Split4"
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "1"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "video_id": datasets.Value("string"),
+                "video_feature": datasets.Array2D(shape=(None, 2048), dtype="float32"),
+                "video_label": datasets.Sequence(datasets.Value(dtype="int32")),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls_to_download = _URLS
+        data_dir = dl_manager.download_and_extract(urls_to_download)["full"]
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, f"gtea/splits/train.{self.config.name}.bundle"
+                    ),
+                    "featurefolder": os.path.join(data_dir, "gtea/features"),
+                    "gtfolder": os.path.join(data_dir, "gtea/groundTruth"),
+                    "mappingpath": os.path.join(data_dir, "gtea/mapping.txt"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, f"gtea/splits/test.{self.config.name}.bundle"
+                    ),
+                    "featurefolder": os.path.join(data_dir, "gtea/features"),
+                    "gtfolder": os.path.join(data_dir, "gtea/groundTruth"),
+                    "mappingpath": os.path.join(data_dir, "gtea/mapping.txt"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, featurefolder, gtfolder, mappingpath):
+        with open(mappingpath, "r") as f:
+            actions = f.read().splitlines()
+        actions_dict = {}
+        for a in actions:
+            actions_dict[a.split()[1]] = int(a.split()[0])
+
+        with open(filepath, "r") as f:
+            lines = f.read().splitlines()
+            for key, line in enumerate(lines):
+                vid = line[:-4]
+                featurepath = os.path.join(featurefolder, f"{vid}.npy")
+                gtpath = os.path.join(gtfolder, line)
+                feature = np.load(featurepath).T  # T x D
+                with open(gtpath, "r") as f:
+                    content = f.read().splitlines()
+
+                label = np.zeros(min(np.shape(feature)[1], len(content)))
+                for i in range(len(label)):
+                    label[i] = actions_dict[content[i]]
+                yield key, {
+                    "video_id": vid,
+                    "video_feature": feature,
+                    "video_label": label,
+                }