Add dataset builder.

Set5.py

@@ -0,0 +1,133 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""Set5 dataset: An evaluation dataset for the image super resolution task"""
+
+
+import datasets
+from pathlib import Path
+
+
+_CITATION = """
+@article{bevilacqua2012low,
+  title={Low-complexity single-image super-resolution based on nonnegative neighbor embedding},
+  author={Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line},
+  year={2012},
+  publisher={BMVA press}
+}
+"""
+
+_DESCRIPTION = """
+Set5 is a evaluation dataset with 5 RGB images for the image super resolution task.
+"""
+
+_HOMEPAGE = "http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html"
+
+_LICENSE = "UNK"
+
+_DL_URL = "https://huggingface.co/datasets/eugenesiow/Set5/resolve/main/data/"
+
+_DEFAULT_CONFIG = "bicubic_x2"
+
+_DATA_OPTIONS = {
+    "bicubic_x2": {
+        "hr": _DL_URL + "Set5_HR.tar.gz",
+        "lr": _DL_URL + "Set5_LR_x2.tar.gz",
+    },
+    "bicubic_x3": {
+        "hr": _DL_URL + "Set5_HR.tar.gz",
+        "lr": _DL_URL + "Set5_LR_x3.tar.gz",
+    },
+    "bicubic_x4": {
+        "hr": _DL_URL + "Set5_HR.tar.gz",
+        "lr": _DL_URL + "Set5_LR_x4.tar.gz",
+    }
+}
+
+
+class Set5Config(datasets.BuilderConfig):
+    """BuilderConfig for Set5."""
+
+    def __init__(
+        self,
+        name,
+        hr_url,
+        lr_url,
+        **kwargs,
+    ):
+        if name not in _DATA_OPTIONS:
+            raise ValueError("data must be one of %s" % _DATA_OPTIONS)
+        super(Set5Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.hr_url = hr_url
+        self.lr_url = lr_url
+
+
+class Set5(datasets.GeneratorBasedBuilder):
+    """Set5 dataset for single image super resolution evaluation."""
+
+    BUILDER_CONFIGS = [
+        Set5Config(
+            name=option,
+            hr_url=values['hr'],
+            lr_url=values['lr']
+        ) for option, values in _DATA_OPTIONS.items()
+    ]
+
+    DEFAULT_CONFIG_NAME = _DEFAULT_CONFIG
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "hr": datasets.Value("string"),
+                "lr": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        hr_data_dir = dl_manager.download_and_extract(self.config.hr_url)
+        lr_data_dir = dl_manager.download_and_extract(self.config.lr_url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "lr_path": lr_data_dir,
+                    "hr_path": hr_data_dir
+                },
+            )
+        ]
+
+    def _generate_examples(
+        self, hr_path, lr_path
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+        extensions = {'.jpg', '.jpeg', '.png'}
+        for file_path in sorted(Path(hr_path).glob(".")):
+            if file_path.suffix in extensions:
+                file_path_str = str(file_path.as_posix())
+                yield file_path_str, {
+                    'hr': file_path_str,
+                    'lr': str((Path(lr_path) / file_path.name).as_posix())
+                }