Prototype of dataset loading script that supports JXL.

td01.py

@@ -0,0 +1,88 @@
+# Copyright (c) 2022, texture.design.
+#
+# 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.
+
+import os
+import json
+
+import datasets
+
+
+DESCRIPTION = """\
+"""
+
+REPO_PREFIX = "https://huggingface.co/datasets/texturedesign/td01_natural-ground-textures"
+DOWNLOAD_PREFIX = REPO_PREFIX + "/resolve/main"
+
+INDEX_URLS = {
+    "train": REPO_PREFIX + "/raw/main/train/metadata.jsonl",
+    "test": REPO_PREFIX + "/raw/main/test/metadata.jsonl",
+}
+
+
+class NaturalGroundTextures(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("0.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="4K", version=VERSION, description="The original resolution dataset."),
+        datasets.BuilderConfig(name="2K", version=VERSION, description="Half-resolution dataset."),
+        datasets.BuilderConfig(name="1K", version=VERSION, description="Quarter-resolution dataset."),
+        
+    ]
+
+    DEFAULT_CONFIG_NAME = "2k"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=DESCRIPTION,
+            citation="",
+            homepage="",
+            license="",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "set": datasets.Value("uint8"),
+                }
+            )
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(INDEX_URLS)
+        train_lines = [json.loads(l) for l in open(data_dir["train"], "r", encoding="utf-8").readlines()]
+        test_lines = [json.loads(l) for l in open(data_dir["test"], "r", encoding="utf-8").readlines()]
+
+        train_files = dl_manager.download_and_extract([(DOWNLOAD_PREFIX + '/train/' + row['file_name']) for row in train_lines])
+        test_files = dl_manager.download_and_extract([(DOWNLOAD_PREFIX + '/test/' + row['file_name']) for row in test_lines])
+
+        return [
+            datasets.SplitGenerator(datasets.Split.TRAIN, dict(lines=train_lines, filenames=train_files)),
+            datasets.SplitGenerator(datasets.Split.TEST, dict(lines=test_lines, filenames=test_files)),
+        ]
+
+    def _generate_examples(self, lines, filenames):
+        from jxlpy import JXLImagePlugin
+        import PIL.Image
+
+        for key, (data, filename) in enumerate(zip(lines, filenames)):
+            image = PIL.Image.open(filename, formats=["jxl"])
+            sz = image.size
+
+            if self.config.name == "1K":
+                image = image.resize(size=(sz[0]//4, sz[1]//4), resample=PIL.Image.Resampling.LANCZOS)
+            elif self.config.name == "2K":
+                image = image.resize(size=(sz[0]//2, sz[1]//2), resample=PIL.Image.Resampling.LANCZOS)
+            else:
+                assert self.config.name == "4K"
+
+            yield key, {"image": image, "set": data["set"]}