from pathlib import Path import datasets import pandas as pd _VERSION = "1.2.1" _CITATION = f""" @dataset{{unsplash-lite-dataset, title = {{Unsplash Lite Dataset {_VERSION}}}, url = {{\\url{{https://github.com/unsplash/datasets}}}}, author = {{Unsplash}}, year = {{2023}}, month = {{May}}, day = {{02}}, }} """ _DESCRIPTION = """ This dataset, available for commercial and noncommercial usage, contains 25k nature-themed Unsplash photos, 25k keywords, and 1M searches. """ _HOMEPAGE = f"https://github.com/unsplash/datasets/tree/{_VERSION}" _URL = f"https://unsplash.com/data/lite/{_VERSION}" _LICENSE = "Unsplash Dataset License" _TSV = ( "collections", "colors", "conversions", "keywords", "photos", ) _FEATURES = datasets.Features( { "photo": { "id": datasets.Value("string"), "url": datasets.Value("string"), "image_url": datasets.Value("string"), "submitted_at": datasets.Value("string"), "featured": datasets.Value("bool"), "width": datasets.Value("uint16"), "height": datasets.Value("uint16"), "aspect_ratio": datasets.Value("float32"), "description": datasets.Value("string"), "blur_hash": datasets.Value("string"), }, "photographer": { "username": datasets.Value("string"), "first_name": datasets.Value("string"), "last_name": datasets.Value("string"), }, "exif": { "camera_make": datasets.Value("string"), "camera_model": datasets.Value("string"), "iso": datasets.Value("string"), "aperture_value": datasets.Value("string"), "focal_length": datasets.Value("string"), "exposure_time": datasets.Value("string"), }, "location": { "name": datasets.Value("string"), "latitude": datasets.Value("float32"), "longitude": datasets.Value("float32"), "country": datasets.Value("string"), "city": datasets.Value("string"), }, "stats": { "views": datasets.Value("uint32"), "downloads": datasets.Value("uint32"), }, "ai": { "description": datasets.Value("string"), "primary_landmark_name": datasets.Value("string"), "primary_landmark_latitude": datasets.Value("string"), "primary_landmark_longitude": datasets.Value("string"), "primary_landmark_confidence": datasets.Value("string"), }, "keywords": [ { "keyword": datasets.Value("string"), "ai_service_1_confidence": datasets.Value("string"), "ai_service_2_confidence": datasets.Value("string"), "suggested_by_user": datasets.Value("bool"), }, ], "collections": [ { "collection_id": datasets.Value("string"), "collection_title": datasets.Value("string"), "photo_collected_at": datasets.Value("string"), }, ], "conversions": [ { "converted_at": datasets.Value("string"), "conversion_type": datasets.Value("string"), "keyword": datasets.Value("string"), "anonymous_user_id": datasets.Value("string"), "conversion_country": datasets.Value("string"), }, ], "colors": [ { "hex": datasets.Value("string"), "red": datasets.Value("uint8"), "green": datasets.Value("uint8"), "blue": datasets.Value("uint8"), "keyword": datasets.Value("string"), "ai_coverage": datasets.Value("float32"), "ai_score": datasets.Value("float32"), }, ], }, ) def df_withprefix(df, prefix, exclude=None): columns = [col for col in df.columns if col.startswith(prefix)] if exclude is not None: columns = [col for col in columns if exclude not in col] if "photo_id" not in columns: columns.append("photo_id") return df[columns].rename(columns=lambda col: col.removeprefix(prefix)) class Unsplash(datasets.GeneratorBasedBuilder): """The Unsplash Lite dataset.""" DEFAULT_WRITER_BATCH_SIZE = 100 def _info(self): return datasets.DatasetInfo( features=_FEATURES, supervised_keys=None, description=_DESCRIPTION, homepage=_HOMEPAGE, license=_LICENSE, version=_VERSION, citation=_CITATION, ) def _split_generators(self, dl_manager): archive_path = Path(dl_manager.download_and_extract(_URL)) # read all tsv files dataframes = {} for doc in _TSV: # read all tsv files for this document type frames = [] for filename in archive_path.glob(f"{doc}.tsv*"): frame = pd.read_csv(filename, sep="\t", header=0) frames.append(frame) # concatenate all subframes into one concat_frames = pd.concat(frames, axis=0, ignore_index=True) if doc != "photos": dataframes[doc] = concat_frames else: # split "photos" into "photo", "photographer", "exif", "location", "stats", "ai" dataframes["photo"] = df_withprefix(concat_frames, "photo_", "location") dataframes["photo"]["blur_hash"] = concat_frames["blur_hash"] dataframes["photographer"] = df_withprefix(concat_frames, "photographer_") dataframes["exif"] = df_withprefix(concat_frames, "exif_") dataframes["location"] = df_withprefix(concat_frames, "photo_location_") dataframes["stats"] = df_withprefix(concat_frames, "stats_") dataframes["ai"] = df_withprefix(concat_frames, "ai_") # preprocess some columns dataframes["photo"]["featured"] = dataframes["photo"]["featured"].map({"t": True, "f": False}) dataframes["keywords"]["suggested_by_user"] = dataframes["keywords"]["suggested_by_user"].map({"t": True, "f": False}) # cast columns to appropriate dtypes for doc in dataframes.keys(): if doc in _TSV: features = _FEATURES[doc][0] else: features = _FEATURES[doc] dataframes[doc].astype({ key: features[key].dtype for key in features.keys() }) # groupby "photo_id" if not "photo" dataframe for key in _TSV[:-1]: dataframes[key] = dataframes[key].groupby("photo_id") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"dataframes": dataframes}, ), ] def _generate_examples(self, dataframes): # iterate over rows of "photos" dataframe photo_id_frames = {} for index, row in dataframes["photo"].iterrows(): photo_id = row["id"] photographer = dataframes["photographer"].iloc[index] exif = dataframes["exif"].iloc[index] location = dataframes["location"].iloc[index] stats = dataframes["stats"].iloc[index] ai = dataframes["ai"].iloc[index] for key in _TSV[:-1]: try: photo_id_frames[key] = dataframes[key].get_group(photo_id) except: photo_id_frames[key] = pd.DataFrame() data = { "photo": { "id": photo_id, "url": row["url"], "image_url": row["image_url"], "submitted_at": row["submitted_at"], "featured": row["featured"], "width": row["width"], "height": row["height"], "aspect_ratio": row["aspect_ratio"], "description": row["description"], "blur_hash": row["blur_hash"], }, "photographer": { "username": photographer["username"], "first_name": photographer["first_name"], "last_name": photographer["last_name"], }, "exif": { "camera_make": exif["camera_make"], "camera_model": exif["camera_model"], "iso": exif["iso"], "aperture_value": exif["aperture_value"], "focal_length": exif["focal_length"], "exposure_time": exif["exposure_time"], }, "location": { "name": location["name"], "latitude": location["latitude"], "longitude": location["longitude"], "country": location["country"], "city": location["city"], }, "stats": { "views": stats["views"], "downloads": stats["downloads"], }, "ai": { "description": ai["description"], "primary_landmark_name": ai["primary_landmark_name"], "primary_landmark_latitude": ai["primary_landmark_latitude"], "primary_landmark_longitude": ai["primary_landmark_longitude"], "primary_landmark_confidence": ai["primary_landmark_confidence"], }, "keywords": [ { "keyword": keyword["keyword"], "ai_service_1_confidence": keyword["ai_service_1_confidence"], "ai_service_2_confidence": keyword["ai_service_2_confidence"], "suggested_by_user": keyword["suggested_by_user"], } for _, keyword in photo_id_frames["keywords"].iterrows() ], "collections": [ { "collection_id": collection["collection_id"], "collection_title": str(collection["collection_title"]), "photo_collected_at": collection["photo_collected_at"], } for _, collection in photo_id_frames["collections"].iterrows() ], "conversions": [ { "converted_at": conversion["converted_at"], "conversion_type": conversion["conversion_type"], "keyword": conversion["keyword"], "anonymous_user_id": conversion["anonymous_user_id"], "conversion_country": str(conversion["conversion_country"]), } for _, conversion in photo_id_frames["conversions"].iterrows() ], "colors": [ { "hex": color["hex"], "red": color["red"], "green": color["green"], "blue": color["blue"], "keyword": color["keyword"], "ai_coverage": color["ai_coverage"], "ai_score": color["ai_score"], } for _, color in photo_id_frames["colors"].iterrows() ], } yield index, data