arrow version of dataset

LADI-v2-dataset.py

@@ -1,278 +0,0 @@
-import cv2
-import datasets
-import pandas as pd
-from datasets.data_files import DataFilesDict, sanitize_patterns
-from pathlib import Path
-from PIL import Image, ImageFile
-
-from typing import List, Optional
-
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
-# maps the dataset names to names for the image sets they rely on
-DATA_NAME_MAP = {
-    'v1_damage': 'v1',
-    'v1_infrastructure': 'v1',
-    'v2': 'v2',
-    'v2_resized': 'v2_resized',
-    'v2a': 'v2',
-    'v2a_resized': 'v2_resized'
-}
-
-DATA_URLS = {'v1': "https://ladi.s3.amazonaws.com/ladi_v1.tar.gz", 
-             'v2': 'https://ladi.s3.amazonaws.com/ladi_v2.tar.gz', 
-             'v2_resized': 'https://ladi.s3.amazonaws.com/ladi_v2_resized.tar.gz'}
-
-SPLIT_REL_PATHS = {
-    # note: the v1 datasets don't have separate 'test' and 'val' splits
-    'v1_damage': {'train':'v1/damage_dataset/damage_df_train.csv',
-                            'val':'v1/damage_dataset/damage_df_test.csv',
-                            'test':'v1/damage_dataset/damage_df_test.csv',
-                            'all': 'v1/damage_dataset/damage_df.csv'},
-    'v1_infrastructure': {'train':'v1/infra_dataset/infra_df_train.csv',
-                            'val':'v1/infra_dataset/infra_df_test.csv',
-                            'test':'v1/infra_dataset/infra_df_test.csv',
-                            'all':'v1/infra_dataset/infra_df.csv'},
-    'v2': {'train':'v2/ladi_v2_labels_train.csv',
-                             'val':'v2/ladi_v2_labels_val.csv',
-                             'test':'v2/ladi_v2_labels_test.csv',
-                             'all':'v2/ladi_v2_labels_train_full.csv'},
-    'v2_resized': {'train':'v2/ladi_v2_labels_train_resized.csv',
-                             'val':'v2/ladi_v2_labels_val_resized.csv',
-                             'test':'v2/ladi_v2_labels_test_resized.csv',
-                             'all':'v2/ladi_v2_labels_train_full_resized.csv'},
-    'v2a': {'train':'v2/ladi_v2a_labels_train.csv',
-                             'val':'v2/ladi_v2a_labels_val.csv',
-                             'test':'v2/ladi_v2a_labels_test.csv',
-                             'all':'v2/ladi_v2a_labels_train_full.csv'},
-    'v2a_resized': {'train':'v2/ladi_v2a_labels_train_resized.csv',
-                             'val':'v2/ladi_v2a_labels_val_resized.csv',
-                             'test':'v2/ladi_v2a_labels_test_resized.csv',
-                             'all':'v2/ladi_v2a_labels_train_full_resized.csv'}
-}
-
-class LadiClassifyDatasetConfig(datasets.BuilderConfig):
-    def __init__(self, 
-                 name: str = 'v2a_resized',
-                 base_dir: Optional[str] = None, 
-                 split_csvs = None,
-                 download_ladi = False,
-                 data_name: Optional[str] = None,
-                 label_name: Optional[str] = None,
-                 **kwargs):
-        """
-        split_csvs: a dictionary mapping split names to existing csv files containing annotations
-            if this arg is set, you MUST already have the dataset
-        base_dir: the base directory of the label CSVs and data files.
-        data_name: the version of the data you're using. Used to determine what files to download if
-            you don't specify split_csvs or url_list. Must be in DATA_URLS.keys().
-            
-        If split_csvs is None, the requested data will be downloaded from the hub. Please do NOT 
-            use this feature with streaming=True, you will perform a large download every time.
-        """
-        self.download_ladi = download_ladi
-        self.data_name = DATA_NAME_MAP[name] if data_name is None else data_name
-        self.label_name = name if label_name is None else label_name
-        self.base_dir = None if base_dir is None else Path(base_dir)
-        self.split_csvs = split_csvs
-
-        if self.data_name not in DATA_URLS.keys():
-            raise ValueError(f"Expected data_name to be one of {DATA_URLS.keys()}, got {self.data_name}")
-        
-        if split_csvs is None and download_ladi == False:
-            self.split_csvs = SPLIT_REL_PATHS[self.label_name]
-
-        super(LadiClassifyDatasetConfig, self).__init__(name=name, **kwargs)
-            
-
-class LADIClassifyDataset(datasets.GeneratorBasedBuilder):
-    """
-    Dataset for LADI Classification task
-    """
-    
-    VERSION = datasets.Version("0.2.1")
-    BUILDER_CONFIG_CLASS = LadiClassifyDatasetConfig
-    DEFAULT_CONFIG_NAME = 'v2a_resized'
-    
-    BUILDER_CONFIGS = [
-        LadiClassifyDatasetConfig(
-            name='v1_damage',
-            version=VERSION,
-            description="Dataset for recognizing damage (flood, rubble, misc) from LADI"
-        ),
-        LadiClassifyDatasetConfig(
-            name="v1_infrastructure",
-            version=VERSION,
-            description="Dataset for recognizing infrastructure (buildings, roads) from LADI"
-        ),
-        LadiClassifyDatasetConfig(
-            name="v2",
-            version=VERSION,
-            description="Dataset using the v2 labels for LADI"
-        ),
-        LadiClassifyDatasetConfig(
-            name="v2_resized",
-            version=VERSION,
-            description="Dataset using the v2 labels for LADI, pointing to the lower resolution source images for speed"
-        ),
-        LadiClassifyDatasetConfig(
-            name="v2a",
-            version=VERSION,
-            description="Dataset using the v2a labels for LADI"
-        ),
-        LadiClassifyDatasetConfig(
-            name="v2a_resized",
-            version=VERSION,
-            description="Dataset using the v2a labels for LADI, pointing to the lower resolution source images for speed"
-        ),
-    ]
-    
-    def _info(self):
-        if self.config.label_name == "v1_damage":
-            features = datasets.Features(
-                {
-                    "image":datasets.Image(),
-                    "flood":datasets.Value("bool"),
-                    "rubble":datasets.Value("bool"),
-                    "misc_damage":datasets.Value("bool")
-                }
-            )
-        elif self.config.label_name == "v1_infrastructure":
-            features = datasets.Features(
-                 {
-                    "image":datasets.Image(),
-                    "building":datasets.Value("bool"),
-                    "road":datasets.Value("bool")
-                }
-            )
-        elif self.config.label_name in ["v2", "v2_resized"]:
-            features = datasets.Features(
-                {
-                    "image":datasets.Image(),
-                    "bridges_any": datasets.Value("bool"),
-                    "bridges_damage": datasets.Value("bool"),
-                    "buildings_affected": datasets.Value("bool"),
-                    "buildings_any": datasets.Value("bool"),
-                    "buildings_destroyed": datasets.Value("bool"),
-                    "buildings_major": datasets.Value("bool"),
-                    "buildings_minor": datasets.Value("bool"),
-                    "debris_any": datasets.Value("bool"),
-                    "flooding_any": datasets.Value("bool"),
-                    "flooding_structures": datasets.Value("bool"),
-                    "roads_any": datasets.Value("bool"),
-                    "roads_damage": datasets.Value("bool"),
-                    "trees_any": datasets.Value("bool"),
-                    "trees_damage": datasets.Value("bool"),
-                    "water_any": datasets.Value("bool"),
-                }
-            )
-        elif self.config.label_name in ["v2a", "v2a_resized"]:
-            features = datasets.Features(
-                {
-                    "image":datasets.Image(),
-                    "bridges_any": datasets.Value("bool"),
-                    "buildings_any": datasets.Value("bool"),
-                    "buildings_affected_or_greater": datasets.Value("bool"),
-                    "buildings_minor_or_greater": datasets.Value("bool"),
-                    "debris_any": datasets.Value("bool"),
-                    "flooding_any": datasets.Value("bool"),
-                    "flooding_structures": datasets.Value("bool"),
-                    "roads_any": datasets.Value("bool"),
-                    "roads_damage": datasets.Value("bool"),
-                    "trees_any": datasets.Value("bool"),
-                    "trees_damage": datasets.Value("bool"),
-                    "water_any": datasets.Value("bool"),
-                }
-            )
-        else:
-            raise NotImplementedError
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=f"LADI Dataset for {self.config.label_name} category",
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("image", "label"),
-        )
-    
-    def read_ann_csv(self, fpath):
-        if self.config.data_name == 'v1':
-            return pd.read_csv(fpath, sep='\t', index_col=False)
-        return pd.read_csv(fpath, sep=',', index_col=False)
-
-    def _split_generators(self, dl_manager):
-        generators = []
-        data_files = self.config.split_csvs
-
-        if self.config.download_ladi:
-            # download data files to config.base_dir
-            dl_url = dl_manager.download(DATA_URLS[self.config.data_name])
-            base_dir = Path(self.config.base_dir)
-            tar_iterator = dl_manager.iter_archive(dl_url)
-            base_dir.mkdir(exist_ok=True)
-            for filename, file in tar_iterator:
-                file_path: Path = base_dir/filename
-                file_path.parent.mkdir(parents=True, exist_ok=True)
-                with open(base_dir/filename, 'wb') as f:
-                    f.write(file.read())
-
-        data_files = DataFilesDict.from_local_or_remote(
-            sanitize_patterns(data_files), 
-            base_path=self.config.base_dir
-        )
-
-        if 'train' in data_files.keys():
-            train_df = self.read_ann_csv(data_files['train'][0])
-            label_cols = tuple(label for label in train_df.columns if label not in ['url','local_path'])
-            train_examples = [x._asdict() for x in train_df.itertuples()]
-            generators.append(datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"examples":train_examples,
-                            "label_cols":label_cols}
-            ))
-        if 'val' in data_files.keys():
-            val_df = self.read_ann_csv(data_files['val'][0])
-            label_cols = tuple(label for label in val_df.columns if label not in ['url','local_path'])
-            val_examples = [x._asdict() for x in val_df.itertuples()]
-            generators.append(datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"examples":val_examples,
-                            "label_cols":label_cols}
-            ))
-        if 'test' in data_files.keys():
-            test_df = self.read_ann_csv(data_files['test'][0])
-            label_cols = tuple(label for label in test_df.columns if label not in ['url','local_path'])
-            test_examples = [x._asdict() for x in test_df.itertuples()]
-            generators.append(datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"examples":test_examples,
-                            "label_cols":label_cols}
-            ))
-        if 'all' in data_files.keys():
-            all_df = self.read_ann_csv(data_files['all'][0])
-            label_cols = tuple(label for label in all_df.columns if label not in ['url','local_path'])
-            all_examples = [x._asdict() for x in all_df.itertuples()]
-            generators.append(datasets.SplitGenerator(
-                name=datasets.Split.ALL,
-                gen_kwargs={"examples":all_examples,
-                            "label_cols":label_cols}
-            ))
-
-        return generators
-
-    def _generate_examples(self, examples, label_cols, from_url_list=False):
-        for ex in examples:
-            try:
-                image_path = Path(ex['local_path'])
-                if not image_path.is_absolute():
-                    image_path = str(self.config.base_dir/image_path)
-            except:
-                print(ex)
-                raise
-            
-            image = cv2.imread(image_path)
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            labels = {k:ex[k] for k in label_cols}
-            labels |= {"image":image}
-            yield image_path, labels

data/dataset_dict.json

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data/test/data-00000-of-00003.arrow

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data/test/dataset_info.json

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+  "builder_name": "ladi_classify_dataset",
+  "citation": "",
+  "config_name": "v2a_resized",
+  "dataset_name": "ladi_classify_dataset",
+  "dataset_size": 29147075809,
+  "description": "LADI Dataset for v2a_resized category",
+  "download_checksums": {},
+  "download_size": 0,
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+      "_type": "Value"
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+      "_type": "Value"
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data/test/state.json

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