IAM datasets implemented.

1 files changed, 48 insertions, 51 deletions

diff --git a/src/text_recognizer/datasets/util.py b/src/text_recognizer/datasets/util.py
index 76bd85f..dd16bed 100644
--- a/src/text_recognizer/datasets/util.py
+++ b/src/text_recognizer/datasets/util.py
@@ -1,10 +1,17 @@
 """Util functions for datasets."""
+import hashlib
 import importlib
-from typing import Callable, Dict, List, Type
+import os
+from pathlib import Path
+from typing import Callable, Dict, List, Optional, Type, Union
+from urllib.request import urlopen, urlretrieve
 
+import cv2
+from loguru import logger
 import numpy as np
 from PIL import Image
 from torch.utils.data import DataLoader, Dataset
+from tqdm import tqdm
 
 
 class Transpose:
@@ -15,58 +22,48 @@ class Transpose:
         return np.array(image).swapaxes(0, 1)
 
 
-def fetch_data_loaders(
-    splits: List[str],
-    dataset: str,
-    dataset_args: Dict,
-    batch_size: int = 128,
-    shuffle: bool = False,
-    num_workers: int = 0,
-    cuda: bool = True,
-) -> Dict[str, DataLoader]:
-    """Fetches DataLoaders for given split(s) as a dictionary.
-
-    Loads the dataset class given, and loads it with the dataset arguments, for the number of splits specified. Then
-    calls the DataLoader. Added to a dictionary with the split as key and DataLoader as value.
-
-    Args:
-        splits (List[str]): One or both of the dataset splits "train" and "val".
-        dataset (str): The name of the dataset.
-        dataset_args (Dict): The dataset arguments.
-        batch_size (int): How many samples per batch to load. Defaults to 128.
-        shuffle (bool): Set to True to have the data reshuffled at every epoch. Defaults to False.
-        num_workers (int): How many subprocesses to use for data loading. 0 means that the data will be
-            loaded in the main process. Defaults to 0.
-        cuda (bool): If True, the data loader will copy Tensors into CUDA pinned memory before returning
-            them. Defaults to True.
-
-    Returns:
-        Dict[str, DataLoader]: Dictionary with split as key and PyTorch DataLoader as value.
+def compute_sha256(filename: Union[Path, str]) -> str:
+    """Returns the SHA256 checksum of a file."""
+    with open(filename, "rb") as f:
+        return hashlib.sha256(f.read()).hexdigest()
 
-    """
-
-    def check_dataset_args(args: Dict, split: str) -> Dict:
-        """Adds train flag to the dataset args."""
-        args["train"] = True if split == "train" else False
-        return args
-
-    # Import dataset module.
-    datasets_module = importlib.import_module("text_recognizer.datasets")
-    dataset_ = getattr(datasets_module, dataset)
 
-    data_loaders = {}
+class TqdmUpTo(tqdm):
+    """TQDM progress bar when downloading files.
 
-    for split in ["train", "val"]:
-        if split in splits:
+    From https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py
 
-            data_loader = DataLoader(
-                dataset=dataset_(**check_dataset_args(dataset_args, split)),
-                batch_size=batch_size,
-                shuffle=shuffle,
-                num_workers=num_workers,
-                pin_memory=cuda,
-            )
-
-            data_loaders[split] = data_loader
+    """
 
-    return data_loaders
+    def update_to(
+        self, blocks: int = 1, block_size: int = 1, total_size: Optional[int] = None
+    ) -> None:
+        """Updates the progress bar.
+
+        Args:
+            blocks (int): Number of blocks transferred so far. Defaults to 1.
+            block_size (int): Size of each block, in tqdm units. Defaults to 1.
+            total_size (Optional[int]): Total size in tqdm units. Defaults to None.
+        """
+        if total_size is not None:
+            self.total = total_size  # pylint: disable=attribute-defined-outside-init
+        self.update(blocks * block_size - self.n)
+
+
+def download_url(url: str, filename: str) -> None:
+    """Downloads a file from url to filename, with a progress bar."""
+    with TqdmUpTo(unit="B", unit_scale=True, unit_divisor=1024, miniters=1) as t:
+        urlretrieve(url, filename, reporthook=t.update_to, data=None)  # nosec
+
+
+def _download_raw_dataset(metadata: Dict) -> None:
+    if os.path.exists(metadata["filename"]):
+        return
+    logger.info(f"Downloading raw dataset from {metadata['url']}...")
+    download_url(metadata["url"], metadata["filename"])
+    logger.info("Computing SHA-256...")
+    sha256 = compute_sha256(metadata["filename"])
+    if sha256 != metadata["sha256"]:
+        raise ValueError(
+            "Downloaded data file SHA-256 does not match that listed in metadata document."
+        )