Update terramesh.py

terramesh.py

@@ -21,11 +21,11 @@ import io
 import re
 import zarr
 import torch
+import warnings
 import fsspec
 import braceexpand
-import numpy as np
 import albumentations
-import warnings
+import numpy as np
 import webdataset as wds
 from collections.abc import Callable, Iterable
 from torch.utils.data._utils.collate import default_collate
@@ -75,29 +75,62 @@ statistics = {
 
 def build_terramesh_dataset(
         path: str = "https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh/resolve/main/",
-        modalities: list | str = None,
+        modalities: list[str] | str = None,
         split: str = "val",
         urls: str | None = None,
+        transform: Callable = None,
         batch_size: int = 8,
         return_metadata: bool = False,
         shuffle: bool = True,
-        *args, **kwargs,
+        shardshuffle: int = 100,
+        deterministic: bool = False,
+        seed: int = None,
+        **kwargs,
 ):
+    """
+    Builds a dataset for TerraMesh, see https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh.
+
+    :param path: URL or local path to dataset root that with data structure ./{split}/{modality}/shard_{id}.tar
+    :param modalities: List of modalities or a single modality name
+    :param split: Split name ("train", "val"). Default to "val".
+    :param urls: Specify custom shard urls instead of providing the path, modalities, and split.
+    :param batch_size: Specify batch size to load batches instead of samples via webdataset (Recommended).
+        It requires batch_size=None in the data loader constructor.
+    :param transform: Transform function to apply to the data, use MultimodalTransforms.
+    :param return_metadata: Load center coordinates, timestamp (ns as int) and cloud mask (if available).
+    :param shuffle: Shuffle samples and shards. Default to True.
+    :param shardshuffle: The number of shards to shuffle, or None. Defaults to 100.
+    :param deterministic: Whether to use deterministic shuffling. Defaults to False.
+    :param seed: Random seed for shuffling. Defaults to None which uses random seeds.
+    :param kwargs: Optional keyword arguments for single-modality which are passed to WebDataset constructor.
+    :return: WebDataset (single modality) or DataPipeline (multiple modalities)
+    """
     if len(modalities) == 1:
+        # Single modality
+        modalities = modalities[0]
+
+    if isinstance(modalities, str):
         # Build standard WebDataset for single modality
         dataset = build_wds_dataset(
             path=path,
-            modality=modalities[0],
+            modality=modalities,
             split=split,
             urls=urls,
             batch_size=batch_size,
+            transform=transform,
             return_metadata=return_metadata,
             shuffle=shuffle,
-            *args, **kwargs
+            shardshuffle=shardshuffle,
+            deterministic=deterministic,
+            seed=seed,
+            **kwargs
         )
         return dataset
 
     else:
+        if len(kwargs):
+            warnings.warn(f"keyword arguments ({kwargs}) are ignored for multiple modalities.")
+
         # Build custom multi-modal dataset
         dataset = build_multimodal_dataset(
             path=path,
@@ -105,9 +138,12 @@ def build_terramesh_dataset(
             split=split,
             urls=urls,
             batch_size=batch_size,
+            transform=transform,
             return_metadata=return_metadata,
             shuffle=shuffle,
-            *args, **kwargs,
+            shardshuffle=shardshuffle,
+            deterministic=deterministic,
+            seed=seed,
         )
         return dataset
 
@@ -118,6 +154,10 @@ def zarr_decoder(key, value):
         return zarr.open_consolidated(mapper, mode="r")["bands"][...]
 
 
+# Default decoder
+default_decoder = wds.decode()
+
+
 def zarr_metadata_decoder(sample):
     for key, value in list(sample.items()):
         if key == "zarr.zip" or key.endswith(".zarr.zip"):
@@ -134,7 +174,12 @@ def zarr_metadata_decoder(sample):
             if data["time"][...] > 1e6:  # DEM has no valid timestamp (value = 0)
                 time_key = "time" if key == "zarr.zip" else "time_" + key
                 sample[time_key] = data["time"][...]  # Integer values of type "datetime64[ns]"
-        # TODO Other types are currently not decoded, fall back to autodecode
+        elif isinstance(value, str):
+            # Skip str data
+            pass
+        else:
+            # Fallback to webdataset autodecoder
+            sample[key] = next(wds.decode()([{key: value}]))[key]
 
     return sample
 
@@ -167,6 +212,10 @@ def build_wds_dataset(
         transform: Callable = None,
         shuffle: bool = True,
         return_metadata: bool = False,
+        shardshuffle: int = 100,
+        deterministic: bool = False,
+        seed: int = None,
+        empty_check: bool = False,
         *args, **kwargs
 ):
     if urls is None:
@@ -183,10 +232,23 @@ def build_wds_dataset(
             [os.path.join(path, split, modality, f) for f in files]
         )
 
-    kwargs["shardshuffle"] = kwargs.get("shardshuffle", 100) * shuffle  # Shuffle shard
+    if modality == "S1GRD" and split == "val" and empty_check:
+        # Setting empty_check to True to avoid errors because of a single shard file in SSL4EOS12 S1GRD val split
+        empty_check = False
 
     # Build dataset
-    dataset = wds.WebDataset(urls, *args, **kwargs)
+    dataset = wds.WebDataset(
+        urls,
+        *args,
+        shardshuffle=shardshuffle * shuffle,  # Shuffle shard
+        detshuffle=deterministic,
+        seed=seed,
+        handler=warn_and_continue,
+        nodesplitter=wds.split_by_node,
+        workersplitter=wds.split_by_worker,
+        empty_check=empty_check,
+        **kwargs
+    )
 
     # Decode from bytes to numpy arrays, etc.
     dataset = dataset.map(zarr_metadata_decoder) if return_metadata else dataset.decode(zarr_decoder)
@@ -216,7 +278,10 @@ def build_multimodal_dataset(
         transform: Callable = None,
         shuffle: bool = True,
         return_metadata: bool = False,
-        *args, **kwargs
+        shardshuffle: int = 100,
+        deterministic: bool = False,
+        seed: int = None,
+        empty_check: bool = False,
 ):
     if modalities is None:
         modalities = ["S2L2A", "S2L1C", "S2RGB", "S1GRD", "S1RTC", "DEM", "NDVI", "LULC"]  # Default
@@ -236,21 +301,14 @@ def build_multimodal_dataset(
         urls = (os.path.join(path, split, majortom_mod, split_files["majortom"][split][0])
                 + "::" + os.path.join(path, split, ssl4eos12_mod, split_files["ssl4eos12"][split][0]))
 
-    dataset = build_datapipeline(urls, transform, batch_size, shuffle, return_metadata, *args, **kwargs)
-    return dataset
-
-
-def build_datapipeline(urls, transform, batch_size, shuffle, return_metadata, *args, **kwargs):
-    shardshuffle = kwargs.get("shardshuffle", 100) * shuffle  # Shuffle shard
-    deterministic = kwargs.get("deterministic", False)
-    seed = kwargs.get("seed", 0)
-
-    datapipeline = wds.DataPipeline(
+    dataset = wds.DataPipeline(
         # Infinitely sample shards from the shard list with replacement. Each worker is seeded independently.
         (
-            wds.ResampledShards(urls, deterministic=deterministic, seed=seed)
+            wds.ResampledShards(urls, deterministic=deterministic, seed=seed, empty_check=empty_check)
             if shuffle else wds.SimpleShardList(urls)
         ),
+        wds.split_by_node,
+        wds.split_by_worker,
         multi_tarfile_samples,  # Extract individual samples from multi-modal tar files
         wds.shuffle(shardshuffle, seed=seed),  # Shuffle with a buffer of given size
         (
@@ -271,7 +329,7 @@ def build_datapipeline(urls, transform, batch_size, shuffle, return_metadata, *a
             else wds.map(identity)
         ),
     )
-    return datapipeline
+    return dataset
 
 
 def extract_modality_names(s):
@@ -308,7 +366,6 @@ def remove_extensions(sample):
 
 def multi_tarfile_samples(
         src_iter: Iterable[dict],
-        handler: Callable[[Exception], bool] = warn_and_continue,
 ):
     """
     This function is adapted from https://github.com/apple/ml-4m/blob/main/fourm/data/unified_datasets.py.
@@ -329,7 +386,6 @@ def multi_tarfile_samples(
             e.g. {"url": "shard_root_train_[rgb,caption]/00000.tar"}, {"url": "shard_root_train_[rgb,caption]/00001.tar"}, ...
             This function will also work when no square braces for multiple modalities are used, e.g. {"url": "shard_root_train/00000.tar"}, ...
             It can be a drop-in replacement for wds.tarfile_samples.
-        handler: Function that handles exceptions. If it returns True, the shard is skipped. If it returns False, the function exits.
 
     Yields:
         Dictionary of aligned samples from all modalities.
@@ -375,13 +431,9 @@ def multi_tarfile_samples(
                 yield merged_dict
 
         except Exception as e:
-            print(e)
-            print(f"Exception occurred while processing {src['url']}.")
-            if handler(e):
-                print("Skipping shard...")
-                continue
-            else:
-                break
+            warnings.warn(f"Exception occurred while processing {src['url']}: {repr(e)}."
+                          f"Skipping shard")
+            continue
 
 
 class Transpose(albumentations.ImageOnlyTransform):
@@ -410,7 +462,7 @@ class Transpose(albumentations.ImageOnlyTransform):
 
 def default_non_image_transform(array):
     if hasattr(array, "dtype") and (array.dtype == float or array.dtype == int):
-        return torch.from_numpy(array)
+        return torch.from_numpy(array.copy())
     else:
         return array