Update terramesh.py

terramesh.py

@@ -19,18 +19,20 @@
 import os
 import io
 import re
+
+import numpy
 import zarr
 import fsspec
 import itertools
 import braceexpand
 import numpy as np
+import albumentations
 import webdataset as wds
 from collections.abc import Callable, Iterable
 from sympy.printing.pytorch import torch
 from torch.utils.data._utils.collate import default_collate
 from webdataset.handlers import warn_and_continue
 
-
 # Definition of all shard files in TerraMesh
 split_files = {
     "ssl4eos12": {
@@ -56,7 +58,6 @@ def build_terramesh_dataset(
         batch_size: int = 8,
         *args, **kwargs,
 ):
-
     if len(modalities) == 1:
         # Build standard WebDataset for single modality
         dataset = build_wds_dataset(
@@ -172,12 +173,11 @@ def build_multimodal_dataset(
                 lst.remove(value)
             return lst
 
-
         majortom_mod = f"[{','.join(filter_list(modalities, 'S1GRD'))}]"
         ssl4eos12_mod = f"[{','.join(filter_list(modalities, 'S1RTC'))}]"
 
         # Joins majortom and ssl4eos12 shard files with "::"
-        urls = (os.path.join(path, split, majortom_mod,  split_files["majortom"][split][0])
+        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, *args, **kwargs)
@@ -193,12 +193,12 @@ def build_datapipeline(urls, transform, batch_size, *args, **kwargs):
         wds.decode(zarr_decoder),  # Decode from bytes to PIL images, numpy arrays, etc.
         wds.map(drop_time_dim),  # Remove time dimension from tensors
         wds.map(remove_extensions),  # Remove "file extensions" from dictionary keys
-        (   # Apply transformation
+        (  # Apply transformation
             wds.map(transform)
             if transform is not None
             else wds.map(identity)
         ),
-        (   # Batching
+        (  # Batching
             wds.batched(batch_size, collation_fn=default_collate, partial=False)
             if batch_size is not None
             else wds.map(identity)
@@ -240,8 +240,8 @@ def remove_extensions(sample):
 
 
 def multi_tarfile_samples(
-    src_iter: Iterable[dict],
-    handler: Callable[[Exception], bool] = warn_and_continue,
+        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.
@@ -289,7 +289,7 @@ def multi_tarfile_samples(
                 merged_dict["__url__"] = src["url"]
 
                 for modality_name, modality_dict in zip(
-                    modality_names, multi_tar_files
+                        modality_names, multi_tar_files
                 ):
                     _key = modality_dict.pop("__key__")
                     _url = modality_dict.pop("__url__")
@@ -315,3 +315,78 @@ def multi_tarfile_samples(
                 continue
             else:
                 break
+
+
+class Transpose(albumentations.ImageOnlyTransform):
+    """
+    Rearrange is a generic image transformation that reshapes an input tensor using a custom einops pattern.
+
+    This transform allows flexible reordering of tensor dimensions based on the provided pattern and arguments.
+    """
+
+    def __init__(self, axis: list):
+        """
+        Initialize the Transpose transform.
+
+        Args:
+            axis (list): Axis for numpy.transpose.
+        """
+        super().__init__(p=1)
+        self.axis = axis
+
+    def apply(self, img, **params):
+        return numpy.transpose(img, self.axis)
+
+    def get_transform_init_args_names(self):
+        return "transpose"
+
+
+def default_non_image_transform(array):
+    if hasattr(array, 'dtype') and (array.dtype == float or array.dtype == int):
+        return torch.from_numpy(array)
+    else:
+        return array
+
+
+class MultimodalTransforms:
+    """
+    MultimodalTransforms applies albumentations transforms to multiple image modalities.
+
+    This class supports both shared transformations across modalities and separate transformations for each modality.
+    It also handles non-image modalities by applying a specified non-image transform.
+
+    This code is adapted from https://github.com/IBM/terratorch/blob/main/terratorch/datasets/transforms.py.
+    """
+
+    def __init__(
+            self,
+            transforms: dict | albumentations.Compose,
+            shared: bool = True,
+            non_image_modalities: list[str] | None = None,
+            non_image_transforms: object | None = None,
+    ):
+        """
+        Initialize the MultimodalTransforms.
+
+        Args:
+            transforms (dict or A.Compose): The transformation(s) to apply to the data.
+            non_image_modalities (list[str] | None): List of keys corresponding to non-image modalities.
+            non_image_transforms (object | None): A transform to apply to non-image modalities.
+                If None, a default transform is used.
+        """
+        self.transforms = transforms
+        self.non_image_modalities = non_image_modalities or []
+        self.non_image_transforms = non_image_transforms or default_non_image_transform
+
+    def __call__(self, data: dict):
+        # albumentations requires a key 'image' and treats all other keys as additional targets
+        image_modality = [k for k in data.keys() if k not in self.non_image_modalities][0]
+        data['image'] = data.pop(image_modality)
+        data = self.transforms(**data)
+        data[image_modality] = data.pop('image')
+
+        # Process sequence data which is ignored by albumentations as 'global_label'
+        for modality in self.non_image_modalities:
+            data[modality] = self.non_image_transforms(data[modality])
+
+        return data