Update terramesh.py

terramesh.py

@@ -25,6 +25,7 @@ import fsspec
 import braceexpand
 import numpy as np
 import albumentations
+import warnings
 import webdataset as wds
 from collections.abc import Callable, Iterable
 from torch.utils.data._utils.collate import default_collate
@@ -46,6 +47,31 @@ split_files = {
     }
 }
 
+statistics = {
+    "mean": {
+        "S2L1C": [2357.090, 2137.398, 2018.799, 2082.998, 2295.663, 2854.548, 3122.860, 3040.571, 3306.491, 1473.849,
+                  506.072, 2472.840, 1838.943],
+        "S2L2A": [1390.461, 1503.332, 1718.211, 1853.926, 2199.116, 2779.989, 2987.025, 3083.248, 3132.235, 3162.989,
+                  2424.902, 1857.665],
+        "S2RGB": [110.349, 99.507, 75.843],
+        "S1GRD": [-12.577, -20.265],
+        "S1RTC": [-10.93, -17.329],
+        "NDVI": [0.327],
+        "DEM": [651.663],
+    },
+    "std": {
+        "S2L1C": [1673.639, 1722.641, 1602.205, 1873.138, 1866.055, 1779.839, 1776.496, 1724.114, 1771.041, 1079.786,
+                  512.404, 1340.879, 1172.435],
+        "S2L2A": [2131.157, 2163.666, 2059.311, 2152.477, 2105.179, 1912.773, 1842.326, 1893.568, 1775.656, 1814.907,
+                  1436.282, 1336.155],
+        "S2RGB": [69.905, 53.708, 53.378],
+        "S1GRD": [5.179, 5.872],
+        "S1RTC": [4.391, 4.459],
+        "NDVI": [0.322],
+        "DEM": [928.168]
+    }
+}
+
 
 def build_terramesh_dataset(
         path: str = "https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh/resolve/main/",
@@ -54,6 +80,7 @@ def build_terramesh_dataset(
         urls: str | None = None,
         batch_size: int = 8,
         return_metadata: bool = False,
+        shuffle: bool = True,
         *args, **kwargs,
 ):
     if len(modalities) == 1:
@@ -65,6 +92,7 @@ def build_terramesh_dataset(
             urls=urls,
             batch_size=batch_size,
             return_metadata=return_metadata,
+            shuffle=shuffle,
             *args, **kwargs
         )
         return dataset
@@ -78,6 +106,7 @@ def build_terramesh_dataset(
             urls=urls,
             batch_size=batch_size,
             return_metadata=return_metadata,
+            shuffle=shuffle,
             *args, **kwargs,
         )
         return dataset
@@ -136,6 +165,7 @@ def build_wds_dataset(
         urls: str | None = None,
         batch_size: int = 8,
         transform: Callable = None,
+        shuffle: bool = True,
         return_metadata: bool = False,
         *args, **kwargs
 ):
@@ -153,7 +183,7 @@ def build_wds_dataset(
             [os.path.join(path, split, modality, f) for f in files]
         )
 
-    kwargs["shardshuffle"] = kwargs.get("shardshuffle", 100)  # Shuffle shard by default
+    kwargs["shardshuffle"] = kwargs.get("shardshuffle", 100) * shuffle  # Shuffle shard
 
     # Build dataset
     dataset = wds.WebDataset(urls, *args, **kwargs)
@@ -184,6 +214,7 @@ def build_multimodal_dataset(
         urls: str | None = None,
         batch_size: int = 8,
         transform: Callable = None,
+        shuffle: bool = True,
         return_metadata: bool = False,
         *args, **kwargs
 ):
@@ -205,16 +236,23 @@ 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, return_metadata, *args, **kwargs)
+    dataset = build_datapipeline(urls, transform, batch_size, shuffle, return_metadata, *args, **kwargs)
     return dataset
 
 
-def build_datapipeline(urls, transform, batch_size, return_metadata, *args, **kwargs):
+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(
         # Infinitely sample shards from the shard list with replacement. Each worker is seeded independently.
-        wds.ResampledShards(urls),
+        (
+            wds.ResampledShards(urls, deterministic=deterministic, seed=seed)
+            if shuffle else wds.SimpleShardList(urls)
+        ),
         multi_tarfile_samples,  # Extract individual samples from multi-modal tar files
-        wds.shuffle(100),  # Shuffle with a buffer of given size
+        wds.shuffle(shardshuffle, seed=seed),  # Shuffle with a buffer of given size
         (
             wds.map(zarr_metadata_decoder)
             if return_metadata
@@ -420,3 +458,23 @@ class MultimodalTransforms:
                 data[modality] = self.non_image_transforms(data[modality])
 
         return data
+
+
+class MultimodalNormalize(Callable):
+    def __init__(self, mean: dict[str, list[float]], std: dict[str, list[float]]):
+        super().__init__()
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, **batch):
+        for m in self.mean.keys():
+            if m not in batch.keys():
+                continue
+            batch[m] = (batch[m] - self.mean[m]) / self.std[m]
+        return batch
+
+    def add_targets(self, targets):
+        """
+        Required by albumentations
+        """
+        pass