Update terramesh.py

terramesh.py

@@ -81,10 +81,13 @@ def build_terramesh_dataset(
         transform: Callable = None,
         batch_size: int = 8,
         return_metadata: bool = False,
-        shuffle: bool = True,
+        shuffle: bool = None,
         shardshuffle: int = 100,
         deterministic: bool = False,
         seed: int = None,
+        time_dim: bool = False,
+        partial: bool = None,
+        probs: list[int] = None,
         **kwargs,
 ):
     """
@@ -98,17 +101,26 @@ def build_terramesh_dataset(
         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 shuffle: Shuffle samples and shards. Default to True for train and False for val.
     :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.
+    :param empty_check: Check if shards are empty. Defaults to False.
+    :param time_dim: If True, keeps time dimension. Defaults to False.
+    :param partial: Load partial batch at the end. Defaults to False for train and True for val.
+    :param probs: List of probabilities for each subset (majortom and ssl4eos12). Defaults to [0.8, 0.2].
     :return: WebDataset (single modality) or DataPipeline (multiple modalities)
     """
     if len(modalities) == 1:
         # Single modality
         modalities = modalities[0]
 
+    # No shuffle and partial load for val
+    shuffle = partial if partial is not None else split != "val"
+    partial = partial if partial is not None else split == "val"
+    shardshuffle = shardshuffle * shuffle
+
     if isinstance(modalities, str):
         # Build standard WebDataset for single modality
         dataset = build_wds_dataset(
@@ -119,10 +131,11 @@ def build_terramesh_dataset(
             batch_size=batch_size,
             transform=transform,
             return_metadata=return_metadata,
-            shuffle=shuffle,
             shardshuffle=shardshuffle,
             deterministic=deterministic,
             seed=seed,
+            time_dim=time_dim,
+            partial=partial,
             **kwargs
         )
         return dataset
@@ -140,10 +153,12 @@ def build_terramesh_dataset(
             batch_size=batch_size,
             transform=transform,
             return_metadata=return_metadata,
-            shuffle=shuffle,
             shardshuffle=shardshuffle,
             deterministic=deterministic,
             seed=seed,
+            time_dim=time_dim,
+            partial=partial,
+            probs=probs,
         )
         return dataset
 
@@ -193,14 +208,16 @@ def drop_time_dim(value, dim: int = 0):
     """
     Remove time dimension from data tensors.
     """
-    if isinstance(value, np.ndarray) or isinstance(value, torch.Tensor):
+    if (isinstance(value, np.ndarray) or isinstance(value, torch.Tensor)) and value.shape[dim] == 1:
         return value.squeeze(dim)
 
     elif isinstance(value, dict):
         for k, v in value.items():
-            if isinstance(v, np.ndarray) or isinstance(v, torch.Tensor):
+            if (isinstance(v, np.ndarray) or isinstance(v, torch.Tensor)) and v.shape[dim] == 1:
                 value[k] = v.squeeze(dim)
         return value
+    else:
+        return value
 
 
 def build_wds_dataset(
@@ -210,12 +227,13 @@ def build_wds_dataset(
         urls: str | None = None,
         batch_size: int = 8,
         transform: Callable = None,
-        shuffle: bool = True,
         return_metadata: bool = False,
         shardshuffle: int = 100,
         deterministic: bool = False,
         seed: int = None,
         empty_check: bool = False,
+        time_dim: bool = False,
+        partial: bool = False,
         *args, **kwargs
 ):
     if urls is None:
@@ -240,7 +258,7 @@ def build_wds_dataset(
     dataset = wds.WebDataset(
         urls,
         *args,
-        shardshuffle=shardshuffle * shuffle,  # Shuffle shard
+        shardshuffle=shardshuffle,
         detshuffle=deterministic,
         seed=seed,
         handler=warn_and_continue,
@@ -254,21 +272,41 @@ def build_wds_dataset(
     dataset = dataset.map(zarr_metadata_decoder) if return_metadata else dataset.decode(zarr_decoder)
 
     # Rename modality to "image" and remove temporal dimension
-    dataset = (dataset
-               .rename(image="zarr.zip")
-               .map(drop_time_dim)
-               )
+    dataset = dataset.rename(image="zarr.zip")
+
+    if not time_dim:
+        dataset = dataset.map(drop_time_dim)
 
     if transform is not None:
         dataset = dataset.map(transform)
 
     # Create batches
     if batch_size is not None:
-        dataset = dataset.batched(batch_size)
+        dataset = dataset.batched(batch_size, partial=partial)
 
     return dataset
 
 
+def _subset_pipeline(urls, *, batch_size, shardshuffle, deterministic, seed, empty_check,
+                     return_metadata, transform, time_dim, partial):
+    return wds.DataPipeline(
+        wds.ResampledShards(urls, deterministic=deterministic, seed=seed, empty_check=empty_check)
+            if shardshuffle else wds.SimpleShardList(urls),
+        wds.split_by_node,
+        wds.split_by_worker,
+        # Extract individual samples from multi-modal tar files
+        multi_tarfile_samples,
+        wds.shuffle(shardshuffle, seed=seed),
+        # Decode from bytes to numpy arrays, etc.
+        (wds.map(zarr_metadata_decoder) if return_metadata else wds.decode(zarr_decoder)),
+        # Remove time dimension from tensors
+        wds.map(drop_time_dim) if not time_dim else wds.map(identity),
+        wds.map(remove_extensions),
+        wds.map(transform) if transform is not None else wds.map(identity),
+        wds.batched(batch_size, collation_fn=collate_fn, partial=partial),
+    )
+
+
 def build_multimodal_dataset(
         path: str = "https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh/resolve/main/",
         modalities: list = None,
@@ -276,12 +314,14 @@ def build_multimodal_dataset(
         urls: str | None = None,
         batch_size: int = 8,
         transform: Callable = None,
-        shuffle: bool = True,
         return_metadata: bool = False,
         shardshuffle: int = 100,
         deterministic: bool = False,
         seed: int = None,
         empty_check: bool = False,
+        time_dim: bool = True,
+        partial: bool = False,
+        probs: list[int] = None,
 ):
     if modalities is None:
         modalities = ["S2L2A", "S2L1C", "S2RGB", "S1GRD", "S1RTC", "DEM", "NDVI", "LULC"]  # Default
@@ -294,44 +334,46 @@ 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'))}]"
+        urls_majortom = os.path.join(path, split, f"[{','.join(filter_list(modalities, 'S1GRD'))}]",
+                                     split_files["majortom"][split][0])
+        urls_ssl4eos12 = os.path.join(path, split, f"[{','.join(filter_list(modalities, 'S1RTC'))}]",
+                                      split_files["ssl4eos12"][split][0])
+    else:
+        if "::" in urls:
+            urls_majortom, urls_ssl4eos12 = urls.split("::")
+        else:
+            urls_majortom = urls_ssl4eos12 = urls
 
-        # Joins majortom and ssl4eos12 shard files with "::"
-        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]))
+    ds_mt  = _subset_pipeline(urls_majortom,  batch_size=batch_size, shardshuffle=shardshuffle,
+                              deterministic=deterministic, seed=seed, empty_check=empty_check,
+                              return_metadata=return_metadata, transform=transform,
+                              time_dim=time_dim, partial=partial)
+
+    ds_ssl = _subset_pipeline(urls_ssl4eos12, batch_size=batch_size, shardshuffle=shardshuffle,
+                              deterministic=deterministic, seed=seed, empty_check=empty_check,
+                              return_metadata=return_metadata, transform=transform,
+                              time_dim=time_dim, partial=partial)
+
+    # mix batches (never mixes samples)
+    dataset = wds.RandomMix([ds_mt, ds_ssl], probs=probs or [0.8, 0.2],
+                            longest=not shardshuffle  # Load all samples if shuffle is false
+                            )
 
-    dataset = wds.DataPipeline(
-        # Infinitely sample shards from the shard list with replacement. Each worker is seeded independently.
-        (
-            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
-        (
-            wds.map(zarr_metadata_decoder)
-            if return_metadata
-            else wds.decode(zarr_decoder)  # Decode from bytes to 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
-            wds.map(transform)
-            if transform is not None
-            else wds.map(identity)
-        ),
-        (  # Batching
-            wds.batched(batch_size, collation_fn=default_collate, partial=False)
-            if batch_size is not None
-            else wds.map(identity)
-        ),
-    )
     return dataset
 
 
+def collate_fn(batch):
+    # Wrapper for debugging
+    try:
+        return default_collate(batch)
+    except Exception as e:
+        for s in batch:
+            print(s["__key__"])
+            print(s["__url__"])
+            print(s.keys())
+        raise e
+
+
 def extract_modality_names(s):
     """
     Function from https://github.com/apple/ml-4m/blob/main/fourm/data/unified_datasets.py.