378 add ensuring patch shape for raw dataset (#379)

added eunsre patch shape function in raw dataset

torch_em/data/raw_dataset.py

@@ -7,7 +7,7 @@
 
 from elf.wrapper import RoiWrapper
 
-from ..util import ensure_tensor_with_channels, load_data
+from ..util import ensure_tensor_with_channels, ensure_patch_shape, load_data
 
 
 class RawDataset(torch.utils.data.Dataset):
@@ -107,7 +107,13 @@ def _get_sample(self, index):
                 sample_id += 1
                 if sample_id > self.max_sampling_attempts:
                     raise RuntimeError(f"Could not sample a valid batch in {self.max_sampling_attempts} attempts")
-
+        if self.patch_shape is not None:
+            raw = ensure_patch_shape(
+                raw=raw,
+                labels=None,
+                patch_shape=self.patch_shape,
+                have_raw_channels=self._with_channels
+            )
         # squeeze the singleton spatial axis if we have a spatial shape that is larger by one than self._ndim
         if len(self.patch_shape) == self._ndim + 1:
             raw = raw.squeeze(1 if self._with_channels else 0)

torch_em/util/util.py

@@ -149,14 +149,15 @@ def ensure_patch_shape(
     raw, labels, patch_shape, have_raw_channels=False, have_label_channels=False, channel_first=True
 ):
     raw_shape = raw.shape
-    labels_shape = labels.shape
+    if labels is not None:
+        labels_shape = labels.shape
 
     # In case the inputs has channels and they are channels first
     # IMPORTANT: for ImageCollectionDataset
     if have_raw_channels and channel_first:
         raw_shape = raw_shape[1:]
 
-    if have_label_channels and channel_first:
+    if have_label_channels and channel_first and labels is not None:
         labels_shape = labels_shape[1:]
 
     # Extract the pad_width and pad the raw inputs
@@ -173,7 +174,7 @@ def ensure_patch_shape(
         raw = np.pad(array=raw, pad_width=pad_width)
 
     # Extract the pad width and pad the label inputs
-    if any(sh < psh for sh, psh in zip(labels_shape, patch_shape)):
+    if labels is not None and any(sh < psh for sh, psh in zip(labels_shape, patch_shape)):
         pw = [(0, max(0, psh - sh)) for sh, psh in zip(labels_shape, patch_shape)]
 
         if have_label_channels and channel_first:
@@ -184,8 +185,10 @@ def ensure_patch_shape(
             pad_width = pw
 
         labels = np.pad(array=labels, pad_width=pad_width)
-
-    return raw, labels
+    if labels is None:
+        return raw
+    else:
+        return raw, labels
 
 
 def get_constructor_arguments(obj):