feat(torch): add dataloadaer collator

src/python/pose_format/numpy/pose_body.py

@@ -37,7 +37,8 @@ class NumPyPoseBody(PoseBody):
         confidence array of the pose keypoints.
     """
 
-    tensor_reader = 'unpack_numpy' """Specifies the method name for unpacking a numpy array (Value: 'unpack_numpy')."""
+    """Specifies the method name for unpacking a numpy array (Value: 'unpack_numpy')."""
+    tensor_reader = 'unpack_numpy'
 
     def __init__(self, fps: float, data: Union[ma.MaskedArray, np.ndarray], confidence: np.ndarray):
         """

src/python/pose_format/tensorflow/pose_body.py

@@ -28,7 +28,9 @@ class TensorflowPoseBody(PoseBody):
     confidence : tf.Tensor
         The confidence scores for the pose data.
     """
-    tensor_reader = 'unpack_tensorflow' """str: The method used to read the tensor data. (Type: str)"""
+
+    """str: The method used to read the tensor data. (Type: str)"""
+    tensor_reader = 'unpack_tensorflow'
 
     def __init__(self, fps: float, data: Union[MaskedTensor, tf.Tensor], confidence: tf.Tensor):
         """

src/python/pose_format/torch/masked/collator.py

@@ -0,0 +1,64 @@
+from typing import Dict, List, Tuple, Union
+
+import numpy as np
+import torch
+from pose_format.torch.masked import MaskedTensor, MaskedTorch
+
+
+def pad_tensors(batch: List[Union[torch.Tensor, MaskedTensor]], pad_value=0):
+    datum = batch[0]
+    torch_cls = MaskedTorch if isinstance(datum, MaskedTensor) else torch
+
+    max_len = max(len(t) for t in batch)
+    if max_len == 1:
+        return torch_cls.stack(batch, dim=0)
+
+    new_batch = []
+    for tensor in batch:
+        missing = list(tensor.shape)
+        missing[0] = max_len - tensor.shape[0]
+
+        if missing[0] > 0:
+            padding_tensor = torch.full(missing, fill_value=pad_value, dtype=tensor.dtype, device=tensor.device)
+            tensor = torch_cls.cat([tensor, padding_tensor], dim=0)
+
+        new_batch.append(tensor)
+
+    return torch_cls.stack(new_batch, dim=0)
+
+
+def collate_tensors(batch: List, pad_value=0) -> Union[torch.Tensor, List]:
+    datum = batch[0]
+
+    if isinstance(datum, dict):  # Recurse over dictionaries
+        return zero_pad_collator(batch)
+
+    if isinstance(datum, (int, np.int32)):
+        return torch.tensor(batch, dtype=torch.long)
+
+    if isinstance(datum, (MaskedTensor, torch.Tensor)):
+        return pad_tensors(batch, pad_value=pad_value)
+
+    return batch
+
+
+def zero_pad_collator(batch) -> Union[Dict[str, torch.Tensor], Tuple[torch.Tensor, ...]]:
+    datum = batch[0]
+
+    # For strings
+    if isinstance(datum, str):
+        return batch
+
+    # For tuples
+    if isinstance(datum, tuple):
+        return tuple(collate_tensors([b[i] for b in batch]) for i in range(len(datum)))
+
+    # For tensors
+    if isinstance(datum, MaskedTensor):
+        return collate_tensors(batch)
+
+    # For dictionaries
+    keys = datum.keys()
+    return {k: collate_tensors([b[k] for b in batch]) for k in keys}
+
+

src/python/pose_format/torch/pose_body.py

@@ -15,7 +15,9 @@ class TorchPoseBody(PoseBody):
 
     This class extends the PoseBody class and provides methods for manipulating pose data using PyTorch tensors.
     """
-    tensor_reader = 'unpack_torch' """str: Reader format for unpacking Torch tensors."""
+
+    """str: Reader format for unpacking Torch tensors."""
+    tensor_reader = 'unpack_torch'
 
     def __init__(self, fps: float, data: Union[MaskedTensor, torch.Tensor], confidence: torch.Tensor):
         if isinstance(data, torch.Tensor):  # If array is not masked

src/python/pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "pose_format"
 description = "Library for viewing, augmenting, and handling .pose files"
-version = "0.2.3"
+version = "0.3.0"
 keywords = ["Pose Files", "Pose Interpolation", "Pose Augmentation"]
 authors = [
     { name = "Amit Moryossef", email = "amitmoryossef@gmail.com" },