Rename smpl->smplpytorch.

smplpytorch/pytorch/tensutils.py

@@ -0,0 +1,53 @@
+import torch
+
+from smplpytorch.pytorch import rodrigues_layer
+
+
+def th_posemap_axisang(pose_vectors):
+    '''
+    Converts axis-angle to rotmat
+    pose_vectors (Tensor (batch_size x 72)): pose parameters in axis-angle representation
+    '''
+    rot_nb = int(pose_vectors.shape[1] / 3)
+    rot_mats = []
+    for joint_idx in range(rot_nb):
+        axis_ang = pose_vectors[:, joint_idx * 3:(joint_idx + 1) * 3]
+        rot_mat = rodrigues_layer.batch_rodrigues(axis_ang)
+        rot_mats.append(rot_mat)
+
+    rot_mats = torch.cat(rot_mats, 1)
+    return rot_mats
+
+
+def th_with_zeros(tensor):
+    batch_size = tensor.shape[0]
+    padding = tensor.new([0.0, 0.0, 0.0, 1.0])
+    padding.requires_grad = False
+
+    concat_list = [tensor, padding.view(1, 1, 4).repeat(batch_size, 1, 1)]
+    cat_res = torch.cat(concat_list, 1)
+    return cat_res
+
+
+def th_pack(tensor):
+    batch_size = tensor.shape[0]
+    padding = tensor.new_zeros((batch_size, 4, 3))
+    padding.requires_grad = False
+    pack_list = [padding, tensor]
+    pack_res = torch.cat(pack_list, 2)
+    return pack_res
+
+
+def subtract_flat_id(rot_mats):
+    # Subtracts identity as a flattened tensor
+    id_flat = torch.eye(
+        3, dtype=rot_mats.dtype, device=rot_mats.device).view(1, 9).repeat(
+            rot_mats.shape[0], 23)
+    # id_flat.requires_grad = False
+    results = rot_mats - id_flat
+    return results
+
+
+def make_list(tensor):
+    # type: (List[int]) -> List[int]
+    return tensor