bugfixed: weights transformation between spconv 1.x and spconv 2.x, should...

bugfixed: weights transformation between spconv 1.x and spconv 2.x, should consider both native/implicit spconv 2.x

pcdet/models/detectors/detector3d_template.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 
 from ...ops.iou3d_nms import iou3d_nms_utils
-from ...spconv_utils import find_all_spconv_keys
+from ...utils.spconv_utils import find_all_spconv_keys
 from .. import backbones_2d, backbones_3d, dense_heads, roi_heads
 from ..backbones_2d import map_to_bev
 from ..backbones_3d import pfe, vfe
@@ -335,7 +335,16 @@ class Detector3DTemplate(nn.Module):
         for key, val in model_state_disk.items():
             if key in spconv_keys and key in state_dict and state_dict[key].shape != val.shape:
                 # with different spconv versions, we need to adapt weight shapes for spconv blocks
-                val = val.transpose(-1, -2).contiguous()
+                # adapt spconv weights from version 1.x to version 2.x if you used weights from spconv 1.x
+
+                val_native = val.transpose(-1, -2)  # (k1, k2, k3, c_in, c_out) to (k1, k2, k3, c_out, c_in)
+                if val_native.shape == state_dict[key].shape:
+                    val = val_native.contiguous()
+                else:
+                    assert val.shape.__len__() == 5, 'currently only spconv 3D is supported'
+                    val_implicit = val.permute(4, 0, 1, 2, 3)  # (k1, k2, k3, c_in, c_out) to (c_out, k1, k2, k3, c_in)
+                    if val_implicit.shape == state_dict[key].shape:
+                        val = val_implicit.contiguous()
 
             if key in state_dict and state_dict[key].shape == val.shape:
                 update_model_state[key] = val