size mismatch between ckpt and model

Hi,
When I run  python run_grasp_simulation.py  I encountered the error for size mismatch when loading PointGroupPredictor from artifacts/artifacts-77.
size mismatch for input_conv.0.weight: copying a param with shape torch.Size([3, 3, 3, 6, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 6]).

Full error report section 1. Click to expand
Error(s) in loading state_dict for PointGroup:
size mismatch for input_conv.0.weight: copying a param with shape torch.Size([3, 3, 3, 6, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 6]).
size mismatch for unet.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 16, 32]) from checkpoint, the shape in current model is torch.Size([32, 2, 2, 2, 16]).
size mismatch for unet.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 32, 48]) from checkpoint, the shape in current model is torch.Size([48, 2, 2, 2, 32]).
size mismatch for unet.u.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 48, 64]) from checkpoint, the shape in current model is torch.Size([64, 2, 2, 2, 48]).
size mismatch for unet.u.u.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 64, 80]) from checkpoint, the shape in current model is torch.Size([80, 2, 2, 2, 64]).
size mismatch for unet.u.u.u.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 80, 96]) from checkpoint, the shape in current model is torch.Size([96, 2, 2, 2, 80]).
size mismatch for unet.u.u.u.u.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 96, 112]) from checkpoint, the shape in current model is torch.Size([112, 2, 2, 2, 96]).
size mismatch for unet.u.u.u.u.u.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 112, 112]) from checkpoint, the shape in current model is torch.Size([112, 3, 3, 3, 112]).
size mismatch for unet.u.u.u.u.u.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 112, 112]) from checkpoint, the shape in current model is torch.Size([112, 3, 3, 3, 112]).
size mismatch for unet.u.u.u.u.u.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 112, 112]) from checkpoint, the shape in current model is torch.Size([112, 3, 3, 3, 112]).
size mismatch for unet.u.u.u.u.u.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 112, 112]) from checkpoint, the shape in current model is torch.Size([112, 3, 3, 3, 112]).
size mismatch for unet.u.u.u.u.u.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 112, 96]) from checkpoint, the shape in current model is torch.Size([96, 2, 2, 2, 112]).
size mismatch for unet.u.u.u.u.u.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 192, 96]) from checkpoint, the shape in current model is torch.Size([96, 1, 1, 1, 192]).
size mismatch for unet.u.u.u.u.u.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 192, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 192]).
size mismatch for unet.u.u.u.u.u.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 96, 80]) from checkpoint, the shape in current model is torch.Size([80, 2, 2, 2, 96]).
size mismatch for unet.u.u.u.u.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 160, 80]) from checkpoint, the shape in current model is torch.Size([80, 1, 1, 1, 160]).
size mismatch for unet.u.u.u.u.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 160, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 160]).
size mismatch for unet.u.u.u.u.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 80, 64]) from checkpoint, the shape in current model is torch.Size([64, 2, 2, 2, 80]).
size mismatch for unet.u.u.u.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 128, 64]) from checkpoint, the shape in current model is torch.Size([64, 1, 1, 1, 128]).
size mismatch for unet.u.u.u.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 128, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 128]).
size mismatch for unet.u.u.u.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 64, 48]) from checkpoint, the shape in current model is torch.Size([48, 2, 2, 2, 64]).
size mismatch for unet.u.u.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 96, 48]) from checkpoint, the shape in current model is torch.Size([48, 1, 1, 1, 96]).
size mismatch for unet.u.u.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 96, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 96]).
size mismatch for unet.u.u.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 48, 32]) from checkpoint, the shape in current model is torch.Size([32, 2, 2, 2, 48]).
size mismatch for unet.u.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 64, 32]) from checkpoint, the shape in current model is torch.Size([32, 1, 1, 1, 64]).
size mismatch for unet.u.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 64, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 64]).
size mismatch for unet.u.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 2, 2, 2, 32]).
size mismatch for unet.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 1, 1, 1, 32]).
size mismatch for unet.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 32]).
size mismatch for unet.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 16, 32]) from checkpoint, the shape in current model is torch.Size([32, 2, 2, 2, 16]).
size mismatch for score_unet.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for score_unet.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for score_unet.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for score_unet.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for score_unet.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 2, 2, 2, 32]).
size mismatch for score_unet.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 1, 1, 1, 32]).
size mismatch for score_unet.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 32]).
size mismatch for score_unet.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).

Full error report section 2. Click to expand

Current model layers:
input_conv.0.weight
input_conv.0.bias
unet.blocks.block0.conv_branch.0.weight
unet.blocks.block0.conv_branch.0.bias
unet.blocks.block0.conv_branch.2.weight
unet.blocks.block0.conv_branch.2.bias
unet.blocks.block0.conv_branch.3.weight
unet.blocks.block0.conv_branch.3.bias
unet.blocks.block0.conv_branch.5.weight
unet.blocks.block0.conv_branch.5.bias
unet.blocks.block1.conv_branch.0.weight
unet.blocks.block1.conv_branch.0.bias
unet.blocks.block1.conv_branch.2.weight
unet.blocks.block1.conv_branch.2.bias
unet.blocks.block1.conv_branch.3.weight
unet.blocks.block1.conv_branch.3.bias
unet.blocks.block1.conv_branch.5.weight
unet.blocks.block1.conv_branch.5.bias
unet.conv.0.weight
unet.conv.0.bias
unet.conv.2.weight
unet.conv.2.bias
unet.u.blocks.block0.conv_branch.0.weight
unet.u.blocks.block0.conv_branch.0.bias
unet.u.blocks.block0.conv_branch.2.weight
unet.u.blocks.block0.conv_branch.2.bias
unet.u.blocks.block0.conv_branch.3.weight
unet.u.blocks.block0.conv_branch.3.bias
unet.u.blocks.block0.conv_branch.5.weight
unet.u.blocks.block0.conv_branch.5.bias
unet.u.blocks.block1.conv_branch.0.weight
unet.u.blocks.block1.conv_branch.0.bias
unet.u.blocks.block1.conv_branch.2.weight
unet.u.blocks.block1.conv_branch.2.bias
unet.u.blocks.block1.conv_branch.3.weight
unet.u.blocks.block1.conv_branch.3.bias
unet.u.blocks.block1.conv_branch.5.weight
unet.u.blocks.block1.conv_branch.5.bias
unet.u.conv.0.weight
unet.u.conv.0.bias
unet.u.conv.2.weight
unet.u.conv.2.bias
unet.u.u.blocks.block0.conv_branch.0.weight
unet.u.u.blocks.block0.conv_branch.0.bias
unet.u.u.blocks.block0.conv_branch.2.weight
unet.u.u.blocks.block0.conv_branch.2.bias
unet.u.u.blocks.block0.conv_branch.3.weight
unet.u.u.blocks.block0.conv_branch.3.bias
unet.u.u.blocks.block0.conv_branch.5.weight
unet.u.u.blocks.block0.conv_branch.5.bias
unet.u.u.blocks.block1.conv_branch.0.weight
unet.u.u.blocks.block1.conv_branch.0.bias
unet.u.u.blocks.block1.conv_branch.2.weight
unet.u.u.blocks.block1.conv_branch.2.bias
unet.u.u.blocks.block1.conv_branch.3.weight
unet.u.u.blocks.block1.conv_branch.3.bias
unet.u.u.blocks.block1.conv_branch.5.weight
unet.u.u.blocks.block1.conv_branch.5.bias
unet.u.u.conv.0.weight
unet.u.u.conv.0.bias
unet.u.u.conv.2.weight
unet.u.u.conv.2.bias
unet.u.u.u.blocks.block0.conv_branch.0.weight
unet.u.u.u.blocks.block0.conv_branch.0.bias
unet.u.u.u.blocks.block0.conv_branch.2.weight
unet.u.u.u.blocks.block0.conv_branch.2.bias
unet.u.u.u.blocks.block0.conv_branch.3.weight
unet.u.u.u.blocks.block0.conv_branch.3.bias
unet.u.u.u.blocks.block0.conv_branch.5.weight
unet.u.u.u.blocks.block0.conv_branch.5.bias
unet.u.u.u.blocks.block1.conv_branch.0.weight
unet.u.u.u.blocks.block1.conv_branch.0.bias
unet.u.u.u.blocks.block1.conv_branch.2.weight
unet.u.u.u.blocks.block1.conv_branch.2.bias
unet.u.u.u.blocks.block1.conv_branch.3.weight
unet.u.u.u.blocks.block1.conv_branch.3.bias
unet.u.u.u.blocks.block1.conv_branch.5.weight
unet.u.u.u.blocks.block1.conv_branch.5.bias
unet.u.u.u.conv.0.weight
unet.u.u.u.conv.0.bias
unet.u.u.u.conv.2.weight
unet.u.u.u.conv.2.bias
unet.u.u.u.u.blocks.block0.conv_branch.0.weight
unet.u.u.u.u.blocks.block0.conv_branch.0.bias
unet.u.u.u.u.blocks.block0.conv_branch.2.weight
unet.u.u.u.u.blocks.block0.conv_branch.2.bias
unet.u.u.u.u.blocks.block0.conv_branch.3.weight
unet.u.u.u.u.blocks.block0.conv_branch.3.bias
unet.u.u.u.u.blocks.block0.conv_branch.5.weight
unet.u.u.u.u.blocks.block0.conv_branch.5.bias
unet.u.u.u.u.blocks.block1.conv_branch.0.weight
unet.u.u.u.u.blocks.block1.conv_branch.0.bias
unet.u.u.u.u.blocks.block1.conv_branch.2.weight
unet.u.u.u.u.blocks.block1.conv_branch.2.bias
unet.u.u.u.u.blocks.block1.conv_branch.3.weight
unet.u.u.u.u.blocks.block1.conv_branch.3.bias
unet.u.u.u.u.blocks.block1.conv_branch.5.weight
unet.u.u.u.u.blocks.block1.conv_branch.5.bias
unet.u.u.u.u.conv.0.weight
unet.u.u.u.u.conv.0.bias
unet.u.u.u.u.conv.2.weight
unet.u.u.u.u.conv.2.bias
unet.u.u.u.u.u.blocks.block0.conv_branch.0.weight
unet.u.u.u.u.u.blocks.block0.conv_branch.0.bias
unet.u.u.u.u.u.blocks.block0.conv_branch.2.weight
unet.u.u.u.u.u.blocks.block0.conv_branch.2.bias
unet.u.u.u.u.u.blocks.block0.conv_branch.3.weight
unet.u.u.u.u.u.blocks.block0.conv_branch.3.bias
unet.u.u.u.u.u.blocks.block0.conv_branch.5.weight
unet.u.u.u.u.u.blocks.block0.conv_branch.5.bias
unet.u.u.u.u.u.blocks.block1.conv_branch.0.weight
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ckpt layers:
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unet.blocks.block1.conv_branch.3.running_var
unet.blocks.block1.conv_branch.3.num_batches_tracked
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score_unet.blocks.block1.conv_branch.2.bias
score_unet.blocks.block1.conv_branch.3.weight
score_unet.blocks.block1.conv_branch.3.bias
score_unet.blocks.block1.conv_branch.3.running_mean
score_unet.blocks.block1.conv_branch.3.running_var
score_unet.blocks.block1.conv_branch.3.num_batches_tracked
score_unet.blocks.block1.conv_branch.5.weight
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score_unet.conv.0.weight
score_unet.conv.0.bias
score_unet.conv.0.running_mean
score_unet.conv.0.running_var
score_unet.conv.0.num_batches_tracked
score_unet.conv.2.weight
score_unet.conv.2.bias
score_unet.u.blocks.block0.conv_branch.0.weight
score_unet.u.blocks.block0.conv_branch.0.bias
score_unet.u.blocks.block0.conv_branch.0.running_mean
score_unet.u.blocks.block0.conv_branch.0.running_var
score_unet.u.blocks.block0.conv_branch.0.num_batches_tracked
score_unet.u.blocks.block0.conv_branch.2.weight
score_unet.u.blocks.block0.conv_branch.2.bias
score_unet.u.blocks.block0.conv_branch.3.weight
score_unet.u.blocks.block0.conv_branch.3.bias
score_unet.u.blocks.block0.conv_branch.3.running_mean
score_unet.u.blocks.block0.conv_branch.3.running_var
score_unet.u.blocks.block0.conv_branch.3.num_batches_tracked
score_unet.u.blocks.block0.conv_branch.5.weight
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score_unet.u.blocks.block1.conv_branch.0.weight
score_unet.u.blocks.block1.conv_branch.0.bias
score_unet.u.blocks.block1.conv_branch.0.running_mean
score_unet.u.blocks.block1.conv_branch.0.running_var
score_unet.u.blocks.block1.conv_branch.0.num_batches_tracked
score_unet.u.blocks.block1.conv_branch.2.weight
score_unet.u.blocks.block1.conv_branch.2.bias
score_unet.u.blocks.block1.conv_branch.3.weight
score_unet.u.blocks.block1.conv_branch.3.bias
score_unet.u.blocks.block1.conv_branch.3.running_mean
score_unet.u.blocks.block1.conv_branch.3.running_var
score_unet.u.blocks.block1.conv_branch.3.num_batches_tracked
score_unet.u.blocks.block1.conv_branch.5.weight
score_unet.u.blocks.block1.conv_branch.5.bias
score_unet.deconv.0.weight
score_unet.deconv.0.bias
score_unet.deconv.0.running_mean
score_unet.deconv.0.running_var
score_unet.deconv.0.num_batches_tracked
score_unet.deconv.2.weight
score_unet.deconv.2.bias
score_unet.blocks_tail.block0.i_branch.0.weight
score_unet.blocks_tail.block0.i_branch.0.bias
score_unet.blocks_tail.block0.conv_branch.0.weight
score_unet.blocks_tail.block0.conv_branch.0.bias
score_unet.blocks_tail.block0.conv_branch.0.running_mean
score_unet.blocks_tail.block0.conv_branch.0.running_var
score_unet.blocks_tail.block0.conv_branch.0.num_batches_tracked
score_unet.blocks_tail.block0.conv_branch.2.weight
score_unet.blocks_tail.block0.conv_branch.2.bias
score_unet.blocks_tail.block0.conv_branch.3.weight
score_unet.blocks_tail.block0.conv_branch.3.bias
score_unet.blocks_tail.block0.conv_branch.3.running_mean
score_unet.blocks_tail.block0.conv_branch.3.running_var
score_unet.blocks_tail.block0.conv_branch.3.num_batches_tracked
score_unet.blocks_tail.block0.conv_branch.5.weight
score_unet.blocks_tail.block0.conv_branch.5.bias
score_unet.blocks_tail.block1.conv_branch.0.weight
score_unet.blocks_tail.block1.conv_branch.0.bias
score_unet.blocks_tail.block1.conv_branch.0.running_mean
score_unet.blocks_tail.block1.conv_branch.0.running_var
score_unet.blocks_tail.block1.conv_branch.0.num_batches_tracked
score_unet.blocks_tail.block1.conv_branch.2.weight
score_unet.blocks_tail.block1.conv_branch.2.bias
score_unet.blocks_tail.block1.conv_branch.3.weight
score_unet.blocks_tail.block1.conv_branch.3.bias
score_unet.blocks_tail.block1.conv_branch.3.running_mean
score_unet.blocks_tail.block1.conv_branch.3.running_var
score_unet.blocks_tail.block1.conv_branch.3.num_batches_tracked
score_unet.blocks_tail.block1.conv_branch.5.weight
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score_outputlayer.0.weight
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score_outputlayer.0.running_mean
score_outputlayer.0.running_var
score_outputlayer.0.num_batches_tracked
score_linear.weight
score_linear.bias

Difference:
unet.blocks.block0.conv_branch.0.running_mean not found in cur model
unet.blocks.block0.conv_branch.0.running_var not found in cur model
unet.blocks.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.blocks.block0.conv_branch.3.running_mean not found in cur model
unet.blocks.block0.conv_branch.3.running_var not found in cur model
unet.blocks.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.blocks.block1.conv_branch.0.running_mean not found in cur model
unet.blocks.block1.conv_branch.0.running_var not found in cur model
unet.blocks.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.blocks.block1.conv_branch.3.running_mean not found in cur model
unet.blocks.block1.conv_branch.3.running_var not found in cur model
unet.blocks.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.conv.0.running_mean not found in cur model
unet.conv.0.running_var not found in cur model
unet.conv.0.num_batches_tracked not found in cur model
unet.u.blocks.block0.conv_branch.0.running_mean not found in cur model
unet.u.blocks.block0.conv_branch.0.running_var not found in cur model
unet.u.blocks.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.blocks.block0.conv_branch.3.running_mean not found in cur model
unet.u.blocks.block0.conv_branch.3.running_var not found in cur model
unet.u.blocks.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.blocks.block1.conv_branch.0.running_mean not found in cur model
unet.u.blocks.block1.conv_branch.0.running_var not found in cur model
unet.u.blocks.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.blocks.block1.conv_branch.3.running_mean not found in cur model
unet.u.blocks.block1.conv_branch.3.running_var not found in cur model
unet.u.blocks.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.u.conv.0.running_mean not found in cur model
unet.u.conv.0.running_var not found in cur model
unet.u.conv.0.num_batches_tracked not found in cur model
unet.u.u.blocks.block0.conv_branch.0.running_mean not found in cur model
unet.u.u.blocks.block0.conv_branch.0.running_var not found in cur model
unet.u.u.blocks.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.blocks.block0.conv_branch.3.running_mean not found in cur model
unet.u.u.blocks.block0.conv_branch.3.running_var not found in cur model
unet.u.u.blocks.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.blocks.block1.conv_branch.0.running_mean not found in cur model
unet.u.u.blocks.block1.conv_branch.0.running_var not found in cur model
unet.u.u.blocks.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.blocks.block1.conv_branch.3.running_mean not found in cur model
unet.u.u.blocks.block1.conv_branch.3.running_var not found in cur model
unet.u.u.blocks.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.conv.0.running_mean not found in cur model
unet.u.u.conv.0.running_var not found in cur model
unet.u.u.conv.0.num_batches_tracked not found in cur model
unet.u.u.u.blocks.block0.conv_branch.0.running_mean not found in cur model
unet.u.u.u.blocks.block0.conv_branch.0.running_var not found in cur model
unet.u.u.u.blocks.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.blocks.block0.conv_branch.3.running_mean not found in cur model
unet.u.u.u.blocks.block0.conv_branch.3.running_var not found in cur model
unet.u.u.u.blocks.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.blocks.block1.conv_branch.0.running_mean not found in cur model
unet.u.u.u.blocks.block1.conv_branch.0.running_var not found in cur model
unet.u.u.u.blocks.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.blocks.block1.conv_branch.3.running_mean not found in cur model
unet.u.u.u.blocks.block1.conv_branch.3.running_var not found in cur model
unet.u.u.u.blocks.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.conv.0.running_mean not found in cur model
unet.u.u.u.conv.0.running_var not found in cur model
unet.u.u.u.conv.0.num_batches_tracked not found in cur model
unet.u.u.u.u.blocks.block0.conv_branch.0.running_mean not found in cur model
unet.u.u.u.u.blocks.block0.conv_branch.0.running_var not found in cur model
unet.u.u.u.u.blocks.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.u.blocks.block0.conv_branch.3.running_mean not found in cur model
unet.u.u.u.u.blocks.block0.conv_branch.3.running_var not found in cur model
unet.u.u.u.u.blocks.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.u.blocks.block1.conv_branch.0.running_mean not found in cur model
unet.u.u.u.u.blocks.block1.conv_branch.0.running_var not found in cur model
unet.u.u.u.u.blocks.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.u.blocks.block1.conv_branch.3.running_mean not found in cur model
unet.u.u.u.u.blocks.block1.conv_branch.3.running_var not found in cur model
unet.u.u.u.u.blocks.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.u.conv.0.running_mean not found in cur model
unet.u.u.u.u.conv.0.running_var not found in cur model
unet.u.u.u.u.conv.0.num_batches_tracked not found in cur model
unet.u.u.u.u.u.blocks.block0.conv_branch.0.running_mean not found in cur model
unet.u.u.u.u.u.blocks.block0.conv_branch.0.running_var not found in cur model
unet.u.u.u.u.u.blocks.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.u.u.blocks.block0.conv_branch.3.running_mean not found in cur model
unet.u.u.u.u.u.blocks.block0.conv_branch.3.running_var not found in cur model
unet.u.u.u.u.u.blocks.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.u.u.blocks.block1.conv_branch.0.running_mean not found in cur model
unet.u.u.u.u.u.blocks.block1.conv_branch.0.running_var not found in cur model
unet.u.u.u.u.u.blocks.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.u.u.blocks.block1.conv_branch.3.running_mean not found in cur model
unet.u.u.u.u.u.blocks.block1.conv_branch.3.running_var not found in cur model
unet.u.u.u.u.u.blocks.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.u.u.conv.0.running_mean not found in cur model
unet.u.u.u.u.u.conv.0.running_var not found in cur model
unet.u.u.u.u.u.conv.0.num_batches_tracked not found in cur model
unet.u.u.u.u.u.u.blocks.block0.conv_branch.0.running_mean not found in cur model
unet.u.u.u.u.u.u.blocks.block0.conv_branch.0.running_var not found in cur model
unet.u.u.u.u.u.u.blocks.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.u.u.u.blocks.block0.conv_branch.3.running_mean not found in cur model
unet.u.u.u.u.u.u.blocks.block0.conv_branch.3.running_var not found in cur model
unet.u.u.u.u.u.u.blocks.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.u.u.u.blocks.block1.conv_branch.0.running_mean not found in cur model
unet.u.u.u.u.u.u.blocks.block1.conv_branch.0.running_var not found in cur model
unet.u.u.u.u.u.u.blocks.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.u.u.u.blocks.block1.conv_branch.3.running_mean not found in cur model
unet.u.u.u.u.u.u.blocks.block1.conv_branch.3.running_var not found in cur model
unet.u.u.u.u.u.u.blocks.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.u.u.deconv.0.running_mean not found in cur model
unet.u.u.u.u.u.deconv.0.running_var not found in cur model
unet.u.u.u.u.u.deconv.0.num_batches_tracked not found in cur model
unet.u.u.u.u.u.blocks_tail.block0.conv_branch.0.running_mean not found in cur model
unet.u.u.u.u.u.blocks_tail.block0.conv_branch.0.running_var not found in cur model
unet.u.u.u.u.u.blocks_tail.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.u.u.blocks_tail.block0.conv_branch.3.running_mean not found in cur model
unet.u.u.u.u.u.blocks_tail.block0.conv_branch.3.running_var not found in cur model
unet.u.u.u.u.u.blocks_tail.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.u.u.blocks_tail.block1.conv_branch.0.running_mean not found in cur model
unet.u.u.u.u.u.blocks_tail.block1.conv_branch.0.running_var not found in cur model
unet.u.u.u.u.u.blocks_tail.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.u.u.blocks_tail.block1.conv_branch.3.running_mean not found in cur model
unet.u.u.u.u.u.blocks_tail.block1.conv_branch.3.running_var not found in cur model
unet.u.u.u.u.u.blocks_tail.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.u.deconv.0.running_mean not found in cur model
unet.u.u.u.u.deconv.0.running_var not found in cur model
unet.u.u.u.u.deconv.0.num_batches_tracked not found in cur model
unet.u.u.u.u.blocks_tail.block0.conv_branch.0.running_mean not found in cur model
unet.u.u.u.u.blocks_tail.block0.conv_branch.0.running_var not found in cur model
unet.u.u.u.u.blocks_tail.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.u.blocks_tail.block0.conv_branch.3.running_mean not found in cur model
unet.u.u.u.u.blocks_tail.block0.conv_branch.3.running_var not found in cur model
unet.u.u.u.u.blocks_tail.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.u.blocks_tail.block1.conv_branch.0.running_mean not found in cur model
unet.u.u.u.u.blocks_tail.block1.conv_branch.0.running_var not found in cur model
unet.u.u.u.u.blocks_tail.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.u.blocks_tail.block1.conv_branch.3.running_mean not found in cur model
unet.u.u.u.u.blocks_tail.block1.conv_branch.3.running_var not found in cur model
unet.u.u.u.u.blocks_tail.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.deconv.0.running_mean not found in cur model
unet.u.u.u.deconv.0.running_var not found in cur model
unet.u.u.u.deconv.0.num_batches_tracked not found in cur model
unet.u.u.u.blocks_tail.block0.conv_branch.0.running_mean not found in cur model
unet.u.u.u.blocks_tail.block0.conv_branch.0.running_var not found in cur model
unet.u.u.u.blocks_tail.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.blocks_tail.block0.conv_branch.3.running_mean not found in cur model
unet.u.u.u.blocks_tail.block0.conv_branch.3.running_var not found in cur model
unet.u.u.u.blocks_tail.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.u.blocks_tail.block1.conv_branch.0.running_mean not found in cur model
unet.u.u.u.blocks_tail.block1.conv_branch.0.running_var not found in cur model
unet.u.u.u.blocks_tail.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.u.blocks_tail.block1.conv_branch.3.running_mean not found in cur model
unet.u.u.u.blocks_tail.block1.conv_branch.3.running_var not found in cur model
unet.u.u.u.blocks_tail.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.deconv.0.running_mean not found in cur model
unet.u.u.deconv.0.running_var not found in cur model
unet.u.u.deconv.0.num_batches_tracked not found in cur model
unet.u.u.blocks_tail.block0.conv_branch.0.running_mean not found in cur model
unet.u.u.blocks_tail.block0.conv_branch.0.running_var not found in cur model
unet.u.u.blocks_tail.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.blocks_tail.block0.conv_branch.3.running_mean not found in cur model
unet.u.u.blocks_tail.block0.conv_branch.3.running_var not found in cur model
unet.u.u.blocks_tail.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.u.blocks_tail.block1.conv_branch.0.running_mean not found in cur model
unet.u.u.blocks_tail.block1.conv_branch.0.running_var not found in cur model
unet.u.u.blocks_tail.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.u.blocks_tail.block1.conv_branch.3.running_mean not found in cur model
unet.u.u.blocks_tail.block1.conv_branch.3.running_var not found in cur model
unet.u.u.blocks_tail.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.u.deconv.0.running_mean not found in cur model
unet.u.deconv.0.running_var not found in cur model
unet.u.deconv.0.num_batches_tracked not found in cur model
unet.u.blocks_tail.block0.conv_branch.0.running_mean not found in cur model
unet.u.blocks_tail.block0.conv_branch.0.running_var not found in cur model
unet.u.blocks_tail.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.u.blocks_tail.block0.conv_branch.3.running_mean not found in cur model
unet.u.blocks_tail.block0.conv_branch.3.running_var not found in cur model
unet.u.blocks_tail.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.u.blocks_tail.block1.conv_branch.0.running_mean not found in cur model
unet.u.blocks_tail.block1.conv_branch.0.running_var not found in cur model
unet.u.blocks_tail.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.u.blocks_tail.block1.conv_branch.3.running_mean not found in cur model
unet.u.blocks_tail.block1.conv_branch.3.running_var not found in cur model
unet.u.blocks_tail.block1.conv_branch.3.num_batches_tracked not found in cur model
unet.deconv.0.running_mean not found in cur model
unet.deconv.0.running_var not found in cur model
unet.deconv.0.num_batches_tracked not found in cur model
unet.blocks_tail.block0.conv_branch.0.running_mean not found in cur model
unet.blocks_tail.block0.conv_branch.0.running_var not found in cur model
unet.blocks_tail.block0.conv_branch.0.num_batches_tracked not found in cur model
unet.blocks_tail.block0.conv_branch.3.running_mean not found in cur model
unet.blocks_tail.block0.conv_branch.3.running_var not found in cur model
unet.blocks_tail.block0.conv_branch.3.num_batches_tracked not found in cur model
unet.blocks_tail.block1.conv_branch.0.running_mean not found in cur model
unet.blocks_tail.block1.conv_branch.0.running_var not found in cur model
unet.blocks_tail.block1.conv_branch.0.num_batches_tracked not found in cur model
unet.blocks_tail.block1.conv_branch.3.running_mean not found in cur model
unet.blocks_tail.block1.conv_branch.3.running_var not found in cur model
unet.blocks_tail.block1.conv_branch.3.num_batches_tracked not found in cur model
output_layer.0.running_mean not found in cur model
output_layer.0.running_var not found in cur model
output_layer.0.num_batches_tracked not found in cur model
offset.1.running_mean not found in cur model
offset.1.running_var not found in cur model
offset.1.num_batches_tracked not found in cur model
score_unet.blocks.block0.conv_branch.0.running_mean not found in cur model
score_unet.blocks.block0.conv_branch.0.running_var not found in cur model
score_unet.blocks.block0.conv_branch.0.num_batches_tracked not found in cur model
score_unet.blocks.block0.conv_branch.3.running_mean not found in cur model
score_unet.blocks.block0.conv_branch.3.running_var not found in cur model
score_unet.blocks.block0.conv_branch.3.num_batches_tracked not found in cur model
score_unet.blocks.block1.conv_branch.0.running_mean not found in cur model
score_unet.blocks.block1.conv_branch.0.running_var not found in cur model
score_unet.blocks.block1.conv_branch.0.num_batches_tracked not found in cur model
score_unet.blocks.block1.conv_branch.3.running_mean not found in cur model
score_unet.blocks.block1.conv_branch.3.running_var not found in cur model
score_unet.blocks.block1.conv_branch.3.num_batches_tracked not found in cur model
score_unet.conv.0.running_mean not found in cur model
score_unet.conv.0.running_var not found in cur model
score_unet.conv.0.num_batches_tracked not found in cur model
score_unet.u.blocks.block0.conv_branch.0.running_mean not found in cur model
score_unet.u.blocks.block0.conv_branch.0.running_var not found in cur model
score_unet.u.blocks.block0.conv_branch.0.num_batches_tracked not found in cur model
score_unet.u.blocks.block0.conv_branch.3.running_mean not found in cur model
score_unet.u.blocks.block0.conv_branch.3.running_var not found in cur model
score_unet.u.blocks.block0.conv_branch.3.num_batches_tracked not found in cur model
score_unet.u.blocks.block1.conv_branch.0.running_mean not found in cur model
score_unet.u.blocks.block1.conv_branch.0.running_var not found in cur model
score_unet.u.blocks.block1.conv_branch.0.num_batches_tracked not found in cur model
score_unet.u.blocks.block1.conv_branch.3.running_mean not found in cur model
score_unet.u.blocks.block1.conv_branch.3.running_var not found in cur model
score_unet.u.blocks.block1.conv_branch.3.num_batches_tracked not found in cur model
score_unet.deconv.0.running_mean not found in cur model
score_unet.deconv.0.running_var not found in cur model
score_unet.deconv.0.num_batches_tracked not found in cur model
score_unet.blocks_tail.block0.conv_branch.0.running_mean not found in cur model
score_unet.blocks_tail.block0.conv_branch.0.running_var not found in cur model
score_unet.blocks_tail.block0.conv_branch.0.num_batches_tracked not found in cur model
score_unet.blocks_tail.block0.conv_branch.3.running_mean not found in cur model
score_unet.blocks_tail.block0.conv_branch.3.running_var not found in cur model
score_unet.blocks_tail.block0.conv_branch.3.num_batches_tracked not found in cur model
score_unet.blocks_tail.block1.conv_branch.0.running_mean not found in cur model
score_unet.blocks_tail.block1.conv_branch.0.running_var not found in cur model
score_unet.blocks_tail.block1.conv_branch.0.num_batches_tracked not found in cur model
score_unet.blocks_tail.block1.conv_branch.3.running_mean not found in cur model
score_unet.blocks_tail.block1.conv_branch.3.running_var not found in cur model
score_unet.blocks_tail.block1.conv_branch.3.num_batches_tracked not found in cur model
score_outputlayer.0.running_mean not found in cur model
score_outputlayer.0.running_var not found in cur model
score_outputlayer.0.num_batches_tracked not found in cur model
Traceback (most recent call last):
File "/home/waiyc/catkin_test_catgrasp/catgrasp/Utils.py", line 148, in load_model
model.load_state_dict(state_dict)
File "/home/waiyc/anaconda3/envs/catgrasp_conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1483, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for PointGroup:
size mismatch for input_conv.0.weight: copying a param with shape torch.Size([3, 3, 3, 6, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 6]).
size mismatch for unet.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 16, 32]) from checkpoint, the shape in current model is torch.Size([32, 2, 2, 2, 16]).
size mismatch for unet.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 32, 48]) from checkpoint, the shape in current model is torch.Size([48, 2, 2, 2, 32]).
size mismatch for unet.u.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 48, 64]) from checkpoint, the shape in current model is torch.Size([64, 2, 2, 2, 48]).
size mismatch for unet.u.u.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 64, 80]) from checkpoint, the shape in current model is torch.Size([80, 2, 2, 2, 64]).
size mismatch for unet.u.u.u.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 80, 96]) from checkpoint, the shape in current model is torch.Size([96, 2, 2, 2, 80]).
size mismatch for unet.u.u.u.u.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 96, 112]) from checkpoint, the shape in current model is torch.Size([112, 2, 2, 2, 96]).
size mismatch for unet.u.u.u.u.u.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 112, 112]) from checkpoint, the shape in current model is torch.Size([112, 3, 3, 3, 112]).
size mismatch for unet.u.u.u.u.u.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 112, 112]) from checkpoint, the shape in current model is torch.Size([112, 3, 3, 3, 112]).
size mismatch for unet.u.u.u.u.u.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 112, 112]) from checkpoint, the shape in current model is torch.Size([112, 3, 3, 3, 112]).
size mismatch for unet.u.u.u.u.u.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 112, 112]) from checkpoint, the shape in current model is torch.Size([112, 3, 3, 3, 112]).
size mismatch for unet.u.u.u.u.u.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 112, 96]) from checkpoint, the shape in current model is torch.Size([96, 2, 2, 2, 112]).
size mismatch for unet.u.u.u.u.u.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 192, 96]) from checkpoint, the shape in current model is torch.Size([96, 1, 1, 1, 192]).
size mismatch for unet.u.u.u.u.u.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 192, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 192]).
size mismatch for unet.u.u.u.u.u.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.u.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 96, 96]) from checkpoint, the shape in current model is torch.Size([96, 3, 3, 3, 96]).
size mismatch for unet.u.u.u.u.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 96, 80]) from checkpoint, the shape in current model is torch.Size([80, 2, 2, 2, 96]).
size mismatch for unet.u.u.u.u.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 160, 80]) from checkpoint, the shape in current model is torch.Size([80, 1, 1, 1, 160]).
size mismatch for unet.u.u.u.u.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 160, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 160]).
size mismatch for unet.u.u.u.u.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.u.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 80, 80]) from checkpoint, the shape in current model is torch.Size([80, 3, 3, 3, 80]).
size mismatch for unet.u.u.u.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 80, 64]) from checkpoint, the shape in current model is torch.Size([64, 2, 2, 2, 80]).
size mismatch for unet.u.u.u.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 128, 64]) from checkpoint, the shape in current model is torch.Size([64, 1, 1, 1, 128]).
size mismatch for unet.u.u.u.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 128, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 128]).
size mismatch for unet.u.u.u.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.u.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 64, 64]) from checkpoint, the shape in current model is torch.Size([64, 3, 3, 3, 64]).
size mismatch for unet.u.u.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 64, 48]) from checkpoint, the shape in current model is torch.Size([48, 2, 2, 2, 64]).
size mismatch for unet.u.u.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 96, 48]) from checkpoint, the shape in current model is torch.Size([48, 1, 1, 1, 96]).
size mismatch for unet.u.u.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 96, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 96]).
size mismatch for unet.u.u.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.u.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 48, 48]) from checkpoint, the shape in current model is torch.Size([48, 3, 3, 3, 48]).
size mismatch for unet.u.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 48, 32]) from checkpoint, the shape in current model is torch.Size([32, 2, 2, 2, 48]).
size mismatch for unet.u.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 64, 32]) from checkpoint, the shape in current model is torch.Size([32, 1, 1, 1, 64]).
size mismatch for unet.u.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 64, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 64]).
size mismatch for unet.u.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.u.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for unet.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 2, 2, 2, 32]).
size mismatch for unet.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 1, 1, 1, 32]).
size mismatch for unet.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 32]).
size mismatch for unet.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for unet.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.conv.2.weight: copying a param with shape torch.Size([2, 2, 2, 16, 32]) from checkpoint, the shape in current model is torch.Size([32, 2, 2, 2, 16]).
size mismatch for score_unet.u.blocks.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for score_unet.u.blocks.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for score_unet.u.blocks.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for score_unet.u.blocks.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 32, 32]) from checkpoint, the shape in current model is torch.Size([32, 3, 3, 3, 32]).
size mismatch for score_unet.deconv.2.weight: copying a param with shape torch.Size([2, 2, 2, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 2, 2, 2, 32]).
size mismatch for score_unet.blocks_tail.block0.i_branch.0.weight: copying a param with shape torch.Size([1, 1, 1, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 1, 1, 1, 32]).
size mismatch for score_unet.blocks_tail.block0.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 32, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 32]).
size mismatch for score_unet.blocks_tail.block0.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks_tail.block1.conv_branch.2.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
size mismatch for score_unet.blocks_tail.block1.conv_branch.5.weight: copying a param with shape torch.Size([3, 3, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([16, 3, 3, 3, 16]).
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "run_grasp_simulation.py", line 715, in
seg_predicter = PointGroupPredictor(class_name)
File "/home/waiyc/catkin_test_catgrasp/catgrasp/predicter.py", line 228, in init
self.model = load_model(self.model,ckpt_dir='{}/best_val.pth.tar'.format(artifact_dir))
File "/home/waiyc/catkin_test_catgrasp/catgrasp/Utils.py", line 172, in load_model
raise RuntimeError
RuntimeError

Did you run inside docker container and build as suggested in readme?

Hi @wenbowen123 ,
Thank for the reply.
I have sucessfully run the simulation after a retrain.
I will close this for now.