Hello, I want to use CAD model alone for image segmentation, which script should I use

[wsq1010]

No description provided.

[wsq1010]

I had a problem executing run_inference_custom.py

Traceback (most recent call last):
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/run_inference_custom.py", line 234, in
run_inference(
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/run_inference_custom.py", line 162, in run_inference
model.ref_data["descriptors"] = model.descriptor_model.compute_features(
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/model/dinov2.py", line 150, in compute_features
features = self.forward_by_chunk(images)
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/model/dinov2.py", line 163, in forward_by_chunk
feats = self.compute_features(
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/model/dinov2.py", line 152, in compute_features
features = self.model(images)
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/model/vision_transformer.py", line 321, in forward
ret = self.forward_features(*args, **kwargs)
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/model/vision_transformer.py", line 257, in forward_features
x = blk(x)
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/model/layers/block.py", line 247, in forward
return super().forward(x_or_x_list)
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/model/layers/block.py", line 105, in forward
x = x + attn_residual_func(x)
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/model/layers/block.py", line 84, in attn_residual_func
return self.ls1(self.attn(self.norm1(x)))
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/media/qtwsq/work/pythonProject/SAM-6D/SAM-6D/Instance_Segmentation_Model/model/layers/attention.py", line 76, in forward
x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/xformers/ops/fmha/init.py", line 196, in memory_efficient_attention
return _memory_efficient_attention(
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/xformers/ops/fmha/init.py", line 294, in _memory_efficient_attention
return _memory_efficient_attention_forward(
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/xformers/ops/fmha/init.py", line 310, in _memory_efficient_attention_forward
op = _dispatch_fw(inp)
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/xformers/ops/fmha/dispatch.py", line 98, in _dispatch_fw
return _run_priority_list(
File "/opt/conda/envs/sam6d-ism/lib/python3.9/site-packages/xformers/ops/fmha/dispatch.py", line 73, in _run_priority_list
raise NotImplementedError(msg)
NotImplementedError: No operator found for memory_efficient_attention_forward with inputs:
query : shape=(16, 257, 16, 64) (torch.float32)
key : shape=(16, 257, 16, 64) (torch.float32)
value : shape=(16, 257, 16, 64) (torch.float32)
attn_bias : <class 'NoneType'>
p : 0.0
flshattF is not supported because:
device=cpu (supported: {'cuda'})
dtype=torch.float32 (supported: {torch.bfloat16, torch.float16})
tritonflashattF is not supported because:
device=cpu (supported: {'cuda'})
dtype=torch.float32 (supported: {torch.bfloat16, torch.float16})
Operator wasn't built - see python -m xformers.info for more info
triton is not available
cutlassF is not supported because:
device=cpu (supported: {'cuda'})
smallkF is not supported because:
max(query.shape[-1] != value.shape[-1]) > 32
unsupported embed per head: 64