When I was training the second step to build MobileSAM image encoder (used as teacher model) to export onnx, I encountered the situation listed below, but still generated the required file mobile_sam_image_encoder_bs16.onnx, did I encounter the same situation? What effect will it have on the results?How to avoid？thank you
/home/work/miniforge3/envs/nanosam/lib/python3.8/site-packages/timm/models/layers/init.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
warnings.warn(f"Importing from {name} is deprecated, please import via timm.layers", FutureWarning)
/home/work/miniforge3/envs/nanosam/lib/python3.8/site-packages/timm/models/registry.py:4: FutureWarning: Importing from timm.models.registry is deprecated, please import via timm.models
warnings.warn(f"Importing from {name} is deprecated, please import via timm.models", FutureWarning)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:656: UserWarning: Overwriting tiny_vit_5m_224 in registry with nanosam.mobile_sam.modeling.tiny_vit_sam.tiny_vit_5m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.
return register_model(fn_wrapper)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:656: UserWarning: Overwriting tiny_vit_11m_224 in registry with nanosam.mobile_sam.modeling.tiny_vit_sam.tiny_vit_11m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.
return register_model(fn_wrapper)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:656: UserWarning: Overwriting tiny_vit_21m_224 in registry with nanosam.mobile_sam.modeling.tiny_vit_sam.tiny_vit_21m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.
return register_model(fn_wrapper)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:656: UserWarning: Overwriting tiny_vit_21m_384 in registry with nanosam.mobile_sam.modeling.tiny_vit_sam.tiny_vit_21m_384. This is because the name being registered conflicts with an existing name. Please check if this is not expected.
return register_model(fn_wrapper)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:656: UserWarning: Overwriting tiny_vit_21m_512 in registry with nanosam.mobile_sam.modeling.tiny_vit_sam.tiny_vit_21m_512. This is because the name being registered conflicts with an existing name. Please check if this is not expected.
return register_model(fn_wrapper)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:338: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert L == H * W, "input feature has wrong size"
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:136: TracerWarning: Using len to get tensor shape might cause the trace to be incorrect. Recommended usage would be tensor.shape[0]. Passing a tensor of different shape might lead to errors or silently give incorrect results.
B = len(x)
/home/work/miniforge3/envs/nanosam/lib/python3.8/site-packages/torch/onnx/_internal/jit_utils.py:307: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
_C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version)
/home/work/miniforge3/envs/nanosam/lib/python3.8/site-packages/torch/onnx/utils.py:702: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
_C._jit_pass_onnx_graph_shape_type_inference(
/home/work/miniforge3/envs/nanosam/lib/python3.8/site-packages/torch/onnx/utils.py:1209: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
_C._jit_pass_onnx_graph_shape_type_inference(
When I was training the second step to build MobileSAM image encoder (used as teacher model) to export onnx, I encountered the situation listed below, but still generated the required file mobile_sam_image_encoder_bs16.onnx, did I encounter the same situation? What effect will it have on the results?How to avoid？thank you
/home/work/miniforge3/envs/nanosam/lib/python3.8/site-packages/timm/models/layers/init.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
warnings.warn(f"Importing from {name} is deprecated, please import via timm.layers", FutureWarning)
/home/work/miniforge3/envs/nanosam/lib/python3.8/site-packages/timm/models/registry.py:4: FutureWarning: Importing from timm.models.registry is deprecated, please import via timm.models
warnings.warn(f"Importing from {name} is deprecated, please import via timm.models", FutureWarning)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:656: UserWarning: Overwriting tiny_vit_5m_224 in registry with nanosam.mobile_sam.modeling.tiny_vit_sam.tiny_vit_5m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.
return register_model(fn_wrapper)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:656: UserWarning: Overwriting tiny_vit_11m_224 in registry with nanosam.mobile_sam.modeling.tiny_vit_sam.tiny_vit_11m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.
return register_model(fn_wrapper)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:656: UserWarning: Overwriting tiny_vit_21m_224 in registry with nanosam.mobile_sam.modeling.tiny_vit_sam.tiny_vit_21m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.
return register_model(fn_wrapper)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:656: UserWarning: Overwriting tiny_vit_21m_384 in registry with nanosam.mobile_sam.modeling.tiny_vit_sam.tiny_vit_21m_384. This is because the name being registered conflicts with an existing name. Please check if this is not expected.
return register_model(fn_wrapper)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:656: UserWarning: Overwriting tiny_vit_21m_512 in registry with nanosam.mobile_sam.modeling.tiny_vit_sam.tiny_vit_21m_512. This is because the name being registered conflicts with an existing name. Please check if this is not expected.
return register_model(fn_wrapper)
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:338: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert L == H * W, "input feature has wrong size"
/home/work/nanosam/nanosam/mobile_sam/modeling/tiny_vit_sam.py:136: TracerWarning: Using len to get tensor shape might cause the trace to be incorrect. Recommended usage would be tensor.shape[0]. Passing a tensor of different shape might lead to errors or silently give incorrect results.
B = len(x)
/home/work/miniforge3/envs/nanosam/lib/python3.8/site-packages/torch/onnx/_internal/jit_utils.py:307: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
_C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version)
/home/work/miniforge3/envs/nanosam/lib/python3.8/site-packages/torch/onnx/utils.py:702: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
_C._jit_pass_onnx_graph_shape_type_inference(
/home/work/miniforge3/envs/nanosam/lib/python3.8/site-packages/torch/onnx/utils.py:1209: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
_C._jit_pass_onnx_graph_shape_type_inference(