OpenGVLab
AssertionError：assert qkv.dtype in [torch.float16, torch.bfloat16]
Thank you for your work on this excellent model and providing the example code.
When evaluate the InternViT-6B-224px with a single GPU for semantic segmentation on the ADE20K dataset, the following error occurred: AssertionError：assert qkv.dtype in [torch.float16, torch.bfloat16]
To help resolve it, could you kindly clarify potential causes? Here are the key points I suspect based on the task requirements: use linear probing on InternViT-6B-224px (frozen) : link config ckpt
Environment info: sys.platform: linux Python: 3.9.21 (main, Dec 11 2024, 16:24:11) [GCC 11.2.0] CUDA available: True GPU 0: NVIDIA RTX A6000 CUDA_HOME: /usr/local/cuda-11.3 NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 PyTorch: 1.12.0 PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.3
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.3.2 (built against CUDA 11.5)
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.13.0 OpenCV: 4.11.0 MMCV: 1.7.0 MMCV Compiler: GCC 9.4 MMCV CUDA Compiler: 11.3 MMSegmentation: 0.27.0+89b1e61
Command: python tools/test.py /home/hello/PX/InternVL-MMDetSeg/mmsegmentation/configs/intern_vit_6b/linear_probing/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.py /home/hello/PX/InternVL-MMDetSeg/pretrained/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.pth --show-dir 0530 --eval mIoU
Log:
/home/hello/PX/InternVL-MMDetSeg/mmcv/mmcv/init.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.
warnings.warn(
2025-06-03 10:26:08,109 - mmseg - INFO - Multi-processing start method is None
2025-06-03 10:26:08,109 - mmseg - INFO - OpenCV num_threads is `32
2025-06-03 10:26:08,128 - mmseg - INFO - Loaded 2000 images
2025-06-03 10:26:35,727 - mmseg - INFO - _IncompatibleKeys(missing_keys=[], unexpected_keys=['clip_projector.norm1_q.weight', 'clip_projector.norm1_q.bias', 'clip_projector.norm1_k.weight', 'clip_projector.norm1_k.bias', 'clip_projector.norm1_v.weight', 'clip_projector.norm1_v.bias', 'clip_projector.cross_attn.q_bias', 'clip_projector.cross_attn.k_bias', 'clip_projector.cross_attn.v_bias', 'clip_projector.cross_attn.q.weight', 'clip_projector.cross_attn.k.weight', 'clip_projector.cross_attn.v.weight', 'clip_projector.cross_attn.proj.weight', 'clip_projector.cross_attn.proj.bias'])
INFO:mmseg:_IncompatibleKeys(missing_keys=[], unexpected_keys=['clip_projector.norm1_q.weight', 'clip_projector.norm1_q.bias', 'clip_projector.norm1_k.weight', 'clip_projector.norm1_k.bias', 'clip_projector.norm1_v.weight', 'clip_projector.norm1_v.bias', 'clip_projector.cross_attn.q_bias', 'clip_projector.cross_attn.k_bias', 'clip_projector.cross_attn.v_bias', 'clip_projector.cross_attn.q.weight', 'clip_projector.cross_attn.k.weight', 'clip_projector.cross_attn.v.weight', 'clip_projector.cross_attn.proj.weight', 'clip_projector.cross_attn.proj.bias'])
/home/hello/PX/InternVL-MMDetSeg/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default avg_non_ignore is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set avg_non_ignore=True.
warnings.warn(
load checkpoint from local path: /home/hello/PX/InternVL-MMDetSeg/pretrained/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.pth
The model and loaded state dict do not match exactly
missing keys in source state_dict: backbone.pos_embed, backbone.cls_token, backbone.patch_embed.proj.weight, backbone.patch_embed.proj.bias, backbone.blocks.0.norm1.weight, backbone.blocks.0.attn.qkv.weight, backbone.blocks.0.attn.proj.weight, backbone.blocks.0.attn.proj.bias, backbone.blocks.0.attn.q_norm.weight, backbone.blocks.0.attn.k_norm.weight, backbone.blocks.0.ls1.gamma, backbone.blocks.0.norm2.weight, backbone.blocks.0.mlp.fc1.weight, backbone.blocks.0.mlp.fc1.bias, backbone.blocks.0.mlp.fc2.weight, backbone.blocks.0.mlp.fc2.bias, backbone.blocks.0.ls2.gamma, backbone.blocks.1.norm1.weight, backbone.blocks.1.attn.qkv.weight, backbone.blocks.1.attn.proj.weight, backbone.blocks.1.attn.proj.bias, backbone.blocks.1.attn.q_norm.weight, backbone.blocks.1.attn.k_norm.weight, backbone.blocks.1.ls1.gamma, backbone.blocks.1.norm2.weight, backbone.blocks.1.mlp.fc1.weight, backbone.blocks.1.mlp.fc1.bias, backbone.blocks.1.mlp.fc2.weight, backbone.blocks.1.mlp.fc2.bias, 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"CLASSES" not found in meta, use dataset.CLASSES instead
"PALETTE" not found in meta, use dataset.PALETTE instead
/home/hello/PX/InternVL-MMDetSeg/mmsegmentation/tools/test.py:264: UserWarning: SyncBN is only supported with DDP. To be compatible with DP, we convert SyncBN to BN. Please use dist_train.sh which can avoid this error.
warnings.warn(
[ ] 0/2000, elapsed: 0s, ETA:/home/hello/miniconda3/envs/internvl-mmdetseg/lib/python3.9/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
Traceback (most recent call last):
File "/home/hello/PX/InternVL-MMDetSeg/mmsegmentation/tools/test.py", line 320, in
