chenhongruixuan / mambacd Goto Github PK

Hello, I encountered the following error while trying to install. Can you try to resolve it?
Processing /home/hhy/下载/VMamba-main/kernels/selective_scan
Preparing metadata (setup.py) ... done
Requirement already satisfied: torch in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from selective_scan==0.0.2) (2.1.1+cu118)
Requirement already satisfied: packaging in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from selective_scan==0.0.2) (23.2)
Requirement already satisfied: ninja in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from selective_scan==0.0.2) (1.11.1.1)
Requirement already satisfied: einops in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from selective_scan==0.0.2) (0.7.0)
Requirement already satisfied: filelock in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from torch->selective_scan==0.0.2) (3.9.0)
Requirement already satisfied: typing-extensions in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from torch->selective_scan==0.0.2) (4.8.0)
Requirement already satisfied: sympy in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from torch->selective_scan==0.0.2) (1.12)
Requirement already satisfied: networkx in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from torch->selective_scan==0.0.2) (3.2.1)
Requirement already satisfied: jinja2 in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from torch->selective_scan==0.0.2) (3.1.2)
Requirement already satisfied: fsspec in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from torch->selective_scan==0.0.2) (2024.3.1)
Requirement already satisfied: triton==2.1.0 in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from torch->selective_scan==0.0.2) (2.1.0)
Requirement already satisfied: MarkupSafe>=2.0 in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from jinja2->torch->selective_scan==0.0.2) (2.1.3)
Requirement already satisfied: mpmath>=0.19 in /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages (from sympy->torch->selective_scan==0.0.2) (1.3.0)
Building wheels for collected packages: selective_scan
Building wheel for selective_scan (setup.py) ... error
error: subprocess-exited-with-error

× python setup.py bdist_wheel did not run successfully.
│ exit code: 1
╰─> [118 lines of output]

  torch.__version__  = 2.1.1+cu118
  
  
  
  
  CUDA_HOME = /home/hhy/anaconda3/envs/mamba
  
  
  running bdist_wheel
  running build
  running build_ext
  /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/utils/cpp_extension.py:424: UserWarning: There are no g++ version bounds defined for CUDA version 11.8
    warnings.warn(f'There are no {compiler_name} version bounds defined for CUDA version {cuda_str_version}')
  building 'selective_scan_cuda_core' extension
  creating /home/hhy/下载/VMamba-main/kernels/selective_scan/build
  creating /home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310
  creating /home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc
  creating /home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan
  creating /home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus
  Emitting ninja build file /home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/build.ninja...
  Compiling objects...
  Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
  [1/3] c++ -MMD -MF '/home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan.o'.d -pthread -B /home/hhy/anaconda3/envs/mamba/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /home/hhy/anaconda3/envs/mamba/include -fPIC -O2 -isystem /home/hhy/anaconda3/envs/mamba/include -fPIC '-I/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/TH -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/THC -I/home/hhy/anaconda3/envs/mamba/include -I/home/hhy/anaconda3/envs/mamba/include/python3.10 -c -c '/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan.cpp' -o '/home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan.o' -O3 -std=c++17 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0
  FAILED: /home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan.o
  c++ -MMD -MF '/home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan.o'.d -pthread -B /home/hhy/anaconda3/envs/mamba/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /home/hhy/anaconda3/envs/mamba/include -fPIC -O2 -isystem /home/hhy/anaconda3/envs/mamba/include -fPIC '-I/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/TH -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/THC -I/home/hhy/anaconda3/envs/mamba/include -I/home/hhy/anaconda3/envs/mamba/include/python3.10 -c -c '/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan.cpp' -o '/home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan.o' -O3 -std=c++17 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0
  In file included from /home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan.cpp:5:
  /home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/ATen/cuda/CUDAContext.h:5:10: fatal error: cuda_runtime_api.h: 没有那个文件或目录
      5 | #include <cuda_runtime_api.h>
        |          ^~~~~~~~~~~~~~~~~~~~
  compilation terminated.
  [2/3] /home/hhy/anaconda3/envs/mamba/bin/nvcc  '-I/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/TH -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/THC -I/home/hhy/anaconda3/envs/mamba/include -I/home/hhy/anaconda3/envs/mamba/include/python3.10 -c -c '/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_core_fwd.cu' -o '/home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_fwd.o' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -O3 -std=c++17 -U__CUDA_NO_HALF_OPERATORS__ -U__CUDA_NO_HALF_CONVERSIONS__ -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT16_CONVERSIONS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -U__CUDA_NO_BFLOAT162_CONVERSIONS__ --expt-relaxed-constexpr --expt-extended-lambda --use_fast_math --ptxas-options=-v -lineinfo -gencode arch=compute_70,code=sm_70 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_90,code=sm_90 --threads 4 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0
  FAILED: /home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_fwd.o
  /home/hhy/anaconda3/envs/mamba/bin/nvcc  '-I/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/TH -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/THC -I/home/hhy/anaconda3/envs/mamba/include -I/home/hhy/anaconda3/envs/mamba/include/python3.10 -c -c '/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_core_fwd.cu' -o '/home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_fwd.o' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -O3 -std=c++17 -U__CUDA_NO_HALF_OPERATORS__ -U__CUDA_NO_HALF_CONVERSIONS__ -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT16_CONVERSIONS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -U__CUDA_NO_BFLOAT162_CONVERSIONS__ --expt-relaxed-constexpr --expt-extended-lambda --use_fast_math --ptxas-options=-v -lineinfo -gencode arch=compute_70,code=sm_70 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_90,code=sm_90 --threads 4 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0
  cc1plus: fatal error: cuda_runtime.h: 没有那个文件或目录
  compilation terminated.
  cc1plus: fatal error: cuda_runtime.h: 没有那个文件或目录
  compilation terminated.
  cc1plus: fatal error: cuda_runtime.h: 没有那个文件或目录
  compilation terminated.
  cc1plus: fatal error: cuda_runtime.h: 没有那个文件或目录
  compilation terminated.
  [3/3] /home/hhy/anaconda3/envs/mamba/bin/nvcc  '-I/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/TH -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/THC -I/home/hhy/anaconda3/envs/mamba/include -I/home/hhy/anaconda3/envs/mamba/include/python3.10 -c -c '/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_core_bwd.cu' -o '/home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_bwd.o' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -O3 -std=c++17 -U__CUDA_NO_HALF_OPERATORS__ -U__CUDA_NO_HALF_CONVERSIONS__ -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT16_CONVERSIONS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -U__CUDA_NO_BFLOAT162_CONVERSIONS__ --expt-relaxed-constexpr --expt-extended-lambda --use_fast_math --ptxas-options=-v -lineinfo -gencode arch=compute_70,code=sm_70 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_90,code=sm_90 --threads 4 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0
  FAILED: /home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_bwd.o
  /home/hhy/anaconda3/envs/mamba/bin/nvcc  '-I/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/TH -I/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/include/THC -I/home/hhy/anaconda3/envs/mamba/include -I/home/hhy/anaconda3/envs/mamba/include/python3.10 -c -c '/home/hhy/下载/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_core_bwd.cu' -o '/home/hhy/下载/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_bwd.o' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -O3 -std=c++17 -U__CUDA_NO_HALF_OPERATORS__ -U__CUDA_NO_HALF_CONVERSIONS__ -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT16_CONVERSIONS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -U__CUDA_NO_BFLOAT162_CONVERSIONS__ --expt-relaxed-constexpr --expt-extended-lambda --use_fast_math --ptxas-options=-v -lineinfo -gencode arch=compute_70,code=sm_70 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_90,code=sm_90 --threads 4 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0
  cc1plus: fatal error: cuda_runtime.h: 没有那个文件或目录
  compilation terminated.
  cc1plus: fatal error: cuda_runtime.h: 没有那个文件或目录
  compilation terminated.
  cc1plus: fatal error: cuda_runtime.h: 没有那个文件或目录
  compilation terminated.
  cc1plus: fatal error: cuda_runtime.h: 没有那个文件或目录
  compilation terminated.
  ninja: build stopped: subcommand failed.
  Traceback (most recent call last):
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2100, in _run_ninja_build
      subprocess.run(
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/subprocess.py", line 526, in run
      raise CalledProcessError(retcode, process.args,
  subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
  
  The above exception was the direct cause of the following exception:
  
  Traceback (most recent call last):
    File "<string>", line 2, in <module>
    File "<pip-setuptools-caller>", line 34, in <module>
    File "/home/hhy/下载/VMamba-main/kernels/selective_scan/setup.py", line 140, in <module>
      setup(
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/__init__.py", line 104, in setup
      return distutils.core.setup(**attrs)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/core.py", line 184, in setup
      return run_commands(dist)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/core.py", line 200, in run_commands
      dist.run_commands()
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 969, in run_commands
      self.run_command(cmd)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/dist.py", line 967, in run_command
      super().run_command(command)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 988, in run_command
      cmd_obj.run()
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/wheel/bdist_wheel.py", line 364, in run
      self.run_command("build")
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/cmd.py", line 316, in run_command
      self.distribution.run_command(command)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/dist.py", line 967, in run_command
      super().run_command(command)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 988, in run_command
      cmd_obj.run()
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/command/build.py", line 132, in run
      self.run_command(cmd_name)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/cmd.py", line 316, in run_command
      self.distribution.run_command(command)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/dist.py", line 967, in run_command
      super().run_command(command)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 988, in run_command
      cmd_obj.run()
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/command/build_ext.py", line 91, in run
      _build_ext.run(self)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 359, in run
      self.build_extensions()
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 873, in build_extensions
      build_ext.build_extensions(self)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 479, in build_extensions
      self._build_extensions_serial()
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 505, in _build_extensions_serial
      self.build_extension(ext)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/command/build_ext.py", line 252, in build_extension
      _build_ext.build_extension(self, ext)
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 560, in build_extension
      objects = self.compiler.compile(
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 686, in unix_wrap_ninja_compile
      _write_ninja_file_and_compile_objects(
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1774, in _write_ninja_file_and_compile_objects
      _run_ninja_build(
    File "/home/hhy/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2116, in _run_ninja_build
      raise RuntimeError(message) from e
  RuntimeError: Error compiling objects for extension
  [end of output]

note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for selective_scan
Running setup.py clean for selective_scan
Failed to build selective_scan
ERROR: Could not build wheels for selective_scan, which is required to install pyproject.toml-based projects

IncompatibleKeys(missing_keys=['outnorm0.weight', 'outnorm0.bias', 'outnorm1.weight', 'outnorm1.bias', 'outnorm2.weight', 'outnorm2.bias', 'outnorm3.weight', 'outnorm3.bias'], unexpected_keys=['classifier.norm.weight', 'classifier.norm.bias', 'classifier.head.weight', 'classifier.head.bias'])
Backbone_VSSM load_pretrained

非常感谢这篇MambaCD的工作，我在复现代码的时候发现，代码并没有用到验证集，而是在每个固定的iteration进行了测试集的评估。如：
在sh中指定的是：
--test_dataset_path '<dataset_path>/SYSU/test'
是测试集的路径，而不是验证集
在训练脚本中：
if (itera + 1) % 10 == 0:
print(f'iter is {itera + 1}, overall loss is {final_loss}')
if (itera + 1) % 500 == 0:
self.deep_model.eval()
rec, pre, oa, f1_score, iou, kc = self.validation()
if kc > best_kc:
torch.save(self.deep_model.state_dict(),
os.path.join(self.model_save_path, f'{itera + 1}_model.pth'))
best_kc = kc
best_round = [rec, pre, oa, f1_score, iou, kc]
self.deep_model.train()

print('The accuracy of the best round is ', best_round)

看上去像是在测试集中找到最好的performance，请问论文中报告的performance是否是用这种方式找到的呢？
非常感谢

"Hello, I would like to use the SCD model for prediction, but I only see 'infer_MambaBCD.py' available. Will there be an update to include 'infer_MambaSCD.py'? Thank you very much."

如题，我是语义检测方面的初学者，我不明白如何生成Binary change map~~非常感谢

想问一下作者是使用什么机器进行的实验，我使用单卡4090在bcd任务上跑SYSU数据集的话，batchsize只能设置为8，那么max_iters是不是应该改为640000（论文中训练iters是设置为20000，readme提供的命令行中bs=16，max_iters=320000），这样才能和epoch对应起来，还是说我只需要跟论文中的iters=20000对应起来即可，这样的话我就设置max_iters=40000，相比起来能缩短十几倍计算成本

Hi! Congratulations on your great work! I am wondering what happened to MambaBDA-Tiny on the [xBD] pretrained weights?

Hello,
I'm very interested in your work and I have some questions for you while reading the code. Regarding the data set categories used in the article and the number of categories in the ADE data set, and I see that the number of categories in the code is also 150, do I need to change the type and number of categories in other locations when using it?
Thanks!

Hello, I would like to use the BDA model for prediction, but I only see 'infer_MambaBCD.py' ,'infer_MambaSCD.py 'available. Will there be an update to include 'infer_MambaBDA.py'? Thank you very much."

非常感谢您的指导，成功复现MambaCD而且在2D变化检测达到sota，因为自己刚接触这块领域，关注到在3D提出了SiameseKPConv ，不知道是否针对这两个模型进行过对比呢

Hello,

I am highly interested in your work on "ChangeMamba: Remote Sensing Change Detection with Spatio-Temporal State Space Model" posted on GitHub. I noticed that you have successfully trained the MambaBDA model on the xBD dataset and achieved remarkable results. I am currently engaged in related research and would greatly appreciate the opportunity to test my dataset using your trained weights.

Would it be possible for you to share the weights trained on the xBD dataset? It would be immensely beneficial for my research, and I will ensure to acknowledge and thank your work in my subsequent studies.

Thank you very much for your consideration and assistance!

Best regards,

xczhou

您好，我直接用的WHU-CD 256的数据集，我的机子目前跑不动1024的，跑出来精度很差，并且极容易出现loss为nan的情况，您有什么建议吗，谢谢！

Hi, I am very interested in this awesome job. I've been doing some work related to semantic change detection recently and I'd like to use your pretrained weights to train on my dataset. Can you please provide me the weights for the semantic change detection model?

Hello, author. When I run the BCD code, I encounter an error: torch.cuda.OutOfMemoryError: CUDA out of memory. So, I wanted to ask how much GPU memory is required to run this part of the code?

我发现在自己的数据集上训练MambaSCD时，计算lovasz_softmax会返回一个空的列表，我发先标签文件都是背景，这个有什么办法解决吗？数据集当中不能有负样本吗？

Thank you for sharing your excellent work. And umm, is there a problem with the areas highlighted in red in the image? I think that both label_1 and label_2 are three-channel images, why are they being processed as single-channel images here?

Hello, I am trying to execute the BDA task and ran train-MambaBDA.py. The following error occurred. I thought it was a problem with the dataset, but I downloaded the xbd dataset again and still encountered the same problem. Can you help me answer this? In addition, I saw that the hold out data, which is not useful for executing BDA tasks, was used there?
I also encountered the same problem when running the train-Mambascd.py file.

parser.add_argument('--pretrained_weight_path', type=str,default='/home/hhy/下载/MambaCD-master/pretrained_weight/vssm_small_0229_ckpt_epoch_222.pth'
'')

parser.add_argument('--dataset', type=str, default='xBD')
parser.add_argument('--type', type=str, default='train')
parser.add_argument('--train_dataset_path', type=str, default='/media/hhy/Ventoy/xbd/train')
parser.add_argument('--train_data_list_path', type=str, default='/media/hhy/Ventoy/xbd/train/train.txt')
parser.add_argument('--test_dataset_path', type=str, default='/media/hhy/Ventoy/xbd/test')
parser.add_argument('--test_data_list_path', type=str, default='/media/hhy/Ventoy/xbd/test/test.txt')
parser.add_argument('--shuffle', type=bool, default=True)
parser.add_argument('--batch_size', type=int, default=4)
parser.add_argument('--crop_size', type=int, default=256)
parser.add_argument('--train_data_name_list', type=list)
parser.add_argument('--test_data_name_list', type=list)
parser.add_argument('--start_iter', type=int, default=0)
parser.add_argument('--cuda', type=bool, default=True)
parser.add_argument('--max_iters', type=int, default=800000)
parser.add_argument('--model_type', type=str, default='bda——small')
parser.add_argument('--model_param_path', type=str, default='../saved_models')

parser.add_argument('--resume', type=str)
parser.add_argument('--learning_rate', type=float, default=1e-4)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--weight_decay', type=float, default=5e-3)

False
0%| | 0/200000 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/home/hhy/下载/MambaCD-master/changedetection/script/train_MambaBDA.py", line 235, in
main()
File "/home/hhy/下载/MambaCD-master/changedetection/script/train_MambaBDA.py", line 231, in main
trainer.training()
File "/home/hhy/下载/MambaCD-master/changedetection/script/train_MambaBDA.py", line 97, in training
itera, data = train_enumerator.next()
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 652, in next
data = self._next_data()
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1347, in _next_data
return self._process_data(data)
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1373, in _process_data
data.reraise()
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/torch/_utils.py", line 461, in reraise
raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
data = fetcher.fetch(index)
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 49, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/hhy/下载/MambaCD-master/changedetection/datasets/make_data_loader.py", line 189, in getitem
pre_img = self.loader(pre_path)
File "/home/hhy/下载/MambaCD-master/changedetection/datasets/make_data_loader.py", line 14, in img_loader
img = np.array(imageio.imread(path), np.float32)
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/imageio/init.py", line 97, in imread
return imread_v2(uri, format=format, **kwargs)
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/imageio/v2.py", line 359, in imread
with imopen(uri, "ri", **imopen_args) as file:
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/imageio/core/imopen.py", line 113, in imopen
request = Request(uri, io_mode, format_hint=format_hint, extension=extension)
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/imageio/core/request.py", line 247, in init
self._parse_uri(uri)
File "/home/hhy/anaconda3/envs/mamba/lib/python3.9/site-packages/imageio/core/request.py", line 407, in _parse_uri
raise FileNotFoundError("No such file: '%s'" % fn)
FileNotFoundError: No such file: '/media/hhy/Ventoy/xbd/train/images/hurricane-florence_00000263_pre_disaster_pre_disaster.png.png'

作者您好，感谢分享这份工作！但我在复现MambaSCD_small在SECOND数据集上的实验时，达不到原文中提供的精度指标，sek只有16。超参数均按照原文提供的数值而设置，但在加载预训练权重时，显示：
Successfully load ckpt MambaCD-master/pretrained_weight/vssm_small_0229_ckpt_epoch_222.pth
_IncompatibleKeys(missing_keys=['outnorm0.weight', 'outnorm0.bias', 'outnorm1.weight', 'outnorm1.bias', 'outnorm2.weight', 'outnorm2.bias', 'outnorm3.weight', 'outnorm3.bias'], unexpected_keys=['classifier.norm.weight', 'classifier.norm.bias', 'classifier.head.weight', 'classifier.head.bias'])
能否提供完整预训练权重呢？期待您的回复，十分感谢！

Hi, Thanks for your great contribution on building damage assessment community. I noticed that you release some model weights of BCD and SCD, with the absence of BDA weights. I've reproduced the small version of MambaBDA (batch size is set as 8 due to limited GPU memory), and the highest F1_oa is only up to $80$% $(\pm0.3)$. Could you release the model weights of MambaBDA? 😄

Hello, when I was training mamba-BDA-Tiny, the training results were good, but in the 10500 round of evaluation, I made an error: IndexError: Dimension out of range (expected to be in range of [-1,0], but got 1). I printed the probas.shape correctly afterwards. Is there any way to modify this situation.

11%|█         | 10965/100000 [10:43:56<87:08:49,  3.52s/it]
Traceback (most recent call last):
  File "/home/wg/MambaCD-master/changedetection/script/train_MambaBDA.py", line 236, in
    main()
  File "/home/wg/MambaCD-master/changedetection/script/train_MambaBDA.py", line 232, in main
    trainer.training()
  File "/home/wg/MambaCD-master/changedetection/script/train_MambaBDA.py", line 120, in training
    lovasz_loss_clf = L.lovasz_softmax(F.softmax(output_clf, dim=1), labels_clf, ignore=255)
  File "/home/wg/MambaCD-master/changedetection/utils_func/lovasz_loss.py", line 167, in lovasz_softmax
    loss = lovasz_softmax_flat(*flatten_probas(probas, labels, ignore), classes=classes)
  File "/home/wg/MambaCD-master/changedetection/utils_func/lovasz_loss.py", line 181, in lovasz_softmax_flat
    C = probas.size(1)
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

I want to continue my unfinished model training, how to ressume train

hello！

First of all, I'd like to express my sincere thanks for your great work on the MambaCD project. This project has been of great help to my research and work, for which I am very appreciative and grateful.

While trying to install and use the project, I noticed that the installation process requires a CUDA version of at least 11.6. Due to hardware and other dependency limitations, the CUDA version installed in my environment can only be up to 11.4.

Therefore, I would like to ask a few questions:

1. Does the project absolutely require CUDA 11.6 and above to run properly?
2. Do you have a suggested solution or alternative for environments that only support CUDA 11.4? For example, is it possible to modify the configuration or source code to be compatible with CUDA 11.4?
3. If the code needs to be adjusted to support CUDA 11.4, can you provide some advice or guidance to help me achieve this?

I'd really like to have this work successfully in my environment. Any advice and help on how to resolve this compatibility issue would be extremely valuable and appreciated.

Very much looking forward to your reply！

best wishes！

我发现模型的forward和backward都依赖了selective_scan，我想将训练好的模型迁移到没有nvidia gpu的设备上运行，但selective_scan需要有cuda能完成安装，selective_scan有cpu或者pytorch的实现吗？

loading mdel...
Successfully load ckpt /root/pingcc/MambaCD/pretrained_weight/vssm_tiny_0230_ckpt_epoch_262.pth
_IncompatibleKeys(missing_keys=['outnorm0.weight', 'outnorm0.bias', 'outnorm1.weight', 'outnorm1.bias', 'outnorm2.weight', 'outnorm2.bias', 'outnorm3.weight', 'outnorm3.bias'], unexpected_keys=['classifier.norm.weight', 'classifier.norm.bias', 'classifier.head.weight', 'classifier.head.bias', 'layers.2.blocks.4.norm.weight', 'layers.2.blocks.4.norm.bias', 'layers.2.blocks.4.op.x_proj_weight', 'layers.2.blocks.4.op.dt_projs_weight', 'layers.2.blocks.4.op.dt_projs_bias', 'layers.2.blocks.4.op.A_logs', 'layers.2.blocks.4.op.Ds', 'layers.2.blocks.4.op.out_norm.weight', 'layers.2.blocks.4.op.out_norm.bias', 'layers.2.blocks.4.op.in_proj.weight', 'layers.2.blocks.4.op.conv2d.weight', 'layers.2.blocks.4.op.out_proj.weight', 'layers.2.blocks.4.norm2.weight', 'layers.2.blocks.4.norm2.bias', 'layers.2.blocks.4.mlp.fc1.weight', 'layers.2.blocks.4.mlp.fc1.bias', 'layers.2.blocks.4.mlp.fc2.weight', 'layers.2.blocks.4.mlp.fc2.bias'])
Backbone_VSSM load_pretrained

Hello, where is the code for the "Spatio-temporal relationship learning methods of ChangeMamba" section? I didn't find it. Thanks.

Hello, when I only run the BDA task, in the xBD dataset I downloaded, the train folder only contains the images, targets and labels three files, and then I try to change it to the format in the make_data_loader.py of your paper, but it is always wrong, how did you change this part of the code, is there any changed code to provide it, thank you very much!

当进入imutils.py脚本中的random_crop_mcd时，似乎变化前和变化后的标签维度是(512, 512, 3)，而在这个方法中将其维度视作(512, 512)，这样就会导致维度对应不上，请问应该如何修改呢

Thank you very much for your efforts. I have observed that while running train_MambaSCD.py, the dataset size during the validation stage was not cropped. I would like to inquire about the data processing procedures employed during the validation phase.

#!/bin/bash

python /home/ubuntu20/Desktop/MambaCD1/MambaCD/script/train_MambaBCD.py
--dataset 'LEVIR-CD+'
--batch_size 16
--crop_size 256
--max_iters 320000
--model_type MambaBCD_Small
--model_param_path '/home/ubuntu20/Desktop/MambaCD1/MambaCD/changedetection/saved_models'
--train_dataset_path '/home/ubuntu20/Desktop/train'
--train_data_list_path '/home/ubuntu20/Desktop/train/train.txt'
--test_dataset_path '/home/ubuntu20/Desktop/train'
--test_data_list_path '/home/ubuntu20/Desktop/train/train.txt'
--cfg '/home/ubuntu20/Desktop/MambaCD1/MambaCD/changedetection/configs/vssm1/vssm_small_224.yaml'
--pretrained_weight_path '/home/ubuntu20/Desktop/MambaCD1/MambaCD/pretrained_weight/vssm_small_0229_ckpt_epoch_222.pth'

This is my main file

(mamba) ubuntu20@ubuntu20-System-Product-Name:/Desktop/MambaCD1/MambaCD/changedetection$ sh main.sh
=> merge config from /home/ubuntu20/Desktop/MambaCD1/MambaCD/changedetection/configs/vssm1/vssm_small_224.yaml
Successfully load ckpt /home/ubuntu20/Desktop/MambaCD1/MambaCD/pretrained_weight/vssm_small_0229_ckpt_epoch_222.pth
_IncompatibleKeys(missing_keys=['outnorm0.weight', 'outnorm0.bias', 'outnorm1.weight', 'outnorm1.bias', 'outnorm2.weight', 'outnorm2.bias', 'outnorm3.weight', 'outnorm3.bias'], unexpected_keys=['classifier.norm.weight', 'classifier.norm.bias', 'classifier.head.weight', 'classifier.head.bias'])
0%| | 0/20000 [00:16<?, ?it/s]
Traceback (most recent call last):
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/changedetection/script/train_MambaBCD.py", line 207, in
main()
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/changedetection/script/train_MambaBCD.py", line 203, in main
trainer.training()
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/changedetection/script/train_MambaBCD.py", line 104, in training
output_1 = self.deep_model(pre_change_imgs, post_change_imgs)
File "/media/ubuntu20/EXOS_1/anaconda3/envs/mamba/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/changedetection/models/MambaBCD.py", line 67, in forward
pre_features = self.encoder(pre_data)
File "/media/ubuntu20/EXOS_1/anaconda3/envs/mamba/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/changedetection/models/Mamba_backbone.py", line 50, in forward
o, x = layer_forward(layer, x) # (B, H, W, C)
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/changedetection/models/Mamba_backbone.py", line 43, in layer_forward
x = l.blocks(x)
File "/media/ubuntu20/EXOS_1/anaconda3/envs/mamba/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/media/ubuntu20/EXOS_1/anaconda3/envs/mamba/lib/python3.8/site-packages/torch/nn/modules/container.py", line 139, in forward
input = module(input)
File "/media/ubuntu20/EXOS_1/anaconda3/envs/mamba/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/classification/models/vmamba.py", line 1360, in forward
return self._forward(input)
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/classification/models/vmamba.py", line 1348, in _forward
x = input + self.drop_path(self.op(self.norm(input)))
File "/media/ubuntu20/EXOS_1/anaconda3/envs/mamba/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/classification/models/vmamba.py", line 1147, in forwardv2
y = self.forward_core(x)
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/classification/models/vmamba.py", line 1124, in forward_corev2
return cross_selective_scan(
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/classification/models/vmamba.py", line 406, in cross_selective_scan
ys: torch.Tensor = selective_scan(
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/classification/models/vmamba.py", line 372, in selective_scan
return SelectiveScan.apply(u, delta, A, B, C, D, delta_bias, delta_softplus, nrows, backnrows, ssoflex)
File "/media/ubuntu20/EXOS_1/anaconda3/envs/mamba/lib/python3.8/site-packages/torch/cuda/amp/autocast_mode.py", line 110, in decorate_fwd
return fwd(*args, **kwargs)
File "/home/ubuntu20/Desktop/MambaCD1/MambaCD/classification/models/vmamba.py", line 299, in forward
out, x, *rest = selective_scan_cuda_oflex.fwd(u, delta, A, B, C, D, delta_bias, delta_softplus, 1, oflex)
NameError: name 'selective_scan_cuda_oflex' is not defined
(mamba) ubuntu20@ubuntu20-System-Product-Name:/Desktop/MambaCD1/MambaCD/changedetection$

Training Dataset: SECOND
Environment:

• Python: 3.12.3
• torch: 2.2.2+cu118

the first thing was GT_CD is no correct, but i found GT_CD data is not used to calculate eval score. my evaluation result is here

我想问一下dataset数据集的时候，SCD数据集SECOND里面的GT_CD源数据集没有啊。是要自己生成吗？