[Bug] No module named 'mmengine.models'

Prerequisite

[X] I have searched Issues and Discussions but cannot get the expected help.
[X] The bug has not been fixed in the latest version(https://github.com/open-mmlab/mmengine).

Environment

OrderedDict([('sys.platform', 'win32'), ('Python', '3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]'), ('CUDA available', True), ('MUSA available', False), ('numpy_random_seed', 2147483648), ('GPU 0', 'NVIDIA GeForce RTX 3060 Laptop GPU'), ('CUDA_HOME', 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.1'), ('NVCC', 'Cuda compilation tools, release 11.1, V11.1.105'), ('MSVC', 'Microsoft (R) C/C++ Optimizing Compiler Version 19.35.32216.1 for x64'), ('GCC', 'n/a'), ('PyTorch', '1.11.0+cu113'), ('PyTorch compiling details', 'PyTorch built with:\n - C++ Version: 199711\n - MSVC 192829337\n - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 2019\n - LAPACK is enabled (usually provided by MKL)\n - CPU capability usage: AVX512\n - CUDA Runtime 11.3\n - 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\n - CuDNN 8.2\n - Magma 2.5.4\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=C:/actions-runner/_work/pytorch/pytorch/builder/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -DUSE_PTHREADPOOL -openmp:experimental -IC:/actions-runner/_work/pytorch/pytorch/builder/windows/mkl/include -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, 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=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF, \n'), ('TorchVision', '0.12.0+cu113'), ('OpenCV', '4.6.0'), ('MMEngine', '0.10.3')])

Reproduces the problem - code sample

from mmengine import Registry
import torch.nn as nn
# scope 表示注册器的作用域，如果不设置，默认为包名，例如在 mmdetection 中，它的 scope 为 mmdet
# locations 表示注册在此注册器的模块所存放的位置，注册器会根据预先定义的位置在构建模块时自动 import
ACTIVATION = Registry('activation', scope='mmengine', locations=['mmengine.models.activations'])

#import torch.nn as nn

# 使用注册器管理模块
@ACTIVATION.register_module()
class Sigmoid(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        print('call Sigmoid.forward')
        return x

@ACTIVATION.register_module()
class ReLU(nn.Module):
    def __init__(self, inplace=False):
        super().__init__()

    def forward(self, x):
        print('call ReLU.forward')
        return x

@ACTIVATION.register_module()
class Softmax(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        print('call Softmax.forward')
        return x

print(ACTIVATION.module_dict)

import torch

input = torch.randn(2)

act_cfg = dict(type='Sigmoid')
activation = ACTIVATION.build(act_cfg)
output = activation(input)
# call Sigmoid.forward
print(output)

Reproduces the problem - command or script

from mmengine import Registry
import torch.nn as nn
# scope 表示注册器的作用域，如果不设置，默认为包名，例如在 mmdetection 中，它的 scope 为 mmdet
# locations 表示注册在此注册器的模块所存放的位置，注册器会根据预先定义的位置在构建模块时自动 import
ACTIVATION = Registry('activation', scope='mmengine', locations=['mmengine.models.activations'])

#import torch.nn as nn

# 使用注册器管理模块
@ACTIVATION.register_module()
class Sigmoid(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        print('call Sigmoid.forward')
        return x

@ACTIVATION.register_module()
class ReLU(nn.Module):
    def __init__(self, inplace=False):
        super().__init__()

    def forward(self, x):
        print('call ReLU.forward')
        return x

@ACTIVATION.register_module()
class Softmax(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        print('call Softmax.forward')
        return x

print(ACTIVATION.module_dict)

import torch

input = torch.randn(2)

act_cfg = dict(type='Sigmoid')
activation = ACTIVATION.build(act_cfg)
output = activation(input)
# call Sigmoid.forward
print(output)

Reproduces the problem - error message

from mmengine import Registry
import torch.nn as nn
# scope 表示注册器的作用域，如果不设置，默认为包名，例如在 mmdetection 中，它的 scope 为 mmdet
# locations 表示注册在此注册器的模块所存放的位置，注册器会根据预先定义的位置在构建模块时自动 import
ACTIVATION = Registry('activation', scope='mmengine', locations=['mmengine.models.activations'])

#import torch.nn as nn

# 使用注册器管理模块
@ACTIVATION.register_module()
class Sigmoid(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        print('call Sigmoid.forward')
        return x

@ACTIVATION.register_module()
class ReLU(nn.Module):
    def __init__(self, inplace=False):
        super().__init__()

    def forward(self, x):
        print('call ReLU.forward')
        return x

@ACTIVATION.register_module()
class Softmax(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        print('call Softmax.forward')
        return x

print(ACTIVATION.module_dict)

import torch

input = torch.randn(2)

act_cfg = dict(type='Sigmoid')
activation = ACTIVATION.build(act_cfg)
output = activation(input)
# call Sigmoid.forward
print(output)

Additional information

give some official example for ACTIVATION = Registry('activation', scope='mmengine', locations=['mmengine.models.activations']) when locatoions is set

open-mmlab / mmengine