diff --git a/.circleci/test.yml b/.circleci/test.yml index 2832ea0a1..ace3c5950 100644 --- a/.circleci/test.yml +++ b/.circleci/test.yml @@ -146,7 +146,7 @@ workflows: name: minimum_version_cpu torch: 1.6.0 torchvision: 0.7.0 - python: 3.6.9 # The lowest python 3.6.x version available on CircleCI images + python: 3.7.4 requires: - lint - build_cpu: diff --git a/.github/workflows/merge_stage_test.yml b/.github/workflows/merge_stage_test.yml index 4e3bcdf36..397f4637c 100644 --- a/.github/workflows/merge_stage_test.yml +++ b/.github/workflows/merge_stage_test.yml @@ -22,7 +22,7 @@ jobs: runs-on: ubuntu-18.04 strategy: matrix: - python-version: [3.6, 3.8, 3.9] + python-version: [3.8, 3.9] torch: [1.8.1] include: - torch: 1.8.1 diff --git a/demo/mmselfsup_colab_tutorial.ipynb b/demo/mmselfsup_colab_tutorial.ipynb index d121a5251..a359b1fda 100644 --- a/demo/mmselfsup_colab_tutorial.ipynb +++ b/demo/mmselfsup_colab_tutorial.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"markdown","id":"856bf300","metadata":{"id":"856bf300"},"source":["\"Open"]},{"cell_type":"markdown","id":"a2505b44","metadata":{"id":"a2505b44"},"source":["# MMSelfSup Tutorial\n","In this tutorial, we will introduce the following content:\n","\n","- How to install MMSelfSup\n","- How to train algorithms in MMSelfSup\n","- How to train downstream tasks\n","\n","If you have any other questions, welcome to report issues."]},{"cell_type":"markdown","id":"2a78b9a6","metadata":{"id":"2a78b9a6"},"source":["## How to install MMSelfSup\n","\n","Before using MMSelfSup, we need to prepare the environment with the following steps:\n","\n","1. Install Python, CUDA, C/C++ compiler and git\n","2. Install PyTorch (CUDA version)\n","3. Install dependent codebase (mmengine, mmcv, mmcls)\n","4. Clone mmselfsup source code from GitHub and install it\n","\n","Because this tutorial is on Google Colab and all necessary packages have been installed, we can skip the first two steps."]},{"cell_type":"code","execution_count":1,"id":"4edc9682","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1662048513424,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"4edc9682","outputId":"b9f96ef5-96dc-4a26-850c-b42d80586e38"},"outputs":[{"name":"stdout","output_type":"stream","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Mon_Oct_12_20:09:46_PDT_2020\n","Cuda compilation tools, release 11.1, V11.1.105\n","Build cuda_11.1.TC455_06.29190527_0\n"]}],"source":["# Check nvcc version\n","!nvcc -V"]},{"cell_type":"code","execution_count":2,"id":"f6c86477","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1662048513425,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"f6c86477","outputId":"4e73eded-7146-44a9-c90b-b4fb4f7cb055"},"outputs":[{"name":"stdout","output_type":"stream","text":["gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"source":["# Check GCC version\n","!gcc --version"]},{"cell_type":"code","execution_count":3,"id":"4d45e19e","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2046,"status":"ok","timestamp":1662048515465,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"4d45e19e","outputId":"049a158c-9471-4631-a43a-f24180bf55da"},"outputs":[{"name":"stdout","output_type":"stream","text":["1.12.1+cu113\n","True\n"]}],"source":["# Check PyTorch installation\n","import torch, torchvision\n","print(torch.__version__)\n","print(torch.cuda.is_available())"]},{"cell_type":"markdown","id":"d8b2afc9","metadata":{"id":"d8b2afc9"},"source":["### Install MMEngine and MMCV\n","\n","MMCV is the basic package of all OpenMMLab packages. We have pre-built wheels on Linux, so we can download and install them directly.\n","\n","Please pay attention to PyTorch and CUDA versions to match the wheel.\n","\n","In the above steps, we have checked the version of PyTorch and CUDA, and they are 1.10.2 and 11.3 respectively, so we need to choose the corresponding wheel.\n","\n","In addition, we can also install the full version of mmcv (mmcv-full). It includes full features and various CUDA ops out of the box, but needs a longer time to build."]},{"cell_type":"markdown","id":"12c97bbd","metadata":{"id":"12c97bbd"},"source":["MIM is recommended: https://github.com/open-mmlab/mim"]},{"cell_type":"code","execution_count":4,"id":"ac1462fd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26271,"status":"ok","timestamp":1662048541730,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"ac1462fd","outputId":"9da88069-d3ac-4d0a-959b-9ffe9d60d3e8"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting openmim\n"," Downloading openmim-0.3.0-py2.py3-none-any.whl (49 kB)\n","\u001b[K |████████████████████████████████| 49 kB 3.1 MB/s \n","\u001b[?25hRequirement already satisfied: Click in /usr/local/lib/python3.7/dist-packages (from openmim) 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commonmark, rich, model-index, colorama, openmim\n","Successfully installed colorama-0.4.5 commonmark-0.9.1 model-index-0.1.11 openmim-0.3.0 ordered-set-4.1.0 rich-12.5.1\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: openmim in /usr/local/lib/python3.7/dist-packages (0.3.0)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from openmim) (1.3.5)\n","Requirement already satisfied: pip>=19.3 in /usr/local/lib/python3.7/dist-packages (from openmim) (21.1.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from openmim) (2.23.0)\n","Requirement already satisfied: colorama in /usr/local/lib/python3.7/dist-packages (from openmim) (0.4.5)\n","Requirement already satisfied: rich in /usr/local/lib/python3.7/dist-packages (from openmim) (12.5.1)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages 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requests->openmim) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->openmim) (1.24.3)\n","Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich->openmim) (2.6.1)\n","Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from rich->openmim) (0.9.1)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.12.0/index.html\n","Collecting mmengine==0.1.0\n"," Downloading mmengine-0.1.0-py3-none-any.whl (280 kB)\n","\u001b[K |████████████████████████████████| 280 kB 5.2 MB/s \n","\u001b[?25hCollecting mmcv>=2.0.0rc1\n"," Downloading https://download.openmmlab.com/mmcv/dist/cu113/torch1.12.0/mmcv-2.0.0rc1-cp37-cp37m-manylinux1_x86_64.whl (40.4 MB)\n","\u001b[K 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(21.3)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv>=2.0.0rc1) (7.1.2)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmengine==0.1.0) (2.8.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmengine==0.1.0) (0.11.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmengine==0.1.0) (1.4.4)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmengine==0.1.0) (3.0.9)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->mmengine==0.1.0) (4.1.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->mmengine==0.1.0) (1.15.0)\n","Installing collected packages: yapf, addict, mmengine, mmcv\n","Successfully installed addict-2.4.0 mmcv-2.0.0rc1 mmengine-0.1.0 yapf-0.32.0\n"]}],"source":["!pip3 install openmim\n","!pip install -U openmim\n","!mim install 'mmengine==0.1.0' 'mmcv>=2.0.0rc1'"]},{"cell_type":"code","execution_count":5,"id":"VevRvwZdl8U-","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2455,"status":"ok","timestamp":1662048544177,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"VevRvwZdl8U-","outputId":"11e8964d-83b5-4392-84f4-504d59293fe5"},"outputs":[{"name":"stdout","output_type":"stream","text":["OrderedDict([('sys.platform', 'linux'), ('Python', '3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]'), ('CUDA available', True), ('numpy_random_seed', 2147483648), ('GPU 0', 'Tesla T4'), ('CUDA_HOME', '/usr/local/cuda'), ('NVCC', 'Cuda compilation tools, release 11.1, V11.1.105'), ('GCC', 'x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0'), ('PyTorch', '1.12.1+cu113'), ('PyTorch compiling details', 'PyTorch built with:\\n - GCC 9.3\\n - C++ Version: 201402\\n - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\\n - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\\n - LAPACK is enabled (usually provided by MKL)\\n - NNPACK is enabled\\n - CPU capability usage: AVX2\\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_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\\n - CuDNN 8.3.2 (built against CUDA 11.5)\\n - Magma 2.5.2\\n - 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= -fabi-version=11 -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=1.12.1, 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, \\n'), ('TorchVision', '0.13.1+cu113'), ('OpenCV', '4.6.0'), ('MMEngine', '0.1.0')])\n","2.0.0rc1\n"]}],"source":["# check mmengine install\n","!python -c 'from mmengine.utils.dl_utils import collect_env;print(collect_env())'\n","\n","# check mmcv install\n","import mmcv\n","print(mmcv.__version__)"]},{"cell_type":"markdown","id":"a0fb23e1","metadata":{"id":"a0fb23e1"},"source":["Besides, you can also use pip to install the packages, but you are supposed to check the pytorch and cuda version manually. The example command is provided below, but you need to modify it according to your PyTorch and CUDA version."]},{"cell_type":"markdown","id":"de19e9ee","metadata":{"id":"de19e9ee"},"source":["### Clone and install mmselfsup"]},{"cell_type":"code","execution_count":6,"id":"ee54ef1a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6770,"status":"ok","timestamp":1662048550930,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"ee54ef1a","outputId":"b91f3fea-6821-4017-e030-5745c2e1ceb4"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content\n","Cloning into 'mmselfsup'...\n","remote: Enumerating objects: 6421, done.\u001b[K\n","remote: Counting objects: 100% (279/279), done.\u001b[K\n","remote: Compressing objects: 100% (192/192), done.\u001b[K\n","remote: Total 6421 (delta 123), reused 194 (delta 86), pack-reused 6142\u001b[K\n","Receiving objects: 100% (6421/6421), 2.75 MiB | 12.05 MiB/s, done.\n","Resolving deltas: 100% (4028/4028), done.\n","/content/mmselfsup\n","Branch 'dev-1.x' set up to track remote branch 'dev-1.x' from 'origin'.\n","Switched to a new branch 'dev-1.x'\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Obtaining file:///content/mmselfsup\n","Requirement already satisfied: attrs in /usr/local/lib/python3.7/dist-packages (from mmselfsup==1.0.0rc1) (22.1.0)\n","Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from mmselfsup==1.0.0rc1) (0.16.0)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmselfsup==1.0.0rc1) (3.2.2)\n","Collecting mmcls>=1.0.0rc0\n"," Downloading mmcls-1.0.0rc0-py2.py3-none-any.whl (557 kB)\n","\u001b[K |████████████████████████████████| 557 kB 5.1 MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmselfsup==1.0.0rc1) (1.21.6)\n","Requirement 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oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmselfsup==1.0.0rc1) (3.2.0)\n","Installing collected packages: mmcls, mmselfsup\n"," Running setup.py develop for mmselfsup\n","Successfully installed mmcls-1.0.0rc0 mmselfsup-1.0.0rc1\n"]}],"source":["%cd /content\n","# Clone MMSelfSup repository\n","!git clone https://github.com/open-mmlab/mmselfsup.git\n","%cd mmselfsup/\n","\n","# Install MMSelfSup from source\n","!git checkout dev-1.x\n","!pip install -e . "]},{"cell_type":"code","execution_count":7,"id":"artAFjMutvBt","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1662048550931,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"artAFjMutvBt","outputId":"92d895d2-0270-4baa-e9d1-fae82748e827"},"outputs":[{"name":"stdout","output_type":"stream","text":["1.0.0rc0\n"]}],"source":["# Check MMClassification installation\n","import mmcls\n","print(mmcls.__version__)"]},{"cell_type":"code","execution_count":8,"id":"OpuyBEm9tgyR","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1662048550931,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"OpuyBEm9tgyR","outputId":"bd1786fb-1908-456a-efbc-c0732024bb02"},"outputs":[{"name":"stdout","output_type":"stream","text":["1.0.0rc1\n"]}],"source":["# Check MMSelfSup installation\n","import mmselfsup\n","print(mmselfsup.__version__)"]},{"cell_type":"markdown","id":"8cc33efb","metadata":{"id":"8cc33efb"},"source":["## Example to start a self-supervised task\n","\n","Before you start training, you need to prepare your dataset, please check [prepare_data.md](https://github.com/open-mmlab/mmselfsup/blob/master/docs/en/prepare_data.md) file carefully.\n","\n","**Note**: As we follow the original algorithms to implement our codes, so many algorithms are supposed to run on distributed mode, they are not supported on 1 GPU training officially. You can check it [here](https://github.com/open-mmlab/mmselfsup/blob/master/tools/train.py#L120).\n"]},{"cell_type":"markdown","id":"cece4760","metadata":{"id":"cece4760"},"source":["Here we provide a example and download a small dataset to display the demo."]},{"cell_type":"markdown","id":"AVJ7zKLyahBn","metadata":{"id":"AVJ7zKLyahBn"},"source":["### Prerapre data"]},{"cell_type":"code","execution_count":9,"id":"541169d6","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19423,"status":"ok","timestamp":1662048570348,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"541169d6","outputId":"3aad6344-4a8a-4a19-9858-cc1283608486"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2022-09-01 16:09:11-- https://download.openmmlab.com/mmselfsup/data/imagenet_examples.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.89.140.71\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.89.140.71|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 155496559 (148M) [application/zip]\n","Saving to: ‘imagenet_examples.zip’\n","\n","imagenet_examples.z 100%[===================>] 148.29M 8.65MB/s in 17s \n","\n","2022-09-01 16:09:29 (8.63 MB/s) - ‘imagenet_examples.zip’ saved [155496559/155496559]\n","\n"]}],"source":["!mkdir data\n","!wget https://download.openmmlab.com/mmselfsup/data/imagenet_examples.zip\n","!unzip -q imagenet_examples.zip -d ./data/"]},{"cell_type":"code","execution_count":10,"id":"2fd014a0","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6244,"status":"ok","timestamp":1662048576572,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"2fd014a0","outputId":"1d175f00-1fda-49a2-ea41-84df3d8b60d0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Reading package lists... Done\n","Building dependency tree \n","Reading state information... Done\n","The following package was automatically installed and is no longer required:\n"," libnvidia-common-460\n","Use 'apt autoremove' to remove it.\n","The following NEW packages will be installed:\n"," tree\n","0 upgraded, 1 newly installed, 0 to remove and 20 not upgraded.\n","Need to get 40.7 kB of archives.\n","After this operation, 105 kB of additional disk space will be used.\n","Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tree amd64 1.7.0-5 [40.7 kB]\n","Fetched 40.7 kB in 0s (113 kB/s)\n","Selecting previously unselected package tree.\n","(Reading database ... 155676 files and directories currently installed.)\n","Preparing to unpack .../tree_1.7.0-5_amd64.deb ...\n","Unpacking tree (1.7.0-5) ...\n","Setting up tree (1.7.0-5) ...\n","Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n","./data\n","└── imagenet\n"," ├── meta\n"," └── train\n"," └── n01440764\n","\n","4 directories\n"]}],"source":["# Check data directory\n","!apt-get install tree\n","!tree -d ./data"]},{"cell_type":"markdown","id":"8cfa1b7b","metadata":{"id":"8cfa1b7b"},"source":["### Create a new config file\n","To reuse the common parts of different config files, we support inheriting multiple base config files. For example, to train `relative_loc` algorithm, the new config file can create the model's basic structure by inheriting `configs/_base_/models/relative-loc.py`."]},{"cell_type":"code","execution_count":11,"id":"d2bbc2de","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1662048576573,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"d2bbc2de","outputId":"c035fe91-8d56-4aba-9b4e-02daabac6f21"},"outputs":[{"name":"stdout","output_type":"stream","text":["Writing configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab.py\n"]}],"source":["%%writefile configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab.py\n","_base_ = [\n"," '../_base_/models/relative-loc.py',\n"," '../_base_/datasets/imagenet_relative-loc.py',\n"," '../_base_/schedules/sgd_steplr-200e_in1k.py',\n"," '../_base_/default_runtime.py',\n","]\n","\n","default_hooks = dict(logger=dict(type='LoggerHook', interval=10))\n","\n","# optimizer wrapper\n","optimizer = dict(type='SGD', lr=0.2, momentum=0.9, weight_decay=1e-4)\n","optim_wrapper = dict(\n"," type='OptimWrapper',\n"," optimizer=optimizer,\n"," paramwise_cfg=dict(custom_keys={\n"," 'neck': dict(decay_mult=5.0),\n"," 'head': dict(decay_mult=5.0)\n"," }))\n","\n","# learning rate scheduler\n","param_scheduler = [dict(type='MultiStepLR', by_epoch=True, milestones=[1, 2])]\n","\n","# runtime settings\n","# pre-train for 70 epochs\n","train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=70)\n","# the max_keep_ckpts controls the max number of ckpt file in your work_dirs\n","# if it is 3, when CheckpointHook (in mmcv) saves the 4th ckpt\n","# it will remove the oldest one to keep the number of total ckpts as 3\n","default_hooks = dict(\n"," checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))\n"]},{"cell_type":"markdown","id":"4bc7018d","metadata":{"id":"4bc7018d"},"source":["### Read the config file and modify config\n","\n","We can modify the loaded config file."]},{"cell_type":"code","execution_count":12,"id":"b37379bc","metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1662048576573,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"b37379bc"},"outputs":[],"source":["# Load the basic config file\n","from mmengine.config import Config\n","cfg = Config.fromfile('configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab.py')\n","\n","# Specify the data settings\n","cfg.train_dataloader.batch_size = 8\n","cfg.train_dataloader.num_workers = 2\n","\n","# Specify the optimizer\n","cfg.optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\n","cfg.optim_wrapper.clip_grad = None\n","\n","# Specify the learning rate scheduler\n","cfg.param_scheduler = [dict(type='MultiStepLR', by_epoch=True, milestones=[1, 2])]\n","\n","# Modify runtime setting\n","cfg.train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2)\n","\n","# Specify the work directory\n","cfg.work_dir = './work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab'\n","\n","# Output logs for every 10 iterations\n","cfg.default_hooks.logger.interval = 10\n","# Set the random seed and enable the deterministic option of cuDNN\n","# to keep the results' reproducible.\n","cfg.randomness = dict(seed=0, deterministic=True)"]},{"cell_type":"markdown","id":"b8be1bfb","metadata":{"id":"b8be1bfb"},"source":["### Start self-supervised pre-train task"]},{"cell_type":"code","execution_count":13,"id":"ff82997c","metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1662048576574,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"ff82997c"},"outputs":[],"source":["import os\n","import torch\n","\n","if torch.cuda.is_available():\n"," os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':16:8'"]},{"cell_type":"code","execution_count":14,"id":"369c3734","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":92165,"status":"ok","timestamp":1662048668732,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"369c3734","outputId":"d8d1c353-977f-4558-add2-087ebc972bc0"},"outputs":[{"name":"stdout","output_type":"stream","text":["09/01 16:09:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","------------------------------------------------------------\n","System environment:\n"," sys.platform: linux\n"," Python: 3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]\n"," CUDA available: True\n"," numpy_random_seed: 0\n"," GPU 0: Tesla T4\n"," CUDA_HOME: /usr/local/cuda\n"," NVCC: Cuda compilation tools, release 11.1, V11.1.105\n"," GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n"," PyTorch: 1.12.1+cu113\n"," PyTorch compiling details: PyTorch built with:\n"," - GCC 9.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - LAPACK is enabled (usually provided by MKL)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\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_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\n"," - CuDNN 8.3.2 (built against CUDA 11.5)\n"," - Magma 2.5.2\n"," - 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= -fabi-version=11 -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=1.12.1, 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, \n","\n"," TorchVision: 0.13.1+cu113\n"," OpenCV: 4.6.0\n"," MMEngine: 0.1.0\n","\n","Runtime environment:\n"," cudnn_benchmark: False\n"," mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n"," dist_cfg: {'backend': 'nccl'}\n"," seed: 0\n"," deterministic: True\n"," Distributed launcher: none\n"," Distributed training: False\n"," GPU number: 1\n","------------------------------------------------------------\n","\n","09/01 16:09:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n","model = dict(\n"," type='RelativeLoc',\n"," data_preprocessor=dict(\n"," type='mmselfsup.RelativeLocDataPreprocessor',\n"," mean=[124, 117, 104],\n"," std=[59, 58, 58],\n"," bgr_to_rgb=True),\n"," backbone=dict(\n"," type='ResNet',\n"," depth=50,\n"," in_channels=3,\n"," out_indices=[4],\n"," norm_cfg=dict(type='BN')),\n"," neck=dict(\n"," type='RelativeLocNeck',\n"," in_channels=2048,\n"," out_channels=4096,\n"," with_avg_pool=True),\n"," head=dict(\n"," type='ClsHead',\n"," loss=dict(type='mmcls.CrossEntropyLoss'),\n"," with_avg_pool=False,\n"," in_channels=4096,\n"," num_classes=8,\n"," init_cfg=[\n"," dict(type='Normal', std=0.005, layer='Linear'),\n"," dict(type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm'])\n"," ]))\n","dataset_type = 'mmcls.ImageNet'\n","data_root = 'data/imagenet/'\n","file_client_args = dict(backend='disk')\n","train_pipeline = [\n"," dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n"," dict(type='Resize', scale=292),\n"," dict(type='RandomCrop', size=255),\n"," dict(type='RandomGrayscale', prob=0.66, keep_channels=True),\n"," dict(type='RandomPatchWithLabels'),\n"," dict(\n"," type='PackSelfSupInputs',\n"," pseudo_label_keys=['patch_box', 'patch_label', 'unpatched_img'],\n"," meta_keys=['img_path'])\n","]\n","train_dataloader = dict(\n"," batch_size=8,\n"," num_workers=2,\n"," persistent_workers=True,\n"," sampler=dict(type='DefaultSampler', shuffle=True),\n"," collate_fn=dict(type='default_collate'),\n"," dataset=dict(\n"," type='mmcls.ImageNet',\n"," data_root='data/imagenet/',\n"," ann_file='meta/train.txt',\n"," data_prefix=dict(img_path='train/'),\n"," pipeline=[\n"," dict(\n"," type='LoadImageFromFile',\n"," file_client_args=dict(backend='disk')),\n"," dict(type='Resize', scale=292),\n"," dict(type='RandomCrop', size=255),\n"," dict(type='RandomGrayscale', prob=0.66, keep_channels=True),\n"," dict(type='RandomPatchWithLabels'),\n"," dict(\n"," type='PackSelfSupInputs',\n"," pseudo_label_keys=[\n"," 'patch_box', 'patch_label', 'unpatched_img'\n"," ],\n"," meta_keys=['img_path'])\n"," ]))\n","optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\n","optim_wrapper = dict(\n"," type='OptimWrapper',\n"," optimizer=dict(type='SGD', lr=0.2, weight_decay=0.0001, momentum=0.9),\n"," paramwise_cfg=dict(\n"," custom_keys=dict(neck=dict(decay_mult=5.0), head=dict(\n"," decay_mult=5.0))),\n"," clip_grad=None)\n","param_scheduler = [dict(type='MultiStepLR', by_epoch=True, milestones=[1, 2])]\n","train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2)\n","default_scope = 'mmselfsup'\n","default_hooks = dict(\n"," runtime_info=dict(type='RuntimeInfoHook'),\n"," timer=dict(type='IterTimerHook'),\n"," logger=dict(type='LoggerHook', interval=10),\n"," param_scheduler=dict(type='ParamSchedulerHook'),\n"," checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3),\n"," sampler_seed=dict(type='DistSamplerSeedHook'))\n","env_cfg = dict(\n"," cudnn_benchmark=False,\n"," mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n"," dist_cfg=dict(backend='nccl'))\n","log_processor = dict(\n"," window_size=10,\n"," custom_cfg=[dict(data_src='', method='mean', windows_size='global')])\n","vis_backends = [dict(type='LocalVisBackend')]\n","visualizer = dict(\n"," type='SelfSupVisualizer',\n"," vis_backends=[dict(type='LocalVisBackend')],\n"," name='visualizer')\n","log_level = 'INFO'\n","load_from = None\n","resume = False\n","work_dir = './work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab'\n","randomness = dict(seed=0, deterministic=True)\n","\n","Result has been saved to /content/mmselfsup/work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/modules_statistic_results.json\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.bn1.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.bn1.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn1.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn1.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn2.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn2.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn3.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn3.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.downsample.1.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.downsample.1.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.1.bn1.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- 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backbone.layer4.1.bn1.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer4.1.bn2.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer4.1.bn2.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer4.1.bn3.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer4.1.bn3.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer4.2.bn1.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer4.2.bn1.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer4.2.bn2.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer4.2.bn2.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer4.2.bn3.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer4.2.bn3.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.fc.weight:lr=0.2\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.fc.weight:weight_decay=0.0005\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.fc.weight:decay_mult=5.0\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.fc.bias:lr=0.2\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.fc.bias:weight_decay=0.0005\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.fc.bias:decay_mult=5.0\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.bn.weight:lr=0.2\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.bn.weight:weight_decay=0.0005\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.bn.weight:decay_mult=5.0\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.bn.bias:lr=0.2\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.bn.bias:weight_decay=0.0005\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- neck.bn.bias:decay_mult=5.0\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.weight:lr=0.2\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.weight:weight_decay=0.0005\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.weight:decay_mult=5.0\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.bias:lr=0.2\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.bias:weight_decay=0.0005\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.bias:decay_mult=5.0\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmselfsup/work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab by HardDiskBackend.\n","09/01 16:09:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][10/163] lr: 2.0000e-01 eta: 0:06:02 time: 1.1465 data_time: 0.0440 memory: 1392 loss: 27.9830\n","09/01 16:10:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][20/163] lr: 2.0000e-01 eta: 0:03:28 time: 0.2144 data_time: 0.0218 memory: 1392 loss: 25.6786\n","09/01 16:10:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][30/163] lr: 2.0000e-01 eta: 0:02:34 time: 0.2041 data_time: 0.0192 memory: 1392 loss: 14.5791\n","09/01 16:10:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][40/163] lr: 2.0000e-01 eta: 0:02:06 time: 0.2023 data_time: 0.0192 memory: 1392 loss: 14.2424\n","09/01 16:10:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][50/163] lr: 2.0000e-01 eta: 0:01:48 time: 0.2026 data_time: 0.0202 memory: 1392 loss: 17.9769\n","09/01 16:10:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][60/163] lr: 2.0000e-01 eta: 0:01:36 time: 0.2026 data_time: 0.0196 memory: 1392 loss: 18.9486\n","09/01 16:10:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][70/163] lr: 2.0000e-01 eta: 0:01:26 time: 0.2025 data_time: 0.0200 memory: 1392 loss: 28.0319\n","09/01 16:10:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][80/163] lr: 2.0000e-01 eta: 0:01:19 time: 0.2033 data_time: 0.0198 memory: 1392 loss: 17.6793\n","09/01 16:10:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][90/163] lr: 2.0000e-01 eta: 0:01:12 time: 0.2048 data_time: 0.0195 memory: 1392 loss: 15.4679\n","09/01 16:10:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][100/163] lr: 2.0000e-01 eta: 0:01:07 time: 0.2058 data_time: 0.0206 memory: 1392 loss: 6.8410\n","09/01 16:10:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][110/163] lr: 2.0000e-01 eta: 0:01:02 time: 0.2036 data_time: 0.0203 memory: 1392 loss: 6.3352\n","09/01 16:10:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][120/163] lr: 2.0000e-01 eta: 0:00:58 time: 0.2045 data_time: 0.0200 memory: 1392 loss: 6.0879\n","09/01 16:10:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][130/163] lr: 2.0000e-01 eta: 0:00:54 time: 0.2206 data_time: 0.0241 memory: 1392 loss: 4.7499\n","09/01 16:10:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][140/163] lr: 2.0000e-01 eta: 0:00:50 time: 0.2143 data_time: 0.0199 memory: 1392 loss: 3.4295\n","09/01 16:10:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][150/163] lr: 2.0000e-01 eta: 0:00:47 time: 0.2055 data_time: 0.0199 memory: 1392 loss: 3.2668\n","09/01 16:10:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][160/163] lr: 2.0000e-01 eta: 0:00:43 time: 0.2033 data_time: 0.0188 memory: 1392 loss: 2.7335\n","09/01 16:10:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: relative-loc_resnet50_8xb64-steplr-70e_in1k_colab_20220901_160940\n","09/01 16:10:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 1 epochs\n","09/01 16:10:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][10/163] lr: 2.0000e-02 eta: 0:00:39 time: 0.2302 data_time: 0.0299 memory: 1392 loss: 2.3706\n","09/01 16:10:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][20/163] lr: 2.0000e-02 eta: 0:00:36 time: 0.2050 data_time: 0.0191 memory: 1392 loss: 2.2516\n","09/01 16:10:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][30/163] lr: 2.0000e-02 eta: 0:00:33 time: 0.2100 data_time: 0.0202 memory: 1392 loss: 2.2116\n","09/01 16:10:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][40/163] lr: 2.0000e-02 eta: 0:00:30 time: 0.2073 data_time: 0.0213 memory: 1392 loss: 2.1653\n","09/01 16:10:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][50/163] lr: 2.0000e-02 eta: 0:00:28 time: 0.2103 data_time: 0.0209 memory: 1392 loss: 2.1445\n","09/01 16:10:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][60/163] lr: 2.0000e-02 eta: 0:00:25 time: 0.2064 data_time: 0.0190 memory: 1392 loss: 2.1613\n","09/01 16:10:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][70/163] lr: 2.0000e-02 eta: 0:00:22 time: 0.2084 data_time: 0.0215 memory: 1392 loss: 2.1216\n","09/01 16:10:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][80/163] lr: 2.0000e-02 eta: 0:00:20 time: 0.2060 data_time: 0.0206 memory: 1392 loss: 2.1333\n","09/01 16:10:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][90/163] lr: 2.0000e-02 eta: 0:00:17 time: 0.2072 data_time: 0.0196 memory: 1392 loss: 2.1104\n","09/01 16:10:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][100/163] lr: 2.0000e-02 eta: 0:00:15 time: 0.2073 data_time: 0.0198 memory: 1392 loss: 2.1128\n","09/01 16:10:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][110/163] lr: 2.0000e-02 eta: 0:00:12 time: 0.2056 data_time: 0.0195 memory: 1392 loss: 2.1260\n","09/01 16:10:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][120/163] lr: 2.0000e-02 eta: 0:00:10 time: 0.2072 data_time: 0.0195 memory: 1392 loss: 2.1056\n","09/01 16:11:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][130/163] lr: 2.0000e-02 eta: 0:00:07 time: 0.2100 data_time: 0.0196 memory: 1392 loss: 2.0948\n","09/01 16:11:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][140/163] lr: 2.0000e-02 eta: 0:00:05 time: 0.2067 data_time: 0.0199 memory: 1392 loss: 2.0966\n","09/01 16:11:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][150/163] lr: 2.0000e-02 eta: 0:00:03 time: 0.2082 data_time: 0.0196 memory: 1392 loss: 2.0897\n","09/01 16:11:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][160/163] lr: 2.0000e-02 eta: 0:00:00 time: 0.2043 data_time: 0.0190 memory: 1392 loss: 2.0927\n","09/01 16:11:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: relative-loc_resnet50_8xb64-steplr-70e_in1k_colab_20220901_160940\n","09/01 16:11:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 2 epochs\n"]},{"data":{"text/plain":["RelativeLoc(\n"," (data_preprocessor): RelativeLocDataPreprocessor()\n"," (backbone): ResNet(\n"," (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n"," (layer1): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer2): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (3): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer3): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (3): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (4): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (5): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer4): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," )\n"," init_cfg=[{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n"," (neck): RelativeLocNeck(\n"," (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n"," (fc): Linear(in_features=4096, out_features=4096, bias=True)\n"," (bn): BatchNorm1d(4096, eps=1e-05, momentum=0.003, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (dropout): Dropout(p=0.5, inplace=False)\n"," )\n"," init_cfg=[{'type': 'Normal', 'std': 0.01, 'layer': 'Linear'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n"," (head): ClsHead(\n"," (loss): CrossEntropyLoss()\n"," (fc_cls): Linear(in_features=4096, out_features=8, bias=True)\n"," )\n"," init_cfg=[{'type': 'Normal', 'std': 0.005, 'layer': 'Linear'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n",")"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["from mmengine.config import Config, DictAction\n","from mmengine.runner import Runner\n","\n","from mmselfsup.utils import register_all_modules\n","\n","# register all modules in mmselfsup into the registries\n","# do not init the default scope here because it will be init in the runner\n","register_all_modules(init_default_scope=False)\n","\n","# build the runner from config\n","runner = Runner.from_cfg(cfg)\n","\n","# start training\n","runner.train()"]},{"cell_type":"markdown","id":"a562c2dd","metadata":{"id":"a562c2dd"},"source":["## Example to start a downstream task\n"]},{"cell_type":"markdown","id":"96ea98b2","metadata":{"id":"96ea98b2"},"source":["### Extract backbone weights from pre-train model"]},{"cell_type":"code","execution_count":15,"id":"9fa74770","metadata":{"executionInfo":{"elapsed":2019,"status":"ok","timestamp":1662048670738,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"9fa74770"},"outputs":[],"source":["!python tools/model_converters/extract_backbone_weights.py \\\n"," work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/epoch_2.pth \\\n"," work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth"]},{"cell_type":"markdown","id":"0f137b0e","metadata":{"id":"0f137b0e"},"source":["### Prepare config file\n","\n","Here we create a new config file for demo dataset, actually we provided various config files in directory `configs/benchmarks`."]},{"cell_type":"code","execution_count":16,"id":"65764022","metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1662048670739,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"65764022"},"outputs":[],"source":["# Load the basic config file\n","from mmengine.config import Config\n","benchmark_cfg = Config.fromfile('configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py')\n","\n","# Modify the model\n","checkpoint_file = 'work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth'\n","# Or directly using pre-train model provided by us\n","# checkpoint_file = 'https://download.openmmlab.com/mmselfsup/moco/mocov2_resnet50_8xb32-coslr-200e_in1k_20220225-89e03af4.pth'\n","\n","benchmark_cfg.model.backbone.frozen_stages=4\n","benchmark_cfg.model.backbone.init_cfg = dict(type='Pretrained', checkpoint=checkpoint_file)\n","\n","# As the imagenet_examples dataset folder doesn't have val dataset\n","# Modify the path and meta files of validation dataset\n","benchmark_cfg.val_dataloader.dataset.data_prefix = 'train'\n","benchmark_cfg.val_dataloader.dataset.ann_file = 'meta/train.txt'\n","\n","# Specify the learning rate scheduler\n","benchmark_cfg.param_scheduler = [dict(type='MultiStepLR', by_epoch=True, milestones=[1, 2])]\n","\n","# Output logs for every 10 iterations\n","benchmark_cfg.default_hooks.logger.interval = 10\n","\n","# Modify runtime settings for demo\n","benchmark_cfg.train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2)\n","\n","\n","# Specify the work directory\n","benchmark_cfg.work_dir = './work_dirs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k_colab'\n","\n","# Set the random seed and enable the deterministic option of cuDNN\n","# to keep the results' reproducible.\n","benchmark_cfg.randomness = dict(seed=0, deterministic=True)"]},{"cell_type":"markdown","id":"636e8865","metadata":{"id":"636e8865"},"source":["### Load extracted backbone weights to start a downstream task"]},{"cell_type":"code","execution_count":17,"id":"f9c51d5c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":62032,"status":"ok","timestamp":1662048732765,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"f9c51d5c","outputId":"7c83b7ac-20f3-4944-bedf-53fd2bf83890"},"outputs":[{"name":"stdout","output_type":"stream","text":["09/01 16:11:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","------------------------------------------------------------\n","System environment:\n"," sys.platform: linux\n"," Python: 3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]\n"," CUDA available: True\n"," numpy_random_seed: 0\n"," GPU 0: Tesla T4\n"," CUDA_HOME: /usr/local/cuda\n"," NVCC: Cuda compilation tools, release 11.1, V11.1.105\n"," GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n"," PyTorch: 1.12.1+cu113\n"," PyTorch compiling details: PyTorch built with:\n"," - GCC 9.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - LAPACK is enabled (usually provided by MKL)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\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_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\n"," - CuDNN 8.3.2 (built against CUDA 11.5)\n"," - Magma 2.5.2\n"," - 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= -fabi-version=11 -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=1.12.1, 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, \n","\n"," TorchVision: 0.13.1+cu113\n"," OpenCV: 4.6.0\n"," MMEngine: 0.1.0\n","\n","Runtime environment:\n"," cudnn_benchmark: False\n"," mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n"," dist_cfg: {'backend': 'nccl'}\n"," seed: 0\n"," deterministic: True\n"," Distributed launcher: none\n"," Distributed training: False\n"," GPU number: 1\n","------------------------------------------------------------\n","\n","09/01 16:11:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n","model = dict(\n"," type='ImageClassifier',\n"," data_preprocessor=dict(\n"," mean=[123.675, 116.28, 103.53],\n"," std=[58.395, 57.12, 57.375],\n"," to_rgb=True),\n"," backbone=dict(\n"," type='ResNet',\n"," depth=50,\n"," in_channels=3,\n"," num_stages=4,\n"," norm_cfg=dict(type='BN'),\n"," frozen_stages=4,\n"," init_cfg=dict(\n"," type='Pretrained',\n"," checkpoint=\n"," 'work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth'\n"," )),\n"," neck=dict(type='GlobalAveragePooling'),\n"," head=dict(\n"," type='LinearClsHead',\n"," num_classes=1000,\n"," in_channels=2048,\n"," loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n"," topk=(1, 5)))\n","dataset_type = 'ImageNet'\n","data_root = 'data/imagenet/'\n","file_client_args = dict(backend='disk')\n","train_pipeline = [\n"," dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n"," dict(type='RandomResizedCrop', scale=224, backend='pillow'),\n"," dict(type='RandomFlip', prob=0.5, direction='horizontal'),\n"," dict(type='PackClsInputs')\n","]\n","test_pipeline = [\n"," dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n"," dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),\n"," dict(type='CenterCrop', crop_size=224),\n"," dict(type='PackClsInputs')\n","]\n","train_dataloader = dict(\n"," batch_size=32,\n"," num_workers=4,\n"," dataset=dict(\n"," type='ImageNet',\n"," data_root='data/imagenet',\n"," ann_file='meta/train.txt',\n"," data_prefix='train',\n"," pipeline=[\n"," dict(\n"," type='LoadImageFromFile',\n"," file_client_args=dict(backend='disk')),\n"," dict(type='RandomResizedCrop', scale=224, backend='pillow'),\n"," dict(type='RandomFlip', prob=0.5, direction='horizontal'),\n"," dict(type='PackClsInputs')\n"," ]),\n"," sampler=dict(type='DefaultSampler', shuffle=True),\n"," persistent_workers=True)\n","val_dataloader = dict(\n"," batch_size=32,\n"," num_workers=4,\n"," dataset=dict(\n"," type='ImageNet',\n"," data_root='data/imagenet',\n"," ann_file='meta/train.txt',\n"," data_prefix='train',\n"," pipeline=[\n"," dict(\n"," type='LoadImageFromFile',\n"," file_client_args=dict(backend='disk')),\n"," dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),\n"," dict(type='CenterCrop', crop_size=224),\n"," dict(type='PackClsInputs')\n"," ]),\n"," sampler=dict(type='DefaultSampler', shuffle=False),\n"," persistent_workers=True)\n","val_evaluator = dict(type='mmcls.Accuracy', topk=(1, 5))\n","test_dataloader = dict(\n"," batch_size=32,\n"," num_workers=4,\n"," dataset=dict(\n"," type='ImageNet',\n"," data_root='data/imagenet',\n"," ann_file='meta/val.txt',\n"," data_prefix='val',\n"," pipeline=[\n"," dict(\n"," type='LoadImageFromFile',\n"," file_client_args=dict(backend='disk')),\n"," dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),\n"," dict(type='CenterCrop', crop_size=224),\n"," dict(type='PackClsInputs')\n"," ]),\n"," sampler=dict(type='DefaultSampler', shuffle=False),\n"," persistent_workers=True)\n","test_evaluator = dict(type='mmcls.Accuracy', topk=(1, 5))\n","optimizer = dict(type='SGD', lr=30.0, momentum=0.9, weight_decay=0.0)\n","optim_wrapper = dict(\n"," type='OptimWrapper',\n"," optimizer=dict(type='SGD', lr=30.0, momentum=0.9, weight_decay=0.0))\n","param_scheduler = [dict(type='MultiStepLR', by_epoch=True, milestones=[1, 2])]\n","train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2)\n","val_cfg = dict()\n","test_cfg = dict()\n","default_scope = 'mmcls'\n","custom_imports = dict(\n"," imports=['mmselfsup.models', 'mmselfsup.engine'],\n"," allow_failed_imports=False)\n","default_hooks = dict(\n"," runtime_info=dict(type='RuntimeInfoHook'),\n"," timer=dict(type='IterTimerHook'),\n"," logger=dict(type='LoggerHook', interval=10),\n"," param_scheduler=dict(type='ParamSchedulerHook'),\n"," checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3),\n"," sampler_seed=dict(type='DistSamplerSeedHook'))\n","env_cfg = dict(\n"," cudnn_benchmark=False,\n"," mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n"," dist_cfg=dict(backend='nccl'))\n","log_processor = dict(\n"," window_size=10,\n"," custom_cfg=[dict(data_src='', method='mean', windows_size='global')])\n","vis_backends = [dict(type='LocalVisBackend')]\n","visualizer = dict(\n"," type='ClsVisualizer',\n"," vis_backends=[dict(type='LocalVisBackend')],\n"," name='visualizer')\n","log_level = 'INFO'\n","load_from = None\n","resume = False\n","work_dir = './work_dirs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k_colab'\n","randomness = dict(seed=0, deterministic=True)\n","\n","Result has been saved to /content/mmselfsup/work_dirs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k_colab/modules_statistic_results.json\n","09/01 16:11:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:566: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," cpuset_checked))\n"]},{"name":"stdout","output_type":"stream","text":["09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth\n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - local loads checkpoint from path: work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth\n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.conv1.weight - torch.Size([64, 3, 7, 7]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.bn1.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.bn1.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn1.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn1.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn2.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn2.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn3.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn3.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.downsample.1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.downsample.1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn1.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn1.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn2.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn2.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn3.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn3.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn1.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn1.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn2.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn2.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn3.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn3.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn1.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn1.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn2.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn2.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn3.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn3.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.downsample.1.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.downsample.1.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn1.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn1.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn2.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn2.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn3.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn3.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn1.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn1.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn2.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn2.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn3.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn3.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn1.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn1.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn2.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn2.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn3.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn3.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.downsample.1.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.downsample.1.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn1.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn1.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn2.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn2.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn3.weight - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn3.bias - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.downsample.1.weight - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.downsample.1.bias - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn1.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn1.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn2.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn2.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn3.weight - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn3.bias - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn1.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn1.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn2.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn2.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn3.weight - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn3.bias - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","head.fc.weight - torch.Size([1000, 2048]): \n","NormalInit: mean=0, std=0.01, bias=0 \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","head.fc.bias - torch.Size([1000]): \n","NormalInit: mean=0, std=0.01, bias=0 \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmselfsup/work_dirs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k_colab by HardDiskBackend.\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:566: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," cpuset_checked))\n"]},{"name":"stdout","output_type":"stream","text":["09/01 16:11:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][10/41] lr: 3.0000e+01 eta: 0:00:35 time: 0.4955 data_time: 0.3703 memory: 1392 loss: 1.0352\n","09/01 16:11:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][20/41] lr: 3.0000e+01 eta: 0:00:23 time: 0.2497 data_time: 0.1329 memory: 762 loss: 0.0000\n","09/01 16:11:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][30/41] lr: 3.0000e+01 eta: 0:00:17 time: 0.2528 data_time: 0.1333 memory: 762 loss: 0.0000\n","09/01 16:11:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][40/41] lr: 3.0000e+01 eta: 0:00:12 time: 0.2088 data_time: 0.0967 memory: 762 loss: 0.0000\n","09/01 16:11:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: resnet50_linear-8xb32-steplr-100e_in1k_20220901_161110\n","09/01 16:11:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [1][10/41] eta: 0:00:12 time: 0.4174 data_time: 0.2966 memory: 762 \n","09/01 16:11:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [1][20/41] eta: 0:00:03 time: 0.1886 data_time: 0.0625 memory: 762 \n","09/01 16:11:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [1][30/41] eta: 0:00:02 time: 0.2412 data_time: 0.1149 memory: 762 \n","09/01 16:11:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [1][40/41] eta: 0:00:00 time: 0.2665 data_time: 0.1533 memory: 762 \n","09/01 16:11:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [1][41/41] accuracy/top1: 100.0000 accuracy/top5: 100.0000\n","09/01 16:11:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][10/41] lr: 3.0000e+00 eta: 0:00:09 time: 0.3464 data_time: 0.2278 memory: 762 loss: 0.0000\n","09/01 16:11:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][20/41] lr: 3.0000e+00 eta: 0:00:06 time: 0.2781 data_time: 0.1648 memory: 762 loss: 0.0000\n","09/01 16:11:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][30/41] lr: 3.0000e+00 eta: 0:00:03 time: 0.2383 data_time: 0.1167 memory: 762 loss: 0.0000\n","09/01 16:11:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][40/41] lr: 3.0000e+00 eta: 0:00:00 time: 0.2536 data_time: 0.1397 memory: 762 loss: 0.0000\n","09/01 16:11:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: resnet50_linear-8xb32-steplr-100e_in1k_20220901_161110\n","09/01 16:11:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 2 epochs\n","09/01 16:12:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [2][10/41] eta: 0:00:11 time: 0.3788 data_time: 0.2459 memory: 762 \n","09/01 16:12:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [2][20/41] eta: 0:00:04 time: 0.2033 data_time: 0.0714 memory: 762 \n","09/01 16:12:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [2][30/41] eta: 0:00:02 time: 0.2373 data_time: 0.1092 memory: 762 \n","09/01 16:12:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [2][40/41] eta: 0:00:00 time: 0.2671 data_time: 0.1587 memory: 762 \n","09/01 16:12:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [2][41/41] accuracy/top1: 100.0000 accuracy/top5: 100.0000\n"]},{"data":{"text/plain":["ImageClassifier(\n"," (data_preprocessor): ClsDataPreprocessor()\n"," (backbone): ResNet(\n"," (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n"," (layer1): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer2): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (3): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer3): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (3): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (4): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (5): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer4): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," )\n"," init_cfg={'type': 'Pretrained', 'checkpoint': 'work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth'}\n"," (neck): GlobalAveragePooling(\n"," (gap): AdaptiveAvgPool2d(output_size=(1, 1))\n"," )\n"," (head): LinearClsHead(\n"," (loss_module): CrossEntropyLoss()\n"," (fc): Linear(in_features=2048, out_features=1000, bias=True)\n"," )\n"," init_cfg={'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\n",")"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["from mmengine.config import Config, DictAction\n","from mmengine.runner import Runner\n","\n","from mmselfsup.utils import register_all_modules\n","\n","# register all modules in mmselfsup into the registries\n","# do not init the default scope here because it will be init in the runner\n","register_all_modules(init_default_scope=False)\n","\n","# build the runner from config\n","runner = Runner.from_cfg(benchmark_cfg)\n","\n","# start training\n","runner.train()"]},{"cell_type":"markdown","id":"e1b5b983","metadata":{"id":"e1b5b983"},"source":["**Note: As the demo only has one class in dataset, the model collapsed and the results of loss and acc should be ignored.**"]},{"cell_type":"code","execution_count":17,"id":"4A0WOMeeeZ9E","metadata":{"executionInfo":{"elapsed":33,"status":"ok","timestamp":1662048732766,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"4A0WOMeeeZ9E"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"provenance":[],"toc_visible":true},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.0"},"vscode":{"interpreter":{"hash":"1742319693997e01e5942276ccf039297cd0a474ab9a20f711b7fa536eca5436"}}},"nbformat":4,"nbformat_minor":5} +{"cells":[{"cell_type":"markdown","id":"856bf300","metadata":{"id":"856bf300"},"source":["\"Open"]},{"cell_type":"markdown","id":"a2505b44","metadata":{"id":"a2505b44"},"source":["# MMSelfSup Tutorial\n","In this tutorial, we will introduce the following content:\n","\n","- How to install MMSelfSup\n","- How to train algorithms in MMSelfSup\n","- How to train downstream tasks\n","\n","If you have any other questions, welcome to report issues."]},{"cell_type":"markdown","id":"2a78b9a6","metadata":{"id":"2a78b9a6"},"source":["## How to install MMSelfSup\n","\n","Before using MMSelfSup, we need to prepare the environment with the following steps:\n","\n","1. Install Python, CUDA, C/C++ compiler and git\n","2. Install PyTorch (CUDA version)\n","3. Install dependent codebase (mmengine, mmcv, mmcls)\n","4. Clone mmselfsup source code from GitHub and install it\n","\n","Because this tutorial is on Google Colab and all necessary packages have been installed, we can skip the first two steps."]},{"cell_type":"code","execution_count":1,"id":"4edc9682","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1662048513424,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"4edc9682","outputId":"b9f96ef5-96dc-4a26-850c-b42d80586e38"},"outputs":[{"name":"stdout","output_type":"stream","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Mon_Oct_12_20:09:46_PDT_2020\n","Cuda compilation tools, release 11.1, V11.1.105\n","Build cuda_11.1.TC455_06.29190527_0\n"]}],"source":["# Check nvcc version\n","!nvcc -V"]},{"cell_type":"code","execution_count":2,"id":"f6c86477","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1662048513425,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"f6c86477","outputId":"4e73eded-7146-44a9-c90b-b4fb4f7cb055"},"outputs":[{"name":"stdout","output_type":"stream","text":["gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"source":["# Check GCC version\n","!gcc --version"]},{"cell_type":"code","execution_count":3,"id":"4d45e19e","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2046,"status":"ok","timestamp":1662048515465,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"4d45e19e","outputId":"049a158c-9471-4631-a43a-f24180bf55da"},"outputs":[{"name":"stdout","output_type":"stream","text":["1.12.1+cu113\n","True\n"]}],"source":["# Check PyTorch installation\n","import torch, torchvision\n","print(torch.__version__)\n","print(torch.cuda.is_available())"]},{"cell_type":"markdown","id":"d8b2afc9","metadata":{"id":"d8b2afc9"},"source":["### Install MMEngine and MMCV\n","\n","MMCV is the basic package of all OpenMMLab packages. We have pre-built wheels on Linux, so we can download and install them directly.\n","\n","Please pay attention to PyTorch and CUDA versions to match the wheel.\n","\n","In the above steps, we have checked the version of PyTorch and CUDA, and they are 1.10.2 and 11.3 respectively, so we need to choose the corresponding wheel.\n","\n","In addition, we can also install the full version of mmcv (mmcv-full). It includes full features and various CUDA ops out of the box, but needs a longer time to build."]},{"cell_type":"markdown","id":"12c97bbd","metadata":{"id":"12c97bbd"},"source":["MIM is recommended: https://github.com/open-mmlab/mim"]},{"cell_type":"code","execution_count":4,"id":"ac1462fd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26271,"status":"ok","timestamp":1662048541730,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"ac1462fd","outputId":"9da88069-d3ac-4d0a-959b-9ffe9d60d3e8"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting openmim\n"," Downloading openmim-0.3.0-py2.py3-none-any.whl (49 kB)\n","\u001b[K |████████████████████████████████| 49 kB 3.1 MB/s \n","\u001b[?25hRequirement already satisfied: Click in /usr/local/lib/python3.7/dist-packages (from openmim) (7.1.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from openmim) (2.23.0)\n","Collecting rich\n"," Downloading rich-12.5.1-py3-none-any.whl (235 kB)\n","\u001b[K |████████████████████████████████| 235 kB 9.7 MB/s \n","\u001b[?25hCollecting model-index\n"," Downloading model_index-0.1.11-py3-none-any.whl (34 kB)\n","Requirement already satisfied: pip>=19.3 in /usr/local/lib/python3.7/dist-packages (from openmim) (21.1.3)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from openmim) (0.8.10)\n","Collecting colorama\n"," Downloading colorama-0.4.5-py2.py3-none-any.whl (16 kB)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from openmim) (1.3.5)\n","Collecting ordered-set\n"," Downloading ordered_set-4.1.0-py3-none-any.whl (7.6 kB)\n","Requirement already satisfied: markdown in /usr/local/lib/python3.7/dist-packages (from model-index->openmim) (3.4.1)\n","Requirement 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commonmark, rich, model-index, colorama, openmim\n","Successfully installed colorama-0.4.5 commonmark-0.9.1 model-index-0.1.11 openmim-0.3.0 ordered-set-4.1.0 rich-12.5.1\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: openmim in /usr/local/lib/python3.7/dist-packages (0.3.0)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from openmim) (1.3.5)\n","Requirement already satisfied: pip>=19.3 in /usr/local/lib/python3.7/dist-packages (from openmim) (21.1.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from openmim) (2.23.0)\n","Requirement already satisfied: colorama in /usr/local/lib/python3.7/dist-packages (from openmim) (0.4.5)\n","Requirement already satisfied: rich in /usr/local/lib/python3.7/dist-packages (from openmim) (12.5.1)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages 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|████████████████████████████████| 40.4 MB 12.3 MB/s \n","\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmengine==0.1.0) (6.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmengine==0.1.0) (1.21.6)\n","Collecting yapf\n"," Downloading yapf-0.32.0-py2.py3-none-any.whl (190 kB)\n","\u001b[K |████████████████████████████████| 190 kB 64.2 MB/s \n","\u001b[?25hCollecting addict\n"," Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n","Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmengine==0.1.0) (4.6.0.66)\n","Requirement already satisfied: termcolor in /usr/local/lib/python3.7/dist-packages (from mmengine==0.1.0) (1.1.0)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmengine==0.1.0) (3.2.2)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcv>=2.0.0rc1) (21.3)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv>=2.0.0rc1) (7.1.2)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmengine==0.1.0) (2.8.2)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmengine==0.1.0) (0.11.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmengine==0.1.0) (1.4.4)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmengine==0.1.0) (3.0.9)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->mmengine==0.1.0) (4.1.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->mmengine==0.1.0) (1.15.0)\n","Installing collected packages: yapf, addict, mmengine, mmcv\n","Successfully installed addict-2.4.0 mmcv-2.0.0rc1 mmengine-0.1.0 yapf-0.32.0\n"]}],"source":["!pip3 install openmim\n","!pip install -U openmim\n","!mim install 'mmengine' 'mmcv>=2.0.0rc1'"]},{"cell_type":"code","execution_count":5,"id":"VevRvwZdl8U-","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2455,"status":"ok","timestamp":1662048544177,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"VevRvwZdl8U-","outputId":"11e8964d-83b5-4392-84f4-504d59293fe5"},"outputs":[{"name":"stdout","output_type":"stream","text":["OrderedDict([('sys.platform', 'linux'), ('Python', '3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]'), ('CUDA available', True), ('numpy_random_seed', 2147483648), ('GPU 0', 'Tesla T4'), ('CUDA_HOME', '/usr/local/cuda'), ('NVCC', 'Cuda compilation tools, release 11.1, V11.1.105'), ('GCC', 'x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0'), ('PyTorch', '1.12.1+cu113'), ('PyTorch compiling details', 'PyTorch built with:\\n - GCC 9.3\\n - C++ Version: 201402\\n - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\\n - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\\n - LAPACK is enabled (usually provided by MKL)\\n - NNPACK is enabled\\n - CPU capability usage: AVX2\\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_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\\n - CuDNN 8.3.2 (built against CUDA 11.5)\\n - Magma 2.5.2\\n - 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= -fabi-version=11 -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=1.12.1, 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, \\n'), ('TorchVision', '0.13.1+cu113'), ('OpenCV', '4.6.0'), ('MMEngine', '0.1.0')])\n","2.0.0rc1\n"]}],"source":["# check mmengine install\n","!python -c 'from mmengine.utils.dl_utils import collect_env;print(collect_env())'\n","\n","# check mmcv install\n","import mmcv\n","print(mmcv.__version__)"]},{"cell_type":"markdown","id":"a0fb23e1","metadata":{"id":"a0fb23e1"},"source":["Besides, you can also use pip to install the packages, but you are supposed to check the pytorch and cuda version manually. The example command is provided below, but you need to modify it according to your PyTorch and CUDA version."]},{"cell_type":"markdown","id":"de19e9ee","metadata":{"id":"de19e9ee"},"source":["### Clone and install mmselfsup"]},{"cell_type":"code","execution_count":6,"id":"ee54ef1a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6770,"status":"ok","timestamp":1662048550930,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"ee54ef1a","outputId":"b91f3fea-6821-4017-e030-5745c2e1ceb4"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content\n","Cloning into 'mmselfsup'...\n","remote: Enumerating objects: 6421, done.\u001b[K\n","remote: Counting objects: 100% (279/279), done.\u001b[K\n","remote: Compressing objects: 100% (192/192), done.\u001b[K\n","remote: Total 6421 (delta 123), reused 194 (delta 86), pack-reused 6142\u001b[K\n","Receiving objects: 100% (6421/6421), 2.75 MiB | 12.05 MiB/s, done.\n","Resolving deltas: 100% (4028/4028), done.\n","/content/mmselfsup\n","Branch 'dev-1.x' set up to track remote branch 'dev-1.x' from 'origin'.\n","Switched to a new branch 'dev-1.x'\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Obtaining file:///content/mmselfsup\n","Requirement already satisfied: attrs in /usr/local/lib/python3.7/dist-packages (from mmselfsup==1.0.0rc1) (22.1.0)\n","Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from mmselfsup==1.0.0rc1) (0.16.0)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmselfsup==1.0.0rc1) (3.2.2)\n","Collecting mmcls>=1.0.0rc0\n"," Downloading mmcls-1.0.0rc0-py2.py3-none-any.whl (557 kB)\n","\u001b[K |████████████████████████████████| 557 kB 5.1 MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmselfsup==1.0.0rc1) (1.21.6)\n","Requirement 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oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmselfsup==1.0.0rc1) (3.2.0)\n","Installing collected packages: mmcls, mmselfsup\n"," Running setup.py develop for mmselfsup\n","Successfully installed mmcls-1.0.0rc0 mmselfsup-1.0.0rc1\n"]}],"source":["%cd /content\n","# Clone MMSelfSup repository\n","!git clone https://github.com/open-mmlab/mmselfsup.git\n","%cd mmselfsup/\n","\n","# Install MMSelfSup from source\n","!git checkout dev-1.x\n","!pip install -e . "]},{"cell_type":"code","execution_count":7,"id":"artAFjMutvBt","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1662048550931,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"artAFjMutvBt","outputId":"92d895d2-0270-4baa-e9d1-fae82748e827"},"outputs":[{"name":"stdout","output_type":"stream","text":["1.0.0rc0\n"]}],"source":["# Check MMClassification installation\n","import mmcls\n","print(mmcls.__version__)"]},{"cell_type":"code","execution_count":8,"id":"OpuyBEm9tgyR","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1662048550931,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"OpuyBEm9tgyR","outputId":"bd1786fb-1908-456a-efbc-c0732024bb02"},"outputs":[{"name":"stdout","output_type":"stream","text":["1.0.0rc1\n"]}],"source":["# Check MMSelfSup installation\n","import mmselfsup\n","print(mmselfsup.__version__)"]},{"cell_type":"markdown","id":"8cc33efb","metadata":{"id":"8cc33efb"},"source":["## Example to start a self-supervised task\n","\n","Before you start training, you need to prepare your dataset, please check [prepare_data.md](https://github.com/open-mmlab/mmselfsup/blob/master/docs/en/prepare_data.md) file carefully.\n","\n","**Note**: As we follow the original algorithms to implement our codes, so many algorithms are supposed to run on distributed mode, they are not supported on 1 GPU training officially. You can check it [here](https://github.com/open-mmlab/mmselfsup/blob/master/tools/train.py#L120).\n"]},{"cell_type":"markdown","id":"cece4760","metadata":{"id":"cece4760"},"source":["Here we provide a example and download a small dataset to display the demo."]},{"cell_type":"markdown","id":"AVJ7zKLyahBn","metadata":{"id":"AVJ7zKLyahBn"},"source":["### Prerapre data"]},{"cell_type":"code","execution_count":9,"id":"541169d6","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19423,"status":"ok","timestamp":1662048570348,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"541169d6","outputId":"3aad6344-4a8a-4a19-9858-cc1283608486"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2022-09-01 16:09:11-- https://download.openmmlab.com/mmselfsup/data/imagenet_examples.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.89.140.71\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.89.140.71|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 155496559 (148M) [application/zip]\n","Saving to: ‘imagenet_examples.zip’\n","\n","imagenet_examples.z 100%[===================>] 148.29M 8.65MB/s in 17s \n","\n","2022-09-01 16:09:29 (8.63 MB/s) - ‘imagenet_examples.zip’ saved [155496559/155496559]\n","\n"]}],"source":["!mkdir data\n","!wget https://download.openmmlab.com/mmselfsup/data/imagenet_examples.zip\n","!unzip -q imagenet_examples.zip -d ./data/"]},{"cell_type":"code","execution_count":10,"id":"2fd014a0","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6244,"status":"ok","timestamp":1662048576572,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"2fd014a0","outputId":"1d175f00-1fda-49a2-ea41-84df3d8b60d0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Reading package lists... Done\n","Building dependency tree \n","Reading state information... Done\n","The following package was automatically installed and is no longer required:\n"," libnvidia-common-460\n","Use 'apt autoremove' to remove it.\n","The following NEW packages will be installed:\n"," tree\n","0 upgraded, 1 newly installed, 0 to remove and 20 not upgraded.\n","Need to get 40.7 kB of archives.\n","After this operation, 105 kB of additional disk space will be used.\n","Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tree amd64 1.7.0-5 [40.7 kB]\n","Fetched 40.7 kB in 0s (113 kB/s)\n","Selecting previously unselected package tree.\n","(Reading database ... 155676 files and directories currently installed.)\n","Preparing to unpack .../tree_1.7.0-5_amd64.deb ...\n","Unpacking tree (1.7.0-5) ...\n","Setting up tree (1.7.0-5) ...\n","Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n","./data\n","└── imagenet\n"," ├── meta\n"," └── train\n"," └── n01440764\n","\n","4 directories\n"]}],"source":["# Check data directory\n","!apt-get install tree\n","!tree -d ./data"]},{"cell_type":"markdown","id":"8cfa1b7b","metadata":{"id":"8cfa1b7b"},"source":["### Create a new config file\n","To reuse the common parts of different config files, we support inheriting multiple base config files. For example, to train `relative_loc` algorithm, the new config file can create the model's basic structure by inheriting `configs/_base_/models/relative-loc.py`."]},{"cell_type":"code","execution_count":11,"id":"d2bbc2de","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1662048576573,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"d2bbc2de","outputId":"c035fe91-8d56-4aba-9b4e-02daabac6f21"},"outputs":[{"name":"stdout","output_type":"stream","text":["Writing configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab.py\n"]}],"source":["%%writefile configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab.py\n","_base_ = [\n"," '../_base_/models/relative-loc.py',\n"," '../_base_/datasets/imagenet_relative-loc.py',\n"," '../_base_/schedules/sgd_steplr-200e_in1k.py',\n"," '../_base_/default_runtime.py',\n","]\n","\n","default_hooks = dict(logger=dict(type='LoggerHook', interval=10))\n","\n","# optimizer wrapper\n","optimizer = dict(type='SGD', lr=0.2, momentum=0.9, weight_decay=1e-4)\n","optim_wrapper = dict(\n"," type='OptimWrapper',\n"," optimizer=optimizer,\n"," paramwise_cfg=dict(custom_keys={\n"," 'neck': dict(decay_mult=5.0),\n"," 'head': dict(decay_mult=5.0)\n"," }))\n","\n","# learning rate scheduler\n","param_scheduler = [dict(type='MultiStepLR', by_epoch=True, milestones=[1, 2])]\n","\n","# runtime settings\n","# pre-train for 70 epochs\n","train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=70)\n","# the max_keep_ckpts controls the max number of ckpt file in your work_dirs\n","# if it is 3, when CheckpointHook (in mmcv) saves the 4th ckpt\n","# it will remove the oldest one to keep the number of total ckpts as 3\n","default_hooks = dict(\n"," checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))\n"]},{"cell_type":"markdown","id":"4bc7018d","metadata":{"id":"4bc7018d"},"source":["### Read the config file and modify config\n","\n","We can modify the loaded config file."]},{"cell_type":"code","execution_count":12,"id":"b37379bc","metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1662048576573,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"b37379bc"},"outputs":[],"source":["# Load the basic config file\n","from mmengine.config import Config\n","cfg = Config.fromfile('configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab.py')\n","\n","# Specify the data settings\n","cfg.train_dataloader.batch_size = 8\n","cfg.train_dataloader.num_workers = 2\n","\n","# Specify the optimizer\n","cfg.optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\n","cfg.optim_wrapper.clip_grad = None\n","\n","# Specify the learning rate scheduler\n","cfg.param_scheduler = [dict(type='MultiStepLR', by_epoch=True, milestones=[1, 2])]\n","\n","# Modify runtime setting\n","cfg.train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2)\n","\n","# Specify the work directory\n","cfg.work_dir = './work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab'\n","\n","# Output logs for every 10 iterations\n","cfg.default_hooks.logger.interval = 10\n","# Set the random seed and enable the deterministic option of cuDNN\n","# to keep the results' reproducible.\n","cfg.randomness = dict(seed=0, deterministic=True)"]},{"cell_type":"markdown","id":"b8be1bfb","metadata":{"id":"b8be1bfb"},"source":["### Start self-supervised pre-train task"]},{"cell_type":"code","execution_count":13,"id":"ff82997c","metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1662048576574,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"ff82997c"},"outputs":[],"source":["import os\n","import torch\n","\n","if torch.cuda.is_available():\n"," os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':16:8'"]},{"cell_type":"code","execution_count":14,"id":"369c3734","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":92165,"status":"ok","timestamp":1662048668732,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"369c3734","outputId":"d8d1c353-977f-4558-add2-087ebc972bc0"},"outputs":[{"name":"stdout","output_type":"stream","text":["09/01 16:09:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","------------------------------------------------------------\n","System environment:\n"," sys.platform: linux\n"," Python: 3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]\n"," CUDA available: True\n"," numpy_random_seed: 0\n"," GPU 0: Tesla T4\n"," CUDA_HOME: /usr/local/cuda\n"," NVCC: Cuda compilation tools, release 11.1, V11.1.105\n"," GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n"," PyTorch: 1.12.1+cu113\n"," PyTorch compiling details: PyTorch built with:\n"," - GCC 9.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - LAPACK is enabled (usually provided by MKL)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\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_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\n"," - CuDNN 8.3.2 (built against CUDA 11.5)\n"," - Magma 2.5.2\n"," - 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= -fabi-version=11 -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=1.12.1, 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, \n","\n"," TorchVision: 0.13.1+cu113\n"," OpenCV: 4.6.0\n"," MMEngine: 0.1.0\n","\n","Runtime environment:\n"," cudnn_benchmark: False\n"," mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n"," dist_cfg: {'backend': 'nccl'}\n"," seed: 0\n"," deterministic: True\n"," Distributed launcher: none\n"," Distributed training: False\n"," GPU number: 1\n","------------------------------------------------------------\n","\n","09/01 16:09:40 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n","model = dict(\n"," type='RelativeLoc',\n"," data_preprocessor=dict(\n"," type='mmselfsup.RelativeLocDataPreprocessor',\n"," mean=[124, 117, 104],\n"," std=[59, 58, 58],\n"," bgr_to_rgb=True),\n"," backbone=dict(\n"," type='ResNet',\n"," depth=50,\n"," in_channels=3,\n"," out_indices=[4],\n"," norm_cfg=dict(type='BN')),\n"," neck=dict(\n"," type='RelativeLocNeck',\n"," in_channels=2048,\n"," out_channels=4096,\n"," with_avg_pool=True),\n"," head=dict(\n"," type='ClsHead',\n"," loss=dict(type='mmcls.CrossEntropyLoss'),\n"," with_avg_pool=False,\n"," in_channels=4096,\n"," num_classes=8,\n"," init_cfg=[\n"," dict(type='Normal', std=0.005, layer='Linear'),\n"," dict(type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm'])\n"," ]))\n","dataset_type = 'mmcls.ImageNet'\n","data_root = 'data/imagenet/'\n","file_client_args = dict(backend='disk')\n","train_pipeline = [\n"," dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n"," dict(type='Resize', scale=292),\n"," dict(type='RandomCrop', size=255),\n"," dict(type='RandomGrayscale', prob=0.66, keep_channels=True),\n"," dict(type='RandomPatchWithLabels'),\n"," dict(\n"," type='PackSelfSupInputs',\n"," pseudo_label_keys=['patch_box', 'patch_label', 'unpatched_img'],\n"," meta_keys=['img_path'])\n","]\n","train_dataloader = dict(\n"," batch_size=8,\n"," num_workers=2,\n"," persistent_workers=True,\n"," sampler=dict(type='DefaultSampler', shuffle=True),\n"," collate_fn=dict(type='default_collate'),\n"," dataset=dict(\n"," type='mmcls.ImageNet',\n"," data_root='data/imagenet/',\n"," ann_file='meta/train.txt',\n"," data_prefix=dict(img_path='train/'),\n"," pipeline=[\n"," dict(\n"," type='LoadImageFromFile',\n"," file_client_args=dict(backend='disk')),\n"," dict(type='Resize', scale=292),\n"," dict(type='RandomCrop', size=255),\n"," dict(type='RandomGrayscale', prob=0.66, keep_channels=True),\n"," dict(type='RandomPatchWithLabels'),\n"," dict(\n"," type='PackSelfSupInputs',\n"," pseudo_label_keys=[\n"," 'patch_box', 'patch_label', 'unpatched_img'\n"," ],\n"," meta_keys=['img_path'])\n"," ]))\n","optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\n","optim_wrapper = dict(\n"," type='OptimWrapper',\n"," optimizer=dict(type='SGD', lr=0.2, weight_decay=0.0001, momentum=0.9),\n"," paramwise_cfg=dict(\n"," custom_keys=dict(neck=dict(decay_mult=5.0), head=dict(\n"," decay_mult=5.0))),\n"," clip_grad=None)\n","param_scheduler = [dict(type='MultiStepLR', by_epoch=True, milestones=[1, 2])]\n","train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2)\n","default_scope = 'mmselfsup'\n","default_hooks = dict(\n"," runtime_info=dict(type='RuntimeInfoHook'),\n"," timer=dict(type='IterTimerHook'),\n"," logger=dict(type='LoggerHook', interval=10),\n"," param_scheduler=dict(type='ParamSchedulerHook'),\n"," checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3),\n"," sampler_seed=dict(type='DistSamplerSeedHook'))\n","env_cfg = dict(\n"," cudnn_benchmark=False,\n"," mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n"," dist_cfg=dict(backend='nccl'))\n","log_processor = dict(\n"," window_size=10,\n"," custom_cfg=[dict(data_src='', method='mean', windows_size='global')])\n","vis_backends = [dict(type='LocalVisBackend')]\n","visualizer = dict(\n"," type='SelfSupVisualizer',\n"," vis_backends=[dict(type='LocalVisBackend')],\n"," name='visualizer')\n","log_level = 'INFO'\n","load_from = None\n","resume = False\n","work_dir = './work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab'\n","randomness = dict(seed=0, deterministic=True)\n","\n","Result has been saved to /content/mmselfsup/work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/modules_statistic_results.json\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.bn1.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.bn1.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn1.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn1.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn2.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn2.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn3.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.bn3.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.downsample.1.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.0.downsample.1.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.1.bn1.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.1.bn1.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.1.bn2.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.1.bn2.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.1.bn3.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.1.bn3.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.2.bn1.weight:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- backbone.layer1.2.bn1.bias:weight_decay=0.0001\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- 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\u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.weight:weight_decay=0.0005\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.weight:decay_mult=5.0\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.bias:lr=0.2\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.bias:weight_decay=0.0005\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - paramwise_options -- head.fc_cls.bias:decay_mult=5.0\n","09/01 16:09:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmselfsup/work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab by HardDiskBackend.\n","09/01 16:09:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][10/163] lr: 2.0000e-01 eta: 0:06:02 time: 1.1465 data_time: 0.0440 memory: 1392 loss: 27.9830\n","09/01 16:10:01 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][20/163] lr: 2.0000e-01 eta: 0:03:28 time: 0.2144 data_time: 0.0218 memory: 1392 loss: 25.6786\n","09/01 16:10:03 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][30/163] lr: 2.0000e-01 eta: 0:02:34 time: 0.2041 data_time: 0.0192 memory: 1392 loss: 14.5791\n","09/01 16:10:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][40/163] lr: 2.0000e-01 eta: 0:02:06 time: 0.2023 data_time: 0.0192 memory: 1392 loss: 14.2424\n","09/01 16:10:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][50/163] lr: 2.0000e-01 eta: 0:01:48 time: 0.2026 data_time: 0.0202 memory: 1392 loss: 17.9769\n","09/01 16:10:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][60/163] lr: 2.0000e-01 eta: 0:01:36 time: 0.2026 data_time: 0.0196 memory: 1392 loss: 18.9486\n","09/01 16:10:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][70/163] lr: 2.0000e-01 eta: 0:01:26 time: 0.2025 data_time: 0.0200 memory: 1392 loss: 28.0319\n","09/01 16:10:13 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][80/163] lr: 2.0000e-01 eta: 0:01:19 time: 0.2033 data_time: 0.0198 memory: 1392 loss: 17.6793\n","09/01 16:10:15 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][90/163] lr: 2.0000e-01 eta: 0:01:12 time: 0.2048 data_time: 0.0195 memory: 1392 loss: 15.4679\n","09/01 16:10:17 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][100/163] lr: 2.0000e-01 eta: 0:01:07 time: 0.2058 data_time: 0.0206 memory: 1392 loss: 6.8410\n","09/01 16:10:19 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][110/163] lr: 2.0000e-01 eta: 0:01:02 time: 0.2036 data_time: 0.0203 memory: 1392 loss: 6.3352\n","09/01 16:10:21 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][120/163] lr: 2.0000e-01 eta: 0:00:58 time: 0.2045 data_time: 0.0200 memory: 1392 loss: 6.0879\n","09/01 16:10:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][130/163] lr: 2.0000e-01 eta: 0:00:54 time: 0.2206 data_time: 0.0241 memory: 1392 loss: 4.7499\n","09/01 16:10:26 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][140/163] lr: 2.0000e-01 eta: 0:00:50 time: 0.2143 data_time: 0.0199 memory: 1392 loss: 3.4295\n","09/01 16:10:28 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][150/163] lr: 2.0000e-01 eta: 0:00:47 time: 0.2055 data_time: 0.0199 memory: 1392 loss: 3.2668\n","09/01 16:10:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][160/163] lr: 2.0000e-01 eta: 0:00:43 time: 0.2033 data_time: 0.0188 memory: 1392 loss: 2.7335\n","09/01 16:10:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: relative-loc_resnet50_8xb64-steplr-70e_in1k_colab_20220901_160940\n","09/01 16:10:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 1 epochs\n","09/01 16:10:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][10/163] lr: 2.0000e-02 eta: 0:00:39 time: 0.2302 data_time: 0.0299 memory: 1392 loss: 2.3706\n","09/01 16:10:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][20/163] lr: 2.0000e-02 eta: 0:00:36 time: 0.2050 data_time: 0.0191 memory: 1392 loss: 2.2516\n","09/01 16:10:39 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][30/163] lr: 2.0000e-02 eta: 0:00:33 time: 0.2100 data_time: 0.0202 memory: 1392 loss: 2.2116\n","09/01 16:10:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][40/163] lr: 2.0000e-02 eta: 0:00:30 time: 0.2073 data_time: 0.0213 memory: 1392 loss: 2.1653\n","09/01 16:10:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][50/163] lr: 2.0000e-02 eta: 0:00:28 time: 0.2103 data_time: 0.0209 memory: 1392 loss: 2.1445\n","09/01 16:10:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][60/163] lr: 2.0000e-02 eta: 0:00:25 time: 0.2064 data_time: 0.0190 memory: 1392 loss: 2.1613\n","09/01 16:10:47 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][70/163] lr: 2.0000e-02 eta: 0:00:22 time: 0.2084 data_time: 0.0215 memory: 1392 loss: 2.1216\n","09/01 16:10:49 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][80/163] lr: 2.0000e-02 eta: 0:00:20 time: 0.2060 data_time: 0.0206 memory: 1392 loss: 2.1333\n","09/01 16:10:51 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][90/163] lr: 2.0000e-02 eta: 0:00:17 time: 0.2072 data_time: 0.0196 memory: 1392 loss: 2.1104\n","09/01 16:10:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][100/163] lr: 2.0000e-02 eta: 0:00:15 time: 0.2073 data_time: 0.0198 memory: 1392 loss: 2.1128\n","09/01 16:10:56 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][110/163] lr: 2.0000e-02 eta: 0:00:12 time: 0.2056 data_time: 0.0195 memory: 1392 loss: 2.1260\n","09/01 16:10:58 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][120/163] lr: 2.0000e-02 eta: 0:00:10 time: 0.2072 data_time: 0.0195 memory: 1392 loss: 2.1056\n","09/01 16:11:00 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][130/163] lr: 2.0000e-02 eta: 0:00:07 time: 0.2100 data_time: 0.0196 memory: 1392 loss: 2.0948\n","09/01 16:11:02 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][140/163] lr: 2.0000e-02 eta: 0:00:05 time: 0.2067 data_time: 0.0199 memory: 1392 loss: 2.0966\n","09/01 16:11:04 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][150/163] lr: 2.0000e-02 eta: 0:00:03 time: 0.2082 data_time: 0.0196 memory: 1392 loss: 2.0897\n","09/01 16:11:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][160/163] lr: 2.0000e-02 eta: 0:00:00 time: 0.2043 data_time: 0.0190 memory: 1392 loss: 2.0927\n","09/01 16:11:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: relative-loc_resnet50_8xb64-steplr-70e_in1k_colab_20220901_160940\n","09/01 16:11:06 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 2 epochs\n"]},{"data":{"text/plain":["RelativeLoc(\n"," (data_preprocessor): RelativeLocDataPreprocessor()\n"," (backbone): ResNet(\n"," (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n"," (layer1): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer2): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (3): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer3): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (3): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (4): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (5): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer4): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," )\n"," init_cfg=[{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n"," (neck): RelativeLocNeck(\n"," (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n"," (fc): Linear(in_features=4096, out_features=4096, bias=True)\n"," (bn): BatchNorm1d(4096, eps=1e-05, momentum=0.003, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (dropout): Dropout(p=0.5, inplace=False)\n"," )\n"," init_cfg=[{'type': 'Normal', 'std': 0.01, 'layer': 'Linear'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n"," (head): ClsHead(\n"," (loss): CrossEntropyLoss()\n"," (fc_cls): Linear(in_features=4096, out_features=8, bias=True)\n"," )\n"," init_cfg=[{'type': 'Normal', 'std': 0.005, 'layer': 'Linear'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n",")"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["from mmengine.config import Config, DictAction\n","from mmengine.runner import Runner\n","\n","from mmselfsup.utils import register_all_modules\n","\n","# register all modules in mmselfsup into the registries\n","# do not init the default scope here because it will be init in the runner\n","register_all_modules(init_default_scope=False)\n","\n","# build the runner from config\n","runner = Runner.from_cfg(cfg)\n","\n","# start training\n","runner.train()"]},{"cell_type":"markdown","id":"a562c2dd","metadata":{"id":"a562c2dd"},"source":["## Example to start a downstream task\n"]},{"cell_type":"markdown","id":"96ea98b2","metadata":{"id":"96ea98b2"},"source":["### Extract backbone weights from pre-train model"]},{"cell_type":"code","execution_count":15,"id":"9fa74770","metadata":{"executionInfo":{"elapsed":2019,"status":"ok","timestamp":1662048670738,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"9fa74770"},"outputs":[],"source":["!python tools/model_converters/extract_backbone_weights.py \\\n"," work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/epoch_2.pth \\\n"," work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth"]},{"cell_type":"markdown","id":"0f137b0e","metadata":{"id":"0f137b0e"},"source":["### Prepare config file\n","\n","Here we create a new config file for demo dataset, actually we provided various config files in directory `configs/benchmarks`."]},{"cell_type":"code","execution_count":16,"id":"65764022","metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1662048670739,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"65764022"},"outputs":[],"source":["# Load the basic config file\n","from mmengine.config import Config\n","benchmark_cfg = Config.fromfile('configs/benchmarks/classification/imagenet/resnet50_linear-8xb32-steplr-100e_in1k.py')\n","\n","# Modify the model\n","checkpoint_file = 'work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth'\n","# Or directly using pre-train model provided by us\n","# checkpoint_file = 'https://download.openmmlab.com/mmselfsup/moco/mocov2_resnet50_8xb32-coslr-200e_in1k_20220225-89e03af4.pth'\n","\n","benchmark_cfg.model.backbone.frozen_stages=4\n","benchmark_cfg.model.backbone.init_cfg = dict(type='Pretrained', checkpoint=checkpoint_file)\n","\n","# As the imagenet_examples dataset folder doesn't have val dataset\n","# Modify the path and meta files of validation dataset\n","benchmark_cfg.val_dataloader.dataset.data_prefix = 'train'\n","benchmark_cfg.val_dataloader.dataset.ann_file = 'meta/train.txt'\n","\n","# Specify the learning rate scheduler\n","benchmark_cfg.param_scheduler = [dict(type='MultiStepLR', by_epoch=True, milestones=[1, 2])]\n","\n","# Output logs for every 10 iterations\n","benchmark_cfg.default_hooks.logger.interval = 10\n","\n","# Modify runtime settings for demo\n","benchmark_cfg.train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2)\n","\n","\n","# Specify the work directory\n","benchmark_cfg.work_dir = './work_dirs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k_colab'\n","\n","# Set the random seed and enable the deterministic option of cuDNN\n","# to keep the results' reproducible.\n","benchmark_cfg.randomness = dict(seed=0, deterministic=True)"]},{"cell_type":"markdown","id":"636e8865","metadata":{"id":"636e8865"},"source":["### Load extracted backbone weights to start a downstream task"]},{"cell_type":"code","execution_count":17,"id":"f9c51d5c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":62032,"status":"ok","timestamp":1662048732765,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"f9c51d5c","outputId":"7c83b7ac-20f3-4944-bedf-53fd2bf83890"},"outputs":[{"name":"stdout","output_type":"stream","text":["09/01 16:11:10 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","------------------------------------------------------------\n","System environment:\n"," sys.platform: linux\n"," Python: 3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]\n"," CUDA available: True\n"," numpy_random_seed: 0\n"," GPU 0: Tesla T4\n"," CUDA_HOME: /usr/local/cuda\n"," NVCC: Cuda compilation tools, release 11.1, V11.1.105\n"," GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n"," PyTorch: 1.12.1+cu113\n"," PyTorch compiling details: PyTorch built with:\n"," - GCC 9.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - LAPACK is enabled (usually provided by MKL)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\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_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\n"," - CuDNN 8.3.2 (built against CUDA 11.5)\n"," - Magma 2.5.2\n"," - 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= -fabi-version=11 -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=1.12.1, 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, \n","\n"," TorchVision: 0.13.1+cu113\n"," OpenCV: 4.6.0\n"," MMEngine: 0.1.0\n","\n","Runtime environment:\n"," cudnn_benchmark: False\n"," mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}\n"," dist_cfg: {'backend': 'nccl'}\n"," seed: 0\n"," deterministic: True\n"," Distributed launcher: none\n"," Distributed training: False\n"," GPU number: 1\n","------------------------------------------------------------\n","\n","09/01 16:11:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Config:\n","model = dict(\n"," type='ImageClassifier',\n"," data_preprocessor=dict(\n"," mean=[123.675, 116.28, 103.53],\n"," std=[58.395, 57.12, 57.375],\n"," to_rgb=True),\n"," backbone=dict(\n"," type='ResNet',\n"," depth=50,\n"," in_channels=3,\n"," num_stages=4,\n"," norm_cfg=dict(type='BN'),\n"," frozen_stages=4,\n"," init_cfg=dict(\n"," type='Pretrained',\n"," checkpoint=\n"," 'work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth'\n"," )),\n"," neck=dict(type='GlobalAveragePooling'),\n"," head=dict(\n"," type='LinearClsHead',\n"," num_classes=1000,\n"," in_channels=2048,\n"," loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n"," topk=(1, 5)))\n","dataset_type = 'ImageNet'\n","data_root = 'data/imagenet/'\n","file_client_args = dict(backend='disk')\n","train_pipeline = [\n"," dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n"," dict(type='RandomResizedCrop', scale=224, backend='pillow'),\n"," dict(type='RandomFlip', prob=0.5, direction='horizontal'),\n"," dict(type='PackClsInputs')\n","]\n","test_pipeline = [\n"," dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),\n"," dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),\n"," dict(type='CenterCrop', crop_size=224),\n"," dict(type='PackClsInputs')\n","]\n","train_dataloader = dict(\n"," batch_size=32,\n"," num_workers=4,\n"," dataset=dict(\n"," type='ImageNet',\n"," data_root='data/imagenet',\n"," ann_file='meta/train.txt',\n"," data_prefix='train',\n"," pipeline=[\n"," dict(\n"," type='LoadImageFromFile',\n"," file_client_args=dict(backend='disk')),\n"," dict(type='RandomResizedCrop', scale=224, backend='pillow'),\n"," dict(type='RandomFlip', prob=0.5, direction='horizontal'),\n"," dict(type='PackClsInputs')\n"," ]),\n"," sampler=dict(type='DefaultSampler', shuffle=True),\n"," persistent_workers=True)\n","val_dataloader = dict(\n"," batch_size=32,\n"," num_workers=4,\n"," dataset=dict(\n"," type='ImageNet',\n"," data_root='data/imagenet',\n"," ann_file='meta/train.txt',\n"," data_prefix='train',\n"," pipeline=[\n"," dict(\n"," type='LoadImageFromFile',\n"," file_client_args=dict(backend='disk')),\n"," dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),\n"," dict(type='CenterCrop', crop_size=224),\n"," dict(type='PackClsInputs')\n"," ]),\n"," sampler=dict(type='DefaultSampler', shuffle=False),\n"," persistent_workers=True)\n","val_evaluator = dict(type='mmcls.Accuracy', topk=(1, 5))\n","test_dataloader = dict(\n"," batch_size=32,\n"," num_workers=4,\n"," dataset=dict(\n"," type='ImageNet',\n"," data_root='data/imagenet',\n"," ann_file='meta/val.txt',\n"," data_prefix='val',\n"," pipeline=[\n"," dict(\n"," type='LoadImageFromFile',\n"," file_client_args=dict(backend='disk')),\n"," dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),\n"," dict(type='CenterCrop', crop_size=224),\n"," dict(type='PackClsInputs')\n"," ]),\n"," sampler=dict(type='DefaultSampler', shuffle=False),\n"," persistent_workers=True)\n","test_evaluator = dict(type='mmcls.Accuracy', topk=(1, 5))\n","optimizer = dict(type='SGD', lr=30.0, momentum=0.9, weight_decay=0.0)\n","optim_wrapper = dict(\n"," type='OptimWrapper',\n"," optimizer=dict(type='SGD', lr=30.0, momentum=0.9, weight_decay=0.0))\n","param_scheduler = [dict(type='MultiStepLR', by_epoch=True, milestones=[1, 2])]\n","train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=2)\n","val_cfg = dict()\n","test_cfg = dict()\n","default_scope = 'mmcls'\n","custom_imports = dict(\n"," imports=['mmselfsup.models', 'mmselfsup.engine'],\n"," allow_failed_imports=False)\n","default_hooks = dict(\n"," runtime_info=dict(type='RuntimeInfoHook'),\n"," timer=dict(type='IterTimerHook'),\n"," logger=dict(type='LoggerHook', interval=10),\n"," param_scheduler=dict(type='ParamSchedulerHook'),\n"," checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3),\n"," sampler_seed=dict(type='DistSamplerSeedHook'))\n","env_cfg = dict(\n"," cudnn_benchmark=False,\n"," mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n"," dist_cfg=dict(backend='nccl'))\n","log_processor = dict(\n"," window_size=10,\n"," custom_cfg=[dict(data_src='', method='mean', windows_size='global')])\n","vis_backends = [dict(type='LocalVisBackend')]\n","visualizer = dict(\n"," type='ClsVisualizer',\n"," vis_backends=[dict(type='LocalVisBackend')],\n"," name='visualizer')\n","log_level = 'INFO'\n","load_from = None\n","resume = False\n","work_dir = './work_dirs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k_colab'\n","randomness = dict(seed=0, deterministic=True)\n","\n","Result has been saved to /content/mmselfsup/work_dirs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k_colab/modules_statistic_results.json\n","09/01 16:11:11 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:566: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," cpuset_checked))\n"]},{"name":"stdout","output_type":"stream","text":["09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - load model from: work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth\n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - local loads checkpoint from path: work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth\n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.conv1.weight - torch.Size([64, 3, 7, 7]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.bn1.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.bn1.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn1.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn1.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn2.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn2.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn3.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.bn3.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.downsample.1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.0.downsample.1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn1.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn1.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn2.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn2.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn3.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.1.bn3.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn1.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn1.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn2.weight - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn2.bias - torch.Size([64]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn3.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer1.2.bn3.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn1.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn1.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn2.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn2.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn3.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.bn3.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.downsample.1.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.0.downsample.1.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn1.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn1.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn2.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn2.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn3.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.1.bn3.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn1.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn1.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn2.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn2.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn3.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.2.bn3.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn1.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn1.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn2.weight - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn2.bias - torch.Size([128]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn3.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer2.3.bn3.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.downsample.1.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.0.downsample.1.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.1.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.2.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:24 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.3.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.4.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn1.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn1.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn2.weight - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn2.bias - torch.Size([256]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn3.weight - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer3.5.bn3.bias - torch.Size([1024]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn1.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn1.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn2.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn2.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn3.weight - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.bn3.bias - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.downsample.1.weight - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.0.downsample.1.bias - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn1.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn1.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn2.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn2.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn3.weight - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.1.bn3.bias - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn1.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn1.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn2.weight - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn2.bias - torch.Size([512]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn3.weight - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","backbone.layer4.2.bn3.bias - torch.Size([2048]): \n","PretrainedInit: load from work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","head.fc.weight - torch.Size([1000, 2048]): \n","NormalInit: mean=0, std=0.01, bias=0 \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - \n","head.fc.bias - torch.Size([1000]): \n","NormalInit: mean=0, std=0.01, bias=0 \n"," \n","09/01 16:11:25 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to /content/mmselfsup/work_dirs/benchmarks/classification/imagenet/resnet50_8xb32-steplr-100e_in1k_colab by HardDiskBackend.\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:566: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n"," cpuset_checked))\n"]},{"name":"stdout","output_type":"stream","text":["09/01 16:11:30 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][10/41] lr: 3.0000e+01 eta: 0:00:35 time: 0.4955 data_time: 0.3703 memory: 1392 loss: 1.0352\n","09/01 16:11:32 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][20/41] lr: 3.0000e+01 eta: 0:00:23 time: 0.2497 data_time: 0.1329 memory: 762 loss: 0.0000\n","09/01 16:11:35 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][30/41] lr: 3.0000e+01 eta: 0:00:17 time: 0.2528 data_time: 0.1333 memory: 762 loss: 0.0000\n","09/01 16:11:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [1][40/41] lr: 3.0000e+01 eta: 0:00:12 time: 0.2088 data_time: 0.0967 memory: 762 loss: 0.0000\n","09/01 16:11:37 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: resnet50_linear-8xb32-steplr-100e_in1k_20220901_161110\n","09/01 16:11:41 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [1][10/41] eta: 0:00:12 time: 0.4174 data_time: 0.2966 memory: 762 \n","09/01 16:11:43 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [1][20/41] eta: 0:00:03 time: 0.1886 data_time: 0.0625 memory: 762 \n","09/01 16:11:45 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [1][30/41] eta: 0:00:02 time: 0.2412 data_time: 0.1149 memory: 762 \n","09/01 16:11:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [1][40/41] eta: 0:00:00 time: 0.2665 data_time: 0.1533 memory: 762 \n","09/01 16:11:48 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [1][41/41] accuracy/top1: 100.0000 accuracy/top5: 100.0000\n","09/01 16:11:52 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][10/41] lr: 3.0000e+00 eta: 0:00:09 time: 0.3464 data_time: 0.2278 memory: 762 loss: 0.0000\n","09/01 16:11:54 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][20/41] lr: 3.0000e+00 eta: 0:00:06 time: 0.2781 data_time: 0.1648 memory: 762 loss: 0.0000\n","09/01 16:11:57 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][30/41] lr: 3.0000e+00 eta: 0:00:03 time: 0.2383 data_time: 0.1167 memory: 762 loss: 0.0000\n","09/01 16:11:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(train) [2][40/41] lr: 3.0000e+00 eta: 0:00:00 time: 0.2536 data_time: 0.1397 memory: 762 loss: 0.0000\n","09/01 16:11:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Exp name: resnet50_linear-8xb32-steplr-100e_in1k_20220901_161110\n","09/01 16:11:59 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Saving checkpoint at 2 epochs\n","09/01 16:12:05 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [2][10/41] eta: 0:00:11 time: 0.3788 data_time: 0.2459 memory: 762 \n","09/01 16:12:07 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [2][20/41] eta: 0:00:04 time: 0.2033 data_time: 0.0714 memory: 762 \n","09/01 16:12:09 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [2][30/41] eta: 0:00:02 time: 0.2373 data_time: 0.1092 memory: 762 \n","09/01 16:12:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [2][40/41] eta: 0:00:00 time: 0.2671 data_time: 0.1587 memory: 762 \n","09/01 16:12:12 - mmengine - \u001b[4m\u001b[37mINFO\u001b[0m - Epoch(val) [2][41/41] accuracy/top1: 100.0000 accuracy/top5: 100.0000\n"]},{"data":{"text/plain":["ImageClassifier(\n"," (data_preprocessor): ClsDataPreprocessor()\n"," (backbone): ResNet(\n"," (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n"," (layer1): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer2): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (3): Bottleneck(\n"," (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer3): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (3): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (4): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (5): Bottleneck(\n"," (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," (layer4): ResLayer(\n"," (0): Bottleneck(\n"," (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (downsample): Sequential(\n"," (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n"," (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," )\n"," (drop_path): Identity()\n"," )\n"," (1): Bottleneck(\n"," (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," (2): Bottleneck(\n"," (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n"," (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n"," (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n"," (relu): ReLU(inplace=True)\n"," (drop_path): Identity()\n"," )\n"," )\n"," )\n"," init_cfg={'type': 'Pretrained', 'checkpoint': 'work_dirs/selfsup/relative-loc_resnet50_8xb64-steplr-70e_in1k_colab/relative-loc_backbone-weights.pth'}\n"," (neck): GlobalAveragePooling(\n"," (gap): AdaptiveAvgPool2d(output_size=(1, 1))\n"," )\n"," (head): LinearClsHead(\n"," (loss_module): CrossEntropyLoss()\n"," (fc): Linear(in_features=2048, out_features=1000, bias=True)\n"," )\n"," init_cfg={'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\n",")"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["from mmengine.config import Config, DictAction\n","from mmengine.runner import Runner\n","\n","from mmselfsup.utils import register_all_modules\n","\n","# register all modules in mmselfsup into the registries\n","# do not init the default scope here because it will be init in the runner\n","register_all_modules(init_default_scope=False)\n","\n","# build the runner from config\n","runner = Runner.from_cfg(benchmark_cfg)\n","\n","# start training\n","runner.train()"]},{"cell_type":"markdown","id":"e1b5b983","metadata":{"id":"e1b5b983"},"source":["**Note: As the demo only has one class in dataset, the model collapsed and the results of loss and acc should be ignored.**"]},{"cell_type":"code","execution_count":17,"id":"4A0WOMeeeZ9E","metadata":{"executionInfo":{"elapsed":33,"status":"ok","timestamp":1662048732766,"user":{"displayName":"qin ren","userId":"07205769677379266243"},"user_tz":-480},"id":"4A0WOMeeeZ9E"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"provenance":[],"toc_visible":true},"gpuClass":"standard","kernelspec":{"display_name":"openmmlab","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.0 (default, Oct 9 2018, 10:31:47) \n[GCC 7.3.0]"},"vscode":{"interpreter":{"hash":"5909b3386efe3692f76356628babf720cfd47771f5d858315790cc041eb41361"}}},"nbformat":4,"nbformat_minor":5} diff --git a/docs/en/get_started.md b/docs/en/get_started.md index 88811f900..2213544f4 100644 --- a/docs/en/get_started.md +++ b/docs/en/get_started.md @@ -22,7 +22,7 @@ In this section, we demonstrate how to prepare an environment with PyTorch. -MMSelfSup works on Linux (Windows and macOS are not officially supported). It requires Python 3.6+, CUDA 9.2+ and PyTorch 1.6+. +MMSelfSup works on Linux (Windows and macOS are not officially supported). It requires Python 3.7+, CUDA 9.2+ and PyTorch 1.6+. ```{note} If you are experienced with PyTorch and have already installed it, just skip this part and jump to the next Installation section. Otherwise, you can follow these steps for the preparation. diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md index b836870ed..35ac13d7c 100644 --- a/docs/zh_cn/get_started.md +++ b/docs/zh_cn/get_started.md @@ -22,7 +22,7 @@ 在本节中，我们将演示如何使用 PyTorch 准备环境。 -MMSelfSup 在 Linux 上运行（Windows 和 macOS 不受官方支持）。 它需要 Python 3.6+、CUDA 9.2+ 和 PyTorch 1.6+。 +MMSelfSup 在 Linux 上运行（Windows 和 macOS 不受官方支持）。 它需要 Python 3.7+、CUDA 9.2+ 和 PyTorch 1.6+。 ```{note} 如果您有使用 PyTorch 的经验并且已经安装了它，请跳过这一部分并跳到下一个安装环节。否则，您可以按照如下步骤进行准备。 diff --git a/mmselfsup/models/utils/__init__.py b/mmselfsup/models/utils/__init__.py index 284e62e95..1d6b7f4ba 100644 --- a/mmselfsup/models/utils/__init__.py +++ b/mmselfsup/models/utils/__init__.py @@ -1,6 +1,5 @@ # Copyright (c) OpenMMLab. All rights reserved. from .clip import build_clip_model -from .dall_e import Encoder from .data_preprocessor import (CAEDataPreprocessor, RelativeLocDataPreprocessor, RotationPredDataPreprocessor, @@ -27,8 +26,8 @@ __all__ = [ 'Extractor', 'GatherLayer', 'MultiPooling', 'MultiPrototypes', 'build_2d_sincos_position_embedding', 'Sobel', 'MultiheadAttention', - 'TransformerEncoderLayer', 'CAETransformerRegressorLayer', 'Encoder', - 'CosineEMA', 'SelfSupDataPreprocessor', 'RelativeLocDataPreprocessor', + 'TransformerEncoderLayer', 'CAETransformerRegressorLayer', 'CosineEMA', + 'SelfSupDataPreprocessor', 'RelativeLocDataPreprocessor', 'RotationPredDataPreprocessor', 'CAEDataPreprocessor', 'ResLayerExtraNorm', 'NormEMAVectorQuantizer', 'TwoNormDataPreprocessor', 'PromptTransformerEncoderLayer', 'build_clip_model' diff --git a/mmselfsup/models/utils/dall_e.py b/mmselfsup/models/utils/dall_e.py deleted file mode 100644 index a026d8071..000000000 --- a/mmselfsup/models/utils/dall_e.py +++ /dev/null @@ -1,174 +0,0 @@ -# Copyright (c) -# https://github.com/microsoft/unilm/blob/master/beit/dall_e/encoder.py -# Copied from BEiT -import math -from collections import OrderedDict -from functools import partial - -import attr -import torch -import torch.nn as nn -import torch.nn.functional as F - - -@attr.s(eq=False) -class Conv2d(nn.Module): - n_in: int = attr.ib(validator=lambda i, a, x: x >= 1) - n_out: int = attr.ib(validator=lambda i, a, x: x >= 1) - kw: int = attr.ib(validator=lambda i, a, x: x >= 1 and x % 2 == 1) - - use_float16: bool = attr.ib(default=True) - device: torch.device = attr.ib(default=torch.device('cpu')) - requires_grad: bool = attr.ib(default=False) - - def __attrs_post_init__(self) -> None: - super().__init__() - - w = torch.empty((self.n_out, self.n_in, self.kw, self.kw), - dtype=torch.float32, - device=self.device, - requires_grad=self.requires_grad) - w.normal_(std=1 / math.sqrt(self.n_in * self.kw**2)) - - b = torch.zeros((self.n_out, ), - dtype=torch.float32, - device=self.device, - requires_grad=self.requires_grad) - self.w, self.b = nn.Parameter(w), nn.Parameter(b) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - if self.use_float16 and 'cuda' in self.w.device.type: - if x.dtype != torch.float16: - x = x.half() - - w, b = self.w.half(), self.b.half() - else: - if x.dtype != torch.float32: - x = x.float() - - w, b = self.w, self.b - - return F.conv2d(x, w, b, padding=(self.kw - 1) // 2) - - -@attr.s(eq=False, repr=False) -class EncoderBlock(nn.Module): - n_in: int = attr.ib(validator=lambda i, a, x: x >= 1) - n_out: int = attr.ib(validator=lambda i, a, x: x >= 1 and x % 4 == 0) - n_layers: int = attr.ib(validator=lambda i, a, x: x >= 1) - - device: torch.device = attr.ib(default=None) - requires_grad: bool = attr.ib(default=False) - - def __attrs_post_init__(self) -> None: - super().__init__() - self.n_hid = self.n_out // 4 - self.post_gain = 1 / (self.n_layers**2) - - make_conv = partial( - Conv2d, device=self.device, requires_grad=self.requires_grad) - self.id_path = make_conv( - self.n_in, self.n_out, - 1) if self.n_in != self.n_out else nn.Identity() - self.res_path = nn.Sequential( - OrderedDict([ - ('relu_1', nn.ReLU()), - ('conv_1', make_conv(self.n_in, self.n_hid, 3)), - ('relu_2', nn.ReLU()), - ('conv_2', make_conv(self.n_hid, self.n_hid, 3)), - ('relu_3', nn.ReLU()), - ('conv_3', make_conv(self.n_hid, self.n_hid, 3)), - ('relu_4', nn.ReLU()), - ('conv_4', make_conv(self.n_hid, self.n_out, 1)), - ])) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.id_path(x) + self.post_gain * self.res_path(x) - - -@attr.s(eq=False, repr=False) -class Encoder(nn.Module): - group_count: int = 4 - n_hid: int = attr.ib(default=256, validator=lambda i, a, x: x >= 64) - n_blk_per_group: int = attr.ib(default=2, validator=lambda i, a, x: x >= 1) - input_channels: int = attr.ib(default=3, validator=lambda i, a, x: x >= 1) - vocab_size: int = attr.ib(default=8192, validator=lambda i, a, x: x >= 512) - - device: torch.device = attr.ib(default=torch.device('cpu')) - requires_grad: bool = attr.ib(default=False) - use_mixed_precision: bool = attr.ib(default=True) - - def __attrs_post_init__(self) -> None: - super().__init__() - - blk_range = range(self.n_blk_per_group) - n_layers = self.group_count * self.n_blk_per_group - make_conv = partial( - Conv2d, device=self.device, requires_grad=self.requires_grad) - make_blk = partial( - EncoderBlock, - n_layers=n_layers, - device=self.device, - requires_grad=self.requires_grad) - - self.blocks = nn.Sequential( - OrderedDict([ - ('input', make_conv(self.input_channels, 1 * self.n_hid, 7)), - ('group_1', - nn.Sequential( - OrderedDict([ - *[(f'block_{i + 1}', - make_blk(1 * self.n_hid, 1 * self.n_hid)) - for i in blk_range], - ('pool', nn.MaxPool2d(kernel_size=2)), - ]))), - ('group_2', - nn.Sequential( - OrderedDict([ - *[(f'block_{i + 1}', - make_blk( - 1 * self.n_hid if i == 0 else 2 * self.n_hid, - 2 * self.n_hid)) for i in blk_range], - ('pool', nn.MaxPool2d(kernel_size=2)), - ]))), - ('group_3', - nn.Sequential( - OrderedDict([ - *[(f'block_{i + 1}', - make_blk( - 2 * self.n_hid if i == 0 else 4 * self.n_hid, - 4 * self.n_hid)) for i in blk_range], - ('pool', nn.MaxPool2d(kernel_size=2)), - ]))), - ('group_4', - nn.Sequential( - OrderedDict([ - *[(f'block_{i + 1}', - make_blk( - 4 * self.n_hid if i == 0 else 8 * self.n_hid, - 8 * self.n_hid)) for i in blk_range], - ]))), - ('output', - nn.Sequential( - OrderedDict([ - ('relu', nn.ReLU()), - ('conv', - make_conv( - 8 * self.n_hid, - self.vocab_size, - 1, - use_float16=False)), - ]))), - ])) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = x.float() - if len(x.shape) != 4: - raise ValueError(f'input shape {x.shape} is not 4d') - if x.shape[1] != self.input_channels: - raise ValueError(f'input has {x.shape[1]} channels but model \ - built for {self.input_channels}') - if x.dtype != torch.float32: - raise ValueError('input must have dtype torch.float32') - - return self.blocks(x) diff --git a/setup.py b/setup.py index 532fa2e49..43b397468 100644 --- a/setup.py +++ b/setup.py @@ -176,8 +176,6 @@ def add_mim_extension(): 'License :: OSI Approved :: Apache Software License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.5', - 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9',