| [{"stream_name":"stdout","time":13.800549499,"data":"Collecting git+https://github.com/facebookresearch/detectron2.git\r\n"} | |
| ,{"stream_name":"stdout","time":13.80060226,"data":" Cloning https://github.com/facebookresearch/detectron2.git to /tmp/pip-req-build-bmi0l3o7\r\n"} | |
| ,{"stream_name":"stdout","time":13.850537072,"data":" Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/detectron2.git /tmp/pip-req-build-bmi0l3o7\r\n"} | |
| ,{"stream_name":"stdout","time":15.828396187,"data":" Resolved https://github.com/facebookresearch/detectron2.git to commit 8c4a333ceb8df05348759443d0206302485890e0\r\n"} | |
| ,{"stream_name":"stdout","time":26.694396295,"data":" Preparing metadata (setup.py) ... \u001b[?25ldone\r\n"} | |
| ,{"stream_name":"stdout","time":26.694424157,"data":"\u001b[?25hRequirement already satisfied: Pillow\u003e=7.1 in /opt/conda/lib/python3.10/site-packages (from detectron2==0.6) (9.5.0)\r\n"} | |
| ,{"stream_name":"stdout","time":26.694431161,"data":"Requirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from detectron2==0.6) (3.7.2)\r\n"} | |
| ,{"stream_name":"stdout","time":26.795896143,"data":"Collecting pycocotools\u003e=2.0.2 (from detectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":26.847171206,"data":" Downloading pycocotools-2.0.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (426 kB)\r\n"} | |
| ,{"stream_name":"stdout","time":26.949470176,"data":"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/426.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m307.2/426.2 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m426.2/426.2 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n"} | |
| ,{"stream_name":"stdout","time":26.949521699,"data":"\u001b[?25hRequirement already satisfied: termcolor\u003e=1.1 in /opt/conda/lib/python3.10/site-packages (from detectron2==0.6) (2.3.0)\r\n"} | |
| ,{"stream_name":"stdout","time":27.000086278,"data":"Collecting yacs\u003e=0.1.8 (from detectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":27.000219986,"data":" Downloading yacs-0.1.8-py3-none-any.whl (14 kB)\r\n"} | |
| ,{"stream_name":"stdout","time":27.000226742,"data":"Requirement already satisfied: tabulate in /opt/conda/lib/python3.10/site-packages (from detectron2==0.6) (0.9.0)\r\n"} | |
| ,{"stream_name":"stdout","time":27.000232155,"data":"Requirement already satisfied: cloudpickle in /opt/conda/lib/python3.10/site-packages (from detectron2==0.6) (2.2.1)\r\n"} | |
| ,{"stream_name":"stdout","time":27.000237341,"data":"Requirement already satisfied: tqdm\u003e4.29.0 in /opt/conda/lib/python3.10/site-packages (from detectron2==0.6) (4.66.1)\r\n"} | |
| ,{"stream_name":"stdout","time":27.000244246,"data":"Requirement already satisfied: tensorboard in /opt/conda/lib/python3.10/site-packages (from detectron2==0.6) (2.12.3)\r\n"} | |
| ,{"stream_name":"stdout","time":27.101876277,"data":"Collecting fvcore\u003c0.1.6,\u003e=0.1.5 (from detectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":27.101900256,"data":" Downloading fvcore-0.1.5.post20221221.tar.gz (50 kB)\r\n"} | |
| ,{"stream_name":"stdout","time":27.101907338,"data":"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/50.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.2/50.2 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n"} | |
| ,{"stream_name":"stdout","time":27.913782362,"data":"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\r\n"} | |
| ,{"stream_name":"stdout","time":27.964242158,"data":"\u001b[?25hCollecting iopath\u003c0.1.10,\u003e=0.1.7 (from detectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":27.964265953,"data":" Downloading iopath-0.1.9-py3-none-any.whl (27 kB)\r\n"} | |
| ,{"stream_name":"stdout","time":28.117040412,"data":"Collecting omegaconf\u003c2.4,\u003e=2.1 (from detectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":28.117076197,"data":" Downloading omegaconf-2.3.0-py3-none-any.whl (79 kB)\r\n"} | |
| ,{"stream_name":"stdout","time":28.117083173,"data":"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/79.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.5/79.5 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n"} | |
| ,{"stream_name":"stdout","time":28.168433409,"data":"\u001b[?25hCollecting hydra-core\u003e=1.1 (from detectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":28.220127501,"data":" Downloading hydra_core-1.3.2-py3-none-any.whl (154 kB)\r\n"} | |
| ,{"stream_name":"stdout","time":28.220149554,"data":"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/154.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.5/154.5 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n"} | |
| ,{"stream_name":"stdout","time":28.321818222,"data":"\u001b[?25hCollecting black (from detectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":28.373153964,"data":" Downloading black-23.9.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\r\n"} | |
| ,{"stream_name":"stdout","time":28.42381878,"data":"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m69.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m39.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n"} | |
| ,{"stream_name":"stdout","time":28.423856743,"data":"\u001b[?25hRequirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from detectron2==0.6) (21.3)\r\n"} | |
| ,{"stream_name":"stdout","time":28.423862996,"data":"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from fvcore\u003c0.1.6,\u003e=0.1.5-\u003edetectron2==0.6) (1.23.5)\r\n"} | |
| ,{"stream_name":"stdout","time":28.423868875,"data":"Requirement already satisfied: pyyaml\u003e=5.1 in /opt/conda/lib/python3.10/site-packages (from fvcore\u003c0.1.6,\u003e=0.1.5-\u003edetectron2==0.6) (6.0)\r\n"} | |
| ,{"stream_name":"stdout","time":28.475184929,"data":"Collecting antlr4-python3-runtime==4.9.* (from hydra-core\u003e=1.1-\u003edetectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":28.475225062,"data":" Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\r\n"} | |
| ,{"stream_name":"stdout","time":28.527590455,"data":"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/117.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n"} | |
| ,{"stream_name":"stdout","time":29.33744428,"data":"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\r\n"} | |
| ,{"stream_name":"stdout","time":29.439532864,"data":"\u001b[?25hCollecting portalocker (from iopath\u003c0.1.10,\u003e=0.1.7-\u003edetectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":29.439553947,"data":" Downloading portalocker-2.8.2-py3-none-any.whl (17 kB)\r\n"} | |
| ,{"stream_name":"stdout","time":29.492629557,"data":"Requirement already satisfied: contourpy\u003e=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib-\u003edetectron2==0.6) (1.1.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.492660448,"data":"Requirement already satisfied: cycler\u003e=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib-\u003edetectron2==0.6) (0.11.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.492667944,"data":"Requirement already satisfied: fonttools\u003e=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib-\u003edetectron2==0.6) (4.40.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.492674789,"data":"Requirement already satisfied: kiwisolver\u003e=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib-\u003edetectron2==0.6) (1.4.4)\r\n"} | |
| ,{"stream_name":"stdout","time":29.492680981,"data":"Requirement already satisfied: pyparsing\u003c3.1,\u003e=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib-\u003edetectron2==0.6) (3.0.9)\r\n"} | |
| ,{"stream_name":"stdout","time":29.492687499,"data":"Requirement already satisfied: python-dateutil\u003e=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib-\u003edetectron2==0.6) (2.8.2)\r\n"} | |
| ,{"stream_name":"stdout","time":29.543539608,"data":"Requirement already satisfied: click\u003e=8.0.0 in /opt/conda/lib/python3.10/site-packages (from black-\u003edetectron2==0.6) (8.1.7)\r\n"} | |
| ,{"stream_name":"stdout","time":29.543565355,"data":"Requirement already satisfied: mypy-extensions\u003e=0.4.3 in /opt/conda/lib/python3.10/site-packages (from black-\u003edetectron2==0.6) (1.0.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.645226589,"data":"Collecting packaging (from detectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":29.64524657,"data":" Downloading packaging-23.1-py3-none-any.whl (48 kB)\r\n"} | |
| ,{"stream_name":"stdout","time":29.645261524,"data":"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/48.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.9/48.9 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n"} | |
| ,{"stream_name":"stdout","time":29.695664356,"data":"\u001b[?25hCollecting pathspec\u003e=0.9.0 (from black-\u003edetectron2==0.6)\r\n"} | |
| ,{"stream_name":"stdout","time":29.695683454,"data":" Downloading pathspec-0.11.2-py3-none-any.whl (29 kB)\r\n"} | |
| ,{"stream_name":"stdout","time":29.747370869,"data":"Requirement already satisfied: platformdirs\u003e=2 in /opt/conda/lib/python3.10/site-packages (from black-\u003edetectron2==0.6) (3.10.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.747404532,"data":"Requirement already satisfied: tomli\u003e=1.1.0 in /opt/conda/lib/python3.10/site-packages (from black-\u003edetectron2==0.6) (2.0.1)\r\n"} | |
| ,{"stream_name":"stdout","time":29.747429754,"data":"Requirement already satisfied: typing-extensions\u003e=4.0.1 in /opt/conda/lib/python3.10/site-packages (from black-\u003edetectron2==0.6) (4.6.3)\r\n"} | |
| ,{"stream_name":"stdout","time":29.747435658,"data":"Requirement already satisfied: absl-py\u003e=0.4 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (1.4.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.74744468,"data":"Requirement already satisfied: grpcio\u003e=1.48.2 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (1.51.1)\r\n"} | |
| ,{"stream_name":"stdout","time":29.747450082,"data":"Requirement already satisfied: google-auth\u003c3,\u003e=1.6.3 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (2.20.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.799535672,"data":"Requirement already satisfied: google-auth-oauthlib\u003c1.1,\u003e=0.5 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (1.0.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.79955317,"data":"Requirement already satisfied: markdown\u003e=2.6.8 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (3.4.3)\r\n"} | |
| ,{"stream_name":"stdout","time":29.799559979,"data":"Requirement already satisfied: protobuf\u003e=3.19.6 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (3.20.3)\r\n"} | |
| ,{"stream_name":"stdout","time":29.799573088,"data":"Requirement already satisfied: requests\u003c3,\u003e=2.21.0 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (2.31.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.799581193,"data":"Requirement already satisfied: setuptools\u003e=41.0.0 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (68.0.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.79958711,"data":"Requirement already satisfied: tensorboard-data-server\u003c0.8.0,\u003e=0.7.0 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (0.7.1)\r\n"} | |
| ,{"stream_name":"stdout","time":29.799592979,"data":"Requirement already satisfied: werkzeug\u003e=1.0.1 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (2.3.7)\r\n"} | |
| ,{"stream_name":"stdout","time":29.799598503,"data":"Requirement already satisfied: wheel\u003e=0.26 in /opt/conda/lib/python3.10/site-packages (from tensorboard-\u003edetectron2==0.6) (0.40.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.901994492,"data":"Requirement already satisfied: cachetools\u003c6.0,\u003e=2.0.0 in /opt/conda/lib/python3.10/site-packages (from google-auth\u003c3,\u003e=1.6.3-\u003etensorboard-\u003edetectron2==0.6) (4.2.4)\r\n"} | |
| ,{"stream_name":"stdout","time":29.902019099,"data":"Requirement already satisfied: pyasn1-modules\u003e=0.2.1 in /opt/conda/lib/python3.10/site-packages (from google-auth\u003c3,\u003e=1.6.3-\u003etensorboard-\u003edetectron2==0.6) (0.2.7)\r\n"} | |
| ,{"stream_name":"stdout","time":29.902030352,"data":"Requirement already satisfied: rsa\u003c5,\u003e=3.1.4 in /opt/conda/lib/python3.10/site-packages (from google-auth\u003c3,\u003e=1.6.3-\u003etensorboard-\u003edetectron2==0.6) (4.9)\r\n"} | |
| ,{"stream_name":"stdout","time":29.90203704,"data":"Requirement already satisfied: six\u003e=1.9.0 in /opt/conda/lib/python3.10/site-packages (from google-auth\u003c3,\u003e=1.6.3-\u003etensorboard-\u003edetectron2==0.6) (1.16.0)\r\n"} | |
| ,{"stream_name":"stdout","time":29.902055856,"data":"Requirement already satisfied: urllib3\u003c2.0 in /opt/conda/lib/python3.10/site-packages (from google-auth\u003c3,\u003e=1.6.3-\u003etensorboard-\u003edetectron2==0.6) (1.26.15)\r\n"} | |
| ,{"stream_name":"stdout","time":29.952776028,"data":"Requirement already satisfied: requests-oauthlib\u003e=0.7.0 in /opt/conda/lib/python3.10/site-packages (from google-auth-oauthlib\u003c1.1,\u003e=0.5-\u003etensorboard-\u003edetectron2==0.6) (1.3.1)\r\n"} | |
| ,{"stream_name":"stdout","time":30.054635326,"data":"Requirement already satisfied: charset-normalizer\u003c4,\u003e=2 in /opt/conda/lib/python3.10/site-packages (from requests\u003c3,\u003e=2.21.0-\u003etensorboard-\u003edetectron2==0.6) (3.1.0)\r\n"} | |
| ,{"stream_name":"stdout","time":30.054655378,"data":"Requirement already satisfied: idna\u003c4,\u003e=2.5 in /opt/conda/lib/python3.10/site-packages (from requests\u003c3,\u003e=2.21.0-\u003etensorboard-\u003edetectron2==0.6) (3.4)\r\n"} | |
| ,{"stream_name":"stdout","time":30.054668613,"data":"Requirement already satisfied: certifi\u003e=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests\u003c3,\u003e=2.21.0-\u003etensorboard-\u003edetectron2==0.6) (2023.7.22)\r\n"} | |
| ,{"stream_name":"stdout","time":30.105753386,"data":"Requirement already satisfied: MarkupSafe\u003e=2.1.1 in /opt/conda/lib/python3.10/site-packages (from werkzeug\u003e=1.0.1-\u003etensorboard-\u003edetectron2==0.6) (2.1.3)\r\n"} | |
| ,{"stream_name":"stdout","time":30.207797875,"data":"Requirement already satisfied: pyasn1\u003c0.5.0,\u003e=0.4.6 in /opt/conda/lib/python3.10/site-packages (from pyasn1-modules\u003e=0.2.1-\u003egoogle-auth\u003c3,\u003e=1.6.3-\u003etensorboard-\u003edetectron2==0.6) (0.4.8)\r\n"} | |
| ,{"stream_name":"stdout","time":30.20781625,"data":"Requirement already satisfied: oauthlib\u003e=3.0.0 in /opt/conda/lib/python3.10/site-packages (from requests-oauthlib\u003e=0.7.0-\u003egoogle-auth-oauthlib\u003c1.1,\u003e=0.5-\u003etensorboard-\u003edetectron2==0.6) (3.2.2)\r\n"} | |
| ,{"stream_name":"stdout","time":30.310920006,"data":"Building wheels for collected packages: detectron2, fvcore, antlr4-python3-runtime\r\n"} | |
| ,{"stream_name":"stdout","time":201.216497586,"data":" Building wheel for detectron2 (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \bdone\r\n"} | |
| ,{"stream_name":"stdout","time":201.216546505,"data":"\u001b[?25h Created wheel for detectron2: filename=detectron2-0.6-cp310-cp310-linux_x86_64.whl size=1255108 sha256=c1bb4c192a8a85f1c87b6eff3d9a1bcc8cb87afba91bb90713b6d52f7d2df0de\r\n"} | |
| ,{"stream_name":"stdout","time":201.216554265,"data":" Stored in directory: /tmp/pip-ephem-wheel-cache-94a0tk58/wheels/47/e5/15/94c80df2ba85500c5d76599cc307c0a7079d0e221bb6fc4375\r\n"} | |
| ,{"stream_name":"stdout","time":202.484371077,"data":" Building wheel for fvcore (setup.py) ... \u001b[?25l-\b \bdone\r\n"} | |
| ,{"stream_name":"stdout","time":202.484412358,"data":"\u001b[?25h Created wheel for fvcore: filename=fvcore-0.1.5.post20221221-py3-none-any.whl size=61405 sha256=bbcfeea57f018330174c976c074113dedf0a54d49dc1e2e15cc21ebd5d4805e8\r\n"} | |
| ,{"stream_name":"stdout","time":202.484420144,"data":" Stored in directory: /root/.cache/pip/wheels/01/c0/af/77c1cf53a1be9e42a52b48e5af2169d40ec2e89f7362489dd0\r\n"} | |
| ,{"stream_name":"stdout","time":203.802844341,"data":" Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l-\b \bdone\r\n"} | |
| ,{"stream_name":"stdout","time":203.802882933,"data":"\u001b[?25h Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=9dc6457d743afbce3116c7471223a132c614f7213c1f42c7b1d3e2e6ebbab799\r\n"} | |
| ,{"stream_name":"stdout","time":203.802890735,"data":" Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\r\n"} | |
| ,{"stream_name":"stdout","time":203.802910449,"data":"Successfully built detectron2 fvcore antlr4-python3-runtime\r\n"} | |
| ,{"stream_name":"stdout","time":215.46712805,"data":"Installing collected packages: antlr4-python3-runtime, yacs, portalocker, pathspec, packaging, omegaconf, iopath, hydra-core, black, pycocotools, fvcore, detectron2\r\n"} | |
| ,{"stream_name":"stdout","time":215.669267295,"data":" Attempting uninstall: packaging\r\n"} | |
| ,{"stream_name":"stdout","time":215.669311267,"data":" Found existing installation: packaging 21.3\r\n"} | |
| ,{"stream_name":"stdout","time":215.66931776,"data":" Uninstalling packaging-21.3:\r\n"} | |
| ,{"stream_name":"stdout","time":215.72093058,"data":" Successfully uninstalled packaging-21.3\r\n"} | |
| ,{"stream_name":"stdout","time":217.038332118,"data":"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038369214,"data":"cudf 23.8.0 requires cupy-cuda11x\u003e=12.0.0, which is not installed.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038380177,"data":"cuml 23.8.0 requires cupy-cuda11x\u003e=12.0.0, which is not installed.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038387024,"data":"dask-cudf 23.8.0 requires cupy-cuda11x\u003e=12.0.0, which is not installed.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038392055,"data":"cudf 23.8.0 requires pandas\u003c1.6.0dev0,\u003e=1.3, but you have pandas 2.0.2 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038397376,"data":"cudf 23.8.0 requires protobuf\u003c5,\u003e=4.21, but you have protobuf 3.20.3 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038402616,"data":"cuml 23.8.0 requires dask==2023.7.1, but you have dask 2023.9.0 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.03840767,"data":"dask-cuda 23.8.0 requires dask==2023.7.1, but you have dask 2023.9.0 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038412866,"data":"dask-cuda 23.8.0 requires pandas\u003c1.6.0dev0,\u003e=1.3, but you have pandas 2.0.2 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038417787,"data":"dask-cudf 23.8.0 requires dask==2023.7.1, but you have dask 2023.9.0 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.03842299,"data":"dask-cudf 23.8.0 requires pandas\u003c1.6.0dev0,\u003e=1.3, but you have pandas 2.0.2 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038427905,"data":"distributed 2023.7.1 requires dask==2023.7.1, but you have dask 2023.9.0 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038433206,"data":"google-cloud-bigquery 2.34.4 requires packaging\u003c22.0dev,\u003e=14.3, but you have packaging 23.1 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038438437,"data":"jupyterlab-lsp 4.2.0 requires jupyter-lsp\u003e=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038443813,"data":"momepy 0.6.0 requires shapely\u003e=2, but you have shapely 1.8.5.post1 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038449692,"data":"pymc3 3.11.5 requires numpy\u003c1.22.2,\u003e=1.15.0, but you have numpy 1.23.5 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038459679,"data":"pymc3 3.11.5 requires scipy\u003c1.8.0,\u003e=1.7.3, but you have scipy 1.11.2 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038469082,"data":"raft-dask 23.8.0 requires dask==2023.7.1, but you have dask 2023.9.0 which is incompatible.\r\n"} | |
| ,{"stream_name":"stdout","time":217.038487331,"data":"ydata-profiling 4.3.1 requires scipy\u003c1.11,\u003e=1.4.1, but you have scipy 1.11.2 which is incompatible.\u001b[0m\u001b[31m\r\n"} | |
| ,{"stream_name":"stdout","time":217.038494464,"data":"\u001b[0mSuccessfully installed antlr4-python3-runtime-4.9.3 black-23.9.1 detectron2-0.6 fvcore-0.1.5.post20221221 hydra-core-1.3.2 iopath-0.1.9 omegaconf-2.3.0 packaging-23.1 pathspec-0.11.2 portalocker-2.8.2 pycocotools-2.0.7 yacs-0.1.8\r\n"} | |
| ,{"stream_name":"stdout","time":219.774063631,"data":"2.0.0 True\n"} | |
| ,{"stream_name":"stdout","time":220.700549514,"data":"\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0m\n"} | |
| ,{"stream_name":"stdout","time":220.700602938,"data":"Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n"} | |
| ,{"stream_name":"stdout","time":220.700611439,"data":"\n"} | |
| ,{"stream_name":"stdout","time":220.702501643,"data":"\u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0mLoaded 123 images in COCO format from /kaggle/input/leaf-flower-fruit-annotation/semantic-segmentation-of-plants.v2i.coco-segmentation/train/train.json\n"} | |
| ,{"stream_name":"stdout","time":220.744890243,"data":"\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0m\n"} | |
| ,{"stream_name":"stdout","time":220.744911035,"data":"Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n"} | |
| ,{"stream_name":"stdout","time":220.744927809,"data":"\n"} | |
| ,{"stream_name":"stdout","time":220.746546326,"data":"\u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0mLoaded 123 images in COCO format from /kaggle/input/leaf-flower-fruit-annotation/semantic-segmentation-of-plants.v2i.coco-segmentation/train/train.json\n"} | |
| ,{"stream_name":"stdout","time":220.788803161,"data":"\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0m\n"} | |
| ,{"stream_name":"stdout","time":220.788823346,"data":"Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n"} | |
| ,{"stream_name":"stdout","time":220.788830261,"data":"\n"} | |
| ,{"stream_name":"stdout","time":220.789918143,"data":"\u001b[32m[09/23 07:38:36 d2.data.datasets.coco]: \u001b[0mLoaded 18 images in COCO format from /kaggle/input/leaf-flower-fruit-annotation/semantic-segmentation-of-plants.v2i.coco-segmentation/valid/valid.json\n"} | |
| ,{"stream_name":"stdout","time":227.179968614,"data":"\u001b[32m[09/23 07:38:42 d2.engine.defaults]: \u001b[0mModel:\n"} | |
| ,{"stream_name":"stdout","time":227.180007033,"data":"GeneralizedRCNN(\n"} | |
| ,{"stream_name":"stdout","time":227.180015259,"data":" (backbone): FPN(\n"} | |
| ,{"stream_name":"stdout","time":227.180020811,"data":" (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.180026353,"data":" (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.180031327,"data":" (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.180037021,"data":" (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.180041947,"data":" (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.180046859,"data":" (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.180051985,"data":" (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.180056954,"data":" (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.180072911,"data":" (top_block): LastLevelMaxPool()\n"} | |
| ,{"stream_name":"stdout","time":227.180077739,"data":" (bottom_up): ResNet(\n"} | |
| ,{"stream_name":"stdout","time":227.180084569,"data":" (stem): BasicStem(\n"} | |
| ,{"stream_name":"stdout","time":227.180105523,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180113641,"data":" 3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180118051,"data":" (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180123293,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180128394,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180132861,"data":" (res2): Sequential(\n"} | |
| ,{"stream_name":"stdout","time":227.180137663,"data":" (0): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.180142884,"data":" (shortcut): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180147615,"data":" 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180152143,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.18015687,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.18016138,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180165908,"data":" 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180171071,"data":" (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180176042,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180180789,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180187851,"data":" 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180192613,"data":" (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180197731,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180203034,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180207967,"data":" 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180213024,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180218341,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180223163,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180227909,"data":" (1): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.180232924,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180237973,"data":" 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180243241,"data":" (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180248315,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180253167,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.18026029,"data":" 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180265617,"data":" (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180282245,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180286668,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180291644,"data":" 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180301924,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180306778,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180311389,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180315953,"data":" (2): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.180320733,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180325365,"data":" 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.1803301,"data":" (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180335192,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180339828,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180344423,"data":" 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.18034927,"data":" (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180354118,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180358687,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180363411,"data":" 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180368875,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180373783,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180378451,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180383843,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180388536,"data":" (res3): Sequential(\n"} | |
| ,{"stream_name":"stdout","time":227.18039321,"data":" (0): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.180398469,"data":" (shortcut): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180403798,"data":" 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180408623,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180413364,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.18041866,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.18042418,"data":" 256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180428929,"data":" (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180434756,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.18043941,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180444084,"data":" 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180449185,"data":" (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180454053,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180459049,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180463752,"data":" 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180469139,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180473977,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180480028,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180488874,"data":" (1): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.180493769,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180506633,"data":" 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.18051207,"data":" (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180516953,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180521744,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180526465,"data":" 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180531405,"data":" (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.18053795,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180542618,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180547357,"data":" 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180552208,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180557098,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180561944,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.1805664,"data":" (2): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.18057115,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180575763,"data":" 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180580599,"data":" (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180585333,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180590415,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180595183,"data":" 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180600173,"data":" (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180604947,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180610435,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180615085,"data":" 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180619835,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180625462,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180630042,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180634662,"data":" (3): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.180640148,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180644815,"data":" 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180649923,"data":" (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180654786,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180659695,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180664411,"data":" 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180670372,"data":" (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.18067983,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180684519,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180689705,"data":" 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180700005,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180704891,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.18070948,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180714443,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180718966,"data":" (res4): Sequential(\n"} | |
| ,{"stream_name":"stdout","time":227.180723353,"data":" (0): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.180728198,"data":" (shortcut): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180732851,"data":" 512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180737728,"data":" (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180742506,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180747193,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180751991,"data":" 512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180757062,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180762048,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180766557,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.18077108,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180776155,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180781206,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180786045,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180791662,"data":" 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180797355,"data":" (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180802206,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180806824,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180811377,"data":" (1): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.180816235,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180820949,"data":" 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180825913,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180830626,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180835287,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180839938,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180844728,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180849879,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.18085443,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180859075,"data":" 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180864006,"data":" (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180875643,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180880301,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180884996,"data":" (2): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.18088957,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180894262,"data":" 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.18089929,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180903955,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180908812,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180913675,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180918443,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180923131,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180927761,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180932395,"data":" 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180937224,"data":" (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180942078,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180946653,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180951224,"data":" (3): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.180956201,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180960857,"data":" 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180966914,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.18097187,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180976406,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.180981079,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.180986389,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.180991115,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.180995671,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181000226,"data":" 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181005248,"data":" (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181010191,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181014824,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181019346,"data":" (4): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.181023859,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181028565,"data":" 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181033696,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181038536,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181043588,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181048625,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181058514,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181063493,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181067956,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181072521,"data":" 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181077503,"data":" (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181082536,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181087066,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181102295,"data":" (5): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.181108497,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181113243,"data":" 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181118186,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181122985,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181127606,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181132206,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181138041,"data":" (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181142835,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181147444,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181169302,"data":" 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181174493,"data":" (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181179363,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181184361,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181189231,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181194132,"data":" (res5): Sequential(\n"} | |
| ,{"stream_name":"stdout","time":227.18119915,"data":" (0): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.181204208,"data":" (shortcut): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181209271,"data":" 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.18121447,"data":" (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181219765,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181225295,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181230167,"data":" 1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181235147,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181240345,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.1812452,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181250229,"data":" 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181255323,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.18126061,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181265673,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181275615,"data":" 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181280497,"data":" (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181285533,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181290529,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181295393,"data":" (1): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.181300267,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181305406,"data":" 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181310517,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.18131568,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181320611,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181326367,"data":" 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181331657,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181336564,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181341456,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181346381,"data":" 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181351864,"data":" (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181356955,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181361823,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.18136669,"data":" (2): BottleneckBlock(\n"} | |
| ,{"stream_name":"stdout","time":227.181371641,"data":" (conv1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181376549,"data":" 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181382795,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181388078,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181393547,"data":" (conv2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181398537,"data":" 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181403673,"data":" (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181408696,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181413535,"data":" (conv3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181418905,"data":" 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n"} | |
| ,{"stream_name":"stdout","time":227.181424205,"data":" (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n"} | |
| ,{"stream_name":"stdout","time":227.181429122,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181434063,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181438825,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181443796,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181448638,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181453454,"data":" (proposal_generator): RPN(\n"} | |
| ,{"stream_name":"stdout","time":227.181458465,"data":" (rpn_head): StandardRPNHead(\n"} | |
| ,{"stream_name":"stdout","time":227.181463297,"data":" (conv): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.181472579,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"} | |
| ,{"stream_name":"stdout","time":227.181477859,"data":" (activation): ReLU()\n"} | |
| ,{"stream_name":"stdout","time":227.181482759,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181488225,"data":" (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.181493739,"data":" (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.181498749,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181512003,"data":" (anchor_generator): DefaultAnchorGenerator(\n"} | |
| ,{"stream_name":"stdout","time":227.181517215,"data":" (cell_anchors): BufferList()\n"} | |
| ,{"stream_name":"stdout","time":227.181522254,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181527441,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181532261,"data":" (roi_heads): StandardROIHeads(\n"} | |
| ,{"stream_name":"stdout","time":227.181537249,"data":" (box_pooler): ROIPooler(\n"} | |
| ,{"stream_name":"stdout","time":227.18154221,"data":" (level_poolers): ModuleList(\n"} | |
| ,{"stream_name":"stdout","time":227.181547381,"data":" (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n"} | |
| ,{"stream_name":"stdout","time":227.18155303,"data":" (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n"} | |
| ,{"stream_name":"stdout","time":227.181558271,"data":" (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n"} | |
| ,{"stream_name":"stdout","time":227.181565761,"data":" (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n"} | |
| ,{"stream_name":"stdout","time":227.181729465,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181751722,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181773205,"data":" (box_head): FastRCNNConvFCHead(\n"} | |
| ,{"stream_name":"stdout","time":227.181790567,"data":" (flatten): Flatten(start_dim=1, end_dim=-1)\n"} | |
| ,{"stream_name":"stdout","time":227.181808844,"data":" (fc1): Linear(in_features=12544, out_features=1024, bias=True)\n"} | |
| ,{"stream_name":"stdout","time":227.181826875,"data":" (fc_relu1): ReLU()\n"} | |
| ,{"stream_name":"stdout","time":227.181845128,"data":" (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n"} | |
| ,{"stream_name":"stdout","time":227.181864632,"data":" (fc_relu2): ReLU()\n"} | |
| ,{"stream_name":"stdout","time":227.181883825,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181900094,"data":" (box_predictor): FastRCNNOutputLayers(\n"} | |
| ,{"stream_name":"stdout","time":227.181917776,"data":" (cls_score): Linear(in_features=1024, out_features=5, bias=True)\n"} | |
| ,{"stream_name":"stdout","time":227.181933954,"data":" (bbox_pred): Linear(in_features=1024, out_features=16, bias=True)\n"} | |
| ,{"stream_name":"stdout","time":227.181953794,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.181971907,"data":" (mask_pooler): ROIPooler(\n"} | |
| ,{"stream_name":"stdout","time":227.181989415,"data":" (level_poolers): ModuleList(\n"} | |
| ,{"stream_name":"stdout","time":227.182006215,"data":" (0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n"} | |
| ,{"stream_name":"stdout","time":227.18202305,"data":" (1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n"} | |
| ,{"stream_name":"stdout","time":227.182044622,"data":" (2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n"} | |
| ,{"stream_name":"stdout","time":227.182071865,"data":" (3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n"} | |
| ,{"stream_name":"stdout","time":227.182111882,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.182133524,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.182149704,"data":" (mask_head): MaskRCNNConvUpsampleHead(\n"} | |
| ,{"stream_name":"stdout","time":227.182202938,"data":" (mask_fcn1): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.182221526,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"} | |
| ,{"stream_name":"stdout","time":227.182238354,"data":" (activation): ReLU()\n"} | |
| ,{"stream_name":"stdout","time":227.182255526,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.182291705,"data":" (mask_fcn2): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.182309548,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"} | |
| ,{"stream_name":"stdout","time":227.182325672,"data":" (activation): ReLU()\n"} | |
| ,{"stream_name":"stdout","time":227.182342486,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.182375262,"data":" (mask_fcn3): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.182390364,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"} | |
| ,{"stream_name":"stdout","time":227.182406994,"data":" (activation): ReLU()\n"} | |
| ,{"stream_name":"stdout","time":227.182423953,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.18245527,"data":" (mask_fcn4): Conv2d(\n"} | |
| ,{"stream_name":"stdout","time":227.182472856,"data":" 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n"} | |
| ,{"stream_name":"stdout","time":227.182488279,"data":" (activation): ReLU()\n"} | |
| ,{"stream_name":"stdout","time":227.18255243,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.182569518,"data":" (deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))\n"} | |
| ,{"stream_name":"stdout","time":227.182585696,"data":" (deconv_relu): ReLU()\n"} | |
| ,{"stream_name":"stdout","time":227.182619832,"data":" (predictor): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1))\n"} | |
| ,{"stream_name":"stdout","time":227.182639116,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.182661524,"data":" )\n"} | |
| ,{"stream_name":"stdout","time":227.18267819,"data":")\n"} | |
| ,{"stream_name":"stdout","time":227.194125733,"data":"\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[09/23 07:38:42 d2.data.datasets.coco]: \u001b[0m\n"} | |
| ,{"stream_name":"stdout","time":227.194167193,"data":"Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n"} | |
| ,{"stream_name":"stdout","time":227.194180416,"data":"\n"} | |
| ,{"stream_name":"stdout","time":227.196109555,"data":"\u001b[32m[09/23 07:38:42 d2.data.datasets.coco]: \u001b[0mLoaded 123 images in COCO format from /kaggle/input/leaf-flower-fruit-annotation/semantic-segmentation-of-plants.v2i.coco-segmentation/train/train.json\n"} | |
| ,{"stream_name":"stdout","time":227.202177857,"data":"\u001b[32m[09/23 07:38:42 d2.data.build]: \u001b[0mRemoved 0 images with no usable annotations. 123 images left.\n"} | |
| ,{"stream_name":"stdout","time":227.21499769,"data":"\u001b[32m[09/23 07:38:42 d2.data.build]: \u001b[0mDistribution of instances among all 4 categories:\n"} | |
| ,{"stream_name":"stdout","time":227.215031132,"data":"\u001b[36m| category | #instances | category | #instances | category | #instances |\n"} | |
| ,{"stream_name":"stdout","time":227.2150391,"data":"|:-------------:|:-------------|:-----------|:-------------|:-----------|:-------------|\n"} | |
| ,{"stream_name":"stdout","time":227.215057176,"data":"| leaf, flowe.. | 0 | 0 | 440 | 1 | 164 |\n"} | |
| ,{"stream_name":"stdout","time":227.215063542,"data":"| 2 | 114 | | | | |\n"} | |
| ,{"stream_name":"stdout","time":227.215069607,"data":"| total | 718 | | | | |\u001b[0m\n"} | |
| ,{"stream_name":"stdout","time":227.21704684,"data":"\u001b[32m[09/23 07:38:42 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]\n"} | |
| ,{"stream_name":"stdout","time":227.218765315,"data":"\u001b[32m[09/23 07:38:42 d2.data.build]: \u001b[0mUsing training sampler TrainingSampler\n"} | |
| ,{"stream_name":"stdout","time":227.220234089,"data":"\u001b[32m[09/23 07:38:42 d2.data.common]: \u001b[0mSerializing the dataset using: \u003cclass 'detectron2.data.common._TorchSerializedList'\u003e\n"} | |
| ,{"stream_name":"stdout","time":227.224137052,"data":"\u001b[32m[09/23 07:38:42 d2.data.common]: \u001b[0mSerializing 123 elements to byte tensors and concatenating them all ...\n"} | |
| ,{"stream_name":"stdout","time":227.230644552,"data":"\u001b[32m[09/23 07:38:42 d2.data.common]: \u001b[0mSerialized dataset takes 0.17 MiB\n"} | |
| ,{"stream_name":"stdout","time":227.232131018,"data":"\u001b[32m[09/23 07:38:42 d2.checkpoint.detection_checkpoint]: \u001b[0m[DetectionCheckpointer] Loading from https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl ...\n"} | |
| ,{"stream_name":"stderr","time":228.472521436,"data":"\rmodel_final_f10217.pkl: 0.00B [00:00, ?B/s]\rmodel_final_f10217.pkl: 0%| | 8.19k/178M [00:00\u003c44:21, 66.8kB/s]\rmodel_final_f10217.pkl: 3%|▎ | 4.88M/178M [00:00\u003c00:06, 25.2MB/s]\rmodel_final_f10217.pkl: 7%|▋ | 12.7M/178M [00:00\u003c00:03, 47.7MB/s]\rmodel_final_f10217.pkl: 13%|█▎ | 22.4M/178M [00:00\u003c00:02, 66.1MB/s]\rmodel_final_f10217.pkl: 20%|█▉ | 34.8M/178M [00:00\u003c00:01, 86.4MB/s]\rmodel_final_f10217.pkl: 28%|██▊ | 49.2M/178M [00:00\u003c00:01, 106MB/s] \rmodel_final_f10217.pkl: 38%|███▊ | 67.8M/178M [00:00\u003c00:00, 132MB/s]\rmodel_final_f10217.pkl: 51%|█████ | 90.0M/178M [00:00\u003c00:00, 160MB/s]\rmodel_final_f10217.pkl: 64%|██████▎ | 113M/178M [00:00\u003c00:00, 183MB/s] \rmodel_final_f10217.pkl: 77%|███████▋ | 136M/178M [00:01\u003c00:00, 198MB/s]\rmodel_final_f10217.pkl: 90%|████████▉ | 160M/178M [00:01\u003c00:00, 208MB/s]\rmodel_final_f10217.pkl: 178MB [00:01, 147MB/s] \n"} | |
| ,{"stream_name":"stdout","time":228.653681696,"data":"\u001b[32m[09/23 07:38:44 d2.engine.train_loop]: \u001b[0mStarting training from iteration 0\n"} | |
| ,{"stream_name":"stderr","time":241.278435168,"data":"/opt/conda/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /usr/local/src/pytorch/aten/src/ATen/native/TensorShape.cpp:3483.)\n"} | |
| ,{"stream_name":"stderr","time":241.27850688,"data":" return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n"} | |
| ,{"stream_name":"stdout","time":251.518974705,"data":"\u001b[32m[09/23 07:39:07 d2.utils.events]: \u001b[0m eta: 0:05:39 iter: 19 total_loss: 2.828 loss_cls: 1.447 loss_box_reg: 0.4917 loss_mask: 0.6928 loss_rpn_cls: 0.1044 loss_rpn_loc: 0.02792 time: 0.3470 last_time: 0.3698 data_time: 0.0158 last_data_time: 0.0070 lr: 4.9953e-06 max_mem: 1767M\n"} | |
| ,{"stream_name":"stderr","time":260.105853923,"data":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version \u003e=1.16.5 and \u003c1.23.0 is required for this version of SciPy (detected version 1.23.5\n"} | |
| ,{"stream_name":"stderr","time":260.105889154,"data":" warnings.warn(f\"A NumPy version \u003e={np_minversion} and \u003c{np_maxversion}\"\n"} | |
| ,{"stream_name":"stdout","time":274.857913039,"data":"\u001b[32m[09/23 07:39:30 d2.utils.events]: \u001b[0m eta: 0:05:29 iter: 39 total_loss: 2.735 loss_cls: 1.383 loss_box_reg: 0.5501 loss_mask: 0.688 loss_rpn_cls: 0.02683 loss_rpn_loc: 0.01686 time: 0.4269 last_time: 0.3428 data_time: 0.0080 last_data_time: 0.0089 lr: 9.9902e-06 max_mem: 1772M\n"} | |
| ,{"stream_name":"stdout","time":281.719035339,"data":"\u001b[32m[09/23 07:39:37 d2.utils.events]: \u001b[0m eta: 0:05:22 iter: 59 total_loss: 2.39 loss_cls: 1.152 loss_box_reg: 0.5103 loss_mask: 0.6818 loss_rpn_cls: 0.05638 loss_rpn_loc: 0.02092 time: 0.3976 last_time: 0.4037 data_time: 0.0069 last_data_time: 0.0072 lr: 1.4985e-05 max_mem: 1772M\n"} | |
| ,{"stream_name":"stdout","time":288.837230053,"data":"\u001b[32m[09/23 07:39:44 d2.utils.events]: \u001b[0m eta: 0:05:17 iter: 79 total_loss: 2.393 loss_cls: 0.9233 loss_box_reg: 0.6164 loss_mask: 0.6686 loss_rpn_cls: 0.08493 loss_rpn_loc: 0.02326 time: 0.3868 last_time: 0.3918 data_time: 0.0072 last_data_time: 0.0065 lr: 1.998e-05 max_mem: 1773M\n"} | |
| ,{"stream_name":"stdout","time":295.766690036,"data":"\u001b[32m[09/23 07:39:51 d2.utils.events]: \u001b[0m eta: 0:05:08 iter: 99 total_loss: 2.018 loss_cls: 0.7153 loss_box_reg: 0.4563 loss_mask: 0.6582 loss_rpn_cls: 0.05049 loss_rpn_loc: 0.02335 time: 0.3778 last_time: 0.3366 data_time: 0.0086 last_data_time: 0.0065 lr: 2.4975e-05 max_mem: 1773M\n"} | |
| ,{"stream_name":"stdout","time":302.773351708,"data":"\u001b[32m[09/23 07:39:58 d2.utils.events]: \u001b[0m eta: 0:05:02 iter: 119 total_loss: 1.87 loss_cls: 0.6182 loss_box_reg: 0.5203 loss_mask: 0.6352 loss_rpn_cls: 0.0492 loss_rpn_loc: 0.02022 time: 0.3735 last_time: 0.3489 data_time: 0.0083 last_data_time: 0.0062 lr: 2.997e-05 max_mem: 1774M\n"} | |
| ,{"stream_name":"stdout","time":309.871407386,"data":"\u001b[32m[09/23 07:40:05 d2.utils.events]: \u001b[0m eta: 0:04:58 iter: 139 total_loss: 1.879 loss_cls: 0.5958 loss_box_reg: 0.5152 loss_mask: 0.6125 loss_rpn_cls: 0.03336 loss_rpn_loc: 0.01598 time: 0.3707 last_time: 0.3607 data_time: 0.0081 last_data_time: 0.0070 lr: 3.4965e-05 max_mem: 1774M\n"} | |
| ,{"stream_name":"stdout","time":316.890266425,"data":"\u001b[32m[09/23 07:40:12 d2.utils.events]: \u001b[0m eta: 0:04:51 iter: 159 total_loss: 1.826 loss_cls: 0.5633 loss_box_reg: 0.5863 loss_mask: 0.5935 loss_rpn_cls: 0.0336 loss_rpn_loc: 0.0204 time: 0.3682 last_time: 0.3499 data_time: 0.0078 last_data_time: 0.0063 lr: 3.996e-05 max_mem: 1774M\n"} | |
| ,{"stream_name":"stdout","time":323.911162118,"data":"\u001b[32m[09/23 07:40:19 d2.utils.events]: \u001b[0m eta: 0:04:44 iter: 179 total_loss: 1.708 loss_cls: 0.5234 loss_box_reg: 0.5497 loss_mask: 0.5757 loss_rpn_cls: 0.02893 loss_rpn_loc: 0.02126 time: 0.3662 last_time: 0.3599 data_time: 0.0071 last_data_time: 0.0067 lr: 4.4955e-05 max_mem: 1774M\n"} | |
| ,{"stream_name":"stdout","time":330.976295222,"data":"\u001b[32m[09/23 07:40:26 d2.utils.events]: \u001b[0m eta: 0:04:38 iter: 199 total_loss: 1.733 loss_cls: 0.5085 loss_box_reg: 0.5441 loss_mask: 0.5637 loss_rpn_cls: 0.03238 loss_rpn_loc: 0.02728 time: 0.3646 last_time: 0.3137 data_time: 0.0072 last_data_time: 0.0081 lr: 4.995e-05 max_mem: 1774M\n"} | |
| ,{"stream_name":"stdout","time":338.394322939,"data":"\u001b[32m[09/23 07:40:33 d2.utils.events]: \u001b[0m eta: 0:04:32 iter: 219 total_loss: 1.709 loss_cls: 0.5236 loss_box_reg: 0.5941 loss_mask: 0.5093 loss_rpn_cls: 0.02147 loss_rpn_loc: 0.02077 time: 0.3653 last_time: 0.3540 data_time: 0.0114 last_data_time: 0.0258 lr: 5.4945e-05 max_mem: 1774M\n"} | |
| ,{"stream_name":"stdout","time":345.525876389,"data":"\u001b[32m[09/23 07:40:41 d2.utils.events]: \u001b[0m eta: 0:04:26 iter: 239 total_loss: 1.628 loss_cls: 0.4467 loss_box_reg: 0.5763 loss_mask: 0.5075 loss_rpn_cls: 0.02056 loss_rpn_loc: 0.02806 time: 0.3645 last_time: 0.3969 data_time: 0.0085 last_data_time: 0.0064 lr: 5.994e-05 max_mem: 1774M\n"} | |
| ,{"stream_name":"stdout","time":352.559771032,"data":"\u001b[32m[09/23 07:40:48 d2.utils.events]: \u001b[0m eta: 0:04:20 iter: 259 total_loss: 1.477 loss_cls: 0.416 loss_box_reg: 0.5193 loss_mask: 0.4923 loss_rpn_cls: 0.01588 loss_rpn_loc: 0.0164 time: 0.3634 last_time: 0.3532 data_time: 0.0069 last_data_time: 0.0061 lr: 6.4935e-05 max_mem: 1774M\n"} | |
| ,{"stream_name":"stdout","time":359.769010562,"data":"\u001b[32m[09/23 07:40:55 d2.utils.events]: \u001b[0m eta: 0:04:13 iter: 279 total_loss: 1.503 loss_cls: 0.4283 loss_box_reg: 0.566 loss_mask: 0.4444 loss_rpn_cls: 0.02047 loss_rpn_loc: 0.02029 time: 0.3632 last_time: 0.2831 data_time: 0.0087 last_data_time: 0.0088 lr: 6.993e-05 max_mem: 1775M\n"} | |
| ,{"stream_name":"stdout","time":367.474066393,"data":"\u001b[32m[09/23 07:41:02 d2.utils.events]: \u001b[0m eta: 0:04:07 iter: 299 total_loss: 1.536 loss_cls: 0.4297 loss_box_reg: 0.5559 loss_mask: 0.437 loss_rpn_cls: 0.03421 loss_rpn_loc: 0.02454 time: 0.3645 last_time: 0.3751 data_time: 0.0074 last_data_time: 0.0091 lr: 7.4925e-05 max_mem: 1775M\n"} | |
| ,{"stream_name":"stdout","time":374.662886654,"data":"\u001b[32m[09/23 07:41:10 d2.utils.events]: \u001b[0m eta: 0:04:01 iter: 319 total_loss: 1.412 loss_cls: 0.4156 loss_box_reg: 0.5481 loss_mask: 0.4122 loss_rpn_cls: 0.01921 loss_rpn_loc: 0.01239 time: 0.3642 last_time: 0.3551 data_time: 0.0103 last_data_time: 0.0065 lr: 7.992e-05 max_mem: 1775M\n"} | |
| ,{"stream_name":"stdout","time":382.170783334,"data":"\u001b[32m[09/23 07:41:17 d2.utils.events]: \u001b[0m eta: 0:03:54 iter: 339 total_loss: 1.266 loss_cls: 0.3341 loss_box_reg: 0.4788 loss_mask: 0.3911 loss_rpn_cls: 0.01888 loss_rpn_loc: 0.01569 time: 0.3649 last_time: 0.4293 data_time: 0.0080 last_data_time: 0.0070 lr: 8.4915e-05 max_mem: 1775M\n"} | |
| ,{"stream_name":"stdout","time":389.425823612,"data":"\u001b[32m[09/23 07:41:24 d2.utils.events]: \u001b[0m eta: 0:03:47 iter: 359 total_loss: 1.395 loss_cls: 0.3506 loss_box_reg: 0.5867 loss_mask: 0.3759 loss_rpn_cls: 0.01438 loss_rpn_loc: 0.01705 time: 0.3647 last_time: 0.3646 data_time: 0.0074 last_data_time: 0.0071 lr: 8.991e-05 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":396.96129169,"data":"\u001b[32m[09/23 07:41:32 d2.utils.events]: \u001b[0m eta: 0:03:40 iter: 379 total_loss: 1.451 loss_cls: 0.3727 loss_box_reg: 0.6039 loss_mask: 0.3999 loss_rpn_cls: 0.01364 loss_rpn_loc: 0.01788 time: 0.3653 last_time: 0.4154 data_time: 0.0070 last_data_time: 0.0067 lr: 9.4905e-05 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":404.823707244,"data":"\u001b[32m[09/23 07:41:40 d2.utils.events]: \u001b[0m eta: 0:03:34 iter: 399 total_loss: 1.315 loss_cls: 0.3367 loss_box_reg: 0.5276 loss_mask: 0.3453 loss_rpn_cls: 0.01492 loss_rpn_loc: 0.01715 time: 0.3666 last_time: 0.4236 data_time: 0.0112 last_data_time: 0.0087 lr: 9.99e-05 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":411.905664712,"data":"\u001b[32m[09/23 07:41:47 d2.utils.events]: \u001b[0m eta: 0:03:27 iter: 419 total_loss: 0.9786 loss_cls: 0.2303 loss_box_reg: 0.4012 loss_mask: 0.3317 loss_rpn_cls: 0.009277 loss_rpn_loc: 0.01368 time: 0.3660 last_time: 0.3196 data_time: 0.0081 last_data_time: 0.0076 lr: 0.0001049 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":419.540183238,"data":"\u001b[32m[09/23 07:41:55 d2.utils.events]: \u001b[0m eta: 0:03:21 iter: 439 total_loss: 1.285 loss_cls: 0.2976 loss_box_reg: 0.5277 loss_mask: 0.3637 loss_rpn_cls: 0.01374 loss_rpn_loc: 0.02255 time: 0.3667 last_time: 0.4135 data_time: 0.0069 last_data_time: 0.0070 lr: 0.00010989 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":427.026892669,"data":"\u001b[32m[09/23 07:42:02 d2.utils.events]: \u001b[0m eta: 0:03:14 iter: 459 total_loss: 1.119 loss_cls: 0.2653 loss_box_reg: 0.5093 loss_mask: 0.3336 loss_rpn_cls: 0.008255 loss_rpn_loc: 0.01522 time: 0.3670 last_time: 0.3390 data_time: 0.0081 last_data_time: 0.0072 lr: 0.00011489 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":434.509628645,"data":"\u001b[32m[09/23 07:42:09 d2.utils.events]: \u001b[0m eta: 0:03:08 iter: 479 total_loss: 1.243 loss_cls: 0.3047 loss_box_reg: 0.5412 loss_mask: 0.3295 loss_rpn_cls: 0.01302 loss_rpn_loc: 0.01568 time: 0.3673 last_time: 0.3210 data_time: 0.0090 last_data_time: 0.0100 lr: 0.00011988 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":442.176178234,"data":"\u001b[32m[09/23 07:42:17 d2.utils.events]: \u001b[0m eta: 0:03:00 iter: 499 total_loss: 1.142 loss_cls: 0.2688 loss_box_reg: 0.4826 loss_mask: 0.3046 loss_rpn_cls: 0.006692 loss_rpn_loc: 0.01597 time: 0.3678 last_time: 0.4091 data_time: 0.0070 last_data_time: 0.0064 lr: 0.00012488 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":449.602940172,"data":"\u001b[32m[09/23 07:42:25 d2.utils.events]: \u001b[0m eta: 0:02:53 iter: 519 total_loss: 1.065 loss_cls: 0.2476 loss_box_reg: 0.4258 loss_mask: 0.3209 loss_rpn_cls: 0.0138 loss_rpn_loc: 0.01544 time: 0.3680 last_time: 0.3954 data_time: 0.0083 last_data_time: 0.0073 lr: 0.00012987 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":457.112448665,"data":"\u001b[32m[09/23 07:42:32 d2.utils.events]: \u001b[0m eta: 0:02:46 iter: 539 total_loss: 0.9309 loss_cls: 0.1731 loss_box_reg: 0.4198 loss_mask: 0.2816 loss_rpn_cls: 0.004735 loss_rpn_loc: 0.01888 time: 0.3683 last_time: 0.3994 data_time: 0.0067 last_data_time: 0.0063 lr: 0.00013487 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":465.022043696,"data":"\u001b[32m[09/23 07:42:40 d2.utils.events]: \u001b[0m eta: 0:02:40 iter: 559 total_loss: 1.027 loss_cls: 0.2307 loss_box_reg: 0.4041 loss_mask: 0.3095 loss_rpn_cls: 0.01669 loss_rpn_loc: 0.02477 time: 0.3692 last_time: 0.4273 data_time: 0.0100 last_data_time: 0.0261 lr: 0.00013986 max_mem: 1776M\n"} | |
| ,{"stream_name":"stdout","time":472.342005965,"data":"\u001b[32m[09/23 07:42:47 d2.utils.events]: \u001b[0m eta: 0:02:32 iter: 579 total_loss: 0.7264 loss_cls: 0.1098 loss_box_reg: 0.2802 loss_mask: 0.2807 loss_rpn_cls: 0.003293 loss_rpn_loc: 0.01229 time: 0.3690 last_time: 0.3049 data_time: 0.0084 last_data_time: 0.0069 lr: 0.00014486 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":479.85336258,"data":"\u001b[32m[09/23 07:42:55 d2.utils.events]: \u001b[0m eta: 0:02:25 iter: 599 total_loss: 0.899 loss_cls: 0.2028 loss_box_reg: 0.329 loss_mask: 0.3195 loss_rpn_cls: 0.007076 loss_rpn_loc: 0.02576 time: 0.3693 last_time: 0.3022 data_time: 0.0079 last_data_time: 0.0075 lr: 0.00014985 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":487.280054506,"data":"\u001b[32m[09/23 07:43:02 d2.utils.events]: \u001b[0m eta: 0:02:18 iter: 619 total_loss: 0.8217 loss_cls: 0.1623 loss_box_reg: 0.3018 loss_mask: 0.2941 loss_rpn_cls: 0.005526 loss_rpn_loc: 0.02092 time: 0.3693 last_time: 0.4131 data_time: 0.0076 last_data_time: 0.0073 lr: 0.00015485 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":494.711325657,"data":"\u001b[32m[09/23 07:43:10 d2.utils.events]: \u001b[0m eta: 0:02:11 iter: 639 total_loss: 0.7626 loss_cls: 0.1449 loss_box_reg: 0.3171 loss_mask: 0.2855 loss_rpn_cls: 0.003396 loss_rpn_loc: 0.01711 time: 0.3694 last_time: 0.3385 data_time: 0.0072 last_data_time: 0.0071 lr: 0.00015984 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":502.406562878,"data":"\u001b[32m[09/23 07:43:17 d2.utils.events]: \u001b[0m eta: 0:02:03 iter: 659 total_loss: 0.8223 loss_cls: 0.1529 loss_box_reg: 0.319 loss_mask: 0.2761 loss_rpn_cls: 0.00508 loss_rpn_loc: 0.01968 time: 0.3698 last_time: 0.3346 data_time: 0.0082 last_data_time: 0.0088 lr: 0.00016484 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":510.053115581,"data":"\u001b[32m[09/23 07:43:25 d2.utils.events]: \u001b[0m eta: 0:01:56 iter: 679 total_loss: 0.6646 loss_cls: 0.1136 loss_box_reg: 0.2644 loss_mask: 0.2595 loss_rpn_cls: 0.005713 loss_rpn_loc: 0.01864 time: 0.3702 last_time: 0.3531 data_time: 0.0085 last_data_time: 0.0078 lr: 0.00016983 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":517.562705788,"data":"\u001b[32m[09/23 07:43:33 d2.utils.events]: \u001b[0m eta: 0:01:49 iter: 699 total_loss: 0.6694 loss_cls: 0.1057 loss_box_reg: 0.2457 loss_mask: 0.2652 loss_rpn_cls: 0.003795 loss_rpn_loc: 0.01363 time: 0.3703 last_time: 0.3294 data_time: 0.0076 last_data_time: 0.0089 lr: 0.00017483 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":524.91804221,"data":"\u001b[32m[09/23 07:43:40 d2.utils.events]: \u001b[0m eta: 0:01:42 iter: 719 total_loss: 0.8476 loss_cls: 0.1678 loss_box_reg: 0.311 loss_mask: 0.3076 loss_rpn_cls: 0.008928 loss_rpn_loc: 0.02535 time: 0.3702 last_time: 0.4312 data_time: 0.0072 last_data_time: 0.0072 lr: 0.00017982 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":532.605087723,"data":"\u001b[32m[09/23 07:43:47 d2.utils.events]: \u001b[0m eta: 0:01:35 iter: 739 total_loss: 0.6485 loss_cls: 0.09793 loss_box_reg: 0.2633 loss_mask: 0.2799 loss_rpn_cls: 0.008818 loss_rpn_loc: 0.01637 time: 0.3704 last_time: 0.4122 data_time: 0.0092 last_data_time: 0.0062 lr: 0.00018482 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":539.851856257,"data":"\u001b[32m[09/23 07:43:55 d2.utils.events]: \u001b[0m eta: 0:01:27 iter: 759 total_loss: 0.7775 loss_cls: 0.1542 loss_box_reg: 0.2954 loss_mask: 0.2675 loss_rpn_cls: 0.006361 loss_rpn_loc: 0.03081 time: 0.3703 last_time: 0.3897 data_time: 0.0079 last_data_time: 0.0067 lr: 0.00018981 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":547.255724779,"data":"\u001b[32m[09/23 07:44:02 d2.utils.events]: \u001b[0m eta: 0:01:20 iter: 779 total_loss: 0.5322 loss_cls: 0.07315 loss_box_reg: 0.2183 loss_mask: 0.2537 loss_rpn_cls: 0.002045 loss_rpn_loc: 0.01187 time: 0.3703 last_time: 0.3445 data_time: 0.0081 last_data_time: 0.0062 lr: 0.00019481 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":554.533396611,"data":"\u001b[32m[09/23 07:44:10 d2.utils.events]: \u001b[0m eta: 0:01:13 iter: 799 total_loss: 0.583 loss_cls: 0.1178 loss_box_reg: 0.2244 loss_mask: 0.2243 loss_rpn_cls: 0.002606 loss_rpn_loc: 0.01259 time: 0.3701 last_time: 0.3479 data_time: 0.0088 last_data_time: 0.0061 lr: 0.0001998 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":562.09889552,"data":"\u001b[32m[09/23 07:44:17 d2.utils.events]: \u001b[0m eta: 0:01:05 iter: 819 total_loss: 0.6812 loss_cls: 0.1063 loss_box_reg: 0.2592 loss_mask: 0.2674 loss_rpn_cls: 0.005045 loss_rpn_loc: 0.01705 time: 0.3703 last_time: 0.3368 data_time: 0.0104 last_data_time: 0.0068 lr: 0.0002048 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":569.577541326,"data":"\u001b[32m[09/23 07:44:25 d2.utils.events]: \u001b[0m eta: 0:00:58 iter: 839 total_loss: 0.6475 loss_cls: 0.1354 loss_box_reg: 0.2331 loss_mask: 0.2683 loss_rpn_cls: 0.003228 loss_rpn_loc: 0.01554 time: 0.3703 last_time: 0.3450 data_time: 0.0073 last_data_time: 0.0085 lr: 0.00020979 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":577.105613046,"data":"\u001b[32m[09/23 07:44:32 d2.utils.events]: \u001b[0m eta: 0:00:51 iter: 859 total_loss: 0.5261 loss_cls: 0.08092 loss_box_reg: 0.2062 loss_mask: 0.2242 loss_rpn_cls: 0.001237 loss_rpn_loc: 0.01276 time: 0.3705 last_time: 0.4097 data_time: 0.0084 last_data_time: 0.0081 lr: 0.00021479 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":584.639586156,"data":"\u001b[32m[09/23 07:44:40 d2.utils.events]: \u001b[0m eta: 0:00:43 iter: 879 total_loss: 0.5345 loss_cls: 0.07986 loss_box_reg: 0.2093 loss_mask: 0.1887 loss_rpn_cls: 0.0009999 loss_rpn_loc: 0.008154 time: 0.3706 last_time: 0.3908 data_time: 0.0076 last_data_time: 0.0079 lr: 0.00021978 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":592.133724965,"data":"\u001b[32m[09/23 07:44:47 d2.utils.events]: \u001b[0m eta: 0:00:36 iter: 899 total_loss: 0.6179 loss_cls: 0.1105 loss_box_reg: 0.2395 loss_mask: 0.2561 loss_rpn_cls: 0.008318 loss_rpn_loc: 0.01785 time: 0.3707 last_time: 0.3466 data_time: 0.0090 last_data_time: 0.0072 lr: 0.00022478 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":599.759886771,"data":"\u001b[32m[09/23 07:44:55 d2.utils.events]: \u001b[0m eta: 0:00:29 iter: 919 total_loss: 0.4821 loss_cls: 0.07783 loss_box_reg: 0.1655 loss_mask: 0.2053 loss_rpn_cls: 0.0013 loss_rpn_loc: 0.01239 time: 0.3709 last_time: 0.4113 data_time: 0.0079 last_data_time: 0.0068 lr: 0.00022977 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":607.564687644,"data":"\u001b[32m[09/23 07:45:03 d2.utils.events]: \u001b[0m eta: 0:00:22 iter: 939 total_loss: 0.5789 loss_cls: 0.1175 loss_box_reg: 0.2348 loss_mask: 0.2183 loss_rpn_cls: 0.002596 loss_rpn_loc: 0.01899 time: 0.3713 last_time: 0.3916 data_time: 0.0087 last_data_time: 0.0098 lr: 0.00023477 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":614.932708254,"data":"\u001b[32m[09/23 07:45:10 d2.utils.events]: \u001b[0m eta: 0:00:14 iter: 959 total_loss: 0.5084 loss_cls: 0.08297 loss_box_reg: 0.1746 loss_mask: 0.235 loss_rpn_cls: 0.002214 loss_rpn_loc: 0.01299 time: 0.3713 last_time: 0.3617 data_time: 0.0084 last_data_time: 0.0086 lr: 0.00023976 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":622.746722669,"data":"\u001b[32m[09/23 07:45:18 d2.utils.events]: \u001b[0m eta: 0:00:07 iter: 979 total_loss: 0.5914 loss_cls: 0.08434 loss_box_reg: 0.235 loss_mask: 0.2241 loss_rpn_cls: 0.002513 loss_rpn_loc: 0.01149 time: 0.3716 last_time: 0.3726 data_time: 0.0085 last_data_time: 0.0177 lr: 0.00024476 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":630.929186452,"data":"\u001b[32m[09/23 07:45:26 d2.utils.events]: \u001b[0m eta: 0:00:00 iter: 999 total_loss: 0.6494 loss_cls: 0.1022 loss_box_reg: 0.2411 loss_mask: 0.252 loss_rpn_cls: 0.002364 loss_rpn_loc: 0.0139 time: 0.3716 last_time: 0.3077 data_time: 0.0075 last_data_time: 0.0069 lr: 0.00024975 max_mem: 1777M\n"} | |
| ,{"stream_name":"stdout","time":630.930411314,"data":"\u001b[32m[09/23 07:45:26 d2.engine.hooks]: \u001b[0mOverall training speed: 998 iterations in 0:06:10 (0.3716 s / it)\n"} | |
| ,{"stream_name":"stdout","time":630.932104437,"data":"\u001b[32m[09/23 07:45:26 d2.engine.hooks]: \u001b[0mTotal training time: 0:06:25 (0:00:14 on hooks)\n"} | |
| ,{"stream_name":"stdout","time":631.852853369,"data":"\u001b[32m[09/23 07:45:27 d2.checkpoint.detection_checkpoint]: \u001b[0m[DetectionCheckpointer] Loading from /kaggle/working/outputs/model_final.pth ...\n"} | |
| ,{"stream_name":"stdout","time":633.854559424,"data":"\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[09/23 07:45:29 d2.data.datasets.coco]: \u001b[0m\n"} | |
| ,{"stream_name":"stdout","time":633.854596514,"data":"Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n"} | |
| ,{"stream_name":"stdout","time":633.854603187,"data":"\n"} | |
| ,{"stream_name":"stdout","time":633.855964849,"data":"\u001b[32m[09/23 07:45:29 d2.data.datasets.coco]: \u001b[0mLoaded 18 images in COCO format from /kaggle/input/leaf-flower-fruit-annotation/semantic-segmentation-of-plants.v2i.coco-segmentation/valid/valid.json\n"} | |
| ,{"stream_name":"stdout","time":633.862563388,"data":"\u001b[32m[09/23 07:45:29 d2.data.build]: \u001b[0mDistribution of instances among all 4 categories:\n"} | |
| ,{"stream_name":"stdout","time":633.862601189,"data":"\u001b[36m| category | #instances | category | #instances | category | #instances |\n"} | |
| ,{"stream_name":"stdout","time":633.862608844,"data":"|:-------------:|:-------------|:-----------|:-------------|:-----------|:-------------|\n"} | |
| ,{"stream_name":"stdout","time":633.862614688,"data":"| leaf, flowe.. | 0 | 0 | 27 | 1 | 19 |\n"} | |
| ,{"stream_name":"stdout","time":633.862620603,"data":"| 2 | 36 | | | | |\n"} | |
| ,{"stream_name":"stdout","time":633.862626214,"data":"| total | 82 | | | | |\u001b[0m\n"} | |
| ,{"stream_name":"stdout","time":633.864171798,"data":"\u001b[32m[09/23 07:45:29 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]\n"} | |
| ,{"stream_name":"stdout","time":633.865624594,"data":"\u001b[32m[09/23 07:45:29 d2.data.common]: \u001b[0mSerializing the dataset using: \u003cclass 'detectron2.data.common._TorchSerializedList'\u003e\n"} | |
| ,{"stream_name":"stdout","time":633.867186249,"data":"\u001b[32m[09/23 07:45:29 d2.data.common]: \u001b[0mSerializing 18 elements to byte tensors and concatenating them all ...\n"} | |
| ,{"stream_name":"stdout","time":633.868659769,"data":"\u001b[32m[09/23 07:45:29 d2.data.common]: \u001b[0mSerialized dataset takes 0.02 MiB\n"} | |
| ,{"stream_name":"stdout","time":633.870068823,"data":"\u001b[32m[09/23 07:45:29 d2.evaluation.evaluator]: \u001b[0mStart inference on 18 batches\n"} | |
| ,{"stream_name":"stdout","time":635.094665576,"data":"\u001b[32m[09/23 07:45:30 d2.evaluation.evaluator]: \u001b[0mInference done 11/18. Dataloading: 0.0016 s/iter. Inference: 0.0821 s/iter. Eval: 0.0064 s/iter. Total: 0.0901 s/iter. ETA=0:00:00\n"} | |
| ,{"stream_name":"stdout","time":635.766430367,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.evaluator]: \u001b[0mTotal inference time: 0:00:01.213719 (0.093363 s / iter per device, on 1 devices)\n"} | |
| ,{"stream_name":"stdout","time":635.769214269,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.evaluator]: \u001b[0mTotal inference pure compute time: 0:00:01 (0.080777 s / iter per device, on 1 devices)\n"} | |
| ,{"stream_name":"stdout","time":635.771225527,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mPreparing results for COCO format ...\n"} | |
| ,{"stream_name":"stdout","time":635.773198027,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mSaving results to ./outputs/coco_instances_results.json\n"} | |
| ,{"stream_name":"stdout","time":635.778019995,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mEvaluating predictions with unofficial COCO API...\n"} | |
| ,{"stream_name":"stdout","time":635.779417428,"data":"Loading and preparing results...\n"} | |
| ,{"stream_name":"stdout","time":635.779434434,"data":"DONE (t=0.00s)\n"} | |
| ,{"stream_name":"stdout","time":635.779440883,"data":"creating index...\n"} | |
| ,{"stream_name":"stdout","time":635.77944617,"data":"index created!\n"} | |
| ,{"stream_name":"stdout","time":635.779451855,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mEvaluate annotation type *bbox*\n"} | |
| ,{"stream_name":"stdout","time":635.789318341,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.evaluate() finished in 0.01 seconds.\n"} | |
| ,{"stream_name":"stdout","time":635.797159053,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mAccumulating evaluation results...\n"} | |
| ,{"stream_name":"stdout","time":635.807632373,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.accumulate() finished in 0.02 seconds.\n"} | |
| ,{"stream_name":"stdout","time":635.810643959,"data":" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.302\n"} | |
| ,{"stream_name":"stdout","time":635.810661179,"data":" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.474\n"} | |
| ,{"stream_name":"stdout","time":635.810671544,"data":" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.357\n"} | |
| ,{"stream_name":"stdout","time":635.810677171,"data":" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n"} | |
| ,{"stream_name":"stdout","time":635.810682026,"data":" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.304\n"} | |
| ,{"stream_name":"stdout","time":635.810686646,"data":" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.263\n"} | |
| ,{"stream_name":"stdout","time":635.810691178,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.138\n"} | |
| ,{"stream_name":"stdout","time":635.810695681,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.391\n"} | |
| ,{"stream_name":"stdout","time":635.810700604,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.392\n"} | |
| ,{"stream_name":"stdout","time":635.810705254,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n"} | |
| ,{"stream_name":"stdout","time":635.810710319,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.371\n"} | |
| ,{"stream_name":"stdout","time":635.810714862,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.364\n"} | |
| ,{"stream_name":"stdout","time":635.810719904,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mEvaluation results for bbox: \n"} | |
| ,{"stream_name":"stdout","time":635.810725438,"data":"| AP | AP50 | AP75 | APs | APm | APl |\n"} | |
| ,{"stream_name":"stdout","time":635.81073265,"data":"|:------:|:------:|:------:|:-----:|:------:|:------:|\n"} | |
| ,{"stream_name":"stdout","time":635.81074006,"data":"| 30.219 | 47.387 | 35.729 | nan | 30.435 | 26.317 |\n"} | |
| ,{"stream_name":"stdout","time":635.812444438,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mSome metrics cannot be computed and is shown as NaN.\n"} | |
| ,{"stream_name":"stdout","time":635.814914009,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mPer-category bbox AP: \n"} | |
| ,{"stream_name":"stdout","time":635.814931885,"data":"| category | AP | category | AP | category | AP |\n"} | |
| ,{"stream_name":"stdout","time":635.814941054,"data":"|:-----------------------|:-------|:-----------|:-------|:-----------|:-------|\n"} | |
| ,{"stream_name":"stdout","time":635.814950236,"data":"| leaf, flower and fruit | nan | 0 | 11.429 | 1 | 39.127 |\n"} | |
| ,{"stream_name":"stdout","time":635.814955966,"data":"| 2 | 40.100 | | | | |\n"} | |
| ,{"stream_name":"stdout","time":635.831025164,"data":"Loading and preparing results...\n"} | |
| ,{"stream_name":"stdout","time":635.831060554,"data":"DONE (t=0.00s)\n"} | |
| ,{"stream_name":"stdout","time":635.831066509,"data":"creating index...\n"} | |
| ,{"stream_name":"stdout","time":635.831071302,"data":"index created!\n"} | |
| ,{"stream_name":"stdout","time":635.831076148,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mEvaluate annotation type *segm*\n"} | |
| ,{"stream_name":"stdout","time":635.832967364,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.evaluate() finished in 0.01 seconds.\n"} | |
| ,{"stream_name":"stdout","time":635.834387149,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mAccumulating evaluation results...\n"} | |
| ,{"stream_name":"stdout","time":635.849730149,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.accumulate() finished in 0.02 seconds.\n"} | |
| ,{"stream_name":"stdout","time":635.852968257,"data":" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.268\n"} | |
| ,{"stream_name":"stdout","time":635.853013325,"data":" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.482\n"} | |
| ,{"stream_name":"stdout","time":635.85302032,"data":" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.266\n"} | |
| ,{"stream_name":"stdout","time":635.853026725,"data":" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n"} | |
| ,{"stream_name":"stdout","time":635.853032566,"data":" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.290\n"} | |
| ,{"stream_name":"stdout","time":635.853037529,"data":" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.233\n"} | |
| ,{"stream_name":"stdout","time":635.853043059,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.134\n"} | |
| ,{"stream_name":"stdout","time":635.853052344,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.349\n"} | |
| ,{"stream_name":"stdout","time":635.853058969,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.350\n"} | |
| ,{"stream_name":"stdout","time":635.853080544,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n"} | |
| ,{"stream_name":"stdout","time":635.853086828,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.347\n"} | |
| ,{"stream_name":"stdout","time":635.853104345,"data":" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.324\n"} | |
| ,{"stream_name":"stdout","time":635.853111104,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mEvaluation results for segm: \n"} | |
| ,{"stream_name":"stdout","time":635.853117005,"data":"| AP | AP50 | AP75 | APs | APm | APl |\n"} | |
| ,{"stream_name":"stdout","time":635.853122229,"data":"|:------:|:------:|:------:|:-----:|:------:|:------:|\n"} | |
| ,{"stream_name":"stdout","time":635.853127204,"data":"| 26.769 | 48.197 | 26.573 | nan | 29.003 | 23.334 |\n"} | |
| ,{"stream_name":"stdout","time":635.854207133,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mSome metrics cannot be computed and is shown as NaN.\n"} | |
| ,{"stream_name":"stdout","time":635.85871157,"data":"\u001b[32m[09/23 07:45:31 d2.evaluation.coco_evaluation]: \u001b[0mPer-category segm AP: \n"} | |
| ,{"stream_name":"stdout","time":635.858754408,"data":"| category | AP | category | AP | category | AP |\n"} | |
| ,{"stream_name":"stdout","time":635.858761686,"data":"|:-----------------------|:-------|:-----------|:-------|:-----------|:-------|\n"} | |
| ,{"stream_name":"stdout","time":635.858767208,"data":"| leaf, flower and fruit | nan | 0 | 11.675 | 1 | 31.041 |\n"} | |
| ,{"stream_name":"stdout","time":635.858772523,"data":"| 2 | 37.590 | | | | |\n"} | |
| ,{"stream_name":"stdout","time":635.860120683,"data":"OrderedDict([('bbox', {'AP': 30.218943392039694, 'AP50': 47.386900130198846, 'AP75': 35.72921800473737, 'APs': nan, 'APm': 30.43451625382318, 'APl': 26.31694060377811, 'AP-leaf, flower and fruit': nan, 'AP-0': 11.42892373885882, 'AP-1': 39.12745560270313, 'AP-2': 40.10045083455714}), ('segm', {'AP': 26.768528027464377, 'AP50': 48.19729533929002, 'AP75': 26.573458292849743, 'APs': nan, 'APm': 29.0032162556915, 'APl': 23.334085433968955, 'AP-leaf, flower and fruit': nan, 'AP-0': 11.674782020116336, 'AP-1': 31.04055405540554, 'AP-2': 37.59024800687124})])\n"} | |
| ,{"stream_name":"stderr","time":641.251753977,"data":"/opt/conda/lib/python3.10/site-packages/traitlets/traitlets.py:2930: FutureWarning: --Exporter.preprocessors=[\"remove_papermill_header.RemovePapermillHeader\"] for containers is deprecated in traitlets 5.0. You can pass `--Exporter.preprocessors item` ... multiple times to add items to a list.\n"} | |
| ,{"stream_name":"stderr","time":641.252033612,"data":" warn(\n"} | |
| ,{"stream_name":"stderr","time":641.271251502,"data":"[NbConvertApp] WARNING | Config option `kernel_spec_manager_class` not recognized by `NbConvertApp`.\n"} | |
| ,{"stream_name":"stderr","time":641.299639329,"data":"[NbConvertApp] Converting notebook __notebook__.ipynb to notebook\n"} | |
| ,{"stream_name":"stderr","time":641.812192464,"data":"[NbConvertApp] Writing 1563115 bytes to __notebook__.ipynb\n"} | |
| ,{"stream_name":"stderr","time":643.508675438,"data":"/opt/conda/lib/python3.10/site-packages/traitlets/traitlets.py:2930: FutureWarning: --Exporter.preprocessors=[\"nbconvert.preprocessors.ExtractOutputPreprocessor\"] for containers is deprecated in traitlets 5.0. You can pass `--Exporter.preprocessors item` ... multiple times to add items to a list.\n"} | |
| ,{"stream_name":"stderr","time":643.508732164,"data":" warn(\n"} | |
| ,{"stream_name":"stderr","time":643.511626803,"data":"[NbConvertApp] WARNING | Config option `kernel_spec_manager_class` not recognized by `NbConvertApp`.\n"} | |
| ,{"stream_name":"stderr","time":643.556443524,"data":"[NbConvertApp] Converting notebook __notebook__.ipynb to html\n"} | |
| ,{"stream_name":"stderr","time":644.538437008,"data":"[NbConvertApp] Support files will be in __results___files/\n"} | |
| ,{"stream_name":"stderr","time":644.538518612,"data":"[NbConvertApp] Making directory __results___files\n"} | |
| ,{"stream_name":"stderr","time":644.539060619,"data":"[NbConvertApp] Making directory __results___files\n"} | |
| ,{"stream_name":"stderr","time":644.539504448,"data":"[NbConvertApp] Making directory __results___files\n"} | |
| ,{"stream_name":"stderr","time":644.540311438,"data":"[NbConvertApp] Writing 357130 bytes to __results__.html\n"} | |
| ] |