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- [INFO]: current net device: eth0, ip: 172.28.0.143
- [INFO]: paddle job envs:
- POD_IP=job-994513c33f71513ea641c85da428a5ae-trainer-0.job-994513c33f71513ea641c85da428a5ae
- PADDLE_PORT=12345
- PADDLE_TRAINER_ID=0
- PADDLE_TRAINERS_NUM=1
- PADDLE_USE_CUDA=1
- NCCL_SOCKET_IFNAME=eth0
- PADDLE_IS_LOCAL=1
- OUTPUT_PATH=/root/paddlejob/workspace/output
- LOCAL_LOG_PATH=/root/paddlejob/workspace/log
- LOCAL_MOUNT_PATH=/mnt/code_20220412215401,/mnt/datasets_20220412215402
- JOB_ID=job-994513c33f71513ea641c85da428a5ae
- TRAINING_ROLE=TRAINER
- [INFO]: user command: python run.py
- [INFO]: start trainer
- ~/paddlejob/workspace/code /mnt
- nvcc: NVIDIA (R) Cuda compiler driver
- Copyright (c) 2005-2019 NVIDIA Corporation
- Built on Sun_Jul_28_19:07:16_PDT_2019
- Cuda compilation tools, release 10.1, V10.1.243
- Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple, https://pypi.tuna.tsinghua.edu.cn/simple
- Collecting filelock
- Downloading https://pypi.tuna.tsinghua.edu.cn/packages/cd/f1/ba7dee3de0e9d3b8634d6fbaa5d0d407a7da64620305d147298b683e5c36/filelock-3.6.0-py3-none-any.whl (10.0 kB)
- Installing collected packages: filelock
- Successfully installed filelock-3.6.0
- WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
- WARNING: You are using pip version 21.3.1; however, version 22.0.4 is available.
- You should consider upgrading via the '/opt/_internal/cpython-3.7.0/bin/python -m pip install --upgrade pip' command.
- CCNet.zip
- data
- run.py
- run.sh
- Archive: CCNet.zip
- extracting: .copyright.hook
- extracting: .gitignore
- extracting: .pre-commit-config.yaml
- extracting: .style.yapf
- extracting: .travis.yml
- creating: benchmark/
- creating: benchmark/configs/
- extracting: benchmark/configs/cityscapes_30imgs.yml
- extracting: benchmark/configs/fastscnn.yml
- extracting: benchmark/configs/ocrnet_hrnetw48.yml
- extracting: benchmark/configs/segformer_b0.yml
- extracting: benchmark/deeplabv3p.yml
- extracting: benchmark/hrnet.yml
- extracting: benchmark/hrnet48.yml
- extracting: benchmark/README.md
- extracting: benchmark/README_CN.md
- extracting: benchmark/run_all.sh
- extracting: benchmark/run_benchmark.sh
- extracting: benchmark/run_fp16.sh
- extracting: benchmark/run_fp32.sh
- creating: configs/
- creating: configs/_base_/
- creating: configs/_base_/.ipynb_checkpoints/
- extracting: configs/_base_/.ipynb_checkpoints/cityscapes_769x769-checkpoint.yml
- extracting: configs/_base_/.ipynb_checkpoints/cityscapes_769x769_setr-checkpoint.yml
- extracting: configs/_base_/ade20k.yml
- extracting: configs/_base_/autonue.yml
- extracting: configs/_base_/chase_db1.yml
- extracting: configs/_base_/cityscapes.yml
- extracting: configs/_base_/cityscapes_1024x1024.yml
- extracting: configs/_base_/cityscapes_769x769.yml
- extracting: configs/_base_/cityscapes_769x769_setr.yml
- extracting: configs/_base_/coco_stuff.yml
- extracting: configs/_base_/drive.yml
- extracting: configs/_base_/hrf.yml
- extracting: configs/_base_/pascal_context.yml
- extracting: configs/_base_/pascal_voc12.yml
- extracting: configs/_base_/pascal_voc12aug.yml
- extracting: configs/_base_/stare.yml
- creating: configs/ann/
- extracting: configs/ann/ann_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/ann/ann_resnet101_os8_voc12aug_512x512_40k.yml
- extracting: configs/ann/ann_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/ann/ann_resnet50_os8_voc12aug_512x512_40k.yml
- extracting: configs/ann/README.md
- creating: configs/attention_unet/
- extracting: configs/attention_unet/attention_unet_cityscapes_1024x512_80k.yml
- extracting: configs/attention_unet/README.md
- creating: configs/bisenet/
- extracting: configs/bisenet/bisenet_cityscapes_1024x1024_160k.yml
- extracting: configs/bisenet/README.md
- creating: configs/bisenetv1/
- extracting: configs/bisenetv1/bisenetv1_resnet18_os8_cityscapes_1024x512_160k.yml
- extracting: configs/bisenetv1/README.md
- creating: configs/ccnet/
- extracting: configs/ccnet/ccnet_resnet101_os8_cityscapes_769x769_60k.yml
- creating: configs/danet/
- extracting: configs/danet/danet_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/danet/danet_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/danet/danet_resnet50_os8_voc12aug_512x512_40k.yml
- extracting: configs/danet/README.md
- creating: configs/decoupled_segnet/
- extracting: configs/decoupled_segnet/decoupledsegnet_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/decoupled_segnet/decoupledsegnet_resnet50_os8_cityscapes_832x832_80k.yml
- extracting: configs/decoupled_segnet/README.md
- creating: configs/deeplabv3/
- creating: configs/deeplabv3/.ipynb_checkpoints/
- extracting: configs/deeplabv3/.ipynb_checkpoints/deeplabv3_resnet101_os8_voc12aug_512x512_40k-checkpoint.yml
- extracting: configs/deeplabv3/deeplabv3_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/deeplabv3/deeplabv3_resnet101_os8_voc12aug_512x512_40k.yml
- extracting: configs/deeplabv3/deeplabv3_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/deeplabv3/deeplabv3_resnet50_os8_voc12aug_512x512_40k.yml
- extracting: configs/deeplabv3/README.md
- creating: configs/deeplabv3p/
- extracting: configs/deeplabv3p/deeplabv3p_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/deeplabv3p/deeplabv3p_resnet101_os8_cityscapes_769x769_80k.yml
- extracting: configs/deeplabv3p/deeplabv3p_resnet101_os8_voc12aug_512x512_40k.yml
- extracting: configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k_rmiloss.yml
- extracting: configs/deeplabv3p/deeplabv3p_resnet50_os8_voc12aug_512x512_40k.yml
- extracting: configs/deeplabv3p/README.md
- creating: configs/dmnet/
- extracting: configs/dmnet/dmnet_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/dmnet/README.md
- creating: configs/dnlnet/
- extracting: configs/dnlnet/dnlnet_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/dnlnet/dnlnet_resnet101_os8_voc12aug_512x512_40k.yml
- extracting: configs/dnlnet/dnlnet_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/dnlnet/dnlnet_resnet50_os8_voc12aug_512x512_40k.yml
- extracting: configs/dnlnet/README.md
- creating: configs/emanet/
- extracting: configs/emanet/emanet_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/emanet/emanet_resnet101_os8_voc12aug_512x512_40k.yml
- extracting: configs/emanet/emanet_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/emanet/emanet_resnet50_os8_voc12aug_512x512_40k.yml
- extracting: configs/emanet/README.md
- creating: configs/encnet/
- extracting: configs/encnet/encnet_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/encnet/README.md
- creating: configs/espnet/
- extracting: configs/espnet/espnet_cityscapes_1024x512_120k.yml
- extracting: configs/espnet/README.md
- creating: configs/espnetv1/
- extracting: configs/espnetv1/espnetv1_cityscapes_1024x512_120k.yml
- extracting: configs/espnetv1/README.md
- creating: configs/fastfcn/
- extracting: configs/fastfcn/fastfcn_resnet50_os8_ade20k_480x480_120k.yml
- extracting: configs/fastfcn/README.md
- creating: configs/fastscnn/
- extracting: configs/fastscnn/fastscnn_cityscapes_1024x1024_160k.yml
- extracting: configs/fastscnn/fastscnn_cityscapes_1024x1024_40k.yml
- extracting: configs/fastscnn/fastscnn_cityscapes_1024x1024_40k_SCL.yml
- extracting: configs/fastscnn/README.md
- creating: configs/fcn/
- extracting: configs/fcn/fcn_hrnetw18_cityscapes_1024x512_80k.yml
- extracting: configs/fcn/fcn_hrnetw18_cityscapes_1024x512_80k_bs4.yml
- extracting: configs/fcn/fcn_hrnetw18_cityscapes_1024x512_80k_bs4_SCL.yml
- extracting: configs/fcn/fcn_hrnetw18_pphumanseg14k.yml
- extracting: configs/fcn/fcn_hrnetw18_voc12aug_512x512_40k.yml
- extracting: configs/fcn/fcn_hrnetw48_cityscapes_1024x512_80k.yml
- extracting: configs/fcn/fcn_hrnetw48_voc12aug_512x512_40k.yml
- extracting: configs/fcn/README.md
- creating: configs/gcnet/
- extracting: configs/gcnet/gcnet_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/gcnet/gcnet_resnet101_os8_voc12aug_512x512_40k.yml
- extracting: configs/gcnet/gcnet_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/gcnet/gcnet_resnet50_os8_voc12aug_512x512_40k.yml
- extracting: configs/gcnet/README.md
- creating: configs/ginet/
- extracting: configs/ginet/ginet_resnet101_os8_ade20k_520x520_150k.yml
- extracting: configs/ginet/ginet_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/ginet/ginet_resnet101_os8_voc12aug_512x512_40k.yml
- extracting: configs/ginet/ginet_resnet50_os8_ade20k_520x520_150k.yml
- extracting: configs/ginet/ginet_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/ginet/ginet_resnet50_os8_voc12aug_512x512_40k.yml
- extracting: configs/ginet/README.md
- creating: configs/gscnn/
- extracting: configs/gscnn/gscnn_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/gscnn/README.md
- creating: configs/hardnet/
- extracting: configs/hardnet/hardnet_cityscapes_1024x1024_160k.yml
- extracting: configs/hardnet/README.md
- creating: configs/hrnet_w48_contrast/
- extracting: configs/hrnet_w48_contrast/HRNet_W48_contrast_cityscapes_1024x512_60k.yml
- extracting: configs/hrnet_w48_contrast/README.md
- creating: configs/isanet/
- extracting: configs/isanet/isanet_resnet101_os8_cityscapes_769x769_80k.yml
- extracting: configs/isanet/isanet_resnet101_os8_voc12aug_512x512_40k.yml
- extracting: configs/isanet/isanet_resnet50_os8_cityscapes_769x769_80k.yml
- extracting: configs/isanet/isanet_resnet50_os8_voc12aug_512x512_40k.yml
- extracting: configs/isanet/README.md
- creating: configs/ocrnet/
- extracting: configs/ocrnet/ocrnet_hrnetw18_cityscapes_1024x512_160k.yml
- extracting: configs/ocrnet/ocrnet_hrnetw18_cityscapes_1024x512_160k_lovasz_softmax.yml
- extracting: configs/ocrnet/ocrnet_hrnetw18_road_extraction_768x768_15k.yml
- extracting: configs/ocrnet/ocrnet_hrnetw18_road_extraction_768x768_15k_lovasz_hinge.yml
- extracting: configs/ocrnet/ocrnet_hrnetw18_voc12aug_512x512_40k.yml
- extracting: configs/ocrnet/ocrnet_hrnetw48_cityscapes_1024x512_160k.yml
- extracting: configs/ocrnet/ocrnet_hrnetw48_cityscapes_1024x512_40k.yml
- extracting: configs/ocrnet/ocrnet_hrnetw48_cityscapes_1024x512_40k_SCL.yml
- extracting: configs/ocrnet/ocrnet_hrnetw48_voc12aug_512x512_40k.yml
- extracting: configs/ocrnet/README.md
- creating: configs/pfpn/
- extracting: configs/pfpn/pfpn_resnet101_os8_cityscapes_512x1024_40k.yml
- extracting: configs/pfpn/README.md
- creating: configs/pointrend/
- extracting: configs/pointrend/pointrend_resnet101_os8_cityscapes_1024×512_80k.yml
- extracting: configs/pointrend/pointrend_resnet101_os8_voc12aug_512×512_40k.yml
- extracting: configs/pointrend/pointrend_resnet50_os8_cityscapes_1024×512_80k.yml
- extracting: configs/pointrend/pointrend_resnet50_os8_voc12aug_512×512_40k.yml
- extracting: configs/pointrend/README.md
- creating: configs/portraitnet/
- extracting: configs/portraitnet/portraitnet_eg1800_224x224_46k.yml
- extracting: configs/portraitnet/portraitnet_supervisely_224x224_60k.yml
- extracting: configs/portraitnet/README.md
- creating: configs/pp_humanseg_lite/
- extracting: configs/pp_humanseg_lite/pp_humanseg_lite_export_398x224.yml
- extracting: configs/pp_humanseg_lite/pp_humanseg_lite_mini_supervisely.yml
- extracting: configs/pp_humanseg_lite/pphumanseg_lite.png
- extracting: configs/pp_humanseg_lite/README.md
- creating: configs/pspnet/
- creating: configs/pspnet/.ipynb_checkpoints/
- extracting: configs/pspnet/.ipynb_checkpoints/pspnet_resnet50_os8_cityscapes_1024x512_80k-checkpoint.yml
- extracting: configs/pspnet/pspnet_resnet101_os8_cityscapes_1024x512_80k.yml
- extracting: configs/pspnet/pspnet_resnet101_os8_voc12aug_512x512_40k.yml
- extracting: configs/pspnet/pspnet_resnet50_os8_cityscapes_1024x512_80k.yml
- extracting: configs/pspnet/pspnet_resnet50_os8_voc12aug_512x512_40k.yml
- extracting: configs/pspnet/README.md
- creating: configs/quick_start/
- extracting: configs/quick_start/bisenet_optic_disc_512x512_1k.yml
- extracting: configs/quick_start/deeplabv3p_resnet18_os8_optic_disc_512x512_1k_student.yml
- extracting: configs/quick_start/deeplabv3p_resnet50_os8_optic_disc_512x512_1k_teacher.yml
- extracting: configs/README.md
- extracting: configs/README_cn.md
- creating: configs/segformer/
- extracting: configs/segformer/README.md
- extracting: configs/segformer/segformer_b0_cityscapes_1024x1024_160k.yml
- extracting: configs/segformer/segformer_b0_cityscapes_1024x512_160k.yml
- extracting: configs/segformer/segformer_b1_cityscapes_1024x1024_160k.yml
- extracting: configs/segformer/segformer_b1_cityscapes_1024x512_160k.yml
- extracting: configs/segformer/segformer_b2_cityscapes_1024x1024_160k.yml
- extracting: configs/segformer/segformer_b2_cityscapes_1024x512_160k.yml
- extracting: configs/segformer/segformer_b3_cityscapes_1024x1024_160k.yml
- extracting: configs/segformer/segformer_b3_cityscapes_1024x512_160k.yml
- extracting: configs/segformer/segformer_b4_cityscapes_1024x1024_160k.yml
- extracting: configs/segformer/segformer_b4_cityscapes_1024x512_160k.yml
- extracting: configs/segformer/segformer_b5_cityscapes_1024x1024_160k.yml
- extracting: configs/segformer/segformer_b5_cityscapes_1024x512_160k.yml
- creating: configs/segmenter/
- extracting: configs/segmenter/README.md
- extracting: configs/segmenter/segmenter_vit_base_linear_ade20k_512x512_160k.yml
- extracting: configs/segmenter/segmenter_vit_base_mask_ade20k_512x512_160k.yml
- extracting: configs/segmenter/segmenter_vit_small_linear_ade20k_512x512_160k.yml
- extracting: configs/segmenter/segmenter_vit_small_mask_ade20k_512x512_160k.yml
- creating: configs/segnet/
- extracting: configs/segnet/README.md
- extracting: configs/segnet/segnet_cityscapes_1024x512_80k.yml
- creating: configs/setr/
- extracting: configs/setr/README.md
- extracting: configs/setr/setr_mla_large_cityscapes_769x769_40k.yml
- extracting: configs/setr/setr_naive_large_cityscapes_769x769_40k.yml
- extracting: configs/setr/setr_pup_large_cityscapes_769x769_40k.yml
- creating: configs/sfnet/
- extracting: configs/sfnet/README.md
- extracting: configs/sfnet/sfnet_resnet18_os8_cityscapes_1024x1024_80k.yml
- extracting: configs/sfnet/sfnet_resnet50_os8_cityscapes_1024x1024_80k.yml
- creating: configs/stdcseg/
- extracting: configs/stdcseg/README.md
- extracting: configs/stdcseg/stdc1_seg_cityscapes_1024x512_80k.yml
- extracting: configs/stdcseg/stdc1_seg_voc12aug_512x512_40k.yml
- extracting: configs/stdcseg/stdc2_seg_cityscapes_1024x512_80k.yml
- extracting: configs/stdcseg/stdc2_seg_voc12aug_512x512_40k.yml
- creating: configs/u2net/
- extracting: configs/u2net/README.md
- extracting: configs/u2net/u2net_cityscapes_1024x512_160k.yml
- extracting: configs/u2net/u2netp_cityscapes_1024x512_160k.yml
- creating: configs/unet/
- extracting: configs/unet/README.md
- extracting: configs/unet/unet_chasedb1_128x128_40k.yml
- extracting: configs/unet/unet_cityscapes_1024x512_160k.yml
- extracting: configs/unet/unet_drive_128x128_40k.yml
- extracting: configs/unet/unet_hrf_256x256_40k.yml
- extracting: configs/unet/unet_stare_128x128_40k.yml
- creating: configs/unet_3plus/
- extracting: configs/unet_3plus/README.md
- extracting: configs/unet_3plus/unet_3plus_cityscapes_1024x512_160k.yml
- creating: configs/unet_plusplus/
- extracting: configs/unet_plusplus/README.md
- extracting: configs/unet_plusplus/unet_plusplus_cityscapes_1024x512_160k.yml
- creating: deploy/
- creating: deploy/cpp/
- extracting: deploy/cpp/CMakeLists.txt
- extracting: deploy/cpp/README.md
- extracting: deploy/cpp/README_cn.md
- extracting: deploy/cpp/run_seg_cpu.sh
- extracting: deploy/cpp/run_seg_gpu.sh
- creating: deploy/cpp/src/
- extracting: deploy/cpp/src/test_seg.cc
- creating: deploy/lite/
- creating: deploy/lite/example/
- extracting: deploy/lite/example/human_1.png
- extracting: deploy/lite/example/human_2.png
- extracting: deploy/lite/example/human_3.png
- creating: deploy/lite/human_segmentation_demo/
- extracting: deploy/lite/human_segmentation_demo/.gitignore
- creating: deploy/lite/human_segmentation_demo/app/
- extracting: deploy/lite/human_segmentation_demo/app/.gitignore
- extracting: deploy/lite/human_segmentation_demo/app/build.gradle
- extracting: deploy/lite/human_segmentation_demo/app/gradlew
- extracting: deploy/lite/human_segmentation_demo/app/gradlew.bat
- extracting: deploy/lite/human_segmentation_demo/app/local.properties
- extracting: deploy/lite/human_segmentation_demo/app/proguard-rules.pro
- creating: deploy/lite/human_segmentation_demo/app/src/
- creating: deploy/lite/human_segmentation_demo/app/src/androidTest/
- creating: deploy/lite/human_segmentation_demo/app/src/androidTest/java/
- creating: deploy/lite/human_segmentation_demo/app/src/androidTest/java/com/
- creating: deploy/lite/human_segmentation_demo/app/src/androidTest/java/com/baidu/
- creating: deploy/lite/human_segmentation_demo/app/src/androidTest/java/com/baidu/paddle/
- creating: deploy/lite/human_segmentation_demo/app/src/androidTest/java/com/baidu/paddle/lite/
- creating: deploy/lite/human_segmentation_demo/app/src/androidTest/java/com/baidu/paddle/lite/demo/
- extracting: deploy/lite/human_segmentation_demo/app/src/androidTest/java/com/baidu/paddle/lite/demo/ExampleInstrumentedTest.java
- creating: deploy/lite/human_segmentation_demo/app/src/main/
- extracting: deploy/lite/human_segmentation_demo/app/src/main/AndroidManifest.xml
- creating: deploy/lite/human_segmentation_demo/app/src/main/assets/
- creating: deploy/lite/human_segmentation_demo/app/src/main/assets/image_segmentation/
- creating: deploy/lite/human_segmentation_demo/app/src/main/assets/image_segmentation/images/
- extracting: deploy/lite/human_segmentation_demo/app/src/main/assets/image_segmentation/images/human.jpg
- creating: deploy/lite/human_segmentation_demo/app/src/main/assets/image_segmentation/labels/
- extracting: deploy/lite/human_segmentation_demo/app/src/main/assets/image_segmentation/labels/label_list
- creating: deploy/lite/human_segmentation_demo/app/src/main/java/
- creating: deploy/lite/human_segmentation_demo/app/src/main/java/com/
- creating: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/
- creating: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/
- creating: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/
- creating: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/demo/
- creating: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/
- extracting: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/AppCompatPreferenceActivity.java
- creating: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/config/
- extracting: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/config/Config.java
- extracting: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/MainActivity.java
- extracting: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/Predictor.java
- creating: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/preprocess/
- extracting: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/preprocess/Preprocess.java
- extracting: deploy/lite/human_segmentation_demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/SettingsActivity.java
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- extracting: paddleseg/utils/env/seg_env.py
- extracting: paddleseg/utils/env/sys_env.py
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- extracting: README.md
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- extracting: requirements.txt
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- creating: slim/
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- creating: test_tipc/
- extracting: test_tipc/common_func.sh
- extracting: test_tipc/compare_results.py
- creating: test_tipc/configs/
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- extracting: test_tipc/configs/_base_/ade20k.yml
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- extracting: test_tipc/configs/_base_/cityscapes_1024x1024.yml
- extracting: test_tipc/configs/_base_/cityscapes_769x769.yml
- extracting: test_tipc/configs/_base_/cityscapes_769x769_setr.yml
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- extracting: test_tipc/configs/_base_/pascal_voc12aug.yml
- creating: test_tipc/configs/bisenetv2/
- extracting: test_tipc/configs/bisenetv2/bisenet_cityscapes_1024x1024_160k.yml
- extracting: test_tipc/configs/bisenetv2/train_infer_python.txt
- creating: test_tipc/configs/deeplabv3p_resnet50/
- extracting: test_tipc/configs/deeplabv3p_resnet50/deeplabv3p_resnet50_humanseg_512x512_mini_supervisely.yml
- extracting: test_tipc/configs/deeplabv3p_resnet50/train_infer_python.txt
- creating: test_tipc/configs/fcn_hrnetw18_small/
- extracting: test_tipc/configs/fcn_hrnetw18_small/fcn_hrnetw18_small_v1_humanseg_192x192_mini_supervisely.yml
- extracting: test_tipc/configs/fcn_hrnetw18_small/train_infer_python.txt
- creating: test_tipc/configs/ocrnet_hrnetw18/
- extracting: test_tipc/configs/ocrnet_hrnetw18/ocrnet_hrnetw18_cityscapes_1024x512_160k.yml
- extracting: test_tipc/configs/ocrnet_hrnetw18/train_infer_python.txt
- creating: test_tipc/configs/pfpnnet/
- extracting: test_tipc/configs/pfpnnet/pfpn_resnet101_os8_cityscapes_512x1024_40k.yml
- extracting: test_tipc/configs/pfpnnet/train_infer_python.txt
- creating: test_tipc/configs/pphumanseg_lite/
- extracting: test_tipc/configs/pphumanseg_lite/pphumanseg_lite_mini_supervisely.yml
- extracting: test_tipc/configs/pphumanseg_lite/train_infer_python.txt
- creating: test_tipc/configs/ppmatting/
- extracting: test_tipc/configs/ppmatting/modnet_mobilenetv2.yml
- extracting: test_tipc/configs/ppmatting/train_infer_python.txt
- creating: test_tipc/configs/segformer_b0/
- extracting: test_tipc/configs/segformer_b0/segformer_b0_cityscapes_1024x1024_160k.yml
- extracting: test_tipc/configs/segformer_b0/train_infer_python.txt
- creating: test_tipc/configs/stdc_stdc1/
- extracting: test_tipc/configs/stdc_stdc1/stdc1_seg_cityscapes_1024x512_80k.yml
- extracting: test_tipc/configs/stdc_stdc1/train_infer_python.txt
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- creating: test_tipc/results/
- extracting: test_tipc/results/python_fcn_hrnetw18_small_results_fp16.txt
- extracting: test_tipc/results/python_fcn_hrnetw18_small_results_fp32.txt
- extracting: test_tipc/test_infer_js.sh
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- extracting: test_tipc/val.py
- creating: test_tipc/web/
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- extracting: test_tipc/web/imgs/human.jpg
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- benchmark
- CCNet.zip
- configs
- data
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- export.py
- LICENSE
- paddleseg
- predict.py
- README_CN.md
- README.md
- requirements.txt
- run.py
- run.sh
- setup.py
- slim
- tests
- test_tipc
- tools
- train.py
- val.py
- WARNING 2022-04-12 21:55:03,701 launch.py:423] Not found distinct arguments and compiled with cuda or xpu. Default use collective mode
- INFO 2022-04-12 21:55:03,703 launch_utils.py:528] Local start 4 processes. First process distributed environment info (Only For Debug):
- +=======================================================================================+
- | Distributed Envs Value |
- +---------------------------------------------------------------------------------------+
- | PADDLE_TRAINER_ID 0 |
- | PADDLE_CURRENT_ENDPOINT 127.0.0.1:50615 |
- | PADDLE_TRAINERS_NUM 4 |
- | PADDLE_TRAINER_ENDPOINTS ... 0.1:45747,127.0.0.1:51319,127.0.0.1:42343|
- | PADDLE_RANK_IN_NODE 0 |
- | PADDLE_LOCAL_DEVICE_IDS 0 |
- | PADDLE_WORLD_DEVICE_IDS 0,1,2,3 |
- | FLAGS_selected_gpus 0 |
- | FLAGS_selected_accelerators 0 |
- +=======================================================================================+
-
- INFO 2022-04-12 21:55:03,703 launch_utils.py:532] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0
- ----------- Configuration Arguments -----------
- backend: auto
- elastic_server: None
- force: False
- gpus: None
- heter_devices:
- heter_worker_num: None
- heter_workers:
- host: None
- http_port: None
- ips: 127.0.0.1
- job_id: None
- log_dir: log
- np: None
- nproc_per_node: None
- run_mode: None
- scale: 0
- server_num: None
- servers:
- training_script: train.py
- training_script_args: ['--config', 'configs/ccnet/ccnet_resnet101_os8_cityscapes_769x769_60k.yml', '--num_workers', '8', '--do_eval', '--use_vdl', '--log_iter', '50', '--save_interval', '4000', '--save_dir', '/root/paddlejob/workspace/output']
- worker_num: None
- workers:
- ------------------------------------------------
- launch train in GPU mode!
- launch proc_id:574 idx:0
- launch proc_id:577 idx:1
- launch proc_id:580 idx:2
- launch proc_id:583 idx:3
- 2022-04-12 21:55:51 [INFO]
- ------------Environment Information-------------
- platform: Linux-4.4.0-150-generic-x86_64-with-centos-6.10-Final
- Python: 3.7.0 (default, Nov 24 2018, 08:51:28) [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]
- Paddle compiled with cuda: True
- NVCC: Cuda compilation tools, release 10.1, V10.1.243
- cudnn: 7.6
- GPUs used: 4
- CUDA_VISIBLE_DEVICES: None
- GPU: ['GPU 0: Tesla V100-SXM2-32GB', 'GPU 1: Tesla V100-SXM2-32GB', 'GPU 2: Tesla V100-SXM2-32GB', 'GPU 3: Tesla V100-SXM2-32GB']
- GCC: gcc (GCC) 4.8.2 20140120 (Red Hat 4.8.2-15)
- PaddleSeg: 2.4.0
- PaddlePaddle: 2.2.2
- OpenCV: 4.1.1
- ------------------------------------------------
- 2022-04-12 21:55:51 [INFO]
- ---------------Config Information---------------
- batch_size: 2
- iters: 60000
- loss:
- coef:
- - 1
- - 0.4
- types:
- - ignore_index: 255
- type: OhemCrossEntropyLoss
- - ignore_index: 255
- type: CrossEntropyLoss
- lr_scheduler:
- end_lr: 0.0001
- learning_rate: 0.01
- power: 0.9
- type: PolynomialDecay
- model:
- backbone:
- output_stride: 8
- pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz
- type: ResNet101_vd
- backbone_indices:
- - 2
- - 3
- dropout_prob: 0.1
- enable_auxiliary_loss: true
- recurrence: 2
- type: CCNet
- optimizer:
- momentum: 0.9
- type: sgd
- weight_decay: 4.0e-05
- train_dataset:
- dataset_root: data/cityscapes
- mode: train
- transforms:
- - max_scale_factor: 2.0
- min_scale_factor: 0.5
- scale_step_size: 0.25
- type: ResizeStepScaling
- - crop_size:
- - 769
- - 769
- type: RandomPaddingCrop
- - type: RandomHorizontalFlip
- - brightness_range: 0.4
- contrast_range: 0.4
- saturation_range: 0.4
- type: RandomDistort
- - type: Normalize
- type: Cityscapes
- val_dataset:
- dataset_root: data/cityscapes
- mode: val
- transforms:
- - target_size:
- - 2049
- - 1025
- type: Padding
- - type: Normalize
- type: Cityscapes
- ------------------------------------------------
- W0412 21:55:51.379340 574 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
- W0412 21:55:51.379395 574 device_context.cc:465] device: 0, cuDNN Version: 7.6.
- 2022-04-12 21:56:01 [INFO] Loading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz
- 2022-04-12 21:56:09 [INFO] There are 530/530 variables loaded into ResNet_vd.
- server not ready, wait 3 sec to retry...
- not ready endpoints:['127.0.0.1:42343']
- I0412 21:56:12.100757 574 nccl_context.cc:74] init nccl context nranks: 4 local rank: 0 gpu id: 0 ring id: 0
- I0412 21:56:12.698354 574 nccl_context.cc:107] init nccl context nranks: 4 local rank: 0 gpu id: 0 ring id: 10
- 2022-04-12 21:56:13,052-INFO: [topology.py:169:__init__] HybridParallelInfo: rank_id: 0, mp_degree: 1, sharding_degree: 1, pp_degree: 1, dp_degree: 4, mp_group: [0], sharding_group: [0], pp_group: [0], dp_group: [0, 1, 2, 3], check/clip group: [0]
- /opt/_internal/cpython-3.7.0/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:253: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float32, but right dtype is paddle.int64, the right dtype will convert to paddle.float32
- format(lhs_dtype, rhs_dtype, lhs_dtype))
- 2022-04-12 21:56:57 [INFO] [TRAIN] epoch: 1, iter: 50/60000, loss: 2.5357, lr: 0.009993, batch_cost: 0.8877, reader_cost: 0.05655, ips: 2.2530 samples/sec | ETA 14:46:57
- 2022-04-12 21:57:39 [INFO] [TRAIN] epoch: 1, iter: 100/60000, loss: 2.0075, lr: 0.009985, batch_cost: 0.8343, reader_cost: 0.00021, ips: 2.3971 samples/sec | ETA 13:52:57
- 2022-04-12 21:58:21 [INFO] [TRAIN] epoch: 1, iter: 150/60000, loss: 1.7502, lr: 0.009978, batch_cost: 0.8387, reader_cost: 0.00019, ips: 2.3846 samples/sec | ETA 13:56:38
- 2022-04-12 21:59:03 [INFO] [TRAIN] epoch: 1, iter: 200/60000, loss: 1.6826, lr: 0.009970, batch_cost: 0.8412, reader_cost: 0.00019, ips: 2.3776 samples/sec | ETA 13:58:23
- 2022-04-12 21:59:45 [INFO] [TRAIN] epoch: 1, iter: 250/60000, loss: 1.5432, lr: 0.009963, batch_cost: 0.8430, reader_cost: 0.00020, ips: 2.3726 samples/sec | ETA 13:59:26
- 2022-04-12 22:00:27 [INFO] [TRAIN] epoch: 1, iter: 300/60000, loss: 1.4782, lr: 0.009956, batch_cost: 0.8439, reader_cost: 0.00019, ips: 2.3700 samples/sec | ETA 13:59:39
- 2022-04-12 22:01:09 [INFO] [TRAIN] epoch: 1, iter: 350/60000, loss: 1.7208, lr: 0.009948, batch_cost: 0.8448, reader_cost: 0.00024, ips: 2.3675 samples/sec | ETA 13:59:50
- 2022-04-12 22:01:54 [INFO] [TRAIN] epoch: 2, iter: 400/60000, loss: 1.7254, lr: 0.009941, batch_cost: 0.8992, reader_cost: 0.04687, ips: 2.2242 samples/sec | ETA 14:53:11
- 2022-04-12 22:02:37 [INFO] [TRAIN] epoch: 2, iter: 450/60000, loss: 1.5980, lr: 0.009933, batch_cost: 0.8458, reader_cost: 0.00025, ips: 2.3646 samples/sec | ETA 13:59:28
- 2022-04-12 22:03:19 [INFO] [TRAIN] epoch: 2, iter: 500/60000, loss: 1.5814, lr: 0.009926, batch_cost: 0.8449, reader_cost: 0.00019, ips: 2.3672 samples/sec | ETA 13:57:50
- 2022-04-12 22:04:01 [INFO] [TRAIN] epoch: 2, iter: 550/60000, loss: 1.4465, lr: 0.009918, batch_cost: 0.8488, reader_cost: 0.00020, ips: 2.3563 samples/sec | ETA 14:00:59
- 2022-04-12 22:04:44 [INFO] [TRAIN] epoch: 2, iter: 600/60000, loss: 1.2910, lr: 0.009911, batch_cost: 0.8471, reader_cost: 0.00024, ips: 2.3611 samples/sec | ETA 13:58:36
- 2022-04-12 22:05:26 [INFO] [TRAIN] epoch: 2, iter: 650/60000, loss: 1.3977, lr: 0.009904, batch_cost: 0.8469, reader_cost: 0.00024, ips: 2.3616 samples/sec | ETA 13:57:41
- 2022-04-12 22:06:08 [INFO] [TRAIN] epoch: 2, iter: 700/60000, loss: 1.3979, lr: 0.009896, batch_cost: 0.8460, reader_cost: 0.00022, ips: 2.3640 samples/sec | ETA 13:56:10
- 2022-04-12 22:06:54 [INFO] [TRAIN] epoch: 3, iter: 750/60000, loss: 1.3279, lr: 0.009889, batch_cost: 0.9056, reader_cost: 0.05298, ips: 2.2084 samples/sec | ETA 14:54:18
- 2022-04-12 22:07:36 [INFO] [TRAIN] epoch: 3, iter: 800/60000, loss: 1.2673, lr: 0.009881, batch_cost: 0.8467, reader_cost: 0.00026, ips: 2.3620 samples/sec | ETA 13:55:26
- 2022-04-12 22:08:18 [INFO] [TRAIN] epoch: 3, iter: 850/60000, loss: 1.3308, lr: 0.009874, batch_cost: 0.8466, reader_cost: 0.00020, ips: 2.3625 samples/sec | ETA 13:54:34
- 2022-04-12 22:09:00 [INFO] [TRAIN] epoch: 3, iter: 900/60000, loss: 1.4305, lr: 0.009866, batch_cost: 0.8448, reader_cost: 0.00022, ips: 2.3674 samples/sec | ETA 13:52:08
- 2022-04-12 22:09:43 [INFO] [TRAIN] epoch: 3, iter: 950/60000, loss: 1.3522, lr: 0.009859, batch_cost: 0.8513, reader_cost: 0.00024, ips: 2.3494 samples/sec | ETA 13:57:47
- 2022-04-12 22:10:25 [INFO] [TRAIN] epoch: 3, iter: 1000/60000, loss: 1.3807, lr: 0.009852, batch_cost: 0.8442, reader_cost: 0.00024, ips: 2.3691 samples/sec | ETA 13:50:08
- 2022-04-12 22:11:07 [INFO] [TRAIN] epoch: 3, iter: 1050/60000, loss: 1.3561, lr: 0.009844, batch_cost: 0.8447, reader_cost: 0.00020, ips: 2.3677 samples/sec | ETA 13:49:56
- 2022-04-12 22:11:50 [INFO] [TRAIN] epoch: 3, iter: 1100/60000, loss: 1.3273, lr: 0.009837, batch_cost: 0.8450, reader_cost: 0.00024, ips: 2.3669 samples/sec | ETA 13:49:30
- 2022-04-12 22:12:35 [INFO] [TRAIN] epoch: 4, iter: 1150/60000, loss: 1.2773, lr: 0.009829, batch_cost: 0.8998, reader_cost: 0.05038, ips: 2.2226 samples/sec | ETA 14:42:35
- 2022-04-12 22:13:17 [INFO] [TRAIN] epoch: 4, iter: 1200/60000, loss: 1.4077, lr: 0.009822, batch_cost: 0.8447, reader_cost: 0.00027, ips: 2.3677 samples/sec | ETA 13:47:48
- 2022-04-12 22:13:59 [INFO] [TRAIN] epoch: 4, iter: 1250/60000, loss: 1.2881, lr: 0.009814, batch_cost: 0.8456, reader_cost: 0.00023, ips: 2.3652 samples/sec | ETA 13:47:58
- 2022-04-12 22:14:42 [INFO] [TRAIN] epoch: 4, iter: 1300/60000, loss: 1.2873, lr: 0.009807, batch_cost: 0.8475, reader_cost: 0.00020, ips: 2.3599 samples/sec | ETA 13:49:07
- 2022-04-12 22:15:24 [INFO] [TRAIN] epoch: 4, iter: 1350/60000, loss: 1.2308, lr: 0.009799, batch_cost: 0.8448, reader_cost: 0.00021, ips: 2.3673 samples/sec | ETA 13:45:50
- 2022-04-12 22:16:06 [INFO] [TRAIN] epoch: 4, iter: 1400/60000, loss: 1.2942, lr: 0.009792, batch_cost: 0.8447, reader_cost: 0.00022, ips: 2.3676 samples/sec | ETA 13:45:00
- 2022-04-12 22:16:48 [INFO] [TRAIN] epoch: 4, iter: 1450/60000, loss: 1.2134, lr: 0.009785, batch_cost: 0.8479, reader_cost: 0.00025, ips: 2.3587 samples/sec | ETA 13:47:26
- 2022-04-12 22:17:33 [INFO] [TRAIN] epoch: 5, iter: 1500/60000, loss: 1.2633, lr: 0.009777, batch_cost: 0.8922, reader_cost: 0.03596, ips: 2.2415 samples/sec | ETA 14:29:56
- 2022-04-12 22:18:15 [INFO] [TRAIN] epoch: 5, iter: 1550/60000, loss: 1.2149, lr: 0.009770, batch_cost: 0.8446, reader_cost: 0.00023, ips: 2.3681 samples/sec | ETA 13:42:44
- 2022-04-12 22:18:58 [INFO] [TRAIN] epoch: 5, iter: 1600/60000, loss: 1.2558, lr: 0.009762, batch_cost: 0.8440, reader_cost: 0.00021, ips: 2.3698 samples/sec | ETA 13:41:27
- 2022-04-12 22:19:40 [INFO] [TRAIN] epoch: 5, iter: 1650/60000, loss: 1.2827, lr: 0.009755, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3661 samples/sec | ETA 13:42:00
- 2022-04-12 22:20:22 [INFO] [TRAIN] epoch: 5, iter: 1700/60000, loss: 1.3534, lr: 0.009747, batch_cost: 0.8481, reader_cost: 0.00024, ips: 2.3582 samples/sec | ETA 13:44:03
- 2022-04-12 22:21:04 [INFO] [TRAIN] epoch: 5, iter: 1750/60000, loss: 1.1914, lr: 0.009740, batch_cost: 0.8447, reader_cost: 0.00023, ips: 2.3676 samples/sec | ETA 13:40:05
- 2022-04-12 22:21:47 [INFO] [TRAIN] epoch: 5, iter: 1800/60000, loss: 1.2750, lr: 0.009732, batch_cost: 0.8445, reader_cost: 0.00019, ips: 2.3682 samples/sec | ETA 13:39:12
- 2022-04-12 22:22:29 [INFO] [TRAIN] epoch: 5, iter: 1850/60000, loss: 1.2999, lr: 0.009725, batch_cost: 0.8438, reader_cost: 0.00019, ips: 2.3703 samples/sec | ETA 13:37:46
- 2022-04-12 22:23:14 [INFO] [TRAIN] epoch: 6, iter: 1900/60000, loss: 1.2093, lr: 0.009718, batch_cost: 0.9034, reader_cost: 0.04851, ips: 2.2138 samples/sec | ETA 14:34:49
- 2022-04-12 22:23:56 [INFO] [TRAIN] epoch: 6, iter: 1950/60000, loss: 1.2373, lr: 0.009710, batch_cost: 0.8484, reader_cost: 0.00024, ips: 2.3573 samples/sec | ETA 13:40:50
- 2022-04-12 22:24:39 [INFO] [TRAIN] epoch: 6, iter: 2000/60000, loss: 1.2480, lr: 0.009703, batch_cost: 0.8480, reader_cost: 0.00020, ips: 2.3584 samples/sec | ETA 13:39:45
- 2022-04-12 22:25:21 [INFO] [TRAIN] epoch: 6, iter: 2050/60000, loss: 1.2182, lr: 0.009695, batch_cost: 0.8494, reader_cost: 0.00026, ips: 2.3547 samples/sec | ETA 13:40:20
- 2022-04-12 22:26:04 [INFO] [TRAIN] epoch: 6, iter: 2100/60000, loss: 1.1760, lr: 0.009688, batch_cost: 0.8476, reader_cost: 0.00027, ips: 2.3597 samples/sec | ETA 13:37:54
- 2022-04-12 22:26:46 [INFO] [TRAIN] epoch: 6, iter: 2150/60000, loss: 1.2458, lr: 0.009680, batch_cost: 0.8490, reader_cost: 0.00023, ips: 2.3557 samples/sec | ETA 13:38:35
- 2022-04-12 22:27:28 [INFO] [TRAIN] epoch: 6, iter: 2200/60000, loss: 1.1065, lr: 0.009673, batch_cost: 0.8447, reader_cost: 0.00023, ips: 2.3676 samples/sec | ETA 13:33:46
- 2022-04-12 22:28:13 [INFO] [TRAIN] epoch: 7, iter: 2250/60000, loss: 1.1884, lr: 0.009665, batch_cost: 0.8929, reader_cost: 0.03863, ips: 2.2400 samples/sec | ETA 14:19:22
- 2022-04-12 22:28:56 [INFO] [TRAIN] epoch: 7, iter: 2300/60000, loss: 1.1734, lr: 0.009658, batch_cost: 0.8497, reader_cost: 0.00023, ips: 2.3537 samples/sec | ETA 13:37:08
- 2022-04-12 22:29:38 [INFO] [TRAIN] epoch: 7, iter: 2350/60000, loss: 1.2747, lr: 0.009650, batch_cost: 0.8447, reader_cost: 0.00020, ips: 2.3678 samples/sec | ETA 13:31:35
- 2022-04-12 22:30:20 [INFO] [TRAIN] epoch: 7, iter: 2400/60000, loss: 1.1926, lr: 0.009643, batch_cost: 0.8495, reader_cost: 0.00019, ips: 2.3542 samples/sec | ETA 13:35:33
- 2022-04-12 22:31:03 [INFO] [TRAIN] epoch: 7, iter: 2450/60000, loss: 1.2352, lr: 0.009636, batch_cost: 0.8462, reader_cost: 0.00019, ips: 2.3634 samples/sec | ETA 13:31:41
- 2022-04-12 22:31:45 [INFO] [TRAIN] epoch: 7, iter: 2500/60000, loss: 1.3266, lr: 0.009628, batch_cost: 0.8453, reader_cost: 0.00019, ips: 2.3659 samples/sec | ETA 13:30:07
- 2022-04-12 22:32:27 [INFO] [TRAIN] epoch: 7, iter: 2550/60000, loss: 1.2321, lr: 0.009621, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3632 samples/sec | ETA 13:30:20
- 2022-04-12 22:33:09 [INFO] [TRAIN] epoch: 7, iter: 2600/60000, loss: 1.2124, lr: 0.009613, batch_cost: 0.8436, reader_cost: 0.00019, ips: 2.3709 samples/sec | ETA 13:27:00
- 2022-04-12 22:33:54 [INFO] [TRAIN] epoch: 8, iter: 2650/60000, loss: 1.1831, lr: 0.009606, batch_cost: 0.8954, reader_cost: 0.04254, ips: 2.2337 samples/sec | ETA 14:15:50
- 2022-04-12 22:34:36 [INFO] [TRAIN] epoch: 8, iter: 2700/60000, loss: 1.1920, lr: 0.009598, batch_cost: 0.8454, reader_cost: 0.00025, ips: 2.3657 samples/sec | ETA 13:27:22
- 2022-04-12 22:35:19 [INFO] [TRAIN] epoch: 8, iter: 2750/60000, loss: 1.3135, lr: 0.009591, batch_cost: 0.8466, reader_cost: 0.00020, ips: 2.3624 samples/sec | ETA 13:27:47
- 2022-04-12 22:36:01 [INFO] [TRAIN] epoch: 8, iter: 2800/60000, loss: 1.1638, lr: 0.009583, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3662 samples/sec | ETA 13:25:47
- 2022-04-12 22:36:43 [INFO] [TRAIN] epoch: 8, iter: 2850/60000, loss: 1.0947, lr: 0.009576, batch_cost: 0.8441, reader_cost: 0.00021, ips: 2.3693 samples/sec | ETA 13:24:01
- 2022-04-12 22:37:25 [INFO] [TRAIN] epoch: 8, iter: 2900/60000, loss: 1.1425, lr: 0.009568, batch_cost: 0.8441, reader_cost: 0.00022, ips: 2.3695 samples/sec | ETA 13:23:16
- 2022-04-12 22:38:08 [INFO] [TRAIN] epoch: 8, iter: 2950/60000, loss: 1.2360, lr: 0.009561, batch_cost: 0.8444, reader_cost: 0.00022, ips: 2.3686 samples/sec | ETA 13:22:51
- 2022-04-12 22:38:52 [INFO] [TRAIN] epoch: 9, iter: 3000/60000, loss: 1.1363, lr: 0.009554, batch_cost: 0.8948, reader_cost: 0.04251, ips: 2.2350 samples/sec | ETA 14:10:05
- 2022-04-12 22:39:35 [INFO] [TRAIN] epoch: 9, iter: 3050/60000, loss: 1.2737, lr: 0.009546, batch_cost: 0.8457, reader_cost: 0.00023, ips: 2.3650 samples/sec | ETA 13:22:39
- 2022-04-12 22:40:17 [INFO] [TRAIN] epoch: 9, iter: 3100/60000, loss: 1.2934, lr: 0.009539, batch_cost: 0.8491, reader_cost: 0.00026, ips: 2.3554 samples/sec | ETA 13:25:14
- 2022-04-12 22:40:59 [INFO] [TRAIN] epoch: 9, iter: 3150/60000, loss: 1.2299, lr: 0.009531, batch_cost: 0.8449, reader_cost: 0.00023, ips: 2.3671 samples/sec | ETA 13:20:34
- 2022-04-12 22:41:42 [INFO] [TRAIN] epoch: 9, iter: 3200/60000, loss: 1.1174, lr: 0.009524, batch_cost: 0.8456, reader_cost: 0.00020, ips: 2.3652 samples/sec | ETA 13:20:30
- 2022-04-12 22:42:24 [INFO] [TRAIN] epoch: 9, iter: 3250/60000, loss: 1.0929, lr: 0.009516, batch_cost: 0.8455, reader_cost: 0.00021, ips: 2.3655 samples/sec | ETA 13:19:41
- 2022-04-12 22:43:06 [INFO] [TRAIN] epoch: 9, iter: 3300/60000, loss: 1.1769, lr: 0.009509, batch_cost: 0.8454, reader_cost: 0.00023, ips: 2.3657 samples/sec | ETA 13:18:54
- 2022-04-12 22:43:51 [INFO] [TRAIN] epoch: 10, iter: 3350/60000, loss: 1.2560, lr: 0.009501, batch_cost: 0.8944, reader_cost: 0.04300, ips: 2.2362 samples/sec | ETA 14:04:25
- 2022-04-12 22:44:33 [INFO] [TRAIN] epoch: 10, iter: 3400/60000, loss: 1.2077, lr: 0.009494, batch_cost: 0.8456, reader_cost: 0.00023, ips: 2.3653 samples/sec | ETA 13:17:38
- 2022-04-12 22:45:15 [INFO] [TRAIN] epoch: 10, iter: 3450/60000, loss: 1.0723, lr: 0.009486, batch_cost: 0.8456, reader_cost: 0.00020, ips: 2.3652 samples/sec | ETA 13:16:59
- 2022-04-12 22:45:58 [INFO] [TRAIN] epoch: 10, iter: 3500/60000, loss: 1.2806, lr: 0.009479, batch_cost: 0.8466, reader_cost: 0.00020, ips: 2.3625 samples/sec | ETA 13:17:10
- 2022-04-12 22:46:40 [INFO] [TRAIN] epoch: 10, iter: 3550/60000, loss: 1.1888, lr: 0.009471, batch_cost: 0.8450, reader_cost: 0.00022, ips: 2.3668 samples/sec | ETA 13:15:01
- 2022-04-12 22:47:22 [INFO] [TRAIN] epoch: 10, iter: 3600/60000, loss: 1.1459, lr: 0.009464, batch_cost: 0.8453, reader_cost: 0.00023, ips: 2.3660 samples/sec | ETA 13:14:36
- 2022-04-12 22:48:05 [INFO] [TRAIN] epoch: 10, iter: 3650/60000, loss: 1.1802, lr: 0.009456, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3656 samples/sec | ETA 13:14:01
- 2022-04-12 22:48:47 [INFO] [TRAIN] epoch: 10, iter: 3700/60000, loss: 1.2274, lr: 0.009449, batch_cost: 0.8446, reader_cost: 0.00023, ips: 2.3680 samples/sec | ETA 13:12:31
- 2022-04-12 22:49:32 [INFO] [TRAIN] epoch: 11, iter: 3750/60000, loss: 1.1776, lr: 0.009441, batch_cost: 0.8951, reader_cost: 0.04234, ips: 2.2344 samples/sec | ETA 13:59:09
- 2022-04-12 22:50:14 [INFO] [TRAIN] epoch: 11, iter: 3800/60000, loss: 1.2308, lr: 0.009434, batch_cost: 0.8460, reader_cost: 0.00030, ips: 2.3642 samples/sec | ETA 13:12:22
- 2022-04-12 22:50:56 [INFO] [TRAIN] epoch: 11, iter: 3850/60000, loss: 1.1461, lr: 0.009427, batch_cost: 0.8463, reader_cost: 0.00023, ips: 2.3632 samples/sec | ETA 13:11:59
- 2022-04-12 22:51:38 [INFO] [TRAIN] epoch: 11, iter: 3900/60000, loss: 1.0965, lr: 0.009419, batch_cost: 0.8441, reader_cost: 0.00020, ips: 2.3695 samples/sec | ETA 13:09:11
- 2022-04-12 22:52:21 [INFO] [TRAIN] epoch: 11, iter: 3950/60000, loss: 1.0864, lr: 0.009412, batch_cost: 0.8450, reader_cost: 0.00020, ips: 2.3669 samples/sec | ETA 13:09:21
- 2022-04-12 22:53:03 [INFO] [TRAIN] epoch: 11, iter: 4000/60000, loss: 1.2238, lr: 0.009404, batch_cost: 0.8438, reader_cost: 0.00021, ips: 2.3701 samples/sec | ETA 13:07:35
- 2022-04-12 22:53:03 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4177 - reader cost: 0.0032
- 2022-04-12 22:53:55 [INFO] [EVAL] #Images: 500 mIoU: 0.5949 Acc: 0.9327 Kappa: 0.9125 Dice: 0.7185
- 2022-04-12 22:53:55 [INFO] [EVAL] Class IoU:
- [0.9644 0.742 0.8819 0.2288 0.4396 0.3837 0.5793 0.6856 0.9006 0.5328
- 0.867 0.7136 0.4552 0.8903 0.2612 0.3958 0.3633 0.3257 0.6928]
- 2022-04-12 22:53:55 [INFO] [EVAL] Class Acc:
- [0.9777 0.8474 0.9234 0.8258 0.5594 0.8437 0.744 0.8667 0.9485 0.7338
- 0.8746 0.8757 0.798 0.9176 0.849 0.8829 0.4605 0.6885 0.7893]
- 2022-04-12 22:53:57 [INFO] [EVAL] The model with the best validation mIoU (0.5949) was saved at iter 4000.
- 2022-04-12 22:54:39 [INFO] [TRAIN] epoch: 11, iter: 4050/60000, loss: 1.1856, lr: 0.009397, batch_cost: 0.8479, reader_cost: 0.00030, ips: 2.3588 samples/sec | ETA 13:10:39
- 2022-04-12 22:55:24 [INFO] [TRAIN] epoch: 12, iter: 4100/60000, loss: 1.1514, lr: 0.009389, batch_cost: 0.8952, reader_cost: 0.04738, ips: 2.2340 samples/sec | ETA 13:54:03
- 2022-04-12 22:56:07 [INFO] [TRAIN] epoch: 12, iter: 4150/60000, loss: 1.1284, lr: 0.009382, batch_cost: 0.8473, reader_cost: 0.00030, ips: 2.3605 samples/sec | ETA 13:08:40
- 2022-04-12 22:56:49 [INFO] [TRAIN] epoch: 12, iter: 4200/60000, loss: 1.1815, lr: 0.009374, batch_cost: 0.8460, reader_cost: 0.00023, ips: 2.3641 samples/sec | ETA 13:06:45
- 2022-04-12 22:57:31 [INFO] [TRAIN] epoch: 12, iter: 4250/60000, loss: 1.2089, lr: 0.009367, batch_cost: 0.8465, reader_cost: 0.00020, ips: 2.3626 samples/sec | ETA 13:06:32
- 2022-04-12 22:58:13 [INFO] [TRAIN] epoch: 12, iter: 4300/60000, loss: 1.1576, lr: 0.009359, batch_cost: 0.8450, reader_cost: 0.00020, ips: 2.3668 samples/sec | ETA 13:04:27
- 2022-04-12 22:58:56 [INFO] [TRAIN] epoch: 12, iter: 4350/60000, loss: 1.1249, lr: 0.009352, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3633 samples/sec | ETA 13:04:54
- 2022-04-12 22:59:38 [INFO] [TRAIN] epoch: 12, iter: 4400/60000, loss: 1.1081, lr: 0.009344, batch_cost: 0.8441, reader_cost: 0.00021, ips: 2.3694 samples/sec | ETA 13:02:11
- 2022-04-12 23:00:20 [INFO] [TRAIN] epoch: 12, iter: 4450/60000, loss: 1.1542, lr: 0.009337, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3660 samples/sec | ETA 13:02:36
- 2022-04-12 23:01:05 [INFO] [TRAIN] epoch: 13, iter: 4500/60000, loss: 1.1338, lr: 0.009329, batch_cost: 0.9003, reader_cost: 0.04788, ips: 2.2214 samples/sec | ETA 13:52:48
- 2022-04-12 23:01:48 [INFO] [TRAIN] epoch: 13, iter: 4550/60000, loss: 1.1618, lr: 0.009322, batch_cost: 0.8451, reader_cost: 0.00022, ips: 2.3666 samples/sec | ETA 13:01:00
- 2022-04-12 23:02:30 [INFO] [TRAIN] epoch: 13, iter: 4600/60000, loss: 1.1066, lr: 0.009314, batch_cost: 0.8442, reader_cost: 0.00020, ips: 2.3692 samples/sec | ETA 12:59:27
- 2022-04-12 23:03:12 [INFO] [TRAIN] epoch: 13, iter: 4650/60000, loss: 1.1162, lr: 0.009307, batch_cost: 0.8448, reader_cost: 0.00020, ips: 2.3673 samples/sec | ETA 12:59:21
- 2022-04-12 23:03:54 [INFO] [TRAIN] epoch: 13, iter: 4700/60000, loss: 1.1551, lr: 0.009299, batch_cost: 0.8443, reader_cost: 0.00020, ips: 2.3688 samples/sec | ETA 12:58:10
- 2022-04-12 23:04:36 [INFO] [TRAIN] epoch: 13, iter: 4750/60000, loss: 1.1834, lr: 0.009292, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3654 samples/sec | ETA 12:58:34
- 2022-04-12 23:05:19 [INFO] [TRAIN] epoch: 13, iter: 4800/60000, loss: 1.2597, lr: 0.009284, batch_cost: 0.8457, reader_cost: 0.00025, ips: 2.3648 samples/sec | ETA 12:58:04
- 2022-04-12 23:06:04 [INFO] [TRAIN] epoch: 14, iter: 4850/60000, loss: 1.0715, lr: 0.009277, batch_cost: 0.8988, reader_cost: 0.05080, ips: 2.2253 samples/sec | ETA 13:46:06
- 2022-04-12 23:06:46 [INFO] [TRAIN] epoch: 14, iter: 4900/60000, loss: 1.1727, lr: 0.009269, batch_cost: 0.8451, reader_cost: 0.00022, ips: 2.3665 samples/sec | ETA 12:56:05
- 2022-04-12 23:07:28 [INFO] [TRAIN] epoch: 14, iter: 4950/60000, loss: 1.0844, lr: 0.009262, batch_cost: 0.8457, reader_cost: 0.00020, ips: 2.3650 samples/sec | ETA 12:55:54
- 2022-04-12 23:08:10 [INFO] [TRAIN] epoch: 14, iter: 5000/60000, loss: 1.1654, lr: 0.009254, batch_cost: 0.8446, reader_cost: 0.00020, ips: 2.3680 samples/sec | ETA 12:54:12
- 2022-04-12 23:08:53 [INFO] [TRAIN] epoch: 14, iter: 5050/60000, loss: 1.1893, lr: 0.009247, batch_cost: 0.8442, reader_cost: 0.00023, ips: 2.3692 samples/sec | ETA 12:53:07
- 2022-04-12 23:09:35 [INFO] [TRAIN] epoch: 14, iter: 5100/60000, loss: 1.3049, lr: 0.009239, batch_cost: 0.8464, reader_cost: 0.00024, ips: 2.3630 samples/sec | ETA 12:54:25
- 2022-04-12 23:10:17 [INFO] [TRAIN] epoch: 14, iter: 5150/60000, loss: 1.1832, lr: 0.009232, batch_cost: 0.8451, reader_cost: 0.00020, ips: 2.3667 samples/sec | ETA 12:52:31
- 2022-04-12 23:11:00 [INFO] [TRAIN] epoch: 14, iter: 5200/60000, loss: 1.1284, lr: 0.009224, batch_cost: 0.8455, reader_cost: 0.00019, ips: 2.3656 samples/sec | ETA 12:52:10
- 2022-04-12 23:11:45 [INFO] [TRAIN] epoch: 15, iter: 5250/60000, loss: 1.1741, lr: 0.009217, batch_cost: 0.9063, reader_cost: 0.05835, ips: 2.2069 samples/sec | ETA 13:46:57
- 2022-04-12 23:12:27 [INFO] [TRAIN] epoch: 15, iter: 5300/60000, loss: 1.2013, lr: 0.009210, batch_cost: 0.8454, reader_cost: 0.00028, ips: 2.3656 samples/sec | ETA 12:50:45
- 2022-04-12 23:13:09 [INFO] [TRAIN] epoch: 15, iter: 5350/60000, loss: 1.1135, lr: 0.009202, batch_cost: 0.8444, reader_cost: 0.00025, ips: 2.3685 samples/sec | ETA 12:49:07
- 2022-04-12 23:13:52 [INFO] [TRAIN] epoch: 15, iter: 5400/60000, loss: 1.0447, lr: 0.009195, batch_cost: 0.8448, reader_cost: 0.00023, ips: 2.3674 samples/sec | ETA 12:48:47
- 2022-04-12 23:14:34 [INFO] [TRAIN] epoch: 15, iter: 5450/60000, loss: 1.0697, lr: 0.009187, batch_cost: 0.8475, reader_cost: 0.00023, ips: 2.3599 samples/sec | ETA 12:50:30
- 2022-04-12 23:15:16 [INFO] [TRAIN] epoch: 15, iter: 5500/60000, loss: 1.0785, lr: 0.009180, batch_cost: 0.8459, reader_cost: 0.00025, ips: 2.3645 samples/sec | ETA 12:48:18
- 2022-04-12 23:15:59 [INFO] [TRAIN] epoch: 15, iter: 5550/60000, loss: 1.0711, lr: 0.009172, batch_cost: 0.8512, reader_cost: 0.00024, ips: 2.3496 samples/sec | ETA 12:52:28
- 2022-04-12 23:16:44 [INFO] [TRAIN] epoch: 16, iter: 5600/60000, loss: 1.0892, lr: 0.009165, batch_cost: 0.8996, reader_cost: 0.05133, ips: 2.2232 samples/sec | ETA 13:35:38
- 2022-04-12 23:17:26 [INFO] [TRAIN] epoch: 16, iter: 5650/60000, loss: 1.2575, lr: 0.009157, batch_cost: 0.8466, reader_cost: 0.00025, ips: 2.3624 samples/sec | ETA 12:46:53
- 2022-04-12 23:18:08 [INFO] [TRAIN] epoch: 16, iter: 5700/60000, loss: 1.1322, lr: 0.009150, batch_cost: 0.8447, reader_cost: 0.00022, ips: 2.3677 samples/sec | ETA 12:44:28
- 2022-04-12 23:18:51 [INFO] [TRAIN] epoch: 16, iter: 5750/60000, loss: 1.0545, lr: 0.009142, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3664 samples/sec | ETA 12:44:10
- 2022-04-12 23:19:33 [INFO] [TRAIN] epoch: 16, iter: 5800/60000, loss: 1.2636, lr: 0.009135, batch_cost: 0.8450, reader_cost: 0.00022, ips: 2.3669 samples/sec | ETA 12:43:18
- 2022-04-12 23:20:15 [INFO] [TRAIN] epoch: 16, iter: 5850/60000, loss: 1.1081, lr: 0.009127, batch_cost: 0.8447, reader_cost: 0.00025, ips: 2.3676 samples/sec | ETA 12:42:22
- 2022-04-12 23:20:57 [INFO] [TRAIN] epoch: 16, iter: 5900/60000, loss: 1.0804, lr: 0.009120, batch_cost: 0.8464, reader_cost: 0.00024, ips: 2.3628 samples/sec | ETA 12:43:12
- 2022-04-12 23:21:40 [INFO] [TRAIN] epoch: 16, iter: 5950/60000, loss: 1.0956, lr: 0.009112, batch_cost: 0.8456, reader_cost: 0.00019, ips: 2.3652 samples/sec | ETA 12:41:45
- 2022-04-12 23:22:25 [INFO] [TRAIN] epoch: 17, iter: 6000/60000, loss: 1.1129, lr: 0.009105, batch_cost: 0.9062, reader_cost: 0.04905, ips: 2.2070 samples/sec | ETA 13:35:36
- 2022-04-12 23:23:07 [INFO] [TRAIN] epoch: 17, iter: 6050/60000, loss: 1.1473, lr: 0.009097, batch_cost: 0.8457, reader_cost: 0.00025, ips: 2.3648 samples/sec | ETA 12:40:27
- 2022-04-12 23:23:50 [INFO] [TRAIN] epoch: 17, iter: 6100/60000, loss: 1.1303, lr: 0.009090, batch_cost: 0.8465, reader_cost: 0.00020, ips: 2.3626 samples/sec | ETA 12:40:26
- 2022-04-12 23:24:32 [INFO] [TRAIN] epoch: 17, iter: 6150/60000, loss: 1.0686, lr: 0.009082, batch_cost: 0.8457, reader_cost: 0.00021, ips: 2.3648 samples/sec | ETA 12:39:03
- 2022-04-12 23:25:14 [INFO] [TRAIN] epoch: 17, iter: 6200/60000, loss: 1.1001, lr: 0.009075, batch_cost: 0.8491, reader_cost: 0.00020, ips: 2.3554 samples/sec | ETA 12:41:21
- 2022-04-12 23:25:57 [INFO] [TRAIN] epoch: 17, iter: 6250/60000, loss: 1.2047, lr: 0.009067, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3647 samples/sec | ETA 12:37:39
- 2022-04-12 23:26:39 [INFO] [TRAIN] epoch: 17, iter: 6300/60000, loss: 1.0854, lr: 0.009059, batch_cost: 0.8451, reader_cost: 0.00021, ips: 2.3667 samples/sec | ETA 12:36:19
- 2022-04-12 23:27:24 [INFO] [TRAIN] epoch: 18, iter: 6350/60000, loss: 1.1252, lr: 0.009052, batch_cost: 0.9062, reader_cost: 0.05768, ips: 2.2071 samples/sec | ETA 13:30:16
- 2022-04-12 23:28:07 [INFO] [TRAIN] epoch: 18, iter: 6400/60000, loss: 1.1366, lr: 0.009044, batch_cost: 0.8453, reader_cost: 0.00023, ips: 2.3660 samples/sec | ETA 12:35:08
- 2022-04-12 23:28:49 [INFO] [TRAIN] epoch: 18, iter: 6450/60000, loss: 1.0646, lr: 0.009037, batch_cost: 0.8447, reader_cost: 0.00024, ips: 2.3677 samples/sec | ETA 12:33:53
- 2022-04-12 23:29:31 [INFO] [TRAIN] epoch: 18, iter: 6500/60000, loss: 1.0804, lr: 0.009029, batch_cost: 0.8443, reader_cost: 0.00020, ips: 2.3690 samples/sec | ETA 12:32:47
- 2022-04-12 23:30:13 [INFO] [TRAIN] epoch: 18, iter: 6550/60000, loss: 1.0481, lr: 0.009022, batch_cost: 0.8442, reader_cost: 0.00020, ips: 2.3691 samples/sec | ETA 12:32:03
- 2022-04-12 23:30:55 [INFO] [TRAIN] epoch: 18, iter: 6600/60000, loss: 1.0565, lr: 0.009014, batch_cost: 0.8449, reader_cost: 0.00020, ips: 2.3671 samples/sec | ETA 12:31:59
- 2022-04-12 23:31:38 [INFO] [TRAIN] epoch: 18, iter: 6650/60000, loss: 1.0765, lr: 0.009007, batch_cost: 0.8440, reader_cost: 0.00020, ips: 2.3695 samples/sec | ETA 12:30:29
- 2022-04-12 23:32:23 [INFO] [TRAIN] epoch: 19, iter: 6700/60000, loss: 1.1180, lr: 0.008999, batch_cost: 0.9001, reader_cost: 0.04249, ips: 2.2219 samples/sec | ETA 13:19:37
- 2022-04-12 23:33:05 [INFO] [TRAIN] epoch: 19, iter: 6750/60000, loss: 1.0810, lr: 0.008992, batch_cost: 0.8442, reader_cost: 0.00022, ips: 2.3690 samples/sec | ETA 12:29:14
- 2022-04-12 23:33:47 [INFO] [TRAIN] epoch: 19, iter: 6800/60000, loss: 1.0854, lr: 0.008984, batch_cost: 0.8464, reader_cost: 0.00023, ips: 2.3629 samples/sec | ETA 12:30:29
- 2022-04-12 23:34:29 [INFO] [TRAIN] epoch: 19, iter: 6850/60000, loss: 1.1607, lr: 0.008977, batch_cost: 0.8440, reader_cost: 0.00023, ips: 2.3697 samples/sec | ETA 12:27:37
- 2022-04-12 23:35:12 [INFO] [TRAIN] epoch: 19, iter: 6900/60000, loss: 1.1391, lr: 0.008969, batch_cost: 0.8481, reader_cost: 0.00023, ips: 2.3581 samples/sec | ETA 12:30:35
- 2022-04-12 23:35:54 [INFO] [TRAIN] epoch: 19, iter: 6950/60000, loss: 1.1020, lr: 0.008962, batch_cost: 0.8474, reader_cost: 0.00020, ips: 2.3601 samples/sec | ETA 12:29:16
- 2022-04-12 23:36:36 [INFO] [TRAIN] epoch: 19, iter: 7000/60000, loss: 1.0724, lr: 0.008954, batch_cost: 0.8441, reader_cost: 0.00020, ips: 2.3694 samples/sec | ETA 12:25:37
- 2022-04-12 23:37:19 [INFO] [TRAIN] epoch: 19, iter: 7050/60000, loss: 1.1240, lr: 0.008947, batch_cost: 0.8450, reader_cost: 0.00020, ips: 2.3668 samples/sec | ETA 12:25:43
- 2022-04-12 23:38:04 [INFO] [TRAIN] epoch: 20, iter: 7100/60000, loss: 1.1802, lr: 0.008939, batch_cost: 0.9012, reader_cost: 0.04545, ips: 2.2192 samples/sec | ETA 13:14:34
- 2022-04-12 23:38:46 [INFO] [TRAIN] epoch: 20, iter: 7150/60000, loss: 1.0497, lr: 0.008932, batch_cost: 0.8480, reader_cost: 0.00025, ips: 2.3586 samples/sec | ETA 12:26:54
- 2022-04-12 23:39:28 [INFO] [TRAIN] epoch: 20, iter: 7200/60000, loss: 1.0882, lr: 0.008924, batch_cost: 0.8463, reader_cost: 0.00021, ips: 2.3632 samples/sec | ETA 12:24:45
- 2022-04-12 23:40:11 [INFO] [TRAIN] epoch: 20, iter: 7250/60000, loss: 1.0321, lr: 0.008917, batch_cost: 0.8493, reader_cost: 0.00020, ips: 2.3550 samples/sec | ETA 12:26:38
- 2022-04-12 23:40:53 [INFO] [TRAIN] epoch: 20, iter: 7300/60000, loss: 1.1415, lr: 0.008909, batch_cost: 0.8454, reader_cost: 0.00020, ips: 2.3657 samples/sec | ETA 12:22:34
- 2022-04-12 23:41:35 [INFO] [TRAIN] epoch: 20, iter: 7350/60000, loss: 1.0580, lr: 0.008902, batch_cost: 0.8454, reader_cost: 0.00020, ips: 2.3657 samples/sec | ETA 12:21:51
- 2022-04-12 23:42:18 [INFO] [TRAIN] epoch: 20, iter: 7400/60000, loss: 1.1031, lr: 0.008894, batch_cost: 0.8463, reader_cost: 0.00024, ips: 2.3633 samples/sec | ETA 12:21:54
- 2022-04-12 23:43:03 [INFO] [TRAIN] epoch: 21, iter: 7450/60000, loss: 1.0583, lr: 0.008887, batch_cost: 0.9016, reader_cost: 0.05348, ips: 2.2183 samples/sec | ETA 13:09:39
- 2022-04-12 23:43:45 [INFO] [TRAIN] epoch: 21, iter: 7500/60000, loss: 1.0568, lr: 0.008879, batch_cost: 0.8467, reader_cost: 0.00027, ips: 2.3621 samples/sec | ETA 12:20:51
- 2022-04-12 23:44:27 [INFO] [TRAIN] epoch: 21, iter: 7550/60000, loss: 1.1186, lr: 0.008872, batch_cost: 0.8461, reader_cost: 0.00020, ips: 2.3637 samples/sec | ETA 12:19:40
- 2022-04-12 23:45:10 [INFO] [TRAIN] epoch: 21, iter: 7600/60000, loss: 1.0693, lr: 0.008864, batch_cost: 0.8461, reader_cost: 0.00023, ips: 2.3639 samples/sec | ETA 12:18:54
- 2022-04-12 23:45:52 [INFO] [TRAIN] epoch: 21, iter: 7650/60000, loss: 1.0481, lr: 0.008857, batch_cost: 0.8448, reader_cost: 0.00020, ips: 2.3674 samples/sec | ETA 12:17:06
- 2022-04-12 23:46:34 [INFO] [TRAIN] epoch: 21, iter: 7700/60000, loss: 1.0598, lr: 0.008849, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3660 samples/sec | ETA 12:16:49
- 2022-04-12 23:47:17 [INFO] [TRAIN] epoch: 21, iter: 7750/60000, loss: 1.0310, lr: 0.008841, batch_cost: 0.8456, reader_cost: 0.00021, ips: 2.3653 samples/sec | ETA 12:16:20
- 2022-04-12 23:47:59 [INFO] [TRAIN] epoch: 21, iter: 7800/60000, loss: 1.0949, lr: 0.008834, batch_cost: 0.8442, reader_cost: 0.00022, ips: 2.3690 samples/sec | ETA 12:14:29
- 2022-04-12 23:48:44 [INFO] [TRAIN] epoch: 22, iter: 7850/60000, loss: 1.0464, lr: 0.008826, batch_cost: 0.8991, reader_cost: 0.04928, ips: 2.2245 samples/sec | ETA 13:01:27
- 2022-04-12 23:49:26 [INFO] [TRAIN] epoch: 22, iter: 7900/60000, loss: 1.1354, lr: 0.008819, batch_cost: 0.8452, reader_cost: 0.00025, ips: 2.3664 samples/sec | ETA 12:13:53
- 2022-04-12 23:50:08 [INFO] [TRAIN] epoch: 22, iter: 7950/60000, loss: 1.0282, lr: 0.008811, batch_cost: 0.8454, reader_cost: 0.00026, ips: 2.3659 samples/sec | ETA 12:13:20
- 2022-04-12 23:50:50 [INFO] [TRAIN] epoch: 22, iter: 8000/60000, loss: 1.1538, lr: 0.008804, batch_cost: 0.8450, reader_cost: 0.00023, ips: 2.3668 samples/sec | ETA 12:12:21
- 2022-04-12 23:50:50 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4178 - reader cost: 0.0034
- 2022-04-12 23:51:43 [INFO] [EVAL] #Images: 500 mIoU: 0.6563 Acc: 0.9406 Kappa: 0.9228 Dice: 0.7686
- 2022-04-12 23:51:43 [INFO] [EVAL] Class IoU:
- [0.9704 0.7791 0.8941 0.1254 0.4537 0.5269 0.639 0.7281 0.8948 0.4932
- 0.9317 0.7766 0.5697 0.9261 0.593 0.6301 0.3283 0.4956 0.7143]
- 2022-04-12 23:51:43 [INFO] [EVAL] Class Acc:
- [0.9838 0.8672 0.9226 0.8257 0.5783 0.7305 0.742 0.8677 0.9673 0.6053
- 0.9549 0.8684 0.7054 0.9612 0.7837 0.6938 0.6464 0.7735 0.8882]
- 2022-04-12 23:51:46 [INFO] [EVAL] The model with the best validation mIoU (0.6563) was saved at iter 8000.
- 2022-04-12 23:52:28 [INFO] [TRAIN] epoch: 22, iter: 8050/60000, loss: 1.0410, lr: 0.008796, batch_cost: 0.8497, reader_cost: 0.00026, ips: 2.3538 samples/sec | ETA 12:15:41
- 2022-04-12 23:53:11 [INFO] [TRAIN] epoch: 22, iter: 8100/60000, loss: 1.0847, lr: 0.008789, batch_cost: 0.8455, reader_cost: 0.00019, ips: 2.3654 samples/sec | ETA 12:11:23
- 2022-04-12 23:53:53 [INFO] [TRAIN] epoch: 22, iter: 8150/60000, loss: 1.1060, lr: 0.008781, batch_cost: 0.8448, reader_cost: 0.00019, ips: 2.3675 samples/sec | ETA 12:10:01
- 2022-04-12 23:54:37 [INFO] [TRAIN] epoch: 23, iter: 8200/60000, loss: 1.5569, lr: 0.008774, batch_cost: 0.8899, reader_cost: 0.04431, ips: 2.2476 samples/sec | ETA 12:48:14
- 2022-04-12 23:55:20 [INFO] [TRAIN] epoch: 23, iter: 8250/60000, loss: 1.3043, lr: 0.008766, batch_cost: 0.8489, reader_cost: 0.00022, ips: 2.3561 samples/sec | ETA 12:12:08
- 2022-04-12 23:56:02 [INFO] [TRAIN] epoch: 23, iter: 8300/60000, loss: 1.2788, lr: 0.008759, batch_cost: 0.8495, reader_cost: 0.00020, ips: 2.3544 samples/sec | ETA 12:11:57
- 2022-04-12 23:56:45 [INFO] [TRAIN] epoch: 23, iter: 8350/60000, loss: 1.1639, lr: 0.008751, batch_cost: 0.8457, reader_cost: 0.00020, ips: 2.3648 samples/sec | ETA 12:08:01
- 2022-04-12 23:57:27 [INFO] [TRAIN] epoch: 23, iter: 8400/60000, loss: 1.1496, lr: 0.008744, batch_cost: 0.8467, reader_cost: 0.00020, ips: 2.3621 samples/sec | ETA 12:08:09
- 2022-04-12 23:58:09 [INFO] [TRAIN] epoch: 23, iter: 8450/60000, loss: 1.1821, lr: 0.008736, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3654 samples/sec | ETA 12:06:27
- 2022-04-12 23:58:51 [INFO] [TRAIN] epoch: 23, iter: 8500/60000, loss: 1.1769, lr: 0.008728, batch_cost: 0.8448, reader_cost: 0.00022, ips: 2.3673 samples/sec | ETA 12:05:09
- 2022-04-12 23:59:34 [INFO] [TRAIN] epoch: 23, iter: 8550/60000, loss: 1.0958, lr: 0.008721, batch_cost: 0.8437, reader_cost: 0.00020, ips: 2.3705 samples/sec | ETA 12:03:28
- 2022-04-13 00:00:19 [INFO] [TRAIN] epoch: 24, iter: 8600/60000, loss: 1.1670, lr: 0.008713, batch_cost: 0.8987, reader_cost: 0.04853, ips: 2.2255 samples/sec | ETA 12:49:51
- 2022-04-13 00:01:01 [INFO] [TRAIN] epoch: 24, iter: 8650/60000, loss: 1.0457, lr: 0.008706, batch_cost: 0.8466, reader_cost: 0.00023, ips: 2.3625 samples/sec | ETA 12:04:30
- 2022-04-13 00:01:43 [INFO] [TRAIN] epoch: 24, iter: 8700/60000, loss: 1.0951, lr: 0.008698, batch_cost: 0.8453, reader_cost: 0.00021, ips: 2.3659 samples/sec | ETA 12:02:46
- 2022-04-13 00:02:25 [INFO] [TRAIN] epoch: 24, iter: 8750/60000, loss: 1.0983, lr: 0.008691, batch_cost: 0.8464, reader_cost: 0.00021, ips: 2.3630 samples/sec | ETA 12:02:57
- 2022-04-13 00:03:08 [INFO] [TRAIN] epoch: 24, iter: 8800/60000, loss: 1.0955, lr: 0.008683, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3655 samples/sec | ETA 12:01:28
- 2022-04-13 00:03:50 [INFO] [TRAIN] epoch: 24, iter: 8850/60000, loss: 1.0114, lr: 0.008676, batch_cost: 0.8461, reader_cost: 0.00023, ips: 2.3638 samples/sec | ETA 12:01:16
- 2022-04-13 00:04:32 [INFO] [TRAIN] epoch: 24, iter: 8900/60000, loss: 1.1649, lr: 0.008668, batch_cost: 0.8442, reader_cost: 0.00020, ips: 2.3692 samples/sec | ETA 11:58:57
- 2022-04-13 00:05:17 [INFO] [TRAIN] epoch: 25, iter: 8950/60000, loss: 1.1280, lr: 0.008661, batch_cost: 0.8953, reader_cost: 0.04790, ips: 2.2340 samples/sec | ETA 12:41:43
- 2022-04-13 00:05:59 [INFO] [TRAIN] epoch: 25, iter: 9000/60000, loss: 1.0814, lr: 0.008653, batch_cost: 0.8471, reader_cost: 0.00022, ips: 2.3611 samples/sec | ETA 11:59:59
- 2022-04-13 00:06:42 [INFO] [TRAIN] epoch: 25, iter: 9050/60000, loss: 1.0436, lr: 0.008645, batch_cost: 0.8456, reader_cost: 0.00020, ips: 2.3652 samples/sec | ETA 11:58:03
- 2022-04-13 00:07:24 [INFO] [TRAIN] epoch: 25, iter: 9100/60000, loss: 1.0337, lr: 0.008638, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3662 samples/sec | ETA 11:57:02
- 2022-04-13 00:08:06 [INFO] [TRAIN] epoch: 25, iter: 9150/60000, loss: 1.1458, lr: 0.008630, batch_cost: 0.8440, reader_cost: 0.00021, ips: 2.3695 samples/sec | ETA 11:55:19
- 2022-04-13 00:08:48 [INFO] [TRAIN] epoch: 25, iter: 9200/60000, loss: 1.0649, lr: 0.008623, batch_cost: 0.8450, reader_cost: 0.00020, ips: 2.3667 samples/sec | ETA 11:55:28
- 2022-04-13 00:09:31 [INFO] [TRAIN] epoch: 25, iter: 9250/60000, loss: 1.1003, lr: 0.008615, batch_cost: 0.8447, reader_cost: 0.00020, ips: 2.3678 samples/sec | ETA 11:54:26
- 2022-04-13 00:10:13 [INFO] [TRAIN] epoch: 25, iter: 9300/60000, loss: 1.0609, lr: 0.008608, batch_cost: 0.8445, reader_cost: 0.00018, ips: 2.3683 samples/sec | ETA 11:53:35
- 2022-04-13 00:10:58 [INFO] [TRAIN] epoch: 26, iter: 9350/60000, loss: 1.0342, lr: 0.008600, batch_cost: 0.9003, reader_cost: 0.04123, ips: 2.2214 samples/sec | ETA 12:40:02
- 2022-04-13 00:11:40 [INFO] [TRAIN] epoch: 26, iter: 9400/60000, loss: 1.0991, lr: 0.008593, batch_cost: 0.8453, reader_cost: 0.00023, ips: 2.3659 samples/sec | ETA 11:52:53
- 2022-04-13 00:12:22 [INFO] [TRAIN] epoch: 26, iter: 9450/60000, loss: 1.1286, lr: 0.008585, batch_cost: 0.8454, reader_cost: 0.00020, ips: 2.3656 samples/sec | ETA 11:52:17
- 2022-04-13 00:13:05 [INFO] [TRAIN] epoch: 26, iter: 9500/60000, loss: 1.0626, lr: 0.008578, batch_cost: 0.8458, reader_cost: 0.00019, ips: 2.3645 samples/sec | ETA 11:51:55
- 2022-04-13 00:13:47 [INFO] [TRAIN] epoch: 26, iter: 9550/60000, loss: 1.0432, lr: 0.008570, batch_cost: 0.8451, reader_cost: 0.00023, ips: 2.3667 samples/sec | ETA 11:50:33
- 2022-04-13 00:14:29 [INFO] [TRAIN] epoch: 26, iter: 9600/60000, loss: 1.0268, lr: 0.008562, batch_cost: 0.8475, reader_cost: 0.00023, ips: 2.3598 samples/sec | ETA 11:51:55
- 2022-04-13 00:15:12 [INFO] [TRAIN] epoch: 26, iter: 9650/60000, loss: 1.0467, lr: 0.008555, batch_cost: 0.8455, reader_cost: 0.00021, ips: 2.3656 samples/sec | ETA 11:49:29
- 2022-04-13 00:15:57 [INFO] [TRAIN] epoch: 27, iter: 9700/60000, loss: 1.0262, lr: 0.008547, batch_cost: 0.9046, reader_cost: 0.05307, ips: 2.2109 samples/sec | ETA 12:38:21
- 2022-04-13 00:16:39 [INFO] [TRAIN] epoch: 27, iter: 9750/60000, loss: 1.0877, lr: 0.008540, batch_cost: 0.8455, reader_cost: 0.00024, ips: 2.3653 samples/sec | ETA 11:48:08
- 2022-04-13 00:17:21 [INFO] [TRAIN] epoch: 27, iter: 9800/60000, loss: 1.0187, lr: 0.008532, batch_cost: 0.8475, reader_cost: 0.00020, ips: 2.3600 samples/sec | ETA 11:49:03
- 2022-04-13 00:18:04 [INFO] [TRAIN] epoch: 27, iter: 9850/60000, loss: 1.0381, lr: 0.008525, batch_cost: 0.8438, reader_cost: 0.00020, ips: 2.3701 samples/sec | ETA 11:45:18
- 2022-04-13 00:18:46 [INFO] [TRAIN] epoch: 27, iter: 9900/60000, loss: 1.1746, lr: 0.008517, batch_cost: 0.8445, reader_cost: 0.00020, ips: 2.3682 samples/sec | ETA 11:45:09
- 2022-04-13 00:19:28 [INFO] [TRAIN] epoch: 27, iter: 9950/60000, loss: 1.1059, lr: 0.008510, batch_cost: 0.8452, reader_cost: 0.00021, ips: 2.3662 samples/sec | ETA 11:45:03
- 2022-04-13 00:20:10 [INFO] [TRAIN] epoch: 27, iter: 10000/60000, loss: 1.1134, lr: 0.008502, batch_cost: 0.8455, reader_cost: 0.00021, ips: 2.3654 samples/sec | ETA 11:44:36
- 2022-04-13 00:20:55 [INFO] [TRAIN] epoch: 28, iter: 10050/60000, loss: 1.1135, lr: 0.008494, batch_cost: 0.8968, reader_cost: 0.04259, ips: 2.2302 samples/sec | ETA 12:26:34
- 2022-04-13 00:21:37 [INFO] [TRAIN] epoch: 28, iter: 10100/60000, loss: 1.0451, lr: 0.008487, batch_cost: 0.8442, reader_cost: 0.00023, ips: 2.3692 samples/sec | ETA 11:42:04
- 2022-04-13 00:22:20 [INFO] [TRAIN] epoch: 28, iter: 10150/60000, loss: 1.0512, lr: 0.008479, batch_cost: 0.8444, reader_cost: 0.00021, ips: 2.3686 samples/sec | ETA 11:41:31
- 2022-04-13 00:23:02 [INFO] [TRAIN] epoch: 28, iter: 10200/60000, loss: 1.0536, lr: 0.008472, batch_cost: 0.8446, reader_cost: 0.00021, ips: 2.3679 samples/sec | ETA 11:41:02
- 2022-04-13 00:23:44 [INFO] [TRAIN] epoch: 28, iter: 10250/60000, loss: 1.0474, lr: 0.008464, batch_cost: 0.8456, reader_cost: 0.00023, ips: 2.3651 samples/sec | ETA 11:41:10
- 2022-04-13 00:24:26 [INFO] [TRAIN] epoch: 28, iter: 10300/60000, loss: 1.0524, lr: 0.008457, batch_cost: 0.8452, reader_cost: 0.00021, ips: 2.3662 samples/sec | ETA 11:40:08
- 2022-04-13 00:25:09 [INFO] [TRAIN] epoch: 28, iter: 10350/60000, loss: 1.1260, lr: 0.008449, batch_cost: 0.8492, reader_cost: 0.00021, ips: 2.3551 samples/sec | ETA 11:42:43
- 2022-04-13 00:25:51 [INFO] [TRAIN] epoch: 28, iter: 10400/60000, loss: 1.0435, lr: 0.008441, batch_cost: 0.8438, reader_cost: 0.00026, ips: 2.3702 samples/sec | ETA 11:37:33
- 2022-04-13 00:26:36 [INFO] [TRAIN] epoch: 29, iter: 10450/60000, loss: 1.0720, lr: 0.008434, batch_cost: 0.8970, reader_cost: 0.04930, ips: 2.2296 samples/sec | ETA 12:20:47
- 2022-04-13 00:27:18 [INFO] [TRAIN] epoch: 29, iter: 10500/60000, loss: 1.0907, lr: 0.008426, batch_cost: 0.8486, reader_cost: 0.00025, ips: 2.3567 samples/sec | ETA 11:40:08
- 2022-04-13 00:28:01 [INFO] [TRAIN] epoch: 29, iter: 10550/60000, loss: 1.0676, lr: 0.008419, batch_cost: 0.8465, reader_cost: 0.00026, ips: 2.3628 samples/sec | ETA 11:37:37
- 2022-04-13 00:28:43 [INFO] [TRAIN] epoch: 29, iter: 10600/60000, loss: 1.1582, lr: 0.008411, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3655 samples/sec | ETA 11:36:07
- 2022-04-13 00:29:25 [INFO] [TRAIN] epoch: 29, iter: 10650/60000, loss: 1.0632, lr: 0.008404, batch_cost: 0.8450, reader_cost: 0.00022, ips: 2.3668 samples/sec | ETA 11:35:01
- 2022-04-13 00:30:08 [INFO] [TRAIN] epoch: 29, iter: 10700/60000, loss: 1.1522, lr: 0.008396, batch_cost: 0.8460, reader_cost: 0.00021, ips: 2.3640 samples/sec | ETA 11:35:08
- 2022-04-13 00:30:50 [INFO] [TRAIN] epoch: 29, iter: 10750/60000, loss: 1.0361, lr: 0.008388, batch_cost: 0.8446, reader_cost: 0.00020, ips: 2.3680 samples/sec | ETA 11:33:16
- 2022-04-13 00:31:35 [INFO] [TRAIN] epoch: 30, iter: 10800/60000, loss: 1.0550, lr: 0.008381, batch_cost: 0.8970, reader_cost: 0.03892, ips: 2.2297 samples/sec | ETA 12:15:32
- 2022-04-13 00:32:17 [INFO] [TRAIN] epoch: 30, iter: 10850/60000, loss: 1.0393, lr: 0.008373, batch_cost: 0.8456, reader_cost: 0.00026, ips: 2.3652 samples/sec | ETA 11:32:41
- 2022-04-13 00:32:59 [INFO] [TRAIN] epoch: 30, iter: 10900/60000, loss: 1.0271, lr: 0.008366, batch_cost: 0.8465, reader_cost: 0.00023, ips: 2.3626 samples/sec | ETA 11:32:44
- 2022-04-13 00:33:42 [INFO] [TRAIN] epoch: 30, iter: 10950/60000, loss: 1.0757, lr: 0.008358, batch_cost: 0.8484, reader_cost: 0.00021, ips: 2.3575 samples/sec | ETA 11:33:32
- 2022-04-13 00:34:24 [INFO] [TRAIN] epoch: 30, iter: 11000/60000, loss: 1.0395, lr: 0.008351, batch_cost: 0.8442, reader_cost: 0.00022, ips: 2.3692 samples/sec | ETA 11:29:24
- 2022-04-13 00:35:06 [INFO] [TRAIN] epoch: 30, iter: 11050/60000, loss: 1.0399, lr: 0.008343, batch_cost: 0.8461, reader_cost: 0.00026, ips: 2.3637 samples/sec | ETA 11:30:18
- 2022-04-13 00:35:48 [INFO] [TRAIN] epoch: 30, iter: 11100/60000, loss: 1.0299, lr: 0.008335, batch_cost: 0.8451, reader_cost: 0.00025, ips: 2.3665 samples/sec | ETA 11:28:46
- 2022-04-13 00:36:31 [INFO] [TRAIN] epoch: 30, iter: 11150/60000, loss: 1.2321, lr: 0.008328, batch_cost: 0.8477, reader_cost: 0.00019, ips: 2.3593 samples/sec | ETA 11:30:10
- 2022-04-13 00:37:16 [INFO] [TRAIN] epoch: 31, iter: 11200/60000, loss: 1.1392, lr: 0.008320, batch_cost: 0.9040, reader_cost: 0.04578, ips: 2.2123 samples/sec | ETA 12:15:16
- 2022-04-13 00:37:58 [INFO] [TRAIN] epoch: 31, iter: 11250/60000, loss: 1.1032, lr: 0.008313, batch_cost: 0.8465, reader_cost: 0.00026, ips: 2.3627 samples/sec | ETA 11:27:46
- 2022-04-13 00:38:41 [INFO] [TRAIN] epoch: 31, iter: 11300/60000, loss: 1.1446, lr: 0.008305, batch_cost: 0.8483, reader_cost: 0.00024, ips: 2.3577 samples/sec | ETA 11:28:30
- 2022-04-13 00:39:23 [INFO] [TRAIN] epoch: 31, iter: 11350/60000, loss: 1.1073, lr: 0.008298, batch_cost: 0.8463, reader_cost: 0.00023, ips: 2.3632 samples/sec | ETA 11:26:12
- 2022-04-13 00:40:06 [INFO] [TRAIN] epoch: 31, iter: 11400/60000, loss: 1.1026, lr: 0.008290, batch_cost: 0.8481, reader_cost: 0.00021, ips: 2.3582 samples/sec | ETA 11:26:57
- 2022-04-13 00:40:48 [INFO] [TRAIN] epoch: 31, iter: 11450/60000, loss: 1.0721, lr: 0.008282, batch_cost: 0.8461, reader_cost: 0.00021, ips: 2.3639 samples/sec | ETA 11:24:36
- 2022-04-13 00:41:30 [INFO] [TRAIN] epoch: 31, iter: 11500/60000, loss: 1.0009, lr: 0.008275, batch_cost: 0.8467, reader_cost: 0.00021, ips: 2.3622 samples/sec | ETA 11:24:23
- 2022-04-13 00:42:15 [INFO] [TRAIN] epoch: 32, iter: 11550/60000, loss: 1.0685, lr: 0.008267, batch_cost: 0.8960, reader_cost: 0.04285, ips: 2.2322 samples/sec | ETA 12:03:30
- 2022-04-13 00:42:57 [INFO] [TRAIN] epoch: 32, iter: 11600/60000, loss: 0.9614, lr: 0.008260, batch_cost: 0.8471, reader_cost: 0.00028, ips: 2.3610 samples/sec | ETA 11:23:20
- 2022-04-13 00:43:40 [INFO] [TRAIN] epoch: 32, iter: 11650/60000, loss: 1.0153, lr: 0.008252, batch_cost: 0.8451, reader_cost: 0.00024, ips: 2.3667 samples/sec | ETA 11:20:58
- 2022-04-13 00:44:22 [INFO] [TRAIN] epoch: 32, iter: 11700/60000, loss: 1.0075, lr: 0.008244, batch_cost: 0.8454, reader_cost: 0.00022, ips: 2.3658 samples/sec | ETA 11:20:30
- 2022-04-13 00:45:04 [INFO] [TRAIN] epoch: 32, iter: 11750/60000, loss: 1.1162, lr: 0.008237, batch_cost: 0.8486, reader_cost: 0.00028, ips: 2.3569 samples/sec | ETA 11:22:23
- 2022-04-13 00:45:47 [INFO] [TRAIN] epoch: 32, iter: 11800/60000, loss: 1.1008, lr: 0.008229, batch_cost: 0.8447, reader_cost: 0.00026, ips: 2.3678 samples/sec | ETA 11:18:33
- 2022-04-13 00:46:29 [INFO] [TRAIN] epoch: 32, iter: 11850/60000, loss: 1.0680, lr: 0.008222, batch_cost: 0.8451, reader_cost: 0.00023, ips: 2.3665 samples/sec | ETA 11:18:12
- 2022-04-13 00:47:11 [INFO] [TRAIN] epoch: 32, iter: 11900/60000, loss: 1.0303, lr: 0.008214, batch_cost: 0.8447, reader_cost: 0.00019, ips: 2.3676 samples/sec | ETA 11:17:12
- 2022-04-13 00:47:56 [INFO] [TRAIN] epoch: 33, iter: 11950/60000, loss: 1.0694, lr: 0.008206, batch_cost: 0.8971, reader_cost: 0.04487, ips: 2.2293 samples/sec | ETA 11:58:27
- 2022-04-13 00:48:38 [INFO] [TRAIN] epoch: 33, iter: 12000/60000, loss: 1.0432, lr: 0.008199, batch_cost: 0.8458, reader_cost: 0.00022, ips: 2.3646 samples/sec | ETA 11:16:38
- 2022-04-13 00:48:38 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4176 - reader cost: 0.0034
- 2022-04-13 00:49:30 [INFO] [EVAL] #Images: 500 mIoU: 0.6934 Acc: 0.9490 Kappa: 0.9337 Dice: 0.8047
- 2022-04-13 00:49:30 [INFO] [EVAL] Class IoU:
- [0.9732 0.8029 0.9077 0.3507 0.5156 0.5389 0.6517 0.7419 0.9112 0.5999
- 0.9283 0.7888 0.559 0.9337 0.6199 0.7109 0.4635 0.4397 0.7375]
- 2022-04-13 00:49:30 [INFO] [EVAL] Class Acc:
- [0.9851 0.8793 0.94 0.7593 0.812 0.7843 0.7278 0.8552 0.9544 0.8323
- 0.9416 0.8881 0.7297 0.9636 0.8558 0.7842 0.7557 0.4847 0.8212]
- 2022-04-13 00:49:34 [INFO] [EVAL] The model with the best validation mIoU (0.6934) was saved at iter 12000.
- 2022-04-13 00:50:16 [INFO] [TRAIN] epoch: 33, iter: 12050/60000, loss: 1.0629, lr: 0.008191, batch_cost: 0.8451, reader_cost: 0.00027, ips: 2.3666 samples/sec | ETA 11:15:22
- 2022-04-13 00:50:58 [INFO] [TRAIN] epoch: 33, iter: 12100/60000, loss: 1.1268, lr: 0.008184, batch_cost: 0.8458, reader_cost: 0.00024, ips: 2.3647 samples/sec | ETA 11:15:11
- 2022-04-13 00:51:41 [INFO] [TRAIN] epoch: 33, iter: 12150/60000, loss: 0.9932, lr: 0.008176, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3631 samples/sec | ETA 11:14:57
- 2022-04-13 00:52:23 [INFO] [TRAIN] epoch: 33, iter: 12200/60000, loss: 1.1226, lr: 0.008168, batch_cost: 0.8447, reader_cost: 0.00019, ips: 2.3676 samples/sec | ETA 11:12:57
- 2022-04-13 00:53:05 [INFO] [TRAIN] epoch: 33, iter: 12250/60000, loss: 1.0406, lr: 0.008161, batch_cost: 0.8461, reader_cost: 0.00020, ips: 2.3637 samples/sec | ETA 11:13:22
- 2022-04-13 00:53:50 [INFO] [TRAIN] epoch: 34, iter: 12300/60000, loss: 1.0670, lr: 0.008153, batch_cost: 0.8986, reader_cost: 0.04403, ips: 2.2256 samples/sec | ETA 11:54:24
- 2022-04-13 00:54:32 [INFO] [TRAIN] epoch: 34, iter: 12350/60000, loss: 1.0995, lr: 0.008146, batch_cost: 0.8460, reader_cost: 0.00023, ips: 2.3642 samples/sec | ETA 11:11:50
- 2022-04-13 00:55:15 [INFO] [TRAIN] epoch: 34, iter: 12400/60000, loss: 1.0797, lr: 0.008138, batch_cost: 0.8478, reader_cost: 0.00022, ips: 2.3590 samples/sec | ETA 11:12:36
- 2022-04-13 00:55:57 [INFO] [TRAIN] epoch: 34, iter: 12450/60000, loss: 0.9831, lr: 0.008131, batch_cost: 0.8486, reader_cost: 0.00021, ips: 2.3567 samples/sec | ETA 11:12:32
- 2022-04-13 00:56:39 [INFO] [TRAIN] epoch: 34, iter: 12500/60000, loss: 1.0278, lr: 0.008123, batch_cost: 0.8461, reader_cost: 0.00020, ips: 2.3639 samples/sec | ETA 11:09:48
- 2022-04-13 00:57:22 [INFO] [TRAIN] epoch: 34, iter: 12550/60000, loss: 0.9932, lr: 0.008115, batch_cost: 0.8481, reader_cost: 0.00023, ips: 2.3582 samples/sec | ETA 11:10:42
- 2022-04-13 00:58:04 [INFO] [TRAIN] epoch: 34, iter: 12600/60000, loss: 1.0211, lr: 0.008108, batch_cost: 0.8470, reader_cost: 0.00022, ips: 2.3612 samples/sec | ETA 11:09:09
- 2022-04-13 00:58:49 [INFO] [TRAIN] epoch: 35, iter: 12650/60000, loss: 1.0592, lr: 0.008100, batch_cost: 0.8981, reader_cost: 0.04512, ips: 2.2268 samples/sec | ETA 11:48:46
- 2022-04-13 00:59:31 [INFO] [TRAIN] epoch: 35, iter: 12700/60000, loss: 1.0533, lr: 0.008092, batch_cost: 0.8449, reader_cost: 0.00025, ips: 2.3672 samples/sec | ETA 11:06:02
- 2022-04-13 01:00:14 [INFO] [TRAIN] epoch: 35, iter: 12750/60000, loss: 1.0182, lr: 0.008085, batch_cost: 0.8458, reader_cost: 0.00021, ips: 2.3646 samples/sec | ETA 11:06:04
- 2022-04-13 01:00:56 [INFO] [TRAIN] epoch: 35, iter: 12800/60000, loss: 0.9874, lr: 0.008077, batch_cost: 0.8497, reader_cost: 0.00020, ips: 2.3536 samples/sec | ETA 11:08:28
- 2022-04-13 01:01:38 [INFO] [TRAIN] epoch: 35, iter: 12850/60000, loss: 1.0484, lr: 0.008070, batch_cost: 0.8466, reader_cost: 0.00020, ips: 2.3623 samples/sec | ETA 11:05:19
- 2022-04-13 01:02:21 [INFO] [TRAIN] epoch: 35, iter: 12900/60000, loss: 1.0391, lr: 0.008062, batch_cost: 0.8474, reader_cost: 0.00021, ips: 2.3602 samples/sec | ETA 11:05:11
- 2022-04-13 01:03:03 [INFO] [TRAIN] epoch: 35, iter: 12950/60000, loss: 1.0938, lr: 0.008054, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3632 samples/sec | ETA 11:03:38
- 2022-04-13 01:03:45 [INFO] [TRAIN] epoch: 35, iter: 13000/60000, loss: 1.0589, lr: 0.008047, batch_cost: 0.8447, reader_cost: 0.00022, ips: 2.3678 samples/sec | ETA 11:01:38
- 2022-04-13 01:04:30 [INFO] [TRAIN] epoch: 36, iter: 13050/60000, loss: 1.0000, lr: 0.008039, batch_cost: 0.8936, reader_cost: 0.04619, ips: 2.2382 samples/sec | ETA 11:39:13
- 2022-04-13 01:05:13 [INFO] [TRAIN] epoch: 36, iter: 13100/60000, loss: 0.9788, lr: 0.008032, batch_cost: 0.8489, reader_cost: 0.00028, ips: 2.3561 samples/sec | ETA 11:03:31
- 2022-04-13 01:05:55 [INFO] [TRAIN] epoch: 36, iter: 13150/60000, loss: 1.0955, lr: 0.008024, batch_cost: 0.8462, reader_cost: 0.00024, ips: 2.3635 samples/sec | ETA 11:00:44
- 2022-04-13 01:06:37 [INFO] [TRAIN] epoch: 36, iter: 13200/60000, loss: 1.0553, lr: 0.008016, batch_cost: 0.8459, reader_cost: 0.00023, ips: 2.3643 samples/sec | ETA 10:59:48
- 2022-04-13 01:07:19 [INFO] [TRAIN] epoch: 36, iter: 13250/60000, loss: 1.0584, lr: 0.008009, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3654 samples/sec | ETA 10:58:48
- 2022-04-13 01:08:02 [INFO] [TRAIN] epoch: 36, iter: 13300/60000, loss: 1.0396, lr: 0.008001, batch_cost: 0.8453, reader_cost: 0.00023, ips: 2.3659 samples/sec | ETA 10:57:57
- 2022-04-13 01:08:44 [INFO] [TRAIN] epoch: 36, iter: 13350/60000, loss: 1.0779, lr: 0.007994, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3645 samples/sec | ETA 10:57:38
- 2022-04-13 01:09:29 [INFO] [TRAIN] epoch: 37, iter: 13400/60000, loss: 0.9747, lr: 0.007986, batch_cost: 0.8970, reader_cost: 0.04090, ips: 2.2297 samples/sec | ETA 11:36:40
- 2022-04-13 01:10:11 [INFO] [TRAIN] epoch: 37, iter: 13450/60000, loss: 1.0452, lr: 0.007978, batch_cost: 0.8465, reader_cost: 0.00024, ips: 2.3626 samples/sec | ETA 10:56:46
- 2022-04-13 01:10:54 [INFO] [TRAIN] epoch: 37, iter: 13500/60000, loss: 1.0444, lr: 0.007971, batch_cost: 0.8492, reader_cost: 0.00022, ips: 2.3551 samples/sec | ETA 10:58:08
- 2022-04-13 01:11:36 [INFO] [TRAIN] epoch: 37, iter: 13550/60000, loss: 1.0352, lr: 0.007963, batch_cost: 0.8460, reader_cost: 0.00022, ips: 2.3640 samples/sec | ETA 10:54:57
- 2022-04-13 01:12:18 [INFO] [TRAIN] epoch: 37, iter: 13600/60000, loss: 1.0184, lr: 0.007955, batch_cost: 0.8456, reader_cost: 0.00022, ips: 2.3652 samples/sec | ETA 10:53:55
- 2022-04-13 01:13:00 [INFO] [TRAIN] epoch: 37, iter: 13650/60000, loss: 1.1889, lr: 0.007948, batch_cost: 0.8455, reader_cost: 0.00019, ips: 2.3655 samples/sec | ETA 10:53:07
- 2022-04-13 01:13:43 [INFO] [TRAIN] epoch: 37, iter: 13700/60000, loss: 1.0008, lr: 0.007940, batch_cost: 0.8460, reader_cost: 0.00019, ips: 2.3641 samples/sec | ETA 10:52:49
- 2022-04-13 01:14:25 [INFO] [TRAIN] epoch: 37, iter: 13750/60000, loss: 1.0335, lr: 0.007933, batch_cost: 0.8461, reader_cost: 0.00025, ips: 2.3637 samples/sec | ETA 10:52:13
- 2022-04-13 01:15:10 [INFO] [TRAIN] epoch: 38, iter: 13800/60000, loss: 1.0818, lr: 0.007925, batch_cost: 0.8985, reader_cost: 0.05027, ips: 2.2261 samples/sec | ETA 11:31:48
- 2022-04-13 01:15:52 [INFO] [TRAIN] epoch: 38, iter: 13850/60000, loss: 1.0311, lr: 0.007917, batch_cost: 0.8480, reader_cost: 0.00024, ips: 2.3586 samples/sec | ETA 10:52:13
- 2022-04-13 01:16:35 [INFO] [TRAIN] epoch: 38, iter: 13900/60000, loss: 1.0085, lr: 0.007910, batch_cost: 0.8461, reader_cost: 0.00020, ips: 2.3638 samples/sec | ETA 10:50:04
- 2022-04-13 01:17:17 [INFO] [TRAIN] epoch: 38, iter: 13950/60000, loss: 1.0262, lr: 0.007902, batch_cost: 0.8475, reader_cost: 0.00020, ips: 2.3598 samples/sec | ETA 10:50:28
- 2022-04-13 01:17:59 [INFO] [TRAIN] epoch: 38, iter: 14000/60000, loss: 1.0795, lr: 0.007895, batch_cost: 0.8450, reader_cost: 0.00021, ips: 2.3668 samples/sec | ETA 10:47:51
- 2022-04-13 01:18:42 [INFO] [TRAIN] epoch: 38, iter: 14050/60000, loss: 1.0526, lr: 0.007887, batch_cost: 0.8449, reader_cost: 0.00020, ips: 2.3672 samples/sec | ETA 10:47:02
- 2022-04-13 01:19:24 [INFO] [TRAIN] epoch: 38, iter: 14100/60000, loss: 0.9649, lr: 0.007879, batch_cost: 0.8464, reader_cost: 0.00020, ips: 2.3631 samples/sec | ETA 10:47:27
- 2022-04-13 01:20:09 [INFO] [TRAIN] epoch: 39, iter: 14150/60000, loss: 1.0330, lr: 0.007872, batch_cost: 0.8980, reader_cost: 0.04841, ips: 2.2271 samples/sec | ETA 11:26:13
- 2022-04-13 01:20:51 [INFO] [TRAIN] epoch: 39, iter: 14200/60000, loss: 1.0137, lr: 0.007864, batch_cost: 0.8456, reader_cost: 0.00023, ips: 2.3652 samples/sec | ETA 10:45:27
- 2022-04-13 01:21:33 [INFO] [TRAIN] epoch: 39, iter: 14250/60000, loss: 1.0191, lr: 0.007856, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3655 samples/sec | ETA 10:44:41
- 2022-04-13 01:22:16 [INFO] [TRAIN] epoch: 39, iter: 14300/60000, loss: 1.0059, lr: 0.007849, batch_cost: 0.8444, reader_cost: 0.00022, ips: 2.3686 samples/sec | ETA 10:43:07
- 2022-04-13 01:22:58 [INFO] [TRAIN] epoch: 39, iter: 14350/60000, loss: 1.0268, lr: 0.007841, batch_cost: 0.8454, reader_cost: 0.00023, ips: 2.3657 samples/sec | ETA 10:43:13
- 2022-04-13 01:23:40 [INFO] [TRAIN] epoch: 39, iter: 14400/60000, loss: 0.9949, lr: 0.007833, batch_cost: 0.8452, reader_cost: 0.00023, ips: 2.3663 samples/sec | ETA 10:42:21
- 2022-04-13 01:24:22 [INFO] [TRAIN] epoch: 39, iter: 14450/60000, loss: 1.0640, lr: 0.007826, batch_cost: 0.8447, reader_cost: 0.00022, ips: 2.3676 samples/sec | ETA 10:41:17
- 2022-04-13 01:25:05 [INFO] [TRAIN] epoch: 39, iter: 14500/60000, loss: 1.0028, lr: 0.007818, batch_cost: 0.8448, reader_cost: 0.00024, ips: 2.3674 samples/sec | ETA 10:40:39
- 2022-04-13 01:25:50 [INFO] [TRAIN] epoch: 40, iter: 14550/60000, loss: 1.0290, lr: 0.007811, batch_cost: 0.9029, reader_cost: 0.04184, ips: 2.2150 samples/sec | ETA 11:23:58
- 2022-04-13 01:26:32 [INFO] [TRAIN] epoch: 40, iter: 14600/60000, loss: 1.0121, lr: 0.007803, batch_cost: 0.8522, reader_cost: 0.00024, ips: 2.3468 samples/sec | ETA 10:44:51
- 2022-04-13 01:27:15 [INFO] [TRAIN] epoch: 40, iter: 14650/60000, loss: 0.9880, lr: 0.007795, batch_cost: 0.8455, reader_cost: 0.00026, ips: 2.3656 samples/sec | ETA 10:39:01
- 2022-04-13 01:27:57 [INFO] [TRAIN] epoch: 40, iter: 14700/60000, loss: 1.0201, lr: 0.007788, batch_cost: 0.8463, reader_cost: 0.00025, ips: 2.3633 samples/sec | ETA 10:38:56
- 2022-04-13 01:28:39 [INFO] [TRAIN] epoch: 40, iter: 14750/60000, loss: 1.0700, lr: 0.007780, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3660 samples/sec | ETA 10:37:30
- 2022-04-13 01:29:21 [INFO] [TRAIN] epoch: 40, iter: 14800/60000, loss: 1.0226, lr: 0.007772, batch_cost: 0.8449, reader_cost: 0.00020, ips: 2.3671 samples/sec | ETA 10:36:29
- 2022-04-13 01:30:04 [INFO] [TRAIN] epoch: 40, iter: 14850/60000, loss: 0.9836, lr: 0.007765, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3662 samples/sec | ETA 10:36:02
- 2022-04-13 01:30:49 [INFO] [TRAIN] epoch: 41, iter: 14900/60000, loss: 1.0339, lr: 0.007757, batch_cost: 0.8976, reader_cost: 0.04927, ips: 2.2281 samples/sec | ETA 11:14:43
- 2022-04-13 01:31:31 [INFO] [TRAIN] epoch: 41, iter: 14950/60000, loss: 1.0451, lr: 0.007750, batch_cost: 0.8460, reader_cost: 0.00024, ips: 2.3641 samples/sec | ETA 10:35:12
- 2022-04-13 01:32:13 [INFO] [TRAIN] epoch: 41, iter: 15000/60000, loss: 0.9915, lr: 0.007742, batch_cost: 0.8465, reader_cost: 0.00020, ips: 2.3627 samples/sec | ETA 10:34:52
- 2022-04-13 01:32:55 [INFO] [TRAIN] epoch: 41, iter: 15050/60000, loss: 1.0187, lr: 0.007734, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3654 samples/sec | ETA 10:33:26
- 2022-04-13 01:33:38 [INFO] [TRAIN] epoch: 41, iter: 15100/60000, loss: 1.0073, lr: 0.007727, batch_cost: 0.8460, reader_cost: 0.00022, ips: 2.3641 samples/sec | ETA 10:33:05
- 2022-04-13 01:34:20 [INFO] [TRAIN] epoch: 41, iter: 15150/60000, loss: 1.0163, lr: 0.007719, batch_cost: 0.8449, reader_cost: 0.00022, ips: 2.3672 samples/sec | ETA 10:31:33
- 2022-04-13 01:35:02 [INFO] [TRAIN] epoch: 41, iter: 15200/60000, loss: 1.0813, lr: 0.007711, batch_cost: 0.8466, reader_cost: 0.00021, ips: 2.3624 samples/sec | ETA 10:32:07
- 2022-04-13 01:35:45 [INFO] [TRAIN] epoch: 41, iter: 15250/60000, loss: 1.0651, lr: 0.007704, batch_cost: 0.8453, reader_cost: 0.00023, ips: 2.3662 samples/sec | ETA 10:30:25
- 2022-04-13 01:36:30 [INFO] [TRAIN] epoch: 42, iter: 15300/60000, loss: 1.0724, lr: 0.007696, batch_cost: 0.9020, reader_cost: 0.04803, ips: 2.2173 samples/sec | ETA 11:11:59
- 2022-04-13 01:37:12 [INFO] [TRAIN] epoch: 42, iter: 15350/60000, loss: 0.9909, lr: 0.007688, batch_cost: 0.8464, reader_cost: 0.00023, ips: 2.3630 samples/sec | ETA 10:29:51
- 2022-04-13 01:37:54 [INFO] [TRAIN] epoch: 42, iter: 15400/60000, loss: 1.0247, lr: 0.007681, batch_cost: 0.8453, reader_cost: 0.00023, ips: 2.3661 samples/sec | ETA 10:28:18
- 2022-04-13 01:38:37 [INFO] [TRAIN] epoch: 42, iter: 15450/60000, loss: 0.9671, lr: 0.007673, batch_cost: 0.8453, reader_cost: 0.00023, ips: 2.3660 samples/sec | ETA 10:27:37
- 2022-04-13 01:39:19 [INFO] [TRAIN] epoch: 42, iter: 15500/60000, loss: 1.0201, lr: 0.007665, batch_cost: 0.8476, reader_cost: 0.00023, ips: 2.3595 samples/sec | ETA 10:28:40
- 2022-04-13 01:40:01 [INFO] [TRAIN] epoch: 42, iter: 15550/60000, loss: 0.9885, lr: 0.007658, batch_cost: 0.8455, reader_cost: 0.00026, ips: 2.3656 samples/sec | ETA 10:26:20
- 2022-04-13 01:40:44 [INFO] [TRAIN] epoch: 42, iter: 15600/60000, loss: 1.0571, lr: 0.007650, batch_cost: 0.8461, reader_cost: 0.00025, ips: 2.3639 samples/sec | ETA 10:26:05
- 2022-04-13 01:41:29 [INFO] [TRAIN] epoch: 43, iter: 15650/60000, loss: 1.0799, lr: 0.007642, batch_cost: 0.9010, reader_cost: 0.04225, ips: 2.2199 samples/sec | ETA 11:05:57
- 2022-04-13 01:42:11 [INFO] [TRAIN] epoch: 43, iter: 15700/60000, loss: 0.9966, lr: 0.007635, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3655 samples/sec | ETA 10:24:14
- 2022-04-13 01:42:53 [INFO] [TRAIN] epoch: 43, iter: 15750/60000, loss: 0.9469, lr: 0.007627, batch_cost: 0.8477, reader_cost: 0.00020, ips: 2.3594 samples/sec | ETA 10:25:09
- 2022-04-13 01:43:36 [INFO] [TRAIN] epoch: 43, iter: 15800/60000, loss: 1.0420, lr: 0.007619, batch_cost: 0.8479, reader_cost: 0.00020, ips: 2.3588 samples/sec | ETA 10:24:36
- 2022-04-13 01:44:18 [INFO] [TRAIN] epoch: 43, iter: 15850/60000, loss: 1.0701, lr: 0.007612, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3664 samples/sec | ETA 10:21:53
- 2022-04-13 01:45:00 [INFO] [TRAIN] epoch: 43, iter: 15900/60000, loss: 0.9280, lr: 0.007604, batch_cost: 0.8481, reader_cost: 0.00021, ips: 2.3582 samples/sec | ETA 10:23:20
- 2022-04-13 01:45:43 [INFO] [TRAIN] epoch: 43, iter: 15950/60000, loss: 1.0220, lr: 0.007597, batch_cost: 0.8461, reader_cost: 0.00020, ips: 2.3639 samples/sec | ETA 10:21:08
- 2022-04-13 01:46:28 [INFO] [TRAIN] epoch: 44, iter: 16000/60000, loss: 1.0432, lr: 0.007589, batch_cost: 0.8981, reader_cost: 0.04812, ips: 2.2269 samples/sec | ETA 10:58:36
- 2022-04-13 01:46:28 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4180 - reader cost: 0.0033
- 2022-04-13 01:47:20 [INFO] [EVAL] #Images: 500 mIoU: 0.7231 Acc: 0.9500 Kappa: 0.9349 Dice: 0.8298
- 2022-04-13 01:47:20 [INFO] [EVAL] Class IoU:
- [0.9754 0.8009 0.9019 0.5273 0.4685 0.4726 0.6793 0.7302 0.912 0.5983
- 0.9359 0.7921 0.5977 0.9422 0.6662 0.7111 0.658 0.6099 0.7595]
- 2022-04-13 01:47:20 [INFO] [EVAL] Class Acc:
- [0.9811 0.9335 0.9345 0.7172 0.7711 0.8545 0.821 0.8701 0.9441 0.8182
- 0.953 0.8619 0.8095 0.9758 0.7691 0.8808 0.8699 0.8101 0.8623]
- 2022-04-13 01:47:23 [INFO] [EVAL] The model with the best validation mIoU (0.7231) was saved at iter 16000.
- 2022-04-13 01:48:06 [INFO] [TRAIN] epoch: 44, iter: 16050/60000, loss: 1.0170, lr: 0.007581, batch_cost: 0.8512, reader_cost: 0.00020, ips: 2.3498 samples/sec | ETA 10:23:28
- 2022-04-13 01:48:48 [INFO] [TRAIN] epoch: 44, iter: 16100/60000, loss: 0.9638, lr: 0.007574, batch_cost: 0.8450, reader_cost: 0.00019, ips: 2.3670 samples/sec | ETA 10:18:13
- 2022-04-13 01:49:30 [INFO] [TRAIN] epoch: 44, iter: 16150/60000, loss: 1.0656, lr: 0.007566, batch_cost: 0.8452, reader_cost: 0.00019, ips: 2.3662 samples/sec | ETA 10:17:43
- 2022-04-13 01:50:12 [INFO] [TRAIN] epoch: 44, iter: 16200/60000, loss: 1.0494, lr: 0.007558, batch_cost: 0.8459, reader_cost: 0.00020, ips: 2.3643 samples/sec | ETA 10:17:31
- 2022-04-13 01:50:55 [INFO] [TRAIN] epoch: 44, iter: 16250/60000, loss: 0.9934, lr: 0.007551, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3655 samples/sec | ETA 10:16:29
- 2022-04-13 01:51:37 [INFO] [TRAIN] epoch: 44, iter: 16300/60000, loss: 0.9664, lr: 0.007543, batch_cost: 0.8464, reader_cost: 0.00019, ips: 2.3630 samples/sec | ETA 10:16:26
- 2022-04-13 01:52:19 [INFO] [TRAIN] epoch: 44, iter: 16350/60000, loss: 1.0098, lr: 0.007535, batch_cost: 0.8450, reader_cost: 0.00019, ips: 2.3667 samples/sec | ETA 10:14:46
- 2022-04-13 01:53:04 [INFO] [TRAIN] epoch: 45, iter: 16400/60000, loss: 1.0367, lr: 0.007528, batch_cost: 0.8947, reader_cost: 0.04023, ips: 2.2355 samples/sec | ETA 10:50:07
- 2022-04-13 01:53:46 [INFO] [TRAIN] epoch: 45, iter: 16450/60000, loss: 1.0606, lr: 0.007520, batch_cost: 0.8451, reader_cost: 0.00023, ips: 2.3667 samples/sec | ETA 10:13:22
- 2022-04-13 01:54:28 [INFO] [TRAIN] epoch: 45, iter: 16500/60000, loss: 0.9780, lr: 0.007512, batch_cost: 0.8449, reader_cost: 0.00023, ips: 2.3673 samples/sec | ETA 10:12:31
- 2022-04-13 01:55:11 [INFO] [TRAIN] epoch: 45, iter: 16550/60000, loss: 0.9749, lr: 0.007505, batch_cost: 0.8443, reader_cost: 0.00027, ips: 2.3688 samples/sec | ETA 10:11:24
- 2022-04-13 01:55:53 [INFO] [TRAIN] epoch: 45, iter: 16600/60000, loss: 1.0239, lr: 0.007497, batch_cost: 0.8454, reader_cost: 0.00027, ips: 2.3658 samples/sec | ETA 10:11:29
- 2022-04-13 01:56:35 [INFO] [TRAIN] epoch: 45, iter: 16650/60000, loss: 1.0059, lr: 0.007489, batch_cost: 0.8455, reader_cost: 0.00024, ips: 2.3655 samples/sec | ETA 10:10:51
- 2022-04-13 01:57:17 [INFO] [TRAIN] epoch: 45, iter: 16700/60000, loss: 0.9816, lr: 0.007482, batch_cost: 0.8452, reader_cost: 0.00026, ips: 2.3663 samples/sec | ETA 10:09:57
- 2022-04-13 01:58:02 [INFO] [TRAIN] epoch: 46, iter: 16750/60000, loss: 0.9854, lr: 0.007474, batch_cost: 0.8993, reader_cost: 0.05148, ips: 2.2238 samples/sec | ETA 10:48:16
- 2022-04-13 01:58:45 [INFO] [TRAIN] epoch: 46, iter: 16800/60000, loss: 0.9939, lr: 0.007466, batch_cost: 0.8445, reader_cost: 0.00023, ips: 2.3683 samples/sec | ETA 10:08:01
- 2022-04-13 01:59:27 [INFO] [TRAIN] epoch: 46, iter: 16850/60000, loss: 0.9880, lr: 0.007459, batch_cost: 0.8495, reader_cost: 0.00023, ips: 2.3544 samples/sec | ETA 10:10:55
- 2022-04-13 02:00:09 [INFO] [TRAIN] epoch: 46, iter: 16900/60000, loss: 0.9922, lr: 0.007451, batch_cost: 0.8468, reader_cost: 0.00020, ips: 2.3620 samples/sec | ETA 10:08:15
- 2022-04-13 02:00:52 [INFO] [TRAIN] epoch: 46, iter: 16950/60000, loss: 1.0442, lr: 0.007443, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3632 samples/sec | ETA 10:07:13
- 2022-04-13 02:01:34 [INFO] [TRAIN] epoch: 46, iter: 17000/60000, loss: 0.9524, lr: 0.007436, batch_cost: 0.8457, reader_cost: 0.00020, ips: 2.3649 samples/sec | ETA 10:06:05
- 2022-04-13 02:02:16 [INFO] [TRAIN] epoch: 46, iter: 17050/60000, loss: 0.9654, lr: 0.007428, batch_cost: 0.8446, reader_cost: 0.00020, ips: 2.3679 samples/sec | ETA 10:04:36
- 2022-04-13 02:02:59 [INFO] [TRAIN] epoch: 46, iter: 17100/60000, loss: 1.0043, lr: 0.007420, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3659 samples/sec | ETA 10:04:25
- 2022-04-13 02:03:43 [INFO] [TRAIN] epoch: 47, iter: 17150/60000, loss: 1.0258, lr: 0.007412, batch_cost: 0.8953, reader_cost: 0.04331, ips: 2.2338 samples/sec | ETA 10:39:25
- 2022-04-13 02:04:26 [INFO] [TRAIN] epoch: 47, iter: 17200/60000, loss: 1.0525, lr: 0.007405, batch_cost: 0.8451, reader_cost: 0.00022, ips: 2.3667 samples/sec | ETA 10:02:48
- 2022-04-13 02:05:08 [INFO] [TRAIN] epoch: 47, iter: 17250/60000, loss: 0.9912, lr: 0.007397, batch_cost: 0.8454, reader_cost: 0.00024, ips: 2.3657 samples/sec | ETA 10:02:22
- 2022-04-13 02:05:50 [INFO] [TRAIN] epoch: 47, iter: 17300/60000, loss: 1.0373, lr: 0.007389, batch_cost: 0.8455, reader_cost: 0.00024, ips: 2.3654 samples/sec | ETA 10:01:43
- 2022-04-13 02:06:32 [INFO] [TRAIN] epoch: 47, iter: 17350/60000, loss: 1.0139, lr: 0.007382, batch_cost: 0.8451, reader_cost: 0.00024, ips: 2.3667 samples/sec | ETA 10:00:42
- 2022-04-13 02:07:15 [INFO] [TRAIN] epoch: 47, iter: 17400/60000, loss: 1.0904, lr: 0.007374, batch_cost: 0.8442, reader_cost: 0.00023, ips: 2.3692 samples/sec | ETA 09:59:22
- 2022-04-13 02:07:57 [INFO] [TRAIN] epoch: 47, iter: 17450/60000, loss: 1.0531, lr: 0.007366, batch_cost: 0.8447, reader_cost: 0.00020, ips: 2.3677 samples/sec | ETA 09:59:02
- 2022-04-13 02:08:42 [INFO] [TRAIN] epoch: 48, iter: 17500/60000, loss: 0.9787, lr: 0.007359, batch_cost: 0.8955, reader_cost: 0.04400, ips: 2.2334 samples/sec | ETA 10:34:18
- 2022-04-13 02:09:24 [INFO] [TRAIN] epoch: 48, iter: 17550/60000, loss: 1.0506, lr: 0.007351, batch_cost: 0.8462, reader_cost: 0.00026, ips: 2.3636 samples/sec | ETA 09:58:39
- 2022-04-13 02:10:06 [INFO] [TRAIN] epoch: 48, iter: 17600/60000, loss: 0.9520, lr: 0.007343, batch_cost: 0.8459, reader_cost: 0.00027, ips: 2.3643 samples/sec | ETA 09:57:47
- 2022-04-13 02:10:49 [INFO] [TRAIN] epoch: 48, iter: 17650/60000, loss: 1.0013, lr: 0.007336, batch_cost: 0.8464, reader_cost: 0.00026, ips: 2.3629 samples/sec | ETA 09:57:25
- 2022-04-13 02:11:31 [INFO] [TRAIN] epoch: 48, iter: 17700/60000, loss: 0.9830, lr: 0.007328, batch_cost: 0.8464, reader_cost: 0.00026, ips: 2.3631 samples/sec | ETA 09:56:40
- 2022-04-13 02:12:13 [INFO] [TRAIN] epoch: 48, iter: 17750/60000, loss: 1.0266, lr: 0.007320, batch_cost: 0.8470, reader_cost: 0.00024, ips: 2.3613 samples/sec | ETA 09:56:24
- 2022-04-13 02:12:55 [INFO] [TRAIN] epoch: 48, iter: 17800/60000, loss: 1.0721, lr: 0.007313, batch_cost: 0.8441, reader_cost: 0.00020, ips: 2.3694 samples/sec | ETA 09:53:40
- 2022-04-13 02:13:38 [INFO] [TRAIN] epoch: 48, iter: 17850/60000, loss: 0.9730, lr: 0.007305, batch_cost: 0.8480, reader_cost: 0.00022, ips: 2.3586 samples/sec | ETA 09:55:41
- 2022-04-13 02:14:23 [INFO] [TRAIN] epoch: 49, iter: 17900/60000, loss: 1.0067, lr: 0.007297, batch_cost: 0.9026, reader_cost: 0.04946, ips: 2.2158 samples/sec | ETA 10:33:19
- 2022-04-13 02:15:05 [INFO] [TRAIN] epoch: 49, iter: 17950/60000, loss: 1.1249, lr: 0.007289, batch_cost: 0.8454, reader_cost: 0.00028, ips: 2.3656 samples/sec | ETA 09:52:30
- 2022-04-13 02:15:47 [INFO] [TRAIN] epoch: 49, iter: 18000/60000, loss: 1.0038, lr: 0.007282, batch_cost: 0.8459, reader_cost: 0.00025, ips: 2.3643 samples/sec | ETA 09:52:08
- 2022-04-13 02:16:30 [INFO] [TRAIN] epoch: 49, iter: 18050/60000, loss: 0.9623, lr: 0.007274, batch_cost: 0.8460, reader_cost: 0.00020, ips: 2.3642 samples/sec | ETA 09:51:27
- 2022-04-13 02:17:12 [INFO] [TRAIN] epoch: 49, iter: 18100/60000, loss: 0.9668, lr: 0.007266, batch_cost: 0.8460, reader_cost: 0.00022, ips: 2.3641 samples/sec | ETA 09:50:46
- 2022-04-13 02:17:54 [INFO] [TRAIN] epoch: 49, iter: 18150/60000, loss: 0.9656, lr: 0.007259, batch_cost: 0.8470, reader_cost: 0.00021, ips: 2.3613 samples/sec | ETA 09:50:46
- 2022-04-13 02:18:37 [INFO] [TRAIN] epoch: 49, iter: 18200/60000, loss: 0.9748, lr: 0.007251, batch_cost: 0.8454, reader_cost: 0.00021, ips: 2.3658 samples/sec | ETA 09:48:56
- 2022-04-13 02:19:22 [INFO] [TRAIN] epoch: 50, iter: 18250/60000, loss: 1.0291, lr: 0.007243, batch_cost: 0.8963, reader_cost: 0.05102, ips: 2.2314 samples/sec | ETA 10:23:39
- 2022-04-13 02:20:04 [INFO] [TRAIN] epoch: 50, iter: 18300/60000, loss: 0.9586, lr: 0.007236, batch_cost: 0.8469, reader_cost: 0.00027, ips: 2.3615 samples/sec | ETA 09:48:36
- 2022-04-13 02:20:46 [INFO] [TRAIN] epoch: 50, iter: 18350/60000, loss: 0.9905, lr: 0.007228, batch_cost: 0.8472, reader_cost: 0.00026, ips: 2.3607 samples/sec | ETA 09:48:06
- 2022-04-13 02:21:29 [INFO] [TRAIN] epoch: 50, iter: 18400/60000, loss: 1.0411, lr: 0.007220, batch_cost: 0.8459, reader_cost: 0.00023, ips: 2.3643 samples/sec | ETA 09:46:30
- 2022-04-13 02:22:11 [INFO] [TRAIN] epoch: 50, iter: 18450/60000, loss: 1.0317, lr: 0.007213, batch_cost: 0.8457, reader_cost: 0.00021, ips: 2.3648 samples/sec | ETA 09:45:40
- 2022-04-13 02:22:53 [INFO] [TRAIN] epoch: 50, iter: 18500/60000, loss: 1.0286, lr: 0.007205, batch_cost: 0.8472, reader_cost: 0.00020, ips: 2.3607 samples/sec | ETA 09:45:59
- 2022-04-13 02:23:35 [INFO] [TRAIN] epoch: 50, iter: 18550/60000, loss: 1.0013, lr: 0.007197, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3631 samples/sec | ETA 09:44:40
- 2022-04-13 02:24:18 [INFO] [TRAIN] epoch: 50, iter: 18600/60000, loss: 1.1202, lr: 0.007189, batch_cost: 0.8421, reader_cost: 0.00021, ips: 2.3751 samples/sec | ETA 09:41:01
- 2022-04-13 02:25:02 [INFO] [TRAIN] epoch: 51, iter: 18650/60000, loss: 0.9628, lr: 0.007182, batch_cost: 0.8959, reader_cost: 0.04205, ips: 2.2323 samples/sec | ETA 10:17:26
- 2022-04-13 02:25:45 [INFO] [TRAIN] epoch: 51, iter: 18700/60000, loss: 1.0505, lr: 0.007174, batch_cost: 0.8462, reader_cost: 0.00023, ips: 2.3634 samples/sec | ETA 09:42:29
- 2022-04-13 02:26:27 [INFO] [TRAIN] epoch: 51, iter: 18750/60000, loss: 0.9522, lr: 0.007166, batch_cost: 0.8471, reader_cost: 0.00024, ips: 2.3610 samples/sec | ETA 09:42:23
- 2022-04-13 02:27:09 [INFO] [TRAIN] epoch: 51, iter: 18800/60000, loss: 1.0357, lr: 0.007159, batch_cost: 0.8449, reader_cost: 0.00026, ips: 2.3671 samples/sec | ETA 09:40:09
- 2022-04-13 02:27:52 [INFO] [TRAIN] epoch: 51, iter: 18850/60000, loss: 0.9737, lr: 0.007151, batch_cost: 0.8477, reader_cost: 0.00026, ips: 2.3594 samples/sec | ETA 09:41:21
- 2022-04-13 02:28:34 [INFO] [TRAIN] epoch: 51, iter: 18900/60000, loss: 0.9899, lr: 0.007143, batch_cost: 0.8458, reader_cost: 0.00025, ips: 2.3646 samples/sec | ETA 09:39:22
- 2022-04-13 02:29:16 [INFO] [TRAIN] epoch: 51, iter: 18950/60000, loss: 0.9929, lr: 0.007135, batch_cost: 0.8460, reader_cost: 0.00021, ips: 2.3641 samples/sec | ETA 09:38:47
- 2022-04-13 02:30:01 [INFO] [TRAIN] epoch: 52, iter: 19000/60000, loss: 0.9638, lr: 0.007128, batch_cost: 0.8965, reader_cost: 0.04970, ips: 2.2310 samples/sec | ETA 10:12:35
- 2022-04-13 02:30:43 [INFO] [TRAIN] epoch: 52, iter: 19050/60000, loss: 1.0045, lr: 0.007120, batch_cost: 0.8473, reader_cost: 0.00025, ips: 2.3605 samples/sec | ETA 09:38:16
- 2022-04-13 02:31:26 [INFO] [TRAIN] epoch: 52, iter: 19100/60000, loss: 0.9892, lr: 0.007112, batch_cost: 0.8459, reader_cost: 0.00022, ips: 2.3645 samples/sec | ETA 09:36:35
- 2022-04-13 02:32:08 [INFO] [TRAIN] epoch: 52, iter: 19150/60000, loss: 1.0516, lr: 0.007105, batch_cost: 0.8470, reader_cost: 0.00022, ips: 2.3614 samples/sec | ETA 09:36:38
- 2022-04-13 02:32:50 [INFO] [TRAIN] epoch: 52, iter: 19200/60000, loss: 0.9394, lr: 0.007097, batch_cost: 0.8450, reader_cost: 0.00023, ips: 2.3669 samples/sec | ETA 09:34:35
- 2022-04-13 02:33:33 [INFO] [TRAIN] epoch: 52, iter: 19250/60000, loss: 1.0330, lr: 0.007089, batch_cost: 0.8456, reader_cost: 0.00023, ips: 2.3651 samples/sec | ETA 09:34:19
- 2022-04-13 02:34:15 [INFO] [TRAIN] epoch: 52, iter: 19300/60000, loss: 1.0058, lr: 0.007081, batch_cost: 0.8461, reader_cost: 0.00022, ips: 2.3638 samples/sec | ETA 09:33:56
- 2022-04-13 02:35:00 [INFO] [TRAIN] epoch: 53, iter: 19350/60000, loss: 0.9945, lr: 0.007074, batch_cost: 0.8947, reader_cost: 0.03977, ips: 2.2354 samples/sec | ETA 10:06:08
- 2022-04-13 02:35:42 [INFO] [TRAIN] epoch: 53, iter: 19400/60000, loss: 0.9464, lr: 0.007066, batch_cost: 0.8450, reader_cost: 0.00023, ips: 2.3670 samples/sec | ETA 09:31:45
- 2022-04-13 02:36:24 [INFO] [TRAIN] epoch: 53, iter: 19450/60000, loss: 0.9894, lr: 0.007058, batch_cost: 0.8470, reader_cost: 0.00020, ips: 2.3614 samples/sec | ETA 09:32:24
- 2022-04-13 02:37:07 [INFO] [TRAIN] epoch: 53, iter: 19500/60000, loss: 1.0267, lr: 0.007051, batch_cost: 0.8470, reader_cost: 0.00020, ips: 2.3614 samples/sec | ETA 09:31:41
- 2022-04-13 02:37:49 [INFO] [TRAIN] epoch: 53, iter: 19550/60000, loss: 0.9920, lr: 0.007043, batch_cost: 0.8464, reader_cost: 0.00023, ips: 2.3629 samples/sec | ETA 09:30:37
- 2022-04-13 02:38:31 [INFO] [TRAIN] epoch: 53, iter: 19600/60000, loss: 1.0006, lr: 0.007035, batch_cost: 0.8447, reader_cost: 0.00020, ips: 2.3677 samples/sec | ETA 09:28:46
- 2022-04-13 02:39:14 [INFO] [TRAIN] epoch: 53, iter: 19650/60000, loss: 1.0407, lr: 0.007027, batch_cost: 0.8497, reader_cost: 0.00020, ips: 2.3537 samples/sec | ETA 09:31:26
- 2022-04-13 02:39:56 [INFO] [TRAIN] epoch: 53, iter: 19700/60000, loss: 0.9701, lr: 0.007020, batch_cost: 0.8451, reader_cost: 0.00020, ips: 2.3666 samples/sec | ETA 09:27:37
- 2022-04-13 02:40:41 [INFO] [TRAIN] epoch: 54, iter: 19750/60000, loss: 1.0179, lr: 0.007012, batch_cost: 0.9004, reader_cost: 0.04717, ips: 2.2212 samples/sec | ETA 10:04:01
- 2022-04-13 02:41:23 [INFO] [TRAIN] epoch: 54, iter: 19800/60000, loss: 1.0023, lr: 0.007004, batch_cost: 0.8472, reader_cost: 0.00022, ips: 2.3606 samples/sec | ETA 09:27:38
- 2022-04-13 02:42:06 [INFO] [TRAIN] epoch: 54, iter: 19850/60000, loss: 1.0343, lr: 0.006996, batch_cost: 0.8464, reader_cost: 0.00022, ips: 2.3629 samples/sec | ETA 09:26:23
- 2022-04-13 02:42:48 [INFO] [TRAIN] epoch: 54, iter: 19900/60000, loss: 0.9945, lr: 0.006989, batch_cost: 0.8459, reader_cost: 0.00020, ips: 2.3644 samples/sec | ETA 09:25:20
- 2022-04-13 02:43:30 [INFO] [TRAIN] epoch: 54, iter: 19950/60000, loss: 0.9905, lr: 0.006981, batch_cost: 0.8461, reader_cost: 0.00024, ips: 2.3637 samples/sec | ETA 09:24:47
- 2022-04-13 02:44:12 [INFO] [TRAIN] epoch: 54, iter: 20000/60000, loss: 0.9930, lr: 0.006973, batch_cost: 0.8443, reader_cost: 0.00021, ips: 2.3688 samples/sec | ETA 09:22:51
- 2022-04-13 02:44:12 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4187 - reader cost: 0.0037
- 2022-04-13 02:45:05 [INFO] [EVAL] #Images: 500 mIoU: 0.7302 Acc: 0.9552 Kappa: 0.9417 Dice: 0.8337
- 2022-04-13 02:45:05 [INFO] [EVAL] Class IoU:
- [0.9798 0.8395 0.9152 0.4098 0.5836 0.5809 0.6643 0.7592 0.9179 0.6035
- 0.9365 0.7994 0.6001 0.9328 0.6572 0.7631 0.7085 0.4734 0.7487]
- 2022-04-13 02:45:05 [INFO] [EVAL] Class Acc:
- [0.9869 0.9252 0.9472 0.8538 0.7819 0.8006 0.8843 0.9047 0.9494 0.8216
- 0.9674 0.8686 0.8163 0.9513 0.8969 0.933 0.8825 0.8647 0.8191]
- 2022-04-13 02:45:08 [INFO] [EVAL] The model with the best validation mIoU (0.7302) was saved at iter 20000.
- 2022-04-13 02:45:51 [INFO] [TRAIN] epoch: 54, iter: 20050/60000, loss: 0.9779, lr: 0.006966, batch_cost: 0.8493, reader_cost: 0.00023, ips: 2.3550 samples/sec | ETA 09:25:28
- 2022-04-13 02:46:36 [INFO] [TRAIN] epoch: 55, iter: 20100/60000, loss: 0.9989, lr: 0.006958, batch_cost: 0.8972, reader_cost: 0.04727, ips: 2.2291 samples/sec | ETA 09:56:39
- 2022-04-13 02:47:18 [INFO] [TRAIN] epoch: 55, iter: 20150/60000, loss: 0.9781, lr: 0.006950, batch_cost: 0.8467, reader_cost: 0.00025, ips: 2.3621 samples/sec | ETA 09:22:21
- 2022-04-13 02:48:00 [INFO] [TRAIN] epoch: 55, iter: 20200/60000, loss: 0.9454, lr: 0.006942, batch_cost: 0.8461, reader_cost: 0.00020, ips: 2.3637 samples/sec | ETA 09:21:15
- 2022-04-13 02:48:42 [INFO] [TRAIN] epoch: 55, iter: 20250/60000, loss: 1.0385, lr: 0.006935, batch_cost: 0.8468, reader_cost: 0.00022, ips: 2.3619 samples/sec | ETA 09:20:59
- 2022-04-13 02:49:25 [INFO] [TRAIN] epoch: 55, iter: 20300/60000, loss: 1.0500, lr: 0.006927, batch_cost: 0.8473, reader_cost: 0.00020, ips: 2.3603 samples/sec | ETA 09:20:39
- 2022-04-13 02:50:07 [INFO] [TRAIN] epoch: 55, iter: 20350/60000, loss: 0.9711, lr: 0.006919, batch_cost: 0.8460, reader_cost: 0.00020, ips: 2.3640 samples/sec | ETA 09:19:05
- 2022-04-13 02:50:49 [INFO] [TRAIN] epoch: 55, iter: 20400/60000, loss: 0.9566, lr: 0.006911, batch_cost: 0.8464, reader_cost: 0.00020, ips: 2.3629 samples/sec | ETA 09:18:38
- 2022-04-13 02:51:32 [INFO] [TRAIN] epoch: 55, iter: 20450/60000, loss: 0.9918, lr: 0.006904, batch_cost: 0.8475, reader_cost: 0.00022, ips: 2.3599 samples/sec | ETA 09:18:37
- 2022-04-13 02:52:17 [INFO] [TRAIN] epoch: 56, iter: 20500/60000, loss: 0.9524, lr: 0.006896, batch_cost: 0.8973, reader_cost: 0.04603, ips: 2.2289 samples/sec | ETA 09:50:42
- 2022-04-13 02:52:59 [INFO] [TRAIN] epoch: 56, iter: 20550/60000, loss: 0.9545, lr: 0.006888, batch_cost: 0.8460, reader_cost: 0.00023, ips: 2.3641 samples/sec | ETA 09:16:14
- 2022-04-13 02:53:41 [INFO] [TRAIN] epoch: 56, iter: 20600/60000, loss: 1.0383, lr: 0.006880, batch_cost: 0.8470, reader_cost: 0.00020, ips: 2.3613 samples/sec | ETA 09:16:11
- 2022-04-13 02:54:24 [INFO] [TRAIN] epoch: 56, iter: 20650/60000, loss: 1.0022, lr: 0.006873, batch_cost: 0.8462, reader_cost: 0.00022, ips: 2.3636 samples/sec | ETA 09:14:56
- 2022-04-13 02:55:06 [INFO] [TRAIN] epoch: 56, iter: 20700/60000, loss: 1.0026, lr: 0.006865, batch_cost: 0.8467, reader_cost: 0.00024, ips: 2.3621 samples/sec | ETA 09:14:36
- 2022-04-13 02:55:49 [INFO] [TRAIN] epoch: 56, iter: 20750/60000, loss: 0.9965, lr: 0.006857, batch_cost: 0.8499, reader_cost: 0.00027, ips: 2.3532 samples/sec | ETA 09:15:58
- 2022-04-13 02:56:31 [INFO] [TRAIN] epoch: 56, iter: 20800/60000, loss: 1.0021, lr: 0.006849, batch_cost: 0.8472, reader_cost: 0.00027, ips: 2.3608 samples/sec | ETA 09:13:28
- 2022-04-13 02:57:16 [INFO] [TRAIN] epoch: 57, iter: 20850/60000, loss: 0.9668, lr: 0.006842, batch_cost: 0.8975, reader_cost: 0.05070, ips: 2.2284 samples/sec | ETA 09:45:37
- 2022-04-13 02:57:58 [INFO] [TRAIN] epoch: 57, iter: 20900/60000, loss: 0.9696, lr: 0.006834, batch_cost: 0.8452, reader_cost: 0.00024, ips: 2.3662 samples/sec | ETA 09:10:48
- 2022-04-13 02:58:40 [INFO] [TRAIN] epoch: 57, iter: 20950/60000, loss: 1.0127, lr: 0.006826, batch_cost: 0.8445, reader_cost: 0.00020, ips: 2.3682 samples/sec | ETA 09:09:38
- 2022-04-13 02:59:23 [INFO] [TRAIN] epoch: 57, iter: 21000/60000, loss: 0.9577, lr: 0.006818, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3655 samples/sec | ETA 09:09:33
- 2022-04-13 03:00:05 [INFO] [TRAIN] epoch: 57, iter: 21050/60000, loss: 0.9806, lr: 0.006811, batch_cost: 0.8462, reader_cost: 0.00024, ips: 2.3634 samples/sec | ETA 09:09:20
- 2022-04-13 03:00:47 [INFO] [TRAIN] epoch: 57, iter: 21100/60000, loss: 1.0207, lr: 0.006803, batch_cost: 0.8479, reader_cost: 0.00022, ips: 2.3589 samples/sec | ETA 09:09:41
- 2022-04-13 03:01:29 [INFO] [TRAIN] epoch: 57, iter: 21150/60000, loss: 1.0363, lr: 0.006795, batch_cost: 0.8445, reader_cost: 0.00021, ips: 2.3682 samples/sec | ETA 09:06:49
- 2022-04-13 03:02:12 [INFO] [TRAIN] epoch: 57, iter: 21200/60000, loss: 0.9675, lr: 0.006787, batch_cost: 0.8444, reader_cost: 0.00023, ips: 2.3684 samples/sec | ETA 09:06:04
- 2022-04-13 03:02:57 [INFO] [TRAIN] epoch: 58, iter: 21250/60000, loss: 0.9304, lr: 0.006780, batch_cost: 0.8996, reader_cost: 0.04082, ips: 2.2232 samples/sec | ETA 09:40:59
- 2022-04-13 03:03:39 [INFO] [TRAIN] epoch: 58, iter: 21300/60000, loss: 0.9914, lr: 0.006772, batch_cost: 0.8462, reader_cost: 0.00025, ips: 2.3635 samples/sec | ETA 09:05:47
- 2022-04-13 03:04:21 [INFO] [TRAIN] epoch: 58, iter: 21350/60000, loss: 0.9856, lr: 0.006764, batch_cost: 0.8467, reader_cost: 0.00020, ips: 2.3621 samples/sec | ETA 09:05:24
- 2022-04-13 03:05:04 [INFO] [TRAIN] epoch: 58, iter: 21400/60000, loss: 0.9881, lr: 0.006756, batch_cost: 0.8466, reader_cost: 0.00020, ips: 2.3624 samples/sec | ETA 09:04:38
- 2022-04-13 03:05:46 [INFO] [TRAIN] epoch: 58, iter: 21450/60000, loss: 0.9758, lr: 0.006749, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3645 samples/sec | ETA 09:03:26
- 2022-04-13 03:06:28 [INFO] [TRAIN] epoch: 58, iter: 21500/60000, loss: 1.0023, lr: 0.006741, batch_cost: 0.8460, reader_cost: 0.00021, ips: 2.3640 samples/sec | ETA 09:02:52
- 2022-04-13 03:07:10 [INFO] [TRAIN] epoch: 58, iter: 21550/60000, loss: 0.9577, lr: 0.006733, batch_cost: 0.8447, reader_cost: 0.00022, ips: 2.3678 samples/sec | ETA 09:01:17
- 2022-04-13 03:07:55 [INFO] [TRAIN] epoch: 59, iter: 21600/60000, loss: 0.9323, lr: 0.006725, batch_cost: 0.8951, reader_cost: 0.04792, ips: 2.2345 samples/sec | ETA 09:32:50
- 2022-04-13 03:08:38 [INFO] [TRAIN] epoch: 59, iter: 21650/60000, loss: 0.9322, lr: 0.006718, batch_cost: 0.8465, reader_cost: 0.00023, ips: 2.3626 samples/sec | ETA 09:01:03
- 2022-04-13 03:09:20 [INFO] [TRAIN] epoch: 59, iter: 21700/60000, loss: 1.0176, lr: 0.006710, batch_cost: 0.8464, reader_cost: 0.00020, ips: 2.3630 samples/sec | ETA 09:00:16
- 2022-04-13 03:10:02 [INFO] [TRAIN] epoch: 59, iter: 21750/60000, loss: 1.0211, lr: 0.006702, batch_cost: 0.8473, reader_cost: 0.00023, ips: 2.3604 samples/sec | ETA 09:00:09
- 2022-04-13 03:10:45 [INFO] [TRAIN] epoch: 59, iter: 21800/60000, loss: 0.9722, lr: 0.006694, batch_cost: 0.8488, reader_cost: 0.00021, ips: 2.3564 samples/sec | ETA 09:00:22
- 2022-04-13 03:11:27 [INFO] [TRAIN] epoch: 59, iter: 21850/60000, loss: 0.9829, lr: 0.006686, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3656 samples/sec | ETA 08:57:33
- 2022-04-13 03:12:09 [INFO] [TRAIN] epoch: 59, iter: 21900/60000, loss: 1.0136, lr: 0.006679, batch_cost: 0.8493, reader_cost: 0.00022, ips: 2.3550 samples/sec | ETA 08:59:16
- 2022-04-13 03:12:54 [INFO] [TRAIN] epoch: 60, iter: 21950/60000, loss: 0.9988, lr: 0.006671, batch_cost: 0.8979, reader_cost: 0.04582, ips: 2.2275 samples/sec | ETA 09:29:24
- 2022-04-13 03:13:37 [INFO] [TRAIN] epoch: 60, iter: 22000/60000, loss: 0.9509, lr: 0.006663, batch_cost: 0.8453, reader_cost: 0.00025, ips: 2.3661 samples/sec | ETA 08:55:20
- 2022-04-13 03:14:19 [INFO] [TRAIN] epoch: 60, iter: 22050/60000, loss: 0.9879, lr: 0.006655, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3661 samples/sec | ETA 08:54:38
- 2022-04-13 03:15:01 [INFO] [TRAIN] epoch: 60, iter: 22100/60000, loss: 0.9671, lr: 0.006648, batch_cost: 0.8457, reader_cost: 0.00020, ips: 2.3648 samples/sec | ETA 08:54:12
- 2022-04-13 03:15:43 [INFO] [TRAIN] epoch: 60, iter: 22150/60000, loss: 0.9427, lr: 0.006640, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3661 samples/sec | ETA 08:53:14
- 2022-04-13 03:16:26 [INFO] [TRAIN] epoch: 60, iter: 22200/60000, loss: 1.0047, lr: 0.006632, batch_cost: 0.8476, reader_cost: 0.00021, ips: 2.3595 samples/sec | ETA 08:54:01
- 2022-04-13 03:17:08 [INFO] [TRAIN] epoch: 60, iter: 22250/60000, loss: 0.9266, lr: 0.006624, batch_cost: 0.8464, reader_cost: 0.00021, ips: 2.3630 samples/sec | ETA 08:52:31
- 2022-04-13 03:17:50 [INFO] [TRAIN] epoch: 60, iter: 22300/60000, loss: 0.9453, lr: 0.006617, batch_cost: 0.8445, reader_cost: 0.00020, ips: 2.3681 samples/sec | ETA 08:50:39
- 2022-04-13 03:18:35 [INFO] [TRAIN] epoch: 61, iter: 22350/60000, loss: 0.9841, lr: 0.006609, batch_cost: 0.8961, reader_cost: 0.04512, ips: 2.2320 samples/sec | ETA 09:22:16
- 2022-04-13 03:19:18 [INFO] [TRAIN] epoch: 61, iter: 22400/60000, loss: 0.9467, lr: 0.006601, batch_cost: 0.8483, reader_cost: 0.00025, ips: 2.3577 samples/sec | ETA 08:51:35
- 2022-04-13 03:20:00 [INFO] [TRAIN] epoch: 61, iter: 22450/60000, loss: 1.0382, lr: 0.006593, batch_cost: 0.8459, reader_cost: 0.00023, ips: 2.3644 samples/sec | ETA 08:49:23
- 2022-04-13 03:20:42 [INFO] [TRAIN] epoch: 61, iter: 22500/60000, loss: 0.9748, lr: 0.006585, batch_cost: 0.8441, reader_cost: 0.00021, ips: 2.3695 samples/sec | ETA 08:47:32
- 2022-04-13 03:21:24 [INFO] [TRAIN] epoch: 61, iter: 22550/60000, loss: 0.9312, lr: 0.006578, batch_cost: 0.8446, reader_cost: 0.00021, ips: 2.3680 samples/sec | ETA 08:47:10
- 2022-04-13 03:22:07 [INFO] [TRAIN] epoch: 61, iter: 22600/60000, loss: 0.9445, lr: 0.006570, batch_cost: 0.8449, reader_cost: 0.00021, ips: 2.3671 samples/sec | ETA 08:46:39
- 2022-04-13 03:22:49 [INFO] [TRAIN] epoch: 61, iter: 22650/60000, loss: 1.0325, lr: 0.006562, batch_cost: 0.8449, reader_cost: 0.00021, ips: 2.3671 samples/sec | ETA 08:45:57
- 2022-04-13 03:23:34 [INFO] [TRAIN] epoch: 62, iter: 22700/60000, loss: 1.0117, lr: 0.006554, batch_cost: 0.9023, reader_cost: 0.04930, ips: 2.2165 samples/sec | ETA 09:20:56
- 2022-04-13 03:24:16 [INFO] [TRAIN] epoch: 62, iter: 22750/60000, loss: 0.9820, lr: 0.006546, batch_cost: 0.8463, reader_cost: 0.00024, ips: 2.3632 samples/sec | ETA 08:45:25
- 2022-04-13 03:24:58 [INFO] [TRAIN] epoch: 62, iter: 22800/60000, loss: 0.9837, lr: 0.006539, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3655 samples/sec | ETA 08:44:12
- 2022-04-13 03:25:41 [INFO] [TRAIN] epoch: 62, iter: 22850/60000, loss: 0.9656, lr: 0.006531, batch_cost: 0.8453, reader_cost: 0.00022, ips: 2.3660 samples/sec | ETA 08:43:23
- 2022-04-13 03:26:23 [INFO] [TRAIN] epoch: 62, iter: 22900/60000, loss: 0.9339, lr: 0.006523, batch_cost: 0.8493, reader_cost: 0.00024, ips: 2.3548 samples/sec | ETA 08:45:10
- 2022-04-13 03:27:05 [INFO] [TRAIN] epoch: 62, iter: 22950/60000, loss: 0.9455, lr: 0.006515, batch_cost: 0.8449, reader_cost: 0.00022, ips: 2.3671 samples/sec | ETA 08:41:44
- 2022-04-13 03:27:48 [INFO] [TRAIN] epoch: 62, iter: 23000/60000, loss: 0.9680, lr: 0.006508, batch_cost: 0.8449, reader_cost: 0.00025, ips: 2.3671 samples/sec | ETA 08:41:01
- 2022-04-13 03:28:30 [INFO] [TRAIN] epoch: 62, iter: 23050/60000, loss: 0.9865, lr: 0.006500, batch_cost: 0.8446, reader_cost: 0.00022, ips: 2.3679 samples/sec | ETA 08:40:09
- 2022-04-13 03:29:15 [INFO] [TRAIN] epoch: 63, iter: 23100/60000, loss: 0.9244, lr: 0.006492, batch_cost: 0.8972, reader_cost: 0.05153, ips: 2.2291 samples/sec | ETA 09:11:46
- 2022-04-13 03:29:57 [INFO] [TRAIN] epoch: 63, iter: 23150/60000, loss: 0.9564, lr: 0.006484, batch_cost: 0.8481, reader_cost: 0.00026, ips: 2.3582 samples/sec | ETA 08:40:52
- 2022-04-13 03:30:39 [INFO] [TRAIN] epoch: 63, iter: 23200/60000, loss: 1.0398, lr: 0.006476, batch_cost: 0.8449, reader_cost: 0.00022, ips: 2.3670 samples/sec | ETA 08:38:13
- 2022-04-13 03:31:22 [INFO] [TRAIN] epoch: 63, iter: 23250/60000, loss: 0.9655, lr: 0.006469, batch_cost: 0.8489, reader_cost: 0.00020, ips: 2.3560 samples/sec | ETA 08:39:57
- 2022-04-13 03:32:04 [INFO] [TRAIN] epoch: 63, iter: 23300/60000, loss: 1.0304, lr: 0.006461, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3647 samples/sec | ETA 08:37:19
- 2022-04-13 03:32:46 [INFO] [TRAIN] epoch: 63, iter: 23350/60000, loss: 0.9872, lr: 0.006453, batch_cost: 0.8447, reader_cost: 0.00020, ips: 2.3676 samples/sec | ETA 08:35:59
- 2022-04-13 03:33:29 [INFO] [TRAIN] epoch: 63, iter: 23400/60000, loss: 0.9827, lr: 0.006445, batch_cost: 0.8438, reader_cost: 0.00020, ips: 2.3703 samples/sec | ETA 08:34:42
- 2022-04-13 03:34:13 [INFO] [TRAIN] epoch: 64, iter: 23450/60000, loss: 1.0043, lr: 0.006437, batch_cost: 0.8958, reader_cost: 0.04339, ips: 2.2325 samples/sec | ETA 09:05:43
- 2022-04-13 03:34:56 [INFO] [TRAIN] epoch: 64, iter: 23500/60000, loss: 0.9511, lr: 0.006430, batch_cost: 0.8462, reader_cost: 0.00026, ips: 2.3636 samples/sec | ETA 08:34:45
- 2022-04-13 03:35:38 [INFO] [TRAIN] epoch: 64, iter: 23550/60000, loss: 0.9438, lr: 0.006422, batch_cost: 0.8464, reader_cost: 0.00025, ips: 2.3630 samples/sec | ETA 08:34:10
- 2022-04-13 03:36:20 [INFO] [TRAIN] epoch: 64, iter: 23600/60000, loss: 0.9981, lr: 0.006414, batch_cost: 0.8464, reader_cost: 0.00023, ips: 2.3631 samples/sec | ETA 08:33:27
- 2022-04-13 03:37:03 [INFO] [TRAIN] epoch: 64, iter: 23650/60000, loss: 0.9292, lr: 0.006406, batch_cost: 0.8461, reader_cost: 0.00024, ips: 2.3639 samples/sec | ETA 08:32:34
- 2022-04-13 03:37:45 [INFO] [TRAIN] epoch: 64, iter: 23700/60000, loss: 0.9994, lr: 0.006398, batch_cost: 0.8462, reader_cost: 0.00023, ips: 2.3636 samples/sec | ETA 08:31:55
- 2022-04-13 03:38:27 [INFO] [TRAIN] epoch: 64, iter: 23750/60000, loss: 0.9478, lr: 0.006391, batch_cost: 0.8460, reader_cost: 0.00020, ips: 2.3640 samples/sec | ETA 08:31:08
- 2022-04-13 03:39:10 [INFO] [TRAIN] epoch: 64, iter: 23800/60000, loss: 0.9353, lr: 0.006383, batch_cost: 0.8450, reader_cost: 0.00023, ips: 2.3669 samples/sec | ETA 08:29:48
- 2022-04-13 03:39:55 [INFO] [TRAIN] epoch: 65, iter: 23850/60000, loss: 0.9600, lr: 0.006375, batch_cost: 0.9005, reader_cost: 0.04904, ips: 2.2211 samples/sec | ETA 09:02:31
- 2022-04-13 03:40:37 [INFO] [TRAIN] epoch: 65, iter: 23900/60000, loss: 0.9276, lr: 0.006367, batch_cost: 0.8459, reader_cost: 0.00027, ips: 2.3644 samples/sec | ETA 08:28:55
- 2022-04-13 03:41:19 [INFO] [TRAIN] epoch: 65, iter: 23950/60000, loss: 0.9816, lr: 0.006359, batch_cost: 0.8461, reader_cost: 0.00020, ips: 2.3638 samples/sec | ETA 08:28:21
- 2022-04-13 03:42:01 [INFO] [TRAIN] epoch: 65, iter: 24000/60000, loss: 0.9547, lr: 0.006351, batch_cost: 0.8462, reader_cost: 0.00020, ips: 2.3636 samples/sec | ETA 08:27:41
- 2022-04-13 03:42:01 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4185 - reader cost: 0.0033
- 2022-04-13 03:42:54 [INFO] [EVAL] #Images: 500 mIoU: 0.7580 Acc: 0.9561 Kappa: 0.9430 Dice: 0.8535
- 2022-04-13 03:42:54 [INFO] [EVAL] Class IoU:
- [0.9785 0.8344 0.9174 0.4067 0.6137 0.5942 0.7028 0.7784 0.916 0.598
- 0.9415 0.8101 0.637 0.9498 0.7655 0.8456 0.7606 0.5848 0.7664]
- 2022-04-13 03:42:54 [INFO] [EVAL] Class Acc:
- [0.9883 0.9192 0.9539 0.8326 0.7747 0.7813 0.8143 0.8786 0.9478 0.7232
- 0.9762 0.8701 0.7882 0.9777 0.8403 0.905 0.9345 0.8563 0.8311]
- 2022-04-13 03:42:57 [INFO] [EVAL] The model with the best validation mIoU (0.7580) was saved at iter 24000.
- 2022-04-13 03:43:40 [INFO] [TRAIN] epoch: 65, iter: 24050/60000, loss: 0.9791, lr: 0.006344, batch_cost: 0.8501, reader_cost: 0.00026, ips: 2.3526 samples/sec | ETA 08:29:21
- 2022-04-13 03:44:22 [INFO] [TRAIN] epoch: 65, iter: 24100/60000, loss: 1.0407, lr: 0.006336, batch_cost: 0.8458, reader_cost: 0.00019, ips: 2.3646 samples/sec | ETA 08:26:04
- 2022-04-13 03:45:04 [INFO] [TRAIN] epoch: 65, iter: 24150/60000, loss: 1.0132, lr: 0.006328, batch_cost: 0.8454, reader_cost: 0.00020, ips: 2.3657 samples/sec | ETA 08:25:07
- 2022-04-13 03:45:49 [INFO] [TRAIN] epoch: 66, iter: 24200/60000, loss: 0.9626, lr: 0.006320, batch_cost: 0.9037, reader_cost: 0.04922, ips: 2.2132 samples/sec | ETA 08:59:10
- 2022-04-13 03:46:32 [INFO] [TRAIN] epoch: 66, iter: 24250/60000, loss: 0.9368, lr: 0.006312, batch_cost: 0.8461, reader_cost: 0.00025, ips: 2.3637 samples/sec | ETA 08:24:09
- 2022-04-13 03:47:14 [INFO] [TRAIN] epoch: 66, iter: 24300/60000, loss: 0.9467, lr: 0.006305, batch_cost: 0.8502, reader_cost: 0.00021, ips: 2.3523 samples/sec | ETA 08:25:53
- 2022-04-13 03:47:57 [INFO] [TRAIN] epoch: 66, iter: 24350/60000, loss: 0.9528, lr: 0.006297, batch_cost: 0.8455, reader_cost: 0.00026, ips: 2.3654 samples/sec | ETA 08:22:22
- 2022-04-13 03:48:39 [INFO] [TRAIN] epoch: 66, iter: 24400/60000, loss: 1.0310, lr: 0.006289, batch_cost: 0.8485, reader_cost: 0.00021, ips: 2.3570 samples/sec | ETA 08:23:27
- 2022-04-13 03:49:21 [INFO] [TRAIN] epoch: 66, iter: 24450/60000, loss: 1.0111, lr: 0.006281, batch_cost: 0.8449, reader_cost: 0.00020, ips: 2.3670 samples/sec | ETA 08:20:37
- 2022-04-13 03:50:04 [INFO] [TRAIN] epoch: 66, iter: 24500/60000, loss: 0.9569, lr: 0.006273, batch_cost: 0.8469, reader_cost: 0.00026, ips: 2.3614 samples/sec | ETA 08:21:06
- 2022-04-13 03:50:46 [INFO] [TRAIN] epoch: 66, iter: 24550/60000, loss: 0.9487, lr: 0.006265, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3655 samples/sec | ETA 08:19:32
- 2022-04-13 03:51:31 [INFO] [TRAIN] epoch: 67, iter: 24600/60000, loss: 0.9509, lr: 0.006258, batch_cost: 0.9076, reader_cost: 0.04490, ips: 2.2036 samples/sec | ETA 08:55:29
- 2022-04-13 03:52:14 [INFO] [TRAIN] epoch: 67, iter: 24650/60000, loss: 0.9511, lr: 0.006250, batch_cost: 0.8473, reader_cost: 0.00024, ips: 2.3605 samples/sec | ETA 08:19:11
- 2022-04-13 03:52:56 [INFO] [TRAIN] epoch: 67, iter: 24700/60000, loss: 0.9582, lr: 0.006242, batch_cost: 0.8516, reader_cost: 0.00022, ips: 2.3484 samples/sec | ETA 08:21:03
- 2022-04-13 03:53:38 [INFO] [TRAIN] epoch: 67, iter: 24750/60000, loss: 0.9881, lr: 0.006234, batch_cost: 0.8441, reader_cost: 0.00020, ips: 2.3695 samples/sec | ETA 08:15:53
- 2022-04-13 03:54:21 [INFO] [TRAIN] epoch: 67, iter: 24800/60000, loss: 0.9717, lr: 0.006226, batch_cost: 0.8449, reader_cost: 0.00021, ips: 2.3672 samples/sec | ETA 08:15:39
- 2022-04-13 03:55:03 [INFO] [TRAIN] epoch: 67, iter: 24850/60000, loss: 0.9137, lr: 0.006218, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3654 samples/sec | ETA 08:15:19
- 2022-04-13 03:55:45 [INFO] [TRAIN] epoch: 67, iter: 24900/60000, loss: 0.9626, lr: 0.006211, batch_cost: 0.8493, reader_cost: 0.00020, ips: 2.3550 samples/sec | ETA 08:16:48
- 2022-04-13 03:56:30 [INFO] [TRAIN] epoch: 68, iter: 24950/60000, loss: 0.9352, lr: 0.006203, batch_cost: 0.9008, reader_cost: 0.04966, ips: 2.2203 samples/sec | ETA 08:46:12
- 2022-04-13 03:57:13 [INFO] [TRAIN] epoch: 68, iter: 25000/60000, loss: 0.9502, lr: 0.006195, batch_cost: 0.8453, reader_cost: 0.00028, ips: 2.3659 samples/sec | ETA 08:13:07
- 2022-04-13 03:57:55 [INFO] [TRAIN] epoch: 68, iter: 25050/60000, loss: 0.9709, lr: 0.006187, batch_cost: 0.8448, reader_cost: 0.00021, ips: 2.3675 samples/sec | ETA 08:12:05
- 2022-04-13 03:58:37 [INFO] [TRAIN] epoch: 68, iter: 25100/60000, loss: 1.0267, lr: 0.006179, batch_cost: 0.8451, reader_cost: 0.00020, ips: 2.3665 samples/sec | ETA 08:11:35
- 2022-04-13 03:59:19 [INFO] [TRAIN] epoch: 68, iter: 25150/60000, loss: 0.9350, lr: 0.006171, batch_cost: 0.8445, reader_cost: 0.00020, ips: 2.3682 samples/sec | ETA 08:10:31
- 2022-04-13 04:00:02 [INFO] [TRAIN] epoch: 68, iter: 25200/60000, loss: 0.9745, lr: 0.006164, batch_cost: 0.8467, reader_cost: 0.00021, ips: 2.3622 samples/sec | ETA 08:11:04
- 2022-04-13 04:00:44 [INFO] [TRAIN] epoch: 68, iter: 25250/60000, loss: 0.9429, lr: 0.006156, batch_cost: 0.8443, reader_cost: 0.00020, ips: 2.3687 samples/sec | ETA 08:09:01
- 2022-04-13 04:01:29 [INFO] [TRAIN] epoch: 69, iter: 25300/60000, loss: 0.9569, lr: 0.006148, batch_cost: 0.9015, reader_cost: 0.05367, ips: 2.2186 samples/sec | ETA 08:41:20
- 2022-04-13 04:02:11 [INFO] [TRAIN] epoch: 69, iter: 25350/60000, loss: 1.0250, lr: 0.006140, batch_cost: 0.8464, reader_cost: 0.00023, ips: 2.3631 samples/sec | ETA 08:08:46
- 2022-04-13 04:02:54 [INFO] [TRAIN] epoch: 69, iter: 25400/60000, loss: 1.0028, lr: 0.006132, batch_cost: 0.8487, reader_cost: 0.00023, ips: 2.3567 samples/sec | ETA 08:09:23
- 2022-04-13 04:03:36 [INFO] [TRAIN] epoch: 69, iter: 25450/60000, loss: 0.9112, lr: 0.006124, batch_cost: 0.8447, reader_cost: 0.00021, ips: 2.3678 samples/sec | ETA 08:06:23
- 2022-04-13 04:04:18 [INFO] [TRAIN] epoch: 69, iter: 25500/60000, loss: 1.0501, lr: 0.006117, batch_cost: 0.8447, reader_cost: 0.00022, ips: 2.3678 samples/sec | ETA 08:05:40
- 2022-04-13 04:05:01 [INFO] [TRAIN] epoch: 69, iter: 25550/60000, loss: 1.0079, lr: 0.006109, batch_cost: 0.8468, reader_cost: 0.00021, ips: 2.3617 samples/sec | ETA 08:06:13
- 2022-04-13 04:05:43 [INFO] [TRAIN] epoch: 69, iter: 25600/60000, loss: 1.0531, lr: 0.006101, batch_cost: 0.8444, reader_cost: 0.00020, ips: 2.3686 samples/sec | ETA 08:04:06
- 2022-04-13 04:06:25 [INFO] [TRAIN] epoch: 69, iter: 25650/60000, loss: 1.0288, lr: 0.006093, batch_cost: 0.8453, reader_cost: 0.00023, ips: 2.3662 samples/sec | ETA 08:03:54
- 2022-04-13 04:07:10 [INFO] [TRAIN] epoch: 70, iter: 25700/60000, loss: 1.0144, lr: 0.006085, batch_cost: 0.9068, reader_cost: 0.04735, ips: 2.2054 samples/sec | ETA 08:38:24
- 2022-04-13 04:07:53 [INFO] [TRAIN] epoch: 70, iter: 25750/60000, loss: 0.9402, lr: 0.006077, batch_cost: 0.8458, reader_cost: 0.00024, ips: 2.3647 samples/sec | ETA 08:02:47
- 2022-04-13 04:08:35 [INFO] [TRAIN] epoch: 70, iter: 25800/60000, loss: 1.0030, lr: 0.006069, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3633 samples/sec | ETA 08:02:22
- 2022-04-13 04:09:17 [INFO] [TRAIN] epoch: 70, iter: 25850/60000, loss: 0.9520, lr: 0.006062, batch_cost: 0.8481, reader_cost: 0.00021, ips: 2.3583 samples/sec | ETA 08:02:41
- 2022-04-13 04:10:00 [INFO] [TRAIN] epoch: 70, iter: 25900/60000, loss: 0.9568, lr: 0.006054, batch_cost: 0.8457, reader_cost: 0.00021, ips: 2.3649 samples/sec | ETA 08:00:37
- 2022-04-13 04:10:42 [INFO] [TRAIN] epoch: 70, iter: 25950/60000, loss: 0.9603, lr: 0.006046, batch_cost: 0.8446, reader_cost: 0.00020, ips: 2.3681 samples/sec | ETA 07:59:17
- 2022-04-13 04:11:24 [INFO] [TRAIN] epoch: 70, iter: 26000/60000, loss: 1.0202, lr: 0.006038, batch_cost: 0.8451, reader_cost: 0.00020, ips: 2.3665 samples/sec | ETA 07:58:54
- 2022-04-13 04:12:09 [INFO] [TRAIN] epoch: 71, iter: 26050/60000, loss: 0.9406, lr: 0.006030, batch_cost: 0.8988, reader_cost: 0.04638, ips: 2.2252 samples/sec | ETA 08:28:34
- 2022-04-13 04:12:51 [INFO] [TRAIN] epoch: 71, iter: 26100/60000, loss: 0.9510, lr: 0.006022, batch_cost: 0.8455, reader_cost: 0.00024, ips: 2.3656 samples/sec | ETA 07:57:41
- 2022-04-13 04:13:34 [INFO] [TRAIN] epoch: 71, iter: 26150/60000, loss: 0.9460, lr: 0.006014, batch_cost: 0.8459, reader_cost: 0.00021, ips: 2.3645 samples/sec | ETA 07:57:12
- 2022-04-13 04:14:16 [INFO] [TRAIN] epoch: 71, iter: 26200/60000, loss: 0.9821, lr: 0.006007, batch_cost: 0.8474, reader_cost: 0.00020, ips: 2.3601 samples/sec | ETA 07:57:23
- 2022-04-13 04:14:59 [INFO] [TRAIN] epoch: 71, iter: 26250/60000, loss: 0.9509, lr: 0.005999, batch_cost: 0.8485, reader_cost: 0.00021, ips: 2.3572 samples/sec | ETA 07:57:15
- 2022-04-13 04:15:41 [INFO] [TRAIN] epoch: 71, iter: 26300/60000, loss: 1.0061, lr: 0.005991, batch_cost: 0.8450, reader_cost: 0.00021, ips: 2.3669 samples/sec | ETA 07:54:35
- 2022-04-13 04:16:23 [INFO] [TRAIN] epoch: 71, iter: 26350/60000, loss: 0.9910, lr: 0.005983, batch_cost: 0.8446, reader_cost: 0.00020, ips: 2.3679 samples/sec | ETA 07:53:41
- 2022-04-13 04:17:05 [INFO] [TRAIN] epoch: 71, iter: 26400/60000, loss: 0.9432, lr: 0.005975, batch_cost: 0.8483, reader_cost: 0.00025, ips: 2.3577 samples/sec | ETA 07:55:02
- 2022-04-13 04:17:50 [INFO] [TRAIN] epoch: 72, iter: 26450/60000, loss: 1.0162, lr: 0.005967, batch_cost: 0.8978, reader_cost: 0.04995, ips: 2.2277 samples/sec | ETA 08:22:00
- 2022-04-13 04:18:33 [INFO] [TRAIN] epoch: 72, iter: 26500/60000, loss: 1.0091, lr: 0.005959, batch_cost: 0.8486, reader_cost: 0.00023, ips: 2.3569 samples/sec | ETA 07:53:47
- 2022-04-13 04:19:15 [INFO] [TRAIN] epoch: 72, iter: 26550/60000, loss: 0.9657, lr: 0.005952, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3645 samples/sec | ETA 07:51:33
- 2022-04-13 04:19:57 [INFO] [TRAIN] epoch: 72, iter: 26600/60000, loss: 1.0607, lr: 0.005944, batch_cost: 0.8461, reader_cost: 0.00021, ips: 2.3638 samples/sec | ETA 07:50:59
- 2022-04-13 04:20:40 [INFO] [TRAIN] epoch: 72, iter: 26650/60000, loss: 0.9079, lr: 0.005936, batch_cost: 0.8480, reader_cost: 0.00021, ips: 2.3584 samples/sec | ETA 07:51:22
- 2022-04-13 04:21:22 [INFO] [TRAIN] epoch: 72, iter: 26700/60000, loss: 0.9737, lr: 0.005928, batch_cost: 0.8481, reader_cost: 0.00021, ips: 2.3583 samples/sec | ETA 07:50:40
- 2022-04-13 04:22:04 [INFO] [TRAIN] epoch: 72, iter: 26750/60000, loss: 0.9446, lr: 0.005920, batch_cost: 0.8453, reader_cost: 0.00022, ips: 2.3661 samples/sec | ETA 07:48:24
- 2022-04-13 04:22:49 [INFO] [TRAIN] epoch: 73, iter: 26800/60000, loss: 0.9362, lr: 0.005912, batch_cost: 0.9003, reader_cost: 0.05051, ips: 2.2214 samples/sec | ETA 08:18:11
- 2022-04-13 04:23:32 [INFO] [TRAIN] epoch: 73, iter: 26850/60000, loss: 0.9704, lr: 0.005904, batch_cost: 0.8454, reader_cost: 0.00025, ips: 2.3658 samples/sec | ETA 07:47:04
- 2022-04-13 04:24:14 [INFO] [TRAIN] epoch: 73, iter: 26900/60000, loss: 0.9446, lr: 0.005896, batch_cost: 0.8457, reader_cost: 0.00023, ips: 2.3648 samples/sec | ETA 07:46:33
- 2022-04-13 04:24:56 [INFO] [TRAIN] epoch: 73, iter: 26950/60000, loss: 0.9486, lr: 0.005888, batch_cost: 0.8461, reader_cost: 0.00026, ips: 2.3638 samples/sec | ETA 07:46:03
- 2022-04-13 04:25:39 [INFO] [TRAIN] epoch: 73, iter: 27000/60000, loss: 0.9878, lr: 0.005881, batch_cost: 0.8460, reader_cost: 0.00026, ips: 2.3640 samples/sec | ETA 07:45:19
- 2022-04-13 04:26:21 [INFO] [TRAIN] epoch: 73, iter: 27050/60000, loss: 0.9533, lr: 0.005873, batch_cost: 0.8456, reader_cost: 0.00021, ips: 2.3653 samples/sec | ETA 07:44:20
- 2022-04-13 04:27:03 [INFO] [TRAIN] epoch: 73, iter: 27100/60000, loss: 0.9184, lr: 0.005865, batch_cost: 0.8494, reader_cost: 0.00023, ips: 2.3545 samples/sec | ETA 07:45:46
- 2022-04-13 04:27:46 [INFO] [TRAIN] epoch: 73, iter: 27150/60000, loss: 0.9488, lr: 0.005857, batch_cost: 0.8448, reader_cost: 0.00024, ips: 2.3675 samples/sec | ETA 07:42:31
- 2022-04-13 04:28:31 [INFO] [TRAIN] epoch: 74, iter: 27200/60000, loss: 0.9685, lr: 0.005849, batch_cost: 0.9023, reader_cost: 0.04943, ips: 2.2164 samples/sec | ETA 08:13:17
- 2022-04-13 04:29:13 [INFO] [TRAIN] epoch: 74, iter: 27250/60000, loss: 0.9433, lr: 0.005841, batch_cost: 0.8455, reader_cost: 0.00024, ips: 2.3654 samples/sec | ETA 07:41:30
- 2022-04-13 04:29:55 [INFO] [TRAIN] epoch: 74, iter: 27300/60000, loss: 0.9247, lr: 0.005833, batch_cost: 0.8501, reader_cost: 0.00020, ips: 2.3528 samples/sec | ETA 07:43:17
- 2022-04-13 04:30:38 [INFO] [TRAIN] epoch: 74, iter: 27350/60000, loss: 0.8985, lr: 0.005825, batch_cost: 0.8458, reader_cost: 0.00021, ips: 2.3646 samples/sec | ETA 07:40:15
- 2022-04-13 04:31:20 [INFO] [TRAIN] epoch: 74, iter: 27400/60000, loss: 0.9144, lr: 0.005818, batch_cost: 0.8448, reader_cost: 0.00020, ips: 2.3675 samples/sec | ETA 07:38:59
- 2022-04-13 04:32:02 [INFO] [TRAIN] epoch: 74, iter: 27450/60000, loss: 0.9148, lr: 0.005810, batch_cost: 0.8451, reader_cost: 0.00021, ips: 2.3666 samples/sec | ETA 07:38:27
- 2022-04-13 04:32:45 [INFO] [TRAIN] epoch: 74, iter: 27500/60000, loss: 0.9347, lr: 0.005802, batch_cost: 0.8453, reader_cost: 0.00022, ips: 2.3660 samples/sec | ETA 07:37:52
- 2022-04-13 04:33:29 [INFO] [TRAIN] epoch: 75, iter: 27550/60000, loss: 0.9600, lr: 0.005794, batch_cost: 0.8987, reader_cost: 0.04384, ips: 2.2254 samples/sec | ETA 08:06:03
- 2022-04-13 04:34:12 [INFO] [TRAIN] epoch: 75, iter: 27600/60000, loss: 0.9160, lr: 0.005786, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3656 samples/sec | ETA 07:36:32
- 2022-04-13 04:34:54 [INFO] [TRAIN] epoch: 75, iter: 27650/60000, loss: 0.9287, lr: 0.005778, batch_cost: 0.8491, reader_cost: 0.00020, ips: 2.3555 samples/sec | ETA 07:37:47
- 2022-04-13 04:35:36 [INFO] [TRAIN] epoch: 75, iter: 27700/60000, loss: 0.9790, lr: 0.005770, batch_cost: 0.8446, reader_cost: 0.00023, ips: 2.3680 samples/sec | ETA 07:34:40
- 2022-04-13 04:36:19 [INFO] [TRAIN] epoch: 75, iter: 27750/60000, loss: 0.9414, lr: 0.005762, batch_cost: 0.8476, reader_cost: 0.00024, ips: 2.3595 samples/sec | ETA 07:35:36
- 2022-04-13 04:37:01 [INFO] [TRAIN] epoch: 75, iter: 27800/60000, loss: 0.9756, lr: 0.005754, batch_cost: 0.8478, reader_cost: 0.00022, ips: 2.3591 samples/sec | ETA 07:34:59
- 2022-04-13 04:37:44 [INFO] [TRAIN] epoch: 75, iter: 27850/60000, loss: 0.9485, lr: 0.005746, batch_cost: 0.8461, reader_cost: 0.00023, ips: 2.3637 samples/sec | ETA 07:33:22
- 2022-04-13 04:38:26 [INFO] [TRAIN] epoch: 75, iter: 27900/60000, loss: 0.9569, lr: 0.005739, batch_cost: 0.8444, reader_cost: 0.00024, ips: 2.3685 samples/sec | ETA 07:31:45
- 2022-04-13 04:39:11 [INFO] [TRAIN] epoch: 76, iter: 27950/60000, loss: 0.9590, lr: 0.005731, batch_cost: 0.9034, reader_cost: 0.05286, ips: 2.2138 samples/sec | ETA 08:02:34
- 2022-04-13 04:39:53 [INFO] [TRAIN] epoch: 76, iter: 28000/60000, loss: 0.9797, lr: 0.005723, batch_cost: 0.8469, reader_cost: 0.00024, ips: 2.3616 samples/sec | ETA 07:31:39
- 2022-04-13 04:39:53 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4184 - reader cost: 0.0034
- 2022-04-13 04:40:46 [INFO] [EVAL] #Images: 500 mIoU: 0.7728 Acc: 0.9577 Kappa: 0.9450 Dice: 0.8640
- 2022-04-13 04:40:46 [INFO] [EVAL] Class IoU:
- [0.9784 0.8295 0.9168 0.4417 0.5827 0.6127 0.7028 0.7851 0.9218 0.6391
- 0.9445 0.8139 0.6533 0.95 0.8305 0.8757 0.798 0.6282 0.7787]
- 2022-04-13 04:40:46 [INFO] [EVAL] Class Acc:
- [0.9896 0.9059 0.9476 0.8848 0.8558 0.7849 0.8732 0.8756 0.9485 0.9013
- 0.9759 0.8802 0.8017 0.975 0.8998 0.9153 0.9306 0.7468 0.8539]
- 2022-04-13 04:40:49 [INFO] [EVAL] The model with the best validation mIoU (0.7728) was saved at iter 28000.
- 2022-04-13 04:41:31 [INFO] [TRAIN] epoch: 76, iter: 28050/60000, loss: 0.9501, lr: 0.005715, batch_cost: 0.8462, reader_cost: 0.00029, ips: 2.3636 samples/sec | ETA 07:30:34
- 2022-04-13 04:42:13 [INFO] [TRAIN] epoch: 76, iter: 28100/60000, loss: 0.9423, lr: 0.005707, batch_cost: 0.8449, reader_cost: 0.00020, ips: 2.3671 samples/sec | ETA 07:29:12
- 2022-04-13 04:42:56 [INFO] [TRAIN] epoch: 76, iter: 28150/60000, loss: 0.9877, lr: 0.005699, batch_cost: 0.8446, reader_cost: 0.00020, ips: 2.3681 samples/sec | ETA 07:28:19
- 2022-04-13 04:43:38 [INFO] [TRAIN] epoch: 76, iter: 28200/60000, loss: 0.9502, lr: 0.005691, batch_cost: 0.8451, reader_cost: 0.00019, ips: 2.3665 samples/sec | ETA 07:27:54
- 2022-04-13 04:44:20 [INFO] [TRAIN] epoch: 76, iter: 28250/60000, loss: 0.9370, lr: 0.005683, batch_cost: 0.8434, reader_cost: 0.00019, ips: 2.3713 samples/sec | ETA 07:26:18
- 2022-04-13 04:45:05 [INFO] [TRAIN] epoch: 77, iter: 28300/60000, loss: 0.9613, lr: 0.005675, batch_cost: 0.9048, reader_cost: 0.04971, ips: 2.2104 samples/sec | ETA 07:58:02
- 2022-04-13 04:45:48 [INFO] [TRAIN] epoch: 77, iter: 28350/60000, loss: 0.9102, lr: 0.005667, batch_cost: 0.8446, reader_cost: 0.00023, ips: 2.3679 samples/sec | ETA 07:25:32
- 2022-04-13 04:46:30 [INFO] [TRAIN] epoch: 77, iter: 28400/60000, loss: 0.9621, lr: 0.005659, batch_cost: 0.8449, reader_cost: 0.00020, ips: 2.3672 samples/sec | ETA 07:24:57
- 2022-04-13 04:47:12 [INFO] [TRAIN] epoch: 77, iter: 28450/60000, loss: 0.9470, lr: 0.005652, batch_cost: 0.8458, reader_cost: 0.00019, ips: 2.3647 samples/sec | ETA 07:24:43
- 2022-04-13 04:47:54 [INFO] [TRAIN] epoch: 77, iter: 28500/60000, loss: 0.9014, lr: 0.005644, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3664 samples/sec | ETA 07:23:42
- 2022-04-13 04:48:37 [INFO] [TRAIN] epoch: 77, iter: 28550/60000, loss: 0.9266, lr: 0.005636, batch_cost: 0.8459, reader_cost: 0.00024, ips: 2.3645 samples/sec | ETA 07:23:22
- 2022-04-13 04:49:19 [INFO] [TRAIN] epoch: 77, iter: 28600/60000, loss: 0.9656, lr: 0.005628, batch_cost: 0.8469, reader_cost: 0.00023, ips: 2.3616 samples/sec | ETA 07:23:11
- 2022-04-13 04:50:04 [INFO] [TRAIN] epoch: 78, iter: 28650/60000, loss: 0.9475, lr: 0.005620, batch_cost: 0.8933, reader_cost: 0.04502, ips: 2.2390 samples/sec | ETA 07:46:43
- 2022-04-13 04:50:46 [INFO] [TRAIN] epoch: 78, iter: 28700/60000, loss: 0.9402, lr: 0.005612, batch_cost: 0.8454, reader_cost: 0.00023, ips: 2.3657 samples/sec | ETA 07:21:01
- 2022-04-13 04:51:28 [INFO] [TRAIN] epoch: 78, iter: 28750/60000, loss: 0.9263, lr: 0.005604, batch_cost: 0.8448, reader_cost: 0.00023, ips: 2.3673 samples/sec | ETA 07:20:00
- 2022-04-13 04:52:11 [INFO] [TRAIN] epoch: 78, iter: 28800/60000, loss: 0.8908, lr: 0.005596, batch_cost: 0.8492, reader_cost: 0.00023, ips: 2.3550 samples/sec | ETA 07:21:36
- 2022-04-13 04:52:53 [INFO] [TRAIN] epoch: 78, iter: 28850/60000, loss: 0.9241, lr: 0.005588, batch_cost: 0.8469, reader_cost: 0.00021, ips: 2.3614 samples/sec | ETA 07:19:42
- 2022-04-13 04:53:35 [INFO] [TRAIN] epoch: 78, iter: 28900/60000, loss: 0.9502, lr: 0.005580, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3664 samples/sec | ETA 07:18:04
- 2022-04-13 04:54:17 [INFO] [TRAIN] epoch: 78, iter: 28950/60000, loss: 0.9923, lr: 0.005572, batch_cost: 0.8454, reader_cost: 0.00020, ips: 2.3658 samples/sec | ETA 07:17:29
- 2022-04-13 04:55:00 [INFO] [TRAIN] epoch: 78, iter: 29000/60000, loss: 0.9372, lr: 0.005564, batch_cost: 0.8441, reader_cost: 0.00020, ips: 2.3692 samples/sec | ETA 07:16:08
- 2022-04-13 04:55:45 [INFO] [TRAIN] epoch: 79, iter: 29050/60000, loss: 1.0009, lr: 0.005556, batch_cost: 0.8998, reader_cost: 0.04766, ips: 2.2228 samples/sec | ETA 07:44:07
- 2022-04-13 04:56:27 [INFO] [TRAIN] epoch: 79, iter: 29100/60000, loss: 0.9318, lr: 0.005548, batch_cost: 0.8470, reader_cost: 0.00024, ips: 2.3612 samples/sec | ETA 07:16:12
- 2022-04-13 04:57:09 [INFO] [TRAIN] epoch: 79, iter: 29150/60000, loss: 0.9443, lr: 0.005541, batch_cost: 0.8455, reader_cost: 0.00026, ips: 2.3654 samples/sec | ETA 07:14:44
- 2022-04-13 04:57:52 [INFO] [TRAIN] epoch: 79, iter: 29200/60000, loss: 0.9374, lr: 0.005533, batch_cost: 0.8502, reader_cost: 0.00026, ips: 2.3523 samples/sec | ETA 07:16:27
- 2022-04-13 04:58:34 [INFO] [TRAIN] epoch: 79, iter: 29250/60000, loss: 0.9529, lr: 0.005525, batch_cost: 0.8462, reader_cost: 0.00023, ips: 2.3635 samples/sec | ETA 07:13:40
- 2022-04-13 04:59:16 [INFO] [TRAIN] epoch: 79, iter: 29300/60000, loss: 0.8948, lr: 0.005517, batch_cost: 0.8450, reader_cost: 0.00020, ips: 2.3669 samples/sec | ETA 07:12:21
- 2022-04-13 04:59:59 [INFO] [TRAIN] epoch: 79, iter: 29350/60000, loss: 0.9518, lr: 0.005509, batch_cost: 0.8462, reader_cost: 0.00021, ips: 2.3634 samples/sec | ETA 07:12:16
- 2022-04-13 05:00:43 [INFO] [TRAIN] epoch: 80, iter: 29400/60000, loss: 0.9584, lr: 0.005501, batch_cost: 0.8958, reader_cost: 0.04322, ips: 2.2327 samples/sec | ETA 07:36:50
- 2022-04-13 05:01:26 [INFO] [TRAIN] epoch: 80, iter: 29450/60000, loss: 1.0190, lr: 0.005493, batch_cost: 0.8460, reader_cost: 0.00024, ips: 2.3641 samples/sec | ETA 07:10:45
- 2022-04-13 05:02:08 [INFO] [TRAIN] epoch: 80, iter: 29500/60000, loss: 0.8715, lr: 0.005485, batch_cost: 0.8458, reader_cost: 0.00022, ips: 2.3647 samples/sec | ETA 07:09:56
- 2022-04-13 05:02:50 [INFO] [TRAIN] epoch: 80, iter: 29550/60000, loss: 0.9037, lr: 0.005477, batch_cost: 0.8462, reader_cost: 0.00020, ips: 2.3635 samples/sec | ETA 07:09:26
- 2022-04-13 05:03:33 [INFO] [TRAIN] epoch: 80, iter: 29600/60000, loss: 0.9932, lr: 0.005469, batch_cost: 0.8487, reader_cost: 0.00020, ips: 2.3566 samples/sec | ETA 07:09:59
- 2022-04-13 05:04:15 [INFO] [TRAIN] epoch: 80, iter: 29650/60000, loss: 0.9412, lr: 0.005461, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3647 samples/sec | ETA 07:07:49
- 2022-04-13 05:04:57 [INFO] [TRAIN] epoch: 80, iter: 29700/60000, loss: 1.0694, lr: 0.005453, batch_cost: 0.8475, reader_cost: 0.00021, ips: 2.3600 samples/sec | ETA 07:07:58
- 2022-04-13 05:05:40 [INFO] [TRAIN] epoch: 80, iter: 29750/60000, loss: 0.9571, lr: 0.005445, batch_cost: 0.8456, reader_cost: 0.00019, ips: 2.3652 samples/sec | ETA 07:06:19
- 2022-04-13 05:06:25 [INFO] [TRAIN] epoch: 81, iter: 29800/60000, loss: 0.9196, lr: 0.005437, batch_cost: 0.8990, reader_cost: 0.04540, ips: 2.2246 samples/sec | ETA 07:32:30
- 2022-04-13 05:07:07 [INFO] [TRAIN] epoch: 81, iter: 29850/60000, loss: 0.9475, lr: 0.005429, batch_cost: 0.8452, reader_cost: 0.00022, ips: 2.3662 samples/sec | ETA 07:04:43
- 2022-04-13 05:07:49 [INFO] [TRAIN] epoch: 81, iter: 29900/60000, loss: 0.9888, lr: 0.005421, batch_cost: 0.8456, reader_cost: 0.00022, ips: 2.3652 samples/sec | ETA 07:04:12
- 2022-04-13 05:08:32 [INFO] [TRAIN] epoch: 81, iter: 29950/60000, loss: 0.9580, lr: 0.005413, batch_cost: 0.8466, reader_cost: 0.00020, ips: 2.3625 samples/sec | ETA 07:03:59
- 2022-04-13 05:09:14 [INFO] [TRAIN] epoch: 81, iter: 30000/60000, loss: 0.9258, lr: 0.005405, batch_cost: 0.8450, reader_cost: 0.00021, ips: 2.3668 samples/sec | ETA 07:02:30
- 2022-04-13 05:09:56 [INFO] [TRAIN] epoch: 81, iter: 30050/60000, loss: 0.9301, lr: 0.005397, batch_cost: 0.8458, reader_cost: 0.00023, ips: 2.3645 samples/sec | ETA 07:02:12
- 2022-04-13 05:10:38 [INFO] [TRAIN] epoch: 81, iter: 30100/60000, loss: 0.9491, lr: 0.005390, batch_cost: 0.8445, reader_cost: 0.00020, ips: 2.3683 samples/sec | ETA 07:00:50
- 2022-04-13 05:11:23 [INFO] [TRAIN] epoch: 82, iter: 30150/60000, loss: 0.9699, lr: 0.005382, batch_cost: 0.9019, reader_cost: 0.04843, ips: 2.2175 samples/sec | ETA 07:28:42
- 2022-04-13 05:12:06 [INFO] [TRAIN] epoch: 82, iter: 30200/60000, loss: 0.9143, lr: 0.005374, batch_cost: 0.8464, reader_cost: 0.00024, ips: 2.3630 samples/sec | ETA 07:00:22
- 2022-04-13 05:12:48 [INFO] [TRAIN] epoch: 82, iter: 30250/60000, loss: 0.9419, lr: 0.005366, batch_cost: 0.8472, reader_cost: 0.00020, ips: 2.3607 samples/sec | ETA 07:00:04
- 2022-04-13 05:13:30 [INFO] [TRAIN] epoch: 82, iter: 30300/60000, loss: 0.9725, lr: 0.005358, batch_cost: 0.8469, reader_cost: 0.00024, ips: 2.3616 samples/sec | ETA 06:59:12
- 2022-04-13 05:14:13 [INFO] [TRAIN] epoch: 82, iter: 30350/60000, loss: 0.9419, lr: 0.005350, batch_cost: 0.8460, reader_cost: 0.00023, ips: 2.3640 samples/sec | ETA 06:58:04
- 2022-04-13 05:14:55 [INFO] [TRAIN] epoch: 82, iter: 30400/60000, loss: 0.9797, lr: 0.005342, batch_cost: 0.8461, reader_cost: 0.00025, ips: 2.3639 samples/sec | ETA 06:57:23
- 2022-04-13 05:15:37 [INFO] [TRAIN] epoch: 82, iter: 30450/60000, loss: 0.9363, lr: 0.005334, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3655 samples/sec | ETA 06:56:24
- 2022-04-13 05:16:20 [INFO] [TRAIN] epoch: 82, iter: 30500/60000, loss: 0.9727, lr: 0.005326, batch_cost: 0.8453, reader_cost: 0.00024, ips: 2.3661 samples/sec | ETA 06:55:35
- 2022-04-13 05:17:05 [INFO] [TRAIN] epoch: 83, iter: 30550/60000, loss: 0.9411, lr: 0.005318, batch_cost: 0.8982, reader_cost: 0.04551, ips: 2.2268 samples/sec | ETA 07:20:50
- 2022-04-13 05:17:47 [INFO] [TRAIN] epoch: 83, iter: 30600/60000, loss: 0.9225, lr: 0.005310, batch_cost: 0.8464, reader_cost: 0.00024, ips: 2.3629 samples/sec | ETA 06:54:44
- 2022-04-13 05:18:29 [INFO] [TRAIN] epoch: 83, iter: 30650/60000, loss: 0.9684, lr: 0.005302, batch_cost: 0.8474, reader_cost: 0.00021, ips: 2.3602 samples/sec | ETA 06:54:30
- 2022-04-13 05:19:12 [INFO] [TRAIN] epoch: 83, iter: 30700/60000, loss: 1.0133, lr: 0.005294, batch_cost: 0.8463, reader_cost: 0.00023, ips: 2.3632 samples/sec | ETA 06:53:16
- 2022-04-13 05:19:54 [INFO] [TRAIN] epoch: 83, iter: 30750/60000, loss: 0.9315, lr: 0.005286, batch_cost: 0.8455, reader_cost: 0.00026, ips: 2.3655 samples/sec | ETA 06:52:10
- 2022-04-13 05:20:36 [INFO] [TRAIN] epoch: 83, iter: 30800/60000, loss: 0.9537, lr: 0.005278, batch_cost: 0.8468, reader_cost: 0.00025, ips: 2.3618 samples/sec | ETA 06:52:07
- 2022-04-13 05:21:18 [INFO] [TRAIN] epoch: 83, iter: 30850/60000, loss: 0.9324, lr: 0.005270, batch_cost: 0.8464, reader_cost: 0.00022, ips: 2.3629 samples/sec | ETA 06:51:12
- 2022-04-13 05:22:04 [INFO] [TRAIN] epoch: 84, iter: 30900/60000, loss: 0.9808, lr: 0.005262, batch_cost: 0.9016, reader_cost: 0.05095, ips: 2.2184 samples/sec | ETA 07:17:15
- 2022-04-13 05:22:46 [INFO] [TRAIN] epoch: 84, iter: 30950/60000, loss: 0.9618, lr: 0.005254, batch_cost: 0.8465, reader_cost: 0.00022, ips: 2.3628 samples/sec | ETA 06:49:49
- 2022-04-13 05:23:28 [INFO] [TRAIN] epoch: 84, iter: 31000/60000, loss: 1.0026, lr: 0.005246, batch_cost: 0.8460, reader_cost: 0.00020, ips: 2.3641 samples/sec | ETA 06:48:53
- 2022-04-13 05:24:10 [INFO] [TRAIN] epoch: 84, iter: 31050/60000, loss: 0.8992, lr: 0.005238, batch_cost: 0.8447, reader_cost: 0.00020, ips: 2.3678 samples/sec | ETA 06:47:32
- 2022-04-13 05:24:53 [INFO] [TRAIN] epoch: 84, iter: 31100/60000, loss: 0.9533, lr: 0.005230, batch_cost: 0.8456, reader_cost: 0.00020, ips: 2.3653 samples/sec | ETA 06:47:16
- 2022-04-13 05:25:35 [INFO] [TRAIN] epoch: 84, iter: 31150/60000, loss: 0.9200, lr: 0.005222, batch_cost: 0.8468, reader_cost: 0.00020, ips: 2.3619 samples/sec | ETA 06:47:09
- 2022-04-13 05:26:17 [INFO] [TRAIN] epoch: 84, iter: 31200/60000, loss: 0.9484, lr: 0.005214, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3653 samples/sec | ETA 06:45:51
- 2022-04-13 05:27:03 [INFO] [TRAIN] epoch: 85, iter: 31250/60000, loss: 0.9595, lr: 0.005206, batch_cost: 0.9060, reader_cost: 0.04764, ips: 2.2075 samples/sec | ETA 07:14:07
- 2022-04-13 05:27:45 [INFO] [TRAIN] epoch: 85, iter: 31300/60000, loss: 0.9299, lr: 0.005198, batch_cost: 0.8451, reader_cost: 0.00022, ips: 2.3667 samples/sec | ETA 06:44:13
- 2022-04-13 05:28:27 [INFO] [TRAIN] epoch: 85, iter: 31350/60000, loss: 0.9338, lr: 0.005190, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3660 samples/sec | ETA 06:43:37
- 2022-04-13 05:29:10 [INFO] [TRAIN] epoch: 85, iter: 31400/60000, loss: 0.9653, lr: 0.005182, batch_cost: 0.8475, reader_cost: 0.00021, ips: 2.3599 samples/sec | ETA 06:43:58
- 2022-04-13 05:29:52 [INFO] [TRAIN] epoch: 85, iter: 31450/60000, loss: 0.9520, lr: 0.005174, batch_cost: 0.8472, reader_cost: 0.00026, ips: 2.3607 samples/sec | ETA 06:43:08
- 2022-04-13 05:30:34 [INFO] [TRAIN] epoch: 85, iter: 31500/60000, loss: 0.9643, lr: 0.005166, batch_cost: 0.8459, reader_cost: 0.00024, ips: 2.3642 samples/sec | ETA 06:41:49
- 2022-04-13 05:31:17 [INFO] [TRAIN] epoch: 85, iter: 31550/60000, loss: 1.0084, lr: 0.005158, batch_cost: 0.8462, reader_cost: 0.00020, ips: 2.3634 samples/sec | ETA 06:41:15
- 2022-04-13 05:31:59 [INFO] [TRAIN] epoch: 85, iter: 31600/60000, loss: 0.9926, lr: 0.005150, batch_cost: 0.8443, reader_cost: 0.00020, ips: 2.3687 samples/sec | ETA 06:39:39
- 2022-04-13 05:32:44 [INFO] [TRAIN] epoch: 86, iter: 31650/60000, loss: 0.9642, lr: 0.005142, batch_cost: 0.8963, reader_cost: 0.04688, ips: 2.2315 samples/sec | ETA 07:03:29
- 2022-04-13 05:33:26 [INFO] [TRAIN] epoch: 86, iter: 31700/60000, loss: 0.9627, lr: 0.005134, batch_cost: 0.8479, reader_cost: 0.00025, ips: 2.3588 samples/sec | ETA 06:39:55
- 2022-04-13 05:34:08 [INFO] [TRAIN] epoch: 86, iter: 31750/60000, loss: 0.9551, lr: 0.005126, batch_cost: 0.8444, reader_cost: 0.00023, ips: 2.3684 samples/sec | ETA 06:37:35
- 2022-04-13 05:34:50 [INFO] [TRAIN] epoch: 86, iter: 31800/60000, loss: 0.9545, lr: 0.005118, batch_cost: 0.8468, reader_cost: 0.00025, ips: 2.3619 samples/sec | ETA 06:37:59
- 2022-04-13 05:35:33 [INFO] [TRAIN] epoch: 86, iter: 31850/60000, loss: 0.9073, lr: 0.005110, batch_cost: 0.8475, reader_cost: 0.00027, ips: 2.3599 samples/sec | ETA 06:37:36
- 2022-04-13 05:36:15 [INFO] [TRAIN] epoch: 86, iter: 31900/60000, loss: 0.9358, lr: 0.005102, batch_cost: 0.8448, reader_cost: 0.00025, ips: 2.3673 samples/sec | ETA 06:35:40
- 2022-04-13 05:36:57 [INFO] [TRAIN] epoch: 86, iter: 31950/60000, loss: 0.9220, lr: 0.005094, batch_cost: 0.8450, reader_cost: 0.00023, ips: 2.3669 samples/sec | ETA 06:35:01
- 2022-04-13 05:37:42 [INFO] [TRAIN] epoch: 87, iter: 32000/60000, loss: 0.9842, lr: 0.005086, batch_cost: 0.8932, reader_cost: 0.04274, ips: 2.2390 samples/sec | ETA 06:56:50
- 2022-04-13 05:37:42 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4193 - reader cost: 0.0040
- 2022-04-13 05:38:35 [INFO] [EVAL] #Images: 500 mIoU: 0.7660 Acc: 0.9589 Kappa: 0.9466 Dice: 0.8592
- 2022-04-13 05:38:35 [INFO] [EVAL] Class IoU:
- [0.9816 0.8507 0.921 0.4626 0.6134 0.5827 0.7017 0.7772 0.9208 0.6184
- 0.9472 0.7969 0.5434 0.9513 0.8209 0.8578 0.7676 0.672 0.7659]
- 2022-04-13 05:38:35 [INFO] [EVAL] Class Acc:
- [0.9892 0.9337 0.9474 0.8067 0.8274 0.826 0.8422 0.9138 0.9481 0.8557
- 0.9756 0.8381 0.7845 0.9751 0.9147 0.951 0.904 0.858 0.8961]
- 2022-04-13 05:38:36 [INFO] [EVAL] The model with the best validation mIoU (0.7728) was saved at iter 28000.
- 2022-04-13 05:39:18 [INFO] [TRAIN] epoch: 87, iter: 32050/60000, loss: 0.9464, lr: 0.005078, batch_cost: 0.8449, reader_cost: 0.00026, ips: 2.3671 samples/sec | ETA 06:33:35
- 2022-04-13 05:40:01 [INFO] [TRAIN] epoch: 87, iter: 32100/60000, loss: 0.9291, lr: 0.005070, batch_cost: 0.8455, reader_cost: 0.00021, ips: 2.3655 samples/sec | ETA 06:33:09
- 2022-04-13 05:40:43 [INFO] [TRAIN] epoch: 87, iter: 32150/60000, loss: 0.9905, lr: 0.005062, batch_cost: 0.8461, reader_cost: 0.00020, ips: 2.3637 samples/sec | ETA 06:32:44
- 2022-04-13 05:41:25 [INFO] [TRAIN] epoch: 87, iter: 32200/60000, loss: 0.9289, lr: 0.005054, batch_cost: 0.8473, reader_cost: 0.00020, ips: 2.3603 samples/sec | ETA 06:32:36
- 2022-04-13 05:42:08 [INFO] [TRAIN] epoch: 87, iter: 32250/60000, loss: 0.9410, lr: 0.005046, batch_cost: 0.8464, reader_cost: 0.00021, ips: 2.3629 samples/sec | ETA 06:31:27
- 2022-04-13 05:42:50 [INFO] [TRAIN] epoch: 87, iter: 32300/60000, loss: 1.0254, lr: 0.005038, batch_cost: 0.8451, reader_cost: 0.00021, ips: 2.3666 samples/sec | ETA 06:30:09
- 2022-04-13 05:43:32 [INFO] [TRAIN] epoch: 87, iter: 32350/60000, loss: 0.9369, lr: 0.005030, batch_cost: 0.8464, reader_cost: 0.00024, ips: 2.3630 samples/sec | ETA 06:30:01
- 2022-04-13 05:44:17 [INFO] [TRAIN] epoch: 88, iter: 32400/60000, loss: 0.9419, lr: 0.005022, batch_cost: 0.8977, reader_cost: 0.05000, ips: 2.2280 samples/sec | ETA 06:52:55
- 2022-04-13 05:44:59 [INFO] [TRAIN] epoch: 88, iter: 32450/60000, loss: 0.9730, lr: 0.005014, batch_cost: 0.8445, reader_cost: 0.00023, ips: 2.3684 samples/sec | ETA 06:27:45
- 2022-04-13 05:45:42 [INFO] [TRAIN] epoch: 88, iter: 32500/60000, loss: 0.9619, lr: 0.005006, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3654 samples/sec | ETA 06:27:31
- 2022-04-13 05:46:24 [INFO] [TRAIN] epoch: 88, iter: 32550/60000, loss: 0.8681, lr: 0.004998, batch_cost: 0.8449, reader_cost: 0.00019, ips: 2.3672 samples/sec | ETA 06:26:31
- 2022-04-13 05:47:06 [INFO] [TRAIN] epoch: 88, iter: 32600/60000, loss: 0.9268, lr: 0.004990, batch_cost: 0.8445, reader_cost: 0.00020, ips: 2.3682 samples/sec | ETA 06:25:40
- 2022-04-13 05:47:48 [INFO] [TRAIN] epoch: 88, iter: 32650/60000, loss: 0.9119, lr: 0.004982, batch_cost: 0.8477, reader_cost: 0.00020, ips: 2.3593 samples/sec | ETA 06:26:25
- 2022-04-13 05:48:31 [INFO] [TRAIN] epoch: 88, iter: 32700/60000, loss: 0.9378, lr: 0.004974, batch_cost: 0.8467, reader_cost: 0.00023, ips: 2.3622 samples/sec | ETA 06:25:14
- 2022-04-13 05:49:16 [INFO] [TRAIN] epoch: 89, iter: 32750/60000, loss: 0.8788, lr: 0.004966, batch_cost: 0.8958, reader_cost: 0.04507, ips: 2.2327 samples/sec | ETA 06:46:50
- 2022-04-13 05:49:58 [INFO] [TRAIN] epoch: 89, iter: 32800/60000, loss: 0.9143, lr: 0.004958, batch_cost: 0.8462, reader_cost: 0.00027, ips: 2.3635 samples/sec | ETA 06:23:36
- 2022-04-13 05:50:40 [INFO] [TRAIN] epoch: 89, iter: 32850/60000, loss: 0.9613, lr: 0.004950, batch_cost: 0.8462, reader_cost: 0.00020, ips: 2.3636 samples/sec | ETA 06:22:53
- 2022-04-13 05:51:23 [INFO] [TRAIN] epoch: 89, iter: 32900/60000, loss: 0.9788, lr: 0.004942, batch_cost: 0.8485, reader_cost: 0.00021, ips: 2.3571 samples/sec | ETA 06:23:14
- 2022-04-13 05:52:05 [INFO] [TRAIN] epoch: 89, iter: 32950/60000, loss: 0.9155, lr: 0.004934, batch_cost: 0.8461, reader_cost: 0.00022, ips: 2.3637 samples/sec | ETA 06:21:27
- 2022-04-13 05:52:47 [INFO] [TRAIN] epoch: 89, iter: 33000/60000, loss: 0.9072, lr: 0.004925, batch_cost: 0.8493, reader_cost: 0.00024, ips: 2.3548 samples/sec | ETA 06:22:11
- 2022-04-13 05:53:30 [INFO] [TRAIN] epoch: 89, iter: 33050/60000, loss: 0.9180, lr: 0.004917, batch_cost: 0.8447, reader_cost: 0.00023, ips: 2.3677 samples/sec | ETA 06:19:24
- 2022-04-13 05:54:12 [INFO] [TRAIN] epoch: 89, iter: 33100/60000, loss: 0.9873, lr: 0.004909, batch_cost: 0.8438, reader_cost: 0.00025, ips: 2.3701 samples/sec | ETA 06:18:19
- 2022-04-13 05:54:57 [INFO] [TRAIN] epoch: 90, iter: 33150/60000, loss: 0.8954, lr: 0.004901, batch_cost: 0.9059, reader_cost: 0.04752, ips: 2.2078 samples/sec | ETA 06:45:22
- 2022-04-13 05:55:39 [INFO] [TRAIN] epoch: 90, iter: 33200/60000, loss: 0.9156, lr: 0.004893, batch_cost: 0.8451, reader_cost: 0.00022, ips: 2.3666 samples/sec | ETA 06:17:28
- 2022-04-13 05:56:22 [INFO] [TRAIN] epoch: 90, iter: 33250/60000, loss: 0.9653, lr: 0.004885, batch_cost: 0.8462, reader_cost: 0.00020, ips: 2.3636 samples/sec | ETA 06:17:15
- 2022-04-13 05:57:04 [INFO] [TRAIN] epoch: 90, iter: 33300/60000, loss: 0.9560, lr: 0.004877, batch_cost: 0.8446, reader_cost: 0.00020, ips: 2.3680 samples/sec | ETA 06:15:50
- 2022-04-13 05:57:46 [INFO] [TRAIN] epoch: 90, iter: 33350/60000, loss: 0.9618, lr: 0.004869, batch_cost: 0.8452, reader_cost: 0.00025, ips: 2.3663 samples/sec | ETA 06:15:24
- 2022-04-13 05:58:28 [INFO] [TRAIN] epoch: 90, iter: 33400/60000, loss: 0.9225, lr: 0.004861, batch_cost: 0.8456, reader_cost: 0.00021, ips: 2.3651 samples/sec | ETA 06:14:54
- 2022-04-13 05:59:11 [INFO] [TRAIN] epoch: 90, iter: 33450/60000, loss: 0.8932, lr: 0.004853, batch_cost: 0.8462, reader_cost: 0.00021, ips: 2.3634 samples/sec | ETA 06:14:27
- 2022-04-13 05:59:56 [INFO] [TRAIN] epoch: 91, iter: 33500/60000, loss: 0.9268, lr: 0.004845, batch_cost: 0.8975, reader_cost: 0.04625, ips: 2.2284 samples/sec | ETA 06:36:24
- 2022-04-13 06:00:38 [INFO] [TRAIN] epoch: 91, iter: 33550/60000, loss: 0.9191, lr: 0.004837, batch_cost: 0.8471, reader_cost: 0.00024, ips: 2.3609 samples/sec | ETA 06:13:26
- 2022-04-13 06:01:20 [INFO] [TRAIN] epoch: 91, iter: 33600/60000, loss: 0.9172, lr: 0.004829, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3664 samples/sec | ETA 06:11:52
- 2022-04-13 06:02:03 [INFO] [TRAIN] epoch: 91, iter: 33650/60000, loss: 0.9636, lr: 0.004821, batch_cost: 0.8452, reader_cost: 0.00022, ips: 2.3664 samples/sec | ETA 06:11:10
- 2022-04-13 06:02:45 [INFO] [TRAIN] epoch: 91, iter: 33700/60000, loss: 0.8957, lr: 0.004813, batch_cost: 0.8465, reader_cost: 0.00019, ips: 2.3626 samples/sec | ETA 06:11:03
- 2022-04-13 06:03:27 [INFO] [TRAIN] epoch: 91, iter: 33750/60000, loss: 0.8997, lr: 0.004805, batch_cost: 0.8454, reader_cost: 0.00022, ips: 2.3657 samples/sec | ETA 06:09:52
- 2022-04-13 06:04:09 [INFO] [TRAIN] epoch: 91, iter: 33800/60000, loss: 0.9591, lr: 0.004797, batch_cost: 0.8472, reader_cost: 0.00021, ips: 2.3608 samples/sec | ETA 06:09:55
- 2022-04-13 06:04:52 [INFO] [TRAIN] epoch: 91, iter: 33850/60000, loss: 0.8843, lr: 0.004789, batch_cost: 0.8447, reader_cost: 0.00026, ips: 2.3676 samples/sec | ETA 06:08:10
- 2022-04-13 06:05:37 [INFO] [TRAIN] epoch: 92, iter: 33900/60000, loss: 0.9255, lr: 0.004780, batch_cost: 0.9000, reader_cost: 0.05182, ips: 2.2221 samples/sec | ETA 06:31:31
- 2022-04-13 06:06:19 [INFO] [TRAIN] epoch: 92, iter: 33950/60000, loss: 0.9697, lr: 0.004772, batch_cost: 0.8454, reader_cost: 0.00024, ips: 2.3657 samples/sec | ETA 06:07:03
- 2022-04-13 06:07:01 [INFO] [TRAIN] epoch: 92, iter: 34000/60000, loss: 0.9222, lr: 0.004764, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3656 samples/sec | ETA 06:06:22
- 2022-04-13 06:07:44 [INFO] [TRAIN] epoch: 92, iter: 34050/60000, loss: 0.9615, lr: 0.004756, batch_cost: 0.8449, reader_cost: 0.00020, ips: 2.3671 samples/sec | ETA 06:05:25
- 2022-04-13 06:08:26 [INFO] [TRAIN] epoch: 92, iter: 34100/60000, loss: 0.9315, lr: 0.004748, batch_cost: 0.8446, reader_cost: 0.00020, ips: 2.3679 samples/sec | ETA 06:04:36
- 2022-04-13 06:09:08 [INFO] [TRAIN] epoch: 92, iter: 34150/60000, loss: 0.9333, lr: 0.004740, batch_cost: 0.8449, reader_cost: 0.00023, ips: 2.3672 samples/sec | ETA 06:03:59
- 2022-04-13 06:09:50 [INFO] [TRAIN] epoch: 92, iter: 34200/60000, loss: 0.9596, lr: 0.004732, batch_cost: 0.8456, reader_cost: 0.00025, ips: 2.3653 samples/sec | ETA 06:03:35
- 2022-04-13 06:10:35 [INFO] [TRAIN] epoch: 93, iter: 34250/60000, loss: 0.9623, lr: 0.004724, batch_cost: 0.8983, reader_cost: 0.04903, ips: 2.2264 samples/sec | ETA 06:25:31
- 2022-04-13 06:11:17 [INFO] [TRAIN] epoch: 93, iter: 34300/60000, loss: 0.9360, lr: 0.004716, batch_cost: 0.8458, reader_cost: 0.00027, ips: 2.3646 samples/sec | ETA 06:02:16
- 2022-04-13 06:12:00 [INFO] [TRAIN] epoch: 93, iter: 34350/60000, loss: 0.9513, lr: 0.004708, batch_cost: 0.8476, reader_cost: 0.00024, ips: 2.3595 samples/sec | ETA 06:02:21
- 2022-04-13 06:12:42 [INFO] [TRAIN] epoch: 93, iter: 34400/60000, loss: 0.9016, lr: 0.004700, batch_cost: 0.8465, reader_cost: 0.00023, ips: 2.3626 samples/sec | ETA 06:01:11
- 2022-04-13 06:13:25 [INFO] [TRAIN] epoch: 93, iter: 34450/60000, loss: 0.9452, lr: 0.004692, batch_cost: 0.8476, reader_cost: 0.00023, ips: 2.3596 samples/sec | ETA 06:00:55
- 2022-04-13 06:14:07 [INFO] [TRAIN] epoch: 93, iter: 34500/60000, loss: 0.9298, lr: 0.004684, batch_cost: 0.8467, reader_cost: 0.00020, ips: 2.3621 samples/sec | ETA 05:59:51
- 2022-04-13 06:14:49 [INFO] [TRAIN] epoch: 93, iter: 34550/60000, loss: 0.9503, lr: 0.004675, batch_cost: 0.8460, reader_cost: 0.00024, ips: 2.3641 samples/sec | ETA 05:58:50
- 2022-04-13 06:15:34 [INFO] [TRAIN] epoch: 94, iter: 34600/60000, loss: 0.9547, lr: 0.004667, batch_cost: 0.9001, reader_cost: 0.05201, ips: 2.2219 samples/sec | ETA 06:21:03
- 2022-04-13 06:16:17 [INFO] [TRAIN] epoch: 94, iter: 34650/60000, loss: 0.9374, lr: 0.004659, batch_cost: 0.8466, reader_cost: 0.00023, ips: 2.3625 samples/sec | ETA 05:57:40
- 2022-04-13 06:16:59 [INFO] [TRAIN] epoch: 94, iter: 34700/60000, loss: 0.9089, lr: 0.004651, batch_cost: 0.8476, reader_cost: 0.00020, ips: 2.3596 samples/sec | ETA 05:57:24
- 2022-04-13 06:17:41 [INFO] [TRAIN] epoch: 94, iter: 34750/60000, loss: 0.9242, lr: 0.004643, batch_cost: 0.8465, reader_cost: 0.00020, ips: 2.3626 samples/sec | ETA 05:56:14
- 2022-04-13 06:18:24 [INFO] [TRAIN] epoch: 94, iter: 34800/60000, loss: 0.9579, lr: 0.004635, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3661 samples/sec | ETA 05:55:01
- 2022-04-13 06:19:06 [INFO] [TRAIN] epoch: 94, iter: 34850/60000, loss: 0.9402, lr: 0.004627, batch_cost: 0.8467, reader_cost: 0.00021, ips: 2.3620 samples/sec | ETA 05:54:55
- 2022-04-13 06:19:48 [INFO] [TRAIN] epoch: 94, iter: 34900/60000, loss: 0.9474, lr: 0.004619, batch_cost: 0.8463, reader_cost: 0.00024, ips: 2.3632 samples/sec | ETA 05:54:02
- 2022-04-13 06:20:30 [INFO] [TRAIN] epoch: 94, iter: 34950/60000, loss: 0.9176, lr: 0.004611, batch_cost: 0.8454, reader_cost: 0.00024, ips: 2.3658 samples/sec | ETA 05:52:56
- 2022-04-13 06:21:15 [INFO] [TRAIN] epoch: 95, iter: 35000/60000, loss: 0.8997, lr: 0.004603, batch_cost: 0.8966, reader_cost: 0.04670, ips: 2.2307 samples/sec | ETA 06:13:34
- 2022-04-13 06:21:57 [INFO] [TRAIN] epoch: 95, iter: 35050/60000, loss: 0.9701, lr: 0.004594, batch_cost: 0.8444, reader_cost: 0.00023, ips: 2.3685 samples/sec | ETA 05:51:08
- 2022-04-13 06:22:40 [INFO] [TRAIN] epoch: 95, iter: 35100/60000, loss: 0.9417, lr: 0.004586, batch_cost: 0.8469, reader_cost: 0.00020, ips: 2.3616 samples/sec | ETA 05:51:27
- 2022-04-13 06:23:22 [INFO] [TRAIN] epoch: 95, iter: 35150/60000, loss: 0.9112, lr: 0.004578, batch_cost: 0.8480, reader_cost: 0.00021, ips: 2.3584 samples/sec | ETA 05:51:13
- 2022-04-13 06:24:05 [INFO] [TRAIN] epoch: 95, iter: 35200/60000, loss: 0.9481, lr: 0.004570, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3661 samples/sec | ETA 05:49:22
- 2022-04-13 06:24:47 [INFO] [TRAIN] epoch: 95, iter: 35250/60000, loss: 0.9107, lr: 0.004562, batch_cost: 0.8453, reader_cost: 0.00021, ips: 2.3659 samples/sec | ETA 05:48:41
- 2022-04-13 06:25:29 [INFO] [TRAIN] epoch: 95, iter: 35300/60000, loss: 0.9277, lr: 0.004554, batch_cost: 0.8465, reader_cost: 0.00021, ips: 2.3627 samples/sec | ETA 05:48:28
- 2022-04-13 06:26:14 [INFO] [TRAIN] epoch: 96, iter: 35350/60000, loss: 0.9242, lr: 0.004546, batch_cost: 0.8984, reader_cost: 0.05133, ips: 2.2262 samples/sec | ETA 06:09:04
- 2022-04-13 06:26:56 [INFO] [TRAIN] epoch: 96, iter: 35400/60000, loss: 0.9040, lr: 0.004538, batch_cost: 0.8457, reader_cost: 0.00024, ips: 2.3650 samples/sec | ETA 05:46:43
- 2022-04-13 06:27:39 [INFO] [TRAIN] epoch: 96, iter: 35450/60000, loss: 1.0128, lr: 0.004530, batch_cost: 0.8462, reader_cost: 0.00021, ips: 2.3636 samples/sec | ETA 05:46:13
- 2022-04-13 06:28:21 [INFO] [TRAIN] epoch: 96, iter: 35500/60000, loss: 0.9216, lr: 0.004521, batch_cost: 0.8447, reader_cost: 0.00020, ips: 2.3677 samples/sec | ETA 05:44:55
- 2022-04-13 06:29:03 [INFO] [TRAIN] epoch: 96, iter: 35550/60000, loss: 0.9641, lr: 0.004513, batch_cost: 0.8464, reader_cost: 0.00020, ips: 2.3630 samples/sec | ETA 05:44:53
- 2022-04-13 06:29:46 [INFO] [TRAIN] epoch: 96, iter: 35600/60000, loss: 0.9650, lr: 0.004505, batch_cost: 0.8466, reader_cost: 0.00020, ips: 2.3625 samples/sec | ETA 05:44:16
- 2022-04-13 06:30:28 [INFO] [TRAIN] epoch: 96, iter: 35650/60000, loss: 0.8935, lr: 0.004497, batch_cost: 0.8493, reader_cost: 0.00020, ips: 2.3548 samples/sec | ETA 05:44:40
- 2022-04-13 06:31:10 [INFO] [TRAIN] epoch: 96, iter: 35700/60000, loss: 0.9306, lr: 0.004489, batch_cost: 0.8465, reader_cost: 0.00020, ips: 2.3628 samples/sec | ETA 05:42:48
- 2022-04-13 06:31:55 [INFO] [TRAIN] epoch: 97, iter: 35750/60000, loss: 0.9346, lr: 0.004481, batch_cost: 0.8975, reader_cost: 0.04553, ips: 2.2285 samples/sec | ETA 06:02:43
- 2022-04-13 06:32:37 [INFO] [TRAIN] epoch: 97, iter: 35800/60000, loss: 0.9226, lr: 0.004473, batch_cost: 0.8445, reader_cost: 0.00022, ips: 2.3683 samples/sec | ETA 05:40:36
- 2022-04-13 06:33:20 [INFO] [TRAIN] epoch: 97, iter: 35850/60000, loss: 0.9342, lr: 0.004465, batch_cost: 0.8487, reader_cost: 0.00021, ips: 2.3565 samples/sec | ETA 05:41:36
- 2022-04-13 06:34:02 [INFO] [TRAIN] epoch: 97, iter: 35900/60000, loss: 0.9586, lr: 0.004456, batch_cost: 0.8457, reader_cost: 0.00024, ips: 2.3649 samples/sec | ETA 05:39:41
- 2022-04-13 06:34:44 [INFO] [TRAIN] epoch: 97, iter: 35950/60000, loss: 0.9337, lr: 0.004448, batch_cost: 0.8462, reader_cost: 0.00023, ips: 2.3634 samples/sec | ETA 05:39:11
- 2022-04-13 06:35:27 [INFO] [TRAIN] epoch: 97, iter: 36000/60000, loss: 0.9720, lr: 0.004440, batch_cost: 0.8461, reader_cost: 0.00022, ips: 2.3638 samples/sec | ETA 05:38:26
- 2022-04-13 06:35:27 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4183 - reader cost: 0.0033
- 2022-04-13 06:36:19 [INFO] [EVAL] #Images: 500 mIoU: 0.7770 Acc: 0.9597 Kappa: 0.9477 Dice: 0.8684
- 2022-04-13 06:36:19 [INFO] [EVAL] Class IoU:
- [0.9812 0.8427 0.9244 0.5639 0.6044 0.6099 0.7139 0.7867 0.9233 0.6058
- 0.9489 0.8236 0.6616 0.9485 0.7505 0.8523 0.7827 0.668 0.7701]
- 2022-04-13 06:36:19 [INFO] [EVAL] Class Acc:
- [0.9902 0.9064 0.9581 0.7677 0.75 0.8184 0.8449 0.9006 0.9568 0.8525
- 0.9714 0.9044 0.8204 0.9719 0.8269 0.902 0.9001 0.845 0.8316]
- 2022-04-13 06:36:23 [INFO] [EVAL] The model with the best validation mIoU (0.7770) was saved at iter 36000.
- 2022-04-13 06:37:05 [INFO] [TRAIN] epoch: 97, iter: 36050/60000, loss: 0.9474, lr: 0.004432, batch_cost: 0.8466, reader_cost: 0.00023, ips: 2.3624 samples/sec | ETA 05:37:55
- 2022-04-13 06:37:50 [INFO] [TRAIN] epoch: 98, iter: 36100/60000, loss: 0.9102, lr: 0.004424, batch_cost: 0.8996, reader_cost: 0.04163, ips: 2.2232 samples/sec | ETA 05:58:20
- 2022-04-13 06:38:33 [INFO] [TRAIN] epoch: 98, iter: 36150/60000, loss: 0.9347, lr: 0.004416, batch_cost: 0.8462, reader_cost: 0.00023, ips: 2.3634 samples/sec | ETA 05:36:22
- 2022-04-13 06:39:15 [INFO] [TRAIN] epoch: 98, iter: 36200/60000, loss: 0.9345, lr: 0.004408, batch_cost: 0.8465, reader_cost: 0.00020, ips: 2.3627 samples/sec | ETA 05:35:46
- 2022-04-13 06:39:57 [INFO] [TRAIN] epoch: 98, iter: 36250/60000, loss: 0.9465, lr: 0.004399, batch_cost: 0.8459, reader_cost: 0.00022, ips: 2.3642 samples/sec | ETA 05:34:51
- 2022-04-13 06:40:40 [INFO] [TRAIN] epoch: 98, iter: 36300/60000, loss: 0.9091, lr: 0.004391, batch_cost: 0.8456, reader_cost: 0.00021, ips: 2.3653 samples/sec | ETA 05:33:59
- 2022-04-13 06:41:22 [INFO] [TRAIN] epoch: 98, iter: 36350/60000, loss: 0.9398, lr: 0.004383, batch_cost: 0.8460, reader_cost: 0.00022, ips: 2.3640 samples/sec | ETA 05:33:28
- 2022-04-13 06:42:04 [INFO] [TRAIN] epoch: 98, iter: 36400/60000, loss: 0.9298, lr: 0.004375, batch_cost: 0.8446, reader_cost: 0.00024, ips: 2.3681 samples/sec | ETA 05:32:11
- 2022-04-13 06:42:46 [INFO] [TRAIN] epoch: 98, iter: 36450/60000, loss: 0.8973, lr: 0.004367, batch_cost: 0.8461, reader_cost: 0.00028, ips: 2.3637 samples/sec | ETA 05:32:06
- 2022-04-13 06:43:32 [INFO] [TRAIN] epoch: 99, iter: 36500/60000, loss: 0.9026, lr: 0.004359, batch_cost: 0.9093, reader_cost: 0.05900, ips: 2.1996 samples/sec | ETA 05:56:07
- 2022-04-13 06:44:14 [INFO] [TRAIN] epoch: 99, iter: 36550/60000, loss: 0.9164, lr: 0.004351, batch_cost: 0.8465, reader_cost: 0.00027, ips: 2.3626 samples/sec | ETA 05:30:51
- 2022-04-13 06:44:57 [INFO] [TRAIN] epoch: 99, iter: 36600/60000, loss: 0.9323, lr: 0.004342, batch_cost: 0.8463, reader_cost: 0.00027, ips: 2.3633 samples/sec | ETA 05:30:03
- 2022-04-13 06:45:39 [INFO] [TRAIN] epoch: 99, iter: 36650/60000, loss: 0.8976, lr: 0.004334, batch_cost: 0.8458, reader_cost: 0.00026, ips: 2.3647 samples/sec | ETA 05:29:09
- 2022-04-13 06:46:21 [INFO] [TRAIN] epoch: 99, iter: 36700/60000, loss: 0.9125, lr: 0.004326, batch_cost: 0.8448, reader_cost: 0.00024, ips: 2.3674 samples/sec | ETA 05:28:04
- 2022-04-13 06:47:03 [INFO] [TRAIN] epoch: 99, iter: 36750/60000, loss: 1.0280, lr: 0.004318, batch_cost: 0.8490, reader_cost: 0.00023, ips: 2.3558 samples/sec | ETA 05:28:58
- 2022-04-13 06:47:46 [INFO] [TRAIN] epoch: 99, iter: 36800/60000, loss: 0.9014, lr: 0.004310, batch_cost: 0.8461, reader_cost: 0.00023, ips: 2.3638 samples/sec | ETA 05:27:09
- 2022-04-13 06:48:31 [INFO] [TRAIN] epoch: 100, iter: 36850/60000, loss: 0.9128, lr: 0.004302, batch_cost: 0.8985, reader_cost: 0.04228, ips: 2.2259 samples/sec | ETA 05:46:40
- 2022-04-13 06:49:13 [INFO] [TRAIN] epoch: 100, iter: 36900/60000, loss: 0.9031, lr: 0.004293, batch_cost: 0.8457, reader_cost: 0.00023, ips: 2.3648 samples/sec | ETA 05:25:36
- 2022-04-13 06:49:55 [INFO] [TRAIN] epoch: 100, iter: 36950/60000, loss: 0.8902, lr: 0.004285, batch_cost: 0.8459, reader_cost: 0.00025, ips: 2.3644 samples/sec | ETA 05:24:57
- 2022-04-13 06:50:38 [INFO] [TRAIN] epoch: 100, iter: 37000/60000, loss: 0.8663, lr: 0.004277, batch_cost: 0.8468, reader_cost: 0.00025, ips: 2.3617 samples/sec | ETA 05:24:37
- 2022-04-13 06:51:20 [INFO] [TRAIN] epoch: 100, iter: 37050/60000, loss: 0.9617, lr: 0.004269, batch_cost: 0.8460, reader_cost: 0.00023, ips: 2.3642 samples/sec | ETA 05:23:34
- 2022-04-13 06:52:02 [INFO] [TRAIN] epoch: 100, iter: 37100/60000, loss: 0.8767, lr: 0.004261, batch_cost: 0.8450, reader_cost: 0.00023, ips: 2.3668 samples/sec | ETA 05:22:30
- 2022-04-13 06:52:45 [INFO] [TRAIN] epoch: 100, iter: 37150/60000, loss: 0.9130, lr: 0.004253, batch_cost: 0.8463, reader_cost: 0.00024, ips: 2.3632 samples/sec | ETA 05:22:17
- 2022-04-13 06:53:27 [INFO] [TRAIN] epoch: 100, iter: 37200/60000, loss: 0.9370, lr: 0.004244, batch_cost: 0.8457, reader_cost: 0.00019, ips: 2.3648 samples/sec | ETA 05:21:22
- 2022-04-13 06:54:12 [INFO] [TRAIN] epoch: 101, iter: 37250/60000, loss: 0.9258, lr: 0.004236, batch_cost: 0.8961, reader_cost: 0.04540, ips: 2.2319 samples/sec | ETA 05:39:46
- 2022-04-13 06:54:54 [INFO] [TRAIN] epoch: 101, iter: 37300/60000, loss: 0.9421, lr: 0.004228, batch_cost: 0.8456, reader_cost: 0.00023, ips: 2.3651 samples/sec | ETA 05:19:55
- 2022-04-13 06:55:36 [INFO] [TRAIN] epoch: 101, iter: 37350/60000, loss: 0.8943, lr: 0.004220, batch_cost: 0.8462, reader_cost: 0.00021, ips: 2.3635 samples/sec | ETA 05:19:26
- 2022-04-13 06:56:19 [INFO] [TRAIN] epoch: 101, iter: 37400/60000, loss: 0.9261, lr: 0.004212, batch_cost: 0.8462, reader_cost: 0.00020, ips: 2.3635 samples/sec | ETA 05:18:43
- 2022-04-13 06:57:01 [INFO] [TRAIN] epoch: 101, iter: 37450/60000, loss: 0.8860, lr: 0.004203, batch_cost: 0.8479, reader_cost: 0.00020, ips: 2.3589 samples/sec | ETA 05:18:39
- 2022-04-13 06:57:43 [INFO] [TRAIN] epoch: 101, iter: 37500/60000, loss: 0.9236, lr: 0.004195, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3663 samples/sec | ETA 05:16:57
- 2022-04-13 06:58:25 [INFO] [TRAIN] epoch: 101, iter: 37550/60000, loss: 0.9751, lr: 0.004187, batch_cost: 0.8462, reader_cost: 0.00020, ips: 2.3636 samples/sec | ETA 05:16:36
- 2022-04-13 06:59:10 [INFO] [TRAIN] epoch: 102, iter: 37600/60000, loss: 0.9540, lr: 0.004179, batch_cost: 0.8947, reader_cost: 0.04300, ips: 2.2354 samples/sec | ETA 05:34:01
- 2022-04-13 06:59:53 [INFO] [TRAIN] epoch: 102, iter: 37650/60000, loss: 0.8765, lr: 0.004171, batch_cost: 0.8466, reader_cost: 0.00028, ips: 2.3624 samples/sec | ETA 05:15:21
- 2022-04-13 07:00:35 [INFO] [TRAIN] epoch: 102, iter: 37700/60000, loss: 0.9253, lr: 0.004162, batch_cost: 0.8469, reader_cost: 0.00020, ips: 2.3615 samples/sec | ETA 05:14:46
- 2022-04-13 07:01:17 [INFO] [TRAIN] epoch: 102, iter: 37750/60000, loss: 0.8967, lr: 0.004154, batch_cost: 0.8454, reader_cost: 0.00020, ips: 2.3659 samples/sec | ETA 05:13:29
- 2022-04-13 07:02:00 [INFO] [TRAIN] epoch: 102, iter: 37800/60000, loss: 0.9045, lr: 0.004146, batch_cost: 0.8475, reader_cost: 0.00023, ips: 2.3600 samples/sec | ETA 05:13:33
- 2022-04-13 07:02:42 [INFO] [TRAIN] epoch: 102, iter: 37850/60000, loss: 0.9135, lr: 0.004138, batch_cost: 0.8453, reader_cost: 0.00022, ips: 2.3660 samples/sec | ETA 05:12:03
- 2022-04-13 07:03:24 [INFO] [TRAIN] epoch: 102, iter: 37900/60000, loss: 0.9106, lr: 0.004130, batch_cost: 0.8456, reader_cost: 0.00023, ips: 2.3653 samples/sec | ETA 05:11:27
- 2022-04-13 07:04:09 [INFO] [TRAIN] epoch: 103, iter: 37950/60000, loss: 0.9061, lr: 0.004121, batch_cost: 0.9027, reader_cost: 0.05455, ips: 2.2155 samples/sec | ETA 05:31:44
- 2022-04-13 07:04:51 [INFO] [TRAIN] epoch: 103, iter: 38000/60000, loss: 0.8881, lr: 0.004113, batch_cost: 0.8449, reader_cost: 0.00025, ips: 2.3671 samples/sec | ETA 05:09:48
- 2022-04-13 07:05:34 [INFO] [TRAIN] epoch: 103, iter: 38050/60000, loss: 0.9339, lr: 0.004105, batch_cost: 0.8454, reader_cost: 0.00020, ips: 2.3658 samples/sec | ETA 05:09:15
- 2022-04-13 07:06:16 [INFO] [TRAIN] epoch: 103, iter: 38100/60000, loss: 0.8949, lr: 0.004097, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3664 samples/sec | ETA 05:08:29
- 2022-04-13 07:06:58 [INFO] [TRAIN] epoch: 103, iter: 38150/60000, loss: 0.9343, lr: 0.004089, batch_cost: 0.8467, reader_cost: 0.00020, ips: 2.3620 samples/sec | ETA 05:08:21
- 2022-04-13 07:07:41 [INFO] [TRAIN] epoch: 103, iter: 38200/60000, loss: 0.8805, lr: 0.004080, batch_cost: 0.8495, reader_cost: 0.00020, ips: 2.3542 samples/sec | ETA 05:08:39
- 2022-04-13 07:08:23 [INFO] [TRAIN] epoch: 103, iter: 38250/60000, loss: 0.9363, lr: 0.004072, batch_cost: 0.8457, reader_cost: 0.00021, ips: 2.3649 samples/sec | ETA 05:06:34
- 2022-04-13 07:09:05 [INFO] [TRAIN] epoch: 103, iter: 38300/60000, loss: 0.9018, lr: 0.004064, batch_cost: 0.8468, reader_cost: 0.00020, ips: 2.3620 samples/sec | ETA 05:06:14
- 2022-04-13 07:09:50 [INFO] [TRAIN] epoch: 104, iter: 38350/60000, loss: 0.9029, lr: 0.004056, batch_cost: 0.8982, reader_cost: 0.04584, ips: 2.2268 samples/sec | ETA 05:24:05
- 2022-04-13 07:10:33 [INFO] [TRAIN] epoch: 104, iter: 38400/60000, loss: 0.8856, lr: 0.004048, batch_cost: 0.8462, reader_cost: 0.00026, ips: 2.3634 samples/sec | ETA 05:04:38
- 2022-04-13 07:11:15 [INFO] [TRAIN] epoch: 104, iter: 38450/60000, loss: 0.9259, lr: 0.004039, batch_cost: 0.8463, reader_cost: 0.00026, ips: 2.3632 samples/sec | ETA 05:03:58
- 2022-04-13 07:11:57 [INFO] [TRAIN] epoch: 104, iter: 38500/60000, loss: 0.8963, lr: 0.004031, batch_cost: 0.8486, reader_cost: 0.00026, ips: 2.3567 samples/sec | ETA 05:04:05
- 2022-04-13 07:12:40 [INFO] [TRAIN] epoch: 104, iter: 38550/60000, loss: 0.9099, lr: 0.004023, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3654 samples/sec | ETA 05:02:16
- 2022-04-13 07:13:22 [INFO] [TRAIN] epoch: 104, iter: 38600/60000, loss: 0.9676, lr: 0.004015, batch_cost: 0.8457, reader_cost: 0.00021, ips: 2.3648 samples/sec | ETA 05:01:38
- 2022-04-13 07:14:04 [INFO] [TRAIN] epoch: 104, iter: 38650/60000, loss: 0.8891, lr: 0.004006, batch_cost: 0.8458, reader_cost: 0.00023, ips: 2.3646 samples/sec | ETA 05:00:58
- 2022-04-13 07:14:49 [INFO] [TRAIN] epoch: 105, iter: 38700/60000, loss: 0.9478, lr: 0.003998, batch_cost: 0.8993, reader_cost: 0.04666, ips: 2.2239 samples/sec | ETA 05:19:15
- 2022-04-13 07:15:32 [INFO] [TRAIN] epoch: 105, iter: 38750/60000, loss: 0.9375, lr: 0.003990, batch_cost: 0.8464, reader_cost: 0.00024, ips: 2.3631 samples/sec | ETA 04:59:45
- 2022-04-13 07:16:14 [INFO] [TRAIN] epoch: 105, iter: 38800/60000, loss: 0.9777, lr: 0.003982, batch_cost: 0.8463, reader_cost: 0.00025, ips: 2.3632 samples/sec | ETA 04:59:01
- 2022-04-13 07:16:56 [INFO] [TRAIN] epoch: 105, iter: 38850/60000, loss: 0.8886, lr: 0.003973, batch_cost: 0.8455, reader_cost: 0.00025, ips: 2.3655 samples/sec | ETA 04:58:02
- 2022-04-13 07:17:38 [INFO] [TRAIN] epoch: 105, iter: 38900/60000, loss: 0.9238, lr: 0.003965, batch_cost: 0.8456, reader_cost: 0.00020, ips: 2.3650 samples/sec | ETA 04:57:23
- 2022-04-13 07:18:21 [INFO] [TRAIN] epoch: 105, iter: 38950/60000, loss: 1.0329, lr: 0.003957, batch_cost: 0.8463, reader_cost: 0.00019, ips: 2.3631 samples/sec | ETA 04:56:55
- 2022-04-13 07:19:03 [INFO] [TRAIN] epoch: 105, iter: 39000/60000, loss: 0.9013, lr: 0.003949, batch_cost: 0.8460, reader_cost: 0.00020, ips: 2.3642 samples/sec | ETA 04:56:05
- 2022-04-13 07:19:45 [INFO] [TRAIN] epoch: 105, iter: 39050/60000, loss: 0.9052, lr: 0.003940, batch_cost: 0.8490, reader_cost: 0.00019, ips: 2.3558 samples/sec | ETA 04:56:25
- 2022-04-13 07:20:30 [INFO] [TRAIN] epoch: 106, iter: 39100/60000, loss: 0.8830, lr: 0.003932, batch_cost: 0.8934, reader_cost: 0.04289, ips: 2.2385 samples/sec | ETA 05:11:12
- 2022-04-13 07:21:12 [INFO] [TRAIN] epoch: 106, iter: 39150/60000, loss: 0.8965, lr: 0.003924, batch_cost: 0.8456, reader_cost: 0.00024, ips: 2.3652 samples/sec | ETA 04:53:50
- 2022-04-13 07:21:55 [INFO] [TRAIN] epoch: 106, iter: 39200/60000, loss: 0.8993, lr: 0.003916, batch_cost: 0.8452, reader_cost: 0.00021, ips: 2.3662 samples/sec | ETA 04:53:00
- 2022-04-13 07:22:37 [INFO] [TRAIN] epoch: 106, iter: 39250/60000, loss: 0.9143, lr: 0.003907, batch_cost: 0.8461, reader_cost: 0.00022, ips: 2.3637 samples/sec | ETA 04:52:37
- 2022-04-13 07:23:19 [INFO] [TRAIN] epoch: 106, iter: 39300/60000, loss: 0.9245, lr: 0.003899, batch_cost: 0.8452, reader_cost: 0.00021, ips: 2.3662 samples/sec | ETA 04:51:36
- 2022-04-13 07:24:02 [INFO] [TRAIN] epoch: 106, iter: 39350/60000, loss: 0.8883, lr: 0.003891, batch_cost: 0.8462, reader_cost: 0.00023, ips: 2.3634 samples/sec | ETA 04:51:14
- 2022-04-13 07:24:44 [INFO] [TRAIN] epoch: 106, iter: 39400/60000, loss: 0.9039, lr: 0.003883, batch_cost: 0.8471, reader_cost: 0.00025, ips: 2.3610 samples/sec | ETA 04:50:50
- 2022-04-13 07:25:29 [INFO] [TRAIN] epoch: 107, iter: 39450/60000, loss: 0.9585, lr: 0.003874, batch_cost: 0.8946, reader_cost: 0.04560, ips: 2.2356 samples/sec | ETA 05:06:24
- 2022-04-13 07:26:11 [INFO] [TRAIN] epoch: 107, iter: 39500/60000, loss: 0.9339, lr: 0.003866, batch_cost: 0.8463, reader_cost: 0.00025, ips: 2.3633 samples/sec | ETA 04:49:08
- 2022-04-13 07:26:53 [INFO] [TRAIN] epoch: 107, iter: 39550/60000, loss: 0.8963, lr: 0.003858, batch_cost: 0.8465, reader_cost: 0.00020, ips: 2.3626 samples/sec | ETA 04:48:31
- 2022-04-13 07:27:36 [INFO] [TRAIN] epoch: 107, iter: 39600/60000, loss: 0.9049, lr: 0.003850, batch_cost: 0.8461, reader_cost: 0.00020, ips: 2.3638 samples/sec | ETA 04:47:40
- 2022-04-13 07:28:18 [INFO] [TRAIN] epoch: 107, iter: 39650/60000, loss: 0.9210, lr: 0.003841, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3647 samples/sec | ETA 04:46:51
- 2022-04-13 07:29:00 [INFO] [TRAIN] epoch: 107, iter: 39700/60000, loss: 0.8918, lr: 0.003833, batch_cost: 0.8503, reader_cost: 0.00020, ips: 2.3521 samples/sec | ETA 04:47:41
- 2022-04-13 07:29:43 [INFO] [TRAIN] epoch: 107, iter: 39750/60000, loss: 0.9286, lr: 0.003825, batch_cost: 0.8443, reader_cost: 0.00020, ips: 2.3688 samples/sec | ETA 04:44:57
- 2022-04-13 07:30:25 [INFO] [TRAIN] epoch: 107, iter: 39800/60000, loss: 0.9201, lr: 0.003817, batch_cost: 0.8443, reader_cost: 0.00020, ips: 2.3688 samples/sec | ETA 04:44:15
- 2022-04-13 07:31:10 [INFO] [TRAIN] epoch: 108, iter: 39850/60000, loss: 0.9506, lr: 0.003808, batch_cost: 0.9003, reader_cost: 0.04995, ips: 2.2215 samples/sec | ETA 05:02:21
- 2022-04-13 07:31:52 [INFO] [TRAIN] epoch: 108, iter: 39900/60000, loss: 0.9382, lr: 0.003800, batch_cost: 0.8462, reader_cost: 0.00022, ips: 2.3634 samples/sec | ETA 04:43:29
- 2022-04-13 07:32:35 [INFO] [TRAIN] epoch: 108, iter: 39950/60000, loss: 0.9491, lr: 0.003792, batch_cost: 0.8467, reader_cost: 0.00021, ips: 2.3622 samples/sec | ETA 04:42:55
- 2022-04-13 07:33:17 [INFO] [TRAIN] epoch: 108, iter: 40000/60000, loss: 0.9315, lr: 0.003783, batch_cost: 0.8459, reader_cost: 0.00020, ips: 2.3644 samples/sec | ETA 04:41:57
- 2022-04-13 07:33:17 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4184 - reader cost: 0.0035
- 2022-04-13 07:34:09 [INFO] [EVAL] #Images: 500 mIoU: 0.7730 Acc: 0.9597 Kappa: 0.9477 Dice: 0.8645
- 2022-04-13 07:34:09 [INFO] [EVAL] Class IoU:
- [0.9835 0.8651 0.9203 0.4656 0.5928 0.6185 0.7128 0.7921 0.9195 0.6037
- 0.9394 0.8206 0.6665 0.9472 0.7974 0.866 0.7557 0.6413 0.7794]
- 2022-04-13 07:34:09 [INFO] [EVAL] Class Acc:
- [0.9918 0.9291 0.9454 0.8473 0.7605 0.7987 0.849 0.9076 0.957 0.9067
- 0.9517 0.9047 0.844 0.974 0.8733 0.9272 0.887 0.7419 0.8653]
- 2022-04-13 07:34:11 [INFO] [EVAL] The model with the best validation mIoU (0.7770) was saved at iter 36000.
- 2022-04-13 07:34:53 [INFO] [TRAIN] epoch: 108, iter: 40050/60000, loss: 0.9283, lr: 0.003775, batch_cost: 0.8457, reader_cost: 0.00030, ips: 2.3650 samples/sec | ETA 04:41:10
- 2022-04-13 07:35:35 [INFO] [TRAIN] epoch: 108, iter: 40100/60000, loss: 0.8647, lr: 0.003767, batch_cost: 0.8467, reader_cost: 0.00023, ips: 2.3620 samples/sec | ETA 04:40:49
- 2022-04-13 07:36:18 [INFO] [TRAIN] epoch: 108, iter: 40150/60000, loss: 0.9354, lr: 0.003759, batch_cost: 0.8468, reader_cost: 0.00019, ips: 2.3620 samples/sec | ETA 04:40:08
- 2022-04-13 07:37:03 [INFO] [TRAIN] epoch: 109, iter: 40200/60000, loss: 0.8816, lr: 0.003750, batch_cost: 0.9002, reader_cost: 0.05010, ips: 2.2217 samples/sec | ETA 04:57:04
- 2022-04-13 07:37:45 [INFO] [TRAIN] epoch: 109, iter: 40250/60000, loss: 0.9344, lr: 0.003742, batch_cost: 0.8466, reader_cost: 0.00022, ips: 2.3624 samples/sec | ETA 04:38:40
- 2022-04-13 07:38:27 [INFO] [TRAIN] epoch: 109, iter: 40300/60000, loss: 0.8573, lr: 0.003734, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3655 samples/sec | ETA 04:37:36
- 2022-04-13 07:39:10 [INFO] [TRAIN] epoch: 109, iter: 40350/60000, loss: 0.8919, lr: 0.003725, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3655 samples/sec | ETA 04:36:53
- 2022-04-13 07:39:52 [INFO] [TRAIN] epoch: 109, iter: 40400/60000, loss: 0.8734, lr: 0.003717, batch_cost: 0.8476, reader_cost: 0.00022, ips: 2.3597 samples/sec | ETA 04:36:52
- 2022-04-13 07:40:34 [INFO] [TRAIN] epoch: 109, iter: 40450/60000, loss: 0.9031, lr: 0.003709, batch_cost: 0.8452, reader_cost: 0.00025, ips: 2.3662 samples/sec | ETA 04:35:24
- 2022-04-13 07:41:17 [INFO] [TRAIN] epoch: 109, iter: 40500/60000, loss: 0.8990, lr: 0.003700, batch_cost: 0.8462, reader_cost: 0.00021, ips: 2.3635 samples/sec | ETA 04:35:01
- 2022-04-13 07:42:02 [INFO] [TRAIN] epoch: 110, iter: 40550/60000, loss: 0.9201, lr: 0.003692, batch_cost: 0.9007, reader_cost: 0.04603, ips: 2.2204 samples/sec | ETA 04:51:59
- 2022-04-13 07:42:44 [INFO] [TRAIN] epoch: 110, iter: 40600/60000, loss: 0.8869, lr: 0.003684, batch_cost: 0.8457, reader_cost: 0.00023, ips: 2.3649 samples/sec | ETA 04:33:26
- 2022-04-13 07:43:26 [INFO] [TRAIN] epoch: 110, iter: 40650/60000, loss: 0.8899, lr: 0.003675, batch_cost: 0.8443, reader_cost: 0.00020, ips: 2.3689 samples/sec | ETA 04:32:16
- 2022-04-13 07:44:08 [INFO] [TRAIN] epoch: 110, iter: 40700/60000, loss: 0.9366, lr: 0.003667, batch_cost: 0.8457, reader_cost: 0.00021, ips: 2.3650 samples/sec | ETA 04:32:01
- 2022-04-13 07:44:51 [INFO] [TRAIN] epoch: 110, iter: 40750/60000, loss: 0.9081, lr: 0.003659, batch_cost: 0.8446, reader_cost: 0.00021, ips: 2.3680 samples/sec | ETA 04:30:58
- 2022-04-13 07:45:33 [INFO] [TRAIN] epoch: 110, iter: 40800/60000, loss: 0.9127, lr: 0.003651, batch_cost: 0.8448, reader_cost: 0.00021, ips: 2.3675 samples/sec | ETA 04:30:19
- 2022-04-13 07:46:15 [INFO] [TRAIN] epoch: 110, iter: 40850/60000, loss: 0.9163, lr: 0.003642, batch_cost: 0.8453, reader_cost: 0.00021, ips: 2.3660 samples/sec | ETA 04:29:47
- 2022-04-13 07:46:57 [INFO] [TRAIN] epoch: 110, iter: 40900/60000, loss: 0.8792, lr: 0.003634, batch_cost: 0.8448, reader_cost: 0.00019, ips: 2.3673 samples/sec | ETA 04:28:56
- 2022-04-13 07:47:42 [INFO] [TRAIN] epoch: 111, iter: 40950/60000, loss: 0.8927, lr: 0.003626, batch_cost: 0.8944, reader_cost: 0.04609, ips: 2.2361 samples/sec | ETA 04:43:58
- 2022-04-13 07:48:24 [INFO] [TRAIN] epoch: 111, iter: 41000/60000, loss: 0.8752, lr: 0.003617, batch_cost: 0.8463, reader_cost: 0.00024, ips: 2.3633 samples/sec | ETA 04:27:59
- 2022-04-13 07:49:07 [INFO] [TRAIN] epoch: 111, iter: 41050/60000, loss: 0.9326, lr: 0.003609, batch_cost: 0.8470, reader_cost: 0.00020, ips: 2.3612 samples/sec | ETA 04:27:31
- 2022-04-13 07:49:49 [INFO] [TRAIN] epoch: 111, iter: 41100/60000, loss: 0.9183, lr: 0.003601, batch_cost: 0.8472, reader_cost: 0.00022, ips: 2.3607 samples/sec | ETA 04:26:52
- 2022-04-13 07:50:31 [INFO] [TRAIN] epoch: 111, iter: 41150/60000, loss: 0.9038, lr: 0.003592, batch_cost: 0.8454, reader_cost: 0.00021, ips: 2.3658 samples/sec | ETA 04:25:35
- 2022-04-13 07:51:14 [INFO] [TRAIN] epoch: 111, iter: 41200/60000, loss: 0.8731, lr: 0.003584, batch_cost: 0.8449, reader_cost: 0.00020, ips: 2.3670 samples/sec | ETA 04:24:44
- 2022-04-13 07:51:56 [INFO] [TRAIN] epoch: 111, iter: 41250/60000, loss: 0.8969, lr: 0.003576, batch_cost: 0.8453, reader_cost: 0.00021, ips: 2.3662 samples/sec | ETA 04:24:08
- 2022-04-13 07:52:41 [INFO] [TRAIN] epoch: 112, iter: 41300/60000, loss: 0.9692, lr: 0.003567, batch_cost: 0.9012, reader_cost: 0.04924, ips: 2.2193 samples/sec | ETA 04:40:51
- 2022-04-13 07:53:23 [INFO] [TRAIN] epoch: 112, iter: 41350/60000, loss: 0.9106, lr: 0.003559, batch_cost: 0.8458, reader_cost: 0.00023, ips: 2.3647 samples/sec | ETA 04:22:53
- 2022-04-13 07:54:06 [INFO] [TRAIN] epoch: 112, iter: 41400/60000, loss: 0.9440, lr: 0.003550, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3632 samples/sec | ETA 04:22:21
- 2022-04-13 07:54:48 [INFO] [TRAIN] epoch: 112, iter: 41450/60000, loss: 0.9141, lr: 0.003542, batch_cost: 0.8448, reader_cost: 0.00020, ips: 2.3673 samples/sec | ETA 04:21:11
- 2022-04-13 07:55:30 [INFO] [TRAIN] epoch: 112, iter: 41500/60000, loss: 0.9170, lr: 0.003534, batch_cost: 0.8451, reader_cost: 0.00021, ips: 2.3666 samples/sec | ETA 04:20:34
- 2022-04-13 07:56:12 [INFO] [TRAIN] epoch: 112, iter: 41550/60000, loss: 0.8778, lr: 0.003525, batch_cost: 0.8462, reader_cost: 0.00020, ips: 2.3635 samples/sec | ETA 04:20:12
- 2022-04-13 07:56:55 [INFO] [TRAIN] epoch: 112, iter: 41600/60000, loss: 0.8728, lr: 0.003517, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3654 samples/sec | ETA 04:19:17
- 2022-04-13 07:57:37 [INFO] [TRAIN] epoch: 112, iter: 41650/60000, loss: 0.8757, lr: 0.003509, batch_cost: 0.8457, reader_cost: 0.00022, ips: 2.3648 samples/sec | ETA 04:18:39
- 2022-04-13 07:58:22 [INFO] [TRAIN] epoch: 113, iter: 41700/60000, loss: 0.9448, lr: 0.003500, batch_cost: 0.8992, reader_cost: 0.04980, ips: 2.2242 samples/sec | ETA 04:34:15
- 2022-04-13 07:59:04 [INFO] [TRAIN] epoch: 113, iter: 41750/60000, loss: 0.9032, lr: 0.003492, batch_cost: 0.8496, reader_cost: 0.00024, ips: 2.3540 samples/sec | ETA 04:18:25
- 2022-04-13 07:59:47 [INFO] [TRAIN] epoch: 113, iter: 41800/60000, loss: 0.9275, lr: 0.003484, batch_cost: 0.8467, reader_cost: 0.00025, ips: 2.3621 samples/sec | ETA 04:16:50
- 2022-04-13 08:00:29 [INFO] [TRAIN] epoch: 113, iter: 41850/60000, loss: 0.8790, lr: 0.003475, batch_cost: 0.8450, reader_cost: 0.00023, ips: 2.3668 samples/sec | ETA 04:15:37
- 2022-04-13 08:01:11 [INFO] [TRAIN] epoch: 113, iter: 41900/60000, loss: 0.8740, lr: 0.003467, batch_cost: 0.8466, reader_cost: 0.00022, ips: 2.3625 samples/sec | ETA 04:15:22
- 2022-04-13 08:01:54 [INFO] [TRAIN] epoch: 113, iter: 41950/60000, loss: 0.8732, lr: 0.003459, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3654 samples/sec | ETA 04:14:21
- 2022-04-13 08:02:36 [INFO] [TRAIN] epoch: 113, iter: 42000/60000, loss: 0.9003, lr: 0.003450, batch_cost: 0.8456, reader_cost: 0.00025, ips: 2.3652 samples/sec | ETA 04:13:40
- 2022-04-13 08:03:21 [INFO] [TRAIN] epoch: 114, iter: 42050/60000, loss: 0.8917, lr: 0.003442, batch_cost: 0.9015, reader_cost: 0.04602, ips: 2.2185 samples/sec | ETA 04:29:42
- 2022-04-13 08:04:03 [INFO] [TRAIN] epoch: 114, iter: 42100/60000, loss: 0.9004, lr: 0.003433, batch_cost: 0.8463, reader_cost: 0.00023, ips: 2.3633 samples/sec | ETA 04:12:28
- 2022-04-13 08:04:46 [INFO] [TRAIN] epoch: 114, iter: 42150/60000, loss: 0.8867, lr: 0.003425, batch_cost: 0.8457, reader_cost: 0.00023, ips: 2.3649 samples/sec | ETA 04:11:35
- 2022-04-13 08:05:28 [INFO] [TRAIN] epoch: 114, iter: 42200/60000, loss: 0.9115, lr: 0.003417, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3653 samples/sec | ETA 04:10:50
- 2022-04-13 08:06:10 [INFO] [TRAIN] epoch: 114, iter: 42250/60000, loss: 0.9110, lr: 0.003408, batch_cost: 0.8454, reader_cost: 0.00020, ips: 2.3658 samples/sec | ETA 04:10:05
- 2022-04-13 08:06:52 [INFO] [TRAIN] epoch: 114, iter: 42300/60000, loss: 0.9047, lr: 0.003400, batch_cost: 0.8466, reader_cost: 0.00020, ips: 2.3623 samples/sec | ETA 04:09:45
- 2022-04-13 08:07:35 [INFO] [TRAIN] epoch: 114, iter: 42350/60000, loss: 0.8568, lr: 0.003391, batch_cost: 0.8451, reader_cost: 0.00020, ips: 2.3666 samples/sec | ETA 04:08:36
- 2022-04-13 08:08:17 [INFO] [TRAIN] epoch: 114, iter: 42400/60000, loss: 0.9024, lr: 0.003383, batch_cost: 0.8448, reader_cost: 0.00019, ips: 2.3675 samples/sec | ETA 04:07:48
- 2022-04-13 08:09:02 [INFO] [TRAIN] epoch: 115, iter: 42450/60000, loss: 0.8992, lr: 0.003375, batch_cost: 0.8967, reader_cost: 0.04569, ips: 2.2303 samples/sec | ETA 04:22:17
- 2022-04-13 08:09:44 [INFO] [TRAIN] epoch: 115, iter: 42500/60000, loss: 0.9153, lr: 0.003366, batch_cost: 0.8458, reader_cost: 0.00026, ips: 2.3646 samples/sec | ETA 04:06:41
- 2022-04-13 08:10:26 [INFO] [TRAIN] epoch: 115, iter: 42550/60000, loss: 0.8470, lr: 0.003358, batch_cost: 0.8451, reader_cost: 0.00021, ips: 2.3666 samples/sec | ETA 04:05:46
- 2022-04-13 08:11:09 [INFO] [TRAIN] epoch: 115, iter: 42600/60000, loss: 0.8960, lr: 0.003349, batch_cost: 0.8456, reader_cost: 0.00020, ips: 2.3653 samples/sec | ETA 04:05:12
- 2022-04-13 08:11:51 [INFO] [TRAIN] epoch: 115, iter: 42650/60000, loss: 0.8679, lr: 0.003341, batch_cost: 0.8456, reader_cost: 0.00021, ips: 2.3651 samples/sec | ETA 04:04:31
- 2022-04-13 08:12:33 [INFO] [TRAIN] epoch: 115, iter: 42700/60000, loss: 0.9328, lr: 0.003333, batch_cost: 0.8460, reader_cost: 0.00021, ips: 2.3642 samples/sec | ETA 04:03:55
- 2022-04-13 08:13:15 [INFO] [TRAIN] epoch: 115, iter: 42750/60000, loss: 1.0106, lr: 0.003324, batch_cost: 0.8457, reader_cost: 0.00020, ips: 2.3648 samples/sec | ETA 04:03:09
- 2022-04-13 08:14:00 [INFO] [TRAIN] epoch: 116, iter: 42800/60000, loss: 0.8696, lr: 0.003316, batch_cost: 0.8986, reader_cost: 0.04433, ips: 2.2258 samples/sec | ETA 04:17:35
- 2022-04-13 08:14:43 [INFO] [TRAIN] epoch: 116, iter: 42850/60000, loss: 0.8944, lr: 0.003307, batch_cost: 0.8466, reader_cost: 0.00023, ips: 2.3623 samples/sec | ETA 04:01:59
- 2022-04-13 08:15:25 [INFO] [TRAIN] epoch: 116, iter: 42900/60000, loss: 0.9062, lr: 0.003299, batch_cost: 0.8459, reader_cost: 0.00024, ips: 2.3644 samples/sec | ETA 04:01:04
- 2022-04-13 08:16:08 [INFO] [TRAIN] epoch: 116, iter: 42950/60000, loss: 0.8814, lr: 0.003291, batch_cost: 0.8503, reader_cost: 0.00023, ips: 2.3522 samples/sec | ETA 04:01:37
- 2022-04-13 08:16:50 [INFO] [TRAIN] epoch: 116, iter: 43000/60000, loss: 0.9272, lr: 0.003282, batch_cost: 0.8471, reader_cost: 0.00021, ips: 2.3610 samples/sec | ETA 04:00:00
- 2022-04-13 08:17:32 [INFO] [TRAIN] epoch: 116, iter: 43050/60000, loss: 0.8957, lr: 0.003274, batch_cost: 0.8459, reader_cost: 0.00020, ips: 2.3643 samples/sec | ETA 03:58:58
- 2022-04-13 08:18:14 [INFO] [TRAIN] epoch: 116, iter: 43100/60000, loss: 0.8647, lr: 0.003265, batch_cost: 0.8449, reader_cost: 0.00019, ips: 2.3672 samples/sec | ETA 03:57:58
- 2022-04-13 08:18:57 [INFO] [TRAIN] epoch: 116, iter: 43150/60000, loss: 0.9745, lr: 0.003257, batch_cost: 0.8437, reader_cost: 0.00018, ips: 2.3706 samples/sec | ETA 03:56:56
- 2022-04-13 08:19:42 [INFO] [TRAIN] epoch: 117, iter: 43200/60000, loss: 0.8791, lr: 0.003248, batch_cost: 0.8990, reader_cost: 0.04605, ips: 2.2246 samples/sec | ETA 04:11:43
- 2022-04-13 08:20:24 [INFO] [TRAIN] epoch: 117, iter: 43250/60000, loss: 0.8801, lr: 0.003240, batch_cost: 0.8452, reader_cost: 0.00023, ips: 2.3662 samples/sec | ETA 03:55:57
- 2022-04-13 08:21:06 [INFO] [TRAIN] epoch: 117, iter: 43300/60000, loss: 0.8699, lr: 0.003232, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3633 samples/sec | ETA 03:55:32
- 2022-04-13 08:21:49 [INFO] [TRAIN] epoch: 117, iter: 43350/60000, loss: 0.8990, lr: 0.003223, batch_cost: 0.8518, reader_cost: 0.00022, ips: 2.3480 samples/sec | ETA 03:56:22
- 2022-04-13 08:22:31 [INFO] [TRAIN] epoch: 117, iter: 43400/60000, loss: 0.9093, lr: 0.003215, batch_cost: 0.8479, reader_cost: 0.00024, ips: 2.3588 samples/sec | ETA 03:54:35
- 2022-04-13 08:23:13 [INFO] [TRAIN] epoch: 117, iter: 43450/60000, loss: 0.8847, lr: 0.003206, batch_cost: 0.8465, reader_cost: 0.00025, ips: 2.3626 samples/sec | ETA 03:53:29
- 2022-04-13 08:23:56 [INFO] [TRAIN] epoch: 117, iter: 43500/60000, loss: 0.9029, lr: 0.003198, batch_cost: 0.8449, reader_cost: 0.00022, ips: 2.3671 samples/sec | ETA 03:52:21
- 2022-04-13 08:24:41 [INFO] [TRAIN] epoch: 118, iter: 43550/60000, loss: 0.8764, lr: 0.003189, batch_cost: 0.8974, reader_cost: 0.04981, ips: 2.2288 samples/sec | ETA 04:06:01
- 2022-04-13 08:25:23 [INFO] [TRAIN] epoch: 118, iter: 43600/60000, loss: 0.9246, lr: 0.003181, batch_cost: 0.8455, reader_cost: 0.00024, ips: 2.3655 samples/sec | ETA 03:51:05
- 2022-04-13 08:26:05 [INFO] [TRAIN] epoch: 118, iter: 43650/60000, loss: 0.8942, lr: 0.003172, batch_cost: 0.8472, reader_cost: 0.00020, ips: 2.3606 samples/sec | ETA 03:50:52
- 2022-04-13 08:26:47 [INFO] [TRAIN] epoch: 118, iter: 43700/60000, loss: 0.9149, lr: 0.003164, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3646 samples/sec | ETA 03:49:46
- 2022-04-13 08:27:30 [INFO] [TRAIN] epoch: 118, iter: 43750/60000, loss: 0.9406, lr: 0.003156, batch_cost: 0.8459, reader_cost: 0.00020, ips: 2.3645 samples/sec | ETA 03:49:05
- 2022-04-13 08:28:12 [INFO] [TRAIN] epoch: 118, iter: 43800/60000, loss: 0.9263, lr: 0.003147, batch_cost: 0.8457, reader_cost: 0.00020, ips: 2.3650 samples/sec | ETA 03:48:19
- 2022-04-13 08:28:54 [INFO] [TRAIN] epoch: 118, iter: 43850/60000, loss: 0.8812, lr: 0.003139, batch_cost: 0.8473, reader_cost: 0.00022, ips: 2.3603 samples/sec | ETA 03:48:04
- 2022-04-13 08:29:39 [INFO] [TRAIN] epoch: 119, iter: 43900/60000, loss: 0.8717, lr: 0.003130, batch_cost: 0.8974, reader_cost: 0.04441, ips: 2.2288 samples/sec | ETA 04:00:47
- 2022-04-13 08:30:22 [INFO] [TRAIN] epoch: 119, iter: 43950/60000, loss: 0.8831, lr: 0.003122, batch_cost: 0.8464, reader_cost: 0.00023, ips: 2.3628 samples/sec | ETA 03:46:25
- 2022-04-13 08:31:04 [INFO] [TRAIN] epoch: 119, iter: 44000/60000, loss: 0.8838, lr: 0.003113, batch_cost: 0.8464, reader_cost: 0.00024, ips: 2.3630 samples/sec | ETA 03:45:42
- 2022-04-13 08:31:04 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4187 - reader cost: 0.0034
- 2022-04-13 08:31:56 [INFO] [EVAL] #Images: 500 mIoU: 0.7907 Acc: 0.9623 Kappa: 0.9511 Dice: 0.8767
- 2022-04-13 08:31:56 [INFO] [EVAL] Class IoU:
- [0.9845 0.8642 0.9257 0.5209 0.6365 0.6125 0.7202 0.7921 0.9241 0.6013
- 0.9505 0.8287 0.6721 0.9552 0.8451 0.9029 0.7982 0.6985 0.7909]
- 2022-04-13 08:31:56 [INFO] [EVAL] Class Acc:
- [0.9925 0.9237 0.9532 0.8517 0.8308 0.8147 0.8612 0.9194 0.9503 0.9152
- 0.9702 0.8981 0.8233 0.9756 0.9428 0.9519 0.9313 0.7987 0.8674]
- 2022-04-13 08:32:00 [INFO] [EVAL] The model with the best validation mIoU (0.7907) was saved at iter 44000.
- 2022-04-13 08:32:43 [INFO] [TRAIN] epoch: 119, iter: 44050/60000, loss: 0.8775, lr: 0.003105, batch_cost: 0.8467, reader_cost: 0.00027, ips: 2.3620 samples/sec | ETA 03:45:05
- 2022-04-13 08:33:25 [INFO] [TRAIN] epoch: 119, iter: 44100/60000, loss: 0.8707, lr: 0.003096, batch_cost: 0.8454, reader_cost: 0.00020, ips: 2.3656 samples/sec | ETA 03:44:02
- 2022-04-13 08:34:07 [INFO] [TRAIN] epoch: 119, iter: 44150/60000, loss: 0.9114, lr: 0.003088, batch_cost: 0.8450, reader_cost: 0.00019, ips: 2.3670 samples/sec | ETA 03:43:12
- 2022-04-13 08:34:50 [INFO] [TRAIN] epoch: 119, iter: 44200/60000, loss: 0.9227, lr: 0.003079, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3633 samples/sec | ETA 03:42:51
- 2022-04-13 08:35:32 [INFO] [TRAIN] epoch: 119, iter: 44250/60000, loss: 0.8921, lr: 0.003071, batch_cost: 0.8444, reader_cost: 0.00022, ips: 2.3686 samples/sec | ETA 03:41:38
- 2022-04-13 08:36:17 [INFO] [TRAIN] epoch: 120, iter: 44300/60000, loss: 0.8487, lr: 0.003062, batch_cost: 0.9033, reader_cost: 0.05388, ips: 2.2141 samples/sec | ETA 03:56:21
- 2022-04-13 08:36:59 [INFO] [TRAIN] epoch: 120, iter: 44350/60000, loss: 0.8858, lr: 0.003054, batch_cost: 0.8474, reader_cost: 0.00024, ips: 2.3602 samples/sec | ETA 03:41:01
- 2022-04-13 08:37:42 [INFO] [TRAIN] epoch: 120, iter: 44400/60000, loss: 0.8848, lr: 0.003045, batch_cost: 0.8495, reader_cost: 0.00022, ips: 2.3544 samples/sec | ETA 03:40:51
- 2022-04-13 08:38:24 [INFO] [TRAIN] epoch: 120, iter: 44450/60000, loss: 0.8633, lr: 0.003037, batch_cost: 0.8500, reader_cost: 0.00020, ips: 2.3530 samples/sec | ETA 03:40:17
- 2022-04-13 08:39:07 [INFO] [TRAIN] epoch: 120, iter: 44500/60000, loss: 0.8783, lr: 0.003028, batch_cost: 0.8481, reader_cost: 0.00021, ips: 2.3583 samples/sec | ETA 03:39:05
- 2022-04-13 08:39:49 [INFO] [TRAIN] epoch: 120, iter: 44550/60000, loss: 0.9262, lr: 0.003020, batch_cost: 0.8493, reader_cost: 0.00022, ips: 2.3548 samples/sec | ETA 03:38:42
- 2022-04-13 08:40:31 [INFO] [TRAIN] epoch: 120, iter: 44600/60000, loss: 0.8975, lr: 0.003011, batch_cost: 0.8469, reader_cost: 0.00020, ips: 2.3616 samples/sec | ETA 03:37:22
- 2022-04-13 08:41:16 [INFO] [TRAIN] epoch: 121, iter: 44650/60000, loss: 0.9158, lr: 0.003003, batch_cost: 0.8992, reader_cost: 0.04431, ips: 2.2242 samples/sec | ETA 03:50:02
- 2022-04-13 08:41:59 [INFO] [TRAIN] epoch: 121, iter: 44700/60000, loss: 0.9127, lr: 0.002994, batch_cost: 0.8482, reader_cost: 0.00028, ips: 2.3580 samples/sec | ETA 03:36:17
- 2022-04-13 08:42:41 [INFO] [TRAIN] epoch: 121, iter: 44750/60000, loss: 0.9016, lr: 0.002986, batch_cost: 0.8471, reader_cost: 0.00024, ips: 2.3610 samples/sec | ETA 03:35:18
- 2022-04-13 08:43:24 [INFO] [TRAIN] epoch: 121, iter: 44800/60000, loss: 0.9071, lr: 0.002977, batch_cost: 0.8471, reader_cost: 0.00021, ips: 2.3610 samples/sec | ETA 03:34:35
- 2022-04-13 08:44:06 [INFO] [TRAIN] epoch: 121, iter: 44850/60000, loss: 0.9180, lr: 0.002969, batch_cost: 0.8470, reader_cost: 0.00022, ips: 2.3611 samples/sec | ETA 03:33:52
- 2022-04-13 08:44:48 [INFO] [TRAIN] epoch: 121, iter: 44900/60000, loss: 0.8876, lr: 0.002960, batch_cost: 0.8468, reader_cost: 0.00021, ips: 2.3619 samples/sec | ETA 03:33:06
- 2022-04-13 08:45:31 [INFO] [TRAIN] epoch: 121, iter: 44950/60000, loss: 0.8950, lr: 0.002952, batch_cost: 0.8461, reader_cost: 0.00021, ips: 2.3638 samples/sec | ETA 03:32:13
- 2022-04-13 08:46:13 [INFO] [TRAIN] epoch: 121, iter: 45000/60000, loss: 0.9306, lr: 0.002943, batch_cost: 0.8450, reader_cost: 0.00019, ips: 2.3670 samples/sec | ETA 03:31:14
- 2022-04-13 08:46:58 [INFO] [TRAIN] epoch: 122, iter: 45050/60000, loss: 0.9280, lr: 0.002935, batch_cost: 0.9017, reader_cost: 0.05419, ips: 2.2181 samples/sec | ETA 03:44:39
- 2022-04-13 08:47:40 [INFO] [TRAIN] epoch: 122, iter: 45100/60000, loss: 0.8966, lr: 0.002926, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3655 samples/sec | ETA 03:29:57
- 2022-04-13 08:48:23 [INFO] [TRAIN] epoch: 122, iter: 45150/60000, loss: 0.8683, lr: 0.002918, batch_cost: 0.8471, reader_cost: 0.00020, ips: 2.3610 samples/sec | ETA 03:29:39
- 2022-04-13 08:49:05 [INFO] [TRAIN] epoch: 122, iter: 45200/60000, loss: 0.9014, lr: 0.002909, batch_cost: 0.8459, reader_cost: 0.00023, ips: 2.3642 samples/sec | ETA 03:28:39
- 2022-04-13 08:49:47 [INFO] [TRAIN] epoch: 122, iter: 45250/60000, loss: 0.9279, lr: 0.002901, batch_cost: 0.8464, reader_cost: 0.00024, ips: 2.3629 samples/sec | ETA 03:28:04
- 2022-04-13 08:50:29 [INFO] [TRAIN] epoch: 122, iter: 45300/60000, loss: 0.9011, lr: 0.002892, batch_cost: 0.8456, reader_cost: 0.00022, ips: 2.3652 samples/sec | ETA 03:27:10
- 2022-04-13 08:51:12 [INFO] [TRAIN] epoch: 122, iter: 45350/60000, loss: 0.9092, lr: 0.002883, batch_cost: 0.8457, reader_cost: 0.00022, ips: 2.3650 samples/sec | ETA 03:26:29
- 2022-04-13 08:51:57 [INFO] [TRAIN] epoch: 123, iter: 45400/60000, loss: 0.8750, lr: 0.002875, batch_cost: 0.8989, reader_cost: 0.04639, ips: 2.2249 samples/sec | ETA 03:38:43
- 2022-04-13 08:52:39 [INFO] [TRAIN] epoch: 123, iter: 45450/60000, loss: 0.8358, lr: 0.002866, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3655 samples/sec | ETA 03:25:01
- 2022-04-13 08:53:21 [INFO] [TRAIN] epoch: 123, iter: 45500/60000, loss: 0.9084, lr: 0.002858, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3633 samples/sec | ETA 03:24:30
- 2022-04-13 08:54:03 [INFO] [TRAIN] epoch: 123, iter: 45550/60000, loss: 0.8690, lr: 0.002849, batch_cost: 0.8447, reader_cost: 0.00020, ips: 2.3676 samples/sec | ETA 03:23:26
- 2022-04-13 08:54:46 [INFO] [TRAIN] epoch: 123, iter: 45600/60000, loss: 0.9274, lr: 0.002841, batch_cost: 0.8451, reader_cost: 0.00020, ips: 2.3666 samples/sec | ETA 03:22:49
- 2022-04-13 08:55:28 [INFO] [TRAIN] epoch: 123, iter: 45650/60000, loss: 0.8728, lr: 0.002832, batch_cost: 0.8469, reader_cost: 0.00021, ips: 2.3616 samples/sec | ETA 03:22:32
- 2022-04-13 08:56:10 [INFO] [TRAIN] epoch: 123, iter: 45700/60000, loss: 0.8824, lr: 0.002824, batch_cost: 0.8479, reader_cost: 0.00020, ips: 2.3587 samples/sec | ETA 03:22:05
- 2022-04-13 08:56:53 [INFO] [TRAIN] epoch: 123, iter: 45750/60000, loss: 0.8941, lr: 0.002815, batch_cost: 0.8446, reader_cost: 0.00023, ips: 2.3681 samples/sec | ETA 03:20:35
- 2022-04-13 08:57:38 [INFO] [TRAIN] epoch: 124, iter: 45800/60000, loss: 0.9124, lr: 0.002806, batch_cost: 0.8983, reader_cost: 0.04719, ips: 2.2265 samples/sec | ETA 03:32:35
- 2022-04-13 08:58:20 [INFO] [TRAIN] epoch: 124, iter: 45850/60000, loss: 0.8552, lr: 0.002798, batch_cost: 0.8475, reader_cost: 0.00024, ips: 2.3599 samples/sec | ETA 03:19:52
- 2022-04-13 08:59:02 [INFO] [TRAIN] epoch: 124, iter: 45900/60000, loss: 0.9127, lr: 0.002789, batch_cost: 0.8460, reader_cost: 0.00025, ips: 2.3641 samples/sec | ETA 03:18:48
- 2022-04-13 08:59:45 [INFO] [TRAIN] epoch: 124, iter: 45950/60000, loss: 0.8780, lr: 0.002781, batch_cost: 0.8488, reader_cost: 0.00027, ips: 2.3562 samples/sec | ETA 03:18:46
- 2022-04-13 09:00:27 [INFO] [TRAIN] epoch: 124, iter: 46000/60000, loss: 0.9214, lr: 0.002772, batch_cost: 0.8457, reader_cost: 0.00024, ips: 2.3648 samples/sec | ETA 03:17:20
- 2022-04-13 09:01:09 [INFO] [TRAIN] epoch: 124, iter: 46050/60000, loss: 0.9191, lr: 0.002763, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3631 samples/sec | ETA 03:16:46
- 2022-04-13 09:01:52 [INFO] [TRAIN] epoch: 124, iter: 46100/60000, loss: 0.8925, lr: 0.002755, batch_cost: 0.8476, reader_cost: 0.00021, ips: 2.3596 samples/sec | ETA 03:16:21
- 2022-04-13 09:02:37 [INFO] [TRAIN] epoch: 125, iter: 46150/60000, loss: 0.8686, lr: 0.002746, batch_cost: 0.8998, reader_cost: 0.05153, ips: 2.2228 samples/sec | ETA 03:27:41
- 2022-04-13 09:03:19 [INFO] [TRAIN] epoch: 125, iter: 46200/60000, loss: 0.9020, lr: 0.002738, batch_cost: 0.8453, reader_cost: 0.00023, ips: 2.3660 samples/sec | ETA 03:14:25
- 2022-04-13 09:04:01 [INFO] [TRAIN] epoch: 125, iter: 46250/60000, loss: 0.9095, lr: 0.002729, batch_cost: 0.8468, reader_cost: 0.00020, ips: 2.3618 samples/sec | ETA 03:14:03
- 2022-04-13 09:04:44 [INFO] [TRAIN] epoch: 125, iter: 46300/60000, loss: 0.8770, lr: 0.002720, batch_cost: 0.8474, reader_cost: 0.00022, ips: 2.3601 samples/sec | ETA 03:13:29
- 2022-04-13 09:05:26 [INFO] [TRAIN] epoch: 125, iter: 46350/60000, loss: 0.9141, lr: 0.002712, batch_cost: 0.8452, reader_cost: 0.00021, ips: 2.3664 samples/sec | ETA 03:12:16
- 2022-04-13 09:06:08 [INFO] [TRAIN] epoch: 125, iter: 46400/60000, loss: 0.8583, lr: 0.002703, batch_cost: 0.8461, reader_cost: 0.00023, ips: 2.3637 samples/sec | ETA 03:11:47
- 2022-04-13 09:06:51 [INFO] [TRAIN] epoch: 125, iter: 46450/60000, loss: 0.8786, lr: 0.002695, batch_cost: 0.8473, reader_cost: 0.00020, ips: 2.3605 samples/sec | ETA 03:11:20
- 2022-04-13 09:07:33 [INFO] [TRAIN] epoch: 125, iter: 46500/60000, loss: 0.8858, lr: 0.002686, batch_cost: 0.8445, reader_cost: 0.00022, ips: 2.3684 samples/sec | ETA 03:10:00
- 2022-04-13 09:08:18 [INFO] [TRAIN] epoch: 126, iter: 46550/60000, loss: 0.8935, lr: 0.002677, batch_cost: 0.9033, reader_cost: 0.05086, ips: 2.2142 samples/sec | ETA 03:22:29
- 2022-04-13 09:09:00 [INFO] [TRAIN] epoch: 126, iter: 46600/60000, loss: 0.8688, lr: 0.002669, batch_cost: 0.8479, reader_cost: 0.00023, ips: 2.3588 samples/sec | ETA 03:09:21
- 2022-04-13 09:09:43 [INFO] [TRAIN] epoch: 126, iter: 46650/60000, loss: 0.8636, lr: 0.002660, batch_cost: 0.8468, reader_cost: 0.00023, ips: 2.3618 samples/sec | ETA 03:08:24
- 2022-04-13 09:10:25 [INFO] [TRAIN] epoch: 126, iter: 46700/60000, loss: 0.8982, lr: 0.002651, batch_cost: 0.8450, reader_cost: 0.00020, ips: 2.3669 samples/sec | ETA 03:07:18
- 2022-04-13 09:11:07 [INFO] [TRAIN] epoch: 126, iter: 46750/60000, loss: 0.8972, lr: 0.002643, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3633 samples/sec | ETA 03:06:52
- 2022-04-13 09:11:50 [INFO] [TRAIN] epoch: 126, iter: 46800/60000, loss: 0.9130, lr: 0.002634, batch_cost: 0.8503, reader_cost: 0.00020, ips: 2.3521 samples/sec | ETA 03:07:04
- 2022-04-13 09:12:32 [INFO] [TRAIN] epoch: 126, iter: 46850/60000, loss: 0.8692, lr: 0.002626, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3662 samples/sec | ETA 03:05:14
- 2022-04-13 09:13:17 [INFO] [TRAIN] epoch: 127, iter: 46900/60000, loss: 0.9147, lr: 0.002617, batch_cost: 0.8970, reader_cost: 0.04021, ips: 2.2296 samples/sec | ETA 03:15:50
- 2022-04-13 09:13:59 [INFO] [TRAIN] epoch: 127, iter: 46950/60000, loss: 0.9400, lr: 0.002608, batch_cost: 0.8467, reader_cost: 0.00028, ips: 2.3622 samples/sec | ETA 03:04:09
- 2022-04-13 09:14:42 [INFO] [TRAIN] epoch: 127, iter: 47000/60000, loss: 0.9249, lr: 0.002600, batch_cost: 0.8467, reader_cost: 0.00022, ips: 2.3620 samples/sec | ETA 03:03:27
- 2022-04-13 09:15:24 [INFO] [TRAIN] epoch: 127, iter: 47050/60000, loss: 0.8797, lr: 0.002591, batch_cost: 0.8488, reader_cost: 0.00023, ips: 2.3562 samples/sec | ETA 03:03:12
- 2022-04-13 09:16:06 [INFO] [TRAIN] epoch: 127, iter: 47100/60000, loss: 0.8939, lr: 0.002582, batch_cost: 0.8463, reader_cost: 0.00024, ips: 2.3632 samples/sec | ETA 03:01:57
- 2022-04-13 09:16:49 [INFO] [TRAIN] epoch: 127, iter: 47150/60000, loss: 0.8714, lr: 0.002574, batch_cost: 0.8459, reader_cost: 0.00026, ips: 2.3643 samples/sec | ETA 03:01:09
- 2022-04-13 09:17:31 [INFO] [TRAIN] epoch: 127, iter: 47200/60000, loss: 0.8827, lr: 0.002565, batch_cost: 0.8446, reader_cost: 0.00026, ips: 2.3680 samples/sec | ETA 03:00:10
- 2022-04-13 09:18:16 [INFO] [TRAIN] epoch: 128, iter: 47250/60000, loss: 0.8954, lr: 0.002556, batch_cost: 0.9016, reader_cost: 0.04578, ips: 2.2182 samples/sec | ETA 03:11:35
- 2022-04-13 09:18:58 [INFO] [TRAIN] epoch: 128, iter: 47300/60000, loss: 0.9000, lr: 0.002548, batch_cost: 0.8475, reader_cost: 0.00023, ips: 2.3599 samples/sec | ETA 02:59:22
- 2022-04-13 09:19:41 [INFO] [TRAIN] epoch: 128, iter: 47350/60000, loss: 0.8664, lr: 0.002539, batch_cost: 0.8453, reader_cost: 0.00023, ips: 2.3661 samples/sec | ETA 02:58:12
- 2022-04-13 09:20:23 [INFO] [TRAIN] epoch: 128, iter: 47400/60000, loss: 0.8810, lr: 0.002530, batch_cost: 0.8457, reader_cost: 0.00022, ips: 2.3648 samples/sec | ETA 02:57:36
- 2022-04-13 09:21:05 [INFO] [TRAIN] epoch: 128, iter: 47450/60000, loss: 0.9005, lr: 0.002522, batch_cost: 0.8446, reader_cost: 0.00020, ips: 2.3680 samples/sec | ETA 02:56:39
- 2022-04-13 09:21:47 [INFO] [TRAIN] epoch: 128, iter: 47500/60000, loss: 0.8835, lr: 0.002513, batch_cost: 0.8456, reader_cost: 0.00022, ips: 2.3652 samples/sec | ETA 02:56:10
- 2022-04-13 09:22:30 [INFO] [TRAIN] epoch: 128, iter: 47550/60000, loss: 0.8922, lr: 0.002504, batch_cost: 0.8455, reader_cost: 0.00026, ips: 2.3654 samples/sec | ETA 02:55:26
- 2022-04-13 09:23:12 [INFO] [TRAIN] epoch: 128, iter: 47600/60000, loss: 0.9707, lr: 0.002496, batch_cost: 0.8465, reader_cost: 0.00024, ips: 2.3626 samples/sec | ETA 02:54:56
- 2022-04-13 09:23:57 [INFO] [TRAIN] epoch: 129, iter: 47650/60000, loss: 0.9067, lr: 0.002487, batch_cost: 0.9027, reader_cost: 0.05222, ips: 2.2157 samples/sec | ETA 03:05:47
- 2022-04-13 09:24:39 [INFO] [TRAIN] epoch: 129, iter: 47700/60000, loss: 0.8737, lr: 0.002478, batch_cost: 0.8463, reader_cost: 0.00027, ips: 2.3632 samples/sec | ETA 02:53:29
- 2022-04-13 09:25:22 [INFO] [TRAIN] epoch: 129, iter: 47750/60000, loss: 0.8881, lr: 0.002469, batch_cost: 0.8457, reader_cost: 0.00024, ips: 2.3648 samples/sec | ETA 02:52:40
- 2022-04-13 09:26:04 [INFO] [TRAIN] epoch: 129, iter: 47800/60000, loss: 0.9075, lr: 0.002461, batch_cost: 0.8456, reader_cost: 0.00024, ips: 2.3653 samples/sec | ETA 02:51:55
- 2022-04-13 09:26:46 [INFO] [TRAIN] epoch: 129, iter: 47850/60000, loss: 0.8911, lr: 0.002452, batch_cost: 0.8468, reader_cost: 0.00024, ips: 2.3618 samples/sec | ETA 02:51:28
- 2022-04-13 09:27:29 [INFO] [TRAIN] epoch: 129, iter: 47900/60000, loss: 0.9283, lr: 0.002443, batch_cost: 0.8463, reader_cost: 0.00024, ips: 2.3632 samples/sec | ETA 02:50:40
- 2022-04-13 09:28:11 [INFO] [TRAIN] epoch: 129, iter: 47950/60000, loss: 0.8988, lr: 0.002435, batch_cost: 0.8449, reader_cost: 0.00026, ips: 2.3672 samples/sec | ETA 02:49:40
- 2022-04-13 09:28:56 [INFO] [TRAIN] epoch: 130, iter: 48000/60000, loss: 0.8440, lr: 0.002426, batch_cost: 0.9096, reader_cost: 0.04539, ips: 2.1989 samples/sec | ETA 03:01:54
- 2022-04-13 09:28:56 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4193 - reader cost: 0.0035
- 2022-04-13 09:29:49 [INFO] [EVAL] #Images: 500 mIoU: 0.7934 Acc: 0.9626 Kappa: 0.9515 Dice: 0.8793
- 2022-04-13 09:29:49 [INFO] [EVAL] Class IoU:
- [0.9829 0.8568 0.9285 0.604 0.6352 0.6247 0.7227 0.791 0.926 0.6482
- 0.9479 0.8244 0.6416 0.9542 0.833 0.8987 0.8115 0.6573 0.7849]
- 2022-04-13 09:29:49 [INFO] [EVAL] Class Acc:
- [0.9904 0.936 0.9586 0.8508 0.8081 0.81 0.8279 0.9077 0.9542 0.8541
- 0.9709 0.8821 0.8017 0.9748 0.9234 0.9404 0.9452 0.8559 0.8541]
- 2022-04-13 09:29:52 [INFO] [EVAL] The model with the best validation mIoU (0.7934) was saved at iter 48000.
- 2022-04-13 09:30:35 [INFO] [TRAIN] epoch: 130, iter: 48050/60000, loss: 0.8936, lr: 0.002417, batch_cost: 0.8520, reader_cost: 0.00026, ips: 2.3474 samples/sec | ETA 02:49:41
- 2022-04-13 09:31:17 [INFO] [TRAIN] epoch: 130, iter: 48100/60000, loss: 0.8742, lr: 0.002408, batch_cost: 0.8529, reader_cost: 0.00020, ips: 2.3450 samples/sec | ETA 02:49:09
- 2022-04-13 09:32:00 [INFO] [TRAIN] epoch: 130, iter: 48150/60000, loss: 0.8880, lr: 0.002400, batch_cost: 0.8472, reader_cost: 0.00024, ips: 2.3607 samples/sec | ETA 02:47:19
- 2022-04-13 09:32:42 [INFO] [TRAIN] epoch: 130, iter: 48200/60000, loss: 0.8430, lr: 0.002391, batch_cost: 0.8463, reader_cost: 0.00025, ips: 2.3634 samples/sec | ETA 02:46:25
- 2022-04-13 09:33:24 [INFO] [TRAIN] epoch: 130, iter: 48250/60000, loss: 0.8977, lr: 0.002382, batch_cost: 0.8455, reader_cost: 0.00021, ips: 2.3655 samples/sec | ETA 02:45:34
- 2022-04-13 09:34:07 [INFO] [TRAIN] epoch: 130, iter: 48300/60000, loss: 0.8813, lr: 0.002374, batch_cost: 0.8454, reader_cost: 0.00020, ips: 2.3657 samples/sec | ETA 02:44:51
- 2022-04-13 09:34:49 [INFO] [TRAIN] epoch: 130, iter: 48350/60000, loss: 0.8743, lr: 0.002365, batch_cost: 0.8457, reader_cost: 0.00022, ips: 2.3650 samples/sec | ETA 02:44:12
- 2022-04-13 09:35:34 [INFO] [TRAIN] epoch: 131, iter: 48400/60000, loss: 0.9169, lr: 0.002356, batch_cost: 0.9051, reader_cost: 0.04759, ips: 2.2098 samples/sec | ETA 02:54:58
- 2022-04-13 09:36:16 [INFO] [TRAIN] epoch: 131, iter: 48450/60000, loss: 0.8756, lr: 0.002347, batch_cost: 0.8457, reader_cost: 0.00022, ips: 2.3650 samples/sec | ETA 02:42:47
- 2022-04-13 09:36:59 [INFO] [TRAIN] epoch: 131, iter: 48500/60000, loss: 0.8817, lr: 0.002339, batch_cost: 0.8435, reader_cost: 0.00020, ips: 2.3711 samples/sec | ETA 02:41:40
- 2022-04-13 09:37:41 [INFO] [TRAIN] epoch: 131, iter: 48550/60000, loss: 0.8752, lr: 0.002330, batch_cost: 0.8468, reader_cost: 0.00021, ips: 2.3618 samples/sec | ETA 02:41:35
- 2022-04-13 09:38:23 [INFO] [TRAIN] epoch: 131, iter: 48600/60000, loss: 0.8880, lr: 0.002321, batch_cost: 0.8452, reader_cost: 0.00021, ips: 2.3664 samples/sec | ETA 02:40:34
- 2022-04-13 09:39:05 [INFO] [TRAIN] epoch: 131, iter: 48650/60000, loss: 0.8820, lr: 0.002312, batch_cost: 0.8441, reader_cost: 0.00020, ips: 2.3693 samples/sec | ETA 02:39:41
- 2022-04-13 09:39:48 [INFO] [TRAIN] epoch: 131, iter: 48700/60000, loss: 0.9114, lr: 0.002303, batch_cost: 0.8460, reader_cost: 0.00020, ips: 2.3640 samples/sec | ETA 02:39:20
- 2022-04-13 09:40:33 [INFO] [TRAIN] epoch: 132, iter: 48750/60000, loss: 0.9039, lr: 0.002295, batch_cost: 0.8984, reader_cost: 0.04769, ips: 2.2262 samples/sec | ETA 02:48:27
- 2022-04-13 09:41:15 [INFO] [TRAIN] epoch: 132, iter: 48800/60000, loss: 0.8878, lr: 0.002286, batch_cost: 0.8504, reader_cost: 0.00023, ips: 2.3519 samples/sec | ETA 02:38:44
- 2022-04-13 09:41:57 [INFO] [TRAIN] epoch: 132, iter: 48850/60000, loss: 0.8819, lr: 0.002277, batch_cost: 0.8453, reader_cost: 0.00024, ips: 2.3661 samples/sec | ETA 02:37:04
- 2022-04-13 09:42:40 [INFO] [TRAIN] epoch: 132, iter: 48900/60000, loss: 0.9178, lr: 0.002268, batch_cost: 0.8468, reader_cost: 0.00024, ips: 2.3618 samples/sec | ETA 02:36:39
- 2022-04-13 09:43:22 [INFO] [TRAIN] epoch: 132, iter: 48950/60000, loss: 0.8581, lr: 0.002260, batch_cost: 0.8488, reader_cost: 0.00021, ips: 2.3562 samples/sec | ETA 02:36:19
- 2022-04-13 09:44:05 [INFO] [TRAIN] epoch: 132, iter: 49000/60000, loss: 0.8589, lr: 0.002251, batch_cost: 0.8463, reader_cost: 0.00021, ips: 2.3632 samples/sec | ETA 02:35:09
- 2022-04-13 09:44:47 [INFO] [TRAIN] epoch: 132, iter: 49050/60000, loss: 0.8652, lr: 0.002242, batch_cost: 0.8472, reader_cost: 0.00020, ips: 2.3608 samples/sec | ETA 02:34:36
- 2022-04-13 09:45:29 [INFO] [TRAIN] epoch: 132, iter: 49100/60000, loss: 0.8769, lr: 0.002233, batch_cost: 0.8456, reader_cost: 0.00018, ips: 2.3651 samples/sec | ETA 02:33:37
- 2022-04-13 09:46:14 [INFO] [TRAIN] epoch: 133, iter: 49150/60000, loss: 0.8744, lr: 0.002224, batch_cost: 0.9019, reader_cost: 0.04625, ips: 2.2176 samples/sec | ETA 02:43:05
- 2022-04-13 09:46:57 [INFO] [TRAIN] epoch: 133, iter: 49200/60000, loss: 0.8850, lr: 0.002216, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3655 samples/sec | ETA 02:32:11
- 2022-04-13 09:47:39 [INFO] [TRAIN] epoch: 133, iter: 49250/60000, loss: 0.9557, lr: 0.002207, batch_cost: 0.8456, reader_cost: 0.00020, ips: 2.3650 samples/sec | ETA 02:31:30
- 2022-04-13 09:48:21 [INFO] [TRAIN] epoch: 133, iter: 49300/60000, loss: 0.8509, lr: 0.002198, batch_cost: 0.8457, reader_cost: 0.00022, ips: 2.3650 samples/sec | ETA 02:30:48
- 2022-04-13 09:49:03 [INFO] [TRAIN] epoch: 133, iter: 49350/60000, loss: 0.8903, lr: 0.002189, batch_cost: 0.8461, reader_cost: 0.00022, ips: 2.3639 samples/sec | ETA 02:30:10
- 2022-04-13 09:49:46 [INFO] [TRAIN] epoch: 133, iter: 49400/60000, loss: 0.8397, lr: 0.002180, batch_cost: 0.8472, reader_cost: 0.00023, ips: 2.3607 samples/sec | ETA 02:29:40
- 2022-04-13 09:50:28 [INFO] [TRAIN] epoch: 133, iter: 49450/60000, loss: 0.8732, lr: 0.002171, batch_cost: 0.8471, reader_cost: 0.00022, ips: 2.3611 samples/sec | ETA 02:28:56
- 2022-04-13 09:51:13 [INFO] [TRAIN] epoch: 134, iter: 49500/60000, loss: 0.8663, lr: 0.002163, batch_cost: 0.9002, reader_cost: 0.05020, ips: 2.2218 samples/sec | ETA 02:37:31
- 2022-04-13 09:51:56 [INFO] [TRAIN] epoch: 134, iter: 49550/60000, loss: 0.8721, lr: 0.002154, batch_cost: 0.8462, reader_cost: 0.00024, ips: 2.3634 samples/sec | ETA 02:27:23
- 2022-04-13 09:52:38 [INFO] [TRAIN] epoch: 134, iter: 49600/60000, loss: 0.8481, lr: 0.002145, batch_cost: 0.8463, reader_cost: 0.00023, ips: 2.3631 samples/sec | ETA 02:26:41
- 2022-04-13 09:53:20 [INFO] [TRAIN] epoch: 134, iter: 49650/60000, loss: 0.8883, lr: 0.002136, batch_cost: 0.8455, reader_cost: 0.00024, ips: 2.3654 samples/sec | ETA 02:25:51
- 2022-04-13 09:54:02 [INFO] [TRAIN] epoch: 134, iter: 49700/60000, loss: 0.9231, lr: 0.002127, batch_cost: 0.8460, reader_cost: 0.00023, ips: 2.3642 samples/sec | ETA 02:25:13
- 2022-04-13 09:54:45 [INFO] [TRAIN] epoch: 134, iter: 49750/60000, loss: 0.8800, lr: 0.002118, batch_cost: 0.8448, reader_cost: 0.00020, ips: 2.3675 samples/sec | ETA 02:24:18
- 2022-04-13 09:55:27 [INFO] [TRAIN] epoch: 134, iter: 49800/60000, loss: 0.8511, lr: 0.002109, batch_cost: 0.8443, reader_cost: 0.00020, ips: 2.3688 samples/sec | ETA 02:23:32
- 2022-04-13 09:56:12 [INFO] [TRAIN] epoch: 135, iter: 49850/60000, loss: 0.8732, lr: 0.002101, batch_cost: 0.8977, reader_cost: 0.04778, ips: 2.2279 samples/sec | ETA 02:31:51
- 2022-04-13 09:56:54 [INFO] [TRAIN] epoch: 135, iter: 49900/60000, loss: 0.8940, lr: 0.002092, batch_cost: 0.8464, reader_cost: 0.00023, ips: 2.3629 samples/sec | ETA 02:22:28
- 2022-04-13 09:57:36 [INFO] [TRAIN] epoch: 135, iter: 49950/60000, loss: 0.8643, lr: 0.002083, batch_cost: 0.8477, reader_cost: 0.00020, ips: 2.3593 samples/sec | ETA 02:21:59
- 2022-04-13 09:58:19 [INFO] [TRAIN] epoch: 135, iter: 50000/60000, loss: 0.8689, lr: 0.002074, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3664 samples/sec | ETA 02:20:51
- 2022-04-13 09:59:01 [INFO] [TRAIN] epoch: 135, iter: 50050/60000, loss: 0.8668, lr: 0.002065, batch_cost: 0.8442, reader_cost: 0.00019, ips: 2.3691 samples/sec | ETA 02:19:59
- 2022-04-13 09:59:43 [INFO] [TRAIN] epoch: 135, iter: 50100/60000, loss: 0.9275, lr: 0.002056, batch_cost: 0.8479, reader_cost: 0.00021, ips: 2.3587 samples/sec | ETA 02:19:54
- 2022-04-13 10:00:26 [INFO] [TRAIN] epoch: 135, iter: 50150/60000, loss: 0.8641, lr: 0.002047, batch_cost: 0.8461, reader_cost: 0.00025, ips: 2.3638 samples/sec | ETA 02:18:54
- 2022-04-13 10:01:08 [INFO] [TRAIN] epoch: 135, iter: 50200/60000, loss: 0.8891, lr: 0.002038, batch_cost: 0.8449, reader_cost: 0.00024, ips: 2.3671 samples/sec | ETA 02:18:00
- 2022-04-13 10:01:53 [INFO] [TRAIN] epoch: 136, iter: 50250/60000, loss: 0.8817, lr: 0.002029, batch_cost: 0.9045, reader_cost: 0.05247, ips: 2.2112 samples/sec | ETA 02:26:58
- 2022-04-13 10:02:35 [INFO] [TRAIN] epoch: 136, iter: 50300/60000, loss: 0.8607, lr: 0.002021, batch_cost: 0.8462, reader_cost: 0.00023, ips: 2.3636 samples/sec | ETA 02:16:47
- 2022-04-13 10:03:18 [INFO] [TRAIN] epoch: 136, iter: 50350/60000, loss: 0.8819, lr: 0.002012, batch_cost: 0.8464, reader_cost: 0.00021, ips: 2.3628 samples/sec | ETA 02:16:08
- 2022-04-13 10:04:00 [INFO] [TRAIN] epoch: 136, iter: 50400/60000, loss: 0.8368, lr: 0.002003, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3663 samples/sec | ETA 02:15:14
- 2022-04-13 10:04:42 [INFO] [TRAIN] epoch: 136, iter: 50450/60000, loss: 0.9042, lr: 0.001994, batch_cost: 0.8452, reader_cost: 0.00020, ips: 2.3664 samples/sec | ETA 02:14:31
- 2022-04-13 10:05:25 [INFO] [TRAIN] epoch: 136, iter: 50500/60000, loss: 0.9186, lr: 0.001985, batch_cost: 0.8461, reader_cost: 0.00020, ips: 2.3637 samples/sec | ETA 02:13:58
- 2022-04-13 10:06:07 [INFO] [TRAIN] epoch: 136, iter: 50550/60000, loss: 0.8679, lr: 0.001976, batch_cost: 0.8448, reader_cost: 0.00020, ips: 2.3674 samples/sec | ETA 02:13:03
- 2022-04-13 10:06:52 [INFO] [TRAIN] epoch: 137, iter: 50600/60000, loss: 0.8602, lr: 0.001967, batch_cost: 0.9041, reader_cost: 0.05201, ips: 2.2122 samples/sec | ETA 02:21:38
- 2022-04-13 10:07:34 [INFO] [TRAIN] epoch: 137, iter: 50650/60000, loss: 0.8709, lr: 0.001958, batch_cost: 0.8457, reader_cost: 0.00029, ips: 2.3650 samples/sec | ETA 02:11:46
- 2022-04-13 10:08:17 [INFO] [TRAIN] epoch: 137, iter: 50700/60000, loss: 0.8924, lr: 0.001949, batch_cost: 0.8469, reader_cost: 0.00025, ips: 2.3616 samples/sec | ETA 02:11:16
- 2022-04-13 10:08:59 [INFO] [TRAIN] epoch: 137, iter: 50750/60000, loss: 0.9089, lr: 0.001940, batch_cost: 0.8460, reader_cost: 0.00021, ips: 2.3641 samples/sec | ETA 02:10:25
- 2022-04-13 10:09:41 [INFO] [TRAIN] epoch: 137, iter: 50800/60000, loss: 0.8885, lr: 0.001931, batch_cost: 0.8465, reader_cost: 0.00024, ips: 2.3626 samples/sec | ETA 02:09:47
- 2022-04-13 10:10:24 [INFO] [TRAIN] epoch: 137, iter: 50850/60000, loss: 0.8825, lr: 0.001922, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3646 samples/sec | ETA 02:08:59
- 2022-04-13 10:11:06 [INFO] [TRAIN] epoch: 137, iter: 50900/60000, loss: 0.8461, lr: 0.001913, batch_cost: 0.8459, reader_cost: 0.00020, ips: 2.3644 samples/sec | ETA 02:08:17
- 2022-04-13 10:11:48 [INFO] [TRAIN] epoch: 137, iter: 50950/60000, loss: 0.8950, lr: 0.001904, batch_cost: 0.8451, reader_cost: 0.00023, ips: 2.3666 samples/sec | ETA 02:07:28
- 2022-04-13 10:12:34 [INFO] [TRAIN] epoch: 138, iter: 51000/60000, loss: 0.8689, lr: 0.001895, batch_cost: 0.9104, reader_cost: 0.04673, ips: 2.1967 samples/sec | ETA 02:16:33
- 2022-04-13 10:13:16 [INFO] [TRAIN] epoch: 138, iter: 51050/60000, loss: 0.8882, lr: 0.001886, batch_cost: 0.8455, reader_cost: 0.00023, ips: 2.3654 samples/sec | ETA 02:06:07
- 2022-04-13 10:13:58 [INFO] [TRAIN] epoch: 138, iter: 51100/60000, loss: 0.8466, lr: 0.001877, batch_cost: 0.8470, reader_cost: 0.00024, ips: 2.3612 samples/sec | ETA 02:05:38
- 2022-04-13 10:14:41 [INFO] [TRAIN] epoch: 138, iter: 51150/60000, loss: 0.8457, lr: 0.001868, batch_cost: 0.8457, reader_cost: 0.00026, ips: 2.3648 samples/sec | ETA 02:04:44
- 2022-04-13 10:15:23 [INFO] [TRAIN] epoch: 138, iter: 51200/60000, loss: 0.8722, lr: 0.001859, batch_cost: 0.8448, reader_cost: 0.00021, ips: 2.3673 samples/sec | ETA 02:03:54
- 2022-04-13 10:16:05 [INFO] [TRAIN] epoch: 138, iter: 51250/60000, loss: 0.8582, lr: 0.001850, batch_cost: 0.8477, reader_cost: 0.00021, ips: 2.3594 samples/sec | ETA 02:03:37
- 2022-04-13 10:16:48 [INFO] [TRAIN] epoch: 138, iter: 51300/60000, loss: 0.8607, lr: 0.001841, batch_cost: 0.8463, reader_cost: 0.00020, ips: 2.3631 samples/sec | ETA 02:02:43
- 2022-04-13 10:17:33 [INFO] [TRAIN] epoch: 139, iter: 51350/60000, loss: 0.8996, lr: 0.001832, batch_cost: 0.9031, reader_cost: 0.04662, ips: 2.2146 samples/sec | ETA 02:10:11
- 2022-04-13 10:18:15 [INFO] [TRAIN] epoch: 139, iter: 51400/60000, loss: 0.9072, lr: 0.001823, batch_cost: 0.8489, reader_cost: 0.00029, ips: 2.3560 samples/sec | ETA 02:01:40
- 2022-04-13 10:18:57 [INFO] [TRAIN] epoch: 139, iter: 51450/60000, loss: 0.8885, lr: 0.001814, batch_cost: 0.8456, reader_cost: 0.00022, ips: 2.3652 samples/sec | ETA 02:00:29
- 2022-04-13 10:19:40 [INFO] [TRAIN] epoch: 139, iter: 51500/60000, loss: 0.8984, lr: 0.001805, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3655 samples/sec | ETA 01:59:46
- 2022-04-13 10:20:22 [INFO] [TRAIN] epoch: 139, iter: 51550/60000, loss: 0.8906, lr: 0.001796, batch_cost: 0.8449, reader_cost: 0.00020, ips: 2.3671 samples/sec | ETA 01:58:59
- 2022-04-13 10:21:04 [INFO] [TRAIN] epoch: 139, iter: 51600/60000, loss: 0.8868, lr: 0.001787, batch_cost: 0.8462, reader_cost: 0.00020, ips: 2.3635 samples/sec | ETA 01:58:28
- 2022-04-13 10:21:47 [INFO] [TRAIN] epoch: 139, iter: 51650/60000, loss: 0.8980, lr: 0.001778, batch_cost: 0.8457, reader_cost: 0.00020, ips: 2.3649 samples/sec | ETA 01:57:41
- 2022-04-13 10:22:29 [INFO] [TRAIN] epoch: 139, iter: 51700/60000, loss: 0.8865, lr: 0.001769, batch_cost: 0.8450, reader_cost: 0.00027, ips: 2.3668 samples/sec | ETA 01:56:53
- 2022-04-13 10:23:14 [INFO] [TRAIN] epoch: 140, iter: 51750/60000, loss: 0.8892, lr: 0.001760, batch_cost: 0.9011, reader_cost: 0.04985, ips: 2.2194 samples/sec | ETA 02:03:54
- 2022-04-13 10:23:56 [INFO] [TRAIN] epoch: 140, iter: 51800/60000, loss: 0.9100, lr: 0.001751, batch_cost: 0.8464, reader_cost: 0.00026, ips: 2.3628 samples/sec | ETA 01:55:40
- 2022-04-13 10:24:38 [INFO] [TRAIN] epoch: 140, iter: 51850/60000, loss: 0.8996, lr: 0.001742, batch_cost: 0.8457, reader_cost: 0.00021, ips: 2.3650 samples/sec | ETA 01:54:52
- 2022-04-13 10:25:21 [INFO] [TRAIN] epoch: 140, iter: 51900/60000, loss: 0.9059, lr: 0.001733, batch_cost: 0.8515, reader_cost: 0.00020, ips: 2.3487 samples/sec | ETA 01:54:57
- 2022-04-13 10:26:03 [INFO] [TRAIN] epoch: 140, iter: 51950/60000, loss: 0.8824, lr: 0.001724, batch_cost: 0.8448, reader_cost: 0.00021, ips: 2.3674 samples/sec | ETA 01:53:20
- 2022-04-13 10:26:46 [INFO] [TRAIN] epoch: 140, iter: 52000/60000, loss: 0.8655, lr: 0.001715, batch_cost: 0.8468, reader_cost: 0.00020, ips: 2.3619 samples/sec | ETA 01:52:54
- 2022-04-13 10:26:46 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4177 - reader cost: 0.0033
- 2022-04-13 10:27:38 [INFO] [EVAL] #Images: 500 mIoU: 0.7964 Acc: 0.9636 Kappa: 0.9527 Dice: 0.8812
- 2022-04-13 10:27:38 [INFO] [EVAL] Class IoU:
- [0.9849 0.8677 0.9296 0.5955 0.6317 0.6345 0.7278 0.7988 0.9269 0.6404
- 0.9508 0.824 0.6472 0.9551 0.8207 0.894 0.834 0.6776 0.7901]
- 2022-04-13 10:27:38 [INFO] [EVAL] Class Acc:
- [0.9932 0.9286 0.9585 0.7878 0.8641 0.7972 0.849 0.8974 0.9589 0.8284
- 0.9709 0.8733 0.8331 0.9755 0.9211 0.9431 0.9567 0.8785 0.8625]
- 2022-04-13 10:27:41 [INFO] [EVAL] The model with the best validation mIoU (0.7964) was saved at iter 52000.
- 2022-04-13 10:28:25 [INFO] [TRAIN] epoch: 140, iter: 52050/60000, loss: 0.8664, lr: 0.001706, batch_cost: 0.8488, reader_cost: 0.00027, ips: 2.3561 samples/sec | ETA 01:52:28
- 2022-04-13 10:29:10 [INFO] [TRAIN] epoch: 141, iter: 52100/60000, loss: 0.9019, lr: 0.001697, batch_cost: 0.9032, reader_cost: 0.04731, ips: 2.2145 samples/sec | ETA 01:58:54
- 2022-04-13 10:29:52 [INFO] [TRAIN] epoch: 141, iter: 52150/60000, loss: 0.8565, lr: 0.001688, batch_cost: 0.8466, reader_cost: 0.00026, ips: 2.3625 samples/sec | ETA 01:50:45
- 2022-04-13 10:30:34 [INFO] [TRAIN] epoch: 141, iter: 52200/60000, loss: 0.9474, lr: 0.001678, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3646 samples/sec | ETA 01:49:57
- 2022-04-13 10:31:17 [INFO] [TRAIN] epoch: 141, iter: 52250/60000, loss: 0.8796, lr: 0.001669, batch_cost: 0.8461, reader_cost: 0.00019, ips: 2.3637 samples/sec | ETA 01:49:17
- 2022-04-13 10:31:59 [INFO] [TRAIN] epoch: 141, iter: 52300/60000, loss: 0.8878, lr: 0.001660, batch_cost: 0.8474, reader_cost: 0.00022, ips: 2.3601 samples/sec | ETA 01:48:45
- 2022-04-13 10:32:41 [INFO] [TRAIN] epoch: 141, iter: 52350/60000, loss: 0.8606, lr: 0.001651, batch_cost: 0.8474, reader_cost: 0.00022, ips: 2.3601 samples/sec | ETA 01:48:02
- 2022-04-13 10:33:24 [INFO] [TRAIN] epoch: 141, iter: 52400/60000, loss: 0.8716, lr: 0.001642, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3659 samples/sec | ETA 01:47:04
- 2022-04-13 10:34:06 [INFO] [TRAIN] epoch: 141, iter: 52450/60000, loss: 0.8763, lr: 0.001633, batch_cost: 0.8441, reader_cost: 0.00022, ips: 2.3693 samples/sec | ETA 01:46:13
- 2022-04-13 10:34:51 [INFO] [TRAIN] epoch: 142, iter: 52500/60000, loss: 0.8829, lr: 0.001624, batch_cost: 0.9053, reader_cost: 0.05372, ips: 2.2093 samples/sec | ETA 01:53:09
- 2022-04-13 10:35:33 [INFO] [TRAIN] epoch: 142, iter: 52550/60000, loss: 0.8809, lr: 0.001615, batch_cost: 0.8457, reader_cost: 0.00026, ips: 2.3649 samples/sec | ETA 01:45:00
- 2022-04-13 10:36:16 [INFO] [TRAIN] epoch: 142, iter: 52600/60000, loss: 0.8952, lr: 0.001605, batch_cost: 0.8462, reader_cost: 0.00023, ips: 2.3636 samples/sec | ETA 01:44:21
- 2022-04-13 10:36:58 [INFO] [TRAIN] epoch: 142, iter: 52650/60000, loss: 0.9061, lr: 0.001596, batch_cost: 0.8467, reader_cost: 0.00022, ips: 2.3620 samples/sec | ETA 01:43:43
- 2022-04-13 10:37:40 [INFO] [TRAIN] epoch: 142, iter: 52700/60000, loss: 0.9059, lr: 0.001587, batch_cost: 0.8459, reader_cost: 0.00021, ips: 2.3643 samples/sec | ETA 01:42:55
- 2022-04-13 10:38:23 [INFO] [TRAIN] epoch: 142, iter: 52750/60000, loss: 0.9253, lr: 0.001578, batch_cost: 0.8442, reader_cost: 0.00021, ips: 2.3690 samples/sec | ETA 01:42:00
- 2022-04-13 10:39:05 [INFO] [TRAIN] epoch: 142, iter: 52800/60000, loss: 0.8617, lr: 0.001569, batch_cost: 0.8463, reader_cost: 0.00021, ips: 2.3632 samples/sec | ETA 01:41:33
- 2022-04-13 10:39:50 [INFO] [TRAIN] epoch: 143, iter: 52850/60000, loss: 0.8702, lr: 0.001560, batch_cost: 0.8985, reader_cost: 0.04413, ips: 2.2260 samples/sec | ETA 01:47:04
- 2022-04-13 10:40:32 [INFO] [TRAIN] epoch: 143, iter: 52900/60000, loss: 0.8340, lr: 0.001550, batch_cost: 0.8452, reader_cost: 0.00024, ips: 2.3664 samples/sec | ETA 01:40:00
- 2022-04-13 10:41:14 [INFO] [TRAIN] epoch: 143, iter: 52950/60000, loss: 0.8689, lr: 0.001541, batch_cost: 0.8463, reader_cost: 0.00022, ips: 2.3632 samples/sec | ETA 01:39:26
- 2022-04-13 10:41:57 [INFO] [TRAIN] epoch: 143, iter: 53000/60000, loss: 0.9025, lr: 0.001532, batch_cost: 0.8475, reader_cost: 0.00020, ips: 2.3600 samples/sec | ETA 01:38:52
- 2022-04-13 10:42:39 [INFO] [TRAIN] epoch: 143, iter: 53050/60000, loss: 0.8527, lr: 0.001523, batch_cost: 0.8466, reader_cost: 0.00021, ips: 2.3625 samples/sec | ETA 01:38:03
- 2022-04-13 10:43:21 [INFO] [TRAIN] epoch: 143, iter: 53100/60000, loss: 0.9052, lr: 0.001514, batch_cost: 0.8464, reader_cost: 0.00023, ips: 2.3629 samples/sec | ETA 01:37:20
- 2022-04-13 10:44:04 [INFO] [TRAIN] epoch: 143, iter: 53150/60000, loss: 0.8557, lr: 0.001504, batch_cost: 0.8447, reader_cost: 0.00021, ips: 2.3678 samples/sec | ETA 01:36:25
- 2022-04-13 10:44:49 [INFO] [TRAIN] epoch: 144, iter: 53200/60000, loss: 0.8683, lr: 0.001495, batch_cost: 0.9015, reader_cost: 0.04752, ips: 2.2186 samples/sec | ETA 01:42:09
- 2022-04-13 10:45:31 [INFO] [TRAIN] epoch: 144, iter: 53250/60000, loss: 0.8685, lr: 0.001486, batch_cost: 0.8468, reader_cost: 0.00023, ips: 2.3618 samples/sec | ETA 01:35:15
- 2022-04-13 10:46:13 [INFO] [TRAIN] epoch: 144, iter: 53300/60000, loss: 0.8686, lr: 0.001477, batch_cost: 0.8456, reader_cost: 0.00020, ips: 2.3650 samples/sec | ETA 01:34:25
- 2022-04-13 10:46:56 [INFO] [TRAIN] epoch: 144, iter: 53350/60000, loss: 0.8265, lr: 0.001467, batch_cost: 0.8460, reader_cost: 0.00020, ips: 2.3641 samples/sec | ETA 01:33:45
- 2022-04-13 10:47:38 [INFO] [TRAIN] epoch: 144, iter: 53400/60000, loss: 0.8934, lr: 0.001458, batch_cost: 0.8472, reader_cost: 0.00023, ips: 2.3606 samples/sec | ETA 01:33:11
- 2022-04-13 10:48:20 [INFO] [TRAIN] epoch: 144, iter: 53450/60000, loss: 0.8801, lr: 0.001449, batch_cost: 0.8454, reader_cost: 0.00021, ips: 2.3658 samples/sec | ETA 01:32:17
- 2022-04-13 10:49:02 [INFO] [TRAIN] epoch: 144, iter: 53500/60000, loss: 0.8549, lr: 0.001440, batch_cost: 0.8445, reader_cost: 0.00020, ips: 2.3683 samples/sec | ETA 01:31:29
- 2022-04-13 10:49:45 [INFO] [TRAIN] epoch: 144, iter: 53550/60000, loss: 0.8712, lr: 0.001430, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3656 samples/sec | ETA 01:30:53
- 2022-04-13 10:50:30 [INFO] [TRAIN] epoch: 145, iter: 53600/60000, loss: 0.8833, lr: 0.001421, batch_cost: 0.8956, reader_cost: 0.04321, ips: 2.2331 samples/sec | ETA 01:35:31
- 2022-04-13 10:51:12 [INFO] [TRAIN] epoch: 145, iter: 53650/60000, loss: 0.8936, lr: 0.001412, batch_cost: 0.8462, reader_cost: 0.00022, ips: 2.3634 samples/sec | ETA 01:29:33
- 2022-04-13 10:51:54 [INFO] [TRAIN] epoch: 145, iter: 53700/60000, loss: 0.8671, lr: 0.001402, batch_cost: 0.8472, reader_cost: 0.00021, ips: 2.3607 samples/sec | ETA 01:28:57
- 2022-04-13 10:52:37 [INFO] [TRAIN] epoch: 145, iter: 53750/60000, loss: 0.8621, lr: 0.001393, batch_cost: 0.8475, reader_cost: 0.00020, ips: 2.3599 samples/sec | ETA 01:28:16
- 2022-04-13 10:53:19 [INFO] [TRAIN] epoch: 145, iter: 53800/60000, loss: 0.8523, lr: 0.001384, batch_cost: 0.8455, reader_cost: 0.00022, ips: 2.3655 samples/sec | ETA 01:27:21
- 2022-04-13 10:54:01 [INFO] [TRAIN] epoch: 145, iter: 53850/60000, loss: 0.8722, lr: 0.001375, batch_cost: 0.8445, reader_cost: 0.00020, ips: 2.3683 samples/sec | ETA 01:26:33
- 2022-04-13 10:54:43 [INFO] [TRAIN] epoch: 145, iter: 53900/60000, loss: 0.8783, lr: 0.001365, batch_cost: 0.8460, reader_cost: 0.00021, ips: 2.3641 samples/sec | ETA 01:26:00
- 2022-04-13 10:55:28 [INFO] [TRAIN] epoch: 146, iter: 53950/60000, loss: 0.8625, lr: 0.001356, batch_cost: 0.8996, reader_cost: 0.04841, ips: 2.2232 samples/sec | ETA 01:30:42
- 2022-04-13 10:56:11 [INFO] [TRAIN] epoch: 146, iter: 54000/60000, loss: 0.8316, lr: 0.001347, batch_cost: 0.8461, reader_cost: 0.00024, ips: 2.3638 samples/sec | ETA 01:24:36
- 2022-04-13 10:56:53 [INFO] [TRAIN] epoch: 146, iter: 54050/60000, loss: 0.9243, lr: 0.001337, batch_cost: 0.8463, reader_cost: 0.00021, ips: 2.3632 samples/sec | ETA 01:23:55
- 2022-04-13 10:57:35 [INFO] [TRAIN] epoch: 146, iter: 54100/60000, loss: 0.8784, lr: 0.001328, batch_cost: 0.8464, reader_cost: 0.00021, ips: 2.3631 samples/sec | ETA 01:23:13
- 2022-04-13 10:58:18 [INFO] [TRAIN] epoch: 146, iter: 54150/60000, loss: 0.8370, lr: 0.001318, batch_cost: 0.8455, reader_cost: 0.00024, ips: 2.3654 samples/sec | ETA 01:22:26
- 2022-04-13 10:59:00 [INFO] [TRAIN] epoch: 146, iter: 54200/60000, loss: 0.8848, lr: 0.001309, batch_cost: 0.8467, reader_cost: 0.00026, ips: 2.3620 samples/sec | ETA 01:21:50
- 2022-04-13 10:59:42 [INFO] [TRAIN] epoch: 146, iter: 54250/60000, loss: 0.8568, lr: 0.001300, batch_cost: 0.8473, reader_cost: 0.00025, ips: 2.3606 samples/sec | ETA 01:21:11
- 2022-04-13 11:00:25 [INFO] [TRAIN] epoch: 146, iter: 54300/60000, loss: 0.8542, lr: 0.001290, batch_cost: 0.8458, reader_cost: 0.00020, ips: 2.3646 samples/sec | ETA 01:20:21
- 2022-04-13 11:01:10 [INFO] [TRAIN] epoch: 147, iter: 54350/60000, loss: 0.8784, lr: 0.001281, batch_cost: 0.8991, reader_cost: 0.04396, ips: 2.2245 samples/sec | ETA 01:24:39
- 2022-04-13 11:01:52 [INFO] [TRAIN] epoch: 147, iter: 54400/60000, loss: 0.8892, lr: 0.001271, batch_cost: 0.8458, reader_cost: 0.00023, ips: 2.3646 samples/sec | ETA 01:18:56
- 2022-04-13 11:02:34 [INFO] [TRAIN] epoch: 147, iter: 54450/60000, loss: 0.8867, lr: 0.001262, batch_cost: 0.8466, reader_cost: 0.00025, ips: 2.3625 samples/sec | ETA 01:18:18
- 2022-04-13 11:03:16 [INFO] [TRAIN] epoch: 147, iter: 54500/60000, loss: 0.8674, lr: 0.001253, batch_cost: 0.8460, reader_cost: 0.00025, ips: 2.3641 samples/sec | ETA 01:17:33
- 2022-04-13 11:03:59 [INFO] [TRAIN] epoch: 147, iter: 54550/60000, loss: 0.8493, lr: 0.001243, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3659 samples/sec | ETA 01:16:47
- 2022-04-13 11:04:41 [INFO] [TRAIN] epoch: 147, iter: 54600/60000, loss: 0.8536, lr: 0.001234, batch_cost: 0.8460, reader_cost: 0.00020, ips: 2.3642 samples/sec | ETA 01:16:08
- 2022-04-13 11:05:23 [INFO] [TRAIN] epoch: 147, iter: 54650/60000, loss: 0.8769, lr: 0.001224, batch_cost: 0.8461, reader_cost: 0.00021, ips: 2.3638 samples/sec | ETA 01:15:26
- 2022-04-13 11:06:08 [INFO] [TRAIN] epoch: 148, iter: 54700/60000, loss: 0.8450, lr: 0.001215, batch_cost: 0.9023, reader_cost: 0.04556, ips: 2.2166 samples/sec | ETA 01:19:42
- 2022-04-13 11:06:51 [INFO] [TRAIN] epoch: 148, iter: 54750/60000, loss: 0.8781, lr: 0.001205, batch_cost: 0.8464, reader_cost: 0.00025, ips: 2.3630 samples/sec | ETA 01:14:03
- 2022-04-13 11:07:33 [INFO] [TRAIN] epoch: 148, iter: 54800/60000, loss: 0.8818, lr: 0.001196, batch_cost: 0.8481, reader_cost: 0.00021, ips: 2.3581 samples/sec | ETA 01:13:30
- 2022-04-13 11:08:15 [INFO] [TRAIN] epoch: 148, iter: 54850/60000, loss: 0.8909, lr: 0.001186, batch_cost: 0.8460, reader_cost: 0.00022, ips: 2.3641 samples/sec | ETA 01:12:36
- 2022-04-13 11:08:58 [INFO] [TRAIN] epoch: 148, iter: 54900/60000, loss: 0.8418, lr: 0.001177, batch_cost: 0.8455, reader_cost: 0.00024, ips: 2.3655 samples/sec | ETA 01:11:52
- 2022-04-13 11:09:40 [INFO] [TRAIN] epoch: 148, iter: 54950/60000, loss: 0.8742, lr: 0.001167, batch_cost: 0.8470, reader_cost: 0.00023, ips: 2.3612 samples/sec | ETA 01:11:17
- 2022-04-13 11:10:22 [INFO] [TRAIN] epoch: 148, iter: 55000/60000, loss: 0.8716, lr: 0.001158, batch_cost: 0.8474, reader_cost: 0.00020, ips: 2.3603 samples/sec | ETA 01:10:36
- 2022-04-13 11:11:05 [INFO] [TRAIN] epoch: 148, iter: 55050/60000, loss: 0.8641, lr: 0.001148, batch_cost: 0.8451, reader_cost: 0.00022, ips: 2.3666 samples/sec | ETA 01:09:43
- 2022-04-13 11:11:50 [INFO] [TRAIN] epoch: 149, iter: 55100/60000, loss: 0.8548, lr: 0.001139, batch_cost: 0.8996, reader_cost: 0.04043, ips: 2.2231 samples/sec | ETA 01:13:28
- 2022-04-13 11:12:32 [INFO] [TRAIN] epoch: 149, iter: 55150/60000, loss: 0.8644, lr: 0.001129, batch_cost: 0.8460, reader_cost: 0.00023, ips: 2.3641 samples/sec | ETA 01:08:22
- 2022-04-13 11:13:14 [INFO] [TRAIN] epoch: 149, iter: 55200/60000, loss: 0.9125, lr: 0.001120, batch_cost: 0.8460, reader_cost: 0.00021, ips: 2.3642 samples/sec | ETA 01:07:40
- 2022-04-13 11:13:57 [INFO] [TRAIN] epoch: 149, iter: 55250/60000, loss: 0.8701, lr: 0.001110, batch_cost: 0.8467, reader_cost: 0.00023, ips: 2.3620 samples/sec | ETA 01:07:02
- 2022-04-13 11:14:39 [INFO] [TRAIN] epoch: 149, iter: 55300/60000, loss: 0.8527, lr: 0.001101, batch_cost: 0.8480, reader_cost: 0.00022, ips: 2.3585 samples/sec | ETA 01:06:25
- 2022-04-13 11:15:21 [INFO] [TRAIN] epoch: 149, iter: 55350/60000, loss: 0.8490, lr: 0.001091, batch_cost: 0.8449, reader_cost: 0.00020, ips: 2.3672 samples/sec | ETA 01:05:28
- 2022-04-13 11:16:04 [INFO] [TRAIN] epoch: 149, iter: 55400/60000, loss: 0.8777, lr: 0.001081, batch_cost: 0.8462, reader_cost: 0.00020, ips: 2.3634 samples/sec | ETA 01:04:52
- 2022-04-13 11:16:49 [INFO] [TRAIN] epoch: 150, iter: 55450/60000, loss: 0.8583, lr: 0.001072, batch_cost: 0.9006, reader_cost: 0.04707, ips: 2.2207 samples/sec | ETA 01:08:17
- 2022-04-13 11:17:31 [INFO] [TRAIN] epoch: 150, iter: 55500/60000, loss: 0.8811, lr: 0.001062, batch_cost: 0.8477, reader_cost: 0.00025, ips: 2.3595 samples/sec | ETA 01:03:34
- 2022-04-13 11:18:13 [INFO] [TRAIN] epoch: 150, iter: 55550/60000, loss: 0.8858, lr: 0.001053, batch_cost: 0.8469, reader_cost: 0.00025, ips: 2.3617 samples/sec | ETA 01:02:48
- 2022-04-13 11:18:56 [INFO] [TRAIN] epoch: 150, iter: 55600/60000, loss: 0.8587, lr: 0.001043, batch_cost: 0.8463, reader_cost: 0.00022, ips: 2.3634 samples/sec | ETA 01:02:03
- 2022-04-13 11:19:38 [INFO] [TRAIN] epoch: 150, iter: 55650/60000, loss: 0.8756, lr: 0.001033, batch_cost: 0.8468, reader_cost: 0.00022, ips: 2.3619 samples/sec | ETA 01:01:23
- 2022-04-13 11:20:20 [INFO] [TRAIN] epoch: 150, iter: 55700/60000, loss: 0.9071, lr: 0.001024, batch_cost: 0.8478, reader_cost: 0.00020, ips: 2.3591 samples/sec | ETA 01:00:45
- 2022-04-13 11:21:03 [INFO] [TRAIN] epoch: 150, iter: 55750/60000, loss: 0.8998, lr: 0.001014, batch_cost: 0.8483, reader_cost: 0.00020, ips: 2.3577 samples/sec | ETA 01:00:05
- 2022-04-13 11:21:45 [INFO] [TRAIN] epoch: 150, iter: 55800/60000, loss: 0.8617, lr: 0.001004, batch_cost: 0.8444, reader_cost: 0.00025, ips: 2.3684 samples/sec | ETA 00:59:06
- 2022-04-13 11:22:30 [INFO] [TRAIN] epoch: 151, iter: 55850/60000, loss: 0.8736, lr: 0.000995, batch_cost: 0.9033, reader_cost: 0.05064, ips: 2.2142 samples/sec | ETA 01:02:28
- 2022-04-13 11:23:13 [INFO] [TRAIN] epoch: 151, iter: 55900/60000, loss: 0.8485, lr: 0.000985, batch_cost: 0.8467, reader_cost: 0.00025, ips: 2.3622 samples/sec | ETA 00:57:51
- 2022-04-13 11:23:55 [INFO] [TRAIN] epoch: 151, iter: 55950/60000, loss: 0.8709, lr: 0.000975, batch_cost: 0.8480, reader_cost: 0.00022, ips: 2.3584 samples/sec | ETA 00:57:14
- 2022-04-13 11:24:37 [INFO] [TRAIN] epoch: 151, iter: 56000/60000, loss: 0.8889, lr: 0.000965, batch_cost: 0.8465, reader_cost: 0.00022, ips: 2.3628 samples/sec | ETA 00:56:25
- 2022-04-13 11:24:37 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 52s - batch_cost: 0.4181 - reader cost: 0.0034
- 2022-04-13 11:25:30 [INFO] [EVAL] #Images: 500 mIoU: 0.8021 Acc: 0.9636 Kappa: 0.9527 Dice: 0.8847
- 2022-04-13 11:25:30 [INFO] [EVAL] Class IoU:
- [0.9844 0.8672 0.9284 0.596 0.5911 0.6276 0.7282 0.8052 0.9271 0.6597
- 0.9502 0.8327 0.6781 0.9552 0.8502 0.9013 0.8553 0.7078 0.7939]
- 2022-04-13 11:25:30 [INFO] [EVAL] Class Acc:
- [0.9928 0.9308 0.9558 0.8612 0.8522 0.8079 0.8548 0.9122 0.9545 0.8544
- 0.9724 0.8988 0.8003 0.9745 0.9251 0.9398 0.9466 0.8473 0.8722]
- 2022-04-13 11:25:33 [INFO] [EVAL] The model with the best validation mIoU (0.8021) was saved at iter 56000.
- 2022-04-13 11:26:16 [INFO] [TRAIN] epoch: 151, iter: 56050/60000, loss: 0.8882, lr: 0.000956, batch_cost: 0.8477, reader_cost: 0.00027, ips: 2.3593 samples/sec | ETA 00:55:48
- 2022-04-13 11:26:59 [INFO] [TRAIN] epoch: 151, iter: 56100/60000, loss: 0.8474, lr: 0.000946, batch_cost: 0.8493, reader_cost: 0.00023, ips: 2.3548 samples/sec | ETA 00:55:12
- 2022-04-13 11:27:41 [INFO] [TRAIN] epoch: 151, iter: 56150/60000, loss: 0.8558, lr: 0.000936, batch_cost: 0.8464, reader_cost: 0.00021, ips: 2.3630 samples/sec | ETA 00:54:18
- 2022-04-13 11:28:26 [INFO] [TRAIN] epoch: 152, iter: 56200/60000, loss: 0.8807, lr: 0.000926, batch_cost: 0.9061, reader_cost: 0.05467, ips: 2.2074 samples/sec | ETA 00:57:23
- 2022-04-13 11:29:09 [INFO] [TRAIN] epoch: 152, iter: 56250/60000, loss: 0.8611, lr: 0.000917, batch_cost: 0.8454, reader_cost: 0.00023, ips: 2.3659 samples/sec | ETA 00:52:50
- 2022-04-13 11:29:51 [INFO] [TRAIN] epoch: 152, iter: 56300/60000, loss: 0.8840, lr: 0.000907, batch_cost: 0.8456, reader_cost: 0.00027, ips: 2.3651 samples/sec | ETA 00:52:08
- 2022-04-13 11:30:33 [INFO] [TRAIN] epoch: 152, iter: 56350/60000, loss: 0.8615, lr: 0.000897, batch_cost: 0.8451, reader_cost: 0.00025, ips: 2.3665 samples/sec | ETA 00:51:24
- 2022-04-13 11:31:15 [INFO] [TRAIN] epoch: 152, iter: 56400/60000, loss: 0.8851, lr: 0.000887, batch_cost: 0.8455, reader_cost: 0.00021, ips: 2.3655 samples/sec | ETA 00:50:43
- 2022-04-13 11:31:58 [INFO] [TRAIN] epoch: 152, iter: 56450/60000, loss: 0.8917, lr: 0.000877, batch_cost: 0.8460, reader_cost: 0.00024, ips: 2.3641 samples/sec | ETA 00:50:03
- 2022-04-13 11:32:40 [INFO] [TRAIN] epoch: 152, iter: 56500/60000, loss: 0.8644, lr: 0.000867, batch_cost: 0.8473, reader_cost: 0.00022, ips: 2.3605 samples/sec | ETA 00:49:25
- 2022-04-13 11:33:25 [INFO] [TRAIN] epoch: 153, iter: 56550/60000, loss: 0.8651, lr: 0.000858, batch_cost: 0.9038, reader_cost: 0.04732, ips: 2.2129 samples/sec | ETA 00:51:58
- 2022-04-13 11:34:08 [INFO] [TRAIN] epoch: 153, iter: 56600/60000, loss: 0.8854, lr: 0.000848, batch_cost: 0.8457, reader_cost: 0.00022, ips: 2.3649 samples/sec | ETA 00:47:55
- 2022-04-13 11:34:50 [INFO] [TRAIN] epoch: 153, iter: 56650/60000, loss: 0.8567, lr: 0.000838, batch_cost: 0.8475, reader_cost: 0.00023, ips: 2.3598 samples/sec | ETA 00:47:19
- 2022-04-13 11:35:32 [INFO] [TRAIN] epoch: 153, iter: 56700/60000, loss: 0.8759, lr: 0.000828, batch_cost: 0.8462, reader_cost: 0.00023, ips: 2.3635 samples/sec | ETA 00:46:32
- 2022-04-13 11:36:15 [INFO] [TRAIN] epoch: 153, iter: 56750/60000, loss: 0.8812, lr: 0.000818, batch_cost: 0.8458, reader_cost: 0.00023, ips: 2.3646 samples/sec | ETA 00:45:48
- 2022-04-13 11:36:57 [INFO] [TRAIN] epoch: 153, iter: 56800/60000, loss: 0.8746, lr: 0.000808, batch_cost: 0.8463, reader_cost: 0.00022, ips: 2.3632 samples/sec | ETA 00:45:08
- 2022-04-13 11:37:39 [INFO] [TRAIN] epoch: 153, iter: 56850/60000, loss: 0.8472, lr: 0.000798, batch_cost: 0.8467, reader_cost: 0.00020, ips: 2.3621 samples/sec | ETA 00:44:27
- 2022-04-13 11:38:21 [INFO] [TRAIN] epoch: 153, iter: 56900/60000, loss: 0.8671, lr: 0.000788, batch_cost: 0.8459, reader_cost: 0.00020, ips: 2.3644 samples/sec | ETA 00:43:42
- 2022-04-13 11:39:06 [INFO] [TRAIN] epoch: 154, iter: 56950/60000, loss: 0.8776, lr: 0.000778, batch_cost: 0.9006, reader_cost: 0.05151, ips: 2.2207 samples/sec | ETA 00:45:46
- 2022-04-13 11:39:49 [INFO] [TRAIN] epoch: 154, iter: 57000/60000, loss: 0.8712, lr: 0.000768, batch_cost: 0.8491, reader_cost: 0.00024, ips: 2.3555 samples/sec | ETA 00:42:27
- 2022-04-13 11:40:31 [INFO] [TRAIN] epoch: 154, iter: 57050/60000, loss: 0.8720, lr: 0.000758, batch_cost: 0.8460, reader_cost: 0.00021, ips: 2.3640 samples/sec | ETA 00:41:35
- 2022-04-13 11:41:14 [INFO] [TRAIN] epoch: 154, iter: 57100/60000, loss: 0.9145, lr: 0.000748, batch_cost: 0.8477, reader_cost: 0.00020, ips: 2.3593 samples/sec | ETA 00:40:58
- 2022-04-13 11:41:56 [INFO] [TRAIN] epoch: 154, iter: 57150/60000, loss: 0.9089, lr: 0.000738, batch_cost: 0.8485, reader_cost: 0.00021, ips: 2.3571 samples/sec | ETA 00:40:18
- 2022-04-13 11:42:38 [INFO] [TRAIN] epoch: 154, iter: 57200/60000, loss: 0.8897, lr: 0.000728, batch_cost: 0.8480, reader_cost: 0.00020, ips: 2.3585 samples/sec | ETA 00:39:34
- 2022-04-13 11:43:21 [INFO] [TRAIN] epoch: 154, iter: 57250/60000, loss: 0.8724, lr: 0.000718, batch_cost: 0.8453, reader_cost: 0.00021, ips: 2.3659 samples/sec | ETA 00:38:44
- 2022-04-13 11:44:06 [INFO] [TRAIN] epoch: 155, iter: 57300/60000, loss: 0.8511, lr: 0.000708, batch_cost: 0.8983, reader_cost: 0.04886, ips: 2.2264 samples/sec | ETA 00:40:25
- 2022-04-13 11:44:48 [INFO] [TRAIN] epoch: 155, iter: 57350/60000, loss: 0.8808, lr: 0.000698, batch_cost: 0.8451, reader_cost: 0.00023, ips: 2.3664 samples/sec | ETA 00:37:19
- 2022-04-13 11:45:30 [INFO] [TRAIN] epoch: 155, iter: 57400/60000, loss: 0.8512, lr: 0.000687, batch_cost: 0.8459, reader_cost: 0.00020, ips: 2.3644 samples/sec | ETA 00:36:39
- 2022-04-13 11:46:12 [INFO] [TRAIN] epoch: 155, iter: 57450/60000, loss: 0.8945, lr: 0.000677, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3662 samples/sec | ETA 00:35:55
- 2022-04-13 11:46:55 [INFO] [TRAIN] epoch: 155, iter: 57500/60000, loss: 0.8534, lr: 0.000667, batch_cost: 0.8461, reader_cost: 0.00021, ips: 2.3638 samples/sec | ETA 00:35:15
- 2022-04-13 11:47:37 [INFO] [TRAIN] epoch: 155, iter: 57550/60000, loss: 0.8737, lr: 0.000657, batch_cost: 0.8453, reader_cost: 0.00020, ips: 2.3661 samples/sec | ETA 00:34:30
- 2022-04-13 11:48:19 [INFO] [TRAIN] epoch: 155, iter: 57600/60000, loss: 0.8696, lr: 0.000647, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3655 samples/sec | ETA 00:33:49
- 2022-04-13 11:49:02 [INFO] [TRAIN] epoch: 155, iter: 57650/60000, loss: 0.8474, lr: 0.000636, batch_cost: 0.8455, reader_cost: 0.00020, ips: 2.3656 samples/sec | ETA 00:33:06
- 2022-04-13 11:49:47 [INFO] [TRAIN] epoch: 156, iter: 57700/60000, loss: 0.8642, lr: 0.000626, batch_cost: 0.9050, reader_cost: 0.05211, ips: 2.2098 samples/sec | ETA 00:34:41
- 2022-04-13 11:50:29 [INFO] [TRAIN] epoch: 156, iter: 57750/60000, loss: 0.8223, lr: 0.000616, batch_cost: 0.8460, reader_cost: 0.00022, ips: 2.3639 samples/sec | ETA 00:31:43
- 2022-04-13 11:51:11 [INFO] [TRAIN] epoch: 156, iter: 57800/60000, loss: 0.9023, lr: 0.000605, batch_cost: 0.8464, reader_cost: 0.00020, ips: 2.3629 samples/sec | ETA 00:31:02
- 2022-04-13 11:51:54 [INFO] [TRAIN] epoch: 156, iter: 57850/60000, loss: 0.8794, lr: 0.000595, batch_cost: 0.8456, reader_cost: 0.00020, ips: 2.3652 samples/sec | ETA 00:30:18
- 2022-04-13 11:52:36 [INFO] [TRAIN] epoch: 156, iter: 57900/60000, loss: 0.8819, lr: 0.000585, batch_cost: 0.8484, reader_cost: 0.00023, ips: 2.3573 samples/sec | ETA 00:29:41
- 2022-04-13 11:53:19 [INFO] [TRAIN] epoch: 156, iter: 57950/60000, loss: 0.8835, lr: 0.000574, batch_cost: 0.8466, reader_cost: 0.00022, ips: 2.3625 samples/sec | ETA 00:28:55
- 2022-04-13 11:54:01 [INFO] [TRAIN] epoch: 156, iter: 58000/60000, loss: 0.8555, lr: 0.000564, batch_cost: 0.8484, reader_cost: 0.00023, ips: 2.3573 samples/sec | ETA 00:28:16
- 2022-04-13 11:54:46 [INFO] [TRAIN] epoch: 157, iter: 58050/60000, loss: 0.8494, lr: 0.000553, batch_cost: 0.8983, reader_cost: 0.04587, ips: 2.2263 samples/sec | ETA 00:29:11
- 2022-04-13 11:55:28 [INFO] [TRAIN] epoch: 157, iter: 58100/60000, loss: 0.8583, lr: 0.000543, batch_cost: 0.8460, reader_cost: 0.00023, ips: 2.3640 samples/sec | ETA 00:26:47
- 2022-04-13 11:56:10 [INFO] [TRAIN] epoch: 157, iter: 58150/60000, loss: 0.8535, lr: 0.000532, batch_cost: 0.8464, reader_cost: 0.00019, ips: 2.3628 samples/sec | ETA 00:26:05
- 2022-04-13 11:56:53 [INFO] [TRAIN] epoch: 157, iter: 58200/60000, loss: 0.8626, lr: 0.000522, batch_cost: 0.8469, reader_cost: 0.00025, ips: 2.3615 samples/sec | ETA 00:25:24
- 2022-04-13 11:57:35 [INFO] [TRAIN] epoch: 157, iter: 58250/60000, loss: 0.9117, lr: 0.000511, batch_cost: 0.8458, reader_cost: 0.00024, ips: 2.3646 samples/sec | ETA 00:24:40
- 2022-04-13 11:58:17 [INFO] [TRAIN] epoch: 157, iter: 58300/60000, loss: 0.8534, lr: 0.000501, batch_cost: 0.8457, reader_cost: 0.00020, ips: 2.3650 samples/sec | ETA 00:23:57
- 2022-04-13 11:59:00 [INFO] [TRAIN] epoch: 157, iter: 58350/60000, loss: 0.8791, lr: 0.000490, batch_cost: 0.8470, reader_cost: 0.00021, ips: 2.3613 samples/sec | ETA 00:23:17
- 2022-04-13 11:59:42 [INFO] [TRAIN] epoch: 157, iter: 58400/60000, loss: 0.8636, lr: 0.000480, batch_cost: 0.8446, reader_cost: 0.00021, ips: 2.3679 samples/sec | ETA 00:22:31
- 2022-04-13 12:00:27 [INFO] [TRAIN] epoch: 158, iter: 58450/60000, loss: 0.8262, lr: 0.000469, batch_cost: 0.9021, reader_cost: 0.04334, ips: 2.2170 samples/sec | ETA 00:23:18
- 2022-04-13 12:01:09 [INFO] [TRAIN] epoch: 158, iter: 58500/60000, loss: 0.8506, lr: 0.000458, batch_cost: 0.8468, reader_cost: 0.00027, ips: 2.3618 samples/sec | ETA 00:21:10
- 2022-04-13 12:01:52 [INFO] [TRAIN] epoch: 158, iter: 58550/60000, loss: 0.8405, lr: 0.000447, batch_cost: 0.8481, reader_cost: 0.00021, ips: 2.3582 samples/sec | ETA 00:20:29
- 2022-04-13 12:02:34 [INFO] [TRAIN] epoch: 158, iter: 58600/60000, loss: 0.8231, lr: 0.000437, batch_cost: 0.8469, reader_cost: 0.00024, ips: 2.3614 samples/sec | ETA 00:19:45
- 2022-04-13 12:03:17 [INFO] [TRAIN] epoch: 158, iter: 58650/60000, loss: 0.8556, lr: 0.000426, batch_cost: 0.8471, reader_cost: 0.00024, ips: 2.3610 samples/sec | ETA 00:19:03
- 2022-04-13 12:03:59 [INFO] [TRAIN] epoch: 158, iter: 58700/60000, loss: 0.8616, lr: 0.000415, batch_cost: 0.8463, reader_cost: 0.00023, ips: 2.3633 samples/sec | ETA 00:18:20
- 2022-04-13 12:04:41 [INFO] [TRAIN] epoch: 158, iter: 58750/60000, loss: 0.8871, lr: 0.000404, batch_cost: 0.8467, reader_cost: 0.00020, ips: 2.3621 samples/sec | ETA 00:17:38
- 2022-04-13 12:05:26 [INFO] [TRAIN] epoch: 159, iter: 58800/60000, loss: 0.8538, lr: 0.000393, batch_cost: 0.9056, reader_cost: 0.05159, ips: 2.2085 samples/sec | ETA 00:18:06
- 2022-04-13 12:06:09 [INFO] [TRAIN] epoch: 159, iter: 58850/60000, loss: 0.8385, lr: 0.000382, batch_cost: 0.8463, reader_cost: 0.00023, ips: 2.3633 samples/sec | ETA 00:16:13
- 2022-04-13 12:06:51 [INFO] [TRAIN] epoch: 159, iter: 58900/60000, loss: 0.8581, lr: 0.000371, batch_cost: 0.8479, reader_cost: 0.00020, ips: 2.3587 samples/sec | ETA 00:15:32
- 2022-04-13 12:07:33 [INFO] [TRAIN] epoch: 159, iter: 58950/60000, loss: 0.8846, lr: 0.000360, batch_cost: 0.8462, reader_cost: 0.00025, ips: 2.3636 samples/sec | ETA 00:14:48
- 2022-04-13 12:08:16 [INFO] [TRAIN] epoch: 159, iter: 59000/60000, loss: 0.8604, lr: 0.000349, batch_cost: 0.8461, reader_cost: 0.00023, ips: 2.3637 samples/sec | ETA 00:14:06
- 2022-04-13 12:08:58 [INFO] [TRAIN] epoch: 159, iter: 59050/60000, loss: 0.8647, lr: 0.000337, batch_cost: 0.8462, reader_cost: 0.00023, ips: 2.3636 samples/sec | ETA 00:13:23
- 2022-04-13 12:09:40 [INFO] [TRAIN] epoch: 159, iter: 59100/60000, loss: 0.8454, lr: 0.000326, batch_cost: 0.8447, reader_cost: 0.00021, ips: 2.3677 samples/sec | ETA 00:12:40
- 2022-04-13 12:10:26 [INFO] [TRAIN] epoch: 160, iter: 59150/60000, loss: 0.8346, lr: 0.000315, batch_cost: 0.9038, reader_cost: 0.05394, ips: 2.2129 samples/sec | ETA 00:12:48
- 2022-04-13 12:11:08 [INFO] [TRAIN] epoch: 160, iter: 59200/60000, loss: 0.8738, lr: 0.000304, batch_cost: 0.8478, reader_cost: 0.00024, ips: 2.3590 samples/sec | ETA 00:11:18
- 2022-04-13 12:11:50 [INFO] [TRAIN] epoch: 160, iter: 59250/60000, loss: 0.8765, lr: 0.000292, batch_cost: 0.8506, reader_cost: 0.00020, ips: 2.3513 samples/sec | ETA 00:10:37
- 2022-04-13 12:12:33 [INFO] [TRAIN] epoch: 160, iter: 59300/60000, loss: 0.8572, lr: 0.000280, batch_cost: 0.8464, reader_cost: 0.00020, ips: 2.3629 samples/sec | ETA 00:09:52
- 2022-04-13 12:13:15 [INFO] [TRAIN] epoch: 160, iter: 59350/60000, loss: 0.8628, lr: 0.000269, batch_cost: 0.8467, reader_cost: 0.00020, ips: 2.3622 samples/sec | ETA 00:09:10
- 2022-04-13 12:13:57 [INFO] [TRAIN] epoch: 160, iter: 59400/60000, loss: 0.8501, lr: 0.000257, batch_cost: 0.8470, reader_cost: 0.00020, ips: 2.3611 samples/sec | ETA 00:08:28
- 2022-04-13 12:14:40 [INFO] [TRAIN] epoch: 160, iter: 59450/60000, loss: 0.8539, lr: 0.000245, batch_cost: 0.8487, reader_cost: 0.00027, ips: 2.3567 samples/sec | ETA 00:07:46
- 2022-04-13 12:15:22 [INFO] [TRAIN] epoch: 160, iter: 59500/60000, loss: 0.8639, lr: 0.000233, batch_cost: 0.8469, reader_cost: 0.00027, ips: 2.3614 samples/sec | ETA 00:07:03
- 2022-04-13 12:16:07 [INFO] [TRAIN] epoch: 161, iter: 59550/60000, loss: 0.8396, lr: 0.000221, batch_cost: 0.9044, reader_cost: 0.04502, ips: 2.2114 samples/sec | ETA 00:06:46
- 2022-04-13 12:16:50 [INFO] [TRAIN] epoch: 161, iter: 59600/60000, loss: 0.8616, lr: 0.000209, batch_cost: 0.8468, reader_cost: 0.00024, ips: 2.3617 samples/sec | ETA 00:05:38
- 2022-04-13 12:17:32 [INFO] [TRAIN] epoch: 161, iter: 59650/60000, loss: 0.8786, lr: 0.000197, batch_cost: 0.8457, reader_cost: 0.00020, ips: 2.3650 samples/sec | ETA 00:04:55
- 2022-04-13 12:18:14 [INFO] [TRAIN] epoch: 161, iter: 59700/60000, loss: 0.8397, lr: 0.000184, batch_cost: 0.8468, reader_cost: 0.00020, ips: 2.3619 samples/sec | ETA 00:04:14
- 2022-04-13 12:18:57 [INFO] [TRAIN] epoch: 161, iter: 59750/60000, loss: 0.8439, lr: 0.000172, batch_cost: 0.8460, reader_cost: 0.00020, ips: 2.3641 samples/sec | ETA 00:03:31
- 2022-04-13 12:19:39 [INFO] [TRAIN] epoch: 161, iter: 59800/60000, loss: 0.8566, lr: 0.000159, batch_cost: 0.8463, reader_cost: 0.00024, ips: 2.3631 samples/sec | ETA 00:02:49
- 2022-04-13 12:20:21 [INFO] [TRAIN] epoch: 161, iter: 59850/60000, loss: 0.8421, lr: 0.000145, batch_cost: 0.8457, reader_cost: 0.00022, ips: 2.3649 samples/sec | ETA 00:02:06
- 2022-04-13 12:21:06 [INFO] [TRAIN] epoch: 162, iter: 59900/60000, loss: 0.8532, lr: 0.000132, batch_cost: 0.9014, reader_cost: 0.04808, ips: 2.2188 samples/sec | ETA 00:01:30
- 2022-04-13 12:21:49 [INFO] [TRAIN] epoch: 162, iter: 59950/60000, loss: 0.8625, lr: 0.000117, batch_cost: 0.8516, reader_cost: 0.00028, ips: 2.3487 samples/sec | ETA 00:00:42
- 2022-04-13 12:22:31 [INFO] [TRAIN] epoch: 162, iter: 60000/60000, loss: 0.8606, lr: 0.000100, batch_cost: 0.8475, reader_cost: 0.00028, ips: 2.3598 samples/sec | ETA 00:00:00
- INFO 2022-04-13 12:23:32,924 launch.py:311] Local processes completed.
- 2022-04-13 12:22:31 [INFO] Start evaluating (total_samples: 500, total_iters: 125)...
- 125/125 - 53s - batch_cost: 0.4201 - reader cost: 0.0034
- 2022-04-13 12:23:24 [INFO] [EVAL] #Images: 500 mIoU: 0.8095 Acc: 0.9650 Kappa: 0.9546 Dice: 0.8898
- 2022-04-13 12:23:24 [INFO] [EVAL] Class IoU:
- [0.9853 0.8733 0.9309 0.6023 0.65 0.6354 0.7319 0.8052 0.9284 0.6739
- 0.9507 0.8346 0.6805 0.9574 0.8681 0.92 0.8499 0.7073 0.7955]
- 2022-04-13 12:23:24 [INFO] [EVAL] Class Acc:
- [0.9929 0.9329 0.9589 0.8801 0.8314 0.8149 0.8566 0.9106 0.9563 0.856
- 0.9726 0.8986 0.8066 0.977 0.9343 0.9638 0.9615 0.8463 0.8713]
- 2022-04-13 12:23:29 [INFO] [EVAL] The model with the best validation mIoU (0.8095) was saved at iter 60000.
- <class 'paddle.nn.layer.pooling.AvgPool2D'>'s flops has been counted
- <class 'paddle.nn.layer.conv.Conv2D'>'s flops has been counted
- Customize Function has been applied to <class 'paddle.nn.layer.norm.SyncBatchNorm'>
- <class 'paddle.nn.layer.activation.ReLU'>'s flops has been counted
- Cannot find suitable count function for <class 'paddle.nn.layer.pooling.MaxPool2D'>. Treat it as zero FLOPs.
- Cannot find suitable count function for <class 'paddleseg.models.layers.activation.Activation'>. Treat it as zero FLOPs.
- Cannot find suitable count function for <class 'paddleseg.models.layers.wrap_functions.Add'>. Treat it as zero FLOPs.
- <class 'paddle.nn.layer.common.Dropout'>'s flops has been counted
- <class 'paddle.nn.layer.activation.LeakyReLU'>'s flops has been counted
- Cannot find suitable count function for <class 'paddle.nn.layer.activation.Softmax'>. Treat it as zero FLOPs.
- Total Flops: 639287861680 Total Params: 66562547
- /mnt
- [INFO]: train job success!
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