Skip to content

  • 体验新版
    • 正在加载...
  • 登录
  • PaddlePaddle
  • PaddleSeg
  • Issue
  • #303

P
PaddleSeg
  • 项目概览

PaddlePaddle / PaddleSeg

通知 289
Star 8
Fork 1
  • 代码
    • 文件
    • 提交
    • 分支
    • Tags
    • 贡献者
    • 分支图
    • Diff
  • Issue 53
    • 列表
    • 看板
    • 标记
    • 里程碑
  • 合并请求 3
  • Wiki 0
    • Wiki
  • 分析
    • 仓库
    • DevOps
  • 项目成员
  • Pages
P
PaddleSeg
  • 项目概览
    • 项目概览
    • 详情
    • 发布
  • 仓库
    • 仓库
    • 文件
    • 提交
    • 分支
    • 标签
    • 贡献者
    • 分支图
    • 比较
  • Issue 53
    • Issue 53
    • 列表
    • 看板
    • 标记
    • 里程碑
  • 合并请求 3
    • 合并请求 3
  • Pages
  • 分析
    • 分析
    • 仓库分析
    • DevOps
  • Wiki 0
    • Wiki
  • 成员
    • 成员
  • 收起侧边栏
  • 动态
  • 分支图
  • 创建新Issue
  • 提交
  • Issue看板
已关闭
开放中
Opened 6月 28, 2020 by saxon_zh@saxon_zhGuest

paddleseg项目中的hrnet,出现以下问题,怎么解决

Created by: chang-png

{'AUG': {'AUG_METHOD': 'unpadding', 'FIX_RESIZE_SIZE': (512, 512), 'FLIP': False, 'FLIP_RATIO': 0.5, 'INF_RESIZE_VALUE': 500, 'MAX_RESIZE_VALUE': 600, 'MAX_SCALE_FACTOR': 2.0, 'MIN_RESIZE_VALUE': 400, 'MIN_SCALE_FACTOR': 0.5, 'MIRROR': True, 'RICH_CROP': {'ASPECT_RATIO': 0.33, 'BLUR': False, 'BLUR_RATIO': 0.1, 'BRIGHTNESS_JITTER_RATIO': 0.5, 'CONTRAST_JITTER_RATIO': 0.5, 'ENABLE': True, 'MAX_ROTATION': 15, 'MIN_AREA_RATIO': 0.5, 'SATURATION_JITTER_RATIO': 0.5}, 'SCALE_STEP_SIZE': 0.25}, 'BATCH_SIZE': 2, 'DATALOADER': {'BUF_SIZE': 256, 'NUM_WORKERS': 8}, 'DATASET': {'DATA_DIM': 3, 'DATA_DIR': './dataset/hongguHRNET', 'IGNORE_INDEX': 255, 'IMAGE_TYPE': 'rgb', 'NUM_CLASSES': 30, 'PADDING_VALUE': [127.5, 127.5, 127.5], 'SEPARATOR': ' ', 'TEST_FILE_LIST': './dataset/hongguHRNET/test_list.txt', 'TEST_TOTAL_IMAGES': 772, 'TRAIN_FILE_LIST': './dataset/hongguHRNET/train_list.txt', 'TRAIN_TOTAL_IMAGES': 2317, 'VAL_FILE_LIST': './dataset/hongguHRNET/val_list.txt', 'VAL_TOTAL_IMAGES': 772, 'VIS_FILE_LIST': './dataset/hongguHRNET/test_list.txt'}, 'EVAL_CROP_SIZE': (512, 512), 'FREEZE': {'MODEL_FILENAME': 'model', 'PARAMS_FILENAME': 'params', 'SAVE_DIR': 'freeze_model'}, 'MEAN': [0.5, 0.5, 0.5], 'MODEL': {'BN_MOMENTUM': 0.99, 'DEEPLAB': {'ASPP_WITH_SEP_CONV': True, 'BACKBONE': 'xception_65', 'DECODER_USE_SEP_CONV': True, 'DEPTH_MULTIPLIER': 1.0, 'ENABLE_DECODER': True, 'ENCODER_WITH_ASPP': True, 'OUTPUT_STRIDE': 16}, 'DEFAULT_EPSILON': 1e-05, 'DEFAULT_GROUP_NUMBER': 32, 'DEFAULT_NORM_TYPE': 'bn', 'FP16': False, 'HRNET': {'STAGE2': {'NUM_CHANNELS': [32, 64], 'NUM_MODULES': 1}, 'STAGE3': {'NUM_CHANNELS': [32, 64, 128], 'NUM_MODULES': 4}, 'STAGE4': {'NUM_CHANNELS': [32, 64, 128, 256], 'NUM_MODULES': 3}}, 'ICNET': {'DEPTH_MULTIPLIER': 0.5, 'LAYERS': 50}, 'MODEL_NAME': 'hrnet', 'MULTI_LOSS_WEIGHT': [1.0], 'PSPNET': {'DEPTH_MULTIPLIER': 1, 'LAYERS': 50}, 'SCALE_LOSS': 'DYNAMIC', 'UNET': {'UPSAMPLE_MODE': 'bilinear'}}, 'NUM_TRAINERS': 1, 'SLIM': {'KNOWLEDGE_DISTILL': False, 'KNOWLEDGE_DISTILL_IS_TEACHER': False, 'KNOWLEDGE_DISTILL_TEACHER_MODEL_DIR': '', 'NAS_ADDRESS': '', 'NAS_IS_SERVER': True, 'NAS_PORT': 23333, 'NAS_SEARCH_STEPS': 100, 'NAS_SPACE_NAME': '', 'NAS_START_EVAL_EPOCH': 0, 'PREPROCESS': False, 'PRUNE_PARAMS': '', 'PRUNE_RATIOS': []}, 'SOLVER': {'BEGIN_EPOCH': 1, 'CROSS_ENTROPY_WEIGHT': None, 'DECAY_EPOCH': [10, 20], 'GAMMA': 0.1, 'LOSS': ['softmax_loss'], 'LOSS_WEIGHT': {'BCE_LOSS': 1, 'DICE_LOSS': 1, 'LOVASZ_HINGE_LOSS': 1, 'LOVASZ_SOFTMAX_LOSS': 1, 'SOFTMAX_LOSS': 1}, 'LR': 0.001, 'LR_POLICY': 'poly', 'LR_WARMUP': False, 'LR_WARMUP_STEPS': 2000, 'MOMENTUM': 0.9, 'MOMENTUM2': 0.999, 'NUM_EPOCHS': 2, 'OPTIMIZER': 'adam', 'POWER': 0.9, 'WEIGHT_DECAY': 4e-05}, 'STD': [0.5, 0.5, 0.5], 'TEST': {'TEST_MODEL': './saved_model/hrnet_optic/final'}, 'TRAIN': {'MODEL_SAVE_DIR': './saved_model/hrnet_optic/', 'PRETRAINED_MODEL_DIR': './pretrained_model/hrnet_w18_bn_cityscapes/', 'RESUME_MODEL_DIR': '', 'SNAPSHOT_EPOCH': 1, 'SYNC_BATCH_NORM': False}, 'TRAINER_ID': 0, 'TRAIN_CROP_SIZE': (512, 512)} #Device count: 1 batch_size_per_dev: 2 W0628 19:30:40.148244 981 device_context.cc:252] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 9.2, Runtime API Version: 9.0 W0628 19:30:40.152480 981 device_context.cc:260] device: 0, cuDNN Version: 7.3. Pretrained model dir ./pretrained_model/hrnet_w18_bn_cityscapes/ not exists, training from scratch... Use multi-thread reader epoch=1 step=10 lr=0.00100 loss=1.8163 step/sec=1.999 | ETA 00:19:13 epoch=1 step=20 lr=0.00099 loss=0.3312 step/sec=5.267 | ETA 00:07:15 epoch=1 step=30 lr=0.00099 loss=0.0831 step/sec=5.287 | ETA 00:07:12 epoch=1 step=40 lr=0.00098 loss=0.3121 step/sec=5.156 | ETA 00:07:21 epoch=1 step=50 lr=0.00098 loss=0.5117 step/sec=5.266 | ETA 00:07:10 epoch=1 step=60 lr=0.00098 loss=0.2998 step/sec=5.084 | ETA 00:07:23 epoch=1 step=70 lr=0.00097 loss=0.0360 step/sec=4.989 | ETA 00:07:30 epoch=1 step=80 lr=0.00097 loss=0.1959 step/sec=4.887 | ETA 00:07:37 epoch=1 step=90 lr=0.00097 loss=0.3038 step/sec=5.207 | ETA 00:07:07 epoch=1 step=100 lr=0.00096 loss=0.1172 step/sec=5.181 | ETA 00:07:07 epoch=1 step=110 lr=0.00096 loss=0.0242 step/sec=4.726 | ETA 00:07:46 epoch=1 step=120 lr=0.00095 loss=0.0439 step/sec=4.599 | ETA 00:07:57 epoch=1 step=130 lr=0.00095 loss=0.3059 step/sec=5.155 | ETA 00:07:04 epoch=1 step=140 lr=0.00095 loss=0.4600 step/sec=5.293 | ETA 00:06:51 epoch=1 step=150 lr=0.00094 loss=0.3119 step/sec=5.054 | ETA 00:07:08 epoch=1 step=160 lr=0.00094 loss=0.0946 step/sec=4.800 | ETA 00:07:29 epoch=1 step=170 lr=0.00093 loss=0.0491 step/sec=5.027 | ETA 00:07:06 epoch=1 step=180 lr=0.00093 loss=0.2321 step/sec=5.330 | ETA 00:06:40 epoch=1 step=190 lr=0.00093 loss=0.2925 step/sec=4.854 | ETA 00:07:17 epoch=1 step=200 lr=0.00092 loss=0.2853 step/sec=4.964 | ETA 00:07:06 epoch=1 step=210 lr=0.00092 loss=0.2620 step/sec=5.199 | ETA 00:06:45 epoch=1 step=220 lr=0.00091 loss=0.2789 step/sec=5.323 | ETA 00:06:33 epoch=1 step=230 lr=0.00091 loss=0.2395 step/sec=5.179 | ETA 00:06:42 epoch=1 step=240 lr=0.00091 loss=0.0829 step/sec=5.421 | ETA 00:06:22 epoch=1 step=250 lr=0.00090 loss=0.3178 step/sec=5.494 | ETA 00:06:16 epoch=1 step=260 lr=0.00090 loss=0.0716 step/sec=5.501 | ETA 00:06:13 epoch=1 step=270 lr=0.00089 loss=0.2129 step/sec=5.517 | ETA 00:06:10 epoch=1 step=280 lr=0.00089 loss=0.2477 step/sec=5.573 | ETA 00:06:05 epoch=1 step=290 lr=0.00089 loss=0.3050 step/sec=4.993 | ETA 00:06:45 epoch=1 step=300 lr=0.00088 loss=0.2109 step/sec=4.979 | ETA 00:06:44 epoch=1 step=310 lr=0.00088 loss=0.0884 step/sec=5.377 | ETA 00:06:13 epoch=1 step=320 lr=0.00088 loss=0.1586 step/sec=5.015 | ETA 00:06:38 epoch=1 step=330 lr=0.00087 loss=0.1708 step/sec=5.033 | ETA 00:06:34 epoch=1 step=340 lr=0.00087 loss=0.2549 step/sec=4.560 | ETA 00:07:13 epoch=1 step=350 lr=0.00086 loss=0.2124 step/sec=4.593 | ETA 00:07:08 epoch=1 step=360 lr=0.00086 loss=0.1506 step/sec=4.632 | ETA 00:07:02 epoch=1 step=370 lr=0.00086 loss=0.0837 step/sec=5.515 | ETA 00:05:52 epoch=1 step=380 lr=0.00085 loss=0.3997 step/sec=5.341 | ETA 00:06:02 epoch=1 step=390 lr=0.00085 loss=0.1531 step/sec=5.300 | ETA 00:06:03 epoch=1 step=400 lr=0.00084 loss=0.0563 step/sec=5.238 | ETA 00:06:05 epoch=1 step=410 lr=0.00084 loss=0.1846 step/sec=5.344 | ETA 00:05:56 epoch=1 step=420 lr=0.00084 loss=0.1685 step/sec=5.201 | ETA 00:06:04 epoch=1 step=430 lr=0.00083 loss=0.2323 step/sec=5.364 | ETA 00:05:51 epoch=1 step=440 lr=0.00083 loss=0.1423 step/sec=5.428 | ETA 00:05:45 epoch=1 step=450 lr=0.00082 loss=0.4865 step/sec=5.043 | ETA 00:06:10 epoch=1 step=460 lr=0.00082 loss=0.2687 step/sec=5.324 | ETA 00:05:48 epoch=1 step=470 lr=0.00082 loss=0.0987 step/sec=5.046 | ETA 00:06:05 epoch=1 step=480 lr=0.00081 loss=0.2082 step/sec=5.269 | ETA 00:05:48 epoch=1 step=490 lr=0.00081 loss=0.2386 step/sec=5.502 | ETA 00:05:31 epoch=1 step=500 lr=0.00080 loss=0.3380 step/sec=5.510 | ETA 00:05:29 epoch=1 step=510 lr=0.00080 loss=0.3068 step/sec=5.513 | ETA 00:05:27 epoch=1 step=520 lr=0.00080 loss=0.2105 step/sec=5.412 | ETA 00:05:31 epoch=1 step=530 lr=0.00079 loss=0.2915 step/sec=5.391 | ETA 00:05:31 epoch=1 step=540 lr=0.00079 loss=0.1835 step/sec=5.384 | ETA 00:05:29 epoch=1 step=550 lr=0.00078 loss=0.2699 step/sec=5.254 | ETA 00:05:36 epoch=1 step=560 lr=0.00078 loss=0.1210 step/sec=5.078 | ETA 00:05:45 epoch=1 step=570 lr=0.00078 loss=0.1623 step/sec=5.164 | ETA 00:05:38 epoch=1 step=580 lr=0.00077 loss=0.1774 step/sec=5.444 | ETA 00:05:18 epoch=1 step=590 lr=0.00077 loss=0.2081 step/sec=5.479 | ETA 00:05:15 epoch=1 step=600 lr=0.00076 loss=0.2097 step/sec=5.336 | ETA 00:05:21 epoch=1 step=610 lr=0.00076 loss=0.2646 step/sec=5.413 | ETA 00:05:15 epoch=1 step=620 lr=0.00076 loss=0.1707 step/sec=5.412 | ETA 00:05:13 epoch=1 step=630 lr=0.00075 loss=0.2040 step/sec=5.393 | ETA 00:05:12 epoch=1 step=640 lr=0.00075 loss=0.1695 step/sec=5.425 | ETA 00:05:08 epoch=1 step=650 lr=0.00074 loss=0.2297 step/sec=5.537 | ETA 00:05:00 epoch=1 step=660 lr=0.00074 loss=0.0697 step/sec=5.231 | ETA 00:05:16 epoch=1 step=670 lr=0.00074 loss=0.1947 step/sec=5.392 | ETA 00:05:05 epoch=1 step=680 lr=0.00073 loss=0.1198 step/sec=5.136 | ETA 00:05:18 epoch=1 step=690 lr=0.00073 loss=0.2839 step/sec=5.338 | ETA 00:05:04 epoch=1 step=700 lr=0.00072 loss=0.0373 step/sec=5.415 | ETA 00:04:58 epoch=1 step=710 lr=0.00072 loss=0.2493 step/sec=5.475 | ETA 00:04:53 epoch=1 step=720 lr=0.00072 loss=0.1024 step/sec=5.315 | ETA 00:05:00 epoch=1 step=730 lr=0.00071 loss=0.2814 step/sec=5.405 | ETA 00:04:53 epoch=1 step=740 lr=0.00071 loss=0.3725 step/sec=5.491 | ETA 00:04:47 epoch=1 step=750 lr=0.00070 loss=0.2068 step/sec=5.443 | ETA 00:04:47 epoch=1 step=760 lr=0.00070 loss=0.0839 step/sec=5.409 | ETA 00:04:47 epoch=1 step=770 lr=0.00070 loss=0.1149 step/sec=5.441 | ETA 00:04:44 epoch=1 step=780 lr=0.00069 loss=0.2186 step/sec=5.449 | ETA 00:04:41 epoch=1 step=790 lr=0.00069 loss=0.1558 step/sec=5.358 | ETA 00:04:44 epoch=1 step=800 lr=0.00068 loss=0.1774 step/sec=5.440 | ETA 00:04:38 epoch=1 step=810 lr=0.00068 loss=0.1653 step/sec=5.441 | ETA 00:04:36 epoch=1 step=820 lr=0.00068 loss=0.1685 step/sec=5.320 | ETA 00:04:41 epoch=1 step=830 lr=0.00067 loss=0.2406 step/sec=5.330 | ETA 00:04:38 epoch=1 step=840 lr=0.00067 loss=0.2114 step/sec=4.642 | ETA 00:05:17 epoch=1 step=850 lr=0.00066 loss=0.0567 step/sec=4.909 | ETA 00:04:58 epoch=1 step=860 lr=0.00066 loss=0.2362 step/sec=4.899 | ETA 00:04:57 epoch=1 step=870 lr=0.00066 loss=0.2638 step/sec=4.954 | ETA 00:04:51 epoch=1 step=880 lr=0.00065 loss=0.2902 step/sec=4.766 | ETA 00:05:01 epoch=1 step=890 lr=0.00065 loss=0.1391 step/sec=4.997 | ETA 00:04:45 epoch=1 step=900 lr=0.00064 loss=0.2761 step/sec=5.202 | ETA 00:04:32 epoch=1 step=910 lr=0.00064 loss=0.2664 step/sec=5.476 | ETA 00:04:16 epoch=1 step=920 lr=0.00063 loss=0.1213 step/sec=5.614 | ETA 00:04:08 epoch=1 step=930 lr=0.00063 loss=0.2519 step/sec=5.749 | ETA 00:04:01 epoch=1 step=940 lr=0.00063 loss=0.3305 step/sec=5.545 | ETA 00:04:08 epoch=1 step=950 lr=0.00062 loss=0.1392 step/sec=5.580 | ETA 00:04:04 epoch=1 step=960 lr=0.00062 loss=0.3605 step/sec=5.415 | ETA 00:04:10 epoch=1 step=970 lr=0.00061 loss=0.1370 step/sec=5.743 | ETA 00:03:54 epoch=1 step=980 lr=0.00061 loss=0.1300 step/sec=5.769 | ETA 00:03:51 epoch=1 step=990 lr=0.00061 loss=0.1200 step/sec=5.744 | ETA 00:03:50 epoch=1 step=1000 lr=0.00060 loss=0.1760 step/sec=5.737 | ETA 00:03:49 epoch=1 step=1010 lr=0.00060 loss=0.1097 step/sec=5.438 | ETA 00:04:00 epoch=1 step=1020 lr=0.00059 loss=0.3858 step/sec=5.653 | ETA 00:03:49 epoch=1 step=1030 lr=0.00059 loss=0.1992 step/sec=5.750 | ETA 00:03:43 epoch=1 step=1040 lr=0.00059 loss=0.3470 step/sec=5.794 | ETA 00:03:40 epoch=1 step=1050 lr=0.00058 loss=0.3766 step/sec=5.709 | ETA 00:03:41 epoch=1 step=1060 lr=0.00058 loss=0.2608 step/sec=5.544 | ETA 00:03:46 epoch=1 step=1070 lr=0.00057 loss=0.0838 step/sec=5.548 | ETA 00:03:44 epoch=1 step=1080 lr=0.00057 loss=0.2124 step/sec=5.476 | ETA 00:03:45 epoch=1 step=1090 lr=0.00057 loss=0.2297 step/sec=5.793 | ETA 00:03:31 epoch=1 step=1100 lr=0.00056 loss=0.2067 step/sec=5.748 | ETA 00:03:31 epoch=1 step=1110 lr=0.00056 loss=0.0893 step/sec=5.695 | ETA 00:03:31 epoch=1 step=1120 lr=0.00055 loss=0.4034 step/sec=5.703 | ETA 00:03:29 epoch=1 step=1130 lr=0.00055 loss=0.1206 step/sec=5.772 | ETA 00:03:25 epoch=1 step=1140 lr=0.00054 loss=0.2643 step/sec=5.707 | ETA 00:03:26 epoch=1 step=1150 lr=0.00054 loss=0.2179 step/sec=5.586 | ETA 00:03:28 Save model checkpoint to ./saved_model/hrnet_optic/1 Evaluation start #Device count: 1 load test model: ./saved_model/hrnet_optic/1 Traceback (most recent call last): File "./pdseg/train.py", line 466, in main(args) File "./pdseg/train.py", line 453, in main train(cfg) File "./pdseg/train.py", line 409, in train use_gpu=args.use_gpu) File "/home/aistudio/pdseg/eval.py", line 140, in evaluate conf_mat.calculate(pred, grts, masks) File "/home/aistudio/pdseg/metrics.py", line 47, in calculate shape=(self.num_classes, self.num_classes)) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/scipy/sparse/compressed.py", line 57, in init other = self.class(coo_matrix(arg1, shape=shape)) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/scipy/sparse/coo.py", line 198, in init self._check() File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/scipy/sparse/coo.py", line 285, in _check raise ValueError('row index exceeds matrix dimensions') ValueError: row index exceeds matrix dimensions

指派人
分配到
无
里程碑
无
分配里程碑
工时统计
无
截止日期
无
标识: paddlepaddle/PaddleSeg#303
渝ICP备2023009037号

京公网安备11010502055752号

网络110报警服务 Powered by GitLab CE v13.7
开源知识
Git 入门 Pro Git 电子书 在线学 Git
Markdown 基础入门 IT 技术知识开源图谱
帮助
使用手册 反馈建议 博客
《GitCode 隐私声明》 《GitCode 服务条款》 关于GitCode
Powered by GitLab CE v13.7