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20 hours ago •
whai362
whai362
About the segmentation and detection. I use the imagenet-1k pretrained model to segmentation and detection task. but the mIoU I get is very low
I use pvt_tiny for this two jobs. But the mIoU is very low. we train with this script on two rtx3090, I just change the gpu_multiples to 1:
_base_ = [
'../../_base_/models/fpn_r50.py',
'../../_base_/datasets/ade20k.py',
'../../_base_/default_runtime.py'
]
# model settings
model = dict(
type='EncoderDecoder',
# pretrained='pretrained/pvt_tiny.pth',
pretrained='/home/host/dist1/zx/classification/checkpoints/pvt_tiny/checkpoint.pth',
backbone=dict(
type='pvt_tiny',
style='pytorch'),
neck=dict(in_channels=[64, 128, 320, 512]),
decode_head=dict(num_classes=150))
gpu_multiples = 1 # we use 8 gpu instead of 4 in mmsegmentation, so lr*2 and max_iters/2
# optimizeiiiiiir
optimizer = dict(type='AdamW', lr=0.0001*gpu_multiples, weight_decay=0.0001)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=0.0, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=80000//gpu_multiples)
checkpoint_config = dict(by_epoch=False, interval=8000//gpu_multiples)
evaluation = dict(interval=8000//gpu_multiples, metric='mIoU')
the part of log I get :
Class | IoU | Acc |
+---------------------+-------+-------+
| wall | 54.7 | 81.83 |
| building | 67.78 | 89.28 |
| sky | 89.7 | 94.46 |
| floor | 57.27 | 73.54 |
| tree | 58.49 | 78.73 |
| ceiling | 63.11 | 73.05 |
| road | 64.98 | 79.87 |
| bed | 48.05 | 77.07 |
| windowpane | 40.18 | 60.33 |
| grass | 57.12 | 75.26 |
| cabinet | 32.02 | 46.5 |
| sidewalk | 41.35 | 63.2 |
| person | 40.43 | 64.08 |
| earth | 27.29 | 36.52 |
| door | 5.95 | 6.61 |
| table | 18.18 | 30.23 |
| mountain | 31.44 | 51.38 |
| plant | 33.12 | 43.71 |
| curtain | 38.04 | 52.99 |
| chair | 21.51 | 36.51 |
| car | 51.0 | 71.94 |
| water | 29.28 | 36.67 |
| painting | 31.18 | 51.95 |
| sofa | 24.42 | 43.53 |
| shelf | 13.72 | 23.72 |
| house | 28.35 | 42.56 |
| sea | 42.37 | 68.75 |
| mirror | 11.43 | 14.2 |
| rug | 23.17 | 27.49 |
| field | 22.71 | 47.06 |
| armchair | 0.78 | 0.84 |
| seat | 25.22 | 31.6 |
| fence | 4.03 | 4.5 |
| desk | 10.89 | 15.5 |
| rock | 16.8 | 28.19 |
| wardrobe | 7.48 | 7.94 |
| lamp | 14.81 | 19.2 |
| bathtub | 13.31 | 16.23 |
| railing | 5.92 | 6.01 |
| cushion | 13.75 | 20.18 |
| base | 0.55 | 0.62 |
| box | 1.56 | 1.72 |
| column | 0.0 | 0.0 |
| signboard | 4.54 | 4.79 |
| chest of drawers | 12.02 | 17.29 |
| counter | 0.69 | 0.7 |
| sand | 14.07 | 21.83 |
| sink | 20.21 | 35.94 |
| skyscraper | 31.83 | 37.65 |
| fireplace | 26.12 | 43.07 |
| refrigerator | 18.92 | 23.57 |
| grandstand | 21.16 | 30.05 |
| path | 10.79 | 12.48 |
| stairs | 10.18 | 11.39 |
| runway | 48.93 | 73.64 |
| case | 24.5 | 49.33 |
| pool table | 50.19 | 63.41 |
| pillow | 18.73 | 23.37 |
| screen door | 17.06 | 19.77 |
| stairway | 5.3 | 7.01 |
| river | 9.48 | 16.44 |
| bridge | 0.28 | 0.29 |
| bookcase | 6.67 | 8.51 |
| blind | 1.8 | 1.84 |
| coffee table | 14.99 | 27.61 |
| toilet | 21.48 | 47.07 |
| flower | 14.28 | 23.6 |
| book | 15.55 | 18.32 |
| hill | 3.46 | 3.74 |
| bench | 5.57 | 5.82 |
| countertop | 7.53 | 9.42 |
| stove | 18.68 | 36.78 |
| palm | 10.27 | 11.79 |
| kitchen island | 15.96 | 18.7 |
| computer | 6.35 | 8.5 |
| swivel chair | 12.05 | 20.8 |
| boat | 5.43 | 6.27 |
| bar | 0.04 | 0.04 |
| arcade machine | 5.5 | 6.23 |
| hovel | 3.51 | 4.12 |
| bus | 6.68 | 7.19 |
| towel | 2.35 | 2.37 |
| light | 10.98 | 11.26 |
| truck | 0.1 | 0.13 |
| tower | 10.42 | 10.66 |
| chandelier | 29.94 | 50.31 |
| awning | 0.35 | 0.38 |
| streetlight | 0.28 | 0.28 |
| booth | 0.0 | 0.0 |
| television receiver | 17.11 | 24.19 |
| airplane | 31.53 | 39.65 |
| dirt track | 0.0 | 0.0 |
| apparel | 4.78 | 5.66 |
| pole | 1.96 | 2.17 |
| land | 0.0 | 0.0 |
| bannister | 0.0 | 0.0 |
| escalator | 0.0 | 0.0 |
| ottoman | 0.0 | 0.0 |
| bottle | 0.0 | 0.0 |
| buffet | 0.0 | 0.0 |
| poster | 0.0 | 0.0 |
| stage | 0.0 | 0.0 |
| van | 0.4 | 0.4 |
| ship | 0.0 | 0.0 |
| fountain | 0.0 | 0.0 |
| conveyer belt | 0.0 | 0.0 |
| canopy | 0.0 | 0.0 |
| washer | 0.28 | 0.28 |
| plaything | 0.5 | 0.54 |
| swimming pool | 24.38 | 42.56 |
| stool | 0.0 | 0.0 |
| barrel | 0.0 | 0.0 |
| basket | 0.0 | 0.0 |
| waterfall | 47.73 | 70.95 |
| tent | 31.71 | 47.17 |
| bag | 0.0 | 0.0 |
| minibike | 10.11 | 11.68 |
| cradle | 29.79 | 67.07 |
| oven | 0.0 | 0.0 |
| ball | 0.17 | 0.38 |
| food | 27.43 | 37.67 |
| step | 0.0 | 0.0 |
| tank | 0.0 | 0.0 |
| trade name | 3.35 | 3.46 |
| microwave | 10.61 | 12.33 |
| pot | 0.0 | 0.0 |
| animal | 1.39 | 1.51 |
| bicycle | 0.0 | 0.0 |
| lake | 2.98 | 3.54 |
| dishwasher | 2.75 | 2.79 |
| screen | 21.23 | 22.22 |
| blanket | 0.0 | 0.0 |
| sculpture | 0.0 | 0.0 |
| hood | 3.97 | 5.03 |
| sconce | 1.98 | 2.02 |
| vase | 2.1 | 2.49 |
| traffic light | 0.03 | 0.03 |
| tray | 0.0 | 0.0 |
| ashcan | 0.0 | 0.0 |
| fan | 12.51 | 14.54 |
| pier | 0.68 | 0.68 |
| crt screen | 0.0 | 0.0 |
| plate | 0.0 | 0.0 |
| monitor | 0.0 | 0.0 |
| bulletin board | 0.0 | 0.0 |
| shower | 0.0 | 0.0 |
| radiator | 0.0 | 0.0 |
| glass | 0.0 | 0.0 |
| clock | 0.0 | 0.0 |
| flag | 0.0 | 0.0 |
+---------------------+-------+-------+
2021-12-23 16:21:00,788 - mmseg - INFO - Summary:
2021-12-23 16:21:00,789 - mmseg - INFO -
+-------+-------+-------+
| aAcc | mIoU | mAcc |
+-------+-------+-------+
| 65.06 | 14.33 | 20.34 |
+-------+-------+-------+
2021-12-23 16:21:00,804 - mmseg - INFO - Exp name: fpn_pvt_t_ade20k_40k.py
2021-12-23 16:21:00,805 - mmseg - INFO - Iter(val) [1000] aAcc: 0.6506, mIoU: 0.1433, mAcc: 0.2034, IoU.wall: 0.5470, IoU.building: 0.6778, IoU.sky: 0.8970, IoU.floor: 0.5727, IoU.tree: 0.5849, IoU.ceiling: 0.6311, IoU.road: 0.6498, IoU.bed : 0.4805, IoU.windowpane: 0.4018, IoU.grass: 0.5712, IoU.cabinet: 0.3202, IoU.sidewalk: 0.4135, IoU.person: 0.4043, IoU.earth: 0.2729, IoU.door: 0.0595, IoU.table: 0.1818, IoU.mountain: 0.3144, IoU.plant: 0.3312, IoU.curtain: 0.3804, IoU.chair: 0.2151, IoU.car: 0.5100, IoU.water: 0.2928, IoU.painting: 0.3118, IoU.sofa: 0.2442, IoU.shelf: 0.1372, IoU.house: 0.2835, IoU.sea: 0.4237, IoU.mirror: 0.1143, IoU.rug: 0.2317, IoU.field: 0.2271, IoU.armchair: 0.0078, IoU.seat: 0.2522, IoU.fence: 0.0403, IoU.desk: 0.1089, IoU.rock: 0.1680, IoU.wardrobe: 0.0748, IoU.lamp: 0.1481, IoU.bathtub: 0.1331, IoU.railing: 0.0592, IoU.cushion: 0.1375, IoU.base: 0.0055, IoU.box: 0.0156, IoU.column: 0.0000, IoU.signboard: 0.0454, IoU.chest of drawers: 0.1202, IoU.counter: 0.0069, IoU.sand: 0.1407, IoU.sink: 0.2021, IoU.skyscraper: 0.3183, IoU.fireplace: 0.2612, IoU.refrigerator: 0.1892, IoU.grandstand: 0.2116, IoU.path: 0.1079, IoU.stairs: 0.1018, IoU.runway: 0.4893, IoU.case: 0.2450, IoU.pool table: 0.5019, IoU.pillow: 0.1873, IoU.screen door: 0.1706, IoU.stairway: 0.0530, IoU.river: 0.0948, IoU.bridge: 0.0028, IoU.bookcase: 0.0667, IoU.blind: 0.0180, IoU.coffee table: 0.1499, IoU.toilet: 0.2148, IoU.flower: 0.1428, IoU.book: 0.1555, IoU.hill: 0.0346, IoU.bench: 0.0557, IoU.countertop: 0.0753, IoU.stove: 0.1868, IoU.palm: 0.1027, IoU.kitchen island: 0.1596, IoU.computer: 0.0635, IoU.swivel chair: 0.1205, IoU.boat: 0.0543, IoU.bar: 0.0004, IoU.arcade machine: 0.0550, IoU.hovel: 0.0351, IoU.bus: 0.0668, IoU.towel: 0.0235, IoU.light: 0.1098, IoU.truck: 0.0010, IoU.tower: 0.1042, IoU.chandelier: 0.2994, IoU.awning: 0.0035, IoU.streetlight: 0.0028, IoU.booth: 0.0000, IoU.television receiver: 0.1711, IoU.airplane: 0.3153, IoU.dirt track: 0.0000, IoU.apparel: 0.0478, IoU.pole: 0.0196, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.0000, IoU.ottoman: 0.0000, IoU.bottle: 0.0000, IoU.buffet: 0.0000, IoU.poster: 0.0000, IoU.stage: 0.0000, IoU.van: 0.0040, IoU.ship: 0.0000, IoU.fountain: 0.0000, IoU.conveyer belt: 0.0000, IoU.canopy: 0.0000, IoU.washer: 0.0028, IoU.plaything: 0.0050, IoU.swimming pool: 0.2438, IoU.stool: 0.0000, IoU.barrel: 0.0000, IoU.basket: 0.0000, IoU.waterfall: 0.4773, IoU.tent: 0.3171, IoU.bag: 0.0000, IoU.minibike: 0.1011, IoU.cradle: 0.2979, IoU.oven: 0.0000, IoU.ball: 0.0017, IoU.food: 0.2743, IoU.step: 0.0000, IoU.tank: 0.0000, IoU.trade name: 0.0335, IoU.microwave: 0.1061, IoU.pot: 0.0000, IoU.animal: 0.0139, IoU.bicycle: 0.0000, IoU.lake: 0.0298, IoU.dishwasher: 0.0275, IoU.screen: 0.2123, IoU.blanket: 0.0000, IoU.sculpture: 0.0000, IoU.hood: 0.0397, IoU.sconce: 0.0198, IoU.vase: 0.0210, IoU.traffic light: 0.0003, IoU.tray: 0.0000, IoU.ashcan: 0.0000, IoU.fan: 0.1251, IoU.pier: 0.0068, IoU.crt screen: 0.0000, IoU.plate: 0.0000, IoU.monitor: 0.0000, IoU.bulletin board: 0.0000, IoU.shower: 0.0000, IoU.radiator: 0.0000, IoU.glass: 0.0000, IoU.clock: 0.0000, IoU.flag: 0.0000, Acc.wall: 0.8183, Acc.building: 0.8928, Acc.sky: 0.9446, Acc.floor: 0.7354, Acc.tree: 0.7873, Acc.ceiling: 0.7305, Acc.road: 0.7987, Acc.bed : 0.7707, Acc.windowpane: 0.6033, Acc.grass: 0.7526, Acc.cabinet: 0.4650, Acc.sidewalk: 0.6320, Acc.person: 0.6408, Acc.earth: 0.3652, Acc.door: 0.0661, Acc.table: 0.3023, Acc.mountain: 0.5138, Acc.plant: 0.4371, Acc.curtain: 0.5299, Acc.chair: 0.3651, Acc.car: 0.7194, Acc.water: 0.3667, Acc.painting: 0.5195, Acc.sofa: 0.4353, Acc.shelf: 0.2372, Acc.house: 0.4256, Acc.sea: 0.6875, Acc.mirror: 0.1420, Acc.rug: 0.2749, Acc.field: 0.4706, Acc.armchair: 0.0084, Acc.seat: 0.3160, Acc.fence: 0.0450, Acc.desk: 0.1550, Acc.rock: 0.2819, Acc.wardrobe: 0.0794, Acc.lamp: 0.1920, Acc.bathtub: 0.1623, Acc.railing: 0.0601, Acc.cushion: 0.2018, Acc.base: 0.0062, Acc.box: 0.0172, Acc.column: 0.0000, Acc.signboard: 0.0479, Acc.chest of drawers: 0.1729, Acc.counter: 0.0070, Acc.sand: 0.2183, Acc.sink: 0.3594, Acc.skyscraper: 0.3765, Acc.fireplace: 0.4307, Acc.refrigerator: 0.2357, Acc.grandstand: 0.3005, Acc.path: 0.1248, Acc.stairs: 0.1139, Acc.runway: 0.7364, Acc.case: 0.4933, Acc.pool table: 0.6341, Acc.pillow: 0.2337, Acc.screen door: 0.1977, Acc.stairway: 0.0701, Acc.river: 0.1644, Acc.bridge: 0.0029, Acc.bookcase: 0.0851, Acc.blind: 0.0184, Acc.coffee table: 0.2761, Acc.toilet: 0.4707, Acc.flower: 0.2360, Acc.book: 0.1832, Acc.hill: 0.0374, Acc.bench: 0.0582, Acc.countertop: 0.0942, Acc.stove: 0.3678, Acc.palm: 0.1179, Acc.kitchen island: 0.1870, Acc.computer: 0.0850, Acc.swivel chair: 0.2080, Acc.boat: 0.0627, Acc.bar: 0.0004, Acc.arcade machine: 0.0623, Acc.hovel: 0.0412, Acc.bus: 0.0719, Acc.towel: 0.0237, Acc.light: 0.1126, Acc.truck: 0.0013, Acc.tower: 0.1066, Acc.chandelier: 0.5031, Acc.awning: 0.0038, Acc.streetlight: 0.0028, Acc.booth: 0.0000, Acc.television receiver: 0.2419, Acc.airplane: 0.3965, Acc.dirt track: 0.0000, Acc.apparel: 0.0566, Acc.pole: 0.0217, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.0000, Acc.ottoman: 0.0000, Acc.bottle: 0.0000, Acc.buffet: 0.0000, Acc.poster: 0.0000, Acc.stage: 0.0000, Acc.van: 0.0040, Acc.ship: 0.0000, Acc.fountain: 0.0000, Acc.conveyer belt: 0.0000, Acc.canopy: 0.0000, Acc.washer: 0.0028, Acc.plaything: 0.0054, Acc.swimming pool: 0.4256, Acc.stool: 0.0000, Acc.barrel: 0.0000, Acc.basket: 0.0000, Acc.waterfall: 0.7095, Acc.tent: 0.4717, Acc.bag: 0.0000, Acc.minibike: 0.1168, Acc.cradle: 0.6707, Acc.oven: 0.0000, Acc.ball: 0.0038, Acc.food: 0.3767, Acc.step: 0.0000, Acc.tank: 0.0000, Acc.trade name: 0.0346, Acc.microwave: 0.1233, Acc.pot: 0.0000, Acc.animal: 0.0151, Acc.bicycle: 0.0000, Acc.lake: 0.0354, Acc.dishwasher: 0.0279, Acc.screen: 0.2222, Acc.blanket: 0.0000, Acc.sculpture: 0.0000, Acc.hood: 0.0503, Acc.sconce: 0.0202, Acc.vase: 0.0249, Acc.traffic light: 0.0003, Acc.tray: 0.0000, Acc.ashcan: 0.0000, Acc.fan: 0.1454, Acc.pier: 0.0068, Acc.crt screen: 0.0000, Acc.plate: 0.0000, Acc.monitor: 0.0000, Acc.bulletin board: 0.0000, Acc.shower: 0.0000, Acc.radiator: 0.0000, Acc.glass: 0.0000, Acc.clock: 0.0000, Acc.flag: 0.0000
2021-12-23 16:21:17,443 - mmseg - INFO - Iter [64050/80000] lr: 2.343e-05, eta: 1:26:26, time: 2.451, data_time: 2.127, memory: 10848, decode.loss_ce: 1.1673, decode.acc_seg: 52.5561, loss: 1.1673
2021-12-23 16:21:34,105 - mmseg - INFO - Iter [64100/80000] lr: 2.336e-05, eta: 1:26:10, time: 0.333, data_time: 0.007, memory: 10848, decode.loss_ce: 1.1150, decode.acc_seg: 48.8460, loss: 1.1150
2021-12-23 16:21:50,267 - mmseg - INFO - Iter [64150/80000] lr: 2.330e-05, eta: 1:25:53, time: 0.323, data_time: 0.008, memory: 10848, decode.loss_ce: 1.1292, decode.acc_seg: 48.7623, loss: 1.1292
2021-12-23 16:22:07,223 - mmseg - INFO - Iter [64200/80000] lr: 2.323e-05, eta: 1:25:37, time: 0.339, data_time: 0.007, memory: 10848, decode.loss_ce: 1.1834, decode.acc_seg: 49.9436, loss: 1.1834
2021-12-23 16:22:23,802 - mmseg - INFO - Iter [64250/80000] lr: 2.316e-05, eta: 1:25:21, time: 0.331, data_time: 0.008, memory: 10848, decode.loss_ce: 1.1725, decode.acc_seg: 49.8978, loss: 1.1725
2021-12-23 16:22:40,176 - mmseg - INFO - Iter [64300/80000] lr: 2.310e-05, eta: 1:25:05, time: 0.327, data_time: 0.008, memory: 10848, decode.loss_ce: 1.1656, decode.acc_seg: 50.8126, loss: 1.1656
could you help me to fix this issue
the start of log:
2021-12-23 10:33:49,393 - mmseg - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:39:48) [GCC 9.3.0]
CUDA available: True
GPU 0,1: NVIDIA GeForce RTX 3090
CUDA_HOME: None
GCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0
PyTorch: 1.8.0
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2021.3-Product Build 20210617 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.1
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.0.5
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,
TorchVision: 0.2.1
OpenCV: 4.5.3
MMCV: 1.3.18
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.1
MMSegmentation: 0.19.0+7a1c9a5
------------------------------------------------------------
2021-12-23 10:33:49,394 - mmseg - INFO - Distributed training: True
2021-12-23 10:33:49,597 - mmseg - INFO - Config:
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=
'/home/host/dist1/zx/classification/checkpoints/pvt_tiny/checkpoint.pth',
backbone=dict(
type='pvt_tiny',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 1, 1),
strides=(1, 2, 2, 2),
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
style='pytorch',
contract_dilation=True),
neck=dict(
type='FPN',
in_channels=[64, 128, 320, 512],
out_channels=256,
num_outs=4),
decode_head=dict(
type='FPNHead',
in_channels=[256, 256, 256, 256],
in_index=[0, 1, 2, 3],
feature_strides=[8, 8, 16, 32],
channels=128,
dropout_ratio=0.1,
num_classes=150,
norm_cfg=dict(type='SyncBN', requires_grad=True),
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
train_cfg=dict(),
test_cfg=dict(mode='whole'))
dataset_type = 'ADE20KDataset'
data_root = 'data/ADEChallengeData2016'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 512),
flip=False,
transforms=[
dict(type='AlignResize', keep_ratio=True, size_divisor=32),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=50,
dataset=dict(
type='ADE20KDataset',
data_root='data/ADEChallengeData2016',
img_dir='images/training',
ann_dir='annotations/training',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(
type='Resize',
img_scale=(2048, 512),
ratio_range=(0.5, 2.0)),
dict(
type='RandomCrop',
crop_size=(512, 512),
cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
])),
val=dict(
type='ADE20KDataset',
data_root='data/ADEChallengeData2016',
img_dir='images/validation',
ann_dir='annotations/validation',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 512),
flip=False,
transforms=[
dict(type='AlignResize', keep_ratio=True, size_divisor=32),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='ADE20KDataset',
data_root='data/ADEChallengeData2016',
img_dir='images/validation',
ann_dir='annotations/validation',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 512),
flip=False,
transforms=[
dict(type='AlignResize', keep_ratio=True, size_divisor=32),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
log_config = dict(
interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
gpu_multiples = 1
optimizer = dict(type='AdamW', lr=0.0001, weight_decay=0.0001)
optimizer_config = dict()
lr_config = dict(policy='poly', power=0.9, min_lr=0.0, by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=80000)
checkpoint_config = dict(by_epoch=False, interval=8000)
evaluation = dict(interval=8000, metric='mIoU')
work_dir = './work_dirs/fpn_pvt_t_ade20k_40k'
gpu_ids = range(0, 1)
2021-12-23 10:33:49,947 - mmseg - INFO - EncoderDecoder(
(backbone): pvt_tiny(
(patch_embed1): PatchEmbed(
(proj): Conv2d(3, 64, kernel_size=(4, 4), stride=(4, 4))
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
)
(pos_drop1): Dropout(p=0.0, inplace=False)
(block1): ModuleList(
(0): Block(
(norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(q): Linear(in_features=64, out_features=64, bias=True)
(kv): Linear(in_features=64, out_features=128, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=64, out_features=64, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8))
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
)
(drop_path): Identity()
(norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=64, out_features=512, bias=True)
(act): GELU()
(fc2): Linear(in_features=512, out_features=64, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(1): Block(
(norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(q): Linear(in_features=64, out_features=64, bias=True)
(kv): Linear(in_features=64, out_features=128, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=64, out_features=64, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(sr): Conv2d(64, 64, kernel_size=(8, 8), stride=(8, 8))
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
)
(drop_path): DropPath()
(norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=64, out_features=512, bias=True)
(act): GELU()
(fc2): Linear(in_features=512, out_features=64, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
)
(patch_embed2): PatchEmbed(
(proj): Conv2d(64, 128, kernel_size=(2, 2), stride=(2, 2))
(norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
)
(pos_drop2): Dropout(p=0.0, inplace=False)
(block2): ModuleList(
(0): Block(
(norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(q): Linear(in_features=128, out_features=128, bias=True)
(kv): Linear(in_features=128, out_features=256, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=128, out_features=128, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4))
(norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
)
(drop_path): DropPath()
(norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=128, out_features=1024, bias=True)
(act): GELU()
(fc2): Linear(in_features=1024, out_features=128, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(1): Block(
(norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(q): Linear(in_features=128, out_features=128, bias=True)
(kv): Linear(in_features=128, out_features=256, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=128, out_features=128, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(sr): Conv2d(128, 128, kernel_size=(4, 4), stride=(4, 4))
(norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
)
(drop_path): DropPath()
(norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=128, out_features=1024, bias=True)
(act): GELU()
(fc2): Linear(in_features=1024, out_features=128, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
)
(patch_embed3): PatchEmbed(
(proj): Conv2d(128, 320, kernel_size=(2, 2), stride=(2, 2))
(norm): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
)
(pos_drop3): Dropout(p=0.0, inplace=False)
(block3): ModuleList(
(0): Block(
(norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(q): Linear(in_features=320, out_features=320, bias=True)
(kv): Linear(in_features=320, out_features=640, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=320, out_features=320, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2))
(norm): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
)
(drop_path): DropPath()
(norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=320, out_features=1280, bias=True)
(act): GELU()
(fc2): Linear(in_features=1280, out_features=320, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(1): Block(
(norm1): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(q): Linear(in_features=320, out_features=320, bias=True)
(kv): Linear(in_features=320, out_features=640, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=320, out_features=320, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(sr): Conv2d(320, 320, kernel_size=(2, 2), stride=(2, 2))
(norm): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
)
(drop_path): DropPath()
(norm2): LayerNorm((320,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=320, out_features=1280, bias=True)
(act): GELU()
(fc2): Linear(in_features=1280, out_features=320, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
)
(patch_embed4): PatchEmbed(
(proj): Conv2d(320, 512, kernel_size=(2, 2), stride=(2, 2))
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
)
(pos_drop4): Dropout(p=0.0, inplace=False)
(block4): ModuleList(
(0): Block(
(norm1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(q): Linear(in_features=512, out_features=512, bias=True)
(kv): Linear(in_features=512, out_features=1024, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(1): Block(
(norm1): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(q): Linear(in_features=512, out_features=512, bias=True)
(kv): Linear(in_features=512, out_features=1024, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
)
)
(neck): FPN(
(lateral_convs): ModuleList(
(0): ConvModule(
(conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
)
(1): ConvModule(
(conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
)
(2): ConvModule(
(conv): Conv2d(320, 256, kernel_size=(1, 1), stride=(1, 1))
)
(3): ConvModule(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
)
(fpn_convs): ModuleList(
(0): ConvModule(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): ConvModule(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): ConvModule(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): ConvModule(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
(decode_head): FPNHead(
input_transform=multiple_select, ignore_index=255, align_corners=False
(loss_decode): CrossEntropyLoss()
(conv_seg): Conv2d(128, 150, kernel_size=(1, 1), stride=(1, 1))
(dropout): Dropout2d(p=0.1, inplace=False)
(scale_heads): ModuleList(
(0): Sequential(
(0): ConvModule(
(conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
)
(1): Sequential(
(0): ConvModule(
(conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
)
(2): Sequential(
(0): ConvModule(
(conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(1): Upsample()
)
(3): Sequential(
(0): ConvModule(
(conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(1): Upsample()
(2): ConvModule(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(3): Upsample()
)
)
)
init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
)
2021-12-23 10:33:50,289 - mmseg - INFO - Loaded 20210 images
2021-12-23 10:33:55,045 - mmseg - INFO - Loaded 2000 images
2021-12-23 10:33:55,046 - mmseg - INFO - Start running, host: host@ubuntu20, work_dir: /home/host/mounted1/zxDet/mmsegmentation/work_dirs/fpn_pvt_t_ade20k_40k
2021-12-23 10:33:55,046 - mmseg - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) PolyLrUpdaterHook
(NORMAL ) CheckpointHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_epoch:
(VERY_HIGH ) PolyLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_iter:
(VERY_HIGH ) PolyLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
--------------------
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
after_train_epoch:
(NORMAL ) CheckpointHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_epoch:
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_epoch:
(VERY_LOW ) TextLoggerHook
--------------------
after_run:
(VERY_LOW ) TextLoggerHook
--------------------
2021-12-23 10:33:55,046 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters
The pretrained weight seems not to be loaded because I didn't see a warning about the head.weight.
I use pvt_tiny for this two jobs. But the mIoU is very low. we train with this script on two rtx3090, I just change the gpu_multiples to 1:
_base_ = [ '../../_base_/models/fpn_r50.py', '../../_base_/datasets/ade20k.py', '../../_base_/default_runtime.py' ] # model settings model = dict( type='EncoderDecoder', # pretrained='pretrained/pvt_tiny.pth', pretrained='/home/host/dist1/zx/classification/checkpoints/pvt_tiny/checkpoint.pth', backbone=dict( type='pvt_tiny', style='pytorch'), neck=dict(in_channels=[64, 128, 320, 512]), decode_head=dict(num_classes=150)) gpu_multiples = 1 # we use 8 gpu instead of 4 in mmsegmentation, so lr*2 and max_iters/2 # optimizeiiiiiir optimizer = dict(type='AdamW', lr=0.0001*gpu_multiples, weight_decay=0.0001) optimizer_config = dict() # learning policy lr_config = dict(policy='poly', power=0.9, min_lr=0.0, by_epoch=False) # runtime settings runner = dict(type='IterBasedRunner', max_iters=80000//gpu_multiples) checkpoint_config = dict(by_epoch=False, interval=8000//gpu_multiples) evaluation = dict(interval=8000//gpu_multiples, metric='mIoU')the part of log I get :
Class | IoU | Acc | +---------------------+-------+-------+ | wall | 54.7 | 81.83 | | building | 67.78 | 89.28 | | sky | 89.7 | 94.46 | | floor | 57.27 | 73.54 | | tree | 58.49 | 78.73 | | ceiling | 63.11 | 73.05 | | road | 64.98 | 79.87 | | bed | 48.05 | 77.07 | | windowpane | 40.18 | 60.33 | | grass | 57.12 | 75.26 | | cabinet | 32.02 | 46.5 | | sidewalk | 41.35 | 63.2 | | person | 40.43 | 64.08 | | earth | 27.29 | 36.52 | | door | 5.95 | 6.61 | | table | 18.18 | 30.23 | | mountain | 31.44 | 51.38 | | plant | 33.12 | 43.71 | | curtain | 38.04 | 52.99 | | chair | 21.51 | 36.51 | | car | 51.0 | 71.94 | | water | 29.28 | 36.67 | | painting | 31.18 | 51.95 | | sofa | 24.42 | 43.53 | | shelf | 13.72 | 23.72 | | house | 28.35 | 42.56 | | sea | 42.37 | 68.75 | | mirror | 11.43 | 14.2 | | rug | 23.17 | 27.49 | | field | 22.71 | 47.06 | | armchair | 0.78 | 0.84 | | seat | 25.22 | 31.6 | | fence | 4.03 | 4.5 | | desk | 10.89 | 15.5 | | rock | 16.8 | 28.19 | | wardrobe | 7.48 | 7.94 | | lamp | 14.81 | 19.2 | | bathtub | 13.31 | 16.23 | | railing | 5.92 | 6.01 | | cushion | 13.75 | 20.18 | | base | 0.55 | 0.62 | | box | 1.56 | 1.72 | | column | 0.0 | 0.0 | | signboard | 4.54 | 4.79 | | chest of drawers | 12.02 | 17.29 | | counter | 0.69 | 0.7 | | sand | 14.07 | 21.83 | | sink | 20.21 | 35.94 | | skyscraper | 31.83 | 37.65 | | fireplace | 26.12 | 43.07 | | refrigerator | 18.92 | 23.57 | | grandstand | 21.16 | 30.05 | | path | 10.79 | 12.48 | | stairs | 10.18 | 11.39 | | runway | 48.93 | 73.64 | | case | 24.5 | 49.33 | | pool table | 50.19 | 63.41 | | pillow | 18.73 | 23.37 | | screen door | 17.06 | 19.77 | | stairway | 5.3 | 7.01 | | river | 9.48 | 16.44 | | bridge | 0.28 | 0.29 | | bookcase | 6.67 | 8.51 | | blind | 1.8 | 1.84 | | coffee table | 14.99 | 27.61 | | toilet | 21.48 | 47.07 | | flower | 14.28 | 23.6 | | book | 15.55 | 18.32 | | hill | 3.46 | 3.74 | | bench | 5.57 | 5.82 | | countertop | 7.53 | 9.42 | | stove | 18.68 | 36.78 | | palm | 10.27 | 11.79 | | kitchen island | 15.96 | 18.7 | | computer | 6.35 | 8.5 | | swivel chair | 12.05 | 20.8 | | boat | 5.43 | 6.27 | | bar | 0.04 | 0.04 | | arcade machine | 5.5 | 6.23 | | hovel | 3.51 | 4.12 | | bus | 6.68 | 7.19 | | towel | 2.35 | 2.37 | | light | 10.98 | 11.26 | | truck | 0.1 | 0.13 | | tower | 10.42 | 10.66 | | chandelier | 29.94 | 50.31 | | awning | 0.35 | 0.38 | | streetlight | 0.28 | 0.28 | | booth | 0.0 | 0.0 | | television receiver | 17.11 | 24.19 | | airplane | 31.53 | 39.65 | | dirt track | 0.0 | 0.0 | | apparel | 4.78 | 5.66 | | pole | 1.96 | 2.17 | | land | 0.0 | 0.0 | | bannister | 0.0 | 0.0 | | escalator | 0.0 | 0.0 | | ottoman | 0.0 | 0.0 | | bottle | 0.0 | 0.0 | | buffet | 0.0 | 0.0 | | poster | 0.0 | 0.0 | | stage | 0.0 | 0.0 | | van | 0.4 | 0.4 | | ship | 0.0 | 0.0 | | fountain | 0.0 | 0.0 | | conveyer belt | 0.0 | 0.0 | | canopy | 0.0 | 0.0 | | washer | 0.28 | 0.28 | | plaything | 0.5 | 0.54 | | swimming pool | 24.38 | 42.56 | | stool | 0.0 | 0.0 | | barrel | 0.0 | 0.0 | | basket | 0.0 | 0.0 | | waterfall | 47.73 | 70.95 | | tent | 31.71 | 47.17 | | bag | 0.0 | 0.0 | | minibike | 10.11 | 11.68 | | cradle | 29.79 | 67.07 | | oven | 0.0 | 0.0 | | ball | 0.17 | 0.38 | | food | 27.43 | 37.67 | | step | 0.0 | 0.0 | | tank | 0.0 | 0.0 | | trade name | 3.35 | 3.46 | | microwave | 10.61 | 12.33 | | pot | 0.0 | 0.0 | | animal | 1.39 | 1.51 | | bicycle | 0.0 | 0.0 | | lake | 2.98 | 3.54 | | dishwasher | 2.75 | 2.79 | | screen | 21.23 | 22.22 | | blanket | 0.0 | 0.0 | | sculpture | 0.0 | 0.0 | | hood | 3.97 | 5.03 | | sconce | 1.98 | 2.02 | | vase | 2.1 | 2.49 | | traffic light | 0.03 | 0.03 | | tray | 0.0 | 0.0 | | ashcan | 0.0 | 0.0 | | fan | 12.51 | 14.54 | | pier | 0.68 | 0.68 | | crt screen | 0.0 | 0.0 | | plate | 0.0 | 0.0 | | monitor | 0.0 | 0.0 | | bulletin board | 0.0 | 0.0 | | shower | 0.0 | 0.0 | | radiator | 0.0 | 0.0 | | glass | 0.0 | 0.0 | | clock | 0.0 | 0.0 | | flag | 0.0 | 0.0 | +---------------------+-------+-------+ 2021-12-23 16:21:00,788 - mmseg - INFO - Summary: 2021-12-23 16:21:00,789 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 65.06 | 14.33 | 20.34 | +-------+-------+-------+ 2021-12-23 16:21:00,804 - mmseg - INFO - Exp name: fpn_pvt_t_ade20k_40k.py 2021-12-23 16:21:00,805 - mmseg - INFO - Iter(val) [1000] aAcc: 0.6506, mIoU: 0.1433, mAcc: 0.2034, IoU.wall: 0.5470, IoU.building: 0.6778, IoU.sky: 0.8970, IoU.floor: 0.5727, IoU.tree: 0.5849, IoU.ceiling: 0.6311, IoU.road: 0.6498, IoU.bed : 0.4805, IoU.windowpane: 0.4018, IoU.grass: 0.5712, IoU.cabinet: 0.3202, IoU.sidewalk: 0.4135, IoU.person: 0.4043, IoU.earth: 0.2729, IoU.door: 0.0595, IoU.table: 0.1818, IoU.mountain: 0.3144, IoU.plant: 0.3312, IoU.curtain: 0.3804, IoU.chair: 0.2151, IoU.car: 0.5100, IoU.water: 0.2928, IoU.painting: 0.3118, IoU.sofa: 0.2442, IoU.shelf: 0.1372, IoU.house: 0.2835, IoU.sea: 0.4237, IoU.mirror: 0.1143, IoU.rug: 0.2317, IoU.field: 0.2271, IoU.armchair: 0.0078, IoU.seat: 0.2522, IoU.fence: 0.0403, IoU.desk: 0.1089, IoU.rock: 0.1680, IoU.wardrobe: 0.0748, IoU.lamp: 0.1481, IoU.bathtub: 0.1331, IoU.railing: 0.0592, IoU.cushion: 0.1375, IoU.base: 0.0055, IoU.box: 0.0156, IoU.column: 0.0000, IoU.signboard: 0.0454, IoU.chest of drawers: 0.1202, IoU.counter: 0.0069, IoU.sand: 0.1407, IoU.sink: 0.2021, IoU.skyscraper: 0.3183, IoU.fireplace: 0.2612, IoU.refrigerator: 0.1892, IoU.grandstand: 0.2116, IoU.path: 0.1079, IoU.stairs: 0.1018, IoU.runway: 0.4893, IoU.case: 0.2450, IoU.pool table: 0.5019, IoU.pillow: 0.1873, IoU.screen door: 0.1706, IoU.stairway: 0.0530, IoU.river: 0.0948, IoU.bridge: 0.0028, IoU.bookcase: 0.0667, IoU.blind: 0.0180, IoU.coffee table: 0.1499, IoU.toilet: 0.2148, IoU.flower: 0.1428, IoU.book: 0.1555, IoU.hill: 0.0346, IoU.bench: 0.0557, IoU.countertop: 0.0753, IoU.stove: 0.1868, IoU.palm: 0.1027, IoU.kitchen island: 0.1596, IoU.computer: 0.0635, IoU.swivel chair: 0.1205, IoU.boat: 0.0543, IoU.bar: 0.0004, IoU.arcade machine: 0.0550, IoU.hovel: 0.0351, IoU.bus: 0.0668, IoU.towel: 0.0235, IoU.light: 0.1098, IoU.truck: 0.0010, IoU.tower: 0.1042, IoU.chandelier: 0.2994, IoU.awning: 0.0035, IoU.streetlight: 0.0028, IoU.booth: 0.0000, IoU.television receiver: 0.1711, IoU.airplane: 0.3153, IoU.dirt track: 0.0000, IoU.apparel: 0.0478, IoU.pole: 0.0196, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.0000, IoU.ottoman: 0.0000, IoU.bottle: 0.0000, IoU.buffet: 0.0000, IoU.poster: 0.0000, IoU.stage: 0.0000, IoU.van: 0.0040, IoU.ship: 0.0000, IoU.fountain: 0.0000, IoU.conveyer belt: 0.0000, IoU.canopy: 0.0000, IoU.washer: 0.0028, IoU.plaything: 0.0050, IoU.swimming pool: 0.2438, IoU.stool: 0.0000, IoU.barrel: 0.0000, IoU.basket: 0.0000, IoU.waterfall: 0.4773, IoU.tent: 0.3171, IoU.bag: 0.0000, IoU.minibike: 0.1011, IoU.cradle: 0.2979, IoU.oven: 0.0000, IoU.ball: 0.0017, IoU.food: 0.2743, IoU.step: 0.0000, IoU.tank: 0.0000, IoU.trade name: 0.0335, IoU.microwave: 0.1061, IoU.pot: 0.0000, IoU.animal: 0.0139, IoU.bicycle: 0.0000, IoU.lake: 0.0298, IoU.dishwasher: 0.0275, IoU.screen: 0.2123, IoU.blanket: 0.0000, IoU.sculpture: 0.0000, IoU.hood: 0.0397, IoU.sconce: 0.0198, IoU.vase: 0.0210, IoU.traffic light: 0.0003, IoU.tray: 0.0000, IoU.ashcan: 0.0000, IoU.fan: 0.1251, IoU.pier: 0.0068, IoU.crt screen: 0.0000, IoU.plate: 0.0000, IoU.monitor: 0.0000, IoU.bulletin board: 0.0000, IoU.shower: 0.0000, IoU.radiator: 0.0000, IoU.glass: 0.0000, IoU.clock: 0.0000, IoU.flag: 0.0000, Acc.wall: 0.8183, Acc.building: 0.8928, Acc.sky: 0.9446, Acc.floor: 0.7354, Acc.tree: 0.7873, Acc.ceiling: 0.7305, Acc.road: 0.7987, Acc.bed : 0.7707, Acc.windowpane: 0.6033, Acc.grass: 0.7526, Acc.cabinet: 0.4650, Acc.sidewalk: 0.6320, Acc.person: 0.6408, Acc.earth: 0.3652, Acc.door: 0.0661, Acc.table: 0.3023, Acc.mountain: 0.5138, Acc.plant: 0.4371, Acc.curtain: 0.5299, Acc.chair: 0.3651, Acc.car: 0.7194, Acc.water: 0.3667, Acc.painting: 0.5195, Acc.sofa: 0.4353, Acc.shelf: 0.2372, Acc.house: 0.4256, Acc.sea: 0.6875, Acc.mirror: 0.1420, Acc.rug: 0.2749, Acc.field: 0.4706, Acc.armchair: 0.0084, Acc.seat: 0.3160, Acc.fence: 0.0450, Acc.desk: 0.1550, Acc.rock: 0.2819, Acc.wardrobe: 0.0794, Acc.lamp: 0.1920, Acc.bathtub: 0.1623, Acc.railing: 0.0601, Acc.cushion: 0.2018, Acc.base: 0.0062, Acc.box: 0.0172, Acc.column: 0.0000, Acc.signboard: 0.0479, Acc.chest of drawers: 0.1729, Acc.counter: 0.0070, Acc.sand: 0.2183, Acc.sink: 0.3594, Acc.skyscraper: 0.3765, Acc.fireplace: 0.4307, Acc.refrigerator: 0.2357, Acc.grandstand: 0.3005, Acc.path: 0.1248, Acc.stairs: 0.1139, Acc.runway: 0.7364, Acc.case: 0.4933, Acc.pool table: 0.6341, Acc.pillow: 0.2337, Acc.screen door: 0.1977, Acc.stairway: 0.0701, Acc.river: 0.1644, Acc.bridge: 0.0029, Acc.bookcase: 0.0851, Acc.blind: 0.0184, Acc.coffee table: 0.2761, Acc.toilet: 0.4707, Acc.flower: 0.2360, Acc.book: 0.1832, Acc.hill: 0.0374, Acc.bench: 0.0582, Acc.countertop: 0.0942, Acc.stove: 0.3678, Acc.palm: 0.1179, Acc.kitchen island: 0.1870, Acc.computer: 0.0850, Acc.swivel chair: 0.2080, Acc.boat: 0.0627, Acc.bar: 0.0004, Acc.arcade machine: 0.0623, Acc.hovel: 0.0412, Acc.bus: 0.0719, Acc.towel: 0.0237, Acc.light: 0.1126, Acc.truck: 0.0013, Acc.tower: 0.1066, Acc.chandelier: 0.5031, Acc.awning: 0.0038, Acc.streetlight: 0.0028, Acc.booth: 0.0000, Acc.television receiver: 0.2419, Acc.airplane: 0.3965, Acc.dirt track: 0.0000, Acc.apparel: 0.0566, Acc.pole: 0.0217, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.0000, Acc.ottoman: 0.0000, Acc.bottle: 0.0000, Acc.buffet: 0.0000, Acc.poster: 0.0000, Acc.stage: 0.0000, Acc.van: 0.0040, Acc.ship: 0.0000, Acc.fountain: 0.0000, Acc.conveyer belt: 0.0000, Acc.canopy: 0.0000, Acc.washer: 0.0028, Acc.plaything: 0.0054, Acc.swimming pool: 0.4256, Acc.stool: 0.0000, Acc.barrel: 0.0000, Acc.basket: 0.0000, Acc.waterfall: 0.7095, Acc.tent: 0.4717, Acc.bag: 0.0000, Acc.minibike: 0.1168, Acc.cradle: 0.6707, Acc.oven: 0.0000, Acc.ball: 0.0038, Acc.food: 0.3767, Acc.step: 0.0000, Acc.tank: 0.0000, Acc.trade name: 0.0346, Acc.microwave: 0.1233, Acc.pot: 0.0000, Acc.animal: 0.0151, Acc.bicycle: 0.0000, Acc.lake: 0.0354, Acc.dishwasher: 0.0279, Acc.screen: 0.2222, Acc.blanket: 0.0000, Acc.sculpture: 0.0000, Acc.hood: 0.0503, Acc.sconce: 0.0202, Acc.vase: 0.0249, Acc.traffic light: 0.0003, Acc.tray: 0.0000, Acc.ashcan: 0.0000, Acc.fan: 0.1454, Acc.pier: 0.0068, Acc.crt screen: 0.0000, Acc.plate: 0.0000, Acc.monitor: 0.0000, Acc.bulletin board: 0.0000, Acc.shower: 0.0000, Acc.radiator: 0.0000, Acc.glass: 0.0000, Acc.clock: 0.0000, Acc.flag: 0.0000 2021-12-23 16:21:17,443 - mmseg - INFO - Iter [64050/80000] lr: 2.343e-05, eta: 1:26:26, time: 2.451, data_time: 2.127, memory: 10848, decode.loss_ce: 1.1673, decode.acc_seg: 52.5561, loss: 1.1673 2021-12-23 16:21:34,105 - mmseg - INFO - Iter [64100/80000] lr: 2.336e-05, eta: 1:26:10, time: 0.333, data_time: 0.007, memory: 10848, decode.loss_ce: 1.1150, decode.acc_seg: 48.8460, loss: 1.1150 2021-12-23 16:21:50,267 - mmseg - INFO - Iter [64150/80000] lr: 2.330e-05, eta: 1:25:53, time: 0.323, data_time: 0.008, memory: 10848, decode.loss_ce: 1.1292, decode.acc_seg: 48.7623, loss: 1.1292 2021-12-23 16:22:07,223 - mmseg - INFO - Iter [64200/80000] lr: 2.323e-05, eta: 1:25:37, time: 0.339, data_time: 0.007, memory: 10848, decode.loss_ce: 1.1834, decode.acc_seg: 49.9436, loss: 1.1834 2021-12-23 16:22:23,802 - mmseg - INFO - Iter [64250/80000] lr: 2.316e-05, eta: 1:25:21, time: 0.331, data_time: 0.008, memory: 10848, decode.loss_ce: 1.1725, decode.acc_seg: 49.8978, loss: 1.1725 2021-12-23 16:22:40,176 - mmseg - INFO - Iter [64300/80000] lr: 2.310e-05, eta: 1:25:05, time: 0.327, data_time: 0.008, memory: 10848, decode.loss_ce: 1.1656, decode.acc_seg: 50.8126, loss: 1.1656could you help me to fix this issue
Where can I download the pre training weight？Thank you！
The pretrained weight seems not to be loaded because I didn't see a warning about the head.weight.
I would like to know how the miou was obtained in the paper, I used the method in the paper and the trained miou results were around 15.
