OpenMMLab AI实战营 第七课笔记

OpenMMLab AI实战营 第七课笔记

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目录

• OpenMMLab AI实战营 第七课笔记

import os

import numpy as np
from PIL import Imageimport os.path as osp
from tqdm import tqdmimport mmcv
import mmengine
import matplotlib.pyplot as plt
%matplotlib inline

# 数据集图片和标注路径
data_root = 'data/koto/'
img_dir = 'imgs'
ann_dir = 'annos'# 类别和对应的颜色
classes = ('bg', 'person')
palette = [[0, 0, 0],[255, 255, 255]]
# palette = [[128, 128, 128], [129, 127, 38], [120, 69, 125], [53, 125, 34], 
#           [0, 11, 123], [118, 20, 12], [122, 81, 25], [241, 134, 51]]

Image.open('data/koto/imgs/00001-125.jpg')

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Image.open('data/koto/annos/00001-125.png')

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import matplotlib.patches as mpatches
img = Image.open('data/koto/annos/00001-125.png')
plt.figure(figsize=(8, 6))
im = plt.imshow(np.array(img.convert('RGB')))# 图例小块
patches = [mpatches.Patch(color=np.array(palette[i])/255., label=classes[i]) for i in range(2)]
# 图例
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize='large')
plt.show()

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from mmseg.registry import DATASETS
from mmseg.datasets import BaseSegDataset@DATASETS.register_module()
class StanfordBackgroundDataset(BaseSegDataset):METAINFO = dict(classes = classes, palette = palette)def __init__(self, **kwargs):super().__init__(img_suffix='.jpg', seg_map_suffix='.png', **kwargs)

# 下载 config 文件 和 预训练模型checkpoint权重文件
!mim download mmsegmentation --config pspnet_r50-d8_4xb2-40k_cityscapes-512x1024 --dest .

processing pspnet_r50-d8_4xb2-40k_cityscapes-512x1024...
[32mpspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth exists in /home/linxu/Desktop/OpenMMLab-Space/mmsegmentation[0m
[32mSuccessfully dumped pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py to /home/linxu/Desktop/OpenMMLab-Space/mmsegmentation[0m

from mmengine import Config
cfg = Config.fromfile('configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py')

cfg.norm_cfg = dict(type='BN', requires_grad=True) # 只使用GPU时，BN取代SyncBN
cfg.crop_size = (256, 256)
cfg.model.data_preprocessor.size = cfg.crop_size
cfg.model.backbone.norm_cfg = cfg.norm_cfg
cfg.model.decode_head.norm_cfg = cfg.norm_cfg
cfg.model.auxiliary_head.norm_cfg = cfg.norm_cfg
# modify num classes of the model in decode/auxiliary head
cfg.model.decode_head.num_classes = 2
cfg.model.auxiliary_head.num_classes = 8# 修改数据集的 type 和 root
cfg.dataset_type = 'StanfordBackgroundDataset'
cfg.data_root = data_rootcfg.train_dataloader.batch_size = 8cfg.train_pipeline = [dict(type='LoadImageFromFile'),dict(type='LoadAnnotations'),dict(type='RandomResize', scale=(320, 240), ratio_range=(0.5, 2.0), keep_ratio=True),dict(type='RandomCrop', crop_size=cfg.crop_size, cat_max_ratio=0.75),dict(type='RandomFlip', prob=0.5),dict(type='PackSegInputs')
]cfg.test_pipeline = [dict(type='LoadImageFromFile'),dict(type='Resize', scale=(320, 240), keep_ratio=True),# add loading annotation after ``Resize`` because ground truth# does not need to do resize data transformdict(type='LoadAnnotations'),dict(type='PackSegInputs')
]cfg.train_dataloader.dataset.type = cfg.dataset_type
cfg.train_dataloader.dataset.data_root = cfg.data_root
cfg.train_dataloader.dataset.data_prefix = dict(img_path=img_dir, seg_map_path=ann_dir)
cfg.train_dataloader.dataset.pipeline = cfg.train_pipeline
cfg.train_dataloader.dataset.ann_file = 'data/koto/train_list.txt'cfg.val_dataloader.dataset.type = cfg.dataset_type
cfg.val_dataloader.dataset.data_root = cfg.data_root
cfg.val_dataloader.dataset.data_prefix = dict(img_path=img_dir, seg_map_path=ann_dir)
cfg.val_dataloader.dataset.pipeline = cfg.test_pipeline
cfg.val_dataloader.dataset.ann_file = 'data/koto/valid_list.txtt'cfg.test_dataloader = cfg.val_dataloader# 载入预训练模型权重
cfg.load_from = 'pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'# 工作目录
cfg.work_dir = './work_dirs/tutorial'# 训练迭代次数
cfg.train_cfg.max_iters = 800
# 评估模型间隔
cfg.train_cfg.val_interval = 400
# 日志记录间隔
cfg.default_hooks.logger.interval = 100
# 模型权重保存间隔
cfg.default_hooks.checkpoint.interval = 400# 随机数种子
cfg['randomness'] = dict(seed=0)

print(cfg.pretty_text)

norm_cfg = dict(type='BN', requires_grad=True)
data_preprocessor = dict(type='SegDataPreProcessor',mean=[123.675, 116.28, 103.53],std=[58.395, 57.12, 57.375],bgr_to_rgb=True,pad_val=0,seg_pad_val=255,size=(512, 1024))
model = dict(type='EncoderDecoder',data_preprocessor=dict(type='SegDataPreProcessor',mean=[123.675, 116.28, 103.53],std=[58.395, 57.12, 57.375],bgr_to_rgb=True,pad_val=0,seg_pad_val=255,size=(256, 256)),pretrained='open-mmlab://resnet50_v1c',backbone=dict(type='ResNetV1c',depth=50,num_stages=4,out_indices=(0, 1, 2, 3),dilations=(1, 1, 2, 4),strides=(1, 2, 1, 1),norm_cfg=dict(type='BN', requires_grad=True),norm_eval=False,style='pytorch',contract_dilation=True),decode_head=dict(type='PSPHead',in_channels=2048,in_index=3,channels=512,pool_scales=(1, 2, 3, 6),dropout_ratio=0.1,num_classes=2,norm_cfg=dict(type='BN', requires_grad=True),align_corners=False,loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),auxiliary_head=dict(type='FCNHead',in_channels=1024,in_index=2,channels=256,num_convs=1,concat_input=False,dropout_ratio=0.1,num_classes=8,norm_cfg=dict(type='BN', requires_grad=True),align_corners=False,loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),train_cfg=dict(),test_cfg=dict(mode='whole'))
dataset_type = 'StanfordBackgroundDataset'
data_root = 'data/koto/'
crop_size = (256, 256)
train_pipeline = [dict(type='LoadImageFromFile'),dict(type='LoadAnnotations'),dict(type='RandomResize',scale=(320, 240),ratio_range=(0.5, 2.0),keep_ratio=True),dict(type='RandomCrop', crop_size=(256, 256), cat_max_ratio=0.75),dict(type='RandomFlip', prob=0.5),dict(type='PackSegInputs')
]
test_pipeline = [dict(type='LoadImageFromFile'),dict(type='Resize', scale=(320, 240), keep_ratio=True),dict(type='LoadAnnotations'),dict(type='PackSegInputs')
]
img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
tta_pipeline = [dict(type='LoadImageFromFile', backend_args=dict(backend='local')),dict(type='TestTimeAug',transforms=[[{'type': 'Resize','scale_factor': 0.5,'keep_ratio': True}, {'type': 'Resize','scale_factor': 0.75,'keep_ratio': True}, {'type': 'Resize','scale_factor': 1.0,'keep_ratio': True}, {'type': 'Resize','scale_factor': 1.25,'keep_ratio': True}, {'type': 'Resize','scale_factor': 1.5,'keep_ratio': True}, {'type': 'Resize','scale_factor': 1.75,'keep_ratio': True}],[{'type': 'RandomFlip','prob': 0.0,'direction': 'horizontal'}, {'type': 'RandomFlip','prob': 1.0,'direction': 'horizontal'}], [{'type': 'LoadAnnotations'}], [{'type': 'PackSegInputs'}]])
]
train_dataloader = dict(batch_size=8,num_workers=2,persistent_workers=True,sampler=dict(type='InfiniteSampler', shuffle=True),dataset=dict(type='StanfordBackgroundDataset',data_root='data/koto/',data_prefix=dict(img_path='imgs', seg_map_path='annos'),pipeline=[dict(type='LoadImageFromFile'),dict(type='LoadAnnotations'),dict(type='RandomResize',scale=(320, 240),ratio_range=(0.5, 2.0),keep_ratio=True),dict(type='RandomCrop', crop_size=(256, 256), cat_max_ratio=0.75),dict(type='RandomFlip', prob=0.5),dict(type='PackSegInputs')],ann_file='data/koto/train_list.txt'))
val_dataloader = dict(batch_size=1,num_workers=4,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type='StanfordBackgroundDataset',data_root='data/koto/',data_prefix=dict(img_path='imgs', seg_map_path='annos'),pipeline=[dict(type='LoadImageFromFile'),dict(type='Resize', scale=(320, 240), keep_ratio=True),dict(type='LoadAnnotations'),dict(type='PackSegInputs')],ann_file='data/koto/valid_list.txtt'))
test_dataloader = dict(batch_size=1,num_workers=4,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type='StanfordBackgroundDataset',data_root='data/koto/',data_prefix=dict(img_path='imgs', seg_map_path='annos'),pipeline=[dict(type='LoadImageFromFile'),dict(type='Resize', scale=(320, 240), keep_ratio=True),dict(type='LoadAnnotations'),dict(type='PackSegInputs')],ann_file='data/koto/valid_list.txtt'))
val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
default_scope = 'mmseg'
env_cfg = dict(cudnn_benchmark=True,mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(type='SegLocalVisualizer',vis_backends=[dict(type='LocalVisBackend')],name='visualizer')
log_processor = dict(by_epoch=False)
log_level = 'INFO'
load_from = 'pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
resume = False
tta_model = dict(type='SegTTAModel')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optim_wrapper = dict(type='OptimWrapper',optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005),clip_grad=None)
param_scheduler = [dict(type='PolyLR',eta_min=0.0001,power=0.9,begin=0,end=40000,by_epoch=False)
]
train_cfg = dict(type='IterBasedTrainLoop', max_iters=800, val_interval=400)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(timer=dict(type='IterTimerHook'),logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),param_scheduler=dict(type='ParamSchedulerHook'),checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=400),sampler_seed=dict(type='DistSamplerSeedHook'),visualization=dict(type='SegVisualizationHook'))
work_dir = './work_dirs/tutorial'
randomness = dict(seed=0)

from mmengine.runner import Runner
from mmseg.utils import register_all_modules# register all modules in mmseg into the registries
# do not init the default scope here because it will be init in the runner
register_all_modules(init_default_scope=False)
runner = Runner.from_cfg(cfg)

02/11 22:55:46 - mmengine - [4m[97mINFO[0m - 
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System environment:sys.platform: linuxPython: 3.8.16 (default, Jan 17 2023, 23:13:24) [GCC 11.2.0]CUDA available: Truenumpy_random_seed: 0GPU 0: NVIDIA GeForce RTX 3060 Laptop GPUCUDA_HOME: /usr/local/cuda-11.6NVCC: Cuda compilation tools, release 11.6, V11.6.124GCC: gcc (Uos 8.3.0.3-3+rebuild) 8.3.0PyTorch: 1.13.1+cu116PyTorch compiling details: PyTorch built with:- GCC 9.3- C++ Version: 201402- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications- Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)- OpenMP 201511 (a.k.a. OpenMP 4.5)- LAPACK is enabled (usually provided by MKL)- NNPACK is enabled- CPU capability usage: AVX2- CUDA Runtime 11.6- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86- CuDNN 8.6  (built against CUDA 11.8)- Built with CuDNN 8.3.2- Magma 2.6.1- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, 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, USE_ROCM=OFF, TorchVision: 0.14.1+cu116OpenCV: 4.7.0MMEngine: 0.5.0Runtime environment:cudnn_benchmark: Truemp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}dist_cfg: {'backend': 'nccl'}seed: 0Distributed launcher: noneDistributed training: FalseGPU number: 1
------------------------------------------------------------02/11 22:55:46 - mmengine - [4m[97mINFO[0m - Config:
norm_cfg = dict(type='BN', requires_grad=True)
data_preprocessor = dict(type='SegDataPreProcessor',mean=[123.675, 116.28, 103.53],std=[58.395, 57.12, 57.375],bgr_to_rgb=True,pad_val=0,seg_pad_val=255,size=(512, 1024))
model = dict(type='EncoderDecoder',data_preprocessor=dict(type='SegDataPreProcessor',mean=[123.675, 116.28, 103.53],std=[58.395, 57.12, 57.375],bgr_to_rgb=True,pad_val=0,seg_pad_val=255,size=(256, 256)),pretrained='open-mmlab://resnet50_v1c',backbone=dict(type='ResNetV1c',depth=50,num_stages=4,out_indices=(0, 1, 2, 3),dilations=(1, 1, 2, 4),strides=(1, 2, 1, 1),norm_cfg=dict(type='BN', requires_grad=True),norm_eval=False,style='pytorch',contract_dilation=True),decode_head=dict(type='PSPHead',in_channels=2048,in_index=3,channels=512,pool_scales=(1, 2, 3, 6),dropout_ratio=0.1,num_classes=2,norm_cfg=dict(type='BN', requires_grad=True),align_corners=False,loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),auxiliary_head=dict(type='FCNHead',in_channels=1024,in_index=2,channels=256,num_convs=1,concat_input=False,dropout_ratio=0.1,num_classes=8,norm_cfg=dict(type='BN', requires_grad=True),align_corners=False,loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),train_cfg=dict(),test_cfg=dict(mode='whole'))
dataset_type = 'StanfordBackgroundDataset'
data_root = 'data/koto/'
crop_size = (256, 256)
train_pipeline = [dict(type='LoadImageFromFile'),dict(type='LoadAnnotations'),dict(type='RandomResize',scale=(320, 240),ratio_range=(0.5, 2.0),keep_ratio=True),dict(type='RandomCrop', crop_size=(256, 256), cat_max_ratio=0.75),dict(type='RandomFlip', prob=0.5),dict(type='PackSegInputs')
]
test_pipeline = [dict(type='LoadImageFromFile'),dict(type='Resize', scale=(320, 240), keep_ratio=True),dict(type='LoadAnnotations'),dict(type='PackSegInputs')
]
img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
tta_pipeline = [dict(type='LoadImageFromFile', backend_args=dict(backend='local')),dict(type='TestTimeAug',transforms=[[{'type': 'Resize','scale_factor': 0.5,'keep_ratio': True}, {'type': 'Resize','scale_factor': 0.75,'keep_ratio': True}, {'type': 'Resize','scale_factor': 1.0,'keep_ratio': True}, {'type': 'Resize','scale_factor': 1.25,'keep_ratio': True}, {'type': 'Resize','scale_factor': 1.5,'keep_ratio': True}, {'type': 'Resize','scale_factor': 1.75,'keep_ratio': True}],[{'type': 'RandomFlip','prob': 0.0,'direction': 'horizontal'}, {'type': 'RandomFlip','prob': 1.0,'direction': 'horizontal'}], [{'type': 'LoadAnnotations'}], [{'type': 'PackSegInputs'}]])
]
train_dataloader = dict(batch_size=8,num_workers=2,persistent_workers=True,sampler=dict(type='InfiniteSampler', shuffle=True),dataset=dict(type='StanfordBackgroundDataset',data_root='data/koto/',data_prefix=dict(img_path='imgs', seg_map_path='annos'),pipeline=[dict(type='LoadImageFromFile'),dict(type='LoadAnnotations'),dict(type='RandomResize',scale=(320, 240),ratio_range=(0.5, 2.0),keep_ratio=True),dict(type='RandomCrop', crop_size=(256, 256), cat_max_ratio=0.75),dict(type='RandomFlip', prob=0.5),dict(type='PackSegInputs')],ann_file='data/koto/train_list.txt'))
val_dataloader = dict(batch_size=1,num_workers=4,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type='StanfordBackgroundDataset',data_root='data/koto/',data_prefix=dict(img_path='imgs', seg_map_path='annos'),pipeline=[dict(type='LoadImageFromFile'),dict(type='Resize', scale=(320, 240), keep_ratio=True),dict(type='LoadAnnotations'),dict(type='PackSegInputs')],ann_file='data/koto/valid_list.txtt'))
test_dataloader = dict(batch_size=1,num_workers=4,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type='StanfordBackgroundDataset',data_root='data/koto/',data_prefix=dict(img_path='imgs', seg_map_path='annos'),pipeline=[dict(type='LoadImageFromFile'),dict(type='Resize', scale=(320, 240), keep_ratio=True),dict(type='LoadAnnotations'),dict(type='PackSegInputs')],ann_file='data/koto/valid_list.txtt'))
val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
default_scope = 'mmseg'
env_cfg = dict(cudnn_benchmark=True,mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(type='SegLocalVisualizer',vis_backends=[dict(type='LocalVisBackend')],name='visualizer')
log_processor = dict(by_epoch=False)
log_level = 'INFO'
load_from = 'pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
resume = False
tta_model = dict(type='SegTTAModel')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optim_wrapper = dict(type='OptimWrapper',optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005),clip_grad=None)
param_scheduler = [dict(type='PolyLR',eta_min=0.0001,power=0.9,begin=0,end=40000,by_epoch=False)
]
train_cfg = dict(type='IterBasedTrainLoop', max_iters=800, val_interval=400)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(timer=dict(type='IterTimerHook'),logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),param_scheduler=dict(type='ParamSchedulerHook'),checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=400),sampler_seed=dict(type='DistSamplerSeedHook'),visualization=dict(type='SegVisualizationHook'))
work_dir = './work_dirs/tutorial'
randomness = dict(seed=0)02/11 22:55:46 - mmengine - [5m[4m[33mWARNING[0m - The "visualizer" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:46 - mmengine - [5m[4m[33mWARNING[0m - The "vis_backend" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:47 - mmengine - [5m[4m[33mWARNING[0m - The "model" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead./home/linxu/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" insteadwarnings.warn('DeprecationWarning: pretrained is a deprecated, '
/home/linxu/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a thresholdwarnings.warn('For binary segmentation, we suggest using'
/home/linxu/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` warnings.warn('``build_loss`` would be deprecated soon, please use '
/home/linxu/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.warnings.warn(02/11 22:55:48 - mmengine - [4m[97mINFO[0m - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
02/11 22:55:48 - mmengine - [5m[4m[33mWARNING[0m - The "hook" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:48 - mmengine - [4m[97mINFO[0m - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) RuntimeInfoHook                    
(BELOW_NORMAL) LoggerHook                         -------------------- 
before_train:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(VERY_LOW    ) CheckpointHook                     -------------------- 
before_train_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(NORMAL      ) DistSamplerSeedHook                -------------------- 
before_train_iter:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      -------------------- 
after_train_iter:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SegVisualizationHook               
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     -------------------- 
after_train_epoch:
(NORMAL      ) IterTimerHook                      
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SegVisualizationHook               
(BELOW_NORMAL) LoggerHook                         -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      -------------------- 
after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SegVisualizationHook               
(BELOW_NORMAL) LoggerHook                         -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         -------------------- /home/linxu/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored.warnings.warn('The draw is False, it means that the '

runner.train()

02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The "loop" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The "dataset" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The "transform" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The "data sampler" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The "optimizer wrapper constructor" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The "optimizer" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The "optim_wrapper" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The "parameter scheduler" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The "metric" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead./home/linxu/anaconda3/envs/mmlab2/lib/python3.8/site-packages/mmengine/evaluator/metric.py:47: UserWarning: The prefix is not set in metric class IoUMetric.warnings.warn('The prefix is not set in metric class '02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The "weight initializer" registry in mmseg did not set import location. Fallback to call `mmseg.utils.register_all_modules` instead.
02/11 22:55:54 - mmengine - [4m[97mINFO[0m - load model from: open-mmlab://resnet50_v1c
02/11 22:55:54 - mmengine - [4m[97mINFO[0m - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet50_v1c
02/11 22:55:54 - mmengine - [5m[4m[33mWARNING[0m - The model and loaded state dict do not match exactlyunexpected key in source state_dict: fc.weight, fc.biasLoads checkpoint by local backend from path: pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
The model and loaded state dict do not match exactlysize mismatch for decode_head.conv_seg.weight: copying a param with shape torch.Size([19, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([2, 512, 1, 1]).
size mismatch for decode_head.conv_seg.bias: copying a param with shape torch.Size([19]) from checkpoint, the shape in current model is torch.Size([2]).
size mismatch for auxiliary_head.conv_seg.weight: copying a param with shape torch.Size([19, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([8, 256, 1, 1]).
size mismatch for auxiliary_head.conv_seg.bias: copying a param with shape torch.Size([19]) from checkpoint, the shape in current model is torch.Size([8]).
02/11 22:55:55 - mmengine - [4m[97mINFO[0m - Load checkpoint from pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
02/11 22:55:55 - mmengine - [4m[97mINFO[0m - Checkpoints will be saved to /home/linxu/Desktop/OpenMMLab-Space/mmsegmentation/work_dirs/tutorial.---------------------------------------------------------------------------RuntimeError                              Traceback (most recent call last)Cell In[14], line 1
----> 1 runner.train()File ~/anaconda3/envs/mmlab2/lib/python3.8/site-packages/mmengine/runner/runner.py:1686, in Runner.train(self)1680 # Initiate inner count of `optim_wrapper`.1681 self.optim_wrapper.initialize_count_status(1682     self.model,1683     self._train_loop.iter,  # type: ignore1684     self._train_loop.max_iters)  # type: ignore
-> 1686 model = self.train_loop.run()  # type: ignore1687 self.call_hook('after_run')1688 return modelFile ~/anaconda3/envs/mmlab2/lib/python3.8/site-packages/mmengine/runner/loops.py:264, in IterBasedTrainLoop.run(self)261 self.runner.model.train()263 data_batch = next(self.dataloader_iterator)
--> 264 self.run_iter(data_batch)266 self._decide_current_val_interval()267 if (self.runner.val_loop is not None268         and self._iter >= self.val_begin269         and self._iter % self.val_interval == 0):File ~/anaconda3/envs/mmlab2/lib/python3.8/site-packages/mmengine/runner/loops.py:287, in IterBasedTrainLoop.run_iter(self, data_batch)282 self.runner.call_hook(283     'before_train_iter', batch_idx=self._iter, data_batch=data_batch)284 # Enable gradient accumulation mode and avoid unnecessary gradient285 # synchronization during gradient accumulation process.286 # outputs should be a dict of loss.
--> 287 outputs = self.runner.model.train_step(288     data_batch, optim_wrapper=self.runner.optim_wrapper)290 self.runner.call_hook(291     'after_train_iter',292     batch_idx=self._iter,293     data_batch=data_batch,294     outputs=outputs)295 self._iter += 1File ~/anaconda3/envs/mmlab2/lib/python3.8/site-packages/mmengine/model/base_model/base_model.py:114, in BaseModel.train_step(self, data, optim_wrapper)112 with optim_wrapper.optim_context(self):113     data = self.data_preprocessor(data, True)
--> 114     losses = self._run_forward(data, mode='loss')  # type: ignore115 parsed_losses, log_vars = self.parse_losses(losses)  # type: ignore116 optim_wrapper.update_params(parsed_losses)File ~/anaconda3/envs/mmlab2/lib/python3.8/site-packages/mmengine/model/base_model/base_model.py:326, in BaseModel._run_forward(self, data, mode)316 """Unpacks data for :meth:`forward`317 318 Args:(...)323     dict or list: Results of training or testing mode.324 """325 if isinstance(data, dict):
--> 326     results = self(**data, mode=mode)327 elif isinstance(data, (list, tuple)):328     results = self(*data, mode=mode)File ~/anaconda3/envs/mmlab2/lib/python3.8/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)1190 # If we don't have any hooks, we want to skip the rest of the logic in1191 # this function, and just call forward.1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)1195 # Do not call functions when jit is used1196 full_backward_hooks, non_full_backward_hooks = [], []File ~/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/models/segmentors/base.py:94, in BaseSegmentor.forward(self, inputs, data_samples, mode)64 """The unified entry for a forward process in both training and test.65 66 The method should accept three modes: "tensor", "predict" and "loss":(...)91     - If ``mode="loss"``, return a dict of tensor.92 """93 if mode == 'loss':
---> 94     return self.loss(inputs, data_samples)95 elif mode == 'predict':96     return self.predict(inputs, data_samples)File ~/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py:176, in EncoderDecoder.loss(self, inputs, data_samples)172 x = self.extract_feat(inputs)174 losses = dict()
--> 176 loss_decode = self._decode_head_forward_train(x, data_samples)177 losses.update(loss_decode)179 if self.with_auxiliary_head:File ~/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py:137, in EncoderDecoder._decode_head_forward_train(self, inputs, data_samples)134 """Run forward function and calculate loss for decode head in135 training."""136 losses = dict()
--> 137 loss_decode = self.decode_head.loss(inputs, data_samples,138                                     self.train_cfg)140 losses.update(add_prefix(loss_decode, 'decode'))141 return lossesFile ~/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/models/decode_heads/decode_head.py:262, in BaseDecodeHead.loss(self, inputs, batch_data_samples, train_cfg)249 """Forward function for training.250 251 Args:(...)259     dict[str, Tensor]: a dictionary of loss components260 """261 seg_logits = self.forward(inputs)
--> 262 losses = self.loss_by_feat(seg_logits, batch_data_samples)263 return lossesFile ~/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/models/decode_heads/decode_head.py:336, in BaseDecodeHead.loss_by_feat(self, seg_logits, batch_data_samples)329     else:330         loss[loss_decode.loss_name] += loss_decode(331             seg_logits,332             seg_label,333             weight=seg_weight,334             ignore_index=self.ignore_index)
--> 336 loss['acc_seg'] = accuracy(337     seg_logits, seg_label, ignore_index=self.ignore_index)338 return lossFile ~/Desktop/OpenMMLab-Space/mmsegmentation/mmseg/models/losses/accuracy.py:49, in accuracy(pred, target, topk, thresh, ignore_index)47     correct = correct & (pred_value > thresh).t()48 if ignore_index is not None:
---> 49     correct = correct[:, target != ignore_index]50 res = []51 eps = torch.finfo(torch.float32).epsRuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.