OpenMMLab AI实战营 第七课笔记
import os
import numpy as np
from PIL import Image
import os.path as osp
from tqdm import tqdm
import 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')
Image.open('data/koto/annos/00001-125.png')
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()
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_root
cfg.train_dataloader.batch_size = 8
cfg.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 transform
dict(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 -
------------------------------------------------------------
System environment:
sys.platform: linux
Python: 3.8.16 (default, Jan 17 2023, 23:13:24) [GCC 11.2.0]
CUDA available: True
numpy_random_seed: 0
GPU 0: NVIDIA GeForce RTX 3060 Laptop GPU
CUDA_HOME: /usr/local/cuda-11.6
NVCC: Cuda compilation tools, release 11.6, V11.6.124
GCC: gcc (Uos 8.3.0.3-3+rebuild) 8.3.0
PyTorch: 1.13.1+cu116
PyTorch 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+cu116
OpenCV: 4.7.0
MMEngine: 0.5.0
Runtime environment:
cudnn_benchmark: True
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 0
Distributed launcher: none
Distributed training: False
GPU 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" instead
warnings.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 threshold
warnings.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 exactly
unexpected key in source state_dict: fc.weight, fc.bias
Loads 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 exactly
size 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: ignore
1684 self._train_loop.max_iters) # type: ignore
-> 1686 model = self.train_loop.run() # type: ignore
1687 self.call_hook('after_run')
1688 return model
File ~/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 None
268 and self._iter >= self.val_begin
269 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 gradient
285 # 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 += 1
File ~/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: ignore
115 parsed_losses, log_vars = self.parse_losses(losses) # type: ignore
116 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 in
1191 # this function, and just call forward.
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 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 in
135 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 losses
File ~/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 components
260 """
261 seg_logits = self.forward(inputs)
--> 262 losses = self.loss_by_feat(seg_logits, batch_data_samples)
263 return losses
File ~/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 loss
File ~/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).eps
RuntimeError: 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.