1. Official model conversion MMSegmentation style
If you want to convert the keywords yourself to use the pretrained model from the official repository, we also provide a script swin2mmseg.py in the tools directory to convert the keywords of the model from the official repo to the MMSegmentation style.
python tools/model_converters/swin2mmseg.py ${PRETRAIN_PATH} ${STORE_PATH}
python tools/model_converters/swin2mmseg.py https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth pretrain/swin_base_patch4_window7_224.pth
This script converts the model from PRETRAIN_PATH and stores the converted model in STORE_PATH.
In our default setup, the pretrained model and its corresponding original model model can be defined as follows:
2. Download the model of ADK20
https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth
https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth
https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth
https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth
https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth
https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k/upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k_20220318_015320-48d180dd.pth
https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth
3. Download the Swin Transform pre-training model
#tiny
https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth
#small
https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth
#big
https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_20220317-e9b98025.pth
https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_20220317-55b0104a.pth
https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_22k_20220317-4f79f7c0.pth
https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_22k_20220317-e5c09f74.pth
#large
https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220412-aeecf2aa.pth
https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window12_384_22k_20220412-6580f57d.pth
4. Construct the data directory of ADK20 structure
ADE20k has more than 25,000 images (20ktrain, 2k val, 3ktest) densely annotated with an open dictionary label set. For the 2017 Places Challenge 2, 100 thing and 50 stuff categories covering 89% of all pixels were selected.
There are 150 categories in total.
Idx Ratio Train Val Name
1 0.1576 11664 1172 wall
2 0.1072 6046 612 building, edifice
3 0.0878 8265 796 sky
4 0.0621 9336 917 floor, flooring
5 0.0480 6678 641 tree
6 0.0450 6604 643 ceiling
7 0.0398 4023 408 road, route
8 0.0231 1906 199 bed
9 0.0198 4688 460 windowpane, window
10 0.0183 2423 225 grass
11 0.0181 2874 294 cabinet
12 0.0166 3068 310 sidewalk, pavement
13 0.0160 5075 526 person, individual, someone, somebody, mortal, soul
14 0.0151 1804 190 earth, ground
15 0.0118 6666 796 door, double door
16 0.0110 4269 411 table
17 0.0109 1691 160 mountain, mount
18 0.0104 3999 441 plant, flora, plant life
19 0.0104 2149 217 curtain, drape, drapery, mantle, pall
20 0.0103 3261 318 chair
21 0.0098 3164 306 car, auto, automobile, machine, motorcar
22 0.0074 709 75 water
23 0.0067 3296 315 painting, picture
24 0.0065 1191 106 sofa, couch, lounge
25 0.0061 1516 162 shelf
26 0.0060 667 69 house
27 0.0053 651 57 sea
28 0.0052 1847 224 mirror
29 0.0046 1158 128 rug, carpet, carpeting
30 0.0044 480 44 field
31 0.0044 1172 98 armchair
32 0.0044 1292 184 seat
33 0.0033 1386 138 fence, fencing
34 0.0031 698 61 desk
35 0.0030 781 73 rock, stone
36 0.0027 380 43 wardrobe, closet, press
37 0.0026 3089 302 lamp
38 0.0024 404 37 bathtub, bathing tub, bath, tub
39 0.0024 804 99 railing, rail
40 0.0023 1453 153 cushion
41 0.0023 411 37 base, pedestal, stand
42 0.0022 1440 162 box
43 0.0022 800 77 column, pillar
44 0.0020 2650 298 signboard, sign
45 0.0019 549 46 chest of drawers, chest, bureau, dresser
46 0.0019 367 36 counter
47 0.0018 311 30 sand
48 0.0018 1181 122 sink
49 0.0018 287 23 skyscraper
50 0.0018 468 38 fireplace, hearth, open fireplace
51 0.0018 402 43 refrigerator, icebox
52 0.0018 130 12 grandstand, covered stand
53 0.0018 561 64 path
54 0.0017 880 102 stairs, steps
55 0.0017 86 12 runway
56 0.0017 172 11 case, display case, showcase, vitrine
57 0.0017 198 18 pool table, billiard table, snooker table
58 0.0017 930 109 pillow
59 0.0015 139 18 screen door, screen
60 0.0015 564 52 stairway, staircase
61 0.0015 320 26 river
62 0.0015 261 29 bridge, span
63 0.0014 275 22 bookcase
64 0.0014 335 60 blind, screen
65 0.0014 792 75 coffee table, cocktail table
66 0.0014 395 49 toilet, can, commode, crapper, pot, potty, stool, throne
67 0.0014 1309 138 flower
68 0.0013 1112 113 book
69 0.0013 266 27 hill
70 0.0013 659 66 bench
71 0.0012 331 31 countertop
72 0.0012 531 56 stove, kitchen stove, range, kitchen range, cooking stove
73 0.0012 369 36 palm, palm tree
74 0.0012 144 9 kitchen island
75 0.0011 265 29 computer, computing machine, computing device, data processor, electronic computer, information processing system
76 0.0010 324 33 swivel chair
77 0.0009 304 27 boat
78 0.0009 170 20 bar
79 0.0009 68 6 arcade machine
80 0.0009 65 8 hovel, hut, hutch, shack, shanty
81 0.0009 248 25 bus, autobus, coach, charabanc, double-decker, jitney, motorbus, motorcoach, omnibus, passenger vehicle
82 0.0008 492 49 towel
83 0.0008 2510 269 light, light source
84 0.0008 440 39 truck, motortruck
85 0.0008 147 18 tower
86 0.0008 583 56 chandelier, pendant, pendent
87 0.0007 533 61 awning, sunshade, sunblind
88 0.0007 1989 239 streetlight, street lamp
89 0.0007 71 5 booth, cubicle, stall, kiosk
90 0.0007 618 53 television receiver, television, television set, tv, tv set, idiot box, boob tube, telly, goggle box
91 0.0007 135 12 airplane, aeroplane, plane
92 0.0007 83 5 dirt track
93 0.0007 178 17 apparel, wearing apparel, dress, clothes
94 0.0006 1003 104 pole
95 0.0006 182 12 land, ground, soil
96 0.0006 452 50 bannister, banister, balustrade, balusters, handrail
97 0.0006 42 6 escalator, moving staircase, moving stairway
98 0.0006 307 31 ottoman, pouf, pouffe, puff, hassock
99 0.0006 965 114 bottle
100 0.0006 117 13 buffet, counter, sideboard
101 0.0006 354 35 poster, posting, placard, notice, bill, card
102 0.0006 108 9 stage
103 0.0006 557 55 van
104 0.0006 52 4 ship
105 0.0005 99 5 fountain
106 0.0005 57 4 conveyer belt, conveyor belt, conveyer, conveyor, transporter
107 0.0005 292 31 canopy
108 0.0005 77 9 washer, automatic washer, washing machine
109 0.0005 340 38 plaything, toy
110 0.0005 66 3 swimming pool, swimming bath, natatorium
111 0.0005 465 49 stool
112 0.0005 50 4 barrel, cask
113 0.0005 622 75 basket, handbasket
114 0.0005 80 9 waterfall, falls
115 0.0005 59 3 tent, collapsible shelter
116 0.0005 531 72 bag
117 0.0005 282 30 minibike, motorbike
118 0.0005 73 7 cradle
119 0.0005 435 44 oven
120 0.0005 136 25 ball
121 0.0005 116 24 food, solid food
122 0.0004 266 31 step, stair
123 0.0004 58 12 tank, storage tank
124 0.0004 418 83 trade name, brand name, brand, marque
125 0.0004 319 43 microwave, microwave oven
126 0.0004 1193 139 pot, flowerpot
127 0.0004 97 23 animal, animate being, beast, brute, creature, fauna
128 0.0004 347 36 bicycle, bike, wheel, cycle
129 0.0004 52 5 lake
130 0.0004 246 22 dishwasher, dish washer, dishwashing machine
131 0.0004 108 13 screen, silver screen, projection screen
132 0.0004 201 30 blanket, cover
133 0.0004 285 21 sculpture
134 0.0004 268 27 hood, exhaust hood
135 0.0003 1020 108 sconce
136 0.0003 1282 122 vase
137 0.0003 528 65 traffic light, traffic signal, stoplight
138 0.0003 453 57 tray
139 0.0003 671 100 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
140 0.0003 397 44 fan
141 0.0003 92 8 pier, wharf, wharfage, dock
142 0.0003 228 18 crt screen
143 0.0003 570 59 plate
144 0.0003 217 22 monitor, monitoring device
145 0.0003 206 19 bulletin board, notice board
146 0.0003 130 14 shower
147 0.0003 178 28 radiator
148 0.0002 504 57 glass, drinking glass
149 0.0002 775 96 clock
150 0.0002 421 56 flag
mmsegmentation
├── mmseg
├── tools
├── configs
├── data
│ ├── cityscapes
│ │ ├── leftImg8bit
│ │ │ ├── train
│ │ │ ├── val
│ │ ├── gtFine
│ │ │ ├── train
│ │ │ ├── val
│ ├── VOCdevkit
│ │ ├── VOC2012
│ │ │ ├── JPEGImages
│ │ │ ├── SegmentationClass
│ │ │ ├── ImageSets
│ │ │ │ ├── Segmentation
│ │ ├── VOC2010
│ │ │ ├── JPEGImages
│ │ │ ├── SegmentationClassContext
│ │ │ ├── ImageSets
│ │ │ │ ├── SegmentationContext
│ │ │ │ │ ├── train.txt
│ │ │ │ │ ├── val.txt
│ │ │ ├── trainval_merged.json
│ │ ├── VOCaug
│ │ │ ├── dataset
│ │ │ │ ├── cls
│ ├── ade
│ │ ├── ADEChallengeData2016
│ │ │ ├── annotations
│ │ │ │ ├── training
│ │ │ │ ├── validation
│ │ │ ├── images
│ │ │ │ ├── training
│ │ │ │ ├── validation
5. Modify the basic configuration file
This time we choose the upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K model for training, and the corresponding configuration files are as follows.
The specific configuration information is as follows
_base_ = [
'../_base_/models/upernet_swin.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
use_abs_pos_embed=False,
drop_path_rate=0.3,
patch_norm=True),
decode_head=dict(in_channels=[96, 192, 384, 768], num_classes=150),
auxiliary_head=dict(in_channels=384, num_classes=150))
# AdamW optimizer, no weight decay for position embedding & layer norm
# in backbone
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.00006,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={
'absolute_pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}))
lr_config = dict(
_delete_=True,
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
by_epoch=False)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
1. Set the number of modified categories and load the pre-training model (model architecture configuration file uppernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py)
_base_ = [
'../_base_/models/upernet_swin.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa，这个可以下载后，加载下载后的路径
model = dict(
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
use_abs_pos_embed=False,
drop_path_rate=0.3,
patch_norm=True),
decode_head=dict(in_channels=[96, 192, 384, 768], num_classes=150),
auxiliary_head=dict(in_channels=384, num_classes=150))#num_classes修改为自己的数据类别数，不包括背景，背景自动为0
# AdamW optimizer, no weight decay for position embedding & layer norm
# in backbone
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.00006,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={
'absolute_pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}))
lr_config = dict(
_delete_=True,
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
by_epoch=False)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
2. Modify data information (data type, data main path, etc. and batch-size) ('.../ base /datasets/ade20k.py')
# dataset settings
dataset_type = 'ADE20KDataset'
data_root = 'data/ade/ADEChallengeData2016' #1、修改为自己的数据路径
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) #2、修改为自己的数据的尺寸
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)),#根据img_crop调整img_scale
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=crop_size, 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),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
type=dataset_type,
data_root=data_root,
img_dir='images/training',
ann_dir='annotations/training',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
data_root=data_root,
img_dir='images/validation',
ann_dir='annotations/validation',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
data_root=data_root,
img_dir='images/validation',
ann_dir='annotations/validation',
pipeline=test_pipeline))
3 Modify the category name CLASSES and the suffix name\Ignore the specified label index in the loss calculation (mmseg/datasets/ade.py, mmseg/datasets/custom.py)
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import mmcv
import numpy as np
from PIL import Image
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class ADE20KDataset(CustomDataset):
"""ADE20K dataset.
In segmentation map annotation for ADE20K, 0 stands for background, which
is not included in 150 categories. ``reduce_zero_label`` is fixed to True.
The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is fixed to
'.png'.
"""
CLASSES = (
'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
'clock', 'flag')#修改为自己数据集的类别名称
PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
[4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
[230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
[150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
[143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
[0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
[255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
[255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
[255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
[224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
[255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
[6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
[140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
[255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
[255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
[11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
[0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
[255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
[0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
[173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
[255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
[255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
[255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
[0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
[0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
[143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
[8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
[255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
[92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
[163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
[255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
[255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
[10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
[255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
[41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
[71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
[184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
[102, 255, 0], [92, 0, 255]] #同理可以修改颜色
def __init__(self, **kwargs):
super(ADE20KDataset, self).__init__(
img_suffix='.jpg', #可以修改数据集的后缀格式
seg_map_suffix='.png',#可以修改数据集标签的后缀格式
reduce_zero_label=True,
**kwargs)
def results2img(self, results, imgfile_prefix, to_label_id, indices=None):
"""Write the segmentation results to images.
Args:
results (list[ndarray]): Testing results of the
dataset.
imgfile_prefix (str): The filename prefix of the png files.
If the prefix is "somepath/xxx",
the png files will be named "somepath/xxx.png".
to_label_id (bool): whether convert output to label_id for
submission.
indices (list[int], optional): Indices of input results, if not
set, all the indices of the dataset will be used.
Default: None.
Returns:
list[str: str]: result txt files which contains corresponding
semantic segmentation images.
"""
if indices is None:
indices = list(range(len(self)))
mmcv.mkdir_or_exist(imgfile_prefix)
result_files = []
for result, idx in zip(results, indices):
filename = self.img_infos[idx]['filename']
basename = osp.splitext(osp.basename(filename))[0]
png_filename = osp.join(imgfile_prefix, f'{basename}.png')#这里可以修改.png
# The index range of official requirement is from 0 to 150.
# But the index range of output is from 0 to 149.
# That is because we set reduce_zero_label=True.
result = result + 1
output = Image.fromarray(result.astype(np.uint8))
output.save(png_filename)
result_files.append(png_filename)
return result_files
def format_results(self,
results,
imgfile_prefix,
to_label_id=True,
indices=None):
"""Format the results into dir (standard format for ade20k evaluation).
Args:
results (list): Testing results of the dataset.
imgfile_prefix (str | None): The prefix of images files. It
includes the file path and the prefix of filename, e.g.,
"a/b/prefix".
to_label_id (bool): whether convert output to label_id for
submission. Default: False
indices (list[int], optional): Indices of input results, if not
set, all the indices of the dataset will be used.
Default: None.
Returns:
tuple: (result_files, tmp_dir), result_files is a list containing
the image paths, tmp_dir is the temporal directory created
for saving json/png files when img_prefix is not specified.
"""
if indices is None:
indices = list(range(len(self)))
assert isinstance(results, list), 'results must be a list.'
assert isinstance(indices, list), 'indices must be a list.'
result_files = self.results2img(results, imgfile_prefix, to_label_id,
indices)
return result_files
One thing to note is that if your picture is in jpg format and the mask is in png format, it should be fine. If it is not in these two formats, you need to modify the format of your picture in mmseg/datasets/custom.py.
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from collections import OrderedDict
import mmcv
import numpy as np
from mmcv.utils import print_log
from prettytable import PrettyTable
from torch.utils.data import Dataset
from mmseg.core import eval_metrics, intersect_and_union, pre_eval_to_metrics
from mmseg.utils import get_root_logger
from .builder import DATASETS
from .pipelines import Compose, LoadAnnotations
@DATASETS.register_module()
class CustomDataset(Dataset):
"""Custom dataset for semantic segmentation. An example of file structure
is as followed.
.. code-block:: none
├── data
│ ├── my_dataset
│ │ ├── img_dir
│ │ │ ├── train
│ │ │ │ ├── xxx{
img_suffix}
│ │ │ │ ├── yyy{
img_suffix}
│ │ │ │ ├── zzz{
img_suffix}
│ │ │ ├── val
│ │ ├── ann_dir
│ │ │ ├── train
│ │ │ │ ├── xxx{
seg_map_suffix}
│ │ │ │ ├── yyy{
seg_map_suffix}
│ │ │ │ ├── zzz{
seg_map_suffix}
│ │ │ ├── val
The img/gt_semantic_seg pair of CustomDataset should be of the same
except suffix. A valid img/gt_semantic_seg filename pair should be like
``xxx{
img_suffix}`` and ``xxx{
seg_map_suffix}`` (extension is also included
in the suffix). If split is given, then ``xxx`` is specified in txt file.
Otherwise, all files in ``img_dir/``and ``ann_dir`` will be loaded.
Please refer to ``docs/en/tutorials/new_dataset.md`` for more details.
Args:
pipeline (list[dict]): Processing pipeline
img_dir (str): Path to image directory
img_suffix (str): Suffix of images. Default: '.jpg'
ann_dir (str, optional): Path to annotation directory. Default: None
seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
split (str, optional): Split txt file. If split is specified, only
file with suffix in the splits will be loaded. Otherwise, all
images in img_dir/ann_dir will be loaded. Default: None
data_root (str, optional): Data root for img_dir/ann_dir. Default:
None.
test_mode (bool): If test_mode=True, gt wouldn't be loaded.
ignore_index (int): The label index to be ignored. Default: 255
reduce_zero_label (bool): Whether to mark label zero as ignored.
Default: False
classes (str | Sequence[str], optional): Specify classes to load.
If is None, ``cls.CLASSES`` will be used. Default: None.
palette (Sequence[Sequence[int]]] | np.ndarray | None):
The palette of segmentation map. If None is given, and
self.PALETTE is None, random palette will be generated.
Default: None
gt_seg_map_loader_cfg (dict, optional): build LoadAnnotations to
load gt for evaluation, load from disk by default. Default: None.
file_client_args (dict): Arguments to instantiate a FileClient.
See :class:`mmcv.fileio.FileClient` for details.
Defaults to ``dict(backend='disk')``.
"""
CLASSES = None
PALETTE = None
def __init__(self,
pipeline,
img_dir,
img_suffix='.jpg',#修改
ann_dir=None,
seg_map_suffix='.png',修改
split=None,
data_root=None,
test_mode=False,
ignore_index=255,
reduce_zero_label=False,
classes=None,
palette=None,
gt_seg_map_loader_cfg=None,
file_client_args=dict(backend='disk')):
self.pipeline = Compose(pipeline)
self.img_dir = img_dir
self.img_suffix = img_suffix
self.ann_dir = ann_dir
self.seg_map_suffix = seg_map_suffix
self.split = split
self.data_root = data_root
self.test_mode = test_mode
self.ignore_index = ignore_index
self.reduce_zero_label = reduce_zero_label
self.label_map = None
self.CLASSES, self.PALETTE = self.get_classes_and_palette(
classes, palette)
self.gt_seg_map_loader = LoadAnnotations(
) if gt_seg_map_loader_cfg is None else LoadAnnotations(
**gt_seg_map_loader_cfg)
self.file_client_args = file_client_args
self.file_client = mmcv.FileClient.infer_client(self.file_client_args)
if test_mode:
assert self.CLASSES is not None, \
'`cls.CLASSES` or `classes` should be specified when testing'
# join paths if data_root is specified
if self.data_root is not None:
if not osp.isabs(self.img_dir):
self.img_dir = osp.join(self.data_root, self.img_dir)
if not (self.ann_dir is None or osp.isabs(self.ann_dir)):
self.ann_dir = osp.join(self.data_root, self.ann_dir)
if not (self.split is None or osp.isabs(self.split)):
self.split = osp.join(self.data_root, self.split)
# load annotations
self.img_infos = self.load_annotations(self.img_dir, self.img_suffix,
self.ann_dir,
self.seg_map_suffix, self.split)
def __len__(self):
"""Total number of samples of data."""
return len(self.img_infos)
def load_annotations(self, img_dir, img_suffix, ann_dir, seg_map_suffix,
split):
"""Load annotation from directory.
Args:
img_dir (str): Path to image directory
img_suffix (str): Suffix of images.
ann_dir (str|None): Path to annotation directory.
seg_map_suffix (str|None): Suffix of segmentation maps.
split (str|None): Split txt file. If split is specified, only file
with suffix in the splits will be loaded. Otherwise, all images
in img_dir/ann_dir will be loaded. Default: None
Returns:
list[dict]: All image info of dataset.
"""
img_infos = []
if split is not None:
lines = mmcv.list_from_file(
split, file_client_args=self.file_client_args)
for line in lines:
img_name = line.strip()
img_info = dict(filename=img_name + img_suffix)
if ann_dir is not None:
seg_map = img_name + seg_map_suffix
img_info['ann'] = dict(seg_map=seg_map)
img_infos.append(img_info)
else:
for img in self.file_client.list_dir_or_file(
dir_path=img_dir,
list_dir=False,
suffix=img_suffix,
recursive=True):
img_info = dict(filename=img)
if ann_dir is not None:
seg_map = img.replace(img_suffix, seg_map_suffix)
img_info['ann'] = dict(seg_map=seg_map)
img_infos.append(img_info)
img_infos = sorted(img_infos, key=lambda x: x['filename'])
print_log(f'Loaded {
len(img_infos)} images', logger=get_root_logger())
return img_infos
def get_ann_info(self, idx):
"""Get annotation by index.
Args:
idx (int): Index of data.
Returns:
dict: Annotation info of specified index.
"""
return self.img_infos[idx]['ann']
def pre_pipeline(self, results):
"""Prepare results dict for pipeline."""
results['seg_fields'] = []
results['img_prefix'] = self.img_dir
results['seg_prefix'] = self.ann_dir
if self.custom_classes:
results['label_map'] = self.label_map
def __getitem__(self, idx):
"""Get training/test data after pipeline.
Args:
idx (int): Index of data.
Returns:
dict: Training/test data (with annotation if `test_mode` is set
False).
"""
if self.test_mode:
return self.prepare_test_img(idx)
else:
return self.prepare_train_img(idx)
def prepare_train_img(self, idx):
"""Get training data and annotations after pipeline.
Args:
idx (int): Index of data.
Returns:
dict: Training data and annotation after pipeline with new keys
introduced by pipeline.
"""
img_info = self.img_infos[idx]
ann_info = self.get_ann_info(idx)
results = dict(img_info=img_info, ann_info=ann_info)
self.pre_pipeline(results)
return self.pipeline(results)
def prepare_test_img(self, idx):
"""Get testing data after pipeline.
Args:
idx (int): Index of data.
Returns:
dict: Testing data after pipeline with new keys introduced by
pipeline.
"""
img_info = self.img_infos[idx]
results = dict(img_info=img_info)
self.pre_pipeline(results)
return self.pipeline(results)
def format_results(self, results, imgfile_prefix, indices=None, **kwargs):
"""Place holder to format result to dataset specific output."""
raise NotImplementedError
def get_gt_seg_map_by_idx(self, index):
"""Get one ground truth segmentation map for evaluation."""
ann_info = self.get_ann_info(index)
results = dict(ann_info=ann_info)
self.pre_pipeline(results)
self.gt_seg_map_loader(results)
return results['gt_semantic_seg']
def get_gt_seg_maps(self, efficient_test=None):
"""Get ground truth segmentation maps for evaluation."""
if efficient_test is not None:
warnings.warn(
'DeprecationWarning: ``efficient_test`` has been deprecated '
'since MMSeg v0.16, the ``get_gt_seg_maps()`` is CPU memory '
'friendly by default. ')
for idx in range(len(self)):
ann_info = self.get_ann_info(idx)
results = dict(ann_info=ann_info)
self.pre_pipeline(results)
self.gt_seg_map_loader(results)
yield results['gt_semantic_seg']
def pre_eval(self, preds, indices):
"""Collect eval result from each iteration.
Args:
preds (list[torch.Tensor] | torch.Tensor): the segmentation logit
after argmax, shape (N, H, W).
indices (list[int] | int): the prediction related ground truth
indices.
Returns:
list[torch.Tensor]: (area_intersect, area_union, area_prediction,
area_ground_truth).
"""
# In order to compat with batch inference
if not isinstance(indices, list):
indices = [indices]
if not isinstance(preds, list):
preds = [preds]
pre_eval_results = []
for pred, index in zip(preds, indices):
seg_map = self.get_gt_seg_map_by_idx(index)
pre_eval_results.append(
intersect_and_union(
pred,
seg_map,
len(self.CLASSES),
self.ignore_index,
# as the labels has been converted when dataset initialized
# in `get_palette_for_custom_classes ` this `label_map`
# should be `dict()`, see
# https://github.com/open-mmlab/mmsegmentation/issues/1415
# for more ditails
label_map=dict(),
reduce_zero_label=self.reduce_zero_label))
return pre_eval_results
def get_classes_and_palette(self, classes=None, palette=None):
"""Get class names of current dataset.
Args:
classes (Sequence[str] | str | None): If classes is None, use
default CLASSES defined by builtin dataset. If classes is a
string, take it as a file name. The file contains the name of
classes where each line contains one class name. If classes is
a tuple or list, override the CLASSES defined by the dataset.
palette (Sequence[Sequence[int]]] | np.ndarray | None):
The palette of segmentation map. If None is given, random
palette will be generated. Default: None
"""
if classes is None:
self.custom_classes = False
return self.CLASSES, self.PALETTE
self.custom_classes = True
if isinstance(classes, str):
# take it as a file path
class_names = mmcv.list_from_file(classes)
elif isinstance(classes, (tuple, list)):
class_names = classes
else:
raise ValueError(f'Unsupported type {type(classes)} of classes.')
if self.CLASSES:
if not set(class_names).issubset(self.CLASSES):
raise ValueError('classes is not a subset of CLASSES.')
# dictionary, its keys are the old label ids and its values
# are the new label ids.
# used for changing pixel labels in load_annotations.
self.label_map = {
}
for i, c in enumerate(self.CLASSES):
if c not in class_names:
self.label_map[i] = -1
else:
self.label_map[i] = class_names.index(c)
palette = self.get_palette_for_custom_classes(class_names, palette)
return class_names, palette
def get_palette_for_custom_classes(self, class_names, palette=None):
if self.label_map is not None:
# return subset of palette
palette = []
for old_id, new_id in sorted(
self.label_map.items(), key=lambda x: x[1]):
if new_id != -1:
palette.append(self.PALETTE[old_id])
palette = type(self.PALETTE)(palette)
elif palette is None:
if self.PALETTE is None:
# Get random state before set seed, and restore
# random state later.
# It will prevent loss of randomness, as the palette
# may be different in each iteration if not specified.
# See: https://github.com/open-mmlab/mmdetection/issues/5844
state = np.random.get_state()
np.random.seed(42)
# random palette
palette = np.random.randint(0, 255, size=(len(class_names), 3))
np.random.set_state(state)
else:
palette = self.PALETTE
return palette
def evaluate(self,
results,
metric='mIoU',
logger=None,
gt_seg_maps=None,
**kwargs):
"""Evaluate the dataset.
Args:
results (list[tuple[torch.Tensor]] | list[str]): per image pre_eval
results or predict segmentation map for computing evaluation
metric.
metric (str | list[str]): Metrics to be evaluated. 'mIoU',
'mDice' and 'mFscore' are supported.
logger (logging.Logger | None | str): Logger used for printing
related information during evaluation. Default: None.
gt_seg_maps (generator[ndarray]): Custom gt seg maps as input,
used in ConcatDataset
Returns:
dict[str, float]: Default metrics.
"""
if isinstance(metric, str):
metric = [metric]
allowed_metrics = ['mIoU', 'mDice', 'mFscore']
if not set(metric).issubset(set(allowed_metrics)):
raise KeyError('metric {} is not supported'.format(metric))
eval_results = {
}
# test a list of files
if mmcv.is_list_of(results, np.ndarray) or mmcv.is_list_of(
results, str):
if gt_seg_maps is None:
gt_seg_maps = self.get_gt_seg_maps()
num_classes = len(self.CLASSES)
ret_metrics = eval_metrics(
results,
gt_seg_maps,
num_classes,
self.ignore_index,
metric,
label_map=dict(),
reduce_zero_label=self.reduce_zero_label)
# test a list of pre_eval_results
else:
ret_metrics = pre_eval_to_metrics(results, metric)
# Because dataset.CLASSES is required for per-eval.
if self.CLASSES is None:
class_names = tuple(range(num_classes))
else:
class_names = self.CLASSES
# summary table
ret_metrics_summary = OrderedDict({
ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2)
for ret_metric, ret_metric_value in ret_metrics.items()
})
# each class table
ret_metrics.pop('aAcc', None)
ret_metrics_class = OrderedDict({
ret_metric: np.round(ret_metric_value * 100, 2)
for ret_metric, ret_metric_value in ret_metrics.items()
})
ret_metrics_class.update({
'Class': class_names})
ret_metrics_class.move_to_end('Class', last=False)
# for logger
class_table_data = PrettyTable()
for key, val in ret_metrics_class.items():
class_table_data.add_column(key, val)
summary_table_data = PrettyTable()
for key, val in ret_metrics_summary.items():
if key == 'aAcc':
summary_table_data.add_column(key, [val])
else:
summary_table_data.add_column('m' + key, [val])
print_log('per class results:', logger)
print_log('\n' + class_table_data.get_string(), logger=logger)
print_log('Summary:', logger)
print_log('\n' + summary_table_data.get_string(), logger=logger)
# each metric dict
for key, value in ret_metrics_summary.items():
if key == 'aAcc':
eval_results[key] = value / 100.0
else:
eval_results['m' + key] = value / 100.0
ret_metrics_class.pop('Class', None)
for key, value in ret_metrics_class.items():
eval_results.update({
key + '.' + str(name): value[idx] / 100.0
for idx, name in enumerate(class_names)
})
return eval_results
1. The modified configuration file of custom
2. The voc data type modifies the result of the model evaluation after the specified label index ignored in the calculation
4. Modify the running information configuration (load the pre-trained model and breakpoint training) (configs/-base-/default_runtime.py)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
# dict(type='TensorboardLoggerHook') #开启TensorboardLoggerHook
# dict(type='PaviLoggerHook') # for internal services
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None #从给定的路径加载模型作为预先训练的模型,这不会恢复训练。
resume_from = None #从给定的路径加载模型作为训练后的断点的模型,恢复训练。
workflow = [('train', 1)]
cudnn_benchmark = True
5. Modify the running information configuration (the maximum number of model training, keep a checkpoints for each training, how many times to perform model training, the index of model training evaluation is, automatically retain the best model,) (configs/-base- /schedule_40k.py, ... / base /schedules/schedule_160k.py)
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=160000)#max_iters，模型训练的最大迭代次数
checkpoint_config = dict(by_epoch=False, interval=16000)##interval，模型保存的迭代次数
evaluation = dict(interval=16000, metric='mIoU', pre_eval=True)#interval=16000模型多少间隔训练一次，评估的指标，#save_best='auto'可以保留最好的模型
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook')
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = '/media/lhy/Swin-Transformer-Semantic-Segmentation/checkpoints/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth'
resume_from = '/media/lhy/mmsegmentation-0.27.0/work_dirs/runs/train/road0.5m_1_deeplabv3plus_r101_exp2/best_mIoU_iter_44000.pth'
workflow = [('train', 1)]
cudnn_benchmark = True
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
#调用FP16
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
fp16 = dict()
lr_config = dict(policy='poly', power=0.9, min_lr=0.0001, by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=160000)
checkpoint_config = dict(by_epoch=False, interval=4000)
evaluation = dict(
interval=4000, metric=['mIoU', 'mFscore'], pre_eval=True, save_best='mIoU')#自动保存mIOU最好的模型
work_dir = 'work_dirs/runs/train/road0.5m_1_deeplabv3plus_r101_exp2'
gpu_ids = range(0, 4)
auto_resume = False
Single GPU learning rate lr= LR*(batch_size/16), LR represents the learning rate of 4GPU
6. Modify the inference mode of the model and norm_cfg (…/ base /models/upernet_swin.py)
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)#这里的norm_cfg中，如果是多卡训练，采用“SyncBN”; 如果是单卡训练，将type修改为'BN'即可。
backbone_norm_cfg = dict(type='LN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='SwinTransformer',
pretrain_img_size=224,
embed_dims=96,
patch_size=4,
window_size=7,
mlp_ratio=4,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
strides=(4, 2, 2, 2),
out_indices=(0, 1, 2, 3),
qkv_bias=True,
qk_scale=None,
patch_norm=True,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.3,
use_abs_pos_embed=False,
act_cfg=dict(type='GELU'),
norm_cfg=backbone_norm_cfg),
decode_head=dict(
type='UPerHead',
in_channels=[96, 192, 384, 768],
in_index=[0, 1, 2, 3],
pool_scales=(1, 2, 3, 6),
channels=512,
dropout_ratio=0.1,
num_classes=19,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
auxiliary_head=dict(
type='FCNHead',
in_channels=384,
in_index=2,
channels=256,
num_convs=1,
concat_input=False,
dropout_ratio=0.1,
num_classes=19,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))
#'whole代表全图推理模式',
#滑窗重叠预测可修改为：test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341))
Sliding window code: mmsegmentation/mmseg/models/segmentors/encoder_decoder.py
# TODO refactor
def slide_inference(self, img, img_meta, rescale):
"""Inference by sliding-window with overlap.
If h_crop > h_img or w_crop > w_img, the small patch will be used to
decode without padding.
"""
h_stride, w_stride = self.test_cfg.stride
h_crop, w_crop = self.test_cfg.crop_size
batch_size, _, h_img, w_img = img.size()
num_classes = self.num_classes
h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
preds = img.new_zeros((batch_size, num_classes, h_img, w_img))
count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
for h_idx in range(h_grids):
for w_idx in range(w_grids):
y1 = h_idx * h_stride
x1 = w_idx * w_stride
y2 = min(y1 + h_crop, h_img)
x2 = min(x1 + w_crop, w_img)
y1 = max(y2 - h_crop, 0)
x1 = max(x2 - w_crop, 0)
crop_img = img[:, :, y1:y2, x1:x2]
crop_seg_logit = self.encode_decode(crop_img, img_meta)
preds += F.pad(crop_seg_logit,
(int(x1), int(preds.shape[3] - x2), int(y1),
int(preds.shape[2] - y2)))
count_mat[:, :, y1:y2, x1:x2] += 1
assert (count_mat == 0).sum() == 0
if torch.onnx.is_in_onnx_export():
# cast count_mat to constant while exporting to ONNX
count_mat = torch.from_numpy(
count_mat.cpu().detach().numpy()).to(device=img.device)
preds = preds / count_mat
if rescale:
# remove padding area
resize_shape = img_meta[0]['img_shape'][:2]
preds = preds[:, :, :resize_shape[0], :resize_shape[1]]
preds = resize(
preds,
size=img_meta[0]['ori_shape'][:2],
mode='bilinear',
align_corners=self.align_corners,
warning=False)
return preds
6. Model optimization skills
1. Learning rate optimization techniques
In semantic segmentation, some methods make the LR of the head larger than that of the backbone to achieve better performance or faster convergence.
In MMSegmentation, you can add the following line to the configuration to make the LR of the head 10 times that of the trunk. With this modification, the LR 'head' of any parameter group with an LR name will be multiplied by 10.
Different Learning Rate(LR) for Backbone and Heads
n MMSegmentation, you may add following lines to config to make the LR of heads 10 times of backbone.
optimizer=dict(
paramwise_cfg = dict(
custom_keys={
'head': dict(lr_mult=10.)}))
2、Online Hard Example Mining (OHEM)
We implement pixel sampler here for training sampling. This is an example configuration for PSPNet training with OHEM enabled.
This way, only pixels with confidence scores lower than 0.7 are used for training. We keep at least 100000 pixels during training. If thresh is not specified, min_kept will select the top missing pixels.
Online Hard Example Mining (OHEM)
We implement pixel sampler here for training sampling. Here is an example config of training PSPNet with OHEM enabled.
_base_ = './pspnet_r50-d8_512x1024_40k_cityscapes.py'
model=dict(
decode_head=dict(
sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=100000)) )
3. Class balance loss
For datasets with an imbalanced class distribution, you can change the loss weights for each class. Here is an example of a cityscape dataset. class_weight will be passed to CrossEntropyLoss as the weight parameter
_base_ = './pspnet_r50-d8_512x1024_40k_cityscapes.py'
model=dict(
decode_head=dict(
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0,
# DeepLab used this class weight for cityscapes
class_weight=[0.8373, 0.9180, 0.8660, 1.0345, 1.0166, 0.9969, 0.9754,
1.0489, 0.8786, 1.0023, 0.9539, 0.9843, 1.1116, 0.9037,
1.0865, 1.0955, 1.0865, 1.1529, 1.0507])))
4. Multiple losses
For loss computation, we support multiple loss trainings at the same time. unet Here is an example configuration trained on the dataset DRIVE with a loss function that is a 1:3 sum of weighted CrossEntropyLoss and DiceLoss:
_base_ = './fcn_unet_s5-d16_64x64_40k_drive.py'
model = dict(
decode_head=dict(loss_decode=[dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)]),
auxiliary_head=dict(loss_decode=[dict(type='CrossEntropyLoss', loss_name='loss_ce',loss_weight=1.0),
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)]),
)
In this way, loss_weight and loss_name will be the weight and name in the training log of the corresponding loss respectively.
Note: if you want to include this loss term into the backward graph, loss_ must be prefixed to the name.
5. Ignore the specified label index in the loss calculation
mmseg has written description files and loading codes for various public segmentation data sets. For those who have used PyTorch, it is still very comfortable to learn the description files of various data sets. Only reduce_zero_label is relatively unfamiliar to mmseg novices. Therefore, when building your own mmseg data set, the most confusing thing for novices is probably whether reduce_zero_label should be True or False.
What is it for? Literally translated from the name is "reduce 0 value label". In the multi-class segmentation task, if the 0 value in your data set is used as the background category in the label file, it is recommended to ignore it.
Open the source code fragment of the loaded data and you can see a piece of code that processes reduce_zero_label, which means: if reduce_zero_label is enabled, all labels that were originally 0 are set to 255, which is the default value of the ignore_index parameter in the loss function. The default avoidance value of this parameter is 255 annotations are involved in the loss calculation. The reason for the 150-class ADE data set not shown above is that the reduce zero label is enabled, and the background with a value of 0 is set to ignore_index.
# mmseg/datasets/pipelines/loading.py
...
# reduce zero_label
if self.reduce_zero_label:
# avoid using underflow conversion
gt_semantic_seg[gt_semantic_seg == 0] = 255
gt_semantic_seg = gt_semantic_seg - 1
gt_semantic_seg[gt_semantic_seg == 254] = 255
...
Description of common problems caused by reduce_zero_label
Here we take the source code of the ADE dataset as an example. The default setting of reduce_zero_label is True. However, even if a novice has mastered the reduce_zero_label in the previous section, he may have a superficial understanding of ADE and wonder whether the reduce_zero_label enabled in the configuration file is to use 150 instances The first one in the class is ignored, after all, isn't num_classes 150, and then reduce_zero_label is taken for granted.
Error Cause Analysis
# configs/_base_/datasets/ade20k.py
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True), # ADE中reduce_zero_label默认设置为True
dict(...),
...
]
There are indeed only 150 classes in the label that actually participated in the training, which are defined in CLASSES, but the label file actually contains 151 classes, and the background class (the remaining unmarked, or accidentally ignored areas are classified as background, in the label file The median value is 0) is not included in the 150 CLASSES, and needs to be set to ignore_index during training, so we use the reduce_zero_label in the previous section to extract the background from the 151 classes and set it to ignore_index alone. If we mistakenly set reduce_zero_label Turn it off, then num_classes is 151.
In the default setting, avg_non_ignore=False which means that every pixel counts towards the loss calculation, although some of them belong to ignore index labels.
For loss calculation, we support passing avg_non_ignore and ignore_index which ignore certain labels. In this way, the average loss will only be calculated in the non-ignored labels, which may achieve better performance, here is the reference. unet This is an example configuration for dataset training Cityscapes: in loss calculation it ignores label 0 as background and calculates loss average only on non-ignored labels:
_base_ = './fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py'
model = dict(
decode_head=dict(
ignore_index=0,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0, avg_non_ignore=True),
auxiliary_head=dict(
ignore_index=0,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0, avg_non_ignore=True)),
))
Just add ignore_index decoder header or auxiliary header and add avg_non_ignore=True:
# model settings
...
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0, avg_non_ignore=True),
...