代码拉取完成，页面将自动刷新
ann_dir = 'labels'
crop_size = (
256,
256,
)
data_preprocessor = dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
1024,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor')
data_root = './data/mmseg-semseg/scene/'
dataset_type = 'BaseSegDataset'
default_hooks = dict(
checkpoint=dict(by_epoch=False, interval=200, type='CheckpointHook'),
logger=dict(interval=10, log_metric_by_epoch=False, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(type='SegVisualizationHook'))
default_scope = 'mmseg'
env_cfg = dict(
cudnn_benchmark=True,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
img_dir = 'images'
img_ratios = [
0.5,
0.75,
1.0,
1.25,
1.5,
1.75,
]
load_from = './data/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=False)
metainfo = dict(
classes=(
'sky',
'tree',
'road',
'grass',
'water',
'bldg',
'mntn',
'fg obj',
),
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,
),
])
model = dict(
auxiliary_head=dict(
align_corners=False,
channels=256,
concat_input=False,
dropout_ratio=0.1,
in_channels=1024,
in_index=2,
loss_decode=dict(
loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False),
norm_cfg=dict(requires_grad=True, type='BN'),
num_classes=8,
num_convs=1,
type='FCNHead'),
backbone=dict(
contract_dilation=True,
depth=50,
dilations=(
1,
1,
2,
4,
),
norm_cfg=dict(requires_grad=True, type='BN'),
norm_eval=False,
num_stages=4,
out_indices=(
0,
1,
2,
3,
),
strides=(
1,
2,
1,
1,
),
style='pytorch',
type='ResNetV1c'),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
256,
256,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
channels=512,
dropout_ratio=0.1,
in_channels=2048,
in_index=3,
loss_decode=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
norm_cfg=dict(requires_grad=True, type='BN'),
num_classes=8,
pool_scales=(
1,
2,
3,
6,
),
type='PSPHead'),
pretrained='open-mmlab://resnet50_v1c',
test_cfg=dict(mode='whole'),
train_cfg=dict(),
type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='BN')
num_classes = 8
optim_wrapper = dict(
clip_grad=None,
optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005),
type='OptimWrapper')
optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
param_scheduler = [
dict(
begin=0,
by_epoch=False,
end=40000,
eta_min=0.0001,
power=0.9,
type='PolyLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='splits/val.txt',
data_prefix=dict(img_path='images', seg_map_path='labels'),
data_root='./data/mmseg-semseg/scene/',
img_suffix='.jpg',
metainfo=dict(
classes=(
'sky',
'tree',
'road',
'grass',
'water',
'bldg',
'mntn',
'fg obj',
),
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,
),
]),
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
320,
240,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
seg_map_suffix='.png',
type='BaseSegDataset'),
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
iou_metrics=[
'mIoU',
], type='IoUMetric')
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
320,
240,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
]
train_batch_size_per_gpu = 16
train_cfg = dict(max_iters=200, type='IterBasedTrainLoop', val_interval=200)
train_dataloader = dict(
batch_size=16,
dataset=dict(
ann_file='splits/train.txt',
data_prefix=dict(img_path='images', seg_map_path='labels'),
data_root='./data/mmseg-semseg/scene/',
img_suffix='.jpg',
metainfo=dict(
classes=(
'sky',
'tree',
'road',
'grass',
'water',
'bldg',
'mntn',
'fg obj',
),
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,
),
]),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
320,
240,
),
type='RandomResize'),
dict(
cat_max_ratio=0.75, crop_size=(
256,
256,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PackSegInputs'),
],
seg_map_suffix='.png',
type='BaseSegDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=True, type='InfiniteSampler'))
train_num_workers = 4
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
320,
240,
),
type='RandomResize'),
dict(cat_max_ratio=0.75, crop_size=(
256,
256,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PackSegInputs'),
]
tta_model = dict(type='SegTTAModel')
tta_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(
transforms=[
[
dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
dict(keep_ratio=True, scale_factor=1.75, type='Resize'),
],
[
dict(direction='horizontal', prob=0.0, type='RandomFlip'),
dict(direction='horizontal', prob=1.0, type='RandomFlip'),
],
[
dict(type='LoadAnnotations'),
],
[
dict(type='PackSegInputs'),
],
],
type='TestTimeAug'),
]
val_batch_size_per_gpu = 1
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='splits/val.txt',
data_prefix=dict(img_path='images', seg_map_path='labels'),
data_root='./data/mmseg-semseg/scene/',
img_suffix='.jpg',
metainfo=dict(
classes=(
'sky',
'tree',
'road',
'grass',
'water',
'bldg',
'mntn',
'fg obj',
),
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,
),
]),
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
320,
240,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
seg_map_suffix='.png',
type='BaseSegDataset'),
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
iou_metrics=[
'mIoU',
], type='IoUMetric')
val_num_workers = 2
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='SegLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
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