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crop_size = (
256,
512,
)
data_preprocessor = dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
256,
512,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor')
data_root = '/dataset/cityscapes/'
dataset_type = 'CityscapesDataset'
default_hooks = dict(
checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'),
logger=dict(interval=50, 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_ratios = [
0.5,
0.75,
1.0,
1.25,
1.5,
1.75,
]
launcher = 'pytorch'
load_from = 'work_dirs/fcn-hr48-4xb2-512x1024-80k/fcn-hr48-4xb2-512x1024-80k_ckpt.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
backbone=dict(
extra=dict(
stage1=dict(
block='BOTTLENECK',
num_blocks=(4, ),
num_branches=1,
num_channels=(64, ),
num_modules=1),
stage2=dict(
block='BASIC',
num_blocks=(
4,
4,
),
num_branches=2,
num_channels=(
48,
96,
),
num_modules=1),
stage3=dict(
block='BASIC',
num_blocks=(
4,
4,
4,
),
num_branches=3,
num_channels=(
48,
96,
192,
),
num_modules=4),
stage4=dict(
block='BASIC',
num_blocks=(
4,
4,
4,
4,
),
num_branches=4,
num_channels=(
48,
96,
192,
384,
),
num_modules=3)),
norm_cfg=dict(requires_grad=True, type='SyncBN'),
norm_eval=False,
type='HRNet'),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
256,
512,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
channels=720,
concat_input=False,
dropout_ratio=-1,
in_channels=[
48,
96,
192,
384,
],
in_index=(
0,
1,
2,
3,
),
input_transform='resize_concat',
kernel_size=1,
loss_decode=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
norm_cfg=dict(requires_grad=True, type='SyncBN'),
num_classes=19,
num_convs=1,
type='FCNHead'),
pretrained='open-mmlab://msra/hrnetv2_w48',
test_cfg=dict(mode='whole'),
train_cfg=dict(),
type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='SyncBN')
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=80000,
eta_min=0.0001,
power=0.9,
type='PolyLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
data_prefix=dict(
img_path='leftImg8bit/val', seg_map_path='gtFine/val'),
data_root='/dataset/cityscapes/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
2048,
1024,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='CityscapesDataset'),
num_workers=4,
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=(
2048,
1024,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=80000, type='IterBasedTrainLoop', val_interval=8000)
train_dataloader = dict(
batch_size=2,
dataset=dict(
data_prefix=dict(
img_path='leftImg8bit/train', seg_map_path='gtFine/train'),
data_root='/dataset/cityscapes/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
2048,
1024,
),
type='RandomResize'),
dict(
cat_max_ratio=0.75, crop_size=(
256,
512,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs'),
],
type='CityscapesDataset'),
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
2048,
1024,
),
type='RandomResize'),
dict(cat_max_ratio=0.75, crop_size=(
256,
512,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
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_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=1,
dataset=dict(
data_prefix=dict(
img_path='leftImg8bit/val', seg_map_path='gtFine/val'),
data_root='/dataset/cityscapes/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
2048,
1024,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='CityscapesDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
iou_metrics=[
'mIoU',
], type='IoUMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='SegLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = './work_dirs/fcn-hr48-4xb2-512x1024-80k'
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