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checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth'
crop_size = (
512,
512,
)
data_preprocessor = dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
512,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor')
data_root = 'PanicleDataset/'
dataset_type = 'TzyDataset'
default_hooks = dict(
checkpoint=dict(
by_epoch=False,
interval=2500,
max_keep_ckpts=1,
save_best='mIoU',
type='CheckpointHook'),
logger=dict(interval=100, 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,
]
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
backbone=dict(
attn_drop_rate=0.0,
drop_path_rate=0.1,
drop_rate=0.0,
embed_dims=64,
in_channels=3,
init_cfg=dict(
checkpoint=
'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth',
type='Pretrained'),
mlp_ratio=4,
num_heads=[
1,
2,
5,
8,
],
num_layers=[
3,
6,
40,
3,
],
num_stages=4,
out_indices=(
0,
1,
2,
3,
),
patch_sizes=[
7,
3,
3,
3,
],
qkv_bias=True,
sr_ratios=[
8,
4,
2,
1,
],
type='MixVisionTransformer'),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
512,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
channels=256,
dropout_ratio=0.1,
in_channels=[
64,
128,
320,
512,
],
in_index=[
0,
1,
2,
3,
],
loss_decode=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
norm_cfg=dict(requires_grad=True, type='BN'),
num_classes=2,
type='SegformerHead'),
pretrained=None,
test_cfg=dict(mode='whole'),
train_cfg=dict(),
type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='BN')
optim_wrapper = dict(
optimizer=dict(
betas=(
0.9,
0.999,
), lr=6e-05, type='AdamW', weight_decay=0.01),
paramwise_cfg=dict(
custom_keys=dict(
head=dict(lr_mult=10.0),
norm=dict(decay_mult=0.0),
pos_block=dict(decay_mult=0.0))),
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=1500, start_factor=1e-06,
type='LinearLR'),
dict(
begin=1500,
by_epoch=False,
end=160000,
eta_min=0.0,
power=1.0,
type='PolyLR'),
]
randomness = dict(seed=0)
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
data_root='PanicleDataset/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
2048,
1024,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='TzyDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
iou_metrics=[
'mIoU',
'mDice',
'mFscore',
], 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=20000, type='IterBasedTrainLoop', val_interval=500)
train_dataloader = dict(
batch_size=2,
dataset=dict(
data_prefix=dict(
img_path='img_dir/train', seg_map_path='ann_dir/train'),
data_root='PanicleDataset/',
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=(
512,
512,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs'),
],
type='TzyDataset'),
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=(
512,
512,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs'),
]
tta_model = dict(type='SegTTAModel')
tta_pipeline = [
dict(file_client_args=dict(backend='disk'), 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='img_dir/val', seg_map_path='ann_dir/val'),
data_root='PanicleDataset/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
2048,
1024,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='TzyDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
iou_metrics=[
'mIoU',
'mDice',
'mFscore',
], type='IoUMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='SegLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = './work_dirs/TzyDataset-Segformer-0721'
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