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Develop #3709

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2c42828
try
wifiBlack Jun 14, 2024
f35081e
nothing
wifiBlack Jun 14, 2024
8583a7e
del 1.txt
wifiBlack Jun 14, 2024
78db720
try train chasedb
wifiBlack Jun 14, 2024
491499c
add mmseg_logs to git ignore!
wifiBlack Jun 16, 2024
1d04889
add pascal_context_datasets
wifiBlack Jun 19, 2024
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3 changes: 3 additions & 0 deletions .gitignore
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Expand Up @@ -35,6 +35,9 @@ MANIFEST
pip-log.txt
pip-delete-this-directory.txt

# train logs
mmseg_logs/*

# Unit test / coverage reports
htmlcov/
.tox/
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3 changes: 3 additions & 0 deletions mmseg_logs/chase_db1/20240614_171848/vis_data/20240614_171848.json
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{"lr": 0.009989084308262066, "data_time": 0.0038670063018798827, "loss": 0.6592155277729035, "decode.loss_ce": 0.47149578332901, "decode.acc_seg": 89.7125244140625, "aux.loss_ce": 0.18771974593400956, "aux.acc_seg": 89.7125244140625, "time": 0.39728693962097167, "iter": 50, "memory": 1717, "step": 50}
{"lr": 0.00997794446709763, "data_time": 0.003805875778198242, "loss": 0.6025463283061981, "decode.loss_ce": 0.4323257476091385, "decode.acc_seg": 93.8629150390625, "aux.loss_ce": 0.17022058442234994, "aux.acc_seg": 93.8629150390625, "time": 0.3980520725250244, "iter": 100, "memory": 784, "step": 100}
{"lr": 0.009966803229875268, "data_time": 0.0038983821868896484, "loss": 0.6277212738990784, "decode.loss_ce": 0.448396360874176, "decode.acc_seg": 76.31072998046875, "aux.loss_ce": 0.17932491898536682, "aux.acc_seg": 75.860595703125, "time": 0.3982886791229248, "iter": 150, "memory": 784, "step": 150}
332 changes: 332 additions & 0 deletions mmseg_logs/chase_db1/20240614_171848/vis_data/config.py
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crop_size = (
128,
128,
)
data_preprocessor = dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
128,
128,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor')
data_root = 'data/CHASE_DB1'
dataset_type = 'ChaseDB1Dataset'
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,
]
img_scale = (
960,
999,
)
launcher = 'none'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
auxiliary_head=dict(
align_corners=False,
channels=64,
concat_input=False,
dropout_ratio=0.1,
in_channels=128,
in_index=3,
loss_decode=dict(
loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False),
norm_cfg=dict(requires_grad=True, type='SyncBN'),
num_classes=2,
num_convs=1,
type='FCNHead'),
backbone=dict(
act_cfg=dict(type='ReLU'),
base_channels=64,
conv_cfg=None,
dec_dilations=(
1,
1,
1,
1,
),
dec_num_convs=(
2,
2,
2,
2,
),
downsamples=(
True,
True,
True,
True,
),
enc_dilations=(
1,
1,
1,
1,
1,
),
enc_num_convs=(
2,
2,
2,
2,
2,
),
in_channels=3,
norm_cfg=dict(requires_grad=True, type='SyncBN'),
norm_eval=False,
num_stages=5,
strides=(
1,
1,
1,
1,
1,
),
type='UNet',
upsample_cfg=dict(type='InterpConv'),
with_cp=False),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
128,
128,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
channels=64,
concat_input=False,
dropout_ratio=0.1,
in_channels=64,
in_index=4,
loss_decode=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
norm_cfg=dict(requires_grad=True, type='SyncBN'),
num_classes=2,
num_convs=1,
type='FCNHead'),
pretrained=None,
test_cfg=dict(crop_size=(
128,
128,
), mode='slide', stride=(
85,
85,
)),
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=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(
data_prefix=dict(
img_path='images/validation',
seg_map_path='annotations/validation'),
data_root='data/CHASE_DB1',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
960,
999,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='ChaseDB1Dataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
iou_metrics=[
'mDice',
], type='IoUMetric')
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
960,
999,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=40000, type='IterBasedTrainLoop', val_interval=4000)
train_dataloader = dict(
batch_size=4,
dataset=dict(
dataset=dict(
data_prefix=dict(
img_path='images/training',
seg_map_path='annotations/training'),
data_root='data/CHASE_DB1',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
960,
999,
),
type='RandomResize'),
dict(
cat_max_ratio=0.75,
crop_size=(
128,
128,
),
type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs'),
],
type='ChaseDB1Dataset'),
times=40000,
type='RepeatDataset'),
num_workers=4,
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=(
960,
999,
),
type='RandomResize'),
dict(cat_max_ratio=0.75, crop_size=(
128,
128,
), 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='images/validation',
seg_map_path='annotations/validation'),
data_root='data/CHASE_DB1',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
960,
999,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='ChaseDB1Dataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
iou_metrics=[
'mDice',
], type='IoUMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='SegLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = 'mmseg_logs/chase_db1/'
3 changes: 3 additions & 0 deletions mmseg_logs/chase_db1/20240614_171848/vis_data/scalars.json
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{"lr": 0.009989084308262066, "data_time": 0.0038670063018798827, "loss": 0.6592155277729035, "decode.loss_ce": 0.47149578332901, "decode.acc_seg": 89.7125244140625, "aux.loss_ce": 0.18771974593400956, "aux.acc_seg": 89.7125244140625, "time": 0.39728693962097167, "iter": 50, "memory": 1717, "step": 50}
{"lr": 0.00997794446709763, "data_time": 0.003805875778198242, "loss": 0.6025463283061981, "decode.loss_ce": 0.4323257476091385, "decode.acc_seg": 93.8629150390625, "aux.loss_ce": 0.17022058442234994, "aux.acc_seg": 93.8629150390625, "time": 0.3980520725250244, "iter": 100, "memory": 784, "step": 100}
{"lr": 0.009966803229875268, "data_time": 0.0038983821868896484, "loss": 0.6277212738990784, "decode.loss_ce": 0.448396360874176, "decode.acc_seg": 76.31072998046875, "aux.loss_ce": 0.17932491898536682, "aux.acc_seg": 75.860595703125, "time": 0.3982886791229248, "iter": 150, "memory": 784, "step": 150}
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