delete sub-optimal config files of fast-scnn
johnzja
parent
4102ed38cf
commit
daf93c6355
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@ -1,62 +0,0 @@
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_base_ = [
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'../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py'
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]
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crop_size = (512, 1024)
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cudnn_benchmark = True
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# model training and testing settings
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train_cfg = dict()
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test_cfg = dict(mode='whole')
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# Here: What is parameter 'with_seg'?
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations'), # with_seg=True
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='RandomCrop', crop_size=crop_size),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(2048, 1024),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=3,
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workers_per_gpu=3,
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=0.045, momentum=0.9, weight_decay=4e-5)
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optimizer_config = dict()
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# learning policy
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lr_config = dict(
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policy='poly',
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power=0.9,
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by_epoch=False,
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)
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# runtime settings
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total_epochs = 247000
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evaluation = dict(interval=1000, metric='mIoU')
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checkpoint_config = dict(interval=1000)
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# log config: log by iter.
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log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
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@ -1,64 +0,0 @@
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_base_ = [
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'../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py'
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]
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crop_size = (512, 1024)
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cudnn_benchmark = True
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# model training and testing settings
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train_cfg = dict()
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test_cfg = dict(mode='whole')
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# Here: What is parameter 'with_seg'?
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations'), # with_seg=True
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='RandomCrop', crop_size=crop_size),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(2048, 1024),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=3,
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workers_per_gpu=3,
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=4e-5)
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optimizer_config = dict()
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# learning policy
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lr_config = dict(
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policy='poly',
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power=0.9,
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by_epoch=False,
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)
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# runtime settings
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# total_epochs = 1000
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total_iters = 450000
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evaluation = dict(interval=2000, metric='mIoU')
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checkpoint_config = dict(interval=2000)
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# log config: log by iter.
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log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
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@ -1,64 +0,0 @@
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_base_ = [
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'../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py'
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]
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crop_size = (512, 1024)
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cudnn_benchmark = True
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# model training and testing settings
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train_cfg = dict()
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test_cfg = dict(mode='whole')
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# Here: What is parameter 'with_seg'?
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations'), # with_seg=True
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='RandomCrop', crop_size=crop_size),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(2048, 1024),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=8,
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workers_per_gpu=4,
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=0.045, momentum=0.9, weight_decay=4e-5)
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optimizer_config = dict()
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# learning policy
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lr_config = dict(
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policy='poly',
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power=1.2,
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by_epoch=False,
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)
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# runtime settings
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# total_epochs = 1000
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total_iters = 100000
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evaluation = dict(interval=2000, metric='mIoU')
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checkpoint_config = dict(interval=2000)
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# log config: log by iter.
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log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
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@ -1,64 +0,0 @@
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_base_ = [
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'../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py'
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]
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crop_size = (512, 1024)
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cudnn_benchmark = True
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# model training and testing settings
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train_cfg = dict()
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test_cfg = dict(mode='whole')
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# Here: What is parameter 'with_seg'?
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations'), # with_seg=True
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='RandomCrop', crop_size=crop_size),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(2048, 1024),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=8,
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workers_per_gpu=4,
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=4e-5)
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optimizer_config = dict()
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# learning policy
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lr_config = dict(
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policy='poly',
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power=0.9,
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by_epoch=False,
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)
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# runtime settings
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# total_epochs = 1000
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total_iters = 100000
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evaluation = dict(interval=2000, metric='mIoU')
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checkpoint_config = dict(interval=2000)
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# log config: log by iter.
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log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
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@ -1,64 +0,0 @@
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_base_ = [
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'../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py'
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]
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crop_size = (512, 1024)
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cudnn_benchmark = True
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# model training and testing settings
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train_cfg = dict()
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test_cfg = dict(mode='whole')
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# Here: What is parameter 'with_seg'?
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations'), # with_seg=True
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='RandomCrop', crop_size=crop_size),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(2048, 1024),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=8,
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workers_per_gpu=4,
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5)
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optimizer_config = dict()
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# learning policy
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lr_config = dict(
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policy='poly',
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power=0.9,
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by_epoch=False,
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)
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# runtime settings
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# total_epochs = 1000
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total_iters = 100000
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evaluation = dict(interval=2000, metric='mIoU')
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checkpoint_config = dict(interval=2000)
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# log config: log by iter.
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log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
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@ -1,61 +0,0 @@
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_base_ = [
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'../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py'
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]
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crop_size = (512, 1024)
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cudnn_benchmark = True
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# model training and testing settings
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train_cfg = dict()
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test_cfg = dict(mode='whole')
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# Here: What is parameter 'with_seg'?
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations'), # with_seg=True
|
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
|
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='RandomCrop', crop_size=crop_size),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
|
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
|
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img_scale=(2048, 1024),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
|
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dict(type='Normalize', **img_norm_cfg),
|
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=8,
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workers_per_gpu=4,
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=4e-5)
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optimizer_config = dict()
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# learning policy
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lr_config = dict(
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policy='poly',
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power=0.9,
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by_epoch=False,
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)
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# runtime settings
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# total_epochs = 1000
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total_iters = 100000
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evaluation = dict(interval=2000, metric='mIoU')
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checkpoint_config = dict(interval=2000)
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|
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@ -1,64 +0,0 @@
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_base_ = [
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'../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
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'../_base_/default_runtime.py'
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]
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crop_size = (512, 1024)
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cudnn_benchmark = True
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# model training and testing settings
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train_cfg = dict()
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test_cfg = dict(mode='whole')
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|
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# Here: What is parameter 'with_seg'?
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
|
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations'), # with_seg=True
|
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='PhotoMetricDistortion'),
|
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dict(type='Normalize', **img_norm_cfg),
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dict(type='RandomCrop', crop_size=crop_size),
|
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
|
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test_pipeline = [
|
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dict(type='LoadImageFromFile'),
|
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dict(
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type='MultiScaleFlipAug',
|
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img_scale=(2048, 1024),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
|
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dict(type='Normalize', **img_norm_cfg),
|
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=8,
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workers_per_gpu=4,
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
|
||||
|
||||
# optimizer
|
||||
optimizer = dict(type='SGD', lr=0.045, momentum=0.9, weight_decay=4e-5)
|
||||
optimizer_config = dict()
|
||||
# learning policy
|
||||
lr_config = dict(
|
||||
policy='poly',
|
||||
power=0.9,
|
||||
by_epoch=False,
|
||||
)
|
||||
# runtime settings
|
||||
# total_epochs = 1000
|
||||
total_iters = 100000
|
||||
evaluation = dict(interval=2000, metric='mIoU')
|
||||
checkpoint_config = dict(interval=2000)
|
||||
|
||||
# log config: log by iter.
|
||||
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
|
||||
|
||||
|
|
@ -1,64 +0,0 @@
|
|||
_base_ = [
|
||||
'../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
|
||||
'../_base_/default_runtime.py'
|
||||
]
|
||||
crop_size = (512, 1024)
|
||||
cudnn_benchmark = True
|
||||
# model training and testing settings
|
||||
train_cfg = dict()
|
||||
test_cfg = dict(mode='whole')
|
||||
|
||||
# Here: What is parameter 'with_seg'?
|
||||
img_norm_cfg = dict(
|
||||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
||||
train_pipeline = [
|
||||
dict(type='LoadImageFromFile', to_float32=True),
|
||||
dict(type='LoadAnnotations'), # with_seg=True
|
||||
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
|
||||
dict(type='RandomFlip', flip_ratio=0.5),
|
||||
dict(type='PhotoMetricDistortion'),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
dict(type='RandomCrop', crop_size=crop_size),
|
||||
dict(type='DefaultFormatBundle'),
|
||||
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
|
||||
]
|
||||
test_pipeline = [
|
||||
dict(type='LoadImageFromFile'),
|
||||
dict(
|
||||
type='MultiScaleFlipAug',
|
||||
img_scale=(2048, 1024),
|
||||
# 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=8,
|
||||
workers_per_gpu=4,
|
||||
train=dict(pipeline=train_pipeline),
|
||||
val=dict(pipeline=test_pipeline),
|
||||
test=dict(pipeline=test_pipeline))
|
||||
|
||||
# optimizer
|
||||
optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=4e-5)
|
||||
optimizer_config = dict()
|
||||
# learning policy
|
||||
lr_config = dict(
|
||||
policy='poly',
|
||||
power=0.9,
|
||||
by_epoch=False,
|
||||
)
|
||||
# runtime settings
|
||||
# total_epochs = 1000
|
||||
total_iters = 80000
|
||||
evaluation = dict(interval=2000, metric='mIoU')
|
||||
checkpoint_config = dict(interval=2000)
|
||||
|
||||
# log config: log by iter.
|
||||
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
|
||||
|
||||
Loading…
Reference in New Issue