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[Improvement] Move train_cfg/test_cfg inside model (#341)

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* [Improvement] Move train_cfg/test_cfg inside model

* fixed config dict

* fixed config dict
This commit is contained in:
Jerry Jiarui XU 2021-01-19 17:06:23 -08:00 committed by GitHub
parent 0ee3016558
commit faaf29e668
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GPG Key ID: 4AEE18F83AFDEB23
88 changed files with 266 additions and 247 deletions
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6
configs/_base_/models/ann_r50-d8.py
View File

@ -40,7 +40,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/apcnet_r50-d8.py
View File

@ -38,7 +38,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/ccnet_r50-d8.py
View File

@ -38,7 +38,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/cgnet.py
View File

@ -29,7 +29,7 @@ model = dict(
10.289121, 9.953208, 4.3097677, 9.490387, 7.674431, 9.396905,
10.347791, 6.3927646, 10.226669, 10.241062, 10.280587,
10.396974, 10.055647
])))
])),
# model training and testing settings
train_cfg = dict(sampler=None)
test_cfg = dict(mode='whole')
train_cfg=dict(sampler=None),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/danet_r50-d8.py
View File

@ -38,7 +38,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/deeplabv3_r50-d8.py
View File

@ -38,7 +38,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/deeplabv3_unet_s5-d16.py
View File

@ -44,7 +44,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='slide', crop_size=256, stride=170)
train_cfg=dict(),
test_cfg=dict(mode='slide', crop_size=256, stride=170))

6
configs/_base_/models/deeplabv3plus_r50-d8.py
View File

@ -40,7 +40,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/dmnet_r50-d8.py
View File

@ -38,7 +38,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/dnl_r50-d8.py
View File

@ -40,7 +40,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/emanet_r50-d8.py
View File

@ -41,7 +41,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/encnet_r50-d8.py
View File

@ -42,7 +42,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

7
configs/_base_/models/fast_scnn.py
View File

@ -51,8 +51,7 @@ model = dict(
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
])
],
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/fcn_hr18.py
View File

@ -46,7 +46,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/fcn_r50-d8.py
View File

@ -39,7 +39,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/fcn_unet_s5-d16.py
View File

@ -45,7 +45,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='slide', crop_size=256, stride=170)
train_cfg=dict(),
test_cfg=dict(mode='slide', crop_size=256, stride=170))

6
configs/_base_/models/fpn_r50.py
View File

@ -30,7 +30,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/gcnet_r50-d8.py
View File

@ -40,7 +40,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/lraspp_m-v3-d8.py
View File

@ -19,7 +19,7 @@ model = dict(
act_cfg=dict(type='ReLU'),
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/nonlocal_r50-d8.py
View File

@ -40,7 +40,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/ocrnet_hr18.py
View File

@ -62,7 +62,7 @@ model = dict(
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
])
],
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/ocrnet_r50-d8.py
View File

@ -41,7 +41,7 @@ model = dict(
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
],
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/pointrend_r50.py
View File

@ -45,12 +45,12 @@ model = dict(
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
],
# model training and testing settings
train_cfg=dict(
num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75)
num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75),
test_cfg=dict(
mode='whole',
subdivision_steps=2,
subdivision_num_points=8196,
scale_factor=2)
scale_factor=2))

6
configs/_base_/models/psanet_r50-d8.py
View File

@ -43,7 +43,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/pspnet_r50-d8.py
View File

@ -38,7 +38,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

6
configs/_base_/models/pspnet_unet_s5-d16.py
View File

@ -44,7 +44,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='slide', crop_size=256, stride=170)
train_cfg=dict(),
test_cfg=dict(mode='slide', crop_size=256, stride=170))

6
configs/_base_/models/upernet_r50.py
View File

@ -38,7 +38,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
train_cfg=dict(),
test_cfg=dict(mode='whole'))

4
configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

5
configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context.py
View File

@ -4,6 +4,7 @@ _base_ = [
'../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=60), auxiliary_head=dict(num_classes=60))
test_cfg = dict(mode='slide', crop_size=(480, 480), stride=(320, 320))
decode_head=dict(num_classes=60),
auxiliary_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

5
configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context.py
View File

@ -4,6 +4,7 @@ _base_ = [
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=60), auxiliary_head=dict(num_classes=60))
test_cfg = dict(mode='slide', crop_size=(480, 480), stride=(320, 320))
decode_head=dict(num_classes=60),
auxiliary_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

4
configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

5
configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_40k_pascal_context.py
View File

@ -4,6 +4,7 @@ _base_ = [
'../_base_/schedules/schedule_40k.py'
]
model = dict(
decode_head=dict(num_classes=60), auxiliary_head=dict(num_classes=60))
test_cfg = dict(mode='slide', crop_size=(480, 480), stride=(320, 320))
decode_head=dict(num_classes=60),
auxiliary_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

5
configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_80k_pascal_context.py
View File

@ -4,6 +4,7 @@ _base_ = [
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=60), auxiliary_head=dict(num_classes=60))
test_cfg = dict(mode='slide', crop_size=(480, 480), stride=(320, 320))
decode_head=dict(num_classes=60),
auxiliary_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

4
configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,8 +5,8 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
optimizer = dict(
paramwise_cfg=dict(
custom_keys=dict(theta=dict(wd_mult=0.), phi=dict(wd_mult=0.))))

4
configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

5
configs/fcn/fcn_r50-d8_480x480_40k_pascal_context.py
View File

@ -2,6 +2,7 @@ _base_ = [
'../_base_/models/fcn_r50-d8.py', '../_base_/datasets/pascal_context.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(decode_head=dict(num_classes=60))
test_cfg = dict(mode='slide', crop_size=(480, 480), stride=(320, 320))
model = dict(
decode_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

5
configs/fcn/fcn_r50-d8_480x480_80k_pascal_context.py
View File

@ -2,6 +2,7 @@ _base_ = [
'../_base_/models/fcn_r50-d8.py', '../_base_/datasets/pascal_context.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(decode_head=dict(num_classes=60))
test_cfg = dict(mode='slide', crop_size=(480, 480), stride=(320, 320))
model = dict(
decode_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

4
configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

5
configs/hrnet/fcn_hr18_480x480_40k_pascal_context.py
View File

@ -2,6 +2,7 @@ _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_context.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
model = dict(decode_head=dict(num_classes=60))
test_cfg = dict(mode='slide', crop_size=(480, 480), stride=(320, 320))
model = dict(
decode_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

5
configs/hrnet/fcn_hr18_480x480_80k_pascal_context.py
View File

@ -2,6 +2,7 @@ _base_ = [
'../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_context.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
model = dict(decode_head=dict(num_classes=60))
test_cfg = dict(mode='slide', crop_size=(480, 480), stride=(320, 320))
model = dict(
decode_head=dict(num_classes=60),
test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

4
configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

2
configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
View File

@ -3,5 +3,5 @@ _base_ = [
'../_base_/datasets/chase_db1.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(128, 128), stride=(85, 85))
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
evaluation = dict(metric='mDice')

2
configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
View File

@ -2,5 +2,5 @@ _base_ = [
'../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/stare.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(128, 128), stride=(85, 85))
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
evaluation = dict(metric='mDice')

2
configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
View File

@ -2,5 +2,5 @@ _base_ = [
'../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/hrf.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(256, 256), stride=(170, 170))
model = dict(test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
evaluation = dict(metric='mDice')

2
configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
View File

@ -2,5 +2,5 @@ _base_ = [
'../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/drive.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(64, 64), stride=(42, 42))
model = dict(test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
evaluation = dict(metric='mDice')

2
configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
View File

@ -2,5 +2,5 @@ _base_ = [
'../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/chase_db1.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(128, 128), stride=(85, 85))
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
evaluation = dict(metric='mDice')

2
configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
View File

@ -2,5 +2,5 @@ _base_ = [
'../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/stare.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(128, 128), stride=(85, 85))
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
evaluation = dict(metric='mDice')

2
configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
View File

@ -2,5 +2,5 @@ _base_ = [
'../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/hrf.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(256, 256), stride=(170, 170))
model = dict(test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
evaluation = dict(metric='mDice')

2
configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
View File

@ -2,5 +2,5 @@ _base_ = [
'../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/drive.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(64, 64), stride=(42, 42))
model = dict(test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
evaluation = dict(metric='mDice')

2
configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
View File

@ -3,5 +3,5 @@ _base_ = [
'../_base_/datasets/chase_db1.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(128, 128), stride=(85, 85))
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
evaluation = dict(metric='mDice')

2
configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
View File

@ -2,5 +2,5 @@ _base_ = [
'../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/stare.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(128, 128), stride=(85, 85))
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
evaluation = dict(metric='mDice')

2
configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
View File

@ -2,5 +2,5 @@ _base_ = [
'../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/hrf.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(256, 256), stride=(170, 170))
model = dict(test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
evaluation = dict(metric='mDice')

2
configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
View File

@ -2,5 +2,5 @@ _base_ = [
'../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/drive.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
test_cfg = dict(crop_size=(64, 64), stride=(42, 42))
model = dict(test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
evaluation = dict(metric='mDice')

4
configs/upernet/upernet_r50_769x769_40k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

4
configs/upernet/upernet_r50_769x769_80k_cityscapes.py
View File

@ -5,5 +5,5 @@ _base_ = [
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True))
test_cfg = dict(mode='slide', crop_size=(769, 769), stride=(513, 513))
auxiliary_head=dict(align_corners=True),
test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

3
mmseg/apis/inference.py
View File

@ -27,7 +27,8 @@ def init_segmentor(config, checkpoint=None, device='cuda:0'):
raise TypeError('config must be a filename or Config object, '
'but got {}'.format(type(config)))
config.model.pretrained = None
model = build_segmentor(config.model, test_cfg=config.test_cfg)
config.model.train_cfg = None
model = build_segmentor(config.model, test_cfg=config.get('test_cfg'))
if checkpoint is not None:
checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
model.CLASSES = checkpoint['meta']['CLASSES']

10
mmseg/models/builder.py
View File

@ -1,3 +1,5 @@
import warnings
from mmcv.utils import Registry, build_from_cfg
from torch import nn
@ -53,4 +55,12 @@ def build_loss(cfg):
def build_segmentor(cfg, train_cfg=None, test_cfg=None):
"""Build segmentor."""
if train_cfg is not None or test_cfg is not None:
warnings.warn(
'train_cfg and test_cfg is deprecated, '
'please specify them in model', UserWarning)
assert cfg.get('train_cfg') is None or train_cfg is None, \
'train_cfg specified in both outer field and model field '
assert cfg.get('test_cfg') is None or test_cfg is None, \
'test_cfg specified in both outer field and model field '
return build(cfg, SEGMENTORS, dict(train_cfg=train_cfg, test_cfg=test_cfg))

7
tests/test_config.py
View File

@ -45,8 +45,6 @@ def test_config_build_segmentor():
config_mod = Config.fromfile(config_fpath)
config_mod.model
config_mod.train_cfg
config_mod.test_cfg
print('Building segmentor, config_fpath = {!r}'.format(config_fpath))
# Remove pretrained keys to allow for testing in an offline environment
@ -54,10 +52,7 @@ def test_config_build_segmentor():
config_mod.model['pretrained'] = None
print('building {}'.format(config_fname))
segmentor = build_segmentor(
config_mod.model,
train_cfg=config_mod.train_cfg,
test_cfg=config_mod.test_cfg)
segmentor = build_segmentor(config_mod.model)
assert segmentor is not None
head_config = config_mod.model['decode_head']

11
tests/test_models/test_forward.py
View File

@ -76,12 +76,9 @@ def _get_segmentor_cfg(fname):
These are deep copied to allow for safe modification of parameters without
influencing other tests.
"""
import mmcv
config = _get_config_module(fname)
model = copy.deepcopy(config.model)
train_cfg = mmcv.Config(copy.deepcopy(config.train_cfg))
test_cfg = mmcv.Config(copy.deepcopy(config.test_cfg))
return model, train_cfg, test_cfg
return model
def test_pspnet_forward():
@ -212,12 +209,12 @@ def _convert_batchnorm(module):
_check_input_dim)
@patch('torch.distributed.get_world_size', get_world_size)
def _test_encoder_decoder_forward(cfg_file):
model, train_cfg, test_cfg = _get_segmentor_cfg(cfg_file)
model = _get_segmentor_cfg(cfg_file)
model['pretrained'] = None
test_cfg['mode'] = 'whole'
model['test_cfg']['mode'] = 'whole'
from mmseg.models import build_segmentor
segmentor = build_segmentor(model, train_cfg=train_cfg, test_cfg=test_cfg)
segmentor = build_segmentor(model)
if isinstance(segmentor.decode_head, nn.ModuleList):
num_classes = segmentor.decode_head[-1].num_classes

50
tests/test_models/test_segmentor.py
View File

@ -1,6 +1,6 @@
import mmcv
import numpy as np
import torch
from mmcv import ConfigDict
from torch import nn
from mmseg.models import BACKBONES, HEADS, build_segmentor
@ -123,31 +123,33 @@ def _segmentor_forward_train_test(segmentor):
def test_encoder_decoder():
# test 1 decode head, w.o. aux head
cfg = dict(
cfg = ConfigDict(
type='EncoderDecoder',
backbone=dict(type='ExampleBackbone'),
decode_head=dict(type='ExampleDecodeHead'))
test_cfg = mmcv.Config(dict(mode='whole'))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
decode_head=dict(type='ExampleDecodeHead'),
train_cfg=None,
test_cfg=dict(mode='whole'))
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test slide mode
test_cfg = mmcv.Config(dict(mode='slide', crop_size=(3, 3), stride=(2, 2)))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
cfg.test_cfg = ConfigDict(mode='slide', crop_size=(3, 3), stride=(2, 2))
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 1 aux head
cfg = dict(
cfg = ConfigDict(
type='EncoderDecoder',
backbone=dict(type='ExampleBackbone'),
decode_head=dict(type='ExampleDecodeHead'),
auxiliary_head=dict(type='ExampleDecodeHead'))
test_cfg = mmcv.Config(dict(mode='whole'))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 2 aux head
cfg = dict(
cfg = ConfigDict(
type='EncoderDecoder',
backbone=dict(type='ExampleBackbone'),
decode_head=dict(type='ExampleDecodeHead'),
@ -155,15 +157,15 @@ def test_encoder_decoder():
dict(type='ExampleDecodeHead'),
dict(type='ExampleDecodeHead')
])
test_cfg = mmcv.Config(dict(mode='whole'))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
def test_cascade_encoder_decoder():
# test 1 decode head, w.o. aux head
cfg = dict(
cfg = ConfigDict(
type='CascadeEncoderDecoder',
num_stages=2,
backbone=dict(type='ExampleBackbone'),
@ -171,17 +173,17 @@ def test_cascade_encoder_decoder():
dict(type='ExampleDecodeHead'),
dict(type='ExampleCascadeDecodeHead')
])
test_cfg = mmcv.Config(dict(mode='whole'))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test slide mode
test_cfg = mmcv.Config(dict(mode='slide', crop_size=(3, 3), stride=(2, 2)))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
cfg.test_cfg = ConfigDict(mode='slide', crop_size=(3, 3), stride=(2, 2))
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 1 aux head
cfg = dict(
cfg = ConfigDict(
type='CascadeEncoderDecoder',
num_stages=2,
backbone=dict(type='ExampleBackbone'),
@ -190,12 +192,12 @@ def test_cascade_encoder_decoder():
dict(type='ExampleCascadeDecodeHead')
],
auxiliary_head=dict(type='ExampleDecodeHead'))
test_cfg = mmcv.Config(dict(mode='whole'))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 2 aux head
cfg = dict(
cfg = ConfigDict(
type='CascadeEncoderDecoder',
num_stages=2,
backbone=dict(type='ExampleBackbone'),
@ -207,6 +209,6 @@ def test_cascade_encoder_decoder():
dict(type='ExampleDecodeHead'),
dict(type='ExampleDecodeHead')
])
test_cfg = mmcv.Config(dict(mode='whole'))
segmentor = build_segmentor(cfg, train_cfg=None, test_cfg=test_cfg)
cfg.test_cfg = ConfigDict(mode='whole')
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)

3
tools/benchmark.py
View File

@ -40,7 +40,8 @@ def main():
shuffle=False)
# build the model and load checkpoint
model = build_segmentor(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
cfg.model.train_cfg = None
model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
load_checkpoint(model, args.checkpoint, map_location='cpu')
model = MMDataParallel(model, device_ids=[0])

4
tools/get_flops.py
View File

@ -33,7 +33,9 @@ def main():
cfg = Config.fromfile(args.config)
cfg.model.pretrained = None
model = build_segmentor(
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg).cuda()
cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg')).cuda()
model.eval()
if hasattr(model, 'forward_dummy'):

3
tools/pytorch2onnx.py
View File

@ -181,8 +181,9 @@ if __name__ == '__main__':
cfg.model.pretrained = None
# build the model and load checkpoint
cfg.model.train_cfg = None
segmentor = build_segmentor(
cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
# convert SyncBN to BN
segmentor = _convert_batchnorm(segmentor)

3
tools/test.py
View File

@ -110,7 +110,8 @@ def main():
shuffle=False)
# build the model and load checkpoint
model = build_segmentor(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
cfg.model.train_cfg = None
model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
model.CLASSES = checkpoint['meta']['CLASSES']
model.PALETTE = checkpoint['meta']['PALETTE']

4
tools/train.py
View File

@ -128,7 +128,9 @@ def main():
meta['exp_name'] = osp.basename(args.config)
model = build_segmentor(
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
logger.info(model)