HuQiong
/
PaddleRS
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[Feat] Add Interface to Set Losses (#18)

Browse Source 
* Add interfaces to set losses

* Fix typo

* Fix import bugs

* import custom_models->rs_models
own
Lin Manhui 3 years ago committed by GitHub
parent 22631cf72c
commit 3f8ce38fb0
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 101 additions and 28 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 3
      paddlers/models/__init__.py
  2. 49
      paddlers/tasks/change_detector.py
  3. 35
      paddlers/tasks/classifier.py
  4. 42
      paddlers/tasks/segmenter.py

3
paddlers/models/__init__.py
Unescape View File

@ -13,3 +13,6 @@
# limitations under the License.
from . import ppcls, ppdet, ppseg, ppgan
import paddlers.models.ppseg.models.losses as seg_losses
import paddlers.models.ppdet.modeling.losses as det_losses
import paddlers.models.ppcls.loss as clas_losses

49
paddlers/tasks/change_detector.py
Unescape View File

@ -28,6 +28,7 @@ import paddlers
import paddlers.models.ppseg as ppseg
import paddlers.rs_models.cd as cmcd
import paddlers.utils.logging as logging
from paddlers.models import seg_losses
from paddlers.transforms import Resize, decode_image
from paddlers.utils import get_single_card_bs, DisablePrint
from paddlers.utils.checkpoint import seg_pretrain_weights_dict
@ -45,6 +46,7 @@ class BaseChangeDetector(BaseModel):
model_name,
num_classes=2,
use_mixed_loss=False,
losses=None,
**params):
self.init_params = locals()
if 'with_net' in self.init_params:
@ -56,7 +58,7 @@ class BaseChangeDetector(BaseModel):
self.model_name = model_name
self.num_classes = num_classes
self.use_mixed_loss = use_mixed_loss
self.losses = None
self.losses = losses
self.labels = None
if params.get('with_net', True):
params.pop('with_net', None)
@ -178,13 +180,13 @@ class BaseChangeDetector(BaseModel):
if isinstance(self.use_mixed_loss, bool):
if self.use_mixed_loss:
losses = [
ppseg.models.CrossEntropyLoss(),
ppseg.models.LovaszSoftmaxLoss()
seg_losses.CrossEntropyLoss(),
seg_losses.LovaszSoftmaxLoss()
]
coef = [.8, .2]
loss_type = [ppseg.models.MixedLoss(losses=losses, coef=coef), ]
loss_type = [seg_losses.MixedLoss(losses=losses, coef=coef), ]
else:
loss_type = [ppseg.models.CrossEntropyLoss()]
loss_type = [seg_losses.CrossEntropyLoss()]
else:
losses, coef = list(zip(*self.use_mixed_loss))
if not set(losses).issubset(
@ -192,8 +194,8 @@ class BaseChangeDetector(BaseModel):
raise ValueError(
"Only 'CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss' are supported."
)
losses = [getattr(ppseg.models, loss)() for loss in losses]
loss_type = [ppseg.models.MixedLoss(losses=losses, coef=list(coef))]
losses = [getattr(seg_losses, loss)() for loss in losses]
loss_type = [seg_losses.MixedLoss(losses=losses, coef=list(coef))]
loss_coef = [1.0]
losses = {'types': loss_type, 'coef': loss_coef}
return losses
@ -810,11 +812,17 @@ class BaseChangeDetector(BaseModel):
raise TypeError(
"`transforms.arrange` must be an ArrangeChangeDetector object.")
def set_losses(self, losses, weights=None):
if weights is None:
weights = [1. for _ in range(len(losses))]
self.losses = {'types': losses, 'coef': weights}
class CDNet(BaseChangeDetector):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
in_channels=6,
**params):
params.update({'in_channels': in_channels})
@ -822,6 +830,7 @@ class CDNet(BaseChangeDetector):
model_name='CDNet',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
@ -829,6 +838,7 @@ class FCEarlyFusion(BaseChangeDetector):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
in_channels=6,
use_dropout=False,
**params):
@ -837,6 +847,7 @@ class FCEarlyFusion(BaseChangeDetector):
model_name='FCEarlyFusion',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
@ -844,6 +855,7 @@ class FCSiamConc(BaseChangeDetector):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
in_channels=3,
use_dropout=False,
**params):
@ -852,6 +864,7 @@ class FCSiamConc(BaseChangeDetector):
model_name='FCSiamConc',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
@ -859,6 +872,7 @@ class FCSiamDiff(BaseChangeDetector):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
in_channels=3,
use_dropout=False,
**params):
@ -867,6 +881,7 @@ class FCSiamDiff(BaseChangeDetector):
model_name='FCSiamDiff',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
@ -874,6 +889,7 @@ class STANet(BaseChangeDetector):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
in_channels=3,
att_type='BAM',
ds_factor=1,
@ -887,6 +903,7 @@ class STANet(BaseChangeDetector):
model_name='STANet',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
@ -894,6 +911,7 @@ class BIT(BaseChangeDetector):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
in_channels=3,
backbone='resnet18',
n_stages=4,
@ -925,6 +943,7 @@ class BIT(BaseChangeDetector):
model_name='BIT',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
@ -932,6 +951,7 @@ class SNUNet(BaseChangeDetector):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
in_channels=3,
width=32,
**params):
@ -940,6 +960,7 @@ class SNUNet(BaseChangeDetector):
model_name='SNUNet',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
@ -947,6 +968,7 @@ class DSIFN(BaseChangeDetector):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
use_dropout=False,
**params):
params.update({'use_dropout': use_dropout})
@ -954,13 +976,14 @@ class DSIFN(BaseChangeDetector):
model_name='DSIFN',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
def default_loss(self):
if self.use_mixed_loss is False:
return {
# XXX: make sure the shallow copy works correctly here.
'types': [ppseg.models.CrossEntropyLoss()] * 5,
'types': [seg_losses.CrossEntropyLoss()] * 5,
'coef': [1.0] * 5
}
else:
@ -973,6 +996,7 @@ class DSAMNet(BaseChangeDetector):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
in_channels=3,
ca_ratio=8,
sa_kernel=7,
@ -986,14 +1010,15 @@ class DSAMNet(BaseChangeDetector):
model_name='DSAMNet',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
def default_loss(self):
if self.use_mixed_loss is False:
return {
'types': [
ppseg.models.CrossEntropyLoss(), ppseg.models.DiceLoss(),
ppseg.models.DiceLoss()
seg_losses.CrossEntropyLoss(), seg_losses.DiceLoss(),
seg_losses.DiceLoss()
],
'coef': [1.0, 0.05, 0.05]
}
@ -1007,6 +1032,7 @@ class ChangeStar(BaseChangeDetector):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
mid_channels=256,
inner_channels=16,
num_convs=4,
@ -1022,13 +1048,14 @@ class ChangeStar(BaseChangeDetector):
model_name='ChangeStar',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
def default_loss(self):
if self.use_mixed_loss is False:
return {
# XXX: make sure the shallow copy works correctly here.
'types': [ppseg.models.CrossEntropyLoss()] * 4,
'types': [seglosses.CrossEntropyLoss()] * 4,
'coef': [1.0] * 4
}
else:

35
paddlers/tasks/classifier.py
Unescape View File

@ -28,7 +28,7 @@ import paddlers.rs_models.clas as cmcls
import paddlers.utils.logging as logging
from paddlers.utils import get_single_card_bs, DisablePrint
from paddlers.models.ppcls.metric import build_metrics
from paddlers.models.ppcls.loss import build_loss
from paddlers.models import clas_losses
from paddlers.models.ppcls.data.postprocess import build_postprocess
from paddlers.utils.checkpoint import cls_pretrain_weights_dict
from paddlers.transforms import Resize, decode_image
@ -45,6 +45,7 @@ class BaseClassifier(BaseModel):
in_channels=3,
num_classes=2,
use_mixed_loss=False,
losses=None,
**params):
self.init_params = locals()
if 'with_net' in self.init_params:
@ -59,7 +60,7 @@ class BaseClassifier(BaseModel):
self.num_classes = num_classes
self.use_mixed_loss = use_mixed_loss
self.metrics = None
self.losses = None
self.losses = losses
self.labels = None
self._postprocess = None
if params.get('with_net', True):
@ -145,7 +146,7 @@ class BaseClassifier(BaseModel):
def default_loss(self):
# TODO: use mixed loss and other loss
default_config = [{"CELoss": {"weight": 1.0}}]
return build_loss(default_config)
return clas_losses.build_loss(default_config)
def default_optimizer(self,
parameters,
@ -556,36 +557,56 @@ class BaseClassifier(BaseModel):
class ResNet50_vd(BaseClassifier):
def __init__(self, num_classes=2, use_mixed_loss=False, **params):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
**params):
super(ResNet50_vd, self).__init__(
model_name='ResNet50_vd',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
class MobileNetV3_small_x1_0(BaseClassifier):
def __init__(self, num_classes=2, use_mixed_loss=False, **params):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
**params):
super(MobileNetV3_small_x1_0, self).__init__(
model_name='MobileNetV3_small_x1_0',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
class HRNet_W18_C(BaseClassifier):
def __init__(self, num_classes=2, use_mixed_loss=False, **params):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
**params):
super(HRNet_W18_C, self).__init__(
model_name='HRNet_W18_C',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
class CondenseNetV2_b(BaseClassifier):
def __init__(self, num_classes=2, use_mixed_loss=False, **params):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
**params):
super(CondenseNetV2_b, self).__init__(
model_name='CondenseNetV2_b',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)

42
paddlers/tasks/segmenter.py
Unescape View File

@ -26,10 +26,11 @@ from paddle.static import InputSpec
import paddlers
import paddlers.models.ppseg as ppseg
import paddlers.rs_models.seg as cmseg
from paddlers.utils import get_single_card_bs, DisablePrint
import paddlers.utils.logging as logging
from paddlers.utils.checkpoint import seg_pretrain_weights_dict
from paddlers.models import seg_losses
from paddlers.transforms import Resize, decode_image
from paddlers.utils import get_single_card_bs, DisablePrint
from paddlers.utils.checkpoint import seg_pretrain_weights_dict
from .base import BaseModel
from .utils import seg_metrics as metrics
@ -41,6 +42,7 @@ class BaseSegmenter(BaseModel):
model_name,
num_classes=2,
use_mixed_loss=False,
losses=None,
**params):
self.init_params = locals()
if 'with_net' in self.init_params:
@ -53,7 +55,7 @@ class BaseSegmenter(BaseModel):
self.model_name = model_name
self.num_classes = num_classes
self.use_mixed_loss = use_mixed_loss
self.losses = None
self.losses = losses
self.labels = None
if params.get('with_net', True):
params.pop('with_net', None)
@ -160,13 +162,13 @@ class BaseSegmenter(BaseModel):
if isinstance(self.use_mixed_loss, bool):
if self.use_mixed_loss:
losses = [
ppseg.models.CrossEntropyLoss(),
ppseg.models.LovaszSoftmaxLoss()
seg_losses.CrossEntropyLoss(),
seg_losses.LovaszSoftmaxLoss()
]
coef = [.8, .2]
loss_type = [ppseg.models.MixedLoss(losses=losses, coef=coef), ]
loss_type = [seg_losses.MixedLoss(losses=losses, coef=coef), ]
else:
loss_type = [ppseg.models.CrossEntropyLoss()]
loss_type = [seg_losses.CrossEntropyLoss()]
else:
losses, coef = list(zip(*self.use_mixed_loss))
if not set(losses).issubset(
@ -174,8 +176,8 @@ class BaseSegmenter(BaseModel):
raise ValueError(
"Only 'CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss' are supported."
)
losses = [getattr(ppseg.models, loss)() for loss in losses]
loss_type = [ppseg.models.MixedLoss(losses=losses, coef=list(coef))]
losses = [getattr(seg_losses, loss)() for loss in losses]
loss_type = [seg_losses.MixedLoss(losses=losses, coef=list(coef))]
if self.model_name == 'FastSCNN':
loss_type *= 2
loss_coef = [1.0, 0.4]
@ -771,12 +773,18 @@ class BaseSegmenter(BaseModel):
raise TypeError(
"`transforms.arrange` must be an ArrangeSegmenter object.")
def set_losses(self, losses, weights=None):
if weights is None:
weights = [1. for _ in range(len(losses))]
self.losses = {'types': losses, 'coef': weights}
class UNet(BaseSegmenter):
def __init__(self,
input_channel=3,
num_classes=2,
use_mixed_loss=False,
losses=None,
use_deconv=False,
align_corners=False,
**params):
@ -789,6 +797,7 @@ class UNet(BaseSegmenter):
input_channel=input_channel,
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
@ -798,6 +807,7 @@ class DeepLabV3P(BaseSegmenter):
num_classes=2,
backbone='ResNet50_vd',
use_mixed_loss=False,
losses=None,
output_stride=8,
backbone_indices=(0, 3),
aspp_ratios=(1, 12, 24, 36),
@ -826,6 +836,7 @@ class DeepLabV3P(BaseSegmenter):
model_name='DeepLabV3P',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
@ -833,6 +844,7 @@ class FastSCNN(BaseSegmenter):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
align_corners=False,
**params):
params.update({'align_corners': align_corners})
@ -840,6 +852,7 @@ class FastSCNN(BaseSegmenter):
model_name='FastSCNN',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
@ -848,6 +861,7 @@ class HRNet(BaseSegmenter):
num_classes=2,
width=48,
use_mixed_loss=False,
losses=None,
align_corners=False,
**params):
if width not in (18, 48):
@ -867,6 +881,7 @@ class HRNet(BaseSegmenter):
model_name='FCN',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
self.model_name = 'HRNet'
@ -875,6 +890,7 @@ class BiSeNetV2(BaseSegmenter):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
align_corners=False,
**params):
params.update({'align_corners': align_corners})
@ -882,13 +898,19 @@ class BiSeNetV2(BaseSegmenter):
model_name='BiSeNetV2',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)
class FarSeg(BaseSegmenter):
def __init__(self, num_classes=2, use_mixed_loss=False, **params):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
losses=None,
**params):
super(FarSeg, self).__init__(
model_name='FarSeg',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
losses=losses,
**params)

Write Preview
Loading…
Cancel
Save