def __init__(self, name, split):
self.name = name
self.split = split
self.path = os.path.join(settings.checkpoints, name + '-split_{}'.format(split))
self.net = DualHypercolumnCatRefineNet(
NoPoolDPN92Base(dpn92()),
num_features=128,
block_multiplier=1,
num_features_base=[256 + 80, 512 + 192, 1024 + 528, 2048 + 640],
classifier=lambda c: SmallDropoutRefineNetUpsampleClassifier(2*128, scale_factor=2),
)
self.tta = [
tta.Pipeline([tta.Pad((13, 14, 13, 14))]),
tta.Pipeline([tta.Pad((13, 14, 13, 14)), tta.Flip()])
]
def __init__(self, name, split):
self.name = name
self.split = split
self.path = os.path.join(settings.checkpoints,
name + '-split_{}'.format(split))
self.net = RefineNet(SCSENoPoolResNextBase(se_resnext101_32x4d()),
num_features=128,
classifier=lambda c: RefineNetUpsampleClassifier(
c, scale_factor=2),
block=SCSERefineNetBlock)
self.tta = [
tta.Pipeline([tta.Pad((13, 14, 13, 14))]),
tta.Pipeline([tta.Pad((13, 14, 13, 14)),
tta.Flip()])
]
def __init__(self, name, split):
self.name = name
self.split = split
self.path = os.path.join(settings.checkpoints, name + '-split_{}'.format(split))
self.net = AuxDualHypercolumnCatRefineNet(
SCSENoPoolResNextBase(se_resnext50_32x4d()),
num_features=128,
classifier=lambda c: SmallDropoutRefineNetUpsampleClassifier(2*c, scale_factor=2, dropout=0.1),
block=SCSERefineNetBlock,
crp=[IdentityCRP, CRP, CRP, CRP]
)
self.tta = [
tta.Pipeline([tta.Pad((13, 14, 13, 14))]),
tta.Pipeline([tta.Pad((13, 14, 13, 14)), tta.Flip()])
]
def __init__(self, name, split):
self.name = name
self.split = split
self.path = os.path.join(settings.checkpoints,
name + '-split_{}'.format(split))
self.net = HypercolumnCatRefineNet(
SCSENoPoolResNextBase(se_resnet101()),
num_features=128,
classifier=lambda c: RefineNetUpsampleClassifier(640,
scale_factor=2),
block=SCSERefineNetBlock)
self.tta = [
tta.Pipeline([tta.Resize((128, 128))]),
tta.Pipeline([tta.Resize((128, 128)),
tta.Flip()])
]
def __init__(self, name, split):
self.name = name
self.split = split
self.path = os.path.join(settings.checkpoints,
name + '-split_{}'.format(split))
self.net = RefineNet(NoPoolDPN107Base(dpn107()),
num_features=128,
block_multiplier=1,
num_features_base=[376, 1152, 2432, 2048 + 640],
classifier=lambda c: RefineNetUpsampleClassifier(
c, scale_factor=2))
self.tta = [
tta.Pipeline([tta.Pad((13, 14, 13, 14))]),
tta.Pipeline([tta.Pad((13, 14, 13, 14)),
tta.Flip()])
]
def __init__(self, name, split):
self.name = name
self.split = split
self.path = os.path.join(settings.checkpoints, name + '-split_{}'.format(split))
self.net = DualHypercolumnCatRefineNet(
SCSENoPoolResNextBase(se_resnet152()),
num_features=128,
classifier=lambda c: RefineNetUpsampleClassifier(2*c, scale_factor=2),
block=SCSERefineNetBlock
)
self.tta = [
tta.Pipeline([tta.Pad((13, 14, 13, 14))]),
tta.Pipeline([tta.Pad((13, 14, 13, 14)), tta.Flip()])
]
self.test_predictions = utils.TestPredictions('ensemble-{}'.format(split)).load()
def __init__(self, name, split):
self.name = name
self.split = split
self.path = os.path.join(settings.checkpoints, name + '-split_{}'.format(split))
self.net = DualHypercolumnCatRefineNet(
NoPoolDPN107Base(dpn107()),
num_features=128,
block_multiplier=1,
num_features_base=[376, 1152, 2432, 2048 + 640],
classifier=lambda c: SmallDropoutRefineNetUpsampleClassifier(2 * 128, scale_factor=2),
)
self.tta = [
tta.Pipeline([tta.Pad((13, 14, 13, 14))]),
tta.Pipeline([tta.Pad((13, 14, 13, 14)), tta.Flip()])
]
self.test_predictions = utils.TestPredictions('ensemble_top_6_postprocessed-split_{}'.format(split)).load()
def __init__(self, name, split):
self.name = name
self.split = split
self.path = os.path.join(settings.checkpoints,
name + '-split_{}'.format(split))
self.net = RefineNet(NoPoolDPN98Base(dpn98()),
num_features=128,
block_multiplier=1,
num_features_base=[336, 768, 1728, 2688],
classifier=lambda c: RefineNetUpsampleClassifier(
c, scale_factor=2))
self.optimizer = Adam(self.net.parameters(),
lr=1e-4,
weight_decay=1e-4)
self.tta = [
tta.Pipeline([tta.Pad((13, 14, 13, 14))]),
tta.Pipeline([tta.Pad((13, 14, 13, 14)),
tta.Flip()])
]
self.batch_size = 16
