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 = 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()