76b060f625e037d479a2eb25462a3b3f70af5bb7,chainercv/links/model/segnet/segnet_basic.py,SegNetBasic,__init__,#SegNetBasic#Any#Any#Any#,64

Before Change

    }

    def __init__(self, n_class=None, pretrained_model=None, initialW=None):
        if n_class is None:
            if pretrained_model not in self._models:
                raise ValueError(
                    "The n_class needs to be supplied as an argument.")
            n_class = self._models[pretrained_model]["n_class"]

        if initialW is None:
            initialW = chainer.initializers.HeNormal()

        super(SegNetBasic, self).__init__()
        with self.init_scope():
            self.conv1 = L.Convolution2D(
                None, 64, 7, 1, 3, nobias=True, initialW=initialW)
            self.conv1_bn = L.BatchNormalization(64, initial_beta=0.001)
            self.conv2 = L.Convolution2D(
                64, 64, 7, 1, 3, nobias=True, initialW=initialW)
            self.conv2_bn = L.BatchNormalization(64, initial_beta=0.001)
            self.conv3 = L.Convolution2D(
                64, 64, 7, 1, 3, nobias=True, initialW=initialW)
            self.conv3_bn = L.BatchNormalization(64, initial_beta=0.001)
            self.conv4 = L.Convolution2D(
                64, 64, 7, 1, 3, nobias=True, initialW=initialW)
            self.conv4_bn = L.BatchNormalization(64, initial_beta=0.001)
            self.conv_decode4 = L.Convolution2D(
                64, 64, 7, 1, 3, nobias=True, initialW=initialW)
            self.conv_decode4_bn = L.BatchNormalization(64, initial_beta=0.001)
            self.conv_decode3 = L.Convolution2D(
                64, 64, 7, 1, 3, nobias=True, initialW=initialW)
            self.conv_decode3_bn = L.BatchNormalization(64, initial_beta=0.001)
            self.conv_decode2 = L.Convolution2D(
                64, 64, 7, 1, 3, nobias=True, initialW=initialW)
            self.conv_decode2_bn = L.BatchNormalization(64, initial_beta=0.001)
            self.conv_decode1 = L.Convolution2D(
                64, 64, 7, 1, 3, nobias=True, initialW=initialW)
            self.conv_decode1_bn = L.BatchNormalization(64, initial_beta=0.001)
            self.conv_classifier = L.Convolution2D(
                64, n_class, 1, 1, 0, initialW=initialW)

        self.n_class = n_class

        if pretrained_model in self._models:
            path = download_model(self._models[pretrained_model]["url"])
            chainer.serializers.load_npz(path, self)
        elif pretrained_model:
            chainer.serializers.load_npz(pretrained_model, self)

    def _upsampling_2d(self, x, pool):
        if x.shape != pool.indexes.shape:
            min_h = min(x.shape[2], pool.indexes.shape[2])
            min_w = min(x.shape[3], pool.indexes.shape[3])

After Change

    }

    def __init__(self, n_class=None, pretrained_model=None, initialW=None):
        n_class, path = prepare_link_initialization(
            n_class, self._models, False)

        if initialW is None:
            initialW = chainer.initializers.HeNormal()

Instances