def __init__()

in LaNAS/one-shot_LaNAS/Evaluate/individual_model.py [0:0]

• 36 lines of code
• 5 McCabe index (conditional complexity)

    def __init__(self, supernet_normal, supernet_reduce, layer_type, C, num_classes, layers, auxiliary, steps=4, multiplier=4, stem_multiplier=3):
        super(Network, self).__init__()
        self.supernet_normal = supernet_normal
        self.supernet_reduce = supernet_reduce
        self.layer_type = layer_type
        self._C = C
        self._num_classes = num_classes
        self._layers = layers
        self._steps = steps
        self._multiplier = multiplier
        self._auxiliary = auxiliary
        C_curr = stem_multiplier * C
        self.stem = nn.Sequential(
            nn.Conv2d(3, C_curr, 3, padding=1, bias=False),
            nn.BatchNorm2d(C_curr)
        )

        C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
        self.cells = nn.ModuleList()
        reduction_prev = False

        for i in range(layers):
            if i in [layers // 3, 2 * layers // 3]:
                C_curr *= 2
                reduction = True
                cell = Cell(self.layer_type, len(self.supernet_reduce), multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, self.supernet_reduce)
            else:
                reduction = False
                cell = Cell(self.layer_type, len(self.supernet_normal), multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, self.supernet_normal)

            reduction_prev = reduction
            self.cells += [cell]

            C_prev_prev, C_prev = C_prev, multiplier * C_curr
            if i == 2 * layers // 3:
                C_to_auxiliary = C_prev
        if auxiliary:
            self.auxiliary_head = AuxiliaryHeadCIFAR(C_to_auxiliary, num_classes)
        self.global_pooling = nn.AdaptiveAvgPool2d(1)
        self.classifier = nn.Linear(C_prev, num_classes)