def __init__()

in levit_c.py [0:0]

• 50 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self, in_dim, out_dim, key_dim, num_heads=8,
                 attn_ratio=2,
                 activation=None,
                 stride=2,
                 resolution=14, resolution_=7):
        super().__init__()
        self.num_heads = num_heads
        self.scale = key_dim ** -0.5
        self.key_dim = key_dim
        self.nh_kd = nh_kd = key_dim * num_heads
        self.d = int(attn_ratio * key_dim)
        self.dh = int(attn_ratio * key_dim) * self.num_heads
        self.attn_ratio = attn_ratio
        self.resolution_ = resolution_
        self.resolution_2 = resolution_**2
        h = self.dh + nh_kd
        self.kv = Conv2d_BN(in_dim, h, resolution=resolution)
        self.q = torch.nn.Sequential(
            torch.nn.AvgPool2d(1, stride, 0),
            Conv2d_BN(in_dim, nh_kd, resolution=resolution_))
        self.proj = torch.nn.Sequential(
            activation(), Conv2d_BN(self.d * num_heads, out_dim, resolution=resolution_))

        self.stride = stride
        self.resolution = resolution
        points = list(itertools.product(range(resolution), range(resolution)))
        points_ = list(itertools.product(
            range(resolution_), range(resolution_)))
        N = len(points)
        N_ = len(points_)
        attention_offsets = {}
        idxs = []
        for p1 in points_:
            for p2 in points:
                size = 1
                offset = (
                    abs(p1[0] * stride - p2[0] + (size - 1) / 2),
                    abs(p1[1] * stride - p2[1] + (size - 1) / 2))
                if offset not in attention_offsets:
                    attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(
            torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer('attention_bias_idxs',
                             torch.LongTensor(idxs).view(N_, N))

        global FLOPS_COUNTER
        #queries * keys
        FLOPS_COUNTER += num_heads * \
            (resolution**2) * (resolution_**2) * key_dim
        # softmax
        FLOPS_COUNTER += num_heads * (resolution**2) * (resolution_**2)
        #attention * v
        FLOPS_COUNTER += num_heads * \
            (resolution**2) * (resolution_**2) * self.d