def __init__(self, w_in, w_out, num_classes):
super(EffHead, self).__init__()
dropout_ratio = cfg.EN.DROPOUT_RATIO
self.conv = conv2d(w_in, w_out, 1)
self.conv_bn = norm2d(w_out)
self.conv_af = activation()
self.avg_pool = gap2d(w_out)
self.dropout = Dropout(p=dropout_ratio) if dropout_ratio > 0 else None
self.fc = linear(w_out, num_classes, bias=True)
def __init__(self, w_in, head_width, num_classes):
super(AnyHead, self).__init__()
self.head_width = head_width
if head_width > 0:
self.conv = conv2d(w_in, head_width, 1)
self.bn = norm2d(head_width)
self.af = activation()
w_in = head_width
self.avg_pool = gap2d(w_in)
self.fc = linear(w_in, num_classes, bias=True)
def __init__(self, w_in, mlp_d):
super().__init__()
self.linear_1 = linear(w_in, mlp_d, bias=True)
self.af = activation("gelu")
self.linear_2 = linear(mlp_d, w_in, bias=True)
def __init__(self, w_in, num_classes):
super().__init__()
self.head_fc = linear(w_in, num_classes, bias=True)
def __init__(self, w_in, num_classes):
super(AnyHead, self).__init__()
self.avg_pool = gap2d(w_in)
self.fc = linear(w_in, num_classes, bias=True)