def __init__(self, use_bias, alg, **kwargs):
super(ConvAct, self).__init__(**kwargs)
self.conv0 = nn.Conv2D(channels=64, kernel_size=(3, 3), strides=1, use_bias=use_bias)
if alg == "relu6":
self.act = RELU6()
elif alg == "leakyrelu":
self.act = nn.LeakyReLU(0.25)
elif alg == "gelu":
self.act = nn.GELU()
else:
self.act = nn.Activation(activation = alg)
def __init__(self, alg, use_bias, **kwargs):
super(ConvBNAct, self).__init__(**kwargs)
self.conv0 = nn.Conv2D(channels=64, kernel_size=(3, 3), strides=1, use_bias=use_bias)
self.bn = nn.BatchNorm()
if alg == "relu6":
self.act = RELU6()
elif alg == "leakyrelu":
self.act = nn.LeakyReLU(0.25)
elif alg == "gelu":
self.act = nn.GELU()
elif alg == "gelu_tanh":
self.act = nn.GELU(approximation='tanh')
else:
self.act = nn.Activation(activation = alg)
def __init__(self, alg, use_bias, **kwargs):
super(ConvBNSumAct, self).__init__(**kwargs)
self.conv0 = nn.Conv2D(channels=64, kernel_size=(3, 3), strides=1, use_bias=use_bias)
self.conv1 = nn.Conv2D(channels=64, kernel_size=(3, 3), strides=1)
self.conv1.share_parameters(self.conv0.collect_params())
self.bn = nn.BatchNorm()
if alg == "relu6":
self.act = RELU6()
elif alg == "leakyrelu":
self.act = nn.LeakyReLU(0.25)
elif alg == "gelu":
self.act = nn.GELU()
else:
self.act = nn.Activation(activation = alg)
def __init__(self, use_bias, alg, **kwargs):
super(ConvActAdd, self).__init__(**kwargs)
self.conv0 = nn.Conv2D(channels=64, kernel_size=(3, 3), strides=1, use_bias=use_bias,
weight_initializer=mx.init.Xavier(magnitude=2.24))
if alg == "relu6":
self.act = RELU6()
elif alg == "leakyrelu":
self.act = nn.LeakyReLU(0.25)
elif alg == "gelu":
self.act = nn.GELU()
else:
self.act = nn.Activation(activation = alg)
self.conv1 = nn.Conv2D(channels=64, kernel_size=(3, 3), strides=1, use_bias=use_bias)
self.conv1.share_parameters(self.conv0.collect_params())
