def __init__(self, in_dim, out_dim, dim, n_blk, norm, activ):
super(MLP, self).__init__()
self.model = []
self.model += [LinearBlock(in_dim, dim, norm=norm, activation=activ)]
for i in range(n_blk - 2):
self.model += [LinearBlock(dim, dim, norm=norm, activation=activ)]
self.model += [LinearBlock(dim, out_dim,
norm='none', activation='none')]
self.model = nn.Sequential(*self.model)
def __init__(self, config, out_dim):
super(MLP, self).__init__()
dims = config.mlp_dims
n_blk = len(dims)
norm = 'none'
acti = 'lrelu'
layers = []
for i in range(n_blk - 1):
layers += LinearBlock(dims[i], dims[i + 1], norm=norm, acti=acti)
layers += LinearBlock(dims[-1], out_dim,
norm='none', acti='none')
self.model = nn.Sequential(*layers)