def __init__(self, opt): super(BaseEncoder, self).__init__() self.enc_channels = opt.enc_channels self.num_downsamples = opt.num_downsamples self.padding_layer = get_padding(opt.enc_padding) self.activ_layer = get_activ(opt.enc_activ) self.norm_layer = get_norm(opt.enc_norm) self.p_dropout = opt.enc_dropout self._build_layers(opt)
def __init__(self, opt): super(BasePatchDiscriminator, self).__init__() self.dsc_channels = opt.dsc_channels self.dsc_layers = opt.dsc_layers self.dsc_scales = opt.dsc_scales self.norm_layer = get_norm(opt.dsc_norm) self.activ_layer = get_activ(opt.dsc_activ) self.padding_layer = get_padding(opt.dsc_padding) self.p_dropout = opt.dsc_dropout self.num_scales = len(self.dsc_scales) self._build_layers(opt)
def __init__(self, in_channels, out_channels, num_blocks, num_layers, opt): super(ScalingResidualBlock, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.num_blocks = num_blocks self.num_layers = num_layers self.num_channels = max(self.in_channels, self.out_channels) self.padding_layer = get_padding(opt.latent_padding) self.activ_layer = get_activ(opt.latent_activ) self.norm_layer = get_norm(opt.latent_norm) self.p_dropout = opt.latent_dropout self._build_layers(opt)
def __init__(self, in_channels, out_channels, dropout=0., activ='gelu', norm='instance', padding='reflection'): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.norm_layer = get_norm(norm) self.activ_layer = get_activ(activ) self.padding_layer = get_padding(padding) self.p_dropout = dropout self._build_layers()
def __init__(self, opt, out_channels, type='continous'): super(BaseDecoder, self).__init__() self.out_channels = out_channels self.enc_channels = opt.enc_channels self.dec_channels = opt.dec_channels self.num_upsamples = opt.num_downsamples # these are layer constructors, not implicit layers self.norm_layer = get_norm(opt.dec_norm) self.activ_layer = get_activ(opt.dec_activ) self.padding_layer = get_padding(opt.dec_padding) self.p_dropout = opt.dec_dropout if type == 'continous': self.activ_final = nn.Tanh() if type == 'probabilities': self.activ_final = nn.Sigmoid() self._build_layers(opt)
def __init__(self, opt): super(AggregatedLargeDilationEncoder, self).__init__() self.enc_channels = opt.enc_channels self.num_downsamples = opt.num_downsamples self.dilations = opt.enc_dilations final_dilation = self.dilations[-1] for i in range(1, self.num_downsamples + 1): # for each downsample add an increasing dilation # why? no reason self.dilations = self.dilations + [final_dilation + i * 2] self.dil_channels = opt.dil_channels self.padding_layer = get_padding(opt.enc_padding) self.activ_layer = get_activ(opt.enc_activ) self.norm_layer = get_norm(opt.enc_norm) self.p_dropout = opt.enc_dropout self._build_layers(opt)
def __init__(self, opt, out_channels, type='continous'): super(AggregatedLargeDilationDecoder, self).__init__() self.out_channels = out_channels self.enc_channels = opt.enc_channels self.dec_channels = opt.dec_channels self.num_upsamples = opt.num_downsamples self.dil_channels = opt.dil_channels self.dilations = opt.dec_dilations final_dilation = self.dilations[-1] for i in range(1, self.num_upsamples + 1): self.dilations = self.dilations + [final_dilation + i * 2] # these are layer constructors, not implicit layers self.norm_layer = get_norm(opt.dec_norm) self.activ_layer = get_activ(opt.dec_activ) if type == 'continous': self.activ_final = nn.Tanh() if type == 'probabilities': self.activ_final = nn.Sigmoid() self.padding_layer = get_padding(opt.dec_padding) self.p_dropout = opt.dec_dropout self._build_layers(opt)
def __init__(self, in_channels, out_channels, dil_channels, dilations, dropout=0., activ='gelu', norm='instance', padding='reflection', residual=False): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.dil_channels = dil_channels self.dilations = dilations self.num_dilations = len(dilations) self.residual = residual self.norm_layer = get_norm(norm) self.activ_layer = get_activ(activ) self.padding_layer = get_padding(padding) self.p_dropout = dropout self._build_layers()