def __init__(self, cfg: Dict, traj_dim=256, cont_dim=256, latent_dim=128, mode_dim=3, v_dim=4): super(CVAE, self).__init__() self.Traj_Encoder = basicModel.Trajectory_Encoder(h_dim=traj_dim, v_dim=v_dim) self.Cont_Encoder = basicModel.Context_Encoder(cfg, cont_dim=cont_dim) # 回归均值 self.encoder_mean = nn.Sequential( nn.Linear(in_features=traj_dim + cont_dim, out_features=256), nn.Linear(in_features=256, out_features=latent_dim), # nn.BatchNorm1d(latent_dim) ) # 回归方差 self.encoder_var = nn.Sequential( nn.Linear(in_features=traj_dim + cont_dim, out_features=256), nn.Linear(in_features=256, out_features=latent_dim), # nn.BatchNorm1d(latent_dim) ) self.future_len = cfg["model_params"]["future_num_frames"] num_targets = 2 * self.future_len self.num_preds = num_targets * mode_dim self.num_modes = mode_dim self.decoder_net = nn.Sequential( nn.BatchNorm1d(latent_dim + cont_dim), nn.Linear(in_features=latent_dim + cont_dim, out_features=2048), nn.Linear(in_features=2048, out_features=self.num_preds + mode_dim), )
def __init__(self, cdf: Dict, h_dim=64, cont_dim=256): super(discriminator, self).__init__() self.encoder = basicModel.Trajectory_Encoder(h_dim=h_dim, v_dim=2) self.classifier = nn.Sequential( nn.Linear(in_features=h_dim + cont_dim, out_features=256), nn.Linear(in_features=256, out_features=1), nn.LeakyReLU())
def __init__(self, cnn_model='resnet34', channels=3, traj_dim=256, cont_dim=256, latent_dim=128, mode_dim=3, v_dim=4): super(CVAE_single, self).__init__() self.Traj_Encoder = basicModel.Trajectory_Encoder(h_dim=traj_dim, v_dim=v_dim) self.Cont_Encoder = basicModel.Context_Encoder(cnn_model=cnn_model, channels=channels, cont_dim=cont_dim) # 回归均值 self.encoder_mean = nn.Sequential( nn.Linear(in_features=traj_dim + cont_dim, out_features=256), nn.Linear(in_features=256, out_features=latent_dim), # nn.BatchNorm1d(latent_dim) ) # 回归方差 self.encoder_var = nn.Sequential( nn.Linear(in_features=traj_dim + cont_dim, out_features=256), nn.Linear(in_features=256, out_features=latent_dim), # nn.BatchNorm1d(latent_dim) ) self.future_len = 12 num_targets = 2 * self.future_len self.num_preds = num_targets self.decoder_net = nn.Sequential(nn.BatchNorm1d(latent_dim + cont_dim), )
def __init__(self, cfg: Dict, traj_dim=256, cont_dim=256, mode=3, v_dim=4): super(generator, self).__init__() self.Traj_Encoder = basicModel.Trajectory_Encoder(h_dim=traj_dim, v_dim=v_dim) self.Cont_Encoder = basicModel.Context_Encoder(cfg, cont_dim=cont_dim) self.future_len = cfg["model_params"]["future_num_frames"] self.num_modes = mode self.num_preds = 2 * self.future_len * self.num_modes self.gen = nn.Sequential( nn.BatchNorm1d(traj_dim + cont_dim), nn.Linear(in_features=traj_dim + cont_dim, out_features=traj_dim + cont_dim), nn.Linear(in_features=traj_dim + cont_dim, out_features=self.num_preds + self.num_modes))
def __init__(self, cnn_model='resnet34', channels=3, traj_dim=256, cont_dim=256, mode=3, v_dim=2): super(generator, self).__init__() self.Traj_Encoder = basicModel.Trajectory_Encoder(h_dim=traj_dim, v_dim=v_dim) self.Cont_Encoder = basicModel.Context_Encoder(cnn_model='resnet34', channels=3, cont_dim=cont_dim) self.future_len = 12 self.num_modes = mode self.num_preds = 2 * self.future_len * self.num_modes self.gen = nn.Sequential( nn.BatchNorm1d(traj_dim + cont_dim), nn.Linear(in_features=traj_dim + cont_dim, out_features=512), nn.Linear(in_features=512, out_features=256), nn.Linear(in_features=256, out_features=self.num_preds + self.num_modes), nn.LeakyReLU())