def get_generator(checkpoint, evalArgs):
args = AttrDict(checkpoint['args'])
generator = TrajectoryGenerator(
obs_len=args.obs_len,
pred_len=args.pred_len,
embedding_dim=args.embedding_dim,
encoder_h_dim=args.encoder_h_dim_g,
decoder_h_dim=args.decoder_h_dim_g,
mlp_dim=args.mlp_dim,
num_layers=args.num_layers,
noise_dim=args.noise_dim,
noise_type=args.noise_type,
noise_mix_type=args.noise_mix_type,
pooling_type=args.pooling_type,
pool_every_timestep=args.pool_every_timestep,
dropout=args.dropout,
bottleneck_dim=args.bottleneck_dim,
neighborhood_size=args.neighborhood_size,
grid_size=args.grid_size,
batch_norm=args.batch_norm,
use_gpu=evalArgs.use_gpu)
generator.load_state_dict(checkpoint['g_state'])
if evalArgs.use_gpu:
generator.cuda()
else:
generator.cpu()
generator.train()
return generator
def get_generator(checkpoint):
args = AttrDict(checkpoint['args'])
generator = TrajectoryGenerator(
obs_len=args.obs_len,
pred_len=args.pred_len,
embedding_dim=args.embedding_dim,
encoder_h_dim=args.encoder_h_dim_g,
decoder_h_dim=args.decoder_h_dim_g,
rep_dim=args.rep_dim,
mlp_dim=args.mlp_dim,
encoder_num_layers=args.encoder_num_layers,
decoder_num_layers=args.decoder_num_layers,
noise_dim=args.noise_dim,
noise_type=args.noise_type,
noise_mix_type=args.noise_mix_type,
pooling_type=args.pooling_type,
pool_every_timestep=args.pool_every_timestep,
dropout=args.dropout,
bottleneck_dim=args.bottleneck_dim,
neighborhood_size=args.neighborhood_size,
grid_size=args.grid_size,
batch_norm=args.batch_norm,
pos_embed=args.pos_embed,
pos_embed_freq=args.pos_embed_freq,
)
generator.load_state_dict(checkpoint['g_state'])
generator.cuda()
generator.train()
return generator
def get_generator(self, checkpoint):
generator = TrajectoryGenerator(
obs_len=self.args_.obs_len,
pred_len=self.args_.pred_len,
embedding_dim=self.args_.embedding_dim,
encoder_h_dim=self.args_.encoder_h_dim_g,
decoder_h_dim=self.args_.decoder_h_dim_g,
mlp_dim=self.args_.mlp_dim,
num_layers=self.args_.num_layers,
noise_dim=self.args_.noise_dim,
noise_type=self.args_.noise_type,
noise_mix_type=self.args_.noise_mix_type,
pooling_type=self.args_.pooling_type,
pool_every_timestep=self.args_.pool_every_timestep,
dropout=self.args_.dropout,
bottleneck_dim=self.args_.bottleneck_dim,
neighborhood_size=self.args_.neighborhood_size,
grid_size=self.args_.grid_size,
batch_norm=self.args_.batch_norm,
use_gpu=self.args_.use_gpu)
generator.load_state_dict(checkpoint['g_state'])
if self.args_.use_gpu:
generator.cuda()
generator.train()
return generator
def get_generator(checkpoint):
args = AttrDict(checkpoint['args'])
n_units = ([40] + [int(x)
for x in args.hidden_units.strip().split(",")] + [40])
generator = TrajectoryGenerator(
obs_len=args.obs_len,
pred_len=args.pred_len,
embedding_dim=args.embedding_dim,
encoder_h_dim=args.encoder_h_dim_g,
decoder_h_dim=args.decoder_h_dim_g,
mlp_dim=args.mlp_dim,
num_layers=args.num_layers,
noise_dim=args.noise_dim,
noise_type=args.noise_type,
noise_mix_type=args.noise_mix_type,
pooling_type=args.pooling_type,
pool_every_timestep=args.pool_every_timestep,
dropout=args.dropout,
bottleneck_dim=args.bottleneck_dim,
neighborhood_size=args.neighborhood_size,
grid_size=args.grid_size,
batch_norm=args.batch_norm,
n_units=n_units,
n_heads=args.n_heads,
dropout1=args.dropout1,
alpha=args.alpha).cuda()
generator.load_state_dict(checkpoint['g_state'])
generator.cuda()
generator.train()
return generator
def get_generator(checkpoint, best=0):
args = AttrDict(checkpoint['args'])
print(args.pred_len)
generator = TrajectoryGenerator(
obs_len=args.obs_len,
pred_len=args.pred_len,
embedding_dim=args.embedding_dim,
encoder_h_dim=args.encoder_h_dim_g,
decoder_h_dim=args.decoder_h_dim_g,
mlp_dim=args.mlp_dim,
noise_dim=args.noise_dim,
noise_type=args.noise_type,
noise_mix_type=args.noise_mix_type,
pooling_type=args.pooling_type,
pool_every_timestep=args.pool_every_timestep,
dropout=args.dropout,
bottleneck_dim=args.bottleneck_dim,
neighborhood_size=args.neighborhood_size,
grid_size=args.grid_size,
batch_norm=args.batch_norm,
)
if best:
generator.load_state_dict(checkpoint['g_best_state'])
else:
generator.load_state_dict(checkpoint['g_state'])
generator.cuda()
generator.train()
return generator
