def __init__(self, pool_static_type, down_samples, embedding_dim, h_dim,
bottleneck_dim, activation, batch_norm, dropout, mlp_dim,
num_cells, neighborhood_size):
super(StaticSceneFeatureExtractorRandom, self).__init__()
self.pool_static_type = pool_static_type
self.down_samples = down_samples
self.embedding_dim = embedding_dim
self.mlp_dim = mlp_dim
self.num_cells = num_cells
self.neighborhood_size = neighborhood_size
self.scene_information = {}
if self.down_samples != -1:
self.spatial_embedding = nn.Linear(2 * self.down_samples,
embedding_dim)
else:
self.spatial_embedding = nn.Linear(2 * self.num_cells,
embedding_dim)
mlp_pre_pool_dims = [
embedding_dim + h_dim, self.mlp_dim * 8, bottleneck_dim
]
self.mlp_pre_pool = make_mlp(mlp_pre_pool_dims,
activation=activation,
batch_norm=batch_norm,
dropout=dropout)
def __init__(self, pool_static_type, down_samples, embedding_dim, h_dim,
bottleneck_dim, activation, batch_norm, dropout, mlp_dim,
num_cells, neighborhood_size):
super(StaticSceneFeatureExtractorCNN, self).__init__()
self.pool_static_type = pool_static_type
self.down_samples = down_samples
self.embedding_dim = embedding_dim
self.mlp_dim = mlp_dim
self.num_cells = num_cells
self.neighborhood_size = neighborhood_size
self.scene_information = {}
if self.pool_static_type == 'random_cnn':
self.spatial_embedding = nn.Sequential(
nn.Conv1d(1,
1,
kernel_size=(self.down_samples // self.embedding_dim,
2),
stride=self.down_samples // self.embedding_dim),
nn.LeakyReLU()).to(device)
elif self.pool_static_type == 'random_cnn_atrous':
self.spatial_embedding = nn.Sequential(
nn.Conv1d(1,
1,
kernel_size=(self.down_samples // self.embedding_dim,
2),
stride=1,
dilation=(self.embedding_dim, 1)),
nn.LeakyReLU()).to(device)
else:
print("Error in recognizing the cnn pool static type!")
exit()
mlp_pre_pool_dims = [
embedding_dim + h_dim, self.mlp_dim * 8, bottleneck_dim
]
self.mlp_pre_pool = make_mlp(mlp_pre_pool_dims,
activation=activation,
batch_norm=batch_norm,
dropout=dropout)