(
self,
state_channel=6,
history_channel=9,
dim=128,
hist_steps=21,
use_ego_history=False,
drop_path=0.2,
state_attn_encoder=True,
state_dropout=0.75,
)
| 7 | |
| 8 | class AgentEncoder(nn.Module): |
| 9 | def __init__( |
| 10 | self, |
| 11 | state_channel=6, |
| 12 | history_channel=9, |
| 13 | dim=128, |
| 14 | hist_steps=21, |
| 15 | use_ego_history=False, |
| 16 | drop_path=0.2, |
| 17 | state_attn_encoder=True, |
| 18 | state_dropout=0.75, |
| 19 | ) -> None: |
| 20 | super().__init__() |
| 21 | self.dim = dim |
| 22 | self.state_channel = state_channel |
| 23 | self.use_ego_history = use_ego_history |
| 24 | self.hist_steps = hist_steps |
| 25 | self.state_attn_encoder = state_attn_encoder |
| 26 | |
| 27 | self.history_encoder = NATSequenceEncoder( |
| 28 | in_chans=history_channel, embed_dim=dim // 4, drop_path_rate=drop_path |
| 29 | ) |
| 30 | |
| 31 | if not use_ego_history: |
| 32 | if not self.state_attn_encoder: |
| 33 | self.ego_state_emb = build_mlp(state_channel, [dim] * 2, norm="bn") |
| 34 | else: |
| 35 | self.ego_state_emb = StateAttentionEncoder( |
| 36 | state_channel, dim, state_dropout |
| 37 | ) |
| 38 | |
| 39 | self.type_emb = nn.Embedding(4, dim) |
| 40 | |
| 41 | @staticmethod |
| 42 | def to_vector(feat, valid_mask): |
no test coverage detected