navsim/agents/hydra/hydra_model_expansion.py

from typing import Dict

import numpy as np
import torch
import torch.nn as nn

from navsim.agents.hydra.hydra_backbone_pe import HydraBackbonePE
from navsim.agents.hydra.hydra_config import HydraConfig
from navsim.agents.transfuser.transfuser_model import AgentHead
from navsim.agents.utils.attn import MemoryEffTransformer
from navsim.agents.utils.nerf import nerf_positional_encoding
from navsim.agents.vadv2.vadv2_config import Vadv2Config


class HydraModel(nn.Module):
    def __init__(self, config: HydraConfig):
        super().__init__()

        self._query_splits = [
            config.num_bounding_boxes,
        ]

        self._config = config
        assert config.backbone_type in ['vit', 'intern', 'vov', 'resnet', 'eva', 'moe', 'moe_ult32', 'swin']
        if config.backbone_type == 'eva':
            raise ValueError(f'{config.backbone_type} not supported')
        elif config.backbone_type == 'intern' or config.backbone_type == 'vov' or \
                config.backbone_type == 'swin' or config.backbone_type == 'vit':
            self._backbone = HydraBackbonePE(config)

        img_num = 2 if config.use_back_view else 1
        self._keyval_embedding = nn.Embedding(
            config.img_vert_anchors * config.img_horz_anchors * img_num, config.tf_d_model
        )  # 8x8 feature grid + trajectory
        self._query_embedding = nn.Embedding(sum(self._query_splits), config.tf_d_model)

        # usually, the BEV features are variable in size.
        self.downscale_layer = nn.Conv2d(self._backbone.img_feat_c, config.tf_d_model, kernel_size=1)
        self._status_encoding = nn.Linear((4 + 2 + 2) * config.num_ego_status, config.tf_d_model)

        tf_decoder_layer = nn.TransformerDecoderLayer(
            d_model=config.tf_d_model,
            nhead=config.tf_num_head,
            dim_feedforward=config.tf_d_ffn,
            dropout=config.tf_dropout,
            batch_first=True,
        )

        self._tf_decoder = nn.TransformerDecoder(tf_decoder_layer, config.tf_num_layers)
        self._agent_head = AgentHead(
            num_agents=config.num_bounding_boxes,
            d_ffn=config.tf_d_ffn,
            d_model=config.tf_d_model,
        )

        self._trajectory_head = HydraTrajHead(
            num_poses=config.trajectory_sampling.num_poses,
            d_ffn=config.tf_d_ffn,
            d_model=config.tf_d_model,
            nhead=config.vadv2_head_nhead,
            nlayers=config.vadv2_head_nlayers,
            vocab_path=config.vocab_path,
            config=config
        )

    def img_feat_blc(self, camera_feature):
        img_features = self._backbone(camera_feature)
        img_features = self.downscale_layer(img_features).flatten(-2, -1)
        img_features = img_features.permute(0, 2, 1)
        return img_features

    def forward(self, features: Dict[str, torch.Tensor],
                interpolated_traj=None) -> Dict[str, torch.Tensor]:
        camera_feature: torch.Tensor = features["camera_feature"]
        status_feature: torch.Tensor = features["status_feature"]
        if isinstance(camera_feature, list):
            camera_feature = camera_feature[-1]
        # todo temp fix!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
        # status_feature[:, 0] = 0.0
        # status_feature[:, 1] = 1.0
        # status_feature[:, 2] = 0.0
        # status_feature[:, 3] = 0.0

        batch_size = status_feature.shape[0]

        img_features = self.img_feat_blc(camera_feature)
        if self._config.use_back_view:
            img_features_back = self.img_feat_blc(features["camera_feature_back"])
            img_features = torch.cat([img_features, img_features_back], 1)

        if self._config.num_ego_status == 1 and status_feature.shape[1] == 32:
            status_encoding = self._status_encoding(status_feature[:, :8])
        else:
            status_encoding = self._status_encoding(status_feature)

        keyval = img_features
        keyval += self._keyval_embedding.weight[None, ...]

        query = self._query_embedding.weight[None, ...].repeat(batch_size, 1, 1)
        agents_query = self._tf_decoder(query, keyval)

        output: Dict[str, torch.Tensor] = {}
        trajectory = self._trajectory_head(keyval, status_encoding, interpolated_traj)
        output.update(trajectory)
        agents = self._agent_head(agents_query)
        output.update(agents)

        return output


class HydraTrajHead(nn.Module):
    def __init__(self, num_poses: int, d_ffn: int, d_model: int, vocab_path: str,
                 nhead: int, nlayers: int, config: Vadv2Config = None
                 ):
        super().__init__()
        self._num_poses = num_poses
        self.transformer = nn.TransformerDecoder(
            nn.TransformerDecoderLayer(
                d_model, nhead, d_ffn,
                dropout=0.0, batch_first=True
            ), nlayers
        )
        self.vocab = nn.Parameter(
            torch.from_numpy(np.load(vocab_path)),
            requires_grad=False
        )

        self.heads = nn.ModuleDict({
            'noc': nn.Sequential(
                nn.Linear(d_model, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, 1),
            ),
            'da':
                nn.Sequential(
                    nn.Linear(d_model, d_ffn),
                    nn.ReLU(),
                    nn.Linear(d_ffn, 1),
                ),
            'ttc': nn.Sequential(
                nn.Linear(d_model, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, 1),
            ),
            'comfort': nn.Sequential(
                nn.Linear(d_model, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, 1),
            ),
            'progress': nn.Sequential(
                nn.Linear(d_model, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, 1),
            ),
            'ddc': nn.Sequential(
                nn.Linear(d_model, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, 1),
            ),
            'lk': nn.Sequential(
                nn.Linear(d_model, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, 1),
            ),
            'tl': nn.Sequential(
                nn.Linear(d_model, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, 1),
            ),
            'imi': nn.Sequential(
                nn.Linear(d_model, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, 1),
            )
        })

        self.inference_imi_weight = config.inference_imi_weight
        self.inference_da_weight = config.inference_da_weight
        self.normalize_vocab_pos = config.normalize_vocab_pos
        if self.normalize_vocab_pos:
            self.encoder = MemoryEffTransformer(
                d_model=d_model,
                nhead=nhead,
                dim_feedforward=d_model * 4,
                dropout=0.0
            )
        self.use_nerf = config.use_nerf

        if self.use_nerf:
            self.pos_embed = nn.Sequential(
                nn.Linear(1040, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, d_model),
            )
        else:
            self.pos_embed = nn.Sequential(
                nn.Linear(num_poses * 3, d_ffn),
                nn.ReLU(),
                nn.Linear(d_ffn, d_model),
            )

    def forward(self, bev_feature, status_encoding, interpolated_traj=None) -> Dict[str, torch.Tensor]:
        # todo sinusoidal embedding
        # vocab: 4096, 40, 3
        # bev_feature: B, 32, C
        # embedded_vocab: B, 4096, C
        vocab = self.vocab.data
        L, HORIZON, _ = vocab.shape
        B = bev_feature.shape[0]
        if self.use_nerf:
            vocab = torch.cat(
                [
                    nerf_positional_encoding(vocab[..., :2]),
                    torch.cos(vocab[..., -1])[..., None],
                    torch.sin(vocab[..., -1])[..., None],
                ], dim=-1
            )

        if self.normalize_vocab_pos:
            embedded_vocab = self.pos_embed(vocab.view(L, -1))[None]
            embedded_vocab = self.encoder(embedded_vocab).repeat(B, 1, 1)
        else:
            embedded_vocab = self.pos_embed(vocab.view(L, -1))[None].repeat(B, 1, 1)
        tr_out = self.transformer(embedded_vocab, bev_feature)
        dist_status = tr_out + status_encoding.unsqueeze(1)
        result = {}
        # selected_indices: B,
        for k, head in self.heads.items():
            if k == 'imi':
                result[k] = head(dist_status).squeeze(-1)
            else:
                result[k] = head(dist_status).squeeze(-1).sigmoid()
        scores = (
                0.05 * result['imi'].softmax(-1).log() +
                0.5 * result['tl'].log() +
                0.5 * result['noc'].log() +
                0.5 * result['da'].log() +
                0.5 * result['ddc'].log() +
                8.0 * (5 * result['ttc'] + 2 * result['comfort'] + 5 * result['progress'] + 5 * result['lk']).log()
        )
        selected_indices = scores.argmax(1)
        result["trajectory"] = self.vocab.data[selected_indices]
        result["trajectory_vocab"] = self.vocab.data
        result["selected_indices"] = selected_indices
        return result