experiments/nets/models.py

"""
Model definitions for Experiment 1: Scene Recognition.
Backbones: CNN1D, BiLSTM, Transformer
Fusion: Early (default), Late, Attention, WeightedLate, GatedLate, Stacking, Product, MoE

Supports optional per-modality projection via proj_dim parameter:
  proj_dim > 0: project each modality to proj_dim before backbone
  proj_dim = 0: no projection, use raw features (original behavior)
"""

import math
import torch
import torch.nn as nn
import torch.nn.functional as F


# ============================================================
# Per-modality projection
# ============================================================

class ModalityProjector(nn.Module):
    """Project each modality from its raw dimension to proj_dim."""

    def __init__(self, modality_dims, proj_dim):
        super().__init__()
        self.mod_names = list(modality_dims.keys())
        self.mod_dims = list(modality_dims.values())
        self.proj_dim = proj_dim
        self.projectors = nn.ModuleList()
        for dim in self.mod_dims:
            self.projectors.append(nn.Sequential(
                nn.Linear(dim, proj_dim),
                nn.LayerNorm(proj_dim),
                nn.ReLU(),
            ))

    @property
    def output_dim(self):
        return self.proj_dim * len(self.mod_dims)

    def forward(self, x):
        """x: (B, T, total_raw_dim) -> (B, T, proj_dim * M)"""
        parts = []
        offset = 0
        for i, dim in enumerate(self.mod_dims):
            x_mod = x[:, :, offset:offset + dim]
            offset += dim
            parts.append(self.projectors[i](x_mod))
        return torch.cat(parts, dim=-1)


# ============================================================
# Per-modality hidden dim scaling (used when proj_dim=0)
# ============================================================

def _compute_per_modality_hidden(mod_dim, base_hidden_dim):
    if mod_dim >= 128:
        return max(base_hidden_dim, 48)
    elif mod_dim >= 32:
        return base_hidden_dim
    else:
        return max(16, base_hidden_dim // 2)


# ============================================================
# Backbones
# ============================================================

class CNN1DBackbone(nn.Module):
    def __init__(self, input_dim, hidden_dim=128):
        super().__init__()
        self.conv1 = nn.Sequential(
            nn.Conv1d(input_dim, 64, kernel_size=7, padding=3),
            nn.BatchNorm1d(64), nn.ReLU(), nn.Dropout(0.1),
        )
        self.conv2 = nn.Sequential(
            nn.Conv1d(64, 128, kernel_size=5, padding=2),
            nn.BatchNorm1d(128), nn.ReLU(), nn.Dropout(0.1),
        )
        self.conv3 = nn.Sequential(
            nn.Conv1d(128, hidden_dim, kernel_size=3, padding=1),
            nn.BatchNorm1d(hidden_dim), nn.ReLU(),
        )
        self.output_dim = hidden_dim

    def forward(self, x, mask=None):
        x = x.permute(0, 2, 1)
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        if mask is not None:
            x = (x * mask.unsqueeze(1).float()).sum(2) / mask.sum(1, keepdim=True).float().clamp(min=1)
        else:
            x = x.mean(2)
        return x


class LSTMBackbone(nn.Module):
    def __init__(self, input_dim, hidden_dim=128, num_layers=2, dropout=0.2):
        super().__init__()
        self.lstm = nn.LSTM(
            input_dim, hidden_dim, num_layers=num_layers,
            batch_first=True, bidirectional=True,
            dropout=dropout if num_layers > 1 else 0,
        )
        self.attn = nn.Linear(hidden_dim * 2, 1)
        self.output_dim = hidden_dim * 2

    def forward(self, x, mask=None):
        out, _ = self.lstm(x)
        scores = self.attn(out).squeeze(-1)
        if mask is not None:
            scores = scores.masked_fill(~mask, float('-inf'))
        weights = torch.softmax(scores, dim=1)
        out = (out * weights.unsqueeze(-1)).sum(dim=1)
        return out


class TinyHARBackbone(nn.Module):
    """TinyHAR backbone (Zhou et al., ISWC 2022 Best Paper).

    Lightweight model for human activity recognition from wearable sensors.
    Uses multi-scale temporal convolutions + cross-channel interaction + temporal pooling.

    Input: (B, T, C) with optional mask
    Output: (B, hidden_dim)
    """

    def __init__(self, input_dim, hidden_dim=128, num_scales=4):
        super().__init__()
        scale_dim = max(4, hidden_dim // num_scales)
        actual_hidden = scale_dim * num_scales

        # Multi-scale temporal convolution feature extraction
        self.convs = nn.ModuleList()
        for i in range(num_scales):
            ks = 2 * (i + 1) + 1  # kernel sizes: 3, 5, 7, 9
            self.convs.append(nn.Sequential(
                nn.Conv1d(input_dim, scale_dim, kernel_size=ks, padding=ks // 2),
                nn.BatchNorm1d(scale_dim),
                nn.ReLU(),
            ))

        # Cross-channel interaction via multi-head self-attention
        nhead = max(1, min(4, actual_hidden // 8))
        # Ensure actual_hidden is divisible by nhead
        while actual_hidden % nhead != 0 and nhead > 1:
            nhead -= 1
        self.channel_attn = nn.MultiheadAttention(
            actual_hidden, num_heads=nhead, batch_first=True, dropout=0.1,
        )
        self.channel_norm = nn.LayerNorm(actual_hidden)
        self.channel_ff = nn.Sequential(
            nn.Linear(actual_hidden, actual_hidden),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(actual_hidden, actual_hidden),
        )
        self.ff_norm = nn.LayerNorm(actual_hidden)

        # Temporal attention pooling
        self.temporal_query = nn.Parameter(torch.randn(1, 1, actual_hidden) * 0.02)
        self.temporal_attn = nn.MultiheadAttention(
            actual_hidden, num_heads=1, batch_first=True, dropout=0.1,
        )

        self.output_dim = actual_hidden

    def forward(self, x, mask=None):
        # x: (B, T, C)
        B, T, C = x.shape
        x_t = x.permute(0, 2, 1)  # (B, C, T)

        # Multi-scale feature extraction
        scale_features = [conv(x_t) for conv in self.convs]
        x = torch.cat(scale_features, dim=1)  # (B, actual_hidden, T)
        x = x.permute(0, 2, 1)  # (B, T, actual_hidden)

        # Cross-channel interaction
        key_padding_mask = ~mask if mask is not None else None
        attn_out, _ = self.channel_attn(x, x, x, key_padding_mask=key_padding_mask)
        x = self.channel_norm(x + attn_out)
        x = self.ff_norm(x + self.channel_ff(x))

        # Temporal attention pooling
        query = self.temporal_query.expand(B, -1, -1)  # (B, 1, actual_hidden)
        pooled, _ = self.temporal_attn(query, x, x, key_padding_mask=key_padding_mask)
        return pooled.squeeze(1)  # (B, actual_hidden)


class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:, :x.size(1)]
        return self.dropout(x)


class TransformerBackbone(nn.Module):
    def __init__(self, input_dim, d_model=128, nhead=4, num_layers=2, dropout=0.1):
        super().__init__()
        self.input_proj = nn.Linear(input_dim, d_model)
        self.pos_enc = PositionalEncoding(d_model, dropout=dropout)
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead, dim_feedforward=d_model * 4,
            dropout=dropout, batch_first=True,
        )
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        self.output_dim = d_model

    def forward(self, x, mask=None):
        x = self.input_proj(x)
        x = self.pos_enc(x)
        src_key_padding_mask = ~mask if mask is not None else None
        x = self.encoder(x, src_key_padding_mask=src_key_padding_mask)
        if mask is not None:
            x = (x * mask.unsqueeze(-1).float()).sum(1) / mask.sum(1, keepdim=True).float().clamp(min=1)
        else:
            x = x.mean(1)
        return x


# ============================================================
# Full models
# ============================================================

def get_backbone(name, input_dim, hidden_dim=128):
    if name == 'cnn':
        return CNN1DBackbone(input_dim, hidden_dim)
    elif name == 'lstm':
        return LSTMBackbone(input_dim, hidden_dim)
    elif name == 'transformer':
        return TransformerBackbone(input_dim, hidden_dim)
    elif name == 'tinyhar':
        return TinyHARBackbone(input_dim, hidden_dim)
    elif name == 'deepconvlstm':
        from experiments.published_models import DeepConvLSTMBackbone
        return DeepConvLSTMBackbone(input_dim, hidden_dim)
    elif name == 'inceptiontime':
        from experiments.published_models import InceptionTimeBackbone
        return InceptionTimeBackbone(input_dim, hidden_dim)
    else:
        raise ValueError(f"Unknown backbone: {name}")


def _make_branch(backbone_name, raw_dim, hidden_dim, proj_dim):
    """Create optional projector + backbone for one modality branch."""
    if proj_dim > 0:
        proj = nn.Sequential(
            nn.Linear(raw_dim, proj_dim),
            nn.LayerNorm(proj_dim),
            nn.ReLU(),
        )
        bb_input = proj_dim
        bb_hidden = hidden_dim
    else:
        proj = None
        bb_input = raw_dim
        bb_hidden = _compute_per_modality_hidden(raw_dim, hidden_dim)
    bb = get_backbone(backbone_name, bb_input, bb_hidden)
    return proj, bb


class SingleModel(nn.Module):
    """Single backbone + classifier (early fusion or single-modality)."""

    def __init__(self, backbone_name, input_dim, num_classes, hidden_dim=128,
                 modality_dims=None, proj_dim=0):
        super().__init__()
        self.projector = None
        if proj_dim > 0 and modality_dims:
            self.projector = ModalityProjector(modality_dims, proj_dim)
            actual_input_dim = self.projector.output_dim
        else:
            actual_input_dim = input_dim
        self.backbone = get_backbone(backbone_name, actual_input_dim, hidden_dim)
        self.classifier = nn.Sequential(
            nn.Dropout(0.5),
            nn.Linear(self.backbone.output_dim, num_classes),
        )

    def forward(self, x, mask=None):
        if self.projector is not None:
            x = self.projector(x)
        feat = self.backbone(x, mask)
        return self.classifier(feat)


class LateFusionModel(nn.Module):
    """Late fusion: separate backbone per modality, configurable logit aggregation.

    late_agg='mean': simple average (original)
    late_agg='confidence': entropy-based confidence weighting (0 extra params)
    late_agg='learned': temperature-scaled learned weights (M+1 extra params)
    """

    def __init__(self, backbone_name, modality_dims, num_classes, hidden_dim=64,
                 proj_dim=0, late_agg='mean'):
        super().__init__()
        self.mod_names = list(modality_dims.keys())
        self.mod_dims = list(modality_dims.values())
        self.late_agg = late_agg
        self.projectors = nn.ModuleList()
        self.backbones = nn.ModuleList()
        self.classifiers = nn.ModuleList()
        for dim in self.mod_dims:
            proj, bb = _make_branch(backbone_name, dim, hidden_dim, proj_dim)
            self.projectors.append(proj if proj else nn.Identity())
            self.backbones.append(bb)
            self.classifiers.append(nn.Sequential(
                nn.Dropout(0.5), nn.Linear(bb.output_dim, num_classes),
            ))
        self._has_proj = proj_dim > 0

        M = len(self.mod_dims)
        if late_agg == 'learned':
            self.modality_logits = nn.Parameter(torch.zeros(M))
            self.temperature = nn.Parameter(torch.ones(1))

    def forward(self, x, mask=None):
        offset = 0
        all_logits = []
        for i, dim in enumerate(self.mod_dims):
            x_mod = x[:, :, offset:offset + dim]
            offset += dim
            if self._has_proj:
                x_mod = self.projectors[i](x_mod)
            feat = self.backbones[i](x_mod, mask)
            all_logits.append(self.classifiers[i](feat))

        stacked = torch.stack(all_logits, dim=0)  # (M, B, C)

        if self.late_agg == 'confidence':
            # Weight by confidence: low entropy → high weight
            probs = F.softmax(stacked, dim=-1)                    # (M, B, C)
            entropy = -(probs * (probs + 1e-8).log()).sum(dim=-1)  # (M, B)
            weights = F.softmax(-entropy, dim=0).unsqueeze(-1)     # (M, B, 1)
            return (stacked * weights).sum(dim=0)
        elif self.late_agg == 'learned':
            weights = F.softmax(self.modality_logits / self.temperature, dim=0)
            return (stacked * weights.view(-1, 1, 1)).sum(dim=0)
        else:  # 'mean'
            return stacked.mean(dim=0)


class AttentionFusionModel(nn.Module):
    """Attention fusion: separate encoder per modality -> cross-modal attention -> classifier."""

    def __init__(self, backbone_name, modality_dims, num_classes, hidden_dim=64, proj_dim=0):
        super().__init__()
        self.mod_names = list(modality_dims.keys())
        self.mod_dims = list(modality_dims.values())
        unified_dim = hidden_dim
        self.projectors = nn.ModuleList()
        self.backbones = nn.ModuleList()
        self.feat_projections = nn.ModuleList()
        for dim in self.mod_dims:
            proj, bb = _make_branch(backbone_name, dim, hidden_dim, proj_dim)
            self.projectors.append(proj if proj else nn.Identity())
            self.backbones.append(bb)
            if bb.output_dim != unified_dim:
                self.feat_projections.append(nn.Linear(bb.output_dim, unified_dim))
            else:
                self.feat_projections.append(nn.Identity())
        self._has_proj = proj_dim > 0
        nhead = 4 if unified_dim % 4 == 0 else (2 if unified_dim % 2 == 0 else 1)
        self.cross_attn = nn.TransformerEncoderLayer(
            d_model=unified_dim, nhead=nhead, dim_feedforward=unified_dim * 2,
            dropout=0.1, batch_first=True,
        )
        self.classifier = nn.Sequential(
            nn.Dropout(0.5), nn.Linear(unified_dim, num_classes),
        )

    def forward(self, x, mask=None):
        offset = 0
        mod_features = []
        for i, dim in enumerate(self.mod_dims):
            x_mod = x[:, :, offset:offset + dim]
            offset += dim
            if self._has_proj:
                x_mod = self.projectors[i](x_mod)
            feat = self.backbones[i](x_mod, mask)
            feat = self.feat_projections[i](feat)
            mod_features.append(feat)
        tokens = torch.stack(mod_features, dim=1)
        tokens = self.cross_attn(tokens)
        pooled = tokens.mean(dim=1)
        return self.classifier(pooled)


class WeightedLateFusionModel(nn.Module):
    def __init__(self, backbone_name, modality_dims, num_classes, hidden_dim=64, proj_dim=0):
        super().__init__()
        self.mod_names = list(modality_dims.keys())
        self.mod_dims = list(modality_dims.values())
        self.projectors = nn.ModuleList()
        self.backbones = nn.ModuleList()
        self.classifiers = nn.ModuleList()
        for dim in self.mod_dims:
            proj, bb = _make_branch(backbone_name, dim, hidden_dim, proj_dim)
            self.projectors.append(proj if proj else nn.Identity())
            self.backbones.append(bb)
            self.classifiers.append(nn.Sequential(
                nn.Dropout(0.5), nn.Linear(bb.output_dim, num_classes),
            ))
        self._has_proj = proj_dim > 0
        self.modality_weights = nn.Parameter(torch.ones(len(self.mod_dims)))

    def forward(self, x, mask=None):
        offset = 0
        all_logits = []
        for i, dim in enumerate(self.mod_dims):
            x_mod = x[:, :, offset:offset + dim]
            offset += dim
            if self._has_proj:
                x_mod = self.projectors[i](x_mod)
            feat = self.backbones[i](x_mod, mask)
            all_logits.append(self.classifiers[i](feat))
        weights = F.softmax(self.modality_weights, dim=0)
        stacked = torch.stack(all_logits, dim=0)
        return (stacked * weights.view(-1, 1, 1)).sum(dim=0)


class GatedLateFusionModel(nn.Module):
    def __init__(self, backbone_name, modality_dims, num_classes, hidden_dim=64, proj_dim=0):
        super().__init__()
        self.mod_names = list(modality_dims.keys())
        self.mod_dims = list(modality_dims.values())
        M = len(self.mod_dims)
        self.projectors = nn.ModuleList()
        self.backbones = nn.ModuleList()
        self.classifiers = nn.ModuleList()
        total_feat_dim = 0
        for dim in self.mod_dims:
            proj, bb = _make_branch(backbone_name, dim, hidden_dim, proj_dim)
            self.projectors.append(proj if proj else nn.Identity())
            self.backbones.append(bb)
            total_feat_dim += bb.output_dim
            self.classifiers.append(nn.Sequential(
                nn.Dropout(0.5), nn.Linear(bb.output_dim, num_classes),
            ))
        self._has_proj = proj_dim > 0
        self.gate = nn.Sequential(
            nn.Linear(total_feat_dim, 32), nn.ReLU(), nn.Linear(32, M),
        )

    def forward(self, x, mask=None):
        offset = 0
        all_feats, all_logits = [], []
        for i, dim in enumerate(self.mod_dims):
            x_mod = x[:, :, offset:offset + dim]
            offset += dim
            if self._has_proj:
                x_mod = self.projectors[i](x_mod)
            feat = self.backbones[i](x_mod, mask)
            all_feats.append(feat)
            all_logits.append(self.classifiers[i](feat))
        cat_feats = torch.cat(all_feats, dim=1)
        gate_weights = F.softmax(self.gate(cat_feats), dim=1)
        stacked = torch.stack(all_logits, dim=1)
        return (stacked * gate_weights.unsqueeze(-1)).sum(dim=1)


class StackingFusionModel(nn.Module):
    def __init__(self, backbone_name, modality_dims, num_classes, hidden_dim=64, proj_dim=0):
        super().__init__()
        self.mod_names = list(modality_dims.keys())
        self.mod_dims = list(modality_dims.values())
        M = len(self.mod_dims)
        self.projectors = nn.ModuleList()
        self.backbones = nn.ModuleList()
        self.classifiers = nn.ModuleList()
        for dim in self.mod_dims:
            proj, bb = _make_branch(backbone_name, dim, hidden_dim, proj_dim)
            self.projectors.append(proj if proj else nn.Identity())
            self.backbones.append(bb)
            self.classifiers.append(nn.Sequential(
                nn.Dropout(0.5), nn.Linear(bb.output_dim, num_classes),
            ))
        self._has_proj = proj_dim > 0
        self.meta_learner = nn.Sequential(
            nn.Linear(M * num_classes, 32), nn.ReLU(),
            nn.Dropout(0.5), nn.Linear(32, num_classes),
        )

    def forward(self, x, mask=None):
        offset = 0
        all_logits = []
        for i, dim in enumerate(self.mod_dims):
            x_mod = x[:, :, offset:offset + dim]
            offset += dim
            if self._has_proj:
                x_mod = self.projectors[i](x_mod)
            feat = self.backbones[i](x_mod, mask)
            all_logits.append(self.classifiers[i](feat))
        cat_logits = torch.cat(all_logits, dim=1)
        return self.meta_learner(cat_logits)


class ProductOfExpertsModel(nn.Module):
    def __init__(self, backbone_name, modality_dims, num_classes, hidden_dim=64, proj_dim=0):
        super().__init__()
        self.mod_names = list(modality_dims.keys())
        self.mod_dims = list(modality_dims.values())
        self.projectors = nn.ModuleList()
        self.backbones = nn.ModuleList()
        self.classifiers = nn.ModuleList()
        for dim in self.mod_dims:
            proj, bb = _make_branch(backbone_name, dim, hidden_dim, proj_dim)
            self.projectors.append(proj if proj else nn.Identity())
            self.backbones.append(bb)
            self.classifiers.append(nn.Sequential(
                nn.Dropout(0.5), nn.Linear(bb.output_dim, num_classes),
            ))
        self._has_proj = proj_dim > 0

    def forward(self, x, mask=None):
        offset = 0
        log_probs_sum = None
        for i, dim in enumerate(self.mod_dims):
            x_mod = x[:, :, offset:offset + dim]
            offset += dim
            if self._has_proj:
                x_mod = self.projectors[i](x_mod)
            feat = self.backbones[i](x_mod, mask)
            logits = self.classifiers[i](feat)
            log_p = F.log_softmax(logits, dim=1)
            log_probs_sum = log_p if log_probs_sum is None else log_probs_sum + log_p
        return log_probs_sum


class MoEFusionModel(nn.Module):
    def __init__(self, backbone_name, modality_dims, num_classes, hidden_dim=64, proj_dim=0):
        super().__init__()
        self.mod_names = list(modality_dims.keys())
        self.mod_dims = list(modality_dims.values())
        M = len(self.mod_dims)
        self.top_k = min(2, M)
        self.projectors = nn.ModuleList()
        self.backbones = nn.ModuleList()
        self.classifiers = nn.ModuleList()
        total_feat_dim = 0
        for dim in self.mod_dims:
            proj, bb = _make_branch(backbone_name, dim, hidden_dim, proj_dim)
            self.projectors.append(proj if proj else nn.Identity())
            self.backbones.append(bb)
            total_feat_dim += bb.output_dim
            self.classifiers.append(nn.Sequential(
                nn.Dropout(0.5), nn.Linear(bb.output_dim, num_classes),
            ))
        self._has_proj = proj_dim > 0
        self.router = nn.Linear(total_feat_dim, M)

    def forward(self, x, mask=None):
        offset = 0
        all_feats, all_logits = [], []
        for i, dim in enumerate(self.mod_dims):
            x_mod = x[:, :, offset:offset + dim]
            offset += dim
            if self._has_proj:
                x_mod = self.projectors[i](x_mod)
            feat = self.backbones[i](x_mod, mask)
            all_feats.append(feat)
            all_logits.append(self.classifiers[i](feat))
        cat_feats = torch.cat(all_feats, dim=1)
        router_logits = self.router(cat_feats)
        top_vals, top_idx = router_logits.topk(self.top_k, dim=1)
        top_weights = F.softmax(top_vals, dim=1)
        stacked = torch.stack(all_logits, dim=1)
        top_idx_exp = top_idx.unsqueeze(-1).expand(-1, -1, stacked.size(-1))
        selected = stacked.gather(1, top_idx_exp)
        return (selected * top_weights.unsqueeze(-1)).sum(dim=1)


class FeatureConcatFusionModel(nn.Module):
    """Feature-level late fusion: separate backbones, concatenate features, joint classifier."""

    def __init__(self, backbone_name, modality_dims, num_classes, hidden_dim=64, proj_dim=0):
        super().__init__()
        self.mod_names = list(modality_dims.keys())
        self.mod_dims = list(modality_dims.values())
        self.projectors = nn.ModuleList()
        self.backbones = nn.ModuleList()
        total_feat_dim = 0
        for dim in self.mod_dims:
            proj, bb = _make_branch(backbone_name, dim, hidden_dim, proj_dim)
            self.projectors.append(proj if proj else nn.Identity())
            self.backbones.append(bb)
            total_feat_dim += bb.output_dim
        self._has_proj = proj_dim > 0
        self.classifier = nn.Sequential(
            nn.LayerNorm(total_feat_dim),
            nn.Dropout(0.5),
            nn.Linear(total_feat_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(hidden_dim, num_classes),
        )

    def forward(self, x, mask=None):
        offset = 0
        all_feats = []
        for i, dim in enumerate(self.mod_dims):
            x_mod = x[:, :, offset:offset + dim]
            offset += dim
            if self._has_proj:
                x_mod = self.projectors[i](x_mod)
            feat = self.backbones[i](x_mod, mask)
            all_feats.append(feat)
        cat_feats = torch.cat(all_feats, dim=1)
        return self.classifier(cat_feats)


def build_model(backbone_name, fusion, input_dim, modality_dims, num_classes,
                hidden_dim=128, proj_dim=0, late_agg='mean'):
    """Factory function. proj_dim=0 means no projection (raw features)."""
    if fusion == 'early':
        return SingleModel(backbone_name, input_dim, num_classes, hidden_dim,
                           modality_dims=modality_dims, proj_dim=proj_dim)
    elif fusion == 'late':
        return LateFusionModel(backbone_name, modality_dims, num_classes, hidden_dim,
                               proj_dim, late_agg=late_agg)
    elif fusion == 'attention':
        return AttentionFusionModel(backbone_name, modality_dims, num_classes, hidden_dim, proj_dim)
    elif fusion == 'weighted_late':
        return WeightedLateFusionModel(backbone_name, modality_dims, num_classes, hidden_dim, proj_dim)
    elif fusion == 'gated_late':
        return GatedLateFusionModel(backbone_name, modality_dims, num_classes, hidden_dim, proj_dim)
    elif fusion == 'stacking':
        return StackingFusionModel(backbone_name, modality_dims, num_classes, hidden_dim, proj_dim)
    elif fusion == 'product':
        return ProductOfExpertsModel(backbone_name, modality_dims, num_classes, hidden_dim, proj_dim)
    elif fusion == 'moe':
        return MoEFusionModel(backbone_name, modality_dims, num_classes, hidden_dim, proj_dim)
    elif fusion == 'feat_concat':
        return FeatureConcatFusionModel(backbone_name, modality_dims, num_classes, hidden_dim, proj_dim)
    else:
        raise ValueError(f"Unknown fusion: {fusion}")