""" mjm_2a/b/c: three variants that inject brain-specific spatial/morphological context into the mjm_1 hierarchical encoder-decoder. All variants preserve the EXACT mjm_1 hierarchical logic: X -> E1->h1 -> E2->h2 -> E3->H D1->C1 D2->C2+C1 D3->C3+C2 + ReconDecoder mjm_2a: Laminar Depth Injection - DepthMLP(depth_imputed[B,1]) -> depth_feat[B,dec_hidden_dim] - Injected as residual into C2 (Subclass decoder), after C1 residual - Auxiliary depth regression head on h2 mjm_2b: Morpho-Spatial Fusion - Input = concat(X[B,140], cell_volume_norm[B,1], x_tiled[B,1], y_tiled[B,1]) -> [B,143] - Linear(143->140) projects back to gene-expression space before E1 - Auxiliary volume regression head on H (latent) mjm_2c: CPS-Conditioned FiLM Modulation - ContextMLP([cps, x_tiled, y_tiled] -> [B,3]) -> gamma, beta [B, enc_hidden_dim] - FiLM modulates h1 (E1 output): h1_mod = gamma * h1 + beta - Everything downstream (E2, E3, D1/D2/D3) sees disease-space-conditioned features """ import torch import torch.nn as nn import torch.nn.functional as F # ── Re-use building blocks from mjm.py ───────────────────────────────────── from src.models.mjm import RMSNorm, SwiGLU, FFN, _make_dec_block # ──────────────────────────────────────────────────────────────────────────── # Plan A: Laminar Depth Injection # ──────────────────────────────────────────────────────────────────────────── class mjm_2a(nn.Module): """ mjm_1 architecture + depth context injected at the Subclass decoder level. depth_imputed [B,1] -> DepthMLP -> depth_feat [B, dec_hidden_dim] C2 = D2(h2) + C1 + depth_feat (depth residual at Subclass level) C3 = D3(H) + C2 (Supertype still gets depth via C2) Auxiliary head: depth_head(h2) -> scalar (only supervised on has_depth cells) """ def __init__(self, input_dim=140, latent_dim=20, e_layers=3, d_layers=1, enc_hidden_dim=256, dec_hidden_dim=128, depth_mlp_dim=64, expansion_factor=2.67, dropout=0.3, output_num=[3, 24, 137], residual_mode='feature', ): super().__init__() assert residual_mode == 'feature', "mjm_2a only supports feature residual mode" self.residual_mode = residual_mode # ── Encoders (identical to mjm_1) ─────────────────────────────────── self.E1 = nn.Sequential( nn.Linear(input_dim, enc_hidden_dim), FFN(n_layers=e_layers, model_dim=enc_hidden_dim, expansion_factor=expansion_factor, dropout=dropout), ) self.E2 = FFN(n_layers=e_layers, model_dim=enc_hidden_dim, expansion_factor=expansion_factor, dropout=dropout) self.E3 = nn.Sequential( FFN(n_layers=e_layers, model_dim=enc_hidden_dim, expansion_factor=expansion_factor, dropout=dropout), nn.Linear(enc_hidden_dim, latent_dim), ) # ── Decoders (identical to mjm_1) ─────────────────────────────────── self.D1 = _make_dec_block(enc_hidden_dim, dec_hidden_dim, d_layers, expansion_factor, dropout) self.D2 = _make_dec_block(enc_hidden_dim, dec_hidden_dim, d_layers, expansion_factor, dropout) self.D3 = _make_dec_block(latent_dim, dec_hidden_dim, d_layers, expansion_factor, dropout) # ── Classification heads (identical to mjm_1) ─────────────────────── self.head1 = nn.Linear(dec_hidden_dim, output_num[0]) self.head2 = nn.Linear(dec_hidden_dim, output_num[1]) self.head3 = nn.Linear(dec_hidden_dim, output_num[2]) # ── Reconstruction decoder (identical to mjm_1) ───────────────────── self.recon_decoder = SwiGLU(latent_dim, input_dim, dropout=dropout) # ── NEW: Depth context branch ──────────────────────────────────────── self.depth_mlp = nn.Sequential( nn.Linear(1, depth_mlp_dim), nn.SiLU(), nn.Linear(depth_mlp_dim, dec_hidden_dim), ) # Auxiliary depth regression head (applied on h2) self.depth_head = nn.Linear(enc_hidden_dim, 1) def forward(self, x, depth_imputed): """ Args: x: [B, input_dim] gene expression (log1p normalized) depth_imputed: [B, 1] imputed normalized depth from pia Returns: recon, [logits1, logits2, logits3], H, depth_pred """ # ── Encoding ──────────────────────────────────────────────────────── h1 = self.E1(x) # [B, enc_hidden_dim] h2 = self.E2(h1) # [B, enc_hidden_dim] H = self.E3(h2) # [B, latent_dim] # ── Depth context ──────────────────────────────────────────────────── depth_feat = self.depth_mlp(depth_imputed) # [B, dec_hidden_dim] # ── Hierarchical decoding + classification ─────────────────────────── C1 = self.D1(h1) # [B, dec_hidden_dim] logits1 = self.head1(C1) # [B, n1] # C2: mjm_1 feature residual + depth injection at Subclass level C2 = self.D2(h2) + C1 + depth_feat # [B, dec_hidden_dim] logits2 = self.head2(C2) # [B, n2] # C3: standard feature residual (depth flows through C2) C3 = self.D3(H) + C2 # [B, dec_hidden_dim] logits3 = self.head3(C3) # [B, n3] # ── Reconstruction ─────────────────────────────────────────────────── recon = self.recon_decoder(H) # [B, input_dim] # ── Auxiliary depth prediction ─────────────────────────────────────── depth_pred = self.depth_head(h2) # [B, 1] return recon, [logits1, logits2, logits3], H, depth_pred # ──────────────────────────────────────────────────────────────────────────── # Plan B: Morpho-Spatial Fusion # ──────────────────────────────────────────────────────────────────────────── class mjm_2b(nn.Module): """ mjm_1 + cell morphology (volume) and spatial tiled coords fused at input. Input fusion: [X(140), cell_volume_norm(1), x_tiled_norm(1), y_tiled_norm(1)] -> [B,143] Linear(143->140) -> X_proj [B,140] -> E1->E2->E3 (identical to mjm_1) Auxiliary volume regression head on H (latent): volume_head(H) -> [B,1], only supervised on has_volume cells has_volume mask ensures 80.5% cells without volume still train normally through the spatial tiled coordinates. """ def __init__(self, input_dim=140, latent_dim=20, e_layers=3, d_layers=1, enc_hidden_dim=256, dec_hidden_dim=128, expansion_factor=2.67, dropout=0.3, output_num=[3, 24, 137], residual_mode='feature', ): super().__init__() assert residual_mode == 'feature' self.residual_mode = residual_mode # ── NEW: Input fusion projection ───────────────────────────────────── # X(140) + volume(1) + x_tiled(1) + y_tiled(1) = 143 self.input_proj = nn.Linear(input_dim + 3, input_dim) # ── Encoders (identical to mjm_1) ─────────────────────────────────── self.E1 = nn.Sequential( nn.Linear(input_dim, enc_hidden_dim), FFN(n_layers=e_layers, model_dim=enc_hidden_dim, expansion_factor=expansion_factor, dropout=dropout), ) self.E2 = FFN(n_layers=e_layers, model_dim=enc_hidden_dim, expansion_factor=expansion_factor, dropout=dropout) self.E3 = nn.Sequential( FFN(n_layers=e_layers, model_dim=enc_hidden_dim, expansion_factor=expansion_factor, dropout=dropout), nn.Linear(enc_hidden_dim, latent_dim), ) # ── Decoders (identical to mjm_1) ─────────────────────────────────── self.D1 = _make_dec_block(enc_hidden_dim, dec_hidden_dim, d_layers, expansion_factor, dropout) self.D2 = _make_dec_block(enc_hidden_dim, dec_hidden_dim, d_layers, expansion_factor, dropout) self.D3 = _make_dec_block(latent_dim, dec_hidden_dim, d_layers, expansion_factor, dropout) # ── Classification heads (identical to mjm_1) ─────────────────────── self.head1 = nn.Linear(dec_hidden_dim, output_num[0]) self.head2 = nn.Linear(dec_hidden_dim, output_num[1]) self.head3 = nn.Linear(dec_hidden_dim, output_num[2]) # ── Reconstruction decoder (identical to mjm_1) ───────────────────── self.recon_decoder = SwiGLU(latent_dim, input_dim, dropout=dropout) # ── NEW: Auxiliary volume regression head ──────────────────────────── self.volume_head = nn.Linear(latent_dim, 1) def forward(self, x, cell_volume_norm, spatial_tiled_norm): """ Args: x: [B, 140] gene expression (log1p) cell_volume_norm: [B, 1] z-score normalized volume (0 if missing) spatial_tiled_norm: [B, 2] normalized tiled spatial coords Returns: recon, [logits1, logits2, logits3], H, vol_pred """ # ── Input fusion ──────────────────────────────────────────────────── x_aug = torch.cat([x, cell_volume_norm, spatial_tiled_norm], dim=-1) # [B,143] x_proj = self.input_proj(x_aug) # [B,140] # ── Encoding ──────────────────────────────────────────────────────── h1 = self.E1(x_proj) # [B, enc_hidden_dim] h2 = self.E2(h1) # [B, enc_hidden_dim] H = self.E3(h2) # [B, latent_dim] # ── Hierarchical decoding (identical to mjm_1 feature mode) ───────── C1 = self.D1(h1) logits1 = self.head1(C1) C2 = self.D2(h2) + C1 logits2 = self.head2(C2) C3 = self.D3(H) + C2 logits3 = self.head3(C3) recon = self.recon_decoder(H) # ── Auxiliary volume prediction ────────────────────────────────────── vol_pred = self.volume_head(H) # [B, 1] return recon, [logits1, logits2, logits3], H, vol_pred # ──────────────────────────────────────────────────────────────────────────── # Plan C: CPS-Conditioned FiLM Modulation # ──────────────────────────────────────────────────────────────────────────── class mjm_2c(nn.Module): """ mjm_1 + FiLM modulation of h1 conditioned on CPS and spatial_tiled. Context = [cps(1), x_tiled_norm(1), y_tiled_norm(1)] -> [B,3] ContextMLP(3->film_hidden->enc_hidden_dim*2) -> gamma[B,d], beta[B,d] h1_mod = gamma * h1 + beta Everything downstream (E2->E3->D1/D2/D3) sees disease-and-space-conditioned features. CPS is available for ALL 1.9M cells, so no cells are wasted. No auxiliary loss needed — FiLM supervision comes from the classification and reconstruction losses backpropagating through h1_mod. """ def __init__(self, input_dim=140, latent_dim=20, e_layers=3, d_layers=1, enc_hidden_dim=256, dec_hidden_dim=128, film_hidden=64, expansion_factor=2.67, dropout=0.3, output_num=[3, 24, 137], residual_mode='feature', ): super().__init__() assert residual_mode == 'feature' self.residual_mode = residual_mode self.enc_hidden_dim = enc_hidden_dim # ── Encoders (identical to mjm_1) ─────────────────────────────────── self.E1 = nn.Sequential( nn.Linear(input_dim, enc_hidden_dim), FFN(n_layers=e_layers, model_dim=enc_hidden_dim, expansion_factor=expansion_factor, dropout=dropout), ) self.E2 = FFN(n_layers=e_layers, model_dim=enc_hidden_dim, expansion_factor=expansion_factor, dropout=dropout) self.E3 = nn.Sequential( FFN(n_layers=e_layers, model_dim=enc_hidden_dim, expansion_factor=expansion_factor, dropout=dropout), nn.Linear(enc_hidden_dim, latent_dim), ) # ── Decoders (identical to mjm_1) ─────────────────────────────────── self.D1 = _make_dec_block(enc_hidden_dim, dec_hidden_dim, d_layers, expansion_factor, dropout) self.D2 = _make_dec_block(enc_hidden_dim, dec_hidden_dim, d_layers, expansion_factor, dropout) self.D3 = _make_dec_block(latent_dim, dec_hidden_dim, d_layers, expansion_factor, dropout) # ── Classification heads (identical to mjm_1) ─────────────────────── self.head1 = nn.Linear(dec_hidden_dim, output_num[0]) self.head2 = nn.Linear(dec_hidden_dim, output_num[1]) self.head3 = nn.Linear(dec_hidden_dim, output_num[2]) # ── Reconstruction decoder (identical to mjm_1) ───────────────────── self.recon_decoder = SwiGLU(latent_dim, input_dim, dropout=dropout) # ── NEW: FiLM context network ──────────────────────────────────────── # Input: [cps, x_tiled_norm, y_tiled_norm] -> 3 dims self.film_net = nn.Sequential( nn.Linear(3, film_hidden), nn.SiLU(), nn.Linear(film_hidden, enc_hidden_dim * 2), # -> gamma + beta ) def forward(self, x, cps, spatial_tiled_norm): """ Args: x: [B, 140] gene expression (log1p) cps: [B, 1] continuous pseudo-progression score spatial_tiled_norm: [B, 2] normalized tiled spatial coords Returns: recon, [logits1, logits2, logits3], H """ # ── E1: initial encoding ───────────────────────────────────────────── h1 = self.E1(x) # [B, enc_hidden_dim] # ── FiLM modulation on h1 ──────────────────────────────────────────── context = torch.cat([cps, spatial_tiled_norm], dim=-1) # [B, 3] film_params = self.film_net(context) # [B, enc_hidden_dim*2] gamma, beta = film_params.chunk(2, dim=-1) # each [B, enc_hidden_dim] # scale gamma around 1 (like layer norm scale), beta is bias h1_mod = (1.0 + gamma) * h1 + beta # [B, enc_hidden_dim] # ── E2, E3 ─────────────────────────────────────────────────────────── h2 = self.E2(h1_mod) # [B, enc_hidden_dim] H = self.E3(h2) # [B, latent_dim] # ── Hierarchical decoding (identical to mjm_1 feature mode) ───────── C1 = self.D1(h1_mod) logits1 = self.head1(C1) C2 = self.D2(h2) + C1 logits2 = self.head2(C2) C3 = self.D3(H) + C2 logits3 = self.head3(C3) recon = self.recon_decoder(H) return recon, [logits1, logits2, logits3], H