tc5/model.py

import torch
import torch.nn as nn
from torchaudio.models import Conformer
from huggingface_hub import PyTorchModelHubMixin
from .config import (
    N_MELS,
    CNN_CH,
    N_HEADS,
    D_MODEL,
    FF_DIM,
    N_LAYERS,
    DROPOUT,
    DEPTHWISE_CONV_KERNEL_SIZE,
    HIDDEN_DIM,
    DEVICE,
)


class TaikoConformer5(nn.Module, PyTorchModelHubMixin):
    def __init__(self):
        super().__init__()
        # 1) CNN frontend: frequency-only pooling
        self.cnn = nn.Sequential(
            nn.Conv2d(1, CNN_CH, 3, stride=(2, 1), padding=1),
            nn.BatchNorm2d(CNN_CH),
            nn.GELU(),
            nn.Dropout2d(DROPOUT),
            nn.Conv2d(CNN_CH, CNN_CH, 3, stride=(2, 1), padding=1),
            nn.BatchNorm2d(CNN_CH),
            nn.GELU(),
            nn.Dropout2d(DROPOUT),
        )
        feat_dim = CNN_CH * (N_MELS // 4)

        # 2) Linear projection to model dimension
        self.proj = nn.Linear(feat_dim, D_MODEL)

        # 3) FiLM conditioning for notes_per_second
        self.film = nn.Linear(1, 2 * D_MODEL)

        # 4) Conformer encoder
        self.encoder = Conformer(
            input_dim=D_MODEL,
            num_heads=N_HEADS,
            ffn_dim=FF_DIM,
            num_layers=N_LAYERS,
            depthwise_conv_kernel_size=DEPTHWISE_CONV_KERNEL_SIZE,
            dropout=DROPOUT,
            use_group_norm=False,
            convolution_first=False,
        )

        # 5) Presence regressor head
        self.presence_regressor = nn.Sequential(
            nn.Dropout(DROPOUT),
            nn.Linear(D_MODEL, HIDDEN_DIM),
            nn.GELU(),
            nn.Dropout(DROPOUT),
            nn.Linear(HIDDEN_DIM, 3),  # Don, Ka, DrumRoll energy
            nn.Sigmoid(),  # Output between 0 and 1
        )

        # 6) Initialize weights
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
            elif isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)

    def forward(
        self, mel: torch.Tensor, lengths: torch.Tensor, notes_per_second: torch.Tensor
    ):
        """
        Args:
            mel: (B, 1, N_MELS, T_mel)
            lengths: (B,) lengths after CNN
            notes_per_second: (B,) stream of control values
        Returns:
            Dict with:
                'presence': (B, T_cnn_out, 4)
                'lengths': lengths
        """
        # CNN frontend
        x = self.cnn(mel)  # (B, C, F, T)
        B, C, F, T = x.size()
        x = x.permute(0, 3, 1, 2).reshape(B, T, C * F)

        # Project to model dimension
        x = self.proj(x)  # (B, T, D_MODEL)

        # FiLM conditioning
        nps = notes_per_second.unsqueeze(-1)  # (B, 1)
        gamma_beta = self.film(nps)  # (B, 2*D_MODEL)
        gamma, beta = gamma_beta.chunk(2, dim=-1)
        x = gamma.unsqueeze(1) * x + beta.unsqueeze(1)

        # Conformer encoder
        x, _ = self.encoder(x, lengths=lengths)

        # Presence prediction
        presence = self.presence_regressor(x)
        return {"presence": presence, "lengths": lengths}


if __name__ == "__main__":
    model = TaikoConformer5().to(device=DEVICE)
    print(model)

    for name, param in model.named_parameters():
        if param.requires_grad:
            print(f"{name}: {param.numel():,}")

    params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print(f"Total parameters: {params / 1e6:.2f}M")

    batch_size = 4
    mel_time_steps = 1024
    input_mel = torch.randn(batch_size, 1, N_MELS, mel_time_steps).to(DEVICE)

    conformer_lengths = torch.tensor(
        [mel_time_steps] * batch_size, dtype=torch.long
    ).to(DEVICE)

    notes_per_second_input = torch.tensor([10.0] * batch_size, dtype=torch.float32).to(
        DEVICE
    )

    output = model(input_mel, conformer_lengths, notes_per_second_input)
    print("Output shapes:")
    for key, value in output.items():
        print(f"{key}: {value.shape}")