src/modeling/models.py

from warnings import warn

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import ASTConfig, ASTModel


class StudentAST(nn.Module):
    """
    A student model for audio classification using the AST architecture.

    :param n_classes: The number of classes to classify.
    :type n_classes: int
    :param hidden_size: The number of units in the hidden layers, defaults to 384.
    :type hidden_size: int, optional
    :param num_heads: The number of attention heads to use, defaults to 6.
    :type num_heads: int, optional
    """

    def __init__(self, n_classes: int, hidden_size: int = 384, num_heads: int = 6):
        super().__init__()

        config = ASTConfig(hidden_size=hidden_size, num_attention_heads=num_heads, intermediate_size=hidden_size * 4)
        self.base_model = ASTModel(config=config)
        self.classifier = StudentClassificationHead(hidden_size, n_classes)

    def forward(self, x: torch.Tensor):
        """
        Forward pass of the student model.

        :param x: The input tensor of shape [batch_size, sequence_length, input_dim].
        :type x: torch.Tensor
        :return: The output tensor of shape [batch_size, n_classes].
        :rtype: torch.Tensor
        """

        x = self.base_model(x)[0]
        x = self.classifier(x)
        return x


class StudentClassificationHead(nn.Module):
    """
    A classification head for the student model.

    :param emb_size: The size of the embedding.
    :type emb_size: int
    :param n_classes: The number of classes to classify.
    :type n_classes: int
    """

    def __init__(self, emb_size: int, n_classes: int):
        super().__init__()

        self.cls_head = nn.Linear(emb_size, n_classes)
        self.dist_head = nn.Linear(emb_size, n_classes)

    def forward(self, x: torch.Tensor):
        """
        Forward pass of the classification head.

        :param x: The input tensor of shape [batch_size, emb_size*2].
        :type x: torch.Tensor
        :return: The output tensor of shape [batch_size, n_classes].
        :rtype: torch.Tensor
        """

        x_cls, x_dist = x[:, 0], x[:, 1]
        x_cls_head = self.cls_head(x_cls)
        x_dist_head = self.dist_head(x_dist)

        if self.training:
            x = x_cls_head, x_dist_head
        else:
            x = (x_cls_head + x_dist_head) / 2

        return x


class ASTPretrained(nn.Module):
    """
    This class implements a PyTorch module for a pre-trained Audio Set Transformer (AST) model
    fine-tuned on MIT's dataset for audio event classification.

    :param n_classes: The number of classes for audio event classification.
    :type n_classes: int
    :param dropout: The dropout probability for the fully connected layer, defaults to 0.5.
    :type dropout: float, optional
    :raises ValueError: If n_classes is not positive.
    :raises TypeError: If dropout is not a float or is not between 0 and 1.
    :return: The output tensor of shape [batch_size, n_classes] containing the probabilities of each class.
    :rtype: torch.Tensor
    """

    def __init__(self, n_classes: int, download_weights: bool = True, freeze_body: bool = False, dropout: float = 0.5):
        super().__init__()

        if download_weights:
            self.base_model = ASTModel.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593")
        else:
            config = ASTConfig()
            self.base_model = ASTModel(config=config)

        if freeze_body:
            self.base_model = freeze(self.base_model)

        fc_in = self.base_model.config.hidden_size

        self.classifier = nn.Sequential(
            nn.LayerNorm((fc_in,), eps=1e-12), nn.Dropout(p=dropout), nn.Linear(fc_in, n_classes)
        )

    def forward(self, x):
        """Passes the input tensor through the pre-trained Audio Set Transformer (AST) model
        followed by a fully connected layer.

        :param x: The input tensor of shape [batch_size, seq_len, num_features].
        :type x: torch.Tensor
        :return: The output tensor of shape [batch_size, n_classes] containing the probabilities of each class.
        :rtype: torch.Tensor
        :raises ValueError: If the shape of x is not [batch_size, seq_len, num_features].
        """

        x = self.base_model(x)[1]
        x = self.classifier(x)
        return x


def layerwise_lr_decay(config, model: ASTModel):
    """
    LLRD (Layer-wise Learning Rate Decay) function computes the learning rate for each layer in a deep neural network
    using a specific decay rate and a base learning rate for the optimizer.

    :param config: A configuration object that contains the parameters required for LLRD.
    :type config: Any
    :param model: A PyTorch neural network model.
    :type model: ASTModel

    :raises Warning: If the configuration object does not contain the LLRD parameters.

    :return: A dictionary containing the optimizer parameters (parameters, weight decay, and learning rate)
        for each layer.
    :rtype: dict
    """

    try:
        config = config.LLRD
    except Exception:
        warn("No LLRD found in config. Learner will use single lr for whole model.")
        return None

    lr = config["base_lr"]
    weight_decay = config["weight_decay"]
    no_decay = ["bias", "layernorm"]
    body = ["embeddings", "encoder.layer"]
    head_params = [(n, p) for n, p in model.named_parameters() if not any(body_param in n for body_param in body)]
    optimizer_grouped_parameters = [
        {
            "params": [p for n, p in head_params if not any(nd in n for nd in no_decay)],
            "weight_decay": weight_decay,
            "lr": lr,
        },
        {
            "params": [p for n, p in head_params if any(nd in n for nd in no_decay)],
            "weight_decay": 0.0,
            "lr": lr,
        },
    ]

    # initialize lrs for every layer
    layers = [getattr(model.module, config["body"]).embeddings] + list(
        getattr(model.module, config["body"]).encoder.layer
    )
    layers.reverse()
    for layer in layers:
        lr *= config["lr_decay_rate"]
        optimizer_grouped_parameters += [
            {
                "params": [p for n, p in layer.named_parameters() if not any(nd in n for nd in no_decay)],
                "weight_decay": weight_decay,
                "lr": lr,
            },
            {
                "params": [p for n, p in layer.named_parameters() if any(nd in n for nd in no_decay)],
                "weight_decay": 0.0,
                "lr": lr,
            },
        ]

    return optimizer_grouped_parameters


def freeze(model: nn.Module):
    """
    Freeze function sets the requires_grad attribute to False for all parameters
    in the given PyTorch neural network model. This is used to freeze the weights of
    the model during training or inference.

    :param model: A PyTorch neural network model.
    :type model: nn.Module

    :return: The same model with requires_grad attribute set to False for all parameters.
    :rtype: nn.Module
    """

    model.eval()
    for param in model.parameters():
        param.requires_grad = False

    return model


def unfreeze(model: nn.Module):
    """
    Unfreeze the model by setting requires_grad to True for all parameters.

    :param model: The model to unfreeze.
    :type model: nn.Module
    :return: The unfrozen model.
    :rtype: nn.Module
    """

    model.train()
    for param in model.parameters():
        param.requires_grad = True

    return model


def interpolate_params(student: nn.Module, teacher: nn.Module):
    """
    Interpolate parameters between two models. This function scales the parameters of the
    teacher model to match the shape of the corresponding parameters in the student model
    using bilinear interpolation. If the shapes of the parameters in the two models are already the same,
    the parameters are unchanged.

    :param student: The student model.
    :type student: nn.Module
    :param teacher: The teacher model.
    :type teacher: nn.Module
    :return: A dictionary of interpolated parameters for the student model.
    :rtype: dict
    """

    new_params = {}

    # Iterate over the parameters in the first model
    for name, param in teacher.base_model.named_parameters():
        # Scale the parameter using interpolate if its shape is different from that of the second model
        target_param = student.base_model.state_dict()[name]
        if param.shape != target_param.shape:
            squeeze_count = 0
            permuted = False
            while param.ndim < 4:
                param = param.unsqueeze(0)
                squeeze_count += 1

            if param.shape[0] > 1:
                param = param.permute(1, 2, 3, 0)
                target_param = target_param.permute(1, 2, 3, 0)
                permuted = True

            if target_param.ndim < 2:
                target_param = target_param.unsqueeze(0)

            scaled_param = F.interpolate(param, size=(target_param.shape[-2:]), mode="bilinear")

            while squeeze_count > 0:
                scaled_param = scaled_param.squeeze(0)
                squeeze_count -= 1

            if permuted:
                scaled_param = scaled_param.permute(-1, 0, 1, 2)

        else:
            scaled_param = param
        new_params[name] = scaled_param

    return new_params


def average_model_weights(model_weights_list):
    """
    Compute the average weights of a list of PyTorch models.

    :param model_weights_list: A list of file paths to PyTorch model weight files.
    :type model_weights_list: List[str]
    :raises ValueError: If the input list is empty.
    :return: A dictionary containing the average weights of the models.
    :rtype: Dict[str, torch.Tensor]
    """

    if not model_weights_list:
        raise ValueError("The input list cannot be empty.")

    num_models = len(model_weights_list)
    averaged_weights = {}

    # Load the first model weights
    state_dict = torch.load(model_weights_list[0])

    # Iterate through the remaining models and add their weights to the first model's weights
    for i in range(1, num_models):
        state_dict_i = torch.load(model_weights_list[i])
        for key in state_dict.keys():
            state_dict[key] += state_dict_i[key]

    # Compute the average of the weights
    for key in state_dict.keys():
        averaged_weights[key] = state_dict[key] / num_models

    return averaged_weights