model.py

# Description: Classification models
from transformers import AutoModel, AutoTokenizer, BatchEncoding, TrainingArguments, Trainer
from functools import partial
from huggingface_hub import snapshot_download
from huggingface_hub.constants import HF_HUB_CACHE
from accelerate import Accelerator
from accelerate.utils import find_executable_batch_size as auto_find_batch_size
from datasets import load_dataset, Dataset
from torch.utils.data import DataLoader
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import json
import os
from tqdm import tqdm
import pandas as pd

import matplotlib.pyplot as plt
from sklearn.metrics import (
    ConfusionMatrixDisplay,
    accuracy_score,
    classification_report,
    confusion_matrix,
    f1_score,
    recall_score
)

BASE_PATH = os.path.dirname(os.path.abspath(__file__))


class MultiHeadClassification(nn.Module):
    """
    MultiHeadClassification

    An easy to use multi-head classification model. It takes a backbone model and a dictionary of head configurations.
    It can be used to train multiple classification tasks at once using a single backbone model.

    Apart from joint training, it also supports training individual heads separately, providing a simple way to freeze
    and unfreeze heads.

    Example:
    >>> from transformers import AutoModel, AutoTokenizer
        >>> from torch.optim import AdamW
        >>> import torch
        >>> import time
        >>> import torch.nn as nn
        >>>
        >>> # Manually load backbone model to create model
        >>> backbone = AutoModel.from_pretrained('BAAI/bge-m3')
        >>> model = MultiHeadClassification(backbone, {'binary': 2, 'sentiment': 3, 'something': 4}).to('cuda')
        >>> print(model)
        >>> # Load tokenizer for data preprocessing
        >>> tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-m3')
        >>> # some training data
        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt", padding=True, truncation=True)
        >>> optimizer = AdamW(model.parameters(), lr=5e-4)
        >>> samples = tokenizer(["Hello, my dog is cute", "Hello, my dog is cute", "I like turtles"], return_tensors="pt", padding=True, truncation=True).to('cuda')
        >>> labels = {'binary': torch.tensor([0, 0, 1]), 'sentiment': torch.tensor([0, 1, 2]), 'something': torch.tensor([0, 1, 2])}
        >>> model.freeze_backbone()
        >>> model.train(True)
        >>> for i in range(10):
        ...     optimizer.zero_grad()
        ...     outputs = model(samples)
        ...     loss = sum([nn.CrossEntropyLoss()(outputs[name].cpu(), labels[name]) for name in model.heads.keys()])
        ...     loss.backward()
        ...     optimizer.step()
        ...     print(loss.item())
        ...     #time.sleep(1)
        ... print(model(samples))
        >>> # Save full model
        >>> model.save('model.pth')
        >>> # Save head only
        >>> model.save_head('binary', 'binary.pth')
        >>> # Load full model
        >>> model = MultiHeadClassification(backbone, {}).to('cuda')
        >>> model.load('model.pth')
        >>> # Load head only
        >>> model = MultiHeadClassification(backbone, {}).to('cuda')
        >>> model.load_head('binary', 'binary.pth')
        >>> # Adding new head
        >>> model.add_head('new_head', 3)
        >>> print(model)
        >>> # extend dataset with data for new head
        >>> labels['new_head'] = torch.tensor([0, 1, 2])
        >>> # Freeze all heads and backbone
        >>> model.freeze_all()
        >>> # Only unfreeze new head
        >>> model.unfreeze_head('new_head')
        >>> model.train(True)
        >>> for i in range(10):
        ...     optimizer.zero_grad()
        ...     outputs = model(samples)
        ...     loss = sum([nn.CrossEntropyLoss()(outputs[name].cpu(), labels[name]) for name in model.heads.keys()])
        ...     loss.backward()
        ...     optimizer.step()
        ...     print(loss.item())
        >>> print(model(samples))

    Args:
        backbone (transformers.PreTrainedModel): A pretrained transformer model
        head_config (dict): A dictionary with head configurations. The key is the head name and the value is the number
            of classes for that head.
    """
    def __init__(self, backbone, head_config, dropout=0.1, l2_reg=0.01):
        super().__init__()
        self.backbone = backbone
        self.num_heads = len(head_config)
        self.heads = nn.ModuleDict({
            name: nn.Linear(backbone.config.hidden_size, num_classes)
            for name, num_classes in head_config.items()
        })
        self.do = nn.Dropout(dropout)
        self.l2_reg = l2_reg
        self.device = 'cpu'
        self.torch_dtype = torch.float16
        self.head_config = head_config

    def forward(self, x, head_names=None) -> dict:
        """
        Forward pass of the model.

        Requires tokenizer output as input. The input should be a dictionary with keys 'input_ids', 'attention_mask'.

        Args:
            x (dict): Tokenizer output
            head_names (list): (optional) List of head names to return logits for. If None, returns logits for all heads.
        
        Returns:
            dict: A dictionary with head names as keys and logits as values
        """
        x = self.backbone(**x, return_dict=True, output_hidden_states=True).last_hidden_state[:, 0, :]
        x = self.do(x)
        if head_names is None:
            return {name: head(x) for name, head in self.heads.items()}
        return {name: head(x) for name, head in self.heads.items() if name in head_names}

    def get_l2_loss(self):
        """
        Getter for L2 regularization loss

        Returns:
            torch.Tensor: L2 regularization loss
        """
        l2_loss = torch.tensor(0.).to(self.device)
        for param in self.parameters():
            if param.requires_grad:
                l2_loss += torch.norm(param, 2)
        return (self.l2_reg * l2_loss).to(self.device)

    def to(self, *args, **kwargs):
        super().to(*args, **kwargs)
        if isinstance(args[0], torch.dtype):
            self.torch_dtype = args[0]
        elif isinstance(args[0], str):
            self.device = args[0]
        return self

    def load_head(self, head_name, path):
        """
        Load head from a file

        Args:
            head_name (str): Name of the head
            path (str): Path to the file

        Returns:
            None
        """
        model = torch.load(path)
        if head_name in self.heads:
            num_classes = model['weight'].shape[0]
            self.heads[head_name].load_state_dict(model)
            self.to(self.torch_dtype).to(self.device)
            self.head_config[head_name] = num_classes
            return

        assert model['weight'].shape[1] == self.backbone.config.hidden_size
        num_classes = model['weight'].shape[0]
        self.heads[head_name] = nn.Linear(self.backbone.config.hidden_size, num_classes)
        self.heads[head_name].load_state_dict(model)
        self.head_config[head_name] = num_classes

        self.to(self.torch_dtype).to(self.device)

    def save_head(self, head_name, path):
        """
        Save head to a file

        Args:
            head_name (str): Name of the head
            path (str): Path to the file
        """
        torch.save(self.heads[head_name].state_dict(), path)

    def save(self, path):
        """
        Save the full model to a file

        Args:
            path (str): Path to the file
        """
        torch.save(self.state_dict(), path)

    def load(self, path):
        """
        Load the full model from a file

        Args:
            path (str): Path to the file
        """
        self.load_state_dict(torch.load(path))
        self.to(self.torch_dtype).to(self.device)

    def save_backbone(self, path):
        """
        Save the backbone to a file

        Args:
            path (str): Path to the file
        """
        self.backbone.save_pretrained(path)

    def load_backbone(self, path):
        """
        Load the backbone from a file

        Args:
            path (str): Path to the file
        """
        self.backbone = AutoModel.from_pretrained(path)
        self.to(self.torch_dtype).to(self.device)

    def freeze_backbone(self):
        """ Freeze the backbone """
        for param in self.backbone.parameters():
            param.requires_grad = False

    def unfreeze_backbone(self):
        """ Unfreeze the backbone """
        for param in self.backbone.parameters():
            param.requires_grad = True

    def freeze_head(self, head_name):
        """
        Freeze a head by name

        Args:
            head_name (str): Name of the head
        """
        for param in self.heads[head_name].parameters():
            param.requires_grad = False

    def unfreeze_head(self, head_name):
        """
        Unfreeze a head by name

        Args:
            head_name (str): Name of the head
        """
        for param in self.heads[head_name].parameters():
            param.requires_grad = True

    def freeze_all_heads(self):
        """ Freeze all heads """
        for head_name in self.heads.keys():
            self.freeze_head(head_name)

    def unfreeze_all_heads(self):
        """ Unfreeze all heads """
        for head_name in self.heads.keys():
            self.unfreeze_head(head_name)

    def freeze_all(self):
        """ Freeze all """
        self.freeze_backbone()
        self.freeze_all_heads()

    def unfreeze_all(self):
        """ Unfreeze all """
        self.unfreeze_backbone()
        self.unfreeze_all_heads()

    def add_head(self, head_name, num_classes):
        """
        Add a new head to the model

        Args:
            head_name (str): Name of the head
            num_classes (int): Number of classes for the head
        """
        self.heads[head_name] = nn.Linear(self.backbone.config.hidden_size, num_classes)
        self.heads[head_name].to(self.torch_dtype).to(self.device)
        self.head_config[head_name] = num_classes

    def remove_head(self, head_name):
        """
        Remove a head from the model
        """
        if head_name not in self.heads:
            raise ValueError(f'Head {head_name} not found')
        del self.heads[head_name]
        del self.head_config[head_name]

    @classmethod
    def from_pretrained(cls, model_name, head_config=None, dropout=0.1, l2_reg=0.01):
        """
        Load a pretrained model from Huggingface model hub

        Args:
            model_name (str): Name of the model
            head_config (dict): Head configuration
            dropout (float): Dropout rate
            l2_reg (float): L2 regularization rate
        """
        if head_config is None:
            head_config = {}
        # check if model exists locally
        hf_cache_dir = HF_HUB_CACHE
        model_path = os.path.join(hf_cache_dir, model_name)
        if os.path.exists(model_path):
            return cls._from_directory(model_path, head_config, dropout, l2_reg)

        model_path = snapshot_download(repo_id=model_name, cache_dir=hf_cache_dir)
        return cls._from_directory(model_path, head_config, dropout, l2_reg)

    @classmethod
    def _from_directory(cls, model_path, head_config, dropout=0.1, l2_reg=0.01):
        """
        Load a model from a directory

        Args:
            model_path (str): Path to the model directory
            head_config (dict): Head configuration
            dropout (float): Dropout rate
            l2_reg (float): L2 regularization rate
        """
        backbone = AutoModel.from_pretrained(os.path.join(model_path, 'pretrained/backbone.pth'))
        instance = cls(backbone, head_config, dropout, l2_reg)
        instance.load(os.path.join(model_path, 'pretrained/model.pth'))
        instance.head_config = {k: v. instance.heads}
        return instance

class MultiHeadClassificationTrainer:
    def __init__(self, **kwargs):
        self.model_conf = kwargs.get('model_conf', {})
        self.optimizer_conf = kwargs.get('optimizer_conf', {})
        self.scheduler_conf = kwargs.get('scheduler_conf', {})
        self.dropout = kwargs.get('dropout', 0.1)
        self.l2_loss_weight = kwargs.get('l2_loss_weight', 0.01)
        self.num_epochs = kwargs.get('num_epochs', 100)
        self.device = kwargs.get('device', 'cuda')
        self.train_run = kwargs.get('train_run', 0)
        self.name_prefix = kwargs.get('name_prefix', 'multihead-classification')
        self.use_lr_scheduler = kwargs.get('use_lr_scheduler', True)
        self.gradient_accumulation_steps = kwargs.get('gradient_accumulation_steps', 1)
        self.batch_size = kwargs.get('batch_size', 4)
        self.train_test_split = kwargs.get('train_test_split', 0.2)
        self.load_best = kwargs.get('load_best', True)
        self.auto_find_batch_size = kwargs.get('auto_find_batch_size', False)
        self.test_data = None
        self.accelerator = Accelerator()
        
        self.classifier = MultiHeadClassification(
            **self.model_conf
        ).to(torch.float16)
        self.classifier.freeze_backbone()
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_conf.get('tokenizer', self.classifier.backbone.name_or_path), model_max_length=128)

    def _batch_data(self, batch_size, data):
        return DataLoader(data, shuffle=True, batch_size=batch_size)

    def train(self, dataset_name: str = None, train_data: DataLoader = None, val_data: DataLoader = None, lr: float = None, num_epochs: int = None, target_heads: list[str] = None, batch_size: int = 4, sample_key=None, label_key=None):
        has_dataset = train_data is not None
        assert (dataset_name is not None and not has_dataset) or (has_dataset and dataset_name is None), 'Must provide either dataset or dataset_name'
        if dataset_name is not None:
            assert target_heads is not None, 'target_heads must be provided when using dataset_name'

        if sample_key is None:
            sample_key = 'sample'
        if label_key is None:
            label_key = 'label'

        self.accelerator.free_memory()
        self.classifier = self.accelerator.prepare(self.classifier)

        if dataset_name is not None:
            dataset = load_dataset(dataset_name)['train'].train_test_split(test_size=self.train_test_split)
            train_data = dataset['train']
            val_data = dataset['test'].train_test_split(test_size=0.5)
            self.test_data = val_data['test']
            val_data = val_data['train']

        if batch_size is not None:
            self.batch_size = batch_size

        if isinstance(train_data, Dataset):
            sample = next(iter(train_data))
            print('Tokenizing dataset...', sample, type(sample))
            is_string_dataset = isinstance(sample[0], str) if not isinstance(sample, dict) else isinstance(sample[sample_key], str)

            if is_string_dataset:
                if isinstance(sample, list):
                    train_data = train_data.map(lambda x: self.tokenizer([x[0]] if isinstance(x[0], str) else x[0], return_tensors="pt", padding=True, truncation=True), batched=True)
                    val_data = val_data.map(lambda x: self.tokenizer([x[0]] if isinstance(x[0], str) else x[0], return_tensors="pt", padding=True, truncation=True), batched=True)
                elif isinstance(sample, dict):
                    assert sample_key in sample and label_key in sample, 'Invalid dataset format'
                    train_data = train_data.map(lambda x: self.tokenizer([x[sample_key]] if isinstance(x[sample_key], str) else x[sample_key], return_tensors="pt", padding=True, truncation=True), batched=True)
                    val_data = val_data.map(lambda x: self.tokenizer([x[sample_key]] if isinstance(x[sample_key], str) else x[sample_key], return_tensors="pt", padding=True, truncation=True), batched=True)
                else:
                    raise ValueError('Invalid dataset format')

                create_train_data = partial(self._batch_data, data=train_data)
                create_val_data = partial(self._batch_data, data=val_data)

                if self.auto_find_batch_size:
                    train_data = auto_find_batch_size(create_train_data)()
                    val_data = auto_find_batch_size(create_val_data)()
                else:
                    train_data = create_train_data(self.batch_size)
                    val_data = create_val_data(self.batch_size)
            # otherwise, assume it's already tokenized
        else:
            assert train_data is not None and val_data is not None, 'train_data and val_data must be provided'
            assert isinstance(train_data, DataLoader) and isinstance(val_data, DataLoader), 'train_data and val_data must be DataLoader instances'

        optimizer_name = self.optimizer_conf.pop('optimizer', 'sgd')
        loss_name = self.optimizer_conf.pop('loss', 'crossentropy')
        if lr:
            self.optimizer_conf['lr'] = lr
        if num_epochs:
            self.num_epochs = num_epochs

        self.classifier.unfreeze_all()
        # freeze backbone
        print('Freezing backbone')
        self.classifier.freeze_backbone()
        # freeze all heads that are not in the training data

        if target_heads is None:
            sample = next(iter(train_data))
            if isinstance(sample, dict):
                train_heads = list(sample[label_key].keys())
            elif isinstance(sample, list):
                train_heads = list(sample[1].keys())
            else:
                raise ValueError('Invalid dataset format')
        else:
            train_heads = target_heads

        for head_name in self.classifier.heads.keys():
            if head_name not in train_heads:
                print(f'Freezing head {head_name}')
                self.classifier.freeze_head(head_name)

        self.classifier.to(self.device)
        self.classifier.train(True)
        loss_func = {'crossentropy': nn.CrossEntropyLoss, 'bce': nn.BCELoss}.get(loss_name, nn.CrossEntropyLoss)
        optimizer_class = {'sgd': optim.SGD, 'adam': optim.Adam}.get(optimizer_name, optim.SGD)
        optimizer = optimizer_class(self.classifier.parameters(), **self.optimizer_conf)

        scheduler = None
        if self.use_lr_scheduler:
            scheduler_class = {
                'plateau': optim.lr_scheduler.ReduceLROnPlateau, 
                'step': optim.lr_scheduler.StepLR,
            }.get(self.scheduler_conf.get('scheduler'), optim.lr_scheduler.ReduceLROnPlateau)
            scheduler = scheduler_class(optimizer, 'min', **self.scheduler_conf)

        history = self._train(loss_func(), optimizer, scheduler, self.accelerator.prepare(train_data), self.accelerator.prepare(val_data), train_heads, sample_key, label_key)
        if self.load_best:
            self.classifier.load(os.path.join(BASE_PATH, f'../train_runs/{self.name_prefix}-run-{self.train_run-1}-best-model.pth'))
        return self.classifier, history

    def _train(self, criterion, optimizer, scheduler, dataloader, val_dataloader, head_names, sample_key, label_key):
        average_acc = 0
        losses = []
        precisions = []
        best_prec = 0.0

        val_losses = []
        val_accs = []
        avg_val_acc = 0.0
        
        patience = 50
        reset_patience = 25
        patience_reset_counter = 0
        patience_counter = 0
        current_max = 0
        total_max = 0
        num_samples = len(dataloader)
        pbar = tqdm(total=self.num_epochs * num_samples, desc='Training model...')
        for epoch in range(self.num_epochs):
            self.classifier.train()  # Set the model to training mode
            running_loss = 0.0
            all_preds = {name: [] for name in head_names}
            all_labels = {name: [] for name in head_names}

            for step, sample in enumerate(dataloader):
                labels = {name: sample[label_key] for name in head_names}
                embeddings = BatchEncoding({k: torch.stack(v, dim=1).to(self.device) for k, v in sample.items() if k not in [label_key, sample_key]}).to(self.device)
                outputs = self.classifier(embeddings, head_names=head_names)  # Forward pass
                loss = sum([criterion(outputs[name].to(self.device), labels[name].to(self.device)) for name in labels.keys()])
                loss += self.l2_loss_weight * self.classifier.get_l2_loss().to(self.device)
                running_loss += loss.item()
                loss.backward()  # Backward pass
                if (step + 1) % self.gradient_accumulation_steps == 0:
                    optimizer.step()  # Update model parameters
                    optimizer.zero_grad()  # Zero the parameter gradients
                # Store predictions and labels for precision calculation
                for name in labels.keys():
                    preds = outputs[name][0].argmax().item()
                    all_labels[name].append(labels[name][0].cpu().numpy())
                    all_preds[name].append(preds)

                pbar.update(1)
                # clear memory
                torch.cuda.empty_cache()

            epoch_loss = running_loss / num_samples
            if scheduler:
                scheduler.step(epoch_loss)

            average_acc += np.mean([np.mean(np.abs(np.array(all_labels[name]) - np.array(all_preds[name])) ==  0) for name in head_names])
            average_acc /= 2.0
            if val_dataloader:
                val_loss, val_acc = self.validate(self.classifier, criterion, val_dataloader, head_names, sample_key, label_key)
                avg_val_acc += val_acc.item()
                avg_val_acc /= 2.0
                val_losses.append(val_loss)
                val_accs.append(val_acc)
            losses.append(epoch_loss)
            precisions.append(average_acc)
            if avg_val_acc > current_max:
                current_max = avg_val_acc
                self.classifier.save(os.path.join(BASE_PATH, f'../train_runs/{self.name_prefix}-run-{self.train_run}-best-model.pth'))
            best_prec = max(average_acc, best_prec)
            #print(f"Epoch {epoch+1}/{num_epochs} (LR: {scheduler.get_last_lr()[0]:.4e}), Loss: {epoch_loss:.4f}, Precision: {l:.4f}")
            pbar_data = {
                'epoch': epoch + 1,
                'loss': epoch_loss, 
                'avg_acc': average_acc,
                'acc_max': best_prec
            }
            if scheduler:
                pbar_data['lr'] = scheduler.get_last_lr()[0]
            if val_dataloader:
                pbar_data['val_loss'] = val_loss
                pbar_data['val_acc'] = val_acc
                pbar_data['avg_val_acc'] = avg_val_acc
            pbar.set_postfix(pbar_data)
            # clear memory
            torch.cuda.empty_cache()

        pbar.close()
        param_dict = {
            'dropout': self.dropout,
            'model_conf': {k:v for k, v in self.model_conf.items() if k not in ['tokenizer', 'backbone']},
            'optimizer_conf': self.optimizer_conf,
            'scheduler_conf': self.scheduler_conf,
            'l2_loss_weight': self.l2_loss_weight,
            'num_epochs': self.num_epochs,
            'device': self.device,
            'train_run': self.train_run,
            'name_prefix': self.name_prefix,
            'use_lr_scheduler': self.use_lr_scheduler,
            'metrics': {
                'loss': losses,
                'val_loss': val_losses,
                'precision': precisions,
                'val_precision': val_accs
            }
        }
        with open(os.path.join(BASE_PATH, f'../train_runs/{self.name_prefix}-train-run-{self.train_run}.json'), 'w') as f:
            json.dump(param_dict, f)
        print("Training complete!")
        self.train_run += 1

        return param_dict

    def _plot_history(self, loss, val_loss, precision, val_precision):
        fig = plt.figure(figsize=(15,7))
        ax = plt.subplot(1,2, 1)
        ax.set_title('loss')
        plt.plot(range(len(loss)), loss, 'g--', label='train_loss')
        plt.plot(range(len(loss)), val_loss, 'r--', label='val_loss')
        plt.yscale('log')
        plt.legend()
        ax = plt.subplot(1,2, 2)
        ax.set_title('accuracy')
        plt.plot(range(len(precision)), precision, 'g--', label='prec')
        plt.plot(range(len(precision)), val_precision, 'r--',label='val_prec')
        plt.legend()
        return fig

    def validate(self, model, criterion, dataloader, head_names=None, sample_key='sample', label_key='label'):
        running_loss = 0
        num_samples = len(dataloader)
        if head_names is None:
            sample = next(iter(dataloader))[1]
            head_names = list(sample.keys())

        all_labels = {name: [] for name in head_names}
        all_preds = {name: [] for name in head_names}

        num_labels = {name: model.heads[name].out_features for name in head_names}

        model.train(False)
        for sample in dataloader:
            labels = {name: sample[label_key] for name in head_names}
            embeddings = BatchEncoding({k: torch.stack(v, dim=1).to(self.device) for k, v in sample.items() if k not in [label_key, sample_key]}) 
            outputs = model(embeddings)  # Forward pass
            loss = sum([criterion(outputs[name].to(self.device), labels[name].to(self.device)) for name in head_names])
            loss += self.l2_loss_weight * model.get_l2_loss().to(self.device)
            running_loss += loss.item()
            # Store predictions and labels for precision calculation
            for name in head_names:
                preds = outputs[name][0].argmax().item()
                all_labels[name].append(labels[name][0].cpu().numpy())
                all_preds[name].append(preds)
            torch.cuda.empty_cache()
        return running_loss / num_samples, np.mean([np.mean(np.abs(np.array(all_labels[name]) - np.array(all_preds[name])) ==  0) for name in head_names])

    def eval(self, label_map, test_set=None, sample_key='sample', label_key='label'):
        if test_set is None:
            assert self.test_data is not None, 'No test data provided'
            test_set = self.test_data
            sample = next(iter(test_set))
            is_string_dataset = isinstance(sample[0], str) if not isinstance(sample, dict) else isinstance(sample[sample_key], str)

            if is_string_dataset:
                if isinstance(sample, list):
                    test_set = test_set.map(lambda x: self.tokenizer([x[0]] if isinstance(x[0], str) else x[0], return_tensors="pt", padding=True, truncation=True), batched=True)
                elif isinstance(sample, dict):
                    assert sample_key in sample and label_key in sample, 'Invalid dataset format'
                    test_set = test_set.map(lambda x: self.tokenizer([x[sample_key]] if isinstance(x[sample_key], str) else x[sample_key], return_tensors="pt", padding=True, truncation=True), batched=True)
                else:
                    raise ValueError('Invalid dataset format')

                test_set = DataLoader(test_set, shuffle=True, batch_size=self.batch_size)
        self.classifier.to(self.device)
        return self._eval_model(test_set, label_map, sample_key, label_key)

    def _eval_model(self, dataloader, label_map, sample_key, label_key):
        self.classifier.train(False)
        eval_heads = list(label_map.keys())
        y_pred = {h: [] for h in eval_heads}
        y_test = {h: [] for h in eval_heads}
        for sample in tqdm(dataloader, total=len(dataloader), desc='Evaluating model...'):
            labels = {name: sample[label_key] for name in eval_heads}
            embeddings = BatchEncoding({k: torch.stack(v, dim=1).to(self.device) for k, v in sample.items() if k not in [label_key, sample_key]}) 
            output = self.classifier(embeddings.to('cuda'), head_names=eval_heads)
            for head in eval_heads:
                y_pred[head].extend(output[head].argmax(dim=1).cpu())
                y_test[head].extend(labels[head])
            torch.cuda.empty_cache()

        accuracies = {h: accuracy_score(y_test[h], y_pred[h]) for h in eval_heads}
        f1_scores = {h: f1_score(y_test[h], y_pred[h], average="macro") for h in eval_heads}
        recalls = {h: recall_score(y_test[h], y_pred[h], average='macro') for h in eval_heads}
        
        report = {}
        for head in eval_heads:
            cm = confusion_matrix(y_test[head], y_pred[head], labels=list(label_map[head].keys()))
            disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=list(label_map[head].values()))
            clf_report = classification_report(
                y_test[head], y_pred[head], output_dict=True, target_names=list(label_map[head].values())
            )
            del clf_report["accuracy"]
            clf_report = pd.DataFrame(clf_report).T.reset_index()
            report[head] = dict(
                clf_report=clf_report, confusion_matrix=disp, metrics={'accuracy': accuracies[head], 'f1': f1_scores[head], 'recall': recalls[head]}
            )
        return report