utils.py

import torch.nn as nn
from modeling import DocFormerEncoder,ResNetFeatureExtractor,DocFormerEmbeddings,LanguageFeatureExtractor

class DocFormerForClassification(nn.Module):
  
    def __init__(self, config):
      super(DocFormerForClassification, self).__init__()

      self.resnet = ResNetFeatureExtractor(hidden_dim = config['max_position_embeddings'])
      self.embeddings = DocFormerEmbeddings(config)
      self.lang_emb = LanguageFeatureExtractor()
      self.config = config
      self.dropout = nn.Dropout(config['hidden_dropout_prob'])
      self.linear_layer = nn.Linear(in_features = config['hidden_size'], out_features = 16)  ## Number of Classes
      self.encoder = DocFormerEncoder(config)

    def forward(self, batch_dict):

      x_feat = batch_dict['x_features']
      y_feat = batch_dict['y_features']

      token = batch_dict['input_ids']
      img = batch_dict['resized_scaled_img']

      v_bar_s, t_bar_s = self.embeddings(x_feat,y_feat)
      v_bar = self.resnet(img)
      t_bar = self.lang_emb(token)
      out = self.encoder(t_bar,v_bar,t_bar_s,v_bar_s)
      out = self.linear_layer(out)
      out = out[:, 0, :]
      return out


## Defining pytorch lightning model
import pytorch_lightning as pl
from sklearn.metrics import accuracy_score, confusion_matrix
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import torchmetrics
import wandb
import torch

class DocFormer(pl.LightningModule):

  def __init__(self, config , lr = 5e-5):
    super(DocFormer, self).__init__()
    
    self.save_hyperparameters()
    self.config = config
    self.docformer = DocFormerForClassification(config)
    
    self.num_classes = 16
    self.train_accuracy_metric = torchmetrics.Accuracy()
    self.val_accuracy_metric = torchmetrics.Accuracy()
    self.f1_metric = torchmetrics.F1Score(num_classes=self.num_classes)
    self.precision_macro_metric = torchmetrics.Precision(
            average="macro", num_classes=self.num_classes
        )
    self.recall_macro_metric = torchmetrics.Recall(
            average="macro", num_classes=self.num_classes
        )
    self.precision_micro_metric = torchmetrics.Precision(average="micro")
    self.recall_micro_metric = torchmetrics.Recall(average="micro")

  def forward(self, batch_dict):
    logits = self.docformer(batch_dict)
    return logits

  def training_step(self, batch, batch_idx):
    logits = self.forward(batch)

    loss = nn.CrossEntropyLoss()(logits, batch['label'])
    preds = torch.argmax(logits, 1)

    ## Calculating the accuracy score
    train_acc = self.train_accuracy_metric(preds, batch["label"])

    ## Logging
    self.log('train/loss', loss,prog_bar = True, on_epoch=True, logger=True, on_step=True)
    self.log('train/acc', train_acc, prog_bar = True, on_epoch=True, logger=True, on_step=True)

    return loss
  
  def validation_step(self, batch, batch_idx):
    logits = self.forward(batch)
    loss = nn.CrossEntropyLoss()(logits, batch['label'])
    preds = torch.argmax(logits, 1)
    
    labels = batch['label']
    # Metrics
    valid_acc = self.val_accuracy_metric(preds, labels)
    precision_macro = self.precision_macro_metric(preds, labels)
    recall_macro = self.recall_macro_metric(preds, labels)
    precision_micro = self.precision_micro_metric(preds, labels)
    recall_micro = self.recall_micro_metric(preds, labels)
    f1 = self.f1_metric(preds, labels)

    # Logging metrics
    self.log("valid/loss", loss, prog_bar=True, on_step=True, logger=True)
    self.log("valid/acc", valid_acc, prog_bar=True, on_epoch=True, logger=True, on_step=True)
    self.log("valid/precision_macro", precision_macro, prog_bar=True, on_epoch=True, logger=True, on_step=True)
    self.log("valid/recall_macro", recall_macro, prog_bar=True, on_epoch=True, logger=True, on_step=True)
    self.log("valid/precision_micro", precision_micro, prog_bar=True, on_epoch=True, logger=True, on_step=True)
    self.log("valid/recall_micro", recall_micro, prog_bar=True, on_epoch=True, logger=True, on_step=True)
    self.log("valid/f1", f1, prog_bar=True, on_epoch=True)
    
    return {"label": batch['label'], "logits": logits}

  def validation_epoch_end(self, outputs):
        labels = torch.cat([x["label"] for x in outputs])
        logits = torch.cat([x["logits"] for x in outputs])
        preds = torch.argmax(logits, 1)

        wandb.log({"cm": wandb.sklearn.plot_confusion_matrix(labels.cpu().numpy(), preds.cpu().numpy())})
        self.logger.experiment.log(
            {"roc": wandb.plot.roc_curve(labels.cpu().numpy(), logits.cpu().numpy())}
        )
        
  def configure_optimizers(self):
    return torch.optim.AdamW(self.parameters(), lr = self.hparams['lr'])