Upload utils.py

utils.py

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+import torch.nn as nn
+from modeling import LiLT
+import torch
+## Defining pytorch lightning model
+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 pytorch_lightning as pl
+
+
+id2label = ['scientific_report',
+ 'resume',
+ 'memo',
+ 'file_folder',
+ 'specification',
+ 'news_article',
+ 'letter',
+ 'form',
+ 'budget',
+ 'handwritten',
+ 'email',
+ 'invoice',
+ 'presentation',
+ 'scientific_publication',
+ 'questionnaire',
+ 'advertisement']
+
+class LiLTForClassification(nn.Module):
+      
+    def __init__(self, config):
+      super(LiLTForClassification, self).__init__()
+
+      self.lilt = LiLT(config)
+      self.config = config
+      self.dropout = nn.Dropout(config['hidden_dropout_prob'])
+      self.linear_layer = nn.Linear(in_features = config['hidden_size'] * 2, out_features = len(id2label))  ## Number of Classes
+
+    def forward(self, batch_dict):
+        encodings = self.lilt(batch_dict['input_words'], batch_dict['input_boxes'])
+        final_out = torch.cat([encodings['layout_hidden_states'][-1], 
+                              encodings['text_hidden_states'][-1]
+                              ],
+                             axis = -1)[:, 0, :]
+        final_out = self.linear_layer(final_out)
+        return final_out
+
+
+class LiLTPL(pl.LightningModule):
+
+  def __init__(self, config , lr = 5e-5):
+    super(LiLTPL, self).__init__()
+    
+    self.save_hyperparameters()
+    self.config = config
+    self.lilt = LiLTForClassification(config)
+    
+    self.num_classes = len(id2label)
+    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.lilt(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'])