Upload utils.py

Added utility file

utils.py

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+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'])