Delete utils.py

utils.py

@@ -1,122 +0,0 @@
-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'])