Step 3. Evaluate Model To assess the quality of the model, we will use the mAP metric defined as AP Area under the curve. To do this, you will need to calculate recall andprecision. from sklearn.metrics import auc   def evaluate(model, test_loader, device):   results = []   model.eval()   nbr_boxes = 0

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Step 3. Evaluate Model

To assess the quality of the model, we will use the mAP metric defined as AP Area under the curve. To do this, you will need to calculate recall andprecision.

from sklearn.metrics import auc
 
def evaluate(model, test_loader, device):
  results = []
  model.eval()
  nbr_boxes = 0
  with torch.no_grad():
     for batch, (images, targets_true) inenumerate(test_loader):
     images = list(image.to(device).float() for image in images)
     targets_pred = model(images)

     targets_true = [{k: v.cpu().float() for k, v in t.items()} for t in targets_true]
     targets_pred = [{k: v.cpu().float() for k, v in t.items()} for t in      targets_pred]

     for i inrange(len(targets_true)):
        target_true = targets_true[i]
        target_pred = targets_pred[i]
        nbr_boxes += target_true['labels'].shape[0]

        results.extend(evaluate_sample(target_pred, target_true))

   results = sorted(results, key=lambda k: k['score'], reverse=True)

# compute precision and recall to calculate mAP
 
## YOUR CODE HERE
 
 
return auc(recall, precision)
 

Step 4. Train function

Now define the functions for training the model.

 
def train_one_epoch(model, train_dataloader, optimizer, device):
   # YOUR CODE HERE
   # TRAIN YOUR MODEL ON THE train_dataloader
   pass


def train(model, train_dataloader, val_dataloader, optimizer, device, n_epochs=10):
   for epoch inrange(n_epochs):
   model.eval()
   test_auc = evaluate(model, val_dataloader, device=device)
   print("AUC ON TEST: {:.4f}".format(test_auc))
   model.train()
   train_one_epoch(model, train_dataloader, optimizer, device=device)
 
 
# for refrence and data detail go to   ---> https://colab.research.google.com/github/hse-aml/intro-to-dl-pytorch/blob/main/week03/SGA1_Object_Detection.ipynb#scrollTo=jhmZOkQajpwZ
# for step 1 and 2 go to --->
https://www.bartleby.com/questions-and-answers/step-1.-intersection-over-union-def-intersection_over_uniondt_bbox-gt_bbox-greater-return-iou-step-2/c87fbbd7-7f0e-4016-83a3-a8f488920cba
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