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DEFAULT_SIZE = (256, 512) import torch class LaneDataset(torch.utils.data.Dataset):     def__init__(self, dataset_path, train=True, size=DEFAULT_SIZE):        # code here    def__getitem__(self, idx):       # code here       return image, segmentation_image, instance_image # l x H x W [[0, 1], [2, 0]]     def__len__(self):       # code here   The output dimension of the instance segmentation embedding should be equal to 5.

DEFAULT_SIZE = (256, 512) import torch class LaneDataset(torch.utils.data.Dataset):     def__init__(self, dataset_path, train=True, size=DEFAULT_SIZE):        # code here    def__getitem__(self, idx):       # code here       return image, segmentation_image, instance_image # l x H x W [[0, 1], [2, 0]]     def__len__(self):       # code here   The output dimension of the instance segmentation embedding should be equal to 5.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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DEFAULT_SIZE = (256, 512)

import torch



class LaneDataset(torch.utils.data.Dataset):
    def__init__(self, dataset_path, train=True, size=DEFAULT_SIZE):
       # code here

   def__getitem__(self, idx):
      # code here
      return image, segmentation_image, instance_image # l x H x W [[0, 1], [2, 0]]
 
  def__len__(self):
      # code here
 
The output dimension of the instance segmentation embedding should be equal to 5.
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