nrupatunga · April 12, 2022 10:34
diff --git a/program_test.py b/program_test.py
 # Required packages: torch
 # Desired packages: numpy, matplotlib
 # Add missing imports:

 def main():
    training_images = {x for x in range(1, 100001)}
    priority_images = {x * 100 for x in range(1, 11)}
    # Task:
    # 1. Dataloader needs to return a batch of size 10. Each element is a unique element from `training_images`.
    # 2. Elements from `priority_images` must be present in EVERY batch with the ratio 1:1, meaning that batch will have
    #    50% of images from `priority_images` and 50% from `training_images` containers.
    #    NOTE: elements of `priority_images` are part of the `training_images`.
    # 3. The code needs to be multiprocessing safe.
    # 4. Follow comments below to add extra functionality.

    batch_size = 10
    num_epochs = 10

    for epoch in range(num_epochs):
        pass


 if __name__ == '__main__':
    main()
	# Required packages: torch
	# Desired packages: numpy, matplotlib
	# Add missing imports:

	def main():
	training_images = {x for x in range(1, 100001)}
	priority_images = {x * 100 for x in range(1, 11)}
	# Task:
	# 1. Dataloader needs to return a batch of size 10. Each element is a unique element from `training_images`.
	# 2. Elements from `priority_images` must be present in EVERY batch with the ratio 1:1, meaning that batch will have
	# 50% of images from `priority_images` and 50% from `training_images` containers.
	# NOTE: elements of `priority_images` are part of the `training_images`.
	# 3. The code needs to be multiprocessing safe.
	# 4. Follow comments below to add extra functionality.

	batch_size = 10
	num_epochs = 10

	for epoch in range(num_epochs):
	pass


	if __name__ == '__main__':
	main()