55 lines
2.2 KiB
Python
55 lines
2.2 KiB
Python
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import random
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import torch
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class ImagePool():
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"""This class implements an image buffer that stores previously generated images.
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This buffer enables us to update discriminators using a history of generated images
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rather than the ones produced by the latest generators.
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"""
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def __init__(self, pool_size):
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"""Initialize the ImagePool class
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Parameters:
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pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created
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"""
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self.pool_size = pool_size
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if self.pool_size > 0: # create an empty pool
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self.num_imgs = 0
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self.images = []
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def query(self, images):
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"""Return an image from the pool.
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Parameters:
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images: the latest generated images from the generator
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Returns images from the buffer.
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By 50/100, the buffer will return input images.
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By 50/100, the buffer will return images previously stored in the buffer,
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and insert the current images to the buffer.
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"""
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if self.pool_size == 0: # if the buffer size is 0, do nothing
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return images
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return_images = []
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for image in images:
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image = torch.unsqueeze(image.data, 0)
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if self.num_imgs < self.pool_size: # if the buffer is not full; keep inserting current images to the buffer
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self.num_imgs = self.num_imgs + 1
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self.images.append(image)
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return_images.append(image)
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else:
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p = random.uniform(0, 1)
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if p > 0.5: # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer
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random_id = random.randint(0, self.pool_size - 1) # randint is inclusive
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tmp = self.images[random_id].clone()
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self.images[random_id] = image
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return_images.append(tmp)
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else: # by another 50% chance, the buffer will return the current image
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return_images.append(image)
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return_images = torch.cat(return_images, 0) # collect all the images and return
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return return_images
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