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# Copyright (c) Microsoft Corporation. | |
# Licensed under the MIT License. | |
import random | |
import torch | |
from torch.autograd import Variable | |
class ImagePool: | |
def __init__(self, pool_size): | |
self.pool_size = pool_size | |
if self.pool_size > 0: | |
self.num_imgs = 0 | |
self.images = [] | |
def query(self, images): | |
if self.pool_size == 0: | |
return images | |
return_images = [] | |
for image in images.data: | |
image = torch.unsqueeze(image, 0) | |
if self.num_imgs < self.pool_size: | |
self.num_imgs = self.num_imgs + 1 | |
self.images.append(image) | |
return_images.append(image) | |
else: | |
p = random.uniform(0, 1) | |
if p > 0.5: | |
random_id = random.randint(0, self.pool_size - 1) | |
tmp = self.images[random_id].clone() | |
self.images[random_id] = image | |
return_images.append(tmp) | |
else: | |
return_images.append(image) | |
return_images = Variable(torch.cat(return_images, 0)) | |
return return_images | |