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