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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import paddle
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.
Args:
pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created
"""
def __init__(self, pool_size, prob=0.5):
self.pool_size = pool_size
self.prob = prob
if self.pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, images):
"""Return an image from the pool.
Args:
images (paddle.Tensor): the latest generated images from the generator
Returns images from the buffer.
"""
# if the buffer size is 0, do nothing
if self.pool_size == 0:
return images
return_images = []
for image in images:
image = paddle.unsqueeze(image, 0)
# if the buffer is not full; keep inserting current images to the buffer
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)
# by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer
if p > self.prob:
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:
# by another 50% chance, the buffer will return the current image
return_images.append(image)
# collect all the images and return
return_images = paddle.concat(return_images, 0)
return return_images