67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
import torch
|
|
|
|
|
|
class LossContainer:
|
|
def __init__(self, weight, loss):
|
|
self.weight = weight
|
|
self.loss = loss
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
if self.weight > 0:
|
|
return self.weight * self.loss(*args, **kwargs)
|
|
return 0.0
|
|
|
|
|
|
class GANImageBuffer:
|
|
"""This class implements an image buffer that stores previously
|
|
generated images.
|
|
This buffer allows us to update the discriminator using a history of
|
|
generated images rather than the ones produced by the latest generator
|
|
to reduce model oscillation.
|
|
Args:
|
|
buffer_size (int): The size of image buffer. If buffer_size = 0,
|
|
no buffer will be created.
|
|
buffer_ratio (float): The chance / possibility to use the images
|
|
previously stored in the buffer.
|
|
"""
|
|
|
|
def __init__(self, buffer_size, buffer_ratio=0.5):
|
|
self.buffer_size = buffer_size
|
|
# create an empty buffer
|
|
if self.buffer_size > 0:
|
|
self.img_num = 0
|
|
self.image_buffer = []
|
|
self.buffer_ratio = buffer_ratio
|
|
|
|
def query(self, images):
|
|
"""Query current image batch using a history of generated images.
|
|
Args:
|
|
images (Tensor): Current image batch without history information.
|
|
"""
|
|
if self.buffer_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 the buffer is not full, keep inserting current images
|
|
if self.img_num < self.buffer_size:
|
|
self.img_num = self.img_num + 1
|
|
self.image_buffer.append(image)
|
|
return_images.append(image)
|
|
else:
|
|
use_buffer = torch.rand(1) < self.buffer_ratio
|
|
# by self.buffer_ratio, the buffer will return a previously
|
|
# stored image, and insert the current image into the buffer
|
|
if use_buffer:
|
|
random_id = torch.randint(0, self.buffer_size, (1,)).item()
|
|
image_tmp = self.image_buffer[random_id].clone()
|
|
self.image_buffer[random_id] = image
|
|
return_images.append(image_tmp)
|
|
# by (1 - self.buffer_ratio), the buffer will return the
|
|
# current image
|
|
else:
|
|
return_images.append(image)
|
|
# collect all the images and return
|
|
return_images = torch.cat(return_images, 0)
|
|
return return_images
|