mirror of https://github.com/XingangPan/DragGAN
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
56 lines
2.3 KiB
Python
56 lines
2.3 KiB
Python
import random
|
|
import torch
|
|
|
|
|
|
class LatentCodesPool:
|
|
"""This class implements latent codes buffer that stores previously generated w latent codes.
|
|
This buffer enables us to update discriminators using a history of generated w's
|
|
rather than the ones produced by the latest encoder.
|
|
"""
|
|
|
|
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_ws = 0
|
|
self.ws = []
|
|
|
|
def query(self, ws):
|
|
"""Return w's from the pool.
|
|
Parameters:
|
|
ws: the latest generated w's from the generator
|
|
Returns w's from the buffer.
|
|
By 50/100, the buffer will return input w's.
|
|
By 50/100, the buffer will return w's previously stored in the buffer,
|
|
and insert the current w's to the buffer.
|
|
"""
|
|
if self.pool_size == 0: # if the buffer size is 0, do nothing
|
|
return ws
|
|
return_ws = []
|
|
for w in ws: # ws.shape: (batch, 512) or (batch, n_latent, 512)
|
|
# w = torch.unsqueeze(image.data, 0)
|
|
if w.ndim == 2:
|
|
i = random.randint(0, len(w) - 1) # apply a random latent index as a candidate
|
|
w = w[i]
|
|
self.handle_w(w, return_ws)
|
|
return_ws = torch.stack(return_ws, 0) # collect all the images and return
|
|
return return_ws
|
|
|
|
def handle_w(self, w, return_ws):
|
|
if self.num_ws < self.pool_size: # if the buffer is not full; keep inserting current codes to the buffer
|
|
self.num_ws = self.num_ws + 1
|
|
self.ws.append(w)
|
|
return_ws.append(w)
|
|
else:
|
|
p = random.uniform(0, 1)
|
|
if p > 0.5: # by 50% chance, the buffer will return a previously stored latent code, and insert the current code into the buffer
|
|
random_id = random.randint(0, self.pool_size - 1) # randint is inclusive
|
|
tmp = self.ws[random_id].clone()
|
|
self.ws[random_id] = w
|
|
return_ws.append(tmp)
|
|
else: # by another 50% chance, the buffer will return the current image
|
|
return_ws.append(w)
|