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aigcmn.py
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import torch
import wget
import os
from module import Generator,Classifier,Generator_num,Generator_acgan
from utils import tensor_to_img
device = 'cpu'
MIN_SCORE = 0.85
MAX_ITER = 10
WEIGHTS_URL = "https://raw.githubusercontent.com/heatingma/MNIST_GENERATE/main/{}"
class AiGcMn():
def __init__(self):
self.gen = Generator(10).to(device)
self.gen_num = Generator_num().to(device)
self.acgen = Generator_acgan(10).to(device)
self.cf = Classifier(11).to(device)
self.load_weights()
def load_weights(self):
if not os.path.exists("weights"):
os.makedirs("weights")
gen_weights_path = "weights/generator_weights.pt"
if not os.path.exists(gen_weights_path):
wget.download(WEIGHTS_URL.format(gen_weights_path),gen_weights_path)
self.gen.load_state_dict(torch.load(gen_weights_path))
cf_weights_path = "weights/classifier_weights.pt"
if not os.path.exists(cf_weights_path):
wget.download(WEIGHTS_URL.format(cf_weights_path),cf_weights_path)
self.cf.load_state_dict(torch.load(cf_weights_path))
def generate(self,label:torch.Tensor,retrain=True,mode="all",show=False,pretrain="1"):
if pretrain == "1":
return self.generate1(label,retrain,mode,show)
elif pretrain == "2":
return self.generate2(label,retrain,mode,show)
elif pretrain == "3":
return self.generate3(label,retrain,mode,show)
def generate1(self,label:torch.Tensor,retrain=True,mode="all",show=False):
label = label.int().to(device)
gen_img = self.gen(label)
if show:
tensor_to_img(gen_img,path="1_before_optimize")
if retrain:
if mode == 'part':
retrain_list = self.get_retrain(gen_img,label)
gen_img = self.optimize(gen_img,retrain_list,self.gen)
if show:
tensor_to_img(gen_img,path="1_after_part_optimize")
elif mode == 'all':
retrain_list = self.all_retrain(label)
gen_img = self.optimize(gen_img,retrain_list,self.gen)
if show:
tensor_to_img(gen_img,path="1_after_all_optimize")
else:
raise TypeError(mode + " is not supported!" + " available mode : all / part")
return gen_img
def generate2(self,label:torch.Tensor,retrain=True,mode="all",show=False):
label = label.int().to(device)
gen_img = self.gen_num(len(label))
index = dict()
for i in range(10):
index[str(i)] = list()
for i in range(len(label)):
index[str(label[i].item())].append(i)
for i in range(10):
cur_index = index[str(i)]
length = len(cur_index)
if length == 0:
continue
gen_weights_path = "weights/GEN_{}.pt".format(i)
if not os.path.exists(gen_weights_path):
wget.download(WEIGHTS_URL.format(gen_weights_path),gen_weights_path)
self.gen_num.load_state_dict(torch.load(gen_weights_path))
for j in range(length):
cur_iter = 0
while(cur_iter < MAX_ITER):
img = self.gen_num(100)
score = self.cf(img)[:,i]
max_score_index = torch.argmax(score)
gen_img[cur_index[j]] = img[max_score_index]
if score[max_score_index] > MIN_SCORE:
break
if show:
tensor_to_img(gen_img,path="2_before_optimize")
if retrain:
if mode == 'part':
retrain_list = self.get_retrain(gen_img,label)
gen_img = self.optimize(gen_img,retrain_list,self.gen)
if show:
tensor_to_img(gen_img,path="2_after_part_optimize")
elif mode == 'all':
#retrain_list = self.all_retrain(label)
#gen_img = self.optimize(gen_img,retrain_list,self.gen)
if show:
tensor_to_img(gen_img,path="2_after_all_optimize")
else:
raise TypeError(mode + " is not supported!" + " available mode : all / part")
return gen_img
def generate3(self, label: torch.Tensor, retrain=True, mode="all", show=False):
gen_weights_path = "weights/ac_generator_weights.pt"
if not os.path.exists(gen_weights_path):
wget.download(WEIGHTS_URL.format(gen_weights_path), gen_weights_path)
self.acgen.load_state_dict(torch.load(gen_weights_path))
label = label.int().to(device)
gen_img = self.acgen(label)
if show:
tensor_to_img(gen_img, path="3_before_optimize")
if retrain:
if mode == 'part':
retrain_list = self.get_retrain(gen_img, label)
gen_img = self.optimize(gen_img, retrain_list,self.acgen)
if show:
tensor_to_img(gen_img, path="3_after_part_optimize")
elif mode == 'all':
retrain_list = self.all_retrain(label)
gen_img = self.optimize(gen_img, retrain_list, self.acgen)
if show:
tensor_to_img(gen_img, path="3_after_all_optimize")
else:
raise TypeError(mode + " is not supported!" + " available mode : all / part")
return gen_img
def get_retrain(self,gen_img,label):
cf_score = torch.sigmoid(self.cf(gen_img))
retrain = dict()
for i in range(10):
retrain[str(i)] = list()
for i in range(len(label)):
if cf_score[i][label[i]] < MIN_SCORE:
retrain[str(label[i].item())].append(i)
return retrain
def all_retrain(self,label):
retrain = dict()
for i in range(10):
retrain[str(i)] = list()
for i in range(len(label)):
retrain[str(label[i].item())].append(i)
return retrain
def optimize(self,gen_img,retrain,gen):
for i in range(10):
cur_retrain = retrain[str(i)]
length = len(cur_retrain)
if length == 0:
continue
for j in range(length):
cur_iter = 0
while(cur_iter < MAX_ITER):
cur_iter += 1
new_imgs = gen(torch.ones(10,dtype=int,device=device) * i)
score = self.cf(new_imgs)[:,i]
score_max_index = torch.argmax(score)
gen_img[cur_retrain[j]] = new_imgs[score_max_index]
if score[score_max_index] > MIN_SCORE:
break
return gen_img