Datasets:
jblitzar
/
github-python-corpus

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if __name__ == "__main__":
os.environ["CUDA_VISIBLE_DEVICES"] = '4,5,6,7'
file = "./config/" + dataset_name + ".json"
args = load_json(json_file=file)
file_path = args['dataset']['gan_file_path']
model_name = args['dataset']['model_name']
lr = args[model_name]['lr']
batch_size = args[model_name]['batch_size']
z_dim = args[model_name]['z_dim']
epochs = args[model_name]['epochs']
n_critic = args[model_name]['n_critic']
print("---------------------Training [%s]------------------------------" % model_name)
utils.print_params(args["dataset"], args[model_name])
dataset, dataloader = init_dataloader(args, file_path, batch_size, mode="gan")
G = Generator(z_dim)
DG = DGWGAN(3)
G = torch.nn.DataParallel(G).cuda()
DG = torch.nn.DataParallel(DG).cuda()
dg_optimizer = torch.optim.Adam(DG.parameters(), lr=lr, betas=(0.5, 0.999))
g_optimizer = torch.optim.Adam(G.parameters(), lr=lr, betas=(0.5, 0.999))
step = 0
for epoch in range(epochs):
start = time.time()
for i, imgs in enumerate(dataloader):
step += 1
imgs = imgs.cuda()
bs = imgs.size(0)
freeze(G)
unfreeze(DG)
z = torch.randn(bs, z_dim).cuda()
f_imgs = G(z)
r_logit = DG(imgs)
f_logit = DG(f_imgs)
wd = r_logit.mean() - f_logit.mean() # Wasserstein-1 Distance
gp = gradient_penalty(imgs.data, f_imgs.data)
dg_loss = - wd + gp * 10.0
dg_optimizer.zero_grad()
dg_loss.backward()
dg_optimizer.step()
# train G
if step % n_critic == 0:
freeze(DG)
unfreeze(G)
z = torch.randn(bs, z_dim).cuda()
f_imgs = G(z)
logit_dg = DG(f_imgs)
# calculate g_loss
g_loss = - logit_dg.mean()
g_optimizer.zero_grad()
g_loss.backward()
g_optimizer.step()
end = time.time()
interval = end - start
print("Epoch:%d \t Time:%.2f\t Generator loss:%.2f" % (epoch, interval, g_loss))
if (epoch+1) % 10 == 0:
z = torch.randn(32, z_dim).cuda()
fake_image = G(z)
save_tensor_images(fake_image.detach(), os.path.join(save_img_dir, "result_image_{}.png".format(epoch)), nrow = 8)
torch.save({'state_dict':G.state_dict()}, os.path.join(save_model_dir, "celeba_G.tar"))
torch.save({'state_dict':DG.state_dict()}, os.path.join(save_model_dir, "celeba_D.tar"))
# <FILESEP>
import json
import random
import re
import shutil
import sqlite3
from dataclasses import asdict, dataclass
from html import escape, unescape
from pathlib import Path
from sqlite3 import Connection
from typing import Any, Iterator
try:
from calibre.constants import isfrozen
from .database import (
create_lang_layer,
create_x_ray_db,
get_ll_path,