"/fix_after_image.png")
break
with torch.no_grad():
g_fake_img = model_G(fix_pre_image)
#g_fake_img = model_G( fix_after_image )
save_image(tensor=g_fake_img[0],
filename=os.path.join(args.results_dir, args.exper_name) +
"/fake_image_epoches{}_batch0.png".format(epoch))
save_image(tensor=g_fake_img,
filename=os.path.join(args.results_dir, args.exper_name) +
"/fake_image_epoches{}_batchAll.png".format(epoch))
fake_images_historys.append(g_fake_img[0].transpose(0, 1).transpose(
1, 2).cpu().clone().numpy())
save_image_historys_gif(
fake_images_historys,
os.path.join(args.results_dir, args.exper_name) +
"/fake_image_epoches{}.gif".format(epoch))
save_checkpoint(
model_G, device,
os.path.join(args.save_checkpoints_dir, args.exper_name, "G",
'G_final.pth'), iterations)
save_checkpoint(
model_D, device,
os.path.join(args.save_checkpoints_dir, args.exper_name, "D",
'D_final.pth'), iterations)
print("Finished Training Loop.")
#save_checkpoint( model_G, device, os.path.join(args.save_checkpoints_dir, args.exper_name, "G", 'step_%08d.pth' % (iterations + 1)), iterations )
save_checkpoint( model_G, device, os.path.join(args.save_checkpoints_dir, args.exper_name, "G", 'G_final.pth'), iterations )
#save_checkpoint( model_D, device, os.path.join(args.save_checkpoints_dir, args.exper_name, "D", 'step_%08d.pth' % (iterations + 1)), iterations )
save_checkpoint( model_D, device, os.path.join(args.save_checkpoints_dir, args.exper_name, "D", 'D_final.pth'), iterations )
print( "saved checkpoints" )
n_print -= 1
#====================================================
# 各 Epoch 終了後の処理
#====================================================
for y_label in range(args.n_classes):
eye_tsr = torch.eye( args.n_classes ).to( device )
y_fake_label = torch.full( (args.batch_size,), y_label ).long().to( device )
y_fake_one_hot = eye_tsr[y_fake_label].view( -1, args.n_classes, 1, 1 ).to( device )
# 出力画像の生成＆保存
model_G.eval()
with torch.no_grad():
G_z = model_G( input_noize_fix_z, y_fake_one_hot )
save_image( tensor = G_z[0], filename = os.path.join(args.results_dir, args.exper_name) + "/fake_image_label{}_epoches{}_batch0.png".format( y_label, epoch ) )
save_image( tensor = G_z, filename = os.path.join(args.results_dir, args.exper_name) + "/fake_image_label{}_epoches{}_batchAll.png".format( y_label, epoch ) )
fake_images_historys.append(G_z[0].transpose(0,1).transpose(1,2).cpu().clone().numpy())
save_image_historys_gif( fake_images_historys, os.path.join(args.results_dir, args.exper_name) + "/fake_image_label{}_epoches{}.gif".format( y_label, epoch, iterations ) )
save_checkpoint( model_G, device, os.path.join(args.save_checkpoints_dir, args.exper_name, "G", 'G_final.pth'), iterations )
save_checkpoint( model_D, device, os.path.join(args.save_checkpoints_dir, args.exper_name, "D", 'D_final.pth'), iterations )
print("Finished Training Loop.")