optimizerG = torch.optim.Adam(netG.parameters(), lr=0.0001, betas=(0.0, 0.9)) optimizerD = torch.optim.Adam(netD.parameters(), lr=0.0004, betas=(0.0, 0.9)) if state_epoch != 0: netG.load_state_dict( torch.load('%s/models/netG_%03d.pth' % (output_dir, state_epoch), map_location='cpu')) netD.load_state_dict( torch.load('%s/models/netD_%03d.pth' % (output_dir, state_epoch), map_location='cpu')) netG = netG.cuda() netD = netD.cuda() optimizerG.load_state_dict( torch.load('%s/models/optimizerG.pth' % (output_dir))) optimizerD.load_state_dict( torch.load('%s/models/optimizerD.pth' % (output_dir))) if cfg.B_VALIDATION: count = sampling(text_encoder, netG, dataloader, device) # generate images for the whole valid dataset print('state_epoch: %d' % (state_epoch)) else: count = train(dataloader, netG, netD, text_encoder, optimizerG, optimizerD, state_epoch, batch_size, device, output_dir, logger)
optimizer_d = torch.optim.Adam(netd.parameters(), opt.lr_netd, betas=(opt.beta1, 0.999)) criterion = torch.nn.BCELoss() # 真图片 label 为 1,假图片为 0 true_labels = Variable(torch.ones(opt.batch_size)) fake_labels = Variable(torch.zeros(opt.batch_size)) #fix_noises = Variable(torch.randn(opt.batch_size, opt.nz, 1, 1)) noises = Variable(torch.randn(opt.batch_size, opt.nz, 1, 1)) if opt.gpu: netg.cuda() netd.cuda() criterion.cuda() true_labels = true_labels.cuda() fake_labels = fake_labels.cuda() #fix_noises = fix_noises.cuda() noises = noises.cuda() while True: action = input('Train(t) or Generate(g) or Quit(q)> ').lower() if action == 't': epochs = int(input('Epoch times > ')) train(max_epoch=epochs) elif action == 'g': num_imgs = int(input('How many images > '))
def train(): # change opt # for k_, v_ in kwargs.items(): # setattr(opt, k_, v_) device = torch.device('cuda') if torch.cuda.is_available else torch.device( 'cpu') if opt.vis: from visualizer import Visualizer vis = Visualizer(opt.env) # rescale to -1~1 transform = transforms.Compose([ transforms.Resize(opt.image_size), transforms.CenterCrop(opt.image_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) dataset = datasets.ImageFolder(opt.data_path, transform=transform) dataloader = DataLoader(dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers, drop_last=True) netd = NetD(opt) netg = NetG(opt) map_location = lambda storage, loc: storage if opt.netd_path: netd.load_state_dict(torch.load(opt.netd_path), map_location=map_location) if opt.netg_path: netg.load_state_dict(torch.load(opt.netg_path), map_location=map_location) if torch.cuda.is_available(): netd.to(device) netg.to(device) # 定义优化器和损失 optimizer_g = torch.optim.Adam(netg.parameters(), opt.lr1, betas=(opt.beta1, 0.999)) optimizer_d = torch.optim.Adam(netd.parameters(), opt.lr2, betas=(opt.beta1, 0.999)) criterion = torch.nn.BCELoss().to(device) # 真label为1, noises是输入噪声 true_labels = Variable(torch.ones(opt.batch_size)) fake_labels = Variable(torch.zeros(opt.batch_size)) fix_noises = Variable(torch.randn(opt.batch_size, opt.nz, 1, 1)) noises = Variable(torch.randn(opt.batch_size, opt.nz, 1, 1)) errord_meter = AverageValueMeter() errorg_meter = AverageValueMeter() if torch.cuda.is_available(): netd.cuda() netg.cuda() criterion.cuda() true_labels, fake_labels = true_labels.cuda(), fake_labels.cuda() fix_noises, noises = fix_noises.cuda(), noises.cuda() for epoch in range(opt.max_epoch): print("epoch:", epoch, end='\r') # sys.stdout.flush() for ii, (img, _) in enumerate(dataloader): real_img = Variable(img) if torch.cuda.is_available(): real_img = real_img.cuda() # 训练判别器, real -> 1, fake -> 0 if (ii + 1) % opt.d_every == 0: # real optimizer_d.zero_grad() output = netd(real_img) # print(output.shape, true_labels.shape) error_d_real = criterion(output, true_labels) error_d_real.backward() # fake noises.data.copy_(torch.randn(opt.batch_size, opt.nz, 1, 1)) fake_img = netg(noises).detach() # 随机噪声生成假图 fake_output = netd(fake_img) error_d_fake = criterion(fake_output, fake_labels) error_d_fake.backward() # update optimizer optimizer_d.step() error_d = error_d_fake + error_d_real errord_meter.add(error_d.item()) # 训练生成器, 让生成器得到的图片能够被判别器判别为真 if (ii + 1) % opt.g_every == 0: optimizer_g.zero_grad() noises.data.copy_(torch.randn(opt.batch_size, opt.nz, 1, 1)) fake_img = netg(noises) fake_output = netd(fake_img) error_g = criterion(fake_output, true_labels) error_g.backward() optimizer_g.step() errorg_meter.add(error_g.item()) if opt.vis and ii % opt.plot_every == opt.plot_every - 1: # 进行可视化 # if os.path.exists(opt.debug_file): # import ipdb # ipdb.set_trace() fix_fake_img = netg(fix_noises) vis.images( fix_fake_img.detach().cpu().numpy()[:opt.batch_size] * 0.5 + 0.5, win='fixfake') vis.images(real_img.data.cpu().numpy()[:opt.batch_size] * 0.5 + 0.5, win='real') vis.plot('errord', errord_meter.value()[0]) vis.plot('errorg', errorg_meter.value()[0]) if (epoch + 1) % opt.save_every == 0: # 保存模型、图片 tv.utils.save_image(fix_fake_img.data[:opt.batch_size], '%s/%s.png' % (opt.save_path, epoch), normalize=True, range=(-1, 1)) torch.save(netd.state_dict(), 'checkpoints/netd_%s.pth' % epoch) torch.save(netg.state_dict(), 'checkpoints/netg_%s.pth' % epoch) errord_meter.reset() errorg_meter.reset()
class TACGAN(): def __init__(self, args): self.lr = args.lr self.cuda = args.use_cuda self.batch_size = args.batch_size self.image_size = args.image_size self.epochs = args.epochs self.data_root = args.data_root self.dataset = args.dataset self.num_classes = args.num_cls self.save_dir = args.save_dir self.save_prefix = args.save_prefix self.continue_training = args.continue_training self.netG_path = args.netg_path self.netD_path = args.netd_path self.save_after = args.save_after self.trainset_loader = None self.evalset_loader = None self.num_workers = args.num_workers self.n_z = args.n_z # length of the noise vector self.nl_d = args.nl_d self.nl_g = args.nl_g self.nf_g = args.nf_g self.nf_d = args.nf_d self.bce_loss = nn.BCELoss() self.nll_loss = nn.NLLLoss() self.netD = NetD(n_cls=self.num_classes, n_t=self.nl_d, n_f=self.nf_d) self.netG = NetG(n_z=self.n_z, n_l=self.nl_g, n_c=self.nf_g) # convert to cuda tensors if self.cuda and torch.cuda.is_available(): print('CUDA is enabled') self.netD = self.netD.cuda() self.netG = self.netG.cuda() self.bce_loss = self.bce_loss.cuda() self.nll_loss = self.nll_loss.cuda() # optimizers for netD and netG self.optimizerD = optim.Adam(params=self.netD.parameters(), lr=self.lr, betas=(0.5, 0.999)) self.optimizerG = optim.Adam(params=self.netG.parameters(), lr=self.lr, betas=(0.5, 0.999)) # create dir for saving checkpoints and other results if do not exist if not os.path.exists(self.save_dir): os.makedirs(self.save_dir) if not os.path.exists(os.path.join(self.save_dir, 'netd_checkpoints')): os.makedirs(os.path.join(self.save_dir, 'netd_checkpoints')) if not os.path.exists(os.path.join(self.save_dir, 'netg_checkpoints')): os.makedirs(os.path.join(self.save_dir, 'netg_checkpoints')) if not os.path.exists(os.path.join(self.save_dir, 'generated_images')): os.makedirs(os.path.join(self.save_dir, 'generated_images')) # start training process def train(self): # write to the log file and print it log_msg = '********************************************\n' log_msg += ' Training Parameters\n' log_msg += 'Dataset:%s\nImage size:%dx%d\n' % ( self.dataset, self.image_size, self.image_size) log_msg += 'Batch size:%d\n' % (self.batch_size) log_msg += 'Number of epochs:%d\nlr:%f\n' % (self.epochs, self.lr) log_msg += 'nz:%d\nnl-d:%d\nnl-g:%d\n' % (self.n_z, self.nl_d, self.nl_g) log_msg += 'nf-g:%d\nnf-d:%d\n' % (self.nf_g, self.nf_d) log_msg += '********************************************\n\n' print(log_msg) with open(os.path.join(self.save_dir, 'training_log.txt'), 'a') as log_file: log_file.write(log_msg) # load trainset and evalset imtext_ds = ImTextDataset(data_dir=self.data_root, dataset=self.dataset, train=True, image_size=self.image_size) self.trainset_loader = DataLoader(dataset=imtext_ds, batch_size=self.batch_size, shuffle=True, num_workers=2) print("Dataset loaded successfuly") # load checkpoints for continuing training if args.continue_training: self.loadCheckpoints() # repeat for the number of epochs netd_losses = [] netg_losses = [] for epoch in range(self.epochs): netd_loss, netg_loss = self.trainEpoch(epoch) netd_losses.append(netd_loss) netg_losses.append(netg_loss) self.saveGraph(netd_losses, netg_losses) #self.evalEpoch(epoch) self.saveCheckpoints(epoch) # train epoch def trainEpoch(self, epoch): self.netD.train() # set to train mode self.netG.train() #! set to train mode??? netd_loss_sum = 0 netg_loss_sum = 0 start_time = time() for i, (images, labels, captions, _) in enumerate(self.trainset_loader): batch_size = images.size( 0 ) # !batch size my be different (from self.batch_size) for the last batch images, labels, captions = Variable(images), Variable( labels), Variable(captions) # !labels should be LongTensor labels = labels.type( torch.FloatTensor ) # convert to FloatTensor (from DoubleTensor) lbl_real = Variable(torch.ones(batch_size, 1)) lbl_fake = Variable(torch.zeros(batch_size, 1)) noise = Variable(torch.randn(batch_size, self.n_z)) # create random noise noise.data.normal_(0, 1) # normalize the noise rnd_perm1 = torch.randperm( batch_size ) # random permutations for different sets of training tuples rnd_perm2 = torch.randperm(batch_size) rnd_perm3 = torch.randperm(batch_size) rnd_perm4 = torch.randperm(batch_size) if self.cuda: images, labels, captions = images.cuda(), labels.cuda( ), captions.cuda() lbl_real, lbl_fake = lbl_real.cuda(), lbl_fake.cuda() noise = noise.cuda() rnd_perm1, rnd_perm2, rnd_perm3, rnd_perm4 = rnd_perm1.cuda( ), rnd_perm2.cuda(), rnd_perm3.cuda(), rnd_perm4.cuda() ############### Update NetD ############### self.netD.zero_grad() # train with wrong image, wrong label, real caption outD_wrong, outC_wrong = self.netD(images[rnd_perm1], captions[rnd_perm2]) lossD_wrong = self.bce_loss(outD_wrong, lbl_fake) lossC_wrong = self.bce_loss(outC_wrong, labels[rnd_perm1]) # train with real image, real label, real caption outD_real, outC_real = self.netD(images, captions) lossD_real = self.bce_loss(outD_real, lbl_real) lossC_real = self.bce_loss(outC_real, labels) # train with fake image, real label, real caption fake = self.netG(noise, captions) outD_fake, outC_fake = self.netD(fake.detach(), captions[rnd_perm3]) lossD_fake = self.bce_loss(outD_fake, lbl_fake) lossC_fake = self.bce_loss(outC_fake, labels[rnd_perm3]) # backward and forwad for NetD netD_loss = lossC_wrong + lossC_real + lossC_fake + lossD_wrong + lossD_real + lossD_fake netD_loss.backward() self.optimizerD.step() ########## Update NetG ########## # train with fake data self.netG.zero_grad() noise.data.normal_(0, 1) # normalize the noise vector fake = self.netG(noise, captions[rnd_perm4]) d_fake, c_fake = self.netD(fake, captions[rnd_perm4]) lossD_fake_G = self.bce_loss(d_fake, lbl_real) lossC_fake_G = self.bce_loss(c_fake, labels[rnd_perm4]) netG_loss = lossD_fake_G + lossC_fake_G netG_loss.backward() self.optimizerG.step() netd_loss_sum += netD_loss.data[0] netg_loss_sum += netG_loss.data[0] ### print progress info ### print( 'Epoch %d/%d, %.2f%% completed. Loss_NetD: %.4f, Loss_NetG: %.4f' % (epoch, self.epochs, (float(i) / len(self.trainset_loader)) * 100, netD_loss.data[0], netG_loss.data[0])) end_time = time() netd_avg_loss = netd_loss_sum / len(self.trainset_loader) netg_avg_loss = netg_loss_sum / len(self.trainset_loader) epoch_time = (end_time - start_time) / 60 log_msg = '-------------------------------------------\n' log_msg += 'Epoch %d took %.2f minutes\n' % (epoch, epoch_time) log_msg += 'NetD average loss: %.4f, NetG average loss: %.4f\n\n' % ( netd_avg_loss, netg_avg_loss) print(log_msg) with open(os.path.join(self.save_dir, 'training_log.txt'), 'a') as log_file: log_file.write(log_msg) return netd_avg_loss, netg_avg_loss # eval epoch def evalEpoch(self, epoch): #self.netD.eval() #self.netG.eval() return 0 # draws and saves the loss graph upto the current epoch def saveGraph(self, netd_losses, netg_losses): plt.plot(netd_losses, color='red', label='NetD Loss') plt.plot(netg_losses, color='blue', label='NetG Loss') plt.xlabel('epoch') plt.ylabel('error') plt.legend(loc='best') plt.savefig(os.path.join(self.save_dir, 'loss_graph.png')) plt.close() # save after each epoch def saveCheckpoints(self, epoch): if epoch % self.save_after == 0: name_netD = "netd_checkpoints/netD_" + self.save_prefix + "_epoch_" + str( epoch) + ".pth" name_netG = "netg_checkpoints/netG_" + self.save_prefix + "_epoch_" + str( epoch) + ".pth" torch.save(self.netD.state_dict(), os.path.join(self.save_dir, name_netD)) torch.save(self.netG.state_dict(), os.path.join(self.save_dir, name_netG)) print("Checkpoints for epoch %d saved successfuly" % (epoch)) # load checkpoints to continue training def loadCheckpoints(self): self.netG.load_state_dict(torch.load(self.netG_path)) self.netD.load_state_dict(torch.load(self.netD_path)) print("Checkpoints loaded successfuly")