def generate(**kwargs): ''' random create caton images and chose the highest scords top 60 ''' for k_,v_, in kwargs.items(): setattr(opt,k_,v_) netg,netd = NetG(opt).eval(),NetD(opt).eval() noises = Variable(t.randn(opt.gen_search_num,opt.nz,1,1).normal_(opt.gen_mean,opt.gen_std)) map_location = lambda storage,loc:storage netd.load_state_dict(t.load(opt.netd_path,map_location=map_location)) netg.load_state_dict(t.load(opt.netg_path,map_location=map_location)) if opt.use_gpu is True: noises.cuda() netd.cuda() netg.cuda() ipdb.set_trace() fake_img = netg(noises) scores = netd(fake_img).data indexs = scores.topk(opt.gen_num)[1] result = [] for ii in indexs: result.append(fake_img.data[ii]) tv.utils.save_image(t.stack(result),opt.gen_img,normalize=True,range=(-1,1))
def generate(**kwargs): ''' 随机生成动漫头像,并根据netd的分数选择较好的 ''' for k_, v_ in kwargs.items(): setattr(opt, k_, v_) netg, netd = NetG(opt).eval(), NetD(opt).eval() noises = t.randn(opt.gen_search_num, opt.nz, 1, 1).normal_(opt.gen_mean, opt.gen_std) noises = Variable(noises, volatile=True) map_location = lambda storage, loc: storage netd.load_state_dict(t.load(opt.netd_path, map_location=map_location)) netg.load_state_dict(t.load(opt.netg_path, map_location=map_location)) if opt.gpu: netd.cuda() netg.cuda() noises = noises.cuda() # 生成图片,并计算图片在判别器的分数 fake_img = netg(noises) scores = netd(fake_img).data # 挑选最好的某几张 indexs = scores.topk(opt.gen_num)[1] result = [] for ii in indexs: result.append(fake_img.data[ii]) # 保存图片 tv.utils.save_image(t.stack(result), opt.gen_img, normalize=True, range=(-1, 1))
def generate(**kwargs): ''' 随机生成动漫头像,并根据netd的分数选择较好的 ''' for k_,v_ in kwargs.items(): setattr(opt,k_,v_) netg, netd = NetG(opt).eval(), NetD(opt).eval() noises = t.randn(opt.gen_search_num,opt.nz,1,1).normal_(opt.gen_mean,opt.gen_std) noises = Variable(noises, volatile=True) map_location=lambda storage, loc: storage netd.load_state_dict(t.load(opt.netd_path, map_location = map_location)) netg.load_state_dict(t.load(opt.netg_path, map_location = map_location)) if opt.gpu: netd.cuda() netg.cuda() noises = noises.cuda() # 生成图片,并计算图片在判别器的分数 fake_img = netg(noises) scores = netd(fake_img).data # 挑选最好的某几张 indexs = scores.topk(opt.gen_num)[1] result = [] for ii in indexs: result.append(fake_img.data[ii]) # 保存图片 tv.utils.save_image(t.stack(result),opt.gen_img,normalize=True,range=(-1,1))
def generate(**kwargs): """ 随机生成动漫头像,并根据netd的分数选择较好的 """ for k_, v_ in kwargs.items(): setattr(opt, k_, v_) # 将网络模型置为预测模式 不保存中间结果,加速 netg, netd = NetG(opt).eval(), NetD(opt).eval() # 初始化gen_search_num张噪声,期望生成gen_search_num张预测图像 noises = t.randn(opt.gen_search_num, opt.nz, 1, 1).normal_(opt.gen_mean, opt.gen_std) noises = Variable(noises, volatile=True) # 将模型参数加载到cpu中 map_location = lambda storage, loc: storage netd.load_state_dict(t.load(opt.netd_path, map_location=map_location)) netg.load_state_dict(t.load(opt.netg_path, map_location=map_location)) # 模型和输入噪声转到GPU中 if opt.gpu: netd.cuda() netg.cuda() noises = noises.cuda() # 生成图片,并计算图片在判别器的分数 fake_img = netg(noises) scores = netd(fake_img).data # 挑选最好的某几张 从512章图片中按分数排序,取前64张 的下标 indexs = scores.topk(opt.gen_num)[1] result = [] for ii in indexs: result.append(fake_img.data[ii]) # 保存图片 tv.utils.save_image(t.stack(result), opt.gen_img, normalize=True, range=(-1, 1))
betas=(0.0, 0.9)) optimizerD_enc = torch.optim.Adam(netD.feature_encoder.parameters(), lr=0.0004, betas=(0.0, 0.9)) optimizerD_proj = torch.optim.Adam(netD.COND_DNET.parameters(), lr=0.004, 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))) netG.cuda() netD.cuda() if cfg.B_VALIDATION: sampling(text_encoder, netG, dataloader, device) # generate images for the whole valid dataset logger.info('state_epoch: %d' % (state_epoch)) else: train(dataloader, netG, netD, text_encoder, optimizerG, optimizerD_enc,
def train(**kwargs): for k_, v_ in kwargs.items(): setattr(opt, k_, v_) if opt.vis: from visualize import Visualizer vis = Visualizer(opt.env) transforms = tv.transforms.Compose([ tv.transforms.Resize(opt.image_size), tv.transforms.CenterCrop(opt.image_size), tv.transforms.ToTensor(), tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) dataset = tv.datasets.ImageFolder(opt.data_path, transform=transforms) dataloader = t.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers, drop_last=True) # 定义网络 netg, netd = NetG(opt), NetD(opt) map_location = lambda storage, loc: storage if opt.netd_path: netd.load_state_dict(t.load(opt.netd_path, map_location=map_location)) if opt.netg_path: netg.load_state_dict(t.load(opt.netg_path, map_location=map_location)) # 定义优化器和损失 optimizer_g = t.optim.Adam(netg.parameters(), opt.lr1, betas=(opt.beta1, 0.999)) optimizer_d = t.optim.Adam(netd.parameters(), opt.lr2, betas=(opt.beta1, 0.999)) criterion = t.nn.BCELoss() # 二分类交叉熵 # 真图片label为1,假图片label为0 # noises为生成网络的输入 true_labels = Variable(t.ones(opt.batch_size)) fake_labels = Variable(t.zeros(opt.batch_size)) fix_noises = Variable(t.randn(opt.batch_size, opt.nz, 1, 1)) noises = Variable(t.randn(opt.batch_size, opt.nz, 1, 1)) errord_meter = AverageValueMeter() errorg_meter = AverageValueMeter() if opt.gpu: netd.cuda() netg.cuda() criterion.cuda() true_labels, fake_labels = true_labels.cuda(), fake_labels.cuda() fix_noises, noises = fix_noises.cuda(), noises.cuda() epochs = range(opt.max_epoch) for epoch in iter(epochs): for ii, (img, _) in tqdm.tqdm(enumerate(dataloader)): real_img = Variable(img) if opt.gpu: real_img = real_img.cuda() if ii % opt.d_every == 0: # 训练判别器 optimizer_d.zero_grad() ## 尽可能的把真图片判别为正确 output = netd(real_img) error_d_real = criterion(output, true_labels) error_d_real.backward() ## 尽可能把假图片判别为错误 noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1)) fake_img = netg(noises).detach() # 根据噪声生成假图 output = netd(fake_img) error_d_fake = criterion(output, fake_labels) error_d_fake.backward() optimizer_d.step() error_d = error_d_fake + error_d_real errord_meter.add(error_d.data[0]) if ii % opt.g_every == 0: # 训练生成器 optimizer_g.zero_grad() noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1)) fake_img = netg(noises) output = netd(fake_img) error_g = criterion(output, true_labels) error_g.backward() optimizer_g.step() errorg_meter.add(error_g.data[0]) if opt.vis and ii % opt.plot_every == opt.plot_every - 1: ## 可视化 if os.path.exists(opt.debug_file): ipdb.set_trace() fix_fake_imgs = netg(fix_noises) vis.images(fix_fake_imgs.data.cpu().numpy()[:64] * 0.5 + 0.5, win='fixfake') vis.images(real_img.data.cpu().numpy()[:64] * 0.5 + 0.5, win='real') vis.plot('errord', errord_meter.value()[0]) vis.plot('errorg', errorg_meter.value()[0]) fix_fake_imgs = netg(fix_noises) if epoch % opt.decay_every == 0: # 保存图片 tv.utils.save_image(fix_fake_imgs.data[:64], '%s/%s.png' % (opt.save_path, epoch), normalize=True, range=(-1, 1)) # 保存模型 t.save(netd.state_dict(), 'checkpoints/netd_%s.pth' % epoch) t.save(netg.state_dict(), 'checkpoints/netg_%s.pth' % epoch) errord_meter.reset() errorg_meter.reset() optimizer_g = t.optim.Adam(netg.parameters(), opt.lr1, betas=(opt.beta1, 0.999)) optimizer_d = t.optim.Adam(netd.parameters(), opt.lr2, betas=(opt.beta1, 0.999))
def main(args): # manualSeed to control the noise manualSeed = 100 random.seed(manualSeed) np.random.seed(manualSeed) torch.manual_seed(manualSeed) with open(args.json_file, 'r') as f: dataset_json = json.load(f) # load rnn encoder text_encoder = RNN_ENCODER(dataset_json['n_words'], nhidden=dataset_json['text_embed_dim']) text_encoder_dir = args.rnn_encoder state_dict = torch.load(text_encoder_dir, map_location=lambda storage, loc: storage) text_encoder.load_state_dict(state_dict) # load netG state_dict = torch.load(args.model_path, map_location=torch.device('cpu')) # netG = NetG(int(dataset_json['n_channels']), int(dataset_json['cond_dim'])) netG = NetG(64, int(dataset_json['cond_dim'])) new_state_dict = OrderedDict() for k, v in state_dict.items(): name = k[7:] # remove `module.`nvidia new_state_dict[name] = v model_dict = netG.state_dict() pretrained_dict = {k: v for k, v in new_state_dict.items() if k in model_dict} model_dict.update(pretrained_dict) netG.load_state_dict(model_dict) # use gpu or not, change model to evaluation mode if args.use_gpu: text_encoder.cuda() netG.cuda() caption_idx.cuda() caption_len.cuda() noise.cuda() text_encoder.eval() netG.eval() # generate noise num_noise = 100 noise = torch.FloatTensor(num_noise, 100) # cub bird captions # caption = 'this small bird has a light yellow breast and brown wings' # caption = 'this small bird has a short beak a light gray breast a darker gray crown and black wing tips' # caption = 'this small bird has wings that are gray and has a white belly' # caption = 'this bird has a yellow throat belly abdomen and sides with lots of brown streaks on them' # caption = 'this little bird has a yellow belly and breast with a gray wing with white wingbars' # caption = 'this bird has a white belly and breast wit ha blue crown and nape' # caption = 'a bird with brown and black wings red crown and throat and the bill is short and pointed' # caption = 'this small bird has a yellow crown and a white belly' # caption = 'this bird has a blue crown with white throat and brown secondaries' # caption = 'this bird has wings that are black and has a white belly' # caption = 'a yellow bird has wings with dark stripes and small eyes' # caption = 'a black bird has wings with dark stripes and small eyes' # caption = 'a red bird has wings with dark stripes and small eyes' # caption = 'a white bird has wings with dark stripes and small eyes' # caption = 'a blue bird has wings with dark stripes and small eyes' # caption = 'a pink bird has wings with dark stripes and small eyes' # caption = 'this is a white and grey bird with black wings and a black stripe by its eyes' # caption = 'a small bird with an orange bill and grey crown and breast' # caption = 'a small bird with black gray and white wingbars' # caption = 'this bird is white and light orange in color with a black beak' # caption = 'a small sized bird that has tones of brown and a short pointed bill' # beak? # MS coco captions # caption = 'two men skiing down a snow covered mountain in the evening' # caption = 'a man walking down a grass covered mountain' # caption = 'a close up of a boat on a field under a sunset' # caption = 'a close up of a boat on a field with a clear sky' # caption = 'a herd of black and white cattle standing on a field' # caption = 'a herd of black and white sheep standing on a field' # caption = 'a herd of black and white dogs standing on a field' # caption = 'a herd of brown cattle standing on a field' # caption = 'a herd of black and white cattle standing in a river' # caption = 'some horses in a field of green grass with a sky in the background' # caption = 'some horses in a field of yellow grass with a sky in the background' caption = 'some horses in a field of green grass with a sunset in the background' # convert caption to index caption_idx, caption_len = get_caption_idx(dataset_json, caption) caption_idx = torch.LongTensor(caption_idx) caption_len = torch.LongTensor([caption_len]) caption_idx = caption_idx.view(1, -1) caption_len = caption_len.view(-1) # use rnn encoder to get caption embedding hidden = text_encoder.init_hidden(1) words_embs, sent_emb = text_encoder(caption_idx, caption_len, hidden) # generate fake image noise.data.normal_(0, 1) sent_emb = sent_emb.repeat(num_noise, 1) words_embs = words_embs.repeat(num_noise, 1, 1) with torch.no_grad(): fake_imgs, fusion_mask = netG(noise, sent_emb) # create path to save image, caption and mask cap_number = 10000 main_path = 'result/mani/cap_%s_0_coco_ch64' % (str(cap_number)) img_save_path = '%s/image' % main_path mask_save_path = '%s/mask_' % main_path mkdir_p(img_save_path) for i in range(7): mkdir_p(mask_save_path + str(i)) # save caption as image ixtoword = {v: k for k, v in dataset_json['word2idx'].items()} cap_img = cap2img(ixtoword, caption_idx, caption_len) im = cap_img[0].data.cpu().numpy() im = (im + 1.0) * 127.5 im = im.astype(np.uint8) im = np.transpose(im, (1, 2, 0)) im = Image.fromarray(im) full_path = '%s/caption.png' % main_path im.save(full_path) # save generated images and masks for i in tqdm(range(num_noise)): full_path = '%s/image_%d.png' % (img_save_path, i) im = fake_imgs[i].data.cpu().numpy() im = (im + 1.0) * 127.5 im = im.astype(np.uint8) im = np.transpose(im, (1, 2, 0)) im = Image.fromarray(im) im.save(full_path) for j in range(7): full_path = '%s%1d/mask_%d.png' % (mask_save_path, j, i) im = fusion_mask[j][i][0].data.cpu().numpy() im = im * 255 im = im.astype(np.uint8) im = Image.fromarray(im) im.save(full_path)
def train(**kwargs): for k_,v_ in kwargs.items(): setattr(opt,k_,v_) if opt.vis: from visualize import Visualizer vis = Visualizer(opt.env) transforms = tv.transforms.Compose([ tv.transforms.Scale(opt.image_size), tv.transforms.CenterCrop(opt.image_size), tv.transforms.ToTensor(), tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) dataset = tv.datasets.ImageFolder(opt.data_path,transform=transforms) dataloader = t.utils.data.DataLoader(dataset, batch_size = opt.batch_size, shuffle = True, num_workers= opt.num_workers, drop_last=True ) # 定义网络 netg, netd = NetG(opt), NetD(opt) map_location=lambda storage, loc: storage if opt.netd_path: netd.load_state_dict(t.load(opt.netd_path, map_location = map_location)) if opt.netg_path: netg.load_state_dict(t.load(opt.netg_path, map_location = map_location)) # 定义优化器和损失 optimizer_g = t.optim.Adam(netg.parameters(),opt.lr1,betas=(opt.beta1, 0.999)) optimizer_d = t.optim.Adam(netd.parameters(),opt.lr2,betas=(opt.beta1, 0.999)) criterion = t.nn.BCELoss() # 真图片label为1,假图片label为0 # noises为生成网络的输入 true_labels = Variable(t.ones(opt.batch_size)) fake_labels = Variable(t.zeros(opt.batch_size)) fix_noises = Variable(t.randn(opt.batch_size,opt.nz,1,1)) noises = Variable(t.randn(opt.batch_size,opt.nz,1,1)) errord_meter = AverageValueMeter() errorg_meter = AverageValueMeter() if opt.gpu: netd.cuda() netg.cuda() criterion.cuda() true_labels,fake_labels = true_labels.cuda(), fake_labels.cuda() fix_noises,noises = fix_noises.cuda(),noises.cuda() epochs = range(opt.max_epoch) for epoch in iter(epochs): for ii,(img,_) in tqdm.tqdm(enumerate(dataloader)): real_img = Variable(img) if opt.gpu: real_img=real_img.cuda() if ii%opt.d_every==0: # 训练判别器 optimizer_d.zero_grad() ## 尽可能的把真图片判别为正确 output = netd(real_img) error_d_real = criterion(output,true_labels) error_d_real.backward() ## 尽可能把假图片判别为错误 noises.data.copy_(t.randn(opt.batch_size,opt.nz,1,1)) fake_img = netg(noises).detach() # 根据噪声生成假图 output = netd(fake_img) error_d_fake = criterion(output,fake_labels) error_d_fake.backward() optimizer_d.step() error_d = error_d_fake + error_d_real errord_meter.add(error_d.data[0]) if ii%opt.g_every==0: # 训练生成器 optimizer_g.zero_grad() noises.data.copy_(t.randn(opt.batch_size,opt.nz,1,1)) fake_img = netg(noises) output = netd(fake_img) error_g = criterion(output,true_labels) error_g.backward() optimizer_g.step() errorg_meter.add(error_g.data[0]) if opt.vis and ii%opt.plot_every == opt.plot_every-1: ## 可视化 if os.path.exists(opt.debug_file): ipdb.set_trace() fix_fake_imgs = netg(fix_noises) vis.images(fix_fake_imgs.data.cpu().numpy()[:64]*0.5+0.5,win='fixfake') vis.images(real_img.data.cpu().numpy()[:64]*0.5+0.5,win='real') vis.plot('errord',errord_meter.value()[0]) vis.plot('errorg',errorg_meter.value()[0]) if epoch%opt.decay_every==0: # 保存模型、图片 tv.utils.save_image(fix_fake_imgs.data[:64],'%s/%s.png' %(opt.save_path,epoch),normalize=True,range=(-1,1)) t.save(netd.state_dict(),'checkpoints/netd_%s.pth' %epoch) t.save(netg.state_dict(),'checkpoints/netg_%s.pth' %epoch) errord_meter.reset() errorg_meter.reset() optimizer_g = t.optim.Adam(netg.parameters(),opt.lr1,betas=(opt.beta1, 0.999)) optimizer_d = t.optim.Adam(netd.parameters(),opt.lr2,betas=(opt.beta1, 0.999))
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()
betas=(opt.beta1, 0.999)) 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':
def train(**kwargs): # 读取参数赋值 for k_, v_ in kwargs.items(): setattr(opt, k_, v_) # 可视化 if opt.vis: from visualize import Visualizer vis = Visualizer(opt.env) # 对图片进行操作 transforms = tv.transforms.Compose([ tv.transforms.Scale(opt.image_size), tv.transforms.CenterCrop(opt.image_size), tv.transforms.ToTensor(), # 均值方差 tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) # ImageFolder 使用pytorch原生的方法读取图片,并进行操作 封装数据集 dataset = tv.datasets.ImageFolder(opt.data_path, transform=transforms) #数据加载器 dataloader = t.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers, drop_last=True) # 定义网络 netg, netd = NetG(opt), NetD(opt) # 把map内容加载到CPU中 map_location = lambda storage, loc: storage # 将预训练的模型都先加载到cpu上 if opt.netd_path: netd.load_state_dict(t.load(opt.netd_path, map_location=map_location)) if opt.netg_path: netg.load_state_dict(t.load(opt.netg_path, map_location=map_location)) # 定义优化器和损失 optimizer_g = t.optim.Adam(netg.parameters(), opt.lr1, betas=(opt.beta1, 0.999)) optimizer_d = t.optim.Adam(netd.parameters(), opt.lr2, betas=(opt.beta1, 0.999)) # BinaryCrossEntropy二分类交叉熵,常用于二分类问题,当然也可以用于多分类问题 criterion = t.nn.BCELoss() # 真图片label为1,假图片label为0 # noises为生成网络的输入 true_labels = Variable(t.ones(opt.batch_size)) fake_labels = Variable(t.zeros(opt.batch_size)) # fix_noises是固定值,用来查看每个epoch的变化效果 fix_noises = Variable(t.randn(opt.batch_size, opt.nz, 1, 1)) noises = Variable(t.randn(opt.batch_size, opt.nz, 1, 1)) # AverageValueMeter统计任意添加的变量的方差和均值 可视化的仪表盘 errord_meter = AverageValueMeter() errorg_meter = AverageValueMeter() if opt.gpu: # 网络转移到GPU netd.cuda() netg.cuda() # 损失函数转移到GPU criterion.cuda() # 标签转移到GPU true_labels, fake_labels = true_labels.cuda(), fake_labels.cuda() # 输入噪声转移到GPU fix_noises, noises = fix_noises.cuda(), noises.cuda() epochs = range(opt.max_epoch) for epoch in iter(epochs): for ii, (img, _) in tqdm.tqdm(enumerate(dataloader)): real_img = Variable(img) if opt.gpu: real_img = real_img.cuda() # 每d_every个batch训练判别器 if ii % opt.d_every == 0: # 训练判别器 optimizer_d.zero_grad() ## 尽可能的把真图片判别为正确 #一个batchd的真照片判定为1 并反向传播 output = netd(real_img) error_d_real = criterion(output, true_labels) #反向传播 error_d_real.backward() ## 尽可能把假图片判别为错误 # 一个batchd的假照片判定为0 并反向传播 noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1)) fake_img = netg(noises).detach() # 根据噪声生成假图 output = netd(fake_img) error_d_fake = criterion(output, fake_labels) error_d_fake.backward() #更新可学习参数 optimizer_d.step() # 总误差=识别真实图片误差+假图片误差 error_d = error_d_fake + error_d_real # 将总误差加入仪表板用于可视化显示 errord_meter.add(error_d.data[0]) # 每g_every个batch训练生成器 if ii % opt.g_every == 0: # 训练生成器 optimizer_g.zero_grad() noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1)) # 生成器:噪声生成假图片 fake_img = netg(noises) # 判别器:假图片判别份数 output = netd(fake_img) # 尽量让假图片的份数与真标签接近,让判别器分不出来 error_g = criterion(output, true_labels) error_g.backward() # 更新参数 optimizer_g.step() # 将误差加入仪表板用于可视化显示 errorg_meter.add(error_g.data[0]) if opt.vis and ii % opt.plot_every == opt.plot_every - 1: ## 可视化 # 进入debug模式 if os.path.exists(opt.debug_file): ipdb.set_trace() # 固定噪声生成假图片 fix_fake_imgs = netg(fix_noises) # 可视化 固定噪声产生的假图片 vis.images(fix_fake_imgs.data.cpu().numpy()[:64] * 0.5 + 0.5, win='fixfake') # 可视化一张真图片。作为对比 vis.images(real_img.data.cpu().numpy()[:64] * 0.5 + 0.5, win='real') # 可视化仪表盘 判别器误差 生成器误差 vis.plot('errord', errord_meter.value()[0]) vis.plot('errorg', errorg_meter.value()[0]) # 每decay_every个epoch之后保存一次模型 if epoch % opt.decay_every == 0: # 保存模型、图片 tv.utils.save_image(fix_fake_imgs.data[:64], '%s/%s.png' % (opt.save_path, epoch), normalize=True, range=(-1, 1)) # 保存判别器 生成器 t.save(netd.state_dict(), 'checkpoints/netd_%s.pth' % epoch) t.save(netg.state_dict(), 'checkpoints/netg_%s.pth' % epoch) # 清空误差仪表盘 errord_meter.reset() errorg_meter.reset() # 重置优化器参数为刚开始的参数 optimizer_g = t.optim.Adam(netg.parameters(), opt.lr1, betas=(opt.beta1, 0.999)) optimizer_d = t.optim.Adam(netd.parameters(), opt.lr2, betas=(opt.beta1, 0.999))
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")