def generate(**kwargs): ''' 随机生成动漫头像,并根据netd的分数选择较好的结果 :param kwargs: :return: ''' 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) 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: device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") netd.to(device) netg.to(device) noises.to(device) #生成图片 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 i 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_) device = t.device('cuda') if opt.gpu else t.device('cpu') 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 = noises.to(device) 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)) netd.to(device) netg.to(device) # 生成图片,并计算图片在判别器的分数 fake_img = netg(noises) scores = netd(fake_img).detach() # 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_) device=t.device('cuda') if opt.gpu else t.device('cpu') 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 = noises.to(device) 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)) netd.to(device) netg.to(device) # 生成图片,并计算图片在判别器的分数 fake_img = netg(noises) scores = netd(fake_img).detach() # 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_) device=t.device('cuda') if opt.gpu else t.device('cpu') 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 = noises.to(device) 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)) netd.to(device) netg.to(device) # 生成图片,并计算图片在判别器的分数 fake_img = netg(noises) scores = netd(fake_img).detach() # 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 epoch_num = opt.netd_path[opt.netd_path.find("_") + 1:opt.netd_path.rfind(".")] randid = random.randint(0, 1000) pic_stack_name = "".join(["result_", str(epoch_num), "_", str(randid), ".png"]) tv.utils.save_image(t.stack(result), pic_stack_name, normalize=True, range=(-1, 1)) base_dir = "./imgs/tiny_epoch_{0}_{1}".format(epoch_num, randid) if not os.path.exists(base_dir): os.mkdir(base_dir) for picx in range(len(result)): picx_name = "".join([base_dir, "/picid_", str(picx), ".png"]) tv.utils.save_image(result[picx], picx_name, normalize=True, range=(-1, 1))
import cv2 import torch as th from model import NetG, NetD, NetA from my_dataSet import CASIABDatasetGenerate netg = NetG(nc=1) netd = NetD(nc=1) neta = NetA(nc=1) device = th.device("cuda:0") netg = netg.to(device) netd = netd.to(device) neta = neta.to(device) fineSize = 64 checkpoint = '/home/mg/code/my_GAN_dataSet/snapshots/snapshot_449.t7' checkpoint = th.load(checkpoint) neta.load_state_dict(checkpoint['netA']) netg.load_state_dict(checkpoint['netG']) netd.load_state_dict(checkpoint['netD']) neta.eval() netg.eval() netd.eval() angles = [ '000', '018', '036', '054', '072', '090', '108', '126', '144', '162', '180' ] for cond in ['nm-01', 'nm-02', 'nm-03', 'nm-04', 'cl-01', 'cl-02']: dataset = CASIABDatasetGenerate( data_dir='/home/mg/code/data/GEI_CASIA_B/gei/', cond=cond)
def train(**kwargs): for k_, v_ in kwargs.items(): setattr(opt, k_, v_) device = t.device('cuda') if opt.gpu else t.device('cpu') # if opt.vis: # from visualize import Visualizer # vis = Visualizer(opt.env) # 数据 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(root=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) # 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)) netd.to(device) netg.to(device) # 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().to(device) # 真图片label为1,假图片label为0 # noises为生成网络的输入 true_labels = t.ones(opt.batch_size).to(device) fake_labels = t.zeros(opt.batch_size).to(device) fix_noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device) noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device) errord_meter = AverageValueMeter() errorg_meter = AverageValueMeter() epochs = range(opt.max_epoch) for epoch in iter(epochs): for ii, (img, _) in tqdm.tqdm(enumerate(dataloader)): real_img = img.to(device) 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.item()) 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.item()) 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.detach().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 + 1) % opt.save_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()
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_) device = torch.device('cuda') if opt.gpu else torch.device('cpu') hash_id = hash(opt.gen_id) % 51603 torch.manual_seed(hash_id) netg, netd = NetG(opt).eval(), NetD(opt).eval() noises = torch.randn(opt.gen_search_num, opt.nz, 1, 1).normal_(opt.gen_mean, opt.gen_std) noises = noises.to(device) map_location = lambda storage, loc: storage netd.load_state_dict(torch.load(opt.netd_path, map_location=map_location)) netg.load_state_dict(torch.load(opt.netg_path, map_location=map_location)) netd.to(device) netg.to(device) # 生成图片,并计算图片在判别器的分数 fake_img = netg(noises) scores = netd(fake_img).detach() # 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 # epoch_num = opt.netd_path[opt.netd_path.find("_") + 1:opt.netd_path.rfind(".")] # pic_stack_name = "".join(["result_", str(epoch_num), "_", str(randid), ".png"]) print(result[0]) print(result[0].size()) # np_array = result[0].cpu().numpy() # # trans_array = np.zeros((96,96,3)) # # trans_array[:,:,0] = np_array[0,:,:] # trans_array[:,:,1] = np_array[1,:,:] # trans_array[:,:,2] = np_array[2,:,:] # # print(trans_array.shape) # # img_ = Image.fromarray(np.uint8(trans_array*255)) # img_ = img_.resize((224, 224), Image.BILINEAR) #resize to # t_img = torch.from_numpy(np.array(img_)) # tv.utils.save_image(t_img, "PIL_test_img.png", normalize=True, range=(-0.8, 0.8)) #tv.utils.save_image(torch.stack(result), "save_test.png", normalize=True, range=(-1, 1)) base_dir = opt.gen_dst base_name = opt.base_name if not os.path.exists(base_dir): os.mkdir(base_dir) for picx in range(len(result)): picx_name = "".join([base_dir, base_name, str(picx), ".png"]) tv.utils.save_image(result[picx], picx_name, normalize=True, range=(-0.8, 0.8)) img = Image.open(picx_name) img = img.resize((188, 188), Image.ANTIALIAS) img.save(base_dir + "/" + str(picx) + ".png", "png") os.remove(picx_name)
def train(**kwargs): for k_, v_ in kwargs.items(): setattr(opt, k_, v_) device = t.device("cuda") if opt.gpu else t.device("cpu") # 数据处理,输出规范为-1~1 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) # 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)) netd.to(device) netg.to(device) # 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, noise为生成网络的输入 true_labels = t.ones(opt.batch_size).to(device) fake_labels = t.zeros(opt.batch_size).to(device) fix_noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device) noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device) # 用来结果的均值和标准差 errord_meter = AverageValueMeter() errorg_meter = AverageValueMeter() epochs = range(opt.max_epoch) for epoch in iter(epochs): for ii, (img, _) in tqdm.tqdm(enumerate(dataloader)): real_img = img.to(device) if ii % opt.d_every == 0: # 训练判别器 optimizer_d.zero_grad() ## 尽可能把真图片判别为正 output = netd(real_img) error_d_real = criterion(output, true_labels) error_d_real.backward() ## 尽可能把假图片判断为错误 noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1)) # 使用detach来关闭G求梯度,加速训练 fake_img = netg(noises).detach() 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.item()) 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) # 尽可能把假的图片也判别为1 error_g = criterion(output, true_labels) error_g.backward() optimizer_g.step() errorg_meter.add(error_g.item()) # 可视化 # 保存模型、图片 if (epoch + 1) % opt.save_every == 0: fix_fake_imgs = netg(fix_noises) 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(), r"./checkpoints/netd_%s.pth" % epoch) t.save(netg.state_dict(), r"./checkpoints/netg_%s.pth" % epoch) errord_meter.reset() errorg_meter.reset()
class GANAgent(object): def __init__(self, input_size, output_size, num_env, num_step, gamma, lam=0.95, learning_rate=1e-4, ent_coef=0.01, clip_grad_norm=0.5, epoch=3, batch_size=128, ppo_eps=0.1, update_proportion=0.25, use_gae=True, use_cuda=False, use_noisy_net=False, hidden_dim=512): self.model = CnnActorCriticNetwork(input_size, output_size, use_noisy_net) self.num_env = num_env self.output_size = output_size self.input_size = input_size self.num_step = num_step self.gamma = gamma self.lam = lam self.epoch = epoch self.batch_size = batch_size self.use_gae = use_gae self.ent_coef = ent_coef self.ppo_eps = ppo_eps self.clip_grad_norm = clip_grad_norm self.update_proportion = update_proportion self.device = torch.device('cuda' if use_cuda else 'cpu') self.netG = NetG(z_dim=hidden_dim) #(input_size, z_dim=hidden_dim) self.netD = NetD(z_dim=1) self.netG.apply(weights_init) self.netD.apply(weights_init) self.optimizer_policy = optim.Adam(list(self.model.parameters()), lr=learning_rate) self.optimizer_G = optim.Adam(list(self.netG.parameters()), lr=learning_rate, betas=(0.5, 0.999)) self.optimizer_D = optim.Adam(list(self.netD.parameters()), lr=learning_rate, betas=(0.5, 0.999)) self.netG = self.netG.to(self.device) self.netD = self.netD.to(self.device) self.model = self.model.to(self.device) def reconstruct(self, state): state = torch.Tensor(state).to(self.device) state = state.float() reconstructed = self.vae(state.unsqueeze(0))[0].squeeze(0) return reconstructed.detach().cpu().numpy() def get_action(self, state): state = torch.Tensor(state).to(self.device) state = state.float() policy, value_ext, value_int = self.model(state) action_prob = F.softmax(policy, dim=-1).data.cpu().numpy() action = self.random_choice_prob_index(action_prob) return action, value_ext.data.cpu().numpy().squeeze( ), value_int.data.cpu().numpy().squeeze(), policy.detach() @staticmethod def random_choice_prob_index(p, axis=1): r = np.expand_dims(np.random.rand(p.shape[1 - axis]), axis=axis) return (p.cumsum(axis=axis) > r).argmax(axis=axis) def compute_intrinsic_reward(self, obs): obs = torch.FloatTensor(obs).to(self.device) #embedding = self.vae.representation(obs) #reconstructed_embedding = self.vae.representation(self.vae(obs)[0]) # why use index[0] reconstructed_img, embedding, reconstructed_embedding = self.netG(obs) intrinsic_reward = (embedding - reconstructed_embedding ).pow(2).sum(1) / 2 # Not use reconstructed loss return intrinsic_reward.detach().cpu().numpy() def train_model(self, s_batch, target_ext_batch, target_int_batch, y_batch, adv_batch, next_obs_batch, old_policy): s_batch = torch.FloatTensor(s_batch).to(self.device) target_ext_batch = torch.FloatTensor(target_ext_batch).to(self.device) target_int_batch = torch.FloatTensor(target_int_batch).to(self.device) y_batch = torch.LongTensor(y_batch).to(self.device) adv_batch = torch.FloatTensor(adv_batch).to(self.device) next_obs_batch = torch.FloatTensor(next_obs_batch).to(self.device) sample_range = np.arange(len(s_batch)) #reconstruction_loss = nn.MSELoss(reduction='none')] l_adv = nn.MSELoss(reduction='none') l_con = nn.L1Loss(reduction='none') l_enc = nn.MSELoss(reduction='none') l_bce = nn.BCELoss(reduction='none') with torch.no_grad(): policy_old_list = torch.stack(old_policy).permute( 1, 0, 2).contiguous().view(-1, self.output_size).to(self.device) m_old = Categorical(F.softmax(policy_old_list, dim=-1)) log_prob_old = m_old.log_prob(y_batch) # ------------------------------------------------------------ #recon_losses = np.array([]) #kld_losses = np.array([]) mean_err_g_adv_per_batch = np.array([]) mean_err_g_con_per_batch = np.array([]) mean_err_g_enc_per_batch = np.array([]) mean_err_d_per_batch = np.array([]) for i in range(self.epoch): np.random.shuffle(sample_range) for j in range(int(len(s_batch) / self.batch_size)): sample_idx = sample_range[self.batch_size * j:self.batch_size * (j + 1)] # -------------------------------------------------------------------------------- # for generative curiosity (GAN loss) #gen_next_state, mu, logvar = self.vae(next_obs_batch[sample_idx]) ############### netG forward ############################################## gen_next_state, latent_i, latent_o = self.netG( next_obs_batch[sample_idx]) ############### netD forward ############################################## pred_real, feature_real = self.netD(next_obs_batch[sample_idx]) pred_fake, feature_fake = self.netD(gen_next_state) #d = len(gen_next_state.shape) #recon_loss = reconstruction_loss(gen_next_state, next_obs_batch[sample_idx]).mean(axis=list(range(1, d))) ############### netG backward ############################################# self.optimizer_G.zero_grad() err_g_adv_per_img = l_adv( self.netD(next_obs_batch[sample_idx])[1], self.netD(gen_next_state)[1]).mean( axis=list(range(1, len(feature_real.shape)))) err_g_con_per_img = l_con( next_obs_batch[sample_idx], gen_next_state).mean( axis=list(range(1, len(gen_next_state.shape)))) err_g_enc_per_img = l_enc(latent_i, latent_o).mean(-1) #kld_loss = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()).sum(axis=1) # TODO: keep this proportion of experience used for VAE update? # Proportion of experience used for VAE update img_num = len(err_g_con_per_img) mask = torch.rand(img_num).to(self.device) mask = (mask < self.update_proportion).type( torch.FloatTensor).to(self.device) mean_err_g_adv = (err_g_adv_per_img * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) mean_err_g_con = (err_g_con_per_img * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) mean_err_g_enc = (err_g_enc_per_img * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) # hyperparameter weights: w_adv = 1 w_con = 50 w_enc = 1 mean_err_g = mean_err_g_adv * w_adv +\ mean_err_g_con * w_con +\ mean_err_g_enc * w_enc mean_err_g.backward(retain_graph=True) self.optimizer_G.step() mean_err_g_adv_per_batch = np.append( mean_err_g_adv_per_batch, mean_err_g_adv.detach().cpu().numpy()) mean_err_g_con_per_batch = np.append( mean_err_g_con_per_batch, mean_err_g_con.detach().cpu().numpy()) mean_err_g_enc_per_batch = np.append( mean_err_g_enc_per_batch, mean_err_g_enc.detach().cpu().numpy()) ############## netD backward ############################################## self.optimizer_D.zero_grad() real_label = torch.ones_like(pred_real).to(self.device) fake_label = torch.zeros_like(pred_fake).to(self.device) err_d_real_per_img = l_bce(pred_real, real_label) err_d_fake_per_img = l_bce(pred_fake, fake_label) mean_err_d_real = (err_d_real_per_img * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) mean_err_d_fake = (err_d_fake_per_img * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) mean_err_d = (mean_err_d_real + mean_err_d_fake) / 2 mean_err_d.backward() self.optimizer_D.step() mean_err_d_per_batch = np.append( mean_err_d_per_batch, mean_err_d.detach().cpu().numpy()) if mean_err_d.item() < 1e-5: self.netD.apply(weights_init) print('Reloading net d') ############# policy update ############################################### policy, value_ext, value_int = self.model(s_batch[sample_idx]) m = Categorical(F.softmax(policy, dim=-1)) log_prob = m.log_prob(y_batch[sample_idx]) ratio = torch.exp(log_prob - log_prob_old[sample_idx]) surr1 = ratio * adv_batch[sample_idx] surr2 = torch.clamp(ratio, 1.0 - self.ppo_eps, 1.0 + self.ppo_eps) * adv_batch[sample_idx] actor_loss = -torch.min(surr1, surr2).mean() critic_ext_loss = F.mse_loss(value_ext.sum(1), target_ext_batch[sample_idx]) critic_int_loss = F.mse_loss(value_int.sum(1), target_int_batch[sample_idx]) critic_loss = critic_ext_loss + critic_int_loss entropy = m.entropy().mean() self.optimizer_policy.zero_grad() loss = actor_loss + 0.5 * critic_loss - self.ent_coef * entropy loss.backward() #global_grad_norm_(list(self.model.parameters())+list(self.vae.parameters())) do we need this step #global_grad_norm_(list(self.model.parameter())) or just norm policy self.optimizer_poilicy.step() return mean_err_g_adv_per_batch, mean_err_g_con_per_batch, mean_err_g_enc_per_batch, mean_err_d_per_batch def train_just_vae(self, s_batch, next_obs_batch): s_batch = torch.FloatTensor(s_batch).to(self.device) next_obs_batch = torch.FloatTensor(next_obs_batch).to(self.device) sample_range = np.arange(len(s_batch)) l_adv = nn.MSELoss(reduction='none') l_con = nn.L1Loss(reduction='none') l_enc = nn.MSELoss(reduction='none') l_bce = nn.BCELoss(reduction='none') mean_err_g_adv_per_batch = np.array([]) mean_err_g_con_per_batch = np.array([]) mean_err_g_enc_per_batch = np.array([]) mean_err_d_per_batch = np.array([]) for i in range(self.epoch): np.random.shuffle(sample_range) for j in range(int(len(s_batch) / self.batch_size)): sample_idx = sample_range[self.batch_size * j:self.batch_size * (j + 1)] ############### netG forward ############################################## gen_next_state, latent_i, latent_o = self.netG( next_obs_batch[sample_idx]) ############### netD forward ############################################## pred_real, feature_real = self.netD(next_obs_batch[sample_idx]) pred_fake, feature_fake = self.netD(gen_next_state) #d = len(gen_next_state.shape) #recon_loss = reconstruction_loss(gen_next_state, next_obs_batch[sample_idx]).mean(axis=list(range(1, d))) ############### netG backward ############################################# self.optimizer_G.zero_grad() err_g_adv_per_img = l_adv( self.netD(next_obs_batch[sample_idx])[1], self.netD(gen_next_state)[1]).mean( axis=list(range(1, len(feature_real.shape)))) err_g_con_per_img = l_con( next_obs_batch[sample_idx], gen_next_state).mean( axis=list(range(1, len(gen_next_state.shape)))) err_g_enc_per_img = l_enc(latent_i, latent_o).mean(-1) #kld_loss = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()).sum(axis=1) # TODO: keep this proportion of experience used for VAE update? # Proportion of experience used for VAE update img_num = len(err_g_con_per_img) mask = torch.rand(img_num).to(self.device) mask = (mask < self.update_proportion).type( torch.FloatTensor).to(self.device) mean_err_g_adv = (err_g_adv_per_img * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) mean_err_g_con = (err_g_con_per_img * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) mean_err_g_enc = (err_g_enc_per_img * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) # hyperparameter weights: w_adv = 1 w_con = 50 w_enc = 1 mean_err_g = mean_err_g_adv * w_adv +\ mean_err_g_con * w_con +\ mean_err_g_enc * w_enc mean_err_g.backward(retain_graph=True) self.optimizer_G.step() mean_err_g_adv_per_batch = np.append( mean_err_g_adv_per_batch, mean_err_g_adv.detach().cpu().numpy()) mean_err_g_con_per_batch = np.append( mean_err_g_con_per_batch, mean_err_g_con.detach().cpu().numpy()) mean_err_g_enc_per_batch = np.append( mean_err_g_enc_per_batch, mean_err_g_enc.detach().cpu().numpy()) ############## netD backward ############################################## self.optimizer_D.zero_grad() real_label = torch.ones_like(pred_real).to(self.device) fake_label = torch.zeros_like(pred_fake).to(self.device) err_d_real_per_img = l_bce(pred_real, real_label) err_d_fake_per_img = l_bce(pred_fake, fake_label) mean_err_d_real = (err_d_real_per_img * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) mean_err_d_fake = (err_d_fake_per_img * mask).sum() / torch.max( mask.sum(), torch.Tensor([1]).to(self.device)) mean_err_d = (mean_err_d_real + mean_err_d_fake) / 2 mean_err_d.backward() self.optimizer_D.step() mean_err_d_per_batch = np.append( mean_err_d_per_batch, mean_err_d.detach().cpu().numpy()) return mean_err_g_adv_per_batch, mean_err_g_con_per_batch, mean_err_g_enc_per_batch, mean_err_d_per_batch
def train(**kwargs): for k_, v_ in kwargs.items(): setattr(opt, k_, v_) device=t.device('cuda') if opt.gpu else t.device('cpu') if opt.vis: from visualize import Visualizer vis = Visualizer(opt.env) # 数据 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 ) # 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)) netd.to(device) netg.to(device) # 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().to(device) # 真图片label为1,假图片label为0 # noises为生成网络的输入 true_labels = t.ones(opt.batch_size).to(device) fake_labels = t.zeros(opt.batch_size).to(device) fix_noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device) noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device) errord_meter = AverageValueMeter() errorg_meter = AverageValueMeter() epochs = range(opt.max_epoch) for epoch in iter(epochs): for ii, (img, _) in tqdm.tqdm(enumerate(dataloader)): real_img = img.to(device) 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.item()) 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.item()) 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.detach().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+1) % opt.save_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()
def train(**kwargs): for k_, v_ in kwargs.items(): setattr(opt, k_, v_) device = t.device('cuda') if opt.gpu else t.device('cpu') # 可视化 if opt.vis: from visualize import Visualizer vis = Visualizer(opt.env) # 数据 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, transforms) dataloader = t.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers, drop_last=True) netg, netd = NetG(opt), NetD(opt) if opt.netd_path: netd.load_state_dict( t.load(opt.netd_path, map_location=t.device('cpu'))) if opt.netg_path: netg.load_state_dict( t.load(opt.netg_path, map_location=t.device('cpu'))) netd.to(device) netg.to(device) # 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.lr1, betas=(opt.beta1, 0.999)) criterion = t.nn.BCELoss().to(device) # 真图片label为1,假图片label为0 # noise为生成网络的输入 true_labels = t.ones(opt.batch_size).to(device) fake_labels = t.zeros(opt.batch_size).to(device) fix_noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device) noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device) errord = 0 errorg = 0 epochs = range(opt.max_epoch) for epoch in epochs: for ii, (img, _) in tqdm(enumerate(dataloader)): real_img = img.to(device) if (ii + 1) % opt.d_every == 0: # 训练判别器 optimizer_d.zero_grad() # 把真图片判断为正确 output = netd(real_img) error_d_real = criterion(output, true_labels) error_d_real.backward() # 把假图片(netg通过噪声生成的图片)判断为错误 noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1)) fake_img = netg(noises).detach() output = netd(fake_img) error_d_fake = criterion(output, fake_labels) error_d_fake.backward() optimizer_d.step() errord += (error_d_fake + error_d_real).item() if (ii + 1) % 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 += error_g.item() if opt.vis and (ii + 1) % opt.plot_every == 0: fix_fake_imgs = netg(fix_noises) vis.images(fix_fake_imgs.detach().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 / (opt.plot_every)) vis.plot('errorg', errorg / (opt.plot_every)) errord = 0 errorg = 0 if (epoch + 1) % opt.save_every == 0: 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 + 1)) t.save(netg.state_dict(), 'checkpoints/netg_%s.pth' % (epoch + 1))
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()
ndf = 64 nz = 100 opt = Config() for gen in range(gen_num): Result = random.randint(1000000, 100000000) device = t.device('cuda') if opt.gpu else t.device('cpu') 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 = noises.to(device) 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)) netd.to(device) netg.to(device) fake_img = netg(noises) scores = netd(fake_img).detach() 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), str(Result) + opt.gen_img, normalize=True, range=(-1, 1))
project="mnist", # have to change when you want to change project job_type="anomaly detection", ) state_dict_G = torch.load("weights/G.prm", map_location=lambda storage, loc: storage) state_dict_D = torch.load("weights/D.prm", map_location=lambda storage, loc: storage) G_update = NetG(CONFIG) D_update = NetD(CONFIG) G_update.load_state_dict(state_dict_G) D_update.load_state_dict(state_dict_D) G_update.to(device) D_update.to(device) mean = (0.5, ) std = (0.5, ) test_dataset = Dataset(csv_file=CONFIG.test_csv_file, transform=ImageTransform(mean, std)) test_dataloader = DataLoader(test_dataset, batch_size=CONFIG.test_batch_size, shuffle=True) for sample in test_dataloader: test_img = sample["img"]
def train(**kwargs): ''' 训练函数 :param kwargs: fire传进来的训练参数 :return: ''' opt.parse(kwargs) for k_,v_ in kwargs.items(): setattr(opt,k_,v_) if opt.vis: vis = Visualizer(opt.env) #step1:数据预处理 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) #step2: 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)) #定义优化器和损失函数 optimizer_g = t.optim.Adam(netg.parameters(), opt.lrG, betas=(0.5, 0.999)) optimizer_d = t.optim.Adam(netd.parameters(), opt.lrD, betas=(0.5, 0.999)) criterion = t.nn.BCELoss() #真图片label为1,加图片label为0 #noise为网络的输入 true_labels = t.ones(opt.batch_size) fake_labels = t.zeros(opt.batch_size) fix_noises = t.randn(opt.batch_size,opt.nz,1,1) noises = t.randn(opt.batch_size,opt.nz,1,1) errord_meter = AverageValueMeter() errorg_meter = AverageValueMeter() if opt.gpu: device = t.device("cuda:0" if t.cuda.is_available() else "cpu") netd.to(device) netg.to(device) criterion.to(device) true_labels,fake_labels = true_labels.to(device),fake_labels.to(device) fix_noises,noises = fix_noises.to(device),noises.to(device) epochs = range(140) for epoch in iter(epochs): for ii,(img,_) in tqdm.tqdm(enumerate(dataloader),total=len(dataloader)): if opt.gpu: real_img = img.to(device) if ii%opt.d_every == 0: #每个batch训练一次鉴别器 optimizer_d.zero_grad() output = netd(real_img) #判断真图片(使其尽可能大) error_d_real = criterion(output,true_labels) error_d_real.backward() ##尽可能把假图片判断为错误 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.item()) if ii%opt.g_every == 0: #每5个batch更新一次生成器 #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() errord_meter.add(error_g.item()) if opt.vis and ii%opt.plot_time == opt.plot_time - 1: ##可视化 fix_fake_img = netg(fix_noises) #使用噪声生成图片 vis.images(fix_fake_img.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 == opt.decay_every-1: #保存模型,图片 tv.utils.save_image(fix_fake_img.data[:64],'%s/new%s.png'%(opt.save_path,epoch), normalize=True,range=(-1,1)) t.save(netd.state_dict(), 'checkpoints/new_netd_%s.pth' % epoch) t.save(netg.state_dict(), 'checkpoints/new_netg_%s.pth' % epoch) errord_meter.reset() errorg_meter.reset() optimizer_g = t.optim.Adam(netg.parameters(), opt.lrG, betas=(0.5, 0.999)) optimizer_d = t.optim.Adam(netd.parameters(), opt.lrD, betas=(0.5, 0.999))