class AnoGAN: """AnoGAN Class """ def __init__(self, opt): # super(AnoGAN, self).__init__(opt, dataloader) # Initalize variables. self.opt = opt self.niter = self.opt.niter self.start_iter = 0 self.netd_niter = 5 self.test_iter = 100 self.lr = self.opt.lr self.batchsize = {'train': self.opt.batchsize, 'test': 1} self.pretrained = False self.phase = 'train' self.outf = self.opt.experiment_group self.algorithm = 'wgan' # LOAD DATA SET self.dataloader = { 'train': provider('train', opt.category, batch_size=self.batchsize['train'], num_workers=4), 'test': provider('test', opt.category, batch_size=self.batchsize['test'], num_workers=4) } self.trn_dir = os.path.join(self.outf, self.opt.experiment_name, 'train') self.tst_dir = os.path.join(self.outf, self.opt.experiment_name, 'test') self.test_img_dir = os.path.join(self.outf, self.opt.experiment_name, 'test', 'images') if not os.path.isdir(self.test_img_dir): os.makedirs(self.test_img_dir) self.best_test_dir = os.path.join(self.outf, self.opt.experiment_name, 'test', 'best_images') if not os.path.isdir(self.best_test_dir): os.makedirs(self.best_test_dir) self.weight_dir = os.path.join(self.trn_dir, 'weights') if not os.path.exists(self.weight_dir): os.makedirs(self.weight_dir) # -- Misc attributes self.epoch = 0 self.l_con = l1_loss self.l_enc = l2_loss ## # Create and initialize networks. self.netg = NetG().cuda() self.netd = NetD().cuda() # Setup optimizer self.optimizer_d = optim.RMSprop(self.netd.parameters(), lr=self.lr) self.optimizer_g = optim.Adam(self.netg.parameters(), lr=self.lr) ## self.weight_path = os.path.join(self.outf, self.opt.experiment_name, 'train', 'weights') if os.path.exists(self.weight_path) and len( os.listdir(self.weight_path)) == 2: print("Loading pre-trained networks...\n") self.netg.load_state_dict( torch.load(os.path.join(self.weight_path, 'netG.pth'))['state_dict']) self.netd.load_state_dict( torch.load(os.path.join(self.weight_path, 'netD.pth'))['state_dict']) self.optimizer_g.load_state_dict( torch.load(os.path.join(self.weight_path, 'netG.pth'))['optimizer']) self.optimizer_d.load_state_dict( torch.load(os.path.join(self.weight_path, 'netD.pth'))['optimizer']) self.start_iter = torch.load( os.path.join(self.weight_path, 'netG.pth'))['epoch'] ## def start(self): """ Train the model """ ## # TRAIN # self.total_steps = 0 best_criterion = -1 #float('inf') best_auc = -1 # Train for niter epochs. # print(">> Training model %s." % self.name) for self.epoch in range(self.start_iter, self.niter): # Train for one epoch mean_wass = self.train() (auc, res, best_rec, best_threshold), res_total = self.test() message = '' # message += 'criterion: (%.3f+%.3f)/2=%.3f ' % (best_rec[0], best_rec[1], res) # message += 'best threshold: %.3f ' % best_threshold message += 'Wasserstein Distance:%.3d ' % mean_wass message += 'AUC: %.3f ' % auc print(message) torch.save( { 'epoch': self.epoch + 1, 'state_dict': self.netg.state_dict(), 'optimizer': self.optimizer_g.state_dict() }, '%s/netG.pth' % (self.weight_dir)) torch.save( { 'epoch': self.epoch + 1, 'state_dict': self.netd.state_dict(), 'optimizer': self.optimizer_d.state_dict() }, '%s/netD.pth' % (self.weight_dir)) if auc > best_auc: best_auc = auc new_message = "******** New optimal found, saving state ********" message = message + '\n' + new_message print(new_message) for img in os.listdir(self.best_test_dir): os.remove(os.path.join(self.best_test_dir, img)) for img in os.listdir(self.test_img_dir): shutil.copyfile(os.path.join(self.test_img_dir, img), os.path.join(self.best_test_dir, img)) shutil.copyfile('%s/netG.pth' % (self.weight_dir), '%s/netg_best.pth' % (self.weight_dir)) log_name = os.path.join(self.outf, self.opt.experiment_name, 'loss_log.txt') message = 'Epoch%3d:' % self.epoch + ' ' + message with open(log_name, "a") as log_file: if self.epoch == 0: log_file.write('\n\n') log_file.write('%s\n' % message) print(">> Training %s Done..." % self.opt.experiment_name) ## def train(self): """ Train the model for one epoch. """ print("\n>>> Epoch %d/%d, Running " % (self.epoch + 1, self.niter) + self.opt.experiment_name) self.netg.train() self.netd.train() # for p in self.netg.parameters(): p.requires_grad = True mean_wass = 0 tk0 = tqdm(self.dataloader['train'], total=len(self.dataloader['train'])) for i, itr in enumerate(tk0): input, _ = itr input = input.cuda() wasserstein_d = None # if self.algorithm == 'wgan': # train NetD for _ in range(self.netd_niter): # for p in self.netd.parameters(): p.requires_grad = True self.optimizer_d.zero_grad() # forward_g latent_i = torch.rand(self.batchsize['train'], 64, 1, 1).cuda() fake = self.netg(latent_i) # forward_d _, pred_real = self.netd(input) _, pred_fake = self.netd(fake) # .detach() TODO # Backward-pass wasserstein_d = (pred_fake.mean() - pred_real.mean()) * 1 wasserstein_d.backward() self.optimizer_d.step() for p in self.netd.parameters(): p.data.clamp_(-0.01, 0.01) #<<<<<<< # train netg # for p in self.netd.parameters(): p.requires_grad = False self.optimizer_g.zero_grad() noise = torch.rand(self.batchsize['train'], 64, 1, 1).cuda() fake = self.netg(noise) _, pred_fake = self.netd(fake) err_g_d = -pred_fake.mean() # negative err_g_d.backward() self.optimizer_g.step() errors = { 'loss_netD': wasserstein_d.item(), 'loss_netG': round(err_g_d.item(), 3), } mean_wass += wasserstein_d.item() tk0.set_postfix(errors) if i % 50 == 0: img_dir = os.path.join(self.outf, self.opt.experiment_name, 'train', 'images') if not os.path.isdir(img_dir): os.makedirs(img_dir) self.save_image_cv2(input.data, '%s/reals.png' % img_dir) self.save_image_cv2(fake.data, '%s/fakes%03d.png' % (img_dir, i)) mean_wass /= len(self.dataloader['train']) return mean_wass ## def test(self): """ Test AnoGAN model. Args: dataloader ([type]): Dataloader for the test set Raises: IOError: Model weights not found. """ self.netg.eval() self.netd.eval() # for p in self.netg.parameters(): p.requires_grad = False # for p in self.netd.parameters(): p.requires_grad = False for img in os.listdir(self.test_img_dir): os.remove(os.path.join(self.test_img_dir, img)) self.phase = 'test' meter = Meter_AnoGAN() tk1 = tqdm(self.dataloader['test'], total=len(self.dataloader['test'])) for i, itr in enumerate(tk1): input, target = itr input = input.cuda() latent_i = torch.rand(self.batchsize['test'], 64, 1, 1).cuda() latent_i.requires_grad = True optimizer_latent = optim.Adam([latent_i], lr=self.lr) test_loss = None for _ in range(self.test_iter): optimizer_latent.zero_grad() fake = self.netg(latent_i) residual_loss = self.l_con(input, fake) latent_o, _ = self.netd(fake) discrimination_loss = self.l_enc(latent_i, latent_o) alpha = 0.1 test_loss = ( 1 - alpha) * residual_loss + alpha * discrimination_loss test_loss.backward() optimizer_latent.step() abnormal_score = test_loss meter.update(abnormal_score, target) #<<<TODO # Save test images. combine = torch.cat([input.cpu(), fake.cpu()], dim=0) self.save_image_cv2(combine, '%s/%05d.jpg' % (self.test_img_dir, i + 1)) criterion, res_total = meter.get_metrics() # rename images for i, res in enumerate(res_total): os.rename('%s/%05d.jpg' % (self.test_img_dir, i + 1), '%s/%05d_%s.jpg' % (self.test_img_dir, i + 1, res)) return criterion, res_total @staticmethod def save_image_cv2(tensor, filename): # return from torchvision.utils import make_grid # tensor = (tensor + 1) / 2 grid = make_grid(tensor, 8, 2, 0, False, None, False) ndarray = grid.mul_(255).clamp_(0, 255).permute(1, 2, 0).to( 'cpu', torch.uint8).numpy() cv2.imwrite(filename, ndarray)
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.save_dir = args.save_dir self.save_prefix = args.save_prefix self.continue_training = args.continue_training self.continue_epoch = args.continue_epoch 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.docvec_size = args.docvec_size 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.mse_loss = nn.MSELoss() self.class_filename = args.class_filename class_path = os.path.join(self.data_root, self.dataset, self.class_filename) with open(class_path) as f: self.num_classes = len([l for l in f]) print(self.num_classes) self.netD = NetD(n_cls=self.num_classes, n_t=self.nl_d, n_f=self.nf_d, docvec_size=self.docvec_size) self.netG = NetG(n_z=self.n_z, n_l=self.nl_g, n_c=self.nf_g, n_t=self.docvec_size) if self.continue_training: self.loadCheckpoints() # convert to cuda tensors if self.cuda and torch.cuda.is_available(): print('CUDA is enabled') self.netD = nn.DataParallel(self.netD).cuda() self.netG = nn.DataParallel(self.netG).cuda() self.bce_loss = self.bce_loss.cuda() self.nll_loss = self.nll_loss.cuda() self.mse_loss = self.mse_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=self.num_workers) print("Dataset loaded successfuly") # load checkpoints for continuing training # 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) lossD_wrong = self.bce_loss(outD_wrong, lbl_fake) + self.mse_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) lossD_real = self.bce_loss(outD_real, lbl_real) + self.mse_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) lossD_fake = self.bce_loss(outD_fake, lbl_fake) + self.mse_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) lossD_fake_G = self.mse_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.item() netg_loss_sum += netG_loss.item() ### 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.item(), netG_loss.item())) 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.module.state_dict(), os.path.join(self.save_dir, name_netD)) torch.save(self.netG.module.state_dict(), os.path.join(self.save_dir, name_netG)) print("Checkpoints for epoch %d saved successfuly" %(epoch)) # SAVE: data parallel model => add .module # LOAD: create model and load checkpoints(not add .module) and wrap nn.DataParallel # this is for fitting prefix # load checkpoints to continue training def loadCheckpoints(self): name_netD = "netd_checkpoints/netD_" + self.save_prefix + "_epoch_" + str(self.continue_epoch) + ".pth" name_netG = "netg_checkpoints/netG_" + self.save_prefix + "_epoch_" + str(self.continue_epoch) + ".pth" self.netG.load_state_dict(torch.load(os.path.join(self.save_dir, name_netG))) self.netD.load_state_dict(torch.load(os.path.join(self.save_dir, name_netD))) print("Checkpoints loaded successfuly")
all_mods = itertools.chain(all_mods, [ list(netg.children())[0].children(), list(netd.children())[0].children(), list(neta.children())[0].children() ]) for mod in all_mods: if isinstance(mod, nn.Conv2d) or isinstance(mod, nn.ConvTranspose2d): init.normal_(mod.weight, 0.0, 0.02) elif isinstance(mod, nn.BatchNorm2d): init.normal_(mod.weight, 1.0, 0.02) init.constant_(mod.bias, 0.0) netg = netg.to(device) netd = netd.to(device) neta = neta.to(device) netg.train() netd.train() neta.train() dataset = CASIABDataset(data_dir='../data/GEI_CASIA_B/gei/') iteration = 0 lr = 0.0002 real_label = 1 fake_label = 0 fineSize = 64 label = th.zeros((128, 1), requires_grad=False).to(device) optimG = optim.Adam(netg.parameters(), lr=lr/2) optimD = optim.Adam(netd.parameters(), lr=lr/3) optimA = optim.Adam(neta.parameters(), lr=lr/3) print('Training starts')
def train_network(): init_epoch = 0 best_f1 = 0 total_steps = 0 train_dir = ct.TRAIN_TXT val_dir = ct.VAL_TXT device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') torch.backends.cudnn.benchmark = True train_data = OSCD_TRAIN(train_dir) train_dataloader = DataLoader(train_data, batch_size=ct.BATCH_SIZE, shuffle=True) val_data = OSCD_TEST(val_dir) val_dataloader = DataLoader(val_data, batch_size=1, shuffle=False) netg = NetG(ct.ISIZE, ct.NC * 2, ct.NZ, ct.NDF, ct.EXTRALAYERS).to(device=device) netd = NetD(ct.ISIZE, ct.GT_C, 1, ct.NGF, ct.EXTRALAYERS).to(device=device) netg.apply(weights_init) netd.apply(weights_init) if ct.RESUME: assert os.path.exists(os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netG.pth')) \ and os.path.exists(os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netG.pth')), \ 'There is not found any saved weights' print("\nLoading pre-trained networks.") init_epoch = torch.load( os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netG.pth'))['epoch'] netg.load_state_dict( torch.load(os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netG.pth'))['model_state_dict']) netd.load_state_dict( torch.load(os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netD.pth'))['model_state_dict']) with open(os.path.join(ct.OUTPUTS_DIR, 'f1_score.txt')) as f: lines = f.readlines() best_f1 = float(lines[-2].strip().split(':')[-1]) print("\tDone.\n") l_adv = l2_loss l_con = nn.L1Loss() l_enc = l2_loss l_bce = nn.BCELoss() l_cos = cos_loss dice = DiceLoss() optimizer_d = optim.Adam(netd.parameters(), lr=ct.LR, betas=(0.5, 0.999)) optimizer_g = optim.Adam(netg.parameters(), lr=ct.LR, betas=(0.5, 0.999)) start_time = time.time() for epoch in range(init_epoch + 1, ct.EPOCH): loss_g = [] loss_d = [] netg.train() netd.train() epoch_iter = 0 for i, data in enumerate(train_dataloader): INPUT_SIZE = [ct.ISIZE, ct.ISIZE] x1, x2, gt = data x1 = x1.to(device, dtype=torch.float) x2 = x2.to(device, dtype=torch.float) gt = gt.to(device, dtype=torch.float) gt = gt[:, 0, :, :].unsqueeze(1) x = torch.cat((x1, x2), 1) epoch_iter += ct.BATCH_SIZE total_steps += ct.BATCH_SIZE real_label = torch.ones(size=(x1.shape[0], ), dtype=torch.float32, device=device) fake_label = torch.zeros(size=(x1.shape[0], ), dtype=torch.float32, device=device) #forward fake = netg(x) pred_real = netd(gt) pred_fake = netd(fake).detach() err_d_fake = l_bce(pred_fake, fake_label) err_g = l_con(fake, gt) err_g_total = ct.G_WEIGHT * err_g + ct.D_WEIGHT * err_d_fake pred_fake_ = netd(fake.detach()) err_d_real = l_bce(pred_real, real_label) err_d_fake_ = l_bce(pred_fake_, fake_label) err_d_total = (err_d_real + err_d_fake_) * 0.5 #backward optimizer_g.zero_grad() err_g_total.backward(retain_graph=True) optimizer_g.step() optimizer_d.zero_grad() err_d_total.backward() optimizer_d.step() errors = utils.get_errors(err_d_total, err_g_total) loss_g.append(err_g_total.item()) loss_d.append(err_d_total.item()) counter_ratio = float(epoch_iter) / len(train_dataloader.dataset) if (i % ct.DISPOLAY_STEP == 0 and i > 0): print( 'epoch:', epoch, 'iteration:', i, ' G|D loss is {}|{}'.format(np.mean(loss_g[-51:]), np.mean(loss_d[-51:]))) if ct.DISPLAY: utils.plot_current_errors(epoch, counter_ratio, errors, vis) utils.display_current_images(gt.data, fake.data, vis) utils.save_current_images(epoch, gt.data, fake.data, ct.IM_SAVE_DIR, 'training_output_images') with open(os.path.join(ct.OUTPUTS_DIR, 'train_loss.txt'), 'a') as f: f.write( 'after %s epoch, loss is %g,loss1 is %g,loss2 is %g,loss3 is %g' % (epoch, np.mean(loss_g), np.mean(loss_d), np.mean(loss_g), np.mean(loss_d))) f.write('\n') if not os.path.exists(ct.WEIGHTS_SAVE_DIR): os.makedirs(ct.WEIGHTS_SAVE_DIR) utils.save_weights(epoch, netg, optimizer_g, ct.WEIGHTS_SAVE_DIR, 'netG') utils.save_weights(epoch, netd, optimizer_d, ct.WEIGHTS_SAVE_DIR, 'netD') duration = time.time() - start_time print('training duration is %g' % duration) #val phase print('Validating.................') pretrained_dict = torch.load( os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netG.pth'))['model_state_dict'] device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') net = NetG(ct.ISIZE, ct.NC * 2, ct.NZ, ct.NDF, ct.EXTRALAYERS).to(device=device) net.load_state_dict(pretrained_dict, False) with net.eval() and torch.no_grad(): TP = 0 FN = 0 FP = 0 TN = 0 for k, data in enumerate(val_dataloader): x1, x2, label = data x1 = x1.to(device, dtype=torch.float) x2 = x2.to(device, dtype=torch.float) label = label.to(device, dtype=torch.float) label = label[:, 0, :, :].unsqueeze(1) x = torch.cat((x1, x2), 1) time_i = time.time() v_fake = net(x) tp, fp, tn, fn = eva.f1(v_fake, label) TP += tp FN += fn TN += tn FP += fp precision = TP / (TP + FP + 1e-8) oa = (TP + TN) / (TP + FN + TN + FP + 1e-8) recall = TP / (TP + FN + 1e-8) f1 = 2 * precision * recall / (precision + recall + 1e-8) if not os.path.exists(ct.BEST_WEIGHT_SAVE_DIR): os.makedirs(ct.BEST_WEIGHT_SAVE_DIR) if f1 > best_f1: best_f1 = f1 shutil.copy( os.path.join(ct.WEIGHTS_SAVE_DIR, 'current_netG.pth'), os.path.join(ct.BEST_WEIGHT_SAVE_DIR, 'netG.pth')) print('current F1: {}'.format(f1)) print('best f1: {}'.format(best_f1)) with open(os.path.join(ct.OUTPUTS_DIR, 'f1_score.txt'), 'a') as f: f.write('current epoch:{},current f1:{},best f1:{}'.format( epoch, f1, best_f1)) f.write('\n')