Pix2PixHD也是采用了条件GAN的思想,在Pix2Pix一文中,CGAN表示为式(1)。其中 \mathbf s 是输入图像, \mathbf x 是目标图像。里我们根据上面介绍的模型结构来推导Pix2PixHD的损失函数。 \mathcal L_\text{GAN}(G,D) = \mathbb E_{(\mathbf s,\mathbf x)} [\log D(\mathbf s,\mathbf x)] + \...
no_ganFeat_loss, gpu_ids=self.gpu_ids) ### Encoder network if self.gen_features: self.netE = networks.define_G(opt.output_nc, opt.feat_num, opt.nef, 'encoder', opt.n_downsample_E, norm=opt.norm, gpu_ids=self.gpu_ids) if self.opt.verbose: print('--- Networks initialized...
| pix2pixHD | G_GAN | G_GAN_Feat | G_VGG | D_fake | D_real | | :---: | :---: | --- | --- | --- | :---: | | cityscapes | 0.540 | 6.153 | 3.087 | 0.534 | 0.424 | ## 应用场景 ### 算法类别 `图像超分` ...
criterionGAN(pred_real, True) # GAN loss (Fake Passability Loss) pred_fake = self.netD.forward(torch.cat((input_label, fake_image), dim=1)) loss_G_GAN = self.criterionGAN(pred_fake, True) # GAN feature matching loss loss_G_GAN_Feat = 0 if not self.opt.no_ganFeat_loss: ...
PatchGAN The PatchGAN network tries to classify if each imagepatchis real or not real, as described in thepix2pix paper. Each block in the discriminator is: Convolution -> Batch normalization -> Leaky ReLU. The shape of the output after the last layer is (batch_size, 30, 30, 1). ...
return [l for (l,f) in zip((g_gan,g_gan_feat,g_vgg,d_real,d_fake),flags) if f] return loss_filter 可见init_loss_filter()设置了一些标志位,每个标志位对应一种损失函数,并定义了一个函数,函数中zip()函数的作用是将可迭代的对象作为参数,将对象中对应的元素打包成一个个元组,然后返回由这些...
G_GAN = [] G_GAN_Feat = [] G_VGG = [] G_KL= [] D_real = [] D_fake = [] G_featD = [] featD_real = [] featD_fake = [] total_data =[] for line in lines: if "(epoch" in line: if "G_GAN" in line : ...
master data datasets imgs models __init__.py base_model.py models.py networks.py pix2pixHD_model.py ui_model.py options scripts util .gitignore LICENSE.txt README.md _config.yml encode_features.py precompute_feature_maps.py run_engine.py ...
netG: local ngf: 32 niter: 100 niter_decay: 100 niter_fix_global: 20 no_flip: False no_ganFeat_loss: False no_html: False no_instance: True no_lsgan: False no_vgg_loss: False norm: instance num_D: 3 output_nc: 3 phase: train ...
A CUDA assertion error pops up when setting --no_lsgan. It seems it's because there are negative values thrown into the nn.BCELoss(). Get's fixed applying nn.BCEWithLogitsLoss() instead. (...) /opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THCUNN/BCECriterion.cu:30: Acc...