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+![mrb.png](Training%20Dataset/mrb.png?fileId=35528#mimetype=image%2Fpng&hasPreview=true) + +## UPDATE: + +**May 5, 2021** - I've added a new version of AbeScale (4x_AbeScale_Remixed.pth) that incorporates a bit of another model for improved reliability and texture. It comes at the expense of razor-sharp clarity, but it can be a nicely-balanced alternative to the normal one. + +##### Summary + +This model was originally conceived as a proof of concept to test a theory that an ESRGAN upscaling model could be trained on professionally re-illustrated scenes from the original show. This model is the result of a few long evenings re-building screenshots from the DVD from scratch. + +The re-illustrated frames are exported in raster form to target 4k, but theoretically since the scenes are in a vector format, there is no upper limit to what the show could be upscaled to in the future. An 8k model is immediately possible with the same data set. + +Finally, to finish the model and maintain fidelity of color, it is interpolated against [Adad2.pth](https://drive.google.com/file/d/1weX_LIADpCEdxJRFW0D8pD98TveSXSlE/view), a model trained on American Dad. + +The dataset, as well as the original vector files, are available as well if anyone can think of ways to improve the level of detail on backgrounds. My hunch is that it might require a digital painter to redo those as well and train a linework model and a background model, then combine them via interpolation. Feel free to pick up from here. + +##### Data + +* **License:** GNU Model +* **Architecture:** 4x_ESRGAN +* **Scale:** 4x +* **Iterations:** 110,000 +* **batch_size:** 512 +* **HR_size:** 8 +* **Epoch:** 33,000 +* **n_frames:** +* **Dataset:** LR: 8 | HR: 8 +* **Dataset_size:** 8 + +##### Attribution + +Hi. My name is **msprout**, and I did this model. 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False + n_workers: 8 + batch_size: 32 + HR_size: 128 + lr_downscale: True + lr_downscale_types: [1, 2, 777] + use_flip: True + use_rot: True + hr_rrot: False + lr_blur: False + lr_blur_types: ['gaussian', 'clean', 'clean', 'clean'] + lr_noise: False + lr_noise_types: ['gaussian', 'clean', 'clean', 'clean', 'clean'] + lr_noise2: False + lr_noise_types2: ['dither', 'dither', 'clean', 'clean'] + hr_noise: False + hr_noise_types: ['gaussian', 'clean', 'clean', 'clean', 'clean'] + phase: train + scale: 4 + data_type: img + ] + val:[ + name: sollevante-val + mode: LRHR + dataroot_HR: ['/mnt/8tb-hdd-1/datasets/sollevante/hr/val'] + dataroot_LR: ['/mnt/8tb-hdd-1/datasets/sollevante/lr/val'] + znorm: False + lr_downscale: False + lr_downscale_types: [0, 1] + phase: val + scale: 4 + data_type: img + ] + ] + path:[ + root: /home/owner/github/BasicSR + pretrain_model_G: ../experiments/pretrained_models/RRDB_PSNR_x4.pth + experiments_root: /home/owner/github/BasicSR/experiments/sollevante + models: /home/owner/github/BasicSR/experiments/sollevante/models + training_state: /home/owner/github/BasicSR/experiments/sollevante/training_state + log: /home/owner/github/BasicSR/experiments/sollevante + val_images: /home/owner/github/BasicSR/experiments/sollevante/val_images + ] + network_G:[ + which_model_G: RRDB_net + norm_type: None + mode: CNA + nf: 64 + nb: 23 + in_nc: 3 + out_nc: 3 + gc: 32 + group: 1 + convtype: Conv2D + net_act: leakyrelu + scale: 4 + ] + network_D:[ + which_model_D: discriminator_vgg_128 + norm_type: batch + act_type: leakyrelu + mode: CNA + nf: 64 + in_nc: 3 + ] + train:[ + lr_G: 0.0001 + weight_decay_G: 0 + beta1_G: 0.9 + lr_D: 0.0001 + weight_decay_D: 0 + beta1_D: 0.9 + lr_scheme: MultiStepLR + lr_steps: [50000, 100000, 200000, 300000] + lr_gamma: 0.5 + pixel_criterion: l1 + pixel_weight: 0.01 + feature_criterion: l1 + feature_weight: 1 + gan_type: vanilla + gan_weight: 0.005 + niter: 500000.0 + val_freq: 5000.0 + ] + logger:[ + print_freq: 200 + save_checkpoint_freq: 5000.0 + ] + is_train: True + +20-04-04 23:22:20.575 - INFO: Random seed: 977 +20-04-04 23:22:20.604 - INFO: Dataset [LRHRDataset - sollevante-train] is created. +20-04-04 23:22:20.604 - INFO: Number of train images: 6,309, iters: 198 +20-04-04 23:22:20.604 - INFO: Total epochs needed: 2526 for iters 500,000 +20-04-04 23:22:20.604 - INFO: Dataset [LRHRDataset - sollevante-val] is created. +20-04-04 23:22:20.604 - INFO: Number of val images in [sollevante-val]: 4 +20-04-04 23:22:20.745 - INFO: Initialization method [kaiming] +20-04-04 23:22:23.042 - INFO: Initialization method [kaiming] +20-04-04 23:22:23.119 - INFO: Loading pretrained model for G [../experiments/pretrained_models/RRDB_PSNR_x4.pth] ... +20-04-04 23:22:24.194 - INFO: Remove HFEN loss. +20-04-04 23:22:24.194 - INFO: Remove TV loss. +20-04-04 23:22:24.194 - INFO: Remove SSIM loss. +20-04-04 23:22:24.195 - INFO: Remove LPIPS loss. +20-04-04 23:22:24.195 - INFO: Remove SPL loss. +20-04-04 23:22:24.203 - INFO: Network G structure: DataParallel - RRDBNet, with parameters: 16,697,987 +20-04-04 23:22:24.203 - INFO: RRDBNet( + (model): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): Identity + + |Sequential( + | (0): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (1): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (2): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (3): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (4): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (5): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (6): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (7): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (8): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (9): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (10): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (11): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (12): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (13): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (14): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (15): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (16): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (17): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (18): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (19): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (20): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (21): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (22): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (23): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + |) + (2): Upsample(scale_factor=2.0, mode=nearest) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (4): LeakyReLU(negative_slope=0.2, inplace=True) + (5): Upsample(scale_factor=2.0, mode=nearest) + (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (7): LeakyReLU(negative_slope=0.2, inplace=True) + (8): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (9): LeakyReLU(negative_slope=0.2, inplace=True) + (10): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) +20-04-04 23:22:24.206 - INFO: Network D structure: DataParallel - Discriminator_VGG_128, with parameters: 14,502,281 +20-04-04 23:22:24.206 - INFO: Discriminator_VGG_128( + (features): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): LeakyReLU(negative_slope=0.2, inplace=True) + (2): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (4): LeakyReLU(negative_slope=0.2, inplace=True) + (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (7): LeakyReLU(negative_slope=0.2, inplace=True) + (8): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (9): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (10): LeakyReLU(negative_slope=0.2, inplace=True) + (11): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (13): LeakyReLU(negative_slope=0.2, inplace=True) + (14): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (16): LeakyReLU(negative_slope=0.2, inplace=True) + (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (18): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (19): LeakyReLU(negative_slope=0.2, inplace=True) + (20): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (21): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (22): LeakyReLU(negative_slope=0.2, inplace=True) + (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (24): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (25): LeakyReLU(negative_slope=0.2, inplace=True) + (26): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (27): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (28): LeakyReLU(negative_slope=0.2, inplace=True) + ) + (classifier): Sequential( + (0): Linear(in_features=8192, out_features=100, bias=True) + (1): LeakyReLU(negative_slope=0.2, inplace=True) + (2): Linear(in_features=100, out_features=1, bias=True) + ) +) +20-04-04 23:22:24.206 - INFO: Network F structure: DataParallel - VGGFeatureExtractor, with parameters: 20,024,384 +20-04-04 23:22:24.206 - INFO: VGGFeatureExtractor( + (features): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU(inplace=True) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (3): ReLU(inplace=True) + (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (6): ReLU(inplace=True) + (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (8): ReLU(inplace=True) + (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (11): ReLU(inplace=True) + (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (13): ReLU(inplace=True) + (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (15): ReLU(inplace=True) + (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (17): ReLU(inplace=True) + (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (20): ReLU(inplace=True) + (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (22): ReLU(inplace=True) + (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (24): ReLU(inplace=True) + (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (26): ReLU(inplace=True) + (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (29): ReLU(inplace=True) + (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (31): ReLU(inplace=True) + (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (33): ReLU(inplace=True) + (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) +20-04-04 23:22:24.206 - INFO: Model [SRRaGANModel] is created. +20-04-04 23:22:24.206 - INFO: Start training from epoch: 0, iter: 0 +20-04-04 23:25:36.321 - INFO: l_g_pix: 1.5067e-04 l_g_fea: 6.4297e-01 l_g_gan: 1.9698e-02 l_d_real: 3.8048e-02 l_d_fake: 4.4457e-02 D_real: -4.7199e+00 D_fake: -8.6182e+00 +20-04-04 23:28:46.420 - INFO: l_g_pix: 1.3205e-04 l_g_fea: 5.9754e-01 l_g_gan: 1.2001e-02 l_d_real: 5.7828e-01 l_d_fake: 8.6757e-01 D_real: -3.6852e+00 D_fake: -5.3624e+00 +20-04-04 23:31:57.108 - INFO: l_g_pix: 1.7049e-04 l_g_fea: 6.4581e-01 l_g_gan: 2.1859e-02 l_d_real: 4.5861e-02 l_d_fake: 4.4064e-02 D_real: -2.7642e+01 D_fake: -3.1969e+01 +20-04-04 23:35:08.808 - INFO: l_g_pix: 1.2745e-04 l_g_fea: 4.9038e-01 l_g_gan: 1.5169e-02 l_d_real: 8.2734e-02 l_d_fake: 6.9391e-02 D_real: -4.2445e+01 D_fake: -4.5403e+01 +20-04-04 23:38:20.465 - INFO: l_g_pix: 2.0304e-04 l_g_fea: 6.2301e-01 l_g_gan: 7.7560e-03 l_d_real: 3.7310e-01 l_d_fake: 3.6773e-01 D_real: -2.6110e+01 D_fake: -2.7290e+01 +20-04-04 23:41:32.063 - INFO: l_g_pix: 3.3270e-04 l_g_fea: 6.7334e-01 l_g_gan: 5.2605e-02 l_d_real: 5.1194e-04 l_d_fake: 5.7718e-04 D_real: -4.9848e+01 D_fake: -6.0368e+01 +20-04-04 23:44:42.791 - INFO: l_g_pix: 9.4193e-05 l_g_fea: 4.4962e-01 l_g_gan: 1.9301e-02 l_d_real: 3.2885e-02 l_d_fake: 3.9705e-02 D_real: -6.5057e+01 D_fake: -6.8881e+01 +20-04-04 23:47:59.742 - INFO: l_g_pix: 1.5462e-04 l_g_fea: 4.6072e-01 l_g_gan: 1.6594e-02 l_d_real: 6.2811e-02 l_d_fake: 5.4601e-02 D_real: -3.4867e+01 D_fake: -3.8127e+01 +20-04-04 23:51:11.580 - INFO: l_g_pix: 1.1984e-04 l_g_fea: 5.9564e-01 l_g_gan: 1.0626e-02 l_d_real: 1.3767e-01 l_d_fake: 1.3454e-01 D_real: -4.5837e+01 D_fake: -4.7826e+01 +20-04-04 23:54:23.741 - INFO: l_g_pix: 9.8765e-05 l_g_fea: 4.4679e-01 l_g_gan: 1.5166e-02 l_d_real: 6.7929e-02 l_d_fake: 6.3209e-02 D_real: -6.8619e+01 D_fake: -7.1586e+01 +20-04-04 23:57:35.874 - INFO: l_g_pix: 1.0897e-04 l_g_fea: 4.5104e-01 l_g_gan: 4.7122e-03 l_d_real: 6.0780e-01 l_d_fake: 6.0522e-01 D_real: -7.5787e+01 D_fake: -7.6123e+01 +20-04-05 00:00:48.342 - INFO: l_g_pix: 1.4725e-04 l_g_fea: 5.3169e-01 l_g_gan: 3.9355e-02 l_d_real: 6.3501e-02 l_d_fake: 1.2131e-01 D_real: -9.2933e+01 D_fake: -1.0071e+02 +20-04-05 00:03:59.594 - INFO: l_g_pix: 1.3640e-04 l_g_fea: 4.6290e-01 l_g_gan: 3.1521e-03 l_d_real: 1.3461e+00 l_d_fake: 1.2871e+00 D_real: -9.8746e+01 D_fake: -9.8060e+01 +20-04-05 00:07:10.849 - INFO: l_g_pix: 1.2909e-04 l_g_fea: 5.6723e-01 l_g_gan: 1.8053e-02 l_d_real: 4.0369e-02 l_d_fake: 3.7563e-02 D_real: -1.1559e+02 D_fake: -1.1916e+02 +20-04-05 00:10:22.774 - INFO: l_g_pix: 1.3945e-04 l_g_fea: 5.1106e-01 l_g_gan: 1.7204e-02 l_d_real: 3.5538e-02 l_d_fake: 3.5022e-02 D_real: -1.0952e+02 D_fake: -1.1292e+02 +20-04-05 00:13:34.370 - INFO: l_g_pix: 1.4709e-04 l_g_fea: 4.9654e-01 l_g_gan: 1.9536e-02 l_d_real: 2.5714e-02 l_d_fake: 2.6977e-02 D_real: -9.8401e+01 D_fake: -1.0228e+02 +20-04-05 00:16:46.174 - INFO: l_g_pix: 1.1033e-04 l_g_fea: 4.2519e-01 l_g_gan: 2.7380e-02 l_d_real: 9.6280e-03 l_d_fake: 3.4007e-02 D_real: -1.1473e+02 D_fake: -1.2018e+02 +20-04-05 00:19:57.374 - INFO: l_g_pix: 1.3446e-04 l_g_fea: 5.0493e-01 l_g_gan: 1.2163e-02 l_d_real: 9.8201e-02 l_d_fake: 9.9758e-02 D_real: -6.2817e+01 D_fake: -6.5150e+01 +20-04-05 00:23:08.459 - INFO: l_g_pix: 1.6831e-04 l_g_fea: 4.4347e-01 l_g_gan: 6.8688e-03 l_d_real: 6.0926e-01 l_d_fake: 6.0525e-01 D_real: -1.0362e+02 D_fake: -1.0439e+02 +20-04-05 00:26:19.569 - INFO: l_g_pix: 1.1049e-04 l_g_fea: 3.7011e-01 l_g_gan: 2.6707e-02 l_d_real: 6.7057e-03 l_d_fake: 5.7112e-03 D_real: -8.4363e+01 D_fake: -8.9698e+01 +20-04-05 00:29:46.129 - INFO: l_g_pix: 1.3017e-04 l_g_fea: 5.1142e-01 l_g_gan: 8.4404e-03 l_d_real: 3.2139e-01 l_d_fake: 3.1410e-01 D_real: -4.7024e+01 D_fake: -4.8395e+01 +20-04-05 00:33:38.103 - INFO: l_g_pix: 3.2017e-04 l_g_fea: 5.2084e-01 l_g_gan: 1.3103e-02 l_d_real: 1.9688e-01 l_d_fake: 1.8874e-01 D_real: -1.0358e+02 D_fake: -1.0601e+02 +20-04-05 00:36:49.272 - INFO: l_g_pix: 1.0807e-04 l_g_fea: 5.1753e-01 l_g_gan: 1.3751e-02 l_d_real: 6.8849e-02 l_d_fake: 6.9987e-02 D_real: -7.6408e+01 D_fake: -7.9089e+01 +20-04-05 00:40:00.567 - INFO: l_g_pix: 1.0611e-04 l_g_fea: 5.5450e-01 l_g_gan: 8.6396e-03 l_d_real: 2.6326e-01 l_d_fake: 2.6683e-01 D_real: -1.0310e+02 D_fake: -1.0457e+02 +20-04-05 00:43:11.166 - INFO: l_g_pix: 2.2638e-04 l_g_fea: 5.5730e-01 l_g_gan: 1.1436e-02 l_d_real: 1.3492e-01 l_d_fake: 1.3454e-01 D_real: -9.1531e+01 D_fake: -9.3683e+01 +20-04-05 00:43:11.609 - INFO: Models and training states saved. +20-04-05 00:44:24.317 - INFO: # Validation # PSNR: 27.42, SSIM: 0.84759, LPIPS: 0.048405 +20-04-05 00:44:24.317 - INFO: psnr: 27.42, ssim: 0.84759, lpips: 0.048405 +20-04-05 00:47:50.180 - INFO: l_g_pix: 1.0966e-04 l_g_fea: 4.6482e-01 l_g_gan: 5.3625e-03 l_d_real: 4.9917e-01 l_d_fake: 5.0392e-01 D_real: -3.3431e+01 D_fake: -3.4002e+01 +20-04-05 00:51:24.255 - INFO: l_g_pix: 1.3932e-04 l_g_fea: 4.9510e-01 l_g_gan: 2.0830e-02 l_d_real: 2.0122e-02 l_d_fake: 1.9591e-02 D_real: -5.4988e+01 D_fake: -5.9135e+01 +20-04-05 00:55:48.466 - INFO: l_g_pix: 1.1144e-04 l_g_fea: 4.7469e-01 l_g_gan: 1.5239e-02 l_d_real: 5.4750e-02 l_d_fake: 5.4829e-02 D_real: -1.0424e+02 D_fake: -1.0724e+02 +20-04-05 00:59:48.414 - INFO: l_g_pix: 1.5416e-04 l_g_fea: 6.7273e-01 l_g_gan: 4.3444e-02 l_d_real: 1.1939e-02 l_d_fake: 1.1962e-02 D_real: -1.3331e+02 D_fake: -1.4199e+02 +20-04-05 01:05:26.021 - INFO: l_g_pix: 9.1823e-05 l_g_fea: 3.3324e-01 l_g_gan: 4.4898e-03 l_d_real: 5.3428e-01 l_d_fake: 5.3431e-01 D_real: -7.5958e+01 D_fake: -7.6322e+01 +20-04-05 01:08:37.923 - INFO: l_g_pix: 1.2368e-04 l_g_fea: 5.2056e-01 l_g_gan: 5.6560e-03 l_d_real: 4.1240e-01 l_d_fake: 4.1315e-01 D_real: -1.6377e+02 D_fake: -1.6449e+02 +20-04-05 01:11:49.748 - INFO: l_g_pix: 1.3756e-04 l_g_fea: 4.3860e-01 l_g_gan: 1.8441e-02 l_d_real: 9.2878e-02 l_d_fake: 6.9032e-02 D_real: -1.6591e+02 D_fake: -1.6952e+02 +20-04-05 01:15:01.978 - INFO: l_g_pix: 1.2548e-04 l_g_fea: 4.9449e-01 l_g_gan: 9.0458e-03 l_d_real: 3.5493e-01 l_d_fake: 3.6301e-01 D_real: -1.7461e+02 D_fake: -1.7607e+02 +20-04-05 01:18:14.203 - INFO: l_g_pix: 1.4897e-04 l_g_fea: 5.0925e-01 l_g_gan: 8.6339e-03 l_d_real: 2.2171e-01 l_d_fake: 2.2235e-01 D_real: -7.7653e+01 D_fake: -7.9158e+01 +20-04-05 01:21:27.143 - INFO: l_g_pix: 1.8414e-04 l_g_fea: 5.7507e-01 l_g_gan: 7.8471e-03 l_d_real: 3.4736e-01 l_d_fake: 3.3294e-01 D_real: -7.5933e+01 D_fake: -7.7162e+01 +20-04-05 01:24:39.212 - INFO: l_g_pix: 1.4895e-04 l_g_fea: 5.2409e-01 l_g_gan: 3.4292e-03 l_d_real: 7.2798e-01 l_d_fake: 7.2715e-01 D_real: -1.1462e+02 D_fake: -1.1458e+02 +20-04-05 01:27:50.800 - INFO: l_g_pix: 1.1425e-04 l_g_fea: 4.4721e-01 l_g_gan: 5.8452e-02 l_d_real: 9.5367e-06 l_d_fake: 1.0468e-05 D_real: -1.6559e+02 D_fake: -1.7728e+02 +20-04-05 01:31:02.576 - INFO: l_g_pix: 1.7729e-04 l_g_fea: 4.8372e-01 l_g_gan: 4.5680e-02 l_d_real: 4.8117e-03 l_d_fake: 4.4829e-03 D_real: -1.4455e+02 D_fake: -1.5368e+02 +20-04-05 01:34:14.570 - INFO: l_g_pix: 1.5779e-04 l_g_fea: 5.5841e-01 l_g_gan: 1.7132e-02 l_d_real: 7.3069e-02 l_d_fake: 7.4340e-02 D_real: -1.7440e+02 D_fake: -1.7776e+02 +20-04-05 01:37:26.825 - INFO: l_g_pix: 2.5441e-04 l_g_fea: 4.4781e-01 l_g_gan: 2.6406e-02 l_d_real: 6.2666e-03 l_d_fake: 6.5308e-03 D_real: -7.9625e+01 D_fake: -8.4899e+01 +20-04-05 01:40:38.020 - INFO: l_g_pix: 1.4776e-04 l_g_fea: 4.0763e-01 l_g_gan: 8.9417e-03 l_d_real: 1.9260e-01 l_d_fake: 1.9373e-01 D_real: -1.5198e+02 D_fake: -1.5357e+02 +20-04-05 01:43:50.207 - INFO: l_g_pix: 1.5214e-04 l_g_fea: 5.2309e-01 l_g_gan: 2.6347e-03 l_d_real: 9.9587e-01 l_d_fake: 9.9060e-01 D_real: -1.2830e+02 D_fake: -1.2784e+02 +20-04-05 01:47:01.448 - INFO: l_g_pix: 1.5511e-04 l_g_fea: 4.6955e-01 l_g_gan: 1.0341e-02 l_d_real: 1.4063e-01 l_d_fake: 1.4082e-01 D_real: -1.5247e+02 D_fake: -1.5440e+02 +20-04-05 01:50:13.446 - INFO: l_g_pix: 2.3693e-04 l_g_fea: 4.3622e-01 l_g_gan: 3.9225e-02 l_d_real: 4.3828e-03 l_d_fake: 1.4228e-02 D_real: -3.7370e+01 D_fake: -4.5206e+01 +20-04-05 01:53:25.553 - INFO: l_g_pix: 1.2815e-04 l_g_fea: 6.1019e-01 l_g_gan: 3.7381e-03 l_d_real: 6.4664e-01 l_d_fake: 6.4765e-01 D_real: 2.3021e+00 D_fake: 2.2016e+00 +20-04-05 01:56:38.519 - INFO: l_g_pix: 1.3876e-04 l_g_fea: 5.6056e-01 l_g_gan: 5.1455e-03 l_d_real: 5.5701e-01 l_d_fake: 5.4845e-01 D_real: 1.8974e+01 D_fake: 1.8498e+01 +20-04-05 01:59:49.959 - INFO: l_g_pix: 1.0843e-04 l_g_fea: 4.4856e-01 l_g_gan: 4.4120e-03 l_d_real: 6.2088e-01 l_d_fake: 6.2020e-01 D_real: 1.9923e+01 D_fake: 1.9661e+01 +20-04-05 02:03:03.535 - INFO: l_g_pix: 8.4562e-05 l_g_fea: 3.9300e-01 l_g_gan: 3.5520e-03 l_d_real: 7.1460e-01 l_d_fake: 7.1677e-01 D_real: 3.0395e+01 D_fake: 3.0400e+01 +20-04-05 02:06:36.558 - INFO: l_g_pix: 9.2877e-05 l_g_fea: 4.3395e-01 l_g_gan: 1.1610e-02 l_d_real: 1.3217e-01 l_d_fake: 1.8780e-01 D_real: 6.1600e+01 D_fake: 5.9437e+01 +20-04-05 02:09:53.824 - INFO: l_g_pix: 1.4323e-04 l_g_fea: 4.8017e-01 l_g_gan: 7.5872e-03 l_d_real: 3.5414e-01 l_d_fake: 3.3937e-01 D_real: 5.3492e+01 D_fake: 5.2321e+01 +20-04-05 02:09:54.311 - INFO: Models and training states saved. +20-04-05 02:11:16.150 - INFO: # Validation # PSNR: 30.339, SSIM: 0.85384, LPIPS: 0.052792 +20-04-05 02:11:16.150 - INFO: psnr: 30.339, ssim: 0.85384, lpips: 0.052792 +20-04-05 02:17:51.442 - INFO: l_g_pix: 1.7941e-04 l_g_fea: 4.9958e-01 l_g_gan: 2.7490e-02 l_d_real: 4.3710e-03 l_d_fake: 5.6518e-03 D_real: 3.0442e+01 D_fake: 2.4949e+01 +20-04-05 02:21:56.655 - INFO: l_g_pix: 1.3766e-04 l_g_fea: 4.7753e-01 l_g_gan: 6.3967e-03 l_d_real: 3.3066e-01 l_d_fake: 3.3004e-01 D_real: 2.2847e+01 D_fake: 2.1898e+01 +20-04-05 02:25:48.786 - INFO: l_g_pix: 9.2204e-05 l_g_fea: 3.6176e-01 l_g_gan: 7.0867e-03 l_d_real: 3.0492e-01 l_d_fake: 3.0170e-01 D_real: 7.5086e+01 D_fake: 7.3972e+01 +20-04-05 02:31:01.809 - INFO: l_g_pix: 1.3665e-04 l_g_fea: 4.3750e-01 l_g_gan: 1.8711e-02 l_d_real: 3.9416e-02 l_d_fake: 3.9316e-02 D_real: 6.3716e+01 D_fake: 6.0013e+01 +20-04-05 02:35:05.038 - INFO: l_g_pix: 1.1327e-04 l_g_fea: 4.5747e-01 l_g_gan: 5.7867e-03 l_d_real: 5.3342e-01 l_d_fake: 5.6487e-01 D_real: 5.7523e+01 D_fake: 5.6915e+01 +20-04-05 02:39:03.756 - INFO: l_g_pix: 1.1227e-04 l_g_fea: 3.6477e-01 l_g_gan: 8.6198e-03 l_d_real: 2.1249e-01 l_d_fake: 2.1208e-01 D_real: 6.9024e+01 D_fake: 6.7513e+01 +20-04-05 02:48:57.092 - INFO: l_g_pix: 1.1359e-04 l_g_fea: 4.5343e-01 l_g_gan: 1.7875e-02 l_d_real: 7.5438e-02 l_d_fake: 5.6726e-02 D_real: 5.7613e+01 D_fake: 5.4104e+01 +20-04-05 03:01:55.310 - INFO: l_g_pix: 1.3380e-04 l_g_fea: 4.4611e-01 l_g_gan: 2.6263e-02 l_d_real: 1.1645e-02 l_d_fake: 9.6214e-03 D_real: 9.9127e+01 D_fake: 9.3885e+01 +20-04-05 03:12:30.843 - INFO: l_g_pix: 2.0053e-04 l_g_fea: 5.6256e-01 l_g_gan: 2.6236e-02 l_d_real: 1.6998e-02 l_d_fake: 2.2984e-02 D_real: 9.6441e+01 D_fake: 9.1214e+01 +20-04-05 03:16:23.698 - INFO: l_g_pix: 2.2350e-04 l_g_fea: 4.5646e-01 l_g_gan: 4.6929e-03 l_d_real: 7.5799e-01 l_d_fake: 7.5156e-01 D_real: 8.8795e+00 D_fake: 8.6957e+00 +20-04-05 03:19:34.255 - INFO: l_g_pix: 1.3298e-04 l_g_fea: 4.5436e-01 l_g_gan: 2.3729e-02 l_d_real: 1.2002e-02 l_d_fake: 1.7953e-02 D_real: -2.3014e+01 D_fake: -2.7744e+01 +20-04-05 03:22:44.940 - INFO: l_g_pix: 1.6123e-04 l_g_fea: 4.2556e-01 l_g_gan: 4.1653e-03 l_d_real: 7.4728e-01 l_d_fake: 7.5638e-01 D_real: -1.0066e+02 D_fake: -1.0074e+02 +20-04-05 03:25:56.316 - INFO: l_g_pix: 1.3938e-04 l_g_fea: 5.1131e-01 l_g_gan: 2.0030e-02 l_d_real: 4.3800e-02 l_d_fake: 3.9567e-02 D_real: -7.9354e+01 D_fake: -8.3318e+01 +20-04-05 03:29:07.238 - INFO: l_g_pix: 1.7949e-04 l_g_fea: 5.1459e-01 l_g_gan: 4.2783e-03 l_d_real: 1.0483e+00 l_d_fake: 1.0796e+00 D_real: -5.7608e+01 D_fake: -5.7400e+01 +20-04-05 03:32:18.476 - INFO: l_g_pix: 1.3891e-04 l_g_fea: 4.7053e-01 l_g_gan: 1.8471e-02 l_d_real: 6.5805e-02 l_d_fake: 1.1466e-01 D_real: 2.3635e+01 D_fake: 2.0031e+01 +20-04-05 03:35:34.651 - INFO: l_g_pix: 1.2747e-04 l_g_fea: 4.0689e-01 l_g_gan: 3.2936e-03 l_d_real: 9.5621e-01 l_d_fake: 9.4614e-01 D_real: 3.4579e+01 D_fake: 3.4872e+01 +20-04-05 03:38:45.147 - INFO: l_g_pix: 1.5790e-04 l_g_fea: 4.5461e-01 l_g_gan: 1.3429e-02 l_d_real: 9.1338e-02 l_d_fake: 9.1519e-02 D_real: 7.3964e+01 D_fake: 7.1370e+01 +20-04-05 03:41:56.338 - INFO: l_g_pix: 1.0944e-04 l_g_fea: 4.0473e-01 l_g_gan: 2.4673e-02 l_d_real: 2.4503e-02 l_d_fake: 1.7540e-02 D_real: 1.0918e+02 D_fake: 1.0427e+02 +20-04-05 03:45:07.565 - INFO: l_g_pix: 1.3727e-04 l_g_fea: 5.5744e-01 l_g_gan: 9.1116e-03 l_d_real: 4.8716e-01 l_d_fake: 3.5481e-01 D_real: 1.9779e+01 D_fake: 1.8378e+01 +20-04-05 03:48:19.163 - INFO: l_g_pix: 7.6404e-05 l_g_fea: 4.1030e-01 l_g_gan: 4.6564e-03 l_d_real: 6.4798e-01 l_d_fake: 6.2443e-01 D_real: 7.3285e+01 D_fake: 7.2990e+01 +20-04-05 03:51:31.273 - INFO: l_g_pix: 9.6431e-05 l_g_fea: 3.5347e-01 l_g_gan: 6.9238e-03 l_d_real: 3.3095e-01 l_d_fake: 3.3331e-01 D_real: 7.9702e+01 D_fake: 7.8649e+01 +20-04-05 03:54:42.607 - INFO: l_g_pix: 1.3377e-04 l_g_fea: 4.5942e-01 l_g_gan: 1.2311e-02 l_d_real: 1.0264e-01 l_d_fake: 1.0283e-01 D_real: 9.6617e+01 D_fake: 9.4257e+01 +20-04-05 03:57:53.409 - INFO: l_g_pix: 1.4791e-04 l_g_fea: 5.9720e-01 l_g_gan: 2.5400e-03 l_d_real: 1.2812e+00 l_d_fake: 1.3686e+00 D_real: 9.0988e+01 D_fake: 9.1805e+01 +20-04-05 04:01:04.772 - INFO: l_g_pix: 1.5849e-04 l_g_fea: 5.4738e-01 l_g_gan: 2.3305e-02 l_d_real: 1.7131e-02 l_d_fake: 1.5302e-02 D_real: 9.3262e+01 D_fake: 8.8617e+01 +20-04-05 04:04:16.836 - INFO: l_g_pix: 1.2297e-04 l_g_fea: 4.0103e-01 l_g_gan: 2.2433e-02 l_d_real: 1.7467e-02 l_d_fake: 2.3739e-02 D_real: 9.8018e+01 D_fake: 9.3552e+01 +20-04-05 04:04:17.301 - INFO: Models and training states saved. +20-04-05 04:05:27.848 - INFO: # Validation # PSNR: 30.61, SSIM: 0.85395, LPIPS: 0.061615 +20-04-05 04:05:27.848 - INFO: psnr: 30.61, ssim: 0.85395, lpips: 0.061615 +20-04-05 04:11:27.360 - INFO: l_g_pix: 1.3583e-04 l_g_fea: 5.1930e-01 l_g_gan: 1.3816e-02 l_d_real: 2.2067e-01 l_d_fake: 2.0291e-01 D_real: 5.0017e+01 D_fake: 4.7465e+01 +20-04-05 04:14:37.306 - INFO: l_g_pix: 1.2748e-04 l_g_fea: 4.1374e-01 l_g_gan: 7.9685e-03 l_d_real: 3.0331e-01 l_d_fake: 3.2215e-01 D_real: 6.8373e+01 D_fake: 6.7092e+01 +20-04-05 04:17:48.261 - INFO: l_g_pix: 1.1728e-04 l_g_fea: 4.0533e-01 l_g_gan: 1.8300e-02 l_d_real: 1.2574e-01 l_d_fake: 9.4504e-02 D_real: 9.4030e+01 D_fake: 9.0481e+01 +20-04-05 04:20:59.884 - INFO: l_g_pix: 1.1409e-04 l_g_fea: 4.3466e-01 l_g_gan: 7.3402e-03 l_d_real: 3.5332e-01 l_d_fake: 3.2967e-01 D_real: 4.3820e+01 D_fake: 4.2693e+01 +20-04-05 04:24:11.905 - INFO: l_g_pix: 1.2790e-04 l_g_fea: 5.1740e-01 l_g_gan: 1.8539e-02 l_d_real: 3.1445e-02 l_d_fake: 2.9690e-02 D_real: 5.0848e+00 D_fake: 1.4075e+00 +20-04-05 04:33:59.589 - INFO: l_g_pix: 9.9768e-05 l_g_fea: 3.4239e-01 l_g_gan: 2.0533e-02 l_d_real: 3.5131e-02 l_d_fake: 6.4917e-02 D_real: 6.5871e+01 D_fake: 6.1814e+01 +20-04-05 04:45:17.359 - INFO: l_g_pix: 1.0807e-04 l_g_fea: 5.1946e-01 l_g_gan: 2.0200e-02 l_d_real: 2.5034e-02 l_d_fake: 2.2227e-02 D_real: 5.8098e+01 D_fake: 5.4082e+01 +20-04-05 04:54:56.843 - INFO: l_g_pix: 1.3698e-04 l_g_fea: 4.1263e-01 l_g_gan: 1.6835e-03 l_d_real: 1.4335e+00 l_d_fake: 1.4538e+00 D_real: 4.4212e+00 D_fake: 5.5282e+00 +20-04-05 04:58:09.043 - INFO: l_g_pix: 1.1719e-04 l_g_fea: 5.2378e-01 l_g_gan: 1.7436e-02 l_d_real: 1.3145e-01 l_d_fake: 1.6941e-01 D_real: 5.6944e+01 D_fake: 5.3607e+01 +20-04-05 05:01:22.444 - INFO: l_g_pix: 1.3756e-04 l_g_fea: 4.9501e-01 l_g_gan: 1.0530e-02 l_d_real: 1.6118e-01 l_d_fake: 1.9111e-01 D_real: 2.8000e+01 D_fake: 2.6070e+01 +20-04-05 05:04:33.685 - INFO: l_g_pix: 1.0870e-04 l_g_fea: 4.1902e-01 l_g_gan: 8.4421e-03 l_d_real: 2.4047e-01 l_d_fake: 2.4000e-01 D_real: 5.4945e+01 D_fake: 5.3497e+01 +20-04-05 05:07:48.283 - INFO: l_g_pix: 1.4376e-04 l_g_fea: 5.6535e-01 l_g_gan: 3.4688e-03 l_d_real: 7.6382e-01 l_d_fake: 7.6312e-01 D_real: 4.5692e+01 D_fake: 4.5761e+01 +20-04-05 05:11:02.728 - INFO: l_g_pix: 1.2240e-04 l_g_fea: 5.1111e-01 l_g_gan: 4.6723e-03 l_d_real: 6.2242e-01 l_d_fake: 6.1814e-01 D_real: 6.8959e+01 D_fake: 6.8645e+01 +20-04-05 05:14:16.232 - INFO: l_g_pix: 9.1249e-05 l_g_fea: 4.2670e-01 l_g_gan: 5.0727e-03 l_d_real: 4.7512e-01 l_d_fake: 4.7528e-01 D_real: 6.4244e+01 D_fake: 6.3705e+01 +20-04-05 05:17:29.314 - INFO: l_g_pix: 1.7362e-04 l_g_fea: 5.9170e-01 l_g_gan: 3.4652e-02 l_d_real: 5.0704e-01 l_d_fake: 1.1724e-01 D_real: 8.4865e+01 D_fake: 7.8247e+01 +20-04-05 05:20:43.713 - INFO: l_g_pix: 1.0576e-04 l_g_fea: 5.2478e-01 l_g_gan: 1.3772e-02 l_d_real: 1.0935e-01 l_d_fake: 1.4057e-01 D_real: 4.6748e+01 D_fake: 4.4119e+01 +20-04-05 05:23:55.545 - INFO: l_g_pix: 1.3819e-04 l_g_fea: 4.8284e-01 l_g_gan: 3.7804e-02 l_d_real: 1.3070e-03 l_d_fake: 4.1660e-03 D_real: 5.3804e+01 D_fake: 4.6246e+01 +20-04-05 05:27:07.647 - INFO: l_g_pix: 1.5724e-04 l_g_fea: 4.5573e-01 l_g_gan: 2.7477e-02 l_d_real: 3.7869e-02 l_d_fake: 6.0123e-03 D_real: 1.9302e+01 D_fake: 1.3828e+01 +20-04-05 05:30:18.289 - INFO: l_g_pix: 1.5861e-04 l_g_fea: 5.0817e-01 l_g_gan: 1.8617e-02 l_d_real: 6.0843e-02 l_d_fake: 1.6890e-01 D_real: 3.4733e+01 D_fake: 3.1124e+01 +20-04-05 05:33:29.634 - INFO: l_g_pix: 1.1666e-04 l_g_fea: 4.9935e-01 l_g_gan: 4.4200e-02 l_d_real: 2.5816e-03 l_d_fake: 4.0628e-03 D_real: 1.0297e+02 D_fake: 9.4138e+01 +20-04-05 05:36:41.415 - INFO: l_g_pix: 1.4681e-04 l_g_fea: 5.6348e-01 l_g_gan: 3.1034e-03 l_d_real: 9.3165e-01 l_d_fake: 9.2749e-01 D_real: 5.4189e+01 D_fake: 5.4498e+01 +20-04-05 05:39:53.493 - INFO: l_g_pix: 1.0987e-04 l_g_fea: 4.4675e-01 l_g_gan: 7.2085e-03 l_d_real: 3.0261e-01 l_d_fake: 2.9654e-01 D_real: -1.8303e+00 D_fake: -2.9725e+00 +20-04-05 05:43:05.010 - INFO: l_g_pix: 1.3648e-04 l_g_fea: 4.8161e-01 l_g_gan: 8.3090e-03 l_d_real: 2.7419e-01 l_d_fake: 2.6723e-01 D_real: 2.0894e+01 D_fake: 1.9503e+01 +20-04-05 05:46:16.741 - INFO: l_g_pix: 1.3937e-04 l_g_fea: 6.0278e-01 l_g_gan: 1.9874e-02 l_d_real: 5.9099e-02 l_d_fake: 1.0371e-01 D_real: 2.4686e+01 D_fake: 2.0792e+01 +20-04-05 05:49:28.416 - INFO: l_g_pix: 1.4293e-04 l_g_fea: 4.9796e-01 l_g_gan: 1.0649e-02 l_d_real: 1.5496e-01 l_d_fake: 1.5358e-01 D_real: 9.2601e+01 D_fake: 9.0626e+01 +20-04-05 05:49:28.833 - INFO: Models and training states saved. +20-04-05 05:51:04.404 - INFO: # Validation # PSNR: 30.407, SSIM: 0.85197, LPIPS: 0.058558 +20-04-05 05:51:04.404 - INFO: psnr: 30.407, ssim: 0.85197, lpips: 0.058558 +20-04-05 05:54:16.670 - INFO: l_g_pix: 1.0958e-04 l_g_fea: 3.9336e-01 l_g_gan: 3.8286e-03 l_d_real: 7.6754e-01 l_d_fake: 7.9088e-01 D_real: 2.3147e+01 D_fake: 2.3160e+01 +20-04-05 05:57:27.556 - INFO: l_g_pix: 1.7309e-04 l_g_fea: 4.8402e-01 l_g_gan: 4.0063e-03 l_d_real: 6.5520e-01 l_d_fake: 6.5449e-01 D_real: 8.0578e+01 D_fake: 8.0431e+01 +20-04-05 06:00:38.754 - INFO: l_g_pix: 1.4503e-04 l_g_fea: 5.0857e-01 l_g_gan: 9.7135e-03 l_d_real: 4.2142e-01 l_d_fake: 4.0996e-01 D_real: 6.4081e+01 D_fake: 6.2554e+01 +20-04-05 06:03:49.514 - INFO: l_g_pix: 1.1103e-04 l_g_fea: 4.0605e-01 l_g_gan: 1.8401e-02 l_d_real: 3.8314e-02 l_d_fake: 3.9341e-02 D_real: 2.7303e+01 D_fake: 2.3662e+01 +20-04-05 06:07:01.168 - INFO: l_g_pix: 1.6396e-04 l_g_fea: 4.9360e-01 l_g_gan: 1.6741e-02 l_d_real: 6.2579e-02 l_d_fake: 5.5661e-02 D_real: 2.8263e+01 D_fake: 2.4974e+01 +20-04-05 06:10:12.911 - INFO: l_g_pix: 1.2544e-04 l_g_fea: 4.8418e-01 l_g_gan: 3.7015e-02 l_d_real: 8.4140e-04 l_d_fake: 7.4298e-04 D_real: 6.1078e+01 D_fake: 5.3676e+01 +20-04-05 06:13:25.083 - INFO: l_g_pix: 1.1937e-04 l_g_fea: 4.7374e-01 l_g_gan: 1.2838e-02 l_d_real: 1.1787e-01 l_d_fake: 1.0661e-01 D_real: 5.4067e+01 D_fake: 5.1612e+01 +20-04-05 06:16:37.085 - INFO: l_g_pix: 1.2669e-04 l_g_fea: 6.0884e-01 l_g_gan: 1.3762e-02 l_d_real: 1.4883e-01 l_d_fake: 1.5857e-01 D_real: 7.1430e+01 D_fake: 6.8832e+01 +20-04-05 06:19:48.764 - INFO: l_g_pix: 1.2569e-04 l_g_fea: 4.3566e-01 l_g_gan: 2.0002e-02 l_d_real: 3.4747e-02 l_d_fake: 3.1841e-02 D_real: 5.5475e+01 D_fake: 5.1508e+01 +20-04-05 06:23:00.310 - INFO: l_g_pix: 1.4690e-04 l_g_fea: 4.9539e-01 l_g_gan: 1.4959e-02 l_d_real: 2.3469e-01 l_d_fake: 1.2446e-01 D_real: 7.7275e+01 D_fake: 7.4462e+01 +20-04-05 06:26:12.404 - INFO: l_g_pix: 7.8230e-05 l_g_fea: 3.8007e-01 l_g_gan: 6.7577e-03 l_d_real: 5.4912e-01 l_d_fake: 5.6914e-01 D_real: 1.1008e+02 D_fake: 1.0929e+02 +20-04-05 06:29:24.252 - INFO: l_g_pix: 1.4401e-04 l_g_fea: 4.9048e-01 l_g_gan: 2.5974e-03 l_d_real: 1.6910e+00 l_d_fake: 1.7574e+00 D_real: 5.6348e+01 D_fake: 5.7552e+01 +20-04-05 06:32:36.452 - INFO: l_g_pix: 1.8464e-04 l_g_fea: 5.4651e-01 l_g_gan: 8.8356e-03 l_d_real: 2.2623e-01 l_d_fake: 2.3082e-01 D_real: 2.1756e+01 D_fake: 2.0217e+01 +20-04-05 06:35:47.571 - INFO: l_g_pix: 1.2793e-04 l_g_fea: 4.1144e-01 l_g_gan: 1.6394e-02 l_d_real: 8.9987e-02 l_d_fake: 7.5205e-02 D_real: 9.0303e+01 D_fake: 8.7107e+01 +20-04-05 06:38:58.493 - INFO: l_g_pix: 1.2763e-04 l_g_fea: 5.7044e-01 l_g_gan: 4.1850e-03 l_d_real: 6.4589e-01 l_d_fake: 6.3781e-01 D_real: 3.8112e+01 D_fake: 3.7917e+01 +20-04-05 06:42:10.659 - INFO: l_g_pix: 9.0617e-05 l_g_fea: 4.1188e-01 l_g_gan: 4.0201e-03 l_d_real: 6.2857e-01 l_d_fake: 6.2756e-01 D_real: 1.6890e+01 D_fake: 1.6714e+01 +20-04-05 06:45:22.115 - INFO: l_g_pix: 1.5156e-04 l_g_fea: 5.1461e-01 l_g_gan: 1.4194e-02 l_d_real: 1.1538e-01 l_d_fake: 7.7912e-02 D_real: 3.7848e+01 D_fake: 3.5105e+01 +20-04-05 06:48:33.986 - INFO: l_g_pix: 1.4405e-04 l_g_fea: 5.1331e-01 l_g_gan: 1.8223e-02 l_d_real: 3.5910e-02 l_d_fake: 3.6962e-02 D_real: 6.8930e+01 D_fake: 6.5322e+01 +20-04-05 06:51:45.287 - INFO: l_g_pix: 1.1710e-04 l_g_fea: 5.2701e-01 l_g_gan: 4.7542e-03 l_d_real: 5.8428e-01 l_d_fake: 5.9706e-01 D_real: 6.8899e+01 D_fake: 6.8539e+01 +20-04-05 06:54:57.216 - INFO: l_g_pix: 1.4912e-04 l_g_fea: 4.9467e-01 l_g_gan: 1.1506e-02 l_d_real: 1.4055e-01 l_d_fake: 1.2771e-01 D_real: 4.4317e+01 D_fake: 4.2150e+01 +20-04-05 06:58:08.117 - INFO: l_g_pix: 9.6493e-05 l_g_fea: 3.3197e-01 l_g_gan: 2.4284e-02 l_d_real: 3.8989e-02 l_d_fake: 8.1843e-02 D_real: 6.5290e+01 D_fake: 6.0493e+01 +20-04-05 07:01:19.487 - INFO: l_g_pix: 1.1749e-04 l_g_fea: 4.6268e-01 l_g_gan: 1.1278e-02 l_d_real: 1.8045e-01 l_d_fake: 2.3088e-01 D_real: 5.0656e+01 D_fake: 4.8606e+01 +20-04-05 07:04:31.669 - INFO: l_g_pix: 1.2043e-04 l_g_fea: 4.5734e-01 l_g_gan: 8.3267e-04 l_d_real: 1.9893e+00 l_d_fake: 1.9930e+00 D_real: 3.8933e+01 D_fake: 4.0758e+01 +20-04-05 07:07:42.789 - INFO: l_g_pix: 1.4967e-04 l_g_fea: 4.2834e-01 l_g_gan: 5.1946e-03 l_d_real: 8.0666e-01 l_d_fake: 9.5851e-01 D_real: 6.2169e+01 D_fake: 6.2012e+01 +20-04-05 07:10:54.156 - INFO: l_g_pix: 1.6882e-04 l_g_fea: 5.7499e-01 l_g_gan: 2.7984e-02 l_d_real: 1.7409e-02 l_d_fake: 9.1500e-03 D_real: 8.8061e+01 D_fake: 8.2478e+01 +20-04-05 07:10:54.549 - INFO: Models and training states saved. +20-04-05 07:12:27.560 - INFO: # Validation # PSNR: 30.135, SSIM: 0.85044, LPIPS: 0.055354 +20-04-05 07:12:27.561 - INFO: psnr: 30.135, ssim: 0.85044, lpips: 0.055354 +20-04-05 07:15:41.926 - INFO: l_g_pix: 1.2637e-04 l_g_fea: 5.3184e-01 l_g_gan: 1.4906e-02 l_d_real: 1.0687e-01 l_d_fake: 6.0371e-02 D_real: 4.5664e+01 D_fake: 4.2767e+01 +20-04-05 07:18:53.115 - INFO: l_g_pix: 1.4789e-04 l_g_fea: 4.2606e-01 l_g_gan: 1.2633e-02 l_d_real: 1.5789e-01 l_d_fake: 1.0593e-01 D_real: 5.4078e+01 D_fake: 5.1683e+01 +20-04-05 07:22:04.351 - INFO: l_g_pix: 1.1780e-04 l_g_fea: 4.7561e-01 l_g_gan: 1.2241e-02 l_d_real: 2.4355e-01 l_d_fake: 2.2020e-01 D_real: 5.4741e+01 D_fake: 5.2525e+01 +20-04-05 07:25:15.977 - INFO: l_g_pix: 1.1938e-04 l_g_fea: 5.7092e-01 l_g_gan: 1.6247e-02 l_d_real: 1.1971e-01 l_d_fake: 6.1033e-02 D_real: 5.4382e+01 D_fake: 5.1223e+01 +20-04-05 07:28:27.846 - INFO: l_g_pix: 1.4718e-04 l_g_fea: 4.5887e-01 l_g_gan: 4.3524e-03 l_d_real: 5.7462e-01 l_d_fake: 5.9247e-01 D_real: 4.1259e+01 D_fake: 4.0972e+01 +20-04-05 07:31:39.215 - INFO: l_g_pix: 1.2696e-04 l_g_fea: 5.1286e-01 l_g_gan: 2.4338e-02 l_d_real: 9.2545e-03 l_d_fake: 1.0094e-02 D_real: 6.5586e+01 D_fake: 6.0728e+01 +20-04-05 07:34:55.558 - INFO: l_g_pix: 9.1099e-05 l_g_fea: 4.1520e-01 l_g_gan: 7.8680e-03 l_d_real: 3.7033e-01 l_d_fake: 3.0792e-01 D_real: 2.9776e+01 D_fake: 2.8542e+01 +20-04-05 07:38:07.110 - INFO: l_g_pix: 1.1177e-04 l_g_fea: 3.8471e-01 l_g_gan: 3.6912e-03 l_d_real: 6.9456e-01 l_d_fake: 7.0313e-01 D_real: 1.7224e+01 D_fake: 1.7184e+01 +20-04-05 07:41:19.187 - INFO: l_g_pix: 1.0549e-04 l_g_fea: 3.9845e-01 l_g_gan: 1.2481e-02 l_d_real: 9.9072e-02 l_d_fake: 9.6814e-02 D_real: 7.0486e+01 D_fake: 6.8087e+01 +20-04-05 07:44:31.318 - INFO: l_g_pix: 1.6603e-04 l_g_fea: 4.8068e-01 l_g_gan: 2.9199e-02 l_d_real: 3.8177e-03 l_d_fake: 3.8939e-03 D_real: 4.7203e+01 D_fake: 4.1367e+01 +20-04-05 07:47:43.499 - INFO: l_g_pix: 1.7065e-04 l_g_fea: 5.0862e-01 l_g_gan: 1.1093e-02 l_d_real: 1.8974e-01 l_d_fake: 1.8688e-01 D_real: 4.8533e+01 D_fake: 4.6502e+01 +20-04-05 07:50:55.263 - INFO: l_g_pix: 1.4867e-04 l_g_fea: 5.8563e-01 l_g_gan: 1.0227e-02 l_d_real: 1.7248e-01 l_d_fake: 1.7383e-01 D_real: 4.7300e+01 D_fake: 4.5427e+01 +20-04-05 07:54:07.257 - INFO: l_g_pix: 1.2237e-04 l_g_fea: 4.1491e-01 l_g_gan: 1.4563e-02 l_d_real: 1.2463e-01 l_d_fake: 7.4181e-02 D_real: 7.7877e+01 D_fake: 7.5064e+01 +20-04-05 07:57:18.575 - INFO: l_g_pix: 1.3995e-04 l_g_fea: 5.8000e-01 l_g_gan: 5.2662e-04 l_d_real: 3.4660e+00 l_d_fake: 3.4916e+00 D_real: 6.4069e+01 D_fake: 6.7442e+01 +20-04-05 08:00:30.776 - INFO: l_g_pix: 1.1989e-04 l_g_fea: 4.7103e-01 l_g_gan: 1.9605e-02 l_d_real: 3.2018e-02 l_d_fake: 3.0359e-02 D_real: 7.0819e+01 D_fake: 6.6929e+01 +20-04-05 08:03:42.451 - INFO: l_g_pix: 1.0620e-04 l_g_fea: 3.5113e-01 l_g_gan: 5.1410e-03 l_d_real: 4.8605e-01 l_d_fake: 5.3837e-01 D_real: 5.0564e+01 D_fake: 5.0048e+01 +20-04-05 08:06:53.539 - INFO: l_g_pix: 1.2032e-04 l_g_fea: 4.7931e-01 l_g_gan: 5.5927e-03 l_d_real: 4.3916e-01 l_d_fake: 4.4392e-01 D_real: 4.0472e+01 D_fake: 3.9795e+01 +20-04-05 08:10:05.039 - INFO: l_g_pix: 1.1307e-04 l_g_fea: 4.9898e-01 l_g_gan: 1.0075e-02 l_d_real: 3.2152e-01 l_d_fake: 3.4645e-01 D_real: 5.3302e+01 D_fake: 5.1621e+01 +20-04-05 08:13:16.496 - INFO: l_g_pix: 1.3711e-04 l_g_fea: 4.7454e-01 l_g_gan: 6.4921e-03 l_d_real: 5.9481e-01 l_d_fake: 5.3367e-01 D_real: 7.2585e+01 D_fake: 7.1850e+01 +20-04-05 08:16:27.634 - INFO: l_g_pix: 2.2056e-04 l_g_fea: 5.5211e-01 l_g_gan: 2.6899e-03 l_d_real: 1.4119e+00 l_d_fake: 1.4012e+00 D_real: 3.7467e+01 D_fake: 3.8335e+01 +20-04-05 08:19:38.996 - INFO: l_g_pix: 1.3965e-04 l_g_fea: 4.1996e-01 l_g_gan: 1.0396e-02 l_d_real: 3.2889e-01 l_d_fake: 1.9519e-01 D_real: 6.3811e+01 D_fake: 6.1994e+01 +20-04-05 08:22:50.843 - INFO: l_g_pix: 1.5939e-04 l_g_fea: 5.7214e-01 l_g_gan: 7.4309e-03 l_d_real: 6.2670e-01 l_d_fake: 6.6653e-01 D_real: 5.1849e+01 D_fake: 5.1009e+01 +20-04-05 08:26:02.934 - INFO: l_g_pix: 1.3045e-04 l_g_fea: 4.5165e-01 l_g_gan: 1.5465e-02 l_d_real: 7.4974e-02 l_d_fake: 8.5239e-02 D_real: 8.0918e+01 D_fake: 7.7905e+01 +20-04-05 08:29:14.918 - INFO: l_g_pix: 1.1350e-04 l_g_fea: 4.6056e-01 l_g_gan: 4.7539e-03 l_d_real: 5.1460e-01 l_d_fake: 5.1514e-01 D_real: 7.8635e+01 D_fake: 7.8199e+01 +20-04-05 08:32:27.124 - INFO: l_g_pix: 1.4496e-04 l_g_fea: 5.2086e-01 l_g_gan: 4.9454e-03 l_d_real: 5.6185e-01 l_d_fake: 5.7023e-01 D_real: 6.4399e+01 D_fake: 6.3976e+01 +20-04-05 08:32:27.557 - INFO: Models and training states saved. +20-04-05 08:33:39.115 - INFO: # Validation # PSNR: 30.717, SSIM: 0.85481, LPIPS: 0.05554 +20-04-05 08:33:39.115 - INFO: psnr: 30.717, ssim: 0.85481, lpips: 0.05554 +20-04-05 08:36:51.595 - INFO: l_g_pix: 1.4376e-04 l_g_fea: 4.2486e-01 l_g_gan: 2.5196e-03 l_d_real: 9.9106e-01 l_d_fake: 9.9352e-01 D_real: 6.0036e+01 D_fake: 6.0525e+01 +20-04-05 08:40:02.816 - INFO: l_g_pix: 8.6438e-05 l_g_fea: 2.6899e-01 l_g_gan: 3.2716e-02 l_d_real: 3.3901e-03 l_d_fake: 2.5666e-03 D_real: 5.9409e+01 D_fake: 5.2869e+01 +20-04-05 08:43:14.971 - INFO: l_g_pix: 1.2108e-04 l_g_fea: 3.3922e-01 l_g_gan: 1.7665e-02 l_d_real: 1.5468e-01 l_d_fake: 1.2169e-01 D_real: 8.0675e+01 D_fake: 7.7280e+01 +20-04-05 08:46:27.249 - INFO: l_g_pix: 1.3569e-04 l_g_fea: 3.9088e-01 l_g_gan: 3.5655e-02 l_d_real: 2.1689e-03 l_d_fake: 1.8019e-03 D_real: 8.0665e+01 D_fake: 7.3536e+01 +20-04-05 08:49:39.328 - INFO: l_g_pix: 1.0648e-04 l_g_fea: 3.4060e-01 l_g_gan: 7.9899e-03 l_d_real: 2.5093e-01 l_d_fake: 2.4899e-01 D_real: 5.5580e+01 D_fake: 5.4232e+01 +20-04-05 08:52:51.857 - INFO: l_g_pix: 1.1870e-04 l_g_fea: 3.9497e-01 l_g_gan: 7.5498e-03 l_d_real: 4.2139e-01 l_d_fake: 4.3234e-01 D_real: 8.1514e+01 D_fake: 8.0431e+01 +20-04-05 08:56:03.936 - INFO: l_g_pix: 1.3726e-04 l_g_fea: 4.0466e-01 l_g_gan: 6.4928e-03 l_d_real: 3.3781e-01 l_d_fake: 3.3534e-01 D_real: 9.3257e+01 D_fake: 9.2295e+01 +20-04-05 08:59:15.980 - INFO: l_g_pix: 1.0524e-04 l_g_fea: 3.2938e-01 l_g_gan: 8.0565e-04 l_d_real: 1.9445e+00 l_d_fake: 1.9449e+00 D_real: 6.9997e+01 D_fake: 7.1780e+01 +20-04-05 09:02:27.215 - INFO: l_g_pix: 1.1276e-04 l_g_fea: 5.1044e-01 l_g_gan: 8.7795e-03 l_d_real: 1.9827e-01 l_d_fake: 1.9602e-01 D_real: 6.3299e+01 D_fake: 6.1740e+01 +20-04-05 09:05:38.983 - INFO: l_g_pix: 1.1400e-04 l_g_fea: 3.4924e-01 l_g_gan: 9.9808e-03 l_d_real: 2.2341e-01 l_d_fake: 2.6532e-01 D_real: 5.4170e+01 D_fake: 5.2419e+01 +20-04-05 09:08:50.870 - INFO: l_g_pix: 1.0875e-04 l_g_fea: 4.1015e-01 l_g_gan: 1.2722e-02 l_d_real: 1.0425e-01 l_d_fake: 1.2473e-01 D_real: 6.2342e+01 D_fake: 5.9913e+01 +20-04-05 09:12:02.403 - INFO: l_g_pix: 1.2195e-04 l_g_fea: 4.6183e-01 l_g_gan: 1.0584e-02 l_d_real: 1.3476e-01 l_d_fake: 1.3280e-01 D_real: 7.0893e+01 D_fake: 6.8910e+01 +20-04-05 09:15:14.762 - INFO: l_g_pix: 1.2659e-04 l_g_fea: 3.5268e-01 l_g_gan: 3.8816e-03 l_d_real: 6.8008e-01 l_d_fake: 6.9281e-01 D_real: 5.5789e+01 D_fake: 5.5699e+01 +20-04-05 09:18:26.257 - INFO: l_g_pix: 1.2666e-04 l_g_fea: 4.8591e-01 l_g_gan: 5.4530e-03 l_d_real: 4.1985e-01 l_d_fake: 4.2227e-01 D_real: 3.1203e+01 D_fake: 3.0533e+01 +20-04-05 09:21:37.339 - INFO: l_g_pix: 1.6201e-04 l_g_fea: 5.3655e-01 l_g_gan: 1.4327e-02 l_d_real: 6.9200e-02 l_d_fake: 7.5050e-02 D_real: 7.0279e+01 D_fake: 6.7485e+01 +20-04-05 09:24:49.401 - INFO: l_g_pix: 1.0844e-04 l_g_fea: 6.0343e-01 l_g_gan: 1.2842e-02 l_d_real: 1.5294e-01 l_d_fake: 1.2766e-01 D_real: 1.9659e+01 D_fake: 1.7230e+01 +20-04-05 09:28:00.573 - INFO: l_g_pix: 9.9356e-05 l_g_fea: 4.7016e-01 l_g_gan: 1.1635e-03 l_d_real: 1.9724e+00 l_d_fake: 1.9333e+00 D_real: 4.2763e+01 D_fake: 4.4483e+01 +20-04-05 09:31:12.204 - INFO: l_g_pix: 1.4659e-04 l_g_fea: 5.1246e-01 l_g_gan: 8.3122e-03 l_d_real: 2.6946e-01 l_d_fake: 2.5783e-01 D_real: 6.3433e+01 D_fake: 6.2035e+01 +20-04-05 09:34:24.323 - INFO: l_g_pix: 1.2380e-04 l_g_fea: 4.4718e-01 l_g_gan: 4.5682e-03 l_d_real: 6.0467e-01 l_d_fake: 6.1360e-01 D_real: 5.3626e+01 D_fake: 5.3321e+01 +20-04-05 09:37:36.159 - INFO: l_g_pix: 1.3176e-04 l_g_fea: 4.6164e-01 l_g_gan: 1.2918e-02 l_d_real: 1.4976e-01 l_d_fake: 2.1101e-01 D_real: 7.4712e+01 D_fake: 7.2308e+01 +20-04-05 09:40:48.561 - INFO: l_g_pix: 1.4205e-04 l_g_fea: 5.5461e-01 l_g_gan: 6.3791e-03 l_d_real: 4.7843e-01 l_d_fake: 4.2995e-01 D_real: 6.8169e+01 D_fake: 6.7348e+01 +20-04-05 09:44:00.779 - INFO: l_g_pix: 1.2141e-04 l_g_fea: 5.7466e-01 l_g_gan: 1.2793e-02 l_d_real: 1.1792e-01 l_d_fake: 1.1857e-01 D_real: 1.0832e+02 D_fake: 1.0588e+02 +20-04-05 09:47:12.504 - INFO: l_g_pix: 1.3233e-04 l_g_fea: 5.1577e-01 l_g_gan: 1.1738e-02 l_d_real: 3.8836e-01 l_d_fake: 3.0614e-01 D_real: 9.9001e+01 D_fake: 9.7001e+01 +20-04-05 09:50:23.647 - INFO: l_g_pix: 1.7730e-04 l_g_fea: 6.6304e-01 l_g_gan: 2.9471e-02 l_d_real: 4.0039e-03 l_d_fake: 4.3401e-03 D_real: 1.1075e+02 D_fake: 1.0486e+02 +20-04-05 09:53:34.793 - INFO: l_g_pix: 9.6854e-05 l_g_fea: 4.7499e-01 l_g_gan: 2.3830e-03 l_d_real: 1.0128e+00 l_d_fake: 1.0137e+00 D_real: 7.8301e+01 D_fake: 7.8838e+01 +20-04-05 09:53:35.189 - INFO: Models and training states saved. +20-04-05 09:54:46.002 - INFO: # Validation # PSNR: 30.577, SSIM: 0.85183, LPIPS: 0.050674 +20-04-05 09:54:46.002 - INFO: psnr: 30.577, ssim: 0.85183, lpips: 0.050674 +20-04-05 09:57:57.514 - INFO: l_g_pix: 9.8456e-05 l_g_fea: 4.8019e-01 l_g_gan: 4.1191e-03 l_d_real: 6.2325e-01 l_d_fake: 6.2441e-01 D_real: 5.4023e+01 D_fake: 5.3823e+01 +20-04-05 10:01:08.584 - INFO: l_g_pix: 1.0728e-04 l_g_fea: 4.1800e-01 l_g_gan: 9.8201e-03 l_d_real: 2.0282e-01 l_d_fake: 1.7608e-01 D_real: 3.6988e+01 D_fake: 3.5213e+01 +20-04-05 10:04:21.128 - INFO: l_g_pix: 1.0787e-04 l_g_fea: 4.6435e-01 l_g_gan: 1.5690e-02 l_d_real: 8.6865e-02 l_d_fake: 9.4246e-02 D_real: 8.7636e+01 D_fake: 8.4589e+01 +20-04-05 10:07:32.433 - INFO: l_g_pix: 1.2923e-04 l_g_fea: 4.9223e-01 l_g_gan: 4.4044e-03 l_d_real: 1.1477e+00 l_d_fake: 1.2117e+00 D_real: 7.9436e+01 D_fake: 7.9735e+01 +20-04-05 10:10:44.078 - INFO: l_g_pix: 9.5829e-05 l_g_fea: 4.5462e-01 l_g_gan: 2.7355e-03 l_d_real: 1.2473e+00 l_d_fake: 1.4374e+00 D_real: 6.0516e+01 D_fake: 6.1312e+01 +20-04-05 10:13:56.499 - INFO: l_g_pix: 1.3795e-04 l_g_fea: 5.3337e-01 l_g_gan: 1.3765e-02 l_d_real: 7.5717e-02 l_d_fake: 8.5031e-02 D_real: 4.8269e+01 D_fake: 4.5597e+01 +20-04-05 10:17:08.352 - INFO: l_g_pix: 1.4676e-04 l_g_fea: 4.5075e-01 l_g_gan: 5.1468e-03 l_d_real: 6.2754e-01 l_d_fake: 6.1904e-01 D_real: 2.3598e+01 D_fake: 2.3192e+01 +20-04-05 10:20:19.516 - INFO: l_g_pix: 1.1506e-04 l_g_fea: 4.1257e-01 l_g_gan: 9.9647e-03 l_d_real: 1.7865e-01 l_d_fake: 2.3750e-01 D_real: 4.8991e+01 D_fake: 4.7206e+01 +20-04-05 10:23:30.864 - INFO: l_g_pix: 1.0347e-04 l_g_fea: 5.1085e-01 l_g_gan: 1.1653e-02 l_d_real: 2.5783e-01 l_d_fake: 2.3967e-01 D_real: 6.7391e+01 D_fake: 6.5309e+01 +20-04-05 10:26:42.733 - INFO: l_g_pix: 1.0697e-04 l_g_fea: 3.6257e-01 l_g_gan: 2.3314e-02 l_d_real: 1.4515e-02 l_d_fake: 1.5938e-02 D_real: 9.0584e+01 D_fake: 8.5937e+01 +20-04-05 10:29:53.773 - INFO: l_g_pix: 1.2249e-04 l_g_fea: 4.0753e-01 l_g_gan: 1.4857e-02 l_d_real: 5.4728e-02 l_d_fake: 5.6605e-02 D_real: 6.7660e+01 D_fake: 6.4744e+01 +20-04-05 10:33:04.844 - INFO: l_g_pix: 1.3173e-04 l_g_fea: 5.0859e-01 l_g_gan: 5.3465e-03 l_d_real: 4.5925e-01 l_d_fake: 4.5628e-01 D_real: 6.5083e+01 D_fake: 6.4472e+01 +20-04-05 10:36:16.050 - INFO: l_g_pix: 1.2474e-04 l_g_fea: 4.4627e-01 l_g_gan: 5.1293e-03 l_d_real: 6.9053e-01 l_d_fake: 7.3176e-01 D_real: 5.4902e+01 D_fake: 5.4587e+01 +20-04-05 10:39:28.662 - INFO: l_g_pix: 1.3017e-04 l_g_fea: 5.4499e-01 l_g_gan: 1.9675e-03 l_d_real: 1.4710e+00 l_d_fake: 1.5024e+00 D_real: 8.7725e+01 D_fake: 8.8818e+01 +20-04-05 10:42:39.510 - INFO: l_g_pix: 1.0794e-04 l_g_fea: 4.3410e-01 l_g_gan: 1.3189e-02 l_d_real: 7.6867e-01 l_d_fake: 9.5025e-01 D_real: 6.2138e+01 D_fake: 6.0360e+01 +20-04-05 10:45:50.686 - INFO: l_g_pix: 1.1763e-04 l_g_fea: 4.2042e-01 l_g_gan: 1.8959e-02 l_d_real: 3.0261e-02 l_d_fake: 2.6668e-02 D_real: 7.5912e+01 D_fake: 7.2149e+01 +20-04-05 10:49:02.619 - INFO: l_g_pix: 9.2962e-05 l_g_fea: 3.9887e-01 l_g_gan: 1.1322e-02 l_d_real: 1.3542e-01 l_d_fake: 1.3840e-01 D_real: 8.7431e+01 D_fake: 8.5303e+01 +20-04-05 10:52:14.382 - INFO: l_g_pix: 9.9223e-05 l_g_fea: 2.7460e-01 l_g_gan: 2.8228e-03 l_d_real: 1.1592e+00 l_d_fake: 1.1515e+00 D_real: 4.4257e+01 D_fake: 4.4847e+01 +20-04-05 10:55:26.468 - INFO: l_g_pix: 1.2450e-04 l_g_fea: 4.8342e-01 l_g_gan: 7.5819e-03 l_d_real: 2.7833e-01 l_d_fake: 2.8124e-01 D_real: 6.9135e+01 D_fake: 6.7899e+01 +20-04-05 10:58:39.037 - INFO: l_g_pix: 1.1320e-04 l_g_fea: 4.2505e-01 l_g_gan: 7.6306e-03 l_d_real: 2.7868e-01 l_d_fake: 2.6623e-01 D_real: 9.0365e+01 D_fake: 8.9111e+01 +20-04-05 11:01:51.079 - INFO: l_g_pix: 1.1035e-04 l_g_fea: 4.7605e-01 l_g_gan: 6.2083e-03 l_d_real: 5.4853e-01 l_d_fake: 4.8943e-01 D_real: 6.9697e+01 D_fake: 6.8974e+01 +20-04-05 11:05:03.051 - INFO: l_g_pix: 1.0961e-04 l_g_fea: 5.0820e-01 l_g_gan: 2.0449e-02 l_d_real: 1.8899e-02 l_d_fake: 1.9221e-02 D_real: 7.2712e+01 D_fake: 6.8641e+01 +20-04-05 11:08:19.843 - INFO: l_g_pix: 1.1181e-04 l_g_fea: 4.2330e-01 l_g_gan: 3.6981e-03 l_d_real: 7.0289e-01 l_d_fake: 7.0575e-01 D_real: 3.1671e+01 D_fake: 3.1636e+01 +20-04-05 11:11:31.518 - INFO: l_g_pix: 1.5140e-04 l_g_fea: 5.7993e-01 l_g_gan: 5.7281e-03 l_d_real: 4.9356e-01 l_d_fake: 4.8556e-01 D_real: 5.0609e+01 D_fake: 4.9953e+01 +20-04-05 11:14:43.134 - INFO: l_g_pix: 1.0920e-04 l_g_fea: 4.4715e-01 l_g_gan: 5.6100e-03 l_d_real: 5.3742e-01 l_d_fake: 5.1876e-01 D_real: 5.4734e+01 D_fake: 5.4140e+01 +20-04-05 11:14:43.580 - INFO: Models and training states saved. +20-04-05 11:15:49.377 - INFO: # Validation # PSNR: 31.386, SSIM: 0.84495, LPIPS: 0.048181 +20-04-05 11:15:49.377 - INFO: psnr: 31.386, ssim: 0.84495, lpips: 0.048181 +20-04-05 11:19:58.474 - INFO: l_g_pix: 1.2685e-04 l_g_fea: 4.9225e-01 l_g_gan: 1.0660e-02 l_d_real: 1.5421e-01 l_d_fake: 1.4337e-01 D_real: 4.0630e+01 D_fake: 3.8647e+01 +20-04-05 11:23:08.562 - INFO: l_g_pix: 9.8769e-05 l_g_fea: 3.9846e-01 l_g_gan: 4.7403e-03 l_d_real: 5.6028e-01 l_d_fake: 5.7880e-01 D_real: 2.7592e+01 D_fake: 2.7214e+01 +20-04-05 11:26:20.820 - INFO: l_g_pix: 1.2723e-04 l_g_fea: 4.4484e-01 l_g_gan: 1.7311e-02 l_d_real: 5.7254e-02 l_d_fake: 9.1726e-02 D_real: 3.9868e+01 D_fake: 3.6480e+01 +20-04-05 11:29:31.804 - INFO: l_g_pix: 1.8544e-04 l_g_fea: 5.9183e-01 l_g_gan: 5.3518e-03 l_d_real: 5.9455e-01 l_d_fake: 5.9451e-01 D_real: 6.7670e+01 D_fake: 6.7194e+01 +20-04-05 11:32:43.301 - INFO: l_g_pix: 1.1849e-04 l_g_fea: 5.1815e-01 l_g_gan: 9.5052e-03 l_d_real: 2.4826e-01 l_d_fake: 2.1484e-01 D_real: 5.4876e+01 D_fake: 5.3206e+01 +20-04-05 11:35:55.604 - INFO: l_g_pix: 1.1663e-04 l_g_fea: 4.8193e-01 l_g_gan: 1.5101e-02 l_d_real: 8.5425e-02 l_d_fake: 7.6627e-02 D_real: 7.6111e+01 D_fake: 7.3172e+01 +20-04-05 11:39:07.009 - INFO: l_g_pix: 9.6313e-05 l_g_fea: 4.0155e-01 l_g_gan: 4.7107e-03 l_d_real: 6.4113e-01 l_d_fake: 6.3433e-01 D_real: 2.3952e+01 D_fake: 2.3647e+01 +20-04-05 11:42:18.945 - INFO: l_g_pix: 9.3100e-05 l_g_fea: 4.3510e-01 l_g_gan: 7.0084e-03 l_d_real: 3.6094e-01 l_d_fake: 3.3821e-01 D_real: 6.8729e+01 D_fake: 6.7677e+01 +20-04-05 11:45:30.797 - INFO: l_g_pix: 1.2167e-04 l_g_fea: 5.5781e-01 l_g_gan: 1.1436e-02 l_d_real: 2.2173e-01 l_d_fake: 2.3078e-01 D_real: 4.3251e+01 D_fake: 4.1190e+01 +20-04-05 11:48:41.967 - INFO: l_g_pix: 1.2311e-04 l_g_fea: 5.3283e-01 l_g_gan: 9.6682e-04 l_d_real: 2.0295e+00 l_d_fake: 2.0212e+00 D_real: 8.4029e+01 D_fake: 8.5861e+01 +20-04-05 11:51:53.250 - INFO: l_g_pix: 1.3401e-04 l_g_fea: 5.6085e-01 l_g_gan: 7.4936e-03 l_d_real: 3.4073e-01 l_d_fake: 3.4341e-01 D_real: 5.9674e+01 D_fake: 5.8518e+01 +20-04-05 11:55:04.684 - INFO: l_g_pix: 1.2045e-04 l_g_fea: 5.5020e-01 l_g_gan: 7.2099e-03 l_d_real: 3.3244e-01 l_d_fake: 3.3791e-01 D_real: 7.3253e+01 D_fake: 7.2146e+01 +20-04-05 11:58:16.114 - INFO: l_g_pix: 1.2557e-04 l_g_fea: 4.4908e-01 l_g_gan: 9.3083e-03 l_d_real: 2.6408e-01 l_d_fake: 2.5708e-01 D_real: 6.5425e+01 D_fake: 6.3824e+01 +20-04-05 12:01:27.747 - INFO: l_g_pix: 1.3485e-04 l_g_fea: 4.4919e-01 l_g_gan: 5.5729e-03 l_d_real: 4.3454e-01 l_d_fake: 4.4091e-01 D_real: 5.6159e+01 D_fake: 5.5482e+01 +20-04-05 12:04:39.544 - INFO: l_g_pix: 1.2239e-04 l_g_fea: 5.3135e-01 l_g_gan: 2.4437e-02 l_d_real: 9.9498e-03 l_d_fake: 9.4393e-03 D_real: 8.5759e+01 D_fake: 8.0881e+01 +20-04-05 12:07:51.514 - INFO: l_g_pix: 1.1897e-04 l_g_fea: 3.9450e-01 l_g_gan: 1.9478e-02 l_d_real: 1.3407e-01 l_d_fake: 8.4675e-02 D_real: 4.1335e+01 D_fake: 3.7548e+01 +20-04-05 12:11:03.532 - INFO: l_g_pix: 1.4031e-04 l_g_fea: 4.7463e-01 l_g_gan: 1.4016e-02 l_d_real: 9.5800e-02 l_d_fake: 1.2025e-01 D_real: 6.2894e+01 D_fake: 6.0199e+01 +20-04-05 12:14:15.004 - INFO: l_g_pix: 9.7273e-05 l_g_fea: 4.3236e-01 l_g_gan: 9.3772e-03 l_d_real: 1.9371e-01 l_d_fake: 1.8689e-01 D_real: 4.3269e+01 D_fake: 4.1584e+01 +20-04-05 12:17:26.348 - INFO: l_g_pix: 1.2578e-04 l_g_fea: 4.6159e-01 l_g_gan: 1.4232e-02 l_d_real: 7.3860e-02 l_d_fake: 8.1009e-02 D_real: 8.4708e+01 D_fake: 8.1939e+01 +20-04-05 12:20:37.887 - INFO: l_g_pix: 1.2442e-04 l_g_fea: 4.3466e-01 l_g_gan: 1.5521e-02 l_d_real: 6.1097e-02 l_d_fake: 8.1845e-02 D_real: 6.3488e+01 D_fake: 6.0455e+01 +20-04-05 12:23:49.364 - INFO: l_g_pix: 1.3918e-04 l_g_fea: 4.8088e-01 l_g_gan: 1.5691e-02 l_d_real: 7.4093e-02 l_d_fake: 8.0851e-02 D_real: 7.3121e+01 D_fake: 7.0060e+01 +20-04-05 12:27:00.961 - INFO: l_g_pix: 1.4664e-04 l_g_fea: 4.5672e-01 l_g_gan: 2.8078e-03 l_d_real: 1.4198e+00 l_d_fake: 1.4211e+00 D_real: 7.8120e+01 D_fake: 7.8979e+01 +20-04-05 12:30:12.547 - INFO: l_g_pix: 1.0412e-04 l_g_fea: 4.1781e-01 l_g_gan: 2.2775e-02 l_d_real: 1.2583e-02 l_d_fake: 1.4088e-02 D_real: 1.0495e+02 D_fake: 1.0041e+02 +20-04-05 12:33:24.645 - INFO: l_g_pix: 1.0843e-04 l_g_fea: 4.4278e-01 l_g_gan: 1.9263e-02 l_d_real: 6.0319e-02 l_d_fake: 3.3949e-02 D_real: 1.0287e+02 D_fake: 9.9060e+01 +20-04-05 12:36:35.865 - INFO: l_g_pix: 9.6675e-05 l_g_fea: 3.4590e-01 l_g_gan: 1.0385e-02 l_d_real: 1.6154e-01 l_d_fake: 1.6665e-01 D_real: 1.1961e+02 D_fake: 1.1770e+02 +20-04-05 12:36:36.258 - INFO: Models and training states saved. +20-04-05 12:37:34.815 - INFO: # Validation # PSNR: 31.466, SSIM: 0.85016, LPIPS: 0.050883 +20-04-05 12:37:34.815 - INFO: psnr: 31.466, ssim: 0.85016, lpips: 0.050883 +20-04-05 12:43:17.289 - INFO: l_g_pix: 1.3000e-04 l_g_fea: 5.3198e-01 l_g_gan: 1.3474e-02 l_d_real: 1.2824e-01 l_d_fake: 1.2663e-01 D_real: 6.3261e+01 D_fake: 6.0694e+01 +20-04-05 12:46:44.929 - INFO: l_g_pix: 1.0389e-04 l_g_fea: 4.3914e-01 l_g_gan: 1.4676e-02 l_d_real: 1.4948e-01 l_d_fake: 1.0621e-01 D_real: 8.2722e+01 D_fake: 7.9915e+01 +20-04-05 12:49:57.427 - INFO: l_g_pix: 1.5557e-04 l_g_fea: 5.4565e-01 l_g_gan: 1.7367e-02 l_d_real: 4.7826e-02 l_d_fake: 6.0750e-02 D_real: 8.3076e+01 D_fake: 7.9657e+01 +20-04-05 12:53:09.625 - INFO: l_g_pix: 1.4517e-04 l_g_fea: 5.3818e-01 l_g_gan: 4.9011e-03 l_d_real: 6.2846e-01 l_d_fake: 6.2732e-01 D_real: 6.8253e+01 D_fake: 6.7901e+01 +20-04-05 12:56:21.122 - INFO: l_g_pix: 1.3177e-04 l_g_fea: 4.9817e-01 l_g_gan: 1.1564e-02 l_d_real: 1.9203e-01 l_d_fake: 2.0073e-01 D_real: 9.2693e+01 D_fake: 9.0577e+01 +20-04-05 12:59:33.611 - INFO: l_g_pix: 1.2966e-04 l_g_fea: 4.9001e-01 l_g_gan: 4.8176e-03 l_d_real: 7.3743e-01 l_d_fake: 7.8316e-01 D_real: 1.0054e+02 D_fake: 1.0034e+02 +20-04-05 13:02:45.492 - INFO: l_g_pix: 1.2050e-04 l_g_fea: 4.2132e-01 l_g_gan: 3.5966e-02 l_d_real: 3.8254e-03 l_d_fake: 4.3320e-03 D_real: 9.8377e+01 D_fake: 9.1188e+01 +20-04-05 13:05:57.952 - INFO: l_g_pix: 9.1987e-05 l_g_fea: 3.4127e-01 l_g_gan: 1.3460e-02 l_d_real: 1.3700e-01 l_d_fake: 1.2156e-01 D_real: 4.8566e+01 D_fake: 4.6003e+01 +20-04-05 13:09:09.976 - INFO: l_g_pix: 1.4014e-04 l_g_fea: 5.1057e-01 l_g_gan: 3.6845e-03 l_d_real: 7.4430e-01 l_d_fake: 7.3791e-01 D_real: 4.0676e+01 D_fake: 4.0680e+01 +20-04-05 13:12:21.561 - INFO: l_g_pix: 1.3447e-04 l_g_fea: 4.4234e-01 l_g_gan: 4.9983e-03 l_d_real: 5.9292e-01 l_d_fake: 6.1781e-01 D_real: 4.3928e+01 D_fake: 4.3534e+01 +20-04-05 13:15:33.187 - INFO: l_g_pix: 9.9045e-05 l_g_fea: 4.2540e-01 l_g_gan: 2.3111e-02 l_d_real: 1.5040e-02 l_d_fake: 1.1374e-02 D_real: 5.3800e+01 D_fake: 4.9191e+01 +20-04-05 13:18:45.951 - INFO: l_g_pix: 1.0035e-04 l_g_fea: 4.2187e-01 l_g_gan: 1.2921e-02 l_d_real: 1.4007e-01 l_d_fake: 9.7249e-02 D_real: 7.4705e+01 D_fake: 7.2239e+01 +20-04-05 13:21:58.819 - INFO: l_g_pix: 1.5834e-04 l_g_fea: 5.0104e-01 l_g_gan: 5.1375e-02 l_d_real: 9.6299e-05 l_d_fake: 1.4611e-04 D_real: 1.0447e+02 D_fake: 9.4195e+01 +20-04-05 13:25:10.834 - INFO: l_g_pix: 1.4859e-04 l_g_fea: 4.8303e-01 l_g_gan: 7.5481e-03 l_d_real: 3.6714e-01 l_d_fake: 4.9977e-01 D_real: 5.3552e+01 D_fake: 5.2476e+01 +20-04-05 13:28:23.161 - INFO: l_g_pix: 1.3741e-04 l_g_fea: 4.6177e-01 l_g_gan: 4.5623e-03 l_d_real: 8.6170e-01 l_d_fake: 8.5958e-01 D_real: 6.4294e+01 D_fake: 6.4242e+01 +20-04-05 13:31:35.569 - INFO: l_g_pix: 1.3584e-04 l_g_fea: 5.1463e-01 l_g_gan: 8.8688e-03 l_d_real: 2.2351e-01 l_d_fake: 2.2397e-01 D_real: 7.2369e+01 D_fake: 7.0819e+01 +20-04-05 13:34:48.170 - INFO: l_g_pix: 1.3966e-04 l_g_fea: 4.6427e-01 l_g_gan: 1.8264e-02 l_d_real: 5.9810e-02 l_d_fake: 6.9173e-02 D_real: 6.9300e+01 D_fake: 6.5712e+01 +20-04-05 13:38:00.776 - INFO: l_g_pix: 1.1005e-04 l_g_fea: 4.3754e-01 l_g_gan: 1.1053e-02 l_d_real: 1.7744e-01 l_d_fake: 1.7531e-01 D_real: 4.9496e+01 D_fake: 4.7462e+01 +20-04-05 13:41:13.094 - INFO: l_g_pix: 1.4019e-04 l_g_fea: 5.9753e-01 l_g_gan: 7.8108e-03 l_d_real: 2.5404e-01 l_d_fake: 2.5375e-01 D_real: 1.7001e+01 D_fake: 1.5692e+01 +20-04-05 13:44:24.794 - INFO: l_g_pix: 1.0313e-04 l_g_fea: 4.3558e-01 l_g_gan: 4.8063e-03 l_d_real: 6.8547e-01 l_d_fake: 7.1282e-01 D_real: 3.5931e+01 D_fake: 3.5669e+01 +20-04-05 13:47:37.299 - INFO: l_g_pix: 1.1753e-04 l_g_fea: 4.7352e-01 l_g_gan: 6.5445e-03 l_d_real: 3.8192e-01 l_d_fake: 3.7198e-01 D_real: 3.7250e+01 D_fake: 3.6318e+01 +20-04-05 13:50:48.613 - INFO: l_g_pix: 1.3436e-04 l_g_fea: 5.9035e-01 l_g_gan: 3.1264e-02 l_d_real: 1.5278e-02 l_d_fake: 3.2013e-02 D_real: 4.9505e+01 D_fake: 4.3276e+01 +20-04-05 13:54:00.615 - INFO: l_g_pix: 1.2626e-04 l_g_fea: 4.8451e-01 l_g_gan: 2.4567e-03 l_d_real: 1.0625e+00 l_d_fake: 1.0776e+00 D_real: 4.2441e+01 D_fake: 4.3020e+01 +20-04-05 13:57:13.422 - INFO: l_g_pix: 1.3890e-04 l_g_fea: 4.2702e-01 l_g_gan: 1.4979e-02 l_d_real: 6.5642e-02 l_d_fake: 6.5447e-02 D_real: 6.4440e+01 D_fake: 6.1510e+01 +20-04-05 14:00:26.277 - INFO: l_g_pix: 1.1430e-04 l_g_fea: 4.3665e-01 l_g_gan: 1.4105e-02 l_d_real: 1.0411e-01 l_d_fake: 9.5090e-02 D_real: 2.7839e+01 D_fake: 2.5118e+01 +20-04-05 14:00:26.763 - INFO: Models and training states saved. +20-04-05 14:01:42.540 - INFO: # Validation # PSNR: 30.316, SSIM: 0.8508, LPIPS: 0.052897 +20-04-05 14:01:42.540 - INFO: psnr: 30.316, ssim: 0.8508, lpips: 0.052897 +20-04-05 14:07:38.809 - INFO: l_g_pix: 1.0541e-04 l_g_fea: 4.8841e-01 l_g_gan: 3.8553e-03 l_d_real: 7.8187e-01 l_d_fake: 7.4747e-01 D_real: 7.0532e+01 D_fake: 7.0525e+01 +20-04-05 14:11:20.000 - INFO: l_g_pix: 1.3231e-04 l_g_fea: 3.9216e-01 l_g_gan: 1.2962e-02 l_d_real: 8.9933e-02 l_d_fake: 8.4489e-02 D_real: 6.7862e+01 D_fake: 6.5357e+01 +20-04-05 14:14:31.583 - INFO: l_g_pix: 1.3497e-04 l_g_fea: 4.3413e-01 l_g_gan: 5.7357e-03 l_d_real: 6.4638e-01 l_d_fake: 6.1206e-01 D_real: 4.7457e+01 D_fake: 4.6940e+01 +20-04-05 14:17:43.930 - INFO: l_g_pix: 1.4755e-04 l_g_fea: 5.6002e-01 l_g_gan: 8.9712e-03 l_d_real: 2.5857e-01 l_d_fake: 2.6373e-01 D_real: 9.3510e+01 D_fake: 9.1977e+01 +20-04-05 14:20:54.963 - INFO: l_g_pix: 1.4607e-04 l_g_fea: 4.5742e-01 l_g_gan: 3.9921e-03 l_d_real: 8.9427e-01 l_d_fake: 9.0302e-01 D_real: 6.1857e+01 D_fake: 6.1957e+01 +20-04-05 14:24:06.833 - INFO: l_g_pix: 1.0374e-04 l_g_fea: 4.2556e-01 l_g_gan: 1.5732e-02 l_d_real: 7.3586e-02 l_d_fake: 8.7054e-02 D_real: 6.6666e+01 D_fake: 6.3600e+01 +20-04-05 14:27:18.681 - INFO: l_g_pix: 1.3353e-04 l_g_fea: 5.1132e-01 l_g_gan: 1.4761e-02 l_d_real: 6.4334e-02 l_d_fake: 6.6131e-02 D_real: 5.1262e+01 D_fake: 4.8375e+01 +20-04-05 14:30:30.243 - INFO: l_g_pix: 1.1471e-04 l_g_fea: 4.4886e-01 l_g_gan: 1.1140e-02 l_d_real: 1.4963e-01 l_d_fake: 1.3941e-01 D_real: 7.2024e+01 D_fake: 6.9940e+01 +20-04-05 14:33:42.018 - INFO: l_g_pix: 9.8479e-05 l_g_fea: 3.9131e-01 l_g_gan: 1.6249e-02 l_d_real: 4.9388e-02 l_d_fake: 4.9177e-02 D_real: 5.7685e+01 D_fake: 5.4485e+01 +20-04-05 14:36:53.527 - INFO: l_g_pix: 9.4024e-05 l_g_fea: 4.6835e-01 l_g_gan: 1.1410e-02 l_d_real: 1.9629e-01 l_d_fake: 1.5929e-01 D_real: 7.8160e+01 D_fake: 7.6055e+01 +20-04-05 14:40:04.854 - INFO: l_g_pix: 1.3210e-04 l_g_fea: 3.9711e-01 l_g_gan: 3.3032e-03 l_d_real: 8.0078e-01 l_d_fake: 8.2963e-01 D_real: 7.3069e+01 D_fake: 7.3224e+01 +20-04-05 14:43:16.610 - INFO: l_g_pix: 1.0067e-04 l_g_fea: 4.1428e-01 l_g_gan: 6.3532e-04 l_d_real: 2.4209e+00 l_d_fake: 2.3886e+00 D_real: 9.5939e+01 D_fake: 9.8217e+01 +20-04-05 14:46:33.826 - INFO: l_g_pix: 1.3802e-04 l_g_fea: 4.7975e-01 l_g_gan: 3.4209e-03 l_d_real: 9.6210e-01 l_d_fake: 9.4127e-01 D_real: 8.4198e+01 D_fake: 8.4466e+01 +20-04-05 14:49:46.347 - INFO: l_g_pix: 1.0945e-04 l_g_fea: 5.0123e-01 l_g_gan: 1.3668e-02 l_d_real: 8.1190e-02 l_d_fake: 1.4790e-01 D_real: 6.0155e+01 D_fake: 5.7536e+01 +20-04-05 14:53:32.564 - INFO: l_g_pix: 8.6250e-05 l_g_fea: 3.0655e-01 l_g_gan: 3.4893e-03 l_d_real: 7.5025e-01 l_d_fake: 7.5038e-01 D_real: 6.2103e+01 D_fake: 6.2155e+01 +20-04-05 14:56:44.456 - INFO: l_g_pix: 9.7275e-05 l_g_fea: 4.0998e-01 l_g_gan: 4.1642e-03 l_d_real: 6.1501e-01 l_d_fake: 6.2078e-01 D_real: 3.4784e+01 D_fake: 3.4569e+01 +20-04-05 14:59:55.252 - INFO: l_g_pix: 1.1653e-04 l_g_fea: 4.6983e-01 l_g_gan: 1.1624e-02 l_d_real: 1.4622e-01 l_d_fake: 1.2401e-01 D_real: 8.6083e+01 D_fake: 8.3893e+01 +20-04-05 15:03:06.384 - INFO: l_g_pix: 7.8515e-05 l_g_fea: 3.6646e-01 l_g_gan: 4.7525e-03 l_d_real: 6.5848e-01 l_d_fake: 6.3286e-01 D_real: 4.1929e+01 D_fake: 4.1625e+01 +20-04-05 15:06:18.219 - INFO: l_g_pix: 1.4032e-04 l_g_fea: 3.7418e-01 l_g_gan: 2.9195e-03 l_d_real: 1.0034e+00 l_d_fake: 1.0078e+00 D_real: 7.2040e+01 D_fake: 7.2462e+01 +20-04-05 15:09:31.489 - INFO: l_g_pix: 1.2584e-04 l_g_fea: 4.3494e-01 l_g_gan: 2.0058e-02 l_d_real: 2.6288e-02 l_d_fake: 2.1702e-02 D_real: 6.9792e+01 D_fake: 6.5804e+01 +20-04-05 15:12:43.247 - INFO: l_g_pix: 1.0957e-04 l_g_fea: 4.3571e-01 l_g_gan: 1.0838e-02 l_d_real: 1.4627e-01 l_d_fake: 1.4880e-01 D_real: 6.4256e+01 D_fake: 6.2236e+01 +20-04-05 15:15:54.985 - INFO: l_g_pix: 1.2849e-04 l_g_fea: 5.3642e-01 l_g_gan: 6.3404e-03 l_d_real: 3.8022e-01 l_d_fake: 3.8098e-01 D_real: 6.1165e+01 D_fake: 6.0278e+01 +20-04-05 15:19:06.501 - INFO: l_g_pix: 8.6026e-05 l_g_fea: 3.8743e-01 l_g_gan: 9.9829e-03 l_d_real: 1.9987e-01 l_d_fake: 2.8134e-01 D_real: 2.5884e+01 D_fake: 2.4128e+01 +20-04-05 15:22:17.218 - INFO: l_g_pix: 1.0490e-04 l_g_fea: 5.2962e-01 l_g_gan: 1.0537e-02 l_d_real: 1.7210e-01 l_d_fake: 1.6688e-01 D_real: 4.6848e+01 D_fake: 4.4910e+01 +20-04-05 15:25:29.281 - INFO: l_g_pix: 1.2915e-04 l_g_fea: 5.3088e-01 l_g_gan: 1.9501e-02 l_d_real: 2.5430e-02 l_d_fake: 2.5326e-02 D_real: 3.3376e+01 D_fake: 2.9501e+01 +20-04-05 15:25:29.679 - INFO: Models and training states saved. +20-04-05 15:26:48.263 - INFO: # Validation # PSNR: 31.382, SSIM: 0.82734, LPIPS: 0.032429 +20-04-05 15:26:48.264 - INFO: psnr: 31.382, ssim: 0.82734, lpips: 0.032429 +20-04-05 15:32:42.602 - INFO: l_g_pix: 1.1128e-04 l_g_fea: 4.8174e-01 l_g_gan: 5.0208e-03 l_d_real: 5.0152e-01 l_d_fake: 5.1880e-01 D_real: 3.1138e+01 D_fake: 3.0644e+01 +20-04-05 15:35:54.582 - INFO: l_g_pix: 1.3757e-04 l_g_fea: 5.4563e-01 l_g_gan: 4.3003e-03 l_d_real: 5.8152e-01 l_d_fake: 5.9057e-01 D_real: 5.9566e+01 D_fake: 5.9292e+01 +20-04-05 15:39:05.711 - INFO: l_g_pix: 8.7105e-05 l_g_fea: 3.4698e-01 l_g_gan: 4.5586e-03 l_d_real: 5.8355e-01 l_d_fake: 5.8575e-01 D_real: 2.2125e+01 D_fake: 2.1798e+01 +20-04-05 15:42:17.203 - INFO: l_g_pix: 1.4104e-04 l_g_fea: 5.8447e-01 l_g_gan: 1.6265e-02 l_d_real: 6.1276e-02 l_d_fake: 6.2833e-02 D_real: 4.5254e+01 D_fake: 4.2063e+01 +20-04-05 15:45:29.388 - INFO: l_g_pix: 1.5401e-04 l_g_fea: 5.0801e-01 l_g_gan: 3.1034e-02 l_d_real: 3.0145e-03 l_d_fake: 3.4144e-03 D_real: 6.1716e+01 D_fake: 5.5512e+01 +20-04-05 15:48:40.093 - INFO: l_g_pix: 1.0174e-04 l_g_fea: 4.3288e-01 l_g_gan: 1.6312e-02 l_d_real: 5.5113e-02 l_d_fake: 4.7276e-02 D_real: 5.4393e+01 D_fake: 5.1182e+01 +20-04-05 15:51:50.611 - INFO: l_g_pix: 1.0422e-04 l_g_fea: 4.7680e-01 l_g_gan: 1.5302e-02 l_d_real: 8.2894e-02 l_d_fake: 1.5972e-01 D_real: 5.9667e+01 D_fake: 5.6728e+01 +20-04-05 15:55:01.907 - INFO: l_g_pix: 1.5576e-04 l_g_fea: 5.2135e-01 l_g_gan: 6.3733e-03 l_d_real: 4.5480e-01 l_d_fake: 4.5120e-01 D_real: 4.1522e+01 D_fake: 4.0700e+01 +20-04-05 15:58:12.732 - INFO: l_g_pix: 1.2263e-04 l_g_fea: 4.1814e-01 l_g_gan: 2.6476e-03 l_d_real: 1.0304e+00 l_d_fake: 1.0619e+00 D_real: 4.9366e+01 D_fake: 4.9883e+01 +20-04-05 16:01:24.932 - INFO: l_g_pix: 1.3971e-04 l_g_fea: 5.0068e-01 l_g_gan: 6.5914e-03 l_d_real: 3.8445e-01 l_d_fake: 3.6521e-01 D_real: 4.8299e+01 D_fake: 4.7355e+01 +20-04-05 16:04:57.928 - INFO: l_g_pix: 1.1106e-04 l_g_fea: 5.0837e-01 l_g_gan: 5.1867e-03 l_d_real: 4.8532e-01 l_d_fake: 4.7171e-01 D_real: 4.4106e+01 D_fake: 4.3548e+01 +20-04-05 16:08:30.707 - INFO: l_g_pix: 1.2993e-04 l_g_fea: 4.2947e-01 l_g_gan: 4.6909e-03 l_d_real: 5.8873e-01 l_d_fake: 5.7259e-01 D_real: 4.5536e+01 D_fake: 4.5179e+01 +20-04-05 16:11:41.800 - INFO: l_g_pix: 1.1714e-04 l_g_fea: 4.4436e-01 l_g_gan: 8.8554e-03 l_d_real: 3.3023e-01 l_d_fake: 3.4354e-01 D_real: 8.5204e+01 D_fake: 8.3770e+01 +20-04-05 16:14:52.793 - INFO: l_g_pix: 1.4480e-04 l_g_fea: 4.4475e-01 l_g_gan: 1.5762e-02 l_d_real: 6.4952e-02 l_d_fake: 5.7475e-02 D_real: 4.2276e+01 D_fake: 3.9185e+01 +20-04-05 16:18:03.582 - INFO: l_g_pix: 1.0882e-04 l_g_fea: 4.5977e-01 l_g_gan: 1.0504e-02 l_d_real: 1.5472e-01 l_d_fake: 1.6191e-01 D_real: 5.8284e+01 D_fake: 5.6342e+01 +20-04-05 16:21:14.271 - INFO: l_g_pix: 1.1743e-04 l_g_fea: 4.6778e-01 l_g_gan: 7.1944e-03 l_d_real: 3.3570e-01 l_d_fake: 3.3325e-01 D_real: 4.4325e+01 D_fake: 4.3221e+01 +20-04-05 16:24:25.707 - INFO: l_g_pix: 1.6785e-04 l_g_fea: 4.8632e-01 l_g_gan: 1.5184e-02 l_d_real: 7.5438e-02 l_d_fake: 6.1936e-02 D_real: 4.9655e+01 D_fake: 4.6686e+01 +20-04-05 16:27:37.412 - INFO: l_g_pix: 8.2516e-05 l_g_fea: 3.3530e-01 l_g_gan: 5.4087e-03 l_d_real: 4.8487e-01 l_d_fake: 4.8179e-01 D_real: 5.1157e+01 D_fake: 5.0559e+01 +20-04-05 16:30:48.191 - INFO: l_g_pix: 1.0427e-04 l_g_fea: 3.5497e-01 l_g_gan: 5.8458e-03 l_d_real: 4.0956e-01 l_d_fake: 4.1980e-01 D_real: 3.8498e+01 D_fake: 3.7743e+01 +20-04-05 16:33:58.444 - INFO: l_g_pix: 1.5773e-04 l_g_fea: 5.6541e-01 l_g_gan: 5.6619e-03 l_d_real: 4.9724e-01 l_d_fake: 4.8426e-01 D_real: 3.4545e+01 D_fake: 3.3903e+01 +20-04-05 16:37:09.572 - INFO: l_g_pix: 1.1590e-04 l_g_fea: 4.1810e-01 l_g_gan: 1.0976e-02 l_d_real: 1.3999e-01 l_d_fake: 1.4347e-01 D_real: 6.3443e+01 D_fake: 6.1390e+01 +20-04-05 16:40:20.508 - INFO: l_g_pix: 1.0254e-04 l_g_fea: 4.6844e-01 l_g_gan: 8.7588e-03 l_d_real: 2.3743e-01 l_d_fake: 2.6892e-01 D_real: 3.9619e+01 D_fake: 3.8121e+01 +20-04-05 16:43:31.689 - INFO: l_g_pix: 1.0216e-04 l_g_fea: 4.7529e-01 l_g_gan: 2.3725e-03 l_d_real: 1.3975e+00 l_d_fake: 1.3073e+00 D_real: 7.5470e+01 D_fake: 7.6348e+01 +20-04-05 16:46:42.217 - INFO: l_g_pix: 1.4056e-04 l_g_fea: 5.3361e-01 l_g_gan: 1.8762e-02 l_d_real: 3.3013e-02 l_d_fake: 2.6903e-02 D_real: 8.3300e+01 D_fake: 7.9577e+01 +20-04-05 16:49:54.165 - INFO: l_g_pix: 1.3508e-04 l_g_fea: 5.1079e-01 l_g_gan: 3.3361e-03 l_d_real: 8.1715e-01 l_d_fake: 8.1811e-01 D_real: 5.2654e+01 D_fake: 5.2805e+01 +20-04-05 16:49:54.619 - INFO: Models and training states saved. +20-04-05 16:51:02.491 - INFO: # Validation # PSNR: 31.384, SSIM: 0.82025, LPIPS: 0.035657 +20-04-05 16:51:02.492 - INFO: psnr: 31.384, ssim: 0.82025, lpips: 0.035657 +20-04-05 16:56:44.900 - INFO: l_g_pix: 9.2096e-05 l_g_fea: 5.0447e-01 l_g_gan: 7.9625e-03 l_d_real: 2.8922e-01 l_d_fake: 2.6318e-01 D_real: 3.3142e+01 D_fake: 3.1826e+01 +20-04-05 17:01:07.042 - INFO: l_g_pix: 1.1561e-04 l_g_fea: 5.1223e-01 l_g_gan: 3.1698e-03 l_d_real: 9.8922e-01 l_d_fake: 9.5779e-01 D_real: 2.4575e+01 D_fake: 2.4915e+01 +20-04-05 17:05:06.567 - INFO: l_g_pix: 1.4152e-04 l_g_fea: 4.6966e-01 l_g_gan: 4.4700e-03 l_d_real: 5.7995e-01 l_d_fake: 5.8305e-01 D_real: 2.0291e+01 D_fake: 1.9979e+01 +20-04-05 17:09:33.832 - INFO: l_g_pix: 1.1410e-04 l_g_fea: 4.3763e-01 l_g_gan: 6.8445e-03 l_d_real: 3.5447e-01 l_d_fake: 3.6832e-01 D_real: 6.1742e+01 D_fake: 6.0735e+01 +20-04-05 17:14:15.063 - INFO: l_g_pix: 1.0632e-04 l_g_fea: 4.6603e-01 l_g_gan: 8.4151e-03 l_d_real: 2.2829e-01 l_d_fake: 2.2429e-01 D_real: 4.7006e+01 D_fake: 4.5549e+01 +20-04-05 17:18:04.708 - INFO: l_g_pix: 1.2656e-04 l_g_fea: 3.5015e-01 l_g_gan: 7.0637e-03 l_d_real: 4.3192e-01 l_d_fake: 5.9200e-01 D_real: 5.6372e+01 D_fake: 5.5471e+01 +20-04-05 17:21:42.904 - INFO: l_g_pix: 1.0609e-04 l_g_fea: 4.7831e-01 l_g_gan: 1.6646e-02 l_d_real: 5.2935e-02 l_d_fake: 5.8295e-02 D_real: 5.6937e+01 D_fake: 5.3663e+01 +20-04-05 17:25:15.619 - INFO: l_g_pix: 1.0065e-04 l_g_fea: 3.6989e-01 l_g_gan: 1.6839e-02 l_d_real: 3.8265e-02 l_d_fake: 3.9768e-02 D_real: 5.6737e+01 D_fake: 5.3408e+01 +20-04-05 17:28:39.520 - INFO: l_g_pix: 1.1300e-04 l_g_fea: 5.3760e-01 l_g_gan: 2.3766e-02 l_d_real: 1.1344e-02 l_d_fake: 9.5102e-03 D_real: 8.0107e+01 D_fake: 7.5364e+01 +20-04-05 17:32:06.526 - INFO: l_g_pix: 1.0744e-04 l_g_fea: 4.3478e-01 l_g_gan: 2.4481e-02 l_d_real: 1.1800e-02 l_d_fake: 1.4403e-02 D_real: 6.6973e+01 D_fake: 6.2090e+01 diff --git a/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/train_200405-174240.log b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/train_200405-174240.log new file mode 100644 index 0000000000000000000000000000000000000000..aa34f83ed52bb94de878c86367f4b1a32cd9ad8a --- /dev/null +++ b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/train_200405-174240.log @@ -0,0 +1,2188 @@ +20-04-05 17:42:40.469 - INFO: Set [resume_state] to ../experiments/sollevante/training_state/60000.state +20-04-05 17:42:40.469 - INFO: Resuming training from epoch: 304, iter: 60000. +20-04-05 17:42:40.469 - WARNING: pretrain_model path will be ignored when resuming training. +20-04-05 17:42:40.469 - INFO: Set [pretrain_model_G] to /home/owner/github/BasicSR/experiments/sollevante/models/60000_G.pth +20-04-05 17:42:40.469 - INFO: Set [pretrain_model_D] to /home/owner/github/BasicSR/experiments/sollevante/models/60000_D.pth +20-04-05 17:42:40.470 - INFO: name: sollevante + use_tb_logger: True + model: srragan + scale: 4 + gpu_ids: [0] + datasets:[ + train:[ + name: sollevante-train + mode: LRHR + dataroot_HR: ['/mnt/8tb-hdd-1/datasets/sollevante/hr/train'] + dataroot_LR: ['/mnt/8tb-hdd-1/datasets/sollevante/lr/train'] + subset_file: None + use_shuffle: True + znorm: False + n_workers: 8 + batch_size: 32 + HR_size: 128 + lr_downscale: True + lr_downscale_types: [1, 2, 777] + use_flip: True + use_rot: True + hr_rrot: False + lr_blur: False + lr_blur_types: ['gaussian', 'clean', 'clean', 'clean'] + lr_noise: False + lr_noise_types: ['gaussian', 'clean', 'clean', 'clean', 'clean'] + lr_noise2: False + lr_noise_types2: ['dither', 'dither', 'clean', 'clean'] + hr_noise: False + hr_noise_types: ['gaussian', 'clean', 'clean', 'clean', 'clean'] + phase: train + scale: 4 + data_type: img + ] + val:[ + name: sollevante-val + mode: LRHR + dataroot_HR: ['/mnt/8tb-hdd-1/datasets/sollevante/hr/val'] + dataroot_LR: ['/mnt/8tb-hdd-1/datasets/sollevante/lr/val'] + znorm: False + lr_downscale: False + lr_downscale_types: [0, 1] + phase: val + scale: 4 + data_type: img + ] + ] + path:[ + root: /home/owner/github/BasicSR + pretrain_model_G: /home/owner/github/BasicSR/experiments/sollevante/models/60000_G.pth + resume_state: ../experiments/sollevante/training_state/60000.state + experiments_root: /home/owner/github/BasicSR/experiments/sollevante + models: /home/owner/github/BasicSR/experiments/sollevante/models + training_state: /home/owner/github/BasicSR/experiments/sollevante/training_state + log: /home/owner/github/BasicSR/experiments/sollevante + val_images: /home/owner/github/BasicSR/experiments/sollevante/val_images + pretrain_model_D: /home/owner/github/BasicSR/experiments/sollevante/models/60000_D.pth + ] + network_G:[ + which_model_G: RRDB_net + norm_type: None + mode: CNA + nf: 64 + nb: 23 + in_nc: 3 + out_nc: 3 + gc: 32 + group: 1 + convtype: Conv2D + net_act: leakyrelu + scale: 4 + ] + network_D:[ + which_model_D: discriminator_vgg_128 + norm_type: batch + act_type: leakyrelu + mode: CNA + nf: 64 + in_nc: 3 + ] + train:[ + lr_G: 0.0001 + weight_decay_G: 0 + beta1_G: 0.9 + lr_D: 0.0001 + weight_decay_D: 0 + beta1_D: 0.9 + lr_scheme: MultiStepLR + lr_steps: [50000, 100000, 200000, 300000] + lr_gamma: 0.5 + pixel_criterion: l1 + pixel_weight: 0.01 + feature_criterion: l1 + feature_weight: 1 + gan_type: vanilla + gan_weight: 0.005 + niter: 500000.0 + val_freq: 5000.0 + ] + logger:[ + print_freq: 200 + save_checkpoint_freq: 5000.0 + ] + is_train: True + +20-04-05 17:42:40.575 - INFO: Random seed: 6670 +20-04-05 17:42:40.610 - INFO: Dataset [LRHRDataset - sollevante-train] is created. +20-04-05 17:42:40.610 - INFO: Number of train images: 6,309, iters: 198 +20-04-05 17:42:40.610 - INFO: Total epochs needed: 2526 for iters 500,000 +20-04-05 17:42:40.610 - INFO: Dataset [LRHRDataset - sollevante-val] is created. +20-04-05 17:42:40.610 - INFO: Number of val images in [sollevante-val]: 4 +20-04-05 17:42:40.751 - INFO: Initialization method [kaiming] +20-04-05 17:42:40.958 - INFO: Initialization method [kaiming] +20-04-05 17:42:41.037 - INFO: Loading pretrained model for G [/home/owner/github/BasicSR/experiments/sollevante/models/60000_G.pth] ... +20-04-05 17:42:41.148 - INFO: Loading pretrained model for D [/home/owner/github/BasicSR/experiments/sollevante/models/60000_D.pth] ... +20-04-05 17:42:42.224 - INFO: Remove HFEN loss. +20-04-05 17:42:42.224 - INFO: Remove TV loss. +20-04-05 17:42:42.224 - INFO: Remove SSIM loss. +20-04-05 17:42:42.224 - INFO: Remove LPIPS loss. +20-04-05 17:42:42.224 - INFO: Remove SPL loss. +20-04-05 17:42:42.232 - INFO: Network G structure: DataParallel - RRDBNet, with parameters: 16,697,987 +20-04-05 17:42:42.232 - INFO: RRDBNet( + (model): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): Identity + + |Sequential( + | (0): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (1): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (2): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (3): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (4): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (5): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (6): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (7): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (8): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (9): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (10): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (11): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (12): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (13): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (14): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (15): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (16): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (17): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (18): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (19): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (20): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (21): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (22): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (23): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + |) + (2): Upsample(scale_factor=2.0, mode=nearest) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (4): LeakyReLU(negative_slope=0.2, inplace=True) + (5): Upsample(scale_factor=2.0, mode=nearest) + (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (7): LeakyReLU(negative_slope=0.2, inplace=True) + (8): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (9): LeakyReLU(negative_slope=0.2, inplace=True) + (10): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) +20-04-05 17:42:42.236 - INFO: Network D structure: DataParallel - Discriminator_VGG_128, with parameters: 14,502,281 +20-04-05 17:42:42.236 - INFO: Discriminator_VGG_128( + (features): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): LeakyReLU(negative_slope=0.2, inplace=True) + (2): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (4): LeakyReLU(negative_slope=0.2, inplace=True) + (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (7): LeakyReLU(negative_slope=0.2, inplace=True) + (8): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (9): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (10): LeakyReLU(negative_slope=0.2, inplace=True) + (11): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (13): LeakyReLU(negative_slope=0.2, inplace=True) + (14): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (16): LeakyReLU(negative_slope=0.2, inplace=True) + (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (18): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (19): LeakyReLU(negative_slope=0.2, inplace=True) + (20): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (21): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (22): LeakyReLU(negative_slope=0.2, inplace=True) + (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (24): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (25): LeakyReLU(negative_slope=0.2, inplace=True) + (26): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (27): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (28): LeakyReLU(negative_slope=0.2, inplace=True) + ) + (classifier): Sequential( + (0): Linear(in_features=8192, out_features=100, bias=True) + (1): LeakyReLU(negative_slope=0.2, inplace=True) + (2): Linear(in_features=100, out_features=1, bias=True) + ) +) +20-04-05 17:42:42.236 - INFO: Network F structure: DataParallel - VGGFeatureExtractor, with parameters: 20,024,384 +20-04-05 17:42:42.236 - INFO: VGGFeatureExtractor( + (features): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU(inplace=True) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (3): ReLU(inplace=True) + (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (6): ReLU(inplace=True) + (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (8): ReLU(inplace=True) + (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (11): ReLU(inplace=True) + (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (13): ReLU(inplace=True) + (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (15): ReLU(inplace=True) + (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (17): ReLU(inplace=True) + (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (20): ReLU(inplace=True) + (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (22): ReLU(inplace=True) + (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (24): ReLU(inplace=True) + (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (26): ReLU(inplace=True) + (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (29): ReLU(inplace=True) + (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (31): ReLU(inplace=True) + (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (33): ReLU(inplace=True) + (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) +20-04-05 17:42:42.236 - INFO: Model [SRRaGANModel] is created. +20-04-05 17:42:42.302 - INFO: Start training from epoch: 304, iter: 60000 +20-04-05 17:45:57.654 - INFO: l_g_pix: 1.4920e-04 l_g_fea: 5.0552e-01 l_g_gan: 7.6096e-03 l_d_real: 2.8285e-01 l_d_fake: 2.9871e-01 D_real: 3.7648e+01 D_fake: 3.6417e+01 +20-04-05 17:49:07.082 - INFO: l_g_pix: 1.6805e-04 l_g_fea: 5.6645e-01 l_g_gan: 1.9379e-02 l_d_real: 3.4413e-02 l_d_fake: 3.3444e-02 D_real: 6.7796e+01 D_fake: 6.3954e+01 +20-04-05 17:52:17.385 - INFO: l_g_pix: 1.1755e-04 l_g_fea: 4.1561e-01 l_g_gan: 2.9043e-02 l_d_real: 4.4754e-03 l_d_fake: 5.4263e-03 D_real: 6.4989e+01 D_fake: 5.9185e+01 +20-04-05 17:55:28.231 - INFO: l_g_pix: 9.4591e-05 l_g_fea: 4.0670e-01 l_g_gan: 6.0991e-03 l_d_real: 4.0453e-01 l_d_fake: 3.9927e-01 D_real: 8.5834e+01 D_fake: 8.5016e+01 +20-04-05 17:58:38.863 - INFO: l_g_pix: 1.5079e-04 l_g_fea: 5.0973e-01 l_g_gan: 3.2770e-02 l_d_real: 1.8009e-03 l_d_fake: 1.9827e-03 D_real: 1.2741e+02 D_fake: 1.2086e+02 +20-04-05 18:01:49.487 - INFO: l_g_pix: 8.4722e-05 l_g_fea: 4.6990e-01 l_g_gan: 1.7428e-02 l_d_real: 3.4085e-02 l_d_fake: 3.5274e-02 D_real: 6.7751e+01 D_fake: 6.4300e+01 +20-04-05 18:05:00.374 - INFO: l_g_pix: 1.1428e-04 l_g_fea: 4.0989e-01 l_g_gan: 4.3307e-03 l_d_real: 6.1917e-01 l_d_fake: 6.2407e-01 D_real: 8.3871e+01 D_fake: 8.3627e+01 +20-04-05 18:08:10.828 - INFO: l_g_pix: 1.6183e-04 l_g_fea: 4.9662e-01 l_g_gan: 1.1615e-02 l_d_real: 1.1200e-01 l_d_fake: 1.1834e-01 D_real: 7.9509e+01 D_fake: 7.7302e+01 +20-04-05 18:11:21.336 - INFO: l_g_pix: 1.0846e-04 l_g_fea: 4.2820e-01 l_g_gan: 7.1860e-03 l_d_real: 2.9912e-01 l_d_fake: 3.1064e-01 D_real: 4.3538e+01 D_fake: 4.2406e+01 +20-04-05 18:14:33.280 - INFO: l_g_pix: 1.3505e-04 l_g_fea: 4.4922e-01 l_g_gan: 9.2906e-03 l_d_real: 2.0661e-01 l_d_fake: 2.2380e-01 D_real: 4.4204e+01 D_fake: 4.2561e+01 +20-04-05 18:17:44.886 - INFO: l_g_pix: 1.5551e-04 l_g_fea: 5.1826e-01 l_g_gan: 6.8055e-03 l_d_real: 4.6039e-01 l_d_fake: 4.4047e-01 D_real: 5.8844e+01 D_fake: 5.7933e+01 +20-04-05 18:20:55.932 - INFO: l_g_pix: 1.1975e-04 l_g_fea: 4.4144e-01 l_g_gan: 6.7295e-03 l_d_real: 3.1694e-01 l_d_fake: 3.1806e-01 D_real: 5.4820e+01 D_fake: 5.3791e+01 +20-04-05 18:24:07.726 - INFO: l_g_pix: 1.4878e-04 l_g_fea: 5.4802e-01 l_g_gan: 1.5005e-03 l_d_real: 1.6188e+00 l_d_fake: 1.5636e+00 D_real: 7.4169e+01 D_fake: 7.5460e+01 +20-04-05 18:27:19.823 - INFO: l_g_pix: 1.2529e-04 l_g_fea: 4.5490e-01 l_g_gan: 5.6609e-03 l_d_real: 5.1210e-01 l_d_fake: 5.2366e-01 D_real: 9.9402e+01 D_fake: 9.8788e+01 +20-04-05 18:30:31.157 - INFO: l_g_pix: 1.1334e-04 l_g_fea: 4.7915e-01 l_g_gan: 9.1592e-03 l_d_real: 2.3335e-01 l_d_fake: 2.0819e-01 D_real: 4.5922e+01 D_fake: 4.4311e+01 +20-04-05 18:33:42.300 - INFO: l_g_pix: 1.4822e-04 l_g_fea: 3.9742e-01 l_g_gan: 1.5309e-03 l_d_real: 1.6062e+00 l_d_fake: 1.5861e+00 D_real: 5.3385e+01 D_fake: 5.4675e+01 +20-04-05 18:36:53.561 - INFO: l_g_pix: 1.0364e-04 l_g_fea: 4.1811e-01 l_g_gan: 1.7089e-02 l_d_real: 3.8306e-02 l_d_fake: 3.8939e-02 D_real: 5.9679e+01 D_fake: 5.6300e+01 +20-04-05 18:40:04.713 - INFO: l_g_pix: 1.1940e-04 l_g_fea: 4.0567e-01 l_g_gan: 1.1291e-02 l_d_real: 2.1058e-01 l_d_fake: 1.5440e-01 D_real: 1.0717e+02 D_fake: 1.0510e+02 +20-04-05 18:43:16.060 - INFO: l_g_pix: 7.7891e-05 l_g_fea: 3.2243e-01 l_g_gan: 9.6772e-03 l_d_real: 1.8601e-01 l_d_fake: 1.9287e-01 D_real: 5.9616e+01 D_fake: 5.7870e+01 +20-04-05 18:46:27.462 - INFO: l_g_pix: 1.6022e-04 l_g_fea: 4.5294e-01 l_g_gan: 5.1016e-02 l_d_real: 4.4829e-05 l_d_fake: 5.2743e-05 D_real: 1.2257e+02 D_fake: 1.1237e+02 +20-04-05 18:49:38.469 - INFO: l_g_pix: 8.7632e-05 l_g_fea: 4.5462e-01 l_g_gan: 1.5239e-02 l_d_real: 8.0071e-02 l_d_fake: 6.9572e-02 D_real: 3.0361e+01 D_fake: 2.7388e+01 +20-04-05 18:52:49.615 - INFO: l_g_pix: 1.3296e-04 l_g_fea: 3.9011e-01 l_g_gan: 1.2972e-02 l_d_real: 1.3981e-01 l_d_fake: 1.0698e-01 D_real: 4.6151e+01 D_fake: 4.3680e+01 +20-04-05 18:56:00.516 - INFO: l_g_pix: 1.0356e-04 l_g_fea: 4.4320e-01 l_g_gan: 1.4147e-02 l_d_real: 8.5226e-02 l_d_fake: 8.9239e-02 D_real: 6.1111e+01 D_fake: 5.8369e+01 +20-04-05 18:59:10.984 - INFO: l_g_pix: 1.3901e-04 l_g_fea: 5.7633e-01 l_g_gan: 5.8241e-03 l_d_real: 4.2737e-01 l_d_fake: 4.4253e-01 D_real: 8.6762e+01 D_fake: 8.6032e+01 +20-04-05 19:02:21.827 - INFO: l_g_pix: 1.2603e-04 l_g_fea: 5.0492e-01 l_g_gan: 9.1765e-03 l_d_real: 2.2353e-01 l_d_fake: 2.2360e-01 D_real: 9.4107e+01 D_fake: 9.2496e+01 +20-04-05 19:02:22.260 - INFO: Models and training states saved. +20-04-05 19:03:31.587 - INFO: # Validation # PSNR: 31.38, SSIM: 0.84938, LPIPS: 0.044759 +20-04-05 19:03:31.587 - INFO: psnr: 31.38, ssim: 0.84938, lpips: 0.044759 +20-04-05 19:06:44.936 - INFO: l_g_pix: 1.2679e-04 l_g_fea: 4.0677e-01 l_g_gan: 8.4452e-03 l_d_real: 2.6940e-01 l_d_fake: 2.7573e-01 D_real: 7.8757e+01 D_fake: 7.7341e+01 +20-04-05 19:10:02.737 - INFO: l_g_pix: 9.7548e-05 l_g_fea: 3.8229e-01 l_g_gan: 1.1409e-02 l_d_real: 1.2468e-01 l_d_fake: 1.2742e-01 D_real: 4.4124e+01 D_fake: 4.1968e+01 +20-04-05 19:13:15.259 - INFO: l_g_pix: 1.2933e-04 l_g_fea: 4.1560e-01 l_g_gan: 2.1634e-02 l_d_real: 1.7301e-02 l_d_fake: 1.6573e-02 D_real: 1.1185e+02 D_fake: 1.0754e+02 +20-04-05 19:16:26.840 - INFO: l_g_pix: 8.1568e-05 l_g_fea: 3.9845e-01 l_g_gan: 1.4427e-02 l_d_real: 6.5083e-02 l_d_fake: 6.7502e-02 D_real: 3.1947e+01 D_fake: 2.9128e+01 +20-04-05 19:19:38.743 - INFO: l_g_pix: 9.8380e-05 l_g_fea: 3.5542e-01 l_g_gan: 1.4267e-02 l_d_real: 1.0810e-01 l_d_fake: 8.3081e-02 D_real: 4.5101e+01 D_fake: 4.2343e+01 +20-04-05 19:22:50.521 - INFO: l_g_pix: 1.2862e-04 l_g_fea: 4.6451e-01 l_g_gan: 6.5539e-03 l_d_real: 3.4308e-01 l_d_fake: 3.5218e-01 D_real: 2.0185e+01 D_fake: 1.9222e+01 +20-04-05 19:26:02.542 - INFO: l_g_pix: 1.3824e-04 l_g_fea: 5.6690e-01 l_g_gan: 8.7386e-04 l_d_real: 2.0395e+00 l_d_fake: 2.0461e+00 D_real: 8.2711e+01 D_fake: 8.4579e+01 +20-04-05 19:29:14.322 - INFO: l_g_pix: 9.5914e-05 l_g_fea: 3.7431e-01 l_g_gan: 2.8820e-02 l_d_real: 5.6822e-03 l_d_fake: 4.3164e-03 D_real: 6.0798e+01 D_fake: 5.5039e+01 +20-04-05 19:32:25.288 - INFO: l_g_pix: 1.2153e-04 l_g_fea: 5.0836e-01 l_g_gan: 3.2546e-02 l_d_real: 2.7078e-03 l_d_fake: 4.6793e-03 D_real: 8.3276e+01 D_fake: 7.6771e+01 +20-04-05 19:35:38.536 - INFO: l_g_pix: 1.1148e-04 l_g_fea: 4.7803e-01 l_g_gan: 1.0869e-02 l_d_real: 1.7475e-01 l_d_fake: 1.8454e-01 D_real: 8.4181e+01 D_fake: 8.2187e+01 +20-04-05 19:38:51.421 - INFO: l_g_pix: 1.1626e-04 l_g_fea: 5.3357e-01 l_g_gan: 1.6603e-03 l_d_real: 1.4774e+00 l_d_fake: 1.5017e+00 D_real: 3.7716e+01 D_fake: 3.8873e+01 +20-04-05 19:42:02.651 - INFO: l_g_pix: 9.6205e-05 l_g_fea: 4.0898e-01 l_g_gan: 6.6202e-03 l_d_real: 5.2520e-01 l_d_fake: 5.4577e-01 D_real: 1.1379e+02 D_fake: 1.1300e+02 +20-04-05 19:45:16.119 - INFO: l_g_pix: 1.5530e-04 l_g_fea: 5.2739e-01 l_g_gan: 7.4224e-03 l_d_real: 4.4305e-01 l_d_fake: 4.2896e-01 D_real: 6.0377e+01 D_fake: 5.9328e+01 +20-04-05 19:48:27.990 - INFO: l_g_pix: 1.6948e-04 l_g_fea: 4.6831e-01 l_g_gan: 4.1166e-03 l_d_real: 6.3772e-01 l_d_fake: 6.3360e-01 D_real: 5.6253e+01 D_fake: 5.6066e+01 +20-04-05 19:51:40.126 - INFO: l_g_pix: 1.0173e-04 l_g_fea: 4.7201e-01 l_g_gan: 7.2020e-03 l_d_real: 3.3367e-01 l_d_fake: 3.2499e-01 D_real: 4.2726e+01 D_fake: 4.1615e+01 +20-04-05 19:54:52.210 - INFO: l_g_pix: 8.5836e-05 l_g_fea: 4.3348e-01 l_g_gan: 1.4224e-02 l_d_real: 9.0617e-02 l_d_fake: 7.9907e-02 D_real: 3.2482e+01 D_fake: 2.9722e+01 +20-04-05 19:58:04.392 - INFO: l_g_pix: 1.6081e-04 l_g_fea: 5.6199e-01 l_g_gan: 4.4119e-02 l_d_real: 4.4575e-04 l_d_fake: 8.4644e-04 D_real: 6.6746e+01 D_fake: 5.7923e+01 +20-04-05 20:01:17.776 - INFO: l_g_pix: 1.1866e-04 l_g_fea: 5.3544e-01 l_g_gan: 2.6007e-02 l_d_real: 9.8808e-03 l_d_fake: 8.0435e-03 D_real: 7.9045e+01 D_fake: 7.3852e+01 +20-04-05 20:04:30.145 - INFO: l_g_pix: 1.1879e-04 l_g_fea: 4.2946e-01 l_g_gan: 1.0199e-02 l_d_real: 1.6068e-01 l_d_fake: 1.8729e-01 D_real: 5.6965e+01 D_fake: 5.5099e+01 +20-04-05 20:07:41.983 - INFO: l_g_pix: 1.5510e-04 l_g_fea: 4.9406e-01 l_g_gan: 3.9216e-03 l_d_real: 6.9541e-01 l_d_fake: 6.9207e-01 D_real: 5.3763e+01 D_fake: 5.3673e+01 +20-04-05 20:10:54.342 - INFO: l_g_pix: 7.6413e-05 l_g_fea: 2.8270e-01 l_g_gan: 1.9241e-03 l_d_real: 1.3537e+00 l_d_fake: 1.3252e+00 D_real: 2.0718e+01 D_fake: 2.1672e+01 +20-04-05 20:14:06.435 - INFO: l_g_pix: 1.1433e-04 l_g_fea: 5.5174e-01 l_g_gan: 1.2397e-02 l_d_real: 1.0288e-01 l_d_fake: 1.1626e-01 D_real: 3.7419e+01 D_fake: 3.5049e+01 +20-04-05 20:17:18.776 - INFO: l_g_pix: 1.1581e-04 l_g_fea: 4.1335e-01 l_g_gan: 1.4133e-02 l_d_real: 7.5386e-02 l_d_fake: 7.2928e-02 D_real: 7.7864e+01 D_fake: 7.5111e+01 +20-04-05 20:20:29.913 - INFO: l_g_pix: 8.6314e-05 l_g_fea: 3.6203e-01 l_g_gan: 5.5070e-03 l_d_real: 4.8780e-01 l_d_fake: 5.2471e-01 D_real: 3.7671e+01 D_fake: 3.7076e+01 +20-04-05 20:23:41.989 - INFO: l_g_pix: 1.0402e-04 l_g_fea: 3.7619e-01 l_g_gan: 5.3810e-04 l_d_real: 2.3886e+00 l_d_fake: 2.3888e+00 D_real: 7.1647e+01 D_fake: 7.3928e+01 +20-04-05 20:23:42.449 - INFO: Models and training states saved. +20-04-05 20:24:55.071 - INFO: # Validation # PSNR: 31.291, SSIM: 0.84065, LPIPS: 0.045839 +20-04-05 20:24:55.071 - INFO: psnr: 31.291, ssim: 0.84065, lpips: 0.045839 +20-04-05 20:29:00.878 - INFO: l_g_pix: 9.5370e-05 l_g_fea: 4.3588e-01 l_g_gan: 7.3815e-03 l_d_real: 2.9088e-01 l_d_fake: 2.9675e-01 D_real: 5.4494e+01 D_fake: 5.3312e+01 +20-04-05 20:32:12.492 - INFO: l_g_pix: 1.0381e-04 l_g_fea: 3.7815e-01 l_g_gan: 2.8280e-02 l_d_real: 5.0693e-03 l_d_fake: 5.9760e-03 D_real: 6.5944e+01 D_fake: 6.0293e+01 +20-04-05 20:35:24.641 - INFO: l_g_pix: 9.9022e-05 l_g_fea: 4.2100e-01 l_g_gan: 1.5534e-02 l_d_real: 5.3473e-02 l_d_fake: 6.0102e-02 D_real: 1.0090e+02 D_fake: 9.7850e+01 +20-04-05 20:38:36.219 - INFO: l_g_pix: 1.1036e-04 l_g_fea: 5.2361e-01 l_g_gan: 1.8340e-02 l_d_real: 2.9711e-02 l_d_fake: 2.8735e-02 D_real: 3.4190e+01 D_fake: 3.0551e+01 +20-04-05 20:41:48.442 - INFO: l_g_pix: 1.2033e-04 l_g_fea: 4.9372e-01 l_g_gan: 1.2979e-02 l_d_real: 9.8545e-02 l_d_fake: 8.8529e-02 D_real: 3.3505e+01 D_fake: 3.1003e+01 +20-04-05 20:45:00.236 - INFO: l_g_pix: 8.8315e-05 l_g_fea: 3.9716e-01 l_g_gan: 1.6271e-02 l_d_real: 4.3926e-02 l_d_fake: 4.5248e-02 D_real: 5.8603e+01 D_fake: 5.5394e+01 +20-04-05 20:48:12.266 - INFO: l_g_pix: 1.0622e-04 l_g_fea: 4.0389e-01 l_g_gan: 1.4858e-02 l_d_real: 7.0176e-02 l_d_fake: 6.0527e-02 D_real: 7.4795e+01 D_fake: 7.1889e+01 +20-04-05 20:51:24.343 - INFO: l_g_pix: 1.2625e-04 l_g_fea: 4.6043e-01 l_g_gan: 7.5876e-03 l_d_real: 7.6533e-01 l_d_fake: 8.3687e-01 D_real: 1.7348e+01 D_fake: 1.6632e+01 +20-04-05 20:54:36.882 - INFO: l_g_pix: 1.5823e-04 l_g_fea: 5.6615e-01 l_g_gan: 1.5077e-02 l_d_real: 7.5428e-02 l_d_fake: 7.5071e-02 D_real: 6.3702e+01 D_fake: 6.0762e+01 +20-04-05 20:57:49.432 - INFO: l_g_pix: 1.3097e-04 l_g_fea: 6.0031e-01 l_g_gan: 1.4324e-03 l_d_real: 1.5862e+00 l_d_fake: 1.5801e+00 D_real: 3.9687e+01 D_fake: 4.0984e+01 +20-04-05 21:01:01.624 - INFO: l_g_pix: 1.3976e-04 l_g_fea: 4.8912e-01 l_g_gan: 1.9403e-02 l_d_real: 4.3156e-02 l_d_fake: 4.2251e-02 D_real: 9.2164e+01 D_fake: 8.8326e+01 +20-04-05 21:04:14.282 - INFO: l_g_pix: 9.5218e-05 l_g_fea: 4.6867e-01 l_g_gan: 6.8859e-03 l_d_real: 3.7782e-01 l_d_fake: 3.6981e-01 D_real: 2.7151e+01 D_fake: 2.6148e+01 +20-04-05 21:07:27.742 - INFO: l_g_pix: 8.3319e-05 l_g_fea: 3.7536e-01 l_g_gan: 6.7135e-03 l_d_real: 3.4942e-01 l_d_fake: 3.4626e-01 D_real: 6.3551e+01 D_fake: 6.2556e+01 +20-04-05 21:10:40.332 - INFO: l_g_pix: 9.6919e-05 l_g_fea: 4.5542e-01 l_g_gan: 1.4087e-02 l_d_real: 1.0676e-01 l_d_fake: 9.2252e-02 D_real: 4.7909e+01 D_fake: 4.5191e+01 +20-04-05 21:13:52.780 - INFO: l_g_pix: 1.2455e-04 l_g_fea: 5.5134e-01 l_g_gan: 1.0733e-02 l_d_real: 1.4025e-01 l_d_fake: 1.4419e-01 D_real: 6.0995e+00 D_fake: 4.0951e+00 +20-04-05 21:17:09.888 - INFO: l_g_pix: 1.6963e-04 l_g_fea: 5.5535e-01 l_g_gan: 1.0827e-02 l_d_real: 1.5321e-01 l_d_fake: 1.6310e-01 D_real: 2.3837e+01 D_fake: 2.1830e+01 +20-04-05 21:20:22.780 - INFO: l_g_pix: 1.2665e-04 l_g_fea: 4.3369e-01 l_g_gan: 9.6351e-03 l_d_real: 2.0997e-01 l_d_fake: 2.2676e-01 D_real: 5.6327e+01 D_fake: 5.4618e+01 +20-04-05 21:23:35.614 - INFO: l_g_pix: 9.6544e-05 l_g_fea: 3.9204e-01 l_g_gan: 7.3944e-03 l_d_real: 2.9451e-01 l_d_fake: 2.9414e-01 D_real: 1.7993e+01 D_fake: 1.6809e+01 +20-04-05 21:26:48.441 - INFO: l_g_pix: 1.3089e-04 l_g_fea: 4.9459e-01 l_g_gan: 1.6885e-02 l_d_real: 4.2808e-02 l_d_fake: 4.4288e-02 D_real: 1.2786e+02 D_fake: 1.2453e+02 +20-04-05 21:29:59.814 - INFO: l_g_pix: 1.7063e-04 l_g_fea: 4.9078e-01 l_g_gan: 1.1379e-02 l_d_real: 1.9227e-01 l_d_fake: 1.5310e-01 D_real: 7.5311e+01 D_fake: 7.3208e+01 +20-04-05 21:33:12.843 - INFO: l_g_pix: 9.6206e-05 l_g_fea: 3.1680e-01 l_g_gan: 1.1940e-02 l_d_real: 1.4100e-01 l_d_fake: 1.9718e-01 D_real: 5.2857e+01 D_fake: 5.0638e+01 +20-04-05 21:36:25.038 - INFO: l_g_pix: 9.8604e-05 l_g_fea: 4.3983e-01 l_g_gan: 1.0722e-02 l_d_real: 1.4707e-01 l_d_fake: 1.4378e-01 D_real: 8.0962e+01 D_fake: 7.8963e+01 +20-04-05 21:39:37.541 - INFO: l_g_pix: 1.3580e-04 l_g_fea: 4.9499e-01 l_g_gan: 3.3195e-03 l_d_real: 8.1459e-01 l_d_fake: 8.1129e-01 D_real: 5.1169e+01 D_fake: 5.1318e+01 +20-04-05 21:42:50.650 - INFO: l_g_pix: 1.1214e-04 l_g_fea: 3.6522e-01 l_g_gan: 2.6863e-02 l_d_real: 7.8313e-03 l_d_fake: 5.4607e-03 D_real: 6.1333e+01 D_fake: 5.5967e+01 +20-04-05 21:46:03.824 - INFO: l_g_pix: 8.8217e-05 l_g_fea: 3.8177e-01 l_g_gan: 6.7913e-03 l_d_real: 3.4151e-01 l_d_fake: 3.5130e-01 D_real: 4.9898e+01 D_fake: 4.8886e+01 +20-04-05 21:46:04.319 - INFO: Models and training states saved. +20-04-05 21:47:14.153 - INFO: # Validation # PSNR: 30.979, SSIM: 0.82168, LPIPS: 0.028558 +20-04-05 21:47:14.153 - INFO: psnr: 30.979, ssim: 0.82168, lpips: 0.028558 +20-04-05 21:53:54.664 - INFO: l_g_pix: 9.3217e-05 l_g_fea: 4.4205e-01 l_g_gan: 4.1458e-03 l_d_real: 6.6498e-01 l_d_fake: 6.7775e-01 D_real: 4.8118e+01 D_fake: 4.7960e+01 +20-04-05 21:57:05.472 - INFO: l_g_pix: 1.2870e-04 l_g_fea: 5.5896e-01 l_g_gan: 1.2245e-02 l_d_real: 1.0344e-01 l_d_fake: 1.1300e-01 D_real: 5.7093e+01 D_fake: 5.4752e+01 +20-04-05 22:00:16.870 - INFO: l_g_pix: 1.2876e-04 l_g_fea: 4.7773e-01 l_g_gan: 8.0843e-03 l_d_real: 3.0072e-01 l_d_fake: 3.0604e-01 D_real: 4.4770e+01 D_fake: 4.3456e+01 +20-04-05 22:03:29.395 - INFO: l_g_pix: 9.5181e-05 l_g_fea: 4.0154e-01 l_g_gan: 1.4429e-02 l_d_real: 6.9895e-02 l_d_fake: 7.3274e-02 D_real: 2.6577e+01 D_fake: 2.3763e+01 +20-04-05 22:06:41.622 - INFO: l_g_pix: 9.6997e-05 l_g_fea: 4.8712e-01 l_g_gan: 2.2464e-02 l_d_real: 1.9162e-02 l_d_fake: 1.5439e-02 D_real: 1.8989e+01 D_fake: 1.4514e+01 +20-04-05 22:09:53.793 - INFO: l_g_pix: 7.4495e-05 l_g_fea: 3.3814e-01 l_g_gan: 2.3388e-02 l_d_real: 1.2139e-02 l_d_fake: 1.3385e-02 D_real: 9.5921e+01 D_fake: 9.1256e+01 +20-04-05 22:13:05.559 - INFO: l_g_pix: 1.0060e-04 l_g_fea: 4.3364e-01 l_g_gan: 7.8762e-03 l_d_real: 2.6207e-01 l_d_fake: 2.6200e-01 D_real: 4.8582e+01 D_fake: 4.7269e+01 +20-04-05 22:16:17.879 - INFO: l_g_pix: 9.2321e-05 l_g_fea: 3.9353e-01 l_g_gan: 1.0818e-02 l_d_real: 1.5028e-01 l_d_fake: 1.4293e-01 D_real: 3.5311e+01 D_fake: 3.3294e+01 +20-04-05 22:19:30.326 - INFO: l_g_pix: 1.3964e-04 l_g_fea: 3.6664e-01 l_g_gan: 1.7483e-02 l_d_real: 4.0597e-02 l_d_fake: 3.9690e-02 D_real: 6.6623e+01 D_fake: 6.3166e+01 +20-04-05 22:22:42.831 - INFO: l_g_pix: 9.1248e-05 l_g_fea: 2.9119e-01 l_g_gan: 1.4564e-02 l_d_real: 6.6352e-02 l_d_fake: 6.2918e-02 D_real: 3.3810e+01 D_fake: 3.0961e+01 +20-04-05 22:25:55.773 - INFO: l_g_pix: 1.0822e-04 l_g_fea: 4.0713e-01 l_g_gan: 1.3928e-02 l_d_real: 7.1199e-02 l_d_fake: 7.5041e-02 D_real: 6.1490e+01 D_fake: 5.8777e+01 +20-04-05 22:29:08.020 - INFO: l_g_pix: 1.0444e-04 l_g_fea: 4.9434e-01 l_g_gan: 1.0374e-02 l_d_real: 1.7279e-01 l_d_fake: 1.7715e-01 D_real: 4.9704e+01 D_fake: 4.7804e+01 +20-04-05 22:32:20.477 - INFO: l_g_pix: 1.3019e-04 l_g_fea: 5.1000e-01 l_g_gan: 9.0384e-03 l_d_real: 2.0335e-01 l_d_fake: 2.0602e-01 D_real: 3.1310e+01 D_fake: 2.9707e+01 +20-04-05 22:35:32.506 - INFO: l_g_pix: 1.1788e-04 l_g_fea: 4.2488e-01 l_g_gan: 6.8180e-03 l_d_real: 3.3118e-01 l_d_fake: 3.3849e-01 D_real: 1.6725e+01 D_fake: 1.5696e+01 +20-04-05 22:38:44.763 - INFO: l_g_pix: 8.7950e-05 l_g_fea: 3.6701e-01 l_g_gan: 7.4189e-03 l_d_real: 3.1479e-01 l_d_fake: 3.0519e-01 D_real: 3.6408e+01 D_fake: 3.5234e+01 +20-04-05 22:41:55.885 - INFO: l_g_pix: 7.5351e-05 l_g_fea: 3.3496e-01 l_g_gan: 2.8108e-02 l_d_real: 4.2673e-03 l_d_fake: 5.1055e-03 D_real: 7.1527e+01 D_fake: 6.5910e+01 +20-04-05 22:45:08.888 - INFO: l_g_pix: 1.7411e-04 l_g_fea: 4.9089e-01 l_g_gan: 1.7442e-03 l_d_real: 1.3501e+00 l_d_fake: 1.3442e+00 D_real: 6.7799e+01 D_fake: 6.8797e+01 +20-04-05 22:48:21.416 - INFO: l_g_pix: 1.2379e-04 l_g_fea: 5.2514e-01 l_g_gan: 2.5428e-03 l_d_real: 9.8736e-01 l_d_fake: 9.8824e-01 D_real: 3.6404e+01 D_fake: 3.6884e+01 +20-04-05 22:51:34.442 - INFO: l_g_pix: 1.3326e-04 l_g_fea: 5.2242e-01 l_g_gan: 2.8718e-03 l_d_real: 9.4646e-01 l_d_fake: 9.6100e-01 D_real: 5.7983e+01 D_fake: 5.8362e+01 +20-04-05 22:54:47.853 - INFO: l_g_pix: 1.1698e-04 l_g_fea: 3.9541e-01 l_g_gan: 6.8159e-03 l_d_real: 3.2789e-01 l_d_fake: 3.2515e-01 D_real: 4.3526e+01 D_fake: 4.2489e+01 +20-04-05 22:58:00.245 - INFO: l_g_pix: 1.3551e-04 l_g_fea: 5.2136e-01 l_g_gan: 7.1226e-03 l_d_real: 3.4789e-01 l_d_fake: 3.2462e-01 D_real: 5.0333e+01 D_fake: 4.9245e+01 +20-04-05 23:01:12.676 - INFO: l_g_pix: 1.0618e-04 l_g_fea: 4.2613e-01 l_g_gan: 6.9920e-03 l_d_real: 3.6974e-01 l_d_fake: 4.0990e-01 D_real: 5.9799e+01 D_fake: 5.8791e+01 +20-04-05 23:04:24.949 - INFO: l_g_pix: 1.2520e-04 l_g_fea: 4.5725e-01 l_g_gan: 9.3443e-03 l_d_real: 3.1935e-01 l_d_fake: 5.8851e-01 D_real: 8.9153e+00 D_fake: 7.5003e+00 +20-04-05 23:07:37.342 - INFO: l_g_pix: 9.3940e-05 l_g_fea: 4.2892e-01 l_g_gan: 1.6186e-02 l_d_real: 5.3361e-02 l_d_fake: 6.4054e-02 D_real: 9.7775e+01 D_fake: 9.4596e+01 +20-04-05 23:10:49.473 - INFO: l_g_pix: 1.2051e-04 l_g_fea: 4.1721e-01 l_g_gan: 4.2240e-03 l_d_real: 8.1323e-01 l_d_fake: 8.0429e-01 D_real: -3.3365e+00 D_fake: -3.3725e+00 +20-04-05 23:10:49.942 - INFO: Models and training states saved. +20-04-05 23:11:59.810 - INFO: # Validation # PSNR: 31.559, SSIM: 0.84308, LPIPS: 0.040322 +20-04-05 23:11:59.810 - INFO: psnr: 31.559, ssim: 0.84308, lpips: 0.040322 +20-04-05 23:18:47.713 - INFO: l_g_pix: 9.8384e-05 l_g_fea: 3.4914e-01 l_g_gan: 1.0330e-02 l_d_real: 1.8243e-01 l_d_fake: 1.6470e-01 D_real: 3.3628e+01 D_fake: 3.1736e+01 +20-04-05 23:22:24.990 - INFO: l_g_pix: 1.1291e-04 l_g_fea: 4.8725e-01 l_g_gan: 1.4767e-02 l_d_real: 8.2060e-02 l_d_fake: 7.8888e-02 D_real: 7.2990e+01 D_fake: 7.0118e+01 +20-04-05 23:25:37.332 - INFO: l_g_pix: 1.1011e-04 l_g_fea: 4.3980e-01 l_g_gan: 9.9973e-03 l_d_real: 1.8823e-01 l_d_fake: 2.4990e-01 D_real: 2.4405e+01 D_fake: 2.2625e+01 +20-04-05 23:28:49.977 - INFO: l_g_pix: 1.0988e-04 l_g_fea: 5.1937e-01 l_g_gan: 9.7106e-04 l_d_real: 1.9450e+00 l_d_fake: 1.9441e+00 D_real: 4.4205e+01 D_fake: 4.5955e+01 +20-04-05 23:32:02.200 - INFO: l_g_pix: 1.0441e-04 l_g_fea: 3.6311e-01 l_g_gan: 1.7176e-02 l_d_real: 4.5731e-02 l_d_fake: 3.9288e-02 D_real: 3.2719e+01 D_fake: 2.9326e+01 +20-04-05 23:35:14.538 - INFO: l_g_pix: 1.3902e-04 l_g_fea: 5.1188e-01 l_g_gan: 8.6744e-03 l_d_real: 2.1395e-01 l_d_fake: 2.1352e-01 D_real: 2.7230e+01 D_fake: 2.5709e+01 +20-04-05 23:38:26.531 - INFO: l_g_pix: 7.8460e-05 l_g_fea: 2.8924e-01 l_g_gan: 7.2637e-03 l_d_real: 2.9830e-01 l_d_fake: 2.9506e-01 D_real: 1.9553e+01 D_fake: 1.8397e+01 +20-04-05 23:41:39.136 - INFO: l_g_pix: 9.6056e-05 l_g_fea: 4.4655e-01 l_g_gan: 5.4771e-03 l_d_real: 7.0615e-01 l_d_fake: 7.7776e-01 D_real: -1.2094e+01 D_fake: -1.2447e+01 +20-04-05 23:44:51.551 - INFO: l_g_pix: 1.1434e-04 l_g_fea: 4.2101e-01 l_g_gan: 1.6224e-02 l_d_real: 4.5773e-02 l_d_fake: 4.4168e-02 D_real: 3.3941e+01 D_fake: 3.0741e+01 +20-04-05 23:48:03.795 - INFO: l_g_pix: 1.0595e-04 l_g_fea: 4.6536e-01 l_g_gan: 1.1489e-03 l_d_real: 1.6964e+00 l_d_fake: 1.6996e+00 D_real: 2.5212e+01 D_fake: 2.6681e+01 +20-04-05 23:51:16.590 - INFO: l_g_pix: 9.4009e-05 l_g_fea: 4.1350e-01 l_g_gan: 1.0615e-02 l_d_real: 1.5380e-01 l_d_fake: 1.5422e-01 D_real: 3.1077e+01 D_fake: 2.9108e+01 +20-04-05 23:54:29.009 - INFO: l_g_pix: 1.1125e-04 l_g_fea: 4.5941e-01 l_g_gan: 5.4528e-03 l_d_real: 4.8284e-01 l_d_fake: 4.8366e-01 D_real: 3.7201e+01 D_fake: 3.6594e+01 +20-04-05 23:57:41.317 - INFO: l_g_pix: 1.5666e-04 l_g_fea: 5.8516e-01 l_g_gan: 6.7357e-03 l_d_real: 3.5516e-01 l_d_fake: 3.6780e-01 D_real: 6.9311e+01 D_fake: 6.8325e+01 +20-04-06 00:00:54.045 - INFO: l_g_pix: 1.8380e-04 l_g_fea: 6.5873e-01 l_g_gan: 1.4441e-02 l_d_real: 1.2408e-01 l_d_fake: 1.0976e-01 D_real: 3.1807e-01 D_fake: -2.4532e+00 +20-04-06 00:04:05.688 - INFO: l_g_pix: 1.0745e-04 l_g_fea: 4.0509e-01 l_g_gan: 2.3569e-02 l_d_real: 1.2215e-02 l_d_fake: 1.2777e-02 D_real: 5.0978e+01 D_fake: 4.6277e+01 +20-04-06 00:07:17.550 - INFO: l_g_pix: 8.8643e-05 l_g_fea: 3.4665e-01 l_g_gan: 4.8465e-03 l_d_real: 7.1731e-01 l_d_fake: 7.2761e-01 D_real: 1.9869e+01 D_fake: 1.9622e+01 +20-04-06 00:10:30.210 - INFO: l_g_pix: 1.1436e-04 l_g_fea: 5.1089e-01 l_g_gan: 1.3508e-02 l_d_real: 8.4249e-02 l_d_fake: 8.6175e-02 D_real: 4.1300e+01 D_fake: 3.8684e+01 +20-04-06 00:13:42.617 - INFO: l_g_pix: 1.0718e-04 l_g_fea: 3.9077e-01 l_g_gan: 1.2474e-02 l_d_real: 9.3050e-02 l_d_fake: 9.2880e-02 D_real: 2.9952e+01 D_fake: 2.7550e+01 +20-04-06 00:16:55.329 - INFO: l_g_pix: 7.7241e-05 l_g_fea: 4.5382e-01 l_g_gan: 5.3542e-03 l_d_real: 4.6873e-01 l_d_fake: 4.7492e-01 D_real: 2.2029e+00 D_fake: 1.6039e+00 +20-04-06 00:20:07.739 - INFO: l_g_pix: 1.1062e-04 l_g_fea: 4.0775e-01 l_g_gan: 1.2056e-02 l_d_real: 1.0277e-01 l_d_fake: 1.0619e-01 D_real: 5.8601e+01 D_fake: 5.6294e+01 +20-04-06 00:23:20.278 - INFO: l_g_pix: 1.0991e-04 l_g_fea: 3.7112e-01 l_g_gan: 1.0160e-03 l_d_real: 1.7798e+00 l_d_fake: 1.7739e+00 D_real: 4.2777e+01 D_fake: 4.4350e+01 +20-04-06 00:26:33.239 - INFO: l_g_pix: 8.6244e-05 l_g_fea: 3.2850e-01 l_g_gan: 9.8954e-04 l_d_real: 1.8134e+00 l_d_fake: 1.8132e+00 D_real: 5.6951e+01 D_fake: 5.8566e+01 +20-04-06 00:29:45.496 - INFO: l_g_pix: 1.1353e-04 l_g_fea: 5.0718e-01 l_g_gan: 2.8619e-03 l_d_real: 9.1002e-01 l_d_fake: 9.0016e-01 D_real: 1.7211e+01 D_fake: 1.7544e+01 +20-04-06 00:32:57.438 - INFO: l_g_pix: 9.9039e-05 l_g_fea: 3.8957e-01 l_g_gan: 7.3871e-03 l_d_real: 2.7872e-01 l_d_fake: 2.9031e-01 D_real: 3.0986e+01 D_fake: 2.9793e+01 +20-04-06 00:36:09.329 - INFO: l_g_pix: 1.6535e-04 l_g_fea: 5.0089e-01 l_g_gan: 1.2805e-02 l_d_real: 1.0737e-01 l_d_fake: 1.1583e-01 D_real: 2.4644e+01 D_fake: 2.2195e+01 +20-04-06 00:36:09.764 - INFO: Models and training states saved. +20-04-06 00:37:19.174 - INFO: # Validation # PSNR: 29.643, SSIM: 0.84546, LPIPS: 0.035241 +20-04-06 00:37:19.174 - INFO: psnr: 29.643, ssim: 0.84546, lpips: 0.035241 +20-04-06 00:41:06.035 - INFO: l_g_pix: 1.1386e-04 l_g_fea: 4.9373e-01 l_g_gan: 1.6335e-02 l_d_real: 4.4351e-02 l_d_fake: 4.6560e-02 D_real: 3.0712e+01 D_fake: 2.7490e+01 +20-04-06 00:44:17.686 - INFO: l_g_pix: 1.2712e-04 l_g_fea: 4.6155e-01 l_g_gan: 7.4378e-03 l_d_real: 2.9927e-01 l_d_fake: 3.0194e-01 D_real: 1.7470e+00 D_fake: 5.6009e-01 +20-04-06 00:47:30.457 - INFO: l_g_pix: 1.0759e-04 l_g_fea: 4.2845e-01 l_g_gan: 7.1883e-03 l_d_real: 3.5129e-01 l_d_fake: 3.4076e-01 D_real: -2.1652e+01 D_fake: -2.2743e+01 +20-04-06 00:50:42.715 - INFO: l_g_pix: 1.2562e-04 l_g_fea: 4.8756e-01 l_g_gan: 2.4806e-02 l_d_real: 1.3751e-02 l_d_fake: 1.9699e-02 D_real: 1.6143e+01 D_fake: 1.1199e+01 +20-04-06 00:53:54.853 - INFO: l_g_pix: 1.2144e-04 l_g_fea: 4.2776e-01 l_g_gan: 1.1224e-02 l_d_real: 1.5119e-01 l_d_fake: 1.3876e-01 D_real: -2.4084e+01 D_fake: -2.6184e+01 +20-04-06 00:57:06.486 - INFO: l_g_pix: 8.8914e-05 l_g_fea: 3.7579e-01 l_g_gan: 3.6256e-03 l_d_real: 8.7458e-01 l_d_fake: 8.7757e-01 D_real: 4.4518e+00 D_fake: 4.6028e+00 +20-04-06 01:00:23.048 - INFO: l_g_pix: 9.9648e-05 l_g_fea: 3.5803e-01 l_g_gan: 9.4881e-03 l_d_real: 1.8533e-01 l_d_fake: 1.7920e-01 D_real: 4.2840e+01 D_fake: 4.1125e+01 +20-04-06 01:03:34.943 - INFO: l_g_pix: 1.7324e-04 l_g_fea: 5.5893e-01 l_g_gan: 5.1962e-03 l_d_real: 4.6525e-01 l_d_fake: 4.6673e-01 D_real: 5.6944e+01 D_fake: 5.6370e+01 +20-04-06 01:06:47.048 - INFO: l_g_pix: 1.0078e-04 l_g_fea: 4.3225e-01 l_g_gan: 1.0837e-02 l_d_real: 1.4636e-01 l_d_fake: 1.5258e-01 D_real: -4.3515e+00 D_fake: -6.3694e+00 +20-04-06 01:09:59.479 - INFO: l_g_pix: 1.3777e-04 l_g_fea: 4.8961e-01 l_g_gan: 2.7388e-02 l_d_real: 9.0700e-03 l_d_fake: 1.0262e-02 D_real: 4.8248e+01 D_fake: 4.2780e+01 +20-04-06 01:13:11.102 - INFO: l_g_pix: 1.2734e-04 l_g_fea: 5.3293e-01 l_g_gan: 9.0652e-03 l_d_real: 1.9488e-01 l_d_fake: 1.9548e-01 D_real: 2.6142e+01 D_fake: 2.4525e+01 +20-04-06 01:16:22.753 - INFO: l_g_pix: 7.9968e-05 l_g_fea: 4.5668e-01 l_g_gan: 2.9772e-03 l_d_real: 9.1149e-01 l_d_fake: 9.5246e-01 D_real: 5.4100e+01 D_fake: 5.4436e+01 +20-04-06 01:19:34.859 - INFO: l_g_pix: 9.8112e-05 l_g_fea: 4.4162e-01 l_g_gan: 4.3795e-03 l_d_real: 6.5634e-01 l_d_fake: 6.8056e-01 D_real: 4.3614e+01 D_fake: 4.3407e+01 +20-04-06 01:22:47.243 - INFO: l_g_pix: 1.3582e-04 l_g_fea: 4.5780e-01 l_g_gan: 4.3444e-03 l_d_real: 6.3333e-01 l_d_fake: 6.3169e-01 D_real: 3.5961e+01 D_fake: 3.5724e+01 +20-04-06 01:25:59.862 - INFO: l_g_pix: 7.8986e-05 l_g_fea: 4.4501e-01 l_g_gan: 6.3692e-03 l_d_real: 3.8373e-01 l_d_fake: 3.8431e-01 D_real: 3.0052e+01 D_fake: 2.9163e+01 +20-04-06 01:29:12.900 - INFO: l_g_pix: 8.5203e-05 l_g_fea: 3.8499e-01 l_g_gan: 2.7484e-03 l_d_real: 9.0821e-01 l_d_fake: 9.2303e-01 D_real: 4.6232e+01 D_fake: 4.6598e+01 +20-04-06 01:32:24.847 - INFO: l_g_pix: 1.2273e-04 l_g_fea: 4.8240e-01 l_g_gan: 3.7916e-02 l_d_real: 5.9814e-04 l_d_fake: 6.0615e-04 D_real: 3.4573e+01 D_fake: 2.6991e+01 +20-04-06 01:35:35.864 - INFO: l_g_pix: 1.5716e-04 l_g_fea: 6.0280e-01 l_g_gan: 1.1701e-03 l_d_real: 1.6788e+00 l_d_fake: 1.6746e+00 D_real: 4.1419e+01 D_fake: 4.2862e+01 +20-04-06 01:38:47.846 - INFO: l_g_pix: 1.0387e-04 l_g_fea: 3.9857e-01 l_g_gan: 6.9512e-03 l_d_real: 3.2113e-01 l_d_fake: 3.3056e-01 D_real: 3.0295e+01 D_fake: 2.9231e+01 +20-04-06 01:41:59.465 - INFO: l_g_pix: 1.2964e-04 l_g_fea: 3.3772e-01 l_g_gan: 2.0766e-02 l_d_real: 2.5451e-02 l_d_fake: 2.8335e-02 D_real: 1.0715e+01 D_fake: 6.5891e+00 +20-04-06 01:45:11.568 - INFO: l_g_pix: 1.0055e-04 l_g_fea: 4.2707e-01 l_g_gan: 4.4700e-03 l_d_real: 6.1413e-01 l_d_fake: 6.1768e-01 D_real: -1.3231e+00 D_fake: -1.6012e+00 +20-04-06 01:48:22.835 - INFO: l_g_pix: 1.4415e-04 l_g_fea: 5.0291e-01 l_g_gan: 8.7691e-03 l_d_real: 3.5115e-01 l_d_fake: 3.2385e-01 D_real: 6.5989e+01 D_fake: 6.4573e+01 +20-04-06 01:51:35.392 - INFO: l_g_pix: 8.8652e-05 l_g_fea: 4.3721e-01 l_g_gan: 1.3974e-02 l_d_real: 8.8141e-02 l_d_fake: 1.2844e-01 D_real: 4.5992e+01 D_fake: 4.3305e+01 +20-04-06 01:54:46.657 - INFO: l_g_pix: 9.6692e-05 l_g_fea: 3.9790e-01 l_g_gan: 4.7966e-03 l_d_real: 5.1960e-01 l_d_fake: 5.3964e-01 D_real: 3.4289e+01 D_fake: 3.3859e+01 +20-04-06 01:57:58.338 - INFO: l_g_pix: 6.5640e-05 l_g_fea: 3.1604e-01 l_g_gan: 4.0505e-03 l_d_real: 6.1952e-01 l_d_fake: 6.1909e-01 D_real: 3.6603e+01 D_fake: 3.6412e+01 +20-04-06 01:57:58.791 - INFO: Models and training states saved. +20-04-06 01:59:03.204 - INFO: # Validation # PSNR: 32.176, SSIM: 0.84419, LPIPS: 0.038451 +20-04-06 01:59:03.204 - INFO: psnr: 32.176, ssim: 0.84419, lpips: 0.038451 +20-04-06 02:04:58.944 - INFO: l_g_pix: 1.1739e-04 l_g_fea: 4.8583e-01 l_g_gan: 2.0991e-02 l_d_real: 1.7785e-02 l_d_fake: 1.6376e-02 D_real: 4.4579e+01 D_fake: 4.0397e+01 +20-04-06 02:08:47.913 - INFO: l_g_pix: 1.1844e-04 l_g_fea: 4.9864e-01 l_g_gan: 2.3122e-03 l_d_real: 1.1367e+00 l_d_fake: 1.1577e+00 D_real: 3.7923e+01 D_fake: 3.8608e+01 +20-04-06 02:11:59.981 - INFO: l_g_pix: 1.0121e-04 l_g_fea: 4.1909e-01 l_g_gan: 1.8768e-02 l_d_real: 3.5465e-02 l_d_fake: 3.2248e-02 D_real: 3.9029e+01 D_fake: 3.5310e+01 +20-04-06 02:15:11.884 - INFO: l_g_pix: 1.2624e-04 l_g_fea: 5.1890e-01 l_g_gan: 1.3796e-02 l_d_real: 9.4842e-02 l_d_fake: 9.5618e-02 D_real: 4.2604e+01 D_fake: 3.9940e+01 +20-04-06 02:18:23.955 - INFO: l_g_pix: 1.1444e-04 l_g_fea: 3.4098e-01 l_g_gan: 1.6963e-02 l_d_real: 4.1051e-02 l_d_fake: 5.4013e-02 D_real: 8.9236e+00 D_fake: 5.5786e+00 +20-04-06 02:21:36.453 - INFO: l_g_pix: 1.2952e-04 l_g_fea: 5.4322e-01 l_g_gan: 1.0504e-02 l_d_real: 1.5285e-01 l_d_fake: 1.6997e-01 D_real: 2.0077e+01 D_fake: 1.8138e+01 +20-04-06 02:24:48.364 - INFO: l_g_pix: 1.0118e-04 l_g_fea: 3.8221e-01 l_g_gan: 1.9936e-02 l_d_real: 4.7341e-02 l_d_fake: 2.8363e-02 D_real: 4.3663e+01 D_fake: 3.9713e+01 +20-04-06 02:27:59.654 - INFO: l_g_pix: 1.2597e-04 l_g_fea: 4.9777e-01 l_g_gan: 4.3751e-03 l_d_real: 6.8182e-01 l_d_fake: 7.7565e-01 D_real: 2.5382e+01 D_fake: 2.5236e+01 +20-04-06 02:31:11.483 - INFO: l_g_pix: 1.3466e-04 l_g_fea: 4.4500e-01 l_g_gan: 3.6830e-03 l_d_real: 7.6647e-01 l_d_fake: 7.5616e-01 D_real: 4.8572e+01 D_fake: 4.8597e+01 +20-04-06 02:34:23.835 - INFO: l_g_pix: 1.4962e-04 l_g_fea: 4.8468e-01 l_g_gan: 6.0937e-03 l_d_real: 4.7114e-01 l_d_fake: 5.0201e-01 D_real: 2.6641e+01 D_fake: 2.5909e+01 +20-04-06 02:37:35.923 - INFO: l_g_pix: 1.0037e-04 l_g_fea: 4.5968e-01 l_g_gan: 4.7774e-03 l_d_real: 5.3208e-01 l_d_fake: 5.3632e-01 D_real: 4.8159e+01 D_fake: 4.7738e+01 +20-04-06 02:40:48.240 - INFO: l_g_pix: 1.2146e-04 l_g_fea: 5.0217e-01 l_g_gan: 9.9679e-03 l_d_real: 1.7586e-01 l_d_fake: 1.7309e-01 D_real: 3.9535e+01 D_fake: 3.7715e+01 +20-04-06 02:44:00.115 - INFO: l_g_pix: 1.1169e-04 l_g_fea: 3.5474e-01 l_g_gan: 9.4726e-03 l_d_real: 1.8541e-01 l_d_fake: 1.8207e-01 D_real: 5.5657e+01 D_fake: 5.3947e+01 +20-04-06 02:47:12.589 - INFO: l_g_pix: 1.2497e-04 l_g_fea: 5.2358e-01 l_g_gan: 2.2497e-02 l_d_real: 1.6347e-02 l_d_fake: 1.8973e-02 D_real: 8.2277e+00 D_fake: 3.7461e+00 +20-04-06 02:50:23.844 - INFO: l_g_pix: 1.0264e-04 l_g_fea: 5.1522e-01 l_g_gan: 1.4285e-02 l_d_real: 7.2759e-02 l_d_fake: 7.9220e-02 D_real: 1.5408e+01 D_fake: 1.2627e+01 +20-04-06 02:53:35.877 - INFO: l_g_pix: 9.2471e-05 l_g_fea: 3.9078e-01 l_g_gan: 3.1647e-03 l_d_real: 8.9784e-01 l_d_fake: 8.7691e-01 D_real: 5.4975e+01 D_fake: 5.5229e+01 +20-04-06 02:56:47.584 - INFO: l_g_pix: 9.2238e-05 l_g_fea: 4.3554e-01 l_g_gan: 1.0559e-02 l_d_real: 1.6258e-01 l_d_fake: 1.5527e-01 D_real: 1.9399e+01 D_fake: 1.7446e+01 +20-04-06 02:59:58.972 - INFO: l_g_pix: 7.3131e-05 l_g_fea: 3.4428e-01 l_g_gan: 7.2536e-03 l_d_real: 4.1318e-01 l_d_fake: 4.1901e-01 D_real: 3.4831e+01 D_fake: 3.3796e+01 +20-04-06 03:03:25.298 - INFO: l_g_pix: 1.4645e-04 l_g_fea: 5.1059e-01 l_g_gan: 6.5493e-03 l_d_real: 4.5537e-01 l_d_fake: 4.2630e-01 D_real: 4.2085e+01 D_fake: 4.1216e+01 +20-04-06 03:07:57.062 - INFO: l_g_pix: 9.0970e-05 l_g_fea: 4.1753e-01 l_g_gan: 1.6571e-02 l_d_real: 4.2596e-02 l_d_fake: 4.6833e-02 D_real: 9.2459e+00 D_fake: 5.9764e+00 +20-04-06 03:13:34.439 - INFO: l_g_pix: 1.0019e-04 l_g_fea: 4.2287e-01 l_g_gan: 2.6844e-03 l_d_real: 1.0041e+00 l_d_fake: 1.0267e+00 D_real: 5.1169e+01 D_fake: 5.1647e+01 +20-04-06 03:19:39.847 - INFO: l_g_pix: 9.0567e-05 l_g_fea: 4.3550e-01 l_g_gan: 2.4206e-02 l_d_real: 1.0886e-02 l_d_fake: 1.3279e-02 D_real: 2.8640e+01 D_fake: 2.3811e+01 +20-04-06 03:23:58.686 - INFO: l_g_pix: 1.0523e-04 l_g_fea: 4.0073e-01 l_g_gan: 7.4437e-03 l_d_real: 3.0329e-01 l_d_fake: 2.9665e-01 D_real: 4.1972e+01 D_fake: 4.0783e+01 +20-04-06 03:27:10.308 - INFO: l_g_pix: 1.2408e-04 l_g_fea: 5.4961e-01 l_g_gan: 6.3868e-03 l_d_real: 3.7881e-01 l_d_fake: 3.7875e-01 D_real: 3.4108e+01 D_fake: 3.3209e+01 +20-04-06 03:30:21.683 - INFO: l_g_pix: 1.3878e-04 l_g_fea: 4.4428e-01 l_g_gan: 1.6042e-02 l_d_real: 6.4661e-02 l_d_fake: 4.8524e-02 D_real: 3.1825e+01 D_fake: 2.8673e+01 +20-04-06 03:30:22.170 - INFO: Models and training states saved. +20-04-06 03:31:31.236 - INFO: # Validation # PSNR: 30.396, SSIM: 0.82259, LPIPS: 0.038421 +20-04-06 03:31:31.236 - INFO: psnr: 30.396, ssim: 0.82259, lpips: 0.038421 +20-04-06 03:36:05.301 - INFO: l_g_pix: 8.8346e-05 l_g_fea: 3.5561e-01 l_g_gan: 1.5083e-03 l_d_real: 1.5135e+00 l_d_fake: 1.4998e+00 D_real: -8.2981e+00 D_fake: -7.0931e+00 +20-04-06 03:39:23.136 - INFO: l_g_pix: 1.4885e-04 l_g_fea: 5.5511e-01 l_g_gan: 1.6925e-02 l_d_real: 5.2614e-02 l_d_fake: 5.1241e-02 D_real: 2.6600e+01 D_fake: 2.3267e+01 +20-04-06 03:42:34.376 - INFO: l_g_pix: 8.4864e-05 l_g_fea: 4.0817e-01 l_g_gan: 1.9932e-03 l_d_real: 1.3953e+00 l_d_fake: 1.3978e+00 D_real: 5.0806e+01 D_fake: 5.1804e+01 +20-04-06 03:45:47.874 - INFO: l_g_pix: 8.7329e-05 l_g_fea: 4.2512e-01 l_g_gan: 3.9272e-03 l_d_real: 6.5115e-01 l_d_fake: 6.6277e-01 D_real: 3.7299e+01 D_fake: 3.7171e+01 +20-04-06 03:49:01.418 - INFO: l_g_pix: 1.2706e-04 l_g_fea: 4.8114e-01 l_g_gan: 5.5671e-03 l_d_real: 4.3374e-01 l_d_fake: 4.2823e-01 D_real: 5.5731e+01 D_fake: 5.5048e+01 +20-04-06 03:52:13.015 - INFO: l_g_pix: 1.4184e-04 l_g_fea: 4.3594e-01 l_g_gan: 6.0627e-03 l_d_real: 4.1441e-01 l_d_fake: 3.8460e-01 D_real: 5.7580e+01 D_fake: 5.6767e+01 +20-04-06 03:55:24.442 - INFO: l_g_pix: 1.0513e-04 l_g_fea: 4.4013e-01 l_g_gan: 1.1637e-02 l_d_real: 1.1238e-01 l_d_fake: 1.2031e-01 D_real: 6.0722e+01 D_fake: 5.8511e+01 +20-04-06 03:58:36.447 - INFO: l_g_pix: 1.0875e-04 l_g_fea: 4.2176e-01 l_g_gan: 1.0770e-02 l_d_real: 1.5515e-01 l_d_fake: 1.5209e-01 D_real: 3.3508e+01 D_fake: 3.1508e+01 +20-04-06 04:01:47.974 - INFO: l_g_pix: 8.9749e-05 l_g_fea: 3.5896e-01 l_g_gan: 4.6517e-03 l_d_real: 7.1133e-01 l_d_fake: 7.1810e-01 D_real: 5.5711e+01 D_fake: 5.5495e+01 +20-04-06 04:05:00.258 - INFO: l_g_pix: 8.6013e-05 l_g_fea: 3.8641e-01 l_g_gan: 1.0682e-02 l_d_real: 1.4032e-01 l_d_fake: 1.3767e-01 D_real: 5.8659e+01 D_fake: 5.6661e+01 +20-04-06 04:08:12.173 - INFO: l_g_pix: 1.0515e-04 l_g_fea: 4.2071e-01 l_g_gan: 1.8014e-02 l_d_real: 2.9755e-02 l_d_fake: 3.0810e-02 D_real: 1.3498e+01 D_fake: 9.9252e+00 +20-04-06 04:11:24.352 - INFO: l_g_pix: 8.8061e-05 l_g_fea: 4.6056e-01 l_g_gan: 5.3565e-03 l_d_real: 4.9079e-01 l_d_fake: 4.9962e-01 D_real: -1.1138e+00 D_fake: -1.6899e+00 +20-04-06 04:16:11.259 - INFO: l_g_pix: 1.4577e-04 l_g_fea: 6.0538e-01 l_g_gan: 9.2151e-03 l_d_real: 2.2918e-01 l_d_fake: 1.9961e-01 D_real: 8.1279e+00 D_fake: 6.4993e+00 +20-04-06 04:20:03.873 - INFO: l_g_pix: 1.2638e-04 l_g_fea: 4.1229e-01 l_g_gan: 2.5150e-03 l_d_real: 1.0700e+00 l_d_fake: 1.0670e+00 D_real: 5.2484e+01 D_fake: 5.3050e+01 +20-04-06 04:23:30.736 - INFO: l_g_pix: 1.0326e-04 l_g_fea: 4.7335e-01 l_g_gan: 2.5998e-02 l_d_real: 7.5082e-03 l_d_fake: 7.2395e-03 D_real: 2.9640e+01 D_fake: 2.4448e+01 +20-04-06 04:26:42.667 - INFO: l_g_pix: 1.1792e-04 l_g_fea: 4.3279e-01 l_g_gan: 9.7408e-03 l_d_real: 1.9285e-01 l_d_fake: 1.8775e-01 D_real: 4.8848e+01 D_fake: 4.7091e+01 +20-04-06 04:29:53.790 - INFO: l_g_pix: 1.1929e-04 l_g_fea: 4.1205e-01 l_g_gan: 1.0157e-03 l_d_real: 1.9522e+00 l_d_fake: 1.9463e+00 D_real: 3.4794e+01 D_fake: 3.6540e+01 +20-04-06 04:33:06.016 - INFO: l_g_pix: 1.3240e-04 l_g_fea: 5.8050e-01 l_g_gan: 4.1107e-03 l_d_real: 6.4417e-01 l_d_fake: 6.4095e-01 D_real: 1.5026e+01 D_fake: 1.4846e+01 +20-04-06 04:36:18.772 - INFO: l_g_pix: 7.9056e-05 l_g_fea: 3.9049e-01 l_g_gan: 8.5170e-03 l_d_real: 2.5917e-01 l_d_fake: 2.8167e-01 D_real: 3.3663e+01 D_fake: 3.2230e+01 +20-04-06 04:39:31.230 - INFO: l_g_pix: 1.3381e-04 l_g_fea: 4.3554e-01 l_g_gan: 6.2928e-03 l_d_real: 4.1367e-01 l_d_fake: 3.6945e-01 D_real: 1.7393e+01 D_fake: 1.6526e+01 +20-04-06 04:42:42.426 - INFO: l_g_pix: 1.3350e-04 l_g_fea: 4.0969e-01 l_g_gan: 1.3840e-02 l_d_real: 8.1178e-02 l_d_fake: 8.1088e-02 D_real: 3.8754e+01 D_fake: 3.6067e+01 +20-04-06 04:45:55.006 - INFO: l_g_pix: 1.1234e-04 l_g_fea: 4.6113e-01 l_g_gan: 7.5684e-03 l_d_real: 2.8089e-01 l_d_fake: 2.7728e-01 D_real: -1.9405e+01 D_fake: -2.0640e+01 +20-04-06 04:49:12.406 - INFO: l_g_pix: 1.4335e-04 l_g_fea: 5.0515e-01 l_g_gan: 1.3314e-02 l_d_real: 8.3879e-02 l_d_fake: 8.7967e-02 D_real: -1.9946e+01 D_fake: -2.2523e+01 +20-04-06 04:52:24.453 - INFO: l_g_pix: 1.2481e-04 l_g_fea: 4.1851e-01 l_g_gan: 5.4125e-03 l_d_real: 5.1077e-01 l_d_fake: 5.0094e-01 D_real: -5.0380e+00 D_fake: -5.6147e+00 +20-04-06 04:55:37.162 - INFO: l_g_pix: 1.0093e-04 l_g_fea: 4.1397e-01 l_g_gan: 1.1084e-02 l_d_real: 1.6110e-01 l_d_fake: 1.7905e-01 D_real: -3.9600e+00 D_fake: -6.0068e+00 +20-04-06 04:55:37.605 - INFO: Models and training states saved. +20-04-06 04:56:47.526 - INFO: # Validation # PSNR: 30.517, SSIM: 0.83536, LPIPS: 0.031511 +20-04-06 04:56:47.526 - INFO: psnr: 30.517, ssim: 0.83536, lpips: 0.031511 +20-04-06 05:03:26.474 - INFO: l_g_pix: 1.4561e-04 l_g_fea: 5.4536e-01 l_g_gan: 7.0627e-04 l_d_real: 2.4138e+00 l_d_fake: 2.4358e+00 D_real: -2.0503e+01 D_fake: -1.8219e+01 +20-04-06 05:06:36.573 - INFO: l_g_pix: 7.2745e-05 l_g_fea: 3.0477e-01 l_g_gan: 3.8145e-03 l_d_real: 6.7592e-01 l_d_fake: 6.5342e-01 D_real: -8.3757e+00 D_fake: -8.4739e+00 +20-04-06 05:09:48.464 - INFO: l_g_pix: 9.6193e-05 l_g_fea: 4.4296e-01 l_g_gan: 4.8851e-03 l_d_real: 5.0272e-01 l_d_fake: 5.1233e-01 D_real: 1.5889e+01 D_fake: 1.5420e+01 +20-04-06 05:13:00.890 - INFO: l_g_pix: 1.1357e-04 l_g_fea: 4.6988e-01 l_g_gan: 9.5421e-03 l_d_real: 1.8093e-01 l_d_fake: 1.9458e-01 D_real: 2.4479e+01 D_fake: 2.2758e+01 +20-04-06 05:16:12.611 - INFO: l_g_pix: 1.0529e-04 l_g_fea: 4.0790e-01 l_g_gan: 4.5302e-04 l_d_real: 2.8204e+00 l_d_fake: 2.8137e+00 D_real: 1.3182e+01 D_fake: 1.5909e+01 +20-04-06 05:19:24.564 - INFO: l_g_pix: 1.4201e-04 l_g_fea: 5.5789e-01 l_g_gan: 6.2380e-03 l_d_real: 4.1396e-01 l_d_fake: 3.9773e-01 D_real: 1.5253e+01 D_fake: 1.4411e+01 +20-04-06 05:22:36.075 - INFO: l_g_pix: 1.5445e-04 l_g_fea: 5.1221e-01 l_g_gan: 6.9409e-03 l_d_real: 3.5281e-01 l_d_fake: 3.5565e-01 D_real: 1.1870e+01 D_fake: 1.0836e+01 +20-04-06 05:25:48.611 - INFO: l_g_pix: 1.2366e-04 l_g_fea: 4.0495e-01 l_g_gan: 1.2285e-02 l_d_real: 1.0406e-01 l_d_fake: 1.1736e-01 D_real: -1.7702e+00 D_fake: -4.1165e+00 +20-04-06 05:29:01.298 - INFO: l_g_pix: 8.8971e-05 l_g_fea: 3.7868e-01 l_g_gan: 5.2998e-03 l_d_real: 4.7913e-01 l_d_fake: 4.8068e-01 D_real: 2.9292e+01 D_fake: 2.8712e+01 +20-04-06 05:32:12.855 - INFO: l_g_pix: 9.5434e-05 l_g_fea: 4.4377e-01 l_g_gan: 8.7782e-03 l_d_real: 2.1272e-01 l_d_fake: 2.0748e-01 D_real: 1.2941e+01 D_fake: 1.1395e+01 +20-04-06 05:35:25.217 - INFO: l_g_pix: 1.4169e-04 l_g_fea: 4.7368e-01 l_g_gan: 2.6879e-03 l_d_real: 9.9595e-01 l_d_fake: 9.9327e-01 D_real: 1.5297e+01 D_fake: 1.5754e+01 +20-04-06 05:38:37.303 - INFO: l_g_pix: 1.0028e-04 l_g_fea: 4.4888e-01 l_g_gan: 7.4861e-03 l_d_real: 2.7712e-01 l_d_fake: 2.6611e-01 D_real: 1.4589e+01 D_fake: 1.3363e+01 +20-04-06 05:41:50.007 - INFO: l_g_pix: 8.9829e-05 l_g_fea: 4.5226e-01 l_g_gan: 1.6496e-02 l_d_real: 4.4963e-02 l_d_fake: 4.6066e-02 D_real: 4.5693e+01 D_fake: 4.2439e+01 +20-04-06 05:45:03.201 - INFO: l_g_pix: 8.3811e-05 l_g_fea: 4.2284e-01 l_g_gan: 1.6310e-02 l_d_real: 4.6374e-02 l_d_fake: 4.6451e-02 D_real: 2.9355e+01 D_fake: 2.6139e+01 +20-04-06 05:48:15.864 - INFO: l_g_pix: 9.6930e-05 l_g_fea: 4.2381e-01 l_g_gan: 1.6647e-02 l_d_real: 5.0424e-02 l_d_fake: 4.6462e-02 D_real: 2.7910e+01 D_fake: 2.4629e+01 +20-04-06 05:51:27.621 - INFO: l_g_pix: 7.6525e-05 l_g_fea: 2.9039e-01 l_g_gan: 5.7853e-03 l_d_real: 4.0733e-01 l_d_fake: 4.1919e-01 D_real: 1.6764e+01 D_fake: 1.6021e+01 +20-04-06 05:54:38.961 - INFO: l_g_pix: 9.2490e-05 l_g_fea: 3.4541e-01 l_g_gan: 3.8431e-03 l_d_real: 6.8317e-01 l_d_fake: 6.8526e-01 D_real: 1.0873e+01 D_fake: 1.0788e+01 +20-04-06 05:57:50.805 - INFO: l_g_pix: 1.4730e-04 l_g_fea: 5.0318e-01 l_g_gan: 4.4943e-03 l_d_real: 6.2784e-01 l_d_fake: 6.1158e-01 D_real: 1.6614e+01 D_fake: 1.6335e+01 +20-04-06 06:01:03.068 - INFO: l_g_pix: 1.2416e-04 l_g_fea: 4.5257e-01 l_g_gan: 6.0955e-03 l_d_real: 3.8555e-01 l_d_fake: 3.8084e-01 D_real: 2.4207e+01 D_fake: 2.3371e+01 +20-04-06 06:04:15.399 - INFO: l_g_pix: 1.0383e-04 l_g_fea: 3.8371e-01 l_g_gan: 1.2697e-02 l_d_real: 1.2277e-01 l_d_fake: 1.0538e-01 D_real: 2.7472e+01 D_fake: 2.5047e+01 +20-04-06 06:07:26.866 - INFO: l_g_pix: 9.2955e-05 l_g_fea: 3.9286e-01 l_g_gan: 3.4319e-03 l_d_real: 7.7001e-01 l_d_fake: 7.6670e-01 D_real: 2.3040e+01 D_fake: 2.3122e+01 +20-04-06 06:10:39.566 - INFO: l_g_pix: 1.0417e-04 l_g_fea: 4.7024e-01 l_g_gan: 5.6240e-03 l_d_real: 4.2277e-01 l_d_fake: 4.2550e-01 D_real: 1.5711e+01 D_fake: 1.5011e+01 +20-04-06 06:13:51.791 - INFO: l_g_pix: 8.6189e-05 l_g_fea: 3.5388e-01 l_g_gan: 3.3943e-03 l_d_real: 7.7724e-01 l_d_fake: 7.8090e-01 D_real: 1.5143e+01 D_fake: 1.5243e+01 +20-04-06 06:17:04.228 - INFO: l_g_pix: 1.0039e-04 l_g_fea: 4.1626e-01 l_g_gan: 5.6891e-03 l_d_real: 4.4151e-01 l_d_fake: 4.4655e-01 D_real: 4.3445e+01 D_fake: 4.2751e+01 +20-04-06 06:20:16.729 - INFO: l_g_pix: 8.6857e-05 l_g_fea: 3.9110e-01 l_g_gan: 5.7319e-03 l_d_real: 4.3557e-01 l_d_fake: 4.5832e-01 D_real: 3.9677e+01 D_fake: 3.8978e+01 +20-04-06 06:20:17.146 - INFO: Models and training states saved. +20-04-06 06:21:25.733 - INFO: # Validation # PSNR: 31.392, SSIM: 0.83527, LPIPS: 0.028824 +20-04-06 06:21:25.733 - INFO: psnr: 31.392, ssim: 0.83527, lpips: 0.028824 +20-04-06 06:25:00.092 - INFO: l_g_pix: 1.2757e-04 l_g_fea: 4.9484e-01 l_g_gan: 5.9982e-03 l_d_real: 4.2208e-01 l_d_fake: 4.0888e-01 D_real: 2.7208e+01 D_fake: 2.6423e+01 +20-04-06 06:28:12.390 - INFO: l_g_pix: 1.0454e-04 l_g_fea: 4.2063e-01 l_g_gan: 1.0728e-02 l_d_real: 1.4481e-01 l_d_fake: 1.5471e-01 D_real: 4.9131e+01 D_fake: 4.7135e+01 +20-04-06 06:31:24.697 - INFO: l_g_pix: 1.0692e-04 l_g_fea: 5.6727e-01 l_g_gan: 3.1203e-03 l_d_real: 8.5589e-01 l_d_fake: 8.4544e-01 D_real: 1.3814e+01 D_fake: 1.4040e+01 +20-04-06 06:35:04.378 - INFO: l_g_pix: 8.3986e-05 l_g_fea: 3.8580e-01 l_g_gan: 8.6291e-03 l_d_real: 2.3757e-01 l_d_fake: 2.2447e-01 D_real: 2.5277e+01 D_fake: 2.3782e+01 +20-04-06 06:38:42.139 - INFO: l_g_pix: 1.0076e-04 l_g_fea: 4.2652e-01 l_g_gan: 8.5985e-03 l_d_real: 2.1760e-01 l_d_fake: 2.2926e-01 D_real: 1.2205e+01 D_fake: 1.0709e+01 +20-04-06 06:43:38.751 - INFO: l_g_pix: 9.7204e-05 l_g_fea: 4.4199e-01 l_g_gan: 6.3485e-04 l_d_real: 2.2411e+00 l_d_fake: 2.2566e+00 D_real: 1.8887e+00 D_fake: 4.0106e+00 +20-04-06 06:46:49.973 - INFO: l_g_pix: 1.1822e-04 l_g_fea: 4.1328e-01 l_g_gan: 1.1952e-02 l_d_real: 1.1472e-01 l_d_fake: 1.1434e-01 D_real: 4.3230e+01 D_fake: 4.0954e+01 +20-04-06 06:50:01.442 - INFO: l_g_pix: 1.2280e-04 l_g_fea: 4.2950e-01 l_g_gan: 1.9667e-02 l_d_real: 2.5298e-02 l_d_fake: 2.3837e-02 D_real: 5.1814e+01 D_fake: 4.7905e+01 +20-04-06 06:53:13.292 - INFO: l_g_pix: 1.2847e-04 l_g_fea: 5.1155e-01 l_g_gan: 1.9616e-02 l_d_real: 3.3107e-02 l_d_fake: 2.7090e-02 D_real: 4.6853e+01 D_fake: 4.2960e+01 +20-04-06 06:56:26.272 - INFO: l_g_pix: 8.1787e-05 l_g_fea: 3.9661e-01 l_g_gan: 1.0075e-02 l_d_real: 1.6032e-01 l_d_fake: 1.5622e-01 D_real: 1.4727e+00 D_fake: -3.8410e-01 +20-04-06 06:59:37.097 - INFO: l_g_pix: 1.3658e-04 l_g_fea: 4.9832e-01 l_g_gan: 1.7240e-03 l_d_real: 1.4176e+00 l_d_fake: 1.4360e+00 D_real: 2.5655e+01 D_fake: 2.6737e+01 +20-04-06 07:02:48.396 - INFO: l_g_pix: 9.1057e-05 l_g_fea: 3.9749e-01 l_g_gan: 5.6685e-03 l_d_real: 4.0842e-01 l_d_fake: 4.1196e-01 D_real: 2.8780e+01 D_fake: 2.8057e+01 +20-04-06 07:05:59.734 - INFO: l_g_pix: 1.3336e-04 l_g_fea: 5.7318e-01 l_g_gan: 2.7659e-04 l_d_real: 3.1200e+00 l_d_fake: 3.1221e+00 D_real: 2.0704e+01 D_fake: 2.3770e+01 +20-04-06 07:09:10.399 - INFO: l_g_pix: 1.1988e-04 l_g_fea: 4.5667e-01 l_g_gan: 1.2991e-03 l_d_real: 1.5854e+00 l_d_fake: 1.5818e+00 D_real: 2.7697e+01 D_fake: 2.9021e+01 +20-04-06 07:12:21.565 - INFO: l_g_pix: 1.2443e-04 l_g_fea: 4.2555e-01 l_g_gan: 7.7617e-03 l_d_real: 2.8579e-01 l_d_fake: 2.7119e-01 D_real: 1.6120e+01 D_fake: 1.4846e+01 +20-04-06 07:15:33.352 - INFO: l_g_pix: 1.2046e-04 l_g_fea: 5.3548e-01 l_g_gan: 8.8053e-03 l_d_real: 2.2429e-01 l_d_fake: 2.2579e-01 D_real: 8.8101e+00 D_fake: 7.2740e+00 +20-04-06 07:18:44.159 - INFO: l_g_pix: 1.1456e-04 l_g_fea: 5.1919e-01 l_g_gan: 1.3132e-02 l_d_real: 1.2180e-01 l_d_fake: 1.0157e-01 D_real: 3.1355e+01 D_fake: 2.8840e+01 +20-04-06 07:21:54.782 - INFO: l_g_pix: 1.1393e-04 l_g_fea: 4.7306e-01 l_g_gan: 1.4332e-02 l_d_real: 7.9408e-02 l_d_fake: 8.0915e-02 D_real: 8.9415e+00 D_fake: 6.1552e+00 +20-04-06 07:25:06.712 - INFO: l_g_pix: 1.0207e-04 l_g_fea: 4.0161e-01 l_g_gan: 1.3886e-02 l_d_real: 7.5554e-02 l_d_fake: 7.5496e-02 D_real: 1.7716e+01 D_fake: 1.5015e+01 +20-04-06 07:28:18.078 - INFO: l_g_pix: 1.1573e-04 l_g_fea: 5.8640e-01 l_g_gan: 1.9046e-02 l_d_real: 2.9301e-02 l_d_fake: 2.6735e-02 D_real: 2.3718e+01 D_fake: 1.9937e+01 +20-04-06 07:31:28.858 - INFO: l_g_pix: 1.0509e-04 l_g_fea: 4.1561e-01 l_g_gan: 1.1626e-02 l_d_real: 1.1705e-01 l_d_fake: 1.2561e-01 D_real: -1.2163e-01 D_fake: -2.3256e+00 +20-04-06 07:34:40.630 - INFO: l_g_pix: 9.4396e-05 l_g_fea: 4.8507e-01 l_g_gan: 3.4386e-03 l_d_real: 7.7721e-01 l_d_fake: 7.7388e-01 D_real: 1.8334e+01 D_fake: 1.8422e+01 +20-04-06 07:37:52.196 - INFO: l_g_pix: 8.5954e-05 l_g_fea: 3.7069e-01 l_g_gan: 7.8876e-03 l_d_real: 2.7759e-01 l_d_fake: 2.6011e-01 D_real: 3.0742e+01 D_fake: 2.9434e+01 +20-04-06 07:41:03.782 - INFO: l_g_pix: 8.1595e-05 l_g_fea: 4.4059e-01 l_g_gan: 1.0646e-02 l_d_real: 1.5457e-01 l_d_fake: 1.6037e-01 D_real: 9.6381e+00 D_fake: 7.6665e+00 +20-04-06 07:44:14.796 - INFO: l_g_pix: 1.0638e-04 l_g_fea: 4.8980e-01 l_g_gan: 1.8298e-02 l_d_real: 3.3713e-02 l_d_fake: 3.3030e-02 D_real: 3.5359e+01 D_fake: 3.1733e+01 +20-04-06 07:44:15.195 - INFO: Models and training states saved. +20-04-06 07:45:29.297 - INFO: # Validation # PSNR: 31.356, SSIM: 0.83125, LPIPS: 0.035853 +20-04-06 07:45:29.298 - INFO: psnr: 31.356, ssim: 0.83125, lpips: 0.035853 +20-04-06 07:48:39.122 - INFO: l_g_pix: 9.2829e-05 l_g_fea: 5.1631e-01 l_g_gan: 1.3940e-02 l_d_real: 7.5914e-02 l_d_fake: 7.7449e-02 D_real: 4.0829e+01 D_fake: 3.8118e+01 +20-04-06 07:51:50.538 - INFO: l_g_pix: 7.8808e-05 l_g_fea: 3.1064e-01 l_g_gan: 7.5406e-03 l_d_real: 2.9176e-01 l_d_fake: 2.7908e-01 D_real: 4.1326e+01 D_fake: 4.0103e+01 +20-04-06 07:55:02.974 - INFO: l_g_pix: 9.3559e-05 l_g_fea: 4.3955e-01 l_g_gan: 9.5684e-04 l_d_real: 1.8555e+00 l_d_fake: 1.8552e+00 D_real: -2.5214e+00 D_fake: -8.5739e-01 +20-04-06 07:58:13.773 - INFO: l_g_pix: 8.3338e-05 l_g_fea: 4.1960e-01 l_g_gan: 2.5987e-02 l_d_real: 6.4031e-03 l_d_fake: 6.0567e-03 D_real: 2.4108e+01 D_fake: 1.8917e+01 +20-04-06 08:01:24.169 - INFO: l_g_pix: 8.1903e-05 l_g_fea: 4.0371e-01 l_g_gan: 1.4549e-02 l_d_real: 7.0510e-02 l_d_fake: 8.4321e-02 D_real: -4.7622e-01 D_fake: -3.3086e+00 +20-04-06 08:04:35.779 - INFO: l_g_pix: 1.1624e-04 l_g_fea: 4.8450e-01 l_g_gan: 1.8059e-02 l_d_real: 3.4753e-02 l_d_fake: 3.3665e-02 D_real: 2.3959e+01 D_fake: 2.0382e+01 +20-04-06 08:07:46.648 - INFO: l_g_pix: 1.2599e-04 l_g_fea: 5.5397e-01 l_g_gan: 2.7725e-03 l_d_real: 9.2762e-01 l_d_fake: 9.2589e-01 D_real: 2.6396e+01 D_fake: 2.6768e+01 +20-04-06 08:10:58.567 - INFO: l_g_pix: 1.0836e-04 l_g_fea: 5.1347e-01 l_g_gan: 3.6201e-03 l_d_real: 8.2593e-01 l_d_fake: 8.1079e-01 D_real: 6.1138e+01 D_fake: 6.1232e+01 +20-04-06 08:14:09.855 - INFO: l_g_pix: 1.0913e-04 l_g_fea: 5.1750e-01 l_g_gan: 7.2442e-04 l_d_real: 2.1435e+00 l_d_fake: 2.1392e+00 D_real: 1.7070e+01 D_fake: 1.9066e+01 +20-04-06 08:17:20.877 - INFO: l_g_pix: 9.1084e-05 l_g_fea: 3.9187e-01 l_g_gan: 1.7427e-02 l_d_real: 4.3922e-02 l_d_fake: 4.0305e-02 D_real: 2.2346e+01 D_fake: 1.8902e+01 +20-04-06 08:20:31.365 - INFO: l_g_pix: 1.0560e-04 l_g_fea: 4.6573e-01 l_g_gan: 1.0729e-02 l_d_real: 1.3892e-01 l_d_fake: 1.4481e-01 D_real: 1.1758e+01 D_fake: 9.7545e+00 +20-04-06 08:23:42.949 - INFO: l_g_pix: 1.0386e-04 l_g_fea: 3.8621e-01 l_g_gan: 8.6442e-03 l_d_real: 2.3316e-01 l_d_fake: 2.4245e-01 D_real: 2.8927e+01 D_fake: 2.7436e+01 +20-04-06 08:26:58.302 - INFO: l_g_pix: 8.8541e-05 l_g_fea: 3.2197e-01 l_g_gan: 1.2027e-02 l_d_real: 1.0746e-01 l_d_fake: 1.4098e-01 D_real: 2.4517e+01 D_fake: 2.2235e+01 +20-04-06 08:30:10.497 - INFO: l_g_pix: 1.1803e-04 l_g_fea: 4.3168e-01 l_g_gan: 1.3667e-02 l_d_real: 9.0712e-02 l_d_fake: 1.0506e-01 D_real: 3.2371e+01 D_fake: 2.9735e+01 +20-04-06 08:33:21.757 - INFO: l_g_pix: 1.0558e-04 l_g_fea: 4.7039e-01 l_g_gan: 1.1775e-02 l_d_real: 1.2243e-01 l_d_fake: 1.3746e-01 D_real: -7.5471e+00 D_fake: -9.7721e+00 +20-04-06 08:36:32.591 - INFO: l_g_pix: 9.6281e-05 l_g_fea: 4.3446e-01 l_g_gan: 2.0212e-02 l_d_real: 2.1460e-02 l_d_fake: 2.1971e-02 D_real: 4.2795e+01 D_fake: 3.8775e+01 +20-04-06 08:39:43.548 - INFO: l_g_pix: 6.3095e-05 l_g_fea: 3.1499e-01 l_g_gan: 1.2283e-03 l_d_real: 1.7573e+00 l_d_fake: 1.7606e+00 D_real: 1.3726e+01 D_fake: 1.5239e+01 +20-04-06 08:42:54.364 - INFO: l_g_pix: 9.5715e-05 l_g_fea: 3.7889e-01 l_g_gan: 9.4221e-03 l_d_real: 2.4671e-01 l_d_fake: 2.0817e-01 D_real: 1.1186e+01 D_fake: 9.5287e+00 +20-04-06 08:46:06.157 - INFO: l_g_pix: 1.0027e-04 l_g_fea: 4.3287e-01 l_g_gan: 1.0301e-02 l_d_real: 1.8227e-01 l_d_fake: 1.6766e-01 D_real: 1.6822e+01 D_fake: 1.4936e+01 +20-04-06 08:49:17.346 - INFO: l_g_pix: 1.3150e-04 l_g_fea: 5.5201e-01 l_g_gan: 1.0848e-02 l_d_real: 1.3669e-01 l_d_fake: 1.5054e-01 D_real: 1.2798e+01 D_fake: 1.0772e+01 +20-04-06 08:52:28.643 - INFO: l_g_pix: 7.7726e-05 l_g_fea: 3.7931e-01 l_g_gan: 2.0225e-03 l_d_real: 1.1777e+00 l_d_fake: 1.1734e+00 D_real: 2.6571e+01 D_fake: 2.7342e+01 +20-04-06 08:55:40.078 - INFO: l_g_pix: 6.8930e-05 l_g_fea: 3.7147e-01 l_g_gan: 1.0095e-02 l_d_real: 1.6007e-01 l_d_fake: 1.6471e-01 D_real: 1.2755e+01 D_fake: 1.0898e+01 +20-04-06 08:58:51.571 - INFO: l_g_pix: 1.1753e-04 l_g_fea: 4.3158e-01 l_g_gan: 1.1205e-02 l_d_real: 1.3761e-01 l_d_fake: 1.4060e-01 D_real: 4.9059e+01 D_fake: 4.6957e+01 +20-04-06 09:02:02.835 - INFO: l_g_pix: 1.1561e-04 l_g_fea: 3.9897e-01 l_g_gan: 1.2508e-02 l_d_real: 9.5629e-02 l_d_fake: 1.1251e-01 D_real: 2.9876e+01 D_fake: 2.7478e+01 +20-04-06 09:05:14.330 - INFO: l_g_pix: 7.7388e-05 l_g_fea: 3.7800e-01 l_g_gan: 4.1196e-03 l_d_real: 6.7609e-01 l_d_fake: 6.8914e-01 D_real: 3.0264e+01 D_fake: 3.0123e+01 +20-04-06 09:05:14.732 - INFO: Models and training states saved. +20-04-06 09:06:33.446 - INFO: # Validation # PSNR: 31.994, SSIM: 0.84802, LPIPS: 0.030219 +20-04-06 09:06:33.446 - INFO: psnr: 31.994, ssim: 0.84802, lpips: 0.030219 +20-04-06 09:09:42.415 - INFO: l_g_pix: 1.0546e-04 l_g_fea: 4.4633e-01 l_g_gan: 9.9688e-03 l_d_real: 1.7043e-01 l_d_fake: 1.9994e-01 D_real: 2.0707e+01 D_fake: 1.8898e+01 +20-04-06 09:12:53.724 - INFO: l_g_pix: 1.1362e-04 l_g_fea: 4.3792e-01 l_g_gan: 1.9019e-02 l_d_real: 3.7735e-02 l_d_fake: 3.1654e-02 D_real: 2.8150e+01 D_fake: 2.4381e+01 +20-04-06 09:16:05.279 - INFO: l_g_pix: 7.8403e-05 l_g_fea: 3.6015e-01 l_g_gan: 1.8857e-02 l_d_real: 2.7436e-02 l_d_fake: 3.4048e-02 D_real: 1.8093e+01 D_fake: 1.4352e+01 +20-04-06 09:19:16.983 - INFO: l_g_pix: 9.0140e-05 l_g_fea: 4.2409e-01 l_g_gan: 7.2196e-03 l_d_real: 3.4175e-01 l_d_fake: 3.4633e-01 D_real: 9.4572e+00 D_fake: 8.3574e+00 +20-04-06 09:22:28.663 - INFO: l_g_pix: 9.5864e-05 l_g_fea: 3.9039e-01 l_g_gan: 6.1802e-03 l_d_real: 4.0731e-01 l_d_fake: 4.0928e-01 D_real: 2.2355e+01 D_fake: 2.1527e+01 +20-04-06 09:25:39.892 - INFO: l_g_pix: 1.0479e-04 l_g_fea: 5.4421e-01 l_g_gan: 4.0972e-03 l_d_real: 7.1415e-01 l_d_fake: 7.2286e-01 D_real: 3.3114e+01 D_fake: 3.3013e+01 +20-04-06 09:28:51.152 - INFO: l_g_pix: 1.1184e-04 l_g_fea: 5.1051e-01 l_g_gan: 1.0700e-02 l_d_real: 1.8579e-01 l_d_fake: 1.6162e-01 D_real: 3.8261e+01 D_fake: 3.6295e+01 +20-04-06 09:32:02.624 - INFO: l_g_pix: 8.9542e-05 l_g_fea: 4.1720e-01 l_g_gan: 1.3443e-02 l_d_real: 8.9327e-02 l_d_fake: 9.7671e-02 D_real: 4.6070e+01 D_fake: 4.3475e+01 +20-04-06 09:35:14.481 - INFO: l_g_pix: 8.9703e-05 l_g_fea: 4.2571e-01 l_g_gan: 1.6746e-02 l_d_real: 5.1579e-02 l_d_fake: 5.0479e-02 D_real: 5.8110e+01 D_fake: 5.4812e+01 +20-04-06 09:38:25.505 - INFO: l_g_pix: 8.2469e-05 l_g_fea: 3.8933e-01 l_g_gan: 2.8648e-03 l_d_real: 9.9518e-01 l_d_fake: 1.0003e+00 D_real: 4.2949e+01 D_fake: 4.3374e+01 +20-04-06 09:41:37.072 - INFO: l_g_pix: 8.6484e-05 l_g_fea: 3.9118e-01 l_g_gan: 1.9862e-03 l_d_real: 1.2175e+00 l_d_fake: 1.2120e+00 D_real: 3.1609e+01 D_fake: 3.2427e+01 +20-04-06 09:44:48.596 - INFO: l_g_pix: 8.9355e-05 l_g_fea: 4.2820e-01 l_g_gan: 5.9841e-03 l_d_real: 4.2058e-01 l_d_fake: 4.1879e-01 D_real: 2.5445e+01 D_fake: 2.4668e+01 +20-04-06 09:48:00.234 - INFO: l_g_pix: 1.1506e-04 l_g_fea: 4.7360e-01 l_g_gan: 1.3855e-02 l_d_real: 8.0994e-02 l_d_fake: 8.1147e-02 D_real: 2.0294e+01 D_fake: 1.7604e+01 +20-04-06 09:51:11.027 - INFO: l_g_pix: 8.5247e-05 l_g_fea: 4.2878e-01 l_g_gan: 2.5385e-03 l_d_real: 1.0342e+00 l_d_fake: 1.0341e+00 D_real: 3.0085e+01 D_fake: 3.0612e+01 +20-04-06 09:54:22.024 - INFO: l_g_pix: 1.6431e-04 l_g_fea: 4.7683e-01 l_g_gan: 7.4342e-03 l_d_real: 3.1959e-01 l_d_fake: 3.2111e-01 D_real: 3.1799e+01 D_fake: 3.0632e+01 +20-04-06 09:57:33.554 - INFO: l_g_pix: 1.0876e-04 l_g_fea: 4.6061e-01 l_g_gan: 1.5693e-02 l_d_real: 5.9267e-02 l_d_fake: 5.8275e-02 D_real: 3.0985e+01 D_fake: 2.7905e+01 +20-04-06 10:00:45.060 - INFO: l_g_pix: 8.3412e-05 l_g_fea: 3.6161e-01 l_g_gan: 8.8589e-03 l_d_real: 2.2719e-01 l_d_fake: 2.2268e-01 D_real: 2.5600e+01 D_fake: 2.4053e+01 +20-04-06 10:03:56.244 - INFO: l_g_pix: 1.0076e-04 l_g_fea: 4.3553e-01 l_g_gan: 1.2706e-02 l_d_real: 1.1740e-01 l_d_fake: 1.2300e-01 D_real: 4.3518e+01 D_fake: 4.1097e+01 +20-04-06 10:07:07.590 - INFO: l_g_pix: 8.6453e-05 l_g_fea: 4.3018e-01 l_g_gan: 9.0292e-03 l_d_real: 2.3087e-01 l_d_fake: 2.3602e-01 D_real: 3.7796e+01 D_fake: 3.6223e+01 +20-04-06 10:10:19.238 - INFO: l_g_pix: 7.4910e-05 l_g_fea: 3.3838e-01 l_g_gan: 1.7563e-02 l_d_real: 3.6953e-02 l_d_fake: 3.7314e-02 D_real: 3.4081e+01 D_fake: 3.0605e+01 +20-04-06 10:13:30.701 - INFO: l_g_pix: 6.7668e-05 l_g_fea: 3.6987e-01 l_g_gan: 1.0938e-02 l_d_real: 1.9860e-01 l_d_fake: 2.0364e-01 D_real: 2.3851e+01 D_fake: 2.1864e+01 +20-04-06 10:16:41.323 - INFO: l_g_pix: 1.0985e-04 l_g_fea: 3.8444e-01 l_g_gan: 1.3590e-02 l_d_real: 9.2960e-02 l_d_fake: 8.1866e-02 D_real: 2.9279e+01 D_fake: 2.6648e+01 +20-04-06 10:19:52.845 - INFO: l_g_pix: 1.0480e-04 l_g_fea: 4.5387e-01 l_g_gan: 1.4067e-02 l_d_real: 9.5195e-02 l_d_fake: 8.2214e-02 D_real: 1.8967e+01 D_fake: 1.6243e+01 +20-04-06 10:23:03.795 - INFO: l_g_pix: 7.1391e-05 l_g_fea: 3.1303e-01 l_g_gan: 4.7387e-03 l_d_real: 5.4724e-01 l_d_fake: 5.3156e-01 D_real: 1.7274e+01 D_fake: 1.6866e+01 +20-04-06 10:26:15.673 - INFO: l_g_pix: 1.1363e-04 l_g_fea: 4.5317e-01 l_g_gan: 1.9420e-02 l_d_real: 2.5527e-02 l_d_fake: 2.3685e-02 D_real: 3.0129e+01 D_fake: 2.6269e+01 +20-04-06 10:26:16.090 - INFO: Models and training states saved. +20-04-06 10:27:12.593 - INFO: # Validation # PSNR: 31.726, SSIM: 0.83427, LPIPS: 0.032943 +20-04-06 10:27:12.593 - INFO: psnr: 31.726, ssim: 0.83427, lpips: 0.032943 +20-04-06 10:30:23.393 - INFO: l_g_pix: 7.1495e-05 l_g_fea: 3.7857e-01 l_g_gan: 4.5789e-03 l_d_real: 6.2286e-01 l_d_fake: 6.2885e-01 D_real: 3.7916e+01 D_fake: 3.7626e+01 +20-04-06 10:33:34.133 - INFO: l_g_pix: 1.0613e-04 l_g_fea: 3.8669e-01 l_g_gan: 7.0605e-03 l_d_real: 3.3101e-01 l_d_fake: 3.2127e-01 D_real: 4.2378e+01 D_fake: 4.1292e+01 +20-04-06 10:36:44.812 - INFO: l_g_pix: 1.0356e-04 l_g_fea: 4.6590e-01 l_g_gan: 7.9770e-03 l_d_real: 2.5926e-01 l_d_fake: 2.6789e-01 D_real: 2.1641e+01 D_fake: 2.0309e+01 +20-04-06 10:39:56.070 - INFO: l_g_pix: 1.1834e-04 l_g_fea: 4.9650e-01 l_g_gan: 9.4563e-03 l_d_real: 1.8304e-01 l_d_fake: 1.8610e-01 D_real: 3.1458e+01 D_fake: 2.9751e+01 +20-04-06 10:43:06.657 - INFO: l_g_pix: 8.0799e-05 l_g_fea: 4.6696e-01 l_g_gan: 4.2258e-03 l_d_real: 7.0269e-01 l_d_fake: 7.0912e-01 D_real: 3.2188e+01 D_fake: 3.2048e+01 +20-04-06 10:46:17.760 - INFO: l_g_pix: 8.8504e-05 l_g_fea: 3.1335e-01 l_g_gan: 1.4024e-02 l_d_real: 7.9919e-02 l_d_fake: 6.8957e-02 D_real: 3.5531e+01 D_fake: 3.2801e+01 +20-04-06 10:49:29.410 - INFO: l_g_pix: 7.7756e-05 l_g_fea: 2.9750e-01 l_g_gan: 4.4251e-03 l_d_real: 5.7268e-01 l_d_fake: 5.6762e-01 D_real: 3.6033e+01 D_fake: 3.5718e+01 +20-04-06 10:52:41.285 - INFO: l_g_pix: 1.0665e-04 l_g_fea: 4.7121e-01 l_g_gan: 4.1280e-03 l_d_real: 6.3676e-01 l_d_fake: 6.5889e-01 D_real: 1.6677e+01 D_fake: 1.6499e+01 +20-04-06 10:55:53.039 - INFO: l_g_pix: 9.5287e-05 l_g_fea: 4.5800e-01 l_g_gan: 2.2904e-02 l_d_real: 1.3005e-02 l_d_fake: 1.2487e-02 D_real: 2.4135e+01 D_fake: 1.9567e+01 +20-04-06 10:59:04.004 - INFO: l_g_pix: 1.1223e-04 l_g_fea: 4.2345e-01 l_g_gan: 1.2238e-02 l_d_real: 1.1465e-01 l_d_fake: 1.1369e-01 D_real: 3.7900e+01 D_fake: 3.5567e+01 +20-04-06 11:02:14.778 - INFO: l_g_pix: 9.4731e-05 l_g_fea: 4.1149e-01 l_g_gan: 9.4901e-03 l_d_real: 1.8962e-01 l_d_fake: 1.8348e-01 D_real: 1.2987e+01 D_fake: 1.1276e+01 +20-04-06 11:05:26.243 - INFO: l_g_pix: 1.1472e-04 l_g_fea: 5.2018e-01 l_g_gan: 1.1246e-02 l_d_real: 1.4516e-01 l_d_fake: 1.4282e-01 D_real: 4.3827e+00 D_fake: 2.2775e+00 +20-04-06 11:08:37.688 - INFO: l_g_pix: 1.1014e-04 l_g_fea: 4.9125e-01 l_g_gan: 1.6529e-02 l_d_real: 4.4263e-02 l_d_fake: 4.0972e-02 D_real: 2.8848e+01 D_fake: 2.5585e+01 +20-04-06 11:11:49.518 - INFO: l_g_pix: 1.0358e-04 l_g_fea: 4.8929e-01 l_g_gan: 8.7315e-03 l_d_real: 2.2180e-01 l_d_fake: 2.2302e-01 D_real: 2.1491e+01 D_fake: 1.9967e+01 +20-04-06 11:15:00.291 - INFO: l_g_pix: 1.2175e-04 l_g_fea: 4.9323e-01 l_g_gan: 7.3315e-03 l_d_real: 3.0889e-01 l_d_fake: 2.8703e-01 D_real: 1.1450e+01 D_fake: 1.0281e+01 +20-04-06 11:18:11.158 - INFO: l_g_pix: 7.3431e-05 l_g_fea: 3.4514e-01 l_g_gan: 2.3753e-03 l_d_real: 1.1543e+00 l_d_fake: 1.1414e+00 D_real: 9.4575e+00 D_fake: 1.0130e+01 +20-04-06 11:21:23.014 - INFO: l_g_pix: 9.7817e-05 l_g_fea: 4.3958e-01 l_g_gan: 1.9754e-03 l_d_real: 1.3112e+00 l_d_fake: 1.3191e+00 D_real: 2.3011e+01 D_fake: 2.3931e+01 +20-04-06 11:24:33.595 - INFO: l_g_pix: 1.0572e-04 l_g_fea: 3.9858e-01 l_g_gan: 3.2187e-02 l_d_real: 1.9973e-03 l_d_fake: 1.8102e-03 D_real: 1.1178e+01 D_fake: 4.7429e+00 +20-04-06 11:27:44.087 - INFO: l_g_pix: 8.0538e-05 l_g_fea: 3.6282e-01 l_g_gan: 6.0997e-03 l_d_real: 4.1530e-01 l_d_fake: 4.1475e-01 D_real: 5.3200e+00 D_fake: 4.5151e+00 +20-04-06 11:30:55.511 - INFO: l_g_pix: 9.2693e-05 l_g_fea: 3.8207e-01 l_g_gan: 2.0498e-03 l_d_real: 1.1768e+00 l_d_fake: 1.1682e+00 D_real: 1.6914e+01 D_fake: 1.7677e+01 +20-04-06 11:34:06.002 - INFO: l_g_pix: 6.4122e-05 l_g_fea: 3.0431e-01 l_g_gan: 5.7154e-03 l_d_real: 4.7566e-01 l_d_fake: 4.6392e-01 D_real: 3.8149e+01 D_fake: 3.7476e+01 +20-04-06 11:37:17.133 - INFO: l_g_pix: 1.1216e-04 l_g_fea: 5.0492e-01 l_g_gan: 1.1889e-02 l_d_real: 1.2724e-01 l_d_fake: 1.3241e-01 D_real: 3.3497e+01 D_fake: 3.1249e+01 +20-04-06 11:40:28.703 - INFO: l_g_pix: 6.6295e-05 l_g_fea: 3.2187e-01 l_g_gan: 3.2481e-03 l_d_real: 8.9072e-01 l_d_fake: 9.1616e-01 D_real: 1.3941e+01 D_fake: 1.4195e+01 +20-04-06 11:43:40.387 - INFO: l_g_pix: 8.3004e-05 l_g_fea: 4.3210e-01 l_g_gan: 1.1915e-02 l_d_real: 1.1344e-01 l_d_fake: 1.0926e-01 D_real: 3.7243e+01 D_fake: 3.4971e+01 +20-04-06 11:46:51.590 - INFO: l_g_pix: 1.1851e-04 l_g_fea: 5.1376e-01 l_g_gan: 2.2758e-02 l_d_real: 1.3948e-02 l_d_fake: 1.2778e-02 D_real: 2.1972e+01 D_fake: 1.7434e+01 +20-04-06 11:46:51.952 - INFO: Models and training states saved. +20-04-06 11:47:45.813 - INFO: # Validation # PSNR: 31.123, SSIM: 0.83208, LPIPS: 0.028689 +20-04-06 11:47:45.813 - INFO: psnr: 31.123, ssim: 0.83208, lpips: 0.028689 +20-04-06 11:51:20.886 - INFO: l_g_pix: 1.2894e-04 l_g_fea: 5.4934e-01 l_g_gan: 5.6035e-03 l_d_real: 4.8337e-01 l_d_fake: 4.6683e-01 D_real: 2.3317e+01 D_fake: 2.2671e+01 +20-04-06 11:54:31.341 - INFO: l_g_pix: 1.1323e-04 l_g_fea: 5.1023e-01 l_g_gan: 1.0102e-02 l_d_real: 1.7818e-01 l_d_fake: 1.6242e-01 D_real: 1.4487e+01 D_fake: 1.2637e+01 +20-04-06 11:57:42.876 - INFO: l_g_pix: 6.9303e-05 l_g_fea: 3.8679e-01 l_g_gan: 6.3632e-03 l_d_real: 3.8814e-01 l_d_fake: 3.9548e-01 D_real: 3.5217e+01 D_fake: 3.4336e+01 +20-04-06 12:00:59.921 - INFO: l_g_pix: 1.0054e-04 l_g_fea: 4.6007e-01 l_g_gan: 8.4623e-03 l_d_real: 2.7376e-01 l_d_fake: 2.9194e-01 D_real: 2.3871e+01 D_fake: 2.2462e+01 +20-04-06 12:04:11.393 - INFO: l_g_pix: 8.7393e-05 l_g_fea: 4.6662e-01 l_g_gan: 1.1545e-02 l_d_real: 1.1323e-01 l_d_fake: 1.1290e-01 D_real: 1.7621e+01 D_fake: 1.5425e+01 +20-04-06 12:07:22.160 - INFO: l_g_pix: 1.0193e-04 l_g_fea: 5.3591e-01 l_g_gan: 4.7114e-03 l_d_real: 6.1085e-01 l_d_fake: 6.0445e-01 D_real: 1.5271e+01 D_fake: 1.4936e+01 +20-04-06 12:10:33.175 - INFO: l_g_pix: 1.3114e-04 l_g_fea: 5.3958e-01 l_g_gan: 1.9288e-03 l_d_real: 1.2293e+00 l_d_fake: 1.2503e+00 D_real: 4.6527e+00 D_fake: 5.5067e+00 +20-04-06 12:13:44.809 - INFO: l_g_pix: 1.1421e-04 l_g_fea: 5.5475e-01 l_g_gan: 3.1915e-03 l_d_real: 9.9018e-01 l_d_fake: 1.0062e+00 D_real: 2.9472e+01 D_fake: 2.9832e+01 +20-04-06 12:16:56.038 - INFO: l_g_pix: 1.0718e-04 l_g_fea: 4.4356e-01 l_g_gan: 1.2000e-02 l_d_real: 1.1939e-01 l_d_fake: 1.2414e-01 D_real: 4.3800e+01 D_fake: 4.1522e+01 +20-04-06 12:20:06.838 - INFO: l_g_pix: 1.1157e-04 l_g_fea: 5.3697e-01 l_g_gan: 1.4065e-02 l_d_real: 7.6083e-02 l_d_fake: 6.8837e-02 D_real: 4.1369e+01 D_fake: 3.8628e+01 +20-04-06 12:23:18.636 - INFO: l_g_pix: 9.6301e-05 l_g_fea: 3.6809e-01 l_g_gan: 5.2123e-03 l_d_real: 4.9710e-01 l_d_fake: 4.8207e-01 D_real: 7.1697e+00 D_fake: 6.6168e+00 +20-04-06 12:26:29.857 - INFO: l_g_pix: 9.3769e-05 l_g_fea: 4.9057e-01 l_g_gan: 6.4315e-03 l_d_real: 3.8172e-01 l_d_fake: 3.6702e-01 D_real: 2.2110e+01 D_fake: 2.1198e+01 +20-04-06 12:29:41.272 - INFO: l_g_pix: 9.1961e-05 l_g_fea: 4.3102e-01 l_g_gan: 1.2757e-02 l_d_real: 1.0122e-01 l_d_fake: 1.2196e-01 D_real: 2.2654e+01 D_fake: 2.0214e+01 +20-04-06 12:32:53.173 - INFO: l_g_pix: 1.1889e-04 l_g_fea: 4.8484e-01 l_g_gan: 1.3470e-02 l_d_real: 8.6042e-02 l_d_fake: 9.5389e-02 D_real: 2.8007e+01 D_fake: 2.5404e+01 +20-04-06 12:36:04.392 - INFO: l_g_pix: 9.9932e-05 l_g_fea: 4.2116e-01 l_g_gan: 1.3091e-02 l_d_real: 8.7690e-02 l_d_fake: 8.7748e-02 D_real: 1.7591e+01 D_fake: 1.5061e+01 +20-04-06 12:39:15.591 - INFO: l_g_pix: 8.5304e-05 l_g_fea: 4.1024e-01 l_g_gan: 2.4097e-03 l_d_real: 1.0843e+00 l_d_fake: 1.0874e+00 D_real: 1.6115e+00 D_fake: 2.2153e+00 +20-04-06 12:42:26.758 - INFO: l_g_pix: 1.1269e-04 l_g_fea: 5.0345e-01 l_g_gan: 1.7210e-02 l_d_real: 4.6374e-02 l_d_fake: 4.8863e-02 D_real: 1.5373e+01 D_fake: 1.1979e+01 +20-04-06 12:45:38.147 - INFO: l_g_pix: 9.8923e-05 l_g_fea: 4.2048e-01 l_g_gan: 2.3434e-02 l_d_real: 1.0745e-02 l_d_fake: 2.4112e-02 D_real: 8.6600e+00 D_fake: 3.9906e+00 +20-04-06 12:48:49.643 - INFO: l_g_pix: 1.1506e-04 l_g_fea: 5.3736e-01 l_g_gan: 1.8107e-02 l_d_real: 3.6725e-02 l_d_fake: 3.6878e-02 D_real: 1.5618e+01 D_fake: 1.2033e+01 +20-04-06 12:52:00.393 - INFO: l_g_pix: 9.3395e-05 l_g_fea: 5.1834e-01 l_g_gan: 1.0651e-02 l_d_real: 1.4980e-01 l_d_fake: 1.6109e-01 D_real: 1.5577e+01 D_fake: 1.3602e+01 +20-04-06 12:55:11.788 - INFO: l_g_pix: 9.6027e-05 l_g_fea: 4.7472e-01 l_g_gan: 2.1072e-03 l_d_real: 1.1901e+00 l_d_fake: 1.1941e+00 D_real: 3.2453e+01 D_fake: 3.3224e+01 +20-04-06 12:58:22.668 - INFO: l_g_pix: 9.0795e-05 l_g_fea: 3.9093e-01 l_g_gan: 6.1297e-03 l_d_real: 4.7879e-01 l_d_fake: 4.6738e-01 D_real: 2.7665e+01 D_fake: 2.6912e+01 +20-04-06 13:01:34.521 - INFO: l_g_pix: 1.6147e-04 l_g_fea: 4.4696e-01 l_g_gan: 7.5213e-03 l_d_real: 3.2639e-01 l_d_fake: 3.0046e-01 D_real: 3.0446e+01 D_fake: 2.9255e+01 +20-04-06 13:04:45.192 - INFO: l_g_pix: 1.0528e-04 l_g_fea: 4.6647e-01 l_g_gan: 7.6747e-03 l_d_real: 3.0763e-01 l_d_fake: 2.7951e-01 D_real: 3.8059e+01 D_fake: 3.6817e+01 +20-04-06 13:07:56.764 - INFO: l_g_pix: 1.2149e-04 l_g_fea: 4.4718e-01 l_g_gan: 3.7719e-03 l_d_real: 7.9434e-01 l_d_fake: 7.8833e-01 D_real: 3.2556e+01 D_fake: 3.2593e+01 +20-04-06 13:07:57.182 - INFO: Models and training states saved. +20-04-06 13:09:05.087 - INFO: # Validation # PSNR: 31.207, SSIM: 0.83788, LPIPS: 0.027606 +20-04-06 13:09:05.087 - INFO: psnr: 31.207, ssim: 0.83788, lpips: 0.027606 +20-04-06 13:12:23.516 - INFO: l_g_pix: 9.1232e-05 l_g_fea: 4.4070e-01 l_g_gan: 4.0021e-03 l_d_real: 6.8014e-01 l_d_fake: 6.5354e-01 D_real: 2.1411e+01 D_fake: 2.1278e+01 +20-04-06 13:15:34.703 - INFO: l_g_pix: 5.5016e-05 l_g_fea: 2.9631e-01 l_g_gan: 9.5159e-03 l_d_real: 2.2418e-01 l_d_fake: 2.1520e-01 D_real: 1.2764e+01 D_fake: 1.1081e+01 +20-04-06 13:18:46.248 - INFO: l_g_pix: 1.0848e-04 l_g_fea: 4.0882e-01 l_g_gan: 8.6591e-03 l_d_real: 2.4418e-01 l_d_fake: 2.3149e-01 D_real: 1.8606e+01 D_fake: 1.7112e+01 +20-04-06 13:21:57.407 - INFO: l_g_pix: 1.4305e-04 l_g_fea: 5.6180e-01 l_g_gan: 8.6899e-03 l_d_real: 2.5500e-01 l_d_fake: 2.7774e-01 D_real: 4.3175e+01 D_fake: 4.1704e+01 +20-04-06 13:25:08.771 - INFO: l_g_pix: 7.1893e-05 l_g_fea: 3.3800e-01 l_g_gan: 3.3708e-03 l_d_real: 8.2053e-01 l_d_fake: 7.9802e-01 D_real: 5.0472e+01 D_fake: 5.0607e+01 +20-04-06 13:28:19.426 - INFO: l_g_pix: 9.1326e-05 l_g_fea: 4.8116e-01 l_g_gan: 2.2505e-02 l_d_real: 1.5941e-02 l_d_fake: 1.2901e-02 D_real: 3.9790e+01 D_fake: 3.5304e+01 +20-04-06 13:31:30.093 - INFO: l_g_pix: 1.1944e-04 l_g_fea: 4.7611e-01 l_g_gan: 5.1861e-03 l_d_real: 5.3954e-01 l_d_fake: 5.1574e-01 D_real: 4.4010e+01 D_fake: 4.3500e+01 +20-04-06 13:34:40.966 - INFO: l_g_pix: 1.0410e-04 l_g_fea: 4.4384e-01 l_g_gan: 5.1048e-03 l_d_real: 5.1724e-01 l_d_fake: 5.2704e-01 D_real: 3.3738e+01 D_fake: 3.3239e+01 +20-04-06 13:37:51.732 - INFO: l_g_pix: 1.3090e-04 l_g_fea: 5.1187e-01 l_g_gan: 3.7367e-02 l_d_real: 6.8114e-04 l_d_fake: 7.3195e-04 D_real: 1.7575e+01 D_fake: 1.0102e+01 +20-04-06 13:41:02.379 - INFO: l_g_pix: 1.1807e-04 l_g_fea: 5.2690e-01 l_g_gan: 3.1629e-03 l_d_real: 8.8403e-01 l_d_fake: 9.0251e-01 D_real: 2.6501e+01 D_fake: 2.6762e+01 +20-04-06 13:44:13.151 - INFO: l_g_pix: 7.5353e-05 l_g_fea: 3.8541e-01 l_g_gan: 5.2401e-03 l_d_real: 5.6913e-01 l_d_fake: 5.0334e-01 D_real: 2.0007e+01 D_fake: 1.9495e+01 +20-04-06 13:47:24.746 - INFO: l_g_pix: 1.0125e-04 l_g_fea: 3.0223e-01 l_g_gan: 6.5987e-03 l_d_real: 3.8785e-01 l_d_fake: 3.8249e-01 D_real: 1.4575e+01 D_fake: 1.3640e+01 +20-04-06 13:50:35.519 - INFO: l_g_pix: 1.3099e-04 l_g_fea: 5.5153e-01 l_g_gan: 1.8544e-02 l_d_real: 2.8867e-02 l_d_fake: 2.9937e-02 D_real: 3.0742e+01 D_fake: 2.7063e+01 +20-04-06 13:53:46.229 - INFO: l_g_pix: 9.3837e-05 l_g_fea: 4.6359e-01 l_g_gan: 1.2579e-02 l_d_real: 1.0139e-01 l_d_fake: 1.0096e-01 D_real: 1.2651e+01 D_fake: 1.0236e+01 +20-04-06 13:56:57.574 - INFO: l_g_pix: 6.7706e-05 l_g_fea: 3.5821e-01 l_g_gan: 1.1428e-02 l_d_real: 1.4037e-01 l_d_fake: 1.3345e-01 D_real: 3.3611e+01 D_fake: 3.1462e+01 +20-04-06 14:00:08.099 - INFO: l_g_pix: 1.2084e-04 l_g_fea: 5.9770e-01 l_g_gan: 3.9245e-03 l_d_real: 7.3377e-01 l_d_fake: 7.1983e-01 D_real: 2.0415e+01 D_fake: 2.0357e+01 +20-04-06 14:03:18.852 - INFO: l_g_pix: 1.5306e-04 l_g_fea: 5.3528e-01 l_g_gan: 4.9736e-03 l_d_real: 5.3470e-01 l_d_fake: 5.5654e-01 D_real: 2.7017e+01 D_fake: 2.6568e+01 +20-04-06 14:06:29.698 - INFO: l_g_pix: 1.1965e-04 l_g_fea: 4.7301e-01 l_g_gan: 1.4679e-02 l_d_real: 7.4257e-02 l_d_fake: 7.3971e-02 D_real: 1.6939e+01 D_fake: 1.4077e+01 +20-04-06 14:09:41.447 - INFO: l_g_pix: 1.6054e-04 l_g_fea: 5.5903e-01 l_g_gan: 1.7962e-02 l_d_real: 3.6017e-02 l_d_fake: 3.5597e-02 D_real: 2.9070e+01 D_fake: 2.5514e+01 +20-04-06 14:12:53.054 - INFO: l_g_pix: 1.0286e-04 l_g_fea: 4.2154e-01 l_g_gan: 1.2280e-02 l_d_real: 1.1847e-01 l_d_fake: 1.1357e-01 D_real: 1.4609e+01 D_fake: 1.2269e+01 +20-04-06 14:16:03.719 - INFO: l_g_pix: 1.2299e-04 l_g_fea: 5.3577e-01 l_g_gan: 1.0064e-02 l_d_real: 1.8082e-01 l_d_fake: 1.7196e-01 D_real: 2.3881e+01 D_fake: 2.2045e+01 +20-04-06 14:19:15.204 - INFO: l_g_pix: 7.3022e-05 l_g_fea: 3.7583e-01 l_g_gan: 7.6022e-03 l_d_real: 3.0968e-01 l_d_fake: 3.2644e-01 D_real: 4.0629e+01 D_fake: 3.9427e+01 +20-04-06 14:22:25.905 - INFO: l_g_pix: 1.2130e-04 l_g_fea: 4.6707e-01 l_g_gan: 1.9051e-03 l_d_real: 1.3030e+00 l_d_fake: 1.3099e+00 D_real: 4.0848e+01 D_fake: 4.1773e+01 +20-04-06 14:25:37.380 - INFO: l_g_pix: 1.1336e-04 l_g_fea: 3.6914e-01 l_g_gan: 7.7980e-03 l_d_real: 2.6991e-01 l_d_fake: 2.6860e-01 D_real: 2.4040e+01 D_fake: 2.2750e+01 +20-04-06 14:28:48.231 - INFO: l_g_pix: 6.8967e-05 l_g_fea: 3.6108e-01 l_g_gan: 9.7178e-03 l_d_real: 2.7104e-01 l_d_fake: 2.2636e-01 D_real: 5.3503e+01 D_fake: 5.1808e+01 +20-04-06 14:28:48.643 - INFO: Models and training states saved. +20-04-06 14:29:53.025 - INFO: # Validation # PSNR: 31.706, SSIM: 0.83449, LPIPS: 0.025505 +20-04-06 14:29:53.025 - INFO: psnr: 31.706, ssim: 0.83449, lpips: 0.025505 +20-04-06 14:33:03.528 - INFO: l_g_pix: 1.0373e-04 l_g_fea: 5.0598e-01 l_g_gan: 1.4297e-02 l_d_real: 7.6654e-02 l_d_fake: 8.2509e-02 D_real: 2.0292e+01 D_fake: 1.7513e+01 +20-04-06 14:36:15.181 - INFO: l_g_pix: 6.7405e-05 l_g_fea: 4.1274e-01 l_g_gan: 2.0404e-02 l_d_real: 3.0635e-02 l_d_fake: 3.5680e-02 D_real: 5.8653e-01 D_fake: -3.4611e+00 +20-04-06 14:39:26.831 - INFO: l_g_pix: 1.1168e-04 l_g_fea: 3.7627e-01 l_g_gan: 1.0481e-02 l_d_real: 1.7070e-01 l_d_fake: 1.7337e-01 D_real: 1.4379e+01 D_fake: 1.2455e+01 +20-04-06 14:42:38.606 - INFO: l_g_pix: 1.3547e-04 l_g_fea: 6.1468e-01 l_g_gan: 8.1550e-03 l_d_real: 2.7376e-01 l_d_fake: 2.5797e-01 D_real: 2.4811e+01 D_fake: 2.3446e+01 +20-04-06 14:45:50.338 - INFO: l_g_pix: 1.1382e-04 l_g_fea: 4.0912e-01 l_g_gan: 1.5218e-02 l_d_real: 5.9770e-02 l_d_fake: 5.3919e-02 D_real: 2.0859e+01 D_fake: 1.7872e+01 +20-04-06 14:49:01.597 - INFO: l_g_pix: 8.3937e-05 l_g_fea: 3.5795e-01 l_g_gan: 4.6785e-03 l_d_real: 6.4958e-01 l_d_fake: 6.4684e-01 D_real: 2.0523e+01 D_fake: 2.0235e+01 +20-04-06 14:52:12.983 - INFO: l_g_pix: 8.3621e-05 l_g_fea: 3.9898e-01 l_g_gan: 7.5898e-03 l_d_real: 3.0848e-01 l_d_fake: 2.9906e-01 D_real: 1.0550e+01 D_fake: 9.3354e+00 +20-04-06 14:55:24.153 - INFO: l_g_pix: 1.2267e-04 l_g_fea: 4.7159e-01 l_g_gan: 5.3505e-03 l_d_real: 4.6450e-01 l_d_fake: 4.6556e-01 D_real: 9.8943e+00 D_fake: 9.2892e+00 +20-04-06 14:58:36.394 - INFO: l_g_pix: 9.8557e-05 l_g_fea: 4.8110e-01 l_g_gan: 5.3267e-03 l_d_real: 4.9283e-01 l_d_fake: 5.2159e-01 D_real: 2.2952e+01 D_fake: 2.2394e+01 +20-04-06 15:01:48.239 - INFO: l_g_pix: 7.8668e-05 l_g_fea: 3.9753e-01 l_g_gan: 3.6440e-03 l_d_real: 7.5553e-01 l_d_fake: 7.7161e-01 D_real: 1.7783e+01 D_fake: 1.7817e+01 +20-04-06 15:04:59.913 - INFO: l_g_pix: 9.3558e-05 l_g_fea: 3.7920e-01 l_g_gan: 5.2237e-03 l_d_real: 4.9958e-01 l_d_fake: 4.9418e-01 D_real: 3.5543e+01 D_fake: 3.4995e+01 +20-04-06 15:08:11.347 - INFO: l_g_pix: 6.8956e-05 l_g_fea: 2.5792e-01 l_g_gan: 8.6777e-03 l_d_real: 2.4250e-01 l_d_fake: 2.3671e-01 D_real: 3.9303e+01 D_fake: 3.7807e+01 +20-04-06 15:11:23.008 - INFO: l_g_pix: 5.2583e-05 l_g_fea: 2.5966e-01 l_g_gan: 7.3603e-03 l_d_real: 3.0241e-01 l_d_fake: 3.2575e-01 D_real: 1.9818e+01 D_fake: 1.8660e+01 +20-04-06 15:14:33.625 - INFO: l_g_pix: 7.4977e-05 l_g_fea: 4.4169e-01 l_g_gan: 3.3990e-03 l_d_real: 7.6731e-01 l_d_fake: 7.6930e-01 D_real: 5.2390e+00 D_fake: 5.3276e+00 +20-04-06 15:17:44.623 - INFO: l_g_pix: 1.0210e-04 l_g_fea: 4.2852e-01 l_g_gan: 1.8442e-02 l_d_real: 3.1694e-02 l_d_fake: 3.2187e-02 D_real: 2.2463e+01 D_fake: 1.8806e+01 +20-04-06 15:20:55.133 - INFO: l_g_pix: 6.9520e-05 l_g_fea: 3.4909e-01 l_g_gan: 9.4475e-03 l_d_real: 2.0449e-01 l_d_fake: 1.8077e-01 D_real: 1.7212e+01 D_fake: 1.5515e+01 +20-04-06 15:24:06.473 - INFO: l_g_pix: 1.5006e-04 l_g_fea: 5.4142e-01 l_g_gan: 7.9806e-03 l_d_real: 2.7480e-01 l_d_fake: 2.6515e-01 D_real: 1.6051e+01 D_fake: 1.4725e+01 +20-04-06 15:27:17.938 - INFO: l_g_pix: 1.3855e-04 l_g_fea: 4.3020e-01 l_g_gan: 5.7193e-03 l_d_real: 4.5845e-01 l_d_fake: 4.7838e-01 D_real: 5.2814e+01 D_fake: 5.2139e+01 +20-04-06 15:30:29.251 - INFO: l_g_pix: 9.7705e-05 l_g_fea: 3.6811e-01 l_g_gan: 6.3529e-03 l_d_real: 4.0990e-01 l_d_fake: 4.1346e-01 D_real: 2.7883e+01 D_fake: 2.7024e+01 +20-04-06 15:33:45.188 - INFO: l_g_pix: 9.7513e-05 l_g_fea: 4.1620e-01 l_g_gan: 7.7831e-03 l_d_real: 2.6423e-01 l_d_fake: 2.6221e-01 D_real: 1.4204e+01 D_fake: 1.2911e+01 +20-04-06 15:36:56.841 - INFO: l_g_pix: 7.1032e-05 l_g_fea: 3.0730e-01 l_g_gan: 3.3367e-03 l_d_real: 7.8105e-01 l_d_fake: 7.9056e-01 D_real: 1.3606e+01 D_fake: 1.3724e+01 +20-04-06 15:40:08.639 - INFO: l_g_pix: 1.2608e-04 l_g_fea: 5.0282e-01 l_g_gan: 1.0692e-02 l_d_real: 1.6212e-01 l_d_fake: 1.7743e-01 D_real: 1.8149e+01 D_fake: 1.6180e+01 +20-04-06 15:43:20.157 - INFO: l_g_pix: 8.8116e-05 l_g_fea: 4.2543e-01 l_g_gan: 1.6191e-02 l_d_real: 4.3115e-02 l_d_fake: 4.4798e-02 D_real: 4.4760e+01 D_fake: 4.1566e+01 +20-04-06 15:46:31.641 - INFO: l_g_pix: 1.0235e-04 l_g_fea: 4.7989e-01 l_g_gan: 7.5192e-03 l_d_real: 3.3420e-01 l_d_fake: 3.5759e-01 D_real: 4.4383e+01 D_fake: 4.3225e+01 +20-04-06 15:49:42.114 - INFO: l_g_pix: 1.2283e-04 l_g_fea: 5.1671e-01 l_g_gan: 1.1348e-02 l_d_real: 1.5953e-01 l_d_fake: 1.4616e-01 D_real: 3.2810e+01 D_fake: 3.0693e+01 +20-04-06 15:49:42.563 - INFO: Models and training states saved. +20-04-06 15:50:45.860 - INFO: # Validation # PSNR: 31.45, SSIM: 0.83828, LPIPS: 0.029365 +20-04-06 15:50:45.861 - INFO: psnr: 31.45, ssim: 0.83828, lpips: 0.029365 +20-04-06 15:54:03.333 - INFO: l_g_pix: 1.2933e-04 l_g_fea: 5.8280e-01 l_g_gan: 1.6778e-02 l_d_real: 4.8190e-02 l_d_fake: 5.6292e-02 D_real: 2.4977e+01 D_fake: 2.1673e+01 +20-04-06 15:57:14.055 - INFO: l_g_pix: 1.2731e-04 l_g_fea: 4.5871e-01 l_g_gan: 2.7229e-02 l_d_real: 5.3350e-03 l_d_fake: 5.2207e-03 D_real: 2.1871e+01 D_fake: 1.6430e+01 +20-04-06 16:00:25.287 - INFO: l_g_pix: 9.5928e-05 l_g_fea: 4.6648e-01 l_g_gan: 1.4689e-02 l_d_real: 6.1880e-02 l_d_fake: 6.1285e-02 D_real: 1.1548e+01 D_fake: 8.6713e+00 +20-04-06 16:03:36.014 - INFO: l_g_pix: 1.0577e-04 l_g_fea: 5.1435e-01 l_g_gan: 3.0361e-03 l_d_real: 8.8146e-01 l_d_fake: 8.8346e-01 D_real: 2.0496e+01 D_fake: 2.0771e+01 +20-04-06 16:06:47.631 - INFO: l_g_pix: 7.7997e-05 l_g_fea: 4.1161e-01 l_g_gan: 5.1601e-03 l_d_real: 4.9109e-01 l_d_fake: 4.8135e-01 D_real: 9.7939e+00 D_fake: 9.2481e+00 +20-04-06 16:09:59.138 - INFO: l_g_pix: 1.1763e-04 l_g_fea: 5.2808e-01 l_g_gan: 5.0622e-03 l_d_real: 5.5914e-01 l_d_fake: 5.6187e-01 D_real: 4.0211e+01 D_fake: 3.9760e+01 +20-04-06 16:13:10.567 - INFO: l_g_pix: 1.0544e-04 l_g_fea: 4.4679e-01 l_g_gan: 6.1630e-03 l_d_real: 3.9911e-01 l_d_fake: 4.0398e-01 D_real: 2.7699e+01 D_fake: 2.6868e+01 +20-04-06 16:16:21.411 - INFO: l_g_pix: 9.1172e-05 l_g_fea: 4.5991e-01 l_g_gan: 1.9437e-02 l_d_real: 2.7855e-02 l_d_fake: 2.7090e-02 D_real: 2.6939e+01 D_fake: 2.3079e+01 +20-04-06 16:19:32.641 - INFO: l_g_pix: 9.3523e-05 l_g_fea: 4.3544e-01 l_g_gan: 1.4888e-02 l_d_real: 6.4138e-02 l_d_fake: 6.7888e-02 D_real: 3.7673e+01 D_fake: 3.4761e+01 +20-04-06 16:22:43.888 - INFO: l_g_pix: 1.2289e-04 l_g_fea: 4.7794e-01 l_g_gan: 5.2887e-03 l_d_real: 5.0012e-01 l_d_fake: 5.2489e-01 D_real: 3.3783e+01 D_fake: 3.3238e+01 +20-04-06 16:25:54.978 - INFO: l_g_pix: 9.6641e-05 l_g_fea: 4.0895e-01 l_g_gan: 9.8488e-03 l_d_real: 1.8868e-01 l_d_fake: 1.7409e-01 D_real: 2.4928e+01 D_fake: 2.3139e+01 +20-04-06 16:29:06.517 - INFO: l_g_pix: 8.4108e-05 l_g_fea: 4.1906e-01 l_g_gan: 9.2835e-03 l_d_real: 1.9868e-01 l_d_fake: 2.0777e-01 D_real: 1.6477e+01 D_fake: 1.4824e+01 +20-04-06 16:32:18.100 - INFO: l_g_pix: 9.6105e-05 l_g_fea: 4.6228e-01 l_g_gan: 9.3641e-03 l_d_real: 2.1405e-01 l_d_fake: 2.2183e-01 D_real: 3.6389e+01 D_fake: 3.4734e+01 +20-04-06 16:35:29.206 - INFO: l_g_pix: 9.3495e-05 l_g_fea: 3.1829e-01 l_g_gan: 5.7959e-03 l_d_real: 4.6643e-01 l_d_fake: 4.7404e-01 D_real: 3.3988e+01 D_fake: 3.3299e+01 +20-04-06 16:38:40.259 - INFO: l_g_pix: 9.6606e-05 l_g_fea: 4.9823e-01 l_g_gan: 7.8683e-03 l_d_real: 2.5705e-01 l_d_fake: 2.6043e-01 D_real: 2.5038e+01 D_fake: 2.3723e+01 +20-04-06 16:41:50.778 - INFO: l_g_pix: 7.9119e-05 l_g_fea: 4.0651e-01 l_g_gan: 1.4070e-02 l_d_real: 6.8983e-02 l_d_fake: 6.7446e-02 D_real: 1.5900e+01 D_fake: 1.3154e+01 +20-04-06 16:45:01.879 - INFO: l_g_pix: 9.4727e-05 l_g_fea: 3.8087e-01 l_g_gan: 2.5126e-02 l_d_real: 7.5641e-03 l_d_fake: 9.2403e-03 D_real: 2.2746e+01 D_fake: 1.7730e+01 +20-04-06 16:48:12.609 - INFO: l_g_pix: 8.2986e-05 l_g_fea: 4.3709e-01 l_g_gan: 4.0552e-03 l_d_real: 6.7004e-01 l_d_fake: 6.6506e-01 D_real: 3.8273e+01 D_fake: 3.8129e+01 +20-04-06 16:51:23.485 - INFO: l_g_pix: 8.4433e-05 l_g_fea: 3.4283e-01 l_g_gan: 8.0619e-03 l_d_real: 2.6168e-01 l_d_fake: 2.4676e-01 D_real: 3.2419e+01 D_fake: 3.1061e+01 +20-04-06 16:54:34.915 - INFO: l_g_pix: 8.4115e-05 l_g_fea: 4.3772e-01 l_g_gan: 6.7672e-03 l_d_real: 3.6982e-01 l_d_fake: 3.4955e-01 D_real: 1.4876e+01 D_fake: 1.3882e+01 diff --git a/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/train_200406-173523.log b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/train_200406-173523.log new file mode 100644 index 0000000000000000000000000000000000000000..f691a76e81a0ac2767f0717965ed9811ff2c187a --- /dev/null +++ b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/train_200406-173523.log @@ -0,0 +1,2656 @@ +20-04-06 17:35:23.230 - INFO: Set [resume_state] to ../experiments/sollevante/training_state/140000.state +20-04-06 17:35:23.230 - INFO: Resuming training from epoch: 710, iter: 140000. +20-04-06 17:35:23.230 - WARNING: pretrain_model path will be ignored when resuming training. +20-04-06 17:35:23.230 - INFO: Set [pretrain_model_G] to /home/owner/github/BasicSR/experiments/sollevante/models/140000_G.pth +20-04-06 17:35:23.230 - INFO: Set [pretrain_model_D] to /home/owner/github/BasicSR/experiments/sollevante/models/140000_D.pth +20-04-06 17:35:23.230 - INFO: name: sollevante + use_tb_logger: True + model: srragan + scale: 4 + gpu_ids: [0] + datasets:[ + train:[ + name: sollevante-train + mode: LRHR + dataroot_HR: ['/mnt/8tb-hdd-1/datasets/sollevante/hr/train'] + dataroot_LR: ['/mnt/8tb-hdd-1/datasets/sollevante/lr/train'] + subset_file: None + use_shuffle: True + znorm: False + n_workers: 8 + batch_size: 32 + HR_size: 128 + lr_downscale: True + lr_downscale_types: [1, 2, 777] + use_flip: True + use_rot: True + hr_rrot: False + lr_blur: False + lr_blur_types: ['gaussian', 'clean', 'clean', 'clean'] + lr_noise: False + lr_noise_types: ['gaussian', 'clean', 'clean', 'clean', 'clean'] + lr_noise2: False + lr_noise_types2: ['dither', 'dither', 'clean', 'clean'] + hr_noise: False + hr_noise_types: ['gaussian', 'clean', 'clean', 'clean', 'clean'] + phase: train + scale: 4 + data_type: img + ] + val:[ + name: sollevante-val + mode: LRHR + dataroot_HR: ['/mnt/8tb-hdd-1/datasets/sollevante/hr/val'] + dataroot_LR: ['/mnt/8tb-hdd-1/datasets/sollevante/lr/val'] + znorm: False + lr_downscale: False + lr_downscale_types: [0, 1] + phase: val + scale: 4 + data_type: img + ] + ] + path:[ + root: /home/owner/github/BasicSR + pretrain_model_G: /home/owner/github/BasicSR/experiments/sollevante/models/140000_G.pth + resume_state: ../experiments/sollevante/training_state/140000.state + experiments_root: /home/owner/github/BasicSR/experiments/sollevante + models: /home/owner/github/BasicSR/experiments/sollevante/models + training_state: /home/owner/github/BasicSR/experiments/sollevante/training_state + log: /home/owner/github/BasicSR/experiments/sollevante + val_images: /home/owner/github/BasicSR/experiments/sollevante/val_images + pretrain_model_D: /home/owner/github/BasicSR/experiments/sollevante/models/140000_D.pth + ] + network_G:[ + which_model_G: RRDB_net + norm_type: None + mode: CNA + nf: 64 + nb: 23 + in_nc: 3 + out_nc: 3 + gc: 32 + group: 1 + convtype: Conv2D + net_act: leakyrelu + scale: 4 + ] + network_D:[ + which_model_D: discriminator_vgg_128 + norm_type: batch + act_type: leakyrelu + mode: CNA + nf: 64 + in_nc: 3 + ] + train:[ + lr_G: 0.0001 + weight_decay_G: 0 + beta1_G: 0.9 + lr_D: 0.0001 + weight_decay_D: 0 + beta1_D: 0.9 + lr_scheme: MultiStepLR + lr_steps: [50000, 100000, 200000, 300000] + lr_gamma: 0.5 + pixel_criterion: l1 + pixel_weight: 0.01 + feature_criterion: l1 + feature_weight: 1 + gan_type: vanilla + gan_weight: 0.005 + niter: 500000.0 + val_freq: 5000.0 + ] + logger:[ + print_freq: 200 + save_checkpoint_freq: 5000.0 + ] + is_train: True + +20-04-06 17:35:23.397 - INFO: Random seed: 6974 +20-04-06 17:35:23.466 - INFO: Dataset [LRHRDataset - sollevante-train] is created. +20-04-06 17:35:23.466 - INFO: Number of train images: 6,309, iters: 198 +20-04-06 17:35:23.466 - INFO: Total epochs needed: 2526 for iters 500,000 +20-04-06 17:35:23.467 - INFO: Dataset [LRHRDataset - sollevante-val] is created. +20-04-06 17:35:23.467 - INFO: Number of val images in [sollevante-val]: 4 +20-04-06 17:35:23.598 - INFO: Initialization method [kaiming] +20-04-06 17:35:23.808 - INFO: Initialization method [kaiming] +20-04-06 17:35:23.897 - INFO: Loading pretrained model for G [/home/owner/github/BasicSR/experiments/sollevante/models/140000_G.pth] ... +20-04-06 17:35:24.036 - INFO: Loading pretrained model for D [/home/owner/github/BasicSR/experiments/sollevante/models/140000_D.pth] ... +20-04-06 17:35:25.220 - INFO: Remove HFEN loss. +20-04-06 17:35:25.220 - INFO: Remove TV loss. +20-04-06 17:35:25.220 - INFO: Remove SSIM loss. +20-04-06 17:35:25.220 - INFO: Remove LPIPS loss. +20-04-06 17:35:25.220 - INFO: Remove SPL loss. +20-04-06 17:35:25.227 - INFO: Network G structure: DataParallel - RRDBNet, with parameters: 16,697,987 +20-04-06 17:35:25.227 - INFO: RRDBNet( + (model): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): Identity + + |Sequential( + | (0): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (1): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (2): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (3): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (4): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (5): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (6): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (7): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (8): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (9): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (10): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (11): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (12): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (13): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (14): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (15): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (16): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (17): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (18): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (19): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (20): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (21): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (22): RRDB( + | (RDB1): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB2): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | (RDB3): ResidualDenseBlock_5C( + | (conv1): Sequential( + | (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv2): Sequential( + | (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv3): Sequential( + | (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv4): Sequential( + | (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | (1): LeakyReLU(negative_slope=0.2, inplace=True) + | ) + | (conv5): Sequential( + | (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + | ) + | ) + | ) + | (23): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + |) + (2): Upsample(scale_factor=2.0, mode=nearest) + (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (4): LeakyReLU(negative_slope=0.2, inplace=True) + (5): Upsample(scale_factor=2.0, mode=nearest) + (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (7): LeakyReLU(negative_slope=0.2, inplace=True) + (8): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (9): LeakyReLU(negative_slope=0.2, inplace=True) + (10): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) +20-04-06 17:35:25.230 - INFO: Network D structure: DataParallel - Discriminator_VGG_128, with parameters: 14,502,281 +20-04-06 17:35:25.231 - INFO: Discriminator_VGG_128( + (features): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): LeakyReLU(negative_slope=0.2, inplace=True) + (2): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (4): LeakyReLU(negative_slope=0.2, inplace=True) + (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (7): LeakyReLU(negative_slope=0.2, inplace=True) + (8): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (9): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (10): LeakyReLU(negative_slope=0.2, inplace=True) + (11): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (13): LeakyReLU(negative_slope=0.2, inplace=True) + (14): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (16): LeakyReLU(negative_slope=0.2, inplace=True) + (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (18): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (19): LeakyReLU(negative_slope=0.2, inplace=True) + (20): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (21): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (22): LeakyReLU(negative_slope=0.2, inplace=True) + (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (24): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (25): LeakyReLU(negative_slope=0.2, inplace=True) + (26): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) + (27): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (28): LeakyReLU(negative_slope=0.2, inplace=True) + ) + (classifier): Sequential( + (0): Linear(in_features=8192, out_features=100, bias=True) + (1): LeakyReLU(negative_slope=0.2, inplace=True) + (2): Linear(in_features=100, out_features=1, bias=True) + ) +) +20-04-06 17:35:25.231 - INFO: Network F structure: DataParallel - VGGFeatureExtractor, with parameters: 20,024,384 +20-04-06 17:35:25.231 - INFO: VGGFeatureExtractor( + (features): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ReLU(inplace=True) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (3): ReLU(inplace=True) + (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (6): ReLU(inplace=True) + (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (8): ReLU(inplace=True) + (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (11): ReLU(inplace=True) + (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (13): ReLU(inplace=True) + (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (15): ReLU(inplace=True) + (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (17): ReLU(inplace=True) + (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (20): ReLU(inplace=True) + (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (22): ReLU(inplace=True) + (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (24): ReLU(inplace=True) + (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (26): ReLU(inplace=True) + (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (29): ReLU(inplace=True) + (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (31): ReLU(inplace=True) + (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (33): ReLU(inplace=True) + (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) +20-04-06 17:35:25.231 - INFO: Model [SRRaGANModel] is created. +20-04-06 17:35:25.291 - INFO: Start training from epoch: 710, iter: 140000 +20-04-06 17:53:36.204 - INFO: l_g_pix: 1.1507e-04 l_g_fea: 4.7865e-01 l_g_gan: 2.6858e-02 l_d_real: 5.6415e-03 l_d_fake: 5.7094e-03 D_real: 2.3685e+01 D_fake: 1.8319e+01 +20-04-06 18:06:42.540 - INFO: l_g_pix: 1.0800e-04 l_g_fea: 4.6483e-01 l_g_gan: 1.2777e-02 l_d_real: 1.1181e-01 l_d_fake: 9.8468e-02 D_real: 3.7534e+01 D_fake: 3.5084e+01 +20-04-06 18:09:49.954 - INFO: l_g_pix: 9.7997e-05 l_g_fea: 3.7335e-01 l_g_gan: 2.0344e-03 l_d_real: 1.2293e+00 l_d_fake: 1.2131e+00 D_real: 2.4253e+01 D_fake: 2.5068e+01 +20-04-06 18:13:00.246 - INFO: l_g_pix: 8.9059e-05 l_g_fea: 3.7213e-01 l_g_gan: 6.1999e-03 l_d_real: 4.3973e-01 l_d_fake: 4.0710e-01 D_real: 2.5668e+01 D_fake: 2.4851e+01 +20-04-06 18:16:11.580 - INFO: l_g_pix: 9.0795e-05 l_g_fea: 3.9405e-01 l_g_gan: 6.1968e-03 l_d_real: 3.9848e-01 l_d_fake: 3.8991e-01 D_real: 3.2614e+01 D_fake: 3.1769e+01 +20-04-06 18:19:22.907 - INFO: l_g_pix: 9.2898e-05 l_g_fea: 4.7541e-01 l_g_gan: 2.8397e-03 l_d_real: 9.9776e-01 l_d_fake: 1.0135e+00 D_real: 3.0951e+01 D_fake: 3.1388e+01 +20-04-06 18:22:32.787 - INFO: l_g_pix: 1.0667e-04 l_g_fea: 4.6999e-01 l_g_gan: 2.7073e-03 l_d_real: 9.7046e-01 l_d_fake: 9.5717e-01 D_real: 2.0009e+01 D_fake: 2.0431e+01 +20-04-06 18:25:43.668 - INFO: l_g_pix: 9.6350e-05 l_g_fea: 4.2836e-01 l_g_gan: 4.9044e-03 l_d_real: 5.6308e-01 l_d_fake: 5.5276e-01 D_real: 4.8393e+00 D_fake: 4.4163e+00 +20-04-06 18:28:53.841 - INFO: l_g_pix: 9.9741e-05 l_g_fea: 4.1101e-01 l_g_gan: 2.2144e-02 l_d_real: 1.2531e-02 l_d_fake: 1.5890e-02 D_real: 4.0548e+01 D_fake: 3.6133e+01 +20-04-06 18:32:03.680 - INFO: l_g_pix: 1.0972e-04 l_g_fea: 3.7260e-01 l_g_gan: 1.2063e-02 l_d_real: 1.1288e-01 l_d_fake: 1.1916e-01 D_real: 2.6553e+01 D_fake: 2.4257e+01 +20-04-06 18:35:14.209 - INFO: l_g_pix: 5.3311e-05 l_g_fea: 3.1261e-01 l_g_gan: 1.9266e-03 l_d_real: 1.3162e+00 l_d_fake: 1.3301e+00 D_real: 2.6918e+01 D_fake: 2.7856e+01 +20-04-06 18:38:24.800 - INFO: l_g_pix: 1.0035e-04 l_g_fea: 4.4199e-01 l_g_gan: 1.5696e-02 l_d_real: 5.3728e-02 l_d_fake: 5.2591e-02 D_real: 9.9871e+00 D_fake: 6.9010e+00 +20-04-06 18:41:36.164 - INFO: l_g_pix: 1.2925e-04 l_g_fea: 4.9124e-01 l_g_gan: 7.2088e-03 l_d_real: 3.2486e-01 l_d_fake: 3.0844e-01 D_real: 1.6183e+01 D_fake: 1.5058e+01 +20-04-06 18:44:46.819 - INFO: l_g_pix: 1.0116e-04 l_g_fea: 4.3732e-01 l_g_gan: 1.8024e-02 l_d_real: 3.5209e-02 l_d_fake: 3.2545e-02 D_real: 2.8524e+01 D_fake: 2.4953e+01 +20-04-06 18:47:56.987 - INFO: l_g_pix: 7.8129e-05 l_g_fea: 3.8211e-01 l_g_gan: 1.0412e-03 l_d_real: 1.8785e+00 l_d_fake: 1.8946e+00 D_real: 3.4164e+01 D_fake: 3.5842e+01 +20-04-06 18:51:07.752 - INFO: l_g_pix: 1.5871e-04 l_g_fea: 5.4979e-01 l_g_gan: 9.5531e-03 l_d_real: 2.0465e-01 l_d_fake: 2.0066e-01 D_real: 4.3992e+01 D_fake: 4.2284e+01 +20-04-06 18:54:19.368 - INFO: l_g_pix: 7.9014e-05 l_g_fea: 4.3688e-01 l_g_gan: 1.6953e-03 l_d_real: 1.3650e+00 l_d_fake: 1.3571e+00 D_real: 1.3103e+01 D_fake: 1.4125e+01 +20-04-06 18:57:30.568 - INFO: l_g_pix: 1.0439e-04 l_g_fea: 4.2079e-01 l_g_gan: 1.6215e-02 l_d_real: 4.7181e-02 l_d_fake: 4.4183e-02 D_real: 2.3523e+01 D_fake: 2.0326e+01 +20-04-06 19:00:42.007 - INFO: l_g_pix: 1.1813e-04 l_g_fea: 5.0480e-01 l_g_gan: 9.6063e-03 l_d_real: 1.9043e-01 l_d_fake: 1.8951e-01 D_real: 1.2390e+01 D_fake: 1.0659e+01 +20-04-06 19:03:52.971 - INFO: l_g_pix: 9.7108e-05 l_g_fea: 4.4192e-01 l_g_gan: 1.7436e-02 l_d_real: 3.6511e-02 l_d_fake: 3.4381e-02 D_real: 2.4042e+01 D_fake: 2.0590e+01 +20-04-06 19:07:03.518 - INFO: l_g_pix: 9.7476e-05 l_g_fea: 4.3837e-01 l_g_gan: 1.2253e-02 l_d_real: 1.0973e-01 l_d_fake: 1.1060e-01 D_real: 2.6198e+01 D_fake: 2.3858e+01 +20-04-06 19:10:13.797 - INFO: l_g_pix: 9.6314e-05 l_g_fea: 3.7619e-01 l_g_gan: 1.1410e-02 l_d_real: 1.2462e-01 l_d_fake: 1.2722e-01 D_real: 2.0320e+01 D_fake: 1.8164e+01 +20-04-06 19:13:24.650 - INFO: l_g_pix: 1.0834e-04 l_g_fea: 4.9019e-01 l_g_gan: 4.9512e-03 l_d_real: 5.5097e-01 l_d_fake: 5.4918e-01 D_real: 5.7234e+00 D_fake: 5.2833e+00 +20-04-06 19:16:35.698 - INFO: l_g_pix: 9.5328e-05 l_g_fea: 4.3857e-01 l_g_gan: 7.4216e-03 l_d_real: 3.0748e-01 l_d_fake: 3.0267e-01 D_real: 2.9241e+01 D_fake: 2.8062e+01 +20-04-06 19:19:47.583 - INFO: l_g_pix: 1.3630e-04 l_g_fea: 6.1428e-01 l_g_gan: 2.1194e-02 l_d_real: 1.9276e-02 l_d_fake: 1.7399e-02 D_real: 3.3752e+01 D_fake: 2.9532e+01 +20-04-06 19:19:48.033 - INFO: Models and training states saved. +20-04-06 19:20:48.994 - INFO: # Validation # PSNR: 31.697, SSIM: 0.84278, LPIPS: 0.028717 +20-04-06 19:20:48.994 - INFO: psnr: 31.697, ssim: 0.84278, lpips: 0.028717 +20-04-06 19:23:59.337 - INFO: l_g_pix: 1.2797e-04 l_g_fea: 5.9077e-01 l_g_gan: 7.1958e-03 l_d_real: 3.1597e-01 l_d_fake: 3.0982e-01 D_real: 2.0154e+01 D_fake: 1.9028e+01 +20-04-06 19:27:11.182 - INFO: l_g_pix: 6.9116e-05 l_g_fea: 3.6951e-01 l_g_gan: 4.3363e-04 l_d_real: 2.7285e+00 l_d_fake: 2.7213e+00 D_real: 9.9283e+00 D_fake: 1.2567e+01 +20-04-06 19:30:22.391 - INFO: l_g_pix: 1.0622e-04 l_g_fea: 4.2746e-01 l_g_gan: 1.4611e-03 l_d_real: 1.5185e+00 l_d_fake: 1.5317e+00 D_real: 3.2014e+01 D_fake: 3.3247e+01 +20-04-06 19:33:34.015 - INFO: l_g_pix: 8.9832e-05 l_g_fea: 4.6531e-01 l_g_gan: 1.2144e-02 l_d_real: 1.0978e-01 l_d_fake: 1.2413e-01 D_real: 4.0471e+01 D_fake: 3.8159e+01 +20-04-06 19:36:44.879 - INFO: l_g_pix: 1.1306e-04 l_g_fea: 4.7285e-01 l_g_gan: 1.9480e-02 l_d_real: 2.5432e-02 l_d_fake: 2.4000e-02 D_real: 1.6923e+01 D_fake: 1.3052e+01 +20-04-06 19:39:56.837 - INFO: l_g_pix: 1.0531e-04 l_g_fea: 5.2697e-01 l_g_gan: 1.3605e-02 l_d_real: 8.8231e-02 l_d_fake: 8.4928e-02 D_real: 4.1966e+01 D_fake: 3.9332e+01 +20-04-06 19:43:07.497 - INFO: l_g_pix: 9.8959e-05 l_g_fea: 5.0305e-01 l_g_gan: 1.0966e-02 l_d_real: 1.4552e-01 l_d_fake: 1.3821e-01 D_real: 7.4681e+00 D_fake: 5.4169e+00 +20-04-06 19:46:19.168 - INFO: l_g_pix: 1.0739e-04 l_g_fea: 4.2339e-01 l_g_gan: 3.0016e-02 l_d_real: 3.0106e-03 l_d_fake: 2.9497e-03 D_real: 4.6985e+01 D_fake: 4.0985e+01 +20-04-06 19:49:30.762 - INFO: l_g_pix: 8.9093e-05 l_g_fea: 4.4789e-01 l_g_gan: 1.1636e-02 l_d_real: 1.5900e-01 l_d_fake: 1.5278e-01 D_real: 4.3227e+01 D_fake: 4.1055e+01 +20-04-06 19:52:42.265 - INFO: l_g_pix: 7.8827e-05 l_g_fea: 3.7228e-01 l_g_gan: 4.0710e-03 l_d_real: 7.1548e-01 l_d_fake: 7.6684e-01 D_real: 3.8410e+01 D_fake: 3.8337e+01 +20-04-06 19:55:53.919 - INFO: l_g_pix: 7.5980e-05 l_g_fea: 3.7155e-01 l_g_gan: 5.9059e-03 l_d_real: 4.4389e-01 l_d_fake: 4.5750e-01 D_real: 2.3541e+01 D_fake: 2.2810e+01 +20-04-06 19:59:04.717 - INFO: l_g_pix: 9.9308e-05 l_g_fea: 4.7665e-01 l_g_gan: 6.4921e-03 l_d_real: 3.8293e-01 l_d_fake: 3.6044e-01 D_real: 4.8334e+01 D_fake: 4.7408e+01 +20-04-06 20:02:15.750 - INFO: l_g_pix: 1.2955e-04 l_g_fea: 5.0943e-01 l_g_gan: 7.2590e-03 l_d_real: 3.3362e-01 l_d_fake: 3.2704e-01 D_real: 2.1227e+01 D_fake: 2.0106e+01 +20-04-06 20:05:26.885 - INFO: l_g_pix: 1.0348e-04 l_g_fea: 5.0569e-01 l_g_gan: 3.0263e-03 l_d_real: 8.7754e-01 l_d_fake: 8.7366e-01 D_real: 6.7380e+00 D_fake: 7.0083e+00 +20-04-06 20:08:37.523 - INFO: l_g_pix: 9.2928e-05 l_g_fea: 4.7807e-01 l_g_gan: 1.5960e-02 l_d_real: 5.1154e-02 l_d_fake: 4.6458e-02 D_real: 1.3172e+01 D_fake: 1.0029e+01 +20-04-06 20:11:48.603 - INFO: l_g_pix: 8.7905e-05 l_g_fea: 4.6024e-01 l_g_gan: 2.2826e-02 l_d_real: 1.3784e-02 l_d_fake: 1.2480e-02 D_real: 2.4522e+01 D_fake: 1.9970e+01 +20-04-06 20:14:59.919 - INFO: l_g_pix: 6.8176e-05 l_g_fea: 3.3201e-01 l_g_gan: 1.2601e-02 l_d_real: 1.0177e-01 l_d_fake: 9.3773e-02 D_real: 1.4616e+01 D_fake: 1.2194e+01 +20-04-06 20:18:10.956 - INFO: l_g_pix: 7.5524e-05 l_g_fea: 3.7323e-01 l_g_gan: 7.0275e-03 l_d_real: 3.3093e-01 l_d_fake: 3.3373e-01 D_real: 2.0478e+01 D_fake: 1.9405e+01 +20-04-06 20:21:21.326 - INFO: l_g_pix: 9.3331e-05 l_g_fea: 5.0867e-01 l_g_gan: 2.4728e-02 l_d_real: 9.5834e-03 l_d_fake: 1.1258e-02 D_real: 2.9422e+01 D_fake: 2.4487e+01 +20-04-06 20:24:32.125 - INFO: l_g_pix: 4.6216e-05 l_g_fea: 2.6441e-01 l_g_gan: 7.2058e-03 l_d_real: 3.5128e-01 l_d_fake: 3.9579e-01 D_real: 9.7836e+00 D_fake: 8.7160e+00 +20-04-06 20:27:41.718 - INFO: l_g_pix: 7.3576e-05 l_g_fea: 3.3430e-01 l_g_gan: 1.2853e-03 l_d_real: 1.6370e+00 l_d_fake: 1.6231e+00 D_real: 1.5796e+01 D_fake: 1.7169e+01 +20-04-06 20:30:52.358 - INFO: l_g_pix: 1.0982e-04 l_g_fea: 4.2047e-01 l_g_gan: 3.1425e-03 l_d_real: 8.2826e-01 l_d_fake: 8.3781e-01 D_real: 2.9931e+01 D_fake: 3.0136e+01 +20-04-06 20:34:03.270 - INFO: l_g_pix: 1.1962e-04 l_g_fea: 3.6793e-01 l_g_gan: 1.3871e-02 l_d_real: 7.3223e-02 l_d_fake: 8.1700e-02 D_real: 4.1575e+01 D_fake: 3.8878e+01 +20-04-06 20:37:14.914 - INFO: l_g_pix: 9.0546e-05 l_g_fea: 3.8677e-01 l_g_gan: 3.3895e-03 l_d_real: 7.7960e-01 l_d_fake: 7.5210e-01 D_real: 3.0930e+00 D_fake: 3.1809e+00 +20-04-06 20:40:25.947 - INFO: l_g_pix: 1.1699e-04 l_g_fea: 4.4026e-01 l_g_gan: 5.5534e-03 l_d_real: 4.2308e-01 l_d_fake: 4.3413e-01 D_real: 1.5463e+01 D_fake: 1.4781e+01 +20-04-06 20:40:26.440 - INFO: Models and training states saved. +20-04-06 20:41:27.697 - INFO: # Validation # PSNR: 31.331, SSIM: 0.84292, LPIPS: 0.029 +20-04-06 20:41:27.697 - INFO: psnr: 31.331, ssim: 0.84292, lpips: 0.029 +20-04-06 20:44:56.174 - INFO: l_g_pix: 9.3089e-05 l_g_fea: 3.6989e-01 l_g_gan: 1.7584e-02 l_d_real: 5.0402e-02 l_d_fake: 4.2185e-02 D_real: 2.1743e+01 D_fake: 1.8273e+01 +20-04-06 20:48:07.297 - INFO: l_g_pix: 6.3311e-05 l_g_fea: 2.7528e-01 l_g_gan: 4.5435e-03 l_d_real: 6.4685e-01 l_d_fake: 6.5037e-01 D_real: 2.1802e+01 D_fake: 2.1542e+01 +20-04-06 20:51:18.125 - INFO: l_g_pix: 1.2887e-04 l_g_fea: 5.5054e-01 l_g_gan: 8.2612e-03 l_d_real: 2.6972e-01 l_d_fake: 2.6972e-01 D_real: 2.2013e+01 D_fake: 2.0630e+01 +20-04-06 20:54:28.650 - INFO: l_g_pix: 8.2011e-05 l_g_fea: 4.0962e-01 l_g_gan: 1.0500e-02 l_d_real: 1.5812e-01 l_d_fake: 1.9048e-01 D_real: 3.7665e+00 D_fake: 1.8409e+00 +20-04-06 20:57:39.966 - INFO: l_g_pix: 7.8662e-05 l_g_fea: 3.9117e-01 l_g_gan: 2.2884e-03 l_d_real: 1.1305e+00 l_d_fake: 1.0909e+00 D_real: 1.3348e+01 D_fake: 1.4001e+01 +20-04-06 21:00:49.881 - INFO: l_g_pix: 9.7746e-05 l_g_fea: 3.5502e-01 l_g_gan: 2.0552e-03 l_d_real: 1.1886e+00 l_d_fake: 1.1762e+00 D_real: 1.0007e+01 D_fake: 1.0778e+01 +20-04-06 21:03:59.788 - INFO: l_g_pix: 1.1807e-04 l_g_fea: 4.8796e-01 l_g_gan: 1.6001e-02 l_d_real: 5.0357e-02 l_d_fake: 5.1865e-02 D_real: 4.8895e+01 D_fake: 4.5746e+01 +20-04-06 21:07:09.297 - INFO: l_g_pix: 8.6850e-05 l_g_fea: 3.3416e-01 l_g_gan: 1.5094e-02 l_d_real: 7.9169e-02 l_d_fake: 6.1621e-02 D_real: 3.9792e+01 D_fake: 3.6843e+01 +20-04-06 21:10:19.761 - INFO: l_g_pix: 1.2095e-04 l_g_fea: 4.9078e-01 l_g_gan: 7.4699e-03 l_d_real: 3.0789e-01 l_d_fake: 2.9450e-01 D_real: 9.4213e+00 D_fake: 8.2285e+00 +20-04-06 21:13:29.688 - INFO: l_g_pix: 9.2636e-05 l_g_fea: 4.7680e-01 l_g_gan: 5.0665e-03 l_d_real: 5.2992e-01 l_d_fake: 5.0508e-01 D_real: 6.2767e+00 D_fake: 5.7809e+00 +20-04-06 21:16:40.602 - INFO: l_g_pix: 1.2677e-04 l_g_fea: 5.0991e-01 l_g_gan: 9.9706e-03 l_d_real: 1.8044e-01 l_d_fake: 1.7935e-01 D_real: 2.7388e+01 D_fake: 2.5573e+01 +20-04-06 21:19:52.346 - INFO: l_g_pix: 1.1719e-04 l_g_fea: 4.7243e-01 l_g_gan: 6.4863e-03 l_d_real: 3.9244e-01 l_d_fake: 4.2279e-01 D_real: 3.9175e+01 D_fake: 3.8286e+01 +20-04-06 21:23:03.199 - INFO: l_g_pix: 1.2326e-04 l_g_fea: 4.4408e-01 l_g_gan: 5.6184e-03 l_d_real: 4.3842e-01 l_d_fake: 4.5147e-01 D_real: 1.5176e+01 D_fake: 1.4497e+01 +20-04-06 21:26:14.553 - INFO: l_g_pix: 9.7369e-05 l_g_fea: 3.6895e-01 l_g_gan: 1.6705e-02 l_d_real: 4.9405e-02 l_d_fake: 4.4896e-02 D_real: 1.5730e+01 D_fake: 1.2437e+01 +20-04-06 21:29:26.096 - INFO: l_g_pix: 1.0125e-04 l_g_fea: 5.1386e-01 l_g_gan: 2.5413e-02 l_d_real: 7.5410e-03 l_d_fake: 7.5468e-03 D_real: 3.5630e+01 D_fake: 3.0555e+01 +20-04-06 21:32:43.258 - INFO: l_g_pix: 6.2357e-05 l_g_fea: 3.1266e-01 l_g_gan: 4.2724e-03 l_d_real: 6.3457e-01 l_d_fake: 6.3232e-01 D_real: 2.6072e+01 D_fake: 2.5851e+01 +20-04-06 21:35:54.957 - INFO: l_g_pix: 8.5430e-05 l_g_fea: 4.3615e-01 l_g_gan: 3.9842e-03 l_d_real: 7.0261e-01 l_d_fake: 7.2293e-01 D_real: 1.3982e+01 D_fake: 1.3898e+01 +20-04-06 21:39:06.727 - INFO: l_g_pix: 8.5250e-05 l_g_fea: 4.9256e-01 l_g_gan: 5.1934e-03 l_d_real: 5.5256e-01 l_d_fake: 5.4408e-01 D_real: 3.3142e+01 D_fake: 3.2651e+01 +20-04-06 21:42:18.060 - INFO: l_g_pix: 1.0298e-04 l_g_fea: 4.4724e-01 l_g_gan: 4.3945e-03 l_d_real: 6.2897e-01 l_d_fake: 6.1974e-01 D_real: 7.0917e+00 D_fake: 6.8372e+00 +20-04-06 21:45:29.303 - INFO: l_g_pix: 6.8839e-05 l_g_fea: 3.2446e-01 l_g_gan: 1.9610e-02 l_d_real: 2.3694e-02 l_d_fake: 2.7259e-02 D_real: 2.1040e+01 D_fake: 1.7143e+01 +20-04-06 21:48:39.659 - INFO: l_g_pix: 1.0441e-04 l_g_fea: 4.7168e-01 l_g_gan: 9.4820e-03 l_d_real: 2.1629e-01 l_d_fake: 2.0093e-01 D_real: 5.3355e+00 D_fake: 3.6477e+00 +20-04-06 21:51:51.123 - INFO: l_g_pix: 9.6181e-05 l_g_fea: 4.4261e-01 l_g_gan: 3.1172e-03 l_d_real: 8.6930e-01 l_d_fake: 8.6212e-01 D_real: 1.5071e+01 D_fake: 1.5313e+01 +20-04-06 21:55:02.170 - INFO: l_g_pix: 8.7292e-05 l_g_fea: 4.0722e-01 l_g_gan: 2.3225e-03 l_d_real: 1.1451e+00 l_d_fake: 1.1164e+00 D_real: 2.0779e+01 D_fake: 2.1445e+01 +20-04-06 21:58:12.759 - INFO: l_g_pix: 1.1404e-04 l_g_fea: 4.8779e-01 l_g_gan: 1.1690e-02 l_d_real: 1.2277e-01 l_d_fake: 1.2839e-01 D_real: 3.5935e+01 D_fake: 3.3723e+01 +20-04-06 22:01:23.654 - INFO: l_g_pix: 9.5320e-05 l_g_fea: 4.0111e-01 l_g_gan: 1.6745e-02 l_d_real: 4.8723e-02 l_d_fake: 5.0691e-02 D_real: 1.0966e+01 D_fake: 7.6663e+00 +20-04-06 22:01:24.075 - INFO: Models and training states saved. +20-04-06 22:02:23.796 - INFO: # Validation # PSNR: 31.695, SSIM: 0.8385, LPIPS: 0.023378 +20-04-06 22:02:23.796 - INFO: psnr: 31.695, ssim: 0.8385, lpips: 0.023378 +20-04-06 22:07:58.516 - INFO: l_g_pix: 1.0655e-04 l_g_fea: 4.7402e-01 l_g_gan: 9.4430e-03 l_d_real: 1.9058e-01 l_d_fake: 1.9812e-01 D_real: 2.7345e+01 D_fake: 2.5651e+01 +20-04-06 22:11:08.200 - INFO: l_g_pix: 1.1351e-04 l_g_fea: 5.2778e-01 l_g_gan: 6.2539e-03 l_d_real: 3.8560e-01 l_d_fake: 3.9500e-01 D_real: 2.0052e+01 D_fake: 1.9191e+01 +20-04-06 22:14:18.986 - INFO: l_g_pix: 7.1926e-05 l_g_fea: 3.9387e-01 l_g_gan: 2.3445e-02 l_d_real: 1.2768e-02 l_d_fake: 1.2157e-02 D_real: 2.8773e+01 D_fake: 2.4096e+01 +20-04-06 22:17:31.136 - INFO: l_g_pix: 1.0759e-04 l_g_fea: 4.3252e-01 l_g_gan: 8.6515e-03 l_d_real: 2.3071e-01 l_d_fake: 2.4108e-01 D_real: 3.1115e+01 D_fake: 2.9620e+01 +20-04-06 22:20:42.997 - INFO: l_g_pix: 8.0576e-05 l_g_fea: 4.2041e-01 l_g_gan: 3.1083e-03 l_d_real: 9.1157e-01 l_d_fake: 8.8844e-01 D_real: 6.5027e+00 D_fake: 6.7811e+00 +20-04-06 22:23:53.728 - INFO: l_g_pix: 9.6721e-05 l_g_fea: 4.2556e-01 l_g_gan: 4.6194e-03 l_d_real: 6.4010e-01 l_d_fake: 6.1990e-01 D_real: 4.8499e+01 D_fake: 4.8205e+01 +20-04-06 22:27:04.630 - INFO: l_g_pix: 1.1237e-04 l_g_fea: 5.3562e-01 l_g_gan: 1.2572e-02 l_d_real: 1.0187e-01 l_d_fake: 1.0539e-01 D_real: 1.9604e+01 D_fake: 1.7194e+01 +20-04-06 22:30:15.062 - INFO: l_g_pix: 1.1615e-04 l_g_fea: 4.6854e-01 l_g_gan: 6.4795e-03 l_d_real: 4.3517e-01 l_d_fake: 3.9999e-01 D_real: 2.6797e+01 D_fake: 2.5918e+01 +20-04-06 22:33:26.167 - INFO: l_g_pix: 1.5827e-04 l_g_fea: 6.3745e-01 l_g_gan: 3.0470e-03 l_d_real: 8.9192e-01 l_d_fake: 8.7024e-01 D_real: 1.6194e+01 D_fake: 1.6466e+01 +20-04-06 22:36:36.814 - INFO: l_g_pix: 8.9528e-05 l_g_fea: 4.2774e-01 l_g_gan: 8.2597e-03 l_d_real: 2.3882e-01 l_d_fake: 2.5007e-01 D_real: 2.6509e+01 D_fake: 2.5102e+01 +20-04-06 22:39:47.185 - INFO: l_g_pix: 8.4386e-05 l_g_fea: 3.4387e-01 l_g_gan: 2.9554e-03 l_d_real: 8.5509e-01 l_d_fake: 8.7193e-01 D_real: 1.3843e+01 D_fake: 1.4116e+01 +20-04-06 22:42:58.246 - INFO: l_g_pix: 1.2692e-04 l_g_fea: 4.8136e-01 l_g_gan: 2.4309e-02 l_d_real: 1.2135e-02 l_d_fake: 1.1512e-02 D_real: 2.6844e+01 D_fake: 2.1994e+01 +20-04-06 22:46:09.750 - INFO: l_g_pix: 1.0411e-04 l_g_fea: 4.6085e-01 l_g_gan: 1.0689e-02 l_d_real: 1.5263e-01 l_d_fake: 1.5076e-01 D_real: 2.5920e+01 D_fake: 2.3934e+01 +20-04-06 22:49:20.254 - INFO: l_g_pix: 1.1796e-04 l_g_fea: 4.2278e-01 l_g_gan: 7.5363e-03 l_d_real: 3.0384e-01 l_d_fake: 2.8919e-01 D_real: 1.9697e+01 D_fake: 1.8486e+01 +20-04-06 22:52:31.327 - INFO: l_g_pix: 6.1158e-05 l_g_fea: 3.5801e-01 l_g_gan: 1.3449e-02 l_d_real: 1.1896e-01 l_d_fake: 8.4647e-02 D_real: 3.8974e+01 D_fake: 3.6386e+01 +20-04-06 22:55:42.328 - INFO: l_g_pix: 9.7116e-05 l_g_fea: 4.6212e-01 l_g_gan: 6.8993e-03 l_d_real: 3.4029e-01 l_d_fake: 3.4051e-01 D_real: 5.3814e+01 D_fake: 5.2775e+01 +20-04-06 22:58:52.739 - INFO: l_g_pix: 7.8989e-05 l_g_fea: 3.1889e-01 l_g_gan: 1.3988e-02 l_d_real: 9.5432e-02 l_d_fake: 7.5781e-02 D_real: 2.1971e+01 D_fake: 1.9259e+01 +20-04-06 23:02:03.016 - INFO: l_g_pix: 1.2014e-04 l_g_fea: 5.4696e-01 l_g_gan: 1.4618e-02 l_d_real: 6.8979e-02 l_d_fake: 7.1021e-02 D_real: 1.6031e+01 D_fake: 1.3177e+01 +20-04-06 23:05:13.226 - INFO: l_g_pix: 6.4046e-05 l_g_fea: 2.7821e-01 l_g_gan: 6.0582e-03 l_d_real: 4.8205e-01 l_d_fake: 4.2885e-01 D_real: 1.8899e+01 D_fake: 1.8142e+01 +20-04-06 23:08:23.731 - INFO: l_g_pix: 9.8282e-05 l_g_fea: 3.8082e-01 l_g_gan: 6.7308e-03 l_d_real: 3.4324e-01 l_d_fake: 3.6088e-01 D_real: 2.8387e+01 D_fake: 2.7393e+01 +20-04-06 23:11:34.321 - INFO: l_g_pix: 1.1962e-04 l_g_fea: 5.4053e-01 l_g_gan: 1.2869e-02 l_d_real: 1.3036e-01 l_d_fake: 1.0704e-01 D_real: 3.5383e+01 D_fake: 3.2928e+01 +20-04-06 23:14:44.880 - INFO: l_g_pix: 1.3142e-04 l_g_fea: 5.4090e-01 l_g_gan: 7.5418e-03 l_d_real: 3.4640e-01 l_d_fake: 3.5216e-01 D_real: 2.1078e+01 D_fake: 1.9919e+01 +20-04-06 23:17:55.996 - INFO: l_g_pix: 1.0330e-04 l_g_fea: 4.4211e-01 l_g_gan: 5.5257e-03 l_d_real: 4.8468e-01 l_d_fake: 4.6034e-01 D_real: 1.8957e+01 D_fake: 1.8324e+01 +20-04-06 23:21:07.357 - INFO: l_g_pix: 8.7233e-05 l_g_fea: 4.4314e-01 l_g_gan: 5.5878e-03 l_d_real: 4.5854e-01 l_d_fake: 4.4156e-01 D_real: 2.2787e+01 D_fake: 2.2119e+01 +20-04-06 23:24:18.857 - INFO: l_g_pix: 1.0917e-04 l_g_fea: 5.0601e-01 l_g_gan: 8.2571e-03 l_d_real: 2.5143e-01 l_d_fake: 2.4977e-01 D_real: 1.4794e+01 D_fake: 1.3393e+01 +20-04-06 23:24:19.312 - INFO: Models and training states saved. +20-04-06 23:25:17.775 - INFO: # Validation # PSNR: 31.963, SSIM: 0.83669, LPIPS: 0.022879 +20-04-06 23:25:17.775 - INFO: psnr: 31.963, ssim: 0.83669, lpips: 0.022879 +20-04-06 23:30:06.070 - INFO: l_g_pix: 1.1528e-04 l_g_fea: 4.4952e-01 l_g_gan: 1.7838e-03 l_d_real: 1.4068e+00 l_d_fake: 1.3946e+00 D_real: 4.4088e+01 D_fake: 4.5132e+01 +20-04-06 23:33:16.833 - INFO: l_g_pix: 1.3075e-04 l_g_fea: 4.9160e-01 l_g_gan: 4.9399e-04 l_d_real: 2.5241e+00 l_d_fake: 2.5276e+00 D_real: 1.6290e+01 D_fake: 1.8717e+01 +20-04-06 23:36:29.237 - INFO: l_g_pix: 1.1083e-04 l_g_fea: 5.8659e-01 l_g_gan: 8.9413e-03 l_d_real: 3.0460e-01 l_d_fake: 2.6506e-01 D_real: 1.3538e+01 D_fake: 1.2034e+01 +20-04-06 23:39:41.281 - INFO: l_g_pix: 1.1791e-04 l_g_fea: 3.9837e-01 l_g_gan: 1.4976e-02 l_d_real: 6.6961e-02 l_d_fake: 6.3625e-02 D_real: 1.3188e+01 D_fake: 1.0258e+01 +20-04-06 23:42:53.983 - INFO: l_g_pix: 6.7426e-05 l_g_fea: 3.8197e-01 l_g_gan: 2.7616e-03 l_d_real: 9.3338e-01 l_d_fake: 9.4741e-01 D_real: 1.7733e+01 D_fake: 1.8121e+01 +20-04-06 23:46:05.315 - INFO: l_g_pix: 1.1252e-04 l_g_fea: 5.2415e-01 l_g_gan: 1.7786e-03 l_d_real: 1.3167e+00 l_d_fake: 1.3048e+00 D_real: 2.3935e+01 D_fake: 2.4890e+01 +20-04-06 23:49:17.357 - INFO: l_g_pix: 8.9389e-05 l_g_fea: 4.3808e-01 l_g_gan: 9.0252e-03 l_d_real: 2.4579e-01 l_d_fake: 2.2651e-01 D_real: 4.0655e+01 D_fake: 3.9087e+01 +20-04-06 23:52:29.716 - INFO: l_g_pix: 1.0583e-04 l_g_fea: 3.8686e-01 l_g_gan: 2.0450e-03 l_d_real: 1.1920e+00 l_d_fake: 1.1823e+00 D_real: 6.4827e+00 D_fake: 7.2609e+00 +20-04-06 23:55:41.317 - INFO: l_g_pix: 9.4949e-05 l_g_fea: 4.0195e-01 l_g_gan: 1.0888e-02 l_d_real: 1.4859e-01 l_d_fake: 1.6151e-01 D_real: 1.4599e+01 D_fake: 1.2577e+01 +20-04-06 23:58:52.608 - INFO: l_g_pix: 1.0739e-04 l_g_fea: 4.3118e-01 l_g_gan: 4.6463e-03 l_d_real: 5.5684e-01 l_d_fake: 5.4806e-01 D_real: 1.9724e+01 D_fake: 1.9347e+01 +20-04-07 00:02:10.516 - INFO: l_g_pix: 9.0438e-05 l_g_fea: 4.0511e-01 l_g_gan: 3.6736e-03 l_d_real: 7.5884e-01 l_d_fake: 7.7149e-01 D_real: 2.9313e+01 D_fake: 2.9344e+01 +20-04-07 00:15:00.457 - INFO: l_g_pix: 9.0399e-05 l_g_fea: 3.7677e-01 l_g_gan: 3.6008e-03 l_d_real: 8.5527e-01 l_d_fake: 8.9490e-01 D_real: 2.9442e+01 D_fake: 2.9597e+01 +20-04-07 00:25:29.656 - INFO: l_g_pix: 9.4367e-05 l_g_fea: 4.1620e-01 l_g_gan: 3.7490e-03 l_d_real: 7.2679e-01 l_d_fake: 7.2442e-01 D_real: 2.3392e+01 D_fake: 2.3368e+01 +20-04-07 00:29:48.288 - INFO: l_g_pix: 1.1581e-04 l_g_fea: 5.7483e-01 l_g_gan: 2.1095e-02 l_d_real: 2.8933e-02 l_d_fake: 2.2575e-02 D_real: 2.8175e+01 D_fake: 2.3982e+01 +20-04-07 00:32:59.273 - INFO: l_g_pix: 9.1784e-05 l_g_fea: 4.0096e-01 l_g_gan: 7.2647e-03 l_d_real: 3.3006e-01 l_d_fake: 3.2894e-01 D_real: 1.2193e+01 D_fake: 1.1070e+01 +20-04-07 00:36:10.645 - INFO: l_g_pix: 1.0118e-04 l_g_fea: 4.4751e-01 l_g_gan: 8.0892e-03 l_d_real: 2.7033e-01 l_d_fake: 3.1962e-01 D_real: 1.2709e+01 D_fake: 1.1386e+01 +20-04-07 00:39:21.733 - INFO: l_g_pix: 1.4262e-04 l_g_fea: 5.6434e-01 l_g_gan: 7.4839e-03 l_d_real: 2.8951e-01 l_d_fake: 2.8109e-01 D_real: 2.8951e+01 D_fake: 2.7739e+01 +20-04-07 00:42:34.124 - INFO: l_g_pix: 7.2620e-05 l_g_fea: 4.9334e-01 l_g_gan: 1.5470e-02 l_d_real: 6.2620e-02 l_d_fake: 7.4573e-02 D_real: 1.1435e+01 D_fake: 8.4094e+00 +20-04-07 00:45:44.526 - INFO: l_g_pix: 1.0692e-04 l_g_fea: 4.6457e-01 l_g_gan: 5.0045e-03 l_d_real: 5.3175e-01 l_d_fake: 5.5510e-01 D_real: 3.1808e+01 D_fake: 3.1351e+01 +20-04-07 00:48:55.287 - INFO: l_g_pix: 1.1800e-04 l_g_fea: 5.2614e-01 l_g_gan: 1.5920e-02 l_d_real: 5.6981e-02 l_d_fake: 4.9867e-02 D_real: 1.3355e+01 D_fake: 1.0225e+01 +20-04-07 00:52:07.109 - INFO: l_g_pix: 7.7204e-05 l_g_fea: 3.3665e-01 l_g_gan: 2.1023e-02 l_d_real: 2.2960e-02 l_d_fake: 1.6878e-02 D_real: 1.7039e+01 D_fake: 1.2854e+01 +20-04-07 00:55:19.116 - INFO: l_g_pix: 8.6757e-05 l_g_fea: 3.6219e-01 l_g_gan: 2.5732e-03 l_d_real: 1.0705e+00 l_d_fake: 1.0399e+00 D_real: 4.0790e+01 D_fake: 4.1330e+01 +20-04-07 00:58:30.797 - INFO: l_g_pix: 1.3939e-04 l_g_fea: 5.8999e-01 l_g_gan: 1.5480e-02 l_d_real: 5.4100e-02 l_d_fake: 5.4544e-02 D_real: 3.1214e+01 D_fake: 2.8172e+01 +20-04-07 01:01:41.544 - INFO: l_g_pix: 8.8381e-05 l_g_fea: 3.7909e-01 l_g_gan: 1.7825e-02 l_d_real: 3.8176e-02 l_d_fake: 3.9712e-02 D_real: 2.6231e+01 D_fake: 2.2705e+01 +20-04-07 01:04:52.876 - INFO: l_g_pix: 8.0345e-05 l_g_fea: 4.9798e-01 l_g_gan: 1.4052e-02 l_d_real: 7.3343e-02 l_d_fake: 7.2913e-02 D_real: 3.3341e+01 D_fake: 3.0603e+01 +20-04-07 01:04:53.341 - INFO: Models and training states saved. +20-04-07 01:05:54.634 - INFO: # Validation # PSNR: 31.61, SSIM: 0.83506, LPIPS: 0.023187 +20-04-07 01:05:54.635 - INFO: psnr: 31.61, ssim: 0.83506, lpips: 0.023187 +20-04-07 01:09:16.176 - INFO: l_g_pix: 9.9088e-05 l_g_fea: 4.4703e-01 l_g_gan: 7.4892e-03 l_d_real: 3.5213e-01 l_d_fake: 3.3912e-01 D_real: 5.0425e+00 D_fake: 3.8903e+00 +20-04-07 01:12:26.694 - INFO: l_g_pix: 1.0971e-04 l_g_fea: 4.4257e-01 l_g_gan: 8.6845e-03 l_d_real: 2.2836e-01 l_d_fake: 2.3326e-01 D_real: 2.6958e+01 D_fake: 2.5452e+01 +20-04-07 01:15:37.802 - INFO: l_g_pix: 8.1014e-05 l_g_fea: 3.7760e-01 l_g_gan: 1.3319e-02 l_d_real: 8.9764e-02 l_d_fake: 1.0090e-01 D_real: 3.4834e+01 D_fake: 3.2266e+01 +20-04-07 01:18:48.840 - INFO: l_g_pix: 7.2965e-05 l_g_fea: 4.0557e-01 l_g_gan: 4.7534e-03 l_d_real: 5.7139e-01 l_d_fake: 5.8324e-01 D_real: 3.1743e+01 D_fake: 3.1370e+01 +20-04-07 01:21:59.723 - INFO: l_g_pix: 1.1403e-04 l_g_fea: 4.4749e-01 l_g_gan: 5.8376e-03 l_d_real: 4.3404e-01 l_d_fake: 4.3208e-01 D_real: 3.0715e+01 D_fake: 2.9980e+01 +20-04-07 01:25:11.194 - INFO: l_g_pix: 7.9253e-05 l_g_fea: 4.9453e-01 l_g_gan: 2.4840e-03 l_d_real: 1.0284e+00 l_d_fake: 1.0460e+00 D_real: 4.0684e+01 D_fake: 4.1224e+01 +20-04-07 01:28:28.228 - INFO: l_g_pix: 9.3680e-05 l_g_fea: 4.5582e-01 l_g_gan: 1.2345e-02 l_d_real: 1.0596e-01 l_d_fake: 1.1220e-01 D_real: 2.4831e+01 D_fake: 2.2471e+01 +20-04-07 01:31:39.716 - INFO: l_g_pix: 1.1272e-04 l_g_fea: 4.1782e-01 l_g_gan: 4.2245e-03 l_d_real: 6.4602e-01 l_d_fake: 6.3322e-01 D_real: 2.9247e+01 D_fake: 2.9041e+01 +20-04-07 01:34:51.120 - INFO: l_g_pix: 5.0303e-05 l_g_fea: 2.7842e-01 l_g_gan: 9.6282e-03 l_d_real: 1.8628e-01 l_d_fake: 1.9114e-01 D_real: 1.0407e+01 D_fake: 8.6700e+00 +20-04-07 01:38:02.983 - INFO: l_g_pix: 8.0983e-05 l_g_fea: 3.5347e-01 l_g_gan: 7.8240e-04 l_d_real: 2.0902e+00 l_d_fake: 2.0940e+00 D_real: 1.8306e+01 D_fake: 2.0242e+01 +20-04-07 01:41:14.305 - INFO: l_g_pix: 1.0048e-04 l_g_fea: 3.9211e-01 l_g_gan: 1.0795e-02 l_d_real: 1.4620e-01 l_d_fake: 1.5398e-01 D_real: 3.2807e+01 D_fake: 3.0798e+01 +20-04-07 01:44:25.060 - INFO: l_g_pix: 6.7106e-05 l_g_fea: 3.3115e-01 l_g_gan: 8.9904e-03 l_d_real: 2.1380e-01 l_d_fake: 2.0998e-01 D_real: 2.5433e+01 D_fake: 2.3847e+01 +20-04-07 01:47:36.413 - INFO: l_g_pix: 1.1656e-04 l_g_fea: 5.0655e-01 l_g_gan: 3.2954e-03 l_d_real: 8.2490e-01 l_d_fake: 8.4485e-01 D_real: 3.7889e+01 D_fake: 3.8065e+01 +20-04-07 01:50:48.078 - INFO: l_g_pix: 8.3052e-05 l_g_fea: 3.9145e-01 l_g_gan: 1.6921e-03 l_d_real: 1.3564e+00 l_d_fake: 1.3425e+00 D_real: 3.0941e+01 D_fake: 3.1952e+01 +20-04-07 01:53:59.379 - INFO: l_g_pix: 7.8075e-05 l_g_fea: 3.6181e-01 l_g_gan: 3.3274e-03 l_d_real: 8.9116e-01 l_d_fake: 9.0816e-01 D_real: 2.8311e+01 D_fake: 2.8545e+01 +20-04-07 01:57:10.222 - INFO: l_g_pix: 1.0673e-04 l_g_fea: 5.5604e-01 l_g_gan: 1.0472e-02 l_d_real: 1.5855e-01 l_d_fake: 1.7449e-01 D_real: 1.9447e+01 D_fake: 1.7519e+01 +20-04-07 02:00:21.765 - INFO: l_g_pix: 1.2027e-04 l_g_fea: 4.7359e-01 l_g_gan: 3.7155e-03 l_d_real: 7.5885e-01 l_d_fake: 7.4434e-01 D_real: 1.6309e+01 D_fake: 1.6318e+01 +20-04-07 02:03:34.380 - INFO: l_g_pix: 6.7006e-05 l_g_fea: 3.3915e-01 l_g_gan: 8.5980e-04 l_d_real: 2.0802e+00 l_d_fake: 2.0684e+00 D_real: 1.4060e+01 D_fake: 1.5962e+01 +20-04-07 02:06:45.541 - INFO: l_g_pix: 9.1556e-05 l_g_fea: 4.1533e-01 l_g_gan: 1.9628e-02 l_d_real: 2.7460e-02 l_d_fake: 2.3305e-02 D_real: 2.5672e+01 D_fake: 2.1771e+01 +20-04-07 02:09:56.218 - INFO: l_g_pix: 9.6788e-05 l_g_fea: 4.5360e-01 l_g_gan: 3.1704e-03 l_d_real: 8.3375e-01 l_d_fake: 8.4311e-01 D_real: 4.3536e+01 D_fake: 4.3740e+01 +20-04-07 02:13:07.628 - INFO: l_g_pix: 1.2459e-04 l_g_fea: 4.6794e-01 l_g_gan: 1.5004e-03 l_d_real: 1.5270e+00 l_d_fake: 1.5573e+00 D_real: 5.8444e+01 D_fake: 5.9686e+01 +20-04-07 02:16:19.071 - INFO: l_g_pix: 1.1361e-04 l_g_fea: 4.6076e-01 l_g_gan: 5.1806e-03 l_d_real: 5.1197e-01 l_d_fake: 5.2164e-01 D_real: 2.2673e+01 D_fake: 2.2154e+01 +20-04-07 02:19:29.614 - INFO: l_g_pix: 7.1056e-05 l_g_fea: 4.2198e-01 l_g_gan: 9.9431e-03 l_d_real: 1.6674e-01 l_d_fake: 1.6920e-01 D_real: 2.2130e+01 D_fake: 2.0310e+01 +20-04-07 02:22:41.121 - INFO: l_g_pix: 9.7593e-05 l_g_fea: 3.9786e-01 l_g_gan: 6.0580e-03 l_d_real: 3.9195e-01 l_d_fake: 3.9926e-01 D_real: 2.8089e+01 D_fake: 2.7273e+01 +20-04-07 02:25:52.443 - INFO: l_g_pix: 1.2716e-04 l_g_fea: 5.1927e-01 l_g_gan: 9.2061e-03 l_d_real: 2.3926e-01 l_d_fake: 2.2534e-01 D_real: 2.9937e+01 D_fake: 2.8328e+01 +20-04-07 02:25:53.206 - INFO: Models and training states saved. +20-04-07 02:26:52.996 - INFO: # Validation # PSNR: 31.58, SSIM: 0.83902, LPIPS: 0.027647 +20-04-07 02:26:52.996 - INFO: psnr: 31.58, ssim: 0.83902, lpips: 0.027647 +20-04-07 02:35:27.391 - INFO: l_g_pix: 1.2764e-04 l_g_fea: 4.3834e-01 l_g_gan: 2.1096e-02 l_d_real: 2.1618e-02 l_d_fake: 1.9233e-02 D_real: 1.2216e+01 D_fake: 8.0170e+00 +20-04-07 02:40:44.714 - INFO: l_g_pix: 9.0111e-05 l_g_fea: 3.9545e-01 l_g_gan: 1.7553e-02 l_d_real: 3.3441e-02 l_d_fake: 3.4506e-02 D_real: 2.8982e+01 D_fake: 2.5505e+01 +20-04-07 02:44:13.371 - INFO: l_g_pix: 9.9573e-05 l_g_fea: 4.0952e-01 l_g_gan: 1.4553e-02 l_d_real: 6.3191e-02 l_d_fake: 6.5561e-02 D_real: 9.7164e+00 D_fake: 6.8701e+00 +20-04-07 02:47:26.710 - INFO: l_g_pix: 8.7037e-05 l_g_fea: 4.7339e-01 l_g_gan: 1.0351e-02 l_d_real: 1.5836e-01 l_d_fake: 1.5944e-01 D_real: 3.7127e+01 D_fake: 3.5216e+01 +20-04-07 02:50:37.565 - INFO: l_g_pix: 9.8870e-05 l_g_fea: 3.1365e-01 l_g_gan: 4.3106e-03 l_d_real: 6.7708e-01 l_d_fake: 6.5255e-01 D_real: 2.4899e-01 D_fake: 5.1688e-02 +20-04-07 02:53:47.930 - INFO: l_g_pix: 1.2921e-04 l_g_fea: 4.7327e-01 l_g_gan: 1.5112e-02 l_d_real: 7.4586e-02 l_d_fake: 6.5481e-02 D_real: 4.0052e+01 D_fake: 3.7099e+01 +20-04-07 02:57:05.301 - INFO: l_g_pix: 8.7704e-05 l_g_fea: 5.0413e-01 l_g_gan: 5.1031e-03 l_d_real: 5.0842e-01 l_d_fake: 5.1700e-01 D_real: 1.4972e+01 D_fake: 1.4464e+01 +20-04-07 03:00:25.359 - INFO: l_g_pix: 8.4821e-05 l_g_fea: 3.6684e-01 l_g_gan: 7.9410e-03 l_d_real: 2.5789e-01 l_d_fake: 2.7393e-01 D_real: 1.7691e+01 D_fake: 1.6369e+01 +20-04-07 03:03:35.488 - INFO: l_g_pix: 6.1943e-05 l_g_fea: 2.5548e-01 l_g_gan: 1.5078e-02 l_d_real: 5.9901e-02 l_d_fake: 5.9396e-02 D_real: 1.5886e+01 D_fake: 1.2930e+01 +20-04-07 03:06:46.005 - INFO: l_g_pix: 7.4025e-05 l_g_fea: 3.7062e-01 l_g_gan: 1.0556e-02 l_d_real: 1.8360e-01 l_d_fake: 1.8904e-01 D_real: 2.4072e+01 D_fake: 2.2147e+01 +20-04-07 03:09:56.921 - INFO: l_g_pix: 1.0754e-04 l_g_fea: 4.8959e-01 l_g_gan: 1.6297e-02 l_d_real: 5.4274e-02 l_d_fake: 5.4653e-02 D_real: 3.0409e+01 D_fake: 2.7204e+01 +20-04-07 03:13:07.805 - INFO: l_g_pix: 8.6287e-05 l_g_fea: 3.7102e-01 l_g_gan: 9.6248e-03 l_d_real: 1.7706e-01 l_d_fake: 1.7383e-01 D_real: 1.4036e+01 D_fake: 1.2286e+01 +20-04-07 03:16:18.388 - INFO: l_g_pix: 1.3596e-04 l_g_fea: 5.3102e-01 l_g_gan: 8.8085e-03 l_d_real: 2.2084e-01 l_d_fake: 2.3107e-01 D_real: 2.2032e+01 D_fake: 2.0496e+01 +20-04-07 03:19:29.715 - INFO: l_g_pix: 7.2211e-05 l_g_fea: 3.9978e-01 l_g_gan: 6.5938e-03 l_d_real: 3.5716e-01 l_d_fake: 3.5845e-01 D_real: 3.0833e+01 D_fake: 2.9872e+01 +20-04-07 03:22:40.425 - INFO: l_g_pix: 8.1634e-05 l_g_fea: 3.7296e-01 l_g_gan: 1.2655e-02 l_d_real: 9.3975e-02 l_d_fake: 9.5629e-02 D_real: 1.9518e+01 D_fake: 1.7081e+01 +20-04-07 03:25:51.016 - INFO: l_g_pix: 1.0277e-04 l_g_fea: 3.9445e-01 l_g_gan: 1.9395e-03 l_d_real: 1.1848e+00 l_d_fake: 1.1793e+00 D_real: 1.3367e+01 D_fake: 1.4161e+01 +20-04-07 03:29:01.990 - INFO: l_g_pix: 1.0363e-04 l_g_fea: 3.7722e-01 l_g_gan: 5.1715e-03 l_d_real: 4.9830e-01 l_d_fake: 5.0758e-01 D_real: 1.7179e+01 D_fake: 1.6648e+01 +20-04-07 03:32:12.866 - INFO: l_g_pix: 1.3883e-04 l_g_fea: 5.3536e-01 l_g_gan: 3.9509e-03 l_d_real: 7.0123e-01 l_d_fake: 7.6290e-01 D_real: 2.8994e+01 D_fake: 2.8936e+01 +20-04-07 03:35:22.526 - INFO: l_g_pix: 9.2318e-05 l_g_fea: 4.6356e-01 l_g_gan: 3.4732e-03 l_d_real: 7.9793e-01 l_d_fake: 7.8094e-01 D_real: -1.8344e+00 D_fake: -1.7396e+00 +20-04-07 03:38:32.762 - INFO: l_g_pix: 1.0378e-04 l_g_fea: 4.2409e-01 l_g_gan: 7.0365e-03 l_d_real: 3.2826e-01 l_d_fake: 3.2034e-01 D_real: 4.0690e+01 D_fake: 3.9607e+01 +20-04-07 03:41:43.196 - INFO: l_g_pix: 8.6806e-05 l_g_fea: 4.4318e-01 l_g_gan: 3.4395e-03 l_d_real: 8.0946e-01 l_d_fake: 8.4135e-01 D_real: 2.2921e+01 D_fake: 2.3058e+01 +20-04-07 03:44:54.718 - INFO: l_g_pix: 1.2798e-04 l_g_fea: 5.6732e-01 l_g_gan: 6.3733e-03 l_d_real: 4.4472e-01 l_d_fake: 3.9119e-01 D_real: 2.1441e+01 D_fake: 2.0584e+01 +20-04-07 03:48:05.862 - INFO: l_g_pix: 8.3686e-05 l_g_fea: 4.2973e-01 l_g_gan: 4.4412e-03 l_d_real: 6.2739e-01 l_d_fake: 6.3813e-01 D_real: 6.4287e+00 D_fake: 6.1732e+00 +20-04-07 03:51:16.294 - INFO: l_g_pix: 7.5582e-05 l_g_fea: 4.0834e-01 l_g_gan: 4.4709e-03 l_d_real: 6.8627e-01 l_d_fake: 6.7028e-01 D_real: 9.4137e+00 D_fake: 9.1978e+00 +20-04-07 03:54:26.368 - INFO: l_g_pix: 9.6535e-05 l_g_fea: 4.0054e-01 l_g_gan: 1.8922e-03 l_d_real: 1.4498e+00 l_d_fake: 1.4065e+00 D_real: 1.8194e+01 D_fake: 1.9244e+01 +20-04-07 03:54:26.822 - INFO: Models and training states saved. +20-04-07 03:55:26.412 - INFO: # Validation # PSNR: 31.683, SSIM: 0.83672, LPIPS: 0.033828 +20-04-07 03:55:26.415 - INFO: psnr: 31.683, ssim: 0.83672, lpips: 0.033828 +20-04-07 03:59:16.804 - INFO: l_g_pix: 1.2171e-04 l_g_fea: 4.2262e-01 l_g_gan: 7.9238e-03 l_d_real: 2.8531e-01 l_d_fake: 2.8024e-01 D_real: 2.0913e+01 D_fake: 1.9611e+01 +20-04-07 04:02:29.199 - INFO: l_g_pix: 1.1994e-04 l_g_fea: 4.9633e-01 l_g_gan: 1.8392e-02 l_d_real: 3.3386e-02 l_d_fake: 3.5047e-02 D_real: 3.3524e+01 D_fake: 2.9880e+01 +20-04-07 04:05:40.403 - INFO: l_g_pix: 9.2509e-05 l_g_fea: 4.6289e-01 l_g_gan: 8.4341e-03 l_d_real: 2.6550e-01 l_d_fake: 2.5738e-01 D_real: 1.7486e+01 D_fake: 1.6060e+01 +20-04-07 04:08:51.480 - INFO: l_g_pix: 1.1113e-04 l_g_fea: 4.5315e-01 l_g_gan: 1.0874e-03 l_d_real: 1.8689e+00 l_d_fake: 1.8495e+00 D_real: 2.4290e+01 D_fake: 2.5932e+01 +20-04-07 04:12:03.038 - INFO: l_g_pix: 9.2665e-05 l_g_fea: 4.3289e-01 l_g_gan: 3.3092e-03 l_d_real: 8.7812e-01 l_d_fake: 8.5258e-01 D_real: 7.3535e+00 D_fake: 7.5570e+00 +20-04-07 04:15:14.461 - INFO: l_g_pix: 1.2774e-04 l_g_fea: 4.7417e-01 l_g_gan: 1.6190e-02 l_d_real: 4.6368e-02 l_d_fake: 4.5863e-02 D_real: 4.1158e+01 D_fake: 3.7967e+01 +20-04-07 04:18:25.986 - INFO: l_g_pix: 1.1873e-04 l_g_fea: 4.0993e-01 l_g_gan: 2.0146e-03 l_d_real: 1.2079e+00 l_d_fake: 1.1910e+00 D_real: 1.6061e+01 D_fake: 1.6858e+01 +20-04-07 04:21:36.317 - INFO: l_g_pix: 1.0537e-04 l_g_fea: 4.1907e-01 l_g_gan: 5.0232e-03 l_d_real: 5.1529e-01 l_d_fake: 5.4862e-01 D_real: 1.5989e+01 D_fake: 1.5516e+01 +20-04-07 04:24:47.491 - INFO: l_g_pix: 6.4255e-05 l_g_fea: 3.4115e-01 l_g_gan: 1.1623e-02 l_d_real: 1.2584e-01 l_d_fake: 1.4154e-01 D_real: 2.4585e+01 D_fake: 2.2394e+01 +20-04-07 04:28:00.156 - INFO: l_g_pix: 9.7502e-05 l_g_fea: 4.1415e-01 l_g_gan: 1.9436e-02 l_d_real: 2.3434e-02 l_d_fake: 2.3856e-02 D_real: 1.7033e+01 D_fake: 1.3170e+01 +20-04-07 04:31:19.445 - INFO: l_g_pix: 8.6337e-05 l_g_fea: 3.6040e-01 l_g_gan: 7.9667e-03 l_d_real: 2.8416e-01 l_d_fake: 2.7564e-01 D_real: 1.8850e+01 D_fake: 1.7536e+01 +20-04-07 04:34:31.146 - INFO: l_g_pix: 8.9004e-05 l_g_fea: 4.1852e-01 l_g_gan: 1.6971e-02 l_d_real: 4.1238e-02 l_d_fake: 4.3861e-02 D_real: 2.5697e+01 D_fake: 2.2345e+01 +20-04-07 04:37:42.685 - INFO: l_g_pix: 7.4782e-05 l_g_fea: 3.6515e-01 l_g_gan: 9.6730e-03 l_d_real: 1.8344e-01 l_d_fake: 1.7917e-01 D_real: 2.2075e+01 D_fake: 2.0321e+01 +20-04-07 04:40:56.459 - INFO: l_g_pix: 1.0207e-04 l_g_fea: 4.5868e-01 l_g_gan: 2.0321e-02 l_d_real: 2.3020e-02 l_d_fake: 2.4423e-02 D_real: 1.2662e+01 D_fake: 8.6214e+00 +20-04-07 04:44:07.158 - INFO: l_g_pix: 7.8904e-05 l_g_fea: 5.1371e-01 l_g_gan: 3.0711e-03 l_d_real: 8.9644e-01 l_d_fake: 8.9423e-01 D_real: 3.8641e+01 D_fake: 3.8922e+01 +20-04-07 04:47:18.936 - INFO: l_g_pix: 8.7123e-05 l_g_fea: 3.4547e-01 l_g_gan: 1.5421e-02 l_d_real: 5.6357e-02 l_d_fake: 5.6357e-02 D_real: 6.6072e+00 D_fake: 3.5793e+00 +20-04-07 04:50:29.880 - INFO: l_g_pix: 9.1039e-05 l_g_fea: 4.5293e-01 l_g_gan: 1.2090e-02 l_d_real: 1.2680e-01 l_d_fake: 1.0993e-01 D_real: 5.5900e+01 D_fake: 5.3600e+01 +20-04-07 04:53:41.001 - INFO: l_g_pix: 7.1218e-05 l_g_fea: 2.6922e-01 l_g_gan: 4.1511e-03 l_d_real: 7.3488e-01 l_d_fake: 6.8520e-01 D_real: 1.4659e+01 D_fake: 1.4539e+01 +20-04-07 04:56:51.664 - INFO: l_g_pix: 9.4947e-05 l_g_fea: 3.8866e-01 l_g_gan: 1.1921e-02 l_d_real: 1.0871e-01 l_d_fake: 1.1260e-01 D_real: 2.5617e+01 D_fake: 2.3343e+01 +20-04-07 05:00:03.274 - INFO: l_g_pix: 7.8603e-05 l_g_fea: 3.1954e-01 l_g_gan: 8.8721e-03 l_d_real: 2.3890e-01 l_d_fake: 2.3379e-01 D_real: 3.3186e+01 D_fake: 3.1648e+01 +20-04-07 05:03:15.501 - INFO: l_g_pix: 6.8128e-05 l_g_fea: 3.1568e-01 l_g_gan: 2.4545e-03 l_d_real: 9.9273e-01 l_d_fake: 1.0042e+00 D_real: 1.0820e+01 D_fake: 1.1327e+01 +20-04-07 05:06:27.662 - INFO: l_g_pix: 1.1343e-04 l_g_fea: 3.8088e-01 l_g_gan: 8.7922e-03 l_d_real: 2.2248e-01 l_d_fake: 2.1945e-01 D_real: 2.0650e+01 D_fake: 1.9113e+01 +20-04-07 05:09:43.326 - INFO: l_g_pix: 9.0946e-05 l_g_fea: 4.2271e-01 l_g_gan: 1.3765e-02 l_d_real: 7.3177e-02 l_d_fake: 7.1555e-02 D_real: 4.3034e-01 D_fake: -2.2504e+00 +20-04-07 05:12:54.961 - INFO: l_g_pix: 8.1609e-05 l_g_fea: 3.8940e-01 l_g_gan: 1.6085e-03 l_d_real: 1.4981e+00 l_d_fake: 1.4932e+00 D_real: 3.0954e+01 D_fake: 3.2128e+01 +20-04-07 05:16:07.425 - INFO: l_g_pix: 7.2849e-05 l_g_fea: 3.1170e-01 l_g_gan: 3.9890e-03 l_d_real: 7.0443e-01 l_d_fake: 6.9761e-01 D_real: 1.1622e+00 D_fake: 1.0655e+00 +20-04-07 05:16:07.964 - INFO: Models and training states saved. +20-04-07 05:17:07.709 - INFO: # Validation # PSNR: 31.946, SSIM: 0.84157, LPIPS: 0.027429 +20-04-07 05:17:07.709 - INFO: psnr: 31.946, ssim: 0.84157, lpips: 0.027429 +20-04-07 05:22:45.943 - INFO: l_g_pix: 8.2907e-05 l_g_fea: 3.8273e-01 l_g_gan: 1.6556e-02 l_d_real: 4.3029e-02 l_d_fake: 4.2902e-02 D_real: 2.7982e+01 D_fake: 2.4713e+01 +20-04-07 05:25:56.408 - INFO: l_g_pix: 1.1936e-04 l_g_fea: 4.7374e-01 l_g_gan: 1.2100e-02 l_d_real: 1.0624e-01 l_d_fake: 1.0729e-01 D_real: 2.1257e+01 D_fake: 1.8944e+01 +20-04-07 05:29:07.339 - INFO: l_g_pix: 1.2381e-04 l_g_fea: 5.2590e-01 l_g_gan: 4.0092e-03 l_d_real: 7.0654e-01 l_d_fake: 7.3797e-01 D_real: 2.6260e+01 D_fake: 2.6180e+01 +20-04-07 05:32:19.176 - INFO: l_g_pix: 9.2869e-05 l_g_fea: 4.3520e-01 l_g_gan: 1.3393e-02 l_d_real: 8.2258e-02 l_d_fake: 7.9405e-02 D_real: 3.8955e-02 D_fake: -2.5589e+00 +20-04-07 05:35:31.209 - INFO: l_g_pix: 1.2600e-04 l_g_fea: 5.2463e-01 l_g_gan: 7.7816e-03 l_d_real: 2.9197e-01 l_d_fake: 2.7325e-01 D_real: 2.3969e+01 D_fake: 2.2696e+01 +20-04-07 05:38:43.230 - INFO: l_g_pix: 1.2125e-04 l_g_fea: 4.9304e-01 l_g_gan: 1.0183e-02 l_d_real: 1.6375e-01 l_d_fake: 1.6815e-01 D_real: 1.8531e+01 D_fake: 1.6660e+01 +20-04-07 05:41:55.988 - INFO: l_g_pix: 1.1077e-04 l_g_fea: 4.0915e-01 l_g_gan: 1.5150e-02 l_d_real: 5.9293e-02 l_d_fake: 6.1104e-02 D_real: 2.1585e+01 D_fake: 1.8615e+01 +20-04-07 05:45:08.274 - INFO: l_g_pix: 1.0363e-04 l_g_fea: 4.5420e-01 l_g_gan: 5.0202e-03 l_d_real: 5.3777e-01 l_d_fake: 5.3963e-01 D_real: 1.5261e+01 D_fake: 1.4796e+01 +20-04-07 05:48:19.402 - INFO: l_g_pix: 1.0343e-04 l_g_fea: 4.5304e-01 l_g_gan: 6.2314e-03 l_d_real: 3.9579e-01 l_d_fake: 3.8167e-01 D_real: -1.9327e+00 D_fake: -2.7902e+00 +20-04-07 05:51:30.344 - INFO: l_g_pix: 1.2263e-04 l_g_fea: 4.9020e-01 l_g_gan: 5.9056e-03 l_d_real: 4.2644e-01 l_d_fake: 4.3824e-01 D_real: 1.9910e+01 D_fake: 1.9161e+01 +20-04-07 05:54:42.338 - INFO: l_g_pix: 8.1359e-05 l_g_fea: 4.3197e-01 l_g_gan: 2.3511e-02 l_d_real: 1.2790e-02 l_d_fake: 1.1417e-02 D_real: 9.8403e+00 D_fake: 5.1502e+00 +20-04-07 05:57:54.329 - INFO: l_g_pix: 1.0858e-04 l_g_fea: 3.8532e-01 l_g_gan: 1.1960e-02 l_d_real: 1.0333e-01 l_d_fake: 1.0412e-01 D_real: 1.6609e+01 D_fake: 1.4321e+01 +20-04-07 06:01:06.368 - INFO: l_g_pix: 8.6626e-05 l_g_fea: 3.7350e-01 l_g_gan: 5.7949e-03 l_d_real: 4.5427e-01 l_d_fake: 4.6060e-01 D_real: 3.3492e+01 D_fake: 3.2790e+01 +20-04-07 06:04:18.194 - INFO: l_g_pix: 9.7411e-05 l_g_fea: 3.9043e-01 l_g_gan: 8.2611e-03 l_d_real: 2.4548e-01 l_d_fake: 2.5540e-01 D_real: 1.2690e+01 D_fake: 1.1288e+01 +20-04-07 06:07:29.239 - INFO: l_g_pix: 8.9891e-05 l_g_fea: 4.1851e-01 l_g_gan: 2.1230e-03 l_d_real: 1.2503e+00 l_d_fake: 1.2131e+00 D_real: 2.8388e+01 D_fake: 2.9195e+01 +20-04-07 06:10:41.358 - INFO: l_g_pix: 1.1814e-04 l_g_fea: 4.8307e-01 l_g_gan: 1.6302e-02 l_d_real: 4.9485e-02 l_d_fake: 5.2980e-02 D_real: 2.2340e+01 D_fake: 1.9131e+01 +20-04-07 06:13:52.019 - INFO: l_g_pix: 1.2887e-04 l_g_fea: 4.6443e-01 l_g_gan: 1.3323e-02 l_d_real: 8.4966e-02 l_d_fake: 8.4775e-02 D_real: 2.5379e+01 D_fake: 2.2799e+01 +20-04-07 06:17:03.818 - INFO: l_g_pix: 9.3463e-05 l_g_fea: 3.1139e-01 l_g_gan: 3.4707e-04 l_d_real: 2.9277e+00 l_d_fake: 2.9404e+00 D_real: 7.2077e+00 D_fake: 1.0072e+01 +20-04-07 06:20:15.214 - INFO: l_g_pix: 9.6421e-05 l_g_fea: 3.7585e-01 l_g_gan: 4.7294e-03 l_d_real: 6.5514e-01 l_d_fake: 5.8876e-01 D_real: 3.3757e+01 D_fake: 3.3433e+01 +20-04-07 06:23:26.690 - INFO: l_g_pix: 8.5309e-05 l_g_fea: 4.8662e-01 l_g_gan: 7.7989e-03 l_d_real: 2.7490e-01 l_d_fake: 2.8052e-01 D_real: 3.7436e+01 D_fake: 3.6154e+01 +20-04-07 06:26:37.921 - INFO: l_g_pix: 1.0573e-04 l_g_fea: 3.4099e-01 l_g_gan: 3.1805e-02 l_d_real: 2.2538e-03 l_d_fake: 2.5295e-03 D_real: 2.8265e+01 D_fake: 2.1906e+01 +20-04-07 06:29:49.592 - INFO: l_g_pix: 9.0825e-05 l_g_fea: 3.8108e-01 l_g_gan: 1.6821e-02 l_d_real: 6.1976e-02 l_d_fake: 4.1941e-02 D_real: 4.3790e+01 D_fake: 4.0478e+01 +20-04-07 06:33:00.816 - INFO: l_g_pix: 9.7822e-05 l_g_fea: 4.9336e-01 l_g_gan: 7.7627e-03 l_d_real: 2.8760e-01 l_d_fake: 2.7538e-01 D_real: 2.0041e+01 D_fake: 1.8770e+01 +20-04-07 06:36:11.926 - INFO: l_g_pix: 1.1132e-04 l_g_fea: 4.7019e-01 l_g_gan: 1.9284e-02 l_d_real: 2.5776e-02 l_d_fake: 2.8165e-02 D_real: 2.3139e+01 D_fake: 1.9309e+01 +20-04-07 06:39:22.834 - INFO: l_g_pix: 1.0031e-04 l_g_fea: 3.9802e-01 l_g_gan: 1.5063e-02 l_d_real: 6.2496e-02 l_d_fake: 5.6327e-02 D_real: 1.7350e+01 D_fake: 1.4397e+01 +20-04-07 06:39:23.310 - INFO: Models and training states saved. +20-04-07 06:40:22.094 - INFO: # Validation # PSNR: 31.665, SSIM: 0.83291, LPIPS: 0.023863 +20-04-07 06:40:22.094 - INFO: psnr: 31.665, ssim: 0.83291, lpips: 0.023863 +20-04-07 06:45:18.172 - INFO: l_g_pix: 8.7782e-05 l_g_fea: 3.9909e-01 l_g_gan: 1.2342e-02 l_d_real: 1.0342e-01 l_d_fake: 1.1612e-01 D_real: 2.3821e+01 D_fake: 2.1463e+01 +20-04-07 06:48:37.803 - INFO: l_g_pix: 1.1784e-04 l_g_fea: 4.8070e-01 l_g_gan: 6.7302e-03 l_d_real: 3.5268e-01 l_d_fake: 3.5293e-01 D_real: 1.4499e+01 D_fake: 1.3506e+01 +20-04-07 06:51:49.464 - INFO: l_g_pix: 7.5785e-05 l_g_fea: 3.9178e-01 l_g_gan: 3.6389e-03 l_d_real: 7.5450e-01 l_d_fake: 7.3463e-01 D_real: 3.4076e+01 D_fake: 3.4093e+01 +20-04-07 06:55:01.811 - INFO: l_g_pix: 7.1904e-05 l_g_fea: 3.6602e-01 l_g_gan: 1.1132e-02 l_d_real: 1.3288e-01 l_d_fake: 1.2739e-01 D_real: -2.9491e+00 D_fake: -5.0453e+00 +20-04-07 06:58:13.647 - INFO: l_g_pix: 9.5459e-05 l_g_fea: 4.6757e-01 l_g_gan: 9.4323e-03 l_d_real: 1.8628e-01 l_d_fake: 1.9331e-01 D_real: -5.4917e+00 D_fake: -7.1884e+00 +20-04-07 07:01:26.397 - INFO: l_g_pix: 1.2525e-04 l_g_fea: 4.6612e-01 l_g_gan: 6.2335e-03 l_d_real: 4.1554e-01 l_d_fake: 4.3675e-01 D_real: 1.0348e+01 D_fake: 9.5273e+00 +20-04-07 07:04:38.758 - INFO: l_g_pix: 1.0634e-04 l_g_fea: 3.8389e-01 l_g_gan: 1.5283e-02 l_d_real: 6.3103e-02 l_d_fake: 7.1416e-02 D_real: 8.5531e+00 D_fake: 5.5637e+00 +20-04-07 07:07:50.387 - INFO: l_g_pix: 1.1758e-04 l_g_fea: 4.7000e-01 l_g_gan: 1.0842e-02 l_d_real: 1.4108e-01 l_d_fake: 1.4135e-01 D_real: 3.0598e+01 D_fake: 2.8571e+01 +20-04-07 07:11:01.867 - INFO: l_g_pix: 8.7271e-05 l_g_fea: 4.8243e-01 l_g_gan: 1.2760e-02 l_d_real: 9.2637e-02 l_d_fake: 1.0177e-01 D_real: 1.9367e+00 D_fake: -5.1804e-01 +20-04-07 07:14:13.269 - INFO: l_g_pix: 1.0807e-04 l_g_fea: 5.3739e-01 l_g_gan: 1.1559e-02 l_d_real: 1.4498e-01 l_d_fake: 1.3462e-01 D_real: 3.6292e+01 D_fake: 3.4120e+01 +20-04-07 07:17:24.347 - INFO: l_g_pix: 1.0812e-04 l_g_fea: 4.4153e-01 l_g_gan: 8.9536e-03 l_d_real: 2.1076e-01 l_d_fake: 2.3652e-01 D_real: 2.1477e+00 D_fake: 5.8059e-01 +20-04-07 07:20:36.213 - INFO: l_g_pix: 1.0494e-04 l_g_fea: 4.7561e-01 l_g_gan: 8.1226e-03 l_d_real: 2.7402e-01 l_d_fake: 2.6022e-01 D_real: 2.2158e+01 D_fake: 2.0800e+01 +20-04-07 07:23:48.269 - INFO: l_g_pix: 9.8871e-05 l_g_fea: 4.1757e-01 l_g_gan: 1.1610e-02 l_d_real: 1.3411e-01 l_d_fake: 1.5158e-01 D_real: 1.6909e+01 D_fake: 1.4730e+01 +20-04-07 07:27:00.197 - INFO: l_g_pix: 1.1944e-04 l_g_fea: 5.0336e-01 l_g_gan: 6.3447e-03 l_d_real: 3.8533e-01 l_d_fake: 3.9446e-01 D_real: 2.6198e+01 D_fake: 2.5319e+01 +20-04-07 07:30:12.428 - INFO: l_g_pix: 1.0266e-04 l_g_fea: 4.2720e-01 l_g_gan: 3.0124e-03 l_d_real: 9.0740e-01 l_d_fake: 8.9679e-01 D_real: 2.9605e+01 D_fake: 2.9904e+01 +20-04-07 07:33:23.750 - INFO: l_g_pix: 1.0635e-04 l_g_fea: 4.9994e-01 l_g_gan: 7.3220e-03 l_d_real: 3.0443e-01 l_d_fake: 3.1538e-01 D_real: 1.8787e+01 D_fake: 1.7632e+01 +20-04-07 07:36:46.328 - INFO: l_g_pix: 9.5015e-05 l_g_fea: 4.6592e-01 l_g_gan: 9.5551e-03 l_d_real: 2.2543e-01 l_d_fake: 1.9613e-01 D_real: 4.5607e+01 D_fake: 4.3907e+01 +20-04-07 07:39:57.644 - INFO: l_g_pix: 8.4061e-05 l_g_fea: 3.9321e-01 l_g_gan: 1.7612e-02 l_d_real: 3.9007e-02 l_d_fake: 3.6929e-02 D_real: 7.3337e+00 D_fake: 3.8492e+00 +20-04-07 07:43:09.318 - INFO: l_g_pix: 9.5396e-05 l_g_fea: 4.0301e-01 l_g_gan: 1.6661e-02 l_d_real: 4.0622e-02 l_d_fake: 4.4334e-02 D_real: 2.5949e+01 D_fake: 2.2660e+01 +20-04-07 07:46:21.363 - INFO: l_g_pix: 1.1136e-04 l_g_fea: 4.6115e-01 l_g_gan: 1.6076e-02 l_d_real: 4.4857e-02 l_d_fake: 4.9733e-02 D_real: 1.8071e+01 D_fake: 1.4903e+01 +20-04-07 07:49:33.790 - INFO: l_g_pix: 7.4329e-05 l_g_fea: 3.7480e-01 l_g_gan: 4.3084e-03 l_d_real: 6.4712e-01 l_d_fake: 6.6220e-01 D_real: -1.6778e+00 D_fake: -1.8848e+00 +20-04-07 07:52:45.953 - INFO: l_g_pix: 8.4351e-05 l_g_fea: 4.5761e-01 l_g_gan: 5.9205e-03 l_d_real: 3.9333e-01 l_d_fake: 4.0130e-01 D_real: -9.8098e+00 D_fake: -1.0597e+01 +20-04-07 07:55:57.671 - INFO: l_g_pix: 1.1834e-04 l_g_fea: 4.6174e-01 l_g_gan: 3.7472e-03 l_d_real: 7.4955e-01 l_d_fake: 7.5000e-01 D_real: 6.1038e+00 D_fake: 6.1041e+00 +20-04-07 07:59:09.705 - INFO: l_g_pix: 1.0088e-04 l_g_fea: 4.2269e-01 l_g_gan: 4.5783e-03 l_d_real: 5.8212e-01 l_d_fake: 5.6625e-01 D_real: 1.2609e+01 D_fake: 1.2268e+01 +20-04-07 08:02:20.823 - INFO: l_g_pix: 9.8974e-05 l_g_fea: 4.3651e-01 l_g_gan: 7.8230e-03 l_d_real: 2.8020e-01 l_d_fake: 2.8825e-01 D_real: 1.2559e+01 D_fake: 1.1278e+01 +20-04-07 08:02:21.331 - INFO: Models and training states saved. +20-04-07 08:03:21.523 - INFO: # Validation # PSNR: 31.214, SSIM: 0.82375, LPIPS: 0.02648 +20-04-07 08:03:21.524 - INFO: psnr: 31.214, ssim: 0.82375, lpips: 0.02648 +20-04-07 08:07:52.768 - INFO: l_g_pix: 1.0005e-04 l_g_fea: 4.6134e-01 l_g_gan: 1.2462e-02 l_d_real: 1.0478e-01 l_d_fake: 1.0249e-01 D_real: 1.3149e+00 D_fake: -1.0740e+00 +20-04-07 08:11:09.023 - INFO: l_g_pix: 1.0611e-04 l_g_fea: 5.5280e-01 l_g_gan: 1.3985e-02 l_d_real: 9.5096e-02 l_d_fake: 7.6585e-02 D_real: 9.2824e+00 D_fake: 6.5711e+00 +20-04-07 08:14:21.080 - INFO: l_g_pix: 9.1646e-05 l_g_fea: 3.2726e-01 l_g_gan: 6.5187e-03 l_d_real: 3.7913e-01 l_d_fake: 3.7789e-01 D_real: 5.3746e+00 D_fake: 4.4494e+00 +20-04-07 08:17:32.701 - INFO: l_g_pix: 1.1113e-04 l_g_fea: 5.0216e-01 l_g_gan: 1.1066e-02 l_d_real: 1.6672e-01 l_d_fake: 1.5934e-01 D_real: 2.0898e+01 D_fake: 1.8848e+01 +20-04-07 08:20:44.333 - INFO: l_g_pix: 1.0367e-04 l_g_fea: 4.5086e-01 l_g_gan: 5.6848e-03 l_d_real: 4.6242e-01 l_d_fake: 4.6023e-01 D_real: 4.2293e+01 D_fake: 4.1617e+01 +20-04-07 08:23:56.391 - INFO: l_g_pix: 1.1953e-04 l_g_fea: 5.4056e-01 l_g_gan: 1.2532e-04 l_d_real: 3.9151e+00 l_d_fake: 3.9186e+00 D_real: -7.7594e-01 D_fake: 3.1159e+00 +20-04-07 08:27:08.118 - INFO: l_g_pix: 7.8532e-05 l_g_fea: 4.7477e-01 l_g_gan: 1.7239e-02 l_d_real: 3.6707e-02 l_d_fake: 3.9668e-02 D_real: 1.5355e+00 D_fake: -1.8741e+00 +20-04-07 08:30:19.584 - INFO: l_g_pix: 1.3514e-04 l_g_fea: 4.7561e-01 l_g_gan: 2.5911e-03 l_d_real: 1.0480e+00 l_d_fake: 1.0384e+00 D_real: 5.2780e+00 D_fake: 5.8031e+00 +20-04-07 08:33:31.277 - INFO: l_g_pix: 1.0680e-04 l_g_fea: 4.0699e-01 l_g_gan: 3.4651e-03 l_d_real: 8.0530e-01 l_d_fake: 8.1513e-01 D_real: 1.5339e+01 D_fake: 1.5456e+01 +20-04-07 08:36:43.620 - INFO: l_g_pix: 1.1874e-04 l_g_fea: 3.9642e-01 l_g_gan: 1.1493e-02 l_d_real: 1.2379e-01 l_d_fake: 1.2825e-01 D_real: 1.6695e+01 D_fake: 1.4522e+01 +20-04-07 08:39:54.800 - INFO: l_g_pix: 1.0539e-04 l_g_fea: 3.9347e-01 l_g_gan: 6.1438e-03 l_d_real: 4.2074e-01 l_d_fake: 4.1641e-01 D_real: 3.4553e+01 D_fake: 3.3742e+01 +20-04-07 08:43:06.205 - INFO: l_g_pix: 9.4895e-05 l_g_fea: 4.1496e-01 l_g_gan: 1.0331e-02 l_d_real: 1.6578e-01 l_d_fake: 1.5521e-01 D_real: 2.6391e+01 D_fake: 2.4485e+01 +20-04-07 08:46:21.697 - INFO: l_g_pix: 7.7517e-05 l_g_fea: 3.8344e-01 l_g_gan: 6.4433e-03 l_d_real: 3.8186e-01 l_d_fake: 3.6547e-01 D_real: 6.0216e+00 D_fake: 5.1066e+00 +20-04-07 08:49:32.853 - INFO: l_g_pix: 7.4185e-05 l_g_fea: 3.4585e-01 l_g_gan: 8.3906e-03 l_d_real: 2.3890e-01 l_d_fake: 2.3591e-01 D_real: 1.5694e+01 D_fake: 1.4253e+01 +20-04-07 08:52:44.672 - INFO: l_g_pix: 1.0530e-04 l_g_fea: 4.4879e-01 l_g_gan: 5.1774e-03 l_d_real: 5.1194e-01 l_d_fake: 5.3216e-01 D_real: 4.1685e+01 D_fake: 4.1172e+01 +20-04-07 08:55:55.665 - INFO: l_g_pix: 8.9339e-05 l_g_fea: 3.9234e-01 l_g_gan: 1.2197e-02 l_d_real: 1.3470e-01 l_d_fake: 1.0870e-01 D_real: 1.7333e+01 D_fake: 1.5015e+01 +20-04-07 08:59:07.751 - INFO: l_g_pix: 8.1816e-05 l_g_fea: 3.5649e-01 l_g_gan: 9.4852e-03 l_d_real: 1.9280e-01 l_d_fake: 1.9712e-01 D_real: 1.7445e+01 D_fake: 1.5743e+01 +20-04-07 09:02:18.715 - INFO: l_g_pix: 9.7975e-05 l_g_fea: 5.1259e-01 l_g_gan: 8.3126e-03 l_d_real: 2.4467e-01 l_d_fake: 2.2904e-01 D_real: 2.6699e+01 D_fake: 2.5273e+01 +20-04-07 09:05:29.766 - INFO: l_g_pix: 1.0599e-04 l_g_fea: 4.0875e-01 l_g_gan: 1.3369e-02 l_d_real: 8.5692e-02 l_d_fake: 8.5865e-02 D_real: 3.1959e+01 D_fake: 2.9371e+01 +20-04-07 09:08:41.753 - INFO: l_g_pix: 1.1777e-04 l_g_fea: 4.4335e-01 l_g_gan: 1.0721e-02 l_d_real: 1.5204e-01 l_d_fake: 1.5254e-01 D_real: 2.5107e+01 D_fake: 2.3116e+01 +20-04-07 09:11:53.591 - INFO: l_g_pix: 1.1513e-04 l_g_fea: 5.0981e-01 l_g_gan: 8.9285e-03 l_d_real: 2.2525e-01 l_d_fake: 2.1733e-01 D_real: 2.8580e+01 D_fake: 2.7016e+01 +20-04-07 09:15:05.117 - INFO: l_g_pix: 8.6754e-05 l_g_fea: 3.8196e-01 l_g_gan: 1.3849e-02 l_d_real: 7.6439e-02 l_d_fake: 8.0082e-02 D_real: 2.9058e+01 D_fake: 2.6366e+01 +20-04-07 09:18:16.217 - INFO: l_g_pix: 1.0633e-04 l_g_fea: 3.7271e-01 l_g_gan: 1.8994e-03 l_d_real: 1.3004e+00 l_d_fake: 1.3009e+00 D_real: 2.9178e+01 D_fake: 3.0099e+01 +20-04-07 09:21:28.108 - INFO: l_g_pix: 1.2693e-04 l_g_fea: 4.8079e-01 l_g_gan: 8.9460e-03 l_d_real: 2.1541e-01 l_d_fake: 2.1952e-01 D_real: 3.4521e+01 D_fake: 3.2949e+01 +20-04-07 09:24:38.669 - INFO: l_g_pix: 1.2236e-04 l_g_fea: 5.3430e-01 l_g_gan: 2.5928e-02 l_d_real: 6.8616e-03 l_d_fake: 6.5243e-03 D_real: -9.5299e-01 D_fake: -6.1320e+00 +20-04-07 09:24:39.093 - INFO: Models and training states saved. +20-04-07 09:25:38.479 - INFO: # Validation # PSNR: 31.704, SSIM: 0.83812, LPIPS: 0.026149 +20-04-07 09:25:38.479 - INFO: psnr: 31.704, ssim: 0.83812, lpips: 0.026149 +20-04-07 09:31:21.525 - INFO: l_g_pix: 9.7191e-05 l_g_fea: 4.2785e-01 l_g_gan: 1.1153e-02 l_d_real: 1.3934e-01 l_d_fake: 1.4413e-01 D_real: 1.4483e+01 D_fake: 1.2395e+01 +20-04-07 09:34:31.995 - INFO: l_g_pix: 8.5487e-05 l_g_fea: 3.6522e-01 l_g_gan: 4.2539e-03 l_d_real: 6.6742e-01 l_d_fake: 6.5278e-01 D_real: 2.7219e+01 D_fake: 2.7029e+01 +20-04-07 09:37:42.558 - INFO: l_g_pix: 9.3871e-05 l_g_fea: 3.3438e-01 l_g_gan: 2.2951e-02 l_d_real: 1.2210e-02 l_d_fake: 1.4593e-02 D_real: 1.0255e+01 D_fake: 5.6783e+00 +20-04-07 09:40:53.119 - INFO: l_g_pix: 9.1135e-05 l_g_fea: 4.7308e-01 l_g_gan: 1.2687e-02 l_d_real: 9.2110e-02 l_d_fake: 9.4202e-02 D_real: 1.2415e+01 D_fake: 9.9710e+00 +20-04-07 09:44:04.738 - INFO: l_g_pix: 1.1042e-04 l_g_fea: 5.0236e-01 l_g_gan: 2.1175e-03 l_d_real: 1.1882e+00 l_d_fake: 1.1853e+00 D_real: -1.0318e-01 D_fake: 6.6006e-01 +20-04-07 09:47:16.494 - INFO: l_g_pix: 6.5942e-05 l_g_fea: 3.7990e-01 l_g_gan: 1.8674e-02 l_d_real: 3.2804e-02 l_d_fake: 3.4311e-02 D_real: 1.0226e+01 D_fake: 6.5243e+00 +20-04-07 09:50:28.376 - INFO: l_g_pix: 7.9373e-05 l_g_fea: 3.6859e-01 l_g_gan: 5.0994e-03 l_d_real: 5.9646e-01 l_d_fake: 5.8527e-01 D_real: 3.5151e+01 D_fake: 3.4722e+01 +20-04-07 09:53:40.888 - INFO: l_g_pix: 1.1746e-04 l_g_fea: 4.6359e-01 l_g_gan: 1.7267e-02 l_d_real: 3.8728e-02 l_d_fake: 3.8779e-02 D_real: 1.4292e+01 D_fake: 1.0878e+01 +20-04-07 09:56:53.518 - INFO: l_g_pix: 1.1346e-04 l_g_fea: 5.1021e-01 l_g_gan: 1.0591e-02 l_d_real: 1.5308e-01 l_d_fake: 1.4865e-01 D_real: 2.2008e+01 D_fake: 2.0041e+01 +20-04-07 10:00:05.419 - INFO: l_g_pix: 9.8030e-05 l_g_fea: 4.8559e-01 l_g_gan: 8.3009e-03 l_d_real: 2.4399e-01 l_d_fake: 2.5215e-01 D_real: 1.1235e+01 D_fake: 9.8228e+00 +20-04-07 10:03:17.932 - INFO: l_g_pix: 9.5566e-05 l_g_fea: 4.2739e-01 l_g_gan: 7.5171e-03 l_d_real: 3.2032e-01 l_d_fake: 3.0355e-01 D_real: 2.4896e+01 D_fake: 2.3704e+01 +20-04-07 10:06:31.183 - INFO: l_g_pix: 7.2529e-05 l_g_fea: 3.5129e-01 l_g_gan: 1.0571e-02 l_d_real: 1.5947e-01 l_d_fake: 1.4582e-01 D_real: 3.4020e+01 D_fake: 3.2059e+01 +20-04-07 10:09:44.241 - INFO: l_g_pix: 9.4893e-05 l_g_fea: 4.6386e-01 l_g_gan: 7.7420e-03 l_d_real: 3.1473e-01 l_d_fake: 2.8743e-01 D_real: 1.9788e+01 D_fake: 1.8540e+01 +20-04-07 10:12:57.594 - INFO: l_g_pix: 9.8753e-05 l_g_fea: 4.4839e-01 l_g_gan: 2.3476e-02 l_d_real: 1.0178e-02 l_d_fake: 1.0445e-02 D_real: 2.0222e+01 D_fake: 1.5537e+01 +20-04-07 10:16:09.724 - INFO: l_g_pix: 9.5883e-05 l_g_fea: 4.0532e-01 l_g_gan: 5.7187e-03 l_d_real: 4.7043e-01 l_d_fake: 4.6292e-01 D_real: 6.5518e+00 D_fake: 5.8748e+00 +20-04-07 10:19:22.922 - INFO: l_g_pix: 9.4438e-05 l_g_fea: 4.1331e-01 l_g_gan: 2.0038e-02 l_d_real: 2.5897e-02 l_d_fake: 2.5157e-02 D_real: 4.6517e+01 D_fake: 4.2535e+01 +20-04-07 10:22:35.875 - INFO: l_g_pix: 9.5421e-05 l_g_fea: 4.6481e-01 l_g_gan: 9.7475e-03 l_d_real: 2.0200e-01 l_d_fake: 2.0346e-01 D_real: 2.7010e+01 D_fake: 2.5264e+01 +20-04-07 10:25:48.239 - INFO: l_g_pix: 7.5124e-05 l_g_fea: 4.4416e-01 l_g_gan: 6.8819e-03 l_d_real: 3.6047e-01 l_d_fake: 3.4967e-01 D_real: 2.2461e+01 D_fake: 2.1440e+01 +20-04-07 10:29:00.346 - INFO: l_g_pix: 9.9089e-05 l_g_fea: 5.0949e-01 l_g_gan: 1.1458e-02 l_d_real: 1.4639e-01 l_d_fake: 1.6083e-01 D_real: 3.8258e+01 D_fake: 3.6120e+01 +20-04-07 10:32:12.961 - INFO: l_g_pix: 1.1005e-04 l_g_fea: 4.4848e-01 l_g_gan: 1.1792e-02 l_d_real: 1.1354e-01 l_d_fake: 1.1685e-01 D_real: 3.7430e+01 D_fake: 3.5187e+01 +20-04-07 10:35:25.157 - INFO: l_g_pix: 8.9775e-05 l_g_fea: 4.1255e-01 l_g_gan: 9.3715e-03 l_d_real: 2.1301e-01 l_d_fake: 2.0710e-01 D_real: 3.9651e+01 D_fake: 3.7987e+01 +20-04-07 10:38:37.690 - INFO: l_g_pix: 1.1649e-04 l_g_fea: 3.8812e-01 l_g_gan: 6.9457e-03 l_d_real: 3.6710e-01 l_d_fake: 3.6490e-01 D_real: 2.9676e+01 D_fake: 2.8652e+01 +20-04-07 10:41:50.240 - INFO: l_g_pix: 8.8930e-05 l_g_fea: 3.8771e-01 l_g_gan: 4.7152e-03 l_d_real: 5.8545e-01 l_d_fake: 5.5386e-01 D_real: 2.1122e+01 D_fake: 2.0749e+01 +20-04-07 10:45:03.111 - INFO: l_g_pix: 1.0340e-04 l_g_fea: 5.1716e-01 l_g_gan: 4.3587e-03 l_d_real: 6.3389e-01 l_d_fake: 6.3692e-01 D_real: 4.6496e+01 D_fake: 4.6259e+01 +20-04-07 10:48:14.873 - INFO: l_g_pix: 9.1910e-05 l_g_fea: 3.6459e-01 l_g_gan: 6.4850e-03 l_d_real: 3.7289e-01 l_d_fake: 3.7336e-01 D_real: 1.2655e+01 D_fake: 1.1732e+01 +20-04-07 10:48:15.381 - INFO: Models and training states saved. +20-04-07 10:49:15.157 - INFO: # Validation # PSNR: 31.364, SSIM: 0.84553, LPIPS: 0.023954 +20-04-07 10:49:15.158 - INFO: psnr: 31.364, ssim: 0.84553, lpips: 0.023954 +20-04-07 10:52:45.341 - INFO: l_g_pix: 8.2879e-05 l_g_fea: 3.2852e-01 l_g_gan: 2.1426e-02 l_d_real: 1.5666e-02 l_d_fake: 1.7615e-02 D_real: 1.6149e+01 D_fake: 1.1881e+01 +20-04-07 10:55:57.029 - INFO: l_g_pix: 9.9942e-05 l_g_fea: 4.7307e-01 l_g_gan: 1.1642e-02 l_d_real: 1.2442e-01 l_d_fake: 1.1849e-01 D_real: 4.0542e+01 D_fake: 3.8335e+01 +20-04-07 10:59:08.390 - INFO: l_g_pix: 1.1339e-04 l_g_fea: 4.6661e-01 l_g_gan: 1.2417e-02 l_d_real: 1.1841e-01 l_d_fake: 1.1615e-01 D_real: 4.2421e+01 D_fake: 4.0055e+01 +20-04-07 11:02:20.219 - INFO: l_g_pix: 9.9349e-05 l_g_fea: 4.4408e-01 l_g_gan: 2.2585e-02 l_d_real: 1.3256e-02 l_d_fake: 1.2515e-02 D_real: 3.2422e+01 D_fake: 2.7918e+01 +20-04-07 11:05:32.369 - INFO: l_g_pix: 7.5578e-05 l_g_fea: 3.6739e-01 l_g_gan: 2.5507e-03 l_d_real: 1.0307e+00 l_d_fake: 1.0279e+00 D_real: 2.5773e+01 D_fake: 2.6292e+01 +20-04-07 11:08:44.145 - INFO: l_g_pix: 8.8843e-05 l_g_fea: 3.5055e-01 l_g_gan: 3.3983e-03 l_d_real: 8.2352e-01 l_d_fake: 8.3397e-01 D_real: 2.9109e+01 D_fake: 2.9258e+01 +20-04-07 11:11:55.742 - INFO: l_g_pix: 9.0275e-05 l_g_fea: 5.0068e-01 l_g_gan: 1.1984e-02 l_d_real: 1.3508e-01 l_d_fake: 1.3336e-01 D_real: 3.8266e+01 D_fake: 3.6003e+01 +20-04-07 11:15:07.242 - INFO: l_g_pix: 1.1533e-04 l_g_fea: 5.1001e-01 l_g_gan: 2.2104e-02 l_d_real: 1.5201e-02 l_d_fake: 1.6533e-02 D_real: 2.1970e+01 D_fake: 1.7565e+01 +20-04-07 11:18:19.150 - INFO: l_g_pix: 1.1368e-04 l_g_fea: 5.1358e-01 l_g_gan: 1.7114e-02 l_d_real: 3.9160e-02 l_d_fake: 4.0653e-02 D_real: 4.5397e+01 D_fake: 4.2014e+01 +20-04-07 11:21:30.599 - INFO: l_g_pix: 9.4392e-05 l_g_fea: 4.2684e-01 l_g_gan: 1.0681e-02 l_d_real: 1.6480e-01 l_d_fake: 1.4984e-01 D_real: 2.4691e+01 D_fake: 2.2712e+01 +20-04-07 11:24:42.649 - INFO: l_g_pix: 9.1887e-05 l_g_fea: 3.2583e-01 l_g_gan: 6.5429e-03 l_d_real: 3.9103e-01 l_d_fake: 3.6512e-01 D_real: 2.5689e+01 D_fake: 2.4758e+01 +20-04-07 11:27:55.090 - INFO: l_g_pix: 7.3119e-05 l_g_fea: 3.6253e-01 l_g_gan: 9.4104e-03 l_d_real: 1.9482e-01 l_d_fake: 2.1471e-01 D_real: 2.4256e+01 D_fake: 2.2579e+01 +20-04-07 11:31:07.509 - INFO: l_g_pix: 9.3996e-05 l_g_fea: 4.1526e-01 l_g_gan: 1.6995e-02 l_d_real: 4.0008e-02 l_d_fake: 4.0672e-02 D_real: 2.9726e+01 D_fake: 2.6368e+01 +20-04-07 11:34:18.788 - INFO: l_g_pix: 6.5882e-05 l_g_fea: 3.5133e-01 l_g_gan: 1.7889e-02 l_d_real: 3.7391e-02 l_d_fake: 3.8203e-02 D_real: 2.3532e+01 D_fake: 1.9993e+01 +20-04-07 11:37:30.465 - INFO: l_g_pix: 7.1957e-05 l_g_fea: 3.4219e-01 l_g_gan: 1.1075e-03 l_d_real: 1.8287e+00 l_d_fake: 1.8218e+00 D_real: 2.1073e+01 D_fake: 2.2677e+01 +20-04-07 11:40:42.195 - INFO: l_g_pix: 1.2306e-04 l_g_fea: 5.9424e-01 l_g_gan: 1.0204e-02 l_d_real: 1.7020e-01 l_d_fake: 1.9274e-01 D_real: 3.3198e+01 D_fake: 3.1339e+01 +20-04-07 11:43:54.531 - INFO: l_g_pix: 1.1678e-04 l_g_fea: 4.4220e-01 l_g_gan: 1.9825e-04 l_d_real: 3.5994e+00 l_d_fake: 3.5925e+00 D_real: 4.1232e+01 D_fake: 4.4788e+01 +20-04-07 11:47:06.925 - INFO: l_g_pix: 1.0992e-04 l_g_fea: 4.2166e-01 l_g_gan: 1.1604e-02 l_d_real: 1.2336e-01 l_d_fake: 1.3012e-01 D_real: 2.3545e+01 D_fake: 2.1351e+01 +20-04-07 11:50:19.043 - INFO: l_g_pix: 8.1852e-05 l_g_fea: 4.1141e-01 l_g_gan: 5.0032e-03 l_d_real: 5.5996e-01 l_d_fake: 5.5306e-01 D_real: 2.5118e+01 D_fake: 2.4674e+01 +20-04-07 11:53:31.054 - INFO: l_g_pix: 1.1323e-04 l_g_fea: 4.6997e-01 l_g_gan: 1.5002e-02 l_d_real: 5.6030e-02 l_d_fake: 5.9501e-02 D_real: 3.6442e+01 D_fake: 3.3499e+01 +20-04-07 11:56:42.172 - INFO: l_g_pix: 1.0293e-04 l_g_fea: 4.2306e-01 l_g_gan: 1.4680e-02 l_d_real: 6.6534e-02 l_d_fake: 6.6514e-02 D_real: 3.2828e+01 D_fake: 2.9959e+01 +20-04-07 11:59:53.216 - INFO: l_g_pix: 7.0059e-05 l_g_fea: 3.8631e-01 l_g_gan: 1.6418e-02 l_d_real: 5.1513e-02 l_d_fake: 5.3048e-02 D_real: 2.2724e+01 D_fake: 1.9492e+01 +20-04-07 12:03:04.658 - INFO: l_g_pix: 1.0075e-04 l_g_fea: 4.0776e-01 l_g_gan: 1.6815e-02 l_d_real: 4.7525e-02 l_d_fake: 4.7198e-02 D_real: 3.4346e+01 D_fake: 3.1030e+01 +20-04-07 12:06:16.726 - INFO: l_g_pix: 9.5210e-05 l_g_fea: 5.1122e-01 l_g_gan: 3.8052e-03 l_d_real: 8.0014e-01 l_d_fake: 7.9286e-01 D_real: 2.2072e+01 D_fake: 2.2108e+01 +20-04-07 12:09:29.551 - INFO: l_g_pix: 1.0554e-04 l_g_fea: 4.8036e-01 l_g_gan: 1.2884e-02 l_d_real: 1.4127e-01 l_d_fake: 1.0674e-01 D_real: 4.3700e+01 D_fake: 4.1247e+01 +20-04-07 12:09:29.972 - INFO: Models and training states saved. +20-04-07 12:10:31.371 - INFO: # Validation # PSNR: 31.463, SSIM: 0.83763, LPIPS: 0.023403 +20-04-07 12:10:31.371 - INFO: psnr: 31.463, ssim: 0.83763, lpips: 0.023403 +20-04-07 12:14:46.232 - INFO: l_g_pix: 6.6944e-05 l_g_fea: 3.4570e-01 l_g_gan: 9.4820e-03 l_d_real: 2.0235e-01 l_d_fake: 2.0236e-01 D_real: 1.4156e+01 D_fake: 1.2462e+01 +20-04-07 12:17:58.219 - INFO: l_g_pix: 9.7275e-05 l_g_fea: 4.3650e-01 l_g_gan: 1.2502e-02 l_d_real: 9.8519e-02 l_d_fake: 9.1800e-02 D_real: 3.1271e+01 D_fake: 2.8866e+01 +20-04-07 12:21:09.474 - INFO: l_g_pix: 9.4252e-05 l_g_fea: 4.0811e-01 l_g_gan: 1.6860e-03 l_d_real: 1.4447e+00 l_d_fake: 1.4655e+00 D_real: 3.3764e+01 D_fake: 3.4881e+01 +20-04-07 12:24:26.092 - INFO: l_g_pix: 1.0552e-04 l_g_fea: 4.4131e-01 l_g_gan: 7.4951e-03 l_d_real: 3.0299e-01 l_d_fake: 3.0304e-01 D_real: 2.8565e+01 D_fake: 2.7369e+01 +20-04-07 12:27:37.585 - INFO: l_g_pix: 1.1292e-04 l_g_fea: 4.2530e-01 l_g_gan: 3.8104e-03 l_d_real: 7.2902e-01 l_d_fake: 7.4166e-01 D_real: 1.1728e+01 D_fake: 1.1701e+01 +20-04-07 12:30:49.446 - INFO: l_g_pix: 9.9493e-05 l_g_fea: 4.5631e-01 l_g_gan: 1.0819e-02 l_d_real: 1.5098e-01 l_d_fake: 1.5441e-01 D_real: 3.6212e+01 D_fake: 3.4201e+01 +20-04-07 12:34:01.522 - INFO: l_g_pix: 1.1165e-04 l_g_fea: 4.9014e-01 l_g_gan: 3.9878e-03 l_d_real: 7.1279e-01 l_d_fake: 6.8520e-01 D_real: 2.9922e+01 D_fake: 2.9823e+01 +20-04-07 12:37:12.692 - INFO: l_g_pix: 1.0290e-04 l_g_fea: 4.8901e-01 l_g_gan: 7.7681e-03 l_d_real: 2.6965e-01 l_d_fake: 2.7524e-01 D_real: 2.5978e+01 D_fake: 2.4697e+01 +20-04-07 12:40:25.017 - INFO: l_g_pix: 7.3741e-05 l_g_fea: 4.6494e-01 l_g_gan: 6.1365e-03 l_d_real: 3.9710e-01 l_d_fake: 4.0507e-01 D_real: 1.4852e+01 D_fake: 1.4026e+01 +20-04-07 12:43:37.943 - INFO: l_g_pix: 8.8406e-05 l_g_fea: 4.0725e-01 l_g_gan: 4.6126e-03 l_d_real: 6.2826e-01 l_d_fake: 6.1316e-01 D_real: 3.2600e+01 D_fake: 3.2298e+01 +20-04-07 12:46:49.747 - INFO: l_g_pix: 7.5715e-05 l_g_fea: 4.2811e-01 l_g_gan: 1.0489e-02 l_d_real: 1.5970e-01 l_d_fake: 1.7152e-01 D_real: 2.4410e+01 D_fake: 2.2478e+01 +20-04-07 12:50:00.357 - INFO: l_g_pix: 1.0122e-04 l_g_fea: 4.6225e-01 l_g_gan: 8.7208e-03 l_d_real: 2.2630e-01 l_d_fake: 2.3654e-01 D_real: 4.0839e+01 D_fake: 3.9326e+01 +20-04-07 12:53:11.244 - INFO: l_g_pix: 9.0528e-05 l_g_fea: 4.4979e-01 l_g_gan: 1.2651e-02 l_d_real: 1.0913e-01 l_d_fake: 1.1093e-01 D_real: 2.9823e+01 D_fake: 2.7403e+01 +20-04-07 12:56:23.146 - INFO: l_g_pix: 6.3453e-05 l_g_fea: 3.7228e-01 l_g_gan: 9.0104e-03 l_d_real: 2.5367e-01 l_d_fake: 2.1869e-01 D_real: 2.2114e+01 D_fake: 2.0548e+01 +20-04-07 12:59:36.600 - INFO: l_g_pix: 1.0771e-04 l_g_fea: 5.1117e-01 l_g_gan: 1.4369e-02 l_d_real: 7.0227e-02 l_d_fake: 7.2031e-02 D_real: 3.5305e+01 D_fake: 3.2502e+01 +20-04-07 13:02:48.849 - INFO: l_g_pix: 7.4884e-05 l_g_fea: 3.2920e-01 l_g_gan: 7.1110e-03 l_d_real: 3.0972e-01 l_d_fake: 3.1012e-01 D_real: 3.4450e+01 D_fake: 3.3337e+01 +20-04-07 13:06:00.953 - INFO: l_g_pix: 1.1385e-04 l_g_fea: 4.8766e-01 l_g_gan: 9.5682e-03 l_d_real: 2.1184e-01 l_d_fake: 1.9503e-01 D_real: 3.6152e+01 D_fake: 3.4442e+01 +20-04-07 13:09:13.444 - INFO: l_g_pix: 1.0228e-04 l_g_fea: 5.0675e-01 l_g_gan: 5.5294e-03 l_d_real: 5.3693e-01 l_d_fake: 5.0171e-01 D_real: 2.5905e+01 D_fake: 2.5319e+01 +20-04-07 13:12:25.169 - INFO: l_g_pix: 1.1154e-04 l_g_fea: 4.4534e-01 l_g_gan: 8.3613e-03 l_d_real: 2.7003e-01 l_d_fake: 2.4710e-01 D_real: 3.0340e+01 D_fake: 2.8926e+01 +20-04-07 13:15:36.683 - INFO: l_g_pix: 7.0946e-05 l_g_fea: 3.7711e-01 l_g_gan: 9.6007e-03 l_d_real: 2.0968e-01 l_d_fake: 1.9609e-01 D_real: 1.5107e+01 D_fake: 1.3390e+01 +20-04-07 13:18:48.464 - INFO: l_g_pix: 9.8937e-05 l_g_fea: 5.1688e-01 l_g_gan: 9.5067e-03 l_d_real: 1.8608e-01 l_d_fake: 2.0479e-01 D_real: 3.1094e+01 D_fake: 2.9388e+01 +20-04-07 13:22:00.073 - INFO: l_g_pix: 8.6331e-05 l_g_fea: 3.8574e-01 l_g_gan: 5.3188e-03 l_d_real: 5.1919e-01 l_d_fake: 5.5023e-01 D_real: 2.1309e+01 D_fake: 2.0780e+01 +20-04-07 13:25:11.553 - INFO: l_g_pix: 9.0672e-05 l_g_fea: 4.6434e-01 l_g_gan: 6.8860e-03 l_d_real: 3.1806e-01 l_d_fake: 3.4738e-01 D_real: 2.6790e+01 D_fake: 2.5746e+01 +20-04-07 13:28:23.294 - INFO: l_g_pix: 1.4364e-04 l_g_fea: 6.2525e-01 l_g_gan: 4.8031e-03 l_d_real: 5.3803e-01 l_d_fake: 5.5028e-01 D_real: 3.5310e+01 D_fake: 3.4893e+01 +20-04-07 13:31:35.408 - INFO: l_g_pix: 7.6676e-05 l_g_fea: 3.9788e-01 l_g_gan: 5.1645e-03 l_d_real: 5.2515e-01 l_d_fake: 4.9977e-01 D_real: 2.3583e+01 D_fake: 2.3062e+01 +20-04-07 13:31:35.872 - INFO: Models and training states saved. +20-04-07 13:32:33.653 - INFO: # Validation # PSNR: 31.631, SSIM: 0.83805, LPIPS: 0.027015 +20-04-07 13:32:33.653 - INFO: psnr: 31.631, ssim: 0.83805, lpips: 0.027015 +20-04-07 13:40:48.052 - INFO: l_g_pix: 9.0580e-05 l_g_fea: 4.2270e-01 l_g_gan: 1.1333e-02 l_d_real: 1.2466e-01 l_d_fake: 1.2228e-01 D_real: 3.0760e+01 D_fake: 2.8617e+01 +20-04-07 13:45:57.918 - INFO: l_g_pix: 1.0541e-04 l_g_fea: 5.5393e-01 l_g_gan: 1.3598e-02 l_d_real: 9.3595e-02 l_d_fake: 1.1036e-01 D_real: 3.8454e+01 D_fake: 3.5836e+01 +20-04-07 13:49:08.413 - INFO: l_g_pix: 1.1669e-04 l_g_fea: 3.6093e-01 l_g_gan: 1.1197e-02 l_d_real: 1.5662e-01 l_d_fake: 1.5907e-01 D_real: 2.0935e+01 D_fake: 1.8854e+01 +20-04-07 13:52:20.689 - INFO: l_g_pix: 8.0906e-05 l_g_fea: 3.0305e-01 l_g_gan: 1.8443e-04 l_d_real: 3.5671e+00 l_d_fake: 3.5601e+00 D_real: 1.4598e+01 D_fake: 1.8125e+01 +20-04-07 13:55:32.611 - INFO: l_g_pix: 7.5993e-05 l_g_fea: 3.5010e-01 l_g_gan: 2.7375e-02 l_d_real: 5.0356e-03 l_d_fake: 5.3377e-03 D_real: 1.7660e+01 D_fake: 1.2190e+01 +20-04-07 13:58:44.925 - INFO: l_g_pix: 1.1176e-04 l_g_fea: 4.0670e-01 l_g_gan: 2.2275e-02 l_d_real: 1.3051e-02 l_d_fake: 1.3815e-02 D_real: 2.1869e+01 D_fake: 1.7428e+01 +20-04-07 14:01:57.423 - INFO: l_g_pix: 1.2746e-04 l_g_fea: 4.9047e-01 l_g_gan: 1.0182e-02 l_d_real: 1.5667e-01 l_d_fake: 1.6591e-01 D_real: 3.1878e+01 D_fake: 3.0003e+01 +20-04-07 14:05:09.472 - INFO: l_g_pix: 8.3994e-05 l_g_fea: 3.9411e-01 l_g_gan: 3.7968e-03 l_d_real: 7.2147e-01 l_d_fake: 7.3298e-01 D_real: 2.5822e+01 D_fake: 2.5790e+01 +20-04-07 14:08:21.464 - INFO: l_g_pix: 8.4097e-05 l_g_fea: 3.6136e-01 l_g_gan: 2.6596e-03 l_d_real: 9.8415e-01 l_d_fake: 9.7989e-01 D_real: 4.7086e+01 D_fake: 4.7536e+01 +20-04-07 14:11:33.467 - INFO: l_g_pix: 8.7866e-05 l_g_fea: 4.3794e-01 l_g_gan: 6.1711e-03 l_d_real: 3.7053e-01 l_d_fake: 3.7945e-01 D_real: 1.2914e+01 D_fake: 1.2054e+01 +20-04-07 14:14:45.480 - INFO: l_g_pix: 9.0478e-05 l_g_fea: 4.4545e-01 l_g_gan: 8.4489e-03 l_d_real: 2.3567e-01 l_d_fake: 2.3975e-01 D_real: 3.6142e+01 D_fake: 3.4690e+01 +20-04-07 14:17:57.325 - INFO: l_g_pix: 1.1720e-04 l_g_fea: 5.1620e-01 l_g_gan: 8.6992e-03 l_d_real: 2.4092e-01 l_d_fake: 2.5496e-01 D_real: 3.8899e+01 D_fake: 3.7407e+01 +20-04-07 14:21:09.473 - INFO: l_g_pix: 1.1007e-04 l_g_fea: 5.3916e-01 l_g_gan: 1.3818e-02 l_d_real: 8.4154e-02 l_d_fake: 9.8056e-02 D_real: 3.7459e+01 D_fake: 3.4787e+01 +20-04-07 14:24:22.069 - INFO: l_g_pix: 9.5396e-05 l_g_fea: 4.6185e-01 l_g_gan: 6.3402e-03 l_d_real: 3.7334e-01 l_d_fake: 3.8494e-01 D_real: 2.4689e+01 D_fake: 2.3800e+01 +20-04-07 14:27:34.300 - INFO: l_g_pix: 6.0420e-05 l_g_fea: 3.1626e-01 l_g_gan: 1.9170e-03 l_d_real: 1.3116e+00 l_d_fake: 1.3370e+00 D_real: 3.2844e+01 D_fake: 3.3785e+01 +20-04-07 14:30:46.464 - INFO: l_g_pix: 8.0755e-05 l_g_fea: 4.0057e-01 l_g_gan: 6.9277e-03 l_d_real: 3.6250e-01 l_d_fake: 3.2479e-01 D_real: 3.9448e+01 D_fake: 3.8407e+01 +20-04-07 14:33:58.837 - INFO: l_g_pix: 1.0180e-04 l_g_fea: 4.6539e-01 l_g_gan: 2.8820e-02 l_d_real: 3.5819e-03 l_d_fake: 5.3803e-03 D_real: 3.7810e+01 D_fake: 3.2050e+01 +20-04-07 14:38:07.860 - INFO: l_g_pix: 8.3773e-05 l_g_fea: 4.7273e-01 l_g_gan: 9.6163e-03 l_d_real: 2.0654e-01 l_d_fake: 1.9251e-01 D_real: 2.1346e+01 D_fake: 1.9622e+01 +20-04-07 14:41:20.436 - INFO: l_g_pix: 1.1222e-04 l_g_fea: 5.5688e-01 l_g_gan: 1.0183e-02 l_d_real: 1.6122e-01 l_d_fake: 1.7187e-01 D_real: 2.8802e+01 D_fake: 2.6932e+01 +20-04-07 14:44:33.023 - INFO: l_g_pix: 7.0673e-05 l_g_fea: 3.2529e-01 l_g_gan: 1.1710e-02 l_d_real: 1.2449e-01 l_d_fake: 1.2850e-01 D_real: 9.4079e+00 D_fake: 7.1925e+00 +20-04-07 14:47:45.352 - INFO: l_g_pix: 5.9993e-05 l_g_fea: 3.4770e-01 l_g_gan: 1.0668e-02 l_d_real: 1.4966e-01 l_d_fake: 1.4856e-01 D_real: 1.7019e+01 D_fake: 1.5035e+01 +20-04-07 14:50:58.032 - INFO: l_g_pix: 1.2040e-04 l_g_fea: 4.5151e-01 l_g_gan: 3.7589e-02 l_d_real: 7.0049e-04 l_d_fake: 6.3966e-04 D_real: 2.1838e+01 D_fake: 1.4320e+01 +20-04-07 14:54:10.834 - INFO: l_g_pix: 6.3848e-05 l_g_fea: 3.5864e-01 l_g_gan: 1.6564e-02 l_d_real: 5.0309e-02 l_d_fake: 4.7521e-02 D_real: 3.2347e+01 D_fake: 2.9083e+01 +20-04-07 14:57:22.522 - INFO: l_g_pix: 9.3500e-05 l_g_fea: 3.5867e-01 l_g_gan: 1.5960e-02 l_d_real: 5.2033e-02 l_d_fake: 5.3254e-02 D_real: 2.8989e+01 D_fake: 2.5849e+01 +20-04-07 15:00:33.987 - INFO: l_g_pix: 1.0288e-04 l_g_fea: 4.8087e-01 l_g_gan: 3.9139e-03 l_d_real: 6.9550e-01 l_d_fake: 6.8559e-01 D_real: 4.0920e+01 D_fake: 4.0827e+01 +20-04-07 15:00:34.415 - INFO: Models and training states saved. +20-04-07 15:01:34.929 - INFO: # Validation # PSNR: 32.057, SSIM: 0.84512, LPIPS: 0.024246 +20-04-07 15:01:34.929 - INFO: psnr: 32.057, ssim: 0.84512, lpips: 0.024246 +20-04-07 15:06:11.533 - INFO: l_g_pix: 5.1245e-05 l_g_fea: 2.8254e-01 l_g_gan: 1.9991e-03 l_d_real: 1.2649e+00 l_d_fake: 1.2347e+00 D_real: 2.1577e+01 D_fake: 2.2427e+01 +20-04-07 15:09:27.324 - INFO: l_g_pix: 9.8226e-05 l_g_fea: 4.9589e-01 l_g_gan: 2.5041e-03 l_d_real: 1.1368e+00 l_d_fake: 1.1262e+00 D_real: 2.4883e+01 D_fake: 2.5514e+01 +20-04-07 15:12:39.115 - INFO: l_g_pix: 1.2057e-04 l_g_fea: 3.9914e-01 l_g_gan: 1.5284e-02 l_d_real: 6.2513e-02 l_d_fake: 6.2099e-02 D_real: 3.9698e+01 D_fake: 3.6703e+01 +20-04-07 15:15:50.692 - INFO: l_g_pix: 1.0053e-04 l_g_fea: 5.0052e-01 l_g_gan: 1.2524e-02 l_d_real: 1.0209e-01 l_d_fake: 1.0973e-01 D_real: 1.7333e+01 D_fake: 1.4934e+01 +20-04-07 15:19:02.287 - INFO: l_g_pix: 8.2831e-05 l_g_fea: 4.1148e-01 l_g_gan: 2.4288e-02 l_d_real: 9.2700e-03 l_d_fake: 8.8981e-03 D_real: 3.9314e+01 D_fake: 3.4465e+01 +20-04-07 15:22:50.328 - INFO: l_g_pix: 1.1742e-04 l_g_fea: 5.6078e-01 l_g_gan: 2.5743e-03 l_d_real: 1.0519e+00 l_d_fake: 1.0840e+00 D_real: 4.1176e+01 D_fake: 4.1729e+01 +20-04-07 15:26:54.300 - INFO: l_g_pix: 9.8655e-05 l_g_fea: 4.1459e-01 l_g_gan: 8.2367e-03 l_d_real: 2.6951e-01 l_d_fake: 2.4861e-01 D_real: 2.6426e+01 D_fake: 2.5038e+01 +20-04-07 15:30:22.315 - INFO: l_g_pix: 6.3891e-05 l_g_fea: 4.0496e-01 l_g_gan: 1.1657e-02 l_d_real: 1.3519e-01 l_d_fake: 1.2902e-01 D_real: 2.9192e+01 D_fake: 2.6992e+01 +20-04-07 15:33:33.236 - INFO: l_g_pix: 7.2063e-05 l_g_fea: 3.6682e-01 l_g_gan: 5.2254e-03 l_d_real: 5.1358e-01 l_d_fake: 4.7505e-01 D_real: 2.1264e+01 D_fake: 2.0713e+01 +20-04-07 15:36:43.771 - INFO: l_g_pix: 1.0386e-04 l_g_fea: 4.4958e-01 l_g_gan: 3.0003e-03 l_d_real: 1.0305e+00 l_d_fake: 1.0035e+00 D_real: 2.7183e+01 D_fake: 2.7600e+01 +20-04-07 15:39:54.556 - INFO: l_g_pix: 1.0725e-04 l_g_fea: 4.5832e-01 l_g_gan: 1.6849e-02 l_d_real: 4.1772e-02 l_d_fake: 4.3872e-02 D_real: 2.5787e+01 D_fake: 2.2460e+01 +20-04-07 15:43:06.235 - INFO: l_g_pix: 1.0239e-04 l_g_fea: 4.6965e-01 l_g_gan: 1.2206e-02 l_d_real: 1.2090e-01 l_d_fake: 1.0796e-01 D_real: 3.3550e+01 D_fake: 3.1224e+01 +20-04-07 15:46:17.943 - INFO: l_g_pix: 9.3479e-05 l_g_fea: 4.7225e-01 l_g_gan: 7.7724e-04 l_d_real: 2.1382e+00 l_d_fake: 2.1496e+00 D_real: 1.4656e+01 D_fake: 1.6645e+01 +20-04-07 15:49:29.297 - INFO: l_g_pix: 8.3955e-05 l_g_fea: 4.0774e-01 l_g_gan: 1.9919e-02 l_d_real: 2.3665e-02 l_d_fake: 2.6989e-02 D_real: 2.8606e+01 D_fake: 2.4647e+01 +20-04-07 15:52:40.114 - INFO: l_g_pix: 9.3537e-05 l_g_fea: 4.4557e-01 l_g_gan: 9.6048e-03 l_d_real: 2.1936e-01 l_d_fake: 2.0952e-01 D_real: 2.7453e+01 D_fake: 2.5747e+01 +20-04-07 15:55:51.946 - INFO: l_g_pix: 1.0116e-04 l_g_fea: 4.2437e-01 l_g_gan: 6.7699e-03 l_d_real: 3.7058e-01 l_d_fake: 3.7539e-01 D_real: 2.2521e+01 D_fake: 2.1540e+01 +20-04-07 15:59:03.143 - INFO: l_g_pix: 8.1032e-05 l_g_fea: 4.2376e-01 l_g_gan: 9.0527e-03 l_d_real: 2.1384e-01 l_d_fake: 2.1470e-01 D_real: 3.4532e+01 D_fake: 3.2936e+01 +20-04-07 16:02:14.199 - INFO: l_g_pix: 9.0082e-05 l_g_fea: 4.3028e-01 l_g_gan: 4.6354e-03 l_d_real: 5.5442e-01 l_d_fake: 5.5105e-01 D_real: 3.3938e+01 D_fake: 3.3564e+01 +20-04-07 16:05:25.992 - INFO: l_g_pix: 8.6470e-05 l_g_fea: 3.6774e-01 l_g_gan: 9.4842e-03 l_d_real: 2.0730e-01 l_d_fake: 1.9062e-01 D_real: 2.8398e+01 D_fake: 2.6700e+01 +20-04-07 16:08:42.100 - INFO: l_g_pix: 1.1564e-04 l_g_fea: 4.6854e-01 l_g_gan: 8.9240e-03 l_d_real: 2.1626e-01 l_d_fake: 2.2582e-01 D_real: 4.3142e+01 D_fake: 4.1578e+01 +20-04-07 16:11:54.049 - INFO: l_g_pix: 1.1125e-04 l_g_fea: 5.3311e-01 l_g_gan: 1.1438e-02 l_d_real: 1.4572e-01 l_d_fake: 1.3327e-01 D_real: 4.3429e+01 D_fake: 4.1281e+01 +20-04-07 16:15:06.074 - INFO: l_g_pix: 1.2365e-04 l_g_fea: 5.4282e-01 l_g_gan: 6.6732e-03 l_d_real: 3.6019e-01 l_d_fake: 4.0348e-01 D_real: 3.8542e+01 D_fake: 3.7590e+01 +20-04-07 16:18:17.847 - INFO: l_g_pix: 1.1207e-04 l_g_fea: 4.7452e-01 l_g_gan: 8.1590e-04 l_d_real: 2.0953e+00 l_d_fake: 2.0965e+00 D_real: 1.9449e+01 D_fake: 2.1382e+01 +20-04-07 16:21:29.187 - INFO: l_g_pix: 1.0994e-04 l_g_fea: 5.0470e-01 l_g_gan: 1.1114e-02 l_d_real: 1.5746e-01 l_d_fake: 1.5597e-01 D_real: 3.7759e+01 D_fake: 3.5693e+01 +20-04-07 16:24:41.178 - INFO: l_g_pix: 1.2216e-04 l_g_fea: 5.5299e-01 l_g_gan: 6.1253e-03 l_d_real: 4.3166e-01 l_d_fake: 4.2286e-01 D_real: 4.3451e+01 D_fake: 4.2653e+01 +20-04-07 16:24:41.650 - INFO: Models and training states saved. +20-04-07 16:25:40.187 - INFO: # Validation # PSNR: 31.849, SSIM: 0.84396, LPIPS: 0.024639 +20-04-07 16:25:40.187 - INFO: psnr: 31.849, ssim: 0.84396, lpips: 0.024639 +20-04-07 16:30:04.372 - INFO: l_g_pix: 9.8676e-05 l_g_fea: 3.4117e-01 l_g_gan: 1.3082e-02 l_d_real: 9.5615e-02 l_d_fake: 9.2269e-02 D_real: 1.8200e+01 D_fake: 1.5678e+01 +20-04-07 16:33:15.505 - INFO: l_g_pix: 6.9538e-05 l_g_fea: 3.2228e-01 l_g_gan: 8.8284e-03 l_d_real: 2.1709e-01 l_d_fake: 2.1696e-01 D_real: 3.8394e+01 D_fake: 3.6845e+01 +20-04-07 16:36:26.409 - INFO: l_g_pix: 1.1860e-04 l_g_fea: 4.4812e-01 l_g_gan: 1.3674e-02 l_d_real: 9.6391e-02 l_d_fake: 8.4878e-02 D_real: 2.7703e+01 D_fake: 2.5058e+01 +20-04-07 16:39:37.566 - INFO: l_g_pix: 8.0222e-05 l_g_fea: 4.2493e-01 l_g_gan: 1.2043e-02 l_d_real: 1.3261e-01 l_d_fake: 1.1012e-01 D_real: 1.2840e+01 D_fake: 1.0553e+01 +20-04-07 16:42:48.853 - INFO: l_g_pix: 8.1887e-05 l_g_fea: 4.0560e-01 l_g_gan: 1.8502e-02 l_d_real: 2.8695e-02 l_d_fake: 4.4068e-02 D_real: 4.0540e+01 D_fake: 3.6876e+01 +20-04-07 16:46:00.398 - INFO: l_g_pix: 7.8996e-05 l_g_fea: 4.8778e-01 l_g_gan: 2.5560e-02 l_d_real: 7.4642e-03 l_d_fake: 7.0362e-03 D_real: 2.4847e+01 D_fake: 1.9742e+01 +20-04-07 16:49:10.694 - INFO: l_g_pix: 1.0944e-04 l_g_fea: 4.9865e-01 l_g_gan: 5.7008e-03 l_d_real: 4.5043e-01 l_d_fake: 4.5039e-01 D_real: 2.7259e+01 D_fake: 2.6569e+01 +20-04-07 16:52:21.327 - INFO: l_g_pix: 8.4202e-05 l_g_fea: 3.8755e-01 l_g_gan: 1.3417e-02 l_d_real: 8.2254e-02 l_d_fake: 8.5424e-02 D_real: 1.7272e+01 D_fake: 1.4673e+01 +20-04-07 16:55:32.253 - INFO: l_g_pix: 1.3614e-04 l_g_fea: 5.4424e-01 l_g_gan: 4.6155e-03 l_d_real: 5.7691e-01 l_d_fake: 5.5867e-01 D_real: 2.8361e+01 D_fake: 2.8005e+01 +20-04-07 16:58:42.819 - INFO: l_g_pix: 8.3941e-05 l_g_fea: 4.5526e-01 l_g_gan: 1.3851e-03 l_d_real: 1.5769e+00 l_d_fake: 1.5779e+00 D_real: 2.9285e+01 D_fake: 3.0586e+01 +20-04-07 17:01:53.899 - INFO: l_g_pix: 8.3124e-05 l_g_fea: 4.0161e-01 l_g_gan: 4.7192e-03 l_d_real: 5.7271e-01 l_d_fake: 5.7105e-01 D_real: 3.1229e+01 D_fake: 3.0857e+01 +20-04-07 17:05:05.510 - INFO: l_g_pix: 1.3124e-04 l_g_fea: 5.1385e-01 l_g_gan: 1.3898e-02 l_d_real: 9.2157e-02 l_d_fake: 8.3659e-02 D_real: 3.3171e+01 D_fake: 3.0479e+01 +20-04-07 17:08:16.823 - INFO: l_g_pix: 1.1213e-04 l_g_fea: 5.7789e-01 l_g_gan: 4.2839e-03 l_d_real: 6.6161e-01 l_d_fake: 6.5234e-01 D_real: 3.3328e+01 D_fake: 3.3128e+01 +20-04-07 17:11:27.456 - INFO: l_g_pix: 1.0978e-04 l_g_fea: 5.3182e-01 l_g_gan: 3.0520e-03 l_d_real: 9.0835e-01 l_d_fake: 9.1905e-01 D_real: 3.2713e+01 D_fake: 3.3016e+01 +20-04-07 17:14:38.662 - INFO: l_g_pix: 1.2019e-04 l_g_fea: 4.9053e-01 l_g_gan: 5.7149e-03 l_d_real: 4.8401e-01 l_d_fake: 4.9036e-01 D_real: 3.0760e+01 D_fake: 3.0104e+01 +20-04-07 17:17:50.664 - INFO: l_g_pix: 1.0205e-04 l_g_fea: 4.6593e-01 l_g_gan: 1.5951e-02 l_d_real: 5.0595e-02 l_d_fake: 5.9689e-02 D_real: 4.0081e+01 D_fake: 3.6946e+01 +20-04-07 17:21:01.975 - INFO: l_g_pix: 1.2577e-04 l_g_fea: 4.3714e-01 l_g_gan: 1.8721e-02 l_d_real: 3.3249e-02 l_d_fake: 3.0710e-02 D_real: 3.2542e+01 D_fake: 2.8830e+01 +20-04-07 17:24:13.411 - INFO: l_g_pix: 5.9717e-05 l_g_fea: 3.3259e-01 l_g_gan: 1.7725e-02 l_d_real: 5.2116e-02 l_d_fake: 3.3015e-02 D_real: 2.4460e+01 D_fake: 2.0958e+01 +20-04-07 17:27:25.200 - INFO: l_g_pix: 9.3599e-05 l_g_fea: 5.0970e-01 l_g_gan: 1.5282e-03 l_d_real: 1.4373e+00 l_d_fake: 1.4483e+00 D_real: 2.4450e+01 D_fake: 2.5587e+01 +20-04-07 17:30:35.898 - INFO: l_g_pix: 1.2870e-04 l_g_fea: 5.7838e-01 l_g_gan: 1.7076e-02 l_d_real: 5.0353e-02 l_d_fake: 4.8279e-02 D_real: 2.7001e+01 D_fake: 2.3635e+01 +20-04-07 17:33:47.230 - INFO: l_g_pix: 1.0053e-04 l_g_fea: 4.8570e-01 l_g_gan: 2.1487e-02 l_d_real: 1.5658e-02 l_d_fake: 1.6305e-02 D_real: 3.2383e+01 D_fake: 2.8101e+01 +20-04-07 17:36:58.183 - INFO: l_g_pix: 9.5170e-05 l_g_fea: 4.3688e-01 l_g_gan: 7.1476e-03 l_d_real: 3.2548e-01 l_d_fake: 3.5117e-01 D_real: 2.9422e+01 D_fake: 2.8331e+01 +20-04-07 17:40:09.400 - INFO: l_g_pix: 1.1957e-04 l_g_fea: 5.0894e-01 l_g_gan: 5.6440e-03 l_d_real: 4.5347e-01 l_d_fake: 4.6968e-01 D_real: 3.9242e+01 D_fake: 3.8575e+01 +20-04-07 17:43:21.380 - INFO: l_g_pix: 9.6244e-05 l_g_fea: 5.1917e-01 l_g_gan: 1.1117e-02 l_d_real: 1.4526e-01 l_d_fake: 1.5000e-01 D_real: 3.0448e+01 D_fake: 2.8372e+01 +20-04-07 17:46:32.161 - INFO: l_g_pix: 6.9062e-05 l_g_fea: 3.5123e-01 l_g_gan: 3.1156e-03 l_d_real: 8.4602e-01 l_d_fake: 8.4158e-01 D_real: 2.2646e+01 D_fake: 2.2866e+01 +20-04-07 17:46:32.557 - INFO: Models and training states saved. +20-04-07 17:47:27.326 - INFO: # Validation # PSNR: 31.943, SSIM: 0.8444, LPIPS: 0.028102 +20-04-07 17:47:27.326 - INFO: psnr: 31.943, ssim: 0.8444, lpips: 0.028102 +20-04-07 17:50:37.293 - INFO: l_g_pix: 7.6954e-05 l_g_fea: 4.1097e-01 l_g_gan: 3.3836e-03 l_d_real: 7.9339e-01 l_d_fake: 7.9193e-01 D_real: 3.4921e+01 D_fake: 3.5037e+01 +20-04-07 17:53:49.316 - INFO: l_g_pix: 1.0182e-04 l_g_fea: 5.0430e-01 l_g_gan: 5.2247e-03 l_d_real: 5.1385e-01 l_d_fake: 5.2875e-01 D_real: 3.8354e+01 D_fake: 3.7830e+01 +20-04-07 17:57:00.239 - INFO: l_g_pix: 8.7728e-05 l_g_fea: 4.1311e-01 l_g_gan: 3.1908e-04 l_d_real: 2.9599e+00 l_d_fake: 2.9527e+00 D_real: 1.8166e+01 D_fake: 2.1059e+01 +20-04-07 18:00:12.269 - INFO: l_g_pix: 1.0471e-04 l_g_fea: 4.3955e-01 l_g_gan: 1.3201e-02 l_d_real: 9.5834e-02 l_d_fake: 8.9744e-02 D_real: 2.7069e+01 D_fake: 2.4522e+01 +20-04-07 18:03:24.005 - INFO: l_g_pix: 1.1839e-04 l_g_fea: 5.1045e-01 l_g_gan: 1.5503e-02 l_d_real: 6.2844e-02 l_d_fake: 5.7861e-02 D_real: 3.0942e+01 D_fake: 2.7902e+01 +20-04-07 18:06:35.832 - INFO: l_g_pix: 6.2202e-05 l_g_fea: 3.1855e-01 l_g_gan: 1.6087e-02 l_d_real: 4.7133e-02 l_d_fake: 4.7545e-02 D_real: 2.1548e+01 D_fake: 1.8378e+01 +20-04-07 18:09:46.723 - INFO: l_g_pix: 1.0527e-04 l_g_fea: 4.8053e-01 l_g_gan: 9.0375e-03 l_d_real: 2.1400e-01 l_d_fake: 2.2893e-01 D_real: 2.5057e+01 D_fake: 2.3471e+01 +20-04-07 18:12:57.271 - INFO: l_g_pix: 1.4204e-04 l_g_fea: 5.4411e-01 l_g_gan: 1.2986e-02 l_d_real: 8.9645e-02 l_d_fake: 9.3136e-02 D_real: 4.3784e+01 D_fake: 4.1278e+01 +20-04-07 18:16:08.257 - INFO: l_g_pix: 9.1400e-05 l_g_fea: 3.6386e-01 l_g_gan: 2.6704e-02 l_d_real: 6.0674e-03 l_d_fake: 5.4170e-03 D_real: 9.2962e+00 D_fake: 3.9611e+00 +20-04-07 18:19:19.649 - INFO: l_g_pix: 9.6573e-05 l_g_fea: 4.1436e-01 l_g_gan: 5.8153e-03 l_d_real: 4.8951e-01 l_d_fake: 4.4117e-01 D_real: 2.2717e+01 D_fake: 2.2019e+01 +20-04-07 18:22:30.565 - INFO: l_g_pix: 1.2926e-04 l_g_fea: 4.8405e-01 l_g_gan: 1.3534e-02 l_d_real: 8.2658e-02 l_d_fake: 9.0213e-02 D_real: 2.4989e+01 D_fake: 2.2369e+01 +20-04-07 18:25:41.918 - INFO: l_g_pix: 1.0747e-04 l_g_fea: 5.2787e-01 l_g_gan: 5.6249e-03 l_d_real: 4.7149e-01 l_d_fake: 5.0319e-01 D_real: 3.1641e+01 D_fake: 3.1003e+01 +20-04-07 18:28:53.433 - INFO: l_g_pix: 1.0755e-04 l_g_fea: 4.0169e-01 l_g_gan: 7.4276e-03 l_d_real: 3.0485e-01 l_d_fake: 3.3254e-01 D_real: 3.0266e+01 D_fake: 2.9099e+01 +20-04-07 18:32:05.034 - INFO: l_g_pix: 9.1004e-05 l_g_fea: 4.2183e-01 l_g_gan: 1.4581e-02 l_d_real: 7.4015e-02 l_d_fake: 8.6964e-02 D_real: 2.5930e+01 D_fake: 2.3095e+01 +20-04-07 18:35:16.512 - INFO: l_g_pix: 6.5625e-05 l_g_fea: 4.1639e-01 l_g_gan: 6.4830e-03 l_d_real: 4.1377e-01 l_d_fake: 4.0200e-01 D_real: 2.7630e+01 D_fake: 2.6741e+01 +20-04-07 18:38:28.723 - INFO: l_g_pix: 1.0459e-04 l_g_fea: 3.7008e-01 l_g_gan: 8.1936e-04 l_d_real: 2.0170e+00 l_d_fake: 2.0224e+00 D_real: 2.6130e+01 D_fake: 2.7986e+01 +20-04-07 18:41:40.580 - INFO: l_g_pix: 1.1399e-04 l_g_fea: 5.9013e-01 l_g_gan: 2.5934e-02 l_d_real: 8.0628e-03 l_d_fake: 1.7761e-02 D_real: 2.7596e+01 D_fake: 2.2422e+01 +20-04-07 18:44:52.490 - INFO: l_g_pix: 1.1663e-04 l_g_fea: 4.5589e-01 l_g_gan: 2.0812e-02 l_d_real: 2.0213e-02 l_d_fake: 1.8383e-02 D_real: 4.2647e+01 D_fake: 3.8504e+01 +20-04-07 18:48:04.250 - INFO: l_g_pix: 8.1726e-05 l_g_fea: 4.7411e-01 l_g_gan: 1.2279e-02 l_d_real: 1.2625e-01 l_d_fake: 1.0832e-01 D_real: 2.9043e+01 D_fake: 2.6705e+01 +20-04-07 18:51:15.108 - INFO: l_g_pix: 1.3593e-04 l_g_fea: 5.1073e-01 l_g_gan: 7.4029e-03 l_d_real: 3.0818e-01 l_d_fake: 2.9497e-01 D_real: 3.1562e+01 D_fake: 3.0383e+01 +20-04-07 18:54:26.799 - INFO: l_g_pix: 8.3563e-05 l_g_fea: 4.3034e-01 l_g_gan: 8.2446e-03 l_d_real: 2.4504e-01 l_d_fake: 2.5387e-01 D_real: 4.0524e+01 D_fake: 3.9124e+01 +20-04-07 18:57:38.603 - INFO: l_g_pix: 7.6552e-05 l_g_fea: 3.8877e-01 l_g_gan: 5.6990e-03 l_d_real: 4.7371e-01 l_d_fake: 5.3225e-01 D_real: 1.5481e+01 D_fake: 1.4845e+01 +20-04-07 19:00:50.422 - INFO: l_g_pix: 9.6802e-05 l_g_fea: 4.5356e-01 l_g_gan: 1.1252e-02 l_d_real: 1.3195e-01 l_d_fake: 1.3670e-01 D_real: 2.7101e+01 D_fake: 2.4985e+01 +20-04-07 19:04:01.457 - INFO: l_g_pix: 7.1933e-05 l_g_fea: 3.9736e-01 l_g_gan: 4.1583e-03 l_d_real: 7.2279e-01 l_d_fake: 7.4141e-01 D_real: 3.6044e+01 D_fake: 3.5945e+01 +20-04-07 19:07:13.579 - INFO: l_g_pix: 9.5022e-05 l_g_fea: 4.7074e-01 l_g_gan: 2.2702e-03 l_d_real: 1.1801e+00 l_d_fake: 1.1700e+00 D_real: 3.4208e+01 D_fake: 3.4929e+01 +20-04-07 19:07:13.999 - INFO: Models and training states saved. +20-04-07 19:08:05.807 - INFO: # Validation # PSNR: 32.13, SSIM: 0.8461, LPIPS: 0.02568 +20-04-07 19:08:05.814 - INFO: psnr: 32.13, ssim: 0.8461, lpips: 0.02568 +20-04-07 19:11:28.786 - INFO: l_g_pix: 8.3560e-05 l_g_fea: 4.1845e-01 l_g_gan: 7.9292e-03 l_d_real: 3.0285e-01 l_d_fake: 2.8518e-01 D_real: 2.6463e+01 D_fake: 2.5171e+01 +20-04-07 19:14:39.996 - INFO: l_g_pix: 1.1696e-04 l_g_fea: 4.2248e-01 l_g_gan: 8.0834e-03 l_d_real: 2.9204e-01 l_d_fake: 2.7150e-01 D_real: 4.1010e+01 D_fake: 3.9675e+01 +20-04-07 19:17:52.104 - INFO: l_g_pix: 7.8222e-05 l_g_fea: 3.3616e-01 l_g_gan: 4.8567e-03 l_d_real: 5.6003e-01 l_d_fake: 5.7240e-01 D_real: 2.9129e+01 D_fake: 2.8724e+01 +20-04-07 19:21:03.977 - INFO: l_g_pix: 9.3314e-05 l_g_fea: 4.6948e-01 l_g_gan: 2.1300e-02 l_d_real: 1.7013e-02 l_d_fake: 2.4001e-02 D_real: 1.9592e+01 D_fake: 1.5352e+01 +20-04-07 19:24:15.083 - INFO: l_g_pix: 7.2488e-05 l_g_fea: 3.8808e-01 l_g_gan: 2.1817e-03 l_d_real: 1.2212e+00 l_d_fake: 1.2321e+00 D_real: 3.4278e+01 D_fake: 3.5068e+01 +20-04-07 19:27:26.626 - INFO: l_g_pix: 1.1780e-04 l_g_fea: 4.9246e-01 l_g_gan: 2.2485e-04 l_d_real: 3.4025e+00 l_d_fake: 3.4081e+00 D_real: 2.6653e+01 D_fake: 3.0014e+01 +20-04-07 19:30:38.623 - INFO: l_g_pix: 1.0561e-04 l_g_fea: 4.4306e-01 l_g_gan: 2.0104e-02 l_d_real: 2.0334e-02 l_d_fake: 2.5510e-02 D_real: 4.1678e+01 D_fake: 3.7680e+01 +20-04-07 19:33:50.697 - INFO: l_g_pix: 6.9429e-05 l_g_fea: 2.9071e-01 l_g_gan: 2.8332e-03 l_d_real: 1.0433e+00 l_d_fake: 1.0201e+00 D_real: 2.0289e+01 D_fake: 2.0754e+01 +20-04-07 19:37:02.444 - INFO: l_g_pix: 1.0129e-04 l_g_fea: 4.9022e-01 l_g_gan: 9.0155e-03 l_d_real: 2.3675e-01 l_d_fake: 2.3134e-01 D_real: 3.7774e+01 D_fake: 3.6205e+01 +20-04-07 19:40:18.650 - INFO: l_g_pix: 8.8748e-05 l_g_fea: 4.5619e-01 l_g_gan: 1.4950e-02 l_d_real: 7.0270e-02 l_d_fake: 6.5246e-02 D_real: 2.8982e+01 D_fake: 2.6059e+01 +20-04-07 19:43:30.286 - INFO: l_g_pix: 9.2392e-05 l_g_fea: 4.3746e-01 l_g_gan: 7.7942e-03 l_d_real: 2.8861e-01 l_d_fake: 2.8898e-01 D_real: 2.1736e+01 D_fake: 2.0466e+01 +20-04-07 19:46:42.272 - INFO: l_g_pix: 9.4903e-05 l_g_fea: 3.9869e-01 l_g_gan: 1.3455e-02 l_d_real: 8.9390e-02 l_d_fake: 9.0075e-02 D_real: 3.0802e+01 D_fake: 2.8201e+01 +20-04-07 19:49:53.102 - INFO: l_g_pix: 9.4612e-05 l_g_fea: 3.8958e-01 l_g_gan: 3.9085e-03 l_d_real: 7.0639e-01 l_d_fake: 7.0566e-01 D_real: 2.1570e+01 D_fake: 2.1494e+01 +20-04-07 19:53:03.881 - INFO: l_g_pix: 1.0475e-04 l_g_fea: 3.7780e-01 l_g_gan: 1.4340e-02 l_d_real: 6.8385e-02 l_d_fake: 7.9615e-02 D_real: 4.2069e+01 D_fake: 3.9275e+01 +20-04-07 19:56:15.849 - INFO: l_g_pix: 1.1953e-04 l_g_fea: 5.2948e-01 l_g_gan: 1.1130e-02 l_d_real: 1.5288e-01 l_d_fake: 1.6229e-01 D_real: 1.9367e+01 D_fake: 1.7298e+01 +20-04-07 19:59:27.078 - INFO: l_g_pix: 1.0738e-04 l_g_fea: 4.5661e-01 l_g_gan: 1.7715e-02 l_d_real: 3.4582e-02 l_d_fake: 3.7126e-02 D_real: 2.4454e+01 D_fake: 2.0947e+01 +20-04-07 20:02:38.500 - INFO: l_g_pix: 9.1945e-05 l_g_fea: 3.9619e-01 l_g_gan: 1.0441e-02 l_d_real: 1.6512e-01 l_d_fake: 1.5718e-01 D_real: 2.9271e+01 D_fake: 2.7344e+01 +20-04-07 20:05:49.566 - INFO: l_g_pix: 9.9937e-05 l_g_fea: 4.5514e-01 l_g_gan: 1.0026e-02 l_d_real: 1.9927e-01 l_d_fake: 1.8089e-01 D_real: 2.5795e+01 D_fake: 2.3980e+01 +20-04-07 20:09:00.574 - INFO: l_g_pix: 7.4262e-05 l_g_fea: 3.3719e-01 l_g_gan: 2.8434e-02 l_d_real: 7.1477e-03 l_d_fake: 4.3835e-03 D_real: 2.4421e+01 D_fake: 1.8740e+01 +20-04-07 20:12:12.101 - INFO: l_g_pix: 9.9998e-05 l_g_fea: 5.0195e-01 l_g_gan: 2.2441e-02 l_d_real: 1.4703e-02 l_d_fake: 1.3904e-02 D_real: 2.7444e+01 D_fake: 2.2970e+01 +20-04-07 20:15:24.084 - INFO: l_g_pix: 1.1971e-04 l_g_fea: 3.5201e-01 l_g_gan: 4.1418e-03 l_d_real: 6.7856e-01 l_d_fake: 6.9086e-01 D_real: 2.9607e+01 D_fake: 2.9464e+01 +20-04-07 20:18:35.068 - INFO: l_g_pix: 7.8480e-05 l_g_fea: 3.8197e-01 l_g_gan: 4.0696e-04 l_d_real: 2.8875e+00 l_d_fake: 2.9170e+00 D_real: 1.0397e+01 D_fake: 1.3217e+01 +20-04-07 20:21:47.024 - INFO: l_g_pix: 1.0729e-04 l_g_fea: 4.7206e-01 l_g_gan: 1.2269e-02 l_d_real: 1.3573e-01 l_d_fake: 1.1545e-01 D_real: 3.9527e+01 D_fake: 3.7199e+01 +20-04-07 20:24:58.720 - INFO: l_g_pix: 1.0679e-04 l_g_fea: 4.8982e-01 l_g_gan: 1.5566e-03 l_d_real: 1.4924e+00 l_d_fake: 1.4999e+00 D_real: 2.2515e+01 D_fake: 2.3700e+01 +20-04-07 20:28:09.774 - INFO: l_g_pix: 1.2704e-04 l_g_fea: 5.2917e-01 l_g_gan: 3.1115e-03 l_d_real: 8.7584e-01 l_d_fake: 8.7018e-01 D_real: 3.0579e+01 D_fake: 3.0830e+01 +20-04-07 20:28:10.202 - INFO: Models and training states saved. +20-04-07 20:29:06.859 - INFO: # Validation # PSNR: 32.041, SSIM: 0.84476, LPIPS: 0.023769 +20-04-07 20:29:06.859 - INFO: psnr: 32.041, ssim: 0.84476, lpips: 0.023769 +20-04-07 20:32:18.467 - INFO: l_g_pix: 5.8310e-05 l_g_fea: 2.7034e-01 l_g_gan: 1.2289e-02 l_d_real: 1.4870e-01 l_d_fake: 1.1804e-01 D_real: 2.6985e+01 D_fake: 2.4661e+01 +20-04-07 20:35:29.339 - INFO: l_g_pix: 6.3148e-05 l_g_fea: 2.9127e-01 l_g_gan: 1.0177e-02 l_d_real: 1.7663e-01 l_d_fake: 2.0343e-01 D_real: 2.5238e+01 D_fake: 2.3392e+01 +20-04-07 20:38:40.783 - INFO: l_g_pix: 1.0143e-04 l_g_fea: 3.9370e-01 l_g_gan: 1.2175e-02 l_d_real: 1.0122e-01 l_d_fake: 1.0141e-01 D_real: 3.0438e+01 D_fake: 2.8104e+01 +20-04-07 20:41:52.698 - INFO: l_g_pix: 5.9956e-05 l_g_fea: 3.1488e-01 l_g_gan: 3.9779e-02 l_d_real: 5.0421e-04 l_d_fake: 4.8432e-04 D_real: 2.4736e+01 D_fake: 1.6781e+01 +20-04-07 20:45:04.440 - INFO: l_g_pix: 1.0649e-04 l_g_fea: 4.8138e-01 l_g_gan: 1.7994e-02 l_d_real: 3.3278e-02 l_d_fake: 3.9145e-02 D_real: 3.4273e+01 D_fake: 3.0710e+01 +20-04-07 20:48:15.851 - INFO: l_g_pix: 7.1833e-05 l_g_fea: 3.6634e-01 l_g_gan: 6.1071e-03 l_d_real: 4.0678e-01 l_d_fake: 4.0752e-01 D_real: 1.4098e+01 D_fake: 1.3284e+01 +20-04-07 20:51:27.325 - INFO: l_g_pix: 1.1193e-04 l_g_fea: 4.4400e-01 l_g_gan: 5.1187e-03 l_d_real: 5.1190e-01 l_d_fake: 4.9649e-01 D_real: 2.9990e+01 D_fake: 2.9470e+01 +20-04-07 20:54:38.896 - INFO: l_g_pix: 1.1063e-04 l_g_fea: 4.1924e-01 l_g_gan: 9.7044e-03 l_d_real: 1.8453e-01 l_d_fake: 1.9808e-01 D_real: 3.0034e+01 D_fake: 2.8284e+01 +20-04-07 20:57:50.402 - INFO: l_g_pix: 8.1616e-05 l_g_fea: 3.9076e-01 l_g_gan: 1.7288e-02 l_d_real: 4.0091e-02 l_d_fake: 3.7733e-02 D_real: 2.7968e+01 D_fake: 2.4549e+01 +20-04-07 21:01:01.413 - INFO: l_g_pix: 8.3220e-05 l_g_fea: 4.1197e-01 l_g_gan: 3.2840e-03 l_d_real: 9.4116e-01 l_d_fake: 9.0787e-01 D_real: 3.5790e+01 D_fake: 3.6057e+01 +20-04-07 21:04:12.475 - INFO: l_g_pix: 1.2207e-04 l_g_fea: 4.9121e-01 l_g_gan: 3.2301e-03 l_d_real: 8.9133e-01 l_d_fake: 9.1661e-01 D_real: 2.5881e+01 D_fake: 2.6139e+01 +20-04-07 21:07:23.457 - INFO: l_g_pix: 6.8172e-05 l_g_fea: 2.9918e-01 l_g_gan: 1.7810e-02 l_d_real: 6.4436e-02 l_d_fake: 3.6725e-02 D_real: 2.3038e+01 D_fake: 1.9527e+01 +20-04-07 21:10:35.569 - INFO: l_g_pix: 1.1284e-04 l_g_fea: 4.1606e-01 l_g_gan: 6.8965e-03 l_d_real: 3.3833e-01 l_d_fake: 3.3826e-01 D_real: 2.8275e+01 D_fake: 2.7234e+01 +20-04-07 21:13:47.616 - INFO: l_g_pix: 1.0014e-04 l_g_fea: 4.6978e-01 l_g_gan: 1.0474e-02 l_d_real: 1.5888e-01 l_d_fake: 1.6965e-01 D_real: 3.4504e+01 D_fake: 3.2573e+01 +20-04-07 21:16:59.624 - INFO: l_g_pix: 1.3352e-04 l_g_fea: 4.6706e-01 l_g_gan: 1.6899e-02 l_d_real: 4.1626e-02 l_d_fake: 4.8306e-02 D_real: 4.0917e+01 D_fake: 3.7582e+01 +20-04-07 21:20:11.433 - INFO: l_g_pix: 9.1390e-05 l_g_fea: 3.6267e-01 l_g_gan: 2.4042e-03 l_d_real: 1.1090e+00 l_d_fake: 1.1361e+00 D_real: 1.8142e+01 D_fake: 1.8784e+01 +20-04-07 21:23:23.301 - INFO: l_g_pix: 1.1038e-04 l_g_fea: 4.6540e-01 l_g_gan: 6.3451e-03 l_d_real: 3.9797e-01 l_d_fake: 4.2215e-01 D_real: 2.7722e+01 D_fake: 2.6863e+01 +20-04-07 21:26:34.379 - INFO: l_g_pix: 1.2162e-04 l_g_fea: 4.5500e-01 l_g_gan: 4.7140e-03 l_d_real: 5.7030e-01 l_d_fake: 6.1013e-01 D_real: 3.0707e+01 D_fake: 3.0354e+01 +20-04-07 21:29:46.227 - INFO: l_g_pix: 6.3005e-05 l_g_fea: 3.6671e-01 l_g_gan: 9.7121e-03 l_d_real: 2.0902e-01 l_d_fake: 1.9750e-01 D_real: 3.3854e+01 D_fake: 3.2114e+01 +20-04-07 21:32:58.090 - INFO: l_g_pix: 1.1260e-04 l_g_fea: 4.4964e-01 l_g_gan: 1.0077e-02 l_d_real: 1.6449e-01 l_d_fake: 1.6456e-01 D_real: 3.0979e+01 D_fake: 2.9128e+01 +20-04-07 21:36:09.641 - INFO: l_g_pix: 9.8192e-05 l_g_fea: 3.8170e-01 l_g_gan: 6.2681e-03 l_d_real: 4.0908e-01 l_d_fake: 3.8902e-01 D_real: 2.6164e+01 D_fake: 2.5310e+01 +20-04-07 21:39:21.025 - INFO: l_g_pix: 1.0162e-04 l_g_fea: 4.0255e-01 l_g_gan: 9.0290e-03 l_d_real: 2.3760e-01 l_d_fake: 2.4260e-01 D_real: 2.4688e+01 D_fake: 2.3122e+01 +20-04-07 21:42:32.039 - INFO: l_g_pix: 9.9809e-05 l_g_fea: 4.6978e-01 l_g_gan: 1.4436e-02 l_d_real: 8.1643e-02 l_d_fake: 7.0104e-02 D_real: 3.2504e+01 D_fake: 2.9692e+01 +20-04-07 21:45:44.089 - INFO: l_g_pix: 6.1121e-05 l_g_fea: 3.3223e-01 l_g_gan: 4.9468e-03 l_d_real: 5.5564e-01 l_d_fake: 5.2871e-01 D_real: 1.4495e+01 D_fake: 1.4048e+01 +20-04-07 21:48:55.390 - INFO: l_g_pix: 7.4798e-05 l_g_fea: 3.6924e-01 l_g_gan: 8.9544e-04 l_d_real: 2.0445e+00 l_d_fake: 2.0456e+00 D_real: 2.7657e+01 D_fake: 2.9523e+01 +20-04-07 21:48:55.804 - INFO: Models and training states saved. +20-04-07 21:49:50.670 - INFO: # Validation # PSNR: 31.886, SSIM: 0.84322, LPIPS: 0.025586 +20-04-07 21:49:50.670 - INFO: psnr: 31.886, ssim: 0.84322, lpips: 0.025586 +20-04-07 21:53:02.658 - INFO: l_g_pix: 8.0706e-05 l_g_fea: 4.3821e-01 l_g_gan: 8.7131e-03 l_d_real: 3.1515e-01 l_d_fake: 2.3129e-01 D_real: 2.9818e+01 D_fake: 2.8348e+01 +20-04-07 21:56:14.292 - INFO: l_g_pix: 1.1031e-04 l_g_fea: 5.0855e-01 l_g_gan: 1.5546e-02 l_d_real: 5.9424e-02 l_d_fake: 5.6108e-02 D_real: 2.5635e+01 D_fake: 2.2584e+01 +20-04-07 21:59:26.251 - INFO: l_g_pix: 1.0366e-04 l_g_fea: 5.5940e-01 l_g_gan: 1.9172e-02 l_d_real: 2.9933e-02 l_d_fake: 2.9775e-02 D_real: 4.3171e+01 D_fake: 3.9366e+01 +20-04-07 22:02:37.571 - INFO: l_g_pix: 1.0762e-04 l_g_fea: 4.7432e-01 l_g_gan: 1.1089e-02 l_d_real: 1.4264e-01 l_d_fake: 1.4709e-01 D_real: 4.6878e+01 D_fake: 4.4805e+01 +20-04-07 22:05:49.061 - INFO: l_g_pix: 8.2196e-05 l_g_fea: 4.2335e-01 l_g_gan: 9.0676e-03 l_d_real: 2.0660e-01 l_d_fake: 1.9797e-01 D_real: 1.8454e+01 D_fake: 1.6843e+01 +20-04-07 22:09:00.923 - INFO: l_g_pix: 1.0522e-04 l_g_fea: 4.2620e-01 l_g_gan: 1.5508e-02 l_d_real: 5.9129e-02 l_d_fake: 6.6552e-02 D_real: 3.4069e+00 D_fake: 3.6813e-01 +20-04-07 22:12:12.059 - INFO: l_g_pix: 8.0422e-05 l_g_fea: 3.1535e-01 l_g_gan: 2.0326e-02 l_d_real: 2.6421e-02 l_d_fake: 5.2593e-02 D_real: 1.6168e+01 D_fake: 1.2142e+01 +20-04-07 22:15:23.801 - INFO: l_g_pix: 9.1209e-05 l_g_fea: 4.2236e-01 l_g_gan: 1.9813e-02 l_d_real: 2.4283e-02 l_d_fake: 2.4644e-02 D_real: 3.2311e+01 D_fake: 2.8372e+01 +20-04-07 22:18:35.679 - INFO: l_g_pix: 8.4341e-05 l_g_fea: 3.1792e-01 l_g_gan: 2.2706e-02 l_d_real: 1.4554e-02 l_d_fake: 1.2504e-02 D_real: 3.0981e+01 D_fake: 2.6453e+01 +20-04-07 22:21:46.968 - INFO: l_g_pix: 8.1220e-05 l_g_fea: 3.7835e-01 l_g_gan: 9.7600e-03 l_d_real: 1.8332e-01 l_d_fake: 1.8931e-01 D_real: 2.3845e+01 D_fake: 2.2080e+01 +20-04-07 22:24:58.678 - INFO: l_g_pix: 1.1100e-04 l_g_fea: 5.6664e-01 l_g_gan: 1.0372e-02 l_d_real: 1.6884e-01 l_d_fake: 1.6570e-01 D_real: 3.0521e+01 D_fake: 2.8614e+01 +20-04-07 22:28:09.332 - INFO: l_g_pix: 1.0193e-04 l_g_fea: 4.2672e-01 l_g_gan: 4.6703e-03 l_d_real: 6.3406e-01 l_d_fake: 6.2405e-01 D_real: 2.4035e+01 D_fake: 2.3730e+01 +20-04-07 22:31:21.078 - INFO: l_g_pix: 9.3796e-05 l_g_fea: 4.4951e-01 l_g_gan: 4.7886e-03 l_d_real: 5.5087e-01 l_d_fake: 5.7380e-01 D_real: 2.7911e+01 D_fake: 2.7516e+01 +20-04-07 22:34:32.831 - INFO: l_g_pix: 8.8041e-05 l_g_fea: 3.5116e-01 l_g_gan: 5.0752e-03 l_d_real: 4.9872e-01 l_d_fake: 5.5730e-01 D_real: 3.0002e+01 D_fake: 2.9515e+01 +20-04-07 22:37:44.789 - INFO: l_g_pix: 7.6639e-05 l_g_fea: 3.3324e-01 l_g_gan: 1.3468e-02 l_d_real: 7.8235e-02 l_d_fake: 8.8610e-02 D_real: 1.3072e+01 D_fake: 1.0462e+01 +20-04-07 22:40:56.432 - INFO: l_g_pix: 9.0122e-05 l_g_fea: 3.9430e-01 l_g_gan: 1.4775e-02 l_d_real: 7.1941e-02 l_d_fake: 6.2065e-02 D_real: 2.8872e+01 D_fake: 2.5983e+01 +20-04-07 22:44:08.648 - INFO: l_g_pix: 6.4775e-05 l_g_fea: 3.6094e-01 l_g_gan: 8.0736e-03 l_d_real: 2.8749e-01 l_d_fake: 2.5553e-01 D_real: 2.5255e+01 D_fake: 2.3912e+01 +20-04-07 22:47:20.849 - INFO: l_g_pix: 9.8523e-05 l_g_fea: 4.8959e-01 l_g_gan: 7.8474e-03 l_d_real: 2.7885e-01 l_d_fake: 2.8645e-01 D_real: 2.5966e+01 D_fake: 2.4679e+01 +20-04-07 22:50:32.457 - INFO: l_g_pix: 1.0816e-04 l_g_fea: 4.5201e-01 l_g_gan: 1.6447e-02 l_d_real: 4.4796e-02 l_d_fake: 5.1144e-02 D_real: 2.6471e+01 D_fake: 2.3229e+01 +20-04-07 22:53:44.405 - INFO: l_g_pix: 7.9282e-05 l_g_fea: 4.8799e-01 l_g_gan: 1.0372e-02 l_d_real: 1.9136e-01 l_d_fake: 1.6952e-01 D_real: 2.3231e+01 D_fake: 2.1337e+01 +20-04-07 22:56:55.536 - INFO: l_g_pix: 7.2884e-05 l_g_fea: 4.3137e-01 l_g_gan: 2.1959e-02 l_d_real: 1.6598e-02 l_d_fake: 1.4384e-02 D_real: 2.7442e+01 D_fake: 2.3065e+01 +20-04-07 23:00:07.729 - INFO: l_g_pix: 9.5787e-05 l_g_fea: 3.9156e-01 l_g_gan: 8.4159e-04 l_d_real: 2.0204e+00 l_d_fake: 2.0297e+00 D_real: 2.5396e+01 D_fake: 2.7253e+01 +20-04-07 23:03:19.324 - INFO: l_g_pix: 8.6494e-05 l_g_fea: 4.8363e-01 l_g_gan: 3.3739e-03 l_d_real: 8.1928e-01 l_d_fake: 8.3423e-01 D_real: 3.5831e+01 D_fake: 3.5983e+01 +20-04-07 23:06:31.032 - INFO: l_g_pix: 1.1967e-04 l_g_fea: 5.7388e-01 l_g_gan: 6.0027e-03 l_d_real: 4.3500e-01 l_d_fake: 4.4540e-01 D_real: 2.2151e+01 D_fake: 2.1390e+01 +20-04-07 23:09:43.156 - INFO: l_g_pix: 7.6197e-05 l_g_fea: 3.2369e-01 l_g_gan: 1.0404e-02 l_d_real: 1.6382e-01 l_d_fake: 1.5396e-01 D_real: 1.7797e+01 D_fake: 1.5875e+01 +20-04-07 23:09:43.501 - INFO: Models and training states saved. +20-04-07 23:10:40.927 - INFO: # Validation # PSNR: 32.004, SSIM: 0.84224, LPIPS: 0.020475 +20-04-07 23:10:40.927 - INFO: psnr: 32.004, ssim: 0.84224, lpips: 0.020475 +20-04-07 23:13:59.128 - INFO: l_g_pix: 1.2233e-04 l_g_fea: 4.9128e-01 l_g_gan: 1.6789e-02 l_d_real: 4.5569e-02 l_d_fake: 5.1444e-02 D_real: 3.9817e+01 D_fake: 3.6508e+01 +20-04-07 23:17:10.427 - INFO: l_g_pix: 8.8728e-05 l_g_fea: 4.5852e-01 l_g_gan: 1.5141e-02 l_d_real: 6.5468e-02 l_d_fake: 6.8849e-02 D_real: 4.0945e+01 D_fake: 3.7983e+01 +20-04-07 23:20:22.410 - INFO: l_g_pix: 5.7834e-05 l_g_fea: 2.8393e-01 l_g_gan: 1.6670e-02 l_d_real: 4.4082e-02 l_d_fake: 4.0157e-02 D_real: 2.4120e+01 D_fake: 2.0828e+01 +20-04-07 23:23:33.536 - INFO: l_g_pix: 9.9895e-05 l_g_fea: 5.0382e-01 l_g_gan: 3.6484e-03 l_d_real: 7.9083e-01 l_d_fake: 7.9202e-01 D_real: 2.8578e+01 D_fake: 2.8640e+01 +20-04-07 23:26:45.293 - INFO: l_g_pix: 9.8363e-05 l_g_fea: 4.6059e-01 l_g_gan: 5.3117e-03 l_d_real: 5.3795e-01 l_d_fake: 5.1303e-01 D_real: 3.5935e+01 D_fake: 3.5398e+01 +20-04-07 23:29:56.423 - INFO: l_g_pix: 9.7500e-05 l_g_fea: 4.5897e-01 l_g_gan: 1.6644e-02 l_d_real: 4.2312e-02 l_d_fake: 4.2077e-02 D_real: 2.3886e+01 D_fake: 2.0599e+01 +20-04-07 23:33:07.294 - INFO: l_g_pix: 1.1030e-04 l_g_fea: 4.7151e-01 l_g_gan: 1.0393e-02 l_d_real: 1.8120e-01 l_d_fake: 1.7602e-01 D_real: 2.2500e+01 D_fake: 2.0600e+01 +20-04-07 23:36:19.027 - INFO: l_g_pix: 1.0825e-04 l_g_fea: 5.0089e-01 l_g_gan: 1.3921e-02 l_d_real: 8.1873e-02 l_d_fake: 8.9343e-02 D_real: 3.0576e+01 D_fake: 2.7877e+01 +20-04-07 23:39:31.053 - INFO: l_g_pix: 9.2094e-05 l_g_fea: 4.0312e-01 l_g_gan: 7.6032e-03 l_d_real: 3.2918e-01 l_d_fake: 3.0122e-01 D_real: 2.5667e+01 D_fake: 2.4462e+01 +20-04-07 23:42:42.352 - INFO: l_g_pix: 6.7104e-05 l_g_fea: 3.0379e-01 l_g_gan: 5.2096e-03 l_d_real: 5.4954e-01 l_d_fake: 4.9497e-01 D_real: 2.3562e+01 D_fake: 2.3042e+01 +20-04-07 23:45:54.136 - INFO: l_g_pix: 9.1979e-05 l_g_fea: 4.3461e-01 l_g_gan: 2.0079e-02 l_d_real: 2.1818e-02 l_d_fake: 2.2180e-02 D_real: 3.9480e+01 D_fake: 3.5486e+01 +20-04-07 23:49:06.478 - INFO: l_g_pix: 7.5201e-05 l_g_fea: 4.0663e-01 l_g_gan: 4.0978e-03 l_d_real: 6.6099e-01 l_d_fake: 6.6364e-01 D_real: 2.0420e+01 D_fake: 2.0263e+01 +20-04-07 23:52:18.135 - INFO: l_g_pix: 1.0832e-04 l_g_fea: 4.5060e-01 l_g_gan: 1.4565e-02 l_d_real: 7.2037e-02 l_d_fake: 7.6595e-02 D_real: 3.1729e+01 D_fake: 2.8890e+01 +20-04-07 23:55:29.470 - INFO: l_g_pix: 8.5861e-05 l_g_fea: 3.7072e-01 l_g_gan: 2.0070e-02 l_d_real: 2.4857e-02 l_d_fake: 2.3630e-02 D_real: 3.1792e+01 D_fake: 2.7802e+01 +20-04-07 23:58:41.589 - INFO: l_g_pix: 7.0846e-05 l_g_fea: 3.8605e-01 l_g_gan: 1.1257e-02 l_d_real: 1.3456e-01 l_d_fake: 1.2934e-01 D_real: 1.4925e+01 D_fake: 1.2805e+01 +20-04-08 00:01:53.727 - INFO: l_g_pix: 1.0924e-04 l_g_fea: 4.2143e-01 l_g_gan: 1.4123e-02 l_d_real: 6.9089e-02 l_d_fake: 6.8985e-02 D_real: 2.5243e+01 D_fake: 2.2487e+01 +20-04-08 00:05:05.348 - INFO: l_g_pix: 6.4474e-05 l_g_fea: 3.9342e-01 l_g_gan: 2.1280e-02 l_d_real: 1.7006e-02 l_d_fake: 1.9328e-02 D_real: 3.0756e+01 D_fake: 2.6518e+01 +20-04-08 00:08:17.041 - INFO: l_g_pix: 5.7268e-05 l_g_fea: 3.2917e-01 l_g_gan: 1.5127e-02 l_d_real: 6.3140e-02 l_d_fake: 8.3007e-02 D_real: 1.3995e+01 D_fake: 1.1042e+01 +20-04-08 00:11:28.993 - INFO: l_g_pix: 8.7317e-05 l_g_fea: 3.5987e-01 l_g_gan: 1.8333e-02 l_d_real: 3.9325e-02 l_d_fake: 3.0401e-02 D_real: 2.6897e+01 D_fake: 2.3265e+01 +20-04-08 00:14:40.050 - INFO: l_g_pix: 7.8295e-05 l_g_fea: 3.9401e-01 l_g_gan: 5.6109e-03 l_d_real: 5.1458e-01 l_d_fake: 4.9173e-01 D_real: 2.7724e+01 D_fake: 2.7105e+01 +20-04-08 00:17:52.500 - INFO: l_g_pix: 7.8730e-05 l_g_fea: 4.0155e-01 l_g_gan: 1.5500e-02 l_d_real: 5.8344e-02 l_d_fake: 6.3615e-02 D_real: 2.5027e+01 D_fake: 2.1988e+01 +20-04-08 00:21:03.556 - INFO: l_g_pix: 9.4683e-05 l_g_fea: 4.3163e-01 l_g_gan: 2.4691e-02 l_d_real: 1.8592e-02 l_d_fake: 1.2361e-02 D_real: 4.0639e+01 D_fake: 3.5716e+01 +20-04-08 00:24:15.065 - INFO: l_g_pix: 9.8407e-05 l_g_fea: 4.9662e-01 l_g_gan: 9.1197e-03 l_d_real: 2.1094e-01 l_d_fake: 2.2096e-01 D_real: 2.4348e+01 D_fake: 2.2740e+01 +20-04-08 00:27:26.350 - INFO: l_g_pix: 9.9675e-05 l_g_fea: 4.3930e-01 l_g_gan: 6.0945e-03 l_d_real: 4.0637e-01 l_d_fake: 4.5218e-01 D_real: 2.3098e+01 D_fake: 2.2308e+01 +20-04-08 00:30:38.476 - INFO: l_g_pix: 8.9757e-05 l_g_fea: 4.6250e-01 l_g_gan: 1.1457e-02 l_d_real: 1.4388e-01 l_d_fake: 1.3075e-01 D_real: 3.4833e+01 D_fake: 3.2679e+01 +20-04-08 00:30:38.884 - INFO: Models and training states saved. +20-04-08 00:31:34.945 - INFO: # Validation # PSNR: 32.062, SSIM: 0.84325, LPIPS: 0.02338 +20-04-08 00:31:34.945 - INFO: psnr: 32.062, ssim: 0.84325, lpips: 0.02338 +20-04-08 00:34:45.089 - INFO: l_g_pix: 7.8659e-05 l_g_fea: 3.8018e-01 l_g_gan: 4.6634e-03 l_d_real: 6.2363e-01 l_d_fake: 6.2262e-01 D_real: 2.0640e+01 D_fake: 2.0330e+01 +20-04-08 00:37:57.299 - INFO: l_g_pix: 8.3373e-05 l_g_fea: 3.3256e-01 l_g_gan: 8.1582e-03 l_d_real: 2.6679e-01 l_d_fake: 2.7668e-01 D_real: 1.8595e+01 D_fake: 1.7235e+01 +20-04-08 00:41:09.323 - INFO: l_g_pix: 9.7799e-05 l_g_fea: 4.2738e-01 l_g_gan: 6.1236e-03 l_d_real: 4.2947e-01 l_d_fake: 4.5658e-01 D_real: 2.0596e+01 D_fake: 1.9814e+01 +20-04-08 00:44:20.953 - INFO: l_g_pix: 8.6142e-05 l_g_fea: 3.3504e-01 l_g_gan: 1.4746e-02 l_d_real: 6.1797e-02 l_d_fake: 6.9949e-02 D_real: 2.4372e+01 D_fake: 2.1489e+01 +20-04-08 00:47:32.584 - INFO: l_g_pix: 8.4643e-05 l_g_fea: 4.2785e-01 l_g_gan: 2.6282e-02 l_d_real: 7.2036e-03 l_d_fake: 8.7467e-03 D_real: 2.4771e+01 D_fake: 1.9523e+01 +20-04-08 00:50:43.796 - INFO: l_g_pix: 1.0043e-04 l_g_fea: 5.5052e-01 l_g_gan: 9.3814e-03 l_d_real: 2.1264e-01 l_d_fake: 2.0857e-01 D_real: 2.7986e+01 D_fake: 2.6321e+01 +20-04-08 00:53:54.984 - INFO: l_g_pix: 8.5563e-05 l_g_fea: 4.5866e-01 l_g_gan: 1.1186e-02 l_d_real: 1.4972e-01 l_d_fake: 1.5572e-01 D_real: 2.3253e+01 D_fake: 2.1168e+01 +20-04-08 00:57:06.820 - INFO: l_g_pix: 1.1280e-04 l_g_fea: 4.7672e-01 l_g_gan: 1.6161e-02 l_d_real: 4.7145e-02 l_d_fake: 5.9335e-02 D_real: 1.9081e+01 D_fake: 1.5902e+01 +20-04-08 01:00:18.472 - INFO: l_g_pix: 9.7442e-05 l_g_fea: 4.3645e-01 l_g_gan: 1.5502e-02 l_d_real: 6.3405e-02 l_d_fake: 5.7800e-02 D_real: 2.5045e+01 D_fake: 2.2005e+01 +20-04-08 01:03:30.216 - INFO: l_g_pix: 7.3638e-05 l_g_fea: 3.4292e-01 l_g_gan: 1.0455e-02 l_d_real: 1.7912e-01 l_d_fake: 1.8283e-01 D_real: 2.2896e+01 D_fake: 2.0986e+01 +20-04-08 01:06:41.697 - INFO: l_g_pix: 9.4315e-05 l_g_fea: 3.9172e-01 l_g_gan: 9.4440e-04 l_d_real: 1.9934e+00 l_d_fake: 2.0013e+00 D_real: 1.9704e+01 D_fake: 2.1512e+01 +20-04-08 01:09:53.572 - INFO: l_g_pix: 7.3325e-05 l_g_fea: 3.7880e-01 l_g_gan: 2.0100e-02 l_d_real: 2.4330e-02 l_d_fake: 2.2026e-02 D_real: 3.3362e+01 D_fake: 2.9366e+01 +20-04-08 01:13:05.041 - INFO: l_g_pix: 8.0825e-05 l_g_fea: 4.7655e-01 l_g_gan: 8.9462e-03 l_d_real: 2.8269e-01 l_d_fake: 2.1961e-01 D_real: 2.4945e+01 D_fake: 2.3406e+01 +20-04-08 01:16:16.678 - INFO: l_g_pix: 5.9478e-05 l_g_fea: 3.9616e-01 l_g_gan: 1.2909e-02 l_d_real: 1.0738e-01 l_d_fake: 1.2097e-01 D_real: 2.8503e+01 D_fake: 2.6035e+01 +20-04-08 01:19:28.929 - INFO: l_g_pix: 8.6111e-05 l_g_fea: 4.1738e-01 l_g_gan: 1.3101e-03 l_d_real: 1.6133e+00 l_d_fake: 1.6103e+00 D_real: 2.2165e+01 D_fake: 2.3515e+01 +20-04-08 01:22:40.608 - INFO: l_g_pix: 8.8348e-05 l_g_fea: 3.5016e-01 l_g_gan: 9.9344e-03 l_d_real: 1.8008e-01 l_d_fake: 1.8401e-01 D_real: 3.6856e+01 D_fake: 3.5051e+01 +20-04-08 01:25:52.932 - INFO: l_g_pix: 5.5739e-05 l_g_fea: 3.3035e-01 l_g_gan: 1.3779e-02 l_d_real: 1.0624e-01 l_d_fake: 8.7827e-02 D_real: 2.4827e+01 D_fake: 2.2168e+01 +20-04-08 01:29:04.984 - INFO: l_g_pix: 8.4896e-05 l_g_fea: 3.6910e-01 l_g_gan: 1.2301e-02 l_d_real: 1.0406e-01 l_d_fake: 1.1417e-01 D_real: 2.1440e+01 D_fake: 1.9089e+01 +20-04-08 01:32:15.824 - INFO: l_g_pix: 7.1538e-05 l_g_fea: 4.0089e-01 l_g_gan: 1.8991e-02 l_d_real: 3.0462e-02 l_d_fake: 2.9979e-02 D_real: 3.9619e+01 D_fake: 3.5851e+01 +20-04-08 01:35:26.667 - INFO: l_g_pix: 8.8926e-05 l_g_fea: 4.8545e-01 l_g_gan: 2.4703e-02 l_d_real: 9.1921e-03 l_d_fake: 9.1210e-03 D_real: 1.8959e+01 D_fake: 1.4027e+01 +20-04-08 01:38:38.182 - INFO: l_g_pix: 7.8254e-05 l_g_fea: 4.2831e-01 l_g_gan: 1.1873e-02 l_d_real: 1.3517e-01 l_d_fake: 1.2652e-01 D_real: 2.0083e+01 D_fake: 1.7839e+01 +20-04-08 01:41:50.034 - INFO: l_g_pix: 8.3800e-05 l_g_fea: 3.9249e-01 l_g_gan: 1.9929e-02 l_d_real: 2.3716e-02 l_d_fake: 2.1296e-02 D_real: 3.0768e+01 D_fake: 2.6805e+01 +20-04-08 01:45:02.070 - INFO: l_g_pix: 8.7141e-05 l_g_fea: 4.5632e-01 l_g_gan: 6.1064e-03 l_d_real: 4.2622e-01 l_d_fake: 4.1464e-01 D_real: 3.2035e+01 D_fake: 3.1234e+01 +20-04-08 01:48:13.595 - INFO: l_g_pix: 7.8888e-05 l_g_fea: 4.4259e-01 l_g_gan: 1.4176e-02 l_d_real: 8.4603e-02 l_d_fake: 8.3710e-02 D_real: 3.0443e+01 D_fake: 2.7692e+01 +20-04-08 01:51:24.126 - INFO: l_g_pix: 8.2682e-05 l_g_fea: 4.2055e-01 l_g_gan: 4.7119e-03 l_d_real: 5.6515e-01 l_d_fake: 5.8038e-01 D_real: 3.3918e+01 D_fake: 3.3548e+01 +20-04-08 01:51:24.543 - INFO: Models and training states saved. +20-04-08 01:52:20.612 - INFO: # Validation # PSNR: 31.699, SSIM: 0.84084, LPIPS: 0.022714 +20-04-08 01:52:20.613 - INFO: psnr: 31.699, ssim: 0.84084, lpips: 0.022714 +20-04-08 01:55:51.154 - INFO: l_g_pix: 1.1069e-04 l_g_fea: 4.7917e-01 l_g_gan: 1.1223e-02 l_d_real: 1.3519e-01 l_d_fake: 1.5182e-01 D_real: 2.9938e+01 D_fake: 2.7837e+01 +20-04-08 01:59:03.262 - INFO: l_g_pix: 8.8161e-05 l_g_fea: 3.5629e-01 l_g_gan: 1.5009e-03 l_d_real: 1.5233e+00 l_d_fake: 1.5311e+00 D_real: 1.8632e+01 D_fake: 1.9859e+01 +20-04-08 02:02:15.777 - INFO: l_g_pix: 1.0218e-04 l_g_fea: 4.0039e-01 l_g_gan: 1.3073e-02 l_d_real: 8.9795e-02 l_d_fake: 9.5623e-02 D_real: 3.0494e+01 D_fake: 2.7972e+01 +20-04-08 02:05:27.172 - INFO: l_g_pix: 1.2551e-04 l_g_fea: 5.0540e-01 l_g_gan: 9.6284e-03 l_d_real: 2.0383e-01 l_d_fake: 1.8468e-01 D_real: 2.5471e+01 D_fake: 2.3740e+01 +20-04-08 02:08:38.853 - INFO: l_g_pix: 8.5044e-05 l_g_fea: 4.3174e-01 l_g_gan: 1.7384e-02 l_d_real: 4.5843e-02 l_d_fake: 3.8679e-02 D_real: 3.2961e+01 D_fake: 2.9526e+01 +20-04-08 02:11:50.944 - INFO: l_g_pix: 8.7796e-05 l_g_fea: 4.0306e-01 l_g_gan: 1.6205e-02 l_d_real: 4.9223e-02 l_d_fake: 5.0021e-02 D_real: 2.1173e+01 D_fake: 1.7981e+01 +20-04-08 02:15:03.002 - INFO: l_g_pix: 6.9670e-05 l_g_fea: 3.7990e-01 l_g_gan: 1.0495e-02 l_d_real: 1.7825e-01 l_d_fake: 1.7331e-01 D_real: 1.6793e+01 D_fake: 1.4870e+01 +20-04-08 02:18:15.426 - INFO: l_g_pix: 7.8471e-05 l_g_fea: 3.5867e-01 l_g_gan: 5.5953e-03 l_d_real: 4.7819e-01 l_d_fake: 4.9061e-01 D_real: 3.0499e+01 D_fake: 2.9865e+01 +20-04-08 02:21:27.611 - INFO: l_g_pix: 7.5656e-05 l_g_fea: 4.4653e-01 l_g_gan: 1.0998e-02 l_d_real: 1.5517e-01 l_d_fake: 1.5888e-01 D_real: 2.2515e+01 D_fake: 2.0472e+01 +20-04-08 02:24:39.932 - INFO: l_g_pix: 1.0495e-04 l_g_fea: 4.0407e-01 l_g_gan: 2.1292e-02 l_d_real: 1.6731e-02 l_d_fake: 1.8422e-02 D_real: 2.3414e+01 D_fake: 1.9173e+01 +20-04-08 02:27:52.715 - INFO: l_g_pix: 8.0083e-05 l_g_fea: 3.5306e-01 l_g_gan: 1.5100e-02 l_d_real: 5.8994e-02 l_d_fake: 5.3857e-02 D_real: 3.8346e+01 D_fake: 3.5383e+01 +20-04-08 02:31:05.650 - INFO: l_g_pix: 1.0216e-04 l_g_fea: 4.1149e-01 l_g_gan: 2.6368e-02 l_d_real: 6.5210e-03 l_d_fake: 6.3239e-03 D_real: 2.9092e+01 D_fake: 2.3825e+01 +20-04-08 02:34:18.070 - INFO: l_g_pix: 8.6745e-05 l_g_fea: 4.0421e-01 l_g_gan: 1.6911e-03 l_d_real: 1.4291e+00 l_d_fake: 1.4651e+00 D_real: 3.4352e+01 D_fake: 3.5461e+01 +20-04-08 02:37:31.613 - INFO: l_g_pix: 9.9682e-05 l_g_fea: 4.7404e-01 l_g_gan: 7.9992e-03 l_d_real: 3.0261e-01 l_d_fake: 3.1268e-01 D_real: 2.2517e+01 D_fake: 2.1225e+01 +20-04-08 02:40:44.021 - INFO: l_g_pix: 7.7351e-05 l_g_fea: 3.8913e-01 l_g_gan: 1.7229e-02 l_d_real: 4.0381e-02 l_d_fake: 3.8885e-02 D_real: 1.4958e+01 D_fake: 1.1552e+01 +20-04-08 02:43:56.854 - INFO: l_g_pix: 6.3066e-05 l_g_fea: 3.3361e-01 l_g_gan: 8.5395e-03 l_d_real: 2.4269e-01 l_d_fake: 2.6308e-01 D_real: 1.5430e+01 D_fake: 1.3975e+01 +20-04-08 02:47:16.575 - INFO: l_g_pix: 1.2489e-04 l_g_fea: 4.7668e-01 l_g_gan: 9.0908e-03 l_d_real: 2.2069e-01 l_d_fake: 2.1940e-01 D_real: 2.2878e+01 D_fake: 2.1279e+01 +20-04-08 02:50:28.910 - INFO: l_g_pix: 1.0512e-04 l_g_fea: 4.6918e-01 l_g_gan: 2.8346e-02 l_d_real: 3.9256e-03 l_d_fake: 3.9666e-03 D_real: 2.5343e+01 D_fake: 1.9677e+01 +20-04-08 02:53:42.120 - INFO: l_g_pix: 9.6669e-05 l_g_fea: 4.6311e-01 l_g_gan: 9.9503e-03 l_d_real: 1.6912e-01 l_d_fake: 1.6793e-01 D_real: 2.3937e+01 D_fake: 2.2115e+01 +20-04-08 02:56:53.643 - INFO: l_g_pix: 9.9880e-05 l_g_fea: 4.5892e-01 l_g_gan: 8.6734e-03 l_d_real: 2.3119e-01 l_d_fake: 2.7750e-01 D_real: 2.2192e+01 D_fake: 2.0712e+01 +20-04-08 03:00:05.513 - INFO: l_g_pix: 8.3741e-05 l_g_fea: 4.6165e-01 l_g_gan: 1.7846e-02 l_d_real: 3.8891e-02 l_d_fake: 3.9934e-02 D_real: 2.4904e+01 D_fake: 2.1374e+01 +20-04-08 03:03:17.892 - INFO: l_g_pix: 1.1873e-04 l_g_fea: 5.3122e-01 l_g_gan: 2.0765e-03 l_d_real: 1.1671e+00 l_d_fake: 1.1744e+00 D_real: 2.2385e+01 D_fake: 2.3141e+01 +20-04-08 03:06:29.641 - INFO: l_g_pix: 1.0198e-04 l_g_fea: 5.3730e-01 l_g_gan: 1.6245e-02 l_d_real: 5.2532e-02 l_d_fake: 5.6359e-02 D_real: 3.7114e+01 D_fake: 3.3919e+01 +20-04-08 03:09:42.115 - INFO: l_g_pix: 1.0052e-04 l_g_fea: 4.1721e-01 l_g_gan: 1.4175e-02 l_d_real: 7.0827e-02 l_d_fake: 6.8493e-02 D_real: 3.6238e+01 D_fake: 3.3472e+01 +20-04-08 03:12:54.840 - INFO: l_g_pix: 1.1488e-04 l_g_fea: 4.6887e-01 l_g_gan: 1.3495e-02 l_d_real: 8.3039e-02 l_d_fake: 9.2048e-02 D_real: 2.7600e+01 D_fake: 2.4989e+01 +20-04-08 03:12:55.280 - INFO: Models and training states saved. +20-04-08 03:13:55.112 - INFO: # Validation # PSNR: 31.614, SSIM: 0.84113, LPIPS: 0.021587 +20-04-08 03:13:55.112 - INFO: psnr: 31.614, ssim: 0.84113, lpips: 0.021587 +20-04-08 03:18:31.013 - INFO: l_g_pix: 7.9822e-05 l_g_fea: 4.1351e-01 l_g_gan: 1.4294e-02 l_d_real: 1.0444e-01 l_d_fake: 7.3299e-02 D_real: 3.5424e+01 D_fake: 3.2654e+01 +20-04-08 03:21:42.375 - INFO: l_g_pix: 1.2174e-04 l_g_fea: 4.2413e-01 l_g_gan: 3.4015e-02 l_d_real: 1.4101e-03 l_d_fake: 1.5288e-03 D_real: 1.9912e+01 D_fake: 1.3110e+01 +20-04-08 03:24:53.687 - INFO: l_g_pix: 9.7695e-05 l_g_fea: 4.3364e-01 l_g_gan: 2.8740e-02 l_d_real: 3.6871e-03 l_d_fake: 3.6826e-03 D_real: 3.9492e+01 D_fake: 3.3747e+01 +20-04-08 03:28:05.581 - INFO: l_g_pix: 1.1028e-04 l_g_fea: 3.9396e-01 l_g_gan: 2.6018e-02 l_d_real: 6.4080e-03 l_d_fake: 6.9936e-03 D_real: 3.1542e+01 D_fake: 2.6345e+01 +20-04-08 03:31:17.192 - INFO: l_g_pix: 1.0130e-04 l_g_fea: 4.5492e-01 l_g_gan: 1.2564e-02 l_d_real: 1.0075e-01 l_d_fake: 9.0752e-02 D_real: 3.4453e+01 D_fake: 3.2036e+01 +20-04-08 03:34:28.617 - INFO: l_g_pix: 7.0143e-05 l_g_fea: 3.4905e-01 l_g_gan: 2.1396e-02 l_d_real: 1.6909e-02 l_d_fake: 1.7760e-02 D_real: 2.4417e+01 D_fake: 2.0155e+01 +20-04-08 03:37:39.821 - INFO: l_g_pix: 9.1826e-05 l_g_fea: 4.7335e-01 l_g_gan: 3.0384e-02 l_d_real: 2.8500e-03 l_d_fake: 2.9455e-03 D_real: 2.3829e+01 D_fake: 1.7755e+01 +20-04-08 03:40:51.774 - INFO: l_g_pix: 1.3798e-04 l_g_fea: 5.6570e-01 l_g_gan: 2.0551e-02 l_d_real: 4.2303e-02 l_d_fake: 2.5276e-02 D_real: 3.6170e+01 D_fake: 3.2094e+01 +20-04-08 03:44:02.644 - INFO: l_g_pix: 1.0222e-04 l_g_fea: 4.6461e-01 l_g_gan: 3.0579e-02 l_d_real: 3.4677e-03 l_d_fake: 3.0952e-03 D_real: 4.2741e+01 D_fake: 3.6629e+01 +20-04-08 03:47:15.927 - INFO: l_g_pix: 8.3630e-05 l_g_fea: 3.8573e-01 l_g_gan: 2.0997e-02 l_d_real: 2.3465e-02 l_d_fake: 2.0607e-02 D_real: 3.4270e+01 D_fake: 3.0093e+01 +20-04-08 03:50:27.824 - INFO: l_g_pix: 8.5159e-05 l_g_fea: 4.4921e-01 l_g_gan: 1.0137e-02 l_d_real: 1.7509e-01 l_d_fake: 1.6801e-01 D_real: 2.5849e+01 D_fake: 2.3993e+01 +20-04-08 03:53:38.468 - INFO: l_g_pix: 7.3127e-05 l_g_fea: 3.7694e-01 l_g_gan: 1.0806e-03 l_d_real: 2.0534e+00 l_d_fake: 2.0496e+00 D_real: 2.1426e+01 D_fake: 2.3261e+01 +20-04-08 03:56:49.546 - INFO: l_g_pix: 7.1260e-05 l_g_fea: 4.4603e-01 l_g_gan: 1.6133e-02 l_d_real: 4.7393e-02 l_d_fake: 6.5739e-02 D_real: 2.6361e+01 D_fake: 2.3191e+01 +20-04-08 04:00:00.163 - INFO: l_g_pix: 7.0480e-05 l_g_fea: 3.7819e-01 l_g_gan: 8.6643e-03 l_d_real: 2.3081e-01 l_d_fake: 2.2143e-01 D_real: 2.0779e+01 D_fake: 1.9273e+01 +20-04-08 04:03:10.929 - INFO: l_g_pix: 8.8817e-05 l_g_fea: 3.5044e-01 l_g_gan: 7.0281e-03 l_d_real: 3.3243e-01 l_d_fake: 3.5165e-01 D_real: 2.7009e+01 D_fake: 2.5945e+01 +20-04-08 04:06:22.806 - INFO: l_g_pix: 1.2756e-04 l_g_fea: 4.4671e-01 l_g_gan: 2.3742e-03 l_d_real: 1.1294e+00 l_d_fake: 1.1452e+00 D_real: 2.8481e+01 D_fake: 2.9143e+01 +20-04-08 04:09:33.404 - INFO: l_g_pix: 1.1857e-04 l_g_fea: 5.7771e-01 l_g_gan: 9.7314e-03 l_d_real: 2.1959e-01 l_d_fake: 2.2494e-01 D_real: 2.7029e+01 D_fake: 2.5305e+01 +20-04-08 04:12:44.725 - INFO: l_g_pix: 1.0606e-04 l_g_fea: 4.2910e-01 l_g_gan: 7.4346e-03 l_d_real: 3.2035e-01 l_d_fake: 3.1540e-01 D_real: 3.5480e+01 D_fake: 3.4311e+01 +20-04-08 04:15:56.301 - INFO: l_g_pix: 9.8605e-05 l_g_fea: 4.4931e-01 l_g_gan: 1.3119e-02 l_d_real: 1.0330e-01 l_d_fake: 1.0292e-01 D_real: 1.1376e+01 D_fake: 8.8552e+00 +20-04-08 04:19:08.434 - INFO: l_g_pix: 1.0070e-04 l_g_fea: 3.7996e-01 l_g_gan: 1.0031e-02 l_d_real: 1.9927e-01 l_d_fake: 1.8268e-01 D_real: 3.0475e+01 D_fake: 2.8660e+01 +20-04-08 04:22:20.548 - INFO: l_g_pix: 8.5595e-05 l_g_fea: 4.0327e-01 l_g_gan: 1.4538e-02 l_d_real: 9.9166e-02 l_d_fake: 7.7373e-02 D_real: 4.0763e+01 D_fake: 3.7944e+01 +20-04-08 04:25:32.410 - INFO: l_g_pix: 8.3651e-05 l_g_fea: 4.2601e-01 l_g_gan: 1.7402e-02 l_d_real: 3.7015e-02 l_d_fake: 3.9129e-02 D_real: 1.6503e+01 D_fake: 1.3061e+01 +20-04-08 04:28:43.639 - INFO: l_g_pix: 9.4079e-05 l_g_fea: 4.5142e-01 l_g_gan: 6.7814e-03 l_d_real: 4.2294e-01 l_d_fake: 3.9353e-01 D_real: 3.3933e+01 D_fake: 3.2985e+01 +20-04-08 04:31:54.817 - INFO: l_g_pix: 1.0110e-04 l_g_fea: 4.5829e-01 l_g_gan: 7.4098e-03 l_d_real: 3.6509e-01 l_d_fake: 3.4913e-01 D_real: 3.6593e+01 D_fake: 3.5468e+01 +20-04-08 04:35:06.663 - INFO: l_g_pix: 1.5423e-04 l_g_fea: 6.0010e-01 l_g_gan: 1.1835e-02 l_d_real: 1.2545e-01 l_d_fake: 1.6145e-01 D_real: 3.9119e+01 D_fake: 3.6895e+01 +20-04-08 04:35:07.082 - INFO: Models and training states saved. +20-04-08 04:36:04.450 - INFO: # Validation # PSNR: 31.57, SSIM: 0.84031, LPIPS: 0.022215 +20-04-08 04:36:04.450 - INFO: psnr: 31.57, ssim: 0.84031, lpips: 0.022215 +20-04-08 04:39:24.799 - INFO: l_g_pix: 7.9786e-05 l_g_fea: 3.8878e-01 l_g_gan: 5.9343e-03 l_d_real: 4.1342e-01 l_d_fake: 4.0897e-01 D_real: 3.0729e+01 D_fake: 2.9954e+01 +20-04-08 04:42:36.612 - INFO: l_g_pix: 1.1966e-04 l_g_fea: 5.4026e-01 l_g_gan: 2.4362e-02 l_d_real: 1.0927e-02 l_d_fake: 9.1572e-03 D_real: 2.5704e+01 D_fake: 2.0842e+01 +20-04-08 04:45:47.907 - INFO: l_g_pix: 1.1915e-04 l_g_fea: 4.4886e-01 l_g_gan: 5.8938e-03 l_d_real: 4.6888e-01 l_d_fake: 4.9085e-01 D_real: 3.5972e+01 D_fake: 3.5273e+01 +20-04-08 04:49:00.196 - INFO: l_g_pix: 1.1061e-04 l_g_fea: 4.5238e-01 l_g_gan: 2.1724e-02 l_d_real: 1.8491e-02 l_d_fake: 1.6626e-02 D_real: 3.9654e+01 D_fake: 3.5327e+01 +20-04-08 04:52:12.348 - INFO: l_g_pix: 9.6693e-05 l_g_fea: 4.9414e-01 l_g_gan: 5.3182e-03 l_d_real: 5.0767e-01 l_d_fake: 5.0968e-01 D_real: 2.6566e+01 D_fake: 2.6011e+01 +20-04-08 04:55:24.383 - INFO: l_g_pix: 1.1925e-04 l_g_fea: 4.3083e-01 l_g_gan: 1.2067e-02 l_d_real: 1.1401e-01 l_d_fake: 1.1329e-01 D_real: 2.0135e+01 D_fake: 1.7835e+01 +20-04-08 04:58:35.563 - INFO: l_g_pix: 9.5110e-05 l_g_fea: 4.8268e-01 l_g_gan: 8.5587e-03 l_d_real: 2.2941e-01 l_d_fake: 2.6104e-01 D_real: 3.7067e+01 D_fake: 3.5600e+01 +20-04-08 05:01:47.708 - INFO: l_g_pix: 7.3554e-05 l_g_fea: 4.3616e-01 l_g_gan: 1.4531e-02 l_d_real: 7.0784e-02 l_d_fake: 7.5914e-02 D_real: 3.4065e+01 D_fake: 3.1232e+01 +20-04-08 05:04:59.417 - INFO: l_g_pix: 1.2039e-04 l_g_fea: 5.3701e-01 l_g_gan: 1.5440e-02 l_d_real: 5.4414e-02 l_d_fake: 6.2404e-02 D_real: 4.4409e+01 D_fake: 4.1379e+01 +20-04-08 05:08:10.730 - INFO: l_g_pix: 5.2068e-05 l_g_fea: 2.6131e-01 l_g_gan: 1.0108e-02 l_d_real: 1.9910e-01 l_d_fake: 1.7583e-01 D_real: 3.3732e+01 D_fake: 3.1898e+01 +20-04-08 05:11:22.893 - INFO: l_g_pix: 1.3674e-04 l_g_fea: 5.4341e-01 l_g_gan: 1.8062e-03 l_d_real: 1.4305e+00 l_d_fake: 1.4309e+00 D_real: 2.7392e+01 D_fake: 2.8461e+01 +20-04-08 05:14:33.787 - INFO: l_g_pix: 7.4827e-05 l_g_fea: 4.0945e-01 l_g_gan: 9.3793e-03 l_d_real: 2.0279e-01 l_d_fake: 2.1587e-01 D_real: 2.7759e+01 D_fake: 2.6092e+01 +20-04-08 05:17:45.830 - INFO: l_g_pix: 7.1083e-05 l_g_fea: 3.9190e-01 l_g_gan: 7.4829e-03 l_d_real: 3.0229e-01 l_d_fake: 3.1794e-01 D_real: 3.5265e+01 D_fake: 3.4079e+01 +20-04-08 05:20:57.162 - INFO: l_g_pix: 1.2718e-04 l_g_fea: 5.5744e-01 l_g_gan: 1.5681e-02 l_d_real: 6.5313e-02 l_d_fake: 5.9289e-02 D_real: 4.2758e+01 D_fake: 3.9684e+01 +20-04-08 05:24:09.189 - INFO: l_g_pix: 7.1733e-05 l_g_fea: 4.0430e-01 l_g_gan: 3.2392e-02 l_d_real: 1.8060e-03 l_d_fake: 2.0372e-03 D_real: 4.1638e+01 D_fake: 3.5162e+01 +20-04-08 05:27:20.846 - INFO: l_g_pix: 9.6544e-05 l_g_fea: 4.3669e-01 l_g_gan: 3.9717e-03 l_d_real: 7.1183e-01 l_d_fake: 7.3506e-01 D_real: 3.4883e+01 D_fake: 3.4812e+01 +20-04-08 05:30:32.801 - INFO: l_g_pix: 1.0598e-04 l_g_fea: 4.9498e-01 l_g_gan: 7.6200e-03 l_d_real: 3.2981e-01 l_d_fake: 2.7915e-01 D_real: 2.1460e+01 D_fake: 2.0240e+01 +20-04-08 05:33:45.481 - INFO: l_g_pix: 7.6132e-05 l_g_fea: 4.0431e-01 l_g_gan: 1.0657e-02 l_d_real: 1.7238e-01 l_d_fake: 1.4992e-01 D_real: 2.3625e+01 D_fake: 2.1655e+01 +20-04-08 05:36:57.380 - INFO: l_g_pix: 1.0326e-04 l_g_fea: 4.7421e-01 l_g_gan: 1.3296e-03 l_d_real: 1.5909e+00 l_d_fake: 1.5953e+00 D_real: 2.8551e+01 D_fake: 2.9878e+01 +20-04-08 05:40:09.302 - INFO: l_g_pix: 9.4739e-05 l_g_fea: 4.4410e-01 l_g_gan: 1.4226e-02 l_d_real: 8.1852e-02 l_d_fake: 6.9717e-02 D_real: 2.8519e+01 D_fake: 2.5750e+01 +20-04-08 05:43:21.530 - INFO: l_g_pix: 7.7192e-05 l_g_fea: 4.4899e-01 l_g_gan: 8.7608e-03 l_d_real: 2.1085e-01 l_d_fake: 2.1205e-01 D_real: 2.7280e+01 D_fake: 2.5739e+01 +20-04-08 05:46:32.596 - INFO: l_g_pix: 8.0292e-05 l_g_fea: 4.3937e-01 l_g_gan: 5.0931e-03 l_d_real: 5.2597e-01 l_d_fake: 5.0572e-01 D_real: 3.3617e+01 D_fake: 3.3114e+01 +20-04-08 05:49:43.682 - INFO: l_g_pix: 9.2280e-05 l_g_fea: 4.3561e-01 l_g_gan: 1.1131e-02 l_d_real: 1.2742e-01 l_d_fake: 1.3736e-01 D_real: 3.9630e+01 D_fake: 3.7536e+01 +20-04-08 05:52:55.465 - INFO: l_g_pix: 7.9818e-05 l_g_fea: 4.4336e-01 l_g_gan: 1.6947e-02 l_d_real: 5.2658e-02 l_d_fake: 4.1608e-02 D_real: 4.2761e+01 D_fake: 3.9418e+01 +20-04-08 05:56:07.116 - INFO: l_g_pix: 6.2484e-05 l_g_fea: 4.3677e-01 l_g_gan: 9.7110e-03 l_d_real: 2.2363e-01 l_d_fake: 2.2639e-01 D_real: 2.9127e+01 D_fake: 2.7409e+01 +20-04-08 05:56:07.576 - INFO: Models and training states saved. +20-04-08 05:57:04.924 - INFO: # Validation # PSNR: 31.986, SSIM: 0.84651, LPIPS: 0.023124 +20-04-08 05:57:04.924 - INFO: psnr: 31.986, ssim: 0.84651, lpips: 0.023124 +20-04-08 06:00:14.733 - INFO: l_g_pix: 1.0686e-04 l_g_fea: 4.5913e-01 l_g_gan: 1.3025e-03 l_d_real: 1.6408e+00 l_d_fake: 1.6489e+00 D_real: 2.3407e+01 D_fake: 2.4791e+01 +20-04-08 06:03:25.895 - INFO: l_g_pix: 9.7232e-05 l_g_fea: 4.6931e-01 l_g_gan: 9.1404e-03 l_d_real: 1.9574e-01 l_d_fake: 2.0242e-01 D_real: 2.7751e+01 D_fake: 2.6122e+01 +20-04-08 06:06:37.220 - INFO: l_g_pix: 1.0168e-04 l_g_fea: 4.7708e-01 l_g_gan: 6.1251e-03 l_d_real: 4.1680e-01 l_d_fake: 3.9723e-01 D_real: 3.1011e+01 D_fake: 3.0193e+01 +20-04-08 06:09:48.436 - INFO: l_g_pix: 1.0365e-04 l_g_fea: 4.9227e-01 l_g_gan: 9.7651e-03 l_d_real: 1.7966e-01 l_d_fake: 1.8094e-01 D_real: 2.8235e+01 D_fake: 2.6462e+01 +20-04-08 06:13:00.317 - INFO: l_g_pix: 8.3358e-05 l_g_fea: 4.0788e-01 l_g_gan: 9.9770e-03 l_d_real: 2.0723e-01 l_d_fake: 1.9403e-01 D_real: 3.1420e+01 D_fake: 2.9625e+01 +20-04-08 06:16:12.025 - INFO: l_g_pix: 7.7076e-05 l_g_fea: 3.7277e-01 l_g_gan: 1.0697e-02 l_d_real: 1.5269e-01 l_d_fake: 1.5861e-01 D_real: 3.8879e+01 D_fake: 3.6896e+01 +20-04-08 06:19:28.081 - INFO: l_g_pix: 1.0422e-04 l_g_fea: 5.1854e-01 l_g_gan: 2.9212e-03 l_d_real: 9.1077e-01 l_d_fake: 8.9456e-01 D_real: 3.9554e+01 D_fake: 3.9872e+01 +20-04-08 06:22:40.238 - INFO: l_g_pix: 7.4524e-05 l_g_fea: 3.7772e-01 l_g_gan: 1.2093e-02 l_d_real: 1.2191e-01 l_d_fake: 1.0836e-01 D_real: 2.1164e+01 D_fake: 1.8861e+01 +20-04-08 06:25:52.282 - INFO: l_g_pix: 8.1268e-05 l_g_fea: 3.6236e-01 l_g_gan: 2.2388e-02 l_d_real: 1.8460e-02 l_d_fake: 1.4067e-02 D_real: 2.6081e+01 D_fake: 2.1620e+01 +20-04-08 06:29:03.411 - INFO: l_g_pix: 1.0533e-04 l_g_fea: 4.5484e-01 l_g_gan: 4.8026e-03 l_d_real: 5.5615e-01 l_d_fake: 5.5851e-01 D_real: 3.0885e+01 D_fake: 3.0482e+01 +20-04-08 06:32:15.316 - INFO: l_g_pix: 1.1649e-04 l_g_fea: 5.2115e-01 l_g_gan: 2.2180e-02 l_d_real: 1.4389e-02 l_d_fake: 1.6531e-02 D_real: 4.3689e+01 D_fake: 3.9269e+01 +20-04-08 06:35:27.118 - INFO: l_g_pix: 8.9650e-05 l_g_fea: 4.7110e-01 l_g_gan: 2.4228e-02 l_d_real: 1.0266e-02 l_d_fake: 1.2658e-02 D_real: 3.4904e+01 D_fake: 3.0070e+01 +20-04-08 06:38:38.169 - INFO: l_g_pix: 1.1659e-04 l_g_fea: 5.0386e-01 l_g_gan: 1.9543e-02 l_d_real: 2.9770e-02 l_d_fake: 2.5644e-02 D_real: 3.9075e+01 D_fake: 3.5195e+01 +20-04-08 06:41:50.598 - INFO: l_g_pix: 8.2142e-05 l_g_fea: 4.2099e-01 l_g_gan: 2.4896e-03 l_d_real: 1.0278e+00 l_d_fake: 1.0321e+00 D_real: 2.4115e+01 D_fake: 2.4647e+01 +20-04-08 06:45:02.796 - INFO: l_g_pix: 7.4963e-05 l_g_fea: 4.1772e-01 l_g_gan: 4.1797e-03 l_d_real: 7.2384e-01 l_d_fake: 6.8020e-01 D_real: 3.2697e+01 D_fake: 3.2563e+01 +20-04-08 06:48:13.811 - INFO: l_g_pix: 7.8044e-05 l_g_fea: 4.5371e-01 l_g_gan: 2.8119e-02 l_d_real: 4.6645e-03 l_d_fake: 4.4868e-03 D_real: 3.2207e+01 D_fake: 2.6587e+01 +20-04-08 06:51:25.517 - INFO: l_g_pix: 9.6214e-05 l_g_fea: 4.7960e-01 l_g_gan: 3.5276e-03 l_d_real: 7.6897e-01 l_d_fake: 7.9854e-01 D_real: 3.7360e+01 D_fake: 3.7438e+01 +20-04-08 06:54:36.792 - INFO: l_g_pix: 9.4278e-05 l_g_fea: 4.8125e-01 l_g_gan: 1.8333e-03 l_d_real: 1.3190e+00 l_d_fake: 1.3133e+00 D_real: 2.7507e+01 D_fake: 2.8457e+01 +20-04-08 06:57:48.015 - INFO: l_g_pix: 1.1527e-04 l_g_fea: 3.4075e-01 l_g_gan: 3.7479e-02 l_d_real: 6.3047e-04 l_d_fake: 1.4104e-03 D_real: 4.5216e+01 D_fake: 3.7721e+01 +20-04-08 07:00:58.644 - INFO: l_g_pix: 8.6659e-05 l_g_fea: 3.7910e-01 l_g_gan: 5.5117e-03 l_d_real: 4.8678e-01 l_d_fake: 5.3726e-01 D_real: 1.1811e+01 D_fake: 1.1221e+01 +20-04-08 07:04:10.649 - INFO: l_g_pix: 1.1717e-04 l_g_fea: 4.1490e-01 l_g_gan: 8.3617e-03 l_d_real: 2.5505e-01 l_d_fake: 2.7017e-01 D_real: 3.5461e+01 D_fake: 3.4052e+01 +20-04-08 07:07:22.908 - INFO: l_g_pix: 6.5254e-05 l_g_fea: 3.7989e-01 l_g_gan: 4.0145e-03 l_d_real: 6.7588e-01 l_d_fake: 6.8037e-01 D_real: 2.4416e+01 D_fake: 2.4291e+01 +20-04-08 07:10:34.784 - INFO: l_g_pix: 1.0866e-04 l_g_fea: 5.2439e-01 l_g_gan: 7.0736e-03 l_d_real: 3.0943e-01 l_d_fake: 3.1432e-01 D_real: 1.8980e+01 D_fake: 1.7877e+01 +20-04-08 07:13:46.862 - INFO: l_g_pix: 7.6344e-05 l_g_fea: 4.3217e-01 l_g_gan: 3.3212e-02 l_d_real: 1.9832e-03 l_d_fake: 1.5043e-03 D_real: 2.0932e+01 D_fake: 1.4292e+01 +20-04-08 07:16:58.314 - INFO: l_g_pix: 1.2602e-04 l_g_fea: 5.2138e-01 l_g_gan: 1.4510e-02 l_d_real: 7.9862e-02 l_d_fake: 7.3947e-02 D_real: 3.2360e+01 D_fake: 2.9535e+01 +20-04-08 07:16:58.742 - INFO: Models and training states saved. +20-04-08 07:17:55.014 - INFO: # Validation # PSNR: 31.657, SSIM: 0.83257, LPIPS: 0.024597 +20-04-08 07:17:55.014 - INFO: psnr: 31.657, ssim: 0.83257, lpips: 0.024597 +20-04-08 07:21:05.126 - INFO: l_g_pix: 8.0085e-05 l_g_fea: 3.7303e-01 l_g_gan: 1.1215e-02 l_d_real: 1.5663e-01 l_d_fake: 1.3938e-01 D_real: 2.2874e+01 D_fake: 2.0779e+01 +20-04-08 07:24:17.066 - INFO: l_g_pix: 7.4854e-05 l_g_fea: 3.9288e-01 l_g_gan: 6.1182e-03 l_d_real: 4.4058e-01 l_d_fake: 4.4340e-01 D_real: 2.9554e+01 D_fake: 2.8773e+01 +20-04-08 07:27:28.087 - INFO: l_g_pix: 7.9392e-05 l_g_fea: 4.0249e-01 l_g_gan: 5.0321e-03 l_d_real: 5.0759e-01 l_d_fake: 5.1233e-01 D_real: 3.3445e+01 D_fake: 3.2948e+01 +20-04-08 07:30:39.261 - INFO: l_g_pix: 1.0363e-04 l_g_fea: 3.8226e-01 l_g_gan: 2.3755e-02 l_d_real: 9.8376e-03 l_d_fake: 1.1224e-02 D_real: 2.0207e+01 D_fake: 1.5466e+01 +20-04-08 07:33:50.577 - INFO: l_g_pix: 8.2607e-05 l_g_fea: 4.1728e-01 l_g_gan: 1.2604e-02 l_d_real: 9.6685e-02 l_d_fake: 9.5818e-02 D_real: 2.8800e+01 D_fake: 2.6375e+01 +20-04-08 07:37:01.807 - INFO: l_g_pix: 1.0320e-04 l_g_fea: 4.6071e-01 l_g_gan: 9.4457e-03 l_d_real: 1.9435e-01 l_d_fake: 2.0578e-01 D_real: 2.7468e+01 D_fake: 2.5779e+01 +20-04-08 07:40:13.595 - INFO: l_g_pix: 6.6222e-05 l_g_fea: 3.2977e-01 l_g_gan: 9.3728e-03 l_d_real: 2.3275e-01 l_d_fake: 1.9476e-01 D_real: 3.1447e+01 D_fake: 2.9786e+01 +20-04-08 07:43:25.461 - INFO: l_g_pix: 1.1336e-04 l_g_fea: 4.1566e-01 l_g_gan: 1.0357e-02 l_d_real: 1.5670e-01 l_d_fake: 1.5654e-01 D_real: 3.7586e+01 D_fake: 3.5671e+01 +20-04-08 07:46:36.449 - INFO: l_g_pix: 8.2036e-05 l_g_fea: 3.7768e-01 l_g_gan: 1.0620e-02 l_d_real: 1.5454e-01 l_d_fake: 1.5748e-01 D_real: 2.1451e+01 D_fake: 1.9483e+01 +20-04-08 07:49:47.539 - INFO: l_g_pix: 9.2097e-05 l_g_fea: 3.5611e-01 l_g_gan: 1.0520e-02 l_d_real: 1.5547e-01 l_d_fake: 1.4434e-01 D_real: 2.0924e+01 D_fake: 1.8970e+01 +20-04-08 07:52:59.205 - INFO: l_g_pix: 5.9372e-05 l_g_fea: 2.7015e-01 l_g_gan: 1.1117e-02 l_d_real: 1.5020e-01 l_d_fake: 1.3795e-01 D_real: 3.0877e+01 D_fake: 2.8798e+01 +20-04-08 07:56:10.346 - INFO: l_g_pix: 9.2175e-05 l_g_fea: 3.9737e-01 l_g_gan: 1.5672e-02 l_d_real: 5.4722e-02 l_d_fake: 5.9626e-02 D_real: 2.5246e+01 D_fake: 2.2168e+01 +20-04-08 07:59:21.242 - INFO: l_g_pix: 8.5338e-05 l_g_fea: 4.6089e-01 l_g_gan: 1.3214e-02 l_d_real: 9.1970e-02 l_d_fake: 1.1207e-01 D_real: 2.0746e+01 D_fake: 1.8205e+01 +20-04-08 08:02:33.147 - INFO: l_g_pix: 9.7109e-05 l_g_fea: 4.7752e-01 l_g_gan: 1.9590e-02 l_d_real: 2.8684e-02 l_d_fake: 3.0313e-02 D_real: 1.0823e+01 D_fake: 6.9342e+00 +20-04-08 08:05:45.064 - INFO: l_g_pix: 8.4171e-05 l_g_fea: 4.1460e-01 l_g_gan: 5.4966e-03 l_d_real: 4.7249e-01 l_d_fake: 4.7311e-01 D_real: 3.0430e+01 D_fake: 2.9804e+01 +20-04-08 08:08:57.184 - INFO: l_g_pix: 1.3734e-04 l_g_fea: 5.2781e-01 l_g_gan: 1.2275e-02 l_d_real: 1.0703e-01 l_d_fake: 1.0887e-01 D_real: 3.3079e+01 D_fake: 3.0732e+01 +20-04-08 08:12:08.653 - INFO: l_g_pix: 7.3764e-05 l_g_fea: 3.8712e-01 l_g_gan: 1.9928e-02 l_d_real: 2.1217e-02 l_d_fake: 2.5953e-02 D_real: 2.6814e+01 D_fake: 2.2852e+01 +20-04-08 08:15:20.378 - INFO: l_g_pix: 1.0687e-04 l_g_fea: 5.2801e-01 l_g_gan: 5.1434e-03 l_d_real: 5.1886e-01 l_d_fake: 5.4059e-01 D_real: 3.6270e+01 D_fake: 3.5771e+01 +20-04-08 08:18:32.755 - INFO: l_g_pix: 9.3673e-05 l_g_fea: 5.0417e-01 l_g_gan: 1.2812e-02 l_d_real: 9.6203e-02 l_d_fake: 1.0867e-01 D_real: 2.7694e+01 D_fake: 2.5234e+01 +20-04-08 08:21:43.832 - INFO: l_g_pix: 9.4973e-05 l_g_fea: 4.2524e-01 l_g_gan: 1.3785e-02 l_d_real: 9.9427e-02 l_d_fake: 9.9881e-02 D_real: 9.2024e+00 D_fake: 6.5451e+00 +20-04-08 08:24:55.370 - INFO: l_g_pix: 1.0060e-04 l_g_fea: 3.5300e-01 l_g_gan: 1.7546e-03 l_d_real: 1.3666e+00 l_d_fake: 1.3511e+00 D_real: 2.1078e+01 D_fake: 2.2086e+01 +20-04-08 08:28:06.161 - INFO: l_g_pix: 1.1292e-04 l_g_fea: 3.8534e-01 l_g_gan: 2.7232e-02 l_d_real: 5.8769e-03 l_d_fake: 5.6764e-03 D_real: 4.2217e+01 D_fake: 3.6777e+01 +20-04-08 08:31:17.056 - INFO: l_g_pix: 8.1588e-05 l_g_fea: 3.8778e-01 l_g_gan: 7.0729e-04 l_d_real: 2.2222e+00 l_d_fake: 2.2252e+00 D_real: 2.8291e+01 D_fake: 3.0373e+01 +20-04-08 08:34:27.804 - INFO: l_g_pix: 6.1974e-05 l_g_fea: 3.0981e-01 l_g_gan: 8.0531e-04 l_d_real: 2.0707e+00 l_d_fake: 2.0633e+00 D_real: 3.0084e+01 D_fake: 3.1990e+01 +20-04-08 08:37:39.679 - INFO: l_g_pix: 8.5358e-05 l_g_fea: 3.8629e-01 l_g_gan: 5.9036e-03 l_d_real: 4.3968e-01 l_d_fake: 4.5546e-01 D_real: 2.3875e+01 D_fake: 2.3142e+01 +20-04-08 08:37:40.097 - INFO: Models and training states saved. +20-04-08 08:38:36.884 - INFO: # Validation # PSNR: 31.441, SSIM: 0.82752, LPIPS: 0.025231 +20-04-08 08:38:36.884 - INFO: psnr: 31.441, ssim: 0.82752, lpips: 0.025231 +20-04-08 08:41:47.994 - INFO: l_g_pix: 9.7796e-05 l_g_fea: 4.2942e-01 l_g_gan: 3.0419e-02 l_d_real: 2.6629e-03 l_d_fake: 2.8978e-03 D_real: 3.7430e+01 D_fake: 3.1349e+01 +20-04-08 08:44:59.476 - INFO: l_g_pix: 1.0100e-04 l_g_fea: 3.7605e-01 l_g_gan: 9.1544e-03 l_d_real: 2.0095e-01 l_d_fake: 2.0001e-01 D_real: 3.5018e+01 D_fake: 3.3388e+01 +20-04-08 08:48:10.480 - INFO: l_g_pix: 9.9731e-05 l_g_fea: 3.4087e-01 l_g_gan: 2.2503e-02 l_d_real: 1.3996e-02 l_d_fake: 1.5178e-02 D_real: 2.4917e+01 D_fake: 2.0431e+01 +20-04-08 08:51:22.093 - INFO: l_g_pix: 8.7910e-05 l_g_fea: 4.9727e-01 l_g_gan: 5.3588e-04 l_d_real: 2.5835e+00 l_d_fake: 2.5718e+00 D_real: 2.3393e+01 D_fake: 2.5863e+01 +20-04-08 08:54:32.994 - INFO: l_g_pix: 8.4406e-05 l_g_fea: 3.4109e-01 l_g_gan: 2.7650e-03 l_d_real: 1.0238e+00 l_d_fake: 1.0492e+00 D_real: 2.5912e+01 D_fake: 2.6396e+01 +20-04-08 08:57:44.073 - INFO: l_g_pix: 9.4222e-05 l_g_fea: 4.5868e-01 l_g_gan: 1.9136e-02 l_d_real: 2.7601e-02 l_d_fake: 2.9624e-02 D_real: 2.3905e+01 D_fake: 2.0107e+01 +20-04-08 09:00:55.272 - INFO: l_g_pix: 8.7386e-05 l_g_fea: 5.5382e-01 l_g_gan: 1.5097e-02 l_d_real: 6.8963e-02 l_d_fake: 7.1083e-02 D_real: 3.0052e+01 D_fake: 2.7103e+01 +20-04-08 09:04:07.237 - INFO: l_g_pix: 1.2838e-04 l_g_fea: 5.1577e-01 l_g_gan: 1.0866e-02 l_d_real: 1.6699e-01 l_d_fake: 1.5076e-01 D_real: 3.4134e+01 D_fake: 3.2119e+01 +20-04-08 09:07:19.365 - INFO: l_g_pix: 9.9342e-05 l_g_fea: 4.6854e-01 l_g_gan: 1.0484e-02 l_d_real: 1.7374e-01 l_d_fake: 1.4891e-01 D_real: 2.9684e+01 D_fake: 2.7749e+01 +20-04-08 09:10:30.945 - INFO: l_g_pix: 1.0192e-04 l_g_fea: 4.3753e-01 l_g_gan: 1.6545e-02 l_d_real: 5.5744e-02 l_d_fake: 4.6529e-02 D_real: 3.1202e+01 D_fake: 2.7944e+01 +20-04-08 09:13:41.789 - INFO: l_g_pix: 1.1830e-04 l_g_fea: 5.2406e-01 l_g_gan: 4.2257e-04 l_d_real: 2.8237e+00 l_d_fake: 2.8273e+00 D_real: 4.7974e+01 D_fake: 5.0715e+01 +20-04-08 09:16:53.383 - INFO: l_g_pix: 1.1653e-04 l_g_fea: 5.7844e-01 l_g_gan: 5.9346e-03 l_d_real: 4.8250e-01 l_d_fake: 4.4699e-01 D_real: 3.4963e+01 D_fake: 3.4241e+01 +20-04-08 09:20:05.008 - INFO: l_g_pix: 8.5429e-05 l_g_fea: 4.7717e-01 l_g_gan: 1.7410e-02 l_d_real: 3.4633e-02 l_d_fake: 3.7381e-02 D_real: 9.4437e+00 D_fake: 5.9976e+00 +20-04-08 09:23:15.957 - INFO: l_g_pix: 1.2061e-04 l_g_fea: 5.5578e-01 l_g_gan: 6.1519e-03 l_d_real: 4.4004e-01 l_d_fake: 4.2337e-01 D_real: 2.7595e+01 D_fake: 2.6797e+01 +20-04-08 09:26:27.517 - INFO: l_g_pix: 9.5826e-05 l_g_fea: 4.8418e-01 l_g_gan: 2.4306e-02 l_d_real: 1.0298e-02 l_d_fake: 1.0216e-02 D_real: 2.3189e+01 D_fake: 1.8338e+01 +20-04-08 09:29:38.435 - INFO: l_g_pix: 8.5117e-05 l_g_fea: 3.4450e-01 l_g_gan: 1.1944e-02 l_d_real: 1.0828e-01 l_d_fake: 1.1030e-01 D_real: 1.2998e+01 D_fake: 1.0718e+01 +20-04-08 09:32:49.714 - INFO: l_g_pix: 1.0205e-04 l_g_fea: 4.8886e-01 l_g_gan: 1.0579e-02 l_d_real: 1.6033e-01 l_d_fake: 1.6471e-01 D_real: 2.0002e+01 D_fake: 1.8049e+01 +20-04-08 09:36:01.120 - INFO: l_g_pix: 1.1001e-04 l_g_fea: 4.5362e-01 l_g_gan: 8.0652e-03 l_d_real: 3.3428e-01 l_d_fake: 3.2455e-01 D_real: 2.5624e+01 D_fake: 2.4340e+01 +20-04-08 09:39:12.273 - INFO: l_g_pix: 1.2126e-04 l_g_fea: 5.1230e-01 l_g_gan: 2.0656e-02 l_d_real: 1.9699e-02 l_d_fake: 2.1893e-02 D_real: 4.8569e+01 D_fake: 4.4458e+01 +20-04-08 09:42:24.147 - INFO: l_g_pix: 7.3767e-05 l_g_fea: 3.2770e-01 l_g_gan: 2.4880e-03 l_d_real: 1.0686e+00 l_d_fake: 1.0792e+00 D_real: 1.6350e+01 D_fake: 1.6926e+01 +20-04-08 09:45:35.868 - INFO: l_g_pix: 8.4606e-05 l_g_fea: 3.6863e-01 l_g_gan: 6.3807e-03 l_d_real: 3.6625e-01 l_d_fake: 3.5980e-01 D_real: 1.9190e+01 D_fake: 1.8277e+01 +20-04-08 09:48:47.274 - INFO: l_g_pix: 7.3162e-05 l_g_fea: 3.1048e-01 l_g_gan: 2.1649e-02 l_d_real: 1.6482e-02 l_d_fake: 1.8593e-02 D_real: 1.8818e+01 D_fake: 1.4505e+01 +20-04-08 09:52:03.070 - INFO: l_g_pix: 1.0304e-04 l_g_fea: 4.7376e-01 l_g_gan: 2.4265e-02 l_d_real: 1.0438e-02 l_d_fake: 1.1091e-02 D_real: 3.5038e+01 D_fake: 3.0196e+01 +20-04-08 09:55:15.529 - INFO: l_g_pix: 1.0903e-04 l_g_fea: 4.8354e-01 l_g_gan: 1.3430e-02 l_d_real: 1.0221e-01 l_d_fake: 8.9349e-02 D_real: 3.1732e+01 D_fake: 2.9141e+01 +20-04-08 09:58:26.305 - INFO: l_g_pix: 7.7658e-05 l_g_fea: 3.9087e-01 l_g_gan: 3.4445e-03 l_d_real: 8.2454e-01 l_d_fake: 8.3774e-01 D_real: 2.3893e+01 D_fake: 2.4035e+01 +20-04-08 09:58:26.999 - INFO: Models and training states saved. +20-04-08 09:59:24.978 - INFO: # Validation # PSNR: 31.586, SSIM: 0.83728, LPIPS: 0.021512 +20-04-08 09:59:24.979 - INFO: psnr: 31.586, ssim: 0.83728, lpips: 0.021512 +20-04-08 10:02:34.506 - INFO: l_g_pix: 9.1295e-05 l_g_fea: 4.2238e-01 l_g_gan: 1.6923e-02 l_d_real: 4.0475e-02 l_d_fake: 3.9031e-02 D_real: 1.4646e+01 D_fake: 1.1301e+01 +20-04-08 10:05:46.251 - INFO: l_g_pix: 7.2124e-05 l_g_fea: 3.6736e-01 l_g_gan: 1.6383e-02 l_d_real: 4.7529e-02 l_d_fake: 4.9381e-02 D_real: 3.3638e+01 D_fake: 3.0410e+01 +20-04-08 10:08:58.172 - INFO: l_g_pix: 1.0157e-04 l_g_fea: 5.0587e-01 l_g_gan: 2.1766e-02 l_d_real: 1.6283e-02 l_d_fake: 1.8271e-02 D_real: 3.2044e+01 D_fake: 2.7708e+01 +20-04-08 10:12:09.972 - INFO: l_g_pix: 7.1377e-05 l_g_fea: 3.3536e-01 l_g_gan: 1.6226e-02 l_d_real: 4.8220e-02 l_d_fake: 4.9480e-02 D_real: 2.8594e+01 D_fake: 2.5398e+01 +20-04-08 10:15:21.803 - INFO: l_g_pix: 9.4143e-05 l_g_fea: 5.1620e-01 l_g_gan: 5.1369e-03 l_d_real: 5.6143e-01 l_d_fake: 5.7347e-01 D_real: 2.8430e+01 D_fake: 2.7970e+01 +20-04-08 10:18:33.948 - INFO: l_g_pix: 1.0077e-04 l_g_fea: 4.5644e-01 l_g_gan: 4.3270e-03 l_d_real: 6.5939e-01 l_d_fake: 6.6515e-01 D_real: 3.0419e+01 D_fake: 3.0216e+01 +20-04-08 10:21:45.177 - INFO: l_g_pix: 8.4642e-05 l_g_fea: 3.7343e-01 l_g_gan: 1.0512e-02 l_d_real: 1.5449e-01 l_d_fake: 1.5429e-01 D_real: 3.9429e+01 D_fake: 3.7481e+01 +20-04-08 10:24:56.424 - INFO: l_g_pix: 1.0092e-04 l_g_fea: 4.8560e-01 l_g_gan: 2.2550e-02 l_d_real: 1.3632e-02 l_d_fake: 1.3008e-02 D_real: 2.1308e+01 D_fake: 1.6811e+01 +20-04-08 10:28:08.468 - INFO: l_g_pix: 1.0174e-04 l_g_fea: 4.6663e-01 l_g_gan: 1.3549e-02 l_d_real: 8.5988e-02 l_d_fake: 8.5829e-02 D_real: 2.2660e+01 D_fake: 2.0036e+01 +20-04-08 10:31:20.384 - INFO: l_g_pix: 7.8339e-05 l_g_fea: 3.7394e-01 l_g_gan: 3.6930e-03 l_d_real: 6.9525e-01 l_d_fake: 6.9162e-01 D_real: 3.4160e+01 D_fake: 3.4115e+01 +20-04-08 10:34:31.404 - INFO: l_g_pix: 9.1582e-05 l_g_fea: 4.0301e-01 l_g_gan: 1.1981e-02 l_d_real: 1.1124e-01 l_d_fake: 1.2597e-01 D_real: 2.0815e+01 D_fake: 1.8538e+01 +20-04-08 10:37:43.340 - INFO: l_g_pix: 7.2129e-05 l_g_fea: 3.1080e-01 l_g_gan: 1.6200e-02 l_d_real: 5.9550e-02 l_d_fake: 4.5534e-02 D_real: 2.6344e+01 D_fake: 2.3156e+01 +20-04-08 10:40:54.330 - INFO: l_g_pix: 1.0504e-04 l_g_fea: 4.9712e-01 l_g_gan: 1.4326e-02 l_d_real: 7.6811e-02 l_d_fake: 6.9327e-02 D_real: 2.7594e+01 D_fake: 2.4802e+01 +20-04-08 10:44:05.649 - INFO: l_g_pix: 1.4520e-04 l_g_fea: 4.7054e-01 l_g_gan: 1.4995e-02 l_d_real: 6.4199e-02 l_d_fake: 6.6485e-02 D_real: 4.1234e+01 D_fake: 3.8300e+01 +20-04-08 10:47:16.587 - INFO: l_g_pix: 1.0025e-04 l_g_fea: 4.9274e-01 l_g_gan: 1.0547e-02 l_d_real: 1.6118e-01 l_d_fake: 1.4785e-01 D_real: 2.8588e+01 D_fake: 2.6633e+01 +20-04-08 10:50:28.168 - INFO: l_g_pix: 7.4848e-05 l_g_fea: 4.6097e-01 l_g_gan: 1.6849e-02 l_d_real: 4.1367e-02 l_d_fake: 4.6369e-02 D_real: 3.2983e+01 D_fake: 2.9657e+01 +20-04-08 10:53:39.803 - INFO: l_g_pix: 9.2224e-05 l_g_fea: 5.0241e-01 l_g_gan: 1.4287e-02 l_d_real: 7.5710e-02 l_d_fake: 7.1675e-02 D_real: 3.9628e+01 D_fake: 3.6844e+01 +20-04-08 10:56:51.621 - INFO: l_g_pix: 8.5605e-05 l_g_fea: 4.0358e-01 l_g_gan: 3.5205e-02 l_d_real: 1.0775e-03 l_d_fake: 1.1094e-03 D_real: 3.3091e+01 D_fake: 2.6051e+01 +20-04-08 11:00:03.476 - INFO: l_g_pix: 8.4902e-05 l_g_fea: 4.3069e-01 l_g_gan: 9.4613e-03 l_d_real: 2.1155e-01 l_d_fake: 2.0178e-01 D_real: 2.0850e+01 D_fake: 1.9165e+01 +20-04-08 11:03:14.972 - INFO: l_g_pix: 9.5420e-05 l_g_fea: 3.6463e-01 l_g_gan: 1.5749e-02 l_d_real: 5.0805e-02 l_d_fake: 6.2382e-02 D_real: 3.8599e+01 D_fake: 3.5506e+01 +20-04-08 11:06:26.640 - INFO: l_g_pix: 8.6489e-05 l_g_fea: 4.6337e-01 l_g_gan: 7.0711e-03 l_d_real: 3.4648e-01 l_d_fake: 3.4381e-01 D_real: 1.5251e+01 D_fake: 1.4182e+01 +20-04-08 11:09:38.551 - INFO: l_g_pix: 9.1203e-05 l_g_fea: 3.6313e-01 l_g_gan: 6.5567e-03 l_d_real: 3.6930e-01 l_d_fake: 3.6268e-01 D_real: 1.7044e+01 D_fake: 1.6099e+01 +20-04-08 11:12:49.829 - INFO: l_g_pix: 6.2295e-05 l_g_fea: 3.0174e-01 l_g_gan: 1.3300e-03 l_d_real: 1.6453e+00 l_d_fake: 1.6255e+00 D_real: 2.5245e+01 D_fake: 2.6615e+01 +20-04-08 11:16:00.862 - INFO: l_g_pix: 8.6407e-05 l_g_fea: 3.6828e-01 l_g_gan: 2.3695e-02 l_d_real: 9.6441e-03 l_d_fake: 9.6197e-03 D_real: 1.8451e+01 D_fake: 1.3721e+01 +20-04-08 11:19:12.816 - INFO: l_g_pix: 1.3937e-04 l_g_fea: 4.7746e-01 l_g_gan: 5.2107e-03 l_d_real: 4.8281e-01 l_d_fake: 5.0336e-01 D_real: 3.1397e+01 D_fake: 3.0848e+01 +20-04-08 11:19:13.236 - INFO: Models and training states saved. +20-04-08 11:20:07.533 - INFO: # Validation # PSNR: 31.707, SSIM: 0.83736, LPIPS: 0.025025 +20-04-08 11:20:07.533 - INFO: psnr: 31.707, ssim: 0.83736, lpips: 0.025025 +20-04-08 11:23:17.011 - INFO: l_g_pix: 8.4399e-05 l_g_fea: 3.9475e-01 l_g_gan: 8.3826e-03 l_d_real: 2.3837e-01 l_d_fake: 2.2884e-01 D_real: 2.8196e+01 D_fake: 2.6753e+01 +20-04-08 11:26:28.856 - INFO: l_g_pix: 5.8676e-05 l_g_fea: 3.4068e-01 l_g_gan: 9.7273e-03 l_d_real: 1.7016e-01 l_d_fake: 1.6700e-01 D_real: 1.0091e+01 D_fake: 8.3137e+00 +20-04-08 11:29:39.748 - INFO: l_g_pix: 9.8421e-05 l_g_fea: 4.4285e-01 l_g_gan: 1.2992e-02 l_d_real: 1.0682e-01 l_d_fake: 1.0580e-01 D_real: 4.5131e+01 D_fake: 4.2639e+01 +20-04-08 11:32:51.689 - INFO: l_g_pix: 1.1132e-04 l_g_fea: 5.8481e-01 l_g_gan: 4.7971e-03 l_d_real: 5.7820e-01 l_d_fake: 5.8865e-01 D_real: 3.0161e+01 D_fake: 2.9785e+01 +20-04-08 11:36:03.037 - INFO: l_g_pix: 1.0315e-04 l_g_fea: 4.0235e-01 l_g_gan: 1.1810e-02 l_d_real: 1.3489e-01 l_d_fake: 1.3044e-01 D_real: 2.7536e+01 D_fake: 2.5307e+01 +20-04-08 11:39:14.283 - INFO: l_g_pix: 7.9736e-05 l_g_fea: 3.9716e-01 l_g_gan: 3.5707e-03 l_d_real: 7.6029e-01 l_d_fake: 7.8424e-01 D_real: 1.7076e+01 D_fake: 1.7134e+01 +20-04-08 11:42:26.232 - INFO: l_g_pix: 1.2331e-04 l_g_fea: 5.4366e-01 l_g_gan: 1.8817e-02 l_d_real: 4.7194e-02 l_d_fake: 3.3282e-02 D_real: 3.8620e+01 D_fake: 3.4896e+01 +20-04-08 11:45:37.724 - INFO: l_g_pix: 1.2734e-04 l_g_fea: 5.4089e-01 l_g_gan: 3.7312e-02 l_d_real: 7.5509e-04 l_d_fake: 7.3124e-04 D_real: 3.4474e+01 D_fake: 2.7013e+01 +20-04-08 11:48:48.753 - INFO: l_g_pix: 9.7564e-05 l_g_fea: 3.8219e-01 l_g_gan: 1.8210e-02 l_d_real: 3.6166e-02 l_d_fake: 4.0302e-02 D_real: 2.2230e+01 D_fake: 1.8626e+01 +20-04-08 11:51:59.993 - INFO: l_g_pix: 1.1054e-04 l_g_fea: 5.2619e-01 l_g_gan: 4.9798e-03 l_d_real: 5.4437e-01 l_d_fake: 5.6429e-01 D_real: 2.2976e+01 D_fake: 2.2534e+01 +20-04-08 11:55:12.187 - INFO: l_g_pix: 1.1443e-04 l_g_fea: 4.8981e-01 l_g_gan: 1.3574e-02 l_d_real: 9.5324e-02 l_d_fake: 7.9442e-02 D_real: 2.0999e+01 D_fake: 1.8371e+01 +20-04-08 11:58:23.951 - INFO: l_g_pix: 1.0960e-04 l_g_fea: 4.7269e-01 l_g_gan: 2.2948e-02 l_d_real: 1.2960e-02 l_d_fake: 1.3537e-02 D_real: 3.6144e+01 D_fake: 3.1567e+01 +20-04-08 12:01:34.910 - INFO: l_g_pix: 6.7839e-05 l_g_fea: 3.8172e-01 l_g_gan: 8.5582e-05 l_d_real: 4.4169e+00 l_d_fake: 4.4136e+00 D_real: 3.2520e+01 D_fake: 3.6919e+01 +20-04-08 12:04:46.857 - INFO: l_g_pix: 7.5788e-05 l_g_fea: 3.8964e-01 l_g_gan: 7.7760e-03 l_d_real: 2.8510e-01 l_d_fake: 2.7152e-01 D_real: 2.8408e+01 D_fake: 2.7131e+01 +20-04-08 12:07:58.456 - INFO: l_g_pix: 8.2118e-05 l_g_fea: 4.5710e-01 l_g_gan: 2.0561e-02 l_d_real: 1.9605e-02 l_d_fake: 2.0079e-02 D_real: 2.8136e+01 D_fake: 2.4043e+01 +20-04-08 12:11:10.392 - INFO: l_g_pix: 1.0983e-04 l_g_fea: 5.2484e-01 l_g_gan: 1.3031e-02 l_d_real: 9.1141e-02 l_d_fake: 8.8119e-02 D_real: 2.3221e+01 D_fake: 2.0704e+01 +20-04-08 12:14:22.316 - INFO: l_g_pix: 8.6402e-05 l_g_fea: 3.9055e-01 l_g_gan: 1.7114e-02 l_d_real: 3.9201e-02 l_d_fake: 4.0036e-02 D_real: 2.6510e+01 D_fake: 2.3126e+01 +20-04-08 12:17:33.975 - INFO: l_g_pix: 7.1973e-05 l_g_fea: 3.0789e-01 l_g_gan: 2.1463e-02 l_d_real: 2.1142e-02 l_d_fake: 1.7941e-02 D_real: 2.1724e+01 D_fake: 1.7451e+01 +20-04-08 12:20:45.327 - INFO: l_g_pix: 7.7106e-05 l_g_fea: 3.9132e-01 l_g_gan: 8.6475e-03 l_d_real: 2.5509e-01 l_d_fake: 2.7735e-01 D_real: 3.3331e+01 D_fake: 3.1868e+01 +20-04-08 12:23:57.236 - INFO: l_g_pix: 1.0091e-04 l_g_fea: 4.5115e-01 l_g_gan: 2.5025e-02 l_d_real: 8.7806e-03 l_d_fake: 8.3273e-03 D_real: 2.3145e+01 D_fake: 1.8148e+01 +20-04-08 12:27:09.234 - INFO: l_g_pix: 1.1384e-04 l_g_fea: 4.6083e-01 l_g_gan: 3.6259e-03 l_d_real: 7.7017e-01 l_d_fake: 7.4768e-01 D_real: 2.2844e+01 D_fake: 2.2878e+01 +20-04-08 12:30:20.498 - INFO: l_g_pix: 1.0351e-04 l_g_fea: 4.6644e-01 l_g_gan: 5.1701e-03 l_d_real: 4.9612e-01 l_d_fake: 5.3940e-01 D_real: 2.0242e+01 D_fake: 1.9725e+01 +20-04-08 12:33:32.392 - INFO: l_g_pix: 8.8206e-05 l_g_fea: 4.7922e-01 l_g_gan: 1.8928e-02 l_d_real: 2.6224e-02 l_d_fake: 2.8529e-02 D_real: 4.4897e+01 D_fake: 4.1139e+01 +20-04-08 12:36:44.319 - INFO: l_g_pix: 1.0034e-04 l_g_fea: 4.6636e-01 l_g_gan: 5.5660e-03 l_d_real: 5.0863e-01 l_d_fake: 5.1069e-01 D_real: 2.5749e+01 D_fake: 2.5146e+01 +20-04-08 12:39:56.421 - INFO: l_g_pix: 1.0487e-04 l_g_fea: 4.0737e-01 l_g_gan: 5.5554e-03 l_d_real: 4.6801e-01 l_d_fake: 4.5923e-01 D_real: 2.6374e+01 D_fake: 2.5727e+01 +20-04-08 12:39:56.837 - INFO: Models and training states saved. +20-04-08 12:40:52.455 - INFO: # Validation # PSNR: 31.666, SSIM: 0.82885, LPIPS: 0.025048 +20-04-08 12:40:52.455 - INFO: psnr: 31.666, ssim: 0.82885, lpips: 0.025048 +20-04-08 12:44:02.584 - INFO: l_g_pix: 7.8820e-05 l_g_fea: 3.8524e-01 l_g_gan: 5.8498e-03 l_d_real: 4.6295e-01 l_d_fake: 4.9778e-01 D_real: 1.2058e+01 D_fake: 1.1368e+01 +20-04-08 12:47:14.042 - INFO: l_g_pix: 6.8576e-05 l_g_fea: 3.3123e-01 l_g_gan: 2.1452e-03 l_d_real: 1.1652e+00 l_d_fake: 1.1403e+00 D_real: 9.1002e+00 D_fake: 9.8239e+00 +20-04-08 12:50:25.448 - INFO: l_g_pix: 9.6194e-05 l_g_fea: 4.4334e-01 l_g_gan: 7.0167e-03 l_d_real: 3.5388e-01 l_d_fake: 3.5685e-01 D_real: 2.0074e+01 D_fake: 1.9026e+01 +20-04-08 12:53:37.348 - INFO: l_g_pix: 1.1051e-04 l_g_fea: 4.4968e-01 l_g_gan: 1.8630e-02 l_d_real: 2.9275e-02 l_d_fake: 3.0316e-02 D_real: 2.3347e+01 D_fake: 1.9651e+01 +20-04-08 12:56:49.032 - INFO: l_g_pix: 9.8892e-05 l_g_fea: 5.2699e-01 l_g_gan: 1.6232e-02 l_d_real: 5.4319e-02 l_d_fake: 5.0333e-02 D_real: 3.8190e+01 D_fake: 3.4996e+01 +20-04-08 13:00:01.038 - INFO: l_g_pix: 7.7757e-05 l_g_fea: 3.7518e-01 l_g_gan: 7.7682e-03 l_d_real: 2.8400e-01 l_d_fake: 3.0365e-01 D_real: 2.4205e+01 D_fake: 2.2946e+01 +20-04-08 13:03:12.193 - INFO: l_g_pix: 1.2059e-04 l_g_fea: 5.8988e-01 l_g_gan: 1.3475e-02 l_d_real: 9.1237e-02 l_d_fake: 8.8869e-02 D_real: 3.0993e+01 D_fake: 2.8388e+01 +20-04-08 13:06:23.444 - INFO: l_g_pix: 9.1041e-05 l_g_fea: 4.0999e-01 l_g_gan: 2.1810e-02 l_d_real: 1.5954e-02 l_d_fake: 1.5805e-02 D_real: 2.8318e+01 D_fake: 2.3971e+01 +20-04-08 13:09:35.562 - INFO: l_g_pix: 6.4771e-05 l_g_fea: 3.2672e-01 l_g_gan: 1.0535e-02 l_d_real: 1.5564e-01 l_d_fake: 1.5709e-01 D_real: 9.9335e+00 D_fake: 7.9828e+00 +20-04-08 13:12:47.019 - INFO: l_g_pix: 8.0479e-05 l_g_fea: 3.8922e-01 l_g_gan: 5.1614e-03 l_d_real: 5.1113e-01 l_d_fake: 5.3120e-01 D_real: 2.2392e+01 D_fake: 2.1881e+01 +20-04-08 13:15:58.770 - INFO: l_g_pix: 8.3518e-05 l_g_fea: 4.7560e-01 l_g_gan: 1.7979e-02 l_d_real: 3.5327e-02 l_d_fake: 3.4638e-02 D_real: 2.8953e+01 D_fake: 2.5392e+01 +20-04-08 13:19:10.185 - INFO: l_g_pix: 1.1550e-04 l_g_fea: 4.8845e-01 l_g_gan: 1.0048e-02 l_d_real: 1.9036e-01 l_d_fake: 1.9479e-01 D_real: 3.9686e+01 D_fake: 3.7869e+01 +20-04-08 13:22:21.184 - INFO: l_g_pix: 7.0514e-05 l_g_fea: 3.4656e-01 l_g_gan: 1.1059e-02 l_d_real: 1.5587e-01 l_d_fake: 1.4680e-01 D_real: 2.9530e+01 D_fake: 2.7469e+01 +20-04-08 13:25:38.327 - INFO: l_g_pix: 6.6443e-05 l_g_fea: 3.3086e-01 l_g_gan: 1.4206e-02 l_d_real: 7.5666e-02 l_d_fake: 8.1044e-02 D_real: 3.7354e+01 D_fake: 3.4591e+01 +20-04-08 13:28:49.614 - INFO: l_g_pix: 7.2023e-05 l_g_fea: 3.1264e-01 l_g_gan: 1.8857e-03 l_d_real: 1.4302e+00 l_d_fake: 1.4197e+00 D_real: 2.1124e+01 D_fake: 2.2172e+01 +20-04-08 13:32:00.710 - INFO: l_g_pix: 8.1460e-05 l_g_fea: 4.4652e-01 l_g_gan: 1.3038e-02 l_d_real: 8.5912e-02 l_d_fake: 8.4123e-02 D_real: 2.8659e+01 D_fake: 2.6136e+01 +20-04-08 13:35:12.887 - INFO: l_g_pix: 1.2252e-04 l_g_fea: 5.0701e-01 l_g_gan: 2.5019e-02 l_d_real: 8.2340e-03 l_d_fake: 1.0609e-02 D_real: 3.9224e+01 D_fake: 3.4229e+01 +20-04-08 13:38:24.844 - INFO: l_g_pix: 7.3968e-05 l_g_fea: 4.0305e-01 l_g_gan: 2.6276e-03 l_d_real: 1.0387e+00 l_d_fake: 1.0202e+00 D_real: 2.4876e+01 D_fake: 2.5380e+01 +20-04-08 13:41:35.769 - INFO: l_g_pix: 7.6268e-05 l_g_fea: 2.9916e-01 l_g_gan: 4.1112e-03 l_d_real: 6.9703e-01 l_d_fake: 6.8263e-01 D_real: 2.2986e+01 D_fake: 2.2854e+01 +20-04-08 13:44:47.657 - INFO: l_g_pix: 9.6125e-05 l_g_fea: 4.7088e-01 l_g_gan: 1.9905e-02 l_d_real: 2.2077e-02 l_d_fake: 2.2189e-02 D_real: 1.5032e+01 D_fake: 1.1074e+01 +20-04-08 13:47:59.434 - INFO: l_g_pix: 7.9819e-05 l_g_fea: 3.6222e-01 l_g_gan: 2.5169e-02 l_d_real: 8.2170e-03 l_d_fake: 7.4424e-03 D_real: 2.3286e+01 D_fake: 1.8260e+01 +20-04-08 13:51:11.418 - INFO: l_g_pix: 1.3457e-04 l_g_fea: 4.3195e-01 l_g_gan: 2.5928e-03 l_d_real: 1.0308e+00 l_d_fake: 1.0338e+00 D_real: 4.1259e+01 D_fake: 4.1772e+01 +20-04-08 13:54:23.508 - INFO: l_g_pix: 9.3337e-05 l_g_fea: 4.7789e-01 l_g_gan: 1.0285e-03 l_d_real: 1.8700e+00 l_d_fake: 1.8685e+00 D_real: 3.2047e+01 D_fake: 3.3710e+01 +20-04-08 13:57:34.871 - INFO: l_g_pix: 1.1104e-04 l_g_fea: 4.2618e-01 l_g_gan: 2.4884e-02 l_d_real: 8.9633e-03 l_d_fake: 8.7205e-03 D_real: 4.3170e+01 D_fake: 3.8202e+01 +20-04-08 14:00:45.930 - INFO: l_g_pix: 6.9943e-05 l_g_fea: 3.4139e-01 l_g_gan: 2.2304e-02 l_d_real: 1.3776e-02 l_d_fake: 1.3839e-02 D_real: 1.7973e+01 D_fake: 1.3526e+01 +20-04-08 14:00:46.328 - INFO: Models and training states saved. +20-04-08 14:01:39.876 - INFO: # Validation # PSNR: 31.957, SSIM: 0.84511, LPIPS: 0.02156 +20-04-08 14:01:39.876 - INFO: psnr: 31.957, ssim: 0.84511, lpips: 0.02156 +20-04-08 14:05:02.197 - INFO: l_g_pix: 1.0871e-04 l_g_fea: 4.2472e-01 l_g_gan: 1.7997e-02 l_d_real: 3.5486e-02 l_d_fake: 3.3870e-02 D_real: 3.6817e+01 D_fake: 3.3252e+01 +20-04-08 14:08:13.945 - INFO: l_g_pix: 9.6418e-05 l_g_fea: 4.7009e-01 l_g_gan: 7.4825e-03 l_d_real: 3.2376e-01 l_d_fake: 3.0691e-01 D_real: 2.5192e+01 D_fake: 2.4011e+01 +20-04-08 14:11:25.944 - INFO: l_g_pix: 7.4044e-05 l_g_fea: 3.3910e-01 l_g_gan: 5.7545e-03 l_d_real: 4.2743e-01 l_d_fake: 4.2661e-01 D_real: 1.5348e+01 D_fake: 1.4624e+01 +20-04-08 14:14:37.721 - INFO: l_g_pix: 6.2489e-05 l_g_fea: 3.5519e-01 l_g_gan: 7.1782e-03 l_d_real: 2.9517e-01 l_d_fake: 3.1425e-01 D_real: 2.7780e+01 D_fake: 2.6649e+01 +20-04-08 14:17:49.692 - INFO: l_g_pix: 7.9266e-05 l_g_fea: 3.7570e-01 l_g_gan: 1.3471e-02 l_d_real: 8.3435e-02 l_d_fake: 8.5440e-02 D_real: 2.8382e+01 D_fake: 2.5773e+01 +20-04-08 14:21:02.488 - INFO: l_g_pix: 1.1850e-04 l_g_fea: 4.6456e-01 l_g_gan: 6.2571e-03 l_d_real: 3.8878e-01 l_d_fake: 4.2184e-01 D_real: 2.8625e+01 D_fake: 2.7779e+01 +20-04-08 14:24:14.027 - INFO: l_g_pix: 1.2145e-04 l_g_fea: 5.1518e-01 l_g_gan: 1.0213e-02 l_d_real: 1.6966e-01 l_d_fake: 1.7173e-01 D_real: 2.6755e+01 D_fake: 2.4883e+01 +20-04-08 14:27:26.490 - INFO: l_g_pix: 1.3483e-04 l_g_fea: 5.6430e-01 l_g_gan: 2.0977e-02 l_d_real: 2.0083e-02 l_d_fake: 2.0102e-02 D_real: 3.7861e+01 D_fake: 3.3686e+01 +20-04-08 14:30:39.120 - INFO: l_g_pix: 1.0602e-04 l_g_fea: 4.6912e-01 l_g_gan: 3.1023e-02 l_d_real: 2.5201e-03 l_d_fake: 2.8452e-03 D_real: 4.4509e+01 D_fake: 3.8307e+01 +20-04-08 14:33:50.861 - INFO: l_g_pix: 7.3960e-05 l_g_fea: 3.2469e-01 l_g_gan: 1.7368e-02 l_d_real: 3.7590e-02 l_d_fake: 3.8938e-02 D_real: 3.9339e+01 D_fake: 3.5904e+01 +20-04-08 14:37:02.828 - INFO: l_g_pix: 7.5043e-05 l_g_fea: 4.1653e-01 l_g_gan: 2.1849e-02 l_d_real: 1.6154e-02 l_d_fake: 1.6063e-02 D_real: 3.6629e+01 D_fake: 3.2275e+01 +20-04-08 14:40:15.100 - INFO: l_g_pix: 8.6163e-05 l_g_fea: 3.5848e-01 l_g_gan: 3.3526e-02 l_d_real: 1.6393e-03 l_d_fake: 1.6348e-03 D_real: 3.2096e+01 D_fake: 2.5393e+01 +20-04-08 14:44:05.715 - INFO: l_g_pix: 8.0959e-05 l_g_fea: 5.0482e-01 l_g_gan: 1.1946e-02 l_d_real: 1.2655e-01 l_d_fake: 1.1669e-01 D_real: 2.2652e+01 D_fake: 2.0384e+01 +20-04-08 14:47:17.095 - INFO: l_g_pix: 9.3229e-05 l_g_fea: 4.1806e-01 l_g_gan: 2.7294e-02 l_d_real: 6.0480e-03 l_d_fake: 5.0726e-03 D_real: 2.7390e+01 D_fake: 2.1937e+01 +20-04-08 14:50:28.473 - INFO: l_g_pix: 8.5905e-05 l_g_fea: 4.1882e-01 l_g_gan: 9.9754e-03 l_d_real: 2.0278e-01 l_d_fake: 1.9007e-01 D_real: 2.4695e+01 D_fake: 2.2896e+01 +20-04-08 14:53:40.927 - INFO: l_g_pix: 1.1244e-04 l_g_fea: 4.6423e-01 l_g_gan: 1.1189e-02 l_d_real: 1.3119e-01 l_d_fake: 1.2884e-01 D_real: 3.0634e+01 D_fake: 2.8526e+01 +20-04-08 14:56:52.755 - INFO: l_g_pix: 4.7035e-05 l_g_fea: 2.7042e-01 l_g_gan: 7.0516e-05 l_d_real: 4.5909e+00 l_d_fake: 4.5927e+00 D_real: 2.3144e+01 D_fake: 2.7721e+01 +20-04-08 15:00:04.626 - INFO: l_g_pix: 9.2497e-05 l_g_fea: 4.0032e-01 l_g_gan: 1.6503e-02 l_d_real: 5.0588e-02 l_d_fake: 4.8899e-02 D_real: 2.9905e+01 D_fake: 2.6654e+01 +20-04-08 15:03:16.793 - INFO: l_g_pix: 8.8123e-05 l_g_fea: 4.3612e-01 l_g_gan: 6.3293e-03 l_d_real: 4.1527e-01 l_d_fake: 4.1298e-01 D_real: 3.3342e+01 D_fake: 3.2490e+01 +20-04-08 15:06:29.116 - INFO: l_g_pix: 8.7832e-05 l_g_fea: 4.3456e-01 l_g_gan: 1.2519e-02 l_d_real: 1.1098e-01 l_d_fake: 1.0076e-01 D_real: 3.3816e+01 D_fake: 3.1418e+01 +20-04-08 15:09:41.128 - INFO: l_g_pix: 6.8710e-05 l_g_fea: 3.0844e-01 l_g_gan: 1.6576e-02 l_d_real: 3.9853e-02 l_d_fake: 4.1557e-02 D_real: 2.5945e+01 D_fake: 2.2671e+01 +20-04-08 15:12:53.634 - INFO: l_g_pix: 1.1139e-04 l_g_fea: 4.1150e-01 l_g_gan: 1.5117e-02 l_d_real: 5.8802e-02 l_d_fake: 6.6528e-02 D_real: 2.3641e+01 D_fake: 2.0681e+01 +20-04-08 15:16:05.063 - INFO: l_g_pix: 9.9405e-05 l_g_fea: 4.4981e-01 l_g_gan: 3.1657e-02 l_d_real: 2.1164e-03 l_d_fake: 2.1385e-03 D_real: 3.9192e+01 D_fake: 3.2863e+01 +20-04-08 15:19:16.416 - INFO: l_g_pix: 7.1188e-05 l_g_fea: 3.7420e-01 l_g_gan: 3.6964e-02 l_d_real: 7.8114e-04 l_d_fake: 7.6195e-04 D_real: 3.1047e+01 D_fake: 2.3655e+01 +20-04-08 15:22:27.639 - INFO: l_g_pix: 8.1274e-05 l_g_fea: 4.2020e-01 l_g_gan: 2.0803e-02 l_d_real: 1.9963e-02 l_d_fake: 2.2775e-02 D_real: 3.9032e+01 D_fake: 3.4893e+01 +20-04-08 15:22:28.071 - INFO: Models and training states saved. +20-04-08 15:23:28.728 - INFO: # Validation # PSNR: 31.911, SSIM: 0.84011, LPIPS: 0.021993 +20-04-08 15:23:28.728 - INFO: psnr: 31.911, ssim: 0.84011, lpips: 0.021993 +20-04-08 15:27:22.320 - INFO: l_g_pix: 8.9808e-05 l_g_fea: 5.2652e-01 l_g_gan: 1.2289e-02 l_d_real: 1.1273e-01 l_d_fake: 1.0778e-01 D_real: 2.9828e+01 D_fake: 2.7480e+01 +20-04-08 15:30:33.938 - INFO: l_g_pix: 7.0342e-05 l_g_fea: 3.8350e-01 l_g_gan: 3.1734e-03 l_d_real: 8.5146e-01 l_d_fake: 8.5725e-01 D_real: 3.2520e+01 D_fake: 3.2740e+01 +20-04-08 15:33:45.751 - INFO: l_g_pix: 7.3725e-05 l_g_fea: 4.5527e-01 l_g_gan: 5.6294e-04 l_d_real: 2.3903e+00 l_d_fake: 2.3982e+00 D_real: 2.4902e+01 D_fake: 2.7183e+01 +20-04-08 15:36:57.551 - INFO: l_g_pix: 1.0095e-04 l_g_fea: 4.4889e-01 l_g_gan: 1.9923e-03 l_d_real: 1.2473e+00 l_d_fake: 1.2527e+00 D_real: 3.8294e+01 D_fake: 3.9145e+01 +20-04-08 15:40:09.536 - INFO: l_g_pix: 1.0308e-04 l_g_fea: 4.5778e-01 l_g_gan: 2.3922e-03 l_d_real: 1.1193e+00 l_d_fake: 1.1194e+00 D_real: 3.2152e+01 D_fake: 3.2793e+01 +20-04-08 15:43:21.104 - INFO: l_g_pix: 6.1324e-05 l_g_fea: 2.9656e-01 l_g_gan: 6.8867e-03 l_d_real: 3.4754e-01 l_d_fake: 3.3801e-01 D_real: 2.2963e+01 D_fake: 2.1929e+01 +20-04-08 15:46:33.341 - INFO: l_g_pix: 1.0459e-04 l_g_fea: 4.8492e-01 l_g_gan: 1.4599e-02 l_d_real: 6.7046e-02 l_d_fake: 6.7745e-02 D_real: 2.7539e+01 D_fake: 2.4686e+01 +20-04-08 15:49:45.427 - INFO: l_g_pix: 1.0668e-04 l_g_fea: 3.7541e-01 l_g_gan: 1.6204e-02 l_d_real: 5.2408e-02 l_d_fake: 5.1632e-02 D_real: 3.0700e+01 D_fake: 2.7511e+01 +20-04-08 15:52:57.369 - INFO: l_g_pix: 1.0826e-04 l_g_fea: 4.7315e-01 l_g_gan: 7.7782e-03 l_d_real: 3.0886e-01 l_d_fake: 2.9322e-01 D_real: 4.0521e+01 D_fake: 3.9266e+01 +20-04-08 15:56:08.759 - INFO: l_g_pix: 8.1546e-05 l_g_fea: 4.0017e-01 l_g_gan: 1.8402e-02 l_d_real: 3.8400e-02 l_d_fake: 3.7732e-02 D_real: 2.7926e+01 D_fake: 2.4284e+01 +20-04-08 15:59:20.703 - INFO: l_g_pix: 8.9337e-05 l_g_fea: 4.7962e-01 l_g_gan: 5.8663e-03 l_d_real: 4.2844e-01 l_d_fake: 4.5749e-01 D_real: 3.6208e+01 D_fake: 3.5478e+01 +20-04-08 16:02:32.185 - INFO: l_g_pix: 8.8001e-05 l_g_fea: 4.0189e-01 l_g_gan: 9.7976e-03 l_d_real: 1.7284e-01 l_d_fake: 1.9486e-01 D_real: 3.7713e+01 D_fake: 3.5937e+01 diff --git a/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/val_200404-232220.log b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/val_200404-232220.log new file mode 100644 index 0000000000000000000000000000000000000000..e34fb87b933f9bbbdc274413d4cbf163431feada --- /dev/null +++ b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/val_200404-232220.log @@ -0,0 +1,12 @@ +20-04-05 00:44:24.317 - INFO: psnr: 27.42, ssim: 0.84759, lpips: 0.048405 +20-04-05 02:11:16.150 - INFO: psnr: 30.339, ssim: 0.85384, lpips: 0.052792 +20-04-05 04:05:27.848 - INFO: psnr: 30.61, ssim: 0.85395, lpips: 0.061615 +20-04-05 05:51:04.404 - INFO: psnr: 30.407, ssim: 0.85197, lpips: 0.058558 +20-04-05 07:12:27.561 - INFO: psnr: 30.135, ssim: 0.85044, lpips: 0.055354 +20-04-05 08:33:39.115 - INFO: psnr: 30.717, ssim: 0.85481, lpips: 0.05554 +20-04-05 09:54:46.002 - INFO: psnr: 30.577, ssim: 0.85183, lpips: 0.050674 +20-04-05 11:15:49.377 - INFO: psnr: 31.386, ssim: 0.84495, lpips: 0.048181 +20-04-05 12:37:34.815 - INFO: psnr: 31.466, ssim: 0.85016, lpips: 0.050883 +20-04-05 14:01:42.540 - INFO: psnr: 30.316, ssim: 0.8508, lpips: 0.052897 +20-04-05 15:26:48.264 - INFO: psnr: 31.382, ssim: 0.82734, lpips: 0.032429 +20-04-05 16:51:02.492 - INFO: psnr: 31.384, ssim: 0.82025, lpips: 0.035657 diff --git a/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/val_200405-174240.log b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/val_200405-174240.log new file mode 100644 index 0000000000000000000000000000000000000000..a6436d026f5fd67e9db1873d64601ab5d0a915b3 --- /dev/null +++ b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/val_200405-174240.log @@ -0,0 +1,16 @@ +20-04-05 19:03:31.587 - INFO: psnr: 31.38, ssim: 0.84938, lpips: 0.044759 +20-04-05 20:24:55.071 - INFO: psnr: 31.291, ssim: 0.84065, lpips: 0.045839 +20-04-05 21:47:14.153 - INFO: psnr: 30.979, ssim: 0.82168, lpips: 0.028558 +20-04-05 23:11:59.810 - INFO: psnr: 31.559, ssim: 0.84308, lpips: 0.040322 +20-04-06 00:37:19.174 - INFO: psnr: 29.643, ssim: 0.84546, lpips: 0.035241 +20-04-06 01:59:03.204 - INFO: psnr: 32.176, ssim: 0.84419, lpips: 0.038451 +20-04-06 03:31:31.236 - INFO: psnr: 30.396, ssim: 0.82259, lpips: 0.038421 +20-04-06 04:56:47.526 - INFO: psnr: 30.517, ssim: 0.83536, lpips: 0.031511 +20-04-06 06:21:25.733 - INFO: psnr: 31.392, ssim: 0.83527, lpips: 0.028824 +20-04-06 07:45:29.298 - INFO: psnr: 31.356, ssim: 0.83125, lpips: 0.035853 +20-04-06 09:06:33.446 - INFO: psnr: 31.994, ssim: 0.84802, lpips: 0.030219 +20-04-06 10:27:12.593 - INFO: psnr: 31.726, ssim: 0.83427, lpips: 0.032943 +20-04-06 11:47:45.813 - INFO: psnr: 31.123, ssim: 0.83208, lpips: 0.028689 +20-04-06 13:09:05.087 - INFO: psnr: 31.207, ssim: 0.83788, lpips: 0.027606 +20-04-06 14:29:53.025 - INFO: psnr: 31.706, ssim: 0.83449, lpips: 0.025505 +20-04-06 15:50:45.861 - INFO: psnr: 31.45, ssim: 0.83828, lpips: 0.029365 diff --git a/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/val_200406-173523.log b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/val_200406-173523.log new file mode 100644 index 0000000000000000000000000000000000000000..78f58f99d3358e088e4bb12d6efc5b9e8198aac0 --- /dev/null +++ b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/val_200406-173523.log @@ -0,0 +1,33 @@ +20-04-06 19:20:48.994 - INFO: psnr: 31.697, ssim: 0.84278, lpips: 0.028717 +20-04-06 20:41:27.697 - INFO: psnr: 31.331, ssim: 0.84292, lpips: 0.029 +20-04-06 22:02:23.796 - INFO: psnr: 31.695, ssim: 0.8385, lpips: 0.023378 +20-04-06 23:25:17.775 - INFO: psnr: 31.963, ssim: 0.83669, lpips: 0.022879 +20-04-07 01:05:54.635 - INFO: psnr: 31.61, ssim: 0.83506, lpips: 0.023187 +20-04-07 02:26:52.996 - INFO: psnr: 31.58, ssim: 0.83902, lpips: 0.027647 +20-04-07 03:55:26.415 - INFO: psnr: 31.683, ssim: 0.83672, lpips: 0.033828 +20-04-07 05:17:07.709 - INFO: psnr: 31.946, ssim: 0.84157, lpips: 0.027429 +20-04-07 06:40:22.094 - INFO: psnr: 31.665, ssim: 0.83291, lpips: 0.023863 +20-04-07 08:03:21.524 - INFO: psnr: 31.214, ssim: 0.82375, lpips: 0.02648 +20-04-07 09:25:38.479 - INFO: psnr: 31.704, ssim: 0.83812, lpips: 0.026149 +20-04-07 10:49:15.158 - INFO: psnr: 31.364, ssim: 0.84553, lpips: 0.023954 +20-04-07 12:10:31.371 - INFO: psnr: 31.463, ssim: 0.83763, lpips: 0.023403 +20-04-07 13:32:33.653 - INFO: psnr: 31.631, ssim: 0.83805, lpips: 0.027015 +20-04-07 15:01:34.929 - INFO: psnr: 32.057, ssim: 0.84512, lpips: 0.024246 +20-04-07 16:25:40.187 - INFO: psnr: 31.849, ssim: 0.84396, lpips: 0.024639 +20-04-07 17:47:27.326 - INFO: psnr: 31.943, ssim: 0.8444, lpips: 0.028102 +20-04-07 19:08:05.814 - INFO: psnr: 32.13, ssim: 0.8461, lpips: 0.02568 +20-04-07 20:29:06.859 - INFO: psnr: 32.041, ssim: 0.84476, lpips: 0.023769 +20-04-07 21:49:50.670 - INFO: psnr: 31.886, ssim: 0.84322, lpips: 0.025586 +20-04-07 23:10:40.927 - INFO: psnr: 32.004, ssim: 0.84224, lpips: 0.020475 +20-04-08 00:31:34.945 - INFO: psnr: 32.062, ssim: 0.84325, lpips: 0.02338 +20-04-08 01:52:20.613 - INFO: psnr: 31.699, ssim: 0.84084, lpips: 0.022714 +20-04-08 03:13:55.112 - INFO: psnr: 31.614, ssim: 0.84113, lpips: 0.021587 +20-04-08 04:36:04.450 - INFO: psnr: 31.57, ssim: 0.84031, lpips: 0.022215 +20-04-08 05:57:04.924 - INFO: psnr: 31.986, ssim: 0.84651, lpips: 0.023124 +20-04-08 07:17:55.014 - INFO: psnr: 31.657, ssim: 0.83257, lpips: 0.024597 +20-04-08 08:38:36.884 - INFO: psnr: 31.441, ssim: 0.82752, lpips: 0.025231 +20-04-08 09:59:24.979 - INFO: psnr: 31.586, ssim: 0.83728, lpips: 0.021512 +20-04-08 11:20:07.533 - INFO: psnr: 31.707, ssim: 0.83736, lpips: 0.025025 +20-04-08 12:40:52.455 - INFO: psnr: 31.666, ssim: 0.82885, lpips: 0.025048 +20-04-08 14:01:39.876 - INFO: psnr: 31.957, ssim: 0.84511, lpips: 0.02156 +20-04-08 15:23:28.728 - INFO: psnr: 31.911, ssim: 0.84011, lpips: 0.021993 diff --git a/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/readme.txt b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/readme.txt new file mode 100644 index 0000000000000000000000000000000000000000..2102ac50eec24f216b405b341ee468b81def7766 --- /dev/null +++ b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/readme.txt @@ -0,0 +1,6 @@ +NTSC DVD-spec encode x4 scale super-resolution for Anime Drawing style content. The dataset has a LOT of data throughout almost every frame, so it had a lot of stuff to learn. The resulting DVD-spec encode also had some blocking at times so it also learned to fight off blocking. + +I would recommend anyone working on NTSC DVD models to use this as a base-model for anime models, I wouldn't recommend doing so for anything else. + +HR: http://download.opencontent.netflix.com.s3.amazonaws.com/SolLevante/hdr10/SolLevante_HDR10_r2020_ST2084_UHD_24fps_1000nit.mov downscaled to 2880x1920 (720*4x480*4) +LR: Encoded the original HR file to NTSC DVD-spec using DVDStyler and remuxed it to MKV using MakeMKV which it then had every frame exported as a png frame sequence, untouched. \ No newline at end of file diff --git a/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/training_options_file.yml b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/training_options_file.yml new file mode 100644 index 0000000000000000000000000000000000000000..093f434390cd793990b948a7040c589c4edb8751 --- /dev/null +++ b/unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/training_options_file.yml @@ -0,0 +1,302 @@ + +# Reading and Operating this file +# ~ is basically Null/None/False e.t.c, you can either use ~ as the value, or comment out the line +# A commented-out line's value is typically it's default but is disabled due to it being commented out +# When a line starts with two #'s (comment line starting with #), then when un-commenting, it's intended to be disabled +# for example, under network_G everything but ESRGAN's values are commented-out, so to show which should still be +# enabled/disabled, a double-comment is used + +name: sollevante # put "debug" in the name to enable debug mode, only enable to test code works!!! +use_tb_logger: true +model: srragan # srragan | sr | srgan | ppon | asrragan +scale: 4 +gpu_ids: +- 0 + +datasets: + train: + name: sollevante-train + mode: LRHR + # high resolution / ground truth images + dataroot_HR: + - /mnt/8tb-hdd-1/datasets/sollevante/hr/train + # low resolution images. If there are missing LR images, they will be generated on the fly from HR + dataroot_LR: + - /mnt/8tb-hdd-1/datasets/sollevante/lr/train + subset_file: ~ + use_shuffle: true + znorm: false # true | false // To normalize images in [-1, 1] range. Default = None (range [0,1]). Can use with activation function like tanh. + n_workers: 8 # 0 to disable CPU multithreading, or an integrer representing CPU threads to use for dataloading + batch_size: 32 + HR_size: 128 #patch size. Default: 128. Needs to be coordinated with the patch size of the features network + # Color space conversion: "color" for both LR and HR, "color_LR" for LR independently, "color_HR" for HR independently + #color: y # remove for no conversion (RGB) | "y" for Y in YCbCr | "gray" to convert RGB to grayscale | "RGB" to convert gray to RGB + #color_LR: y # remove for no conversion (RGB) | "y" for Y in YCbCr | "gray" to convert RGB to grayscale | "RGB" to convert gray to RGB + #color_HR: y # remove for no conversion (RGB) | "y" for Y in YCbCr | "gray" to convert RGB to grayscale | "RGB" to convert gray to RGB + # LR and HR modifiers. Random flip LR and HR or ignore provided LRs and generate new ones on the fly with defined probability: + #rand_flip_LR_HR: false # flip LR and HR during training. + #flip_chance: 0.05 # Example: 0.05 = 5% chance of LR and HR flipping during training. + #aug_downscale: 0.2 # Example: 0.6 = 60% chance of generating LR on the fly, even if LR dataset exists + + # If manually configuring on the fly generation of LR: (else, it will automatically default to Matlab-like downscale algorithm (777) when/if required + lr_downscale: true + lr_downscale_types: + # select from [0,1,2,3,4,5,777] where each number is: (scale algorithm) + # - cv2.INTER_NEAREST + # - cv2.INTER_LINEAR + # - cv2.INTER_CUBIC + # - cv2.INTER_AREA + # - cv2.INTER_LANCZOS4 + # - cv2.INTER_LINEAR_EXACT + # - matlab.bicubic + - 1 + - 2 + - 777 + + # Rotations augmentations: + use_flip: true # flip lr with hr and hr with lr + use_rot: true # rotate images in 90 degree angles, + hr_rrot: false # rotate images in random degrees between -45 and 45 + + # Noise and blur augmentations: + lr_blur: false + lr_blur_types: + # select from: average | box | gaussian | bilateral | clean, `median` and `motion` aren't working yet + - gaussian + - clean + - clean + - clean + lr_noise: false + lr_noise_types: + # select from: gaussian | JPEG | quantize | poisson | dither | s&p | speckle | clean + - gaussian + - clean + - clean + - clean + - clean + lr_noise2: false + lr_noise_types2: + # select from: gaussian | JPEG | quantize | poisson | dither | s&p | speckle | clean + - dither + - dither + - clean + - clean + hr_noise: false + hr_noise_types: + # select from: gaussian | JPEG | quantize | poisson | dither | s&p | speckle | clean + - gaussian + - clean + - clean + - clean + - clean + + # Color augmentations + #lr_fringes: true + #lr_fringes_chance: 0.4 + #auto_levels: HR # "HR" | "LR" | "Both" //add auto levels to the images to expand dynamic range. Can use with SPL loss or (MS)-SSIM. + #rand_auto_levels: 0.7 # Example: 0.4 = 40% chance of adding auto levels to images on the fly + #unsharp_mask: true # add a un-sharpening mask to HR images. Can work well together with the HFEN loss function. + #rand_unsharp: 1 # Example: 0.5 = 50% chance of adding un-sharpening mask to HR images on the fly + + # Augmentations for classification or (maybe) in-painting networks: + #lr_cutout: false + #lr_erasing: false + val: + name: sollevante-val + mode: LRHR + dataroot_HR: + - /mnt/8tb-hdd-1/datasets/sollevante/hr/val + dataroot_LR: + - /mnt/8tb-hdd-1/datasets/sollevante/lr/val + znorm: false # true | false // To normalize images in [-1, 1] range. Default = None (range [0,1]). Can use with activation function like tanh. + + # Color space conversion: "color" for both LR and HR, "color_LR" for LR independently, "color_HR" for HR independently + #color: y # remove for no conversion (RGB) | "y" for Y in YCbCr | "gray" to convert RGB to grayscale | "RGB" to convert gray to RGB + #color_LR: y # remove for no conversion (RGB) | "y" for Y in YCbCr | "gray" to convert RGB to grayscale | "RGB" to convert gray to RGB + #color_HR: y # remove for no conversion (RGB) | "y" for Y in YCbCr | "gray" to convert RGB to grayscale | "RGB" to convert gray to RGB + + #hr_crop: false + lr_downscale: false + lr_downscale_types: + # select from [0,1,2,3,4,5] where each number is: (scale algorithm) + # - cv2.INTER_NEAREST + # - cv2.INTER_LINEAR + # - cv2.INTER_CUBIC + # - cv2.INTER_AREA + # - cv2.INTER_LANCZOS4 + # - cv2.INTER_LINEAR_EXACT + - 0 + - 1 +path: + root: /home/owner/github/BasicSR + pretrain_model_G: ../experiments/pretrained_models/RRDB_PSNR_x4.pth + resume_state: ../experiments/sollevante/training_state/140000.state + +# Generator options: +network_G: + # ESRGAN: + which_model_G: RRDB_net # RRDB_net | sr_resnet + norm_type: ~ + mode: CNA + nf: 64 # number of discriminator filters in the first convolution layer + nb: 23 + in_nc: 3 # of input image channels: 3 for RGB and 1 for grayscale + out_nc: 3 # of output image channels: 3 for RGB and 1 for grayscale + gc: 32 + group: 1 + convtype: Conv2D # convolution, Conv2D | PartialConv2D + net_act: leakyrelu # network activation, swish | leakyrelu + #finalact: tanh # in testing, activation function to make outputs fit in [-1,1] range. Coordinate with znorm + + # ASRGAN: + #which_model_G: asr_resnet # asr_resnet | asr_cnn + #nf: 64 + + # PPON: + #which_model_G: ppon + ##norm_type: ~ + #mode: CNA + #nf: 64 + #nb: 24 + #in_nc: 3 + #out_nc: 3 + ##gc: 32 + #group: 1 + ##convtype: Conv2D # Conv2D | PartialConv2D + + # SRGAN: + #which_model_G: sr_resnet # RRDB_net | sr_resnet + #norm_type: ~ + #mode: CNA + #nf: 64 + #nb: 16 + #in_nc: 3 + #out_nc: 3 + + # SR: + #which_model_G: RRDB_net # RRDB_net | sr_resnet + #norm_type: ~ + #mode: CNA + #nf: 64 + #nb: 23 + #in_nc: 3 + #out_nc: 3 + #gc: 32 + #group: 1 + +# Discriminator options: +network_D: + # ESRGAN (default) | PPON + which_model_D: discriminator_vgg_128 # discriminator_vgg_128 | discriminator_vgg + norm_type: batch + act_type: leakyrelu + mode: CNA # CNA | NAC + nf: 64 + in_nc: 3 + + # ASRGAN (feature extraction) + #which_model_D: discriminator_vgg_128_fea + #norm_type: batch + #act_type: leakyrelu + #mode: CNA # CNA | NAC + #nf: 64 + #in_nc: 3 + #spectral_norm: true + #self_attention: true + #max_pool: true + #poolsize: 4 + +# Schedulers options: +train: + lr_G: !!float 1e-4 # starting lr_g + weight_decay_G: 0 + beta1_G: 0.9 + lr_D: !!float 1e-4 # starting lr_d + weight_decay_D: 0 + beta1_D: 0.9 + + # For MultiStepLR (ESRGAN, default):, + lr_scheme: MultiStepLR + lr_steps: + - 50000 + - 100000 + - 200000 + - 300000 + lr_gamma: 0.5 # lr change at every step (multiplied by) + + # For StepLR_Restart (PPON): + # lr_gamma: 0.9 # lr change at every step (multiplied by) + # lr_scheme: StepLR_Restart # MultiStepLR | MultiStepLR_Restart | StepLR | StepLR_Restart | CosineAnnealingLR_Restart + # lr_step_sizes: # Steps for each restart for "StepLR_Restart" + # - 200 + # - 100 + # - 250 + # restarts: # Restart iterations for MultiStepLR_Restart | StepLR_Restart | CosineAnnealingLR_Restart + # - 138000 + # - 172500 + # restart_weights: # lr_() * each weight in "restart_weights" for each restart in "restarts" + # - 1 + # - 0.5 + # - 0.5 + ## clear_state: true + + # For MultiStepLR_Restart: + # lr_gamma: 0.9 + # lr_scheme: MultiStepLR_Restart # MultiStepLR | MultiStepLR_Restart | StepLR | StepLR_Restart | CosineAnnealingLR_Restart + # lr_steps: # For "MultiStepLR" and "MultiStepLR_Restart" + # - 34500 + # - 69000 + # - 103500 + # - 155250 + # - 189750 + # - 241500 + # restarts: # Restart iterations for MultiStepLR_Restart | StepLR_Restart | CosineAnnealingLR_Restart + # - 138000 + # - 172500 + # restart_weights: # lr_() * each weight in "restart_weights" for each restart in "restarts" + # - 0.5 + # - 0.5 + ## clear_state: true + + # Losses: + pixel_criterion: l1 # pixel loss, l1 | l2 | cb | elastic | relativel1 | l1cosinesim + pixel_weight: !!float 1e-2 # 1e-2 | 1 + feature_criterion: l1 # feature loss (VGG feature network), l1 | l2 | cb | elastic + feature_weight: 1 + #dis_feature_criterion: l1 # discriminator feature loss, asrragan only, l1 | l2 | cb | elastic + #dis_feature_weight: 1 + #hfen_criterion: l1 # helps in deblurring and finding edges/lines, l1 | l2 | rel_l1 | rel_l2 + #hfen_weight: !!float 1e-1 + #tv_type: normal # helps in denoising, reducing upscale artefacts + #tv_weight: !!float 1e-6 + #tv_norm: 1 # 1 == l1 or 2 == l2. Change "tv_weight" so the l_g_tv is around 1e-02 + #ssim_type: ms-ssim # ssim | ms-ssim # helps to maintain luminance, contrast and covariance between SR and HR + #ssim_weight: 1 + gan_type: vanilla # vanilla | basic + gan_weight: !!float 5e-3 + #lpips_weight: 1 # perceptual loss + #lpips_type: net-lin # net-lin | net + #lpips_net: squeeze # vgg | alex | squeeze + #spl_weight: !!float 1e-3 # SPL loss function. note: needs to add a cap in the generator (finalcap) or it becomes unstable + #spl_type: spl # spl | gpl | cpl + + # for wgan-gp: + #D_update_ratio: 1 + #D_init_iters: 0 + #gp_weigth: 10 + + # For PPON: + #train_phase: 1 # Training starting phase, can skip the first phases + #phase1_s: 100 # -1 to skip. Need to coordinate with the LR steps. //COBranch: lr = 2e−4, decreased by the factor of 2 for every 1000 epochs (1.38e+5 iterations) 138k + #phase2_s: 200 # -1 to skip. Need to coordinate with the LR steps. //SOBranch: λ = 1e+3 (?), lr = 1e−4 and halved at every 250 epochs (3.45e+4iterations) 34.5k + #phase3_s: 5000000 # -1 to skip. Need to coordinate with the LR steps. //POBranch: η = 5e−3, lr = 1e−4 and halved at every 250 epochs (3.45e+4iterations) 34.5k + #phase4_s: 100100 + + # Other training options: + #finalcap: tanh # Test. Cap Generator outputs to fit in: [-1,1] range ("tanh"), rescale tanh to [0,1] range ("scaltanh"), cap ("sigmoid") or clamp ("clamp") to [0,1] range. Default = None. Coordinate with znorm. + #manual_seed: 0, # only set if you want reproducibility as it will incur a performance cut, 0 to 2^32-1 + niter: !!float 5e5 + val_freq: !!float 5e3 +logger: + print_freq: 200 + save_checkpoint_freq: !!float 5e3 \ No newline at end of file