How to prepare my own customized data?

[sjrnf-j]

I want to know how to prepare my own customized data for finetuning part (data is in .mp4 format). I would like to train the model using my own video to improve the resolution. May you explain the procedure in details?

Thank you.

[Markfryazino]

No particular procedure is required for preparing the data. You can use any .mp4 video, as soon as it contains a talking face in high resolution. It is possible to use multiple videos, but then you need to run the frame-saving script multiple times. All the necessary commands are listed in Colab Notebook.

If you still have questions, feel free to ask.

[AaronWong]

Finetune ESRGAN error

FileNotFoundError: [Errno 2] No such file or directory: '/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/net_g_67500.pth'

[Yathish27]

FileNotFoundError: [Errno 2] No such file or directory: '/content/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/train_001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb_20210714_155113.log'

[agassan]

Same here FileNotFoundError: [Errno 2] No such file or directory: '/content/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/net_g_67500.pth'

[agassan]

Cannot import deform_conv_ext. Error: cannot import name 'deform_conv_ext' from 'basicsr.ops.dcn' (/usr/local/lib/python3.7/dist-packages/basicsr/ops/dcn/init.py). You may need to:

1. compile with BASICSR_EXT=True. or
2. set BASICSR_JIT=True during running Cannot import fused_act_ext. Error: cannot import name 'fused_act_ext' from 'basicsr.ops.fused_act' (/usr/local/lib/python3.7/dist-packages/basicsr/ops/fused_act/init.py). You may need to:
3. compile with BASICSR_EXT=True. or
4. set BASICSR_JIT=True during running Cannot import upfirdn2d_ext. Error: cannot import name 'upfirdn2d_ext' from 'basicsr.ops.upfirdn2d' (/usr/local/lib/python3.7/dist-packages/basicsr/ops/upfirdn2d/init.py). You may need to:
5. compile with BASICSR_EXT=True. or
6. set BASICSR_JIT=True during running Disable distributed. 2021-07-21 18:29:18,654 INFO: ____ _ _____ ____ / __ ) ____ _ _____ ()____/ / / __
   / __ |/ __ `// // // /_ \ / // / / // // // /( )/ // / / // _, _/ // _,///// ___///// ||

   ---

   / / ____ / / / / __ __ _____ / / / / / / __ / __ \ / __ \ / __ / / / / / / // // /// / / / // // // // // // // / / // // // / / /< // _/ _/ _/ _/ //_/ __///|| (_)

Version Information: BasicSR: 1.3.3.8 PyTorch: 1.9.0+cu102 TorchVision: 0.10.0+cu102 2021-07-21 18:29:18,654 INFO: name: 001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb model_type: ESRGANModel scale: 4 num_gpu: 1 manual_seed: 0 datasets:[ train:[ name: face_dataset type: PairedImageDataset dataroot_gt: data/hq dataroot_lq: data/lq filename_tmpl: {} io_backend:[ type: disk ] gt_size: 384 use_flip: True use_rot: True use_shuffle: True num_worker_per_gpu: 1 batch_size_per_gpu: 4 dataset_enlarge_ratio: 1 prefetch_mode: None phase: train scale: 4 ] ] network_g:[ type: RRDBNet num_in_ch: 3 num_out_ch: 3 num_feat: 64 num_block: 23 ] network_d:[ type: VGGStyleDiscriminator128 num_in_ch: 3 num_feat: 64 ] path:[ pretrain_network_g: None strict_load_g: True resume_state: checkpoints/pretrained.state experiments_root: /content/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb models: /content/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models training_states: /content/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/training_states log: /content/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb visualization: /content/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/visualization ] train:[ optim_g:[ type: Adam lr: 0.0001 weight_decay: 0 betas: [0.9, 0.99] ] optim_d:[ type: Adam lr: 0.0001 weight_decay: 0 betas: [0.9, 0.99] ] scheduler:[ type: MultiStepLR milestones: [50000, 100000, 200000, 300000] gamma: 0.5 ] total_iter: 150000 warmup_iter: -1 pixel_opt:[ type: L1Loss loss_weight: 0.01 reduction: mean ] perceptual_opt:[ type: PerceptualLoss layer_weights:[ conv5_4: 1 ] vgg_type: vgg19 use_input_norm: True range_norm: False perceptual_weight: 1.0 style_weight: 0 criterion: l1 ] gan_opt:[ type: GANLoss gan_type: vanilla real_label_val: 1.0 fake_label_val: 0.0 loss_weight: 0.005 ] net_d_iters: 1 net_d_init_iters: 0 ] val:[ val_freq: 2500.0 save_img: True metrics:[ psnr:[ type: calculate_psnr crop_border: 4 test_y_channel: False ] ] ] logger:[ print_freq: 100 save_checkpoint_freq: 2500.0 use_tb_logger: True wandb:[ project: None resume_id: None ] ] dist_params:[ backend: nccl port: 29500 ] is_train: True dist: False rank: 0 world_size: 1

2021-07-21 18:29:18.884290: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0 2021-07-21 18:29:20,245 INFO: Dataset [PairedImageDataset] - face_dataset is built. 2021-07-21 18:29:20,246 INFO: Training statistics: Number of train images: 3307 Dataset enlarge ratio: 1 Batch size per gpu: 4 World size (gpu number): 1 Require iter number per epoch: 827 Total epochs: 182; iters: 150000. 2021-07-21 18:29:20,246 INFO: Set pretrain_network_g to /content/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/net_g_67500.pth 2021-07-21 18:29:20,246 INFO: Set pretrain_network_d to /content/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/net_d_67500.pth 2021-07-21 18:29:20,591 INFO: Network [RRDBNet] is created. 2021-07-21 18:29:20,653 INFO: Network: RRDBNet, with parameters: 16,697,987 2021-07-21 18:29:20,653 INFO: RRDBNet( (conv_first): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (body): Sequential( (0): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (1): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (2): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (3): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (4): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (5): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (6): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (7): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (8): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (9): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (10): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (11): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (12): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (13): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (14): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (15): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (16): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (17): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (18): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (19): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (20): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (21): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) (22): RRDB( (rdb1): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb2): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) (rdb3): ResidualDenseBlock( (conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv4): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv5): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) ) ) (conv_body): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv_up1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv_up2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv_hr): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv_last): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (lrelu): LeakyReLU(negative_slope=0.2, inplace=True) ) 2021-07-21 18:29:20,679 INFO: Loading RRDBNet model from /content/wav2lip-hq/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/net_g_67500.pth. Traceback (most recent call last): File "basicsr/train.py", line 221, in train_pipeline(root_path) File "basicsr/train.py", line 138, in train_pipeline model = build_model(opt) File "/usr/local/lib/python3.7/dist-packages/basicsr/models/init.py", line 27, in build_model model = MODEL_REGISTRY.get(opt['model_type'])(opt) File "/usr/local/lib/python3.7/dist-packages/basicsr/models/sr_model.py", line 29, in init self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True)) File "/usr/local/lib/python3.7/dist-packages/basicsr/models/base_model.py", line 254, in load_network load_net = load_net[param_key] KeyError: 'params'

[agassan]

I tried to use these model https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG

[freecui]

@agassan How to use these model you said?

[vokshin]

Same problem as @agassan 's : KeyError: 'params'

[freecui]

@agassan @vokshin How do you get net_d_67500 and net_g_67500 models ?

[vokshin]

@freecui Unfortunately, I did not find any information here where to get these weights. So I used the pretrained.state weights from colab and got the same result as @agassan 's.

[freecui]

vokshin ， I use the pretrained.state weights, but there is also Same problem as @agassan 's : KeyError: 'params'. How to solve it?

[freecui]

This is about https://github.com/xinntao/BasicSR