GLEAN docs

recipes/glean/README.md

@@ -0,0 +1,25 @@
+# GLEAN
+
+DLAS contains an attempt at implementing [GLEAN](https://ckkelvinchan.github.io/papers/glean.pdf), which performs image
+super-resolution guided by pretrained StyleGAN networks. Since this paper is currently closed-source, it was 
+implemented entirely on what information I could glean from the paper.
+
+## Training
+
+GLEAN requires a pre-trained StyleGAN network to operate. DLAS currently only has support for StyleGAN2 models, so
+you will need to use one of those. The pre-eminent StyleGAN 2 model is the one trained on FFHQ faces, so I will use
+that in this training example.
+
+1. Download the ffhq model from [nVidias Drive](https://drive.google.com/drive/folders/1yanUI9m4b4PWzR0eurKNq6JR1Bbfbh6L).
+   This repo currently only supports the "-f.pkl" files without further modifications, so choose one of those.
+1. Download and extract the [FFHQ dataset](https://github.com/NVlabs/ffhq-dataset).
+1. Convert the TF model to a Pytorch one supported by DLAS:
+
+   `python scripts/stylegan2/convert_weights_rosinality.py stylegan2-ffhq-config-f.pkl`
+   
+1. The above conversion script outputs a *.pth file as well as JPG preview of model outputs. Check the JPG to ensure
+   the StyleGAN is performing as expected. If so, copy the *.pth file to your experiments/ directory within DLAS.
+1. Edit the provided trainer configuration. Find comments starting with '<--' and make changes as indicated.
+1. Train the model:
+
+   `python train.py -opt train_ffhq_glean.yml`

recipes/glean/train_ffhq_glean.yml

@@ -0,0 +1,133 @@
+#### general settings
+name: train_faces_glean
+use_tb_logger: true
+model: extensibletrainer
+scale: 8
+gpu_ids: [0]
+fp16: false
+start_step: -1
+checkpointing_enabled: true
+wandb: false
+
+datasets:
+  train:
+    n_workers: 4
+    batch_size: 32
+    name: ffhq
+    mode: imagefolder
+    paths: /content/flickr_faces_hq # <-- Put your data path here.
+    target_size: 256
+    scale: 8
+
+networks:
+  generator:
+    type: generator
+    which_model_G: glean
+    nf: 64
+    pretrained_stylegan: ../experiments/stylegan2-ffhq-config-f.pth
+
+  feature_discriminator:
+    type: discriminator
+    which_model_D: discriminator_vgg_128_gn
+    extra_conv: true
+    scale: 2
+    nf: 64
+    in_nc: 3
+    image_size: 256
+
+#### path
+path:
+  #pretrain_model_generator: <insert pretrained *GLEAN* model path if desired. Pretrained stylegan goes above.>
+  strict_load: true
+  #resume_state: ../experiments/train_faces_glean/training_state/0.state # <-- Uncomment to continue training at a checkpoint.
+
+steps:
+  feature_discriminator:
+    training: feature_discriminator
+    after: 10000  # Delays starting discriminator training until step 10k
+
+    # Optimizer params
+    lr: !!float 2e-4
+    weight_decay: 0
+    beta1: 0.9
+    beta2: 0.99
+
+    injectors:
+      dgen_inj:
+        type: generator
+        generator: generator
+        grad: false
+        in: lq
+        out: dgen
+
+    losses:
+      gan_disc_img:
+        type: discriminator_gan
+        gan_type: gan
+        weight: 1
+        noise: .004
+        gradient_penalty: true
+        real: hq
+        fake: dgen
+        
+  generator:
+    training: generator
+
+    optimizer_params:
+      # Optimizer params
+      lr: !!float 2e-4
+      weight_decay: 0
+      beta1: 0.9
+      beta2: 0.99
+
+    injectors:
+      gen_inj:
+        type: generator
+        generator: generator
+        in: lq
+        out: gen
+        
+    losses:
+      pix:
+        type: pix
+        weight: .05
+        criterion: l1
+        real: hq
+        fake: gen
+      feature:
+        type: feature
+        after: 5000
+        which_model_F: vgg
+        criterion: l1
+        weight: 1
+        real: hq
+        fake: gen
+      gan_gen_img:
+        after: 10000
+        type: generator_gan
+        gan_type: gan
+        weight: .02
+        noise: .004
+        discriminator: feature_discriminator
+        fake: gen
+        real: hq
+
+train:
+  niter: 500000
+  warmup_iter: -1
+  mega_batch_factor: 1    # <-- Gradient accumulation factor. If you are running OOM, increase this to [2,4,8].
+  val_freq: 4000  # No validation currently in this config, this is irrelevant.
+
+  # Default LR scheduler options
+  default_lr_scheme: MultiStepLR
+  gen_lr_steps: [40000, 80000, 100000, 120000]
+  lr_gamma: 0.5
+
+eval:
+  output_state: gen
+
+logger:
+  print_freq: 30
+  save_checkpoint_freq: 2000
+  visuals: [gen, hq, lq]
+  visual_debug_rate: 100