vqvae docs (unfinished)

recipes/srflow/train_div2k_srflow.yml

@@ -60,7 +60,6 @@ steps:
     training: generator
 
     optimizer_params:
-      # Optimizer params
       lr: !!float 2e-4
       weight_decay: 0
       beta1: 0.9

recipes/vqvae2/README.md

@@ -0,0 +1,22 @@
+# VQVAE2 in Pytorch
+
+[VQVAE2](https://arxiv.org/pdf/1906.00446.pdf) is a generative autoencoder developed by Deepmind. It's unique innovation is
+discretizing the latent space into a fixed set of "codebook" vectors.  This codebook
+can then be used in downstream tasks to rebuild images from the training set.
+
+This model is in DLAS thanks to work [@rosinality](https://github.com/rosinality) did 
+[converting the Deepmind model](https://github.com/rosinality/vq-vae-2-pytorch) to Pytorch.
+
+# Training VQVAE2
+
+VQVAE2 is trained in two steps:
+
+## Training the autoencoder
+
+This first step is to train the autoencoder itself. The config file `train_imgnet_vqvae_stage1.yml` provided shows how to do this
+for imagenet with the hyperparameters specified by deepmind. You'll need to bring your own imagenet folder for this.
+
+## Training the PixelCNN encoder
+
+The second step is to train the PixelCNN model which will create "codebook" vectors given an
+input image.

recipes/vqvae2/train_imgnet_vqvae_stage1.yml

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+name: train_imgnet_vqvae_stage1
+model: extensibletrainer
+scale: 1
+gpu_ids: [0]
+start_step: -1
+checkpointing_enabled: true  # <-- Gradient checkpointing. Enable for huge GPU memory savings. Disable for distributed training.
+fp16: false
+wandb: false  # <-- enable to log to wandb. tensorboard logging is always enabled.
+
+datasets:
+  train:
+    name: imgnet
+    n_workers: 8
+    batch_size: 128
+    mode: imagefolder
+    paths: /content/imagenet  # <-- Put your imagenet path here.
+    target_size: 224
+    scale: 1
+  val:
+    name: val
+    mode: fullimage
+    dataroot_GT: /content/imagenet_val
+    min_tile_size: 32
+    scale: 1
+    force_multiple: 16
+
+networks:
+  generator:
+    type: generator
+    which_model_G: vqvae
+    kwargs:
+      #  Hyperparameters specified from VQVAE2 paper.
+      in_channel: 3
+      channel: 128
+      n_res_block: 2
+      n_res_channel: 32
+      codebook_dim: 64
+      codebook_size: 512
+
+#### path
+path:
+  #pretrain_model_generator: <insert pretrained model path if desired>
+  strict_load: true
+  #resume_state: ../experiments/train_imgnet_vqvae_stage1/training_state/0.state   # <-- Set this to resume from a previous training state.
+
+steps:        
+  generator:
+    training: generator
+
+    optimizer_params:
+      lr: !!float 3e-4
+      weight_decay: 0
+      beta1: 0.9
+      beta2: 0.99
+
+    injectors:
+      # Cool hack for more training diversity:
+      # Make sure to change below references to `hq` to `cropped`.
+      #random_crop:
+      #  train: true
+      #  type: random_crop
+      #  dim_in: 224
+      #  dim_out: 192
+      #  in: hq
+      #  out: cropped
+      gen_inj_train:
+        train: true
+        type: generator
+        generator: generator
+        in: hq
+        out: [gen, codebook_commitment_loss]
+    losses:
+      pixel_mse_loss:
+        type: pix
+        criterion: l2
+        weight: 1
+        fake: gen
+        real: hq
+      commitment_loss:
+        type: direct
+        weight: .25
+        key: codebook_commitment_loss
+
+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
+
+  # Optimizer/LR schedule was not specified in the paper. Using an arbitrary default one.
+  default_lr_scheme: MultiStepLR
+  gen_lr_steps: [50000, 100000, 140000, 180000]
+  lr_gamma: 0.5
+
+eval:
+  output_state: gen
+  injectors:
+    gen_inj_eval:
+      type: generator
+      generator: generator
+      in: hq
+      out: [gen, codebook_commitment_loss]
+
+logger:
+  print_freq: 30
+  save_checkpoint_freq: 2000
+  visuals: [gen, hq, cropped]
+  visual_debug_rate: 100