switch to unsupervised

hsv1.txt

@@ -0,0 +1,86 @@
+# lightning.pytorch==2.1.3
+seed_everything: 1387
+trainer:
+  accelerator: auto
+  strategy: auto
+  devices: auto
+  num_nodes: 1
+  precision: null
+  logger: null
+  callbacks:
+  - class_path: callbacks.SaveImageCallback
+    init_args:
+      save_interval: 0
+      final_dir: out
+  fast_dev_run: false
+  max_epochs: 10
+  min_epochs: 10
+  max_steps: -1
+  min_steps: null
+  max_time: null
+  limit_train_batches: null
+  limit_val_batches: 50
+  limit_test_batches: null
+  limit_predict_batches: null
+  overfit_batches: 0.0
+  val_check_interval: null
+  check_val_every_n_epoch: 1
+  num_sanity_val_steps: null
+  log_every_n_steps: 3
+  enable_checkpointing: null
+  enable_progress_bar: null
+  enable_model_summary: null
+  accumulate_grad_batches: 1
+  gradient_clip_val: null
+  gradient_clip_algorithm: null
+  deterministic: null
+  benchmark: null
+  inference_mode: true
+  use_distributed_sampler: true
+  profiler: null
+  detect_anomaly: false
+  barebones: false
+  plugins: null
+  sync_batchnorm: false
+  reload_dataloaders_every_n_epochs: 0
+  default_root_dir: null
+model:
+  transform: tanh
+  width: 128
+  depth: 4
+  bias: true
+  alpha: 0.0
+data:
+  val_size: 10000
+  train_size: 10000
+  batch_size: 256
+  num_workers: 3
+ckpt_path: null
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 0.001
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+    weight_decay: 0.0
+    amsgrad: false
+    foreach: null
+    maximize: false
+    capturable: false
+    differentiable: false
+    fused: null
+lr_scheduler:
+  class_path: lightning.pytorch.cli.ReduceLROnPlateau
+  init_args:
+    monitor: hp_metric
+    mode: min
+    factor: 0.05
+    patience: 5
+    threshold: 0.0001
+    threshold_mode: rel
+    cooldown: 10
+    min_lr: 0.0
+    eps: 1.0e-08
+    verbose: true

hsv2.txt

@@ -0,0 +1,86 @@
+# lightning.pytorch==2.1.3
+seed_everything: 31
+trainer:
+  accelerator: auto
+  strategy: auto
+  devices: auto
+  num_nodes: 1
+  precision: null
+  logger: null
+  callbacks:
+  - class_path: callbacks.SaveImageCallback
+    init_args:
+      save_interval: 0
+      final_dir: out
+  fast_dev_run: false
+  max_epochs: 10
+  min_epochs: 10
+  max_steps: -1
+  min_steps: null
+  max_time: null
+  limit_train_batches: null
+  limit_val_batches: 50
+  limit_test_batches: null
+  limit_predict_batches: null
+  overfit_batches: 0.0
+  val_check_interval: null
+  check_val_every_n_epoch: 1
+  num_sanity_val_steps: null
+  log_every_n_steps: 3
+  enable_checkpointing: null
+  enable_progress_bar: null
+  enable_model_summary: null
+  accumulate_grad_batches: 1
+  gradient_clip_val: null
+  gradient_clip_algorithm: null
+  deterministic: null
+  benchmark: null
+  inference_mode: true
+  use_distributed_sampler: true
+  profiler: null
+  detect_anomaly: false
+  barebones: false
+  plugins: null
+  sync_batchnorm: false
+  reload_dataloaders_every_n_epochs: 0
+  default_root_dir: null
+model:
+  transform: tanh
+  width: 256
+  depth: 8
+  bias: true
+  alpha: 0.0
+data:
+  val_size: 10000
+  train_size: 10000
+  batch_size: 256
+  num_workers: 3
+ckpt_path: null
+optimizer:
+  class_path: torch.optim.AdamW
+  init_args:
+    lr: 0.001
+    betas:
+    - 0.9
+    - 0.999
+    eps: 1.0e-08
+    weight_decay: 0.01
+    amsgrad: false
+    maximize: false
+    foreach: null
+    capturable: false
+    differentiable: false
+    fused: null
+lr_scheduler:
+  class_path: lightning.pytorch.cli.ReduceLROnPlateau
+  init_args:
+    monitor: hp_metric
+    mode: min
+    factor: 0.05
+    patience: 5
+    threshold: 0.0001
+    threshold_mode: rel
+    cooldown: 10
+    min_lr: 0.0
+    eps: 1.0e-08
+    verbose: true

model.py

@@ -56,23 +56,29 @@ class ColorTransformerModel(L.LightningModule):
         #     torch.norm(outputs - labels), torch.norm(1 + outputs - labels)
         # ).mean()
         distance = torch.norm(outputs - labels).mean()
-        loss = p_loss
 
-        self.log("train_loss", distance)
+        # Backprop with this:
+        loss = p_loss
+        # p_loss is unsupervised
+        # distance is supervised.
         self.log("hp_metric", loss)
-        self.log("p_loss", p_loss)
+
-        return distance
+        # Log all losses individually
+        self.log("train_mse", distance)
+        self.log("train_pres", p_loss)
+        return loss
 
     def validation_step(self, batch):
         inputs, labels = batch  # these are true HSV labels - no learning allowed.
         outputs = self.forward(inputs)
-        distance = torch.minimum(
+        # distance = torch.minimum(
-            torch.norm(outputs - labels), torch.norm(1 + outputs - labels)
+        #     torch.norm(outputs - labels), torch.norm(1 + outputs - labels)
-        )
+        # )
+        distance = torch.norm(outputs - labels)
         mean_loss = torch.mean(distance)
         max_loss = torch.max(distance)
-        self.log("val_mean_loss", mean_loss)
+        self.log("val_mse", mean_loss)
-        self.log("val_max_loss", max_loss)
+        self.log("val_max", max_loss)
         return mean_loss
 
     def configure_optimizers(self):