🔧 Update CIFAR10 experiments (script + config files)

Author: alafage

experiments/classification/cifar10/configs/resnet.yaml

@@ -1,4 +1,3 @@
-# lightning.pytorch==2.1.3
 seed_everything: false
 eval_after_fit: true
 trainer:
@@ -23,22 +22,34 @@ trainer:
         patience: 1000
         check_finite: true
 model:
+  model:
+    class_path: torch_uncertainty.models.classification.batched_resnet
+    init_args:
+      in_channels: 3
+      num_classes: 10
+      arch: 18
+      num_estimators: 4
+      style: cifar
   num_classes: 10
-  in_channels: 3
   loss: CrossEntropyLoss
-  version: batched
-  arch: 18
-  style: cifar
-  num_estimators: 4
+  is_ensemble: true
+  format_batch_fn:
+    class_path: torch_uncertainty.transforms.RepeatTarget
+    init_args:
+      num_repeats: 4
 data:
   root: ./data
   batch_size: 128
 optimizer:
-  lr: 0.05
-  momentum: 0.9
-  weight_decay: 5e-4
+  class_path: torch.optim.SGD
+  init_args:
+    lr: 0.05
+    momentum: 0.9
+    weight_decay: 5e-4
 lr_scheduler:
-  milestones:
-    - 25
-    - 50
-  gamma: 0.1
+  class_path: torch.optim.lr_scheduler.MultiStepLR
+  init_args:
+    milestones:
+      - 25
+      - 50
+    gamma: 0.1

experiments/classification/cifar10/configs/resnet18/batched.yaml

@@ -0,0 +1,62 @@
+# lightning.pytorch==2.1.3
+seed_everything: false
+eval_after_fit: true
+trainer:
+  accelerator: gpu
+  devices: 1
+  precision: 16-mixed
+  max_epochs: 75
+  logger:
+    class_path: lightning.pytorch.loggers.TensorBoardLogger
+    init_args:
+      save_dir: logs/resnet18
+      name: deep_ensembles
+      default_hp_metric: false
+  callbacks:
+    - class_path: torch_uncertainty.callbacks.TUClsCheckpoint
+    - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+      init_args:
+        logging_interval: step
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/cls/Acc
+        patience: 1000
+        check_finite: true
+model:
+  model:
+    class_path: torch_uncertainty.models.deep_ensembles
+    init_args:
+      core_models:
+        class_path: torch_uncertainty.models.classification.resnet
+        init_args:
+          in_channels: 3
+          num_classes: 10
+          arch: 18
+          style: cifar
+      num_estimators: 4
+      task: classification
+      # eventually you can pass the checkpoints of standard resnet18 models here
+      # ckpt_paths: [path/to/ckpt1, path/to/ckpt2, path/to/ckpt3, path/to/ckpt4]
+  num_classes: 10
+  loss: CrossEntropyLoss
+  is_ensemble: true
+  format_batch_fn:
+    class_path: torch_uncertainty.transforms.RepeatTarget
+    init_args:
+      num_repeats: 4
+data:
+  root: ./data
+  batch_size: 128
+optimizer:
+  class_path: torch.optim.SGD
+  init_args:
+    lr: 0.2  # initial learning rate times 4 (num_estimators)
+    momentum: 0.9
+    weight_decay: 5e-4
+lr_scheduler:
+  class_path: torch.optim.lr_scheduler.MultiStepLR
+  init_args:
+    milestones:
+      - 25
+      - 50
+    gamma: 0.1

experiments/classification/cifar10/configs/resnet18/deep_ensembles.yaml

@@ -23,23 +23,34 @@ trainer:
         patience: 1000
         check_finite: true
 model:
+  model:
+    class_path: torch_uncertainty.models.classification.masked_resnet
+    init_args:
+      in_channels: 3
+      num_classes: 10
+      arch: 18
+      num_estimators: 4
+      scale: 2
+      style: cifar
   num_classes: 10
-  in_channels: 3
   loss: CrossEntropyLoss
-  version: masked
-  arch: 18
-  style: cifar
-  num_estimators: 4
-  scale: 2
+  format_batch_fn:
+    class_path: torch_uncertainty.transforms.RepeatTarget
+    init_args:
+      num_repeats: 4
 data:
   root: ./data
   batch_size: 128
 optimizer:
-  lr: 0.05
-  momentum: 0.9
-  weight_decay: 5e-4
+  class_path: torch.optim.SGD
+  init_args:
+    lr: 0.05
+    momentum: 0.9
+    weight_decay: 5e-4
 lr_scheduler:
-  milestones:
-    - 25
-    - 50
-  gamma: 0.1
+  class_path: torch.optim.lr_scheduler.MultiStepLR
+  init_args:
+    milestones:
+      - 25
+      - 50
+    gamma: 0.1

experiments/classification/cifar10/configs/resnet18/masked.yaml

@@ -23,23 +23,36 @@ trainer:
         patience: 1000
         check_finite: true
 model:
+  model:
+    class_path: torch_uncertainty.models.classification.mimo_resnet
+    init_args:
+      in_channels: 3
+      num_classes: 10
+      arch: 18
+      num_estimators: 4
+      style: cifar
   num_classes: 10
-  in_channels: 3
   loss: CrossEntropyLoss
-  version: mimo
-  arch: 18
-  style: cifar
-  num_estimators: 4
-  rho: 1.0
+  is_ensemble: true
+  format_batch_fn:
+    class_path: torch_uncertainty.transforms.MIMOBatchFormat
+    init_args:
+      num_estimators: 4
+      rho: 1.0
+      batch_repeat: 1
 data:
   root: ./data
   batch_size: 128
 optimizer:
-  lr: 0.05
-  momentum: 0.9
-  weight_decay: 5e-4
+  class_path: torch.optim.SGD
+  init_args:
+    lr: 0.05
+    momentum: 0.9
+    weight_decay: 5e-4
 lr_scheduler:
-  milestones:
-    - 25
-    - 50
-  gamma: 0.1
+  class_path: torch.optim.lr_scheduler.MultiStepLR
+  init_args:
+    milestones:
+      - 25
+      - 50
+    gamma: 0.1

experiments/classification/cifar10/configs/resnet18/mimo.yaml

@@ -23,24 +23,36 @@ trainer:
         patience: 1000
         check_finite: true
 model:
+  model:
+    class_path: torch_uncertainty.models.classification.packed_resnet
+    init_args:
+      in_channels: 3
+      num_classes: 10
+      arch: 18
+      style: cifar
+      num_estimators: 4
+      alpha: 2
+      gamma: 2
   num_classes: 10
-  in_channels: 3
   loss: CrossEntropyLoss
-  version: packed
-  arch: 18
-  style: cifar
-  num_estimators: 4
-  alpha: 2
-  gamma: 2
+  is_ensemble: true
+  format_batch_fn:
+    class_path: torch_uncertainty.transforms.RepeatTarget
+    init_args:
+      num_repeats: 4
 data:
   root: ./data
   batch_size: 128
 optimizer:
-  lr: 0.05
-  momentum: 0.9
-  weight_decay: 5e-4
+  class_path: torch.optim.SGD
+  init_args:
+    lr: 0.05
+    momentum: 0.9
+    weight_decay: 5e-4
 lr_scheduler:
-  milestones:
-    - 25
-    - 50
-  gamma: 0.1
+  class_path: torch.optim.lr_scheduler.MultiStepLR
+  init_args:
+    milestones:
+      - 25
+      - 50
+    gamma: 0.1

experiments/classification/cifar10/configs/resnet18/packed.yaml

@@ -23,21 +23,28 @@ trainer:
         patience: 1000
         check_finite: true
 model:
+  model:
+    class_path: torch_uncertainty.models.classification.resnet
+    init_args:
+      in_channels: 3
+      num_classes: 10
+      arch: 18
+      style: cifar
   num_classes: 10
-  in_channels: 3
   loss: CrossEntropyLoss
-  version: std
-  arch: 18
-  style: cifar
 data:
   root: ./data
   batch_size: 128
 optimizer:
-  lr: 0.05
-  momentum: 0.9
-  weight_decay: 5e-4
+  class_path: torch.optim.SGD
+  init_args:
+    lr: 0.05
+    momentum: 0.9
+    weight_decay: 5e-4
 lr_scheduler:
-  milestones:
-    - 25
-    - 50
-  gamma: 0.1
+  class_path: torch.optim.lr_scheduler.MultiStepLR
+  init_args:
+    milestones:
+      - 25
+      - 50
+    gamma: 0.1

experiments/classification/cifar10/configs/resnet18/standard.yaml

@@ -23,24 +23,36 @@ trainer:
       patience: 1000
       check_finite: true
 model:
+  model:
+    class_path: torch_uncertainty.models.classification.batched_resnet
+    init_args:
+      in_channels: 3
+      num_classes: 10
+      arch: 50
+      num_estimators: 4
+      style: cifar
   num_classes: 10
-  in_channels: 3
   loss: CrossEntropyLoss
-  version: batched
-  arch: 50
-  style: cifar
-  num_estimators: 4
+  is_ensemble: true
+  format_batch_fn:
+    class_path: torch_uncertainty.transforms.RepeatTarget
+    init_args:
+      num_repeats: 4
 data:
   root: ./data
   batch_size: 128
 optimizer:
-  lr: 0.08
-  momentum: 0.9
-  weight_decay: 5e-4
-  nesterov: true
+  class_path: torch.optim.SGD
+  init_args:
+    lr: 0.08
+    momentum: 0.9
+    weight_decay: 5e-4
+    nesterov: true
 lr_scheduler:
-  milestones:
-  - 60
-  - 120
-  - 160
-  gamma: 0.2
+  class_path: torch.optim.lr_scheduler.MultiStepLR
+  init_args:
+    milestones:
+    - 60
+    - 120
+    - 160
+    gamma: 0.2