[Anomaly Task] Expose more params to the UI

external/anomaly/anomaly_classification/configs/padim/configuration.py

@@ -17,15 +17,11 @@
 # and limitations under the License.
 
 from attr import attrs
-from ote_anomalib.configs.configuration import BaseAnomalyConfig
-from ote_sdk.configuration.elements import string_attribute
+from ote_anomalib.configs.padim import PadimAnomalyBaseConfig
 
 
 @attrs
-class PadimAnomalyClassificationConfig(BaseAnomalyConfig):
+class PadimAnomalyClassificationConfig(PadimAnomalyBaseConfig):
     """
     Configurable parameters for PADIM anomaly classification task.
     """
-
-    header = string_attribute("Configuration for Padim")
-    description = header

external/anomaly/anomaly_classification/configs/padim/configuration.yaml

@@ -3,6 +3,8 @@ dataset:
   header: Dataset Parameters
   num_workers:
     affects_outcome_of: NONE
+    auto_hpo_state: not_possible
+    auto_hpo_value: null
     default_value: 8
     description:
       Increasing this value might improve training speed however it might
@@ -18,10 +20,13 @@ dataset:
       operator: AND
       rules: []
       type: UI_RULES
+    value: 8
     visible_in_ui: true
     warning: null
   train_batch_size:
     affects_outcome_of: TRAINING
+    auto_hpo_state: not_possible
+    auto_hpo_value: null
     default_value: 32
     description:
       The number of training samples seen in each iteration of training.
@@ -37,6 +42,7 @@ dataset:
       operator: AND
       rules: []
       type: UI_RULES
+    value: 32
     visible_in_ui: true
     warning:
       Increasing this value may cause the system to use more memory than available,
@@ -45,50 +51,56 @@ dataset:
   visible_in_ui: true
 description: Configuration for Padim
 header: Configuration for Padim
-pot_parameters:
-  description: POT Parameters
-  header: POT Parameters
-  preset:
+id: ""
+model:
+  backbone:
     affects_outcome_of: NONE
-    default_value: Performance
-    description: Quantization preset that defines quantization scheme
+    auto_hpo_state: not_possible
+    auto_hpo_value: null
+    default_value: resnet18
+    description: Pre-trained backbone used for feature extraction
     editable: true
-    enum_name: POTQuantizationPreset
-    header: Preset
+    enum_name: ModelBackbone
+    header: Model Backbone
     options:
-      MIXED: Mixed
-      PERFORMANCE: Performance
+      RESNET18: resnet18
+      WIDE_RESNET_50: wide_resnet50_2
     type: SELECTABLE
     ui_rules:
       action: DISABLE_EDITING
       operator: AND
       rules: []
       type: UI_RULES
+    value: resnet18
     visible_in_ui: true
     warning: null
-  stat_subset_size:
+  description: Model Parameters
+  header: Model Parameters
+  type: PARAMETER_GROUP
+  visible_in_ui: true
+nncf_optimization:
+  description: Optimization by NNCF
+  enable_pruning:
     affects_outcome_of: NONE
-    default_value: 300
-    description: Number of data samples used for post-training optimization
+    auto_hpo_state: not_possible
+    auto_hpo_value: null
+    default_value: false
+    description: Enable filter pruning algorithm
     editable: true
-    header: Number of data samples
-    max_value: 9223372036854775807
-    min_value: 1
-    type: INTEGER
+    header: Enable filter pruning algorithm
+    type: BOOLEAN
     ui_rules:
       action: DISABLE_EDITING
       operator: AND
       rules: []
       type: UI_RULES
+    value: false
     visible_in_ui: true
     warning: null
-  type: PARAMETER_GROUP
-  visible_in_ui: true
-nncf_optimization:
-  description: Optimization by NNCF
-  header: Optimization by NNCF
   enable_quantization:
-    affects_outcome_of: TRAINING
+    affects_outcome_of: NONE
+    auto_hpo_state: not_possible
+    auto_hpo_value: null
     default_value: true
     description: Enable quantization algorithm
     editable: true
@@ -102,12 +114,15 @@ nncf_optimization:
     value: true
     visible_in_ui: true
     warning: null
-  enable_pruning:
+  header: Optimization by NNCF
+  pruning_supported:
     affects_outcome_of: TRAINING
+    auto_hpo_state: not_possible
+    auto_hpo_value: null
     default_value: false
-    description: Enable filter pruning algorithm
+    description: Whether filter pruning is supported
     editable: true
-    header: Enable filter pruning algorithm
+    header: Whether filter pruning is supported
     type: BOOLEAN
     ui_rules:
       action: DISABLE_EDITING
@@ -117,20 +132,50 @@ nncf_optimization:
     value: false
     visible_in_ui: true
     warning: null
-  pruning_supported:
-    affects_outcome_of: TRAINING
-    default_value: false
-    description: Whether filter pruning is supported
-    editable: false
-    header: Whether filter pruning is supported
-    type: BOOLEAN
+  type: PARAMETER_GROUP
+  visible_in_ui: true
+pot_parameters:
+  description: POT Parameters
+  header: POT Parameters
+  preset:
+    affects_outcome_of: NONE
+    auto_hpo_state: not_possible
+    auto_hpo_value: null
+    default_value: Performance
+    description: Quantization preset that defines quantization scheme
+    editable: true
+    enum_name: POTQuantizationPreset
+    header: Preset
+    options:
+      MIXED: Mixed
+      PERFORMANCE: Performance
+    type: SELECTABLE
     ui_rules:
       action: DISABLE_EDITING
       operator: AND
       rules: []
       type: UI_RULES
-    value: false
-    visible_in_ui: false
+    value: Performance
+    visible_in_ui: true
+    warning: null
+  stat_subset_size:
+    affects_outcome_of: NONE
+    auto_hpo_state: not_possible
+    auto_hpo_value: null
+    default_value: 300
+    description: Number of data samples used for post-training optimization
+    editable: true
+    header: Number of data samples
+    max_value: 9223372036854775807
+    min_value: 1
+    type: INTEGER
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: 300
+    visible_in_ui: true
     warning: null
   type: PARAMETER_GROUP
   visible_in_ui: false

external/anomaly/anomaly_classification/configs/stfpm/configuration.py

@@ -17,64 +17,11 @@
 # and limitations under the License.
 
 from attr import attrs
-from ote_anomalib.configs.configuration import BaseAnomalyConfig
-from ote_anomalib.configs.configuration_enums import EarlyStoppingMetrics
-from ote_sdk.configuration.elements import (
-    ParameterGroup,
-    add_parameter_group,
-    configurable_integer,
-    selectable,
-    string_attribute,
-)
-from ote_sdk.configuration.model_lifecycle import ModelLifecycle
+from ote_anomalib.configs.stfpm import STFPMAnomalyBaseConfig
 
 
 @attrs
-class STFPMAnomalyClassificationConfig(BaseAnomalyConfig):
+class STFPMAnomalyClassificationConfig(STFPMAnomalyBaseConfig):
     """
     Configurable parameters for STFPM anomaly classification task.
     """
-
-    header = string_attribute("Configuration for STFPM")
-    description = header
-
-    @attrs
-    class ModelParameters(ParameterGroup):
-        """
-        Parameter Group for training model
-        """
-
-        header = string_attribute("Model Parameters")
-        description = header
-
-        @attrs
-        class EarlyStoppingParameters(ParameterGroup):
-            """
-            Early stopping parameters
-            """
-
-            header = string_attribute("Early Stopping Parameters")
-            description = header
-
-            metric = selectable(
-                default_value=EarlyStoppingMetrics.IMAGE_F1,
-                header="Early Stopping Metric",
-                description="The metric used to determine if the model should stop training",
-            )
-
-            patience = configurable_integer(
-                default_value=10,
-                min_value=1,
-                max_value=100,
-                header="Early Stopping Patience",
-                description="Number of epochs to wait for an improvement in the monitored metric. If the metric has "
-                "not improved for this many epochs, the training will stop and the best model will be "
-                "returned.",
-                warning="Setting this value too low might lead to underfitting. Setting the value too high will "
-                "increase the training time and might lead to overfitting.",
-                affects_outcome_of=ModelLifecycle.TRAINING,
-            )
-
-        early_stopping = add_parameter_group(EarlyStoppingParameters)
-
-    model = add_parameter_group(ModelParameters)