[MPA] Class-incremental Learning for instance-segmentation

external/model-preparation-algorithm/configs/instance-segmentation/configuration.yaml

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+description: Configuration for an instance segmentation task
+header: Configuration for an instance segmentation task
+learning_parameters:
+  batch_size:
+    affects_outcome_of: TRAINING
+    default_value: 5
+    description:
+      The number of training samples seen in each iteration of training.
+      Increasing this value improves training time and may make the training more
+      stable. A larger batch size has higher memory requirements.
+    editable: true
+    header: Batch size
+    max_value: 512
+    min_value: 1
+    type: INTEGER
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: 5
+    visible_in_ui: true
+    warning:
+      Increasing this value may cause the system to use more memory than available,
+      potentially causing out of memory errors, please update with caution.
+  description: Learning Parameters
+  header: Learning Parameters
+  learning_rate:
+    affects_outcome_of: TRAINING
+    default_value: 0.01
+    description:
+      Increasing this value will speed up training convergence but might
+      make it unstable.
+    editable: true
+    header: Learning rate
+    max_value: 0.1
+    min_value: 1.0e-07
+    type: FLOAT
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: 0.01
+    visible_in_ui: true
+    warning: null
+  learning_rate_warmup_iters:
+    affects_outcome_of: TRAINING
+    default_value: 100
+    description: ""
+    editable: true
+    header: Number of iterations for learning rate warmup
+    max_value: 10000
+    min_value: 0
+    type: INTEGER
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: 100
+    visible_in_ui: true
+    warning: null
+  num_checkpoints:
+    affects_outcome_of: NONE
+    default_value: 5
+    description: ""
+    editable: true
+    header: Number of checkpoints that is done during the single training round
+    max_value: 100
+    min_value: 1
+    type: INTEGER
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: 5
+    visible_in_ui: true
+    warning: null
+  num_iters:
+    affects_outcome_of: TRAINING
+    default_value: 1
+    description:
+      Increasing this value causes the results to be more robust but training
+      time will be longer.
+    editable: true
+    header: Number of training iterations
+    max_value: 100000
+    min_value: 1
+    type: INTEGER
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: 1
+    visible_in_ui: true
+    warning: null
+  num_workers:
+    affects_outcome_of: NONE
+    default_value: 5
+    description:
+      Increasing this value might improve training speed however it might
+      cause out of memory errors. If the number of workers is set to zero, data loading
+      will happen in the main training thread.
+    editable: true
+    header: Number of cpu threads to use during batch generation
+    max_value: 8
+    min_value: 0
+    type: INTEGER
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: 0
+    visible_in_ui: true
+    warning: null
+  type: PARAMETER_GROUP
+  visible_in_ui: true
+postprocessing:
+  confidence_threshold:
+    affects_outcome_of: INFERENCE
+    default_value: 0.35
+    description:
+      This threshold only takes effect if the threshold is not set based
+      on the result.
+    editable: true
+    header: Confidence threshold
+    max_value: 1
+    min_value: 0
+    type: FLOAT
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    # value: 0.35
+    value: 0.01
+    visible_in_ui: true
+    warning: null
+  description: Postprocessing
+  header: Postprocessing
+  result_based_confidence_threshold:
+    affects_outcome_of: INFERENCE
+    default_value: true
+    description: Confidence threshold is derived from the results
+    editable: true
+    header: Result based confidence threshold
+    type: BOOLEAN
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: true
+    visible_in_ui: true
+    warning: null
+  type: PARAMETER_GROUP
+  visible_in_ui: true
+algo_backend:
+  description: parameters for algo backend
+  header: Algo backend parameters
+  train_type:
+    affects_outcome_of: NONE
+    default_value: Incremental
+    description: Quantization preset that defines quantization scheme
+    editable: false
+    enum_name: TrainType
+    header: train type
+    options:
+      SemiSupervised: "SemiSupervised"
+      SelfSupervised: "SelfSupervised"
+      Incremental: "Incremental"
+    type: SELECTABLE
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: SelfSupervised
+    visible_in_ui: True
+    warning: null
+  type: PARAMETER_GROUP
+  visible_in_ui: true
+type: CONFIGURABLE_PARAMETERS
+visible_in_ui: true
+pot_parameters:
+  description: POT Parameters
+  header: POT Parameters
+  preset:
+    affects_outcome_of: NONE
+    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: Performance
+    visible_in_ui: True
+    warning: null
+  stat_subset_size:
+    affects_outcome_of: NONE
+    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: true
+nncf_optimization:
+  description: Optimization by NNCF
+  header: Optimization by NNCF
+  enable_quantization:
+    affects_outcome_of: INFERENCE
+    default_value: True
+    description: Enable quantization algorithm
+    editable: false
+    header: Enable quantization algorithm
+    type: BOOLEAN
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: true
+    visible_in_ui: false
+    warning: null
+  enable_pruning:
+    affects_outcome_of: INFERENCE
+    default_value: false
+    description: Enable filter pruning algorithm
+    editable: true
+    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
+  maximal_accuracy_degradation:
+    affects_outcome_of: NONE
+    default_value: 1.0
+    description: The maximal allowed accuracy metric drop in absolute values
+    editable: True
+    header: Maximum accuracy degradation
+    max_value: 100.0
+    min_value: 0.0
+    type: FLOAT
+    ui_rules:
+      action: DISABLE_EDITING
+      operator: AND
+      rules: []
+      type: UI_RULES
+    value: 1.0
+    visible_in_ui: True
+    warning: null
+  type: PARAMETER_GROUP
+  visible_in_ui: True

external/model-preparation-algorithm/configs/instance-segmentation/efficientnetb2b_maskrcnn/coco_data_pipeline.py

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+dataset_type = 'CocoDataset'
+img_size = (1024, 1024)
+
+img_norm_cfg = dict(
+    mean=(103.53, 116.28, 123.675), std=(1.0, 1.0, 1.0), to_rgb=False)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True,
+         with_mask=True, poly2mask=False),
+    dict(type='Resize', img_scale=img_size, keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_size,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+__dataset_type = 'CocoDataset'
+__data_root = 'data/coco/'
+
+__samples_per_gpu = 4
+
+data = dict(
+    samples_per_gpu=__samples_per_gpu,
+    workers_per_gpu=2,
+    train=dict(
+        type=__dataset_type,
+        ann_file=__data_root + 'annotations/instances_train2017.json',
+        img_prefix=__data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=__dataset_type,
+        ann_file=__data_root + 'annotations/instances_val2017.json',
+        img_prefix=__data_root + 'val2017/',
+        test_mode=True,
+        pipeline=test_pipeline),
+    test=dict(
+        type=__dataset_type,
+        ann_file=__data_root + 'annotations/instances_val2017.json',
+        img_prefix=__data_root + 'val2017/',
+        test_mode=True,
+        pipeline=test_pipeline)
+)

external/model-preparation-algorithm/configs/instance-segmentation/efficientnetb2b_maskrcnn/hpo_config.yaml

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+metric: mAP
+search_algorithm: smbo
+early_stop: None
+hp_space:
+  learning_parameters.learning_rate:
+    param_type: quniform
+    range:
+      - 0.001
+      - 0.1
+      - 0.001
+  learning_parameters.batch_size:
+    param_type: qloguniform
+    range:
+      - 2
+      - 4
+      - 2

external/model-preparation-algorithm/configs/instance-segmentation/efficientnetb2b_maskrcnn/model.py

@@ -0,0 +1,4 @@
+_base_ = [
+  '../../../submodule/samples/cfgs/models/backbones/efficientnet_b2b.yaml',
+  '../../../submodule/recipes/stages/_base_/models/detectors/efficientnetb2b_maskrcnn.custom.py'
+]