Update person detection template

configs/ote/person-detection/person-detection-0200/compression_config.json

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+{
+    "base": {
+        "find_unused_parameters": true,
+        "nncf_config": {
+            "input_info": {
+                "sample_size": [
+                    1,
+                    3,
+                    256,
+                    256
+                ]
+            },
+            "compression": [],
+            "log_dir": "."
+        }
+    },
+    "nncf_quantization": {
+        "optimizer": {
+            "type": "SGD",
+            "lr": 0.00005,
+            "momentum": 0.9,
+            "weight_decay": 0.0005
+        },
+        "runner": {
+            "max_epochs": 2
+        },
+        "nncf_config": {
+            "compression": [
+                {
+                    "algorithm": "quantization",
+                    "initializer": {
+                        "range": {
+                            "num_init_samples": 10
+                        },
+                        "batchnorm_adaptation": {
+                            "num_bn_adaptation_samples": 30
+                        }
+                    }
+                }
+            ]
+        }
+    },
+    "order_of_parts": [
+        "nncf_quantization"
+    ]
+}

configs/ote/person-detection/person-detection-0200/model.py

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+# model settings
+input_size = 256
+image_width, image_height = input_size, input_size
+width_mult = 1.0
+model = dict(
+    type='SingleStageDetector',
+    backbone=dict(
+        type='mobilenetv2_w1',
+        out_indices=(4, 5),
+        frozen_stages=-1,
+        norm_eval=False,
+        pretrained=True
+    ),
+    neck=None,
+    bbox_head=dict(
+        type='SSDHead',
+        num_classes=1,
+        in_channels=(int(width_mult * 96), int(width_mult * 320)),
+        anchor_generator=dict(
+            type='SSDAnchorGeneratorClustered',
+            strides=(16, 32),
+            widths=[
+                [image_width * x for x in
+                 [0.015411783166343854, 0.033018232306549156, 0.04467156688464953,
+                  0.0610697815328886]],
+                [image_width * x for x in
+                 [0.0789599954420517, 0.10113984043326349, 0.12805187473050397, 0.16198319380154758,
+                  0.21636496806213493]],
+
+            ],
+            heights=[
+                [image_height * x for x in
+                 [0.05032631418898226, 0.10070800135152037, 0.15806180366055939,
+                  0.22343401646383804]],
+                [image_height * x for x in
+                 [0.300881401352503, 0.393181898580379, 0.4998807213337051, 0.6386035764261081,
+                  0.8363451552091518]],
+
+            ],
+        ),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=(.0, .0, .0, .0),
+            target_stds=(0.1, 0.1, 0.2, 0.2), ),
+        depthwise_heads=True,
+        depthwise_heads_activations='relu',
+        loss_balancing=True),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.4,
+            neg_iou_thr=0.4,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        smoothl1_beta=1.,
+        use_giou=False,
+        use_focal=False,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms=dict(type='nms', iou_threshold=0.45),
+        min_bbox_size=0,
+        score_thr=0.02,
+        max_per_img=200))
+cudnn_benchmark = True
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '../../data/airport/'
+img_norm_cfg = dict(mean=[0, 0, 0], std=[255, 255, 255], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.1),
+    dict(type='Resize', img_scale=(input_size, input_size), keep_ratio=False),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(input_size, input_size),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=122,
+    workers_per_gpu=3,
+    train=dict(
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            labels=('person',),
+            ann_file=data_root + 'annotation_person_train.json',
+            min_size=20,
+            img_prefix=data_root + 'train',
+            pipeline=train_pipeline
+        )
+    ),
+    val=dict(
+        type=dataset_type,
+        labels=('person',),
+        ann_file=data_root + 'annotation_person_val.json',
+        img_prefix=data_root + 'val',
+        test_mode=True,
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        labels=('person',),
+        ann_file=data_root + 'annotation_person_val.json',
+        img_prefix=data_root + 'val',
+        test_mode=True,
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=0.05, momentum=0.9, weight_decay=0.0005)
+optimizer_config = dict()
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1200,
+    warmup_ratio=1.0 / 3,
+    step=[8, 15, 18])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=1,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=20)
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = 'output'
+load_from = 'https://download.01.org/opencv/openvino_training_extensions/models/object_detection/v2/person-detection-0200-1.pth'
+resume_from = None
+evaluation = dict(interval=1, metric='mAP', save_best='mAP')
+workflow = [('train', 1)]

configs/ote/person-detection/person-detection-0200/template_experimental.yaml

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+# Description.
+model_template_id: Person_Detection_0200
+name: person-detection-0200
+task_type: DETECTION
+task_family: VISION
+instantiation: "CLASS"
+summary: Fastest Person Detection model for large and simple objects (MobileNetV2-SSD).
+application:
+  ~
+
+# Algo backend.
+framework: OTEDetection v2.9.1
+
+# Task implementations.
+entrypoints:
+  base: mmdet.apis.ote.apis.detection.OTEDetectionTrainingTask
+  openvino: mmdet.apis.ote.apis.detection.OpenVINODetectionTask
+  nncf: mmdet.apis.ote.apis.detection.OTEDetectionNNCFTask
+
+# Capabilities.
+capabilities:
+  - compute_representations
+
+# Hyperparameters.
+hyper_parameters:
+  base_path: ../../../../mmdet/apis/ote/apis/detection/configuration.yaml
+  parameter_overrides:
+    learning_parameters:
+      batch_size:
+        default_value: 122
+      learning_rate:
+        default_value: 0.05
+      learning_rate_warmup_iters:
+        default_value: 1200
+      num_iters:
+        default_value: 20
+    nncf_optimization:
+      enable_quantization:
+        default_value: true
+      enable_pruning:
+        default_value: false
+      maximal_accuracy_degradation:
+        default_value: 0.01
+
+# Training resources.
+max_nodes: 1
+training_targets:
+  - GPU
+  - CPU
+
+# Stats.
+gigaflops: 0.82 #to be checked with model-analyzer
+size: 1.83
+
+# Obsolete
+# gpu_nums: 2

configs/ote/person-detection/person-detection-0201/compression_config.json

@@ -0,0 +1,46 @@
+{
+    "base": {
+        "find_unused_parameters": true,
+        "nncf_config": {
+            "input_info": {
+                "sample_size": [
+                    1,
+                    3,
+                    384,
+                    384
+                ]
+            },
+            "compression": [],
+            "log_dir": "."
+        }
+    },
+    "nncf_quantization": {
+        "optimizer": {
+            "type": "SGD",
+            "lr": 0.00005,
+            "momentum": 0.9,
+            "weight_decay": 0.0005
+        },
+        "runner": {
+            "max_epochs": 2
+        },
+        "nncf_config": {
+            "compression": [
+                {
+                    "algorithm": "quantization",
+                    "initializer": {
+                        "range": {
+                            "num_init_samples": 10
+                        },
+                        "batchnorm_adaptation": {
+                            "num_bn_adaptation_samples": 30
+                        }
+                    }
+                }
+            ]
+        }
+    },
+    "order_of_parts": [
+        "nncf_quantization"
+    ]
+}