Yolox improve with REPConv/ASFF/TOOD

configs/detection/yolox/yolox_l_8xb8_300e_coco.py

@@ -1,4 +1,4 @@
-_base_ = './yolox_s_8xb16_300e_coco.py'
+_base_ = 'configs/detection/yolox/yolox_s_8xb16_300e_coco.py'
 
 # model settings
 model = dict(model_type='l')

configs/detection/yolox/yolox_s_8xb16_300e_coco.py

@@ -3,10 +3,23 @@
 # model settings s m l x
 model = dict(
     type='YOLOX',
-    num_classes=80,
-    model_type='s',  # s m l x tiny nano
     test_conf=0.01,
-    nms_thre=0.65)
+    nms_thre=0.65,
+    backbone=dict(
+        type='YOLOPAFPN',
+        backbone='CSPDarknet',
+        model_type='s',  # s m l x tiny nano
+        use_att=None,
+        neck='yolo'
+    ),
+    head=dict(
+        type='YOLOXHead',
+        model_type='s',
+        obj_loss_type='BCE',
+        reg_loss_type='giou',
+        num_classes=80
+    )
+)
 
 # s m l x
 img_scale = (640, 640)
@@ -35,7 +48,9 @@
 ]
 
 # dataset settings
-data_root = 'data/coco/'
+# data_root = '/apsarapangu/disk2/xinyi.zxy/data/coco/'
+data_root = '/mnt/data/nas/data/detection/coco/'
+
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 

configs/detection/yolox/yolox_s_8xb16_300e_coco_asff_tood3.py

@@ -0,0 +1,198 @@
+_base_ = '../../base.py'
+
+# model settings s m l x
+model = dict(
+    type='YOLOX',
+    test_conf=0.01,
+    nms_thre=0.65,
+    backbone=dict(
+        type='YOLOPAFPN',
+        backbone='RepVGGYOLOX',
+        model_type='s',  # s m l x tiny nano
+        use_att='ASFF',
+        neck='yolo'
+    ),
+    head=dict(
+        type='TOODHead',
+        model_type='s',
+        obj_loss_type='BCE',
+        reg_loss_type='giou',
+        num_classes=80
+    )
+)
+
+# s m l x
+img_scale = (640, 640)
+random_size = (14, 26)
+scale_ratio = (0.1, 2)
+
+# tiny nano without mixup
+# img_scale = (416, 416)
+# random_size = (10, 20)
+# scale_ratio = (0.5, 1.5)
+
+CLASSES = [
+    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
+    'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
+    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
+    'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
+    'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
+    'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
+    'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
+    'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
+    'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
+    'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+    'hair drier', 'toothbrush'
+]
+
+# dataset settings
+# data_root = '/apsarapangu/disk2/xinyi.zxy/data/coco/'
+# data_root = '/mnt/data/nas/data/detection/coco/'
+data_root = '/root/workspace/data/coco/'
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='MMMosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='MMRandomAffine',
+        scaling_ratio_range=scale_ratio,
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(
+        type='MMMixUp',  # s m x l; tiny nano will detele
+        img_scale=img_scale,
+        ratio_range=(0.8, 1.6),
+        pad_val=114.0),
+    dict(
+        type='MMPhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='MMRandomFlip', flip_ratio=0.5),
+    dict(type='MMResize', keep_ratio=True),
+    dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
+    dict(type='MMNormalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='MMResize', img_scale=img_scale, keep_ratio=True),
+    dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
+    dict(type='MMNormalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img'])
+]
+
+train_dataset = dict(
+    type='DetImagesMixDataset',
+    data_source=dict(
+        type='DetSourceCoco',
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile', to_float32=True),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        classes=CLASSES,
+        filter_empty_gt=False,
+        iscrowd=False),
+    pipeline=train_pipeline,
+    dynamic_scale=img_scale)
+
+val_dataset = dict(
+    type='DetImagesMixDataset',
+    imgs_per_gpu=2,
+    data_source=dict(
+        type='DetSourceCoco',
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile', to_float32=True),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        classes=CLASSES,
+        filter_empty_gt=False,
+        iscrowd=True),
+    pipeline=test_pipeline,
+    dynamic_scale=None,
+    label_padding=False)
+
+data = dict(
+    imgs_per_gpu=16, workers_per_gpu=4, train=train_dataset, val=val_dataset)
+
+# additional hooks
+interval = 10
+custom_hooks = [
+    dict(
+        type='YOLOXModeSwitchHook',
+        no_aug_epochs=15,
+        skip_type_keys=('MMMosaic', 'MMRandomAffine', 'MMMixUp'),
+        priority=48),
+    dict(
+        type='SyncRandomSizeHook',
+        ratio_range=random_size,
+        img_scale=img_scale,
+        interval=interval,
+        priority=48),
+    dict(
+        type='SyncNormHook',
+        num_last_epochs=15,
+        interval=interval,
+        priority=48)
+]
+
+# evaluation
+eval_config = dict(
+    interval=10,
+    gpu_collect=False,
+    visualization_config=dict(
+        vis_num=10,
+        score_thr=0.5,
+    )  # show by TensorboardLoggerHookV2 and WandbLoggerHookV2
+)
+eval_pipelines = [
+    dict(
+        mode='test',
+        data=data['val'],
+        evaluators=[dict(type='CocoDetectionEvaluator', classes=CLASSES)],
+    )
+]
+
+checkpoint_config = dict(interval=interval)
+
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.02, momentum=0.9, weight_decay=5e-4, nesterov=True)
+optimizer_config = {}
+
+# learning policy
+lr_config = dict(
+    policy='YOLOX',
+    warmup='exp',
+    by_epoch=False,
+    warmup_by_epoch=True,
+    warmup_ratio=1,
+    warmup_iters=5,  # 5 epoch
+    num_last_epochs=15,
+    min_lr_ratio=0.05)
+
+# exponetial model average
+ema = dict(decay=0.9998)
+
+# runtime settings
+total_epochs = 300
+
+# yapf:disable
+log_config = dict(
+    interval=100,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='TensorboardLoggerHookV2'),
+        # dict(type='WandbLoggerHookV2'),
+    ])
+
+export = dict(use_jit=False, export_blade=False, end2end=False)