add yolof in yolo project

yolo/config/__init__.py

@@ -7,6 +7,7 @@ from .yolov5_af_config import build_yolov5af_config
 from .yolov7_af_config import build_yolov7af_config
 from .yolov8_config    import build_yolov8_config
 from .gelan_config     import build_gelan_config
+from .yolof_config     import build_yolof_config
 from .rtdetr_config    import build_rtdetr_config
 
 
@@ -30,6 +31,8 @@ def build_config(args):
         cfg = build_yolov8_config(args)
     elif 'gelan' in args.model:
         cfg = build_gelan_config(args)
+    elif 'yolof' in args.model:
+        cfg = build_yolof_config(args)
     # ----------- RT-DETR -----------
     elif 'rtdetr' in args.model:
         cfg = build_rtdetr_config(args)

yolo/config/yolof_config.py

@@ -0,0 +1,186 @@
+# Modified You Only Look One-level Feature
+
+def build_yolof_config(args):
+    if   args.model == 'yolof_n':
+        return YolofNConfig()
+    elif args.model == 'yolof_s':
+        return YolofSConfig()
+    elif args.model == 'yolof_m':
+        return YolofMConfig()
+    elif args.model == 'yolof_l':
+        return YolofLConfig()
+        
+    else:
+        raise NotImplementedError("No config for model: {}".format(args.model))
+
+
+# --------------- Base configuration ---------------
+class YolofBaseConfig(object):
+    def __init__(self):
+        # --------- Backbone ---------
+        self.width = 1.0
+        self.depth = 1.0
+        self.ratio = 1.0
+        self.scale = "b"
+        self.max_stride = 32
+        self.out_stride = 16
+        ## Backbone
+        self.bk_act   = 'silu'
+        self.bk_norm  = 'BN'
+        self.bk_depthwise = False
+        self.use_pretrained = True
+
+        # --------- Neck ---------
+        self.upscale_factor = 2
+        self.neck_dilations = [2, 4, 6, 8]
+        self.neck_expand_ratio = 0.5
+        self.neck_act = 'silu'
+        self.neck_norm = 'BN'
+        self.neck_depthwise = False
+
+        # --------- Head ---------
+        self.head_dim     = 512
+        self.num_cls_head = 4
+        self.num_reg_head = 4
+        self.head_act     = 'silu'
+        self.head_norm    = 'BN'
+        self.head_depthwise = False
+        self.anchor_size  = [[16, 16],
+                             [32, 32],
+                             [64, 64],
+                             [128, 128],
+                             [256, 256],
+                             [512, 512]]
+
+        # --------- Post-process ---------
+        ## Post process
+        self.val_topk = 1000
+        self.val_conf_thresh = 0.001
+        self.val_nms_thresh  = 0.7
+        self.test_topk = 300
+        self.test_conf_thresh = 0.4
+        self.test_nms_thresh  = 0.5
+
+        # --------- Label Assignment ---------
+        ## Matcher
+        self.ota_soft_center_radius = 3.0
+        self.ota_topk_candidates = 8
+        ## Loss weight
+        self.loss_cls = 1.0
+        self.loss_box = 2.0
+
+        # ---------------- ModelEMA config ----------------
+        self.use_ema = True
+        self.ema_decay = 0.9998
+        self.ema_tau   = 2000
+
+        # ---------------- Optimizer config ----------------
+        self.trainer      = 'yolo'
+        self.optimizer    = 'adamw'
+        self.per_image_lr = 0.001 / 64
+        self.base_lr      = None      # base_lr = per_image_lr * batch_size
+        self.min_lr_ratio = 0.01      # min_lr  = base_lr * min_lr_ratio
+        self.momentum     = 0.9
+        self.weight_decay = 0.05
+        self.clip_max_norm   = 35.0
+        self.warmup_bias_lr  = 0.1
+        self.warmup_momentum = 0.8
+
+        # ---------------- Lr Scheduler config ----------------
+        self.warmup_epoch = 3
+        self.lr_scheduler = "cosine"
+        self.max_epoch    = 300
+        self.eval_epoch   = 10
+        self.no_aug_epoch = 20
+
+        # ---------------- Data process config ----------------
+        self.aug_type = 'yolo'
+        self.box_format = 'xyxy'
+        self.normalize_coords = False
+        self.mosaic_prob = 0.0
+        self.mixup_prob  = 0.0
+        self.copy_paste  = 0.0           # approximated by the YOLOX's mixup
+        self.multi_scale = [0.5, 1.25]   # multi scale: [img_size * 0.5, img_size * 1.25]
+        ## Pixel mean & std
+        self.pixel_mean = [123.675, 116.28, 103.53]   # RGB format
+        self.pixel_std  = [58.395, 57.12, 57.375]     # RGB format
+        ## Transforms
+        self.train_img_size = 640
+        self.test_img_size  = 640
+        self.use_ablu = True
+        self.affine_params = {
+            'degrees': 0.0,
+            'translate': 0.2,
+            'scale': [0.1, 2.0],
+            'shear': 0.0,
+            'perspective': 0.0,
+            'hsv_h': 0.015,
+            'hsv_s': 0.7,
+            'hsv_v': 0.4,
+        }
+
+    def print_config(self):
+        config_dict = {key: value for key, value in self.__dict__.items() if not key.startswith('__')}
+        for k, v in config_dict.items():
+            print("{} : {}".format(k, v))
+
+# --------------- Modified YOLOF ---------------
+class YolofNConfig(YolofBaseConfig):
+    def __init__(self) -> None:
+        super().__init__()
+        # ---------------- Model config ----------------
+        self.width = 0.25
+        self.depth = 0.34
+        self.ratio = 2.0
+        self.scale = "n"
+        self.head_dim = 128
+
+        # ---------------- Data process config ----------------
+        self.mosaic_prob = 1.0
+        self.mixup_prob  = 0.0
+        self.copy_paste  = 0.5
+
+class YolofSConfig(YolofBaseConfig):
+    def __init__(self) -> None:
+        super().__init__()
+        # ---------------- Model config ----------------
+        self.width = 0.50
+        self.depth = 0.34
+        self.ratio = 2.0
+        self.scale = "s"
+        self.head_dim = 256
+
+        # ---------------- Data process config ----------------
+        self.mosaic_prob = 1.0
+        self.mixup_prob  = 0.0
+        self.copy_paste  = 0.5
+
+class YolofMConfig(YolofSConfig):
+    def __init__(self) -> None:
+        super().__init__()
+        # ---------------- Model config ----------------
+        self.width = 0.75
+        self.depth = 0.67
+        self.ratio = 1.5
+        self.scale = "m"
+        self.head_dim = 384
+
+        # ---------------- Data process config ----------------
+        self.mosaic_prob = 1.0
+        self.mixup_prob  = 0.1
+        self.copy_paste  = 0.5
+
+class YolofLConfig(YolofSConfig):
+    def __init__(self) -> None:
+        super().__init__()
+        # ---------------- Model config ----------------
+        self.width = 1.0
+        self.depth = 1.0
+        self.ratio = 1.0
+        self.scale = "l"
+        self.head_dim = 512
+
+        # ---------------- Data process config ----------------
+        self.mosaic_prob = 1.0
+        self.mixup_prob  = 0.1
+        self.copy_paste  = 0.5