add a README

yolo/config/__init__.py

@@ -8,7 +8,6 @@ from .yolov6_config     import build_yolov6_config
 from .yolov8_config     import build_yolov8_config
 from .yolov8_e2e_config import build_yolov8_e2e_config
 from .gelan_config      import build_gelan_config
-from .rtcdet_config     import build_rtcdet_config
 from .rtdetr_config     import build_rtdetr_config
 
 
@@ -34,8 +33,6 @@ def build_config(args):
         cfg = build_yolov8_config(args)
     elif 'gelan' in args.model:
         cfg = build_gelan_config(args)
-    elif 'rtcdet' in args.model:
-        cfg = build_rtcdet_config(args)
         
     # ----------- RT-DETR -----------
     elif 'rtdetr' in args.model:

yolo/config/rtcdet_config.py

@@ -1,217 +0,0 @@
-# RTCDet config
-
-
-def build_rtcdet_config(args):
-    if   args.model == 'rtcdet_n':
-        return RTCDet_Nano_Config()
-    elif args.model == 'rtcdet_t':
-        return RTCDet_Tiny_Config()
-    elif args.model == 'rtcdet_s':
-        return RTCDet_Small_Config()
-    elif args.model == 'rtcdet_m':
-        return RTCDet_Medium_Config()
-    elif args.model == 'rtcdet_l':
-        return RTCDet_Large_Config()
-    elif args.model == 'rtcdet_x':
-        return RTCDet_xLarge_Config()
-    else:
-        raise NotImplementedError("No config for model: {}".format(args.model))
-    
-# RTCDet-Base config
-class RTCDetBaseConfig(object):
-    def __init__(self) -> None:
-        # ---------------- Model config ----------------
-        self.stage_dims  = [64, 128, 256, 512, 512]
-        self.stage_depth = [3, 6, 6, 3]
-        self.width    = 1.0
-        self.depth    = 1.0
-        self.ratio    = 1.0
-        self.reg_max  = 16
-        self.out_stride = [8, 16, 32]
-        self.max_stride = 32
-        self.num_levels = 3
-        ## Backbone
-        self.bk_block    = 'elan_layer'
-        self.bk_ds_block = 'conv'
-        self.bk_act      = 'silu'
-        self.bk_norm     = 'bn'
-        self.bk_depthwise   = False
-        ## Neck
-        self.neck_act       = 'silu'
-        self.neck_norm      = 'bn'
-        self.neck_depthwise = False
-        self.neck_expand_ratio = 0.5
-        self.spp_pooling_size  = 5
-        ## FPN
-        self.fpn_block     = 'elan_layer'
-        self.fpn_ds_block  = 'conv'
-        self.fpn_act       = 'silu'
-        self.fpn_norm      = 'bn'
-        self.fpn_depthwise = False
-        ## Head
-        self.head_act  = 'silu'
-        self.head_norm = 'bn'
-        self.head_depthwise = False
-        self.num_cls_head   = 2
-        self.num_reg_head   = 2
-
-        # ---------------- Post-process config ----------------
-        ## Post process
-        self.val_topk = 1000
-        self.val_conf_thresh = 0.001
-        self.val_nms_thresh  = 0.7
-        self.test_topk = 100
-        self.test_conf_thresh = 0.2
-        self.test_nms_thresh  = 0.5
-
-        # ---------------- Assignment & Loss config ----------------
-        self.loss_cls_type = "bce"
-        self.matcher_dict = {"tal_alpha": 0.5, "tal_beta": 6.0, "topk_candidates": 10}
-        self.weight_dict  = {"loss_cls": 0.5, "loss_box": 7.5, "loss_dfl": 1.5}
-
-        # ---------------- Assignment & Loss config ----------------
-        # self.loss_cls_type = "vfl"
-        # self.matcher_dict = {"tal_alpha": 1.0, "tal_beta": 6.0, "topk_candidates": 13}   # For VFL
-        # self.weight_dict  = {"loss_cls": 1.0, "loss_box": 2.5, "loss_dfl": 0.5}   # For VFL
-
-        # ---------------- ModelEMA config ----------------
-        self.use_ema = True
-        self.ema_decay = 0.9998
-        self.ema_tau   = 2000
-
-        # ---------------- Optimizer config ----------------
-        self.trainer      = 'yolo'
-        self.no_norm_decay = True
-        self.no_bias_decay = True
-        self.batch_size_base = 64
-        self.optimizer    = 'adamw'
-        self.base_lr      = 0.001
-        self.min_lr_ratio = 0.05      # 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
-        self.use_fp16        = True  # use mixing precision
-
-        # ---------------- Lr Scheduler config ----------------
-        self.warmup_epoch = 3
-        self.lr_scheduler = "cosine"
-        self.max_epoch    = 500
-        self.eval_epoch   = 10
-        self.no_aug_epoch = 15
-
-        # ---------------- 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, 1.5]   # multi scale: [img_size * 0.5, img_size * 1.5]
-        ## Pixel mean & std
-        self.pixel_mean = [0., 0., 0.]
-        self.pixel_std  = [255., 255., 255.]
-        ## Transforms
-        self.train_img_size = 640
-        self.test_img_size  = 640
-        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))
-
-# RTCDet-N
-class RTCDet_Nano_Config(RTCDetBaseConfig):
-    def __init__(self) -> None:
-        super().__init__()
-        # ---------------- Model config ----------------
-        self.width = 0.25
-        self.depth = 0.34
-        self.ratio = 2.0
-
-        # ---------------- Data process config ----------------
-        self.mosaic_prob = 1.0
-        self.mixup_prob  = 0.0
-        self.copy_paste  = 1.0
-
-# RTCDet-T
-class RTCDet_Tiny_Config(RTCDetBaseConfig):
-    def __init__(self) -> None:
-        super().__init__()
-        # ---------------- Model config ----------------
-        self.width = 0.375
-        self.depth = 0.34
-        self.ratio = 2.0
-
-        # ---------------- Data process config ----------------
-        self.mosaic_prob = 1.0
-        self.mixup_prob  = 0.0
-        self.copy_paste  = 1.0
-
-# RTCDet-S
-class RTCDet_Small_Config(RTCDetBaseConfig):
-    def __init__(self) -> None:
-        super().__init__()
-        # ---------------- Model config ----------------
-        self.width = 0.50
-        self.depth = 0.34
-        self.ratio = 2.0
-
-        # ---------------- Data process config ----------------
-        self.mosaic_prob = 1.0
-        self.mixup_prob  = 0.05
-        self.copy_paste  = 1.0
-
-# RTCDet-M
-class RTCDet_Medium_Config(RTCDetBaseConfig):
-    def __init__(self) -> None:
-        super().__init__()
-        # ---------------- Model config ----------------
-        self.width = 0.75
-        self.depth = 0.67
-        self.ratio = 1.5
-
-        # ---------------- Data process config ----------------
-        self.mosaic_prob = 1.0
-        self.mixup_prob  = 0.1
-        self.copy_paste  = 1.0
-
-# RTCDet-L
-class RTCDet_Large_Config(RTCDetBaseConfig):
-    def __init__(self) -> None:
-        super().__init__()
-        # ---------------- Model config ----------------
-        self.width = 1.0
-        self.depth = 1.0
-        self.ratio = 1.0
-
-        # ---------------- Data process config ----------------
-        self.mosaic_prob = 1.0
-        self.mixup_prob  = 0.15
-        self.copy_paste  = 1.0
-
-# RTCDet-X
-class RTCDet_xLarge_Config(RTCDetBaseConfig):
-    def __init__(self) -> None:
-        super().__init__()
-        # ---------------- Model config ----------------
-        self.width = 1.25
-        self.depth = 1.0
-        self.ratio = 1.0
-
-        # ---------------- Data process config ----------------
-        self.mosaic_prob = 1.0
-        self.mixup_prob  = 0.2
-        self.copy_paste  = 1.0
-        

yolo/models/__init__.py

@@ -11,7 +11,6 @@ from .yolov6.build     import build_yolov6
 from .yolov8.build     import build_yolov8
 from .yolov8_e2e.build import build_yolov8_e2e
 from .gelan.build      import build_gelan
-from .rtcdet.build     import build_rtcdet
 from .rtdetr.build     import build_rtdetr
 
 
@@ -45,9 +44,6 @@ def build_model(args, cfg, is_val=False):
     ## GElan
     elif 'gelan' in args.model:
         model, criterion = build_gelan(cfg, is_val)
-    ## RTCDet
-    elif 'rtcdet' in args.model:
-        model, criterion = build_rtcdet(cfg, is_val)
     ## RT-DETR
     elif 'rtdetr' in args.model:
         model, criterion = build_rtdetr(cfg, is_val)

yolo/models/rtcdet/README.md

@@ -1,56 +0,0 @@
-# RTCDet: My Empirical Study of Real-Time Convolutional Object Detectors.
-
-- VOC
-
-|     Model   | Batch | Scale | AP<sup>val<br>0.5 | Weight |  Logs  |
-|-------------|-------|-------|-------------------|--------|--------|
-| RTCDet-S    | 1xb16 |  640  |               |  |  |
-
-- COCO
-
-|    Model    | Batch | Scale | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |  Logs  |
-|-------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|--------|
-| RTCDet-S    | 1xb16 |  640  |                    |               |   26.9            |   8.9             |  |  |
-
-
-
-## Train RTCDet
-### Single GPU
-Taking training RTCDet-S on COCO as the example,
-```Shell
-python train.py --cuda -d coco --root path/to/coco -m rtcdet_s -bs 16 --fp16 
-```
-
-### Multi GPU
-Taking training RTCDet-S on COCO as the example,
-```Shell
-python -m torch.distributed.run --nproc_per_node=8 train.py --cuda --distributed -d coco --root path/to/coco -m rtcdet_s -bs 256 --fp16 
-```
-
-## Test RTCDet
-Taking testing RTCDet-S on COCO-val as the example,
-```Shell
-python test.py --cuda -d coco --root path/to/coco -m rtcdet_s --weight path/to/RTCDet.pth --show 
-```
-
-## Evaluate RTCDet
-Taking evaluating RTCDet-S on COCO-val as the example,
-```Shell
-python eval.py --cuda -d coco --root path/to/coco -m rtcdet_s --weight path/to/RTCDet.pth 
-```
-
-## Demo
-### Detect with Image
-```Shell
-python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m rtcdet_s --weight path/to/weight --show
-```
-
-### Detect with Video
-```Shell
-python demo.py --mode video --path_to_vid path/to/video --cuda -m rtcdet_s --weight path/to/weight --show --gif
-```
-
-### Detect with Camera
-```Shell
-python demo.py --mode camera --cuda -m rtcdet_s --weight path/to/weight --show --gif
-```

yolo/models/rtcdet/build.py

@@ -1,18 +0,0 @@
-import torch.nn as nn
-
-from .loss import SetCriterion
-from .rtcdet import RTCDet
-
-
-# build object detector
-def build_rtcdet(cfg, is_val=False):
-    # -------------- Build YOLO --------------
-    model = RTCDet(cfg, is_val)
-
-    # -------------- Build criterion --------------
-    criterion = None
-    if is_val:
-        # build criterion for training
-        criterion = SetCriterion(cfg)
-        
-    return model, criterion

yolo/models/rtcdet/loss.py

@@ -1,197 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from utils.box_ops import bbox2dist, bbox_iou
-from utils.distributed_utils import get_world_size, is_dist_avail_and_initialized
-
-from .matcher import TaskAlignedAssigner
-
-
-# ---------- Criterion for RTCDet ----------
-class SetCriterion(object):
-    def __init__(self, cfg):
-        # --------------- Basic parameters ---------------
-        self.cfg = cfg
-        self.reg_max = cfg.reg_max
-        self.num_classes   = cfg.num_classes
-        self.loss_cls_type = cfg.loss_cls_type
-        self.matcher_dict  = cfg.matcher_dict
-        # --------------- Loss config ---------------
-        self.loss_cls_weight = cfg.weight_dict["loss_cls"]
-        self.loss_box_weight = cfg.weight_dict["loss_box"]
-        self.loss_dfl_weight = cfg.weight_dict["loss_dfl"]
-        # --------------- Matcher config ---------------
-        self.matcher = TaskAlignedAssigner(num_classes     = cfg.num_classes,
-                                           topk_candidates = self.matcher_dict["topk_candidates"],
-                                           alpha           = self.matcher_dict["tal_alpha"],
-                                           beta            = self.matcher_dict["tal_beta"],
-                                           )
-
-    def loss_classes(self, pred_cls, gt_score):
-        # Compute VFL loss
-        if self.loss_cls_type == "vfl":
-            alpha, gamma = 0.75, 2.0
-            pred_sigmoid = pred_cls.sigmoid()
-            focal_weight = gt_score * (gt_score > 0.0).float() + \
-                alpha * (pred_sigmoid - gt_score).abs().pow(gamma) * \
-                (gt_score <= 0.0).float()
-            
-            loss_cls = F.binary_cross_entropy_with_logits(
-                pred_cls, gt_score, reduction='none') * focal_weight
-        # Compute BCE loss
-        else:
-            loss_cls = F.binary_cross_entropy_with_logits(pred_cls, gt_score, reduction='none')
-
-        return loss_cls
-    
-    def loss_bboxes(self, pred_box, gt_box, bbox_weight):
-        # regression loss
-        ious = bbox_iou(pred_box, gt_box, xywh=False, CIoU=True)
-        loss_box = (1.0 - ious.squeeze(-1)) * bbox_weight
-
-        return loss_box
-    
-    def loss_dfl(self, pred_reg, gt_box, anchor, stride, bbox_weight=None):
-        # rescale coords by stride
-        gt_box_s = gt_box / stride
-        anchor_s = anchor / stride
-
-        # compute deltas
-        gt_ltrb_s = bbox2dist(anchor_s, gt_box_s, self.reg_max - 1)
-
-        gt_left = gt_ltrb_s.to(torch.long)
-        gt_right = gt_left + 1
-
-        weight_left = gt_right.to(torch.float) - gt_ltrb_s
-        weight_right = 1 - weight_left
-
-        # loss left
-        loss_left = F.cross_entropy(
-            pred_reg.view(-1, self.reg_max),
-            gt_left.view(-1),
-            reduction='none').view(gt_left.shape) * weight_left
-        # loss right
-        loss_right = F.cross_entropy(
-            pred_reg.view(-1, self.reg_max),
-            gt_right.view(-1),
-            reduction='none').view(gt_left.shape) * weight_right
-
-        loss_dfl = (loss_left + loss_right).mean(-1)
-        
-        if bbox_weight is not None:
-            loss_dfl *= bbox_weight
-
-        return loss_dfl
-
-    def __call__(self, outputs, targets):        
-        """
-            outputs['pred_cls']: List(Tensor) [B, M, C]
-            outputs['pred_reg']: List(Tensor) [B, M, 4*(reg_max+1)]
-            outputs['pred_box']: List(Tensor) [B, M, 4]
-            outputs['anchors']: List(Tensor) [M, 2]
-            outputs['strides']: List(Int) [8, 16, 32] output stride
-            outputs['stride_tensor']: List(Tensor) [M, 1]
-            targets: (List) [dict{'boxes': [...], 
-                                 'labels': [...], 
-                                 'orig_size': ...}, ...]
-        """
-        # preds: [B, M, C]
-        cls_preds = torch.cat(outputs['pred_cls'], dim=1)
-        reg_preds = torch.cat(outputs['pred_reg'], dim=1)
-        box_preds = torch.cat(outputs['pred_box'], dim=1)
-        delta_preds = torch.cat(outputs['pred_delta'], dim=1)
-        bs, num_anchors = cls_preds.shape[:2]
-        device = cls_preds.device
-        anchors = torch.cat(outputs['anchors'], dim=0)
-        strides = torch.cat(outputs['stride_tensor'], dim=0)
-
-        # --------------- label assignment ---------------
-        gt_score_targets = []
-        gt_bbox_targets = []
-        fg_masks = []
-        for batch_idx in range(bs):
-            tgt_labels = targets[batch_idx]["labels"].to(device)     # [Mp,]
-            tgt_boxs = targets[batch_idx]["boxes"].to(device)        # [Mp, 4]
-
-            if self.cfg.normalize_coords:
-                img_h, img_w = outputs['image_size']
-                tgt_boxs[..., [0, 2]] *= img_w
-                tgt_boxs[..., [1, 3]] *= img_h
-            
-            if self.cfg.box_format == 'xywh':
-                tgt_boxs_x1y1 = tgt_boxs[..., :2] - 0.5 * tgt_boxs[..., 2:]
-                tgt_boxs_x2y2 = tgt_boxs[..., :2] + 0.5 * tgt_boxs[..., 2:]
-                tgt_boxs = torch.cat([tgt_boxs_x1y1, tgt_boxs_x2y2], dim=-1)
-
-            # check target
-            if len(tgt_labels) == 0 or tgt_boxs.max().item() == 0.:
-                # There is no valid gt
-                fg_mask  = cls_preds.new_zeros(1, num_anchors).bool()               #[1, M,]
-                gt_score = cls_preds.new_zeros((1, num_anchors, self.num_classes)) #[1, M, C]
-                gt_box   = cls_preds.new_zeros((1, num_anchors, 4))                  #[1, M, 4]
-            else:
-                tgt_labels = tgt_labels[None, :, None]      # [1, Mp, 1]
-                tgt_boxs = tgt_boxs[None]                   # [1, Mp, 4]
-                (
-                    _,
-                    gt_box,     # [1, M, 4]
-                    gt_score,   # [1, M, C]
-                    fg_mask,    # [1, M,]
-                    _
-                ) = self.matcher(
-                    pd_scores = cls_preds[batch_idx:batch_idx+1].detach().sigmoid(), 
-                    pd_bboxes = box_preds[batch_idx:batch_idx+1].detach(),
-                    anc_points = anchors,
-                    gt_labels = tgt_labels,
-                    gt_bboxes = tgt_boxs
-                    )
-            gt_score_targets.append(gt_score)
-            gt_bbox_targets.append(gt_box)
-            fg_masks.append(fg_mask)
-
-        # List[B, 1, M, C] -> Tensor[B, M, C] -> Tensor[BM, C]
-        fg_masks = torch.cat(fg_masks, 0).view(-1)                                    # [BM,]
-        gt_score_targets = torch.cat(gt_score_targets, 0).view(-1, self.num_classes)  # [BM, C]
-        gt_bbox_targets = torch.cat(gt_bbox_targets, 0).view(-1, 4)                   # [BM, 4]
-        num_fgs = gt_score_targets.sum()
-        
-        # Average loss normalizer across all the GPUs
-        if is_dist_avail_and_initialized():
-            torch.distributed.all_reduce(num_fgs)
-        num_fgs = (num_fgs / get_world_size()).clamp(1.0)
-
-        # ------------------ Classification loss ------------------
-        cls_preds = cls_preds.view(-1, self.num_classes)
-        loss_cls = self.loss_classes(cls_preds, gt_score_targets)
-        loss_cls = loss_cls.sum() / num_fgs
-
-        # ------------------ Regression loss ------------------
-        box_preds_pos = box_preds.view(-1, 4)[fg_masks]
-        box_targets_pos = gt_bbox_targets.view(-1, 4)[fg_masks]
-        bbox_weight = gt_score_targets[fg_masks].sum(-1)
-        loss_box = self.loss_bboxes(box_preds_pos, box_targets_pos, bbox_weight)
-        loss_box = loss_box.sum() / num_fgs
-
-        # ------------------ Distribution focal loss ------------------
-        reg_preds_pos = reg_preds.view(-1, 4*self.reg_max)[fg_masks]
-        anchors_pos = anchors[None].repeat(bs, 1, 1).view(-1, 2)[fg_masks]
-        stride_pos  = strides[None].repeat(bs, 1, 1).view(-1, 1)[fg_masks]
-        loss_dfl = self.loss_dfl(reg_preds_pos, box_targets_pos, anchors_pos, stride_pos, bbox_weight)
-        loss_dfl = loss_dfl.sum() / num_fgs
-
-        # Compute total loss
-        losses = loss_cls * self.loss_cls_weight + \
-                 loss_box * self.loss_box_weight + \
-                 loss_dfl * self.loss_dfl_weight 
-        loss_dict = dict(
-                loss_cls = loss_cls,
-                loss_box = loss_box,
-                loss_dfl = loss_dfl,
-                losses = losses
-        )
-
-        return loss_dict
-    
-
-if __name__ == "__main__":
-    pass

yolo/models/rtcdet/matcher.py

@@ -1,202 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from utils.box_ops import bbox_iou
-
-
-# -------------------------- Task Aligned Assigner --------------------------
-class TaskAlignedAssigner(nn.Module):
-    """
-        This code referenced to https://github.com/ultralytics/ultralytics
-    """
-    def __init__(self,
-                 num_classes     = 80,
-                 topk_candidates = 10,
-                 alpha           = 0.5,
-                 beta            = 6.0, 
-                 eps             = 1e-9):
-        super(TaskAlignedAssigner, self).__init__()
-        self.topk_candidates = topk_candidates
-        self.num_classes = num_classes
-        self.bg_idx = num_classes
-        self.alpha = alpha
-        self.beta = beta
-        self.eps = eps
-
-    @torch.no_grad()
-    def forward(self,
-                pd_scores,
-                pd_bboxes,
-                anc_points,
-                gt_labels,
-                gt_bboxes):
-        self.bs = pd_scores.size(0)
-        self.n_max_boxes = gt_bboxes.size(1)
-
-        mask_pos, align_metric, overlaps = self.get_pos_mask(
-            pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points)
-
-        target_gt_idx, fg_mask, mask_pos = select_highest_overlaps(
-            mask_pos, overlaps, self.n_max_boxes)
-
-        # Assigned target
-        target_labels, target_bboxes, target_scores = self.get_targets(
-            gt_labels, gt_bboxes, target_gt_idx, fg_mask)
-
-        # normalize
-        align_metric *= mask_pos
-        pos_align_metrics = align_metric.amax(axis=-1, keepdim=True)  # b, max_num_obj
-        pos_overlaps = (overlaps * mask_pos).amax(axis=-1, keepdim=True)  # b, max_num_obj
-        norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1)
-        target_scores = target_scores * norm_align_metric
-
-        return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx
-
-    def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points):
-        # get in_gts mask, (b, max_num_obj, h*w)
-        mask_in_gts = select_candidates_in_gts(anc_points, gt_bboxes)
-        # get anchor_align metric, (b, max_num_obj, h*w)
-        align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts)
-        # get topk_metric mask, (b, max_num_obj, h*w)
-        mask_topk = self.select_topk_candidates(align_metric)
-        # merge all mask to a final mask, (b, max_num_obj, h*w)
-        mask_pos = mask_topk * mask_in_gts
-
-        return mask_pos, align_metric, overlaps
-
-    def get_box_metrics(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts):
-        """Compute alignment metric given predicted and ground truth bounding boxes."""
-        na = pd_bboxes.shape[-2]
-        mask_in_gts = mask_in_gts.bool()  # b, max_num_obj, h*w
-        overlaps = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_bboxes.dtype, device=pd_bboxes.device)
-        bbox_scores = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_scores.dtype, device=pd_scores.device)
-
-        ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long)  # 2, b, max_num_obj
-        ind[0] = torch.arange(end=self.bs).view(-1, 1).expand(-1, self.n_max_boxes)  # b, max_num_obj
-        ind[1] = gt_labels.squeeze(-1)  # b, max_num_obj
-        # Get the scores of each grid for each gt cls
-        bbox_scores[mask_in_gts] = pd_scores[ind[0], :, ind[1]][mask_in_gts]  # b, max_num_obj, h*w
-
-        # (b, max_num_obj, 1, 4), (b, 1, h*w, 4)
-        pd_boxes = pd_bboxes.unsqueeze(1).expand(-1, self.n_max_boxes, -1, -1)[mask_in_gts]
-        gt_boxes = gt_bboxes.unsqueeze(2).expand(-1, -1, na, -1)[mask_in_gts]
-        overlaps[mask_in_gts] = bbox_iou(gt_boxes, pd_boxes, xywh=False, CIoU=True).squeeze(-1).clamp_(0)
-
-        align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
-        return align_metric, overlaps
-
-    def select_topk_candidates(self, metrics, largest=True):
-        """
-        Args:
-            metrics: (b, max_num_obj, h*w).
-            topk_mask: (b, max_num_obj, topk) or None
-        """
-        # (b, max_num_obj, topk)
-        topk_metrics, topk_idxs = torch.topk(metrics, self.topk_candidates, dim=-1, largest=largest)
-        topk_mask = (topk_metrics.max(-1, keepdim=True)[0] > self.eps).expand_as(topk_idxs)
-        # (b, max_num_obj, topk)
-        topk_idxs.masked_fill_(~topk_mask, 0)
-
-        # (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w)
-        count_tensor = torch.zeros(metrics.shape, dtype=torch.int8, device=topk_idxs.device)
-        ones = torch.ones_like(topk_idxs[:, :, :1], dtype=torch.int8, device=topk_idxs.device)
-        for k in range(self.topk_candidates):
-            # Expand topk_idxs for each value of k and add 1 at the specified positions
-            count_tensor.scatter_add_(-1, topk_idxs[:, :, k:k + 1], ones)
-        # count_tensor.scatter_add_(-1, topk_idxs, torch.ones_like(topk_idxs, dtype=torch.int8, device=topk_idxs.device))
-        # Filter invalid bboxes
-        count_tensor.masked_fill_(count_tensor > 1, 0)
-
-        return count_tensor.to(metrics.dtype)
-
-    def get_targets(self, gt_labels, gt_bboxes, target_gt_idx, fg_mask):
-        # Assigned target labels, (b, 1)
-        batch_ind = torch.arange(end=self.bs, dtype=torch.int64, device=gt_labels.device)[..., None]
-        target_gt_idx = target_gt_idx + batch_ind * self.n_max_boxes  # (b, h*w)
-        target_labels = gt_labels.long().flatten()[target_gt_idx]  # (b, h*w)
-
-        # Assigned target boxes, (b, max_num_obj, 4) -> (b, h*w, 4)
-        target_bboxes = gt_bboxes.view(-1, 4)[target_gt_idx]
-
-        # Assigned target scores
-        target_labels.clamp_(0)
-
-        # 10x faster than F.one_hot()
-        target_scores = torch.zeros((target_labels.shape[0], target_labels.shape[1], self.num_classes),
-                                    dtype=torch.int64,
-                                    device=target_labels.device)  # (b, h*w, 80)
-        target_scores.scatter_(2, target_labels.unsqueeze(-1), 1)
-
-        fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.num_classes)  # (b, h*w, 80)
-        target_scores = torch.where(fg_scores_mask > 0, target_scores, 0)
-
-        return target_labels, target_bboxes, target_scores
-    
-
-# -------------------------- Basic Functions --------------------------
-def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
-    """select the positive anchors's center in gt
-    Args:
-        xy_centers (Tensor): shape(bs*n_max_boxes, num_total_anchors, 4)
-        gt_bboxes (Tensor): shape(bs, n_max_boxes, 4)
-    Return:
-        (Tensor): shape(bs, n_max_boxes, num_total_anchors)
-    """
-    n_anchors = xy_centers.size(0)
-    bs, n_max_boxes, _ = gt_bboxes.size()
-    _gt_bboxes = gt_bboxes.reshape([-1, 4])
-    xy_centers = xy_centers.unsqueeze(0).repeat(bs * n_max_boxes, 1, 1)
-    gt_bboxes_lt = _gt_bboxes[:, 0:2].unsqueeze(1).repeat(1, n_anchors, 1)
-    gt_bboxes_rb = _gt_bboxes[:, 2:4].unsqueeze(1).repeat(1, n_anchors, 1)
-    b_lt = xy_centers - gt_bboxes_lt
-    b_rb = gt_bboxes_rb - xy_centers
-    bbox_deltas = torch.cat([b_lt, b_rb], dim=-1)
-    bbox_deltas = bbox_deltas.reshape([bs, n_max_boxes, n_anchors, -1])
-    return (bbox_deltas.min(axis=-1)[0] > eps).to(gt_bboxes.dtype)
-
-def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
-    """if an anchor box is assigned to multiple gts,
-        the one with the highest iou will be selected.
-    Args:
-        mask_pos (Tensor): shape(bs, n_max_boxes, num_total_anchors)
-        overlaps (Tensor): shape(bs, n_max_boxes, num_total_anchors)
-    Return:
-        target_gt_idx (Tensor): shape(bs, num_total_anchors)
-        fg_mask (Tensor): shape(bs, num_total_anchors)
-        mask_pos (Tensor): shape(bs, n_max_boxes, num_total_anchors)
-    """
-    fg_mask = mask_pos.sum(-2)
-    if fg_mask.max() > 1:  # one anchor is assigned to multiple gt_bboxes
-        mask_multi_gts = (fg_mask.unsqueeze(1) > 1).expand(-1, n_max_boxes, -1)  # (b, n_max_boxes, h*w)
-        max_overlaps_idx = overlaps.argmax(1)  # (b, h*w)
-
-        is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device)
-        is_max_overlaps.scatter_(1, max_overlaps_idx.unsqueeze(1), 1)
-
-        mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos).float()  # (b, n_max_boxes, h*w)
-        fg_mask = mask_pos.sum(-2)
-    # Find each grid serve which gt(index)
-    target_gt_idx = mask_pos.argmax(-2)  # (b, h*w)
-
-    return target_gt_idx, fg_mask, mask_pos
-
-def iou_calculator(box1, box2, eps=1e-9):
-    """Calculate iou for batch
-    Args:
-        box1 (Tensor): shape(bs, n_max_boxes, 1, 4)
-        box2 (Tensor): shape(bs, 1, num_total_anchors, 4)
-    Return:
-        (Tensor): shape(bs, n_max_boxes, num_total_anchors)
-    """
-    box1 = box1.unsqueeze(2)  # [N, M1, 4] -> [N, M1, 1, 4]
-    box2 = box2.unsqueeze(1)  # [N, M2, 4] -> [N, 1, M2, 4]
-    px1y1, px2y2 = box1[:, :, :, 0:2], box1[:, :, :, 2:4]
-    gx1y1, gx2y2 = box2[:, :, :, 0:2], box2[:, :, :, 2:4]
-    x1y1 = torch.maximum(px1y1, gx1y1)
-    x2y2 = torch.minimum(px2y2, gx2y2)
-    overlap = (x2y2 - x1y1).clip(0).prod(-1)
-    area1 = (px2y2 - px1y1).clip(0).prod(-1)
-    area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
-    union = area1 + area2 - overlap + eps
-
-    return overlap / union

yolo/models/rtcdet/rtcdet.py

@@ -1,148 +0,0 @@
-# --------------- Torch components ---------------
-import torch
-import torch.nn as nn
-
-# --------------- Model components ---------------
-from .rtcdet_backbone import RTCBackbone
-from .rtcdet_neck     import SPPF
-from .rtcdet_pafpn    import RTCPaFPN
-from .rtcdet_head     import MSDetHead
-from .rtcdet_pred     import MSDetPredLayer
-
-# --------------- External components ---------------
-from utils.misc import multiclass_nms
-
-
-# Real-time Convolutional Detector
-class RTCDet(nn.Module):
-    def __init__(self,
-                 cfg,
-                 is_val = False,
-                 ) -> None:
-        super(RTCDet, self).__init__()
-        # ---------------------- Basic setting ----------------------
-        self.cfg = cfg
-        self.num_classes = cfg.num_classes
-        ## Post-process parameters
-        self.topk_candidates = cfg.val_topk        if is_val else cfg.test_topk
-        self.conf_thresh     = cfg.val_conf_thresh if is_val else cfg.test_conf_thresh
-        self.nms_thresh      = cfg.val_nms_thresh  if is_val else cfg.test_nms_thresh
-        self.no_multi_labels = False if is_val else True
-        
-        # ---------------------- Network Parameters ----------------------
-        ## Backbone
-        self.backbone = RTCBackbone(cfg)
-        self.neck     = SPPF(cfg, self.backbone.pyramid_feat_dims[-1], self.backbone.pyramid_feat_dims[-1])
-        self.fpn      = RTCPaFPN(cfg, self.backbone.pyramid_feat_dims)
-        self.head     = MSDetHead(cfg, self.fpn.out_dims)
-        self.pred     = MSDetPredLayer(cfg, self.head.cls_head_dim, self.head.reg_head_dim)
-
-    def post_process(self, cls_preds, box_preds):
-        """
-        We process predictions at each scale hierarchically
-        Input:
-            cls_preds: List[torch.Tensor] -> [[B, M, C], ...], B=1
-            box_preds: List[torch.Tensor] -> [[B, M, 4], ...], B=1
-        Output:
-            bboxes: np.array -> [N, 4]
-            scores: np.array -> [N,]
-            labels: np.array -> [N,]
-        """
-        all_scores = []
-        all_labels = []
-        all_bboxes = []
-        
-        for cls_pred_i, box_pred_i in zip(cls_preds, box_preds):
-            cls_pred_i = cls_pred_i[0]
-            box_pred_i = box_pred_i[0]
-            if self.no_multi_labels:
-                # [M,]
-                scores, labels = torch.max(cls_pred_i.sigmoid(), dim=1)
-
-                # Keep top k top scoring indices only.
-                num_topk = min(self.topk_candidates, box_pred_i.size(0))
-
-                # topk candidates
-                predicted_prob, topk_idxs = scores.sort(descending=True)
-                topk_scores = predicted_prob[:num_topk]
-                topk_idxs = topk_idxs[:num_topk]
-
-                # filter out the proposals with low confidence score
-                keep_idxs = topk_scores > self.conf_thresh
-                scores = topk_scores[keep_idxs]
-                topk_idxs = topk_idxs[keep_idxs]
-
-                labels = labels[topk_idxs]
-                bboxes = box_pred_i[topk_idxs]
-            else:
-                # [M, C] -> [MC,]
-                scores_i = cls_pred_i.sigmoid().flatten()
-
-                # Keep top k top scoring indices only.
-                num_topk = min(self.topk_candidates, box_pred_i.size(0))
-
-                # torch.sort is actually faster than .topk (at least on GPUs)
-                predicted_prob, topk_idxs = scores_i.sort(descending=True)
-                topk_scores = predicted_prob[:num_topk]
-                topk_idxs = topk_idxs[:num_topk]
-
-                # filter out the proposals with low confidence score
-                keep_idxs = topk_scores > self.conf_thresh
-                scores = topk_scores[keep_idxs]
-                topk_idxs = topk_idxs[keep_idxs]
-
-                anchor_idxs = torch.div(topk_idxs, self.num_classes, rounding_mode='floor')
-                labels = topk_idxs % self.num_classes
-
-                bboxes = box_pred_i[anchor_idxs]
-
-            all_scores.append(scores)
-            all_labels.append(labels)
-            all_bboxes.append(bboxes)
-
-        scores = torch.cat(all_scores, dim=0)
-        labels = torch.cat(all_labels, dim=0)
-        bboxes = torch.cat(all_bboxes, dim=0)
-
-        # to cpu & numpy
-        scores = scores.cpu().numpy()
-        labels = labels.cpu().numpy()
-        bboxes = bboxes.cpu().numpy()
-
-        # nms
-        scores, labels, bboxes = multiclass_nms(
-            scores, labels, bboxes, self.nms_thresh, self.num_classes)
-        
-        return bboxes, scores, labels
-    
-    def forward(self, x):
-        # ---------------- Backbone ----------------
-        pyramid_feats = self.backbone(x)
-        
-        # ---------------- Neck: SPP ----------------
-        pyramid_feats[-1] = self.neck(pyramid_feats[-1])
-
-        # ---------------- Neck: PaFPN ----------------
-        pyramid_feats = self.fpn(pyramid_feats)
-
-        # ---------------- Heads ----------------
-        cls_feats, reg_feats = self.head(pyramid_feats)
-
-        # ---------------- Preds ----------------
-        outputs = self.pred(cls_feats, reg_feats)
-        outputs['image_size'] = [x.shape[2], x.shape[3]]
-
-        if not self.training:
-            all_cls_preds = outputs['pred_cls']
-            all_box_preds = outputs['pred_box']
-
-            # post process
-            bboxes, scores, labels = self.post_process(all_cls_preds, all_box_preds)
-            outputs = {
-                "scores": scores,
-                "labels": labels,
-                "bboxes": bboxes
-            }
-        
-        return outputs
-    

yolo/models/rtcdet/rtcdet_backbone.py

@@ -1,135 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .rtcdet_basic import BasicConv, ElanLayer, MDown, ADown
-except:
-    from  rtcdet_basic import BasicConv, ElanLayer, MDown, ADown
-
-
-# ------------------ Basic functions ------------------
-class RTCBackbone(nn.Module):
-    def __init__(self, cfg):
-        super(RTCBackbone, self).__init__()
-        # ------------------ Basic setting ------------------
-        self.stage_depth = [round(nb  * cfg.depth) for nb  in cfg.stage_depth]
-        self.stage_dims  = [round(dim * cfg.width * cfg.ratio) if i == len(cfg.stage_dims) - 1
-                            else round(dim * cfg.width) for i, dim in enumerate(cfg.stage_dims)]
-        self.pyramid_feat_dims = self.stage_dims[-3:]
-        
-        # ------------------ Model setting ------------------
-        ## P1/2
-        self.layer_1 = BasicConv(3, self.stage_dims[0], kernel_size=6, padding=2, stride=2,
-                                 act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise)
-        # P2/4
-        self.layer_2 = nn.Sequential(
-            self.make_downsample_block(cfg, self.stage_dims[0], self.stage_dims[1]),
-            self.make_stage_block(cfg, self.stage_dims[1], self.stage_dims[1], self.stage_depth[0])
-        )
-        # P3/8
-        self.layer_3 = nn.Sequential(
-            self.make_downsample_block(cfg, self.stage_dims[1], self.stage_dims[2]),
-            self.make_stage_block(cfg, self.stage_dims[2], self.stage_dims[2], self.stage_depth[1])
-        )
-        # P4/16
-        self.layer_4 = nn.Sequential(
-            self.make_downsample_block(cfg, self.stage_dims[2], self.stage_dims[3]),
-            self.make_stage_block(cfg, self.stage_dims[3], self.stage_dims[3], self.stage_depth[2])
-        )
-        # P5/32
-        self.layer_5 = nn.Sequential(
-            self.make_downsample_block(cfg, self.stage_dims[3], self.stage_dims[4]),
-            self.make_stage_block(cfg, self.stage_dims[4], self.stage_dims[4], self.stage_depth[3])
-        )
-
-        # Initialize all layers
-        self.init_weights()
-                        
-    def init_weights(self):
-        """Initialize the parameters."""
-        for m in self.modules():
-            if isinstance(m, torch.nn.Conv2d):
-                m.reset_parameters()
-
-    def make_downsample_block(self, cfg, in_dim, out_dim):
-        if cfg.bk_ds_block == "conv":
-            return BasicConv(in_dim, out_dim, kernel_size=3, padding=1, stride=2,
-                             act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise)
-        if cfg.bk_ds_block == "mdown":
-            return MDown(in_dim, out_dim, cfg.bk_act, cfg.bk_norm, cfg.bk_depthwise)
-        if cfg.bk_ds_block == "adown":
-            return ADown(in_dim, out_dim, cfg.bk_act, cfg.bk_norm, cfg.bk_depthwise)
-        if cfg.bk_ds_block == "maxpool":
-            assert in_dim == out_dim
-            return nn.MaxPool2d((2, 2), stride=2)
-        else:
-            raise NotImplementedError("Unknown fpn downsample block: {}".format(cfg.fpn_ds_block))
-        
-    def make_stage_block(self, cfg, in_dim, out_dim, stage_depth):
-        if cfg.bk_block == "elan_layer":
-            return ElanLayer(in_dim     = in_dim,
-                             out_dim    = out_dim,
-                             num_blocks = stage_depth,
-                             expansion  = 0.5,
-                             shortcut   = True,
-                             act_type   = cfg.bk_act,
-                             norm_type  = cfg.bk_norm,
-                             depthwise  = cfg.bk_depthwise)
-        else:
-            raise NotImplementedError("Unknown stage block: {}".format(cfg.bk_block))
-        
-    def forward(self, x):
-        c1 = self.layer_1(x)
-        c2 = self.layer_2(c1)
-        c3 = self.layer_3(c2)
-        c4 = self.layer_4(c3)
-        c5 = self.layer_5(c4)
-        outputs = [c3, c4, c5]
-
-        return outputs
-
-
-# ------------------ Functions ------------------
-## build Yolo's Backbone
-def build_backbone(cfg): 
-    # model
-    backbone = RTCBackbone(cfg)
-        
-    return backbone
-
-
-if __name__ == '__main__':
-    import time
-    from thop import profile
-    class BaseConfig(object):
-        def __init__(self) -> None:
-            self.stage_dims =  [64, 128, 256, 512, 512]
-            self.stage_depth = [3, 6, 6, 3]
-            self.bk_block = "elan_layer"
-            self.bk_ds_block = "mdown"
-            self.bk_act = 'silu'
-            self.bk_norm = 'bn'
-            self.bk_depthwise = False
-            self.use_pretrained = False
-            self.width = 0.5
-            self.depth = 0.34
-            self.ratio = 2.0
-
-    cfg = BaseConfig()
-    model = build_backbone(cfg).cuda()
-    x = torch.randn(1, 3, 640, 640).cuda()
-
-    for _ in range(5):
-        t0 = time.time()
-        outputs = model(x)
-        t1 = time.time()
-        print('Time: ', t1 - t0)
-        
-    for out in outputs:
-        print(out.shape)
-
-    print('==============================')
-    flops, params = profile(model, inputs=(x, ), verbose=False)
-    print('==============================')
-    print('GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
-    print('Params : {:.2f} M'.format(params / 1e6))

yolo/models/rtcdet/rtcdet_basic.py

@@ -1,313 +0,0 @@
-import torch
-import torch.nn as nn
-from typing import List
-
-
-# --------------------- Basic modules ---------------------
-def get_conv2d(c1, c2, k, p, s, d=1, g=1, bias=False):
-    conv = nn.Conv2d(c1, c2, k, stride=s, padding=p, dilation=d, groups=g, bias=bias)
-
-    return conv
-
-def get_activation(act_type=None):
-    if act_type == 'relu':
-        return nn.ReLU(inplace=True)
-    elif act_type == 'lrelu':
-        return nn.LeakyReLU(0.1, inplace=True)
-    elif act_type == 'mish':
-        return nn.Mish(inplace=True)
-    elif act_type == 'silu':
-        return nn.SiLU(inplace=True)
-    elif act_type is None:
-        return nn.Identity()
-    else:
-        raise NotImplementedError
-        
-def get_norm(norm_type, dim):
-    if norm_type == 'bn':
-        return nn.BatchNorm2d(dim)
-    elif norm_type == 'gn':
-        return nn.GroupNorm(num_groups=32, num_channels=dim)
-    elif norm_type is None:
-        return nn.Identity()
-    else:
-        raise NotImplementedError
-
-class BasicConv(nn.Module):
-    def __init__(self, 
-                 in_dim,                   # in channels
-                 out_dim,                  # out channels 
-                 kernel_size=1,            # kernel size 
-                 padding=0,                # padding
-                 stride=1,                 # padding
-                 dilation=1,               # dilation
-                 groups=1,                 # group
-                 act_type  :str = 'lrelu', # activation
-                 norm_type :str = 'bn',    # normalization
-                 depthwise :bool = False
-                ):
-        super(BasicConv, self).__init__()
-        self.depthwise = depthwise
-        use_bias = False if norm_type is not None else True
-        if not depthwise:
-            self.conv = get_conv2d(in_dim, out_dim, k=kernel_size, p=padding, s=stride, d=dilation, g=groups, bias=use_bias)
-            self.norm = get_norm(norm_type, out_dim)
-        else:
-            self.conv1 = get_conv2d(in_dim, in_dim, k=kernel_size, p=padding, s=stride, d=dilation, g=in_dim, bias=use_bias)
-            self.norm1 = get_norm(norm_type, in_dim)
-            self.conv2 = get_conv2d(in_dim, out_dim, k=1, p=0, s=1, d=1, g=1, bias=use_bias)
-            self.norm2 = get_norm(norm_type, out_dim)
-        self.act  = get_activation(act_type)
-
-    def forward(self, x):
-        if not self.depthwise:
-            return self.act(self.norm(self.conv(x)))
-        else:
-            # Depthwise conv
-            x = self.act(self.norm1(self.conv1(x)))
-            # Pointwise conv
-            x = self.act(self.norm2(self.conv2(x)))
-            return x
-
-class DWConv(nn.Module):
-    def __init__(self, 
-                 in_dim      :int,           # in channels
-                 out_dim     :int,           # out channels 
-                 kernel_size :int = 1,       # kernel size 
-                 padding     :int = 0,       # padding
-                 stride      :int = 1,       # padding
-                 dilation    :int = 1,       # dilation
-                 act_type    :str = 'lrelu', # activation
-                 norm_type   :str = 'BN',    # normalization
-                ):
-        super(DWConv, self).__init__()
-        assert in_dim == out_dim
-        use_bias = False if norm_type is not None else True
-        self.conv = get_conv2d(in_dim, out_dim, k=kernel_size, p=padding, s=stride, d=dilation, g=out_dim, bias=use_bias)
-        self.norm = get_norm(norm_type, out_dim)
-        self.act  = get_activation(act_type)
-
-    def forward(self, x):
-        return self.act(self.norm(self.conv(x)))
-
-
-# --------------------- Downsample modules ---------------------
-class ADown(nn.Module):
-    def __init__(self,
-                 in_dim    :int,
-                 out_dim   :int,
-                 act_type  :str  = "silu",
-                 norm_type :str  = "bn",
-                 depthwise :bool = False):
-        super().__init__()
-        inter_dim = out_dim // 2
-        self.conv_layer_1 = BasicConv(in_dim // 2, inter_dim, kernel_size=3, padding=1, stride=2,
-                                      act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-        self.conv_layer_2 = BasicConv(in_dim // 2, inter_dim, kernel_size=1,
-                                      act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-    def forward(self, x):
-        # Split
-        x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True)
-        x1,x2 = x.chunk(2, 1)
-
-        # Downsample branch - 1
-        x1 = self.conv_layer_1(x1)
-
-        # Downsample branch - 2
-        x2 = torch.nn.functional.max_pool2d(x2, 3, 2, 1)
-        x2 = self.conv_layer_2(x2)
-
-        return torch.cat([x1, x2], dim=1)
-
-class MDown(nn.Module):
-    def __init__(self,
-                 in_dim    :int,
-                 out_dim   :int,
-                 act_type  :str   = 'silu',
-                 norm_type :str   = 'BN',
-                 depthwise :bool  = False,
-                 ) -> None:
-        super().__init__()
-        inter_dim = out_dim // 2
-        self.downsample_1 = nn.Sequential(
-            nn.MaxPool2d((2, 2), stride=2),
-            BasicConv(in_dim, inter_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
-        )
-        self.downsample_2 = nn.Sequential(
-            BasicConv(in_dim, inter_dim, kernel_size=1, act_type=act_type, norm_type=norm_type),
-            BasicConv(inter_dim, inter_dim,
-                      kernel_size=3, padding=1, stride=2,
-                      act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-        )
-
-    def forward(self, x):
-        x1 = self.downsample_1(x)
-        x2 = self.downsample_2(x)
-
-        return torch.cat([x1, x2], dim=1)
-
-
-# --------------------- Feature processing modules ---------------------
-class MBottleneck(nn.Module):
-    def __init__(self,
-                 in_dim    :int,
-                 out_dim   :int,
-                 expansion :float = 0.5,
-                 shortcut  :bool  = False,
-                 act_type  :str   = 'silu',
-                 norm_type :str   = 'bn',
-                 depthwise :bool  = False,
-                 ) -> None:
-        super(MBottleneck, self).__init__()
-        inter_dim = int(out_dim * expansion)
-        # ----------------- Network setting -----------------
-        self.conv_layer = nn.Sequential(
-            # 3x3 conv + bn + silu
-            BasicConv(in_dim, inter_dim, kernel_size=3, padding=1, stride=1,
-                      act_type=act_type, norm_type=norm_type, depthwise=depthwise),
-            # 5x5 dw conv
-            DWConv(inter_dim, inter_dim, kernel_size=5, padding=2, stride=1,
-                   act_type=None, norm_type=norm_type),
-            # 3x3 conv + bn + silu
-            BasicConv(inter_dim, out_dim, kernel_size=3, padding=1, stride=1,
-                      act_type=act_type, norm_type=norm_type, depthwise=depthwise),
-        )
-        self.shortcut = shortcut and in_dim == out_dim
-
-    def forward(self, x):
-        h = self.conv_layer(x)
-
-        return x + h if self.shortcut else h
-
-class CSPLayer(nn.Module):
-    # CSP Bottleneck
-    def __init__(self,
-                 in_dim      :int,
-                 out_dim     :int,
-                 num_blocks  :int   = 1,
-                 expansion   :float = 0.5,
-                 shortcut    :bool  = True,
-                 act_type    :str   = 'silu',
-                 norm_type   :str   = 'bn',
-                 depthwise   :bool  = False,
-                 ) -> None:
-        super().__init__()
-        inter_dim = round(out_dim * expansion)
-        self.input_proj = BasicConv(in_dim, out_dim, kernel_size=1, act_type=None, norm_type=norm_type, depthwise=depthwise)
-        self.module = nn.Sequential(*[MBottleneck(inter_dim,
-                                                  inter_dim,
-                                                  expansion   = 1.0,
-                                                  shortcut    = shortcut,
-                                                  act_type    = act_type,
-                                                  norm_type   = norm_type,
-                                                  depthwise   = depthwise,
-                                                  ) for _ in range(num_blocks)])
-
-    def forward(self, x):
-        # Split
-        x1, x2 = torch.chunk(self.input_proj(x), chunks=2, dim=1)
-
-        # Branch
-        x2 = self.module(x2)
-
-        # Output proj
-        out = torch.cat([x1, x2], dim=1)
-
-        return out
-
-class ElanLayer(nn.Module):
-    def __init__(self,
-                 in_dim,
-                 out_dim,
-                 expansion  :float = 0.5,
-                 num_blocks :int   = 1,
-                 shortcut   :bool  = False,
-                 act_type   :str   = 'silu',
-                 norm_type  :str   = 'bn',
-                 depthwise  :bool  = False,
-                 ) -> None:
-        super(ElanLayer, self).__init__()
-        inter_dim = round(out_dim * expansion)
-        self.input_proj  = BasicConv(in_dim, inter_dim * 2, kernel_size=1, act_type=act_type, norm_type=norm_type)
-        self.output_proj = BasicConv((2 + num_blocks) * inter_dim, out_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
-        self.module      = nn.ModuleList([MBottleneck(inter_dim,
-                                                      inter_dim,
-                                                      expansion   = 1.0,
-                                                      shortcut    = shortcut,
-                                                      act_type    = act_type,
-                                                      norm_type   = norm_type,
-                                                      depthwise   = depthwise)
-                                                      for _ in range(num_blocks)])
-
-    def forward(self, x):
-        # Input proj
-        x1, x2 = torch.chunk(self.input_proj(x), 2, dim=1)
-        out = list([x1, x2])
-
-        # Bottleneck
-        out.extend(m(out[-1]) for m in self.module)
-
-        # Output proj
-        out = self.output_proj(torch.cat(out, dim=1))
-
-        return out
-    
-class GElanLayer(nn.Module):
-    """Modified YOLOv9's GELAN module"""
-    def __init__(self,
-                 in_dim     :int,
-                 inter_dims :List,
-                 out_dim    :int,
-                 num_blocks :int   = 1,
-                 shortcut   :bool  = False,
-                 act_type   :str   = 'silu',
-                 norm_type  :str   = 'bn',
-                 depthwise  :bool  = False,
-                 ) -> None:
-        super(GElanLayer, self).__init__()
-        # ----------- Basic parameters -----------
-        self.in_dim = in_dim
-        self.inter_dims = inter_dims
-        self.out_dim = out_dim
-
-        # ----------- Network parameters -----------
-        self.conv_layer_1  = BasicConv(in_dim, inter_dims[0], kernel_size=1, act_type=act_type, norm_type=norm_type)
-        self.elan_module_1 = nn.Sequential(
-             CSPLayer(inter_dims[0]//2,
-                      inter_dims[1],
-                      num_blocks  = num_blocks,
-                      shortcut    = shortcut,
-                      expansion   = 0.5,
-                      act_type    = act_type,
-                      norm_type   = norm_type,
-                      depthwise   = depthwise),
-            BasicConv(inter_dims[1], inter_dims[1], kernel_size=3, padding=1,
-                      act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-        )
-        self.elan_module_2 = nn.Sequential(
-             CSPLayer(inter_dims[1],
-                      inter_dims[1],
-                      num_blocks  = num_blocks,
-                      shortcut    = shortcut,
-                      expansion   = 0.5,
-                      act_type    = act_type,
-                      norm_type   = norm_type,
-                      depthwise   = depthwise),
-            BasicConv(inter_dims[1], inter_dims[1], kernel_size=3, padding=1,
-                      act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-        )
-        self.conv_layer_2 = BasicConv(inter_dims[0] + 2*self.inter_dims[1], out_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
-
-    def forward(self, x):
-        # Input proj
-        x1, x2 = torch.chunk(self.conv_layer_1(x), 2, dim=1)
-        out = list([x1, x2])
-
-        # ELAN module
-        out.append(self.elan_module_1(out[-1]))
-        out.append(self.elan_module_2(out[-1]))
-
-        # Output proj
-        out = self.conv_layer_2(torch.cat(out, dim=1))
-
-        return out

yolo/models/rtcdet/rtcdet_head.py

@@ -1,280 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .rtcdet_basic import BasicConv
-except:
-    from  rtcdet_basic import BasicConv
-    
-
-# -------------------- Detection Head --------------------
-## Single-level Detection Head
-class DetHead(nn.Module):
-    def __init__(self,
-                 in_dim       :int  = 256,
-                 cls_head_dim :int  = 256,
-                 reg_head_dim :int  = 256,
-                 num_cls_head :int  = 2,
-                 num_reg_head :int  = 2,
-                 act_type     :str  = "silu",
-                 norm_type    :str  = "BN",
-                 depthwise    :bool = False):
-        super().__init__()
-        # --------- Basic Parameters ----------
-        self.in_dim = in_dim
-        self.num_cls_head = num_cls_head
-        self.num_reg_head = num_reg_head
-        self.act_type = act_type
-        self.norm_type = norm_type
-        self.depthwise = depthwise
-        
-        # --------- Network Parameters ----------
-        ## cls head
-        cls_feats = []
-        self.cls_head_dim = cls_head_dim
-        for i in range(num_cls_head):
-            if i == 0:
-                cls_feats.append(
-                    BasicConv(in_dim, self.cls_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=act_type,
-                              norm_type=norm_type,
-                              depthwise=depthwise)
-                              )
-            else:
-                cls_feats.append(
-                    BasicConv(self.cls_head_dim, self.cls_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=act_type,
-                              norm_type=norm_type,
-                              depthwise=depthwise)
-                              )
-        ## reg head
-        reg_feats = []
-        self.reg_head_dim = reg_head_dim
-        for i in range(num_reg_head):
-            if i == 0:
-                reg_feats.append(
-                    BasicConv(in_dim, self.reg_head_dim,
-                              kernel_size=3, padding=1, stride=1, groups=4, 
-                              act_type=act_type,
-                              norm_type=norm_type,
-                              depthwise=depthwise)
-                              )
-            else:
-                reg_feats.append(
-                    BasicConv(self.reg_head_dim, self.reg_head_dim,
-                              kernel_size=3, padding=1, stride=1, groups=4,
-                              act_type=act_type,
-                              norm_type=norm_type,
-                              depthwise=depthwise)
-                              )
-        self.cls_feats = nn.Sequential(*cls_feats)
-        self.reg_feats = nn.Sequential(*reg_feats)
-
-        self.init_weights()
-        
-    def init_weights(self):
-        """Initialize the parameters."""
-        for m in self.modules():
-            if isinstance(m, torch.nn.Conv2d):
-                # In order to be consistent with the source code,
-                # reset the Conv2d initialization parameters
-                m.reset_parameters()
-
-    def forward(self, x):
-        """
-            in_feats: (Tensor) [B, C, H, W]
-        """
-        cls_feats = self.cls_feats(x)
-        reg_feats = self.reg_feats(x)
-
-        return cls_feats, reg_feats
-    
-## Multi-scales Detection Head
-class MSDetHead(nn.Module):
-    def __init__(self, cfg, in_dims):
-        super().__init__()
-        ## ----------- Network Parameters -----------
-        self.multi_level_heads = nn.ModuleList(
-            [DetHead(in_dim       = in_dims[level],
-                     cls_head_dim = max(in_dims[0], min(cfg.num_classes, 128)),
-                     reg_head_dim = max(in_dims[0]//4, 16, 4*cfg.reg_max),
-                     num_cls_head = cfg.num_cls_head,
-                     num_reg_head = cfg.num_reg_head,
-                     act_type     = cfg.head_act,
-                     norm_type    = cfg.head_norm,
-                     depthwise    = cfg.head_depthwise)
-                     for level in range(cfg.num_levels)
-                     ])
-        # --------- Basic Parameters ----------
-        self.in_dims = in_dims
-        self.cls_head_dim = self.multi_level_heads[0].cls_head_dim
-        self.reg_head_dim = self.multi_level_heads[0].reg_head_dim
-
-
-    def forward(self, feats):
-        """
-            feats: List[(Tensor)] [[B, C, H, W], ...]
-        """
-        cls_feats = []
-        reg_feats = []
-        for feat, head in zip(feats, self.multi_level_heads):
-            # ---------------- Pred ----------------
-            cls_feat, reg_feat = head(feat)
-
-            cls_feats.append(cls_feat)
-            reg_feats.append(reg_feat)
-
-        return cls_feats, reg_feats
-
-
-# -------------------- Segmentation Head --------------------
-## Single-level Segmentation Head
-class SegHead(nn.Module):
-    def __init__(self,
-                 in_dim       :int  = 256,
-                 cls_head_dim :int  = 256,
-                 reg_head_dim :int  = 256,
-                 seg_head_dim :int  = 256,
-                 num_cls_head :int  = 2,
-                 num_reg_head :int  = 2,
-                 num_seg_head :int  = 2,
-                 act_type     :str  = "silu",
-                 norm_type    :str  = "BN",
-                 depthwise    :bool = False):
-        super().__init__()
-        # --------- Basic Parameters ----------
-        self.in_dim = in_dim
-        self.num_cls_head = num_cls_head
-        self.num_reg_head = num_reg_head
-        self.num_seg_head = num_reg_head
-        self.act_type = act_type
-        self.norm_type = norm_type
-        self.depthwise = depthwise
-        
-        # --------- Network Parameters ----------
-        ## cls head
-        cls_feats = []
-        self.cls_head_dim = cls_head_dim
-        for i in range(num_cls_head):
-            if i == 0:
-                cls_feats.append(
-                    BasicConv(in_dim, self.cls_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=act_type,
-                              norm_type=norm_type,
-                              depthwise=depthwise)
-                              )
-            else:
-                cls_feats.append(
-                    BasicConv(self.cls_head_dim, self.cls_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=act_type,
-                              norm_type=norm_type,
-                              depthwise=depthwise)
-                              )
-        ## reg head
-        reg_feats = []
-        self.reg_head_dim = reg_head_dim
-        for i in range(num_reg_head):
-            if i == 0:
-                reg_feats.append(
-                    BasicConv(in_dim, self.reg_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=act_type,
-                              norm_type=norm_type,
-                              depthwise=depthwise)
-                              )
-            else:
-                reg_feats.append(
-                    BasicConv(self.reg_head_dim, self.reg_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=act_type,
-                              norm_type=norm_type,
-                              depthwise=depthwise)
-                              )
-        ## seg head
-        seg_feats = []
-        self.seg_head_dim = seg_head_dim
-        for i in range(num_seg_head):
-            if i == 0:
-                seg_feats.append(
-                    BasicConv(in_dim, self.seg_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=act_type,
-                              norm_type=norm_type,
-                              depthwise=depthwise)
-                              )
-            else:
-                seg_feats.append(
-                    BasicConv(self.seg_head_dim, self.seg_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=act_type,
-                              norm_type=norm_type,
-                              depthwise=depthwise)
-                              )
-        self.cls_feats = nn.Sequential(*cls_feats)
-        self.reg_feats = nn.Sequential(*reg_feats)
-        self.seg_feats = nn.Sequential(*seg_feats)
-
-        self.init_weights()
-        
-    def init_weights(self):
-        """Initialize the parameters."""
-        for m in self.modules():
-            if isinstance(m, torch.nn.Conv2d):
-                # In order to be consistent with the source code,
-                # reset the Conv2d initialization parameters
-                m.reset_parameters()
-
-    def forward(self, x):
-        """
-            in_feats: (Tensor) [B, C, H, W]
-        """
-        cls_feats = self.cls_feats(x)
-        reg_feats = self.reg_feats(x)
-        seg_feats = self.reg_feats(x)
-
-        return cls_feats, reg_feats, seg_feats
-    
-## Multi-scales Segmentation Head
-class MSSegHead(nn.Module):
-    def __init__(self, cfg, in_dims):
-        super().__init__()
-        ## ----------- Network Parameters -----------
-        self.multi_level_heads = nn.ModuleList(
-            [SegHead(in_dim       = in_dims[level],
-                     cls_head_dim = max(in_dims[0], min(cfg.num_classes, 128)),
-                     reg_head_dim = max(in_dims[0]//4, 16, 4*cfg.reg_max),
-                     seg_head_dim = in_dims[0],
-                     num_cls_head = cfg.num_cls_head,
-                     num_reg_head = cfg.num_reg_head,
-                     num_seg_head = cfg.num_seg_head,
-                     act_type     = cfg.head_act,
-                     norm_type    = cfg.head_norm,
-                     depthwise    = cfg.head_depthwise)
-                     for level in range(cfg.num_levels)
-                     ])
-        # --------- Basic Parameters ----------
-        self.in_dims = in_dims
-        self.cls_head_dim = self.multi_level_heads[0].cls_head_dim
-        self.reg_head_dim = self.multi_level_heads[0].reg_head_dim
-        self.seg_head_dim = self.multi_level_heads[0].seg_head_dim
-
-    def forward(self, feats):
-        """
-            feats: List[(Tensor)] [[B, C, H, W], ...]
-        """
-        cls_feats = []
-        reg_feats = []
-        seg_feats = []
-        for feat, head in zip(feats, self.multi_level_heads):
-            # ---------------- Pred ----------------
-            cls_feat, reg_feat, seg_feat = head(feat)
-
-            cls_feats.append(cls_feat)
-            reg_feats.append(reg_feat)
-            seg_feats.append(seg_feat)
-
-        return cls_feats, reg_feats, seg_feats

yolo/models/rtcdet/rtcdet_neck.py

@@ -1,39 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .rtcdet_basic import BasicConv
-
-
-# -------------- Neck network --------------
-class SPPF(nn.Module):
-    """
-        This code referenced to https://github.com/ultralytics/yolov5
-    """
-    def __init__(self, cfg, in_dim, out_dim):
-        super().__init__()
-        ## ----------- Basic Parameters -----------
-        inter_dim = round(in_dim * cfg.neck_expand_ratio)
-        self.out_dim = out_dim
-        ## ----------- Network Parameters -----------
-        self.input_proj  = BasicConv(in_dim, inter_dim, kernel_size=1,
-                                     act_type=cfg.neck_act, norm_type=cfg.neck_norm)
-        self.output_proj = BasicConv(inter_dim * 4, out_dim, kernel_size=1,
-                                     act_type=cfg.neck_act, norm_type=cfg.neck_norm)
-        self.module = nn.MaxPool2d(cfg.spp_pooling_size, stride=1, padding=cfg.spp_pooling_size//2)
-
-        # Initialize all layers
-        self.init_weights()
-                
-    def init_weights(self):
-        """Initialize the parameters."""
-        for m in self.modules():
-            if isinstance(m, torch.nn.Conv2d):
-                m.reset_parameters()
-
-    def forward(self, x):
-        x = self.input_proj(x)
-        y1 = self.module(x)
-        y2 = self.module(y1)
-
-        return self.output_proj(torch.cat((x, y1, y2, self.module(y2)), 1))
-    

yolo/models/rtcdet/rtcdet_pafpn.py

@@ -1,108 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from typing import List
-
-try:
-    from .rtcdet_basic import BasicConv, DWConv, ElanLayer, MDown, ADown
-except:
-    from  rtcdet_basic import BasicConv, DWConv, ElanLayer, MDown, ADown
-
-
-# -------------- Feature pyramid network --------------
-class RTCPaFPN(nn.Module):
-    def __init__(self,
-                 cfg,
-                 in_dims :List = [256, 512, 1024],
-                 ) -> None:
-        super(RTCPaFPN, self).__init__()
-        print('==============================')
-        print('FPN: {}'.format("RTC-PaFPN"))
-        # ----------- Basic Parameters -----------
-        self.in_dims = in_dims[::-1]
-
-        # ----------- Yolov8's Top-down FPN -----------
-        ## P5 -> P4
-        self.top_down_layer_1 = self.make_fpn_block(cfg, self.in_dims[0] + self.in_dims[1], round(512*cfg.width), round(3 * cfg.depth))
-        ## P4 -> P3
-        self.top_down_layer_2 = self.make_fpn_block(cfg, self.in_dims[2] + round(512*cfg.width), round(256*cfg.width), round(3 * cfg.depth))
-
-        # ----------- Yolov8's Bottom-up PAN -----------
-        ## P3 -> P4
-        self.dowmsample_layer_1 = self.make_downsample_block(cfg, round(256*cfg.width), round(256*cfg.width))
-        self.bottom_up_layer_1  = self.make_fpn_block(cfg, round(256*cfg.width) + round(512*cfg.width), round(512*cfg.width), round(3 * cfg.depth))
-        ## P4 -> P5
-        self.dowmsample_layer_2 = self.make_downsample_block(cfg, round(512*cfg.width), round(512*cfg.width))
-        self.bottom_up_layer_2  = self.make_fpn_block(cfg, round(512*cfg.width) + self.in_dims[0], round(512*cfg.width*cfg.ratio), round(3 * cfg.depth))
-
-        # ----------- Output projection -----------
-        self.out_layers = nn.ModuleList([
-            BasicConv(in_dim, round(256*cfg.width), kernel_size=1, act_type=cfg.fpn_act, norm_type=cfg.fpn_norm)
-                      for in_dim in [round(256*cfg.width), round(512*cfg.width), round(512*cfg.width*cfg.ratio)]])
-        self.out_dims = [round(256*cfg.width)] * 3
-
-        self.init_weights()
-        
-    def init_weights(self):
-        """Initialize the parameters."""
-        for m in self.modules():
-            if isinstance(m, torch.nn.Conv2d):
-                m.reset_parameters()
-
-    def make_downsample_block(self, cfg, in_dim, out_dim):
-        if cfg.fpn_ds_block == "conv":
-            return BasicConv(in_dim, out_dim, kernel_size=3, padding=1, stride=2,
-                             act_type=cfg.fpn_act, norm_type=cfg.fpn_norm, depthwise=cfg.fpn_depthwise)
-        if cfg.fpn_ds_block == "dw_conv":
-            return DWConv(in_dim, out_dim, kernel_size=3, padding=1, stride=2,
-                             act_type=cfg.fpn_act, norm_type=cfg.fpn_norm)
-        if cfg.fpn_ds_block == "mdown":
-            return MDown(in_dim, out_dim, cfg.bk_act, cfg.bk_norm, cfg.bk_depthwise)
-        if cfg.fpn_ds_block == "adown":
-            return ADown(in_dim, out_dim, cfg.bk_act, cfg.bk_norm, cfg.bk_depthwise)
-        else:
-            raise NotImplementedError("Unknown fpn downsample block: {}".format(cfg.fpn_ds_block))
-        
-    def make_fpn_block(self, cfg, in_dim, out_dim, block_depth):
-        if cfg.fpn_block == "elan_layer":
-            return ElanLayer(in_dim     = in_dim,
-                             out_dim    = out_dim,
-                             num_blocks = block_depth,
-                             expansion  = 0.5,
-                             shortcut   = False,
-                             act_type   = cfg.fpn_act,
-                             norm_type  = cfg.fpn_norm,
-                             depthwise  = cfg.fpn_depthwise)
-        else:
-            raise NotImplementedError("Unknown stage block: {}".format(cfg.bk_block))
-        
-    def forward(self, features):
-        c3, c4, c5 = features
-
-        # ------------------ Top down FPN ------------------
-        ## P5 -> P4
-        p5_up = F.interpolate(c5, scale_factor=2.0)
-        p4 = self.top_down_layer_1(torch.cat([p5_up, c4], dim=1))
-
-        ## P4 -> P3
-        p4_up = F.interpolate(p4, scale_factor=2.0)
-        p3 = self.top_down_layer_2(torch.cat([p4_up, c3], dim=1))
-
-        # ------------------ Bottom up FPN ------------------
-        ## p3 -> P4
-        p3_ds = self.dowmsample_layer_1(p3)
-        p4 = self.bottom_up_layer_1(torch.cat([p3_ds, p4], dim=1))
-
-        ## P4 -> 5
-        p4_ds = self.dowmsample_layer_2(p4)
-        p5 = self.bottom_up_layer_2(torch.cat([p4_ds, c5], dim=1))
-
-        out_feats = [p3, p4, p5] # [P3, P4, P5]
-                
-        # ------------------ Output projection ------------------
-        out_feats_proj = []
-        for feat, layer in zip(out_feats, self.out_layers):
-            out_feats_proj.append(layer(feat))
-            
-        return out_feats_proj
-    

yolo/models/rtcdet/rtcdet_pred.py

@@ -1,330 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-# -------------------- Detection Pred Layer --------------------
-## Single-level pred layer
-class DetPredLayer(nn.Module):
-    def __init__(self,
-                 cls_dim     :int = 256,
-                 reg_dim     :int = 256,
-                 stride      :int = 32,
-                 num_classes :int = 80,
-                 num_coords  :int = 4):
-        super().__init__()
-        # --------- Basic Parameters ----------
-        self.stride = stride
-        self.cls_dim = cls_dim
-        self.reg_dim = reg_dim
-        self.num_classes = num_classes
-        self.num_coords = num_coords
-
-        # --------- Network Parameters ----------
-        self.cls_pred = nn.Conv2d(cls_dim, num_classes, kernel_size=1)
-        self.reg_pred = nn.Conv2d(reg_dim, num_coords,  kernel_size=1, groups=4)                
-
-        self.init_bias()
-        
-    def init_bias(self):
-        # cls pred bias
-        b = self.cls_pred.bias.view(1, -1)
-        b.data.fill_(math.log(5 / self.num_classes / (640. / self.stride) ** 2))
-        self.cls_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-        # reg pred bias
-        b = self.reg_pred.bias.view(-1, )
-        b.data.fill_(1.0)
-        self.reg_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-        w = self.reg_pred.weight
-        w.data.fill_(0.)
-        self.reg_pred.weight = torch.nn.Parameter(w, requires_grad=True)
-
-    def generate_anchors(self, fmp_size):
-        """
-            fmp_size: (List) [H, W]
-        """
-        # generate grid cells
-        fmp_h, fmp_w = fmp_size
-        anchor_y, anchor_x = torch.meshgrid([torch.arange(fmp_h), torch.arange(fmp_w)])
-        # [H, W, 2] -> [HW, 2]
-        anchors = torch.stack([anchor_x, anchor_y], dim=-1).float().view(-1, 2)
-        anchors += 0.5  # add center offset
-        anchors *= self.stride
-
-        return anchors
-        
-    def forward(self, cls_feat, reg_feat):
-        # pred
-        cls_pred = self.cls_pred(cls_feat)
-        reg_pred = self.reg_pred(reg_feat)
-
-        # generate anchor boxes: [M, 4]
-        B, _, H, W = cls_pred.size()
-        fmp_size = [H, W]
-        anchors = self.generate_anchors(fmp_size)
-        anchors = anchors.to(cls_pred.device)
-        # stride tensor: [M, 1]
-        stride_tensor = torch.ones_like(anchors[..., :1]) * self.stride
-        
-        # [B, C, H, W] -> [B, H, W, C] -> [B, M, C]
-        cls_pred = cls_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, self.num_classes)
-        reg_pred = reg_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, self.num_coords)
-        
-        # output dict
-        outputs = {"pred_cls": cls_pred,            # List(Tensor) [B, M, C]
-                   "pred_reg": reg_pred,            # List(Tensor) [B, M, 4*(reg_max)]
-                   "anchors": anchors,              # List(Tensor) [M, 2]
-                   "strides": self.stride,          # List(Int) = [8, 16, 32]
-                   "stride_tensor": stride_tensor   # List(Tensor) [M, 1]
-                   }
-
-        return outputs
-
-## Multi-scales pred layer
-class MSDetPredLayer(nn.Module):
-    def __init__(self,
-                 cfg,
-                 cls_dim,
-                 reg_dim,
-                 ):
-        super().__init__()
-        # --------- Basic Parameters ----------
-        self.cfg = cfg
-        self.cls_dim = cls_dim
-        self.reg_dim = reg_dim
-        self.reg_max    = cfg.reg_max
-        self.num_levels = cfg.num_levels
-        self.out_stride = cfg.out_stride
-
-        # ----------- Network Parameters -----------
-        ## pred layers
-        self.multi_level_preds = nn.ModuleList(
-            [DetPredLayer(cls_dim     = cls_dim,
-                          reg_dim     = reg_dim,
-                          stride      = cfg.out_stride[level],
-                          num_classes = cfg.num_classes,
-                          num_coords  = cfg.reg_max * 4)
-                          for level in range(cfg.num_levels)
-                          ])
-        ## proj conv
-        proj_init = torch.arange(cfg.reg_max, dtype=torch.float)
-        self.proj_conv = nn.Conv2d(cfg.reg_max, 1, kernel_size=1, bias=False).requires_grad_(False)
-        self.proj_conv.weight.data[:] = nn.Parameter(proj_init.view([1, cfg.reg_max, 1, 1]), requires_grad=False)
-
-    def forward(self, cls_feats, reg_feats):
-        all_anchors = []
-        all_strides = []
-        all_cls_preds = []
-        all_reg_preds = []
-        all_box_preds = []
-        all_delta_preds = []
-        for level in range(self.num_levels):
-            # -------------- Single-level prediction --------------
-            outputs = self.multi_level_preds[level](cls_feats[level], reg_feats[level])
-
-            # -------------- Decode bbox --------------
-            B, M = outputs["pred_reg"].shape[:2]
-            # [B, M, 4*(reg_max)] -> [B, M, 4, reg_max]
-            delta_pred = outputs["pred_reg"].reshape([B, M, 4, self.reg_max])
-            # [B, M, 4, reg_max] -> [B, reg_max, 4, M]
-            delta_pred = delta_pred.permute(0, 3, 2, 1).contiguous()
-            # [B, reg_max, 4, M] -> [B, 1, 4, M]
-            delta_pred = self.proj_conv(F.softmax(delta_pred, dim=1))
-            # [B, 1, 4, M] -> [B, 4, M] -> [B, M, 4]
-            delta_pred = delta_pred.view(B, 4, M).permute(0, 2, 1).contiguous()
-            ## tlbr -> xyxy
-            x1y1_pred = outputs["anchors"][None] - delta_pred[..., :2] * self.out_stride[level]
-            x2y2_pred = outputs["anchors"][None] + delta_pred[..., 2:] * self.out_stride[level]
-            box_pred = torch.cat([x1y1_pred, x2y2_pred], dim=-1)
-
-            # collect results
-            all_cls_preds.append(outputs["pred_cls"])
-            all_reg_preds.append(outputs["pred_reg"])
-            all_box_preds.append(box_pred)
-            all_delta_preds.append(delta_pred)
-            all_anchors.append(outputs["anchors"])
-            all_strides.append(outputs["stride_tensor"])
-        
-        # output dict
-        outputs = {"pred_cls":      all_cls_preds,     # List(Tensor) [B, M, C]
-                   "pred_reg":      all_reg_preds,     # List(Tensor) [B, M, 4*(reg_max)]
-                   "pred_box":      all_box_preds,     # List(Tensor) [B, M, 4]
-                   "pred_delta":    all_delta_preds,   # List(Tensor) [B, M, 4]
-                   "anchors":       all_anchors,       # List(Tensor) [M, 2]
-                   "stride_tensor": all_strides,       # List(Tensor) [M, 1]
-                   "strides":       self.out_stride,   # List(Int) = [8, 16, 32]
-                   }
-
-        return outputs
-
-
-# -------------------- Segmentation Pred Layer --------------------
-## Single-level pred layer
-class SegPredLayer(nn.Module):
-    def __init__(self,
-                 cls_dim     :int = 256,
-                 reg_dim     :int = 256,
-                 seg_dim     :int = 256,
-                 stride      :int = 32,
-                 num_classes :int = 80,
-                 num_coords  :int = 4,
-                 num_masks   :int = 1):
-        super().__init__()
-        # --------- Basic Parameters ----------
-        self.stride = stride
-        self.cls_dim = cls_dim
-        self.reg_dim = reg_dim
-        self.seg_dim = seg_dim
-        self.num_classes = num_classes
-        self.num_coords = num_coords
-        self.num_masks = num_masks
-
-        # --------- Network Parameters ----------
-        self.cls_pred = nn.Conv2d(cls_dim, num_classes, kernel_size=1)
-        self.reg_pred = nn.Conv2d(reg_dim, num_coords, kernel_size=1)                
-        self.seg_pred = nn.Conv2d(seg_dim, num_masks, kernel_size=1)                
-
-        self.init_bias()
-        
-    def init_bias(self):
-        # cls pred bias
-        b = self.cls_pred.bias.view(1, -1)
-        b.data.fill_(math.log(5 / self.num_classes / (640. / self.stride) ** 2))
-        self.cls_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-        # reg pred bias
-        b = self.reg_pred.bias.view(-1, )
-        b.data.fill_(1.0)
-        self.reg_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-        w = self.reg_pred.weight
-        w.data.fill_(0.)
-        self.reg_pred.weight = torch.nn.Parameter(w, requires_grad=True)
-        # seg pred bias
-        b = self.seg_pred.bias.view(-1, )
-        b.data.fill_(1.0)
-        self.seg_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-        w = self.seg_pred.weight
-        w.data.fill_(0.)
-        self.seg_pred.weight = torch.nn.Parameter(w, requires_grad=True)
-
-    def generate_anchors(self, fmp_size):
-        """
-            fmp_size: (List) [H, W]
-        """
-        # generate grid cells
-        fmp_h, fmp_w = fmp_size
-        anchor_y, anchor_x = torch.meshgrid([torch.arange(fmp_h), torch.arange(fmp_w)])
-        # [H, W, 2] -> [HW, 2]
-        anchors = torch.stack([anchor_x, anchor_y], dim=-1).float().view(-1, 2)
-        anchors += 0.5  # add center offset
-        anchors *= self.stride
-
-        return anchors
-        
-    def forward(self, cls_feat, reg_feat, seg_feat):
-        # pred
-        cls_pred = self.cls_pred(cls_feat)
-        reg_pred = self.reg_pred(reg_feat)
-        seg_pred = self.seg_pred(seg_feat)
-
-        # generate anchor boxes: [M, 4]
-        B, _, H, W = cls_pred.size()
-        fmp_size = [H, W]
-        anchors = self.generate_anchors(fmp_size)
-        anchors = anchors.to(cls_pred.device)
-        # stride tensor: [M, 1]
-        stride_tensor = torch.ones_like(anchors[..., :1]) * self.stride
-        
-        # [B, C, H, W] -> [B, H, W, C] -> [B, M, C]
-        cls_pred = cls_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, self.num_classes)
-        reg_pred = reg_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, self.num_coords)
-        seg_pred = seg_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, self.num_masks)
-        
-        # output dict
-        outputs = {"pred_cls": cls_pred,            # List(Tensor) [B, M, Nc]
-                   "pred_reg": reg_pred,            # List(Tensor) [B, M, Na]
-                   "pred_seg": seg_pred,            # List(Tensor) [B, M, Nm]
-                   "anchors": anchors,              # List(Tensor) [M, 2]
-                   "strides": self.stride,          # List(Int) = [8, 16, 32]
-                   "stride_tensor": stride_tensor   # List(Tensor) [M, 1]
-                   }
-
-        return outputs
-
-## Multi-level pred layer
-class RTCSegPredLayer(nn.Module):
-    def __init__(self,
-                 cfg,
-                 cls_dim,
-                 reg_dim,
-                 seg_dim,
-                 ):
-        super().__init__()
-        # --------- Basic Parameters ----------
-        self.cfg = cfg
-        self.cls_dim = cls_dim
-        self.reg_dim = reg_dim
-        self.seg_dim = seg_dim
-
-        # ----------- Network Parameters -----------
-        ## pred layers
-        self.multi_level_preds = nn.ModuleList(
-            [SegPredLayer(cls_dim     = cls_dim,
-                          reg_dim     = reg_dim,
-                          seg_dim     = seg_dim,
-                          stride      = cfg.out_stride[level],
-                          num_classes = cfg.num_classes,
-                          num_coords  = cfg.reg_max * 4,
-                          num_masks   = cfg.mask_dim)
-                          for level in range(cfg.num_levels)
-                          ])
-        ## proj conv
-        proj_init = torch.arange(cfg.reg_max, dtype=torch.float)
-        self.proj_conv = nn.Conv2d(cfg.reg_max, 1, kernel_size=1, bias=False).requires_grad_(False)
-        self.proj_conv.weight.data[:] = nn.Parameter(proj_init.view([1, cfg.reg_max, 1, 1]), requires_grad=False)
-
-    def forward(self, cls_feats, reg_feats, seg_feats):
-        all_anchors = []
-        all_strides = []
-        all_cls_preds = []
-        all_reg_preds = []
-        all_box_preds = []
-        all_seg_preds = []
-        for level in range(self.cfg.num_levels):
-            # -------------- Single-level prediction --------------
-            outputs = self.multi_level_preds[level](cls_feats[level], reg_feats[level], seg_feats[level])
-
-            # -------------- Decode bbox --------------
-            B, M = outputs["pred_reg"].shape[:2]
-            # [B, M, 4*(reg_max)] -> [B, M, 4, reg_max]
-            delta_pred = outputs["pred_reg"].reshape([B, M, 4, self.cfg.reg_max])
-            # [B, M, 4, reg_max] -> [B, reg_max, 4, M]
-            delta_pred = delta_pred.permute(0, 3, 2, 1).contiguous()
-            # [B, reg_max, 4, M] -> [B, 1, 4, M]
-            delta_pred = self.proj_conv(F.softmax(delta_pred, dim=1))
-            # [B, 1, 4, M] -> [B, 4, M] -> [B, M, 4]
-            delta_pred = delta_pred.view(B, 4, M).permute(0, 2, 1).contiguous()
-            ## tlbr -> xyxy
-            x1y1_pred = outputs["anchors"][None] - delta_pred[..., :2] * self.cfg.out_stride[level]
-            x2y2_pred = outputs["anchors"][None] + delta_pred[..., 2:] * self.cfg.out_stride[level]
-            box_pred = torch.cat([x1y1_pred, x2y2_pred], dim=-1)
-
-            # collect results
-            all_cls_preds.append(outputs["pred_cls"])
-            all_reg_preds.append(outputs["pred_reg"])
-            all_seg_preds.append(outputs["pred_seg"])
-            all_box_preds.append(box_pred)
-            all_anchors.append(outputs["anchors"])
-            all_strides.append(outputs["stride_tensor"])
-        
-        # output dict
-        outputs = {"pred_cls":      all_cls_preds,         # List(Tensor) [B, M, C]
-                   "pred_reg":      all_reg_preds,         # List(Tensor) [B, M, 4*(reg_max)]
-                   "pred_box":      all_box_preds,         # List(Tensor) [B, M, 4]
-                   "pred_seg":      all_seg_preds,         # List(Tensor) [B, M, 4]
-                   "anchors":       all_anchors,           # List(Tensor) [M, 2]
-                   "stride_tensor": all_strides,           # List(Tensor) [M, 1]
-                   "strides":       self.cfg.out_stride,   # List(Int) = [8, 16, 32]
-                   }
-
-        return outputs