remove yolov4, since it is similar to yolov5

config/__init__.py

@@ -2,11 +2,10 @@
 from .yolov1_config   import build_yolov1_config
 from .yolov2_config   import build_yolov2_config
 from .yolov3_config   import build_yolov3_config
-from .yolov4_config   import build_yolov4_config
 from .yolov5_config   import build_yolov5_config
 from .yolox_config    import build_yolox_config
 from .yolov8_config   import build_yolov8_config
-from .rtdetr_config import build_rtdetr_config
+from .rtdetr_config   import build_rtdetr_config
 
 def build_config(args):
     print('==============================')
@@ -18,8 +17,6 @@ def build_config(args):
         cfg = build_yolov2_config(args)
     elif 'yolov3' in args.model:
         cfg = build_yolov3_config(args)
-    elif 'yolov4' in args.model:
-        cfg = build_yolov4_config(args)
     elif 'yolov5' in args.model:
         cfg = build_yolov5_config(args)
     elif 'yolox' in args.model:

config/yolov4_config.py

@@ -1,129 +0,0 @@
-# yolo Config
-
-
-def build_yolov4_config(args):
-    if args.model == 'yolov4_s':
-        return Yolov4SConfig()
-    else:
-        raise NotImplementedError("No config for model: {}".format(args.model))
-    
-# YOLOv4-Base config
-class Yolov4BaseConfig(object):
-    def __init__(self) -> None:
-        # ---------------- Model config ----------------
-        self.width    = 1.0
-        self.depth    = 1.0
-        self.out_stride = [8, 16, 32]
-        self.max_stride = 32
-        self.num_levels = 3
-        self.scale      = "b"
-        ## Backbone
-        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_act  = 'silu'
-        self.fpn_norm = 'BN'
-        self.fpn_depthwise = False
-        ## Head
-        self.head_act  = 'silu'
-        self.head_norm = 'BN'
-        self.head_depthwise = False
-        self.head_dim       = 256
-        self.num_cls_head   = 2
-        self.num_reg_head   = 2
-        self.anchor_size    = {0: [[10, 13],   [16, 30],   [33, 23]],
-                               1: [[30, 61],   [62, 45],   [59, 119]],
-                               2: [[116, 90],  [156, 198], [373, 326]]}
-
-        # ---------------- 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 config ----------------
-        ## Matcher
-        self.iou_thresh = 0.5
-        ## Loss weight
-        self.loss_obj = 1.0
-        self.loss_cls = 1.0
-        self.loss_box = 5.0
-
-        # ---------------- 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   = -1.
-        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 = 1.0
-        self.mixup_prob  = 0.15
-        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 = [0., 0., 0.]
-        self.pixel_std  = [255., 255., 255.]
-        ## Transforms
-        self.train_img_size = 640
-        self.test_img_size  = 640
-        self.use_ablu = True
-        self.affine_params = {
-            'degrees': 0.0,
-            'translate': 0.1,
-            '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))
-
-# YOLOv4-S
-class Yolov4SConfig(Yolov4BaseConfig):
-    def __init__(self) -> None:
-        super().__init__()
-        # ---------------- Model config ----------------
-        self.width = 0.50
-        self.depth = 0.34
-        self.scale = "s"
-
-        # ---------------- Data process config ----------------
-        self.mosaic_prob = 1.0
-        self.mixup_prob  = 0.0
-        self.copy_paste  = 0.0

models/__init__.py

@@ -5,7 +5,6 @@ import torch
 from .yolov1.build import build_yolov1
 from .yolov2.build import build_yolov2
 from .yolov3.build import build_yolov3
-from .yolov4.build import build_yolov4
 from .yolov5.build import build_yolov5
 from .yolox.build  import build_yolox
 from .yolov8.build import build_yolov8
@@ -23,9 +22,6 @@ def build_model(args, cfg, is_val=False):
     ## Modified YOLOv3
     elif 'yolov3' in args.model:
         model, criterion = build_yolov3(cfg, is_val)
-    ## Modified YOLOv4
-    elif 'yolov4' in args.model:
-        model, criterion = build_yolov4(cfg, is_val)
     ## Modified YOLOv5
     elif 'yolov5' in args.model:
         model, criterion = build_yolov5(cfg, is_val)

models/yolov4/build.py

@@ -1,24 +0,0 @@
-import torch.nn as nn
-
-from .loss import SetCriterion
-from .yolov4 import Yolov4
-
-
-# build object detector
-def build_yolov4(cfg, is_val=False):
-    # -------------- Build YOLO --------------
-    model = Yolov4(cfg, is_val)
-
-    # -------------- Initialize YOLO --------------
-    for m in model.modules():
-        if isinstance(m, nn.BatchNorm2d):
-            m.eps = 1e-3
-            m.momentum = 0.03    
-            
-    # -------------- Build criterion --------------
-    criterion = None
-    if is_val:
-        # build criterion for training
-        criterion = SetCriterion(cfg)
-        
-    return model, criterion

models/yolov4/loss.py

@@ -1,101 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from utils.box_ops import get_ious
-from utils.distributed_utils import get_world_size, is_dist_avail_and_initialized
-
-from .matcher import Yolov4Matcher
-
-
-class SetCriterion(object):
-    def __init__(self, cfg):
-        self.cfg = cfg
-        self.num_classes = cfg.num_classes
-        self.loss_obj_weight = cfg.loss_obj
-        self.loss_cls_weight = cfg.loss_cls
-        self.loss_box_weight = cfg.loss_box
-
-        # matcher
-        anchor_size = cfg.anchor_size[0] + cfg.anchor_size[1] + cfg.anchor_size[2]
-        self.matcher = Yolov4Matcher(cfg.num_classes, 3, anchor_size, cfg.iou_thresh)
-
-    def loss_objectness(self, pred_obj, gt_obj):
-        loss_obj = F.binary_cross_entropy_with_logits(pred_obj, gt_obj, reduction='none')
-
-        return loss_obj
-    
-    def loss_classes(self, pred_cls, gt_label):
-        loss_cls = F.binary_cross_entropy_with_logits(pred_cls, gt_label, reduction='none')
-
-        return loss_cls
-
-    def loss_bboxes(self, pred_box, gt_box):
-        # regression loss
-        ious = get_ious(pred_box,
-                        gt_box,
-                        box_mode="xyxy",
-                        iou_type='giou')
-        loss_box = 1.0 - ious
-
-        return loss_box, ious
-
-    def __call__(self, outputs, targets):
-        device = outputs['pred_cls'][0].device
-        fpn_strides = outputs['strides']
-        fmp_sizes = outputs['fmp_sizes']
-        (
-            gt_objectness, 
-            gt_classes, 
-            gt_bboxes,
-            ) = self.matcher(fmp_sizes=fmp_sizes, 
-                             fpn_strides=fpn_strides, 
-                             targets=targets)
-        # List[B, M, C] -> [B, M, C] -> [BM, C]
-        pred_obj = torch.cat(outputs['pred_obj'], dim=1).view(-1)                      # [BM,]
-        pred_cls = torch.cat(outputs['pred_cls'], dim=1).view(-1, self.num_classes)    # [BM, C]
-        pred_box = torch.cat(outputs['pred_box'], dim=1).view(-1, 4)                   # [BM, 4]
-       
-        gt_objectness = gt_objectness.view(-1).to(device).float()               # [BM,]
-        gt_classes = gt_classes.view(-1, self.num_classes).to(device).float()   # [BM, C]
-        gt_bboxes = gt_bboxes.view(-1, 4).to(device).float()                    # [BM, 4]
-
-        pos_masks = (gt_objectness > 0)
-        num_fgs = pos_masks.sum()
-
-        if is_dist_avail_and_initialized():
-            torch.distributed.all_reduce(num_fgs)
-        num_fgs = (num_fgs / get_world_size()).clamp(1.0)
-
-        # box loss
-        pred_box_pos = pred_box[pos_masks]
-        gt_bboxes_pos = gt_bboxes[pos_masks]
-        loss_box, ious = self.loss_bboxes(pred_box_pos, gt_bboxes_pos)
-        loss_box = loss_box.sum() / num_fgs
-        
-        # cls loss
-        pred_cls_pos = pred_cls[pos_masks]
-        gt_classes_pos = gt_classes[pos_masks] * ious.unsqueeze(-1).clamp(0.)
-        loss_cls = self.loss_classes(pred_cls_pos, gt_classes_pos)
-        loss_cls = loss_cls.sum() / num_fgs
-
-        # obj loss
-        loss_obj = self.loss_objectness(pred_obj, gt_objectness)
-        loss_obj = loss_obj.sum() / num_fgs
-
-        # total loss
-        losses = self.loss_obj_weight * loss_obj + \
-                 self.loss_cls_weight * loss_cls + \
-                 self.loss_box_weight * loss_box
-
-        loss_dict = dict(
-                loss_obj = loss_obj,
-                loss_cls = loss_cls,
-                loss_box = loss_box,
-                losses = losses
-        )
-
-        return loss_dict
-    
-    
-if __name__ == "__main__":
-    pass

models/yolov4/matcher.py

@@ -1,166 +0,0 @@
-import numpy as np
-import torch
-
-
-class Yolov4Matcher(object):
-    def __init__(self, num_classes, num_anchors, anchor_size, iou_thresh):
-        self.num_classes = num_classes
-        self.num_anchors = num_anchors
-        self.iou_thresh = iou_thresh
-        self.anchor_boxes = np.array(
-            [[0., 0., anchor[0], anchor[1]]
-            for anchor in anchor_size]
-            )  # [KA, 4]
-
-    def compute_iou(self, anchor_boxes, gt_box):
-        """
-            anchor_boxes : ndarray -> [KA, 4] (cx, cy, bw, bh).
-            gt_box       : ndarray -> [1, 4] (cx, cy, bw, bh).
-        """
-        # anchors: [KA, 4]
-        anchors_xyxy = np.zeros_like(anchor_boxes)
-        anchors_area = anchor_boxes[..., 2] * anchor_boxes[..., 3]
-        # convert [cx, cy, bw, bh] -> [x1, y1, x2, y2]
-        anchors_xyxy[..., :2] = anchor_boxes[..., :2] - anchor_boxes[..., 2:] * 0.5  # x1y1
-        anchors_xyxy[..., 2:] = anchor_boxes[..., :2] + anchor_boxes[..., 2:] * 0.5  # x2y2
-        
-        # expand gt_box: [1, 4] -> [KA, 4]
-        gt_box = np.array(gt_box).reshape(-1, 4)
-        gt_box = np.repeat(gt_box, anchors_xyxy.shape[0], axis=0)
-        gt_box_area = gt_box[..., 2] * gt_box[..., 3]
-        # convert [cx, cy, bw, bh] -> [x1, y1, x2, y2]
-        gt_box_xyxy = np.zeros_like(gt_box)
-        gt_box_xyxy[..., :2] = gt_box[..., :2] - gt_box[..., 2:] * 0.5  # x1y1
-        gt_box_xyxy[..., 2:] = gt_box[..., :2] + gt_box[..., 2:] * 0.5  # x2y2
-
-        # intersection
-        inter_w = np.minimum(anchors_xyxy[:, 2], gt_box_xyxy[:, 2]) - \
-                  np.maximum(anchors_xyxy[:, 0], gt_box_xyxy[:, 0])
-        inter_h = np.minimum(anchors_xyxy[:, 3], gt_box_xyxy[:, 3]) - \
-                  np.maximum(anchors_xyxy[:, 1], gt_box_xyxy[:, 1])
-        inter_area = inter_w * inter_h
-        
-        # union
-        union_area = anchors_area + gt_box_area - inter_area
-
-        # iou
-        iou = inter_area / union_area
-        iou = np.clip(iou, a_min=1e-10, a_max=1.0)
-        
-        return iou
-
-    @torch.no_grad()
-    def __call__(self, fmp_sizes, fpn_strides, targets):
-        """
-            fmp_size: (List) [fmp_h, fmp_w]
-            fpn_strides: (List) -> [8, 16, 32, ...] stride of network output.
-            targets: (Dict) dict{'boxes': [...], 
-                                 'labels': [...], 
-                                 'orig_size': ...}
-        """
-        assert len(fmp_sizes) == len(fpn_strides)
-        # prepare
-        bs = len(targets)
-        gt_objectness = [
-            torch.zeros([bs, fmp_h, fmp_w, self.num_anchors, 1]) 
-            for (fmp_h, fmp_w) in fmp_sizes
-            ]
-        gt_classes = [
-            torch.zeros([bs, fmp_h, fmp_w, self.num_anchors, self.num_classes]) 
-            for (fmp_h, fmp_w) in fmp_sizes
-            ]
-        gt_bboxes = [
-            torch.zeros([bs, fmp_h, fmp_w, self.num_anchors, 4]) 
-            for (fmp_h, fmp_w) in fmp_sizes
-            ]
-
-        for batch_index in range(bs):
-            targets_per_image = targets[batch_index]
-            # [N,]
-            tgt_cls = targets_per_image["labels"].numpy()
-            # [N, 4]
-            tgt_box = targets_per_image['boxes'].numpy()
-
-            for gt_box, gt_label in zip(tgt_box, tgt_cls):
-                # get a bbox coords
-                x1, y1, x2, y2 = gt_box.tolist()
-                # xyxy -> cxcywh
-                xc, yc = (x2 + x1) * 0.5, (y2 + y1) * 0.5
-                bw, bh = x2 - x1, y2 - y1
-                gt_box = [0, 0, bw, bh]
-
-                # check target
-                if bw < 1. or bh < 1.:
-                    # invalid target
-                    continue
-
-                # compute IoU
-                iou = self.compute_iou(self.anchor_boxes, gt_box)
-                iou_mask = (iou > self.iou_thresh)
-
-                label_assignment_results = []
-                if iou_mask.sum() == 0:
-                    # We assign the anchor box with highest IoU score.
-                    iou_ind = np.argmax(iou)
-
-                    level = iou_ind // self.num_anchors              # pyramid level
-                    anchor_idx = iou_ind - level * self.num_anchors  # anchor index
-
-                    # get the corresponding stride
-                    stride = fpn_strides[level]
-
-                    # compute the grid cell
-                    xc_s = xc / stride
-                    yc_s = yc / stride
-                    grid_x = int(xc_s)
-                    grid_y = int(yc_s)
-
-                    label_assignment_results.append([grid_x, grid_y, level, anchor_idx])
-                else:            
-                    for iou_ind, iou_m in enumerate(iou_mask):
-                        if iou_m:
-                            level = iou_ind // self.num_anchors              # pyramid level
-                            anchor_idx = iou_ind - level * self.num_anchors  # anchor index
-
-                            # get the corresponding stride
-                            stride = fpn_strides[level]
-
-                            # compute the gride cell
-                            xc_s = xc / stride
-                            yc_s = yc / stride
-                            grid_x = int(xc_s)
-                            grid_y = int(yc_s)
-
-                            label_assignment_results.append([grid_x, grid_y, level, anchor_idx])
-
-                # label assignment
-                for result in label_assignment_results:
-                    grid_x, grid_y, level, anchor_idx = result
-                    stride = fpn_strides[level]
-                    x1s, y1s = x1 / stride, y1 / stride
-                    x2s, y2s = x2 / stride, y2 / stride
-                    fmp_h, fmp_w = fmp_sizes[level]
-
-                    # 3x3 center sampling
-                    for j in range(grid_y - 1, grid_y + 2):
-                        for i in range(grid_x - 1, grid_x + 2):
-                            is_in_box = (j >= y1s and j < y2s) and (i >= x1s and i < x2s)
-                            is_valid = (j >= 0 and j < fmp_h) and (i >= 0 and i < fmp_w)
-
-                            if is_in_box and is_valid:
-                                # obj
-                                gt_objectness[level][batch_index, j, i, anchor_idx] = 1.0
-                                # cls
-                                cls_ont_hot = torch.zeros(self.num_classes)
-                                cls_ont_hot[int(gt_label)] = 1.0
-                                gt_classes[level][batch_index, j, i, anchor_idx] = cls_ont_hot
-                                # box
-                                gt_bboxes[level][batch_index, j, i, anchor_idx] = torch.as_tensor([x1, y1, x2, y2])
-
-        # [B, M, C]
-        gt_objectness = torch.cat([gt.view(bs, -1, 1) for gt in gt_objectness], dim=1).float()
-        gt_classes = torch.cat([gt.view(bs, -1, self.num_classes) for gt in gt_classes], dim=1).float()
-        gt_bboxes = torch.cat([gt.view(bs, -1, 4) for gt in gt_bboxes], dim=1).float()
-
-        return gt_objectness, gt_classes, gt_bboxes
-    

models/yolov4/yolov4.py

@@ -1,155 +0,0 @@
-# --------------- Torch components ---------------
-import torch
-import torch.nn as nn
-
-# --------------- Model components ---------------
-from .yolov4_backbone import Yolov4Backbone
-from .yolov4_neck     import SPPF
-from .yolov4_pafpn    import Yolov4PaFPN
-from .yolov4_head     import Yolov4DetHead
-from .yolov4_pred     import Yolov4DetPredLayer
-
-# --------------- External components ---------------
-from utils.misc import multiclass_nms
-
-
-# YOLOv4
-class Yolov4(nn.Module):
-    def __init__(self,
-                 cfg,
-                 is_val = False,
-                 ) -> None:
-        super(Yolov4, 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 = Yolov4Backbone(cfg)
-        self.pyramid_feat_dims = self.backbone.feat_dims[-3:]
-        ## Neck: SPP
-        self.neck     = SPPF(cfg, self.pyramid_feat_dims[-1], self.pyramid_feat_dims[-1])
-        ## Neck: FPN
-        self.fpn      = Yolov4PaFPN(cfg, self.pyramid_feat_dims)
-        ## Head
-        self.head     = Yolov4DetHead(cfg, self.fpn.out_dims)
-        ## Pred
-        self.pred     = Yolov4DetPredLayer(cfg)
-
-    def post_process(self, obj_preds, cls_preds, box_preds):
-        """
-        We process predictions at each scale hierarchically
-        Input:
-            obj_preds: List[torch.Tensor] -> [[B, M, 1], ...], B=1
-            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 obj_pred_i, cls_pred_i, box_pred_i in zip(obj_preds, cls_preds, box_preds):
-            obj_pred_i = obj_pred_i[0]
-            cls_pred_i = cls_pred_i[0]
-            box_pred_i = box_pred_i[0]
-            if self.no_multi_labels:
-                # [M,]
-                scores, labels = torch.max(
-                    torch.sqrt(obj_pred_i.sigmoid() * 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 = torch.sqrt(obj_pred_i.sigmoid() * 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_obj_preds = outputs['pred_obj']
-            all_cls_preds = outputs['pred_cls']
-            all_box_preds = outputs['pred_box']
-
-            # post process
-            bboxes, scores, labels = self.post_process(all_obj_preds, all_cls_preds, all_box_preds)
-            outputs = {
-                "scores": scores,
-                "labels": labels,
-                "bboxes": bboxes
-            }
-        
-        return outputs 

models/yolov4/yolov4_backbone.py

@@ -1,135 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .yolov4_basic import BasicConv, CSPBlock
-except:
-    from  yolov4_basic import BasicConv, CSPBlock
-
-
-# --------------------- Yolov3's Backbone -----------------------
-## Modified DarkNet
-class Yolov4Backbone(nn.Module):
-    def __init__(self, cfg):
-        super(Yolov4Backbone, self).__init__()
-        # ------------------ Basic setting ------------------
-        self.model_scale = cfg.scale
-        self.feat_dims = [round(64   * cfg.width),
-                          round(128  * cfg.width),
-                          round(256  * cfg.width),
-                          round(512  * cfg.width),
-                          round(1024 * cfg.width)]
-        
-        # ------------------ Network setting ------------------
-        ## P1/2
-        self.layer_1 = BasicConv(3, self.feat_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(
-            BasicConv(self.feat_dims[0], self.feat_dims[1],
-                      kernel_size=3, padding=1, stride=2,
-                      act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise),
-            CSPBlock(in_dim     = self.feat_dims[1],
-                     out_dim    = self.feat_dims[1],
-                     num_blocks = round(3*cfg.depth),
-                     expansion  = 0.5,
-                     shortcut   = True,
-                     act_type   = cfg.bk_act,
-                     norm_type  = cfg.bk_norm,
-                     depthwise  = cfg.bk_depthwise)
-        )
-        # P3/8
-        self.layer_3 = nn.Sequential(
-            BasicConv(self.feat_dims[1], self.feat_dims[2],
-                      kernel_size=3, padding=1, stride=2,
-                      act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise),
-            CSPBlock(in_dim     = self.feat_dims[2],
-                     out_dim    = self.feat_dims[2],
-                     num_blocks = round(9*cfg.depth),
-                     expansion  = 0.5,
-                     shortcut   = True,
-                     act_type   = cfg.bk_act,
-                     norm_type  = cfg.bk_norm,
-                     depthwise  = cfg.bk_depthwise)
-        )
-        # P4/16
-        self.layer_4 = nn.Sequential(
-            BasicConv(self.feat_dims[2], self.feat_dims[3],
-                      kernel_size=3, padding=1, stride=2,
-                      act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise),
-            CSPBlock(in_dim     = self.feat_dims[3],
-                     out_dim    = self.feat_dims[3],
-                     num_blocks = round(9*cfg.depth),
-                     expansion  = 0.5,
-                     shortcut   = True,
-                     act_type   = cfg.bk_act,
-                     norm_type  = cfg.bk_norm,
-                     depthwise  = cfg.bk_depthwise)
-        )
-        # P5/32
-        self.layer_5 = nn.Sequential(
-            BasicConv(self.feat_dims[3], self.feat_dims[4],
-                      kernel_size=3, padding=1, stride=2,
-                      act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise),
-            CSPBlock(in_dim     = self.feat_dims[4],
-                     out_dim    = self.feat_dims[4],
-                     num_blocks = round(3*cfg.depth),
-                     expansion  = 0.5,
-                     shortcut   = True,
-                     act_type   = cfg.bk_act,
-                     norm_type  = cfg.bk_norm,
-                     depthwise  = cfg.bk_depthwise)
-        )
-
-        # Initialize all layers
-        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):
-        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
-
-
-if __name__ == '__main__':
-    import time
-    from thop import profile
-    class BaseConfig(object):
-        def __init__(self) -> None:
-            self.bk_act = 'silu'
-            self.bk_norm = 'BN'
-            self.bk_depthwise = False
-            self.width = 0.5
-            self.depth = 0.34
-            self.scale = "s"
-            self.use_pretrained = True
-
-    cfg = BaseConfig()
-    model = Yolov4Backbone(cfg)
-    x = torch.randn(1, 3, 640, 640)
-    t0 = time.time()
-    outputs = model(x)
-    t1 = time.time()
-    print('Time: ', t1 - t0)
-    for out in outputs:
-        print(out.shape)
-
-    x = torch.randn(1, 3, 640, 640)
-    print('==============================')
-    flops, params = profile(model, inputs=(x, ), verbose=False)
-    print('==============================')
-    print('GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
-    print('Params : {:.2f} M'.format(params / 1e6))

models/yolov4/yolov4_basic.py

@@ -1,137 +0,0 @@
-import torch
-import torch.nn as nn
-from typing import List
-
-
-# --------------------- Basic modules ---------------------
-def get_conv2d(c1, c2, k, p, s, d, g, 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
-                 act_type  :str = 'lrelu', # activation
-                 norm_type :str = 'BN',    # normalization
-                 depthwise :bool = False
-                ):
-        super(BasicConv, self).__init__()
-        self.depthwise = depthwise
-        if not depthwise:
-            self.conv = get_conv2d(in_dim, out_dim, k=kernel_size, p=padding, s=stride, d=dilation, g=1)
-            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)
-            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)
-            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.norm1(self.conv1(x))
-            # Pointwise conv
-            x = self.norm2(self.conv2(x))
-            return x
-
-
-# ---------------------------- Basic Modules ----------------------------
-class YoloBottleneck(nn.Module):
-    def __init__(self,
-                 in_dim       :int,
-                 out_dim      :int,
-                 kernel_size  :List  = [1, 3],
-                 expansion    :float = 0.5,
-                 shortcut     :bool  = False,
-                 act_type     :str   = 'silu',
-                 norm_type    :str   = 'BN',
-                 depthwise    :bool  = False,
-                 ) -> None:
-        super(YoloBottleneck, self).__init__()
-        inter_dim = int(out_dim * expansion)
-        # ----------------- Network setting -----------------
-        self.conv_layer1 = BasicConv(in_dim, inter_dim,
-                                     kernel_size=kernel_size[0], padding=kernel_size[0]//2, stride=1,
-                                     act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-        self.conv_layer2 = BasicConv(inter_dim, out_dim,
-                                     kernel_size=kernel_size[1], padding=kernel_size[1]//2, 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_layer2(self.conv_layer1(x))
-
-        return x + h if self.shortcut else h
-
-class CSPBlock(nn.Module):
-    def __init__(self,
-                 in_dim,
-                 out_dim,
-                 num_blocks   :int   = 1,
-                 expansion    :float = 0.5,
-                 shortcut     :bool  = False,
-                 act_type     :str   = 'silu',
-                 norm_type    :str   = 'BN',
-                 depthwise    :bool  = False,
-                 ):
-        super(CSPBlock, self).__init__()
-        # ---------- Basic parameters ----------
-        self.num_blocks = num_blocks
-        self.expansion = expansion
-        self.shortcut = shortcut
-        inter_dim = round(out_dim * expansion)
-        # ---------- Model parameters ----------
-        self.conv_layer_1 = BasicConv(in_dim, inter_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
-        self.conv_layer_2 = BasicConv(in_dim, inter_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
-        self.conv_layer_3 = BasicConv(inter_dim * 2, out_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
-        self.module       = nn.Sequential(*[YoloBottleneck(inter_dim,
-                                                           inter_dim,
-                                                           kernel_size  = [1, 3],
-                                                           expansion    = 1.0,
-                                                           shortcut     = shortcut,
-                                                           act_type     = act_type,
-                                                           norm_type    = norm_type,
-                                                           depthwise    = depthwise)
-                                                           for _ in range(num_blocks)
-                                                           ])
-
-    def forward(self, x):
-        x1 = self.conv_layer_1(x)
-        x2 = self.module(self.conv_layer_2(x))
-        out = self.conv_layer_3(torch.cat([x1, x2], dim=1))
-
-        return out
-    

models/yolov4/yolov4_head.py

@@ -1,171 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .yolov4_basic import BasicConv
-except:
-    from  yolov4_basic import BasicConv
-
-
-## 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, 
-                              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)
-                              )
-        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-level Detection Head
-class Yolov4DetHead(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 = round(cfg.head_dim * cfg.width),
-                     reg_head_dim = round(cfg.head_dim * cfg.width),
-                     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 = cfg.head_dim
-        self.reg_head_dim = cfg.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
-
-
-if __name__=='__main__':
-    import time
-    from thop import profile
-    # Model config
-    
-    # YOLOv3-Base config
-    class Yolov4BaseConfig(object):
-        def __init__(self) -> None:
-            # ---------------- Model config ----------------
-            self.out_stride = 32
-            self.max_stride = 32
-            self.num_levels = 3
-            ## Head
-            self.head_act  = 'lrelu'
-            self.head_norm = 'BN'
-            self.head_depthwise = False
-            self.head_dim  = 256
-            self.num_cls_head   = 2
-            self.num_reg_head   = 2
-
-    cfg = Yolov4BaseConfig()
-    # Build a head
-    pyramid_feats = [torch.randn(1, cfg.head_dim, 80, 80),
-                     torch.randn(1, cfg.head_dim, 40, 40),
-                     torch.randn(1, cfg.head_dim, 20, 20)]
-    head = Yolov4DetHead(cfg, [cfg.head_dim]*3)
-
-
-    # Inference
-    t0 = time.time()
-    cls_feats, reg_feats = head(pyramid_feats)
-    t1 = time.time()
-    print('Time: ', t1 - t0)
-    for cls_f, reg_f in zip(cls_feats, reg_feats):
-        print(cls_f.shape, reg_f.shape)
-
-    print('==============================')
-    flops, params = profile(head, inputs=(pyramid_feats, ), verbose=False)
-    print('==============================')
-    print('GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
-    print('Params : {:.2f} M'.format(params / 1e6))    

models/yolov4/yolov4_neck.py

@@ -1,33 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .yolov4_basic import BasicConv
-
-
-# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
-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.cv1 = BasicConv(in_dim, inter_dim,
-                             kernel_size=1, padding=0, stride=1,
-                             act_type=cfg.neck_act, norm_type=cfg.neck_norm)
-        self.cv2 = BasicConv(inter_dim * 4, out_dim,
-                             kernel_size=1, padding=0, stride=1,
-                             act_type=cfg.neck_act, norm_type=cfg.neck_norm)
-        self.m = nn.MaxPool2d(kernel_size=cfg.spp_pooling_size,
-                              stride=1,
-                              padding=cfg.spp_pooling_size // 2)
-
-    def forward(self, x):
-        x = self.cv1(x)
-        y1 = self.m(x)
-        y2 = self.m(y1)
-
-        return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))

models/yolov4/yolov4_pafpn.py

@@ -1,102 +0,0 @@
-from typing import List
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .yolov4_basic import BasicConv, CSPBlock
-
-
-# Yolov4FPN
-class Yolov4PaFPN(nn.Module):
-    def __init__(self, cfg, in_dims: List = [256, 512, 1024],
-                 ):
-        super(Yolov4PaFPN, self).__init__()
-        self.in_dims = in_dims
-        c3, c4, c5 = in_dims
-
-        # ---------------------- Yolov4's Top down FPN ----------------------
-        ## P5 -> P4
-        self.reduce_layer_1   = BasicConv(c5, round(512*cfg.width), kernel_size=1, act_type=cfg.fpn_act, norm_type=cfg.fpn_norm)
-        self.top_down_layer_1 = CSPBlock(in_dim     = c4 + round(512*cfg.width),
-                                         out_dim    = round(512*cfg.width),
-                                         num_blocks = round(3*cfg.depth),
-                                         expansion  = 0.5,
-                                         shortcut   = False,
-                                         act_type   = cfg.fpn_act,
-                                         norm_type  = cfg.fpn_norm,
-                                         depthwise  = cfg.fpn_depthwise)
-
-        ## P4 -> P3
-        self.reduce_layer_2   = BasicConv(round(512*cfg.width), round(256*cfg.width), kernel_size=1, act_type=cfg.fpn_act, norm_type=cfg.fpn_norm)
-        self.top_down_layer_2 = CSPBlock(in_dim     = c3 + round(256*cfg.width),
-                                         out_dim    = round(256*cfg.width),
-                                         num_blocks = round(3*cfg.depth),
-                                         expansion  = 0.5,
-                                         shortcut   = False,
-                                         act_type   = cfg.fpn_act,
-                                         norm_type  = cfg.fpn_norm,
-                                         depthwise  = cfg.fpn_depthwise)
-        
-        # ---------------------- Yolov4's Bottom up FPN ----------------------
-        ## P3 -> P4
-        self.downsample_layer_1 = BasicConv(round(256*cfg.width), round(256*cfg.width),
-                                            kernel_size=3, padding=1, stride=2,
-                                            act_type=cfg.fpn_act, norm_type=cfg.fpn_norm, depthwise=cfg.fpn_depthwise)
-        self.bottom_up_layer_1  = CSPBlock(in_dim     = round(256*cfg.width) + round(256*cfg.width),
-                                           out_dim    = round(512*cfg.width),
-                                           num_blocks = round(3*cfg.depth),
-                                           expansion  = 0.5,
-                                           shortcut   = False,
-                                           act_type   = cfg.fpn_act,
-                                           norm_type  = cfg.fpn_norm,
-                                           depthwise  = cfg.fpn_depthwise)
-        ## P4 -> P5
-        self.downsample_layer_2 = BasicConv(round(512*cfg.width), round(512*cfg.width),
-                                            kernel_size=3, padding=1, stride=2,
-                                            act_type=cfg.fpn_act, norm_type=cfg.fpn_norm, depthwise=cfg.fpn_depthwise)
-        self.bottom_up_layer_2  = CSPBlock(in_dim     = round(512*cfg.width) + round(512*cfg.width),
-                                           out_dim    = round(1024*cfg.width),
-                                           num_blocks = round(3*cfg.depth),
-                                           expansion  = 0.5,
-                                           shortcut   = False,
-                                           act_type   = cfg.fpn_act,
-                                           norm_type  = cfg.fpn_norm,
-                                           depthwise  = cfg.fpn_depthwise)
-
-        # ---------------------- Yolov4's output projection ----------------------
-        self.out_layers = nn.ModuleList([
-            BasicConv(in_dim, round(cfg.head_dim*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(1024*cfg.width)]
-                      ])
-        self.out_dims = [round(cfg.head_dim*cfg.width)] * 3
-
-    def forward(self, features):
-        c3, c4, c5 = features
-        
-        # P5 -> P4
-        p5 = self.reduce_layer_1(c5)
-        p5_up = F.interpolate(p5, scale_factor=2.0)
-        p4 = self.top_down_layer_1(torch.cat([c4, p5_up], dim=1))
-
-        # P4 -> P3
-        p4 = self.reduce_layer_2(p4)
-        p4_up = F.interpolate(p4, scale_factor=2.0)
-        p3 = self.top_down_layer_2(torch.cat([c3, p4_up], dim=1))
-
-        # P3 -> P4
-        p3_ds = self.downsample_layer_1(p3)
-        p4 = self.bottom_up_layer_1(torch.cat([p4, p3_ds], dim=1))
-
-        # P4 -> P5
-        p4_ds = self.downsample_layer_2(p4)
-        p5 = self.bottom_up_layer_2(torch.cat([p5, p4_ds], dim=1))
-
-        out_feats = [p3, p4, p5]
-
-        # output proj layers
-        out_feats_proj = []
-        for feat, layer in zip(out_feats, self.out_layers):
-            out_feats_proj.append(layer(feat))
-            
-        return out_feats_proj

models/yolov4/yolov4_pred.py

@@ -1,159 +0,0 @@
-import torch
-import torch.nn as nn
-from typing import List
-
-# -------------------- Detection Pred Layer --------------------
-## Single-level pred layer
-class DetPredLayer(nn.Module):
-    def __init__(self,
-                 cls_dim      :int,
-                 reg_dim      :int,
-                 stride       :int,
-                 num_classes  :int,
-                 anchor_sizes :List,
-                 ):
-        super().__init__()
-        # --------- Basic Parameters ----------
-        self.stride  = stride
-        self.cls_dim = cls_dim
-        self.reg_dim = reg_dim
-        self.num_classes = num_classes
-        # ------------------- Anchor box -------------------
-        self.anchor_size = torch.as_tensor(anchor_sizes).float().view(-1, 2) # [A, 2]
-        self.num_anchors = self.anchor_size.shape[0]
-
-        # --------- Network Parameters ----------
-        self.obj_pred = nn.Conv2d(self.cls_dim, 1 * self.num_anchors, kernel_size=1)
-        self.cls_pred = nn.Conv2d(self.cls_dim, num_classes * self.num_anchors, kernel_size=1)
-        self.reg_pred = nn.Conv2d(self.reg_dim, 4 * self.num_anchors, kernel_size=1)                
-
-        self.init_bias()
-        
-    def init_bias(self):
-        # Init bias
-        init_prob = 0.01
-        bias_value = -torch.log(torch.tensor((1. - init_prob) / init_prob))
-        # obj pred
-        b = self.obj_pred.bias.view(1, -1)
-        b.data.fill_(bias_value.item())
-        self.obj_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-        # cls pred
-        b = self.cls_pred.bias.view(1, -1)
-        b.data.fill_(bias_value.item())
-        self.cls_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-        # reg pred
-        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)
-
-    def generate_anchors(self, fmp_size):
-        """
-            fmp_size: (List) [H, W]
-        """
-        # 特征图的宽和高
-        fmp_h, fmp_w = fmp_size
-
-        # 生成网格的x坐标和y坐标
-        anchor_y, anchor_x = torch.meshgrid([torch.arange(fmp_h), torch.arange(fmp_w)])
-
-        # 将xy两部分的坐标拼起来：[H, W, 2] -> [HW, 2]
-        anchor_xy = torch.stack([anchor_x, anchor_y], dim=-1).float().view(-1, 2)
-        # [HW, 2] -> [HW, A, 2] -> [M, 2], M=HWA
-        anchor_xy = anchor_xy.unsqueeze(1).repeat(1, self.num_anchors, 1).view(-1, 2)
-
-        # 加入亚像素坐标（anchor坐标偏移至中心点）
-        anchor_xy += 0.5
-
-        # [A, 2] -> [1, A, 2] -> [HW, A, 2] -> [M, 2], M=HWA
-        anchor_wh = self.anchor_size.unsqueeze(0).repeat(fmp_h*fmp_w, 1, 1)
-        anchor_wh = anchor_wh.view(-1, 2)
-
-        anchors = torch.cat([anchor_xy, anchor_wh], dim=-1)
-
-        return anchors
-        
-    def forward(self, cls_feat, reg_feat):
-        # 预测层
-        obj_pred = self.obj_pred(reg_feat)
-        cls_pred = self.cls_pred(cls_feat)
-        reg_pred = self.reg_pred(reg_feat)
-
-        # 生成网格坐标
-        B, _, H, W = cls_pred.size()
-        fmp_size = [H, W]
-        anchors = self.generate_anchors(fmp_size)
-        anchors = anchors.to(cls_pred.device)
-
-        # 对 pred 的size做一些view调整，便于后续的处理
-        # [B, C*A, H, W] -> [B, H, W, C*A] -> [B, H*W*A, C]
-        obj_pred = obj_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, 1)
-        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, 4)
-        
-        # 解算边界框坐标
-        cxcy_pred = (torch.sigmoid(reg_pred[..., :2]) * 3.0 - 1.5 + anchors[..., :2]) * self.stride
-        bwbh_pred = torch.exp(reg_pred[..., 2:]) * anchors[..., 2:]
-        pred_x1y1 = cxcy_pred - bwbh_pred * 0.5
-        pred_x2y2 = cxcy_pred + bwbh_pred * 0.5
-        box_pred = torch.cat([pred_x1y1, pred_x2y2], dim=-1)
-
-        # output dict
-        outputs = {"pred_obj": obj_pred,       # (torch.Tensor) [B, M, 1]
-                   "pred_cls": cls_pred,       # (torch.Tensor) [B, M, C]
-                   "pred_reg": reg_pred,       # (torch.Tensor) [B, M, 4]
-                   "pred_box": box_pred,       # (torch.Tensor) [B, M, 4]
-                   "anchors" : anchors,        # (torch.Tensor) [M, 2]
-                   "fmp_size": fmp_size,
-                   "stride"  : self.stride,    # (Int)
-                   }
-
-        return outputs
-
-## Multi-level pred layer
-class Yolov4DetPredLayer(nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-        # --------- Basic Parameters ----------
-        self.cfg = cfg
-
-        # ----------- Network Parameters -----------
-        ## pred layers
-        self.multi_level_preds = nn.ModuleList(
-            [DetPredLayer(cls_dim      = round(cfg.head_dim * cfg.width),
-                          reg_dim      = round(cfg.head_dim * cfg.width),
-                          stride       = cfg.out_stride[level],
-                          anchor_sizes = cfg.anchor_size[level],
-                          num_classes  = cfg.num_classes,)
-                          for level in range(cfg.num_levels)
-                          ])
-
-    def forward(self, cls_feats, reg_feats):
-        all_anchors = []
-        all_fmp_sizes = []
-        all_obj_preds = []
-        all_cls_preds = []
-        all_reg_preds = []
-        all_box_preds = []
-        for level in range(self.cfg.num_levels):
-            # -------------- Single-level prediction --------------
-            outputs = self.multi_level_preds[level](cls_feats[level], reg_feats[level])
-
-            # collect results
-            all_obj_preds.append(outputs["pred_obj"])
-            all_cls_preds.append(outputs["pred_cls"])
-            all_reg_preds.append(outputs["pred_reg"])
-            all_box_preds.append(outputs["pred_box"])
-            all_fmp_sizes.append(outputs["fmp_size"])
-            all_anchors.append(outputs["anchors"])
-        
-        # output dict
-        outputs = {"pred_obj":  all_obj_preds,         # List(Tensor) [B, M, 1]
-                   "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]
-                   "fmp_sizes": all_fmp_sizes,         # List(Tensor) [M, 1]
-                   "anchors":   all_anchors,           # List(Tensor) [M, 2]
-                   "strides":   self.cfg.out_stride,   # List(Int) = [8, 16, 32]
-                   }
-
-        return outputs

models/yolov5/yolov5_pred.py

@@ -50,17 +50,14 @@ class DetPredLayer(nn.Module):
         """
             fmp_size: (List) [H, W]
         """
-        # 特征图的宽和高
         fmp_h, fmp_w = fmp_size
-
-        # 生成网格的x坐标和y坐标
         anchor_y, anchor_x = torch.meshgrid([torch.arange(fmp_h), torch.arange(fmp_w)])
 
-        # 将xy两部分的坐标拼起来：[H, W, 2] -> [HW, 2]
+        # [H, W, 2] -> [HW, 2]
         anchor_xy = torch.stack([anchor_x, anchor_y], dim=-1).float().view(-1, 2)
+
         # [HW, 2] -> [HW, A, 2] -> [M, 2], M=HWA
-        anchor_xy = anchor_xy.unsqueeze(1).repeat(1, self.num_anchors, 1)
-        anchor_xy = anchor_xy.view(-1, 2)
+        anchor_xy = anchor_xy.unsqueeze(1).repeat(1, self.num_anchors, 1).view(-1, 2)
 
         # [A, 2] -> [1, A, 2] -> [HW, A, 2] -> [M, 2], M=HWA
         anchor_wh = self.anchor_size.unsqueeze(0).repeat(fmp_h*fmp_w, 1, 1)
@@ -127,7 +124,6 @@ class Yolov5DetPredLayer(nn.Module):
 
     def forward(self, cls_feats, reg_feats):
         all_anchors = []
-        all_strides = []
         all_fmp_sizes = []
         all_obj_preds = []
         all_cls_preds = []