update

yolo/config/__init__.py

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

yolo/config/yolof_config.py

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

yolo/models/__init__.py

@@ -10,7 +10,6 @@ from .yolov5_af.build import build_yolov5af
 from .yolov7_af.build import build_yolov7af
 from .yolov8.build    import build_yolov8
 from .gelan.build     import build_gelan
-from .yolof.build     import build_yolof
 from .rtdetr.build    import build_rtdetr
 
 
@@ -41,9 +40,6 @@ def build_model(args, cfg, is_val=False):
     ## GElan
     elif 'gelan' in args.model:
         model, criterion = build_gelan(cfg, is_val)
-    ## YOLOF
-    elif 'yolof' in args.model:
-        model, criterion = build_yolof(cfg, is_val)
     ## RT-DETR
     elif 'rtdetr' in args.model:
         model, criterion = build_rtdetr(cfg, is_val)

yolo/models/gelan/gelan_basic.py

@@ -64,7 +64,7 @@ class BasicConv(nn.Module):
             return self.act(self.norm(self.conv(x)))
         else:
             # Depthwise conv
-            x = self.act(self.norm1(self.conv1(x)))
+            x = self.norm1(self.conv1(x))
             # Pointwise conv
             x = self.act(self.norm2(self.conv2(x)))
             return x

yolo/models/yolof/build.py

@@ -1,16 +0,0 @@
-from .loss import SetCriterion
-from .yolof import Yolof
-
-
-# build object detector
-def build_yolof(cfg, is_val=False):
-    # -------------- Build YOLO --------------
-    model = Yolof(cfg, is_val)
-  
-    # -------------- Build criterion --------------
-    criterion = None
-    if is_val:
-        # build criterion for training
-        criterion = SetCriterion(cfg)
-        
-    return model, criterion

yolo/models/yolof/loss.py

@@ -1,131 +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 SimOtaMatcher
-
-
-class SetCriterion(object):
-    def __init__(self, cfg):
-        self.cfg = cfg
-        self.num_classes = cfg.num_classes
-        # --------------- Loss config ---------------
-        self.loss_cls_weight = cfg.loss_cls
-        self.loss_box_weight = cfg.loss_box
-        # --------------- Matcher config ---------------
-        self.matcher = SimOtaMatcher(soft_center_radius = cfg.ota_soft_center_radius,
-                                     topk_candidates    = cfg.ota_topk_candidates,
-                                     num_classes        = cfg.num_classes,
-                                     )
-
-    def loss_classes(self, pred_cls, target, beta=2.0):
-        # Quality FocalLoss
-        """
-            pred_cls: (torch.Tensor): [N, C]。
-            target:   (tuple([torch.Tensor], [torch.Tensor])): label -> (N,), score -> (N)
-        """
-        label, score = target
-        pred_sigmoid = pred_cls.sigmoid()
-        scale_factor = pred_sigmoid
-        zerolabel = scale_factor.new_zeros(pred_cls.shape)
-
-        ce_loss = F.binary_cross_entropy_with_logits(
-            pred_cls, zerolabel, reduction='none') * scale_factor.pow(beta)
-        
-        bg_class_ind = pred_cls.shape[-1]
-        pos = ((label >= 0) & (label < bg_class_ind)).nonzero().squeeze(1)
-        if pos.shape[0] > 0:
-            pos_label = label[pos].long()
-
-            scale_factor = score[pos] - pred_sigmoid[pos, pos_label]
-
-            ce_loss[pos, pos_label] = F.binary_cross_entropy_with_logits(
-                pred_cls[pos, pos_label], score[pos],
-                reduction='none') * scale_factor.abs().pow(beta)
-
-        return ce_loss
-    
-    def loss_bboxes(self, pred_box, gt_box, bbox_weight=None):
-        ious = get_ious(pred_box, gt_box, box_mode="xyxy", iou_type='giou')
-        loss_box = 1.0 - ious
-
-        if bbox_weight is not None:
-            loss_box = loss_box.squeeze(-1) * bbox_weight
-
-        return loss_box
-
-    def __call__(self, outputs, targets):        
-        """
-            outputs['pred_cls']: List(Tensor) [B, M, C]
-            outputs['pred_reg']: List(Tensor) [B, M, 4]
-            outputs['pred_box']: List(Tensor) [B, M, 4]
-            outputs['strides']: List(Int) [8, 16, 32] output stride
-            targets: (List) [dict{'boxes': [...], 
-                                 'labels': [...], 
-                                 'orig_size': ...}, ...]
-        """
-        bs          = outputs['pred_cls'].shape[0]
-        device      = outputs['pred_cls'].device
-        anchors     = outputs['anchors']
-        stride      = outputs['stride']
-        # preds: [B, M, C]
-        cls_preds = outputs['pred_cls']
-        box_preds = outputs['pred_box']
-        
-        # --------------- label assignment ---------------
-        cls_targets = []
-        box_targets = []
-        assign_metrics = []
-        for batch_idx in range(bs):
-            tgt_labels = targets[batch_idx]["labels"].to(device)  # [N,]
-            tgt_bboxes = targets[batch_idx]["boxes"].to(device)   # [N, 4]
-            assigned_result = self.matcher(stride=stride,
-                                           anchors=anchors[..., :2],
-                                           pred_cls=cls_preds[batch_idx].detach(),
-                                           pred_box=box_preds[batch_idx].detach(),
-                                           gt_labels=tgt_labels,
-                                           gt_bboxes=tgt_bboxes
-                                           )
-            cls_targets.append(assigned_result['assigned_labels'])
-            box_targets.append(assigned_result['assigned_bboxes'])
-            assign_metrics.append(assigned_result['assign_metrics'])
-
-        # List[B, M, C] -> Tensor[BM, C]
-        cls_targets = torch.cat(cls_targets, dim=0)
-        box_targets = torch.cat(box_targets, dim=0)
-        assign_metrics = torch.cat(assign_metrics, dim=0)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        bg_class_ind = self.num_classes
-        pos_inds = ((cls_targets >= 0) & (cls_targets < bg_class_ind)).nonzero().squeeze(1)
-        num_fgs = assign_metrics.sum()
-
-        if is_dist_avail_and_initialized():
-            torch.distributed.all_reduce(num_fgs)
-        num_fgs = (num_fgs / get_world_size()).clamp(1.0).item()
-        bbox_weight = assign_metrics[pos_inds]
-
-        # ------------------ Classification loss ------------------
-        cls_preds = cls_preds.view(-1, self.num_classes)
-        loss_cls = self.loss_classes(cls_preds, (cls_targets, assign_metrics))
-        loss_cls = loss_cls.sum() / num_fgs
-
-        # ------------------ Regression loss ------------------
-        box_preds_pos = box_preds.view(-1, 4)[pos_inds]
-        box_targets_pos = box_targets[pos_inds]
-        loss_box = self.loss_bboxes(box_preds_pos, box_targets_pos, bbox_weight)
-        loss_box = loss_box.sum() / num_fgs
-
-        # total loss
-        losses = self.loss_cls_weight * loss_cls + \
-                 self.loss_box_weight * loss_box
-        loss_dict = dict(
-                loss_cls = loss_cls,
-                loss_box = loss_box,
-                losses = losses
-        )
-
-        return loss_dict
-    

yolo/models/yolof/matcher.py

@@ -1,160 +0,0 @@
-# ---------------------------------------------------------------------
-# Copyright (c) Megvii Inc. All rights reserved.
-# ---------------------------------------------------------------------
-
-import math
-import torch
-import torch.nn.functional as F
-
-from utils.box_ops import *
-
-
-class SimOtaMatcher(object):
-    def __init__(self, num_classes, soft_center_radius=3.0, topk_candidates=13):
-        self.num_classes = num_classes
-        self.soft_center_radius = soft_center_radius
-        self.topk_candidates = topk_candidates
-
-    @torch.no_grad()
-    def __call__(self, 
-                 stride, 
-                 anchors, 
-                 pred_cls, 
-                 pred_box,
-                 gt_labels,
-                 gt_bboxes):
-        # List[F, M, 2] -> [M, 2]
-        num_gt = len(gt_labels)
-
-        # check gt
-        if num_gt == 0 or gt_bboxes.max().item() == 0.:
-            return {
-                'assigned_labels': gt_labels.new_full(pred_cls[..., 0].shape,
-                                                      self.num_classes,
-                                                      dtype=torch.long),
-                'assigned_bboxes': gt_bboxes.new_full(pred_box.shape, 0),
-                'assign_metrics': gt_bboxes.new_full(pred_cls[..., 0].shape, 0)
-            }
-        
-        # get inside points: [N, M]
-        is_in_gt = self.find_inside_points(gt_bboxes, anchors)
-        valid_mask = is_in_gt.sum(dim=0) > 0  # [M,]
-
-        # ----------------------------------- soft center prior -----------------------------------
-        gt_center = (gt_bboxes[..., :2] + gt_bboxes[..., 2:]) / 2.0
-        distance = (anchors.unsqueeze(0) - gt_center.unsqueeze(1)
-                    ).pow(2).sum(-1).sqrt() / stride  # [N, M]
-        distance = distance * valid_mask.unsqueeze(0)
-        soft_center_prior = torch.pow(10, distance - self.soft_center_radius)
-
-        # ----------------------------------- regression cost -----------------------------------
-        pair_wise_ious, _ = box_iou(gt_bboxes, pred_box)  # [N, M]
-        pair_wise_ious_loss = -torch.log(pair_wise_ious + 1e-8) * 3.0
-
-        # ----------------------------------- classification cost -----------------------------------
-        ## select the predicted scores corresponded to the gt_labels
-        pairwise_pred_scores = pred_cls.permute(1, 0)  # [M, C] -> [C, M]
-        pairwise_pred_scores = pairwise_pred_scores[gt_labels.long(), :].float()   # [N, M]
-        ## scale factor
-        scale_factor = (pair_wise_ious - pairwise_pred_scores.sigmoid()).abs().pow(2.0)
-        ## cls cost
-        pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
-            pairwise_pred_scores, pair_wise_ious,
-            reduction="none") * scale_factor # [N, M]
-            
-        del pairwise_pred_scores
-
-        ## foreground cost matrix
-        cost_matrix = pair_wise_cls_loss + pair_wise_ious_loss + soft_center_prior
-        max_pad_value = torch.ones_like(cost_matrix) * 1e9
-        cost_matrix = torch.where(valid_mask[None].repeat(num_gt, 1),   # [N, M]
-                                  cost_matrix, max_pad_value)
-
-        # ----------------------------------- dynamic label assignment -----------------------------------
-        matched_pred_ious, matched_gt_inds, fg_mask_inboxes = self.dynamic_k_matching(
-            cost_matrix, pair_wise_ious, num_gt)
-        del pair_wise_cls_loss, cost_matrix, pair_wise_ious, pair_wise_ious_loss
-
-        # -----------------------------------process assigned labels -----------------------------------
-        assigned_labels = gt_labels.new_full(pred_cls[..., 0].shape,
-                                             self.num_classes)  # [M,]
-        assigned_labels[fg_mask_inboxes] = gt_labels[matched_gt_inds].squeeze(-1)
-        assigned_labels = assigned_labels.long()  # [M,]
-
-        assigned_bboxes = gt_bboxes.new_full(pred_box.shape, 0)        # [M, 4]
-        assigned_bboxes[fg_mask_inboxes] = gt_bboxes[matched_gt_inds]  # [M, 4]
-
-        assign_metrics = gt_bboxes.new_full(pred_cls[..., 0].shape, 0) # [M,]
-        assign_metrics[fg_mask_inboxes] = matched_pred_ious            # [M,]
-
-        assigned_dict = dict(
-            assigned_labels=assigned_labels,
-            assigned_bboxes=assigned_bboxes,
-            assign_metrics=assign_metrics
-            )
-        
-        return assigned_dict
-
-    def find_inside_points(self, gt_bboxes, anchors):
-        """
-            gt_bboxes: Tensor -> [N, 2]
-            anchors:   Tensor -> [M, 2]
-        """
-        num_anchors = anchors.shape[0]
-        num_gt = gt_bboxes.shape[0]
-
-        anchors_expand = anchors.unsqueeze(0).repeat(num_gt, 1, 1)           # [N, M, 2]
-        gt_bboxes_expand = gt_bboxes.unsqueeze(1).repeat(1, num_anchors, 1)  # [N, M, 4]
-
-        # offset
-        lt = anchors_expand - gt_bboxes_expand[..., :2]
-        rb = gt_bboxes_expand[..., 2:] - anchors_expand
-        bbox_deltas = torch.cat([lt, rb], dim=-1)
-
-        is_in_gts = bbox_deltas.min(dim=-1).values > 0
-
-        return is_in_gts
-    
-    def dynamic_k_matching(self, cost_matrix, pairwise_ious, num_gt):
-        """Use IoU and matching cost to calculate the dynamic top-k positive
-        targets.
-
-        Args:
-            cost_matrix (Tensor): Cost matrix.
-            pairwise_ious (Tensor): Pairwise iou matrix.
-            num_gt (int): Number of gt.
-            valid_mask (Tensor): Mask for valid bboxes.
-        Returns:
-            tuple: matched ious and gt indexes.
-        """
-        matching_matrix = torch.zeros_like(cost_matrix, dtype=torch.uint8)
-        # select candidate topk ious for dynamic-k calculation
-        # candidate_topk = min(self.topk_candidates, pairwise_ious.size(1))
-        candidate_topk = self.topk_candidates
-        topk_ious, _ = torch.topk(pairwise_ious, candidate_topk, dim=1)
-        # calculate dynamic k for each gt
-        dynamic_ks = torch.clamp(topk_ious.sum(1).int(), min=1)
-
-        # sorting the batch cost matirx is faster than topk
-        _, sorted_indices = torch.sort(cost_matrix, dim=1)
-        for gt_idx in range(num_gt):
-            topk_ids = sorted_indices[gt_idx, :dynamic_ks[gt_idx]]
-            matching_matrix[gt_idx, :][topk_ids] = 1
-
-        del topk_ious, dynamic_ks, topk_ids
-
-        prior_match_gt_mask = matching_matrix.sum(0) > 1
-        if prior_match_gt_mask.sum() > 0:
-            cost_min, cost_argmin = torch.min(
-                cost_matrix[:, prior_match_gt_mask], dim=0)
-            matching_matrix[:, prior_match_gt_mask] *= 0
-            matching_matrix[cost_argmin, prior_match_gt_mask] = 1
-
-        # get foreground mask inside box and center prior
-        fg_mask_inboxes = matching_matrix.sum(0) > 0
-        matched_pred_ious = (matching_matrix *
-                             pairwise_ious).sum(0)[fg_mask_inboxes]
-        matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-
-        return matched_pred_ious, matched_gt_inds, fg_mask_inboxes
-        

yolo/models/yolof/yolof.py

@@ -1,139 +0,0 @@
-# --------------- Torch components ---------------
-import torch
-import torch.nn as nn
-
-# --------------- Model components ---------------
-from .yolof_backbone  import YolofBackbone
-from .yolof_upsampler import YolofUpsampler
-from .yolof_encoder   import YolofEncoder
-from .yolof_decoder   import YolofDecoder
-
-# --------------- External components ---------------
-from utils.misc import multiclass_nms
-
-
-# Yolof
-class Yolof(nn.Module):
-    def __init__(self,
-                 cfg,
-                 is_val = False,
-                 ) -> None:
-        super(Yolof, 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 ----------------------
-        self.backbone  = YolofBackbone(cfg)
-        self.upsampler = YolofUpsampler(cfg, self.backbone.feat_dims, cfg.head_dim)
-        self.encoder   = YolofEncoder(cfg, cfg.head_dim, cfg.head_dim)
-        self.decoder   = YolofDecoder(cfg, self.encoder.out_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)
-
-        # ---------------- Encoder ----------------
-        x = self.upsampler(pyramid_feats)
-        x = self.encoder(x)
-
-        # ---------------- Decoder ----------------
-        outputs = self.decoder(x)
-        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/yolof/yolof_backbone.py

@@ -1,181 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .yolof_basic import BasicConv, ELANLayer
-except:
-    from  yolof_basic import BasicConv, ELANLayer
-
-# IN1K pretrained weight
-pretrained_urls = {
-    'n': "https://github.com/yjh0410/ICLab/releases/download/in1k_pretrained/elandarknet_n_in1k_62.1.pth",
-    's': "https://github.com/yjh0410/ICLab/releases/download/in1k_pretrained/elandarknet_s_in1k_71.3.pth",
-    'm': None,
-    'l': None,
-    'x': None,
-}
-
-# ---------------------------- Basic functions ----------------------------
-class YolofBackbone(nn.Module):
-    def __init__(self, cfg):
-        super(YolofBackbone, 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(512 * cfg.width * cfg.ratio)]
-        
-        # ------------------ Network setting ------------------
-        ## P1/2
-        self.layer_1 = BasicConv(3, self.feat_dims[0],
-                                 kernel_size=3, padding=1, 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),
-            ELANLayer(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),
-            ELANLayer(in_dim     = self.feat_dims[2],
-                      out_dim    = self.feat_dims[2],
-                      num_blocks = round(6*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),
-            ELANLayer(in_dim     = self.feat_dims[3],
-                      out_dim    = self.feat_dims[3],
-                      num_blocks = round(6*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),
-            ELANLayer(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()
-        
-        # Load imagenet pretrained weight
-        if cfg.use_pretrained:
-            self.load_pretrained()
-        
-    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 load_pretrained(self):
-        url = pretrained_urls[self.model_scale]
-        if url is not None:
-            print('Loading backbone pretrained weight from : {}'.format(url))
-            # checkpoint state dict
-            checkpoint = torch.hub.load_state_dict_from_url(
-                url=url, map_location="cpu", check_hash=True)
-            checkpoint_state_dict = checkpoint.pop("model")
-            # model state dict
-            model_state_dict = self.state_dict()
-            # check
-            for k in list(checkpoint_state_dict.keys()):
-                if k in model_state_dict:
-                    shape_model = tuple(model_state_dict[k].shape)
-                    shape_checkpoint = tuple(checkpoint_state_dict[k].shape)
-                    if shape_model != shape_checkpoint:
-                        checkpoint_state_dict.pop(k)
-                else:
-                    checkpoint_state_dict.pop(k)
-                    print('Unused key: ', k)
-            # load the weight
-            self.load_state_dict(checkpoint_state_dict)
-        else:
-            print('No pretrained weight for model scale: {}.'.format(self.model_scale))
-
-    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 = YolofBackbone(cfg)
-        
-    return backbone
-
-
-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.use_pretrained = True
-            self.width = 0.50
-            self.depth = 0.34
-            self.ratio = 2.0
-            self.scale = "s"
-
-    cfg = BaseConfig()
-    model = build_backbone(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))

yolo/models/yolof/yolof_basic.py

@@ -1,172 +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
-        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=1, 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)
-            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
-
-
-# --------------------- Yolov8 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 CSPLayer(nn.Module):
-    # CSP Bottleneck with 3 convolutions
-    def __init__(self,
-                 in_dim      :int,
-                 out_dim     :int,
-                 num_blocks  :int   = 1,
-                 kernel_size :List = [3, 3],
-                 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_1 = BasicConv(in_dim, inter_dim, kernel_size=1, act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-        self.input_proj_2 = BasicConv(in_dim, inter_dim, kernel_size=1, act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-        self.output_proj  = BasicConv(2 * inter_dim, out_dim, kernel_size=1, act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-        self.module       = nn.Sequential(*[YoloBottleneck(inter_dim,
-                                                           inter_dim,
-                                                           kernel_size,
-                                                           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.input_proj_1(x)
-        x2 = self.input_proj_2(x)
-        x2 = self.module(x2)
-        out = self.output_proj(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([YoloBottleneck(inter_dim,
-                                                         inter_dim,
-                                                         kernel_size = [3, 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):
-        # Input proj
-        x1, x2 = torch.chunk(self.input_proj(x), 2, dim=1)
-        out = list([x1, x2])
-
-        # Bottlenecl
-        out.extend(m(out[-1]) for m in self.module)
-
-        # Output proj
-        out = self.output_proj(torch.cat(out, dim=1))
-
-        return out

yolo/models/yolof/yolof_decoder.py

@@ -1,150 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-
-try:
-    from .yolof_basic import BasicConv
-except:
-    from  yolof_basic import BasicConv
-    
-
-class YolofDecoder(nn.Module):
-    def __init__(self, cfg, in_dim):
-        super().__init__()
-        # ------------------ Basic parameters -------------------
-        self.cfg = cfg
-        self.in_dim = in_dim
-        self.stride       = cfg.out_stride
-        self.num_classes  = cfg.num_classes
-        self.num_cls_head = cfg.num_cls_head
-        self.num_reg_head = cfg.num_reg_head
-        # Anchor config
-        self.anchor_size = torch.as_tensor(cfg.anchor_size)
-        self.num_anchors = len(cfg.anchor_size)
-
-        # ------------------ Network parameters -------------------
-        ## cls head
-        cls_heads = []
-        self.cls_head_dim = cfg.head_dim
-        for i in range(self.num_cls_head):
-            if i == 0:
-                cls_heads.append(
-                    BasicConv(in_dim, self.cls_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=cfg.head_act, norm_type=cfg.head_norm, depthwise=cfg.head_depthwise)
-                              )
-            else:
-                cls_heads.append(
-                    BasicConv(self.cls_head_dim, self.cls_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=cfg.head_act, norm_type=cfg.head_norm, depthwise=cfg.head_depthwise)
-                              )
-        ## reg head
-        reg_heads = []
-        self.reg_head_dim = cfg.head_dim
-        for i in range(self.num_reg_head):
-            if i == 0:
-                reg_heads.append(
-                    BasicConv(in_dim, self.reg_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=cfg.head_act, norm_type=cfg.head_norm, depthwise=cfg.head_depthwise)
-                              )
-            else:
-                reg_heads.append(
-                    BasicConv(self.reg_head_dim, self.reg_head_dim,
-                              kernel_size=3, padding=1, stride=1, 
-                              act_type=cfg.head_act, norm_type=cfg.head_norm, depthwise=cfg.head_depthwise)
-                              )
-        self.cls_heads = nn.Sequential(*cls_heads)
-        self.reg_heads = nn.Sequential(*reg_heads)
-
-        # pred layer
-        self.cls_pred = nn.Conv2d(self.cls_head_dim, self.num_classes * self.num_anchors, kernel_size=1)
-        self.reg_pred = nn.Conv2d(self.reg_head_dim, 4 * self.num_anchors, kernel_size=1)
-
-        self.init_weights()
-        
-    def init_weights(self):
-        # Init bias
-        init_prob = 0.01
-        bias_value = -torch.log(torch.tensor((1. - init_prob) / init_prob))
-        # 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)
-        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]
-        """
-        # 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)
-        anchor_xy = anchor_xy.view(-1, 2) + 0.5
-        anchor_xy *= self.stride
-
-        # [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 decode_boxes(self, anchors, reg_pred):
-        """
-            anchors:  (List[tensor]) [1, M, 4]
-            reg_pred: (List[tensor]) [B, M, 4]
-        """
-        cxcy_pred = anchors[..., :2] + reg_pred[..., :2] * self.stride
-        bwbh_pred = anchors[..., 2:] * torch.exp(reg_pred[..., 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)
-
-        return box_pred
-
-    def forward(self, x):
-        # ------------------- Decoupled head -------------------
-        cls_feats = self.cls_heads(x)
-        reg_feats = self.reg_heads(x)
-
-        # ------------------- Prediction -------------------
-        cls_pred = self.cls_pred(cls_feats)
-        reg_pred = self.reg_pred(reg_feats)
-
-        # ------------------- Generate anchor box -------------------
-        B, _, H, W = cls_pred.size()
-        anchors = self.generate_anchors([H, W])   # [M, 4]
-        anchors = anchors.to(cls_feats.device)
-
-        # ------------------- Precoess preds -------------------
-        # [B, C*A, H, W] -> [B, H, W, C*A] -> [B, H*W*A, 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, 4)
-
-        ## Decode bbox
-        box_pred = self.decode_boxes(anchors[None], reg_pred)  # [B, M, 4]
-
-        outputs = {"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]
-                   "stride":   self.stride,
-                   "anchors":  anchors,    # (torch.Tensor) [M, C]
-                   }
-
-        return outputs 

yolo/models/yolof/yolof_encoder.py

@@ -1,95 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .yolof_basic import BasicConv
-except:
-    from  yolof_basic import BasicConv
-
-
-# BottleNeck
-class Bottleneck(nn.Module):
-    def __init__(self,
-                 in_dim       :int,
-                 out_dim      :int,
-                 dilation     :int,
-                 expand_ratio :float = 0.5,
-                 shortcut     :bool  = False,
-                 act_type     :str   = 'relu',
-                 norm_type    :str   = 'BN',
-                 depthwise    :bool  = False,):
-        super(Bottleneck, self).__init__()
-        # ------------------ Basic parameters -------------------
-        self.in_dim = in_dim
-        self.out_dim = out_dim
-        self.dilation = dilation
-        self.expand_ratio = expand_ratio
-        self.shortcut = shortcut and in_dim == out_dim
-        inter_dim = round(in_dim * expand_ratio)
-        # ------------------ Network parameters -------------------
-        self.branch = nn.Sequential(
-            BasicConv(in_dim, inter_dim,
-                      kernel_size=1, padding=0, stride=1,
-                      act_type=act_type, norm_type=norm_type),
-            BasicConv(inter_dim, inter_dim,
-                      kernel_size=3, padding=dilation, dilation=dilation, stride=1,
-                      act_type=act_type, norm_type=norm_type, depthwise=depthwise),
-            BasicConv(inter_dim, in_dim,
-                      kernel_size=1, padding=0, stride=1,
-                      act_type=act_type, norm_type=norm_type)
-        )
-
-    def forward(self, x):
-        h = self.branch(x)
-
-        return x + self.branch(x) if self.shortcut else h
-
-# ELAN-style Dilated Encoder
-class YolofEncoder(nn.Module):
-    def __init__(self, cfg, in_dim, out_dim):
-        super(YolofEncoder, self).__init__()
-        # ------------------ Basic parameters -------------------
-        self.in_dim = in_dim
-        self.out_dim = out_dim
-        self.expand_ratio = cfg.neck_expand_ratio
-        self.dilations    = cfg.neck_dilations
-        # ------------------ Network parameters -------------------
-        ## input layer
-        self.input_proj = BasicConv(in_dim, out_dim, kernel_size=1, act_type=cfg.neck_act, norm_type=cfg.neck_norm)
-        ## dilated layers
-        self.module = nn.ModuleList([Bottleneck(in_dim       = out_dim,
-                                                out_dim      = out_dim,
-                                                dilation     = dilation,
-                                                expand_ratio = self.expand_ratio,
-                                                shortcut     = True,
-                                                act_type     = cfg.neck_act,
-                                                norm_type    = cfg.neck_norm,
-                                                depthwise    = cfg.neck_depthwise,
-                                                ) for dilation in self.dilations])
-        ## output layer
-        self.output_proj = BasicConv(out_dim * (len(self.dilations) + 1), out_dim,
-                                     kernel_size=1, padding=0, stride=1,
-                                     act_type=cfg.neck_act, norm_type=cfg.neck_norm)
-
-        # 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):
-        x = self.input_proj(x)
-
-        out = [x]
-        for m in self.module:
-            x = m(x)
-            out.append(x)
-
-        out = self.output_proj(torch.cat(out, dim=1))
-
-        return out

yolo/models/yolof/yolof_upsampler.py

@@ -1,30 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .yolof_basic import BasicConv
-except:
-    from  yolof_basic import BasicConv
-
-
-class YolofUpsampler(nn.Module):
-    def __init__(self, cfg, in_dims, out_dim):
-        super(YolofUpsampler, self).__init__()
-        # ----------- Model parameters -----------
-        self.input_proj_1 = BasicConv(in_dims[-1], out_dim, kernel_size=1, act_type=cfg.neck_act, norm_type=cfg.neck_norm)
-        self.input_proj_2 = BasicConv(in_dims[-2], out_dim, kernel_size=1, act_type=cfg.neck_act, norm_type=cfg.neck_norm)
-        self.output_proj  = nn.Sequential(
-            BasicConv(out_dim * 2, out_dim, kernel_size=1, act_type=cfg.neck_act, norm_type=cfg.neck_norm),
-            BasicConv(out_dim, out_dim, kernel_size=3, padding=1, stride=1, act_type=cfg.neck_act, norm_type=cfg.neck_norm),
-        )
-
-    def forward(self, pyramid_feats):
-        x1 = self.input_proj_1(pyramid_feats[-1])
-        x2 = self.input_proj_2(pyramid_feats[-2])
-        
-        x1_up = nn.functional.interpolate(x1, scale_factor=2.0)
-
-        x3 = torch.cat([x2, x1_up], dim=1)
-        out = self.output_proj(x3)
-        
-        return out

yolo/models/yolov1/yolov1_basic.py

@@ -63,7 +63,7 @@ class BasicConv(nn.Module):
             return self.act(self.norm(self.conv(x)))
         else:
             # Depthwise conv
-            x = self.act(self.norm1(self.conv1(x)))
+            x = self.norm1(self.conv1(x))
             # Pointwise conv
             x = self.act(self.norm2(self.conv2(x)))
             return x

yolo/models/yolov2/yolov2_basic.py

@@ -63,7 +63,7 @@ class BasicConv(nn.Module):
             return self.act(self.norm(self.conv(x)))
         else:
             # Depthwise conv
-            x = self.act(self.norm1(self.conv1(x)))
+            x = self.norm1(self.conv1(x))
             # Pointwise conv
             x = self.act(self.norm2(self.conv2(x)))
             return x

yolo/models/yolov3/yolov3_basic.py

@@ -63,7 +63,7 @@ class BasicConv(nn.Module):
             return self.act(self.norm(self.conv(x)))
         else:
             # Depthwise conv
-            x = self.act(self.norm1(self.conv1(x)))
+            x = self.norm1(self.conv1(x))
             # Pointwise conv
             x = self.act(self.norm2(self.conv2(x)))
             return x

yolo/models/yolov5/yolov5_basic.py

@@ -63,7 +63,7 @@ class BasicConv(nn.Module):
             return self.act(self.norm(self.conv(x)))
         else:
             # Depthwise conv
-            x = self.act(self.norm1(self.conv1(x)))
+            x = self.norm1(self.conv1(x))
             # Pointwise conv
             x = self.act(self.norm2(self.conv2(x)))
             return x

yolo/models/yolov5_af/yolov5_af_basic.py

@@ -63,7 +63,7 @@ class BasicConv(nn.Module):
             return self.act(self.norm(self.conv(x)))
         else:
             # Depthwise conv
-            x = self.act(self.norm1(self.conv1(x)))
+            x = self.norm1(self.conv1(x))
             # Pointwise conv
             x = self.act(self.norm2(self.conv2(x)))
             return x

yolo/models/yolov7_af/yolov7_af_basic.py

@@ -63,7 +63,7 @@ class BasicConv(nn.Module):
             return self.act(self.norm(self.conv(x)))
         else:
             # Depthwise conv
-            x = self.act(self.norm1(self.conv1(x)))
+            x = self.norm1(self.conv1(x))
             # Pointwise conv
             x = self.act(self.norm2(self.conv2(x)))
             return x

yolo/models/yolov8/yolov8_basic.py

@@ -63,7 +63,7 @@ class BasicConv(nn.Module):
             return self.act(self.norm(self.conv(x)))
         else:
             # Depthwise conv
-            x = self.act(self.norm1(self.conv1(x)))
+            x = self.norm1(self.conv1(x))
             # Pointwise conv
             x = self.act(self.norm2(self.conv2(x)))
             return x

yolo/train.py

@@ -196,7 +196,7 @@ def train():
         trainer.eval(model_eval)
         return
 
-    garbage = torch.randn(640, 1024, 73, 73).to(device) # 15 G
+    # garbage = torch.randn(640, 1024, 73, 73).to(device) # 15 G
 
     # ---------------------------- Train pipeline ----------------------------
     trainer.train(model)