add yolov7-af

config/__init__.py

@@ -4,6 +4,7 @@ from .yolov2_config   import build_yolov2_config
 from .yolov3_config   import build_yolov3_config
 from .yolov5_config   import build_yolov5_config
 from .yolox_config    import build_yolox_config
+from .yolov7_config   import build_yolov7_config
 from .yolov8_config   import build_yolov8_config
 from .rtdetr_config   import build_rtdetr_config
 
@@ -21,6 +22,8 @@ def build_config(args):
         cfg = build_yolov5_config(args)
     elif 'yolox' in args.model:
         cfg = build_yolox_config(args)
+    elif 'yolov7' in args.model:
+        cfg = build_yolov7_config(args)
     elif 'yolov8' in args.model:
         cfg = build_yolov8_config(args)
     # RT-DETR

config/yolov3_config.py

@@ -100,7 +100,7 @@ class Yolov3BaseConfig(object):
         self.use_ablu = True
         self.affine_params = {
             'degrees': 0.0,
-            'translate': 0.1,
+            'translate': 0.2,
             'scale': [0.1, 2.0],
             'shear': 0.0,
             'perspective': 0.0,

config/yolov5_config.py

@@ -100,7 +100,7 @@ class Yolov5BaseConfig(object):
         self.use_ablu = True
         self.affine_params = {
             'degrees': 0.0,
-            'translate': 0.1,
+            'translate': 0.2,
             'scale': [0.1, 2.0],
             'shear': 0.0,
             'perspective': 0.0,

config/yolov7_config.py

@@ -0,0 +1,130 @@
+# yolo Config
+
+
+def build_yolov7_config(args):
+    if args.model == 'yolov7_s':
+        return Yolov7SConfig()
+    else:
+        raise NotImplementedError("No config for model: {}".format(args.model))
+    
+# YOLOv7-Base config
+class Yolov7BaseConfig(object):
+    def __init__(self) -> None:
+        # ---------------- Model config ----------------
+        self.width    = 1.0
+        self.depth    = 1.0
+        self.reg_max  = 16
+        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
+        self.use_pretrained = 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
+
+        # ---------------- 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.tal_topk_candidates = 13
+        self.tal_alpha = 1.0
+        self.tal_beta  = 6.0
+        ## Loss weight
+        self.loss_cls = 1.0
+        self.loss_box = 2.5
+        self.loss_dfl = 0.5
+
+        # ---------------- 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))
+
+# YOLOv7-S
+class Yolov7SConfig(Yolov7BaseConfig):
+    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

config/yolox_config.py

@@ -86,8 +86,8 @@ class YoloxBaseConfig(object):
         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.mixup_prob  = 0.0
+        self.copy_paste  = 1.0           # 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.]
@@ -98,7 +98,7 @@ class YoloxBaseConfig(object):
         self.use_ablu = True
         self.affine_params = {
             'degrees': 0.0,
-            'translate': 0.1,
+            'translate': 0.2,
             'scale': [0.1, 2.0],
             'shear': 0.0,
             'perspective': 0.0,
@@ -124,4 +124,4 @@ class YoloxSConfig(YoloxBaseConfig):
         # ---------------- Data process config ----------------
         self.mosaic_prob = 1.0
         self.mixup_prob  = 0.0
-        self.copy_paste  = 0.0
+        self.copy_paste  = 1.0

evaluator/voc_evaluator.py

@@ -37,7 +37,7 @@ class VOCAPIEvaluator():
         self.transform = transform
 
         # path
-        time_stamp = time.strftime('%Y-%m-%d_%H:%M:%S',time.localtime(time.time()))
+        time_stamp = time.strftime('%Y-%m-%d_%H-%M-%S',time.localtime(time.time()))
         self.devkit_path = os.path.join(data_dir, 'VOC' + year)
         self.annopath = os.path.join(data_dir, 'VOC2007', 'Annotations', '%s.xml')
         self.imgpath = os.path.join(data_dir, 'VOC2007', 'JPEGImages', '%s.jpg')

models/__init__.py

@@ -7,6 +7,7 @@ from .yolov2.build import build_yolov2
 from .yolov3.build import build_yolov3
 from .yolov5.build import build_yolov5
 from .yolox.build  import build_yolox
+from .yolov7.build import build_yolov7
 from .yolov8.build import build_yolov8
 from .rtdetr.build import build_rtdetr
 
@@ -28,6 +29,9 @@ def build_model(args, cfg, is_val=False):
     ## YOLOX
     elif 'yolox' in args.model:
         model, criterion = build_yolox(cfg, is_val)
+    ## YOLOv7
+    elif 'yolov7' in args.model:
+        model, criterion = build_yolov7(cfg, is_val)
     ## YOLOv8
     elif 'yolov8' in args.model:
         model, criterion = build_yolov8(cfg, is_val)

models/yolov5/yolov5_pafpn.py

@@ -8,8 +8,7 @@ from .yolov5_basic import BasicConv, CSPBlock
 
 # Yolov5FPN
 class Yolov5PaFPN(nn.Module):
-    def __init__(self, cfg, in_dims: List = [256, 512, 1024],
-                 ):
+    def __init__(self, cfg, in_dims: List = [256, 512, 1024]):
         super(Yolov5PaFPN, self).__init__()
         self.in_dims = in_dims
         c3, c4, c5 = in_dims

models/yolov7/build.py

@@ -0,0 +1,24 @@
+import torch.nn as nn
+
+from .loss import SetCriterion
+from .yolov7 import Yolov7
+
+
+# build object detector
+def build_yolov7(cfg, is_val=False):
+    # -------------- Build YOLO --------------
+    model = Yolov7(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/yolov7/loss.py

@@ -0,0 +1,187 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from utils.box_ops import bbox2dist, bbox_iou
+from utils.distributed_utils import get_world_size, is_dist_avail_and_initialized
+
+from .matcher import TaskAlignedAssigner
+
+
+class SetCriterion(object):
+    def __init__(self, cfg):
+        # --------------- Basic parameters ---------------
+        self.cfg = cfg
+        self.reg_max = cfg.reg_max
+        self.num_classes = cfg.num_classes
+        # --------------- Loss config ---------------
+        self.loss_cls_weight = cfg.loss_cls
+        self.loss_box_weight = cfg.loss_box
+        self.loss_dfl_weight = cfg.loss_dfl
+        # --------------- Matcher config ---------------
+        self.matcher = TaskAlignedAssigner(num_classes     = cfg.num_classes,
+                                           topk_candidates = cfg.tal_topk_candidates,
+                                           alpha           = cfg.tal_alpha,
+                                           beta            = cfg.tal_beta
+                                           )
+
+    def loss_classes(self, pred_cls, gt_score):
+        # compute bce loss
+        loss_cls = F.binary_cross_entropy_with_logits(pred_cls, gt_score, reduction='none')
+
+        return loss_cls
+    
+    def loss_bboxes(self, pred_box, gt_box, bbox_weight):
+        # regression loss
+        ious = bbox_iou(pred_box, gt_box, xywh=False, CIoU=True)
+        loss_box = (1.0 - ious.squeeze(-1)) * bbox_weight
+
+        return loss_box
+    
+    def loss_dfl(self, pred_reg, gt_box, anchor, stride, bbox_weight=None):
+        # rescale coords by stride
+        gt_box_s = gt_box / stride
+        anchor_s = anchor / stride
+
+        # compute deltas
+        gt_ltrb_s = bbox2dist(anchor_s, gt_box_s, self.reg_max - 1)
+
+        gt_left = gt_ltrb_s.to(torch.long)
+        gt_right = gt_left + 1
+
+        weight_left = gt_right.to(torch.float) - gt_ltrb_s
+        weight_right = 1 - weight_left
+
+        # loss left
+        loss_left = F.cross_entropy(
+            pred_reg.view(-1, self.reg_max),
+            gt_left.view(-1),
+            reduction='none').view(gt_left.shape) * weight_left
+        # loss right
+        loss_right = F.cross_entropy(
+            pred_reg.view(-1, self.reg_max),
+            gt_right.view(-1),
+            reduction='none').view(gt_left.shape) * weight_right
+
+        loss_dfl = (loss_left + loss_right).mean(-1)
+        
+        if bbox_weight is not None:
+            loss_dfl *= bbox_weight
+
+        return loss_dfl
+
+    def __call__(self, outputs, targets):        
+        """
+            outputs['pred_cls']: List(Tensor) [B, M, C]
+            outputs['pred_reg']: List(Tensor) [B, M, 4*(reg_max+1)]
+            outputs['pred_box']: List(Tensor) [B, M, 4]
+            outputs['anchors']: List(Tensor) [M, 2]
+            outputs['strides']: List(Int) [8, 16, 32] output stride
+            outputs['stride_tensor']: List(Tensor) [M, 1]
+            targets: (List) [dict{'boxes': [...], 
+                                 'labels': [...], 
+                                 'orig_size': ...}, ...]
+        """
+        # preds: [B, M, C]
+        cls_preds = torch.cat(outputs['pred_cls'], dim=1)
+        reg_preds = torch.cat(outputs['pred_reg'], dim=1)
+        box_preds = torch.cat(outputs['pred_box'], dim=1)
+        bs, num_anchors = cls_preds.shape[:2]
+        device = cls_preds.device
+        anchors = torch.cat(outputs['anchors'], dim=0)
+        
+        # --------------- label assignment ---------------
+        gt_score_targets = []
+        gt_bbox_targets = []
+        fg_masks = []
+        for batch_idx in range(bs):
+            tgt_labels = targets[batch_idx]["labels"].to(device)     # [Mp,]
+            tgt_boxs = targets[batch_idx]["boxes"].to(device)        # [Mp, 4]
+
+            if self.cfg.normalize_coords:
+                img_h, img_w = outputs['image_size']
+                tgt_boxs[..., [0, 2]] *= img_w
+                tgt_boxs[..., [1, 3]] *= img_h
+            
+            if self.cfg.box_format == 'xywh':
+                tgt_boxs_x1y1 = tgt_boxs[..., :2] - 0.5 * tgt_boxs[..., 2:]
+                tgt_boxs_x2y2 = tgt_boxs[..., :2] + 0.5 * tgt_boxs[..., 2:]
+                tgt_boxs = torch.cat([tgt_boxs_x1y1, tgt_boxs_x2y2], dim=-1)
+
+            # check target
+            if len(tgt_labels) == 0 or tgt_boxs.max().item() == 0.:
+                # There is no valid gt
+                fg_mask  = cls_preds.new_zeros(1, num_anchors).bool()               #[1, M,]
+                gt_score = cls_preds.new_zeros((1, num_anchors, self.num_classes)) #[1, M, C]
+                gt_box   = cls_preds.new_zeros((1, num_anchors, 4))                  #[1, M, 4]
+            else:
+                tgt_labels = tgt_labels[None, :, None]      # [1, Mp, 1]
+                tgt_boxs = tgt_boxs[None]                   # [1, Mp, 4]
+                (
+                    _,
+                    gt_box,     # [1, M, 4]
+                    gt_score,   # [1, M, C]
+                    fg_mask,    # [1, M,]
+                    _
+                ) = self.matcher(
+                    pd_scores = cls_preds[batch_idx:batch_idx+1].detach().sigmoid(), 
+                    pd_bboxes = box_preds[batch_idx:batch_idx+1].detach(),
+                    anc_points = anchors,
+                    gt_labels = tgt_labels,
+                    gt_bboxes = tgt_boxs
+                    )
+            gt_score_targets.append(gt_score)
+            gt_bbox_targets.append(gt_box)
+            fg_masks.append(fg_mask)
+
+        # List[B, 1, M, C] -> Tensor[B, M, C] -> Tensor[BM, C]
+        fg_masks = torch.cat(fg_masks, 0).view(-1)                                    # [BM,]
+        gt_score_targets = torch.cat(gt_score_targets, 0).view(-1, self.num_classes)  # [BM, C]
+        gt_bbox_targets = torch.cat(gt_bbox_targets, 0).view(-1, 4)                   # [BM, 4]
+        num_fgs = gt_score_targets.sum()
+        
+        # Average loss normalizer across all the GPUs
+        if is_dist_avail_and_initialized():
+            torch.distributed.all_reduce(num_fgs)
+        num_fgs = (num_fgs / get_world_size()).clamp(1.0)
+
+        # ------------------ Classification loss ------------------
+        cls_preds = cls_preds.view(-1, self.num_classes)
+        loss_cls = self.loss_classes(cls_preds, gt_score_targets)
+        loss_cls = loss_cls.sum() / num_fgs
+
+        # ------------------ Regression loss ------------------
+        box_preds_pos = box_preds.view(-1, 4)[fg_masks]
+        box_targets_pos = gt_bbox_targets.view(-1, 4)[fg_masks]
+        bbox_weight = gt_score_targets[fg_masks].sum(-1)
+        loss_box = self.loss_bboxes(box_preds_pos, box_targets_pos, bbox_weight)
+        loss_box = loss_box.sum() / num_fgs
+
+        # ------------------ Distribution focal loss  ------------------
+        ## process anchors
+        anchors = anchors[None].repeat(bs, 1, 1).view(-1, 2)
+        ## process stride tensors
+        strides = torch.cat(outputs['stride_tensor'], dim=0)
+        strides = strides.unsqueeze(0).repeat(bs, 1, 1).view(-1, 1)
+        ## fg preds
+        reg_preds_pos = reg_preds.view(-1, 4*self.reg_max)[fg_masks]
+        anchors_pos = anchors[fg_masks]
+        strides_pos = strides[fg_masks]
+        ## compute dfl
+        loss_dfl = self.loss_dfl(reg_preds_pos, box_targets_pos, anchors_pos, strides_pos, bbox_weight)
+        loss_dfl = loss_dfl.sum() / num_fgs
+
+        # total loss
+        losses = loss_cls * self.loss_cls_weight + loss_box * self.loss_box_weight + loss_dfl * self.loss_dfl_weight
+        loss_dict = dict(
+                loss_cls = loss_cls,
+                loss_box = loss_box,
+                loss_dfl = loss_dfl,
+                losses = losses
+        )
+
+        return loss_dict
+    
+
+if __name__ == "__main__":
+    pass

models/yolov7/matcher.py

@@ -0,0 +1,199 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from utils.box_ops import bbox_iou
+
+
+# -------------------------- Task Aligned Assigner --------------------------
+class TaskAlignedAssigner(nn.Module):
+    def __init__(self,
+                 num_classes     = 80,
+                 topk_candidates = 10,
+                 alpha           = 0.5,
+                 beta            = 6.0, 
+                 eps             = 1e-9):
+        super(TaskAlignedAssigner, self).__init__()
+        self.topk_candidates = topk_candidates
+        self.num_classes = num_classes
+        self.bg_idx = num_classes
+        self.alpha = alpha
+        self.beta = beta
+        self.eps = eps
+
+    @torch.no_grad()
+    def forward(self,
+                pd_scores,
+                pd_bboxes,
+                anc_points,
+                gt_labels,
+                gt_bboxes):
+        self.bs = pd_scores.size(0)
+        self.n_max_boxes = gt_bboxes.size(1)
+
+        mask_pos, align_metric, overlaps = self.get_pos_mask(
+            pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points)
+
+        target_gt_idx, fg_mask, mask_pos = select_highest_overlaps(
+            mask_pos, overlaps, self.n_max_boxes)
+
+        # Assigned target
+        target_labels, target_bboxes, target_scores = self.get_targets(
+            gt_labels, gt_bboxes, target_gt_idx, fg_mask)
+
+        # normalize
+        align_metric *= mask_pos
+        pos_align_metrics = align_metric.amax(axis=-1, keepdim=True)  # b, max_num_obj
+        pos_overlaps = (overlaps * mask_pos).amax(axis=-1, keepdim=True)  # b, max_num_obj
+        norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1)
+        target_scores = target_scores * norm_align_metric
+
+        return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx
+
+    def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points):
+        # get in_gts mask, (b, max_num_obj, h*w)
+        mask_in_gts = select_candidates_in_gts(anc_points, gt_bboxes)
+        # get anchor_align metric, (b, max_num_obj, h*w)
+        align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts)
+        # get topk_metric mask, (b, max_num_obj, h*w)
+        mask_topk = self.select_topk_candidates(align_metric)
+        # merge all mask to a final mask, (b, max_num_obj, h*w)
+        mask_pos = mask_topk * mask_in_gts
+
+        return mask_pos, align_metric, overlaps
+
+    def get_box_metrics(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts):
+        """Compute alignment metric given predicted and ground truth bounding boxes."""
+        na = pd_bboxes.shape[-2]
+        mask_in_gts = mask_in_gts.bool()  # b, max_num_obj, h*w
+        overlaps = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_bboxes.dtype, device=pd_bboxes.device)
+        bbox_scores = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_scores.dtype, device=pd_scores.device)
+
+        ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long)  # 2, b, max_num_obj
+        ind[0] = torch.arange(end=self.bs).view(-1, 1).expand(-1, self.n_max_boxes)  # b, max_num_obj
+        ind[1] = gt_labels.squeeze(-1)  # b, max_num_obj
+        # Get the scores of each grid for each gt cls
+        bbox_scores[mask_in_gts] = pd_scores[ind[0], :, ind[1]][mask_in_gts]  # b, max_num_obj, h*w
+
+        # (b, max_num_obj, 1, 4), (b, 1, h*w, 4)
+        pd_boxes = pd_bboxes.unsqueeze(1).expand(-1, self.n_max_boxes, -1, -1)[mask_in_gts]
+        gt_boxes = gt_bboxes.unsqueeze(2).expand(-1, -1, na, -1)[mask_in_gts]
+        overlaps[mask_in_gts] = bbox_iou(gt_boxes, pd_boxes, xywh=False, CIoU=True).squeeze(-1).clamp_(0)
+
+        align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
+        return align_metric, overlaps
+
+    def select_topk_candidates(self, metrics, largest=True):
+        """
+        Args:
+            metrics: (b, max_num_obj, h*w).
+            topk_mask: (b, max_num_obj, topk) or None
+        """
+        # (b, max_num_obj, topk)
+        topk_metrics, topk_idxs = torch.topk(metrics, self.topk_candidates, dim=-1, largest=largest)
+        topk_mask = (topk_metrics.max(-1, keepdim=True)[0] > self.eps).expand_as(topk_idxs)
+        # (b, max_num_obj, topk)
+        topk_idxs.masked_fill_(~topk_mask, 0)
+
+        # (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w)
+        count_tensor = torch.zeros(metrics.shape, dtype=torch.int8, device=topk_idxs.device)
+        ones = torch.ones_like(topk_idxs[:, :, :1], dtype=torch.int8, device=topk_idxs.device)
+        for k in range(self.topk_candidates):
+            # Expand topk_idxs for each value of k and add 1 at the specified positions
+            count_tensor.scatter_add_(-1, topk_idxs[:, :, k:k + 1], ones)
+        # count_tensor.scatter_add_(-1, topk_idxs, torch.ones_like(topk_idxs, dtype=torch.int8, device=topk_idxs.device))
+        # Filter invalid bboxes
+        count_tensor.masked_fill_(count_tensor > 1, 0)
+
+        return count_tensor.to(metrics.dtype)
+
+    def get_targets(self, gt_labels, gt_bboxes, target_gt_idx, fg_mask):
+        # Assigned target labels, (b, 1)
+        batch_ind = torch.arange(end=self.bs, dtype=torch.int64, device=gt_labels.device)[..., None]
+        target_gt_idx = target_gt_idx + batch_ind * self.n_max_boxes  # (b, h*w)
+        target_labels = gt_labels.long().flatten()[target_gt_idx]  # (b, h*w)
+
+        # Assigned target boxes, (b, max_num_obj, 4) -> (b, h*w, 4)
+        target_bboxes = gt_bboxes.view(-1, 4)[target_gt_idx]
+
+        # Assigned target scores
+        target_labels.clamp_(0)
+
+        # 10x faster than F.one_hot()
+        target_scores = torch.zeros((target_labels.shape[0], target_labels.shape[1], self.num_classes),
+                                    dtype=torch.int64,
+                                    device=target_labels.device)  # (b, h*w, 80)
+        target_scores.scatter_(2, target_labels.unsqueeze(-1), 1)
+
+        fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.num_classes)  # (b, h*w, 80)
+        target_scores = torch.where(fg_scores_mask > 0, target_scores, 0)
+
+        return target_labels, target_bboxes, target_scores
+    
+
+# -------------------------- Basic Functions --------------------------
+def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
+    """select the positive anchors's center in gt
+    Args:
+        xy_centers (Tensor): shape(bs*n_max_boxes, num_total_anchors, 4)
+        gt_bboxes (Tensor): shape(bs, n_max_boxes, 4)
+    Return:
+        (Tensor): shape(bs, n_max_boxes, num_total_anchors)
+    """
+    n_anchors = xy_centers.size(0)
+    bs, n_max_boxes, _ = gt_bboxes.size()
+    _gt_bboxes = gt_bboxes.reshape([-1, 4])
+    xy_centers = xy_centers.unsqueeze(0).repeat(bs * n_max_boxes, 1, 1)
+    gt_bboxes_lt = _gt_bboxes[:, 0:2].unsqueeze(1).repeat(1, n_anchors, 1)
+    gt_bboxes_rb = _gt_bboxes[:, 2:4].unsqueeze(1).repeat(1, n_anchors, 1)
+    b_lt = xy_centers - gt_bboxes_lt
+    b_rb = gt_bboxes_rb - xy_centers
+    bbox_deltas = torch.cat([b_lt, b_rb], dim=-1)
+    bbox_deltas = bbox_deltas.reshape([bs, n_max_boxes, n_anchors, -1])
+    return (bbox_deltas.min(axis=-1)[0] > eps).to(gt_bboxes.dtype)
+
+def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
+    """if an anchor box is assigned to multiple gts,
+        the one with the highest iou will be selected.
+    Args:
+        mask_pos (Tensor): shape(bs, n_max_boxes, num_total_anchors)
+        overlaps (Tensor): shape(bs, n_max_boxes, num_total_anchors)
+    Return:
+        target_gt_idx (Tensor): shape(bs, num_total_anchors)
+        fg_mask (Tensor): shape(bs, num_total_anchors)
+        mask_pos (Tensor): shape(bs, n_max_boxes, num_total_anchors)
+    """
+    fg_mask = mask_pos.sum(-2)
+    if fg_mask.max() > 1:  # one anchor is assigned to multiple gt_bboxes
+        mask_multi_gts = (fg_mask.unsqueeze(1) > 1).expand(-1, n_max_boxes, -1)  # (b, n_max_boxes, h*w)
+        max_overlaps_idx = overlaps.argmax(1)  # (b, h*w)
+
+        is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device)
+        is_max_overlaps.scatter_(1, max_overlaps_idx.unsqueeze(1), 1)
+
+        mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos).float()  # (b, n_max_boxes, h*w)
+        fg_mask = mask_pos.sum(-2)
+    # Find each grid serve which gt(index)
+    target_gt_idx = mask_pos.argmax(-2)  # (b, h*w)
+
+    return target_gt_idx, fg_mask, mask_pos
+
+def iou_calculator(box1, box2, eps=1e-9):
+    """Calculate iou for batch
+    Args:
+        box1 (Tensor): shape(bs, n_max_boxes, 1, 4)
+        box2 (Tensor): shape(bs, 1, num_total_anchors, 4)
+    Return:
+        (Tensor): shape(bs, n_max_boxes, num_total_anchors)
+    """
+    box1 = box1.unsqueeze(2)  # [N, M1, 4] -> [N, M1, 1, 4]
+    box2 = box2.unsqueeze(1)  # [N, M2, 4] -> [N, 1, M2, 4]
+    px1y1, px2y2 = box1[:, :, :, 0:2], box1[:, :, :, 2:4]
+    gx1y1, gx2y2 = box2[:, :, :, 0:2], box2[:, :, :, 2:4]
+    x1y1 = torch.maximum(px1y1, gx1y1)
+    x2y2 = torch.minimum(px2y2, gx2y2)
+    overlap = (x2y2 - x1y1).clip(0).prod(-1)
+    area1 = (px2y2 - px1y1).clip(0).prod(-1)
+    area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
+    union = area1 + area2 - overlap + eps
+
+    return overlap / union

models/yolov7/yolov7.py

@@ -0,0 +1,152 @@
+# --------------- Torch components ---------------
+import torch
+import torch.nn as nn
+
+# --------------- Model components ---------------
+from .yolov7_backbone import Yolov7Backbone
+from .yolov7_neck     import SPPFBlockCSP
+from .yolov7_pafpn    import Yolov7PaFPN
+from .yolov7_head     import Yolov7DetHead
+from .yolov7_pred     import Yolov7DetPredLayer
+
+# --------------- External components ---------------
+from utils.misc import multiclass_nms
+
+
+# YOLOv7
+class Yolov7(nn.Module):
+    def __init__(self,
+                 cfg,
+                 is_val = False,
+                 ) -> None:
+        super(Yolov7, 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 = Yolov7Backbone(cfg)
+        self.pyramid_feat_dims = self.backbone.feat_dims[-3:]
+        ## Neck
+        self.neck     = SPPFBlockCSP(cfg, self.pyramid_feat_dims[-1], self.pyramid_feat_dims[-1]//2)
+        self.pyramid_feat_dims[-1] = self.neck.out_dim
+        ## Neck: PaFPN
+        self.fpn      = Yolov7PaFPN(cfg, self.pyramid_feat_dims)
+        ## Head
+        self.head     = Yolov7DetHead(cfg, self.fpn.out_dims)
+        ## Pred
+        self.pred     = Yolov7DetPredLayer(cfg, self.head.cls_head_dim, self.head.reg_head_dim)
+
+    def post_process(self, cls_preds, box_preds):
+        """
+        We process predictions at each scale hierarchically
+        Input:
+            cls_preds: List[torch.Tensor] -> [[B, M, C], ...], B=1
+            box_preds: List[torch.Tensor] -> [[B, M, 4], ...], B=1
+        Output:
+            bboxes: np.array -> [N, 4]
+            scores: np.array -> [N,]
+            labels: np.array -> [N,]
+        """
+        all_scores = []
+        all_labels = []
+        all_bboxes = []
+        
+        for cls_pred_i, box_pred_i in zip(cls_preds, box_preds):
+            cls_pred_i = cls_pred_i[0]
+            box_pred_i = box_pred_i[0]
+            if self.no_multi_labels:
+                # [M,]
+                scores, labels = torch.max(cls_pred_i.sigmoid(), dim=1)
+
+                # Keep top k top scoring indices only.
+                num_topk = min(self.topk_candidates, box_pred_i.size(0))
+
+                # topk candidates
+                predicted_prob, topk_idxs = scores.sort(descending=True)
+                topk_scores = predicted_prob[:num_topk]
+                topk_idxs = topk_idxs[:num_topk]
+
+                # filter out the proposals with low confidence score
+                keep_idxs = topk_scores > self.conf_thresh
+                scores = topk_scores[keep_idxs]
+                topk_idxs = topk_idxs[keep_idxs]
+
+                labels = labels[topk_idxs]
+                bboxes = box_pred_i[topk_idxs]
+            else:
+                # [M, C] -> [MC,]
+                scores_i = cls_pred_i.sigmoid().flatten()
+
+                # Keep top k top scoring indices only.
+                num_topk = min(self.topk_candidates, box_pred_i.size(0))
+
+                # torch.sort is actually faster than .topk (at least on GPUs)
+                predicted_prob, topk_idxs = scores_i.sort(descending=True)
+                topk_scores = predicted_prob[:num_topk]
+                topk_idxs = topk_idxs[:num_topk]
+
+                # filter out the proposals with low confidence score
+                keep_idxs = topk_scores > self.conf_thresh
+                scores = topk_scores[keep_idxs]
+                topk_idxs = topk_idxs[keep_idxs]
+
+                anchor_idxs = torch.div(topk_idxs, self.num_classes, rounding_mode='floor')
+                labels = topk_idxs % self.num_classes
+
+                bboxes = box_pred_i[anchor_idxs]
+
+            all_scores.append(scores)
+            all_labels.append(labels)
+            all_bboxes.append(bboxes)
+
+        scores = torch.cat(all_scores, dim=0)
+        labels = torch.cat(all_labels, dim=0)
+        bboxes = torch.cat(all_bboxes, dim=0)
+
+        # to cpu & numpy
+        scores = scores.cpu().numpy()
+        labels = labels.cpu().numpy()
+        bboxes = bboxes.cpu().numpy()
+
+        # nms
+        scores, labels, bboxes = multiclass_nms(
+            scores, labels, bboxes, self.nms_thresh, self.num_classes)
+        
+        return bboxes, scores, labels
+    
+    def forward(self, x):
+        # ---------------- Backbone ----------------
+        pyramid_feats = self.backbone(x)
+        # ---------------- Neck: SPP ----------------
+        pyramid_feats[-1] = self.neck(pyramid_feats[-1])
+
+        # ---------------- Neck: PaFPN ----------------
+        pyramid_feats = self.fpn(pyramid_feats)
+
+        # ---------------- Heads ----------------
+        cls_feats, reg_feats = self.head(pyramid_feats)
+
+        # ---------------- Preds ----------------
+        outputs = self.pred(cls_feats, reg_feats)
+        outputs['image_size'] = [x.shape[2], x.shape[3]]
+
+        if not self.training:
+            all_cls_preds = outputs['pred_cls']
+            all_box_preds = outputs['pred_box']
+
+            # post process
+            bboxes, scores, labels = self.post_process(all_cls_preds, all_box_preds)
+            outputs = {
+                "scores": scores,
+                "labels": labels,
+                "bboxes": bboxes
+            }
+        
+        return outputs 

models/yolov7/yolov7_backbone.py

@@ -0,0 +1,110 @@
+import torch
+import torch.nn as nn
+
+try:
+    from .yolov7_basic import BasicConv, MDown, ELANLayer
+except:
+    from  yolov7_basic import BasicConv, MDown, ELANLayer
+
+
+# ELANNet
+class Yolov7Backbone(nn.Module):
+    def __init__(self, cfg):
+        super(Yolov7Backbone, self).__init__()
+        # ---------------- Basic parameters ----------------
+        self.model_scale = cfg.scale
+        if self.model_scale in ["l", "x"]:
+            self.feat_dims = [round(64   * cfg.width), round(128  * cfg.width), round(256  * cfg.width),
+                              round(512  * cfg.width), round(1024 * cfg.width), round(1024 * cfg.width)]
+            self.last_stage_eratio = 0.25
+        if self.model_scale in ["n", "s"]:
+            self.feat_dims = [round(64   * cfg.width), round(64  * cfg.width), round(128  * cfg.width),
+                              round(256  * cfg.width), round(512 * cfg.width), round(1024 * cfg.width)]
+            self.last_stage_eratio = 0.5
+
+        # ---------------- Model parameters ----------------
+        
+        # large backbone
+        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)
+        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(self.feat_dims[1], self.feat_dims[2],
+                      expansion=0.5, num_blocks=round(3*cfg.depth),
+                      act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise),      
+        )
+        self.layer_3 = nn.Sequential(
+            MDown(self.feat_dims[2], self.feat_dims[2],
+                  act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise),             
+            ELANLayer(self.feat_dims[2], self.feat_dims[3],
+                      expansion=0.5, num_blocks=round(3*cfg.depth),
+                      act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise),      
+        )
+        self.layer_4 = nn.Sequential(
+            MDown(self.feat_dims[3], self.feat_dims[3],
+                  act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise),             
+            ELANLayer(self.feat_dims[3], self.feat_dims[4],
+                      expansion=0.5, num_blocks=round(3*cfg.depth),
+                      act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise),      
+        )
+        self.layer_5 = nn.Sequential(
+            MDown(self.feat_dims[4], self.feat_dims[4],
+                  act_type=cfg.bk_act, norm_type=cfg.bk_norm, depthwise=cfg.bk_depthwise),             
+            ELANLayer(self.feat_dims[4], self.feat_dims[5],
+                      expansion=self.last_stage_eratio, num_blocks=round(3*cfg.depth),
+                      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"
+
+    cfg = BaseConfig()
+    model = Yolov7Backbone(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/yolov7/yolov7_basic.py

@@ -0,0 +1,138 @@
+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 MDown(nn.Module):
+    def __init__(self,
+                 in_dim    :int,
+                 out_dim   :int,
+                 act_type  :str   = 'silu',
+                 norm_type :str   = 'BN',
+                 depthwise :bool  = False,
+                 ) -> None:
+        super().__init__()
+        inter_dim = in_dim // 2
+        self.downsample_1 = nn.Sequential(
+            nn.MaxPool2d((2, 2), stride=2),
+            BasicConv(in_dim, inter_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
+        )
+        self.downsample_2 = nn.Sequential(
+            BasicConv(in_dim, inter_dim, kernel_size=1, act_type=act_type, norm_type=norm_type),
+            BasicConv(inter_dim, inter_dim,
+                      kernel_size=3, padding=1, stride=2,
+                      act_type=act_type, norm_type=norm_type, depthwise=depthwise)
+        )
+        if in_dim == out_dim:
+            self.output_proj = nn.Identity()
+        else:
+            self.output_proj = BasicConv(inter_dim * 2, out_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
+
+    def forward(self, x):
+        x1 = self.downsample_1(x)
+        x2 = self.downsample_2(x)
+
+        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,
+                 act_type   :str   = 'silu',
+                 norm_type  :str   = 'BN',
+                 depthwise  :bool  = False,
+                 ) -> None:
+        super(ELANLayer, self).__init__()
+        self.inter_dim = round(in_dim * expansion)
+        self.conv_layer_1 = BasicConv(in_dim, self.inter_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
+        self.conv_layer_2 = BasicConv(in_dim, self.inter_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
+        self.conv_layer_3 = BasicConv(self.inter_dim * 4, out_dim, kernel_size=1, act_type=act_type, norm_type=norm_type)
+        self.elan_layer_1 = nn.Sequential(*[BasicConv(self.inter_dim, self.inter_dim,
+                                                      kernel_size=3, padding=1,
+                                                      act_type=act_type, norm_type=norm_type, depthwise=depthwise)
+                                                      for _ in range(num_blocks)])
+        self.elan_layer_2 = nn.Sequential(*[BasicConv(self.inter_dim, self.inter_dim,
+                                                      kernel_size=3, padding=1,
+                                                      act_type=act_type, norm_type=norm_type, depthwise=depthwise)
+                                                      for _ in range(num_blocks)])
+
+    def forward(self, x):
+        # Input proj
+        x1 = self.conv_layer_1(x)
+        x2 = self.conv_layer_2(x)
+        x3 = self.elan_layer_1(x2)
+        x4 = self.elan_layer_2(x3)
+    
+        out = self.conv_layer_3(torch.cat([x1, x2, x3, x4], dim=1))
+
+        return out

models/yolov7/yolov7_head.py

@@ -0,0 +1,126 @@
+import torch
+import torch.nn as nn
+
+from .yolov7_basic import BasicConv
+
+
+# -------------------- Detection Head --------------------
+## Single-level Detection Head
+class DetHead(nn.Module):
+    def __init__(self,
+                 in_dim       :int  = 256,
+                 cls_head_dim :int  = 256,
+                 reg_head_dim :int  = 256,
+                 num_cls_head :int  = 2,
+                 num_reg_head :int  = 2,
+                 act_type     :str  = "silu",
+                 norm_type    :str  = "BN",
+                 depthwise    :bool = False):
+        super().__init__()
+        # --------- Basic Parameters ----------
+        self.in_dim = in_dim
+        self.num_cls_head = num_cls_head
+        self.num_reg_head = num_reg_head
+        self.act_type = act_type
+        self.norm_type = norm_type
+        self.depthwise = depthwise
+        
+        # --------- Network Parameters ----------
+        ## cls head
+        cls_feats = []
+        self.cls_head_dim = cls_head_dim
+        for i in range(num_cls_head):
+            if i == 0:
+                cls_feats.append(
+                    BasicConv(in_dim, self.cls_head_dim,
+                              kernel_size=3, padding=1, stride=1, 
+                              act_type=act_type,
+                              norm_type=norm_type,
+                              depthwise=depthwise)
+                              )
+            else:
+                cls_feats.append(
+                    BasicConv(self.cls_head_dim, self.cls_head_dim,
+                              kernel_size=3, padding=1, stride=1, 
+                              act_type=act_type,
+                              norm_type=norm_type,
+                              depthwise=depthwise)
+                              )
+        ## reg head
+        reg_feats = []
+        self.reg_head_dim = reg_head_dim
+        for i in range(num_reg_head):
+            if i == 0:
+                reg_feats.append(
+                    BasicConv(in_dim, self.reg_head_dim,
+                              kernel_size=3, padding=1, stride=1, 
+                              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 Yolov7DetHead(nn.Module):
+    def __init__(self, cfg, in_dims):
+        super().__init__()
+        ## ----------- Network Parameters -----------
+        self.multi_level_heads = nn.ModuleList(
+            [DetHead(in_dim       = in_dims[level],
+                     cls_head_dim = max(in_dims[0], min(cfg.num_classes, 100)),
+                     reg_head_dim = max(in_dims[0]//4, 16, 4*cfg.reg_max),
+                     num_cls_head = cfg.num_cls_head,
+                     num_reg_head = cfg.num_reg_head,
+                     act_type     = cfg.head_act,
+                     norm_type    = cfg.head_norm,
+                     depthwise    = cfg.head_depthwise)
+                     for level in range(cfg.num_levels)
+                     ])
+        # --------- Basic Parameters ----------
+        self.in_dims = in_dims
+        self.cls_head_dim = self.multi_level_heads[0].cls_head_dim
+        self.reg_head_dim = self.multi_level_heads[0].reg_head_dim
+
+
+    def forward(self, feats):
+        """
+            feats: List[(Tensor)] [[B, C, H, W], ...]
+        """
+        cls_feats = []
+        reg_feats = []
+        for feat, head in zip(feats, self.multi_level_heads):
+            # ---------------- Pred ----------------
+            cls_feat, reg_feat = head(feat)
+
+            cls_feats.append(cls_feat)
+            reg_feats.append(reg_feat)
+
+        return cls_feats, reg_feats

models/yolov7/yolov7_neck.py

@@ -0,0 +1,60 @@
+import torch
+import torch.nn as nn
+from .yolov7_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, expansion=0.5):
+        super().__init__()
+        ## ----------- Basic Parameters -----------
+        inter_dim = round(in_dim * expansion)
+        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))
+
+# SPPF block with CSP module
+class SPPFBlockCSP(nn.Module):
+    """
+        CSP Spatial Pyramid Pooling Block
+    """
+    def __init__(self, cfg, in_dim, out_dim):
+        super(SPPFBlockCSP, self).__init__()
+        inter_dim = int(in_dim * cfg.neck_expand_ratio)
+        self.out_dim = out_dim
+        self.cv1 = BasicConv(in_dim, inter_dim, kernel_size=1, act_type=cfg.neck_act, norm_type=cfg.neck_norm)
+        self.cv2 = BasicConv(in_dim, inter_dim, kernel_size=1, act_type=cfg.neck_act, norm_type=cfg.neck_norm)
+        self.module = nn.Sequential(
+            BasicConv(inter_dim, inter_dim, kernel_size=3, padding=1, 
+                      act_type=cfg.neck_act, norm_type=cfg.neck_norm, depthwise=cfg.neck_depthwise),
+            SPPF(cfg, inter_dim, inter_dim, expansion=1.0),
+            BasicConv(inter_dim, inter_dim, kernel_size=3, padding=1, 
+                      act_type=cfg.neck_act, norm_type=cfg.neck_norm, depthwise=cfg.neck_depthwise),
+                      )
+        self.cv3 = BasicConv(inter_dim * 2, self.out_dim, kernel_size=1, act_type=cfg.neck_act, norm_type=cfg.neck_norm)
+
+        
+    def forward(self, x):
+        x1 = self.cv1(x)
+        x2 = self.module(self.cv2(x))
+        y = self.cv3(torch.cat([x1, x2], dim=1))
+
+        return y

models/yolov7/yolov7_pafpn.py

@@ -0,0 +1,121 @@
+from typing import List
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .yolov7_basic import BasicConv, ELANLayer, MDown
+
+
+# PaFPN-ELAN (YOLOv7's)
+class Yolov7PaFPN(nn.Module):
+    def __init__(self, cfg, in_dims: List = [512, 1024, 512]):
+        super(Yolov7PaFPN, self).__init__()
+        # ----------------------------- Basic parameters -----------------------------
+        self.in_dims = in_dims
+        c3, c4, c5 = in_dims
+
+        # ----------------------------- Top-down FPN -----------------------------
+        ## P5 -> P4
+        self.reduce_layer_1 = BasicConv(c5, round(256*cfg.width),
+                                        kernel_size=1, act_type=cfg.fpn_act, norm_type=cfg.fpn_norm)
+        self.reduce_layer_2 = BasicConv(c4, round(256*cfg.width),
+                                        kernel_size=1, act_type=cfg.fpn_act, norm_type=cfg.fpn_norm)
+        self.top_down_layer_1 = ELANLayer(in_dim     = round(256*cfg.width) + round(256*cfg.width),
+                                          out_dim    = round(256*cfg.width),
+                                          expansion  = 0.5,
+                                          num_blocks = round(3*cfg.depth),
+                                          act_type   = cfg.fpn_act,
+                                          norm_type  = cfg.fpn_norm,
+                                          depthwise  = cfg.fpn_depthwise,
+                                          )
+        ## P4 -> P3
+        self.reduce_layer_3 = BasicConv(round(256*cfg.width), round(128*cfg.width),
+                                        kernel_size=1, act_type=cfg.fpn_act, norm_type=cfg.fpn_norm)
+        self.reduce_layer_4 = BasicConv(c3, round(128*cfg.width),
+                                        kernel_size=1, act_type=cfg.fpn_act, norm_type=cfg.fpn_norm)
+        self.top_down_layer_2 = ELANLayer(in_dim     = round(128*cfg.width) + round(128*cfg.width),
+                                          out_dim    = round(128*cfg.width),
+                                          expansion  = 0.5,
+                                          num_blocks = round(3*cfg.depth),
+                                          act_type   = cfg.fpn_act,
+                                          norm_type  = cfg.fpn_norm,
+                                          depthwise  = cfg.fpn_depthwise,
+                                          )
+        # ----------------------------- Bottom-up FPN -----------------------------
+        ## P3 -> P4
+        self.downsample_layer_1 = MDown(round(128*cfg.width), round(256*cfg.width),
+                                        act_type=cfg.fpn_act, norm_type=cfg.fpn_norm)
+        self.bottom_up_layer_1 = ELANLayer(in_dim     = round(256*cfg.width) + round(256*cfg.width),
+                                           out_dim    = round(256*cfg.width),
+                                           expansion  = 0.5,
+                                           num_blocks = round(3*cfg.depth),
+                                           act_type   = cfg.fpn_act,
+                                           norm_type  = cfg.fpn_norm,
+                                           depthwise  = cfg.fpn_depthwise,
+                                           )
+        ## P4 -> P5
+        self.downsample_layer_2 = MDown(round(256*cfg.width), round(512*cfg.width),
+                                        act_type=cfg.fpn_act, norm_type=cfg.fpn_norm)
+        self.bottom_up_layer_2 = ELANLayer(in_dim     = round(512*cfg.width) + c5,
+                                           out_dim    = round(512*cfg.width),
+                                           expansion  = 0.5,
+                                           num_blocks = round(3*cfg.depth),
+                                           act_type   = cfg.fpn_act,
+                                           norm_type  = cfg.fpn_norm,
+                                           depthwise  = cfg.fpn_depthwise,
+                                           )
+
+        # ----------------------------- Head conv layers -----------------------------
+        ## Head convs
+        self.head_conv_1 = BasicConv(round(128*cfg.width), round(256*cfg.width),
+                                     kernel_size=3, padding=1, stride=1,
+                                     act_type=cfg.fpn_act, norm_type=cfg.fpn_norm, depthwise=cfg.fpn_depthwise)
+        self.head_conv_2 = BasicConv(round(256*cfg.width), round(512*cfg.width),
+                                     kernel_size=3, padding=1, stride=1,
+                                     act_type=cfg.fpn_act, norm_type=cfg.fpn_norm, depthwise=cfg.fpn_depthwise)
+        self.head_conv_3 = BasicConv(round(512*cfg.width), round(1024*cfg.width),
+                                     kernel_size=3, padding=1, stride=1,
+                                     act_type=cfg.fpn_act, norm_type=cfg.fpn_norm, depthwise=cfg.fpn_depthwise)
+
+        # ---------------------- Yolov5'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.reduce_layer_2(c4)
+        p4 = self.top_down_layer_1(torch.cat([p5_up, p4], dim=1))
+
+        ## P4 -> P3
+        p4_in = self.reduce_layer_3(p4)
+        p4_up = F.interpolate(p4_in, scale_factor=2.0)
+        p3 = self.reduce_layer_4(c3)
+        p3 = self.top_down_layer_2(torch.cat([p4_up, p3], dim=1))
+
+        ## P3 -> P4
+        p3_ds = self.downsample_layer_1(p3)
+        p4 = torch.cat([p3_ds, p4], dim=1)
+        p4 = self.bottom_up_layer_1(p4)
+
+        ## P4 -> P5
+        p4_ds = self.downsample_layer_2(p4)
+        p5 = torch.cat([p4_ds, c5], dim=1)
+        p5 = self.bottom_up_layer_2(p5)
+
+        out_feats = [self.head_conv_1(p3), self.head_conv_2(p4), self.head_conv_3(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/yolov7/yolov7_pred.py

@@ -0,0 +1,153 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# -------------------- Detection Pred Layer --------------------
+## Single-level pred layer
+class DetPredLayer(nn.Module):
+    def __init__(self,
+                 cls_dim     :int = 256,
+                 reg_dim     :int = 256,
+                 stride      :int = 32,
+                 reg_max     :int = 16,
+                 num_classes :int = 80,
+                 num_coords  :int = 4):
+        super().__init__()
+        # --------- Basic Parameters ----------
+        self.stride = stride
+        self.cls_dim = cls_dim
+        self.reg_dim = reg_dim
+        self.reg_max = reg_max
+        self.num_classes = num_classes
+        self.num_coords = num_coords
+
+        # --------- Network Parameters ----------
+        self.cls_pred = nn.Conv2d(cls_dim, num_classes, kernel_size=1)
+        self.reg_pred = nn.Conv2d(reg_dim, num_coords, kernel_size=1)                
+
+        self.init_bias()
+        
+    def init_bias(self):
+        # cls pred bias
+        b = self.cls_pred.bias.view(1, -1)
+        b.data.fill_(math.log(5 / self.num_classes / (640. / self.stride) ** 2))
+        self.cls_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
+        # reg pred bias
+        b = self.reg_pred.bias.view(-1, )
+        b.data.fill_(1.0)
+        self.reg_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
+
+    def generate_anchors(self, fmp_size):
+        """
+            fmp_size: (List) [H, W]
+        """
+        # generate grid cells
+        fmp_h, fmp_w = fmp_size
+        anchor_y, anchor_x = torch.meshgrid([torch.arange(fmp_h), torch.arange(fmp_w)])
+        # [H, W, 2] -> [HW, 2]
+        anchors = torch.stack([anchor_x, anchor_y], dim=-1).float().view(-1, 2)
+        anchors += 0.5  # add center offset
+        anchors *= self.stride
+
+        return anchors
+        
+    def forward(self, cls_feat, reg_feat):
+        # pred
+        cls_pred = self.cls_pred(cls_feat)
+        reg_pred = self.reg_pred(reg_feat)
+
+        # generate anchor boxes: [M, 4]
+        B, _, H, W = cls_pred.size()
+        fmp_size = [H, W]
+        anchors = self.generate_anchors(fmp_size)
+        anchors = anchors.to(cls_pred.device)
+        # stride tensor: [M, 1]
+        stride_tensor = torch.ones_like(anchors[..., :1]) * self.stride
+        
+        # [B, C, H, W] -> [B, H, W, C] -> [B, M, C]
+        cls_pred = cls_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, self.num_classes)
+        reg_pred = reg_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, 4*self.reg_max)
+        
+        # output dict
+        outputs = {"pred_cls": cls_pred,            # List(Tensor) [B, M, C]
+                   "pred_reg": reg_pred,            # List(Tensor) [B, M, 4*(reg_max)]
+                   "anchors": anchors,              # List(Tensor) [M, 2]
+                   "strides": self.stride,          # List(Int) = [8, 16, 32]
+                   "stride_tensor": stride_tensor   # List(Tensor) [M, 1]
+                   }
+
+        return outputs
+
+## Multi-level pred layer
+class Yolov7DetPredLayer(nn.Module):
+    def __init__(self,
+                 cfg,
+                 cls_dim,
+                 reg_dim,
+                 ):
+        super().__init__()
+        # --------- Basic Parameters ----------
+        self.cfg = cfg
+        self.cls_dim = cls_dim
+        self.reg_dim = reg_dim
+
+        # ----------- Network Parameters -----------
+        ## pred layers
+        self.multi_level_preds = nn.ModuleList(
+            [DetPredLayer(cls_dim     = cls_dim,
+                          reg_dim     = reg_dim,
+                          stride      = cfg.out_stride[level],
+                          reg_max     = cfg.reg_max,
+                          num_classes = cfg.num_classes,
+                          num_coords  = 4 * cfg.reg_max)
+                          for level in range(cfg.num_levels)
+                          ])
+        ## proj conv
+        proj_init = torch.arange(cfg.reg_max, dtype=torch.float)
+        self.proj_conv = nn.Conv2d(cfg.reg_max, 1, kernel_size=1, bias=False).requires_grad_(False)
+        self.proj_conv.weight.data[:] = nn.Parameter(proj_init.view([1, cfg.reg_max, 1, 1]), requires_grad=False)
+
+    def forward(self, cls_feats, reg_feats):
+        all_anchors = []
+        all_strides = []
+        all_cls_preds = []
+        all_reg_preds = []
+        all_box_preds = []
+        for level in range(self.cfg.num_levels):
+            # -------------- Single-level prediction --------------
+            outputs = self.multi_level_preds[level](cls_feats[level], reg_feats[level])
+
+            # -------------- Decode bbox --------------
+            B, M = outputs["pred_reg"].shape[:2]
+            # [B, M, 4*(reg_max)] -> [B, M, 4, reg_max]
+            delta_pred = outputs["pred_reg"].reshape([B, M, 4, self.cfg.reg_max])
+            # [B, M, 4, reg_max] -> [B, reg_max, 4, M]
+            delta_pred = delta_pred.permute(0, 3, 2, 1).contiguous()
+            # [B, reg_max, 4, M] -> [B, 1, 4, M]
+            delta_pred = self.proj_conv(F.softmax(delta_pred, dim=1))
+            # [B, 1, 4, M] -> [B, 4, M] -> [B, M, 4]
+            delta_pred = delta_pred.view(B, 4, M).permute(0, 2, 1).contiguous()
+            ## tlbr -> xyxy
+            x1y1_pred = outputs["anchors"][None] - delta_pred[..., :2] * self.cfg.out_stride[level]
+            x2y2_pred = outputs["anchors"][None] + delta_pred[..., 2:] * self.cfg.out_stride[level]
+            box_pred = torch.cat([x1y1_pred, x2y2_pred], dim=-1)
+
+            # collect results
+            all_cls_preds.append(outputs["pred_cls"])
+            all_reg_preds.append(outputs["pred_reg"])
+            all_box_preds.append(box_pred)
+            all_anchors.append(outputs["anchors"])
+            all_strides.append(outputs["stride_tensor"])
+        
+        # output dict
+        outputs = {"pred_cls":      all_cls_preds,         # List(Tensor) [B, M, C]
+                   "pred_reg":      all_reg_preds,         # List(Tensor) [B, M, 4*(reg_max)]
+                   "pred_box":      all_box_preds,         # List(Tensor) [B, M, 4]
+                   "anchors":       all_anchors,           # List(Tensor) [M, 2]
+                   "stride_tensor": all_strides,           # List(Tensor) [M, 1]
+                   "strides":       self.cfg.out_stride,   # List(Int) = [8, 16, 32]
+                   }
+
+        return outputs