update

config/model_config/yolov7_config.py

@@ -35,8 +35,8 @@ yolov7_cfg = {
         'head_depthwise': False,
         # ---------------- Train config ----------------
         ## input
-        'trans_type': 'yolox_small',
-        'multi_scale': [0.7, 1.25], # 448 -> 800
+        'trans_type': 'yolov5_small',
+        'multi_scale': [0.5, 1.5], # 320 -> 960
         # ---------------- Assignment config ----------------
         ## matcher
         'matcher': {'center_sampling_radius': 2.5,
@@ -47,7 +47,7 @@ yolov7_cfg = {
         'loss_cls_weight': 1.0,
         'loss_box_weight': 5.0,
         # ---------------- Train config ----------------
-        'trainer_type': 'yolox',
+        'trainer_type': 'yolov8',
     },
 
     'yolov7':{
@@ -84,8 +84,8 @@ yolov7_cfg = {
         'head_depthwise': False,
         # ---------------- Train config ----------------
         ## input
-        'trans_type': 'yolox_large',
-        'multi_scale': [0.7, 1.25], # 448 -> 800
+        'trans_type': 'yolov5_large',
+        'multi_scale': [0.5, 1.5], # 320 -> 960
         # ---------------- Assignment config ----------------
         ## matcher
         'matcher': {'center_sampling_radius': 2.5,
@@ -96,7 +96,7 @@ yolov7_cfg = {
         'loss_cls_weight': 1.0,
         'loss_box_weight': 5.0,
         # ---------------- Train config ----------------
-        'trainer_type': 'yolox',
+        'trainer_type': 'yolov8',
     },
 
     'yolov7_x':{
@@ -133,8 +133,8 @@ yolov7_cfg = {
         'head_depthwise': False,
         # ---------------- Train config ----------------
         ## input
-        'trans_type': 'yolox_huge',
-        'multi_scale': [0.7, 1.25], # 448 -> 800
+        'trans_type': 'yolov5_huge',
+        'multi_scale': [0.5, 1.5], # 320 -> 960
         # ---------------- Assignment config ----------------
         ## matcher
         'matcher': {'center_sampling_radius': 2.5,
@@ -145,7 +145,7 @@ yolov7_cfg = {
         'loss_cls_weight': 1.0,
         'loss_box_weight': 5.0,
         # ---------------- Train config ----------------
-        'trainer_type': 'yolox',
+        'trainer_type': 'yolov8',
     },
 
 }

models/detectors/yolov7/loss.py

@@ -52,20 +52,6 @@ class Criterion(object):
         return loss_box
 
 
-    def loss_bboxes_aux(self, pred_reg, gt_box, anchors, stride_tensors):
-        # xyxy -> cxcy&bwbh
-        gt_cxcy = (gt_box[..., :2] + gt_box[..., 2:]) * 0.5
-        gt_bwbh = gt_box[..., 2:] - gt_box[..., :2]
-        # encode gt box
-        gt_cxcy_encode = (gt_cxcy - anchors) / stride_tensors
-        gt_bwbh_encode = torch.log(gt_bwbh / stride_tensors)
-        gt_box_encode = torch.cat([gt_cxcy_encode, gt_bwbh_encode], dim=-1)
-        # l1 loss
-        loss_box_aux = F.l1_loss(pred_reg, gt_box_encode, reduction='none')
-
-        return loss_box_aux
-
-
     def __call__(self, outputs, targets, epoch=0):        
         """
             outputs['pred_obj']: List(Tensor) [B, M, 1]
@@ -159,40 +145,13 @@ class Criterion(object):
                  self.loss_cls_weight * loss_cls + \
                  self.loss_box_weight * loss_box
 
-        # ------------------ Aux regression loss ------------------
-        loss_box_aux = None
-        if epoch >= (self.max_epoch - self.no_aug_epoch - 1):
-            ## reg_preds
-            reg_preds = torch.cat(outputs['pred_reg'], dim=1)
-            reg_preds_pos = reg_preds.view(-1, 4)[fg_masks]
-            ## anchor tensors
-            anchors_tensors = torch.cat(outputs['anchors'], dim=0)[None].repeat(bs, 1, 1)
-            anchors_tensors_pos = anchors_tensors.view(-1, 2)[fg_masks]
-            ## stride tensors
-            stride_tensors = torch.cat(outputs['stride_tensors'], dim=0)[None].repeat(bs, 1, 1)
-            stride_tensors_pos = stride_tensors.view(-1, 1)[fg_masks]
-            ## aux loss
-            loss_box_aux = self.loss_bboxes_aux(reg_preds_pos, box_targets, anchors_tensors_pos, stride_tensors_pos)
-            loss_box_aux = loss_box_aux.sum() / num_fgs
-
-            losses += loss_box_aux
-
         # Loss dict
-        if loss_box_aux is None:
-            loss_dict = dict(
-                    loss_obj = loss_obj,
-                    loss_cls = loss_cls,
-                    loss_box = loss_box,
-                    losses = losses
-            )
-        else:
-            loss_dict = dict(
-                    loss_obj = loss_obj,
-                    loss_cls = loss_cls,
-                    loss_box = loss_box,
-                    loss_box_aux = loss_box_aux,
-                    losses = losses
-                    )
+        loss_dict = dict(
+                loss_obj = loss_obj,
+                loss_cls = loss_cls,
+                loss_box = loss_box,
+                losses = losses
+        )
 
         return loss_dict