fix gt_num 0D tensor

Author: LokeZhou

ppdet/modeling/losses/yolov5_loss.py

@@ -198,18 +198,20 @@ def yolov5_loss(self, pi, t_cls, t_box, t_indices, t_anchor, balance):
     def forward(self, inputs, targets, anchors):
         yolo_losses = dict()
         #tcls, tbox, indices, anch = self.build_targets(inputs, targets, anchors)
-        tcls, tbox, indices, anch = self.build_targets_paddle(inputs, targets, anchors)
-        
+        tcls, tbox, indices, anch = self.build_targets_paddle(inputs, targets,
+                                                              anchors)
+
         for i, (p_det, balance) in enumerate(zip(inputs, self.balance)):
             t_cls = tcls[i]
             t_box = tbox[i]
             t_anchor = anch[i]
             t_indices = indices[i]
 
             bs, ch, h, w = p_det.shape
-            pi = p_det.reshape((bs, self.na, int(ch/self.na), h, w)).transpose(
-                (0, 1, 3, 4, 2))
-            
+            pi = p_det.reshape(
+                (bs, self.na, int(ch / self.na), h, w)).transpose(
+                    (0, 1, 3, 4, 2))
+
             yolo_loss = self.yolov5_loss(pi, t_cls, t_box, t_indices, t_anchor,
                                          balance)
 
@@ -222,13 +224,12 @@ def forward(self, inputs, targets, anchors):
         loss = 0
         for k, v in yolo_losses.items():
             loss += v
-  
+
         batch_size = inputs[0].shape[0]
         num_gpus = targets.get('num_gpus', 8)
         yolo_losses['loss'] = loss * batch_size * num_gpus
         return yolo_losses
 
-
     def build_targets_paddle(self, outputs, targets, anchors):
         # targets['gt_class'] [bs, max_gt_nums, 1]
         # targets['gt_bbox'] [bs, max_gt_nums, 4]
@@ -239,19 +240,28 @@ def build_targets_paddle(self, outputs, targets, anchors):
         na = anchors.shape[1]  # not len(anchors)
         tcls, tbox, indices, anch = [], [], [], []
 
-        gain = paddle.ones([7], dtype=np.float32)  # normalized to gridspace gain
-        ai = paddle.tile(paddle.arange(na,dtype=np.float32).reshape([na, 1]), [1, nt])
+        gain = paddle.ones(
+            [7], dtype=np.float32)  # normalized to gridspace gain
+        ai = paddle.tile(
+            paddle.arange(
+                na, dtype=np.float32).reshape([na, 1]), [1, nt])
 
         batch_size = outputs[0].shape[0]
         gt_labels = []
         for idx in range(batch_size):
-            gt_num = gt_nums[idx].astype("int32")
+            gt_num = gt_nums[idx:idx + 1].astype("int32")
             if gt_num == 0:
                 continue
+
             gt_bbox = targets['gt_bbox'][idx][:gt_num]
             gt_class = targets['gt_class'][idx][:gt_num] * 1.0
-            img_idx = paddle.repeat_interleave(paddle.to_tensor(idx),gt_num,axis=0)[None,:].astype(paddle.float32).T
-            gt_labels.append(paddle.concat((img_idx, gt_class, gt_bbox),axis=-1))
+            img_idx = paddle.repeat_interleave(
+                paddle.to_tensor([idx]), gt_num,
+                axis=0)[None, :].astype(paddle.float32).T
+            gt_labels.append(
+                paddle.concat(
+                    (img_idx, gt_class, gt_bbox), axis=-1))
+
         if (len(gt_labels)):
             gt_labels = paddle.concat(gt_labels)
         else:
@@ -264,29 +274,33 @@ def build_targets_paddle(self, outputs, targets, anchors):
         for i in range(len(anchors)):
             anchor = anchors[i] / self.downsample_ratios[i]
             gain[2:6] = paddle.to_tensor(
-                outputs[i].shape, dtype=paddle.float32)[[3, 2, 3, 2]]  # xyxy gain
+                outputs[i].shape,
+                dtype=paddle.float32)[[3, 2, 3, 2]]  # xyxy gain
 
             # Match targets_labels to
             t = targets_labels * gain
             if nt:
                 # Matches
                 r = t[:, :, 4:6] / anchor[:, None]
                 j = paddle.maximum(r, 1 / r).max(2) < self.anchor_t
-                t = paddle.flatten(t,0,1)
-                j = paddle.flatten(j.astype(paddle.int32),0,1).astype(paddle.bool)
+                t = paddle.flatten(t, 0, 1)
+                j = paddle.flatten(j.astype(paddle.int32), 0,
+                                   1).astype(paddle.bool)
                 t = t[j]  # filter
-            
 
                 # Offsets
                 gxy = t[:, 2:4]  # grid xy
                 gxi = gain[[2, 3]] - gxy  # inverse
                 j, k = ((gxy % 1 < g) & (gxy > 1)).T.astype(paddle.int64)
                 l, m = ((gxi % 1 < g) & (gxi > 1)).T.astype(paddle.int64)
-                j = paddle.flatten(paddle.stack((paddle.ones_like(j), j, k, l, m)),0,1).astype(paddle.bool)
-                t = paddle.flatten(paddle.tile(t, [5, 1, 1]),0,1)
+                j = paddle.flatten(
+                    paddle.stack((paddle.ones_like(j), j, k, l, m)), 0,
+                    1).astype(paddle.bool)
+                t = paddle.flatten(paddle.tile(t, [5, 1, 1]), 0, 1)
                 t = t[j]
-                offsets = paddle.zeros_like(gxy)[None,:] + paddle.to_tensor(self.off)[:,None]
-                offsets = paddle.flatten(offsets,0,1)[j]
+                offsets = paddle.zeros_like(gxy)[None, :] + paddle.to_tensor(
+                    self.off)[:, None]
+                offsets = paddle.flatten(offsets, 0, 1)[j]
             else:
                 t = targets_labels[0]
                 offsets = 0
@@ -297,14 +311,14 @@ def build_targets_paddle(self, outputs, targets, anchors):
             gwh = t[:, 4:6]  # grid wh
             gij = (gxy - offsets).astype(paddle.int64)
             gi, gj = gij.T  # grid xy indices
-          
+
             # Append
             a = t[:, 6].astype(paddle.int64)  # anchor indices
             gj, gi = gj.clip(0, gain[3] - 1), gi.clip(0, gain[2] - 1)
             indices.append(
                 (b, a, gj.astype(paddle.int64), gi.astype(paddle.int64)))
             tbox.append(
-                    paddle.concat((gxy - gij, gwh), 1).astype(paddle.float32))
+                paddle.concat((gxy - gij, gwh), 1).astype(paddle.float32))
             anch.append(anchor[a])
             tcls.append(c)
         return tcls, tbox, indices, anch