add picodet ppyoloe_crn_s_300e_coco static training (#7859)

ppdet/engine/export_utils.py

@@ -142,6 +142,46 @@ TO_STATIC_SPEC = {
         'centerness4': paddle.static.InputSpec(
             name='centerness4', shape=[-1, 10, 10, 1], dtype='float32'),
     }],
+    'picodet_s_320_coco_lcnet': [{
+        'im_id': paddle.static.InputSpec(
+            name='im_id', shape=[-1, 1], dtype='float32'),
+        'is_crowd': paddle.static.InputSpec(
+            name='is_crowd', shape=[-1, -1, 1], dtype='float32'),
+        'gt_class': paddle.static.InputSpec(
+            name='gt_class', shape=[-1, -1, 1], dtype='int32'),
+        'gt_bbox': paddle.static.InputSpec(
+            name='gt_bbox', shape=[-1, -1, 4], dtype='float32'),
+        'curr_iter': paddle.static.InputSpec(
+            name='curr_iter', shape=[-1], dtype='float32'),
+        'image': paddle.static.InputSpec(
+            name='image', shape=[-1, 3, -1, -1], dtype='float32'),
+        'im_shape': paddle.static.InputSpec(
+            name='im_shape', shape=[-1, 2], dtype='float32'),
+        'scale_factor': paddle.static.InputSpec(
+            name='scale_factor', shape=[-1, 2], dtype='float32'),
+        'pad_gt_mask': paddle.static.InputSpec(
+            name='pad_gt_mask', shape=[-1, -1, 1], dtype='float32'),
+    }],
+    'ppyoloe_crn_s_300e_coco': [{
+        'im_id': paddle.static.InputSpec(
+            name='im_id', shape=[-1, 1], dtype='float32'),
+        'is_crowd': paddle.static.InputSpec(
+            name='is_crowd', shape=[-1, -1, 1], dtype='float32'),
+        'gt_class': paddle.static.InputSpec(
+            name='gt_class', shape=[-1, -1, 1], dtype='int32'),
+        'gt_bbox': paddle.static.InputSpec(
+            name='gt_bbox', shape=[-1, -1, 4], dtype='float32'),
+        'curr_iter': paddle.static.InputSpec(
+            name='curr_iter', shape=[-1], dtype='float32'),
+        'image': paddle.static.InputSpec(
+            name='image', shape=[-1, 3, -1, -1], dtype='float32'),
+        'im_shape': paddle.static.InputSpec(
+            name='im_shape', shape=[-1, 2], dtype='float32'),
+        'scale_factor': paddle.static.InputSpec(
+            name='scale_factor', shape=[-1, 2], dtype='float32'),
+        'pad_gt_mask': paddle.static.InputSpec(
+            name='pad_gt_mask', shape=[-1, -1, 1], dtype='float32'),
+    }],
 }
 
 

ppdet/modeling/assigners/atss_assigner.py

@@ -169,8 +169,9 @@ class ATSSAssigner(nn.Layer):
         # the one with the highest iou will be selected.
         mask_positive_sum = mask_positive.sum(axis=-2)
         if mask_positive_sum.max() > 1:
-            mask_multiple_gts = (mask_positive_sum.unsqueeze(1) > 1).tile(
-                [1, num_max_boxes, 1])
+            mask_multiple_gts = (
+                mask_positive_sum.unsqueeze(1) > 1).astype('int32').tile(
+                    [1, num_max_boxes, 1]).astype('bool')
             if self.sm_use:
                 is_max_iou = compute_max_iou_anchor(ious * mask_positive)
             else:
@@ -221,4 +222,4 @@ class ATSSAssigner(nn.Layer):
                                          paddle.zeros_like(gather_scores))
             assigned_scores *= gather_scores.unsqueeze(-1)
 
-        return assigned_labels, assigned_bboxes, assigned_scores, mask_positive
+        return assigned_labels, assigned_bboxes, assigned_scores

ppdet/modeling/assigners/task_aligned_assigner.py

@@ -190,4 +190,4 @@ class TaskAlignedAssigner(nn.Layer):
         alignment_metrics = alignment_metrics.max(-2).unsqueeze(-1)
         assigned_scores = assigned_scores * alignment_metrics
 
-        return assigned_labels, assigned_bboxes, assigned_scores, mask_positive
+        return assigned_labels, assigned_bboxes, assigned_scores

ppdet/modeling/assigners/task_aligned_assigner_cr.py

@@ -178,4 +178,4 @@ class TaskAlignedAssigner_CR(nn.Layer):
         alignment_metrics = alignment_metrics.max(-2).unsqueeze(-1)
         assigned_scores = assigned_scores * alignment_metrics
 
-        return assigned_labels, assigned_bboxes, assigned_scores, mask_positive
+        return assigned_labels, assigned_bboxes, assigned_scores

ppdet/modeling/heads/pico_head.py

@@ -651,7 +651,7 @@ class PicoHeadV2(GFLHead):
 
         # label assignment
         if gt_meta['epoch_id'] < self.static_assigner_epoch:
-            assigned_labels, assigned_bboxes, assigned_scores, _ = self.static_assigner(
+            assigned_labels, assigned_bboxes, assigned_scores = self.static_assigner(
                 anchors,
                 num_anchors_list,
                 gt_labels,
@@ -662,7 +662,7 @@ class PicoHeadV2(GFLHead):
                 pred_bboxes=pred_bboxes.detach() * stride_tensor_list)
 
         else:
-            assigned_labels, assigned_bboxes, assigned_scores, _ = self.assigner(
+            assigned_labels, assigned_bboxes, assigned_scores = self.assigner(
                 pred_scores.detach(),
                 pred_bboxes.detach() * stride_tensor_list,
                 centers,

ppdet/modeling/heads/ppyoloe_contrast_head.py

@@ -121,7 +121,7 @@ class PPYOLOEContrastHead(PPYOLOEHead):
         pad_gt_mask = gt_meta['pad_gt_mask']
         # label assignment
         if gt_meta['epoch_id'] < self.static_assigner_epoch:
-            assigned_labels, assigned_bboxes, assigned_scores, _ = \
+            assigned_labels, assigned_bboxes, assigned_scores = \
                 self.static_assigner(
                     anchors,
                     num_anchors_list,
@@ -133,7 +133,7 @@ class PPYOLOEContrastHead(PPYOLOEHead):
             alpha_l = 0.25
         else:
             if self.sm_use:
-                assigned_labels, assigned_bboxes, assigned_scores, _ = \
+                assigned_labels, assigned_bboxes, assigned_scores = \
                     self.assigner(
                     pred_scores.detach(),
                     pred_bboxes.detach() * stride_tensor,
@@ -144,7 +144,7 @@ class PPYOLOEContrastHead(PPYOLOEHead):
                     pad_gt_mask,
                     bg_index=self.num_classes)
             else:
-                assigned_labels, assigned_bboxes, assigned_scores, _ = \
+                assigned_labels, assigned_bboxes, assigned_scores = \
                     self.assigner(
                     pred_scores.detach(),
                     pred_bboxes.detach() * stride_tensor,

ppdet/modeling/heads/ppyoloe_head.py

@@ -337,7 +337,8 @@ class PPYOLOEHead(nn.Layer):
         # pos/neg loss
         if num_pos > 0:
             # l1 + iou
-            bbox_mask = mask_positive.unsqueeze(-1).tile([1, 1, 4])
+            bbox_mask = mask_positive.astype('int32').unsqueeze(-1).tile(
+                [1, 1, 4]).astype('bool')
             pred_bboxes_pos = paddle.masked_select(pred_bboxes,
                                                    bbox_mask).reshape([-1, 4])
             assigned_bboxes_pos = paddle.masked_select(
@@ -351,8 +352,8 @@ class PPYOLOEHead(nn.Layer):
                                      assigned_bboxes_pos) * bbox_weight
             loss_iou = loss_iou.sum() / assigned_scores_sum
 
-            dist_mask = mask_positive.unsqueeze(-1).tile(
-                [1, 1, self.reg_channels * 4])
+            dist_mask = mask_positive.unsqueeze(-1).astype('int32').tile(
+                [1, 1, self.reg_channels * 4]).astype('bool')
             pred_dist_pos = paddle.masked_select(
                 pred_dist, dist_mask).reshape([-1, 4, self.reg_channels])
             assigned_ltrb = self._bbox2distance(anchor_points, assigned_bboxes)
@@ -387,7 +388,7 @@ class PPYOLOEHead(nn.Layer):
         pad_gt_mask = gt_meta['pad_gt_mask']
         # label assignment
         if gt_meta['epoch_id'] < self.static_assigner_epoch:
-            assigned_labels, assigned_bboxes, assigned_scores, mask_positive = \
+            assigned_labels, assigned_bboxes, assigned_scores = \
                 self.static_assigner(
                     anchors,
                     num_anchors_list,
@@ -400,7 +401,7 @@ class PPYOLOEHead(nn.Layer):
         else:
             if self.sm_use:
                 # only used in smalldet of PPYOLOE-SOD model
-                assigned_labels, assigned_bboxes, assigned_scores, mask_positive = \
+                assigned_labels, assigned_bboxes, assigned_scores = \
                     self.assigner(
                     pred_scores.detach(),
                     pred_bboxes.detach() * stride_tensor,
@@ -413,7 +414,7 @@ class PPYOLOEHead(nn.Layer):
             else:
                 if aux_pred is None:
                     if not hasattr(self, "assigned_labels"):
-                        assigned_labels, assigned_bboxes, assigned_scores, mask_positive = \
+                        assigned_labels, assigned_bboxes, assigned_scores = \
                             self.assigner(
                             pred_scores.detach(),
                             pred_bboxes.detach() * stride_tensor,
@@ -427,15 +428,15 @@ class PPYOLOEHead(nn.Layer):
                             self.assigned_labels = assigned_labels
                             self.assigned_bboxes = assigned_bboxes
                             self.assigned_scores = assigned_scores
-                            self.mask_positive = mask_positive
+
                     else:
                         # only used in distill
                         assigned_labels = self.assigned_labels
                         assigned_bboxes = self.assigned_bboxes
                         assigned_scores = self.assigned_scores
-                        mask_positive = self.mask_positive
+
                 else:
-                    assigned_labels, assigned_bboxes, assigned_scores, mask_positive = \
+                    assigned_labels, assigned_bboxes, assigned_scores = \
                             self.assigner(
                             pred_scores_aux.detach(),
                             pred_bboxes_aux.detach() * stride_tensor,
@@ -451,14 +452,12 @@ class PPYOLOEHead(nn.Layer):
 
         assign_out_dict = self.get_loss_from_assign(
             pred_scores, pred_distri, pred_bboxes, anchor_points_s,
-            assigned_labels, assigned_bboxes, assigned_scores, mask_positive,
-            alpha_l)
+            assigned_labels, assigned_bboxes, assigned_scores, alpha_l)
 
         if aux_pred is not None:
             assign_out_dict_aux = self.get_loss_from_assign(
                 aux_pred[0], aux_pred[1], pred_bboxes_aux, anchor_points_s,
-                assigned_labels, assigned_bboxes, assigned_scores,
-                mask_positive, alpha_l)
+                assigned_labels, assigned_bboxes, assigned_scores, alpha_l)
             loss = {}
             for key in assign_out_dict.keys():
                 loss[key] = assign_out_dict[key] + assign_out_dict_aux[key]
@@ -469,7 +468,7 @@ class PPYOLOEHead(nn.Layer):
 
     def get_loss_from_assign(self, pred_scores, pred_distri, pred_bboxes,
                              anchor_points_s, assigned_labels, assigned_bboxes,
-                             assigned_scores, mask_positive, alpha_l):
+                             assigned_scores, alpha_l):
         # cls loss
         if self.use_varifocal_loss:
             one_hot_label = F.one_hot(assigned_labels,
@@ -490,7 +489,7 @@ class PPYOLOEHead(nn.Layer):
             self.distill_pairs['pred_cls_scores'] = pred_scores
             self.distill_pairs['pos_num'] = assigned_scores_sum
             self.distill_pairs['assigned_scores'] = assigned_scores
-            self.distill_pairs['mask_positive'] = mask_positive
+
             one_hot_label = F.one_hot(assigned_labels,
                                       self.num_classes + 1)[..., :-1]
             self.distill_pairs['target_labels'] = one_hot_label

test_tipc/configs/picodet/picodet_s_320_coco_lcnet_train_infer_python.txt

@@ -58,3 +58,5 @@ repeat:25
 flags:null
 ===========================infer_benchmark_params===========================
 numpy_infer_input:3x320x320_2.npy
+===========================to_static_train_benchmark_params===========================
+to_static_train:--to_static
\ No newline at end of file