To static (#7851)

* [TIPC] fcos add static train

* tinypose_128x96 add training static

* [TIPC] tinypose add static training

* disable maskrcnn static training

ppdet/engine/export_utils.py

@@ -82,6 +82,66 @@ TO_STATIC_SPEC = {
         'target2': paddle.static.InputSpec(
             name='target2', shape=[-1, 3, 86, -1, -1], dtype='float32'),
     }],
+    'tinypose_128x96': [{
+        'center': paddle.static.InputSpec(
+            name='center', shape=[-1, 2], dtype='float32'),
+        'scale': paddle.static.InputSpec(
+            name='scale', shape=[-1, 2], dtype='float32'),
+        'im_id': paddle.static.InputSpec(
+            name='im_id', shape=[-1, 1], dtype='float32'),
+        'image': paddle.static.InputSpec(
+            name='image', shape=[-1, 3, 128, 96], dtype='float32'),
+        'score': paddle.static.InputSpec(
+            name='score', shape=[-1], dtype='float32'),
+        'rotate': paddle.static.InputSpec(
+            name='rotate', shape=[-1], dtype='float32'),
+        'target': paddle.static.InputSpec(
+            name='target', shape=[-1, 17, 32, 24], dtype='float32'),
+        'target_weight': paddle.static.InputSpec(
+            name='target_weight', shape=[-1, 17, 1], dtype='float32'),
+    }],
+    'fcos_r50_fpn_1x_coco': [{
+        'im_id': paddle.static.InputSpec(
+            name='im_id', shape=[-1, 1], 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'),
+        'reg_target0': paddle.static.InputSpec(
+            name='reg_target0', shape=[-1, 160, 160, 4], dtype='float32'),
+        'labels0': paddle.static.InputSpec(
+            name='labels0', shape=[-1, 160, 160, 1], dtype='int32'),
+        'centerness0': paddle.static.InputSpec(
+            name='centerness0', shape=[-1, 160, 160, 1], dtype='float32'),
+        'reg_target1': paddle.static.InputSpec(
+            name='reg_target1', shape=[-1, 80, 80, 4], dtype='float32'),
+        'labels1': paddle.static.InputSpec(
+            name='labels1', shape=[-1, 80, 80, 1], dtype='int32'),
+        'centerness1': paddle.static.InputSpec(
+            name='centerness1', shape=[-1, 80, 80, 1], dtype='float32'),
+        'reg_target2': paddle.static.InputSpec(
+            name='reg_target2', shape=[-1, 40, 40, 4], dtype='float32'),
+        'labels2': paddle.static.InputSpec(
+            name='labels2', shape=[-1, 40, 40, 1], dtype='int32'),
+        'centerness2': paddle.static.InputSpec(
+            name='centerness2', shape=[-1, 40, 40, 1], dtype='float32'),
+        'reg_target3': paddle.static.InputSpec(
+            name='reg_target3', shape=[-1, 20, 20, 4], dtype='float32'),
+        'labels3': paddle.static.InputSpec(
+            name='labels3', shape=[-1, 20, 20, 1], dtype='int32'),
+        'centerness3': paddle.static.InputSpec(
+            name='centerness3', shape=[-1, 20, 20, 1], dtype='float32'),
+        'reg_target4': paddle.static.InputSpec(
+            name='reg_target4', shape=[-1, 10, 10, 4], dtype='float32'),
+        'labels4': paddle.static.InputSpec(
+            name='labels4', shape=[-1, 10, 10, 1], dtype='int32'),
+        'centerness4': paddle.static.InputSpec(
+            name='centerness4', shape=[-1, 10, 10, 1], dtype='float32'),
+    }],
 }
 
 

ppdet/modeling/losses/fcos_loss.py

@@ -69,12 +69,12 @@ class FCOSLoss(nn.Layer):
         self.reg_weights = reg_weights
         self.quality = quality
 
-    def __iou_loss(self,
-                   pred,
-                   targets,
-                   positive_mask,
-                   weights=None,
-                   return_iou=False):
+    def _iou_loss(self,
+                  pred,
+                  targets,
+                  positive_mask,
+                  weights=None,
+                  return_iou=False):
         """
         Calculate the loss for location prediction
         Args:
@@ -216,7 +216,7 @@ class FCOSLoss(nn.Layer):
             # 2. bboxes_reg: giou_loss
             mask_positive_float = paddle.squeeze(mask_positive_float, axis=-1)
             tag_center_flatten = paddle.squeeze(tag_center_flatten, axis=-1)
-            reg_loss = self.__iou_loss(
+            reg_loss = self._iou_loss(
                 bboxes_reg_flatten,
                 tag_bboxes_flatten,
                 mask_positive_float,
@@ -233,7 +233,7 @@ class FCOSLoss(nn.Layer):
             # 2. bboxes_reg: giou_loss
             mask_positive_float = paddle.squeeze(mask_positive_float, axis=-1)
             tag_center_flatten = paddle.squeeze(tag_center_flatten, axis=-1)
-            reg_loss = self.__iou_loss(
+            reg_loss = self._iou_loss(
                 bboxes_reg_flatten,
                 tag_bboxes_flatten,
                 mask_positive_float,
@@ -243,7 +243,7 @@ class FCOSLoss(nn.Layer):
 
             # 3. centerness: sigmoid_cross_entropy_with_logits_loss
             centerness_flatten = paddle.squeeze(centerness_flatten, axis=-1)
-            gt_ious = self.__iou_loss(
+            gt_ious = self._iou_loss(
                 bboxes_reg_flatten,
                 tag_bboxes_flatten,
                 mask_positive_float,

test_tipc/configs/fcos/fcos_r50_fpn_1x_coco_train_infer_python.txt

@@ -57,4 +57,6 @@ repeat:3
 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
 flags:null
 ===========================infer_benchmark_params===========================
-numpy_infer_input:3x800x1344_2.npy
+numpy_infer_input:3x800x1344_2.npy
\ No newline at end of file
+===========================to_static_train_benchmark_params===========================
+to_static_train:--to_static
\ No newline at end of file

test_tipc/configs/keypoint/tinypose_128x96_train_infer_python.txt

@@ -57,4 +57,6 @@ repeat:1
 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
 flags:null
 ===========================infer_benchmark_params===========================
-random_infer_input:[{float32,[3,128,96]}]
+random_infer_input:[{float32,[3,128,96]}]
\ No newline at end of file
+===========================to_static_train_benchmark_params===========================
+to_static_train:--to_static
\ No newline at end of file