From 50ac93f080c48e96693d9f69ee39c94f6cd2e03d Mon Sep 17 00:00:00 2001
From: nemonameless <nemonameless@qq.com>
Date: Thu, 29 Sep 2022 07:41:06 +0000
Subject: [PATCH] fix UT in in_dygraph_mode

---
 .../tests/unittests/test_box_coder_op.py      |   1 +
 .../tests/unittests/test_prior_box_op.py      |   2 -
 python/paddle/vision/ops.py                   | 103 +++++++++---------
 3 files changed, 55 insertions(+), 51 deletions(-)

diff --git a/python/paddle/fluid/tests/unittests/test_box_coder_op.py b/python/paddle/fluid/tests/unittests/test_box_coder_op.py
index 406f2c213e970..a1ed0115415cf 100644
--- a/python/paddle/fluid/tests/unittests/test_box_coder_op.py
+++ b/python/paddle/fluid/tests/unittests/test_box_coder_op.py
@@ -337,4 +337,5 @@ def test_dygraph_with_static(self):
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     unittest.main()
diff --git a/python/paddle/fluid/tests/unittests/test_prior_box_op.py b/python/paddle/fluid/tests/unittests/test_prior_box_op.py
index 7592079d63af9..0b05aef4f5e23 100644
--- a/python/paddle/fluid/tests/unittests/test_prior_box_op.py
+++ b/python/paddle/fluid/tests/unittests/test_prior_box_op.py
@@ -110,8 +110,6 @@ def init_test_params(self):
         self.flip = True
         self.set_min_max_aspect_ratios_order()
         self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
-        self.aspect_ratios = np.array(self.aspect_ratios,
-                                      dtype=np.float64).flatten()
         self.variances = [0.1, 0.1, 0.2, 0.2]
         self.variances = np.array(self.variances, dtype=np.float64).flatten()
 
diff --git a/python/paddle/vision/ops.py b/python/paddle/vision/ops.py
index 038ed40becf41..3c6a0c3724ce9 100755
--- a/python/paddle/vision/ops.py
+++ b/python/paddle/vision/ops.py
@@ -710,25 +710,6 @@ def prior_box(input,
             clip=True,
             flip=True)
     """
-    if in_dygraph_mode():
-        step_w, step_h = steps
-        if max_sizes == None:
-            max_sizes = []
-        box, var = _C_ops.prior_box(input, image, min_sizes, aspect_ratios,
-                                    variance, max_sizes, flip, clip, step_w,
-                                    step_h, offset, min_max_aspect_ratios_order)
-        return box, var
-
-    if _non_static_mode():
-        step_w, step_h = steps
-        if max_sizes == None:
-            max_sizes = []
-        box, var = _legacy_C_ops.prior_box(input, image, min_sizes,
-                                           aspect_ratios, variance, max_sizes,
-                                           flip, clip, step_w, step_h, offset,
-                                           min_max_aspect_ratios_order)
-        return box, var
-
     helper = LayerHelper("prior_box", **locals())
     dtype = helper.input_dtype()
     check_variable_and_dtype(input, 'input',
@@ -750,39 +731,63 @@ def _is_list_or_tuple_(data):
     aspect_ratios = list(map(float, aspect_ratios))
     steps = list(map(float, steps))
 
-    attrs = {
-        'min_sizes': min_sizes,
-        'aspect_ratios': aspect_ratios,
-        'variances': variance,
-        'flip': flip,
-        'clip': clip,
-        'step_w': steps[0],
-        'step_h': steps[1],
-        'offset': offset,
-        'min_max_aspect_ratios_order': min_max_aspect_ratios_order
-    }
+    cur_max_sizes = None
     if max_sizes is not None and len(max_sizes) > 0 and max_sizes[0] > 0:
         if not _is_list_or_tuple_(max_sizes):
             max_sizes = [max_sizes]
-        attrs['max_sizes'] = max_sizes
-
-    box = helper.create_variable_for_type_inference(dtype)
-    var = helper.create_variable_for_type_inference(dtype)
-    helper.append_op(
-        type="prior_box",
-        inputs={
-            "Input": input,
-            "Image": image
-        },
-        outputs={
-            "Boxes": box,
-            "Variances": var
-        },
-        attrs=attrs,
-    )
-    box.stop_gradient = True
-    var.stop_gradient = True
-    return box, var
+        cur_max_sizes = max_sizes
+
+    if in_dygraph_mode():
+        step_w, step_h = steps
+        if max_sizes == None:
+            max_sizes = []
+        box, var = _C_ops.prior_box(input, image, min_sizes, aspect_ratios,
+                                    variance, max_sizes, flip, clip, step_w,
+                                    step_h, offset, min_max_aspect_ratios_order)
+        return box, var
+
+    if _non_static_mode():
+        attrs = ('min_sizes', min_sizes, 'aspect_ratios', aspect_ratios,
+                 'variances', variance, 'flip', flip, 'clip', clip, 'step_w',
+                 steps[0], 'step_h', steps[1], 'offset', offset,
+                 'min_max_aspect_ratios_order', min_max_aspect_ratios_order)
+        if cur_max_sizes is not None:
+            attrs += ('max_sizes', cur_max_sizes)
+        box, var = _legacy_C_ops.prior_box(input, image, *attrs)
+        return box, var
+    else:
+        attrs = {
+            'min_sizes': min_sizes,
+            'aspect_ratios': aspect_ratios,
+            'variances': variance,
+            'flip': flip,
+            'clip': clip,
+            'step_w': steps[0],
+            'step_h': steps[1],
+            'offset': offset,
+            'min_max_aspect_ratios_order': min_max_aspect_ratios_order
+        }
+
+        if cur_max_sizes is not None:
+            attrs['max_sizes'] = cur_max_sizes
+
+        box = helper.create_variable_for_type_inference(dtype)
+        var = helper.create_variable_for_type_inference(dtype)
+        helper.append_op(
+            type="prior_box",
+            inputs={
+                "Input": input,
+                "Image": image
+            },
+            outputs={
+                "Boxes": box,
+                "Variances": var
+            },
+            attrs=attrs,
+        )
+        box.stop_gradient = True
+        var.stop_gradient = True
+        return box, var
 
 
 def box_coder(prior_box,