[cherry pick] fix sample code diff for OP(retinanet_detection_output,...

[cherry pick] fix sample code diff for OP(retinanet_detection_output, retinanet_target_assign), fix  default value for OP(sigmoid_focal_loss) (#23744). test=release/2.0-beta (#23858)

python/paddle/fluid/layers/detection.py

@@ -233,7 +233,7 @@ def retinanet_target_assign(bbox_pred,
                             dtype='float32')
           is_crowd = fluid.data(name='is_crowd', shape=[1],
                             dtype='float32')
-          im_info = fluid.data(name='im_infoss', shape=[1, 3],
+          im_info = fluid.data(name='im_info', shape=[1, 3],
                             dtype='float32')
           score_pred, loc_pred, score_target, loc_target, bbox_inside_weight, fg_num = \\
                 fluid.layers.retinanet_target_assign(bbox_pred, cls_logits, anchor_box,
@@ -452,7 +452,7 @@ def rpn_target_assign(bbox_pred,
     return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox, bbox_inside_weight
 
 
-def sigmoid_focal_loss(x, label, fg_num, gamma=2, alpha=0.25):
+def sigmoid_focal_loss(x, label, fg_num, gamma=2.0, alpha=0.25):
     """
     **Sigmoid Focal Loss Operator.**
 
@@ -493,9 +493,9 @@ def sigmoid_focal_loss(x, label, fg_num, gamma=2, alpha=0.25):
             is int32.
         fg_num(Variable): A 1-D tensor with shape [1] represents the number of positive samples in a
             mini-batch, which should be obtained before this OP. The data type of :attr:`fg_num` is int32.
-        gamma(float): Hyper-parameter to balance the easy and hard examples. Default value is
+        gamma(int|float): Hyper-parameter to balance the easy and hard examples. Default value is
             set to 2.0.
-        alpha(float): Hyper-parameter to balance the positive and negative example. Default value
+        alpha(int|float): Hyper-parameter to balance the positive and negative example. Default value
             is set to 0.25.
 
     Returns:
@@ -514,7 +514,7 @@ def sigmoid_focal_loss(x, label, fg_num, gamma=2, alpha=0.25):
             loss = fluid.layers.sigmoid_focal_loss(x=input,
                                                    label=label,
                                                    fg_num=fg_num,
-                                                   gamma=2.,
+                                                   gamma=2.0,
                                                    alpha=0.25)
     """
 
@@ -2914,7 +2914,7 @@ def retinanet_detection_output(bboxes,
                                nms_top_k=1000,
                                keep_top_k=100,
                                nms_threshold=0.3,
-                               nms_eta=1.):
+                               nms_eta=1.0):
     """
     **Detection Output Layer for the detector RetinaNet.**
 
@@ -3010,15 +3010,15 @@ def retinanet_detection_output(bboxes,
            im_info = fluid.data(
                name="im_info", shape=[1, 3], dtype='float32')
            nmsed_outs = fluid.layers.retinanet_detection_output(
-                                          bboxes=[bboxes_low, bboxes_high],
-                                          scores=[scores_low, scores_high],
-                                          anchors=[anchors_low, anchors_high],
-                                          im_info=im_info,
-                                          score_threshold=0.05,
-                                          nms_top_k=1000,
-                                          keep_top_k=100,
-                                          nms_threshold=0.45,
-                                          nms_eta=1.)
+               bboxes=[bboxes_low, bboxes_high],
+               scores=[scores_low, scores_high],
+               anchors=[anchors_low, anchors_high],
+               im_info=im_info,
+               score_threshold=0.05,
+               nms_top_k=1000,
+               keep_top_k=100,
+               nms_threshold=0.45,
+               nms_eta=1.0)
     """
 
     check_type(bboxes, 'bboxes', (list), 'retinanet_detection_output')