Split some APIs from nn.py to loss.py (#21117)

* Split some APIs from nn.py to loss.py

test=develop

* fix test_detection unit-test

test=develop

python/paddle/fluid/layers/__init__.py

@@ -28,6 +28,8 @@ from . import device
 from .device import *
 from . import math_op_patch
 from .math_op_patch import *
+from . import loss
+from .loss import *
 from . import detection
 from .detection import *
 from . import metric_op
@@ -50,6 +52,7 @@ __all__ += metric_op.__all__
 __all__ += learning_rate_scheduler.__all__
 __all__ += distributions.__all__
 __all__ += sequence_lod.__all__
+__all__ += loss.__all__
 __all__ += rnn.__all__
 
 from .rnn import *

python/paddle/fluid/layers/detection.py

@@ -21,6 +21,7 @@ from .layer_function_generator import generate_layer_fn
 from .layer_function_generator import autodoc, templatedoc
 from ..layer_helper import LayerHelper
 from ..framework import Variable
+from .loss import softmax_with_cross_entropy
 from . import tensor
 from . import nn
 from . import ops
@@ -1540,7 +1541,7 @@ def ssd_loss(location,
     target_label = tensor.cast(x=target_label, dtype='int64')
     target_label = __reshape_to_2d(target_label)
     target_label.stop_gradient = True
-    conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
+    conf_loss = softmax_with_cross_entropy(confidence, target_label)
     # 3. Mining hard examples
     actual_shape = nn.slice(conf_shape, axes=[0], starts=[0], ends=[2])
     actual_shape.stop_gradient = True
@@ -1594,7 +1595,7 @@ def ssd_loss(location,
     target_label = __reshape_to_2d(target_label)
     target_label = tensor.cast(x=target_label, dtype='int64')
 
-    conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
+    conf_loss = softmax_with_cross_entropy(confidence, target_label)
     target_conf_weight = __reshape_to_2d(target_conf_weight)
     conf_loss = conf_loss * target_conf_weight