（fluid清理）Remove filter by instag in nn.py under fluid (#47929)

python/paddle/fluid/layers/nn.py

@@ -23,7 +23,7 @@ import numpy as np
 import paddle
 from ..layer_helper import LayerHelper
 from paddle.fluid.framework import _in_legacy_dygraph
-from ..initializer import Normal, Constant, NumpyArrayInitializer
+from ..initializer import Normal, Constant
 from ..framework import (
     Variable,
     OpProtoHolder,
@@ -182,7 +182,6 @@ __all__ = [
     'sign',
     'unfold',
     'deformable_roi_pooling',
-    'filter_by_instag',
     'shard_index',
     'hard_swish',
     'mish',
@@ -9951,75 +9950,6 @@ def flatten(x, axis=1, name=None):
     return out
 
 
-@templatedoc(op_type="filter_by_instag")
-def filter_by_instag(ins, ins_tag, filter_tag, is_lod, out_val_if_empty=0):
-    """
-    **Filter By Instag Layer**
-
-    This function filter a batch of ins by instag,
-    There are multiple ins, and every ins belongs to some tags.
-    We can specify some tags we want. So the ins which belongs to that tags
-    remains in the output, and others removed.
-
-    For example, one batch has 4 ins. Every ins has its tag list.
-
-       | Ins   |   Ins_Tag |
-       |:-----:|:------:|
-       |  0    |   0, 1 |
-       |  1    |   1, 3 |
-       |  2    |   0, 3 |
-       |  3    |   2, 6 |
-
-    And Lod is [1,1,1,1]
-
-    And the filter tags [1]
-
-    From the definition above, ins which has tag 1 can pass the filter
-    So Ins 0 and Ins 1 can pass and be seen in the output,
-    Ins 2 and 3 cannot pass because they do not has tag 1.
-
-    Actually, if is_lod is false, it is normal tensor that equals to
-    lod_tensor with all 1, similar to the example above.
-
-    Args:
-        ins (Variable): Input Variable (LoDTensor), usually it is 2D tensor
-                        And first dimension can have lod info or not.
-        ins_tag (Variable): Input Variable (LoDTensor), usually it is 1D list
-                        And split them by lod info
-        filter_tag (Variable): Input Variable (1D Tensor/List), usually it is
-                        list that holds the tags.
-        is_lod (Bool): Boolean value to indicate ins is lod tensor or not.
-        out_val_if_empty(Int64): If the output after filter is empty, this value
-                        will be set to Output tensor.
-
-    Returns:
-        Variable: filtered ins (LoDTensor) and loss weight (Tensor)
-
-    Examples:
-        .. code-block:: python
-
-          import paddle.fluid.layers as layers
-          ins = layers.data(name='Ins', shape=[-1,32], lod_level=0, dtype='float64')
-          ins_tag = layers.data(name='Ins_tag', shape=[-1,16], lod_level=0, dtype='int64')
-          filter_tag = layers.data(name='Filter_tag', shape=[-1,16], dtype='int64')
-          out, loss_weight = layers.filter_by_instag(ins,  ins_tag,  filter_tag, True)
-
-    """
-    helper = LayerHelper('filter_by_instag', **locals())
-
-    out = helper.create_variable_for_type_inference(dtype=ins.dtype)
-    loss_weight = helper.create_variable_for_type_inference(dtype=np.float64)
-    mmap = helper.create_variable_for_type_inference(dtype=ins_tag.dtype)
-    helper.append_op(
-        type='filter_by_instag',
-        inputs={'Ins': ins, 'Ins_tag': ins_tag, 'Filter_tag': filter_tag},
-        outputs={'Out': out, 'LossWeight': loss_weight, 'IndexMap': mmap},
-        attrs={'is_lod': is_lod, 'out_val_if_empty': out_val_if_empty},
-    )
-
-    return [out, loss_weight]
-
-
 @deprecated(since='2.0.0', update_to="paddle.expand")
 def expand(x, expand_times, name=None):
     """

python/paddle/fluid/tests/unittests/test_layers.py

@@ -4284,29 +4284,6 @@ class TestBook(LayerTest):
             )
             return out
 
-    def test_filter_by_instag(self):
-        # TODO(minqiyang): dygraph do not support lod now
-        with self.static_graph():
-            x1 = layers.data(
-                name='Ins', shape=[32, 1], dtype='float32', lod_level=0
-            )
-            x2 = layers.data(
-                name='Ins_tag',
-                shape=[32, 1],
-                dtype='int64',
-                lod_level=0,
-                stop_gradient=True,
-            )
-            x3 = layers.create_global_var(
-                shape=[1, 1],
-                value=20,
-                dtype='int64',
-                persistable=True,
-                force_cpu=True,
-                name='Filter_tag',
-            )
-            out1, out2 = layers.filter_by_instag(x1, x2, x3, is_lod=True)
-
     def test_shuffle_batch(self):
         # TODO(minqiyang): dygraph do not support lod now
         with self.static_graph():