Add Identity OP

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

@@ -0,0 +1,53 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle
+
+
+class TestIdentityAPI(unittest.TestCase):
+    def setUp(self):
+        self.shape = [4, 4]
+        self.x = np.random.random((4, 4)).astype(np.float32)
+        self.place = paddle.CPUPlace()
+
+    def test_api_static(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.fluid.data('X', self.shape)
+            id_layer = paddle.nn.Identity()
+            out = id_layer(x)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x}, fetch_list=[out])
+
+        out_ref = self.x
+        for out in res:
+            self.assertEqual(np.allclose(out, out_ref, rtol=1e-08), True)
+
+    def test_api_dygraph(self):
+        paddle.disable_static(self.place)
+        x_tensor = paddle.to_tensor(self.x)
+        id_layer = paddle.nn.Identity()
+        out = id_layer(x_tensor)
+
+        out_ref = self.x
+        self.assertEqual(np.allclose(out.numpy(), out_ref, rtol=1e-08), True)
+        paddle.enable_static()
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/nn/__init__.py

@@ -55,6 +55,7 @@
 from .layer.common import CosineSimilarity  # noqa: F401
 from .layer.common import Embedding  # noqa: F401
 from .layer.common import Linear  # noqa: F401
+from .layer.common import Identity  # noqa: F401
 from .layer.common import Flatten  # noqa: F401
 from .layer.common import Upsample  # noqa: F401
 from .layer.common import UpsamplingNearest2D  # noqa: F401

python/paddle/nn/layer/__init__.py

@@ -32,6 +32,7 @@
 from .common import CosineSimilarity  # noqa: F401
 from .common import Embedding  # noqa: F401
 from .common import Linear  # noqa: F401
+from .common import Identity  # noqa: F401
 from .common import Flatten  # noqa: F401
 from .common import Upsample  # noqa: F401
 from .common import Dropout  # noqa: F401

python/paddle/nn/layer/common.py

@@ -1,4 +1,4 @@
-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -30,6 +30,52 @@ def _npairs(x, n):
     return x
 
 
+class Identity(Layer):
+    r"""
+
+    A placeholder identity operator that is argument-insensitive. For each input :math:`X` ,
+    the output :math:`Out` is:
+
+    .. math::
+
+        Out = X
+
+    Parameters:
+        Any parameters which is not used
+
+    Shape:
+        - input: Multi-dimentional tensor with shape :math:`[batch\_size, n1, n2, ...]` .
+        - output: Multi-dimentional tensor with shape :math:`[batch\_size, n1, n2, ...]` .
+
+    Examples:
+        .. code-block:: python
+
+          import paddle
+          layer = paddle.nn.Identity(54)
+          input_tensor = paddle.to_tensor(paddle.randn(shape=[3, 2]))
+          input_tensor.stop_gradient=False
+          input_tensor = input_tensor+1
+          input_tensor.register_hook(lambda grad: print('input grad', grad))
+          print('input_tensor.grad', input_tensor.grad)
+          out = m(input_tensor)
+          # input_tensor: [[-0.32342386 -1.200079  ]
+          #                [ 0.7979031  -0.90978354]
+          #                [ 0.40597573  1.8095392 ]]
+          # out: [[-0.32342386 -1.200079  ]
+          #      [ 0.7979031  -0.90978354]
+          #      [ 0.40597573  1.8095392 ]]
+          out.backward()
+          print(out.shape, paddle.sum(input_tensor), paddle.sum(out))
+
+    """
+
+    def __init__(self, *args, **kwargs):
+        super(Identity, self).__init__()
+
+    def forward(self, input):
+        return input
+
+
 class Linear(Layer):
     r"""