Add full_like op. (#23364)

* add full_like op. test=develop

* add dygraph support. test=develop

* increase coverage. test=develop

paddle/fluid/inference/tensorrt/convert/activation_op.cc

@@ -49,6 +49,7 @@ class ActivationOpConverter : public OpConverter {
       layer->setAlpha(0.);
       layer->setBeta(6.);
     }
+    g
 #endif
 
         auto output_name = op_desc.Output("Out")[0];

python/paddle/__init__.py

@@ -56,7 +56,7 @@ from .tensor.creation import zeros_like  #DEFINE_ALIAS
 # from .tensor.creation import eye   #DEFINE_ALIAS
 from .tensor.creation import full  #DEFINE_ALIAS
 # from .tensor.creation import linspace   #DEFINE_ALIAS
-# from .tensor.creation import full_like   #DEFINE_ALIAS
+from .tensor.creation import full_like  #DEFINE_ALIAS
 # from .tensor.creation import triu   #DEFINE_ALIAS
 # from .tensor.creation import tril   #DEFINE_ALIAS
 # from .tensor.creation import meshgrid   #DEFINE_ALIAS

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

@@ -17,6 +17,7 @@ from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
+from paddle.fluid import Program, program_guard
 import paddle.compat as cpt
 import unittest
 import numpy as np
@@ -96,6 +97,60 @@ class TestFillAnyLikeOpFloat16(TestFillAnyLikeOp):
         self.dtype = np.float16
 
 
+class TestFillAnyLikeOp_attr_out(unittest.TestCase):
+    """ Test fill_any_like op(whose API is full_like) for attr out. """
+
+    def test_attr_tensor_API(self):
+        startup_program = fluid.Program()
+        train_program = fluid.Program()
+        with fluid.program_guard(train_program, startup_program):
+            fill_value = 2.0
+            input = fluid.data(name='input', dtype='float32', shape=[2, 3])
+            output = paddle.full_like(input, fill_value)
+
+            place = fluid.CPUPlace()
+            if fluid.core.is_compiled_with_cuda():
+                place = fluid.CUDAPlace(0)
+            exe = fluid.Executor(place)
+            exe.run(startup_program)
+
+            img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
+
+            res = exe.run(train_program,
+                          feed={'input': img},
+                          fetch_list=[output])
+
+            out_np = np.array(res[0])
+            self.assertTrue(
+                not (out_np - np.full_like(img, fill_value)).any(),
+                msg="full_like output is wrong, out = " + str(out_np))
+
+
+class TestFillAnyLikeOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            #for ci coverage
+
+            input_data = fluid.data(name='input', dtype='float32', shape=[2, 3])
+            output = paddle.full_like(input_data, 2.0)
+
+            def test_input_dtype():
+                paddle.full_like
+
+            self.assertRaises(
+                ValueError,
+                paddle.full_like,
+                input=input_data,
+                fill_value=2,
+                dtype='uint4')
+            self.assertRaises(
+                TypeError,
+                paddle.full_like,
+                input=input_data,
+                fill_value=2,
+                dtype='int16')
+
+
 class ApiOnesLikeTest(unittest.TestCase):
     def test_out(self):
         with fluid.program_guard(fluid.Program()):

python/paddle/tensor/__init__.py

@@ -31,7 +31,7 @@ from .creation import linspace  #DEFINE_ALIAS
 # from .creation import eye   #DEFINE_ALIAS
 from .creation import full  # DEFINE_ALIAS
 # from .creation import linspace   #DEFINE_ALIAS
-# from .creation import full_like   #DEFINE_ALIAS
+from .creation import full_like  #DEFINE_ALIAS
 from .creation import triu  #DEFINE_ALIAS
 from .creation import tril  #DEFINE_ALIAS
 # from .creation import meshgrid   #DEFINE_ALIAS

python/paddle/tensor/creation.py

@@ -12,10 +12,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import print_function
+from ..fluid.framework import Variable
+from ..fluid.initializer import Constant
+from ..fluid.layers import core
+from ..fluid.layer_helper import LayerHelper
+from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
+from ..fluid.framework import convert_np_dtype_to_dtype_, in_dygraph_mode, _varbase_creator, device_guard, OpProtoHolder
+from ..fluid.layers import fill_constant
 from paddle.common_ops_import import *
 
 # TODO: define functions to get create a tensor  
-
 __all__ = [
     'create_tensor',
     #            'create_lod_tensor', 
@@ -33,13 +40,64 @@ __all__ = [
     #            'arrange',
     #            'eye',
     'full',
-    #            'full_like',
+    'full_like',
     'triu',
     'tril',
-    #            'meshgrid'
+    #            'meshgrid',
 ]
 
 
+def full_like(input,
+              fill_value,
+              out=None,
+              dtype=None,
+              device=None,
+              stop_gradient=True,
+              name=None):
+    """
+    **full_like**
+    This function creates a tensor filled with `fill_value` which has identical shape and dtype 
+    with `input`.
+    Args:
+        input(Variable): The input tensor which specifies shape and dtype.
+        fill_value: The value to fill the tensor with. Data type can be bool, float32, float64, int32, int64. Default value is 0.
+        out(Variable): The output tensor.
+    Returns:
+        out(Variable): The tensor variable storing the output.
+    Examples:
+        .. code-block:: python
+          import paddle
+          import paddle.fluid as fluid
+          import numpy as np
+
+          input = fluid.data(name='input', dtype='float32', shape=[2, 3])
+          output = paddle.full_like(input, 2.0)
+          exe = fluid.Executor(fluid.CPUPlace())
+          exe.run(fluid.default_startup_program())
+          img=np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
+          res = exe.run(fluid.default_main_program(), feed={'input':img}, fetch_list=[output])
+          print(res) # [array([[2., 2., 2.], [2., 2., 2.]], dtype=float32)]
+    """
+    helper = LayerHelper("full_like", **locals())
+
+    if dtype is None:
+        dtype = 'float32'
+
+    check_dtype(dtype, 'dtype',
+                ['bool', 'float16', 'float32', 'int32', 'int64'], 'full_like')
+
+    if out is None:
+        out = helper.create_variable_for_type_inference(dtype=dtype)
+    helper.append_op(
+        type='fill_any_like',
+        inputs={'X': [input]},
+        attrs={'value': fill_value},
+        outputs={'Out': [out]})
+    out.stop_gradient = stop_gradient
+
+    return out
+
+
 def linspace(start, stop, num, dtype, out=None, device=None, name=None):
     """
     This OP return fixed number of evenly spaced values within a given interval.