refine convert type for numpy type (#22386)

python/paddle/fluid/data_feeder.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 from . import core
-import numpy
+import numpy as np
 import os
 import six
 from six.moves import zip, range, xrange
@@ -47,6 +47,12 @@ def convert_dtype(dtype):
             return 'int64'
         elif dtype == core.VarDesc.VarType.UINT8:
             return 'uint8'
+    elif isinstance(dtype, type):
+        if dtype in [
+                np.bool, np.float16, np.float32, np.float64, np.int8, np.int16,
+                np.int32, np.int64, np.uint8
+        ]:
+            return dtype.__name__
     else:
         if dtype in [
                 'bool', 'float16', 'float32', 'float64', 'int8', 'int16',
@@ -136,7 +142,7 @@ class DataToLoDTensorConverter(object):
                     format(self.shape, shape))
 
     def done(self):
-        arr = numpy.array(self.data, dtype=self.dtype)
+        arr = np.array(self.data, dtype=self.dtype)
         if self.shape:
             if len(arr.shape) != len(self.shape):
                 try:

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

@@ -216,20 +216,24 @@ class TestFillConstantAPI(unittest.TestCase):
         out_5 = fluid.layers.fill_constant(
             shape=shape_tensor_int64, dtype="float32", value=1.1)
 
+        out_6 = fluid.layers.fill_constant(
+            shape=shape_tensor_int64, dtype=np.float32, value=1.1)
+
         exe = fluid.Executor(place=fluid.CPUPlace())
-        res_1, res_2, res_3, res_4, res_5 = exe.run(
+        res_1, res_2, res_3, res_4, res_5, res_6 = exe.run(
             fluid.default_main_program(),
             feed={
                 "shape_tensor_int32": np.array([1, 2]).astype("int32"),
                 "shape_tensor_int64": np.array([1, 2]).astype("int64"),
             },
-            fetch_list=[out_1, out_2, out_3, out_4, out_5])
+            fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6])
 
         assert np.array_equal(res_1, np.full([1, 2], 1.1, dtype="float32"))
         assert np.array_equal(res_2, np.full([1, 2], 1.1, dtype="float32"))
         assert np.array_equal(res_3, np.full([1, 2], 1.1, dtype="float32"))
         assert np.array_equal(res_4, np.full([1, 2], 1.1, dtype="float32"))
         assert np.array_equal(res_5, np.full([1, 2], 1.1, dtype="float32"))
+        assert np.array_equal(res_6, np.full([1, 2], 1.1, dtype="float32"))
 
 
 class TestFillConstantOpError(unittest.TestCase):