text: #   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.
  # 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.
  
  from __future__ import print_function
  
  import numpy as np
  import unittest
  
  import paddle
  import paddle.nn as nn
  import paddle.fluid as fluid
  import paddle.fluid.framework as framework
  import paddle.nn.initializer as initializer
  from paddle.fluid.core import VarDesc
  
  DELTA = 0.00001
  
  
  def get_uniform_min_and_max(weight):
      min_value = np.min(weight)
      max_value = np.max(weight)
      return min_value, max_value
  
  
  def check_cast_op(op):
      return op.type == 'cast' and \
             op.attr('in_dtype') == VarDesc.VarType.FP32 and \
             op.attr('out_dtype') in [VarDesc.VarType.FP16, VarDesc.VarType.BF16]
  
  
  class TestConstantInitializer(unittest.TestCase):
      def static_test_constant_initializer_common(self,
                                                  init_inst,
                                                  dtype="float32",
                                                  value_target=0.0):
          paddle.enable_static()
          program = framework.Program()
          block = program.global_block()
          for _ in range(2):
              block.create_parameter(
                  dtype=dtype,
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=init_inst)
          num_ops = 1
          self.assertEqual(len(block.ops), num_ops)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'fill_constant')
          self.assertAlmostEqual(init_op.attr('value'), value_target, delta=DELTA)
          paddle.disable_static()
          return block
  
      def test_constant_initializer_default_value_static(self, dtype="float32"):
          """Test the constant initializer with default value in static graph
          """
          block = self.static_test_constant_initializer_common(
              init_inst=initializer.Constant(), dtype=dtype, value_target=0.0)
          return block
  
      def test_constant_initializer_default_value_dygraph(self, dtype="float32"):
          """Test constant initializer with supplied value in dygraph
          """
          with fluid.dygraph.guard():
              linear = nn.Linear(2, 4, weight_attr=nn.initializer.Constant())
              mat_target = np.ones((2, 4), dtype=dtype) * 0.0
              mat_linear = linear.weight.numpy()
              mismatch = np.sum(
                  (mat_target - mat_linear) * (mat_target - mat_linear))
              self.assertAlmostEqual(mismatch, 0.0, delta=DELTA)
  
      def test_constant_initializer_static(self, dtype="float32"):
          """Test constant initializer with supplied value in static graph
          """
          block = self.static_test_constant_initializer_common(
              init_inst=initializer.Constant(2.3), dtype=dtype, value_target=2.3)
          return block
  
      def test_constant_initializer_dygraph(self, dtype="float32"):
          """Test constant initializer with supplied value in dygraph
          """
          with fluid.dygraph.guard():
              linear = nn.Linear(
                  2, 4, weight_attr=nn.initializer.Constant(value=2.0))
              mat_target = np.ones((2, 4), dtype=dtype) * 2.0
              mat_linear = linear.weight.numpy()
              mismatch = np.sum(
                  (mat_target - mat_linear) * (mat_target - mat_linear))
              self.assertAlmostEqual(mismatch, 0.0, delta=DELTA)
  
      def test_constant_initializer_fp16(self):
          """Test constant initializer with float16
          """
          block = self.test_constant_initializer_default_value_static("float16")
          block = self.test_constant_initializer_static("float16")
          self.test_constant_initializer_default_value_dygraph("float16")
          self.test_constant_initializer_dygraph("float16")
  
      def test_constant_initializer_bf16(self):
          """Test constant initializer with bfloat16
              No cast operator has been added here
          """
          self.test_constant_initializer_default_value_static("uint16")  #bfloat16
          self.test_constant_initializer_static("uint16")  #bfloat16
  
  
  class TestKaimingInitializer(unittest.TestCase):
      def static_test_kaiming_initializer_common(self,
                                                 init_inst,
                                                 dtype="float32",
                                                 uniform=False,
                                                 is_conv=False):
          paddle.enable_static()
          program = framework.Program()
          block = program.global_block()
          shape_mat = [5, 10, 15, 20] if is_conv else [5, 10]
          for _ in range(2):
              param = block.create_parameter(
                  dtype="float32",
                  shape=shape_mat,
                  lod_level=0,
                  name="param",
                  initializer=init_inst)
          self.assertEqual(len(block.ops), 1)
          init_op = block.ops[0]
          if uniform:
              self.assertEqual(init_op.type, 'uniform_random')
              if is_conv:
                  receptive_field_size = float(15 * 20)
                  limit = np.sqrt(6.0 / (param.shape[1] * receptive_field_size))
              else:
                  limit = np.sqrt(6.0 / param.shape[0])
              self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
              self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
          else:
              self.assertEqual(init_op.type, 'gaussian_random')
              if is_conv:
                  receptive_field_size = float(15 * 20)
                  std = np.sqrt(2.0 / (param.shape[1] * receptive_field_size))
              else:
                  std = np.sqrt(2.0 / param.shape[0])
              self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
              self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
          paddle.disable_static()
  
      def dygraph_test_kaiming_initializer_common(self,
                                                  init_inst,
                                                  dtype="float32",
                                                  uniform=False):
          linear = nn.Linear(40, 20, weight_attr=init_inst)
  
      def test_kaiming_dygraph(self):
          self.dygraph_test_kaiming_initializer_common(
              init_inst=initializer.KaimingUniform(),
              dtype="float32",
              uniform=True)
          self.dygraph_test_kaiming_initializer_common(
              init_inst=initializer.KaimingNormal(),
              dtype="float32",
              uniform=False)
  
      def test_kaiming_uniform_initializer_static(self):
          """Test Kaiming unorm initializer for matrix multiply.
          """
          self.static_test_kaiming_initializer_common(
              init_inst=initializer.KaimingUniform(),
              dtype="float32",
              uniform=True,
              is_conv=False)
  
      def test_kaiming_uniform_initializer_conv_static(self):
          """Test Kaiming unorm initializer for convolutions.
          """
          self.static_test_kaiming_initializer_common(
              init_inst=initializer.KaimingUniform(),
              dtype="float32",
              uniform=True,
              is_conv=True)
  
      def test_kaiming_normal_initializer_static(self):
          """Test Kaiming normal initializer for matrix multiply.
          """
          self.static_test_kaiming_initializer_common(
              init_inst=initializer.KaimingNormal(),
              dtype="float32",
              uniform=False,
              is_conv=False)
  
      def test_kaiming_normal_initializer_conv_static(self):
          """Test Kaiming normal initializer for convolutions.
          """
          self.static_test_kaiming_initializer_common(
              init_inst=initializer.KaimingNormal(),
              dtype="float32",
              uniform=False,
              is_conv=True)
  
  
  class TestUniform(unittest.TestCase):
      def test_uniform_common(self, dtype="float32", seed=0):
          """Test the uniform initializer with default value
          """
          paddle.enable_static()
  
          program = framework.Program()
          program.random_seed = seed
          block = program.global_block()
          for _ in range(2):
              block.create_parameter(
                  dtype=dtype,
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=initializer.Uniform())
          num_ops = 2 if dtype == "float16" else 1
          self.assertEqual(len(block.ops), num_ops)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'uniform_random')
          self.assertAlmostEqual(init_op.attr('min'), -1.0, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('max'), 1.0, delta=DELTA)
          self.assertEqual(init_op.attr('seed'), seed)
  
          paddle.disable_static()
  
          return block
  
      def test_uniform_initializer_default_value(self,
                                                 dtype="float32",
                                                 seed=0,
                                                 min_value=-1.0,
                                                 max_vlaue=1.0):
          """Test the uniform initializer with default value
          """
          paddle.enable_static()
  
          program = framework.Program()
          program.random_seed = seed
          block = program.global_block()
          for _ in range(2):
              block.create_parameter(
                  dtype=dtype,
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=initializer.Uniform())
          num_ops = 2 if dtype == "float16" else 1
          self.assertEqual(len(block.ops), num_ops)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'uniform_random')
          self.assertAlmostEqual(init_op.attr('min'), min_value, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('max'), max_vlaue, delta=DELTA)
          self.assertEqual(init_op.attr('seed'), seed)
  
          paddle.disable_static()
  
          return block
  
      def test_uniform_initializer(self,
                                   dtype="float32",
                                   seed=0,
                                   min_value=-4.2,
                                   max_vlaue=3.1):
          """Test uniform initializer with supplied attributes
          """
          paddle.enable_static()
  
          program = framework.Program()
          program.random_seed = seed
          block = program.global_block()
          for _ in range(2):
              block.create_parameter(
                  dtype=dtype,
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=initializer.Uniform(min_value, max_vlaue))
          num_ops = 2 if dtype == "float16" else 1
          self.assertEqual(len(block.ops), num_ops)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'uniform_random')
          self.assertAlmostEqual(init_op.attr('min'), min_value, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('max'), max_vlaue, delta=DELTA)
  
          paddle.disable_static()
  
          return block
  
      def test_uniform_initializer_two_op(self,
                                          dtype="float32",
                                          seed=123,
                                          min_value=-4.2,
                                          max_vlaue=0.0):
          """Test uniform initializer with supplied attributes
          """
          paddle.enable_static()
  
          program = framework.Program()
          program.random_seed = seed
          block = program.global_block()
          for i in range(2):
              block.create_parameter(
                  dtype=dtype,
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=initializer.Uniform(min_value, float(i)))
          num_ops = 2 if dtype == "float16" else 1
          self.assertEqual(len(block.ops), num_ops)
          init_op0 = block.ops[0]
          self.assertEqual(init_op0.type, 'uniform_random')
          self.assertAlmostEqual(init_op0.attr('min'), min_value, delta=DELTA)
          self.assertAlmostEqual(init_op0.attr('max'), 0.0, delta=DELTA)
          self.assertEqual(init_op0.attr("seed"), seed)
  
          paddle.disable_static()
  
          return block
  
      def test_uniform_initializer_fp16(self):
          """Test uniform initializer with float16
          """
          block = self.test_uniform_initializer_default_value("float16")
          self.assertTrue(check_cast_op(block.ops[1]))
          block = self.test_uniform_initializer(dtype="float16")
          self.assertTrue(check_cast_op(block.ops[1]))
          block = self.test_uniform_initializer_two_op("float16")
          self.assertTrue(check_cast_op(block.ops[1]))
  
      def test_uniform_initializer_bf16(self):
          """Test uniform initializer with bfloat16
          """
          block = self.test_uniform_initializer_default_value("uint16")  #bfloat16
          block = self.test_uniform_initializer(dtype="uint16")  #bfloat16
          block = self.test_uniform_initializer_two_op("uint16")  #bfloat16
  
      def test_uniform_initializer_dygraph(self):
          """Test uniform initializer in dygraph model.
          """
          paddle.disable_static()
  
          weight_attr = paddle.framework.ParamAttr(
              name="linear_weight",
              initializer=paddle.nn.initializer.Uniform(
                  low=-0.5, high=0.5))
          linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
  
          min_value, max_value = get_uniform_min_and_max(linear.weight.numpy())
          self.assertTrue(min_value >= -0.5,
                          'min value {} should >= -0.5'.format(min_value))
          self.assertTrue(max_value <= 0.5,
                          'max value {} should <= 0.5'.format(max_value))
  
  
  class TestNormal(unittest.TestCase):
      def test_normal_initializer_default_value(self):
          """Test the normal initializer with default value
          """
          paddle.enable_static()
  
          program = framework.Program()
          block = program.global_block()
          for _ in range(2):
              block.create_parameter(
                  dtype="float32",
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=initializer.Normal())
          self.assertEqual(len(block.ops), 1)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'gaussian_random')
          self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('std'), 1.0, delta=DELTA)
          self.assertEqual(init_op.attr('seed'), 0)
  
          paddle.disable_static()
  
      def test_normal_initializer(self, dtype="float32"):
          """Test normal initializer with supplied attributes
          """
          paddle.enable_static()
  
          program = framework.Program()
          block = program.global_block()
          for _ in range(2):
              block.create_parameter(
                  dtype=dtype,
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=initializer.Normal(2.3, 1.9))
          num_ops = 2 if dtype in ["float16", "uint16"] else 1
          self.assertEqual(len(block.ops), num_ops)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'gaussian_random')
          self.assertAlmostEqual(init_op.attr('mean'), 2.3, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('std'), 1.9, delta=DELTA)
  
          paddle.disable_static()
  
          return block
  
      def test_normal_initializer_fp16(self):
          """Test normal initializer with float16
          """
          block = self.test_normal_initializer("float16")
  
      def test_normal_initializer_bf16(self):
          """Test normal initializer with bfloat16
          """
          block = self.test_normal_initializer("uint16")  #bfloat16
  
      def test_normal_initializer_dygraph(self):
          """Test normal initializer in dygraph model.
          """
          paddle.disable_static()
  
          weight_attr = paddle.framework.ParamAttr(
              name="linear_weight",
              initializer=paddle.nn.initializer.Normal(
                  mean=0.0, std=2.0))
          linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
  
  
  class TestTruncatedNormal(unittest.TestCase):
      def test_truncated_normal_initializer_default_value(self):
          """Test the truncated normal initializer with default value
          """
          paddle.enable_static()
  
          program = framework.Program()
          block = program.global_block()
          for _ in range(2):
              block.create_parameter(
                  dtype="float32",
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=initializer.TruncatedNormal())
          self.assertEqual(len(block.ops), 1)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'truncated_gaussian_random')
          self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('std'), 1.0, delta=DELTA)
          self.assertEqual(init_op.attr('seed'), 0)
  
          paddle.disable_static()
  
      def test_truncated_normal_initializer(self, dtype="float32"):
          """Test truncated normal initializer with supplied attributes
          """
          paddle.enable_static()
  
          program = framework.Program()
          block = program.global_block()
          for _ in range(2):
              block.create_parameter(
                  dtype=dtype,
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=initializer.TruncatedNormal(2.3, 1.9))
          num_ops = 2 if dtype in ["float16", "uint16"] else 1
          self.assertEqual(len(block.ops), num_ops)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'truncated_gaussian_random')
          self.assertAlmostEqual(init_op.attr('mean'), 2.3, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('std'), 1.9, delta=DELTA)
  
          paddle.disable_static()
  
          return block
  
      def test_truncated_normal_initializer_fp16(self):
          """Test truncated normal initializer with float16
          """
          paddle.enable_static()
  
          block = self.test_truncated_normal_initializer("float16")
          self.assertTrue(check_cast_op(block.ops[1]))
  
      def test_truncated_normal_initializer_bf16(self):
          """Test truncated normal initializer with bfloat16
          """
          paddle.enable_static()
  
          block = self.test_truncated_normal_initializer("uint16")  #bfloat16
          self.assertTrue(check_cast_op(block.ops[1]))
  
      def test_truncated_normal_initializer_dygraph(self):
          """Test truncated normal initializer in dygraph model.
          """
          paddle.disable_static()
  
          weight_attr = paddle.framework.ParamAttr(
              name="linear_weight",
              initializer=paddle.nn.initializer.TruncatedNormal(
                  mean=0.0, std=2.0))
          linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
  
  
  class TestXavierUniform(unittest.TestCase):
      def test_xavier_uniform_initializer(self):
          """Test Xavier initializer with uniform distribution on
             for matrix multiply.
          """
          paddle.enable_static()
  
          program = framework.Program()
          block = program.global_block()
          for _ in range(2):
              param = block.create_parameter(
                  dtype="float32",
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=initializer.XavierUniform())
          self.assertEqual(len(block.ops), 1)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'uniform_random')
          limit = np.sqrt(6.0 / (param.shape[0] + param.shape[1]))
          self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
          self.assertEqual(init_op.attr('seed'), 0)
  
          paddle.disable_static()
  
      def test_xavier_uniform_initializer_conv(self):
          """Test Xavier initializer with uniform distribution on
             for convolutions.
          """
          paddle.enable_static()
  
          program = framework.Program()
          block = program.global_block()
          for _ in range(2):
              param = block.create_parameter(
                  dtype="float32",
                  shape=[5, 10, 15, 20],
                  lod_level=0,
                  name="param",
                  initializer=initializer.XavierUniform())
          self.assertEqual(len(block.ops), 1)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'uniform_random')
          receptive_field_size = float(15 * 20)
          limit = np.sqrt(6.0 / (
              (param.shape[0] + param.shape[1]) * receptive_field_size))
          self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
          self.assertEqual(init_op.attr('seed'), 0)
  
      def test_xavier_uniform_initializer_dygraph(self):
          """Test xavier uniform initializer in dygraph model.
          """
          paddle.disable_static()
  
          weight_attr = paddle.framework.ParamAttr(
              name="linear_weight",
              initializer=paddle.nn.initializer.XavierUniform())
          linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
  
  
  class TestXavierNormal(unittest.TestCase):
      def test_xavier_normal_initializer(self):
          """Test Xavier initializer with normal distribution on
             for matrix multiply.
          """
          paddle.enable_static()
  
          program = framework.Program()
          block = program.global_block()
          for _ in range(2):
              param = block.create_parameter(
                  dtype="float32",
                  shape=[5, 10],
                  lod_level=0,
                  name="param",
                  initializer=initializer.XavierNormal())
          self.assertEqual(len(block.ops), 1)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'gaussian_random')
          std = np.sqrt(2.0 / (param.shape[0] + param.shape[1]))
          self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
          self.assertEqual(init_op.attr('seed'), 0)
  
          paddle.disable_static()
  
      def test_xavier_normal_initializer_conv(self):
          """Test Xavier initializer with normal distribution on
             for convolutions.
          """
          paddle.enable_static()
  
          program = framework.Program()
          block = program.global_block()
          for _ in range(2):
              param = block.create_parameter(
                  dtype="float32",
                  shape=[5, 10, 15, 20],
                  lod_level=0,
                  name="param",
                  initializer=initializer.XavierNormal())
          self.assertEqual(len(block.ops), 1)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'gaussian_random')
          receptive_field_size = float(15 * 20)
          std = np.sqrt(2.0 / (
              (param.shape[0] + param.shape[1]) * receptive_field_size))
          self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
          self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
          self.assertEqual(init_op.attr('seed'), 0)
  
          paddle.disable_static()
  
      def test_xavier_normal_initializer_dygraph(self):
          """Test xavier normal initializer in dygraph model.
          """
          paddle.disable_static()
  
          weight_attr = paddle.framework.ParamAttr(
              name="linear_weight",
              initializer=paddle.nn.initializer.XavierNormal())
          linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
  
  
  class TestAssign(unittest.TestCase):
      def test_assign_initializer(self, dtype="float32"):
          """Test the numpy array initializer with supplied arguments
          """
          paddle.enable_static()
  
          import numpy
          program = framework.Program()
          block = program.global_block()
          np_array = numpy.random.random((10000)).astype(dtype)
          for _ in range(2):
              block.create_parameter(
                  dtype=np_array.dtype,
                  shape=np_array.shape,
                  lod_level=0,
                  name="param",
                  initializer=initializer.Assign(np_array))
          num_ops = 2 if dtype in ["float16", "uint16"] else 1
          self.assertEqual(len(block.ops), num_ops)
          init_op = block.ops[0]
          self.assertEqual(init_op.type, 'assign_value')
          assert (init_op.attr('fp32_values') == np_array).all()
  
          paddle.disable_static()
  
          return block
  
      def test_assign_initializer_fp16(self):
          """Test the numpy array initializer with float16
          """
          block = self.test_assign_initializer("float16")
          self.assertTrue(block.ops[1])
  
      def test_assign_initializer_bf16(self):
          """Test the numpy array initializer with bfloat16
          """
          block = self.test_assign_initializer("uint16")  #bfloat16
          self.assertTrue(block.ops[1])
  
      def test_assign_initializer_dygraph_1(self):
          """Test assign initializer in dygraph model.
          """
          paddle.disable_static()
  
          weight_attr_1 = paddle.framework.ParamAttr(
              name="linear_weight_1",
              initializer=paddle.nn.initializer.Assign(np.array([2, 2])))
          linear_1 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_1)
  
          self.assertTrue((linear_1.weight.numpy() == [2.0, 2.0]).all(), '')
  
      def test_assign_initializer_dygraph_2(self):
          """Test assign initializer in dygraph model.
          """
          paddle.disable_static()
  
          weight_attr_2 = paddle.framework.ParamAttr(
              name="linear_weight_2",
              initializer=paddle.nn.initializer.Assign([2, 2]))
          linear_2 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_2)
  
          self.assertTrue((linear_2.weight.numpy() == [2.0, 2.0]).all(), '')
  
      def test_assign_initializer_dygraph_3(self):
          """Test assign initializer in dygraph model.
          """
          paddle.disable_static()
  
          weight_attr_3 = paddle.framework.ParamAttr(
              name="linear_weight_3",
              initializer=paddle.nn.initializer.Assign(paddle.full([2], 2)))
          linear_3 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_3)
  
          self.assertTrue((linear_3.weight.numpy() == [2.0, 2.0]).all(), '')
  
      def test_assign_initializer_dygraph_4(self):
          """Test assign initializer in dygraph model.
          """
          paddle.disable_static()
  
          weight_attr_4 = paddle.framework.ParamAttr(
              name="linear_weight_4",
              initializer=paddle.nn.initializer.Assign((2, 2)))
          linear_4 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_4)
  
          self.assertTrue((linear_4.weight.numpy() == [2.0, 2.0]).all(), '')
  
  
  if __name__ == '__main__':
      unittest.main()
  