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687 lines
25 KiB
687 lines
25 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import numpy as np
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import unittest
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import paddle
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import paddle.nn as nn
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import paddle.fluid as fluid
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import paddle.fluid.framework as framework
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import paddle.nn.initializer as initializer
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from paddle.fluid.core import VarDesc
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DELTA = 0.00001
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def get_uniform_min_and_max(weight):
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min_value = np.min(weight)
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max_value = np.max(weight)
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return min_value, max_value
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def check_cast_op(op):
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return op.type == 'cast' and \
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op.attr('in_dtype') == VarDesc.VarType.FP32 and \
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op.attr('out_dtype') == VarDesc.VarType.FP16
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class TestConstantInitializer(unittest.TestCase):
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def static_test_constant_initializer_common(self,
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init_inst,
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dtype="float32",
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value_target=0.0):
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paddle.enable_static()
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program = framework.Program()
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block = program.global_block()
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for _ in range(2):
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block.create_parameter(
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dtype=dtype,
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shape=[5, 10],
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lod_level=0,
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name="param",
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initializer=init_inst)
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num_ops = 2 if dtype == "float16" else 1
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self.assertEqual(len(block.ops), num_ops)
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init_op = block.ops[0]
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self.assertEqual(init_op.type, 'fill_constant')
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self.assertAlmostEqual(init_op.attr('value'), value_target, delta=DELTA)
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paddle.disable_static()
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return block
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def test_constant_initializer_default_value_static(self, dtype="float32"):
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"""Test the constant initializer with default value in static graph
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"""
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block = self.static_test_constant_initializer_common(
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init_inst=initializer.Constant(), dtype=dtype, value_target=0.0)
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return block
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def test_constant_initializer_default_value_dygraph(self, dtype="float32"):
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"""Test constant initializer with supplied value in dygraph
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"""
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with fluid.dygraph.guard():
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linear = nn.Linear(2, 4, weight_attr=nn.initializer.Constant())
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mat_target = np.ones((2, 4), dtype=dtype) * 0.0
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mat_linear = linear.weight.numpy()
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mismatch = np.sum(
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(mat_target - mat_linear) * (mat_target - mat_linear))
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self.assertAlmostEqual(mismatch, 0.0, delta=DELTA)
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def test_constant_initializer_static(self, dtype="float32"):
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"""Test constant initializer with supplied value in static graph
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"""
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block = self.static_test_constant_initializer_common(
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init_inst=initializer.Constant(2.3), dtype=dtype, value_target=2.3)
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return block
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def test_constant_initializer_dygraph(self, dtype="float32"):
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"""Test constant initializer with supplied value in dygraph
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"""
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with fluid.dygraph.guard():
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linear = nn.Linear(
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2, 4, weight_attr=nn.initializer.Constant(value=2.0))
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mat_target = np.ones((2, 4), dtype=dtype) * 2.0
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mat_linear = linear.weight.numpy()
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mismatch = np.sum(
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(mat_target - mat_linear) * (mat_target - mat_linear))
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self.assertAlmostEqual(mismatch, 0.0, delta=DELTA)
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def test_constant_initializer_fp16(self):
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"""Test constant initializer with float16
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"""
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block = self.test_constant_initializer_default_value_static("float16")
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self.assertTrue(check_cast_op(block.ops[1]))
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block = self.test_constant_initializer_static("float16")
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self.assertTrue(check_cast_op(block.ops[1]))
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self.test_constant_initializer_default_value_dygraph("float16")
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self.test_constant_initializer_dygraph("float16")
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class TestKaimingInitializer(unittest.TestCase):
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def static_test_kaiming_initializer_common(self,
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init_inst,
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dtype="float32",
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uniform=False,
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is_conv=False):
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paddle.enable_static()
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program = framework.Program()
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block = program.global_block()
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shape_mat = [5, 10, 15, 20] if is_conv else [5, 10]
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for _ in range(2):
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param = block.create_parameter(
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dtype="float32",
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shape=shape_mat,
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lod_level=0,
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name="param",
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initializer=init_inst)
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self.assertEqual(len(block.ops), 1)
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init_op = block.ops[0]
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if uniform:
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self.assertEqual(init_op.type, 'uniform_random')
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if is_conv:
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receptive_field_size = float(15 * 20)
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limit = np.sqrt(6.0 / (param.shape[1] * receptive_field_size))
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else:
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limit = np.sqrt(6.0 / param.shape[0])
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self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
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self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
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else:
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self.assertEqual(init_op.type, 'gaussian_random')
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if is_conv:
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receptive_field_size = float(15 * 20)
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std = np.sqrt(2.0 / (param.shape[1] * receptive_field_size))
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else:
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std = np.sqrt(2.0 / param.shape[0])
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self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
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self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
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paddle.disable_static()
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def dygraph_test_kaiming_initializer_common(self,
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init_inst,
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dtype="float32",
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uniform=False):
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linear = nn.Linear(40, 20, weight_attr=init_inst)
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def test_kaiming_dygraph(self):
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self.dygraph_test_kaiming_initializer_common(
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init_inst=initializer.KaimingUniform(),
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dtype="float32",
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uniform=True)
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self.dygraph_test_kaiming_initializer_common(
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init_inst=initializer.KaimingNormal(),
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dtype="float32",
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uniform=False)
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def test_kaiming_uniform_initializer_static(self):
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"""Test Kaiming unorm initializer for matrix multiply.
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"""
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self.static_test_kaiming_initializer_common(
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init_inst=initializer.KaimingUniform(),
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dtype="float32",
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uniform=True,
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is_conv=False)
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def test_kaiming_uniform_initializer_conv_static(self):
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"""Test Kaiming unorm initializer for convolutions.
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"""
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self.static_test_kaiming_initializer_common(
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init_inst=initializer.KaimingUniform(),
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dtype="float32",
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uniform=True,
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is_conv=True)
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def test_kaiming_normal_initializer_static(self):
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"""Test Kaiming normal initializer for matrix multiply.
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"""
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self.static_test_kaiming_initializer_common(
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init_inst=initializer.KaimingNormal(),
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dtype="float32",
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uniform=False,
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is_conv=False)
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def test_kaiming_normal_initializer_conv_static(self):
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"""Test Kaiming normal initializer for convolutions.
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"""
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self.static_test_kaiming_initializer_common(
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init_inst=initializer.KaimingNormal(),
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dtype="float32",
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uniform=False,
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is_conv=True)
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class TestUniform(unittest.TestCase):
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def test_uniform_common(self, dtype="float32", seed=0):
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"""Test the uniform initializer with default value
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"""
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paddle.enable_static()
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program = framework.Program()
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program.random_seed = seed
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block = program.global_block()
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for _ in range(2):
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block.create_parameter(
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dtype=dtype,
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shape=[5, 10],
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lod_level=0,
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name="param",
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initializer=initializer.Uniform())
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num_ops = 2 if dtype == "float16" else 1
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self.assertEqual(len(block.ops), num_ops)
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init_op = block.ops[0]
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self.assertEqual(init_op.type, 'uniform_random')
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self.assertAlmostEqual(init_op.attr('min'), -1.0, delta=DELTA)
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self.assertAlmostEqual(init_op.attr('max'), 1.0, delta=DELTA)
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self.assertEqual(init_op.attr('seed'), seed)
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paddle.disable_static()
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return block
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def test_uniform_initializer_default_value(self,
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dtype="float32",
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seed=0,
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min_value=-1.0,
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max_vlaue=1.0):
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"""Test the uniform initializer with default value
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"""
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paddle.enable_static()
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program = framework.Program()
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program.random_seed = seed
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block = program.global_block()
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for _ in range(2):
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block.create_parameter(
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dtype=dtype,
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shape=[5, 10],
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lod_level=0,
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name="param",
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initializer=initializer.Uniform())
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num_ops = 2 if dtype == "float16" else 1
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self.assertEqual(len(block.ops), num_ops)
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init_op = block.ops[0]
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self.assertEqual(init_op.type, 'uniform_random')
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self.assertAlmostEqual(init_op.attr('min'), min_value, delta=DELTA)
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self.assertAlmostEqual(init_op.attr('max'), max_vlaue, delta=DELTA)
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self.assertEqual(init_op.attr('seed'), seed)
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paddle.disable_static()
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return block
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def test_uniform_initializer(self,
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dtype="float32",
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seed=0,
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min_value=-4.2,
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max_vlaue=3.1):
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"""Test uniform initializer with supplied attributes
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"""
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paddle.enable_static()
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program = framework.Program()
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program.random_seed = seed
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block = program.global_block()
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for _ in range(2):
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block.create_parameter(
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dtype=dtype,
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shape=[5, 10],
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lod_level=0,
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name="param",
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initializer=initializer.Uniform(min_value, max_vlaue))
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num_ops = 2 if dtype == "float16" else 1
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self.assertEqual(len(block.ops), num_ops)
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init_op = block.ops[0]
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self.assertEqual(init_op.type, 'uniform_random')
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self.assertAlmostEqual(init_op.attr('min'), min_value, delta=DELTA)
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self.assertAlmostEqual(init_op.attr('max'), max_vlaue, delta=DELTA)
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paddle.disable_static()
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return block
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def test_uniform_initializer_two_op(self,
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dtype="float32",
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seed=123,
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min_value=-4.2,
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max_vlaue=0.0):
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"""Test uniform initializer with supplied attributes
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"""
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paddle.enable_static()
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program = framework.Program()
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program.random_seed = seed
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block = program.global_block()
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for i in range(2):
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block.create_parameter(
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dtype=dtype,
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shape=[5, 10],
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lod_level=0,
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name="param",
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initializer=initializer.Uniform(min_value, float(i)))
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num_ops = 2 if dtype == "float16" else 1
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self.assertEqual(len(block.ops), num_ops)
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init_op0 = block.ops[0]
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self.assertEqual(init_op0.type, 'uniform_random')
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self.assertAlmostEqual(init_op0.attr('min'), min_value, delta=DELTA)
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self.assertAlmostEqual(init_op0.attr('max'), 0.0, delta=DELTA)
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self.assertEqual(init_op0.attr("seed"), seed)
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paddle.disable_static()
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return block
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def test_uniform_initializer_fp16(self):
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"""Test uniform initializer with float16
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"""
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block = self.test_uniform_initializer_default_value("float16")
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self.assertTrue(check_cast_op(block.ops[1]))
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block = self.test_uniform_initializer(dtype="float16")
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self.assertTrue(check_cast_op(block.ops[1]))
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block = self.test_uniform_initializer_two_op("float16")
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self.assertTrue(check_cast_op(block.ops[1]))
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def test_uniform_initializer_dygraph(self):
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"""Test uniform initializer in dygraph model.
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"""
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paddle.disable_static()
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weight_attr = paddle.framework.ParamAttr(
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name="linear_weight",
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initializer=paddle.nn.initializer.Uniform(
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low=-0.5, high=0.5))
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linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
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min_value, max_value = get_uniform_min_and_max(linear.weight.numpy())
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self.assertTrue(min_value >= -0.5,
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'min value {} should >= -0.5'.format(min_value))
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self.assertTrue(max_value <= 0.5,
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'max value {} should <= 0.5'.format(max_value))
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class TestNormal(unittest.TestCase):
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def test_normal_initializer_default_value(self):
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"""Test the normal initializer with default value
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"""
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paddle.enable_static()
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program = framework.Program()
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block = program.global_block()
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for _ in range(2):
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block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="param",
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initializer=initializer.Normal())
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self.assertEqual(len(block.ops), 1)
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init_op = block.ops[0]
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self.assertEqual(init_op.type, 'gaussian_random')
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self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
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self.assertAlmostEqual(init_op.attr('std'), 1.0, delta=DELTA)
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self.assertEqual(init_op.attr('seed'), 0)
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paddle.disable_static()
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def test_normal_initializer(self, dtype="float32"):
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"""Test normal initializer with supplied attributes
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"""
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paddle.enable_static()
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program = framework.Program()
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block = program.global_block()
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for _ in range(2):
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block.create_parameter(
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dtype=dtype,
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shape=[5, 10],
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lod_level=0,
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name="param",
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initializer=initializer.Normal(2.3, 1.9))
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num_ops = 2 if dtype == "float16" else 1
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self.assertEqual(len(block.ops), num_ops)
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init_op = block.ops[0]
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self.assertEqual(init_op.type, 'gaussian_random')
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self.assertAlmostEqual(init_op.attr('mean'), 2.3, delta=DELTA)
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self.assertAlmostEqual(init_op.attr('std'), 1.9, delta=DELTA)
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paddle.disable_static()
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return block
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def test_normal_initializer_fp16(self):
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"""Test normal initializer with float16
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"""
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block = self.test_normal_initializer("float16")
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self.assertTrue(check_cast_op(block.ops[1]))
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def test_normal_initializer_dygraph(self):
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"""Test normal initializer in dygraph model.
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"""
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paddle.disable_static()
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weight_attr = paddle.framework.ParamAttr(
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name="linear_weight",
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initializer=paddle.nn.initializer.Normal(
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mean=0.0, std=2.0))
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linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
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class TestTruncatedNormal(unittest.TestCase):
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def test_truncated_normal_initializer_default_value(self):
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"""Test the truncated normal initializer with default value
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"""
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paddle.enable_static()
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program = framework.Program()
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block = program.global_block()
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for _ in range(2):
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block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="param",
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initializer=initializer.TruncatedNormal())
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self.assertEqual(len(block.ops), 1)
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init_op = block.ops[0]
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self.assertEqual(init_op.type, 'truncated_gaussian_random')
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self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
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self.assertAlmostEqual(init_op.attr('std'), 1.0, delta=DELTA)
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self.assertEqual(init_op.attr('seed'), 0)
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paddle.disable_static()
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def test_truncated_normal_initializer(self, dtype="float32"):
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"""Test truncated normal initializer with supplied attributes
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"""
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paddle.enable_static()
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program = framework.Program()
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block = program.global_block()
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for _ in range(2):
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block.create_parameter(
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dtype=dtype,
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shape=[5, 10],
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lod_level=0,
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name="param",
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initializer=initializer.TruncatedNormal(2.3, 1.9))
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num_ops = 2 if dtype == "float16" else 1
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self.assertEqual(len(block.ops), num_ops)
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init_op = block.ops[0]
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self.assertEqual(init_op.type, 'truncated_gaussian_random')
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self.assertAlmostEqual(init_op.attr('mean'), 2.3, delta=DELTA)
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self.assertAlmostEqual(init_op.attr('std'), 1.9, delta=DELTA)
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paddle.disable_static()
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return block
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def test_truncated_normal_initializer_fp16(self):
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"""Test truncated normal initializer with float16
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"""
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paddle.enable_static()
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block = self.test_truncated_normal_initializer("float16")
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self.assertTrue(check_cast_op(block.ops[1]))
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def test_truncated_normal_initializer_dygraph(self):
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"""Test truncated normal initializer in dygraph model.
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"""
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paddle.disable_static()
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weight_attr = paddle.framework.ParamAttr(
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name="linear_weight",
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initializer=paddle.nn.initializer.TruncatedNormal(
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mean=0.0, std=2.0))
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linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
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class TestXavierUniform(unittest.TestCase):
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def test_xavier_uniform_initializer(self):
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"""Test Xavier initializer with uniform distribution on
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|
for matrix multiply.
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|
"""
|
|
paddle.enable_static()
|
|
|
|
program = framework.Program()
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|
block = program.global_block()
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|
for _ in range(2):
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|
param = block.create_parameter(
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|
dtype="float32",
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|
shape=[5, 10],
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lod_level=0,
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|
name="param",
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|
initializer=initializer.XavierUniform())
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|
self.assertEqual(len(block.ops), 1)
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|
init_op = block.ops[0]
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self.assertEqual(init_op.type, 'uniform_random')
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|
limit = np.sqrt(6.0 / (param.shape[0] + param.shape[1]))
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self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
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|
self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
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|
self.assertEqual(init_op.attr('seed'), 0)
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|
|
|
paddle.disable_static()
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|
|
|
def test_xavier_uniform_initializer_conv(self):
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|
"""Test Xavier initializer with uniform distribution on
|
|
for convolutions.
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|
"""
|
|
paddle.enable_static()
|
|
|
|
program = framework.Program()
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|
block = program.global_block()
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|
for _ in range(2):
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|
param = block.create_parameter(
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|
dtype="float32",
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|
shape=[5, 10, 15, 20],
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|
lod_level=0,
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|
name="param",
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|
initializer=initializer.XavierUniform())
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|
self.assertEqual(len(block.ops), 1)
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|
init_op = block.ops[0]
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|
self.assertEqual(init_op.type, 'uniform_random')
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|
receptive_field_size = float(15 * 20)
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|
limit = np.sqrt(6.0 / (
|
|
(param.shape[0] + param.shape[1]) * receptive_field_size))
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|
self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
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|
self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
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|
self.assertEqual(init_op.attr('seed'), 0)
|
|
|
|
def test_xavier_uniform_initializer_dygraph(self):
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|
"""Test xavier uniform initializer in dygraph model.
|
|
"""
|
|
paddle.disable_static()
|
|
|
|
weight_attr = paddle.framework.ParamAttr(
|
|
name="linear_weight",
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|
initializer=paddle.nn.initializer.XavierUniform())
|
|
linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr)
|
|
|
|
|
|
class TestXavierNormal(unittest.TestCase):
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|
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)
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|
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 == "float16" 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_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(), '')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|