wehub-resource-sync
196 lines
7.8 KiB
Python
196 lines
7.8 KiB
Python
# Copyright (c) 2018 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 copy
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
from paddle.base.dygraph import guard
|
|
from paddle.base.executor import Executor
|
|
from paddle.base.framework import Variable, default_main_program
|
|
|
|
paddle.enable_static()
|
|
main_program = default_main_program()
|
|
|
|
|
|
class ParameterChecks(unittest.TestCase):
|
|
def test_parameter(self):
|
|
with paddle.pir_utils.OldIrGuard():
|
|
shape = [784, 100]
|
|
val = 1.0625
|
|
b = main_program.global_block()
|
|
param = b.create_parameter(
|
|
name='fc.w',
|
|
shape=shape,
|
|
dtype='float32',
|
|
initializer=paddle.nn.initializer.Constant(val),
|
|
)
|
|
self.assertIsNotNone(param)
|
|
self.assertEqual('fc.w', param.name)
|
|
self.assertEqual((784, 100), param.shape)
|
|
self.assertEqual(paddle.float32, param.dtype)
|
|
self.assertEqual(0, param.block.idx)
|
|
exe = Executor(paddle.CPUPlace())
|
|
p = exe.run(main_program, fetch_list=[param])[0]
|
|
np.testing.assert_array_equal(p, np.ones(shape) * val)
|
|
|
|
zero_dim_param = b.create_parameter(
|
|
name='x', shape=[], dtype='float32'
|
|
)
|
|
self.assertEqual(zero_dim_param.shape, ())
|
|
|
|
def test_parambase(self):
|
|
with guard():
|
|
linear = paddle.nn.Linear(10, 10)
|
|
param = linear.weight
|
|
|
|
memo = {}
|
|
param_copy = copy.deepcopy(param, memo)
|
|
self.assertEqual(param_copy.shape, param.shape)
|
|
self.assertEqual(param_copy.type, param.type)
|
|
self.assertEqual(param_copy.dtype, param.dtype)
|
|
self.assertEqual(str(param_copy.place), str(param.place))
|
|
np.testing.assert_array_equal(param_copy.numpy(), param.numpy())
|
|
self.assertEqual(param_copy.optimize_attr, param.optimize_attr)
|
|
self.assertEqual(param_copy.regularizer, param.regularizer)
|
|
self.assertEqual(
|
|
param_copy.do_model_average, param.do_model_average
|
|
)
|
|
self.assertEqual(param_copy.need_clip, param.need_clip)
|
|
self.assertEqual(param_copy.is_distributed, param.is_distributed)
|
|
|
|
pram_copy2 = copy.deepcopy(param, memo)
|
|
self.assertEqual(id(param_copy), id(pram_copy2))
|
|
|
|
def test_create_0_size_param(self):
|
|
with guard():
|
|
shape = [0, 4]
|
|
for dtype in [
|
|
paddle.float32,
|
|
paddle.float64,
|
|
]:
|
|
zero_size_param = paddle.create_parameter(
|
|
shape,
|
|
dtype,
|
|
)
|
|
self.assertEqual(zero_size_param.shape, shape)
|
|
self.assertEqual(zero_size_param.data_ptr(), 0)
|
|
self.assertEqual(zero_size_param.strides, [4, 1])
|
|
|
|
def func_exception(self):
|
|
b = main_program.global_block()
|
|
with self.assertRaises(ValueError):
|
|
b.create_parameter(
|
|
name='test', shape=None, dtype='float32', initializer=None
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
b.create_parameter(
|
|
name='test', shape=[1], dtype=None, initializer=None
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
b.create_parameter(
|
|
name='test', shape=[], dtype='float32', initializer=None
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
b.create_parameter(
|
|
name='test', shape=[-1], dtype='float32', initializer=None
|
|
)
|
|
|
|
def test_parambase_to_vector(self):
|
|
with guard():
|
|
initializer = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(3.0)
|
|
)
|
|
linear1 = paddle.nn.Linear(10, 15, initializer)
|
|
|
|
vec = paddle.nn.utils.parameters_to_vector(linear1.parameters())
|
|
self.assertEqual(linear1.weight.shape, [10, 15])
|
|
self.assertEqual(linear1.bias.shape, [15])
|
|
self.assertTrue(isinstance(vec, Variable))
|
|
self.assertTrue(vec.shape, [165])
|
|
|
|
linear2 = paddle.nn.Linear(10, 15)
|
|
paddle.nn.utils.vector_to_parameters(vec, linear2.parameters())
|
|
self.assertEqual(linear2.weight.shape, [10, 15])
|
|
self.assertEqual(linear2.bias.shape, [15])
|
|
np.testing.assert_array_equal(
|
|
linear1.weight.numpy(), linear2.weight.numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
linear1.bias.numpy(), linear2.bias.numpy()
|
|
)
|
|
self.assertTrue(linear2.weight.is_leaf, True)
|
|
self.assertTrue(linear2.bias.is_leaf, True)
|
|
|
|
def test_parambase_to_vector_zero(self):
|
|
with guard():
|
|
initializer = paddle.ParamAttr(
|
|
initializer=paddle.nn.initializer.Constant(3.0)
|
|
)
|
|
linear1 = paddle.nn.Linear(0, 15, initializer)
|
|
|
|
vec = paddle.nn.utils.parameters_to_vector(linear1.parameters())
|
|
self.assertEqual(linear1.weight.shape, [0, 15])
|
|
self.assertEqual(linear1.bias.shape, [15])
|
|
self.assertTrue(isinstance(vec, Variable))
|
|
self.assertEqual(vec.shape, [15])
|
|
|
|
|
|
class TestVectorToParam(unittest.TestCase):
|
|
def test_vector_to_param_zerosize(self):
|
|
# test the case that the parameters contains zero size tensor
|
|
with guard():
|
|
vec = paddle.randn([18], dtype='float32')
|
|
param1 = paddle.empty([5], dtype='float32')
|
|
param2 = paddle.empty([5], dtype='float32')
|
|
param3 = paddle.empty([8], dtype='float32')
|
|
param4 = paddle.empty([0], dtype='float32')
|
|
params = [param1, param2, param3, param4]
|
|
paddle.nn.utils.vector_to_parameters(vec, params)
|
|
# concat the parameters and get the original vector
|
|
vec_ = paddle.concat(params, axis=0)
|
|
np.testing.assert_array_equal(vec_.numpy(), vec.numpy())
|
|
|
|
def test_vector_to_param1(self):
|
|
# test the case that the sum of parameter's elements less than vector elements
|
|
with guard():
|
|
vec = paddle.randn([18], dtype='float32')
|
|
param1 = paddle.empty([5], dtype='float32')
|
|
param2 = paddle.empty([5], dtype='float32')
|
|
param3 = paddle.empty([7], dtype='float32')
|
|
params = [param1, param2, param3]
|
|
paddle.nn.utils.vector_to_parameters(vec, params)
|
|
# concat the parameters and get the original vector
|
|
vec_ = paddle.concat(params, axis=0)
|
|
np.testing.assert_array_equal(vec_.numpy(), vec[:17].numpy())
|
|
|
|
def test_vector_to_param2(self):
|
|
# test the case that the sum of parameter's elements grater than vector elements
|
|
def _test_vector_to_param():
|
|
with guard():
|
|
vec = paddle.randn([18], dtype='float32')
|
|
param1 = paddle.empty([5], dtype='float32')
|
|
param2 = paddle.empty([5], dtype='float32')
|
|
param3 = paddle.empty([9], dtype='float32')
|
|
params = [param1, param2, param3]
|
|
paddle.nn.utils.vector_to_parameters(vec, params)
|
|
|
|
self.assertRaises(ValueError, _test_vector_to_param)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|