You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
104 lines
3.9 KiB
104 lines
3.9 KiB
# 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.
|
|
|
|
from __future__ import print_function
|
|
|
|
import op_test
|
|
import numpy as np
|
|
import unittest
|
|
import paddle.fluid.core as core
|
|
from paddle.fluid.op import Operator
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid import compiler, Program, program_guard
|
|
from paddle.fluid.backward import append_backward
|
|
|
|
|
|
class TestAssignOp(op_test.OpTest):
|
|
def setUp(self):
|
|
self.op_type = "assign"
|
|
x = np.random.random(size=(100, 10)).astype('float64')
|
|
self.inputs = {'X': x}
|
|
self.outputs = {'Out': x}
|
|
|
|
def test_forward(self):
|
|
self.check_output()
|
|
|
|
def test_backward(self):
|
|
self.check_grad(['X'], 'Out')
|
|
|
|
|
|
class TestAssignFP16Op(op_test.OpTest):
|
|
def setUp(self):
|
|
self.op_type = "assign"
|
|
x = np.random.random(size=(100, 10)).astype('float16')
|
|
self.inputs = {'X': x}
|
|
self.outputs = {'Out': x}
|
|
|
|
def test_forward(self):
|
|
self.check_output()
|
|
|
|
def test_backward(self):
|
|
self.check_grad(['X'], 'Out')
|
|
|
|
|
|
class TestAssignOpWithLoDTensorArray(unittest.TestCase):
|
|
def test_assign_LoDTensorArray(self):
|
|
main_program = Program()
|
|
startup_program = Program()
|
|
with program_guard(main_program):
|
|
x = fluid.data(name='x', shape=[100, 10], dtype='float32')
|
|
x.stop_gradient = False
|
|
y = fluid.layers.fill_constant(
|
|
shape=[100, 10], dtype='float32', value=1)
|
|
z = fluid.layers.elementwise_add(x=x, y=y)
|
|
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
|
|
init_array = fluid.layers.array_write(x=z, i=i)
|
|
array = fluid.layers.assign(init_array)
|
|
sums = fluid.layers.array_read(array=init_array, i=i)
|
|
mean = fluid.layers.mean(sums)
|
|
append_backward(mean)
|
|
|
|
place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
|
|
) else fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
feed_x = np.random.random(size=(100, 10)).astype('float32')
|
|
ones = np.ones((100, 10)).astype('float32')
|
|
feed_add = feed_x + ones
|
|
res = exe.run(main_program,
|
|
feed={'x': feed_x},
|
|
fetch_list=[sums.name, x.grad_name])
|
|
self.assertTrue(np.allclose(res[0], feed_add))
|
|
self.assertTrue(np.allclose(res[1], ones / 1000.0))
|
|
|
|
|
|
class TestAssignOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
# The type of input must be Variable or numpy.ndarray.
|
|
x1 = fluid.create_lod_tensor(
|
|
np.array([[-1]]), [[1]], fluid.CPUPlace())
|
|
self.assertRaises(TypeError, fluid.layers.assign, x1)
|
|
# When the type of input is Variable, the dtype of input must be float16, float32, float64, int32, int64, bool.
|
|
x3 = fluid.layers.data(name='x3', shape=[4], dtype="uint8")
|
|
self.assertRaises(TypeError, fluid.layers.assign, x3)
|
|
# When the type of input is numpy.ndarray, the dtype of input must be float32, int32.
|
|
x4 = np.array([[2.5, 2.5]], dtype='float64')
|
|
self.assertRaises(TypeError, fluid.layers.assign, x4)
|
|
x5 = np.array([[2.5, 2.5]], dtype='uint8')
|
|
self.assertRaises(TypeError, fluid.layers.assign, x5)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|