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.
220 lines
7.1 KiB
220 lines
7.1 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.
|
|
|
|
import numpy as np
|
|
import unittest
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
from op_test import OpTest
|
|
|
|
|
|
class TestStackOpBase(OpTest):
|
|
def initDefaultParameters(self):
|
|
self.num_inputs = 4
|
|
self.input_dim = (5, 6, 7)
|
|
self.axis = 0
|
|
self.dtype = 'float64'
|
|
|
|
def initParameters(self):
|
|
pass
|
|
|
|
def get_x_names(self):
|
|
x_names = []
|
|
for i in range(self.num_inputs):
|
|
x_names.append('x{}'.format(i))
|
|
return x_names
|
|
|
|
def setUp(self):
|
|
self.initDefaultParameters()
|
|
self.initParameters()
|
|
self.op_type = 'stack'
|
|
self.x = []
|
|
for i in range(self.num_inputs):
|
|
self.x.append(
|
|
np.random.random(size=self.input_dim).astype(self.dtype))
|
|
|
|
tmp = []
|
|
x_names = self.get_x_names()
|
|
for i in range(self.num_inputs):
|
|
tmp.append((x_names[i], self.x[i]))
|
|
|
|
self.inputs = {'X': tmp}
|
|
self.outputs = {'Y': np.stack(self.x, axis=self.axis)}
|
|
self.attrs = {'axis': self.axis}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(self.get_x_names(), 'Y')
|
|
|
|
|
|
class TestStackOp1(TestStackOpBase):
|
|
def initParameters(self):
|
|
self.num_inputs = 16
|
|
|
|
|
|
class TestStackOp2(TestStackOpBase):
|
|
def initParameters(self):
|
|
self.num_inputs = 20
|
|
|
|
|
|
class TestStackOp3(TestStackOpBase):
|
|
def initParameters(self):
|
|
self.axis = -1
|
|
|
|
|
|
class TestStackOp4(TestStackOpBase):
|
|
def initParameters(self):
|
|
self.axis = -4
|
|
|
|
|
|
class TestStackOp5(TestStackOpBase):
|
|
def initParameters(self):
|
|
self.axis = 1
|
|
|
|
|
|
class TestStackOp6(TestStackOpBase):
|
|
def initParameters(self):
|
|
self.axis = 3
|
|
|
|
|
|
class TestStackAPIWithLoDTensorArray(unittest.TestCase):
|
|
"""
|
|
Test stack api when the input(x) is a LoDTensorArray.
|
|
"""
|
|
|
|
def setUp(self):
|
|
self.axis = 1
|
|
self.iter_num = 3
|
|
self.input_shape = [2, 3]
|
|
self.x = np.random.random(self.input_shape).astype("float32")
|
|
self.place = fluid.CUDAPlace(0) \
|
|
if fluid.is_compiled_with_cuda() else fluid.CPUPlace()
|
|
self.set_program()
|
|
|
|
def set_program(self):
|
|
self.program = fluid.Program()
|
|
with fluid.program_guard(self.program):
|
|
input = fluid.layers.assign(self.x)
|
|
tensor_array = fluid.layers.create_array(dtype='float32')
|
|
zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64")
|
|
|
|
for i in range(self.iter_num):
|
|
fluid.layers.array_write(input, zero + i, tensor_array)
|
|
|
|
self.out_var = fluid.layers.stack(tensor_array, axis=self.axis)
|
|
|
|
def test_case(self):
|
|
self.assertTrue(self.out_var.shape[self.axis] == -1)
|
|
exe = fluid.Executor(self.place)
|
|
res = exe.run(self.program, fetch_list=self.out_var)
|
|
self.assertTrue(
|
|
np.array_equal(
|
|
res[0], np.stack(
|
|
[self.x] * self.iter_num, axis=self.axis)))
|
|
|
|
|
|
class TestTensorStackAPIWithLoDTensorArray(unittest.TestCase):
|
|
"""
|
|
Test stack api when the input(x) is a LoDTensorArray.
|
|
"""
|
|
|
|
def setUp(self):
|
|
self.axis = 1
|
|
self.iter_num = 3
|
|
self.input_shape = [2, 3]
|
|
self.x = np.random.random(self.input_shape).astype("float32")
|
|
self.place = fluid.CUDAPlace(0) \
|
|
if fluid.is_compiled_with_cuda() else fluid.CPUPlace()
|
|
self.set_program()
|
|
|
|
def set_program(self):
|
|
self.program = fluid.Program()
|
|
with fluid.program_guard(self.program):
|
|
input = fluid.layers.assign(self.x)
|
|
tensor_array = fluid.layers.create_array(dtype='float32')
|
|
zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64")
|
|
|
|
for i in range(self.iter_num):
|
|
fluid.layers.array_write(input, zero + i, tensor_array)
|
|
|
|
self.out_var = paddle.stack(tensor_array, axis=self.axis)
|
|
|
|
def test_case(self):
|
|
self.assertTrue(self.out_var.shape[self.axis] == -1)
|
|
exe = fluid.Executor(self.place)
|
|
res = exe.run(self.program, fetch_list=self.out_var)
|
|
self.assertTrue(
|
|
np.array_equal(
|
|
res[0], np.stack(
|
|
[self.x] * self.iter_num, axis=self.axis)))
|
|
|
|
|
|
class API_test(unittest.TestCase):
|
|
def test_out(self):
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
data1 = fluid.layers.data('data1', shape=[1, 2], dtype='float64')
|
|
data2 = fluid.layers.data('data2', shape=[1, 2], dtype='float64')
|
|
data3 = fluid.layers.data('data3', shape=[1, 2], dtype='float64')
|
|
result_stack = paddle.stack([data1, data2, data3], axis=0)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
input1 = np.random.random([1, 2]).astype('float64')
|
|
input2 = np.random.random([1, 2]).astype('float64')
|
|
input3 = np.random.random([1, 2]).astype('float64')
|
|
result, = exe.run(
|
|
feed={"data1": input1,
|
|
"data2": input2,
|
|
"data3": input3},
|
|
fetch_list=[result_stack])
|
|
expected_result = np.stack([input1, input2, input3], axis=0)
|
|
self.assertTrue(np.allclose(expected_result, result))
|
|
|
|
def test_single_tensor_error(self):
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
x = paddle.rand([2, 3])
|
|
self.assertRaises(TypeError, paddle.stack, x)
|
|
|
|
|
|
class API_DygraphTest(unittest.TestCase):
|
|
def test_out(self):
|
|
data1 = np.array([[1.0, 2.0]])
|
|
data2 = np.array([[3.0, 4.0]])
|
|
data3 = np.array([[5.0, 6.0]])
|
|
with fluid.dygraph.guard():
|
|
x1 = fluid.dygraph.to_variable(data1)
|
|
x2 = fluid.dygraph.to_variable(data2)
|
|
x3 = fluid.dygraph.to_variable(data3)
|
|
result = paddle.stack([x1, x2, x3])
|
|
result_np = result.numpy()
|
|
expected_result = np.stack([data1, data2, data3])
|
|
self.assertTrue(np.allclose(expected_result, result_np))
|
|
|
|
with fluid.dygraph.guard():
|
|
y1 = fluid.dygraph.to_variable(data1)
|
|
result = paddle.stack([y1], axis=0)
|
|
result_np_2 = result.numpy()
|
|
expected_result_2 = np.stack([data1], axis=0)
|
|
self.assertTrue(np.allclose(expected_result_2, result_np_2))
|
|
|
|
def test_single_tensor_error(self):
|
|
with fluid.dygraph.guard():
|
|
x = paddle.to_tensor([1, 2, 3])
|
|
self.assertRaises(Exception, paddle.stack, x)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|