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.
209 lines
7.0 KiB
209 lines
7.0 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 unittest
|
|
import numpy as np
|
|
from op_test import OpTest
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid import Program, program_guard
|
|
|
|
|
|
class TestTransposeOp(OpTest):
|
|
def setUp(self):
|
|
self.init_op_type()
|
|
self.initTestCase()
|
|
self.inputs = {'X': np.random.random(self.shape).astype("float64")}
|
|
self.attrs = {
|
|
'axis': list(self.axis),
|
|
'use_mkldnn': self.use_mkldnn,
|
|
}
|
|
self.outputs = {
|
|
'XShape': np.random.random(self.shape).astype("float64"),
|
|
'Out': self.inputs['X'].transpose(self.axis)
|
|
}
|
|
|
|
def init_op_type(self):
|
|
self.op_type = "transpose2"
|
|
self.use_mkldnn = False
|
|
|
|
def test_check_output(self):
|
|
self.check_output(no_check_set=['XShape'])
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['X'], 'Out')
|
|
|
|
def initTestCase(self):
|
|
self.shape = (3, 40)
|
|
self.axis = (1, 0)
|
|
|
|
|
|
class TestCase0(TestTransposeOp):
|
|
def initTestCase(self):
|
|
self.shape = (100, )
|
|
self.axis = (0, )
|
|
|
|
|
|
class TestCase1(TestTransposeOp):
|
|
def initTestCase(self):
|
|
self.shape = (3, 4, 10)
|
|
self.axis = (0, 2, 1)
|
|
|
|
|
|
class TestCase2(TestTransposeOp):
|
|
def initTestCase(self):
|
|
self.shape = (2, 3, 4, 5)
|
|
self.axis = (0, 2, 3, 1)
|
|
|
|
|
|
class TestCase3(TestTransposeOp):
|
|
def initTestCase(self):
|
|
self.shape = (2, 3, 4, 5, 6)
|
|
self.axis = (4, 2, 3, 1, 0)
|
|
|
|
|
|
class TestCase4(TestTransposeOp):
|
|
def initTestCase(self):
|
|
self.shape = (2, 3, 4, 5, 6, 1)
|
|
self.axis = (4, 2, 3, 1, 0, 5)
|
|
|
|
|
|
class TestCase5(TestTransposeOp):
|
|
def initTestCase(self):
|
|
self.shape = (2, 16, 96)
|
|
self.axis = (0, 2, 1)
|
|
|
|
|
|
class TestCase6(TestTransposeOp):
|
|
def initTestCase(self):
|
|
self.shape = (2, 10, 12, 16)
|
|
self.axis = (3, 1, 2, 0)
|
|
|
|
|
|
class TestCase7(TestTransposeOp):
|
|
def initTestCase(self):
|
|
self.shape = (2, 10, 2, 16)
|
|
self.axis = (0, 1, 3, 2)
|
|
|
|
|
|
class TestTransposeOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
x = fluid.layers.data(name='x', shape=[10, 5, 3], dtype='float64')
|
|
|
|
def test_x_Variable_check():
|
|
# the Input(x)'s type must be Variable
|
|
fluid.layers.transpose("not_variable", perm=[1, 0, 2])
|
|
|
|
self.assertRaises(TypeError, test_x_Variable_check)
|
|
|
|
def test_x_dtype_check():
|
|
# the Input(x)'s dtype must be one of [float16, float32, float64, int32, int64]
|
|
x1 = fluid.layers.data(
|
|
name='x1', shape=[10, 5, 3], dtype='bool')
|
|
fluid.layers.transpose(x1, perm=[1, 0, 2])
|
|
|
|
self.assertRaises(TypeError, test_x_dtype_check)
|
|
|
|
def test_perm_list_check():
|
|
# Input(perm)'s type must be list
|
|
fluid.layers.transpose(x, perm="[1, 0, 2]")
|
|
|
|
self.assertRaises(TypeError, test_perm_list_check)
|
|
|
|
def test_perm_length_and_x_dim_check():
|
|
# Input(perm) is the permutation of dimensions of Input(input)
|
|
# its length should be equal to dimensions of Input(input)
|
|
fluid.layers.transpose(x, perm=[1, 0, 2, 3, 4])
|
|
|
|
self.assertRaises(ValueError, test_perm_length_and_x_dim_check)
|
|
|
|
def test_each_elem_value_check():
|
|
# Each element in Input(perm) should be less than Input(x)'s dimension
|
|
fluid.layers.transpose(x, perm=[3, 5, 7])
|
|
|
|
self.assertRaises(ValueError, test_each_elem_value_check)
|
|
|
|
|
|
class TestTAPI(unittest.TestCase):
|
|
def test_out(self):
|
|
with fluid.program_guard(fluid.Program()):
|
|
data = fluid.data(shape=[10], dtype="float64", name="data")
|
|
data_t = paddle.t(data)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
data_np = np.random.random([10]).astype("float64")
|
|
result, = exe.run(feed={"data": data_np}, fetch_list=[data_t])
|
|
expected_result = np.transpose(data_np)
|
|
self.assertEqual((result == expected_result).all(), True)
|
|
|
|
with fluid.program_guard(fluid.Program()):
|
|
data = fluid.data(shape=[10, 5], dtype="float64", name="data")
|
|
data_t = paddle.t(data)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
data_np = np.random.random([10, 5]).astype("float64")
|
|
result, = exe.run(feed={"data": data_np}, fetch_list=[data_t])
|
|
expected_result = np.transpose(data_np)
|
|
self.assertEqual((result == expected_result).all(), True)
|
|
|
|
with fluid.program_guard(fluid.Program()):
|
|
data = fluid.data(shape=[1, 5], dtype="float64", name="data")
|
|
data_t = paddle.t(data)
|
|
place = fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
data_np = np.random.random([1, 5]).astype("float64")
|
|
result, = exe.run(feed={"data": data_np}, fetch_list=[data_t])
|
|
expected_result = np.transpose(data_np)
|
|
self.assertEqual((result == expected_result).all(), True)
|
|
|
|
with fluid.dygraph.guard():
|
|
np_x = np.random.random([10]).astype("float64")
|
|
data = fluid.dygraph.to_variable(np_x)
|
|
z = paddle.t(data)
|
|
np_z = z.numpy()
|
|
z_expected = np.array(np.transpose(np_x))
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
with fluid.dygraph.guard():
|
|
np_x = np.random.random([10, 5]).astype("float64")
|
|
data = fluid.dygraph.to_variable(np_x)
|
|
z = paddle.t(data)
|
|
np_z = z.numpy()
|
|
z_expected = np.array(np.transpose(np_x))
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
with fluid.dygraph.guard():
|
|
np_x = np.random.random([1, 5]).astype("float64")
|
|
data = fluid.dygraph.to_variable(np_x)
|
|
z = paddle.t(data)
|
|
np_z = z.numpy()
|
|
z_expected = np.array(np.transpose(np_x))
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
def test_errors(self):
|
|
with fluid.program_guard(fluid.Program()):
|
|
x = fluid.data(name='x', shape=[10, 5, 3], dtype='float64')
|
|
|
|
def test_x_dimension_check():
|
|
paddle.t(x)
|
|
|
|
self.assertRaises(ValueError, test_x_dimension_check)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|