test_transpose_op.py

#   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, convert_float_to_uint16
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import paddle.fluid.core as core
import gradient_checker
from decorator_helper import prog_scope
import paddle.fluid.layers as layers

paddle.enable_static()


class TestTransposeOp(OpTest):

    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.python_api = paddle.transpose
        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'], check_eager=True)

    def test_check_grad(self):
        self.check_grad(['X'], 'Out', check_eager=True)

    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 TestCase8(TestTransposeOp):

    def initTestCase(self):
        self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
        self.axis = (0, 1, 3, 2, 4, 5, 6, 7)


class TestCase9(TestTransposeOp):

    def initTestCase(self):
        self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
        self.axis = (6, 1, 3, 5, 0, 2, 4, 7)


class TestAutoTuneTransposeOp(OpTest):

    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.python_api = paddle.transpose
        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 initTestCase(self):
        fluid.core.set_autotune_range(0, 3)
        fluid.core.update_autotune_status()
        fluid.core.enable_autotune()
        self.shape = (1, 12, 256, 1)
        self.axis = (0, 3, 2, 1)

    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'], check_eager=True)
        fluid.core.disable_autotune()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out', check_eager=True)


class TestTransposeBF16Op(OpTest):

    def setUp(self):
        self.init_op_type()
        self.initTestCase()
        self.dtype = np.uint16
        x = np.random.random(self.shape).astype("float32")

        self.inputs = {'X': convert_float_to_uint16(x)}
        self.attrs = {
            'axis': list(self.axis),
            'use_mkldnn': self.use_mkldnn,
        }
        self.outputs = {
            'XShape':
            convert_float_to_uint16(
                np.random.random(self.shape).astype("float32")),
            '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):
        pass

    def initTestCase(self):
        self.shape = (3, 2)
        self.axis = (1, 0)


class TestTransposeOpBool(TestTransposeOp):

    def test_check_grad(self):
        pass


class TestTransposeOpBool1D(TestTransposeOpBool):

    def initTestCase(self):
        self.shape = (100, )
        self.axis = (0, )
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis)
        }


class TestTransposeOpBool2D(TestTransposeOpBool):

    def initTestCase(self):
        self.shape = (3, 40)
        self.axis = (1, 0)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis)
        }


class TestTransposeOpBool3D(TestTransposeOpBool):

    def initTestCase(self):
        self.shape = (3, 4, 10)
        self.axis = (0, 2, 1)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis)
        }


class TestTransposeOpBool4D(TestTransposeOpBool):

    def initTestCase(self):
        self.shape = (2, 3, 4, 5)
        self.axis = (0, 2, 3, 1)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis)
        }


class TestTransposeOpBool5D(TestTransposeOpBool):

    def initTestCase(self):
        self.shape = (2, 3, 4, 5, 6)
        self.axis = (4, 2, 3, 1, 0)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis)
        }


class TestTransposeOpBool6D(TestTransposeOpBool):

    def initTestCase(self):
        self.shape = (2, 3, 4, 5, 6, 1)
        self.axis = (4, 2, 3, 1, 0, 5)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis)
        }


class TestTransposeOpBool7D(TestTransposeOpBool):

    def initTestCase(self):
        self.shape = (2, 3, 2, 3, 2, 4, 3)
        self.axis = (0, 1, 3, 2, 4, 5, 6)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis)
        }


class TestTransposeOpBool8D(TestTransposeOpBool):

    def initTestCase(self):
        self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
        self.axis = (6, 1, 3, 5, 0, 2, 4, 7)
        self.inputs = {'X': np.random.random(self.shape).astype("bool")}
        self.outputs = {
            'XShape': np.random.random(self.shape).astype("bool"),
            'Out': self.inputs['X'].transpose(self.axis)
        }


class TestTransposeOpError(unittest.TestCase):

    def test_errors(self):
        paddle.enable_static()
        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 [bool, float16, float32, float64, int32, int64]
                x1 = fluid.layers.data(name='x1',
                                       shape=[10, 5, 3],
                                       dtype='int8')
                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 TestTransposeApi(unittest.TestCase):

    def test_static_out(self):
        paddle.enable_static()
        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data(name='x', shape=[2, 3, 4], dtype='float32')
            x_trans1 = paddle.transpose(x, perm=[1, 0, 2])
            x_trans2 = paddle.transpose(x, perm=(2, 1, 0))
            place = paddle.CPUPlace()
            exe = paddle.static.Executor(place)
            x_np = np.random.random([2, 3, 4]).astype("float32")
            result1, result2 = exe.run(feed={"x": x_np},
                                       fetch_list=[x_trans1, x_trans2])
            expected_result1 = np.transpose(x_np, [1, 0, 2])
            expected_result2 = np.transpose(x_np, (2, 1, 0))

            np.testing.assert_array_equal(result1, expected_result1)
            np.testing.assert_array_equal(result2, expected_result2)

    def test_dygraph_out(self):
        # This is an old test before 2.0 API so we need to disable static
        # to trigger dygraph
        paddle.disable_static()
        x = paddle.randn([2, 3, 4])
        x_trans1 = paddle.transpose(x, perm=[1, 0, 2])
        x_trans2 = paddle.transpose(x, perm=(2, 1, 0))
        x_np = x.numpy()
        expected_result1 = np.transpose(x_np, [1, 0, 2])
        expected_result2 = np.transpose(x_np, (2, 1, 0))

        np.testing.assert_array_equal(x_trans1.numpy(), expected_result1)
        np.testing.assert_array_equal(x_trans2.numpy(), expected_result2)
        # This is an old test before 2.0 API so we enable static again after
        # dygraph test
        paddle.enable_static()


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)


class TestMoveAxis(unittest.TestCase):

    def test_moveaxis1(self):
        x_np = np.random.randn(2, 3, 4, 5, 7)
        expected = np.moveaxis(x_np, [0, 4, 3, 2], [1, 3, 2, 0])
        paddle.enable_static()
        with paddle.static.program_guard(fluid.Program()):
            x = paddle.static.data("x", shape=[2, 3, 4, 5, 7], dtype='float64')
            out = paddle.moveaxis(x, [0, 4, 3, 2], [1, 3, 2, 0])

            exe = paddle.static.Executor()
            out_np = exe.run(feed={"x": x_np}, fetch_list=[out])[0]

        np.testing.assert_array_equal(out_np, expected)

        paddle.disable_static()
        x = paddle.to_tensor(x_np)
        out = paddle.moveaxis(x, [0, 4, 3, 2], [1, 3, 2, 0])
        self.assertEqual(out.shape, [4, 2, 5, 7, 3])
        np.testing.assert_array_equal(out.numpy(), expected)
        paddle.enable_static()

    def test_moveaxis2(self):
        x_np = np.random.randn(2, 3, 5)
        expected = np.moveaxis(x_np, -2, -1)
        paddle.enable_static()
        with paddle.static.program_guard(fluid.Program()):
            x = paddle.static.data("x", shape=[2, 3, 5], dtype='float64')
            out = x.moveaxis(-2, -1)

            exe = paddle.static.Executor()
            out_np = exe.run(feed={"x": x_np}, fetch_list=[out])[0]

        np.testing.assert_array_equal(out_np, expected)

        paddle.disable_static()
        x = paddle.to_tensor(x_np)
        out = x.moveaxis(-2, -1)
        self.assertEqual(out.shape, [2, 5, 3])
        np.testing.assert_array_equal(out.numpy(), expected)
        paddle.enable_static()

    def test_moveaxis3(self):
        paddle.disable_static()
        x = paddle.to_tensor([[1 + 1j, -1 - 1j], [1 + 1j, -1 - 1j],
                              [1 + 1j, -1 - 1j]])
        out = x.moveaxis(0, 1)
        self.assertEqual(out.shape, [2, 3])
        paddle.enable_static()

    def test_error(self):
        x = paddle.randn([2, 3, 4, 5])
        # src must have the same number with dst
        with self.assertRaises(AssertionError):
            paddle.moveaxis(x, [1, 0], [2])

        # each element of src must be unique
        with self.assertRaises(ValueError):
            paddle.moveaxis(x, [1, 1], [0, 2])

        # each element of dst must be unique
        with self.assertRaises(ValueError):
            paddle.moveaxis(x, [0, 1], [2, 2])

        # each element of src must be integer
        with self.assertRaises(AssertionError):
            paddle.moveaxis(x, [0.5], [1])

        # each element of dst must be integer
        with self.assertRaises(AssertionError):
            paddle.moveaxis(x, [0], [1.5])

        # each element of src must be in the range of [-4, 3)
        with self.assertRaises(AssertionError):
            paddle.moveaxis(x, [-10, 1], [2, 3])

        # each element of dst must be in the range of [-4, 3)
        with self.assertRaises(AssertionError):
            paddle.moveaxis(x, [2, 1], [10, 3])


class TestTransposeDoubleGradCheck(unittest.TestCase):

    def transpose_wrapper(self, x):
        return paddle.transpose(x[0], [1, 0, 2])

    @prog_scope()
    def func(self, place):
        # the shape of input variable should be clearly specified, not inlcude -1.
        eps = 0.005
        dtype = np.float32

        data = layers.data('data', [2, 3, 4], False, dtype)
        data.persistable = True
        out = paddle.transpose(data, [1, 0, 2])
        data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)

        gradient_checker.double_grad_check([data],
                                           out,
                                           x_init=[data_arr],
                                           place=place,
                                           eps=eps)
        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
        gradient_checker.double_grad_check_for_dygraph(self.transpose_wrapper,
                                                       [data],
                                                       out,
                                                       x_init=[data_arr],
                                                       place=place)

    def test_grad(self):
        paddle.enable_static()
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for p in places:
            self.func(p)


class TestTransposeTripleGradCheck(unittest.TestCase):

    def transpose_wrapper(self, x):
        return paddle.transpose(x[0], [1, 0, 2])

    @prog_scope()
    def func(self, place):
        # the shape of input variable should be clearly specified, not inlcude -1.
        eps = 0.005
        dtype = np.float32

        data = layers.data('data', [2, 3, 4], False, dtype)
        data.persistable = True
        out = paddle.transpose(data, [1, 0, 2])
        data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)

        gradient_checker.triple_grad_check([data],
                                           out,
                                           x_init=[data_arr],
                                           place=place,
                                           eps=eps)
        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
        gradient_checker.triple_grad_check_for_dygraph(self.transpose_wrapper,
                                                       [data],
                                                       out,
                                                       x_init=[data_arr],
                                                       place=place)

    def test_grad(self):
        paddle.enable_static()
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for p in places:
            self.func(p)


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()