test_expand_v2_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
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard, core
import paddle
from paddle.fluid.framework import _test_eager_guard
import gradient_checker
from decorator_helper import prog_scope
import paddle.fluid.layers as layers


# Situation 1: shape is a list(without tensor)
class TestExpandV2OpRank1(OpTest):

    def setUp(self):
        self.op_type = "expand_v2"
        self.init_data()
        self.python_api = paddle.expand

        self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")}
        self.attrs = {'shape': self.shape}
        output = np.tile(self.inputs['X'], self.expand_times)
        self.outputs = {'Out': output}

    def init_data(self):
        self.ori_shape = [100]
        self.shape = [100]
        self.expand_times = [1]

    def test_check_output(self):
        self.check_output(check_eager=True)

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


class TestExpandV2OpRank2_DimExpanding(TestExpandV2OpRank1):

    def init_data(self):
        self.ori_shape = [120]
        self.shape = [2, 120]
        self.expand_times = [2, 1]


class TestExpandV2OpRank2(TestExpandV2OpRank1):

    def init_data(self):
        self.ori_shape = [1, 140]
        self.shape = [12, 140]
        self.expand_times = [12, 1]


class TestExpandV2OpRank3_Corner(TestExpandV2OpRank1):

    def init_data(self):
        self.ori_shape = (2, 10, 5)
        self.shape = (2, 10, 5)
        self.expand_times = (1, 1, 1)


class TestExpandV2OpRank4(TestExpandV2OpRank1):

    def init_data(self):
        self.ori_shape = (2, 4, 5, 7)
        self.shape = (-1, -1, -1, -1)
        self.expand_times = (1, 1, 1, 1)


# Situation 2: shape is a list(with tensor)
class TestExpandV2OpRank1_tensor_attr(OpTest):

    def setUp(self):
        self.op_type = "expand_v2"
        self.init_data()
        expand_shapes_tensor = []
        for index, ele in enumerate(self.expand_shape):
            expand_shapes_tensor.append(("x" + str(index), np.ones(
                (1)).astype('int32') * ele))

        self.inputs = {
            'X': np.random.random(self.ori_shape).astype("float64"),
            'expand_shapes_tensor': expand_shapes_tensor,
        }
        self.attrs = {"shape": self.infer_expand_shape}
        output = np.tile(self.inputs['X'], self.expand_times)
        self.outputs = {'Out': output}

    def init_data(self):
        self.ori_shape = [100]
        self.expand_times = [1]
        self.expand_shape = [100]
        self.infer_expand_shape = [-1]

    def test_check_output(self):
        self.check_output()

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


class TestExpandV2OpRank2_Corner_tensor_attr(TestExpandV2OpRank1_tensor_attr):

    def init_data(self):
        self.ori_shape = [12, 14]
        self.expand_times = [1, 1]
        self.expand_shape = [12, 14]
        self.infer_expand_shape = [12, -1]


# Situation 3: shape is a tensor
class TestExpandV2OpRank1_tensor(OpTest):

    def setUp(self):
        self.op_type = "expand_v2"
        self.init_data()

        self.inputs = {
            'X': np.random.random(self.ori_shape).astype("float64"),
            'Shape': np.array(self.expand_shape).astype("int32"),
        }
        self.attrs = {}
        output = np.tile(self.inputs['X'], self.expand_times)
        self.outputs = {'Out': output}

    def init_data(self):
        self.ori_shape = [100]
        self.expand_times = [2, 1]
        self.expand_shape = [2, 100]

    def test_check_output(self):
        self.check_output()

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


# Situation 4: input x is Integer
class TestExpandV2OpInteger(OpTest):

    def setUp(self):
        self.op_type = "expand_v2"
        self.inputs = {
            'X': np.random.randint(10, size=(2, 4, 5)).astype("int32")
        }
        self.attrs = {'shape': [2, 4, 5]}
        output = np.tile(self.inputs['X'], (1, 1, 1))
        self.outputs = {'Out': output}

    def test_check_output(self):
        self.check_output()


# Situation 5: input x is Bool
class TestExpandV2OpBoolean(OpTest):

    def setUp(self):
        self.op_type = "expand_v2"
        self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
        self.attrs = {'shape': [2, 4, 5]}
        output = np.tile(self.inputs['X'], (1, 1, 1))
        self.outputs = {'Out': output}

    def test_check_output(self):
        self.check_output()


# Situation 56: input x is Integer
class TestExpandV2OpInt64_t(OpTest):

    def setUp(self):
        self.op_type = "expand_v2"
        self.inputs = {
            'X': np.random.randint(10, size=(2, 4, 5)).astype("int64")
        }
        self.attrs = {'shape': [2, 4, 5]}
        output = np.tile(self.inputs['X'], (1, 1, 1))
        self.outputs = {'Out': output}

    def test_check_output(self):
        self.check_output()


class TestExpandV2Error(unittest.TestCase):

    def test_errors(self):
        with program_guard(Program(), Program()):
            x1 = fluid.create_lod_tensor(np.array([[-1]]), [[1]],
                                         fluid.CPUPlace())
            shape = [2, 2]
            self.assertRaises(TypeError, paddle.tensor.expand, x1, shape)
            x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
            self.assertRaises(TypeError, paddle.tensor.expand, x2, shape)
            x3 = fluid.layers.data(name='x3', shape=[4], dtype="bool")
            x3.stop_gradient = False
            self.assertRaises(ValueError, paddle.tensor.expand, x3, shape)


# Test python API
class TestExpandV2API(unittest.TestCase):

    def test_api(self):
        input = np.random.random([12, 14]).astype("float32")
        x = fluid.layers.data(name='x',
                              shape=[12, 14],
                              append_batch_size=False,
                              dtype="float32")

        positive_2 = fluid.layers.fill_constant([1], "int32", 12)
        expand_shape = fluid.layers.data(name="expand_shape",
                                         shape=[2],
                                         append_batch_size=False,
                                         dtype="int32")

        out_1 = paddle.expand(x, shape=[12, 14])
        out_2 = paddle.expand(x, shape=[positive_2, 14])
        out_3 = paddle.expand(x, shape=expand_shape)

        g0 = fluid.backward.calc_gradient(out_2, x)

        exe = fluid.Executor(place=fluid.CPUPlace())
        res_1, res_2, res_3 = exe.run(fluid.default_main_program(),
                                      feed={
                                          "x":
                                          input,
                                          "expand_shape":
                                          np.array([12, 14]).astype("int32")
                                      },
                                      fetch_list=[out_1, out_2, out_3])
        assert np.array_equal(res_1, np.tile(input, (1, 1)))
        assert np.array_equal(res_2, np.tile(input, (1, 1)))
        assert np.array_equal(res_3, np.tile(input, (1, 1)))


class TestExpandInferShape(unittest.TestCase):

    def test_shape_with_var(self):
        with program_guard(Program(), Program()):
            x = paddle.static.data(shape=[-1, 1, 3], name='x')
            fake_var = paddle.randn([2, 3])
            target_shape = [
                -1, paddle.shape(fake_var)[0],
                paddle.shape(fake_var)[1]
            ]
            out = paddle.expand(x, shape=target_shape)
            self.assertListEqual(list(out.shape), [-1, -1, -1])


# Test python Dygraph API
class TestExpandV2DygraphAPI(unittest.TestCase):

    def test_expand_times_is_tensor(self):
        with paddle.fluid.dygraph.guard():
            with _test_eager_guard():
                paddle.seed(1)
                a = paddle.rand([2, 5])
                egr_expand_1 = paddle.expand(a, shape=[2, 5])
                np_array = np.array([2, 5])
                egr_expand_2 = paddle.expand(a, shape=np_array)

            paddle.seed(1)
            a = paddle.rand([2, 5])
            expand_1 = paddle.expand(a, shape=[2, 5])
            np_array = np.array([2, 5])
            expand_2 = paddle.expand(a, shape=np_array)

            np.testing.assert_array_equal(egr_expand_1.numpy(),
                                          egr_expand_2.numpy())
            np.testing.assert_array_equal(expand_1.numpy(), expand_2.numpy())
            np.testing.assert_array_equal(expand_1.numpy(),
                                          egr_expand_1.numpy())


class TestExpandDoubleGradCheck(unittest.TestCase):

    def expand_wrapper(self, x):
        return paddle.expand(x[0], [2, 3])

    @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], False, dtype)
        data.persistable = True
        out = paddle.expand(data, [2, 3])
        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.expand_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 TestExpandTripleGradCheck(unittest.TestCase):

    def expand_wrapper(self, x):
        return paddle.expand(x[0], [2, 3])

    @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], False, dtype)
        data.persistable = True
        out = paddle.expand(data, [2, 3])
        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.expand_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()