test_slice_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
import paddle.fluid.core as core
from op_test import OpTest, convert_float_to_uint16
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle
from paddle.fluid.framework import _test_eager_guard, _enable_legacy_dygraph
import gradient_checker
from decorator_helper import prog_scope
import paddle.fluid.layers as layers

paddle.enable_static()


# Situation 1: starts(list, no tensor), ends(list, no tensor)
# 1.1 without attr(decrease)
class TestSliceOp(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()
        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [3, 3, 4]
        self.axes = [0, 1, 2]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[1:3, 0:3, 2:4, :]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


class TestCase1(TestSliceOp):

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [-3, 0, 2]
        self.ends = [3, 100, -1]
        self.axes = [0, 1, 2]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[-3:3, 0:100, 2:-1, :]


class TestCase2(TestSliceOp):

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [-3, 0, 2]
        self.ends = [3, 100, -1]
        self.axes = [0, 1, 3]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[-3:3, 0:100, :, 2:-1]


class TestSliceZerosShapeTensor(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()
        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
            'use_mkldnn': True
        }

    def config(self):
        self.input = np.random.random([0, 0, 0]).astype("float32")
        self.starts = [1]
        self.ends = [2]
        self.axes = [0]
        self.infer_flags = []
        self.out = self.input[1:2]

    def test_check_output(self):
        self.check_output_with_place(paddle.CPUPlace())


# 1.2 with attr(decrease)
class TestSliceOp_decs_dim(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()
        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
            'decrease_axis': self.decrease_axis,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 3, 4]
        self.axes = [0, 1, 2]
        self.decrease_axis = [0]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[1, 0:3, 2:4, :]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim):

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 1, 4]
        self.axes = [0, 1, 2]
        self.decrease_axis = [0, 1]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[1, 0, 2:4, :]


class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim):

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [-1, 0, 2]
        self.ends = [1000000, 1, 4]
        self.axes = [0, 1, 2]
        self.decrease_axis = [0, 1]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[-1, 0, 2:4, :]


class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim):

    def config(self):
        self.input = np.random.random([3, 4, 5, 7]).astype("float64")
        self.starts = [0, 1, 2, 3]
        self.ends = [1, 2, 3, 4]
        self.axes = [0, 1, 2, 3]
        self.decrease_axis = [0, 1, 2, 3]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[0, 1, 2, 3:4]


class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim):

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [-1]
        self.ends = [1000000]
        self.axes = [3]
        self.decrease_axis = [3]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[:, :, :, -1]


class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim):

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [0, 1, 2, 3]
        self.ends = [1, 2, 3, 4]
        self.axes = [0, 1, 2, 3]
        self.decrease_axis = [0, 1, 2, 3]
        self.infer_flags = [1, 1, 1]
        self.out = self.input[0, 1, 2, 3:4]


# Situation 2: starts(list, have tensor), ends(list, no tensor)
# without attr(decrease)
class TestSliceOp_starts_ListTensor(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()

        starts_tensor = []
        for index, ele in enumerate(self.starts):
            starts_tensor.append(("x" + str(index), np.ones(
                (1)).astype('int64') * ele))

        self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts_infer,
            'ends': self.ends,
            'infer_flags': self.infer_flags
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [3, 3, 4]
        self.axes = [0, 1, 2]
        self.infer_flags = [-1, 1, -1]
        self.out = self.input[1:3, 0:3, 2:4, :]

        self.starts_infer = [-1, 0, -1]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


# Situation 2: starts(list, have tensor), ends(list, no tensor)
#  with attr(decrease)
class TestSliceOp_decs_dim_starts_ListTensor(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()

        starts_tensor = []
        for index, ele in enumerate(self.starts):
            starts_tensor.append(("x" + str(index), np.ones(
                (1)).astype('int32') * ele))

        self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}

        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts_infer,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
            'decrease_axis': self.decrease_axis,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 3, 4]
        self.axes = [0, 1, 2]
        self.decrease_axis = [0]
        self.infer_flags = [1, -1, 1]
        self.out = self.input[1, 0:3, 2:4, :]

        self.starts_infer = [1, -1, 2]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


class TestSliceOp_decs_dim_5_starts_ListTensor(
        TestSliceOp_decs_dim_starts_ListTensor):

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [-1]
        self.ends = [1000000]
        self.axes = [3]
        self.decrease_axis = [3]
        self.infer_flags = [-1]
        self.out = self.input[:, :, :, -1]

        self.starts_infer = [-1]


# Situation 3: starts(tensor), ends(list, no tensor)
# with attr(decrease)
class TestSliceOp_decs_dim_starts_OneTensor(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()
        self.inputs = {
            'Input': self.input,
            "StartsTensor": np.array(self.starts, dtype="int32")
        }
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            #'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags,
            'decrease_axis': self.decrease_axis,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 3, 4]
        self.axes = [0, 1, 2]
        self.decrease_axis = [0]
        self.infer_flags = [-1, -1, -1]
        self.out = self.input[1, 0:3, 2:4, :]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


# Situation 4: starts(tensor), ends(tensor)
#  without attr(decrease)
class TestSliceOp_starts_OneTensor_ends_OneTensor(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()

        self.inputs = {
            'Input': self.input,
            "StartsTensor": np.array(self.starts, dtype="int64"),
            "EndsTensor": np.array(self.ends, dtype="int32")
        }
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            #'starts': self.starts,
            #'ends': self.ends_infer,
            'infer_flags': self.infer_flags
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [3, 3, 4]
        self.axes = [0, 1, 2]
        self.infer_flags = [-1, -1, -1]
        self.out = self.input[1:3, 0:3, 2:4, :]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


# Situation 5: starts(tensor), ends(tensor)
#  with attr(decrease)
class TestSliceOp_decs_dim_starts_and_ends_OneTensor(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()
        self.inputs = {
            'Input': self.input,
            "StartsTensor": np.array(self.starts, dtype="int32"),
            "EndsTensor": np.array(self.ends, dtype="int32")
        }
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            #'starts': self.starts,
            #'ends': self.ends,
            'infer_flags': self.infer_flags,
            'decrease_axis': self.decrease_axis,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 1, 4]
        self.axes = [0, 1, 2]
        self.decrease_axis = [0, 1]
        self.infer_flags = [-1, -1, -1]
        self.out = self.input[1, 0, 2:4, :]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


# Situation 6: starts(tensor), ends(list, have tensor)
# without attr(decrease)
class TestSliceOp_starts_OneTensor_ends_ListTensor(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()

        ends_tensor = []
        for index, ele in enumerate(self.ends):
            ends_tensor.append(("y" + str(index), np.ones(
                (1)).astype('int32') * ele))

        self.inputs = {
            'Input': self.input,
            "StartsTensor": np.array(self.starts, dtype="int32"),
            'EndsTensorList': ends_tensor
        }
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            #'starts': self.starts,
            'ends': self.ends_infer,
            'infer_flags': self.infer_flags
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [3, 3, 4]
        self.axes = [0, 1, 2]
        self.infer_flags = [-1, -1, -1]
        self.out = self.input[1:3, 0:3, 2:4, :]

        self.ends_infer = [-1, 3, 4]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


# Test CUDA float16
@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestFP16(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()
        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags
        }

    def config(self):
        self.dtype = "float16"
        self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
        self.starts = [-3, 0, 2]
        self.ends = [3, 100, -1]
        self.axes = [0, 1, 3]
        self.out = self.input[-3:3, 0:100, :, 2:-1]
        self.infer_flags = [1, 1, 1]

    def test_check_output(self):
        place = core.CUDAPlace(0)
        if core.is_float16_supported(place):
            self.check_output_with_place(place, atol=1e-5)

    def test_check_grad_normal(self):
        place = core.CUDAPlace(0)
        if core.is_float16_supported(place):
            self.check_grad_with_place(place, ['Input'],
                                       'Out',
                                       max_relative_error=0.006)


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestFP16_2(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()
        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.out}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags
        }

    def config(self):
        self.dtype = "float16"
        self.input = np.random.random([3, 4, 10]).astype(self.dtype)
        self.starts = [0]
        self.ends = [1]
        self.axes = [1]
        self.out = self.input[:, 0:1, :]
        self.infer_flags = [1]

    def test_check_output(self):
        place = core.CUDAPlace(0)
        if core.is_float16_supported(place):
            self.check_output_with_place(place, atol=1e-5)

    def test_check_grad_normal(self):
        place = core.CUDAPlace(0)
        if core.is_float16_supported(place):
            self.check_grad_with_place(place, ['Input'],
                                       'Out',
                                       max_relative_error=0.006,
                                       numeric_grad_delta=0.5)


class TestBF16(OpTest):

    def setUp(self):
        self.op_type = "slice"
        self.config()
        self.inputs = {'Input': convert_float_to_uint16(self.input)}
        self.outputs = {'Out': convert_float_to_uint16(self.out)}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'infer_flags': self.infer_flags
        }

    def config(self):
        self.dtype = np.uint16
        self.input = np.random.random([3, 4, 5, 6]).astype(np.float32)
        self.starts = [-3, 0, 2]
        self.ends = [3, 100, -1]
        self.axes = [0, 1, 3]
        self.out = self.input[-3:3, 0:100, :, 2:-1]
        self.infer_flags = [1, 1, 1]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out')


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

    def test_1(self):
        input = np.random.random([3, 4, 5, 6]).astype("float64")
        minus_1 = fluid.layers.fill_constant([1], "int32", -1)
        minus_3 = fluid.layers.fill_constant([1], "int64", -3)
        starts = fluid.layers.data(name='starts',
                                   shape=[1, 3],
                                   append_batch_size=False)
        ends = fluid.layers.data(name='ends',
                                 shape=[3],
                                 append_batch_size=False)

        x = fluid.layers.data(name="x",
                              shape=[3, 4, 5, 6],
                              append_batch_size=False,
                              dtype="float64")

        # value_int64 is greater than 2147483647 which is the max of int32
        value_int64 = fluid.layers.fill_constant([1], "int64", 2147483648)

        out_1 = paddle.slice(x,
                             axes=[0, 1, 2],
                             starts=[-3, 0, 2],
                             ends=[value_int64, 100, -1])
        out_2 = paddle.slice(x,
                             axes=[0, 1, 3],
                             starts=[minus_3, 0, 2],
                             ends=[3, 100, -1])
        out_3 = paddle.slice(x,
                             axes=[0, 1, 3],
                             starts=[minus_3, 0, 2],
                             ends=[3, 100, minus_1])
        out_4 = paddle.slice(x, axes=[0, 1, 2], starts=starts, ends=ends)

        out_5 = x[-3:3, 0:100, 2:-1]
        out_6 = x[minus_3:3, 0:100, :, 2:-1]
        out_7 = x[minus_1, 0:100, :, 2:minus_1]

        exe = fluid.Executor(place=fluid.CPUPlace())
        res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
            fluid.default_main_program(),
            feed={
                "x": input,
                'starts': np.array([-3, 0, 2]).astype("int32"),
                'ends': np.array([3, 100, -1]).astype("int32")
            },
            fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7])

        assert np.array_equal(res_1, input[-3:3, 0:100, 2:-1, :])
        assert np.array_equal(res_2, input[-3:3, 0:100, :, 2:-1])
        assert np.array_equal(res_3, input[-3:3, 0:100, :, 2:-1])
        assert np.array_equal(res_4, input[-3:3, 0:100, 2:-1, :])
        assert np.array_equal(res_5, input[-3:3, 0:100, 2:-1, :])
        assert np.array_equal(res_6, input[-3:3, 0:100, :, 2:-1])
        assert np.array_equal(res_7, input[-1, 0:100, :, 2:-1])


class TestSliceApiWithTensor(unittest.TestCase):

    def test_starts_ends_is_tensor(self):
        with paddle.fluid.dygraph.guard():
            a = paddle.rand(shape=[4, 5, 6], dtype='float32')
            axes = [0, 1, 2]
            starts = [-3, 0, 2]
            ends = [3, 2, 4]
            a_1 = paddle.slice(a,
                               axes=axes,
                               starts=paddle.to_tensor(starts, dtype='int32'),
                               ends=paddle.to_tensor(ends, dtype='int32'))
            a_2 = paddle.slice(a, axes=axes, starts=starts, ends=ends)

            np.testing.assert_array_equal(a_1.numpy(), a_2.numpy())

    def test_bool_tensor(self):
        with paddle.fluid.dygraph.guard():
            array = (np.arange(60).reshape([3, 4, 5]) % 3).astype('bool')
            tt = paddle.to_tensor(array)
            tt.stop_gradient = False

            starts = [0, 1, 2]
            ends = [3, 5, 4]
            axes = [0, 1, 2]

            y_paddle = paddle.slice(tt, axes, starts, ends)
            y_np = tt[0:3, 1:5, 2:4]

            self.assertTrue(paddle.bool == y_paddle.dtype)
            np.testing.assert_array_equal(y_paddle.numpy(), y_np)


class TestSliceApiEager(unittest.TestCase):

    def test_slice_api(self):
        with paddle.fluid.dygraph.guard():
            with _test_eager_guard():
                a = paddle.rand(shape=[4, 5, 6], dtype='float32')
                a.stop_gradient = False
                axes = [0, 1, 2]
                starts = [-3, 0, 2]
                ends = [3, 2, 4]
                a_1 = paddle.slice(a, axes=axes, starts=starts, ends=ends)

                a_2 = paddle.slice(a,
                                   axes=axes,
                                   starts=paddle.to_tensor(starts),
                                   ends=paddle.to_tensor(ends))
                np.testing.assert_array_equal(a_1.numpy(), a_2.numpy())
                a_1.backward()
                grad_truth = paddle.zeros_like(a)
                grad_truth[-3:3, 0:2, 2:4] = 1
                np.testing.assert_array_equal(grad_truth, a.gradient())

                np.testing.assert_allclose(a_1.numpy(),
                                           a[-3:3, 0:2, 2:4],
                                           rtol=1e-05)


class TestSliceApiWithLoDTensorArray(unittest.TestCase):

    def setUp(self):
        self.shape = (3, 4)
        self.data = np.random.random(size=self.shape).astype('float32')
        self.idx = 0
        self.start = 0
        self.end = 2
        self.axis = 1

        self.place = fluid.CUDAPlace(
            0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace()
        self.exe = fluid.Executor(self.place)

    def set_program_and_run(self, main_program, case_num):
        with fluid.program_guard(main_program):
            x = [
                fluid.data(name='x0', shape=self.shape, dtype="float32"),
                fluid.data(name='x1', shape=self.shape, dtype="float32"),
                fluid.data(name='x2', shape=self.shape, dtype="float32")
            ]

            for each_x in x:
                each_x.stop_gradient = False

            arr = layers.create_array(dtype="float32")
            for i in range(3):
                idx = layers.array_length(arr)
                arr = layers.array_write(x=x[i], i=idx, array=arr)

            if case_num == 1:
                self.sliced_arr = output = arr[0]

            elif case_num == 2:
                end = fluid.layers.array_length(
                    arr) - 1  # dtype of end is int64
                self.sliced_arr = slice_arr = arr[self.start:end]
                output, _ = fluid.layers.tensor_array_to_tensor(slice_arr,
                                                                axis=self.axis,
                                                                use_stack=True)
            elif case_num == 3:
                value_int64 = fluid.layers.fill_constant([1], "int64",
                                                         2147483648)
                self.sliced_arr = slice_arr = arr[self.start:value_int64]
                output, _ = fluid.layers.tensor_array_to_tensor(slice_arr,
                                                                axis=self.axis,
                                                                use_stack=True)

            loss = fluid.layers.reduce_sum(output)
            fluid.backward.append_backward(loss)
            g_vars = list(
                map(main_program.global_block().var,
                    [each_x.name + "@GRAD" for each_x in x]))
            self.out, self.g_x0, self.g_x1, self.g_x2 = \
                self.exe.run(main_program,
                             feed = {'x0': self.data,
                                     'x1': self.data,
                                     'x2': self.data},
                             fetch_list=[output] + g_vars)

    def test_case_1(self):
        main_program = fluid.Program()
        self.set_program_and_run(main_program, 1)

        self.assertTrue(self.sliced_arr.type == core.VarDesc.VarType.LOD_TENSOR)
        self.assertEqual(self.sliced_arr.shape, self.shape)
        np.testing.assert_array_equal(self.out, self.data)
        np.testing.assert_array_equal(self.g_x0, np.ones_like(self.data))
        np.testing.assert_array_equal(self.g_x1, np.zeros_like(self.data))
        np.testing.assert_array_equal(self.g_x2, np.zeros_like(self.data))

    def test_case_2(self):
        main_program = fluid.Program()
        self.set_program_and_run(main_program, 2)

        self.assertTrue(
            self.sliced_arr.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY)
        self.assertEqual(self.sliced_arr.shape, self.shape)
        np.testing.assert_array_equal(
            self.out, np.stack([self.data, self.data], axis=self.axis))
        np.testing.assert_array_equal(self.g_x0, np.ones_like(self.data))
        np.testing.assert_array_equal(self.g_x1, np.ones_like(self.data))
        np.testing.assert_array_equal(self.g_x2, np.zeros_like(self.data))

    def test_case_3(self):
        main_program = fluid.Program()
        self.set_program_and_run(main_program, 3)

        self.assertTrue(
            self.sliced_arr.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY)
        self.assertEqual(self.sliced_arr.shape, self.shape)
        np.testing.assert_array_equal(
            self.out, np.stack([self.data, self.data, self.data],
                               axis=self.axis))
        np.testing.assert_array_equal(self.g_x0, np.ones_like(self.data))
        np.testing.assert_array_equal(self.g_x1, np.ones_like(self.data))
        np.testing.assert_array_equal(self.g_x2, np.ones_like(self.data))


class TestImperativeVarBaseGetItem(unittest.TestCase):

    def test_getitem_with_long(self):
        with fluid.dygraph.guard():
            data = np.random.random((2, 80, 16128)).astype('float32')
            var = fluid.dygraph.to_variable(data)
            sliced = var[:, 10:, :var.shape[1]]  # var.shape[1] is 80L here
            self.assertEqual(sliced.shape, [2, 70, 80])

            sliced = var[:, var.shape[0]:, var.shape[0]:var.shape[1]]
            self.assertEqual(sliced.shape, [2, 78, 78])

    def test_getitem_with_float(self):

        def test_float_in_slice_item():
            with fluid.dygraph.guard():
                data = np.random.random((2, 80, 16128)).astype('float32')
                var = fluid.dygraph.to_variable(data)
                sliced = var[:, 1.1:, :var.shape[1]]

        self.assertRaises(Exception, test_float_in_slice_item)

        def test_float_in_index():
            with fluid.dygraph.guard():
                data = np.random.random((2, 80, 16128)).astype('float32')
                var = fluid.dygraph.to_variable(data)
                sliced = var[1.1]

        self.assertRaises(Exception, test_float_in_index)


class TestInferShape(unittest.TestCase):

    def test(self):
        x = paddle.ones(shape=[3, 4, 5])
        x.desc.set_shape([3, -1, 5])
        self.assertEqual(x.shape, (3, -1, 5))

        out0 = paddle.slice(x, axes=[1], starts=[0], ends=[3])
        self.assertEqual(out0.shape, (3, -1, 5))

    def test_axis_less_than_zero(self):
        # Using paddle.disable_static will make other unittests fail.
        with fluid.dygraph.guard():
            x_arr = np.arange(0, 24, dtype=np.float32).reshape([2, 3, 4])
            x = paddle.to_tensor(x_arr)

            pp_slice = paddle.slice(x, [
                100,
            ], [0], [1])
            np_slice = x_arr[:, :, 0:1]
            np.testing.assert_array_equal(pp_slice, np_slice)

            pp_slice = paddle.slice(x, (-100, ), [0], [1])
            np_slice = x_arr[0:1]
            np.testing.assert_array_equal(pp_slice, np_slice)

            x_arr = np.array([], dtype=np.float32)
            x = paddle.to_tensor(np.reshape(x_arr, (0, 0, 0)))

            starts = paddle.to_tensor(
                np.reshape(np.array([], dtype=np.int32), (0, )))
            ends = paddle.to_tensor(
                np.reshape(np.array([], dtype=np.int32), (0, )))

            with self.assertRaises(ValueError):
                paddle.slice(x, [-1000000], starts, ends)

            with self.assertRaises(ValueError):
                paddle.slice(x, [1000000], starts, ends)

            with self.assertRaises(ValueError):
                paddle.slice(x, [], starts, ends)

            with self.assertRaises(ValueError):
                paddle.slice(x, 0, starts, ends)


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestImperativeCUDAPinnedInput(unittest.TestCase):

    def test_input_cuda_pinned_var(self):
        _enable_legacy_dygraph()
        with fluid.dygraph.guard():
            data = np.random.random((2, 80, 16128)).astype('float32')
            var = core.VarBase(value=data,
                               name='',
                               persistable=False,
                               place=fluid.CUDAPinnedPlace(),
                               zero_copy=False)
            sliced = var[:, 10:, :var.shape[1]]
            self.assertEqual(sliced.shape, [2, 70, 80])


class TestSliceDoubleGradCheck(unittest.TestCase):

    def slice_wrapper(self, x):
        return paddle.slice(x[0],
                            axes=[0, 1, 2],
                            starts=[-3, 0, 2],
                            ends=[3, 2, 4])

    @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', [4, 5, 6], False, dtype)
        data.persistable = True
        out = paddle.slice(data,
                           axes=[0, 1, 2],
                           starts=[-3, 0, 2],
                           ends=[3, 2, 4])
        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.slice_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 TestSliceTripleGradCheck(unittest.TestCase):

    def slice_wrapper(self, x):
        return paddle.slice(x[0],
                            axes=[0, 1, 2],
                            starts=[-3, 0, 2],
                            ends=[3, 2, 4])

    @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', [4, 5, 6], False, dtype)
        data.persistable = True
        out = paddle.slice(data,
                           axes=[0, 1, 2],
                           starts=[-3, 0, 2],
                           ends=[3, 2, 4])
        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.slice_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()