test_mean_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.

import unittest
import numpy as np
from op_test import OpTest, OpTestTool
import paddle
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
from test_sum_op import TestReduceOPTensorAxisBase
import gradient_checker
from decorator_helper import prog_scope
import paddle.fluid.layers as layers

np.random.seed(10)


def mean_wrapper(x, axis=None, keepdim=False, reduce_all=False):
    if reduce_all:
        return paddle.mean(x, range(len(x.shape)), keepdim)
    return paddle.mean(x, axis, keepdim)


def reduce_mean_wrapper(x, axis=0, keepdim=False, reduce_all=False):
    if reduce_all:
        return paddle.mean(x, range(len(x.shape)), keepdim)
    return paddle.mean(x, axis, keepdim)


class TestMeanOp(OpTest):
    def setUp(self):
        self.op_type = "mean"
        self.python_api = paddle.mean
        self.dtype = np.float64
        self.init_dtype_type()
        self.inputs = {'X': np.random.random((10, 10)).astype(self.dtype)}
        self.outputs = {'Out': np.mean(self.inputs["X"])}

    def init_dtype_type(self):
        pass

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

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


class TestMeanOp_ZeroDim(OpTest):
    def setUp(self):
        self.op_type = "mean"
        self.python_api = paddle.mean
        self.dtype = np.float64
        self.inputs = {'X': np.random.random([]).astype(self.dtype)}
        self.outputs = {'Out': np.mean(self.inputs["X"])}

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

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


class TestMeanOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # The input type of mean_op must be Variable.
            input1 = 12
            self.assertRaises(TypeError, paddle.mean, input1)
            # The input dtype of mean_op must be float16, float32, float64.
            input2 = fluid.layers.data(
                name='input2', shape=[12, 10], dtype="int32"
            )
            self.assertRaises(TypeError, paddle.mean, input2)
            input3 = fluid.layers.data(
                name='input3', shape=[4], dtype="float16"
            )
            fluid.layers.softmax(input3)


@unittest.skipIf(
    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
)
class TestFP16MeanOp(TestMeanOp):
    def init_dtype_type(self):
        self.dtype = np.float16
        self.__class__.no_need_check_grad = True

    def test_check_output(self):
        place = core.CUDAPlace(0)
        if core.is_float16_supported(place):
            self.check_output_with_place(place, check_eager=True)

    def test_checkout_grad(self):
        place = core.CUDAPlace(0)
        if core.is_float16_supported(place):
            with fluid.dygraph.guard():
                x_np = np.random.random((10, 10)).astype(self.dtype)
                x = paddle.to_tensor(x_np)
                x.stop_gradient = False
                y = paddle.mean(x)
                dx = paddle.grad(y, x)[0].numpy()
                dx_expected = self.dtype(1.0 / np.prod(x_np.shape)) * np.ones(
                    x_np.shape
                ).astype(self.dtype)
                np.testing.assert_array_equal(dx, dx_expected)


@OpTestTool.skip_if_not_cpu_bf16()
class TestBF16MeanOp(TestMeanOp):
    def init_dtype_type(self):
        self.dtype = np.uint16

    def test_check_output(self):
        paddle.enable_static()
        self.check_output_with_place(core.CPUPlace(), check_eager=True)

    def test_checkout_grad(self):
        place = core.CPUPlace()
        self.check_grad_with_place(place, ['X'], 'Out', check_eager=True)


def ref_reduce_mean(x, axis=None, keepdim=False, reduce_all=False):
    if isinstance(axis, list):
        axis = tuple(axis)
    if reduce_all:
        axis = None
    return np.mean(x, axis=axis, keepdims=keepdim)


def ref_reduce_mean_grad(x, axis, dtype, reduce_all):
    if reduce_all:
        axis = list(range(x.ndim))

    shape = [x.shape[i] for i in axis]
    return (1.0 / np.prod(shape) * np.ones(shape)).astype(dtype)


class TestReduceMeanOp(OpTest):
    def setUp(self):
        self.op_type = 'reduce_mean'
        self.python_api = reduce_mean_wrapper
        self.dtype = 'float64'
        self.shape = [2, 3, 4, 5]
        self.axis = [0]
        self.keepdim = False
        self.set_attrs()

        np.random.seed(10)
        x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
        if not hasattr(self, "reduce_all"):
            self.reduce_all = (not self.axis) or len(self.axis) == len(x_np)

        out_np = ref_reduce_mean(x_np, self.axis, self.keepdim, self.reduce_all)
        self.inputs = {'X': x_np}
        self.outputs = {'Out': out_np}
        self.attrs = {
            'dim': self.axis,
            'keep_dim': self.keepdim,
            'reduce_all': self.reduce_all,
        }

        if self.dtype == 'float16':
            self.__class__.no_need_check_grad = True

    def set_attrs(self):
        pass

    def test_check_output(self):
        if self.dtype != 'float16':
            self.check_output(check_eager=True)
        else:
            if not core.is_compiled_with_cuda():
                return
            place = paddle.CUDAPlace(0)
            self.check_output_with_place(place=place)

    def test_check_grad(self):
        if self.dtype != 'float16':
            self.check_grad(['X'], ['Out'], check_eager=True)
        else:
            if not core.is_compiled_with_cuda():
                return
            place = paddle.CUDAPlace(0)
            if core.is_float16_supported(place):
                return
            with fluid.dygraph.guard(place=place):
                x = paddle.tensor(self.inputs['X'])
                y = paddle.mean(
                    x, axis=self.attrs['dim'], keepdim=self.attrs['keep_dim']
                )
                dx = paddle.grad(y, x)[0].numpy()
                dx_expected = ref_reduce_mean_grad(
                    self.inputs['X'],
                    self.attrs['dim'],
                    self.dtype,
                    self.attrs['reduce_all'],
                )
                np.testing.assert_array_equal(dx, dx_expected)


class TestReduceMeanOpDefaultAttrs(TestReduceMeanOp):
    def setUp(self):
        self.op_type = 'reduce_mean'
        self.python_api = reduce_mean_wrapper
        self.dtype = 'float64'
        self.shape = [2, 3, 4, 5]

        x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
        out_np = np.mean(x_np, axis=0)
        self.inputs = {'X': x_np}
        self.outputs = {'Out': out_np}


class TestReduceMeanOpFloat32(TestReduceMeanOp):
    def set_attrs(self):
        self.dtype = 'float32'


class TestReduceMeanOpFloat16(TestReduceMeanOp):
    def set_attrs(self):
        self.dtype = 'float16'


class TestReduceMeanOpShape1D(TestReduceMeanOp):
    def set_attrs(self):
        self.shape = [100]


class TestReduceMeanOpShape1DFP16(TestReduceMeanOp):
    def set_attrs(self):
        self.shape = [100]
        self.dtype = 'float16'


class TestReduceMeanOpShape6D(TestReduceMeanOp):
    def set_attrs(self):
        self.shape = [2, 3, 4, 5, 6, 7]


class TestReduceMeanOpShape6DFP16(TestReduceMeanOp):
    def set_attrs(self):
        self.shape = [2, 3, 4, 5, 6, 7]
        self.dtype = 'float16'


class TestReduceMeanOpAxisAll(TestReduceMeanOp):
    def set_attrs(self):
        self.axis = [0, 1, 2, 3]


class TestReduceMeanOpAxisAllFP16(TestReduceMeanOp):
    def set_attrs(self):
        self.axis = [0, 1, 2, 3]
        self.dtype = 'float16'


class TestReduceMeanOpAxisTuple(TestReduceMeanOp):
    def set_attrs(self):
        self.axis = (0, 1, 2)


class TestReduceMeanOpAxisTupleFP16(TestReduceMeanOp):
    def set_attrs(self):
        self.axis = (0, 1, 2)
        self.dtype = 'float16'


class TestReduceMeanOpAxisNegative(TestReduceMeanOp):
    def set_attrs(self):
        self.axis = [-2, -1]


class TestReduceMeanOpAxisNegativeFP16(TestReduceMeanOp):
    def set_attrs(self):
        self.axis = [-2, -1]
        self.dtype = 'float16'


class TestReduceMeanOpKeepdimTrue1(TestReduceMeanOp):
    def set_attrs(self):
        self.keepdim = True


class TestReduceMeanOpKeepdimTrue1FP16(TestReduceMeanOp):
    def set_attrs(self):
        self.keepdim = True
        self.dtype = 'float16'


class TestReduceMeanOpKeepdimTrue2(TestReduceMeanOp):
    def set_attrs(self):
        self.axis = [0, 1, 2, 3]
        self.keepdim = True


class TestReduceMeanOpKeepdimTrue2FP16(TestReduceMeanOp):
    def set_attrs(self):
        self.axis = [0, 1, 2, 3]
        self.keepdim = True
        self.dtype = 'float16'


class TestReduceMeanOpReduceAllTrue(TestReduceMeanOp):
    def set_attrs(self):
        self.reduce_all = True


class TestReduceMeanOpReduceAllTrueFP16(TestReduceMeanOp):
    def set_attrs(self):
        self.reduce_all = True
        self.dtype = 'float16'


class TestMeanAPI(unittest.TestCase):
    # test paddle.tensor.stat.mean

    def setUp(self):
        self.x_shape = [2, 3, 4, 5]
        self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
        self.place = (
            paddle.CUDAPlace(0)
            if core.is_compiled_with_cuda()
            else paddle.CPUPlace()
        )

    def test_api_static(self):
        paddle.enable_static()
        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.fluid.data('X', self.x_shape)
            out1 = paddle.mean(x)
            out2 = paddle.tensor.mean(x)
            out3 = paddle.tensor.stat.mean(x)
            axis = np.arange(len(self.x_shape)).tolist()
            out4 = paddle.mean(x, axis)
            out5 = paddle.mean(x, tuple(axis))

            exe = paddle.static.Executor(self.place)
            res = exe.run(
                feed={'X': self.x}, fetch_list=[out1, out2, out3, out4, out5]
            )
        out_ref = np.mean(self.x)
        for out in res:
            np.testing.assert_allclose(out, out_ref, rtol=0.0001)

    def test_api_dygraph(self):
        paddle.disable_static(self.place)

        def test_case(x, axis=None, keepdim=False):
            x_tensor = paddle.to_tensor(x)
            out = paddle.mean(x_tensor, axis, keepdim)
            if isinstance(axis, list):
                axis = tuple(axis)
                if len(axis) == 0:
                    axis = None
            out_ref = np.mean(x, axis, keepdims=keepdim)
            np.testing.assert_allclose(out.numpy(), out_ref, rtol=0.0001)

        test_case(self.x)
        test_case(self.x, [])
        test_case(self.x, -1)
        test_case(self.x, keepdim=True)
        test_case(self.x, 2, keepdim=True)
        test_case(self.x, [0, 2])
        test_case(self.x, (0, 2))
        test_case(self.x, [0, 1, 2, 3])
        paddle.enable_static()

    def test_fluid_api(self):
        with fluid.program_guard(fluid.Program(), fluid.Program()):
            x = fluid.data("x", shape=[10, 10], dtype="float32")
            out = fluid.layers.reduce_mean(input=x, dim=1)
            place = fluid.CPUPlace()
            exe = fluid.Executor(place)
            x_np = np.random.rand(10, 10).astype(np.float32)
            res = exe.run(feed={"x": x_np}, fetch_list=[out])
        np.testing.assert_allclose(res[0], np.mean(x_np, axis=1), rtol=1e-05)

        with fluid.dygraph.guard():
            x_np = np.random.rand(10, 10).astype(np.float32)
            x = fluid.dygraph.to_variable(x_np)
            out = fluid.layers.reduce_mean(input=x, dim=1)
        np.testing.assert_allclose(
            out.numpy(), np.mean(x_np, axis=1), rtol=1e-05
        )

    def test_errors(self):
        paddle.disable_static()
        x = np.random.uniform(-1, 1, [10, 12]).astype('float32')
        x = paddle.to_tensor(x)
        self.assertRaises(Exception, paddle.mean, x, -3)
        self.assertRaises(Exception, paddle.mean, x, 2)
        paddle.enable_static()
        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.fluid.data('X', [10, 12], 'int32')
            self.assertRaises(TypeError, paddle.mean, x)


class TestMeanWithTensorAxis1(TestReduceOPTensorAxisBase):
    def init_data(self):
        self.pd_api = paddle.mean
        self.np_api = np.mean
        self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
        self.np_axis = np.array([1, 2], dtype='int64')
        self.tensor_axis = paddle.to_tensor([1, 2], dtype='int64')


class TestMeanWithTensorAxis2(TestReduceOPTensorAxisBase):
    def init_data(self):
        self.pd_api = paddle.mean
        self.np_api = np.mean
        self.x = paddle.randn([10, 10, 9, 9], dtype='float64')
        self.np_axis = np.array([0, 1, 2], dtype='int64')
        self.tensor_axis = [
            0,
            paddle.to_tensor([1], 'int64'),
            paddle.to_tensor([2], 'int64'),
        ]


class TestMeanDoubleGradCheck(unittest.TestCase):
    def mean_wrapper(self, x):
        return paddle.mean(x[0])

    @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', [3, 4, 5], False, dtype)
        data.persistable = True
        out = paddle.mean(data)
        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.mean_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 TestMeanTripleGradCheck(unittest.TestCase):
    def mean_wrapper(self, x):
        return paddle.mean(x[0])

    @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', [3, 4, 5], False, dtype)
        data.persistable = True
        out = paddle.mean(data)
        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.mean_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()