test_cross_entropy_loss.py

# Copyright (c) 2020 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 paddle
import paddle.fluid as fluid
import numpy as np
import unittest
from test_softmax_op import stable_softmax
from test_softmax_with_cross_entropy_op import cross_entropy
from paddle.fluid import Program, program_guard
from paddle.fluid.framework import _test_eager_guard


def log_softmax(x, axis=-1):
    softmax_out = np.apply_along_axis(stable_softmax, axis, x)
    return np.log(softmax_out)


def cross_entropy_loss_1d(input,
                          label,
                          weight=None,
                          reduction='mean',
                          ignore_index=-100):
    log_softmax_out = log_softmax(input)
    input_shape = log_softmax_out.shape
    N = input_shape[0]
    C = input_shape[1]
    out = np.zeros_like(label).astype(np.float64)
    total_weight = 0
    ###1. compute softmax cross_entropy (with weight)
    ###   Note: only support hard labels.
    for i in range(N):
        cur_target = label[i]
        if cur_target == ignore_index:
            out[i] = 0
            continue
        cur_weight = weight[cur_target] if weight is not None else 1
        total_weight += cur_weight
        out[i] = -log_softmax_out[i][cur_target] * cur_weight

    ###2. deal with reduction
    if reduction == 'sum':
        return np.sum(out), np.array([total_weight]).astype('float64')
    elif reduction == 'mean':
        out = out.sum() / total_weight if total_weight != 0 else out.sum()
        return out, np.array([total_weight]).astype('float64')
    elif reduction == 'none':
        return out


def cross_entropy_loss_2d(input,
                          label,
                          weight=None,
                          reduction='mean',
                          ignore_index=-100):
    log_softmax_out = log_softmax(input)
    input_shape = log_softmax_out.shape
    N = input_shape[0]
    H = input_shape[1]
    W = input_shape[2]

    out = np.zeros_like(label).astype(np.float64)
    total_weight = 0
    for i in range(N):
        for h in range(H):
            for w in range(W):
                cur_target = label[i][h][w]
                if cur_target == ignore_index:
                    out[i][h][w] = 0
                    continue
                cur_weight = weight[cur_target] if weight is not None else 1
                total_weight += cur_weight
                out[i][h][
                    w] = -log_softmax_out[i][h][w][cur_target] * cur_weight
    if reduction == 'sum':
        return np.sum(out), np.array([total_weight]).astype('float64')
    elif reduction == 'mean':
        out = out.sum() / total_weight if total_weight != 0 else out.sum()
        return out, np.array([total_weight]).astype('float64')
    elif reduction == 'none':
        return out


def cross_entropy_soft(softmax,
                       label,
                       axis,
                       N,
                       weight=None,
                       reduction='mean',
                       ignore_index=-100):
    #1.loss
    loss = cross_entropy(
        softmax,
        label,
        True,  #soft_label,
        axis,
        ignore_index)

    if weight is None and reduction == 'none':
        return loss

    #2.weight
    weighted_loss = loss
    total_weight = N  #for weight is None
    if weight is not None:
        weighted_loss = np.zeros_like(loss).astype(np.float64)
        total_weight = 0
        for i in range(N):
            cur_soft_label = label[i]
            cur_weight = np.dot(weight, cur_soft_label)
            total_weight += cur_weight
            weighted_loss[i] = loss[i] * cur_weight

    #3.reduce
    if reduction == 'none':
        return weighted_loss

    elif reduction == 'mean':
        weighted_loss_sum = np.sum(weighted_loss)
        weighted_loss_mean = weighted_loss_sum / total_weight
        return weighted_loss_mean

    else:
        weighted_loss_sum = np.sum(weighted_loss)
        return weighted_loss_sum


def cross_entropy_soft_2d(softmax,
                          label,
                          axis,
                          N,
                          H,
                          W,
                          weight=None,
                          reduction='mean',
                          ignore_index=-100):
    #1.loss
    loss = cross_entropy(
        softmax,
        label,
        True,  #soft_label,
        axis,
        ignore_index)

    if weight is None and reduction == 'none':
        return loss

    #2.weight
    weighted_loss = loss
    total_weight = N  #for weight is None
    if weight is not None:
        weighted_loss = np.zeros_like(loss).astype(np.float64)
        total_weight = 0
        for i in range(N):
            for h in range(H):
                for w in range(W):
                    cur_soft_label = label[i][h][w]
                    cur_weight = np.dot(weight, cur_soft_label)
                    total_weight += cur_weight
                    weighted_loss[i][h][w] = loss[i][h][w] * cur_weight

    #3.reduce
    if reduction == 'none':
        return weighted_loss

    elif reduction == 'mean':
        weighted_loss_sum = np.sum(weighted_loss)
        weighted_loss_mean = weighted_loss_sum / total_weight
        return weighted_loss_mean

    else:
        weighted_loss_sum = np.sum(weighted_loss)
        return weighted_loss_sum


class CrossEntropyLoss(unittest.TestCase):

    def setUp(self):
        self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
        ) else 'float64'

    ###test for deprecated softmax_with_cross_entropy
    def test_softmax_with_cross_entropy(self):
        self.numeric_stable_mode = False
        self.soft_label = True
        self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
        ) else 'float64'
        self.axis = -1
        self.ignore_index = -100  #should not be changed
        self.N = 4
        self.C = 3
        self.shape = [self.N, self.C]
        self.use_softmax = True
        self.reduction = 'none'
        self.weight = None
        self.logits = getattr(
            self, "logits",
            np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype))
        softmax = np.apply_along_axis(stable_softmax, self.axis, self.logits)

        self.labels = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)
        self.labels /= np.sum(self.labels, axis=self.axis, keepdims=True)

        expected = cross_entropy_soft(softmax,
                                      self.labels,
                                      self.axis,
                                      self.N,
                                      weight=self.weight,
                                      reduction=self.reduction,
                                      ignore_index=self.ignore_index)

        paddle.set_device("cpu")

        paddle.disable_static()
        paddle_loss_swce = paddle.nn.functional.softmax_with_cross_entropy(
            fluid.dygraph.to_variable(self.logits),
            fluid.dygraph.to_variable(self.labels),
            soft_label=True,
            axis=self.axis)

        paddle_loss_ce = paddle.nn.functional.cross_entropy(
            fluid.dygraph.to_variable(self.logits),
            fluid.dygraph.to_variable(self.labels),
            soft_label=True,
            axis=self.axis,
            weight=fluid.dygraph.to_variable(self.weight)
            if self.weight is not None else None,
            reduction=self.reduction)

        np.testing.assert_allclose(paddle_loss_swce.numpy(),
                                   expected,
                                   rtol=1e-05)
        np.testing.assert_allclose(paddle_loss_ce.numpy(), expected, rtol=1e-05)

    ###soft_label test start
    ###soft_label test 1
    def test_cross_entropy_loss_soft_1d(self):
        self.numeric_stable_mode = False
        self.soft_label = True
        self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
        ) else 'float64'
        self.axis = -1
        self.ignore_index = -100  #should not be changed
        self.N = 4
        self.C = 3
        self.shape = [self.N, self.C]
        self.use_softmax = True
        self.reduction = 'none'
        self.weight = None
        self.logits = getattr(
            self, "logits",
            np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype))
        softmax = np.apply_along_axis(stable_softmax, self.axis, self.logits)

        self.labels = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)
        self.labels /= np.sum(self.labels, axis=self.axis, keepdims=True)

        expected = cross_entropy_soft(softmax,
                                      self.labels,
                                      self.axis,
                                      self.N,
                                      weight=self.weight,
                                      reduction=self.reduction,
                                      ignore_index=self.ignore_index)

        paddle.set_device("cpu")

        #2. dygraph
        paddle.disable_static()
        paddle_loss_none_weight = paddle.nn.functional.cross_entropy(
            fluid.dygraph.to_variable(self.logits),
            fluid.dygraph.to_variable(self.labels),
            soft_label=True,
            axis=self.axis,
            weight=fluid.dygraph.to_variable(self.weight)
            if self.weight is not None else None,
            reduction=self.reduction)
        dy_ret_value = paddle_loss_none_weight.numpy()

        #3. static
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[self.N, self.C],
                               dtype=self.dtype)
            label = fluid.data(name='label',
                               shape=[self.N, self.C],
                               dtype=self.dtype)

            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction=self.reduction, soft_label=True)
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': self.logits,
                                     'label': self.labels,
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        paddle.disable_static()

        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    ###soft_label test 2
    def test_cross_entropy_loss_soft_1d_weight(self):
        self.numeric_stable_mode = False
        self.soft_label = True
        self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
        ) else 'float64'
        self.axis = -1
        self.ignore_index = -100  #should not be changed
        self.N = 4
        self.C = 3
        self.shape = [self.N, self.C]
        self.use_softmax = True
        self.reduction = 'none'
        self.weight = np.random.uniform(0.1, 1.0, self.C).astype(self.dtype)
        self.logits = getattr(
            self, "logits",
            np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype))
        softmax = np.apply_along_axis(stable_softmax, self.axis, self.logits)

        if self.soft_label:
            self.labels = np.random.uniform(0.1, 1.0,
                                            self.shape).astype(self.dtype)
            self.labels /= np.sum(self.labels, axis=self.axis, keepdims=True)
        else:
            axis_dim = self.shape[self.axis]
            self.shape[self.axis] = 1
            self.labels = np.random.randint(0,
                                            axis_dim,
                                            self.shape,
                                            dtype="int64")

        #1. numpy
        expected = cross_entropy_soft(softmax,
                                      self.labels,
                                      self.axis,
                                      self.N,
                                      weight=self.weight,
                                      reduction=self.reduction,
                                      ignore_index=self.ignore_index)

        paddle.set_device("cpu")

        #2. dygraph
        paddle.disable_static()
        paddle_loss_none_weight = paddle.nn.functional.cross_entropy(
            fluid.dygraph.to_variable(self.logits),
            fluid.dygraph.to_variable(self.labels),
            soft_label=True,
            axis=self.axis,
            weight=fluid.dygraph.to_variable(self.weight),
            reduction=self.reduction)
        dy_ret_value = paddle_loss_none_weight.numpy()

        # 3.static
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[self.N, self.C],
                               dtype=self.dtype)
            label = fluid.data(name='label',
                               shape=[self.N, self.C],
                               dtype=self.dtype)
            weight = fluid.data(name='weight', shape=[self.C], dtype=self.dtype)

            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=weight, reduction=self.reduction, soft_label=True)
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': self.logits,
                                     'label': self.labels,
                                     "weight": self.weight
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        paddle.disable_static()

        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    ###soft_label test 3
    def test_cross_entropy_loss_soft_1d_mean(self):
        self.numeric_stable_mode = False
        self.soft_label = True
        self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
        ) else 'float64'
        self.axis = -1
        self.ignore_index = -100  #should not be changed
        self.N = 4
        self.C = 3
        self.shape = [self.N, self.C]
        self.use_softmax = True
        self.reduction = 'mean'
        self.weight = None
        self.logits = getattr(
            self, "logits",
            np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype))
        softmax = np.apply_along_axis(stable_softmax, self.axis, self.logits)

        self.labels = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)
        self.labels /= np.sum(self.labels, axis=self.axis, keepdims=True)

        #1. numpy
        expected = cross_entropy_soft(softmax,
                                      self.labels,
                                      self.axis,
                                      self.N,
                                      weight=self.weight,
                                      reduction=self.reduction,
                                      ignore_index=self.ignore_index)

        paddle.set_device("cpu")

        #2 dygraph
        paddle.disable_static()
        paddle_loss_mean = paddle.nn.functional.cross_entropy(
            fluid.dygraph.to_variable(self.logits),
            fluid.dygraph.to_variable(self.labels),
            soft_label=True,
            axis=self.axis,
            weight=self.weight,
            reduction=self.reduction)
        dy_ret_value = paddle_loss_mean.numpy()

        #3. static
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[self.N, self.C],
                               dtype=self.dtype)
            label = fluid.data(name='label',
                               shape=[self.N, self.C],
                               dtype=self.dtype)

            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction=self.reduction, soft_label=True)
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': self.logits,
                                     'label': self.labels
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        paddle.disable_static()

        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    ###soft_label test 4
    def test_cross_entropy_loss_soft_1d_weight_mean(self):
        self.numeric_stable_mode = False
        self.soft_label = True
        self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
        ) else 'float64'
        self.axis = -1
        self.ignore_index = -100  #should not be changed
        self.N = 4
        self.C = 3
        self.shape = [self.N, self.C]
        self.use_softmax = True
        self.reduction = 'mean'
        self.weight = np.random.uniform(0.1, 1.0, self.C).astype(self.dtype)
        self.logits = getattr(
            self, "logits",
            np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype))
        softmax = np.apply_along_axis(stable_softmax, self.axis, self.logits)

        self.labels = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)
        self.labels /= np.sum(self.labels, axis=self.axis, keepdims=True)

        #1. numpy
        expected = cross_entropy_soft(softmax,
                                      self.labels,
                                      self.axis,
                                      self.N,
                                      weight=self.weight,
                                      reduction=self.reduction,
                                      ignore_index=self.ignore_index)

        paddle.set_device("cpu")
        paddle.disable_static()

        #2. dygraph
        paddle_loss_none_weight = paddle.nn.functional.cross_entropy(
            fluid.dygraph.to_variable(self.logits),
            fluid.dygraph.to_variable(self.labels),
            soft_label=True,
            axis=self.axis,
            weight=fluid.dygraph.to_variable(self.weight),
            reduction=self.reduction)
        dy_ret_value = paddle_loss_none_weight.numpy()

        #3. static
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[self.N, self.C],
                               dtype=self.dtype)
            label = fluid.data(name='label',
                               shape=[self.N, self.C],
                               dtype=self.dtype)
            weight = fluid.data(name='weight', shape=[self.C], dtype=self.dtype)

            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=weight, reduction=self.reduction, soft_label=True)
            ret = cross_entropy_loss(input, label)
            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': self.logits,
                                     'label': self.labels,
                                     "weight": self.weight
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        paddle.disable_static()

        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    ###soft_label test 5
    def test_cross_entropy_loss_soft_2d(self):
        self.numeric_stable_mode = False
        self.soft_label = True
        self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
        ) else 'float64'
        self.axis = -1
        self.ignore_index = -100  #should not be changed
        self.N = 3
        self.H = 2
        self.W = 2
        self.C = 5
        self.shape = [self.N, self.H, self.W, self.C]
        self.use_softmax = True
        self.reduction = 'none'
        self.weight = None
        self.logits = getattr(
            self, "logits",
            np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype))
        softmax = np.apply_along_axis(stable_softmax, self.axis, self.logits)

        self.labels = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)
        self.labels /= np.sum(self.labels, axis=self.axis, keepdims=True)

        #1. numpy
        expected = cross_entropy_soft_2d(softmax,
                                         self.labels,
                                         self.axis,
                                         self.N,
                                         self.H,
                                         self.W,
                                         weight=self.weight,
                                         reduction=self.reduction,
                                         ignore_index=self.ignore_index)

        paddle.set_device("cpu")
        paddle.disable_static()

        #2. dygraph
        paddle_loss_none_weight = paddle.nn.functional.cross_entropy(
            fluid.dygraph.to_variable(self.logits),
            fluid.dygraph.to_variable(self.labels),
            soft_label=True,
            axis=self.axis,
            weight=fluid.dygraph.to_variable(self.weight)
            if self.weight is not None else None,
            reduction=self.reduction)
        dy_ret_value = paddle_loss_none_weight.numpy()

        #3. static
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[self.N, self.H, self.W, self.C],
                               dtype=self.dtype)
            label = fluid.data(name='label',
                               shape=[self.N, self.H, self.W, self.C],
                               dtype=self.dtype)

            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction=self.reduction, soft_label=True)
            ret = cross_entropy_loss(input, label)
            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': self.logits,
                                     'label': self.labels,
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        paddle.disable_static()

        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    ###soft_label test 6
    def test_cross_entropy_loss_soft_2d_weight_mean(self):
        self.numeric_stable_mode = False
        self.soft_label = True
        self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
        ) else 'float64'
        self.axis = -1
        self.ignore_index = -100  #should not be changed
        self.N = 3
        self.H = 2
        self.W = 2
        self.C = 5
        self.shape = [self.N, self.H, self.W, self.C]
        self.use_softmax = True
        self.reduction = 'mean'
        self.weight = np.random.uniform(0.1, 1.0, self.C).astype(self.dtype)
        self.logits = getattr(
            self, "logits",
            np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype))
        softmax = np.apply_along_axis(stable_softmax, self.axis, self.logits)

        self.labels = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)
        self.labels /= np.sum(self.labels, axis=self.axis, keepdims=True)

        #1. numpy
        expected = cross_entropy_soft_2d(softmax,
                                         self.labels,
                                         self.axis,
                                         self.N,
                                         self.H,
                                         self.W,
                                         weight=self.weight,
                                         reduction=self.reduction,
                                         ignore_index=self.ignore_index)

        paddle.set_device("cpu")
        paddle.disable_static()

        #2. dygraph
        paddle_loss_none_weight = paddle.nn.functional.cross_entropy(
            fluid.dygraph.to_variable(self.logits),
            fluid.dygraph.to_variable(self.labels),
            soft_label=True,
            axis=self.axis,
            weight=fluid.dygraph.to_variable(self.weight),
            reduction=self.reduction)
        dy_ret_value = paddle_loss_none_weight.numpy()

        #3. static
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[self.N, self.H, self.W, self.C],
                               dtype=self.dtype)
            label = fluid.data(name='label',
                               shape=[self.N, self.H, self.W, self.C],
                               dtype=self.dtype)
            weight = fluid.data(name='weight', shape=[self.C], dtype=self.dtype)

            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=weight, reduction=self.reduction, soft_label=True)
            ret = cross_entropy_loss(input, label)
            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': self.logits,
                                     'label': self.labels,
                                     "weight": self.weight
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        paddle.disable_static()

        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    ###soft_label test end

    def test_cross_entropy_loss_1d_with_mean_ignore(self):
        input_np = np.random.random([2, 4]).astype(self.dtype)
        label_np = np.random.randint(0, 4, size=(2)).astype(np.int64)
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input', shape=[2, 4], dtype=self.dtype)
            label = fluid.data(name='label', shape=[2], dtype='int64')
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(ignore_index=0)
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        expected = cross_entropy_loss_1d(input_np, label_np)[0]

        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(axis=1,
                                                                 ignore_index=0)
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np, label_np, ignore_index=0)[0]
        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_1d_with_mean_ignore_negative(self):
        N = 100
        C = 200
        input_np = np.random.random([N, C]).astype(self.dtype)
        label_np = -np.ones((N)).astype(np.int64)
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input', shape=[N, C], dtype=self.dtype)
            label = fluid.data(name='label', shape=[N], dtype='int64')
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                ignore_index=-1)
            ret = cross_entropy_loss(input, label)
            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)

        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                axis=1, ignore_index=-1)
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np, label_np, ignore_index=-1)[0]

        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_1d_with_weight_mean_ignore(self):
        N = 100
        C = 200
        input_np = np.random.random([N, C]).astype(self.dtype)
        label_np = np.random.randint(0, C, size=(N)).astype(np.int64)
        weight_np = np.random.random([C]).astype(self.dtype)
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input', shape=[N, C], dtype=self.dtype)
            label = fluid.data(name='label', shape=[N], dtype='int64')
            weight = fluid.data(name='weight', shape=[C],
                                dtype=self.dtype)  #weight for each class
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(weight=weight,
                                                                 ignore_index=0)
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                     "weight": weight_np
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)

        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=fluid.dygraph.to_variable(weight_np),
                axis=1,
                ignore_index=0)
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np,
                                         label_np,
                                         weight=weight_np,
                                         ignore_index=0)[0]

        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_1d_with_weight_mean_ignore_exceedlabel(self):
        N = 100
        C = 200
        input_np = np.random.random([N, C]).astype(self.dtype)
        label_np = np.random.randint(0, C, size=(N)).astype(np.int64)
        label_np[0] = 255
        weight_np = np.random.random([C]).astype(self.dtype)

        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=fluid.dygraph.to_variable(weight_np), ignore_index=255)
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np,
                                         label_np,
                                         weight=weight_np,
                                         ignore_index=255)[0]

        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_1d_with_weight_mean(self):
        input_np = np.random.random([2, 4]).astype(self.dtype)
        label_np = np.random.randint(0, 4, size=(2)).astype(np.int64)
        weight_np = np.random.random([4]).astype(self.dtype)  #shape:C
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input', shape=[2, 4], dtype=self.dtype)
            label = fluid.data(name='label', shape=[2], dtype='int64')
            weight = fluid.data(name='weight', shape=[4],
                                dtype=self.dtype)  #weight for each class
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(weight=weight)
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                     "weight": weight_np
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        expected = cross_entropy_loss_1d(input_np, label_np,
                                         weight=weight_np)[0]

        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=fluid.dygraph.to_variable(weight_np), axis=1)
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np, label_np,
                                         weight=weight_np)[0]
        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_1d_with_weight_sum(self):
        input_np = np.random.random([100, 200]).astype(self.dtype)  #N,C
        label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  #N,1
        weight_np = np.random.random([200]).astype(self.dtype)  #C
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
            label = fluid.data(name='label', shape=[100], dtype='int64')
            weight = fluid.data(name='weight', shape=[200], dtype=self.dtype)
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=weight, reduction='sum')
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                     "weight": weight_np
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=fluid.dygraph.to_variable(weight_np), reduction='sum')
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np,
                                         label_np,
                                         weight=weight_np,
                                         reduction='sum')[0]
        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_1d_with_weight_none(self):
        input_np = np.random.random([100, 200]).astype(self.dtype)  #N,C
        label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  #N,1
        weight_np = np.random.random([200]).astype(self.dtype)  #C

        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
            label = fluid.data(name='label', shape=[100], dtype='int64')
            weight = fluid.data(name='weight', shape=[200], dtype=self.dtype)

            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=weight, reduction='none')
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                     "weight": weight_np
                                 },
                                 fetch_list=[ret])
            static_ret = np.squeeze(static_ret)
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=fluid.dygraph.to_variable(weight_np), reduction='none')
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            dy_ret_value = np.squeeze(dy_ret_value)
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np,
                                         label_np,
                                         weight=weight_np,
                                         reduction='none')
        np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_1d_with_weight_none_func(self):
        input_np = np.random.random([100, 200]).astype(self.dtype)  #N,C
        label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  #N
        weight_np = np.random.random([200]).astype(self.dtype)  #C
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
            label = fluid.data(name='label', shape=[100], dtype='int64')
            weight = fluid.data(name='weight', shape=[200], dtype=self.dtype)
            ret = paddle.nn.functional.cross_entropy(input,
                                                     label,
                                                     weight=weight,
                                                     reduction='none')

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                     "weight": weight_np
                                 },
                                 fetch_list=[ret])
            static_ret = np.squeeze(static_ret)
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            dy_ret = paddle.nn.functional.cross_entropy(
                fluid.dygraph.to_variable(input_np),
                fluid.dygraph.to_variable(label_np),
                weight=fluid.dygraph.to_variable(weight_np),
                reduction='none')
            dy_ret_value = dy_ret.numpy()
            dy_ret_value = np.squeeze(dy_ret_value)
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np,
                                         label_np,
                                         weight=weight_np,
                                         reduction='none')
        np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_1d_mean(self):
        input_np = np.random.random([100, 200]).astype(self.dtype)  #N,C
        label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  #N,1
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
            label = fluid.data(name='label', shape=[100], dtype='int64')
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss()
            ret = cross_entropy_loss(input, label)
            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss()
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np, label_np)[0]
        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_1d_sum(self):
        input_np = np.random.random([100, 200]).astype(self.dtype)  #N,C
        label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  #N,1
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
            label = fluid.data(name='label', shape=[100], dtype='int64')
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction='sum')
            ret = cross_entropy_loss(input, label)
            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction='sum')
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np, label_np, reduction='sum')[0]
        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_1d_none(self):
        input_np = np.random.random([100, 200]).astype(self.dtype)  #N,C
        label_np = np.random.randint(0, 100, size=(100)).astype(np.int64)  #N,1
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
            label = fluid.data(name='label', shape=[100], dtype='int64')
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction='none')
            ret = cross_entropy_loss(input, label)
            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np
                                 },
                                 fetch_list=[ret])
            static_ret = np.squeeze(static_ret)
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction='none')
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            dy_ret_value = np.squeeze(dy_ret_value)
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_1d(input_np, label_np, reduction='none')
        np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_2d_with_weight_none(self):
        input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype)  #NHWC
        label_np = np.random.randint(0, 3,
                                     size=(2, 2, 2)).astype(np.int64)  #NHW1
        weight_np = np.random.random(size=(3, )).astype(self.dtype)  #C

        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[2, 2, 2, 3],
                               dtype=self.dtype)
            label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
            weight = fluid.data(name='weight', shape=[3], dtype=self.dtype)
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=weight, reduction='none')
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                     "weight": weight_np
                                 },
                                 fetch_list=[ret])
            static_ret = np.squeeze(static_ret)
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=fluid.dygraph.to_variable(weight_np), reduction='none')
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            dy_ret_value = np.squeeze(dy_ret_value)
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_2d(input_np,
                                         label_np,
                                         weight=weight_np,
                                         reduction='none')
        np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_2d_with_weight_axis_change_mean(self):
        input_np = np.random.random(size=(2, 3, 2, 2)).astype(self.dtype)  #NCHW
        label_np = np.random.randint(0, 3,
                                     size=(2, 2, 2)).astype(np.int64)  #NHW
        weight_np = np.random.random(size=(3, )).astype(self.dtype)  #C

        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[2, 3, 2, 2],
                               dtype=self.dtype)
            label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
            weight = fluid.data(name='weight', shape=[3], dtype=self.dtype)
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=weight, reduction='mean', axis=1)
            # specify the class channels to axis 1
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                     "weight": weight_np
                                 },
                                 fetch_list=[ret])

            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=fluid.dygraph.to_variable(weight_np),
                reduction='mean',
                axis=1)
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_2d(np.transpose(input_np, [0, 2, 3, 1]),
                                         label_np,
                                         weight=weight_np,
                                         reduction='mean')[0]
        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_2d_with_weight_mean_ignore_exceedlabel(self):
        N = 4
        C = 3
        H = 512
        W = 512
        input_np = np.random.random([N, H, W, C]).astype(self.dtype)
        label_np = np.random.randint(0, C, size=(N, H, W)).astype(np.int64)
        label_np[0, 0, 0] = 255
        weight_np = np.random.random([C]).astype(self.dtype)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=fluid.dygraph.to_variable(weight_np), ignore_index=255)
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_2d(input_np,
                                         label_np,
                                         weight=weight_np,
                                         ignore_index=255)[0]
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_2d_with_weight_mean(self):
        input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype)  #NHWC
        label_np = np.random.randint(0, 3,
                                     size=(2, 2, 2)).astype(np.int64)  #NHW
        weight_np = np.random.random(size=(3, )).astype(self.dtype)  #C
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[2, 2, 2, 3],
                               dtype=self.dtype)
            label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
            weight = fluid.data(name='weight', shape=[3], dtype=self.dtype)
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=weight, reduction='mean')
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                     "weight": weight_np
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=fluid.dygraph.to_variable(weight_np), reduction='mean')
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_2d(input_np,
                                         label_np,
                                         weight=weight_np,
                                         reduction='mean')[0]
        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_2d_with_weight_sum(self):
        input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype)  #NHWC
        label_np = np.random.randint(0, 3,
                                     size=(2, 2, 2)).astype(np.int64)  #NHW
        weight_np = np.random.random(size=(3, )).astype(self.dtype)  #C
        paddle.enable_static()

        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[2, 2, 2, 3],
                               dtype=self.dtype)
            label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
            weight = fluid.data(name='weight', shape=[3], dtype=self.dtype)
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=weight, reduction='sum')
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                     "weight": weight_np
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=fluid.dygraph.to_variable(weight_np), reduction='sum')
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_2d(input_np,
                                         label_np,
                                         weight=weight_np,
                                         reduction='sum')[0]
        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_2d_none(self):
        input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype)  #NHWC
        label_np = np.random.randint(0, 3,
                                     size=(2, 2, 2)).astype(np.int64)  #NHW
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[2, 2, 2, 3],
                               dtype=self.dtype)
            label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction='none')
            ret = cross_entropy_loss(input, label)
            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                 },
                                 fetch_list=[ret])
            static_ret = np.squeeze(static_ret)
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction='none')
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            dy_ret_value = np.squeeze(dy_ret_value)
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_2d(input_np, label_np, reduction='none')
        np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_2d_mean(self):
        input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype)  #NHWC
        label_np = np.random.randint(0, 3,
                                     size=(2, 2, 2)).astype(np.int64)  #NHW
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[2, 2, 2, 3],
                               dtype=self.dtype)
            label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction='mean')
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction='mean')
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_2d(input_np, label_np,
                                         reduction='mean')[0]
        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_cross_entropy_loss_2d_sum(self):
        input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype)  #NHWC
        label_np = np.random.randint(0, 3,
                                     size=(2, 2, 2)).astype(np.int64)  #NHW
        paddle.enable_static()
        prog = fluid.Program()
        startup_prog = fluid.Program()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        with fluid.program_guard(prog, startup_prog):
            input = fluid.data(name='input',
                               shape=[2, 2, 2, 3],
                               dtype=self.dtype)
            label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction='sum')
            ret = cross_entropy_loss(input, label)

            exe = fluid.Executor(place)
            static_ret = exe.run(prog,
                                 feed={
                                     'input': input_np,
                                     'label': label_np,
                                 },
                                 fetch_list=[ret])
            self.assertIsNotNone(static_ret)
        with fluid.dygraph.guard():
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                reduction='sum')
            dy_ret = cross_entropy_loss(fluid.dygraph.to_variable(input_np),
                                        fluid.dygraph.to_variable(label_np))
            dy_ret_value = dy_ret.numpy()
            self.assertIsNotNone(dy_ret_value)
        expected = cross_entropy_loss_2d(input_np, label_np, reduction='sum')[0]
        np.testing.assert_allclose(static_ret[0], dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret[0], expected, rtol=1e-05)
        np.testing.assert_allclose(dy_ret_value, expected, rtol=1e-05)

    def test_soft_1d_dygraph_final_state_api(self):
        with _test_eager_guard():
            self.test_cross_entropy_loss_soft_1d()
            self.test_cross_entropy_loss_soft_1d_weight()
            self.test_cross_entropy_loss_soft_1d_mean()
            self.test_cross_entropy_loss_soft_1d_weight_mean()

    # put all testcases in one test will be failed
    def test_soft_2d_dygraph_final_state_api(self):
        with _test_eager_guard():
            self.test_cross_entropy_loss_soft_2d()
            self.test_cross_entropy_loss_soft_2d_weight_mean()

    def test_other_dygraph_final_state_api(self):
        with _test_eager_guard():
            self.test_cross_entropy_loss_1d_with_mean_ignore()
            self.test_cross_entropy_loss_1d_with_mean_ignore_negative()
            self.test_cross_entropy_loss_1d_with_weight_mean_ignore()
            self.test_cross_entropy_loss_1d_with_weight_mean_ignore_exceedlabel(
            )
            self.test_cross_entropy_loss_1d_with_weight_mean()
            self.test_cross_entropy_loss_1d_with_weight_sum()
            self.test_cross_entropy_loss_1d_with_weight_none()
            self.test_cross_entropy_loss_1d_with_weight_none_func()
            self.test_cross_entropy_loss_1d_mean()
            self.test_cross_entropy_loss_1d_sum()
            self.test_cross_entropy_loss_1d_none()
            self.test_cross_entropy_loss_2d_with_weight_none()
            self.test_cross_entropy_loss_2d_with_weight_axis_change_mean()
            self.test_cross_entropy_loss_2d_with_weight_mean_ignore_exceedlabel(
            )
            self.test_cross_entropy_loss_2d_with_weight_mean()
            self.test_cross_entropy_loss_2d_with_weight_sum()
            self.test_cross_entropy_loss_2d_none()
            self.test_cross_entropy_loss_2d_mean()
            self.test_cross_entropy_loss_2d_sum()


class TestCrossEntropyFAPIError(unittest.TestCase):

    def test_errors(self):
        with program_guard(Program(), Program()):

            def test_WeightLength_NotEqual():
                input_data = paddle.rand(shape=[20, 100])
                label_data = paddle.randint(0,
                                            100,
                                            shape=[20, 1],
                                            dtype="int64")
                weight_data = paddle.rand([100 + 1])
                paddle.nn.functional.cross_entropy(input=input_data,
                                                   label=label_data,
                                                   weight=weight_data,
                                                   ignore_index=-100)

            self.assertRaises(ValueError, test_WeightLength_NotEqual)

            def test_LabelValue_ExceedMax():
                input_data = paddle.rand(shape=[20, 100])
                label_data = paddle.randint(0,
                                            100,
                                            shape=[20, 1],
                                            dtype="int64")
                label_data[0] = 100
                weight_data = paddle.rand([100])
                paddle.nn.functional.cross_entropy(input=input_data,
                                                   label=label_data,
                                                   weight=weight_data,
                                                   ignore_index=-100)

            self.assertRaises(ValueError, test_LabelValue_ExceedMax)

            def test_LabelValue_ExceedMin():
                input_data = paddle.rand(shape=[20, 100])
                label_data = paddle.randint(0,
                                            100,
                                            shape=[20, 1],
                                            dtype="int64")
                label_data[0] = -1
                weight_data = paddle.rand([100])
                paddle.nn.functional.cross_entropy(input=input_data,
                                                   label=label_data,
                                                   weight=weight_data,
                                                   ignore_index=-100)

            self.assertRaises(ValueError, test_LabelValue_ExceedMin)

            def static_test_WeightLength_NotEqual():
                input_np = np.random.random([2, 4]).astype('float32')
                label_np = np.random.randint(0, 4, size=(2)).astype(np.int64)
                weight_np = np.random.random([3]).astype('float32')
                paddle.enable_static()
                prog = fluid.Program()
                startup_prog = fluid.Program()
                place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
                ) else fluid.CPUPlace()
                with fluid.program_guard(prog, startup_prog):
                    input = fluid.data(name='input',
                                       shape=[2, 4],
                                       dtype='float32')
                    label = fluid.data(name='label', shape=[2], dtype='int64')
                    weight = fluid.data(name='weight',
                                        shape=[3],
                                        dtype='float32')  #weight for each class
                    cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                        weight=weight)
                    ret = cross_entropy_loss(input, label)

                    exe = fluid.Executor(place)
                    static_ret = exe.run(prog,
                                         feed={
                                             'input': input_np,
                                             'label': label_np,
                                             "weight": weight_np
                                         },
                                         fetch_list=[ret])
                    self.assertIsNotNone(static_ret)

            self.assertRaises(ValueError, static_test_WeightLength_NotEqual)


if __name__ == "__main__":
    unittest.main()