test_layers.py 163.1 KB
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#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# 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
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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
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# 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.

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import unittest

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import contextlib
import numpy as np
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from decorator_helper import prog_scope
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import inspect
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import paddle
import paddle.fluid as fluid
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from paddle.fluid.layers.device import get_places
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import paddle.fluid.nets as nets
from paddle.fluid.framework import Program, program_guard, default_main_program
from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid import core
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from paddle.fluid.initializer import Constant
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import paddle.fluid.layers as layers
from test_imperative_base import new_program_scope
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from paddle.fluid.dygraph import nn
from paddle.fluid.dygraph import base
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from paddle.fluid.dygraph import to_variable
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from paddle.fluid.framework import _test_eager_guard
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from paddle.tensor import random
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import paddle.nn.functional as F
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class LayerTest(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.seed = 111

    @classmethod
    def tearDownClass(cls):
        pass

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    def _get_place(self, force_to_use_cpu=False):
        # this option for ops that only have cpu kernel
        if force_to_use_cpu:
            return core.CPUPlace()
        else:
            if core.is_compiled_with_cuda():
                return core.CUDAPlace(0)
            return core.CPUPlace()
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    @contextlib.contextmanager
    def static_graph(self):
        with new_program_scope():
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            paddle.seed(self.seed)
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            paddle.framework.random._manual_program_seed(self.seed)
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            yield

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    def get_static_graph_result(
        self, feed, fetch_list, with_lod=False, force_to_use_cpu=False
    ):
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        exe = fluid.Executor(self._get_place(force_to_use_cpu))
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        exe.run(fluid.default_startup_program())
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        return exe.run(
            fluid.default_main_program(),
            feed=feed,
            fetch_list=fetch_list,
            return_numpy=(not with_lod),
        )
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    @contextlib.contextmanager
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    def dynamic_graph(self, force_to_use_cpu=False):
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        with fluid.dygraph.guard(
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            self._get_place(force_to_use_cpu=force_to_use_cpu)
        ):
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            paddle.seed(self.seed)
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            paddle.framework.random._manual_program_seed(self.seed)
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            yield


class TestLayer(LayerTest):
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    def test_custom_layer_with_kwargs(self):
        class CustomLayer(fluid.Layer):
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            def __init__(self, input_size, linear1_size=4):
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                super().__init__()
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                self.linear1 = nn.Linear(
                    input_size, linear1_size, bias_attr=False
                )
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                self.linear2 = nn.Linear(linear1_size, 1, bias_attr=False)

            def forward(self, x, do_linear2=False):
                ret = self.linear1(x)
                if do_linear2:
                    ret = self.linear2(ret)
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                return ret

        with self.dynamic_graph():
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            with _test_eager_guard():
                inp = np.ones([3, 3], dtype='float32')
                x = base.to_variable(inp)
                custom = CustomLayer(input_size=3, linear1_size=2)
                ret = custom(x, do_linear2=False)
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                np.testing.assert_array_equal(ret.numpy().shape, [3, 2])
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                ret = custom(x, do_linear2=True)
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                np.testing.assert_array_equal(ret.numpy().shape, [3, 1])
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            inp = np.ones([3, 3], dtype='float32')
            x = base.to_variable(inp)
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            custom = CustomLayer(input_size=3, linear1_size=2)
            ret = custom(x, do_linear2=False)
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            np.testing.assert_array_equal(ret.numpy().shape, [3, 2])
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            ret = custom(x, do_linear2=True)
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            np.testing.assert_array_equal(ret.numpy().shape, [3, 1])
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    def test_dropout(self):
        inp = np.ones([3, 32, 32], dtype='float32')
        with self.static_graph():
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            t = layers.data(
                name='data',
                shape=[3, 32, 32],
                dtype='float32',
                append_batch_size=False,
            )
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            dropout = nn.Dropout(p=0.35, seed=1, is_test=False)
            ret = dropout(t)
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            ret2 = fluid.layers.dropout(
                t, dropout_prob=0.35, seed=1, is_test=False
            )
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            static_ret, static_ret2 = self.get_static_graph_result(
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                feed={'data': inp}, fetch_list=[ret, ret2]
            )
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        with self.dynamic_graph():
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            with _test_eager_guard():
                t = base.to_variable(inp)
                dropout = nn.Dropout(p=0.35, seed=1, is_test=False)
                dy_eager_ret = dropout(t)
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                dy_eager_ret2 = fluid.layers.dropout(
                    t, dropout_prob=0.35, seed=1, is_test=False
                )
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                dy_eager_ret_value = dy_eager_ret.numpy()
                dy_eager_ret2_value = dy_eager_ret2.numpy()

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            t = base.to_variable(inp)
            dropout = nn.Dropout(p=0.35, seed=1, is_test=False)
            dy_ret = dropout(t)
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            dy_ret2 = fluid.layers.dropout(
                t, dropout_prob=0.35, seed=1, is_test=False
            )
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            dy_ret_value = dy_ret.numpy()
            dy_ret2_value = dy_ret2.numpy()

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        np.testing.assert_array_equal(dy_eager_ret_value, dy_eager_ret2_value)
        np.testing.assert_array_equal(static_ret, dy_eager_ret_value)
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        np.testing.assert_array_equal(static_ret, static_ret2)
        np.testing.assert_array_equal(dy_ret_value, dy_ret2_value)
        np.testing.assert_array_equal(static_ret, dy_ret_value)
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    def test_linear(self):
        inp = np.ones([3, 32, 32], dtype='float32')
        with self.static_graph():
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            t = layers.data(
                name='data',
                shape=[3, 32, 32],
                dtype='float32',
                append_batch_size=False,
            )
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            linear = nn.Linear(
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                32, 4, bias_attr=fluid.initializer.ConstantInitializer(value=1)
            )
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            ret = linear(t)
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            static_ret = self.get_static_graph_result(
                feed={'data': inp}, fetch_list=[ret]
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                t = base.to_variable(inp)
                linear = nn.Linear(
                    32,
                    4,
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                    bias_attr=fluid.initializer.ConstantInitializer(value=1),
                )
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                dy_eager_ret = linear(t)
                dy_eager_ret_value = dy_eager_ret.numpy()

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            t = base.to_variable(inp)
            linear = nn.Linear(
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                32, 4, bias_attr=fluid.initializer.ConstantInitializer(value=1)
            )
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            dy_ret = linear(t)
            dy_ret_value = dy_ret.numpy()

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        np.testing.assert_array_equal(static_ret, dy_eager_ret_value)
        np.testing.assert_array_equal(static_ret, dy_ret_value)
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        with self.static_graph():

            # the input of Linear must be Variable.
            def test_Variable():
                inp = np.ones([3, 32, 32], dtype='float32')
                linear = nn.Linear(
                    32,
                    4,
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                    bias_attr=fluid.initializer.ConstantInitializer(value=1),
                )
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                linear_ret1 = linear(inp)

            self.assertRaises(TypeError, test_Variable)

            # the input dtype of Linear must be float16 or float32 or float64
            # float16 only can be set on GPU place
            def test_type():
                inp = np.ones([3, 32, 32], dtype='int32')
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                linear = nn.Linear(
                    32,
                    4,
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                    bias_attr=fluid.initializer.ConstantInitializer(value=1),
                )
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                linear_ret2 = linear(inp)

            self.assertRaises(TypeError, test_type)

    def test_Flatten(self):
        inp = np.ones([3, 4, 4, 5], dtype='float32')
        with self.static_graph():
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            t = layers.data(
                name='data',
                shape=[3, 4, 4, 5],
                dtype='float32',
                append_batch_size=False,
            )
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            flatten = nn.Flatten()
            ret = flatten(t)
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            static_ret = self.get_static_graph_result(
                feed={'data': inp}, fetch_list=[ret]
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                t = base.to_variable(inp)
                flatten = nn.Flatten()
                dy_eager_ret = flatten(t)
                dy_eager_ret_value = dy_eager_ret.numpy()

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            t = base.to_variable(inp)
            flatten = nn.Flatten()
            dy_ret = flatten(t)
            dy_ret_value = dy_ret.numpy()

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        np.testing.assert_array_equal(static_ret, dy_eager_ret_value)
        np.testing.assert_array_equal(static_ret, dy_ret_value)
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        with self.static_graph():

            # the input of Linear must be Variable.
            def test_Variable():
                inp = np.ones([3, 32, 32], dtype='float32')
                linear = nn.Linear(
                    32,
                    4,
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                    bias_attr=fluid.initializer.ConstantInitializer(value=1),
                )
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                linear_ret1 = linear(inp)

            self.assertRaises(TypeError, test_Variable)

            # the input dtype of Linear must be float16 or float32 or float64
            # float16 only can be set on GPU place
            def test_type():
                inp = np.ones([3, 32, 32], dtype='int32')
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                linear = nn.Linear(
                    32,
                    4,
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                    bias_attr=fluid.initializer.ConstantInitializer(value=1),
                )
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                linear_ret2 = linear(inp)

            self.assertRaises(TypeError, test_type)

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    def test_layer_norm(self):
        inp = np.ones([3, 32, 32], dtype='float32')
        with self.static_graph():
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            t = layers.data(
                name='data',
                shape=[3, 32, 32],
                dtype='float32',
                append_batch_size=False,
            )
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            ret = layers.layer_norm(
                t,
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                act='sigmoid',
            )
            static_ret = self.get_static_graph_result(
                feed={'data': inp}, fetch_list=[ret]
            )[0]
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        with self.static_graph():
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            t = layers.data(
                name='data',
                shape=[3, 32, 32],
                dtype='float32',
                append_batch_size=False,
            )
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            lm = nn.LayerNorm(
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                normalized_shape=[32, 32],
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                bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                act='sigmoid',
            )
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            ret = lm(t)
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            static_ret2 = self.get_static_graph_result(
                feed={'data': inp}, fetch_list=[ret]
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                lm = nn.LayerNorm(
                    normalized_shape=[32, 32],
                    bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                    act='sigmoid',
                )
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                dy_eager_ret = lm(base.to_variable(inp))
                dy_eager_ret_value = dy_eager_ret.numpy()

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            lm = nn.LayerNorm(
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                normalized_shape=[32, 32],
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                bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                act='sigmoid',
            )
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            dy_ret = lm(base.to_variable(inp))
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            dy_ret_value = dy_ret.numpy()
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        with self.dynamic_graph():
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            with _test_eager_guard():
                lm = nn.LayerNorm(
                    normalized_shape=[32, 32],
                    shift=False,
                    scale=False,
                    param_attr=fluid.initializer.ConstantInitializer(value=1),
                    bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                    act='sigmoid',
                )
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                lm(base.to_variable(inp))

                self.assertFalse(hasattr(lm, "_scale_w"))
                self.assertFalse(hasattr(lm, "_bias_w"))

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            lm = nn.LayerNorm(
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                normalized_shape=[32, 32],
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                shift=False,
                scale=False,
                param_attr=fluid.initializer.ConstantInitializer(value=1),
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                act='sigmoid',
            )
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            lm(base.to_variable(inp))

            self.assertFalse(hasattr(lm, "_scale_w"))
            self.assertFalse(hasattr(lm, "_bias_w"))
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        np.testing.assert_array_equal(static_ret, static_ret2)
        np.testing.assert_array_equal(dy_eager_ret_value, static_ret2)
        np.testing.assert_array_equal(dy_ret_value, static_ret2)
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        with self.dynamic_graph():
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            with _test_eager_guard():
                lm = nn.LayerNorm(
                    normalized_shape=[16, 32],
                    bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                    act='sigmoid',
                )
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                with self.assertRaises(ValueError):
                    lm(base.to_variable(inp))

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            lm = nn.LayerNorm(
                normalized_shape=[16, 32],
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                act='sigmoid',
            )
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            with self.assertRaises(ValueError):
                lm(base.to_variable(inp))

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    def test_SyncBatchNorm(self):
        if core.is_compiled_with_cuda():
            with self.static_graph():
                t = layers.data(name='t', shape=[-1, 3, 5, 5], dtype='float32')
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                my_sync_bn = paddle.nn.SyncBatchNorm(3)
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                ret = my_sync_bn(t)
                static_ret = self.get_static_graph_result(
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                    feed={'t': np.ones([3, 3, 5, 5], dtype='float32')},
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                    fetch_list=[ret],
                )[0]
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            with self.dynamic_graph():
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                with _test_eager_guard():
                    t = np.ones([3, 3, 5, 5], dtype='float32')
                    my_syncbn = paddle.nn.SyncBatchNorm(3)
                    dy_eager_ret = my_syncbn(base.to_variable(t))
                    dy_eager_ret_value = dy_eager_ret.numpy()

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                t = np.ones([3, 3, 5, 5], dtype='float32')
                my_syncbn = paddle.nn.SyncBatchNorm(3)
                dy_ret = my_syncbn(base.to_variable(t))
                dy_ret_value = dy_ret.numpy()
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            np.testing.assert_array_equal(static_ret, dy_ret_value)
            np.testing.assert_array_equal(static_ret, dy_eager_ret_value)
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    def test_relu(self):
        with self.static_graph():
            t = layers.data(name='t', shape=[3, 3], dtype='float32')
            ret = layers.relu(t)
            static_ret = self.get_static_graph_result(
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                feed={'t': np.ones([3, 3], dtype='float32')}, fetch_list=[ret]
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                t = np.ones([3, 3], dtype='float32')
                dy_eager_ret = layers.relu(base.to_variable(t))
                dy_eager_ret_value = dy_eager_ret.numpy()

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            t = np.ones([3, 3], dtype='float32')
            dy_ret = layers.relu(base.to_variable(t))
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            dy_ret_value = dy_ret.numpy()
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        np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, dy_eager_ret_value, rtol=1e-05)
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    def test_matmul(self):
        with self.static_graph():
            t = layers.data(name='t', shape=[3, 3], dtype='float32')
            t2 = layers.data(name='t2', shape=[3, 3], dtype='float32')
            ret = layers.matmul(t, t2)
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            static_ret = self.get_static_graph_result(
                feed={
                    't': np.ones([3, 3], dtype='float32'),
                    't2': np.ones([3, 3], dtype='float32'),
                },
                fetch_list=[ret],
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                t = np.ones([3, 3], dtype='float32')
                t2 = np.ones([3, 3], dtype='float32')
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                dy_eager_ret = layers.matmul(
                    base.to_variable(t), base.to_variable(t2)
                )
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                dy_eager_ret_value = dy_eager_ret.numpy()

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            t = np.ones([3, 3], dtype='float32')
            t2 = np.ones([3, 3], dtype='float32')
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            dy_ret = layers.matmul(base.to_variable(t), base.to_variable(t2))
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            dy_ret_value = dy_ret.numpy()
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        np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, dy_eager_ret_value, rtol=1e-05)
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    def test_gru_unit(self):
        lod = [[2, 4, 3]]
        D = 5
        T = sum(lod[0])
        N = len(lod[0])

        input = np.random.rand(T, 3 * D).astype('float32')
        hidden_input = np.random.rand(T, D).astype('float32')

        with self.static_graph():
            x = layers.data(name='x', shape=[-1, D * 3], dtype='float32')
            hidden = layers.data(name='hidden', shape=[-1, D], dtype='float32')
            updated_hidden, reset_hidden_pre, gate = layers.gru_unit(
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                input=x, hidden=hidden, size=D * 3
            )
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            static_ret = self.get_static_graph_result(
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                feed={'x': input, 'hidden': hidden_input},
                fetch_list=[updated_hidden, reset_hidden_pre, gate],
            )
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        with self.static_graph():
            x = layers.data(name='x', shape=[-1, D * 3], dtype='float32')
            hidden = layers.data(name='hidden', shape=[-1, D], dtype='float32')
            updated_hidden, reset_hidden_pre, gate = layers.gru_unit(
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                input=x, hidden=hidden, size=D * 3
            )
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            gru = nn.GRUUnit(size=D * 3)
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            updated_hidden, reset_hidden_pre, gate = gru(x, hidden)

            static_ret2 = self.get_static_graph_result(
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                feed={'x': input, 'hidden': hidden_input},
                fetch_list=[updated_hidden, reset_hidden_pre, gate],
            )
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        with self.dynamic_graph():
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            with _test_eager_guard():
                gru = nn.GRUUnit(size=D * 3)
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                dy_eager_ret = gru(
                    base.to_variable(input), base.to_variable(hidden_input)
                )
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                dy_eager_ret_value = []
                for i in range(len(static_ret)):
                    dy_eager_ret_value.append(dy_eager_ret[i].numpy())

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            gru = nn.GRUUnit(size=D * 3)
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            dy_ret = gru(
                base.to_variable(input), base.to_variable(hidden_input)
            )
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            dy_ret_value = []
            for i in range(len(static_ret)):
                dy_ret_value.append(dy_ret[i].numpy())
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        for i in range(len(static_ret)):
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            np.testing.assert_allclose(
                static_ret[i], static_ret2[i], rtol=1e-05
            )
            np.testing.assert_allclose(
                static_ret[i], dy_ret_value[i], rtol=1e-05
            )
            np.testing.assert_allclose(
                static_ret[i], dy_eager_ret_value[i], rtol=1e-05
            )
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        with self.dynamic_graph():
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            with _test_eager_guard():
                custom_weight = np.random.randn(D, D * 3).astype("float32")
                weight_attr = fluid.ParamAttr(
                    initializer=fluid.initializer.NumpyArrayInitializer(
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                        custom_weight
                    )
                )
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                gru1 = nn.GRUUnit(size=D * 3)
                gru2 = nn.GRUUnit(size=D * 3, param_attr=weight_attr)
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                dy_ret1 = gru1(
                    base.to_variable(input), base.to_variable(hidden_input)
                )
                dy_ret2 = gru2(
                    base.to_variable(input), base.to_variable(hidden_input)
                )
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                self.assertFalse(
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                    np.array_equal(gru1.weight.numpy(), gru2.weight.numpy())
                )
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                for o1, o2 in zip(dy_ret1, dy_ret2):
                    self.assertFalse(np.array_equal(o1.numpy(), o2.numpy()))
                gru2.weight.set_value(gru1.weight.numpy())
                gru2.bias.set_value(gru1.bias)
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                dy_ret1 = gru1(
                    base.to_variable(input), base.to_variable(hidden_input)
                )
                dy_ret2 = gru2(
                    base.to_variable(input), base.to_variable(hidden_input)
                )
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                for o1, o2 in zip(dy_ret1, dy_ret2):
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                    np.testing.assert_array_equal(o1.numpy(), o2.numpy())
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                gru2.weight = gru1.weight
                gru2.bias = gru1.bias
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                np.testing.assert_array_equal(
                    gru1.weight.numpy(), gru2.weight.numpy()
                )
                np.testing.assert_array_equal(
                    gru1.bias.numpy(), gru2.bias.numpy()
                )
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            custom_weight = np.random.randn(D, D * 3).astype("float32")
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            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight
                )
            )
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            gru1 = nn.GRUUnit(size=D * 3)
            gru2 = nn.GRUUnit(size=D * 3, param_attr=weight_attr)
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            dy_ret1 = gru1(
                base.to_variable(input), base.to_variable(hidden_input)
            )
            dy_ret2 = gru2(
                base.to_variable(input), base.to_variable(hidden_input)
            )
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            self.assertFalse(
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                np.array_equal(gru1.weight.numpy(), gru2.weight.numpy())
            )
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            for o1, o2 in zip(dy_ret1, dy_ret2):
                self.assertFalse(np.array_equal(o1.numpy(), o2.numpy()))
            gru2.weight.set_value(gru1.weight.numpy())
            gru2.bias.set_value(gru1.bias)
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            dy_ret1 = gru1(
                base.to_variable(input), base.to_variable(hidden_input)
            )
            dy_ret2 = gru2(
                base.to_variable(input), base.to_variable(hidden_input)
            )
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            for o1, o2 in zip(dy_ret1, dy_ret2):
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                np.testing.assert_array_equal(o1.numpy(), o2.numpy())
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            gru2.weight = gru1.weight
            gru2.bias = gru1.bias
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            np.testing.assert_array_equal(
                gru1.weight.numpy(), gru2.weight.numpy()
            )
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            np.testing.assert_array_equal(gru1.bias.numpy(), gru2.bias.numpy())
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    def test_elementwise_math(self):
        n = np.ones([3, 3], dtype='float32')
        n2 = np.ones([3, 3], dtype='float32') * 1.1
        n3 = np.ones([3, 3], dtype='float32') * 2
        n4 = np.ones([3, 3], dtype='float32') * 3
        n5 = np.ones([3, 3], dtype='float32') * 4
        n6 = np.ones([3, 3], dtype='float32') * 5

        with self.static_graph():
            t = layers.data(name='t', shape=[3, 3], dtype='float32')
            t2 = layers.data(name='t2', shape=[3, 3], dtype='float32')
            t3 = layers.data(name='t3', shape=[3, 3], dtype='float32')
            t4 = layers.data(name='t4', shape=[3, 3], dtype='float32')
            t5 = layers.data(name='t5', shape=[3, 3], dtype='float32')
            t6 = layers.data(name='t6', shape=[3, 3], dtype='float32')

            ret = layers.elementwise_add(t, t2)
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            ret = paddle.pow(ret, t3)
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            ret = layers.elementwise_div(ret, t4)
            ret = layers.elementwise_sub(ret, t5)
            ret = layers.elementwise_mul(ret, t6)

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            static_ret = self.get_static_graph_result(
                feed={'t': n, 't2': n2, 't3': n3, 't4': n4, 't5': n5, 't6': n6},
                fetch_list=[ret],
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                ret = layers.elementwise_add(to_variable(n), to_variable(n2))
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                ret = paddle.pow(ret, to_variable(n3))
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                ret = layers.elementwise_div(ret, to_variable(n4))
                ret = layers.elementwise_sub(ret, to_variable(n5))
                dy_eager_ret = layers.elementwise_mul(ret, to_variable(n6))
                dy_eager_ret_value = dy_eager_ret.numpy()

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            ret = layers.elementwise_add(to_variable(n), to_variable(n2))
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            ret = paddle.pow(ret, to_variable(n3))
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            ret = layers.elementwise_div(ret, to_variable(n4))
            ret = layers.elementwise_sub(ret, to_variable(n5))
            dy_ret = layers.elementwise_mul(ret, to_variable(n6))
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            dy_ret_value = dy_ret.numpy()
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        np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, dy_eager_ret_value, rtol=1e-05)
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    def test_elementwise_minmax(self):
        n = np.ones([3, 3], dtype='float32')
        n2 = np.ones([3, 3], dtype='float32') * 2

        with self.dynamic_graph():
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            with _test_eager_guard():
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                min_eager_ret = paddle.minimum(to_variable(n), to_variable(n2))
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                max_eager_ret = paddle.maximum(to_variable(n), to_variable(n2))
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                min_eager_ret_value = min_eager_ret.numpy()
                max_eager_ret_value = max_eager_ret.numpy()

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            min_ret = paddle.minimum(to_variable(n), to_variable(n2))
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            max_ret = paddle.maximum(to_variable(n), to_variable(n2))
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            min_ret_value = min_ret.numpy()
            max_ret_value = max_ret.numpy()
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        np.testing.assert_allclose(n, min_ret_value, rtol=1e-05)
        np.testing.assert_allclose(n2, max_ret_value, rtol=1e-05)
        np.testing.assert_allclose(n, min_eager_ret_value, rtol=1e-05)
        np.testing.assert_allclose(n2, max_eager_ret_value, rtol=1e-05)
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    def test_sequence_conv(self):
        inp_np = np.arange(12).reshape([3, 4]).astype('float32')
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        with self.static_graph():
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            seq = layers.data(
                name='seq_in',
                shape=[3, 4],
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
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            out = layers.sequence_conv(seq, 2, act='sigmoid')
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            static_rlt = self.get_static_graph_result(
                feed={
                    "seq_in": fluid.create_lod_tensor(
                        data=inp_np, recursive_seq_lens=[[1, 1, 1]], place=place
                    )
                },
                fetch_list=[out],
                with_lod=True,
            )[0]
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        with self.static_graph():
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            seq = layers.data(
                name='seq_in',
                shape=[3, 4],
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
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            seq_conv = nn.SequenceConv('seq_conv', num_filters=2, act='sigmoid')
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            out = seq_conv(seq)
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            static_rlt2 = self.get_static_graph_result(
                feed={
                    "seq_in": fluid.create_lod_tensor(
                        data=inp_np, recursive_seq_lens=[[1, 1, 1]], place=place
                    )
                },
                fetch_list=[out],
                with_lod=True,
            )[0]
        np.testing.assert_array_equal(
            np.array(static_rlt), np.array(static_rlt2)
        )
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    def test_conv2d_transpose(self):
        inp_np = np.arange(0, 24).reshape([2, 3, 2, 2]).astype('float32')
        with self.static_graph():
            img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32')
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            out = paddle.static.nn.conv2d_transpose(
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                input=img,
                num_filters=10,
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                filter_size=27,
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                act='sigmoid',
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                bias_attr=fluid.initializer.ConstantInitializer(value=1),
            )
            static_rlt = self.get_static_graph_result(
                feed={'pixel': inp_np}, fetch_list=[out]
            )[0]
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        with self.static_graph():
            img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32')
            conv2d_transpose = nn.Conv2DTranspose(
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                num_channels=3,
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                num_filters=10,
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                filter_size=27,
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                act='sigmoid',
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                bias_attr=fluid.initializer.ConstantInitializer(value=1),
            )
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            out = conv2d_transpose(img)
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            static_rlt2 = self.get_static_graph_result(
                feed={'pixel': inp_np}, fetch_list=[out]
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                conv2d_transpose = nn.Conv2DTranspose(
                    num_channels=3,
                    num_filters=10,
                    filter_size=27,
                    act='sigmoid',
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                    bias_attr=fluid.initializer.ConstantInitializer(value=1),
                )
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                dy_eager_rlt = conv2d_transpose(base.to_variable(inp_np))
                dy_eager_rlt_value = dy_eager_rlt.numpy()

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            conv2d_transpose = nn.Conv2DTranspose(
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                num_channels=3,
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                num_filters=10,
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                filter_size=27,
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                act='sigmoid',
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                bias_attr=fluid.initializer.ConstantInitializer(value=1),
            )
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            dy_rlt = conv2d_transpose(base.to_variable(inp_np))
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            dy_rlt_value = dy_rlt.numpy()
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        np.testing.assert_allclose(static_rlt2, static_rlt, rtol=1e-05)
        np.testing.assert_allclose(dy_rlt_value, static_rlt2, rtol=1e-05)
        np.testing.assert_allclose(dy_eager_rlt_value, static_rlt2, rtol=1e-05)
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        with self.dynamic_graph():
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            with _test_eager_guard():
                images = np.ones([2, 3, 5, 5], dtype='float32')
                custom_weight = np.random.randn(3, 3, 2, 2).astype("float32")
                weight_attr = fluid.ParamAttr(
                    initializer=fluid.initializer.NumpyArrayInitializer(
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                        custom_weight
                    )
                )
                conv2d1 = nn.Conv2DTranspose(
                    num_channels=3, num_filters=3, filter_size=[2, 2]
                )
                conv2d2 = nn.Conv2DTranspose(
                    num_channels=3,
                    num_filters=3,
                    filter_size=[2, 2],
                    param_attr=weight_attr,
                )
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                dy_ret1 = conv2d1(base.to_variable(images))
                dy_ret2 = conv2d2(base.to_variable(images))
                self.assertFalse(
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                    np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())
                )
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                conv2d1_weight_np = conv2d1.weight.numpy()
                conv2d1_bias = conv2d1.bias
                self.assertFalse(
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                    np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy())
                )
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                conv2d2.weight.set_value(conv2d1_weight_np)
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                np.testing.assert_array_equal(
                    conv2d1_weight_np, conv2d2.weight.numpy()
                )
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                conv2d2.bias.set_value(conv2d1_bias)
                dy_ret1 = conv2d1(base.to_variable(images))
                dy_ret2 = conv2d2(base.to_variable(images))
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                np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy())
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                conv2d2.weight = conv2d1.weight
                conv2d2.bias = conv2d1.bias
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                np.testing.assert_array_equal(
                    conv2d1.weight.numpy(), conv2d2.weight.numpy()
                )
                np.testing.assert_array_equal(
                    conv2d1.bias.numpy(), conv2d2.bias.numpy()
                )
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            images = np.ones([2, 3, 5, 5], dtype='float32')
            custom_weight = np.random.randn(3, 3, 2, 2).astype("float32")
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            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight
                )
            )
            conv2d1 = nn.Conv2DTranspose(
                num_channels=3, num_filters=3, filter_size=[2, 2]
            )
            conv2d2 = nn.Conv2DTranspose(
                num_channels=3,
                num_filters=3,
                filter_size=[2, 2],
                param_attr=weight_attr,
            )
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            dy_ret1 = conv2d1(base.to_variable(images))
            dy_ret2 = conv2d2(base.to_variable(images))
            self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv2d1_weight_np = conv2d1.weight.numpy()
            conv2d1_bias = conv2d1.bias
            self.assertFalse(
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                np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy())
            )
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            conv2d2.weight.set_value(conv2d1_weight_np)
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            np.testing.assert_array_equal(
                conv2d1_weight_np, conv2d2.weight.numpy()
            )
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            conv2d2.bias.set_value(conv2d1_bias)
            dy_ret1 = conv2d1(base.to_variable(images))
            dy_ret2 = conv2d2(base.to_variable(images))
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            np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy())
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            conv2d2.weight = conv2d1.weight
            conv2d2.bias = conv2d1.bias
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            np.testing.assert_array_equal(
                conv2d1.weight.numpy(), conv2d2.weight.numpy()
            )
            np.testing.assert_array_equal(
                conv2d1.bias.numpy(), conv2d2.bias.numpy()
            )
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        with self.static_graph():

            # the input of Conv2DTranspose must be Variable.
            def test_Variable():
                images = np.ones([2, 3, 5, 5], dtype='float32')
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                conv2d = nn.Conv2DTranspose(
                    num_channels=3, num_filters=3, filter_size=[2, 2]
                )
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                conv2d_ret1 = conv2d(images)

            self.assertRaises(TypeError, test_Variable)

            # the input dtype of Conv2DTranspose must be float16 or float32 or float64
            # float16 only can be set on GPU place
            def test_type():
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                images = layers.data(
                    name='pixel', shape=[3, 5, 5], dtype='int32'
                )
                conv2d = nn.Conv2DTranspose(
                    num_channels=3, num_filters=3, filter_size=[2, 2]
                )
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                conv2d_ret2 = conv2d(images)

            self.assertRaises(TypeError, test_type)

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    def test_bilinear_tensor_product(self):
        inp_np_x = np.array([[1, 2, 3]]).astype('float32')
        inp_np_y = np.array([[4, 5, 6]]).astype('float32')

        with self.static_graph():
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            data_x = layers.data(
                name='x', shape=[1, 3], dtype="float32", append_batch_size=False
            )
            data_y = layers.data(
                name='y', shape=[1, 3], dtype="float32", append_batch_size=False
            )
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            out = layers.bilinear_tensor_product(
                data_x,
                data_y,
                6,
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                act='sigmoid',
            )
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            static_rlt = self.get_static_graph_result(
                feed={'x': inp_np_x, 'y': inp_np_y}, fetch_list=[out]
            )[0]
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        with self.static_graph():
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            data_x = layers.data(
                name='x', shape=[1, 3], dtype="float32", append_batch_size=False
            )
            data_y = layers.data(
                name='y', shape=[1, 3], dtype="float32", append_batch_size=False
            )
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            btp = nn.BilinearTensorProduct(
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                3,
                3,
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                6,
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                act='sigmoid',
            )
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            out = btp(data_x, data_y)
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            static_rlt2 = self.get_static_graph_result(
                feed={'x': inp_np_x, 'y': inp_np_y}, fetch_list=[out]
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                btp = nn.BilinearTensorProduct(
                    3,
                    3,
                    6,
                    bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                    act='sigmoid',
                )
                dy_eager_rlt = btp(
                    base.to_variable(inp_np_x), base.to_variable(inp_np_y)
                )
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                dy_eager_rlt_value = dy_eager_rlt.numpy()

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            btp = nn.BilinearTensorProduct(
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                3,
                3,
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                6,
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
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                act='sigmoid',
            )
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            dy_rlt = btp(base.to_variable(inp_np_x), base.to_variable(inp_np_y))
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            dy_rlt_value = dy_rlt.numpy()
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        with self.dynamic_graph():
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            with _test_eager_guard():
                btp2 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid')
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                dy_eager_rlt2 = btp2(
                    base.to_variable(inp_np_x), base.to_variable(inp_np_y)
                )
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                dy_eager_rlt2_value = dy_eager_rlt2.numpy()

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            btp2 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid')
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            dy_rlt2 = btp2(
                base.to_variable(inp_np_x), base.to_variable(inp_np_y)
            )
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            dy_rlt2_value = dy_rlt2.numpy()
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        with self.static_graph():
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            data_x2 = layers.data(
                name='x', shape=[1, 3], dtype="float32", append_batch_size=False
            )
            data_y2 = layers.data(
                name='y', shape=[1, 3], dtype="float32", append_batch_size=False
            )
            out2 = layers.bilinear_tensor_product(
                data_x2, data_y2, 6, act='sigmoid'
            )

            static_rlt3 = self.get_static_graph_result(
                feed={'x': inp_np_x, 'y': inp_np_y}, fetch_list=[out2]
            )[0]
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        np.testing.assert_array_equal(dy_rlt2_value, static_rlt3)
        np.testing.assert_array_equal(dy_eager_rlt2_value, static_rlt3)
        np.testing.assert_array_equal(static_rlt2, static_rlt)
        np.testing.assert_array_equal(dy_rlt_value, static_rlt)
        np.testing.assert_array_equal(dy_eager_rlt_value, static_rlt)
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        with self.dynamic_graph():
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            with _test_eager_guard():
                custom_weight = np.random.randn(6, 3, 3).astype("float32")
                weight_attr = fluid.ParamAttr(
                    initializer=fluid.initializer.NumpyArrayInitializer(
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                        custom_weight
                    )
                )
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                btp1 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid')
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                btp2 = nn.BilinearTensorProduct(
                    3, 3, 6, act='sigmoid', param_attr=weight_attr
                )
                dy_rlt1 = btp1(
                    base.to_variable(inp_np_x), base.to_variable(inp_np_y)
                )
                dy_rlt2 = btp2(
                    base.to_variable(inp_np_x), base.to_variable(inp_np_y)
                )
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                self.assertFalse(
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                    np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy())
                )
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                btp2.weight.set_value(btp1.weight.numpy())
                btp2.bias.set_value(btp1.bias)
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                dy_rlt1 = btp1(
                    base.to_variable(inp_np_x), base.to_variable(inp_np_y)
                )
                dy_rlt2 = btp2(
                    base.to_variable(inp_np_x), base.to_variable(inp_np_y)
                )
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                np.testing.assert_array_equal(dy_rlt1.numpy(), dy_rlt2.numpy())
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                btp2.weight = btp1.weight
                btp2.bias = btp1.bias
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                np.testing.assert_array_equal(
                    btp1.weight.numpy(), btp2.weight.numpy()
                )
                np.testing.assert_array_equal(
                    btp1.bias.numpy(), btp2.bias.numpy()
                )
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            custom_weight = np.random.randn(6, 3, 3).astype("float32")
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            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight
                )
            )
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            btp1 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid')
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            btp2 = nn.BilinearTensorProduct(
                3, 3, 6, act='sigmoid', param_attr=weight_attr
            )
            dy_rlt1 = btp1(
                base.to_variable(inp_np_x), base.to_variable(inp_np_y)
            )
            dy_rlt2 = btp2(
                base.to_variable(inp_np_x), base.to_variable(inp_np_y)
            )
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            self.assertFalse(np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()))
            btp2.weight.set_value(btp1.weight.numpy())
            btp2.bias.set_value(btp1.bias)
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            dy_rlt1 = btp1(
                base.to_variable(inp_np_x), base.to_variable(inp_np_y)
            )
            dy_rlt2 = btp2(
                base.to_variable(inp_np_x), base.to_variable(inp_np_y)
            )
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            np.testing.assert_array_equal(dy_rlt1.numpy(), dy_rlt2.numpy())
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            btp2.weight = btp1.weight
            btp2.bias = btp1.bias
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            np.testing.assert_array_equal(
                btp1.weight.numpy(), btp2.weight.numpy()
            )
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            np.testing.assert_array_equal(btp1.bias.numpy(), btp2.bias.numpy())
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    def prelu_test(self, mode):
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        inp_np = np.ones([5, 200, 100, 100]).astype('float32')
        with self.static_graph():
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            data_t = layers.data(
                name="input",
                shape=[5, 200, 100, 100],
                dtype="float32",
                append_batch_size=False,
            )
            out = layers.prelu(
                data_t, mode, param_attr=ParamAttr(initializer=Constant(1.0))
            )
            static_rlt = self.get_static_graph_result(
                feed={"input": inp_np}, fetch_list=[out]
            )[0]
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        with self.static_graph():
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            data_t = layers.data(
                name="input",
                shape=[5, 200, 100, 100],
                dtype="float32",
                append_batch_size=False,
            )
            prelu = nn.PRelu(
                mode=mode,
                channel=inp_np.shape[1],
                input_shape=data_t.shape,
                param_attr=ParamAttr(initializer=Constant(1.0)),
            )
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            out = prelu(data_t)
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            static_rlt2 = self.get_static_graph_result(
                feed={"input": inp_np}, fetch_list=[out]
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                prelu = nn.PRelu(
                    mode=mode,
                    channel=inp_np.shape[1],
                    input_shape=inp_np.shape,
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                    param_attr=ParamAttr(initializer=Constant(1.0)),
                )
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                dy_eager_rlt = prelu(base.to_variable(inp_np))
                dy_eager_rlt_value = dy_eager_rlt.numpy()

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            prelu = nn.PRelu(
                mode=mode,
                channel=inp_np.shape[1],
                input_shape=inp_np.shape,
                param_attr=ParamAttr(initializer=Constant(1.0)),
            )
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            dy_rlt = prelu(base.to_variable(inp_np))
1110
            dy_rlt_value = dy_rlt.numpy()
1111

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        np.testing.assert_allclose(static_rlt2, static_rlt, rtol=1e-05)
        np.testing.assert_allclose(dy_rlt_value, static_rlt, rtol=1e-05)
        np.testing.assert_allclose(dy_eager_rlt_value, static_rlt, rtol=1e-05)
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1116
        with self.dynamic_graph():
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            with _test_eager_guard():
                inp_np = np.random.randn(5, 200, 100, 100).astype("float32")
                inp = base.to_variable(inp_np)
                prelu1 = nn.PRelu(
                    mode=mode,
                    channel=inp_np.shape[1],
                    input_shape=inp_np.shape,
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                    param_attr=ParamAttr(initializer=Constant(2.0)),
                )
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                prelu2 = nn.PRelu(
                    mode=mode,
                    channel=inp_np.shape[1],
                    input_shape=inp_np.shape,
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                    param_attr=ParamAttr(initializer=Constant(1.0)),
                )
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                dy_rlt1 = prelu1(inp)
                dy_rlt2 = prelu2(inp)
                self.assertFalse(
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                    np.array_equal(prelu1.weight.numpy(), prelu2.weight.numpy())
                )
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                self.assertFalse(
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                    np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy())
                )
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                prelu2.weight.set_value(prelu1.weight.numpy())
                dy_rlt1 = prelu1(inp)
                dy_rlt2 = prelu2(inp)
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                np.testing.assert_array_equal(dy_rlt1.numpy(), dy_rlt2.numpy())
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                prelu2.weight = prelu1.weight
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                np.testing.assert_array_equal(
                    prelu1.weight.numpy(), prelu2.weight.numpy()
                )
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            inp_np = np.random.randn(5, 200, 100, 100).astype("float32")
            inp = base.to_variable(inp_np)
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            prelu1 = nn.PRelu(
                mode=mode,
                channel=inp_np.shape[1],
                input_shape=inp_np.shape,
                param_attr=ParamAttr(initializer=Constant(2.0)),
            )
            prelu2 = nn.PRelu(
                mode=mode,
                channel=inp_np.shape[1],
                input_shape=inp_np.shape,
                param_attr=ParamAttr(initializer=Constant(1.0)),
            )
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            dy_rlt1 = prelu1(inp)
            dy_rlt2 = prelu2(inp)
            self.assertFalse(
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                np.array_equal(prelu1.weight.numpy(), prelu2.weight.numpy())
            )
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            self.assertFalse(np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()))
            prelu2.weight.set_value(prelu1.weight.numpy())
            dy_rlt1 = prelu1(inp)
            dy_rlt2 = prelu2(inp)
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            np.testing.assert_array_equal(dy_rlt1.numpy(), dy_rlt2.numpy())
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            prelu2.weight = prelu1.weight
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            np.testing.assert_array_equal(
                prelu1.weight.numpy(), prelu2.weight.numpy()
            )
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    def test_prelu(self):
        self.prelu_test("channel")
        self.prelu_test("element")
        self.prelu_test("all")

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    def test_embeding(self):
        inp_word = np.array([[[1]]]).astype('int64')
        dict_size = 20
        with self.static_graph():
            data_t = layers.data(name='word', shape=[1], dtype='int64')
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            emb = layers.embedding(
                input=data_t,
                size=[dict_size, 32],
                param_attr='emb.w',
                is_sparse=False,
            )
            static_rlt = self.get_static_graph_result(
                feed={'word': inp_word}, fetch_list=[emb]
            )[0]
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        with self.static_graph():
            data_t = layers.data(name='word', shape=[1], dtype='int64')
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            emb2 = nn.Embedding(
                size=[dict_size, 32], param_attr='emb.w', is_sparse=False
            )
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            emb_rlt = emb2(data_t)
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            static_rlt2 = self.get_static_graph_result(
                feed={'word': inp_word}, fetch_list=[emb_rlt]
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
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                emb2 = nn.Embedding(
                    size=[dict_size, 32],
                    param_attr='eager_emb.w',
                    is_sparse=False,
                )
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                dy_eager_rlt = emb2(base.to_variable(inp_word))
                dy_eager_rlt_value = dy_eager_rlt.numpy()

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            emb2 = nn.Embedding(
                size=[dict_size, 32], param_attr='emb.w', is_sparse=False
            )
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            dy_rlt = emb2(base.to_variable(inp_word))
            dy_rlt_value = dy_rlt.numpy()
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        self.assertTrue(np.allclose(static_rlt2, static_rlt))
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        self.assertTrue(np.allclose(dy_rlt_value, static_rlt))
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        self.assertTrue(np.allclose(dy_eager_rlt_value, static_rlt))
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        with self.dynamic_graph():
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            with _test_eager_guard():
                custom_weight = np.random.randn(dict_size, 32).astype("float32")
                weight_attr = fluid.ParamAttr(
                    initializer=fluid.initializer.NumpyArrayInitializer(
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                        custom_weight
                    )
                )
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                emb1 = nn.Embedding(size=[dict_size, 32], is_sparse=False)
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                emb2 = nn.Embedding(
                    size=[dict_size, 32],
                    param_attr=weight_attr,
                    is_sparse=False,
                )
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                rep1 = emb1(base.to_variable(inp_word))
                rep2 = emb2(base.to_variable(inp_word))
                self.assertFalse(
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                    np.array_equal(emb1.weight.numpy(), custom_weight)
                )
                np.testing.assert_array_equal(
                    emb2.weight.numpy(), custom_weight
                )
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                self.assertFalse(np.array_equal(rep1.numpy(), rep2.numpy()))
                emb2.weight.set_value(emb1.weight.numpy())
                rep2 = emb2(base.to_variable(inp_word))
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                np.testing.assert_array_equal(rep1.numpy(), rep2.numpy())
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                emb2.weight = emb1.weight
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                np.testing.assert_array_equal(
                    emb1.weight.numpy(), emb2.weight.numpy()
                )
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            custom_weight = np.random.randn(dict_size, 32).astype("float32")
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            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight
                )
            )
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            emb1 = nn.Embedding(size=[dict_size, 32], is_sparse=False)
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            emb2 = nn.Embedding(
                size=[dict_size, 32], param_attr=weight_attr, is_sparse=False
            )
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            rep1 = emb1(base.to_variable(inp_word))
            rep2 = emb2(base.to_variable(inp_word))
            self.assertFalse(np.array_equal(emb1.weight.numpy(), custom_weight))
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            np.testing.assert_array_equal(emb2.weight.numpy(), custom_weight)
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            self.assertFalse(np.array_equal(rep1.numpy(), rep2.numpy()))
            emb2.weight.set_value(emb1.weight.numpy())
            rep2 = emb2(base.to_variable(inp_word))
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            np.testing.assert_array_equal(rep1.numpy(), rep2.numpy())
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            emb2.weight = emb1.weight
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            np.testing.assert_array_equal(
                emb1.weight.numpy(), emb2.weight.numpy()
            )
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    def test_nce(self):
        window_size = 5
        dict_size = 20
        label_word = int(window_size // 2) + 1
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        inp_word = np.array([[1], [2], [3], [4], [5]]).astype('int64')
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        nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32')
        seed = 1
        with self.static_graph():
            words = []
            for i in range(window_size):
                words.append(
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                    layers.data(
                        name='word_{0}'.format(i), shape=[None], dtype='int64'
                    )
                )
            sample_weights = layers.fill_constant(
                shape=[5, 1], dtype='float32', value=1
            )
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            embs = []
            for i in range(window_size):
                if i == label_word:
                    continue

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                emb = fluid.embedding(
                    input=words[i],
                    size=[dict_size, 32],
                    param_attr='emb.w',
                    is_sparse=False,
                )
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                embs.append(emb)

            embs = layers.concat(input=embs, axis=1)
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            wl = fluid.layers.unsqueeze(words[label_word], axes=[0])
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            nce_loss = paddle.static.nn.nce(
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                input=embs,
                label=wl,
                num_total_classes=dict_size,
                num_neg_samples=2,
                sampler="custom_dist",
                custom_dist=nid_freq_arr.tolist(),
                seed=seed,
                param_attr='nce.w',
                bias_attr='nce.b',
                sample_weight=sample_weights,
            )
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            feed_dict = dict()
            for i in range(window_size):
                feed_dict['word_{0}'.format(i)] = inp_word[i]
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            static_rlt = self.get_static_graph_result(
                feed=feed_dict, fetch_list=[nce_loss]
            )[0]
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        with self.static_graph():
            words = []
            for i in range(window_size):
                words.append(
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                    layers.data(
                        name='word_{0}'.format(i), shape=[None], dtype='int64'
                    )
                )
            sample_weights = layers.fill_constant(
                shape=[5, 1], dtype='float32', value=1
            )
            emb = nn.Embedding(
                size=[dict_size, 32], param_attr='emb.w', is_sparse=False
            )
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            embs2 = []
            for i in range(window_size):
                if i == label_word:
                    continue

                emb_rlt = emb(words[i])
                embs2.append(emb_rlt)

            embs2 = layers.concat(input=embs2, axis=1)
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            nce = nn.NCE(
                num_total_classes=dict_size,
                dim=embs2.shape[1],
                num_neg_samples=2,
                sampler="custom_dist",
                custom_dist=nid_freq_arr.tolist(),
                seed=seed,
                param_attr='nce.w',
                bias_attr='nce.b',
                sample_weight=sample_weights,
            )
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            wl = fluid.layers.unsqueeze(words[label_word], axes=[0])
            nce_loss2 = nce(embs2, wl)
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            feed_dict = dict()
            for i in range(len(words)):
                feed_dict['word_{0}'.format(i)] = inp_word[i]

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            static_rlt2 = self.get_static_graph_result(
                feed=feed_dict, fetch_list=[nce_loss2]
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                words = []
                for i in range(window_size):
                    words.append(base.to_variable(inp_word[i]))
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                sample_weights = layers.fill_constant(
                    shape=[5, 1], dtype='float32', value=1
                )
                emb = nn.Embedding(
                    size=[dict_size, 32],
                    param_attr='eager_emb.w',
                    is_sparse=False,
                )
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                embs3 = []
                for i in range(window_size):
                    if i == label_word:
                        continue

                    emb_rlt = emb(words[i])
                    embs3.append(emb_rlt)

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                embs3 = layers.concat(
                    input=embs3, axis=fluid.dygraph.to_variable(np.array([1]))
                )
                nce = nn.NCE(
                    num_total_classes=dict_size,
                    dim=embs3.shape[1],
                    num_neg_samples=2,
                    sampler="custom_dist",
                    custom_dist=nid_freq_arr.tolist(),
                    seed=seed,
                    param_attr='eager_nce.w',
                    bias_attr='eager_nce.b',
                    sample_weight=sample_weights,
                )
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                wl = fluid.layers.unsqueeze(words[label_word], axes=[0])
                dy_eager_rlt = nce(embs3, wl)
                dy_eager_rlt_value = dy_eager_rlt.numpy()

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            words = []
            for i in range(window_size):
                words.append(base.to_variable(inp_word[i]))
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            sample_weights = layers.fill_constant(
                shape=[5, 1], dtype='float32', value=1
            )
            emb = nn.Embedding(
                size=[dict_size, 32], param_attr='emb.w', is_sparse=False
            )
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            embs3 = []
            for i in range(window_size):
                if i == label_word:
                    continue

                emb_rlt = emb(words[i])
                embs3.append(emb_rlt)

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            embs3 = layers.concat(
                input=embs3, axis=fluid.dygraph.to_variable(np.array([1]))
            )
            nce = nn.NCE(
                num_total_classes=dict_size,
                dim=embs3.shape[1],
                num_neg_samples=2,
                sampler="custom_dist",
                custom_dist=nid_freq_arr.tolist(),
                seed=seed,
                param_attr='nce.w',
                bias_attr='nce.b',
                sample_weight=sample_weights,
            )
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            wl = fluid.layers.unsqueeze(words[label_word], axes=[0])
            dy_rlt = nce(embs3, wl)
1458
            dy_rlt_value = dy_rlt.numpy()
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        np.testing.assert_allclose(static_rlt2, static_rlt, rtol=1e-05)
        np.testing.assert_allclose(dy_rlt_value, static_rlt, rtol=1e-05)
        np.testing.assert_allclose(dy_eager_rlt_value, static_rlt, rtol=1e-05)
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        with self.dynamic_graph():
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            with _test_eager_guard():
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                custom_weight = np.random.randn(dict_size, 128).astype(
                    "float32"
                )
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                weight_attr = fluid.ParamAttr(
                    initializer=fluid.initializer.NumpyArrayInitializer(
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                        custom_weight
                    )
                )
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                words = []
                for i in range(window_size):
                    words.append(base.to_variable(inp_word[i]))
                sample_weights = layers.fill_constant(
                    shape=fluid.dygraph.to_variable(np.array([5, 1])),
                    dtype='float32',
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                    value=1,
                )
                emb = nn.Embedding(
                    size=[dict_size, 32],
                    param_attr='eager_emb.w',
                    is_sparse=False,
                )
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                embs3 = []
                for i in range(window_size):
                    if i == label_word:
                        continue

                    emb_rlt = emb(words[i])
                    embs3.append(emb_rlt)

                embs3 = layers.concat(input=embs3, axis=1)
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                nce1 = nn.NCE(
                    num_total_classes=dict_size,
                    dim=embs3.shape[1],
                    num_neg_samples=2,
                    sampler="custom_dist",
                    custom_dist=nid_freq_arr.tolist(),
                    seed=seed,
                    param_attr='eager_nce1.w',
                    bias_attr='eager_nce1.b',
                    sample_weight=sample_weights,
                )

                nce2 = nn.NCE(
                    num_total_classes=dict_size,
                    dim=embs3.shape[1],
                    num_neg_samples=2,
                    sampler="custom_dist",
                    custom_dist=nid_freq_arr.tolist(),
                    seed=seed,
                    param_attr=weight_attr,
                    bias_attr='eager_nce2.b',
                    sample_weight=sample_weights,
                )
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                wl = fluid.layers.unsqueeze(words[label_word], axes=[0])
                nce1_loss = nce1(embs3, wl)
                nce2_loss = nce2(embs3, wl)
                self.assertFalse(
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                    np.array_equal(nce1_loss.numpy(), nce2_loss.numpy())
                )
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                nce2.weight.set_value(nce1.weight.numpy())
                nce2.bias.set_value(nce1.bias)
                nce1_loss = nce1(embs3, wl)
                nce2_loss = nce2(embs3, wl)
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                np.testing.assert_array_equal(
                    nce1_loss.numpy(), nce2_loss.numpy()
                )
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                nce2.weight = nce1.weight
                nce2.bias = nce1.bias
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                np.testing.assert_array_equal(
                    nce1.weight.numpy(), nce2.weight.numpy()
                )
                np.testing.assert_array_equal(
                    nce1.bias.numpy(), nce2.bias.numpy()
                )
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            custom_weight = np.random.randn(dict_size, 128).astype("float32")
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            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight
                )
            )
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            words = []
            for i in range(window_size):
                words.append(base.to_variable(inp_word[i]))
            sample_weights = layers.fill_constant(
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                shape=fluid.dygraph.to_variable(np.array([5, 1])),
                dtype='float32',
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                value=1,
            )
            emb = nn.Embedding(
                size=[dict_size, 32], param_attr='emb.w', is_sparse=False
            )
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            embs3 = []
            for i in range(window_size):
                if i == label_word:
                    continue

                emb_rlt = emb(words[i])
                embs3.append(emb_rlt)

            embs3 = layers.concat(input=embs3, axis=1)
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            nce1 = nn.NCE(
                num_total_classes=dict_size,
                dim=embs3.shape[1],
                num_neg_samples=2,
                sampler="custom_dist",
                custom_dist=nid_freq_arr.tolist(),
                seed=seed,
                param_attr='nce1.w',
                bias_attr='nce1.b',
                sample_weight=sample_weights,
            )

            nce2 = nn.NCE(
                num_total_classes=dict_size,
                dim=embs3.shape[1],
                num_neg_samples=2,
                sampler="custom_dist",
                custom_dist=nid_freq_arr.tolist(),
                seed=seed,
                param_attr=weight_attr,
                bias_attr='nce2.b',
                sample_weight=sample_weights,
            )
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            wl = fluid.layers.unsqueeze(words[label_word], axes=[0])
            nce1_loss = nce1(embs3, wl)
            nce2_loss = nce2(embs3, wl)
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            self.assertFalse(
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                np.array_equal(nce1_loss.numpy(), nce2_loss.numpy())
            )
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            nce2.weight.set_value(nce1.weight.numpy())
            nce2.bias.set_value(nce1.bias)
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            nce1_loss = nce1(embs3, wl)
            nce2_loss = nce2(embs3, wl)
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            np.testing.assert_array_equal(nce1_loss.numpy(), nce2_loss.numpy())
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            nce2.weight = nce1.weight
            nce2.bias = nce1.bias
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            np.testing.assert_array_equal(
                nce1.weight.numpy(), nce2.weight.numpy()
            )
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            np.testing.assert_array_equal(nce1.bias.numpy(), nce2.bias.numpy())
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    def test_one_hot(self):
        with self.dynamic_graph():
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            with _test_eager_guard():
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                label = fluid.dygraph.to_variable(
                    np.array([[1], [1], [3], [0]])
                )
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                one_hot_label1 = fluid.layers.one_hot(input=label, depth=4)
                one_hot_label2 = fluid.layers.one_hot(
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                    input=label, depth=fluid.dygraph.to_variable(np.array([4]))
                )
                np.testing.assert_array_equal(
                    one_hot_label1.numpy(), one_hot_label2.numpy()
                )
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            label = fluid.dygraph.to_variable(np.array([[1], [1], [3], [0]]))
            one_hot_label1 = fluid.layers.one_hot(input=label, depth=4)
            one_hot_label2 = fluid.layers.one_hot(
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                input=label, depth=fluid.dygraph.to_variable(np.array([4]))
            )
            np.testing.assert_array_equal(
                one_hot_label1.numpy(), one_hot_label2.numpy()
            )
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    def test_split(self):
        with self.dynamic_graph():
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            with _test_eager_guard():
                input = fluid.dygraph.to_variable(np.random.random((3, 8, 5)))
                x0, x1 = fluid.layers.split(input, num_or_sections=2, dim=1)
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                x00, x11 = fluid.layers.split(
                    input,
                    num_or_sections=2,
                    dim=fluid.dygraph.to_variable(np.array([1])),
                )
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                np.testing.assert_array_equal(x0.numpy(), x00.numpy())
                np.testing.assert_array_equal(x1.numpy(), x11.numpy())
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            input = fluid.dygraph.to_variable(np.random.random((3, 8, 5)))
            x0, x1 = fluid.layers.split(input, num_or_sections=2, dim=1)
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            x00, x11 = fluid.layers.split(
                input,
                num_or_sections=2,
                dim=fluid.dygraph.to_variable(np.array([1])),
            )
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            np.testing.assert_array_equal(x0.numpy(), x00.numpy())
            np.testing.assert_array_equal(x1.numpy(), x11.numpy())
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    def test_topk(self):
        with self.dynamic_graph():
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            with _test_eager_guard():
                input = fluid.dygraph.to_variable(np.random.random((13, 11)))
                top5_values1, top5_indices1 = layers.topk(input, k=5)
                top5_values2, top5_indices2 = layers.topk(
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                    input, k=fluid.dygraph.to_variable(np.array([5]))
                )
                np.testing.assert_array_equal(
                    top5_values1.numpy(), top5_values2.numpy()
                )
                np.testing.assert_array_equal(
                    top5_indices1.numpy(), top5_indices2.numpy()
                )
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            input = fluid.dygraph.to_variable(np.random.random((13, 11)))
            top5_values1, top5_indices1 = layers.topk(input, k=5)
            top5_values2, top5_indices2 = layers.topk(
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                input, k=fluid.dygraph.to_variable(np.array([5]))
            )
            np.testing.assert_array_equal(
                top5_values1.numpy(), top5_values2.numpy()
            )
            np.testing.assert_array_equal(
                top5_indices1.numpy(), top5_indices2.numpy()
            )
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    def test_conv3d(self):
        with self.static_graph():
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            images = layers.data(
                name='pixel', shape=[3, 6, 6, 6], dtype='float32'
            )
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            ret = paddle.static.nn.conv3d(
                input=images, num_filters=3, filter_size=2
            )
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            static_ret = self.get_static_graph_result(
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                feed={'pixel': np.ones([2, 3, 6, 6, 6], dtype='float32')},
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                fetch_list=[ret],
            )[0]
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        with self.static_graph():
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            images = layers.data(
                name='pixel', shape=[3, 6, 6, 6], dtype='float32'
            )
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            conv3d = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2)
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            ret = conv3d(images)
            static_ret2 = self.get_static_graph_result(
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                feed={'pixel': np.ones([2, 3, 6, 6, 6], dtype='float32')},
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                fetch_list=[ret],
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                images = np.ones([2, 3, 6, 6, 6], dtype='float32')
                conv3d = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2)
                dy_eager_ret = conv3d(base.to_variable(images))
                dy_eager_rlt_value = dy_eager_ret.numpy()

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            images = np.ones([2, 3, 6, 6, 6], dtype='float32')
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            conv3d = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2)
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            dy_ret = conv3d(base.to_variable(images))
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            dy_rlt_value = dy_ret.numpy()
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        np.testing.assert_allclose(static_ret, dy_rlt_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, dy_eager_rlt_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, static_ret2, rtol=1e-05)
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        with self.dynamic_graph():
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            with _test_eager_guard():
                images = np.ones([2, 3, 6, 6, 6], dtype='float32')
                custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32")
                weight_attr = fluid.ParamAttr(
                    initializer=fluid.initializer.NumpyArrayInitializer(
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                        custom_weight
                    )
                )
                conv3d1 = nn.Conv3D(
                    num_channels=3, num_filters=3, filter_size=2
                )
                conv3d2 = nn.Conv3D(
                    num_channels=3,
                    num_filters=3,
                    filter_size=2,
                    param_attr=weight_attr,
                )
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                dy_ret1 = conv3d1(base.to_variable(images))
                dy_ret2 = conv3d2(base.to_variable(images))
                self.assertFalse(
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                    np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())
                )
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                conv3d1_weight_np = conv3d1.weight.numpy()
                conv3d1_bias = conv3d1.bias
                self.assertFalse(
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                    np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy())
                )
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                conv3d2.weight.set_value(conv3d1_weight_np)
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                np.testing.assert_array_equal(
                    conv3d1_weight_np, conv3d2.weight.numpy()
                )
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                conv3d1.bias.set_value(conv3d1_bias)
                dy_ret1 = conv3d1(base.to_variable(images))
                dy_ret2 = conv3d2(base.to_variable(images))
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                np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy())
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                conv3d2.weight = conv3d1.weight
                conv3d2.bias = conv3d1.bias
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                np.testing.assert_array_equal(
                    conv3d1.weight.numpy(), conv3d2.weight.numpy()
                )
                np.testing.assert_array_equal(
                    conv3d1.bias.numpy(), conv3d2.bias.numpy()
                )
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            images = np.ones([2, 3, 6, 6, 6], dtype='float32')
            custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32")
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            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight
                )
            )
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            conv3d1 = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2)
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            conv3d2 = nn.Conv3D(
                num_channels=3,
                num_filters=3,
                filter_size=2,
                param_attr=weight_attr,
            )
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            dy_ret1 = conv3d1(base.to_variable(images))
            dy_ret2 = conv3d2(base.to_variable(images))
            self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv3d1_weight_np = conv3d1.weight.numpy()
            conv3d1_bias = conv3d1.bias
            self.assertFalse(
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                np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy())
            )
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            conv3d2.weight.set_value(conv3d1_weight_np)
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            np.testing.assert_array_equal(
                conv3d1_weight_np, conv3d2.weight.numpy()
            )
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            conv3d1.bias.set_value(conv3d1_bias)
            dy_ret1 = conv3d1(base.to_variable(images))
            dy_ret2 = conv3d2(base.to_variable(images))
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            np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy())
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            conv3d2.weight = conv3d1.weight
            conv3d2.bias = conv3d1.bias
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            np.testing.assert_array_equal(
                conv3d1.weight.numpy(), conv3d2.weight.numpy()
            )
            np.testing.assert_array_equal(
                conv3d1.bias.numpy(), conv3d2.bias.numpy()
            )
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    def test_row_conv(self):
        input = np.arange(15).reshape([3, 5]).astype('float32')
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        with self.static_graph():
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            x = layers.data(
                name='X',
                shape=[3, 5],
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
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            ret = layers.row_conv(input=x, future_context_size=2)
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            static_ret = self.get_static_graph_result(
                feed={
                    'X': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1, 1]], place=place
                    )
                },
                fetch_list=[ret],
                with_lod=True,
            )[0]
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        with self.static_graph():
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            x = layers.data(
                name='X',
                shape=[3, 5],
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
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            rowConv = nn.RowConv('RowConv', future_context_size=2)
            ret = rowConv(x)
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            static_ret2 = self.get_static_graph_result(
                feed={
                    'X': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1, 1]], place=place
                    )
                },
                fetch_list=[ret],
                with_lod=True,
            )[0]
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        # TODO: dygraph can't support LODTensor
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        np.testing.assert_allclose(static_ret, static_ret2, rtol=1e-05)
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    def func_group_norm(self):
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        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        shape = (2, 4, 3, 3)

        input = np.random.random(shape).astype('float32')

        with self.static_graph():
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            X = fluid.layers.data(
                name='X',
                shape=shape,
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
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            ret = paddle.static.nn.group_norm(
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                input=X,
                groups=2,
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                param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5),
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                bias_attr=fluid.initializer.ConstantInitializer(value=1),
            )
            static_ret = self.get_static_graph_result(
                feed={
                    'X': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1]], place=place
                    )
                },
                fetch_list=[ret],
                with_lod=True,
            )[0]
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        with self.static_graph():
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            X = fluid.layers.data(
                name='X',
                shape=shape,
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
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            groupNorm = nn.GroupNorm(
                channels=shape[1],
                groups=2,
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                param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5),
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                bias_attr=fluid.initializer.ConstantInitializer(value=1),
            )
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            ret = groupNorm(X)
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            static_ret2 = self.get_static_graph_result(
                feed={
                    'X': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1]], place=place
                    )
                },
                fetch_list=[ret],
                with_lod=True,
            )[0]
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        with self.dynamic_graph():
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            groupNorm = nn.GroupNorm(
                channels=shape[1],
                groups=2,
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                param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5),
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                bias_attr=fluid.initializer.ConstantInitializer(value=1),
            )
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            dy_ret = groupNorm(base.to_variable(input))
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            dy_rlt_value = dy_ret.numpy()
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        np.testing.assert_allclose(static_ret, dy_rlt_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, static_ret2, rtol=1e-05)
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    def test_group_norm(self):
        with _test_eager_guard():
            self.func_group_norm()
        self.func_group_norm()

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    def test_instance_norm(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        shape = (2, 4, 3, 3)

        input = np.random.random(shape).astype('float32')

        with self.static_graph():
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            X = fluid.layers.data(
                name='X', shape=shape, dtype='float32', append_batch_size=False
            )
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            ret = paddle.static.nn.instance_norm(input=X)
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            static_ret = self.get_static_graph_result(
                feed={'X': input}, fetch_list=[ret]
            )[0]
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        with self.static_graph():
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            X = fluid.layers.data(
                name='X', shape=shape, dtype='float32', append_batch_size=False
            )
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            instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1])
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            ret = instanceNorm(X)
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            static_ret2 = self.get_static_graph_result(
                feed={'X': input}, fetch_list=[ret]
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
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                instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1])
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                dy_eager_ret = instanceNorm(base.to_variable(input))
                dy_eager_rlt_value = dy_eager_ret.numpy()

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            instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1])
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            dy_ret = instanceNorm(base.to_variable(input))
            dy_rlt_value = dy_ret.numpy()

        with self.dynamic_graph():
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            with _test_eager_guard():
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                instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1])
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                dy_eager_ret = instanceNorm(base.to_variable(input))
                dy_eager_rlt_value2 = dy_eager_ret.numpy()

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            instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1])
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            dy_ret = instanceNorm(base.to_variable(input))
            dy_rlt_value2 = dy_ret.numpy()

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        np.testing.assert_allclose(static_ret, dy_rlt_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, dy_rlt_value2, rtol=1e-05)
        np.testing.assert_allclose(static_ret, dy_eager_rlt_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, dy_eager_rlt_value2, rtol=1e-05)
        np.testing.assert_allclose(static_ret, static_ret2, rtol=1e-05)
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        with self.static_graph():
            # the input of InstanceNorm must be Variable.
            def test_Variable():
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                instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1])
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                ret1 = instanceNorm(input)

            self.assertRaises(TypeError, test_Variable)

            # the input dtype of InstanceNorm must be float32 or float64
            def test_type():
                input = np.random.random(shape).astype('int32')
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                instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1])
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                ret2 = instanceNorm(input)

            self.assertRaises(TypeError, test_type)

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    def test_spectral_norm(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        shape = (2, 4, 3, 3)

        input = np.random.random(shape).astype('float32')

        with self.static_graph():
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            Weight = fluid.layers.data(
                name='Weight',
                shape=shape,
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
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            ret = layers.spectral_norm(weight=Weight, dim=1, power_iters=2)
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            static_ret = self.get_static_graph_result(
                feed={
                    'Weight': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1]], place=place
                    ),
                },
                fetch_list=[ret],
                with_lod=True,
            )[0]
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        with self.static_graph():
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            Weight = fluid.layers.data(
                name='Weight',
                shape=shape,
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
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            spectralNorm = nn.SpectralNorm(shape, dim=1, power_iters=2)
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            ret = spectralNorm(Weight)
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            static_ret2 = self.get_static_graph_result(
                feed={
                    'Weight': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1]], place=place
                    )
                },
                fetch_list=[ret],
                with_lod=True,
            )[0]
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        with self.dynamic_graph():
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            with _test_eager_guard():
                spectralNorm = nn.SpectralNorm(shape, dim=1, power_iters=2)
                dy_eager_ret = spectralNorm(base.to_variable(input))
                dy_eager_rlt_value = dy_eager_ret.numpy()

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            spectralNorm = nn.SpectralNorm(shape, dim=1, power_iters=2)
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            dy_ret = spectralNorm(base.to_variable(input))
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            dy_rlt_value = dy_ret.numpy()
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        np.testing.assert_allclose(static_ret, dy_rlt_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, dy_eager_rlt_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, static_ret2, rtol=1e-05)
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    def test_tree_conv(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        adj_array = [1, 2, 1, 3, 1, 4, 1, 5, 2, 6, 2, 7, 2, 8, 4, 9, 4, 10]
        adj = np.array(adj_array).reshape((1, 9, 2)).astype('int32')
        adj = np.tile(adj, (1, 1, 1))
        vectors = np.random.random((1, 10, 5)).astype('float32')
        with self.static_graph():
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            NodesVector = fluid.layers.data(
                name='NodesVector',
                shape=(1, 10, 5),
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
            EdgeSet = fluid.layers.data(
                name='EdgeSet',
                shape=(1, 9, 2),
                dtype='int32',
                lod_level=1,
                append_batch_size=False,
            )
            ret = fluid.contrib.layers.tree_conv(
                nodes_vector=NodesVector,
                edge_set=EdgeSet,
                output_size=6,
                num_filters=1,
                max_depth=2,
            )
            static_ret = self.get_static_graph_result(
                feed={
                    'NodesVector': fluid.create_lod_tensor(
                        data=vectors, recursive_seq_lens=[[1]], place=place
                    ),
                    'EdgeSet': fluid.create_lod_tensor(
                        data=adj, recursive_seq_lens=[[1]], place=place
                    ),
                },
                fetch_list=[ret],
                with_lod=False,
            )[0]
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        with self.static_graph():
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            NodesVector = fluid.layers.data(
                name='NodesVector',
                shape=(1, 10, 5),
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
            EdgeSet = fluid.layers.data(
                name='EdgeSet',
                shape=(1, 9, 2),
                dtype='int32',
                lod_level=1,
                append_batch_size=False,
            )
            treeConv = nn.TreeConv(
                feature_size=5, output_size=6, num_filters=1, max_depth=2
            )
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            ret = treeConv(NodesVector, EdgeSet)
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            static_ret2 = self.get_static_graph_result(
                feed={
                    'NodesVector': fluid.create_lod_tensor(
                        data=vectors, recursive_seq_lens=[[1]], place=place
                    ),
                    'EdgeSet': fluid.create_lod_tensor(
                        data=adj, recursive_seq_lens=[[1]], place=place
                    ),
                },
                fetch_list=[ret],
                with_lod=False,
            )[0]
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        with self.dynamic_graph():
2153
            with _test_eager_guard():
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                treeConv = nn.TreeConv(
                    feature_size=5, output_size=6, num_filters=1, max_depth=2
                )
                dy_eager_ret = treeConv(
                    base.to_variable(vectors), base.to_variable(adj)
                )
2160 2161
                dy_eager_rlt_value = dy_eager_ret.numpy()

2162 2163 2164
            treeConv = nn.TreeConv(
                feature_size=5, output_size=6, num_filters=1, max_depth=2
            )
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            dy_ret = treeConv(base.to_variable(vectors), base.to_variable(adj))
2166
            dy_rlt_value = dy_ret.numpy()
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        np.testing.assert_allclose(static_ret, static_ret2, rtol=1e-05)
        np.testing.assert_allclose(static_ret, dy_rlt_value, rtol=1e-05)
        np.testing.assert_allclose(static_ret, dy_eager_rlt_value, rtol=1e-05)
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2172
        with self.dynamic_graph():
2173 2174 2175 2176
            with _test_eager_guard():
                custom_weight = np.random.randn(5, 3, 6, 1).astype("float32")
                weight_attr = fluid.ParamAttr(
                    initializer=fluid.initializer.NumpyArrayInitializer(
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                        custom_weight
                    )
                )
                treeConv1 = nn.TreeConv(
                    feature_size=5,
                    output_size=6,
                    num_filters=1,
                    max_depth=2,
                    bias_attr='eager_tc1_b',
                )
                treeConv2 = nn.TreeConv(
                    feature_size=5,
                    output_size=6,
                    num_filters=1,
                    max_depth=2,
                    param_attr=weight_attr,
                    bias_attr='eager_tc2_b',
                )
                dy_ret1 = treeConv1(
                    base.to_variable(vectors), base.to_variable(adj)
                )
                dy_ret2 = treeConv2(
                    base.to_variable(vectors), base.to_variable(adj)
                )
2201
                self.assertFalse(
2202 2203
                    np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())
                )
2204 2205
                treeConv2.weight.set_value(treeConv1.weight.numpy())
                treeConv2.bias.set_value(treeConv1.bias)
2206 2207 2208 2209 2210 2211
                dy_ret1 = treeConv1(
                    base.to_variable(vectors), base.to_variable(adj)
                )
                dy_ret2 = treeConv2(
                    base.to_variable(vectors), base.to_variable(adj)
                )
2212
                np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy())
2213 2214 2215

                treeConv2.weight = treeConv1.weight
                treeConv2.bias = treeConv1.bias
2216 2217 2218 2219 2220 2221
                np.testing.assert_array_equal(
                    treeConv1.weight.numpy(), treeConv2.weight.numpy()
                )
                np.testing.assert_array_equal(
                    treeConv1.bias.numpy(), treeConv2.bias.numpy()
                )
2222

2223
            custom_weight = np.random.randn(5, 3, 6, 1).astype("float32")
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            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight
                )
            )
            treeConv1 = nn.TreeConv(
                feature_size=5,
                output_size=6,
                num_filters=1,
                max_depth=2,
                bias_attr='tc1_b',
            )
            treeConv2 = nn.TreeConv(
                feature_size=5,
                output_size=6,
                num_filters=1,
                max_depth=2,
                param_attr=weight_attr,
                bias_attr='tc2_b',
            )
            dy_ret1 = treeConv1(
                base.to_variable(vectors), base.to_variable(adj)
            )
            dy_ret2 = treeConv2(
                base.to_variable(vectors), base.to_variable(adj)
            )
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            self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))
            treeConv2.weight.set_value(treeConv1.weight.numpy())
            treeConv2.bias.set_value(treeConv1.bias)
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            dy_ret1 = treeConv1(
                base.to_variable(vectors), base.to_variable(adj)
            )
            dy_ret2 = treeConv2(
                base.to_variable(vectors), base.to_variable(adj)
            )
2259
            np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy())
2260 2261 2262

            treeConv2.weight = treeConv1.weight
            treeConv2.bias = treeConv1.bias
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            np.testing.assert_array_equal(
                treeConv1.weight.numpy(), treeConv2.weight.numpy()
            )
            np.testing.assert_array_equal(
                treeConv1.bias.numpy(), treeConv2.bias.numpy()
            )
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    def test_conv3d_transpose(self):
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        input_array = (
            np.arange(0, 48).reshape([2, 3, 2, 2, 2]).astype('float32')
        )
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        with self.static_graph():
            img = layers.data(name='pixel', shape=[3, 2, 2, 2], dtype='float32')
2277
            out = paddle.static.nn.conv3d_transpose(
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                input=img, num_filters=12, filter_size=12, use_cudnn=False
            )
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            static_rlt = self.get_static_graph_result(
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                feed={'pixel': input_array}, fetch_list=[out]
            )[0]
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        with self.static_graph():
            img = layers.data(name='pixel', shape=[3, 2, 2, 2], dtype='float32')
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            conv3d_transpose = nn.Conv3DTranspose(
                num_channels=3, num_filters=12, filter_size=12, use_cudnn=False
            )
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            out = conv3d_transpose(img)
            static_rlt2 = self.get_static_graph_result(
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                feed={'pixel': input_array}, fetch_list=[out]
            )[0]
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        with self.dynamic_graph():
2293
            with _test_eager_guard():
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                conv3d_transpose = nn.Conv3DTranspose(
                    num_channels=3,
                    num_filters=12,
                    filter_size=12,
                    use_cudnn=False,
                )
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                dy_eager_rlt = conv3d_transpose(base.to_variable(input_array))
                dy_eager_rlt_value = dy_eager_rlt.numpy()

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            conv3d_transpose = nn.Conv3DTranspose(
                num_channels=3, num_filters=12, filter_size=12, use_cudnn=False
            )
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            dy_rlt = conv3d_transpose(base.to_variable(input_array))
2307
            dy_rlt_value = dy_rlt.numpy()
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        np.testing.assert_allclose(static_rlt2, static_rlt, rtol=1e-05)
        np.testing.assert_allclose(dy_rlt_value, static_rlt, rtol=1e-05)
        np.testing.assert_allclose(dy_eager_rlt_value, static_rlt, rtol=1e-05)
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2312
        with self.dynamic_graph():
2313 2314 2315 2316 2317
            with _test_eager_guard():
                images = np.ones([2, 3, 6, 6, 6], dtype='float32')
                custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32")
                weight_attr = fluid.ParamAttr(
                    initializer=fluid.initializer.NumpyArrayInitializer(
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                        custom_weight
                    )
                )
                conv3d1 = nn.Conv3DTranspose(
                    num_channels=3,
                    num_filters=3,
                    filter_size=2,
                    bias_attr='eager_conv3d1_b',
                    use_cudnn=False,
                )
                conv3d2 = nn.Conv3DTranspose(
                    num_channels=3,
                    num_filters=3,
                    filter_size=2,
                    param_attr=weight_attr,
                    bias_attr='eager_conv3d2_b',
                    use_cudnn=False,
                )
2336 2337 2338
                dy_ret1 = conv3d1(base.to_variable(images))
                dy_ret2 = conv3d2(base.to_variable(images))
                self.assertFalse(
2339 2340
                    np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())
                )
2341 2342 2343 2344

                conv3d1_weight_np = conv3d1.weight.numpy()
                conv3d1_bias = conv3d1.bias
                self.assertFalse(
2345 2346
                    np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy())
                )
2347
                conv3d2.weight.set_value(conv3d1_weight_np)
2348 2349 2350
                np.testing.assert_array_equal(
                    conv3d1_weight_np, conv3d2.weight.numpy()
                )
2351 2352 2353
                conv3d1.bias.set_value(conv3d1_bias)
                dy_ret1 = conv3d1(base.to_variable(images))
                dy_ret2 = conv3d2(base.to_variable(images))
2354
                np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy())
2355 2356 2357

                conv3d2.weight = conv3d1.weight
                conv3d2.bias = conv3d1.bias
2358 2359 2360 2361 2362 2363
                np.testing.assert_array_equal(
                    conv3d1.weight.numpy(), conv3d2.weight.numpy()
                )
                np.testing.assert_array_equal(
                    conv3d1.bias.numpy(), conv3d2.bias.numpy()
                )
2364

2365 2366
            images = np.ones([2, 3, 6, 6, 6], dtype='float32')
            custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32")
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            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight
                )
            )
            conv3d1 = nn.Conv3DTranspose(
                num_channels=3,
                num_filters=3,
                filter_size=2,
                bias_attr='conv3d1_b',
                use_cudnn=False,
            )
            conv3d2 = nn.Conv3DTranspose(
                num_channels=3,
                num_filters=3,
                filter_size=2,
                param_attr=weight_attr,
                bias_attr='conv3d2_b',
                use_cudnn=False,
            )
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            dy_ret1 = conv3d1(base.to_variable(images))
            dy_ret2 = conv3d2(base.to_variable(images))
            self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv3d1_weight_np = conv3d1.weight.numpy()
            conv3d1_bias = conv3d1.bias
            self.assertFalse(
2394 2395
                np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy())
            )
2396
            conv3d2.weight.set_value(conv3d1_weight_np)
2397 2398 2399
            np.testing.assert_array_equal(
                conv3d1_weight_np, conv3d2.weight.numpy()
            )
2400 2401 2402
            conv3d1.bias.set_value(conv3d1_bias)
            dy_ret1 = conv3d1(base.to_variable(images))
            dy_ret2 = conv3d2(base.to_variable(images))
2403
            np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy())
2404 2405 2406

            conv3d2.weight = conv3d1.weight
            conv3d2.bias = conv3d1.bias
2407 2408 2409 2410 2411 2412
            np.testing.assert_array_equal(
                conv3d1.weight.numpy(), conv3d2.weight.numpy()
            )
            np.testing.assert_array_equal(
                conv3d1.bias.numpy(), conv3d2.bias.numpy()
            )
2413

2414
    def func_while_loop(self):
2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431
        with self.static_graph():
            i = layers.fill_constant(shape=[1], dtype='int64', value=0)
            ten = layers.fill_constant(shape=[1], dtype='int64', value=10)

            def cond(i):
                return layers.less_than(i, ten)

            def body(i):
                return i + 1

            out = layers.while_loop(cond, body, [i])
            static_ret = self.get_static_graph_result(feed={}, fetch_list=out)

        with self.dynamic_graph():
            i = layers.fill_constant(shape=[1], dtype='int64', value=0)
            ten = layers.fill_constant(shape=[1], dtype='int64', value=10)

2432
            def cond1(i):
2433 2434
                return layers.less_than(i, ten)

2435
            def body1(i):
2436 2437
                return i + 1

2438
            dy_ret = layers.while_loop(cond1, body1, [i])
2439 2440 2441 2442 2443 2444
            with self.assertRaises(ValueError):
                j = layers.fill_constant(shape=[1], dtype='int64', value=0)

                def body2(i):
                    return i + 1, i + 2

2445
                layers.while_loop(cond1, body2, [j])
2446

2447
        np.testing.assert_array_equal(static_ret[0], dy_ret[0].numpy())
2448

2449 2450 2451 2452 2453
    def test_while_loop(self):
        with _test_eager_guard():
            self.func_while_loop()
        self.func_while_loop()

2454 2455 2456 2457 2458 2459 2460 2461
    def test_compare(self):
        value_a = np.arange(3)
        value_b = np.arange(3)
        # less than
        with self.static_graph():
            a = layers.data(name='a', shape=[1], dtype='int64')
            b = layers.data(name='b', shape=[1], dtype='int64')
            cond = layers.less_than(x=a, y=b)
2462 2463 2464
            static_ret = self.get_static_graph_result(
                feed={"a": value_a, "b": value_b}, fetch_list=[cond]
            )[0]
2465
        with self.dynamic_graph():
2466 2467 2468 2469 2470 2471 2472 2473
            with _test_eager_guard():
                da = base.to_variable(value_a)
                db = base.to_variable(value_b)
                dcond = layers.less_than(x=da, y=db)

                for i in range(len(static_ret)):
                    self.assertTrue(dcond.numpy()[i] == static_ret[i])

2474 2475 2476 2477
            da = base.to_variable(value_a)
            db = base.to_variable(value_b)
            dcond = layers.less_than(x=da, y=db)

2478 2479
            for i in range(len(static_ret)):
                self.assertTrue(dcond.numpy()[i] == static_ret[i])
2480 2481 2482 2483 2484

        # less equal
        with self.static_graph():
            a1 = layers.data(name='a1', shape=[1], dtype='int64')
            b1 = layers.data(name='b1', shape=[1], dtype='int64')
2485
            cond1 = paddle.less_equal(x=a1, y=b1)
2486 2487 2488
            static_ret1 = self.get_static_graph_result(
                feed={"a1": value_a, "b1": value_b}, fetch_list=[cond1]
            )[0]
2489
        with self.dynamic_graph():
2490 2491 2492
            with _test_eager_guard():
                da1 = base.to_variable(value_a)
                db1 = base.to_variable(value_b)
2493
                dcond1 = paddle.less_equal(x=da1, y=db1)
2494 2495 2496 2497

                for i in range(len(static_ret1)):
                    self.assertTrue(dcond1.numpy()[i] == static_ret1[i])

2498 2499
            da1 = base.to_variable(value_a)
            db1 = base.to_variable(value_b)
2500
            dcond1 = paddle.less_equal(x=da1, y=db1)
2501 2502 2503 2504

            for i in range(len(static_ret1)):
                self.assertTrue(dcond1.numpy()[i] == static_ret1[i])

2505
        # greater than
2506 2507 2508
        with self.static_graph():
            a2 = layers.data(name='a2', shape=[1], dtype='int64')
            b2 = layers.data(name='b2', shape=[1], dtype='int64')
2509
            cond2 = paddle.greater_than(x=a2, y=b2)
2510 2511 2512
            static_ret2 = self.get_static_graph_result(
                feed={"a2": value_a, "b2": value_b}, fetch_list=[cond2]
            )[0]
2513
        with self.dynamic_graph():
2514 2515 2516
            with _test_eager_guard():
                da2 = base.to_variable(value_a)
                db2 = base.to_variable(value_b)
2517
                dcond2 = paddle.greater_than(x=da2, y=db2)
2518 2519 2520 2521

                for i in range(len(static_ret2)):
                    self.assertTrue(dcond2.numpy()[i] == static_ret2[i])

2522 2523
            da2 = base.to_variable(value_a)
            db2 = base.to_variable(value_b)
2524
            dcond2 = paddle.greater_than(x=da2, y=db2)
2525 2526 2527 2528

            for i in range(len(static_ret2)):
                self.assertTrue(dcond2.numpy()[i] == static_ret2[i])

2529
        # greater equal
2530 2531 2532
        with self.static_graph():
            a3 = layers.data(name='a3', shape=[1], dtype='int64')
            b3 = layers.data(name='b3', shape=[1], dtype='int64')
2533
            cond3 = paddle.greater_equal(x=a3, y=b3)
2534 2535 2536
            static_ret3 = self.get_static_graph_result(
                feed={"a3": value_a, "b3": value_b}, fetch_list=[cond3]
            )[0]
2537
        with self.dynamic_graph():
2538 2539 2540
            with _test_eager_guard():
                da3 = base.to_variable(value_a)
                db3 = base.to_variable(value_b)
2541
                dcond3 = paddle.greater_equal(x=da3, y=db3)
2542 2543 2544 2545

                for i in range(len(static_ret3)):
                    self.assertTrue(dcond3.numpy()[i] == static_ret3[i])

2546 2547
            da3 = base.to_variable(value_a)
            db3 = base.to_variable(value_b)
2548
            dcond3 = paddle.greater_equal(x=da3, y=db3)
2549 2550 2551 2552 2553 2554 2555 2556

            for i in range(len(static_ret3)):
                self.assertTrue(dcond3.numpy()[i] == static_ret3[i])

        # equal
        with self.static_graph():
            a4 = layers.data(name='a4', shape=[1], dtype='int64')
            b4 = layers.data(name='b4', shape=[1], dtype='int64')
2557
            cond4 = paddle.equal(x=a4, y=b4)
2558 2559 2560
            static_ret4 = self.get_static_graph_result(
                feed={"a4": value_a, "b4": value_b}, fetch_list=[cond4]
            )[0]
2561
        with self.dynamic_graph():
2562 2563 2564
            with _test_eager_guard():
                da4 = base.to_variable(value_a)
                db4 = base.to_variable(value_b)
2565
                dcond4 = paddle.equal(x=da4, y=db4)
2566 2567 2568 2569

                for i in range(len(static_ret4)):
                    self.assertTrue(dcond4.numpy()[i] == static_ret4[i])

2570 2571
            da4 = base.to_variable(value_a)
            db4 = base.to_variable(value_b)
2572
            dcond4 = paddle.equal(x=da4, y=db4)
2573 2574 2575 2576 2577 2578 2579 2580

            for i in range(len(static_ret4)):
                self.assertTrue(dcond4.numpy()[i] == static_ret4[i])

        # not equal
        with self.static_graph():
            a5 = layers.data(name='a5', shape=[1], dtype='int64')
            b5 = layers.data(name='b5', shape=[1], dtype='int64')
2581
            cond5 = paddle.equal(x=a5, y=b5)
2582 2583 2584
            static_ret5 = self.get_static_graph_result(
                feed={"a5": value_a, "b5": value_b}, fetch_list=[cond5]
            )[0]
2585
        with self.dynamic_graph():
2586 2587 2588
            with _test_eager_guard():
                da5 = base.to_variable(value_a)
                db5 = base.to_variable(value_b)
2589
                dcond5 = paddle.equal(x=da5, y=db5)
2590 2591 2592 2593

                for i in range(len(static_ret5)):
                    self.assertTrue(dcond5.numpy()[i] == static_ret5[i])

2594 2595
            da5 = base.to_variable(value_a)
            db5 = base.to_variable(value_b)
2596
            dcond5 = paddle.equal(x=da5, y=db5)
2597 2598 2599 2600

            for i in range(len(static_ret5)):
                self.assertTrue(dcond5.numpy()[i] == static_ret5[i])

2601 2602 2603 2604 2605 2606 2607 2608
    def test_cond(self):
        def less_than_branch(a, b):
            return fluid.layers.elementwise_add(a, b)

        def greater_equal_branch(a, b):
            return fluid.layers.elementwise_sub(a, b)

        with self.static_graph():
2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624
            a = fluid.layers.fill_constant(
                shape=[1], dtype='float32', value=0.1
            )
            b = fluid.layers.fill_constant(
                shape=[1], dtype='float32', value=0.23
            )
            out = fluid.layers.cond(
                a >= b,
                lambda: greater_equal_branch(a, b),
                lambda: less_than_branch(a, b),
            )
            place = (
                fluid.CUDAPlace(0)
                if core.is_compiled_with_cuda()
                else fluid.CPUPlace()
            )
2625 2626 2627 2628 2629
            exe = fluid.Executor(place)
            ret = exe.run(fetch_list=[out])
            static_res = ret[0]

        with self.dynamic_graph():
2630 2631 2632
            with _test_eager_guard():
                a = fluid.dygraph.to_variable(np.array([0.1]).astype('float32'))
                b = fluid.dygraph.to_variable(
2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644
                    np.array([0.23]).astype('float32')
                )
                out = layers.cond(
                    a < b,
                    lambda: less_than_branch(a, b),
                    lambda: greater_equal_branch(a, b),
                )
                out2 = layers.cond(
                    a >= b,
                    lambda: greater_equal_branch(a, b),
                    lambda: less_than_branch(a, b),
                )
2645 2646
                eager_dynamic_res = out.numpy()
                eager_dynamic_res2 = out2.numpy()
2647 2648 2649
                np.testing.assert_array_equal(
                    eager_dynamic_res, eager_dynamic_res2
                )
2650 2651 2652 2653 2654
                with self.assertRaises(TypeError):
                    layers.cond(a < b, 'str', 'str')
                with self.assertRaises(TypeError):
                    layers.cond(a >= b, 'str', 'str')

2655 2656
            a = fluid.dygraph.to_variable(np.array([0.1]).astype('float32'))
            b = fluid.dygraph.to_variable(np.array([0.23]).astype('float32'))
2657 2658 2659 2660 2661 2662 2663 2664 2665 2666
            out = layers.cond(
                a < b,
                lambda: less_than_branch(a, b),
                lambda: greater_equal_branch(a, b),
            )
            out2 = layers.cond(
                a >= b,
                lambda: greater_equal_branch(a, b),
                lambda: less_than_branch(a, b),
            )
2667 2668
            dynamic_res = out.numpy()
            dynamic_res2 = out2.numpy()
2669
            np.testing.assert_array_equal(dynamic_res, dynamic_res2)
2670 2671 2672 2673 2674
            with self.assertRaises(TypeError):
                layers.cond(a < b, 'str', 'str')
            with self.assertRaises(TypeError):
                layers.cond(a >= b, 'str', 'str')

2675 2676
        np.testing.assert_array_equal(static_res, dynamic_res)
        np.testing.assert_array_equal(static_res, eager_dynamic_res)
2677

2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694
    def test_case(self):
        def fn_1():
            return layers.fill_constant(shape=[1, 2], dtype='float32', value=1)

        def fn_2():
            return layers.fill_constant(shape=[2, 2], dtype='int32', value=2)

        def fn_3():
            return layers.fill_constant(shape=[3], dtype='int32', value=3)

        with self.static_graph():
            x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
            y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
            z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)

            pred_1 = layers.less_than(z, x)  # true: 0.2 < 0.3
            pred_2 = layers.less_than(x, y)  # false: 0.3 < 0.1
2695
            pred_3 = paddle.equal(x, y)  # false: 0.3 == 0.1
2696

2697 2698 2699
            out_1 = layers.case(
                pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3
            )
2700 2701
            out_2 = layers.case(pred_fn_pairs=[(pred_2, fn_2), (pred_3, fn_3)])

2702 2703 2704 2705 2706
            place = (
                fluid.CUDAPlace(0)
                if core.is_compiled_with_cuda()
                else fluid.CPUPlace()
            )
2707 2708 2709 2710
            exe = fluid.Executor(place)
            static_res1, static_res2 = exe.run(fetch_list=[out_1, out_2])

        with self.dynamic_graph():
2711 2712 2713 2714 2715 2716 2717
            with _test_eager_guard():
                x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
                y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
                z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)

                pred_1 = layers.less_than(z, x)  # true: 0.2 < 0.3
                pred_2 = layers.less_than(x, y)  # false: 0.3 < 0.1
2718
                pred_3 = paddle.equal(x, y)  # false: 0.3 == 0.1
2719

2720 2721 2722 2723 2724 2725
                out_1 = layers.case(
                    pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3
                )
                out_2 = layers.case(
                    pred_fn_pairs=[(pred_2, fn_2), (pred_3, fn_3)]
                )
2726 2727 2728
                eager_dynamic_res1 = out_1.numpy()
                eager_dynamic_res2 = out_2.numpy()

2729 2730 2731 2732 2733 2734
            x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
            y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
            z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)

            pred_1 = layers.less_than(z, x)  # true: 0.2 < 0.3
            pred_2 = layers.less_than(x, y)  # false: 0.3 < 0.1
2735
            pred_3 = paddle.equal(x, y)  # false: 0.3 == 0.1
2736

2737 2738 2739
            out_1 = layers.case(
                pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3
            )
2740 2741 2742 2743
            out_2 = layers.case(pred_fn_pairs=[(pred_2, fn_2), (pred_3, fn_3)])
            dynamic_res1 = out_1.numpy()
            dynamic_res2 = out_2.numpy()

2744 2745 2746 2747
        np.testing.assert_array_equal(static_res1, dynamic_res1)
        np.testing.assert_array_equal(static_res2, dynamic_res2)
        np.testing.assert_array_equal(static_res1, eager_dynamic_res1)
        np.testing.assert_array_equal(static_res2, eager_dynamic_res2)
2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762

    def test_switch_case(self):
        def fn_1():
            return layers.fill_constant(shape=[1, 2], dtype='float32', value=1)

        def fn_2():
            return layers.fill_constant(shape=[2, 2], dtype='int32', value=2)

        def fn_3():
            return layers.fill_constant(shape=[3], dtype='int32', value=3)

        with self.static_graph():
            index_1 = layers.fill_constant(shape=[1], dtype='int32', value=1)
            index_2 = layers.fill_constant(shape=[1], dtype='int32', value=2)

2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782
            out_1 = layers.switch_case(
                branch_index=index_1,
                branch_fns={1: fn_1, 2: fn_2},
                default=fn_3,
            )
            out_2 = layers.switch_case(
                branch_index=index_2,
                branch_fns=[(1, fn_1), (2, fn_2)],
                default=fn_3,
            )
            out_3 = layers.switch_case(
                branch_index=index_2,
                branch_fns=[(0, fn_1), (4, fn_2), (7, fn_3)],
            )

            place = (
                fluid.CUDAPlace(0)
                if core.is_compiled_with_cuda()
                else fluid.CPUPlace()
            )
2783 2784
            exe = fluid.Executor(place)
            static_res1, static_res2, static_res3 = exe.run(
2785 2786
                fetch_list=[out_1, out_2, out_3]
            )
2787 2788

        with self.dynamic_graph():
2789
            with _test_eager_guard():
2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810
                index_1 = layers.fill_constant(
                    shape=[1], dtype='int32', value=1
                )
                index_2 = layers.fill_constant(
                    shape=[1], dtype='int32', value=2
                )

                out_1 = layers.switch_case(
                    branch_index=index_1,
                    branch_fns={1: fn_1, 2: fn_2},
                    default=fn_3,
                )
                out_2 = layers.switch_case(
                    branch_index=index_2,
                    branch_fns=[(1, fn_1), (2, fn_2)],
                    default=fn_3,
                )
                out_3 = layers.switch_case(
                    branch_index=index_2,
                    branch_fns=[(0, fn_1), (4, fn_2), (7, fn_3)],
                )
2811 2812 2813 2814 2815

                eager_dynamic_res1 = out_1.numpy()
                eager_dynamic_res2 = out_2.numpy()
                eager_dynamic_res3 = out_3.numpy()

2816 2817 2818
            index_1 = layers.fill_constant(shape=[1], dtype='int32', value=1)
            index_2 = layers.fill_constant(shape=[1], dtype='int32', value=2)

2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832
            out_1 = layers.switch_case(
                branch_index=index_1,
                branch_fns={1: fn_1, 2: fn_2},
                default=fn_3,
            )
            out_2 = layers.switch_case(
                branch_index=index_2,
                branch_fns=[(1, fn_1), (2, fn_2)],
                default=fn_3,
            )
            out_3 = layers.switch_case(
                branch_index=index_2,
                branch_fns=[(0, fn_1), (4, fn_2), (7, fn_3)],
            )
2833 2834 2835 2836 2837

            dynamic_res1 = out_1.numpy()
            dynamic_res2 = out_2.numpy()
            dynamic_res3 = out_3.numpy()

2838 2839 2840 2841 2842 2843
        np.testing.assert_array_equal(static_res1, dynamic_res1)
        np.testing.assert_array_equal(static_res2, dynamic_res2)
        np.testing.assert_array_equal(static_res3, dynamic_res3)
        np.testing.assert_array_equal(static_res1, eager_dynamic_res1)
        np.testing.assert_array_equal(static_res2, eager_dynamic_res2)
        np.testing.assert_array_equal(static_res3, eager_dynamic_res3)
2844

2845 2846 2847 2848
    def test_crop_tensor(self):
        with self.static_graph():
            x = fluid.layers.data(name="x1", shape=[6, 5, 8])

2849 2850 2851 2852 2853 2854
            dim1 = fluid.layers.data(
                name="dim1", shape=[1], append_batch_size=False
            )
            dim2 = fluid.layers.data(
                name="dim2", shape=[1], append_batch_size=False
            )
2855
            crop_shape1 = (1, 2, 4, 4)
2856 2857 2858
            crop_shape2 = fluid.layers.data(
                name="crop_shape", shape=[4], append_batch_size=False
            )
2859 2860
            crop_shape3 = [-1, dim1, dim2, 4]
            crop_offsets1 = [0, 0, 1, 0]
2861 2862 2863
            crop_offsets2 = fluid.layers.data(
                name="crop_offset", shape=[4], append_batch_size=False
            )
2864 2865
            crop_offsets3 = [0, dim1, dim2, 0]

2866 2867 2868
            out1 = paddle.crop(x, shape=crop_shape1, offsets=crop_offsets1)
            out2 = paddle.crop(x, shape=crop_shape2, offsets=crop_offsets2)
            out3 = paddle.crop(x, shape=crop_shape3, offsets=crop_offsets3)
2869 2870 2871 2872 2873

            self.assertIsNotNone(out1)
            self.assertIsNotNone(out2)
            self.assertIsNotNone(out3)

2874 2875 2876
    def test_shard_index(self):
        with self.static_graph():
            x = fluid.layers.data(name="label", shape=[4, 1], dtype='int64')
2877 2878 2879
            shard_label = fluid.layers.shard_index(
                input=x, index_num=20, nshards=2, shard_id=0
            )
2880 2881 2882

        self.assertIsNotNone(shard_label)

2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895
    def test_accuracy(self):
        x = np.random.rand(3, 32, 32).astype("float32")
        y = np.array([[1], [0], [1]])
        with self.static_graph():
            data = fluid.data(name="input", shape=[-1, 32, 32], dtype="float32")
            label = fluid.data(name="label", shape=[-1, 1], dtype="int")
            fc_out = fluid.layers.fc(input=data, size=10)
            predict = fluid.layers.softmax(input=fc_out)
            result = fluid.layers.accuracy(input=predict, label=label, k=5)
            place = fluid.CPUPlace()
            exe = fluid.Executor(place)

            exe.run(fluid.default_startup_program())
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            # x = np.random.rand(3, 32, 32).astype("float32")
            # y = np.array([[1], [0], [1]])
2898 2899 2900
            static_out = exe.run(
                feed={"input": x, "label": y}, fetch_list=result[0]
            )
2901

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        with self.dynamic_graph(force_to_use_cpu=True):
2903 2904 2905 2906 2907 2908
            data = base.to_variable(x)
            label = base.to_variable(y)
            fc_out = fluid.layers.fc(data, size=10)
            predict = fluid.layers.softmax(fc_out)
            dynamic_out = fluid.layers.accuracy(input=predict, label=label, k=5)

2909
        np.testing.assert_array_equal(static_out[0], dynamic_out.numpy())
2910

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2912
class TestBook(LayerTest):
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    def setUp(self):
        self.only_static_set = set({"make_word_embedding"})
2915 2916 2917 2918 2919 2920 2921 2922 2923
        self.not_compare_static_dygraph_set = set(
            {
                "make_gaussian_random",
                "make_kldiv_loss",
                "make_prelu",
                "make_sampling_id",
                "make_uniform_random_batch_size_like",
            }
        )
2924
        self.all_close_compare = set({"make_spectral_norm"})
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2925

2926
    def func_all_layers(self):
2927 2928 2929 2930 2931
        attrs = (getattr(self, name) for name in dir(self))
        methods = filter(inspect.ismethod, attrs)
        for method in methods:
            if not method.__name__.startswith('make_'):
                continue
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2932 2933 2934
            self._low_data_bound = 0
            self._high_data_bound = 2
            self._batch_size = 2
2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946
            self._feed_dict = {}
            self._force_to_use_cpu = False
            with self.static_graph():
                static_var = method()
                if isinstance(static_var, tuple):
                    static_var = static_var[0]

                if static_var is not None:
                    fetch_list = [static_var.name]
                    static_result = self.get_static_graph_result(
                        feed=self._feed_dict,
                        fetch_list=fetch_list,
2947 2948
                        force_to_use_cpu=self._force_to_use_cpu,
                    )
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2950 2951 2952
                else:
                    assert method.__name__ in ('make_get_places')
                    continue
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2953 2954
            if method.__name__ in self.only_static_set:
                continue
2955 2956 2957 2958 2959

            with self.dynamic_graph(self._force_to_use_cpu):
                dy_result = method()
                if isinstance(dy_result, tuple):
                    dy_result = dy_result[0]
2960
                dy_result_value = dy_result.numpy()
2961

2962
            if method.__name__ in self.all_close_compare:
2963 2964 2965 2966 2967 2968
                np.testing.assert_allclose(
                    static_result[0],
                    dy_result_value,
                    rtol=1e-05,
                    atol=0,
                    err_msg='Result of function [{}] compare failed'.format(
2969 2970 2971
                        method.__name__
                    ),
                )
2972 2973
                continue

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            if method.__name__ not in self.not_compare_static_dygraph_set:
2975 2976 2977 2978
                np.testing.assert_array_equal(
                    static_result[0],
                    dy_result_value,
                    err_msg='Result of function [{}] not equal'.format(
2979 2980 2981
                        method.__name__
                    ),
                )
2982

2983 2984 2985 2986 2987
    def test_all_layers(self):
        with _test_eager_guard():
            self.func_all_layers()
        self.func_all_layers()

2988 2989 2990
    def _get_np_data(self, shape, dtype, append_batch_size=True):
        np.random.seed(self.seed)
        if append_batch_size:
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            shape = [self._batch_size] + shape
2992 2993 2994 2995 2996
        if dtype == 'float32':
            return np.random.random(shape).astype(dtype)
        elif dtype == 'float64':
            return np.random.random(shape).astype(dtype)
        elif dtype == 'int32':
2997 2998 2999
            return np.random.randint(
                self._low_data_bound, self._high_data_bound, shape
            ).astype(dtype)
3000
        elif dtype == 'int64':
3001 3002 3003 3004 3005 3006 3007
            return np.random.randint(
                self._low_data_bound, self._high_data_bound, shape
            ).astype(dtype)

    def _get_data(
        self, name, shape, dtype, set_feed_dict=True, append_batch_size=True
    ):
3008
        if base.enabled():
3009 3010 3011 3012 3013
            return base.to_variable(
                value=self._get_np_data(shape, dtype, append_batch_size),
                name=name,
                zero_copy=False,
            )
3014 3015
        else:
            if set_feed_dict:
3016
                self._feed_dict[name] = self._get_np_data(
3017 3018 3019 3020 3021 3022 3023 3024
                    shape, dtype, append_batch_size
                )
            return layers.data(
                name=name,
                shape=shape,
                dtype=dtype,
                append_batch_size=append_batch_size,
            )
3025 3026

    def make_fit_a_line(self):
3027 3028 3029 3030
        with program_guard(
            fluid.default_main_program(),
            startup_program=fluid.default_startup_program(),
        ):
3031
            x = self._get_data(name='x', shape=[13], dtype='float32')
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            y_predict = layers.fc(input=x, size=1, act=None)
3033
            y = self._get_data(name='y', shape=[1], dtype='float32')
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            cost = layers.square_error_cost(input=y_predict, label=y)
3035
            avg_cost = paddle.mean(cost)
3036
            return avg_cost
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3038
    def make_recognize_digits_mlp(self):
3039 3040 3041
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
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            # Change g_program, so the rest layers use `g_program`
3043 3044
            images = self._get_data(name='pixel', shape=[784], dtype='float32')
            label = self._get_data(name='label', shape=[1], dtype='int64')
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            hidden1 = layers.fc(input=images, size=128, act='relu')
            hidden2 = layers.fc(input=hidden1, size=64, act='relu')
3047 3048 3049 3050 3051 3052
            predict = layers.fc(
                input=[hidden2, hidden1],
                size=10,
                act='softmax',
                param_attr=["sftmax.w1", "sftmax.w2"],
            )
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            cost = layers.cross_entropy(input=predict, label=label)
3054
            avg_cost = paddle.mean(cost)
3055
            return avg_cost
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3057
    def make_conv2d_transpose(self):
3058 3059 3060
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3061
            img = self._get_data(name='pixel', shape=[3, 2, 2], dtype='float32')
3062
            return paddle.static.nn.conv2d_transpose(
3063 3064
                input=img, num_filters=10, output_size=28
            )
3065

3066
    def make_recognize_digits_conv(self):
3067 3068 3069 3070 3071 3072
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            images = self._get_data(
                name='pixel', shape=[1, 28, 28], dtype='float32'
            )
3073
            label = self._get_data(name='label', shape=[1], dtype='int64')
3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089
            conv_pool_1 = nets.simple_img_conv_pool(
                input=images,
                filter_size=5,
                num_filters=2,
                pool_size=2,
                pool_stride=2,
                act="relu",
            )
            conv_pool_2 = nets.simple_img_conv_pool(
                input=conv_pool_1,
                filter_size=5,
                num_filters=4,
                pool_size=2,
                pool_stride=2,
                act="relu",
            )
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            predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
            cost = layers.cross_entropy(input=predict, label=label)
3093
            avg_cost = paddle.mean(cost)
3094
            return avg_cost
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3096
    def make_word_embedding(self):
3097 3098 3099
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
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            dict_size = 10000
            embed_size = 32
3102
            first_word = self._get_data(name='firstw', shape=[1], dtype='int64')
3103 3104 3105
            second_word = self._get_data(
                name='secondw', shape=[1], dtype='int64'
            )
3106 3107 3108
            third_word = self._get_data(name='thirdw', shape=[1], dtype='int64')
            forth_word = self._get_data(name='forthw', shape=[1], dtype='int64')
            next_word = self._get_data(name='nextw', shape=[1], dtype='int64')
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3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134
            embed_first = layers.embedding(
                input=first_word,
                size=[dict_size, embed_size],
                dtype='float32',
                param_attr='shared_w',
            )
            embed_second = layers.embedding(
                input=second_word,
                size=[dict_size, embed_size],
                dtype='float32',
                param_attr='shared_w',
            )

            embed_third = layers.embedding(
                input=third_word,
                size=[dict_size, embed_size],
                dtype='float32',
                param_attr='shared_w',
            )
            embed_forth = layers.embedding(
                input=forth_word,
                size=[dict_size, embed_size],
                dtype='float32',
                param_attr='shared_w',
            )
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            concat_embed = layers.concat(
                input=[embed_first, embed_second, embed_third, embed_forth],
3138 3139
                axis=1,
            )
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            hidden1 = layers.fc(input=concat_embed, size=256, act='sigmoid')
3142 3143 3144
            predict_word = layers.fc(
                input=hidden1, size=dict_size, act='softmax'
            )
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            cost = layers.cross_entropy(input=predict_word, label=next_word)
3146
            avg_cost = paddle.mean(cost)
3147
            return avg_cost
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3149
    def make_sigmoid_cross_entropy(self):
3150 3151 3152
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3153 3154
            dat = self._get_data(name='data', shape=[10], dtype='float32')
            lbl = self._get_data(name='label', shape=[10], dtype='float32')
3155
            ignore_index = -1
3156 3157 3158
            return layers.sigmoid_cross_entropy_with_logits(
                x=dat, label=lbl, ignore_index=ignore_index
            )
3159 3160

    def make_pool2d(self):
3161 3162 3163
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3164
            x = self._get_data(name='x', shape=[3, 224, 224], dtype='float32')
3165 3166 3167
            return layers.pool2d(
                x, pool_size=[5, 3], pool_stride=[1, 2], pool_padding=(2, 1)
            )
3168

K
Kaipeng Deng 已提交
3169
    def make_pool2d_infershape(self):
3170 3171 3172
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
K
Kaipeng Deng 已提交
3173
            theta = self._get_data("theta", shape=[2, 3], dtype='float32')
3174 3175 3176
            x = paddle.nn.functional.affine_grid(
                theta, out_shape=[2, 3, 244, 244]
            )
3177 3178 3179
            return layers.pool2d(
                x, pool_size=[5, 3], pool_stride=[1, 2], pool_padding=(2, 1)
            )
K
Kaipeng Deng 已提交
3180

3181
    def make_lstm_unit(self):
3182 3183 3184 3185 3186 3187
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            x_t_data = self._get_data(
                name='x_t_data', shape=[10, 10], dtype='float32'
            )
Y
yangyaming 已提交
3188
            x_t = layers.fc(input=x_t_data, size=10)
3189 3190 3191
            prev_hidden_data = self._get_data(
                name='prev_hidden_data', shape=[10, 30], dtype='float32'
            )
Y
yangyaming 已提交
3192
            prev_hidden = layers.fc(input=prev_hidden_data, size=30)
3193 3194 3195
            prev_cell_data = self._get_data(
                name='prev_cell', shape=[10, 30], dtype='float32'
            )
Y
yangyaming 已提交
3196
            prev_cell = layers.fc(input=prev_cell_data, size=30)
3197 3198 3199
            return layers.lstm_unit(
                x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell
            )
3200

3201
    def make_softmax(self):
3202 3203 3204
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3205
            data = self._get_data(name='data', shape=[10], dtype='float32')
D
dangqingqing 已提交
3206
            hid = layers.fc(input=data, size=20)
3207
            return layers.softmax(hid, axis=1)
D
dangqingqing 已提交
3208

3209
    def make_get_places(self):
3210 3211 3212
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3213
            get_places(device_count=1)
X
xuezhong 已提交
3214

3215
    @prog_scope()
3216
    def make_nce(self):
Y
Yang Yu 已提交
3217 3218
        window_size = 5
        words = []
3219
        for i in range(window_size):
Y
Yang Yu 已提交
3220
            words.append(
3221 3222 3223 3224
                self._get_data(
                    name='word_{0}'.format(i), shape=[1], dtype='int64'
                )
            )
Y
Yang Yu 已提交
3225 3226

        dict_size = 10000
M
minqiyang 已提交
3227
        label_word = int(window_size // 2) + 1
Y
Yang Yu 已提交
3228 3229

        embs = []
3230
        for i in range(window_size):
Y
Yang Yu 已提交
3231 3232 3233
            if i == label_word:
                continue

3234 3235 3236 3237 3238 3239
            emb = layers.embedding(
                input=words[i],
                size=[dict_size, 32],
                param_attr='emb.w',
                is_sparse=True,
            )
Y
Yang Yu 已提交
3240 3241 3242 3243

            embs.append(emb)

        embs = layers.concat(input=embs, axis=1)
3244
        loss = paddle.static.nn.nce(
3245 3246 3247 3248 3249 3250
            input=embs,
            label=words[label_word],
            num_total_classes=dict_size,
            param_attr='nce.w',
            bias_attr='nce.b',
        )
3251
        avg_loss = paddle.mean(loss)
3252
        return avg_loss
Y
Yang Yu 已提交
3253

3254
    def make_multiplex(self):
3255 3256 3257
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3258 3259 3260
            x1 = self._get_data(name='x1', shape=[4], dtype='float32')
            x2 = self._get_data(name='x2', shape=[4], dtype='float32')
            index = self._get_data(name='index', shape=[1], dtype='int32')
3261
            out = layers.multiplex(inputs=[x1, x2], index=index)
3262
            return out
3263 3264

    def make_softmax_with_cross_entropy(self):
3265 3266 3267
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3268 3269
            x = self._get_data(name='x', shape=[16], dtype='float32')
            y = self._get_data(name='label', shape=[1], dtype='int64')
3270
            loss, softmax = layers.softmax_with_cross_entropy(
3271 3272
                x, y, return_softmax=True
            )
3273 3274 3275
            self.assertIsNotNone(loss)
            self.assertIsNotNone(softmax)

3276
            loss = layers.softmax_with_cross_entropy(x, y)
3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290
            self.assertIsNotNone(loss)

            x1 = self._get_data(name='x1', shape=[16, 32, 64], dtype='float32')
            y1 = self._get_data(name='label1', shape=[1, 32, 64], dtype='int64')
            y2 = self._get_data(name='label2', shape=[16, 1, 64], dtype='int64')
            y3 = self._get_data(name='label3', shape=[16, 32, 1], dtype='int64')
            loss1 = layers.softmax_with_cross_entropy(x1, y1, axis=1)
            loss2 = layers.softmax_with_cross_entropy(x1, y2, axis=2)
            loss3 = layers.softmax_with_cross_entropy(x1, y3, axis=3)
            loss4 = layers.softmax_with_cross_entropy(x1, y3, axis=-1)
            self.assertIsNotNone(loss1)
            self.assertIsNotNone(loss2)
            self.assertIsNotNone(loss3)
            self.assertIsNotNone(loss4)
3291
            return loss4
3292 3293

    def make_smooth_l1(self):
3294 3295 3296
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3297 3298
            x = self._get_data(name='x', shape=[4], dtype='float32')
            y = self._get_data(name='label', shape=[4], dtype='float32')
3299
            loss = layers.smooth_l1(x, y)
3300
            return loss
3301

3302
    def make_scatter(self):
3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            x = self._get_data(
                name='x', shape=[3, 3], append_batch_size=False, dtype='float32'
            )
            idx = self._get_data(
                name='idx', shape=[2], append_batch_size=False, dtype='int32'
            )
            updates = self._get_data(
                name='updates',
                shape=[2, 3],
                append_batch_size=False,
                dtype='float32',
            )
3318
            out = paddle.scatter(x, index=idx, updates=updates)
3319
            return out
Y
yangyaming 已提交
3320

3321 3322 3323 3324
    def make_one_hot(self):
        with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
            label = self._get_data(name="label", shape=[1], dtype="int32")
            one_hot_label = layers.one_hot(input=label, depth=10)
3325
            return one_hot_label
3326

3327 3328 3329 3330 3331
    def make_label_smooth(self):
        # TODO(minqiyang): support gpu ut
        self._force_to_use_cpu = True
        with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
            label = self._get_data(name="label", shape=[1], dtype="int32")
3332
            one_hot_label = layers.one_hot(input=label, depth=10)
3333
            smooth_label = F.label_smooth(label=one_hot_label, epsilon=0.1)
3334
            return smooth_label
3335

3336
    def make_topk(self):
3337 3338 3339
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3340 3341
            data = self._get_data(name="label", shape=[200], dtype="float32")
            values, indices = layers.topk(data, k=5)
3342 3343
            return values
            return indices
J
jerrywgz 已提交
3344

3345
    def make_resize_bilinear(self):
3346 3347 3348
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3349
            x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
B
baiyf 已提交
3350
            output = layers.resize_bilinear(x, out_shape=[12, 12])
3351
            return output
K
Kaipeng Deng 已提交
3352 3353

    def make_resize_bilinear_by_scale(self):
3354 3355 3356
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
K
Kaipeng Deng 已提交
3357 3358
            x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
            output = layers.resize_bilinear(x, scale=1.5)
3359
            return output
3360

3361
    def make_resize_nearest(self):
K
Kaipeng Deng 已提交
3362
        try:
3363 3364 3365
            with program_guard(
                fluid.default_main_program(), fluid.default_startup_program()
            ):
K
Kaipeng Deng 已提交
3366 3367 3368 3369 3370 3371
                x = self._get_data(name='x1', shape=[3, 9, 6], dtype="float32")
                output = layers.resize_nearest(x, out_shape=[12, 12])
        except ValueError:
            pass

        try:
3372 3373 3374 3375 3376 3377
            with program_guard(
                fluid.default_main_program(), fluid.default_startup_program()
            ):
                x = self._get_data(
                    name='x2', shape=[3, 9, 6, 7], dtype="float32"
                )
K
Kaipeng Deng 已提交
3378 3379 3380 3381
                output = layers.resize_nearest(x, out_shape=[12, 12, 12])
        except ValueError:
            pass

3382 3383 3384
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3385
            x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
3386
            output = layers.resize_nearest(x, out_shape=[12, 12])
3387
            return output
K
Kaipeng Deng 已提交
3388 3389

    def make_resize_nearest_by_scale(self):
3390 3391 3392
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
K
Kaipeng Deng 已提交
3393 3394
            x = self._get_data(name='x1', shape=[3, 9, 6], dtype="float32")
            output = layers.resize_nearest(x, scale=1.8)
3395
            return output
K
Kaipeng Deng 已提交
3396 3397 3398

    def make_resize_trilinear(self):
        try:
3399 3400 3401
            with program_guard(
                fluid.default_main_program(), fluid.default_startup_program()
            ):
K
Kaipeng Deng 已提交
3402 3403 3404 3405 3406 3407
                x = self._get_data(name='x2', shape=[3, 9, 6], dtype="float32")
                output = layers.resize_trilinear(x, out_shape=[12, 12, 12])
        except ValueError:
            pass

        try:
3408 3409 3410 3411 3412 3413
            with program_guard(
                fluid.default_main_program(), fluid.default_startup_program()
            ):
                x = self._get_data(
                    name='x', shape=[3, 9, 6, 7], dtype="float32"
                )
K
Kaipeng Deng 已提交
3414 3415 3416 3417
                output = layers.resize_trilinear(x, out_shape=[12, 12])
        except ValueError:
            pass

3418 3419 3420
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
K
Kaipeng Deng 已提交
3421 3422
            x = self._get_data(name='x', shape=[3, 9, 6, 7], dtype="float32")
            output = layers.resize_trilinear(x, out_shape=[12, 12, 12])
3423
            return output
K
Kaipeng Deng 已提交
3424 3425

    def make_resize_trilinear_by_scale(self):
3426 3427 3428
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
K
Kaipeng Deng 已提交
3429 3430
            x = self._get_data(name='x', shape=[3, 9, 6, 7], dtype="float32")
            output = layers.resize_trilinear(x, scale=2.1)
3431
            return output
3432

3433
    def make_polygon_box_transform(self):
3434 3435 3436
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3437
            x = self._get_data(name='x', shape=[8, 4, 4], dtype="float32")
3438
            output = layers.polygon_box_transform(input=x)
3439
            return output
3440

3441
    def make_l2_normalize(self):
3442 3443 3444
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3445
            x = self._get_data(name='x', shape=[8, 7, 10], dtype="float32")
3446
            output = layers.l2_normalize(x, axis=1)
3447
            return output
3448

3449
    def make_argsort(self):
3450 3451 3452
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3453
            data = self._get_data(name='x', shape=[2, 3, 3], dtype="float32")
3454
            out, ids = layers.argsort(input=data, axis=1)
3455 3456
            return out
            return ids
3457 3458

    def make_shape(self):
3459 3460 3461 3462 3463 3464
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            input = self._get_data(
                name="input", shape=[3, 100, 100], dtype="float32"
            )
G
fix  
gongweibao 已提交
3465
            out = layers.shape(input)
3466
            return out
B
Bai Yifan 已提交
3467

3468
    def make_pad2d(self):
3469 3470 3471 3472 3473 3474
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            input = self._get_data(
                name="input", shape=[3, 100, 100], dtype="float32"
            )
傅剑寒 已提交
3475 3476 3477

            tmp_pad = paddle.nn.Pad2D(
                padding=[1, 2, 3, 4],
3478 3479 3480 3481
                mode='reflect',
                data_format='NCHW',
                name="shape",
            )
傅剑寒 已提交
3482
            out = tmp_pad(input)
3483
            return out
W
whs 已提交
3484

3485
    def make_prelu(self):
3486 3487 3488 3489 3490 3491
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            input = self._get_data(
                name="input", shape=[5, 200, 100, 100], dtype="float32"
            )
J
jerrywgz 已提交
3492
            mode = 'channel'
3493 3494 3495 3496 3497 3498 3499
            out = layers.prelu(
                input,
                mode,
                param_attr=ParamAttr(initializer=Constant(1.0)),
                name='prelu',
            )
            return out
J
jerrywgz 已提交
3500

K
Kaipeng Deng 已提交
3501
    def make_mish(self):
3502 3503 3504
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
K
Kaipeng Deng 已提交
3505 3506
            input = self._get_data(name="input", shape=[16], dtype="float32")
            out = layers.mish(input, name='mish')
3507
            return out
K
Kaipeng Deng 已提交
3508

3509
    def make_cross_entropy(self):
3510 3511 3512
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3513 3514
            x = self._get_data(name="x", shape=[30, 10], dtype="float32")
            label = self._get_data(name="label", shape=[30, 1], dtype="int64")
3515 3516
            mode = 'channel'
            out = layers.cross_entropy(x, label, False, 4)
3517
            return out
3518

3519
    def make_uniform_random_batch_size_like(self):
3520 3521 3522 3523 3524 3525
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            input = self._get_data(
                name="input", shape=[13, 11], dtype='float32'
            )
3526
            out = random.uniform_random_batch_size_like(input, [-1, 11])
3527
            return out
G
fix  
gongweibao 已提交
3528

3529
    def make_gaussian_random(self):
3530 3531 3532
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
G
fix  
gongweibao 已提交
3533
            out = layers.gaussian_random(shape=[20, 30])
3534
            return out
G
fix  
gongweibao 已提交
3535

3536
    def make_sampling_id(self):
3537 3538 3539 3540 3541 3542 3543 3544 3545
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            x = self._get_data(
                name="X",
                shape=[13, 11],
                dtype='float32',
                append_batch_size=False,
            )
G
fix  
gongweibao 已提交
3546 3547

            out = layers.sampling_id(x)
3548
            return out
G
fix  
gongweibao 已提交
3549

3550
    def make_sum(self):
3551 3552 3553 3554 3555 3556
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            input = self._get_data(
                name="input", shape=[13, 11], dtype='float32'
            )
G
fix  
gongweibao 已提交
3557 3558

            out = layers.sum(input)
3559
            return out
G
fix  
gongweibao 已提交
3560

3561
    def make_slice(self):
G
fix  
gongweibao 已提交
3562 3563 3564 3565
        starts = [1, 0, 2]
        ends = [3, 3, 4]
        axes = [0, 1, 2]

3566 3567 3568 3569 3570 3571
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            input = self._get_data(
                name="input", shape=[3, 4, 5, 6], dtype='float32'
            )
G
fix  
gongweibao 已提交
3572

2
201716010711 已提交
3573
            out = paddle.slice(input, axes=axes, starts=starts, ends=ends)
3574
            return out
G
merge  
gongweibao 已提交
3575

3576
    def make_scale_variable(self):
3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            input = self._get_data(
                name="input", shape=[3, 4, 5, 6], dtype='float32'
            )
            scale_var = self._get_data(
                name="scale",
                shape=[1],
                dtype='float32',
                append_batch_size=False,
            )
2
201716010711 已提交
3589
            out = paddle.scale(input, scale=scale_var)
3590 3591
            return out

M
minqiyang 已提交
3592
    def make_iou_similarity(self):
3593 3594 3595
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
M
minqiyang 已提交
3596 3597
            x = self._get_data(name="x", shape=[4], dtype="float32")
            y = self._get_data(name="y", shape=[4], dtype="float32")
X
Xin Pan 已提交
3598
            out = layers.iou_similarity(x, y, name='iou_similarity')
3599
            return out
3600 3601

    def make_grid_sampler(self):
3602 3603 3604
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3605 3606
            x = self._get_data(name='x', shape=[3, 5, 7], dtype='float32')
            grid = self._get_data(name='grid', shape=[5, 7, 2], dtype='float32')
D
dengkaipeng 已提交
3607
            out = layers.grid_sampler(x, grid)
3608
            return out
3609 3610

    def make_bilinear_tensor_product_layer(self):
3611 3612 3613
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3614 3615 3616 3617
            data = self._get_data(name='data', shape=[4], dtype="float32")

            theta = self._get_data(name="theta", shape=[5], dtype="float32")
            out = layers.bilinear_tensor_product(data, theta, 6)
3618
            return out
3619 3620

    def make_batch_norm(self):
3621 3622 3623 3624 3625 3626
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            data = self._get_data(
                name='data', shape=[32, 128, 128], dtype="float32"
            )
3627
            out = layers.batch_norm(data)
3628
            return out
3629

3630
    def make_batch_norm_momentum_variable(self):
3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            data = self._get_data(
                name='data', shape=[32, 128, 128], dtype="float32"
            )
            momentum = self._get_data(
                name='momentum',
                shape=[1],
                dtype='float32',
                append_batch_size=False,
            )
3643
            out = layers.batch_norm(data, momentum=momentum)
3644
            return out
3645

3646
    def make_range(self):
3647 3648 3649
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
C
ccrrong 已提交
3650 3651 3652
            paddle.arange(0, 10, 2, 'int32')
            paddle.arange(0.1, 10.0, 0.2, 'float32')
            paddle.arange(0.1, 10.0, 0.2, 'float64')
3653 3654 3655
            start = layers.fill_constant(shape=[1], value=0.1, dtype="float32")
            end = layers.fill_constant(shape=[1], value=10.0, dtype="float32")
            step = layers.fill_constant(shape=[1], value=0.2, dtype="float32")
C
ccrrong 已提交
3656
            y = paddle.arange(start, end, step, 'float64')
3657 3658 3659
            return y

    def make_spectral_norm(self):
3660 3661 3662 3663 3664 3665 3666 3667 3668
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            weight = self._get_data(
                name='weight',
                shape=[2, 3, 32, 32],
                dtype="float32",
                append_batch_size=False,
            )
3669
            out = layers.spectral_norm(weight, dim=1, power_iters=1)
3670
            return out
3671 3672

    def make_kldiv_loss(self):
3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            x = self._get_data(
                name='x',
                shape=[32, 128, 128],
                dtype="float32",
                append_batch_size=False,
            )
            target = self._get_data(
                name='target',
                shape=[32, 128, 128],
                dtype="float32",
                append_batch_size=False,
            )
3688 3689 3690
            loss = paddle.nn.functional.kl_div(
                input=x, label=target, reduction='batchmean'
            )
3691
            return loss
3692 3693

    def make_temporal_shift(self):
3694 3695 3696
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3697 3698
            x = self._get_data(name="X", shape=[16, 4, 4], dtype="float32")
            out = layers.temporal_shift(x, seg_num=2, shift_ratio=0.2)
3699
            return out
3700

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    def make_fsp_matrix(self):
3702 3703 3704
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3705 3706 3707
            x = self._get_data(name="X", shape=[16, 4, 4], dtype="float32")
            y = self._get_data(name="Y", shape=[8, 4, 4], dtype="float32")
            out = layers.fsp_matrix(x, y)
3708
            return out
3709

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3710
    def make_pixel_shuffle(self):
3711 3712 3713
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
M
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3714 3715
            x = self._get_data(name="X", shape=[9, 4, 4], dtype="float32")
            out = layers.pixel_shuffle(x, upscale_factor=3)
3716
            return out
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3717

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3718
    def make_mse_loss(self):
3719 3720 3721
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
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3722 3723
            x = self._get_data(name="X", shape=[1], dtype="float32")
            y = self._get_data(name="Y", shape=[1], dtype="float32")
3724
            out = paddle.nn.functional.mse_loss(input=x, label=y)
3725
            return out
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3726

3727
    def make_square_error_cost(self):
3728 3729 3730
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
3731 3732 3733
            x = self._get_data(name="X", shape=[1], dtype="float32")
            y = self._get_data(name="Y", shape=[1], dtype="float32")
            out = layers.square_error_cost(input=x, label=y)
3734
            return out
3735

3736 3737 3738 3739
    def test_dynamic_lstmp(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            hidden_dim, proj_dim = 16, 8
3740 3741 3742
            seq_data = layers.data(
                name='seq_data', shape=[10, 10], dtype='float32', lod_level=1
            )
3743 3744
            fc_out = layers.fc(input=seq_data, size=4 * hidden_dim)
            self.assertIsNotNone(
3745 3746 3747 3748
                layers.dynamic_lstmp(
                    input=fc_out, size=4 * hidden_dim, proj_size=proj_dim
                )
            )
3749 3750 3751 3752 3753 3754

    def test_im2sequence(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
            y = layers.data(name='y', shape=[], dtype='float32')
3755 3756 3757 3758 3759 3760 3761 3762
            output = layers.im2sequence(
                input=x,
                input_image_size=y,
                stride=[1, 1],
                filter_size=[2, 2],
                out_stride=[1, 1],
            )
            return output
3763 3764 3765 3766

    def test_lod_reset(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
3767
            # case 1
3768
            x = layers.data(name='x', shape=[10], dtype='float32')
3769 3770 3771
            y = layers.data(
                name='y', shape=[10, 20], dtype='float32', lod_level=2
            )
3772 3773 3774
            z = layers.lod_reset(x=x, y=y)
            self.assertTrue(z.lod_level == 2)
            # case 2
3775
            lod_tensor_in = layers.data(name='lod_in', shape=[1], dtype='int32')
3776 3777 3778 3779 3780 3781
            z = layers.lod_reset(x=x, y=lod_tensor_in)
            self.assertTrue(z.lod_level == 1)
            # case 3
            z = layers.lod_reset(x=x, target_lod=[1, 2, 3])
            self.assertTrue(z.lod_level == 1)
            return z
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    def test_affine_grid(self):
3784
        with self.static_graph():
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3785 3786 3787 3788
            data = layers.data(name='data', shape=[2, 3, 3], dtype="float32")
            out, ids = layers.argsort(input=data, axis=1)

            theta = layers.data(name="theta", shape=[2, 3], dtype="float32")
3789
            out_shape = layers.data(name="out_shape", shape=[-1], dtype="int32")
3790 3791
            data_0 = paddle.nn.functional.affine_grid(theta, out_shape)
            data_1 = paddle.nn.functional.affine_grid(theta, [5, 3, 28, 28])
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            self.assertIsNotNone(data_0)
            self.assertIsNotNone(data_1)
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    def test_stridedslice(self):
        axes = [0, 1, 2]
        starts = [1, 0, 2]
        ends = [3, 3, 4]
        strides = [1, 1, 1]
        with self.static_graph():
            x = layers.data(name="x", shape=[245, 30, 30], dtype="float32")
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3803
            out = paddle.strided_slice(
3804 3805
                x, axes=axes, starts=starts, ends=ends, strides=strides
            )
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3806 3807
            return out

3808 3809
    def test_fill_constant_batch_size_like(self):
        with self.static_graph():
3810 3811 3812 3813 3814 3815
            like = fluid.layers.fill_constant(
                shape=[1, 200], value=10, dtype='int64'
            )
            out = layers.fill_constant_batch_size_like(
                input=like, shape=[2, 3300], value=1315454564656, dtype='int64'
            )
3816 3817
            return out

3818 3819 3820 3821
    def test_sequence_expand(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[10], dtype='float32')
3822 3823 3824 3825
            y = layers.data(
                name='y', shape=[10, 20], dtype='float32', lod_level=2
            )
            return layers.sequence_expand(x=x, y=y, ref_level=1)
3826

3827 3828 3829 3830 3831
    def test_sequence_reshape(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[8], dtype='float32', lod_level=1)
            out = layers.sequence_reshape(input=x, new_dim=16)
3832
            return out
3833

3834 3835 3836 3837
    def test_sequence_unpad(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[10, 5], dtype='float32')
3838
            length = layers.data(name='length', shape=[], dtype='int64')
3839
            return layers.sequence_unpad(x=x, length=length)
3840

3841 3842 3843
    def test_sequence_softmax(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
3844 3845 3846
            seq_data = layers.data(
                name='seq_data', shape=[10, 10], dtype='float32', lod_level=1
            )
3847
            seq = layers.fc(input=seq_data, size=20)
3848
            return layers.sequence_softmax(seq)
3849

3850 3851 3852 3853 3854
    def test_sequence_unsqueeze(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[8, 2], dtype='float32')
            out = layers.unsqueeze(input=x, axes=[1])
3855
            return out
3856

3857 3858 3859
    def test_sequence_scatter(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876
            x = layers.data(
                name='x', shape=[3, 6], append_batch_size=False, dtype='float32'
            )
            idx = layers.data(
                name='idx',
                shape=[12, 1],
                append_batch_size=False,
                dtype='int32',
                lod_level=1,
            )
            updates = layers.data(
                name='updates',
                shape=[12, 1],
                append_batch_size=False,
                dtype='float32',
                lod_level=1,
            )
3877
            out = layers.sequence_scatter(input=x, index=idx, updates=updates)
3878
            return out
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3880 3881 3882 3883
    def test_sequence_slice(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            import numpy as np
3884 3885 3886 3887

            seqs = layers.data(
                name='x', shape=[10, 5], dtype='float32', lod_level=1
            )
3888 3889
            offset = layers.assign(input=np.array([[0, 1]]).astype('int32'))
            length = layers.assign(input=np.array([[2, 1]]).astype('int32'))
3890 3891 3892 3893
            out = layers.sequence_slice(
                input=seqs, offset=offset, length=length
            )
            return out
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    def test_shuffle_batch(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
3898 3899 3900
            x = layers.data(
                name='X', shape=[4, 50], dtype='float32', lod_level=0
            )
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            out1 = fluid.contrib.layers.shuffle_batch(x)
            default_main_program().random_seed = 1000
            out2 = fluid.contrib.layers.shuffle_batch(x)
            self.assertIsNotNone(out1)
            self.assertIsNotNone(out2)
3906
            return out1
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3908 3909 3910 3911
    def test_partial_sum(self):
        with self.static_graph():
            x = fluid.data(name="x", shape=[None, 3], dtype="float32")
            y = fluid.data(name="y", shape=[None, 3], dtype="float32")
3912 3913 3914 3915
            sum = fluid.contrib.layers.partial_sum(
                [x, y], start_index=0, length=2
            )
            return sum
3916

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3917 3918 3919 3920 3921 3922 3923 3924 3925
    def test_batch_fc(self):
        with self.static_graph():
            input = fluid.data(name="input", shape=[16, 2, 3], dtype="float32")
            out = fluid.contrib.layers.batch_fc(
                input=input,
                param_size=[16, 3, 10],
                param_attr=fluid.ParamAttr(
                    learning_rate=1.0,
                    name="w_0",
3926 3927
                    initializer=fluid.initializer.Xavier(uniform=False),
                ),
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3928 3929 3930 3931
                bias_size=[16, 10],
                bias_attr=fluid.ParamAttr(
                    learning_rate=1.0,
                    name="b_0",
3932 3933 3934 3935 3936
                    initializer=fluid.initializer.Xavier(uniform=False),
                ),
                act="relu",
            )
        return out
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3937

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3938 3939 3940
    def test_rank_attention(self):
        with self.static_graph():
            input = fluid.data(name="input", shape=[None, 2], dtype="float32")
3941 3942 3943
            rank_offset = fluid.data(
                name="rank_offset", shape=[None, 7], dtype="int32"
            )
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3944 3945 3946 3947 3948 3949 3950
            out = fluid.contrib.layers.rank_attention(
                input=input,
                rank_offset=rank_offset,
                rank_param_shape=[18, 3],
                rank_param_attr=fluid.ParamAttr(
                    learning_rate=1.0,
                    name="ubm_rank_param.w_0",
3951 3952 3953 3954 3955
                    initializer=fluid.initializer.Xavier(uniform=False),
                ),
                max_rank=3,
            )
            return out
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3956

3957 3958 3959 3960 3961 3962 3963 3964 3965 3966
    def test_sequence_enumerate(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name="input", shape=[1], dtype='int32', lod_level=1)
            out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0)

    def test_roi_perspective_transform(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
3967 3968 3969
            rois = layers.data(
                name="rois", shape=[8], dtype="float32", lod_level=1
            )
3970
            output = layers.roi_perspective_transform(x, rois, 7, 7, 0.6)
3971
            return output
3972 3973 3974 3975 3976 3977

    def test_row_conv(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[16], dtype='float32', lod_level=1)
            out = layers.row_conv(input=x, future_context_size=2)
3978
            return out
3979 3980 3981 3982

    def test_simple_conv2d(self):
        # TODO(minqiyang): dygraph do not support layers with param now
        with self.static_graph():
3983 3984 3985 3986 3987 3988
            images = layers.data(
                name='pixel', shape=[3, 48, 48], dtype='float32'
            )
            return layers.conv2d(
                input=images, num_filters=3, filter_size=[4, 4]
            )
3989 3990 3991 3992 3993

    def test_squeeze(self):
        # TODO(minqiyang): dygraph do not support layers with param now
        with self.static_graph():
            x = layers.data(name='x', shape=[1, 1, 4], dtype='float32')
3994
            out = paddle.squeeze(x, axis=[2])
3995
            return out
3996 3997 3998 3999

    def test_flatten(self):
        # TODO(minqiyang): dygraph do not support op without kernel now
        with self.static_graph():
4000 4001 4002 4003 4004 4005
            x = layers.data(
                name='x',
                append_batch_size=False,
                shape=[4, 4, 3],
                dtype="float32",
            )
4006
            out = paddle.flatten(x, 1, -1, name="flatten")
4007
            return out
4008

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zhoukunsheng 已提交
4009 4010 4011 4012 4013 4014 4015
    def test_linspace(self):
        program = Program()
        with program_guard(program):
            out = layers.linspace(20, 10, 5, 'float64')
            self.assertIsNotNone(out)
        print(str(program))

4016 4017 4018 4019
    def test_unfold(self):
        with self.static_graph():
            x = layers.data(name='x', shape=[3, 20, 20], dtype='float32')
            out = layers.unfold(x, [3, 3], 1, 1, 1)
4020
            return out
4021

4022 4023 4024 4025
    def test_partial_concat(self):
        with self.static_graph():
            x = fluid.data(name="x", shape=[None, 3], dtype="float32")
            y = fluid.data(name="y", shape=[None, 3], dtype="float32")
4026 4027 4028 4029 4030 4031
            concat1 = fluid.contrib.layers.partial_concat(
                [x, y], start_index=0, length=2
            )
            concat2 = fluid.contrib.layers.partial_concat(
                x, start_index=0, length=-1
            )
4032 4033
            return concat1, concat2

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4034
    def test_deform_roi_pooling(self):
4035 4036 4037 4038 4039 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            input = layers.data(
                name='input',
                shape=[2, 3, 32, 32],
                dtype='float32',
                append_batch_size=False,
            )
            rois = layers.data(
                name="rois", shape=[4], dtype='float32', lod_level=1
            )
            trans = layers.data(
                name="trans",
                shape=[2, 3, 32, 32],
                dtype='float32',
                append_batch_size=False,
            )
            out = layers.deformable_roi_pooling(
                input=input,
                rois=rois,
                trans=trans,
                no_trans=False,
                spatial_scale=1.0,
                group_size=(1, 1),
                pooled_height=8,
                pooled_width=8,
                part_size=(8, 8),
                sample_per_part=4,
                trans_std=0.1,
            )
        return out
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4067

4068
    def test_retinanet_target_assign(self):
4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 4126 4127 4128 4129 4130
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            bbox_pred = layers.data(
                name='bbox_pred',
                shape=[1, 100, 4],
                append_batch_size=False,
                dtype='float32',
            )
            cls_logits = layers.data(
                name='cls_logits',
                shape=[1, 100, 10],
                append_batch_size=False,
                dtype='float32',
            )
            anchor_box = layers.data(
                name='anchor_box',
                shape=[100, 4],
                append_batch_size=False,
                dtype='float32',
            )
            anchor_var = layers.data(
                name='anchor_var',
                shape=[100, 4],
                append_batch_size=False,
                dtype='float32',
            )
            gt_boxes = layers.data(
                name='gt_boxes',
                shape=[10, 4],
                append_batch_size=False,
                dtype='float32',
            )
            gt_labels = layers.data(
                name='gt_labels',
                shape=[10, 1],
                append_batch_size=False,
                dtype='int32',
            )
            is_crowd = layers.data(
                name='is_crowd',
                shape=[1],
                append_batch_size=False,
                dtype='int32',
            )
            im_info = layers.data(
                name='im_info',
                shape=[1, 3],
                append_batch_size=False,
                dtype='float32',
            )
            return layers.retinanet_target_assign(
                bbox_pred,
                cls_logits,
                anchor_box,
                anchor_var,
                gt_boxes,
                gt_labels,
                is_crowd,
                im_info,
                10,
            )
4131

4132
    def test_sigmoid_focal_loss(self):
4133 4134 4135 4136 4137 4138 4139 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 4154
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            input = layers.data(
                name='data',
                shape=[10, 80],
                append_batch_size=False,
                dtype='float32',
            )
            label = layers.data(
                name='label',
                shape=[10, 1],
                append_batch_size=False,
                dtype='int32',
            )
            fg_num = layers.data(
                name='fg_num', shape=[1], append_batch_size=False, dtype='int32'
            )
            out = fluid.layers.sigmoid_focal_loss(
                x=input, label=label, fg_num=fg_num, gamma=2.0, alpha=0.25
            )
            return out
4155

4156
    def test_addmm(self):
4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169 4170 4171
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            input = layers.data(
                name='input_data',
                shape=[3, 3],
                append_batch_size=False,
                dtype='float32',
            )
            x = layers.data(
                name='x', shape=[3, 2], append_batch_size=False, dtype='float32'
            )
            y = layers.data(
                name='y', shape=[2, 3], append_batch_size=False, dtype='float32'
            )
4172 4173

            out = paddle.addmm(input=input, x=x, y=y)
4174
            return out
4175

4176
    def test_retinanet_detection_output(self):
4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197 4198 4199 4200 4201 4202 4203
        with program_guard(
            fluid.default_main_program(), fluid.default_startup_program()
        ):
            bboxes = layers.data(
                name='bboxes',
                shape=[1, 21, 4],
                append_batch_size=False,
                dtype='float32',
            )
            scores = layers.data(
                name='scores',
                shape=[1, 21, 10],
                append_batch_size=False,
                dtype='float32',
            )
            anchors = layers.data(
                name='anchors',
                shape=[21, 4],
                append_batch_size=False,
                dtype='float32',
            )
            im_info = layers.data(
                name="im_info",
                shape=[1, 3],
                append_batch_size=False,
                dtype='float32',
            )
4204 4205 4206 4207 4208 4209 4210 4211 4212
            nmsed_outs = layers.retinanet_detection_output(
                bboxes=[bboxes, bboxes],
                scores=[scores, scores],
                anchors=[anchors, anchors],
                im_info=im_info,
                score_threshold=0.05,
                nms_top_k=1000,
                keep_top_k=100,
                nms_threshold=0.3,
4213 4214 4215
                nms_eta=1.0,
            )
            return nmsed_outs
4216

4217 4218 4219
    def test_warpctc_with_padding(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
4220
            input_length = paddle.static.data(
4221 4222
                name='logits_length', shape=[11], dtype='int64'
            )
4223
            label_length = paddle.static.data(
4224 4225
                name='labels_length', shape=[12], dtype='int64'
            )
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            label = paddle.static.data(
                name='label', shape=[12, 1], dtype='int32'
            )
            predict = paddle.static.data(
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                name='predict', shape=[4, 4, 8], dtype='float32'
            )
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            output = paddle.nn.functional.ctc_loss(
                log_probs=predict,
                labels=label,
                input_lengths=input_length,
                label_lengths=label_length,
                reduction='none',
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            )
            return output
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    def test_basic_gru(self):
        input_size = 128
        hidden_size = 256
        with self.static_graph():
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            input = fluid.data(
                name="input", shape=[None, None, input_size], dtype='float32'
            )
            pre_hidden = fluid.data(
                name="pre_hidden", shape=[None, hidden_size], dtype='float32'
            )
            sequence_length = fluid.data(
                name="sequence_length", shape=[None], dtype='int32'
            )
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            for bidirectional in [True, False]:
                for batch_first in [True, False]:
                    rnn_out, last_hidden = fluid.contrib.layers.basic_gru(
                        input,
                        pre_hidden,
                        hidden_size=256,
                        num_layers=2,
                        sequence_length=sequence_length,
                        dropout_prob=0.5,
                        bidirectional=bidirectional,
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                        batch_first=batch_first,
                    )
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class TestMetricsDetectionMap(unittest.TestCase):
    def test_detection_map(self):
        program = fluid.Program()
        with program_guard(program):
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            detect_res = fluid.layers.data(
                name='detect_res',
                shape=[10, 6],
                append_batch_size=False,
                dtype='float32',
            )
            label = fluid.layers.data(
                name='label',
                shape=[10, 1],
                append_batch_size=False,
                dtype='float32',
            )
            box = fluid.layers.data(
                name='bbox',
                shape=[10, 4],
                append_batch_size=False,
                dtype='float32',
            )
            map_eval = fluid.metrics.DetectionMAP(
                detect_res, label, box, class_num=21
            )
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            cur_map, accm_map = map_eval.get_map_var()
            self.assertIsNotNone(cur_map)
            self.assertIsNotNone(accm_map)
        print(str(program))


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class ExampleNet(paddle.nn.Layer):
    def __init__(self):
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        super().__init__()
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        self.weight = self.create_parameter(
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            shape=[1, 1], attr=paddle.ParamAttr(trainable=False)
        )
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    def forward(self):
        # only for test parameter trainable attr
        pass


class TestLayerParameterTrainableSet(unittest.TestCase):
    def test_layer_parameter_set(self):
        with fluid.dygraph.guard():
            net = ExampleNet()
            self.assertFalse(net.weight.trainable)


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class TestLayerTrainingAttribute(unittest.TestCase):
    def test_set_train_eval_in_dynamic_mode(self):
        with fluid.dygraph.guard():
            net = paddle.nn.Dropout()
            net.train()
            self.assertTrue(net.training)
            net.eval()
            self.assertFalse(net.training)

    def test_set_train_eval_in_static_mode(self):
        net = paddle.nn.Dropout()
        net.train()
        self.assertTrue(net.training)
        net.eval()
        self.assertFalse(net.training)


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class MyLayer(paddle.nn.Layer):
    def __init__(self):
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        super().__init__()
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        self._linear = paddle.nn.Linear(1, 1)
        self._dropout = paddle.nn.Dropout(p=0.5)

    def forward(self, input):
        temp = self._linear(input)
        temp = self._dropout(temp)
        return temp


class MySuperLayer(paddle.nn.Layer):
    def __init__(self):
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        super().__init__()
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        self._mylayer = MyLayer()

    def forward(self, input):
        temp = self._mylayer(input)
        return temp


class TestSubLayerCount(unittest.TestCase):
    def test_sublayer(self):
        with fluid.dygraph.guard():
            mySuperlayer = MySuperLayer()
            self.assertTrue(len(mySuperlayer.sublayers()) == 3)
            self.assertTrue(len(mySuperlayer.sublayers(include_self=True)) == 4)


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