test_imperative_gan.py 6.7 KB
Newer Older
X
Xin Pan 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import contextlib
import unittest
import numpy as np
import six
import sys

import paddle
import paddle.fluid as fluid
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
from test_imperative_base import new_program_scope
X
Xin Pan 已提交
26
from paddle.fluid.imperative.base import to_variable
X
Xin Pan 已提交
27 28 29 30 31 32 33 34


class Discriminator(fluid.imperative.Layer):
    def __init__(self):
        super(Discriminator, self).__init__()
        self._fc1 = FC(size=32, act='elu', name="d_fc1")
        self._fc2 = FC(size=1, name="d_fc2")

X
Xin Pan 已提交
35 36 37
    def parameters(self):
        return self._fc1.parameters() + self._fc2.parameters()

X
Xin Pan 已提交
38 39 40 41 42 43 44 45 46 47 48 49
    def forward(self, inputs):
        x = self._fc1(inputs)
        return self._fc2(x)


class Generator(fluid.imperative.Layer):
    def __init__(self):
        super(Generator, self).__init__()
        self._fc1 = FC(size=64, act='elu', name="g_fc1")
        self._fc2 = FC(size=64, act='elu', name="g_fc2")
        self._fc3 = FC(size=1, name="g_fc3")

X
Xin Pan 已提交
50 51 52 53
    def parameters(self):
        return self._fc1.parameters() + self._fc2.parameters(
        ) + self._fc3.parameters()

X
Xin Pan 已提交
54 55 56 57 58 59 60 61 62 63 64 65 66
    def forward(self, inputs):
        x = self._fc1(inputs)
        x = self._fc2(x)
        return self._fc3(x)


class TestImperativeMnist(unittest.TestCase):
    def test_mnist_cpu_float32(self):
        seed = 90

        startup = fluid.Program()
        startup.random_seed = seed
        discriminate_p = fluid.Program()
X
Xin Pan 已提交
67 68 69 70
        generate_p = fluid.Program()
        discriminate_p.random_seed = seed
        generate_p.random_seed = seed

X
Xin Pan 已提交
71 72 73 74 75 76 77 78 79 80 81 82 83 84
        scope = fluid.core.Scope()
        with new_program_scope(
                main=discriminate_p, startup=startup, scope=scope):
            discriminator = Discriminator()
            generator = Generator()

            img = fluid.layers.data(
                name="img", shape=[2, 1], append_batch_size=False)
            noise = fluid.layers.data(
                name="noise", shape=[2, 2], append_batch_size=False)

            d_real = discriminator(img)
            d_loss_real = fluid.layers.reduce_mean(
                fluid.layers.sigmoid_cross_entropy_with_logits(
X
Xin Pan 已提交
85 86 87
                    x=d_real,
                    label=fluid.layers.fill_constant(
                        shape=[2, 1], dtype='float32', value=1.0)))
X
Xin Pan 已提交
88 89 90 91

            d_fake = discriminator(generator(noise))
            d_loss_fake = fluid.layers.reduce_mean(
                fluid.layers.sigmoid_cross_entropy_with_logits(
X
Xin Pan 已提交
92 93 94
                    x=d_fake,
                    label=fluid.layers.fill_constant(
                        shape=[2, 1], dtype='float32', value=0.0)))
X
Xin Pan 已提交
95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110

            d_loss = d_loss_real + d_loss_fake

            sgd = SGDOptimizer(learning_rate=1e-3)
            sgd.minimize(d_loss)

        with new_program_scope(main=generate_p, startup=startup, scope=scope):
            discriminator = Discriminator()
            generator = Generator()

            noise = fluid.layers.data(
                name="noise", shape=[2, 2], append_batch_size=False)

            d_fake = discriminator(generator(noise))
            g_loss = fluid.layers.reduce_mean(
                fluid.layers.sigmoid_cross_entropy_with_logits(
X
Xin Pan 已提交
111 112 113
                    x=d_fake,
                    label=fluid.layers.fill_constant(
                        shape=[2, 1], dtype='float32', value=1.0)))
X
Xin Pan 已提交
114 115 116 117

            sgd = SGDOptimizer(learning_rate=1e-3)
            sgd.minimize(g_loss)

X
Xin Pan 已提交
118 119
        exe = fluid.Executor(fluid.CPUPlace())
        static_params = dict()
X
Xin Pan 已提交
120 121 122 123
        with fluid.scope_guard(scope):
            img = np.ones([2, 1], np.float32)
            noise = np.ones([2, 2], np.float32)
            exe.run(startup)
X
Xin Pan 已提交
124 125 126 127 128 129 130 131 132
            static_d_loss = exe.run(discriminate_p,
                                    feed={'img': img,
                                          'noise': noise},
                                    fetch_list=[d_loss])[0]
            static_g_loss = exe.run(generate_p,
                                    feed={'noise': noise},
                                    fetch_list=[g_loss])[0]

            # generate_p contains all parameters needed.
X
Xin Pan 已提交
133
            for param in generate_p.global_block().all_parameters():
X
Xin Pan 已提交
134 135 136 137
                static_params[param.name] = np.array(
                    scope.find_var(param.name).get_tensor())

        dy_params = dict()
M
minqiyang 已提交
138
        with fluid.imperative.guard(device=None):
X
Xin Pan 已提交
139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed

            discriminator = Discriminator()
            generator = Generator()
            sgd = SGDOptimizer(learning_rate=1e-3)

            d_real = discriminator(to_variable(np.ones([2, 1], np.float32)))
            d_loss_real = fluid.layers.reduce_mean(
                fluid.layers.sigmoid_cross_entropy_with_logits(
                    x=d_real, label=to_variable(np.ones([2, 1], np.float32))))

            d_fake = discriminator(
                generator(to_variable(np.ones([2, 2], np.float32))))
            d_loss_fake = fluid.layers.reduce_mean(
                fluid.layers.sigmoid_cross_entropy_with_logits(
                    x=d_fake, label=to_variable(np.zeros([2, 1], np.float32))))

            d_loss = d_loss_real + d_loss_fake
            d_loss._backward()
            sgd.minimize(d_loss)
X
Xin Pan 已提交
160 161
            discriminator.clear_gradients()
            generator.clear_gradients()
X
Xin Pan 已提交
162

X
Xin Pan 已提交
163 164 165 166 167 168 169 170 171 172 173
            d_fake = discriminator(
                generator(to_variable(np.ones([2, 2], np.float32))))
            g_loss = fluid.layers.reduce_mean(
                fluid.layers.sigmoid_cross_entropy_with_logits(
                    x=d_fake, label=to_variable(np.ones([2, 1], np.float32))))
            g_loss._backward()
            sgd.minimize(g_loss)
            for p in discriminator.parameters():
                dy_params[p.name] = p._numpy()
            for p in generator.parameters():
                dy_params[p.name] = p._numpy()
X
Xin Pan 已提交
174 175 176 177 178 179 180 181

            dy_g_loss = g_loss._numpy()
            dy_d_loss = d_loss._numpy()

        self.assertEqual(dy_g_loss, static_g_loss)
        self.assertEqual(dy_d_loss, static_d_loss)
        for k, v in six.iteritems(dy_params):
            self.assertTrue(np.allclose(v, static_params[k]))
X
Xin Pan 已提交
182 183 184 185


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