DCGAN.py

#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from network.DCGAN_network import DCGAN_model
from util import utility

import sys
import six
import os
import numpy as np
import time
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import paddle.fluid as fluid


class GTrainer():
    def __init__(self, input, label, cfg):
        self.program = fluid.default_main_program().clone()
        with fluid.program_guard(self.program):
            model = DCGAN_model()
            self.fake = model.network_G(input, name='G')
            self.fake.persistable = True
            self.infer_program = self.program.clone()
            d_fake = model.network_D(self.fake, name="D")
            fake_labels = fluid.layers.fill_constant_batch_size_like(
                input, dtype='float32', shape=[-1, 1], value=1.0)
            self.g_loss = fluid.layers.reduce_mean(
                fluid.layers.sigmoid_cross_entropy_with_logits(
                    x=d_fake, label=fake_labels))
            self.g_loss.persistable = True

            vars = []
            for var in self.program.list_vars():
                if fluid.io.is_parameter(var) and (var.name.startswith("G")):
                    vars.append(var.name)
            optimizer = fluid.optimizer.Adam(
                learning_rate=cfg.learning_rate, beta1=0.5, name="net_G")
            optimizer.minimize(self.g_loss, parameter_list=vars)


class DTrainer():
    def __init__(self, input, labels, cfg):
        self.program = fluid.default_main_program().clone()
        with fluid.program_guard(self.program):
            model = DCGAN_model()
            d_logit = model.network_D(input, name="D")
            self.d_loss = fluid.layers.reduce_mean(
                fluid.layers.sigmoid_cross_entropy_with_logits(
                    x=d_logit, label=labels))
            self.d_loss.persistable = True
            vars = []
            for var in self.program.list_vars():
                if fluid.io.is_parameter(var) and (var.name.startswith("D")):
                    vars.append(var.name)

            optimizer = fluid.optimizer.Adam(
                learning_rate=cfg.learning_rate, beta1=0.5, name="net_D")
            optimizer.minimize(self.d_loss, parameter_list=vars)


class DCGAN(object):
    def add_special_args(self, parser):
        parser.add_argument(
            '--noise_size', type=int, default=100, help="the noise dimension")

        return parser

    def __init__(self, cfg=None, train_reader=None):
        self.cfg = cfg
        self.train_reader = train_reader

    def build_model(self):
        img = fluid.layers.data(name='img', shape=[784], dtype='float32')
        noise = fluid.layers.data(
            name='noise', shape=[self.cfg.noise_size], dtype='float32')
        label = fluid.layers.data(name='label', shape=[1], dtype='float32')

        g_trainer = GTrainer(noise, label, self.cfg)
        d_trainer = DTrainer(img, label, self.cfg)

        # prepare enviorment
        place = fluid.CUDAPlace(0)
        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())

        const_n = np.random.uniform(
            low=-1.0, high=1.0,
            size=[self.cfg.batch_size, self.cfg.noise_size]).astype('float32')

        if self.cfg.init_model:
            utility.init_checkpoints(self.cfg, exe, g_trainer, "net_G")
            utility.init_checkpoints(self.cfg, exe, d_trainer, "net_D")

### memory optim
        build_strategy = fluid.BuildStrategy()
        build_strategy.enable_inplace = True
        build_strategy.memory_optimize = False

        g_trainer_program = fluid.CompiledProgram(
            g_trainer.program).with_data_parallel(
                loss_name=g_trainer.g_loss.name, build_strategy=build_strategy)
        d_trainer_program = fluid.CompiledProgram(
            d_trainer.program).with_data_parallel(
                loss_name=d_trainer.d_loss.name, build_strategy=build_strategy)

        t_time = 0
        losses = [[], []]
        for epoch_id in range(self.cfg.epoch):
            for batch_id, data in enumerate(self.train_reader()):
                if len(data) != self.cfg.batch_size:
                    continue

                noise_data = np.random.uniform(
                    low=-1.0,
                    high=1.0,
                    size=[self.cfg.batch_size, self.cfg.noise_size]).astype(
                        'float32')
                real_image = np.array(list(map(lambda x: x[0], data))).reshape(
                    [-1, 784]).astype('float32')
                real_label = np.ones(
                    shape=[real_image.shape[0], 1], dtype='float32')
                fake_label = np.zeros(
                    shape=[real_image.shape[0], 1], dtype='float32')
                s_time = time.time()

                generate_image = exe.run(g_trainer.infer_program,
                                         feed={'noise': noise_data},
                                         fetch_list=[g_trainer.fake])

                d_real_loss = exe.run(
                    d_trainer_program,
                    feed={'img': real_image,
                          'label': real_label},
                    fetch_list=[d_trainer.d_loss])[0]
                d_fake_loss = exe.run(
                    d_trainer_program,
                    feed={'img': generate_image,
                          'label': fake_label},
                    fetch_list=[d_trainer.d_loss])[0]
                d_loss = d_real_loss + d_fake_loss
                losses[1].append(d_loss)

                for _ in six.moves.xrange(self.cfg.num_generator_time):
                    g_loss = exe.run(g_trainer_program,
                                     feed={'noise': noise_data},
                                     fetch_list=[g_trainer.g_loss])[0]
                    losses[0].append(g_loss)

                batch_time = time.time() - s_time
                t_time += batch_time

                if batch_id % self.cfg.print_freq == 0:
                    image_path = self.cfg.output + '/images'
                    if not os.path.exists(image_path):
                        os.makedirs(image_path)
                    generate_const_image = exe.run(
                        g_trainer.infer_program,
                        feed={'noise': const_n},
                        fetch_list={g_trainer.fake})[0]

                    generate_image_reshape = np.reshape(generate_const_image, (
                        self.cfg.batch_size, -1))
                    total_images = np.concatenate(
                        [real_image, generate_image_reshape])
                    fig = utility.plot(total_images)
                    print(
                        'Epoch ID={} Batch ID={} D_loss={} G_loss={} Batch_time_cost={:.2f}'.
                        format(epoch_id, batch_id, d_loss[0], g_loss[0],
                               batch_time))
                    plt.title('Epoch ID={}, Batch ID={}'.format(epoch_id,
                                                                batch_id))
                    plt.savefig(
                        '{}/{:04d}_{:04d}.png'.format(image_path, epoch_id,
                                                      batch_id),
                        bbox_inches='tight')
                    plt.close(fig)

            if self.cfg.save_checkpoints:
                utility.checkpoints(epoch_id, self.cfg, exe, g_trainer, "net_G")
                utility.checkpoints(epoch_id, self.cfg, exe, d_trainer, "net_D")