test.py

import numpy as np
import scipy.misc
import os
import sys

from model import DCGAN
from utils import pp
import tensorflow as tf

flags = tf.app.flags
# flags.DEFINE_integer("momentum_decay_steps", 100,
#                      "change after 100 iterations of inner loop of G")
# flags.DEFINE_float("momentum_decay_rate", 1.17, "factor of change in momentum")
flags.DEFINE_integer("epoch_pretrain", 1000, "Epoch to train [25]")
flags.DEFINE_integer("epoch_policy", 2000000, "Epochs for policy gradient")
flags.DEFINE_float("learning_rate_D", 0.0002,
                   "Learning rate of for adam [0.0002]")
flags.DEFINE_float("learning_rate_G", 0.0002,
                   "Learning rate of for adam [0.0002]")
flags.DEFINE_float("beta1D", 0.5, "Momentum term of adam [0.5]")
flags.DEFINE_float("beta1G", 0.5, "Momentum term of adam [0.5]")
flags.DEFINE_integer("decay_step", 5000000, "Decay step of learning rate in epochs")
flags.DEFINE_float("decay_rate", 0.8, "Decay rate of learning rate")
flags.DEFINE_float("eps", 1e-5, "Epsilon")
flags.DEFINE_float("var", 1e-5, "Variance")
flags.DEFINE_float("gpu_frac", 0.95, "Gpu fraction")
flags.DEFINE_integer("no_of_samples", 50,
                     "no of samples for each noise vector Z during policy gradient")
flags.DEFINE_boolean("teacher_forcing", False,
                     "True if teacher forcing is enabled")
flags.DEFINE_boolean("label_to_disc", True,
                     "True if labels are passed to the discriminator")
flags.DEFINE_boolean("conditional", True,
                     "True if want to train conditional GAN")
flags.DEFINE_integer("pre_train_iters", 2000,
                      "Number of iterations to pre-train D")
flags.DEFINE_integer("num_keypoints", 68,
                      "Number of keypoints extracted in the face")
flags.DEFINE_float("lam", 0.1,
                      "lam for impainting")

dataset = "video"
comment ="model_weights"

flags.DEFINE_float(
    "margin", 0.3, "Threshold to judge stopping of D and G nets training")
flags.DEFINE_boolean("margin_restriction", True,
                     "whether to use margin restriction to stop D or G nets")
flags.DEFINE_boolean("policy_train", True,
                     "Whether to use PolicyGan training procedure")

flags.DEFINE_string("dataset", dataset,
                    "The name of dataset [celebA, mnist, lsun]")
if dataset == 'celebA':
  flags.DEFINE_string("data_dir", "data/",
                    "Directory name containing the dataset [data]")
else:
  flags.DEFINE_string("data_dir", "data/" + dataset,
                    "Directory name containing the dataset [data]")
flags.DEFINE_string("checkpoint_dir", "checkpoint/" + dataset + "/" + comment,
                    "Directory name to save the checkpoints [checkpoint]")
flags.DEFINE_string("sample_dir", "samples/" + dataset,
                    "Directory name to save the image samples [samples]")
flags.DEFINE_string("log_dir", "logs/" + dataset + "/" + comment,
                    "Directory name to save the logs [logs]")
flags.DEFINE_boolean("load_chkpt", True, "True for loading saved checkpoint")
flags.DEFINE_boolean("inc_score", False, "True for computing inception score")
flags.DEFINE_boolean("gauss_noise", False, "True for adding noise to disc input")
flags.DEFINE_boolean("flip_label", False, "True for flipping the labels")
flags.DEFINE_boolean("error_conceal", False, "True for flipping the labels")
flags.DEFINE_boolean("siamese_net", False, "True for flipping the labels")
flags.DEFINE_boolean("use_tfrecords", False, "True for running error concealment part")
flags.DEFINE_boolean("vgg_loss", False, "True for taking loss on VGG feature maps")

flags.DEFINE_integer("batch_size", 1, "The size of batch images [64]")
flags.DEFINE_integer("z_dim", 100, "Dimension of latent vector.")
flags.DEFINE_integer("sampleInterval", 500, "Dimension of latent vector.")
flags.DEFINE_integer("saveInterval", 2000, "Dimension of latent vector.")

flags.DEFINE_integer("c_dim", 3, "Number of channels in input image")
flags.DEFINE_boolean("is_grayscale", False, "True for grayscale image")
flags.DEFINE_integer("output_size", 128, "True for grayscale image")


FLAGS = flags.FLAGS

def main(_):
    pp.pprint(flags.FLAGS.__flags)

    if not os.path.exists(FLAGS.checkpoint_dir):
        os.makedirs(FLAGS.checkpoint_dir)
    if not os.path.exists(FLAGS.sample_dir):
        os.makedirs(FLAGS.sample_dir)

    gpu_options = tf.GPUOptions(
        per_process_gpu_memory_fraction=FLAGS.gpu_frac)

    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    
    with tf.Session(config = tf.ConfigProto(gpu_options=gpu_options)) as sess:
        dcgan = DCGAN(sess)
        dcgan.predict()
        # dcgan.predict_video()


if __name__ == '__main__':
    tf.app.run()