Models:
Catherine Stones
cathy-stones/
FewShot_GAN-Unet3D
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[4fa73e]: / tensorflow / experiments / proposed_model / main.py

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import os

import pprint

import tensorflow as tf



from model import model

from test import *





# Define flags

flags = tf.app.flags

flags.DEFINE_integer("epoch", 300, "Number of training epochs (default: 300)")

flags.DEFINE_float("learning_rate_D", 0.0001, "Learning rate of Adam optimizer for Discriminator (default: 0.0001)")

flags.DEFINE_float("learning_rate_G", 0.0001, "Learning rate of Adam optimizer for Generator (default: 0.0001)")

flags.DEFINE_float("learning_rate_E", 0.0001, "Learning rate of Adam optimizer for Encoder (default: 0.0001)")

flags.DEFINE_float("beta1D", 0.5, "Momentum term of Adam optimizer for Discriminator (default: 0.5)")

flags.DEFINE_float("beta1G", 0.5, "Momentum term of Adam optimizer for Generator (default: 0.5)")

flags.DEFINE_float("beta1E", 0.5, "Momentum term of Adam optimizer for Encoder (default: 0.5)")



flags.DEFINE_float("gpu_frac", 0.95, "Gpu fraction")

flags.DEFINE_float("tlw", 0.5, "True loss weight")

flags.DEFINE_float("flw", 0.5, "Fake loss weight")

flags.DEFINE_float("vi_weight", 0.01, "Weight of variational inference loss")



flags.DEFINE_integer("number_train_images", 1, "No. of labeled images for training")

flags.DEFINE_integer("number_train_unlab_images", 12, "No. of unlabeled images for training")

flags.DEFINE_integer("number_test_images", 7, "No. of images for testing")



flags.DEFINE_string("data_directory", "../data/iSEG_preprocessed", "Directory name containing the dataset")

flags.DEFINE_string("checkpoint_dir", "checkpoint/current", "Directory name to save the checkpoints [checkpoint]")

flags.DEFINE_string("best_checkpoint_dir", "checkpoint/best", "Directory name to save the best checkpoints [checkpoint]")

flags.DEFINE_string("results_dir", "results/", "Directory name to save the results [results]")



flags.DEFINE_boolean("load_chkpt", False, "True for loading saved checkpoint")

flags.DEFINE_boolean("training", False, "True for Training ")

flags.DEFINE_boolean("testing", False, "True for Testing ")

flags.DEFINE_boolean("badGAN", False, "True if you want to run badGAN based model ")

flags.DEFINE_boolean("use_weighted_fm", False, "True if you want to run Weighted Feature Matching based model ")



flags.DEFINE_integer("batch_size", 30, "The size of batch images [64]")



flags.DEFINE_integer("num_mod", 2, "Number of modalities of the input 3-D image")

flags.DEFINE_integer("num_classes", 4, "Number of output classes to segment")

flags.DEFINE_integer("noise_dim", 200, "Dimension of noise vector")







FLAGS = flags.FLAGS



def main(_):

  # Create required directories

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

    os.makedirs(FLAGS.checkpoint_dir)



  if not os.path.exists(FLAGS.results_dir):

    os.makedirs(FLAGS.results_dir)



  if not os.path.exists(FLAGS.best_checkpoint_dir):

    os.makedirs(FLAGS.best_checkpoint_dir)





  # To configure the GPU fraction

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



  # Parameters of extracted training and testing patches

  patch_shape=(32,32,32)

  extraction_step=(8,8,8)

  testing_extraction_shape=(8,8,8)



  if FLAGS.training:

    # For training the network

    with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:

      network = model(sess,patch_shape,extraction_step)

      network.build_model()

      network.train()

  if FLAGS.testing:

      # For testing the trained network

      test(patch_shape,testing_extraction_shape)





if __name__ == '__main__':

  tf.app.run()