def main(): parser = argparse.ArgumentParser( description='py, test_data_txt, model, outdir') parser.add_argument('--test_data_txt', '-i1', default='') parser.add_argument('--model', '-i2', default='./model_{}'.format(50000)) parser.add_argument('--outdir', '-i3', default='') args = parser.parse_args() # check folder if not (os.path.exists(args.outdir)): os.makedirs(args.outdir) # tf flag flags = tf.flags flags.DEFINE_float("beta", 0.1, "hyperparameter beta") flags.DEFINE_integer("num_of_test", 100, "number of test data") flags.DEFINE_integer("batch_size", 1, "batch size") flags.DEFINE_integer("latent_dim", 2, "latent dim") flags.DEFINE_list("image_size", [512, 512, 1], "image size") FLAGS = flags.FLAGS # read list test_data_list = io.load_list(args.test_data_txt) # test step test_step = FLAGS.num_of_test // FLAGS.batch_size if FLAGS.num_of_test % FLAGS.batch_size != 0: test_step += 1 # load test data test_set = tf.data.TFRecordDataset(test_data_list) test_set = test_set.map( lambda x: _parse_function(x, image_size=FLAGS.image_size), num_parallel_calls=os.cpu_count()) test_set = test_set.batch(FLAGS.batch_size) test_iter = test_set.make_one_shot_iterator() test_data = test_iter.get_next() # initializer init_op = tf.group(tf.initializers.global_variables(), tf.initializers.local_variables()) with tf.Session(config=utils.config) as sess: # set network kwargs = { 'sess': sess, 'outdir': args.outdir, 'beta': FLAGS.beta, 'latent_dim': FLAGS.latent_dim, 'batch_size': FLAGS.batch_size, 'image_size': FLAGS.image_size, 'encoder': cnn_encoder, 'decoder': cnn_decoder } VAE = Variational_Autoencoder(**kwargs) sess.run(init_op) # testing VAE.restore_model(args.model) tbar = tqdm(range(test_step), ascii=True) preds = [] ori = [] for k in tbar: test_data_batch = sess.run(test_data) ori_single = test_data_batch preds_single = VAE.reconstruction_image(ori_single) preds_single = preds_single[0, :, :, 0] ori_single = ori_single[0, :, :, 0] preds.append(preds_single) ori.append(ori_single) # # label ji = [] for j in range(len(preds)): # EUDT eudt_image = sitk.GetImageFromArray(preds[j]) eudt_image.SetSpacing([1, 1]) eudt_image.SetOrigin([0, 0]) label = np.where(preds[j] > 0, 0, 1) label_image = sitk.GetImageFromArray(label) label_image.SetSpacing([1, 1]) label_image.SetOrigin([0, 0]) ori_label = np.where(ori[j] > 0, 0, 1) ori_label_image = sitk.GetImageFromArray(ori_label) ori_label_image.SetSpacing([1, 1]) ori_label_image.SetOrigin([0, 0]) # # calculate ji ji.append(utils.jaccard(label, ori_label)) # output image io.write_mhd_and_raw( eudt_image, '{}.mhd'.format( os.path.join(args.outdir, 'EUDT', 'recon_{}'.format(j)))) io.write_mhd_and_raw( label_image, '{}.mhd'.format( os.path.join(args.outdir, 'label', 'recon_{}'.format(j)))) generalization = np.mean(ji) print('generalization = %f' % generalization) # output csv file with open(os.path.join(args.outdir, 'generalization.csv'), 'w', newline='') as file: writer = csv.writer(file) writer.writerows(ji) writer.writerow(['generalization= ', generalization])
def main(): # tf flag flags = tf.flags flags.DEFINE_string("train_data_txt", "./train.txt", "train data txt") flags.DEFINE_string("val_data_txt", "./val.txt", "validation data txt") flags.DEFINE_string("outdir", "./output/", "outdir") flags.DEFINE_float("beta", 1, "hyperparameter beta") flags.DEFINE_integer("num_of_val", 600, "number of validation data") flags.DEFINE_integer("batch_size", 30, "batch size") flags.DEFINE_integer("num_iteration", 500001, "number of iteration") flags.DEFINE_integer("save_loss_step", 200, "step of save loss") flags.DEFINE_integer("save_model_step", 500, "step of save model and validation") flags.DEFINE_integer("shuffle_buffer_size", 1000, "buffer size of shuffle") flags.DEFINE_integer("latent_dim", 6, "latent dim") flags.DEFINE_list("image_size", [9 * 9 * 9], "image size") flags.DEFINE_string("model", './model/model_{}', "pre training model1") flags.DEFINE_string("model2", './model/model_{}', "pre training model2") flags.DEFINE_boolean("is_n1_opt", True, "n1_opt") FLAGS = flags.FLAGS # check folder if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard', 'train'))): os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard', 'train')) if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard', 'val'))): os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard', 'val')) if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard', 'rec'))): os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard', 'rec')) if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard', 'kl'))): os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard', 'kl')) if not (os.path.exists(os.path.join(FLAGS.outdir, 'model'))): os.makedirs(os.path.join(FLAGS.outdir, 'model')) # read list train_data_list = io.load_list(FLAGS.train_data_txt) val_data_list = io.load_list(FLAGS.val_data_txt) # shuffle list random.shuffle(train_data_list) # val step val_step = FLAGS.num_of_val // FLAGS.batch_size if FLAGS.num_of_val % FLAGS.batch_size != 0: val_step += 1 # load train data and validation data train_set = tf.data.Dataset.list_files(train_data_list) train_set = train_set.apply( tf.contrib.data.parallel_interleave(tf.data.TFRecordDataset, cycle_length=6)) # train_set = tf.data.TFRecordDataset(train_data_list) train_set = train_set.map( lambda x: _parse_function(x, image_size=FLAGS.image_size), num_parallel_calls=os.cpu_count()) train_set = train_set.shuffle(buffer_size=FLAGS.shuffle_buffer_size) train_set = train_set.repeat() train_set = train_set.batch(FLAGS.batch_size) train_iter = train_set.make_one_shot_iterator() train_data = train_iter.get_next() val_set = tf.data.Dataset.list_files(val_data_list) val_set = val_set.apply( tf.contrib.data.parallel_interleave(tf.data.TFRecordDataset, cycle_length=os.cpu_count())) # val_set = tf.data.TFRecordDataset(val_data_list) val_set = val_set.map( lambda x: _parse_function(x, image_size=FLAGS.image_size), num_parallel_calls=os.cpu_count()) val_set = val_set.repeat() val_set = val_set.batch(FLAGS.batch_size) val_iter = val_set.make_one_shot_iterator() val_data = val_iter.get_next() # initializer init_op = tf.group(tf.initializers.global_variables(), tf.initializers.local_variables()) with tf.Session(config=utils.config) as sess: # with tf.Session() as sess: # set network kwargs = { 'sess': sess, 'outdir': FLAGS.outdir, 'beta': FLAGS.beta, 'latent_dim': FLAGS.latent_dim, 'batch_size': FLAGS.batch_size, 'image_size': FLAGS.image_size, 'encoder': encoder_mlp, 'decoder': decoder_mlp, 'is_res': False } VAE = Variational_Autoencoder(**kwargs) kwargs_2 = { 'sess': sess, 'outdir': FLAGS.outdir, 'beta': FLAGS.beta, 'latent_dim': 8, 'batch_size': FLAGS.batch_size, 'image_size': FLAGS.image_size, 'encoder': encoder_mlp2, 'decoder': decoder_mlp_tanh, 'is_res': True, 'is_constraints': False, # 'keep_prob': 0.5 } VAE_2 = Variational_Autoencoder(**kwargs_2) # print parmeters utils.cal_parameter() # prepare tensorboard writer_train = tf.summary.FileWriter( os.path.join(FLAGS.outdir, 'tensorboard', 'train'), sess.graph) writer_val = tf.summary.FileWriter( os.path.join(FLAGS.outdir, 'tensorboard', 'val')) writer_rec = tf.summary.FileWriter( os.path.join(FLAGS.outdir, 'tensorboard', 'rec')) writer_kl = tf.summary.FileWriter( os.path.join(FLAGS.outdir, 'tensorboard', 'kl')) value_loss = tf.Variable(0.0) tf.summary.scalar("loss", value_loss) merge_op = tf.summary.merge_all() # initialize sess.run(init_op) # use pre trained model # ckpt_state = tf.train.get_checkpoint_state(FLAGS.model) # # if ckpt_state: # restore_model = ckpt_state.model_checkpoint_path # # VAE.restore_model(FLAGS.model+'model_{}'.format(FLAGS.itr)) VAE.restore_model(FLAGS.model) if FLAGS.is_n1_opt == True: VAE_2.restore_model(FLAGS.model2) # training tbar = tqdm(range(FLAGS.num_iteration), ascii=True) for i in tbar: train_data_batch = sess.run(train_data) if FLAGS.is_n1_opt == True: VAE.update(train_data_batch) output1 = VAE.reconstruction_image(train_data_batch) train_loss, rec_loss, kl_loss = VAE_2.update2( train_data_batch, output1) if i % FLAGS.save_loss_step is 0: s = "Loss: {:.4f}, rec_loss: {:.4f}, kl_loss: {:.4f}".format( train_loss, rec_loss, kl_loss) tbar.set_description(s) summary_train_loss = sess.run(merge_op, {value_loss: train_loss}) writer_train.add_summary(summary_train_loss, i) summary_rec_loss = sess.run(merge_op, {value_loss: rec_loss}) summary_kl_loss = sess.run(merge_op, {value_loss: kl_loss}) writer_rec.add_summary(summary_rec_loss, i) writer_kl.add_summary(summary_kl_loss, i) if i % FLAGS.save_model_step is 0: # save model VAE.save_model(i) VAE_2.save_model2(i) # validation val_loss = 0. for j in range(val_step): val_data_batch = sess.run(val_data) val_data_batch_output1 = VAE.reconstruction_image( val_data_batch) val_loss += VAE_2.validation2(val_data_batch, val_data_batch_output1) val_loss /= val_step summary_val = sess.run(merge_op, {value_loss: val_loss}) writer_val.add_summary(summary_val, i)
def main(): # tf flag flags = tf.flags flags.DEFINE_string( "test_data_txt", 'F:/data_info/VAE_liver/set_5/TFrecord/fold_1/test.txt', "test data txt") flags.DEFINE_string( "indir", 'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/VAE/axis_5/beta_7', "input dir") flags.DEFINE_string( "outdir", 'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/VAE/axis_5/beta_7/rec', "outdir") flags.DEFINE_integer("model_index", 3300, "index of model") flags.DEFINE_string("gpu_index", "0", "GPU-index") flags.DEFINE_float("beta", 1.0, "hyperparameter beta") flags.DEFINE_integer("num_of_test", 75, "number of test data") flags.DEFINE_integer("batch_size", 1, "batch size") flags.DEFINE_integer("latent_dim", 5, "latent dim") flags.DEFINE_list("image_size", [56, 72, 88, 1], "image size") FLAGS = flags.FLAGS # check folder if not (os.path.exists(FLAGS.outdir)): os.makedirs(FLAGS.outdir) # read list test_data_list = io.load_list(FLAGS.test_data_txt) # test step test_step = FLAGS.num_of_test // FLAGS.batch_size if FLAGS.num_of_test % FLAGS.batch_size != 0: test_step += 1 # load test data test_set = tf.data.TFRecordDataset(test_data_list, compression_type='GZIP') test_set = test_set.map( lambda x: utils._parse_function(x, image_size=FLAGS.image_size), num_parallel_calls=os.cpu_count()) test_set = test_set.batch(FLAGS.batch_size) test_iter = test_set.make_one_shot_iterator() test_data = test_iter.get_next() # initializer init_op = tf.group(tf.initializers.global_variables(), tf.initializers.local_variables()) with tf.Session(config=utils.config(index=FLAGS.gpu_index)) as sess: # set network kwargs = { 'sess': sess, 'outdir': FLAGS.outdir, 'beta': FLAGS.beta, 'latent_dim': FLAGS.latent_dim, 'batch_size': FLAGS.batch_size, 'image_size': FLAGS.image_size, 'encoder': encoder_resblock_bn, 'decoder': decoder_resblock_bn, 'downsampling': down_sampling, 'upsampling': up_sampling, 'is_training': False, 'is_down': False } VAE = Variational_Autoencoder(**kwargs) sess.run(init_op) # testing VAE.restore_model( os.path.join(FLAGS.indir, 'model', 'model_{}'.format(FLAGS.model_index))) tbar = tqdm(range(test_step), ascii=True) preds = [] ori = [] ji = [] for k in tbar: test_data_batch = sess.run(test_data) ori_single = test_data_batch preds_single = VAE.reconstruction_image(ori_single) preds_single = preds_single[0, :, :, :, 0] ori_single = ori_single[0, :, :, :, 0] preds.append(preds_single) ori.append(ori_single) # # label ji = [] for j in range(len(preds)): # EUDT eudt_image = sitk.GetImageFromArray(preds[j]) eudt_image.SetSpacing([1, 1, 1]) eudt_image.SetOrigin([0, 0, 0]) label = np.where(preds[j] > 0.5, 0, 1) # label = np.where(preds[j] > 0.5, 1, 0.5) label = label.astype(np.int16) label_image = sitk.GetImageFromArray(label) label_image.SetSpacing([1, 1, 1]) label_image.SetOrigin([0, 0, 0]) ori_label = np.where(ori[j] > 0.5, 0, 1) ori_label_image = sitk.GetImageFromArray(ori_label) ori_label_image.SetSpacing([1, 1, 1]) ori_label_image.SetOrigin([0, 0, 0]) # # calculate ji ji.append([utils.jaccard(label, ori_label)]) # output image io.write_mhd_and_raw( label_image, '{}.mhd'.format( os.path.join(FLAGS.outdir, 'label', 'recon_{}'.format(j)))) generalization = np.mean(ji) print('generalization = %f' % generalization) # # output csv file with open(os.path.join( FLAGS.outdir, 'generalization_{}.csv'.format(FLAGS.model_index)), 'w', newline='') as file: writer = csv.writer(file) writer.writerows(ji) writer.writerow(['generalization= ', generalization])