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("train_data_txt", "E:/git/beta-VAE/input/CT/shift/train.txt", "train data txt") flags.DEFINE_string("ground_truth_txt", "E:/git/beta-VAE/input/CT/shift/test.txt", "i1") flags.DEFINE_string( "model1", 'D:/vae_result/n1/z6/beta_1/model/model_{}'.format(997500), "i2") flags.DEFINE_string( "model2", 'D:/vae_result/n1+n2/all/sig/beta_1/model/model_{}'.format(197500), "i3") flags.DEFINE_string("outdir", "D:/vae_result/n1+n2/all/sig/beta_1/spe/", "i4") flags.DEFINE_float("beta", 1, "hyperparameter beta") flags.DEFINE_integer("num_of_generate", 5000, "number of generate data") flags.DEFINE_integer("num_of_test", 600, "number of test data") flags.DEFINE_integer("num_of_train", 1804, "number of train data") flags.DEFINE_integer("batch_size", 1, "batch size") flags.DEFINE_integer("latent_dim", 6, "latent dim") flags.DEFINE_list("image_size", [9 * 9 * 9], "image size") flags.DEFINE_boolean("const_bool", False, "if there is sigmoid in front of last output") FLAGS = flags.FLAGS # check folder if not (os.path.exists(FLAGS.outdir)): os.makedirs(FLAGS.outdir + 'spe1/') os.makedirs(FLAGS.outdir + 'spe2/') os.makedirs(FLAGS.outdir + 'spe_all/') # read list test_data_list = io.load_list(FLAGS.ground_truth_txt) train_data_list = io.load_list(FLAGS.train_data_txt) # test step test_step = FLAGS.num_of_generate // FLAGS.batch_size if FLAGS.num_of_generate % FLAGS.batch_size != 0: test_step += 1 # load train data 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.batch(FLAGS.batch_size) train_iter = train_set.make_one_shot_iterator() train_data = train_iter.get_next() # 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 # 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': False, 'is_constraints': FLAGS.const_bool } VAE_2 = Variational_Autoencoder(**kwargs_2) sess.run(init_op) # testing VAE.restore_model(FLAGS.model1) VAE_2.restore_model(FLAGS.model2) tbar = tqdm(range(FLAGS.num_of_generate), ascii=True) specificity = [] spe_mean = [] generate_data = [] generate_data2 = [] ori = [] latent_space = [] latent_space2 = [] patch_side = 9 for i in range(FLAGS.num_of_train): train_data_batch = sess.run(train_data) z = VAE.plot_latent(train_data_batch) z2 = VAE_2.plot_latent(train_data_batch) z = z.flatten() z2 = z2.flatten() latent_space.append(z) latent_space2.append(z2) mu = np.mean(latent_space, axis=0) var = np.var(latent_space, axis=0) mu2 = np.mean(latent_space2, axis=0) var2 = np.var(latent_space2, axis=0) for i in range(FLAGS.num_of_test): test_data_batch = sess.run(test_data) ori_single = test_data_batch ori_single = ori_single[0, :] ori.append(ori_single) file_spe1 = open(FLAGS.outdir + 'spe1/list.txt', 'w') file_spe2 = open(FLAGS.outdir + 'spe2/list.txt', 'w') file_spe_all = open(FLAGS.outdir + 'spe_all/list.txt', 'w') for j in tbar: sample_z = np.random.normal(mu, var, (1, FLAGS.latent_dim)) sample_z2 = np.random.normal(mu2, var2, (1, 8)) generate_data_single = VAE.generate_sample(sample_z) if FLAGS.const_bool is False: generate_data_single2 = VAE_2.generate_sample(sample_z2) generate_data_single = generate_data_single[0, :] generate_data_single2 = generate_data_single2[0, :] generate_data.append(generate_data_single) generate_data2.append(generate_data_single2) gen = np.reshape(generate_data_single, [patch_side, patch_side, patch_side]) gen2 = np.reshape(generate_data_single2, [patch_side, patch_side, patch_side]) generate_data_single_all = generate_data_single + generate_data_single2 gen_all = gen + gen2 if FLAGS.const_bool is True: generate_data_single_all = VAE_2.generate_sample2( sample_z2, generate_data_single) generate_data_single = generate_data_single[0, :] generate_data_single_all = generate_data_single_all[0, :] generate_data.append(generate_data_single) generate_data2.append(generate_data_single_all) gen = np.reshape(generate_data_single, [patch_side, patch_side, patch_side]) gen_all = np.reshape(generate_data_single_all, [patch_side, patch_side, patch_side]) generate_data_single2 = generate_data_single_all - generate_data_single gen2 = gen_all - gen # EUDT gen_image = sitk.GetImageFromArray(gen) gen_image.SetSpacing([0.885, 0.885, 1]) gen_image.SetOrigin([0, 0, 0]) gen2_image = sitk.GetImageFromArray(gen2) gen2_image.SetSpacing([0.885, 0.885, 1]) gen2_image.SetOrigin([0, 0, 0]) gen_all_image = sitk.GetImageFromArray(gen_all) gen_all_image.SetSpacing([0.885, 0.885, 1]) gen_all_image.SetOrigin([0, 0, 0]) # calculation case_min_specificity = 1.0 for image_index in range(FLAGS.num_of_test): specificity_tmp = utils.L1norm(ori[image_index], generate_data_single_all) if specificity_tmp < case_min_specificity: case_min_specificity = specificity_tmp specificity.append([case_min_specificity]) spe = np.mean(specificity) spe_mean.append(spe) io.write_mhd_and_raw( gen_image, '{}.mhd'.format( os.path.join(FLAGS.outdir, 'spe1', 'spe1_{}'.format(j + 1)))) io.write_mhd_and_raw( gen2_image, '{}.mhd'.format( os.path.join(FLAGS.outdir, 'spe2', 'spe2_{}'.format(j + 1)))) io.write_mhd_and_raw( gen_all_image, '{}.mhd'.format( os.path.join(FLAGS.outdir, 'spe_all', 'spe_all_{}'.format(j + 1)))) file_spe1.write('{}.mhd'.format( os.path.join(FLAGS.outdir, 'spe1', 'spe1_{}'.format(j + 1))) + "\n") file_spe2.write('{}.mhd'.format( os.path.join(FLAGS.outdir, 'spe2', 'spe2_{}'.format(j + 1))) + "\n") file_spe_all.write('{}.mhd'.format( os.path.join(FLAGS.outdir, 'spe_all', 'spe_all_{}'.format( j + 1))) + "\n") file_spe1.close() file_spe2.close() file_spe_all.close() print('specificity = %f' % np.mean(specificity)) np.savetxt(os.path.join(FLAGS.outdir, 'specificity.csv'), specificity, delimiter=",") # spe graph plt.plot(spe_mean) plt.grid() # plt.show() plt.savefig(FLAGS.outdir + "Specificity.png")
def main(): # tf flag flags = tf.flags flags.DEFINE_string( "model", 'G:/experiment_result/liver/VAE/set_4/down_64/RBF/alpha_0.1/4/beta_10/model/model_{}' .format(1350), "model") flags.DEFINE_string( "outdir", 'G:/experiment_result/liver/VAE/set_4/down_64/RBF/alpha_0.1/4/beta_10/random', "outdir") flags.DEFINE_string("gpu_index", "0", "GPU-index") flags.DEFINE_float("beta", 1.0, "hyperparameter beta") flags.DEFINE_integer("batch_size", 1, "batch size") flags.DEFINE_integer("latent_dim", 2, "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) # 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(FLAGS.model) # 2 dim vis for j in range(-2, 3): for i in range(-2, 3): mean = [0.37555057, 0.8882291] var = [32.121346, 24.540127] sample_z = [[i, j]] sample_z = np.asarray(mean) + np.sqrt( np.asarray(var)) * sample_z generate_data = VAE.generate_sample(sample_z) generate_data = generate_data[0, :, :, :, 0] # EUDT generate_data = generate_data.astype(np.float32) eudt_image = sitk.GetImageFromArray(generate_data) eudt_image.SetSpacing([1, 1, 1]) eudt_image.SetOrigin([0, 0, 0]) # label label = np.where(generate_data > 0.5, 0, 1) label = label.astype(np.int16) label_image = sitk.GetImageFromArray(label) label_image.SetSpacing([1, 1, 1]) label_image.SetOrigin([0, 0, 0]) io.write_mhd_and_raw( label_image, '{}.mhd'.format( os.path.join(FLAGS.outdir, '2_dim', str(i) + '_' + str(j))))
def main(): parser = argparse.ArgumentParser( description='py, test_data_txt, ground_truth_txt, outdir') parser.add_argument('--ground_truth_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_generate", 1000, "number of generate 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 # load ground truth ground_truth = io.load_matrix_data(args.ground_truth_txt, 'int32') print(ground_truth.shape) # 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(FLAGS.num_of_generate), ascii=True) specificity = [] for i in tbar: sample_z = np.random.normal(0, 1.0, (1, FLAGS.latent_dim)) generate_data = VAE.generate_sample(sample_z) generate_data = generate_data[0, :, :, 0] # EUDT eudt_image = sitk.GetImageFromArray(generate_data) eudt_image.SetSpacing([1, 1]) eudt_image.SetOrigin([0, 0]) # label label = np.where(generate_data > 0, 0, 1) label_image = sitk.GetImageFromArray(label) label_image.SetSpacing([1, 1]) label_image.SetOrigin([0, 0]) # calculate ji case_max_ji = 0. for image_index in range(ground_truth.shape[0]): ji = utils.jaccard(label, ground_truth[image_index]) if ji > case_max_ji: case_max_ji = ji specificity.append([case_max_ji]) # output image io.write_mhd_and_raw( eudt_image, '{}.mhd'.format(os.path.join(args.outdir, 'EUDT', str(i + 1)))) io.write_mhd_and_raw( label_image, '{}.mhd'.format(os.path.join(args.outdir, 'label', str(i + 1)))) print('specificity = %f' % np.mean(specificity)) # output csv file with open(os.path.join(args.outdir, 'specificity.csv'), 'w', newline='') as file: writer = csv.writer(file) writer.writerows(specificity) writer.writerow(['specificity:', np.mean(specificity)])
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( "val_data_txt", 'F:/data_info/VAE_liver/set_5/TFrecord/fold_1/val.txt', "validation data txt") flags.DEFINE_string( "model_dir", 'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/beta_10/model', "dir of model") flags.DEFINE_string( "outdir", 'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/beta_10', "outdir") flags.DEFINE_string("gpu_index", "0", "GPU-index") flags.DEFINE_float("beta", 1, "hyperparameter beta") flags.DEFINE_integer("num_of_val", 76, "number of validation data") flags.DEFINE_integer("train_iteration", 12001, "number of training iteration") flags.DEFINE_integer("batch_size", 1, "batch size") flags.DEFINE_integer( "num_per_val", 150, "number per each validation(equal step of saving model)") flags.DEFINE_integer("latent_dim", 4, "latent dim") flags.DEFINE_list("image_size", [56, 72, 88, 1], "image size") FLAGS = flags.FLAGS # check folder if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard'))): os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard')) # read list val_data_list = io.load_list(FLAGS.val_data_txt) # number of model num_of_model = FLAGS.train_iteration // FLAGS.num_per_val if FLAGS.train_iteration % FLAGS.num_per_val != 0: num_of_model += 1 if FLAGS.train_iteration % FLAGS.num_per_val == 0: num_of_model -= 1 # val_iter num_val_iter = FLAGS.num_of_val // FLAGS.batch_size if FLAGS.num_of_val % FLAGS.batch_size != 0: num_val_iter += 1 # load validation data val_set = tf.data.TFRecordDataset(val_data_list, compression_type='GZIP') val_set = val_set.map( lambda x: utils._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(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) # print parmeters utils.cal_parameter() # prepare tensorboard writer_val = tf.summary.FileWriter( os.path.join(FLAGS.outdir, 'tensorboard', 'val')) writer_val_rec = tf.summary.FileWriter( os.path.join(FLAGS.outdir, 'tensorboard', 'val_rec')) writer_val_kl = tf.summary.FileWriter( os.path.join(FLAGS.outdir, 'tensorboard', 'val_kl')) value_loss = tf.Variable(0.0) tf.summary.scalar("loss", value_loss) merge_op = tf.summary.merge_all() # initialize sess.run(init_op) # # validation tbar = tqdm(range(num_of_model), ascii=True) for i in tbar: VAE.restore_model(FLAGS.model_dir + '/model_{}'.format(i * FLAGS.num_per_val)) val_loss_all = [] val_rec_all = [] val_kl_all = [] for j in range(num_val_iter): val_data_batch = sess.run(val_data) val_loss, val_rec, val_kl = VAE.validation(val_data_batch) val_loss_all.append(val_loss) val_rec_all.append(val_rec) val_kl_all.append(val_kl) val_loss, val_rec, val_kl = np.mean(val_loss_all), np.mean( val_rec_all), np.mean(val_kl_all) s = "val: {:.4f}, val_rec: {:.4f}, val_kl: {:.4f} ".format( val_loss, val_rec, val_kl) tbar.set_description(s) summary_val = sess.run(merge_op, {value_loss: val_loss}) summary_val_rec = sess.run(merge_op, {value_loss: val_rec}) summary_val_kl = sess.run(merge_op, {value_loss: val_kl}) writer_val.add_summary(summary_val, i * FLAGS.num_per_val) writer_val_rec.add_summary(summary_val_rec, i * FLAGS.num_per_val) writer_val_kl.add_summary(summary_val_kl, i * FLAGS.num_per_val) val_loss_all.clear() val_rec_all.clear() val_kl_all.clear()
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])
def main(): # tf flag flags = tf.flags # flags.DEFINE_string("test_data_txt", "./input/CT/patch/test.txt", "i1") flags.DEFINE_string("test_data_txt", "./input/axis2/noise/test.txt", "i1") # flags.DEFINE_string("model", './output/CT/patch/model2/z24/alpha_1e-5/beta_0.1/fine/model/model_{}'.format(244000), "i2") # flags.DEFINE_string("outdir", "./output/CT/patch/model2/z24/alpha_1e-5/beta_0.1/fine/latent/", "i3") flags.DEFINE_string( "model", './output/axis2/noise/model2/z24/alpha_1e-5/model/model_{}'.format( 9072000), "i2") flags.DEFINE_string("outdir", "./output/axis2/noise/model2/z24/alpha_1e-5/latent/", "i3") flags.DEFINE_float("beta", 1, "hyperparameter beta") # flags.DEFINE_integer("num_of_test", 607, "number of test data") flags.DEFINE_integer("num_of_test", 3000, "number of test data") flags.DEFINE_integer("batch_size", 1, "batch size") flags.DEFINE_integer("latent_dim", 24, "latent dim") flags.DEFINE_list("image_size", [9 * 9 * 9], "image size") FLAGS = flags.FLAGS # check folder if not (os.path.exists(FLAGS.outdir)): os.makedirs(FLAGS.outdir + 'morphing/') # 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) 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': 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 } VAE = Variational_Autoencoder(**kwargs) sess.run(init_op) patch_side = 9 patch_center = int(patch_side / 2) # testing VAE.restore_model(FLAGS.model) tbar = tqdm(range(test_step), ascii=True) preds = [] ori = [] latent_space = [] for k in tbar: test_data_batch = sess.run(test_data) ori_single = test_data_batch z = VAE.plot_latent(ori_single) z = z.flatten() latent_space.append(z) latent_space = np.asarray(latent_space) # print("latent_space =",latent_space.shape) # print(latent_space[0]) # print(latent_space[1]) # print(latent_space[2]) # print(latent_space[3]) # print(latent_space[4]) mu = np.mean(latent_space, axis=0) var = np.var(latent_space, axis=0) sigma = np.sqrt(var) plt.figure(figsize=(8, 6)) fig = plt.scatter(latent_space[:, 0], latent_space[:, 1]) plt.xlabel('dim_1') plt.ylabel('dim_2') plt.title('latent distribution') plt.savefig(FLAGS.outdir + "latent_space.png") if FLAGS.latent_dim == 3: if not (os.path.exists(FLAGS.outdir + "3D/")): os.makedirs(FLAGS.outdir + "3D/") utils.matplotlib_plt(latent_space, FLAGS.outdir) # check folder # fig = plt.figure() # ax = fig.add_subplot(111, projection="3d") # ax.scatter(latent_space[:, 0], latent_space[:, 1], latent_space[:, 2], marker="x") # ax.scatter(latent_space[:5, 0], latent_space[:5, 1], latent_space[:5, 2], marker="o", color='orange') plt.figure(figsize=(8, 6)) plt.scatter(latent_space[:, 0], latent_space[:, 1]) plt.scatter(latent_space[:5, 0], latent_space[:5, 1], color='orange') plt.title('latent distribution') plt.xlabel('dim_1') plt.ylabel('dim_2') plt.savefig(FLAGS.outdir + "back_projection.png") # plt.show() #### display a 2D manifold of digits plt.figure() n = 13 digit_size = patch_side figure1 = np.zeros((digit_size * n, digit_size * n)) figure2 = np.zeros((digit_size * n, digit_size * n)) figure3 = np.zeros((digit_size * n, digit_size * n)) # linearly spaced coordinates corresponding to the 2D plot # of digit classes in the latent space grid_x = np.linspace(-3 * sigma[0], 3 * sigma[0], n) grid_y = np.linspace(-3 * sigma[1], 3 * sigma[1], n)[::-1] for i, yi in enumerate(grid_y): for j, xi in enumerate(grid_x): z_sample = [] if FLAGS.latent_dim == 2: z_sample = np.array([[xi, yi]]) if FLAGS.latent_dim == 3: z_sample = np.array([[xi, yi, 0]]) if FLAGS.latent_dim == 4: z_sample = np.array([[xi, yi, 0, 0]]) if FLAGS.latent_dim == 6: z_sample = np.array([[xi, yi, 0, 0, 0, 0]]) if FLAGS.latent_dim == 8: z_sample = np.array([[xi, yi, 0, 0, 0, 0, 0, 0]]) if FLAGS.latent_dim == 24: z_sample = np.array([[ xi, yi, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]]) if FLAGS.latent_dim == 25: z_sample = np.array([[ xi, yi, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]]) x_decoded = VAE.generate_sample(z_sample) generate_data = x_decoded[0].reshape(digit_size, digit_size, digit_size) digit_axial = generate_data[patch_center, :, :] digit_coronal = generate_data[:, patch_center, :] digit_sagital = generate_data[:, :, patch_center] digit1 = np.reshape(digit_axial, [patch_side, patch_side]) digit2 = np.reshape(digit_coronal, [patch_side, patch_side]) digit3 = np.reshape(digit_sagital, [patch_side, patch_side]) fig2 = plt.imshow(digit_axial, cmap='Greys_r', vmin=0, vmax=1, interpolation='none') plt.savefig(FLAGS.outdir + 'morphing/' + str(i) + '@' + str(j) + 'fig.png') figure1[i * digit_size:(i + 1) * digit_size, j * digit_size:(j + 1) * digit_size] = digit1 figure2[i * digit_size:(i + 1) * digit_size, j * digit_size:(j + 1) * digit_size] = digit2 figure3[i * digit_size:(i + 1) * digit_size, j * digit_size:(j + 1) * digit_size] = digit3 # set graph start_range = digit_size // 2 end_range = n * digit_size + start_range + 1 pixel_range = np.arange(start_range, end_range, digit_size) sample_range_x = np.round(grid_x, 1) sample_range_y = np.round(grid_y, 1) # axial plt.figure(figsize=(10, 10)) plt.xticks(pixel_range, sample_range_x) plt.yticks(pixel_range, sample_range_y) plt.xlabel("z[0]") plt.ylabel("z[1]") plt.imshow(figure1, cmap='Greys_r', vmin=0, vmax=1, interpolation='none') plt.savefig(FLAGS.outdir + "digit_axial.png") # plt.show() # coronal plt.figure(figsize=(10, 10)) plt.xticks(pixel_range, sample_range_x) plt.yticks(pixel_range, sample_range_y) plt.xlabel("z[0]") plt.ylabel("z[1]") plt.imshow(figure2, cmap='Greys_r', vmin=0, vmax=1, interpolation='none') plt.savefig(FLAGS.outdir + "digit_coronal.png") # plt.show() # sagital plt.figure(figsize=(10, 10)) plt.xticks(pixel_range, sample_range_x) plt.yticks(pixel_range, sample_range_y) plt.xlabel("z[0]") plt.ylabel("z[1]") plt.imshow(figure3, cmap='Greys_r', vmin=0, vmax=1, interpolation='none') plt.savefig(FLAGS.outdir + "digit_sagital.png")
def main(): # tf flag flags = tf.flags flags.DEFINE_string("test_data_txt", 'F:/data_info/VAE_liver/set_5/TFrecord/fold_1/train.txt', "test data txt") flags.DEFINE_string("dir", 'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/VAE/axis_4/beta_6', "input dir") flags.DEFINE_integer("model_index", 3450 ,"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", 4681, "number of test data") flags.DEFINE_integer("batch_size", 1, "batch size") flags.DEFINE_integer("latent_dim", 4, "latent dim") flags.DEFINE_list("image_size", [56, 72, 88, 1], "image size") FLAGS = flags.FLAGS # check folder if not (os.path.exists(FLAGS.dir)): os.makedirs(FLAGS.dir) # 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.dir, '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.dir,'model','model_{}'.format(FLAGS.model_index))) tbar = tqdm(range(test_step), ascii=True) latent_space = [] for k in tbar: test_data_batch = sess.run(test_data) ori_single = test_data_batch z = VAE.plot_latent(ori_single) z = z.flatten() if FLAGS.latent_dim == 1: z = [z[0], 0] latent_space.append(z) latent_space = np.asarray(latent_space) plt.figure(figsize=(8, 6)) fig = plt.scatter(latent_space[:, 0], latent_space[:, 1], alpha=0.2) plt.title('latent distribution') plt.xlabel('dim_1') plt.ylabel('dim_2') plt.savefig(os.path.join(FLAGS.dir, 'latent_distribution_{}.PNG'.format(FLAGS.model_index))) # filename = open(os.path.join(FLAGS.outdir, 'latent_distribution.pickle'), 'wb') # pickle.dump(fig, filename) # plt.show() latent_space = np.asarray(latent_space) mean = np.average(latent_space, axis=0) var = np.var(latent_space, axis=0, ddof=1) print(mean) print(var) print(np.cov(latent_space.transpose())) print('skew, kurtosis') print(skew(latent_space, axis=0)) print(kurtosis(latent_space, axis=0)) # output mean and var np.savetxt(os.path.join(FLAGS.dir, 'mean_{}.txt'.format(FLAGS.model_index)), mean) np.savetxt(os.path.join(FLAGS.dir, 'var_{}.txt'.format(FLAGS.model_index)), var)
def main(): # tf flag flags = tf.flags flags.DEFINE_string("ground_truth_txt", 'F:/data_info/VAE_liver/set_5/PCA/alpha_0.1/fold_1/test_label.txt', "ground truth txt") flags.DEFINE_string("indir", 'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/VAE/axis_4/beta_6', "input dir") flags.DEFINE_string("outdir", 'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/VAE/axis_4/beta_6/random', "outdir") flags.DEFINE_integer("model_index", 3450 ,"index of model") flags.DEFINE_string("gpu_index", "0", "GPU-index") flags.DEFINE_float("beta", 1.0, "hyperparameter beta") flags.DEFINE_integer("num_of_generate", 1000, "number of generate data") flags.DEFINE_integer("batch_size", 1, "batch size") flags.DEFINE_integer("latent_dim", 4, "latent dim") flags.DEFINE_list("image_size", [56, 72, 88, 1], "image size") FLAGS = flags.FLAGS np.random.seed(1) # check folder if not (os.path.exists(FLAGS.outdir)): os.makedirs(FLAGS.outdir) # load ground truth ground_truth = io.load_matrix_data(FLAGS.ground_truth_txt, 'int32') print(ground_truth.shape) # 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))) mean = np.loadtxt(os.path.join(FLAGS.indir, 'mean_{}.txt'.format(FLAGS.model_index))) var = np.loadtxt(os.path.join(FLAGS.indir, 'var_{}.txt'.format(FLAGS.model_index))) specificity = [] tbar = tqdm(range(FLAGS.num_of_generate), ascii=True) for i in tbar: sample_z = np.random.normal(0, 1.0, (1, FLAGS.latent_dim)) sample_z = np.asarray(mean) + np.sqrt(np.asarray(var)) * sample_z generate_data = VAE.generate_sample(sample_z) generate_data = generate_data[0, :, :, :, 0] # EUDT eudt_image = sitk.GetImageFromArray(generate_data) eudt_image.SetSpacing([1, 1, 1]) eudt_image.SetOrigin([0, 0, 0]) # label label = np.where(generate_data > 0.5, 0, 1) label = label.astype(np.int8) label_image = sitk.GetImageFromArray(label) label_image.SetSpacing([1, 1, 1]) label_image.SetOrigin([0, 0, 0]) # # calculate ji case_max_ji = 0. for image_index in range(ground_truth.shape[0]): ji = utils.jaccard(label, ground_truth[image_index]) if ji > case_max_ji: case_max_ji = ji specificity.append([case_max_ji]) # # output image # io.write_mhd_and_raw(eudt_image, '{}.mhd'.format(os.path.join(FLAGS.outdir, 'EUDT', str(i+1)))) # io.write_mhd_and_raw(label_image, '{}.mhd'.format(os.path.join(FLAGS.outdir, 'label', str(i + 1)))) print('specificity = %f' % np.mean(specificity)) # # output csv file with open(os.path.join(FLAGS.outdir, 'specificity_{}.csv'.format(FLAGS.model_index)), 'w', newline='') as file: writer = csv.writer(file) writer.writerows(specificity) writer.writerow(['specificity:', np.mean(specificity)])
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 = [] latent_space = [] for k in tbar: test_data_batch = sess.run(test_data) ori_single = test_data_batch z = VAE.plot_latent(ori_single) z = z.flatten() latent_space.append(z) latent_space = np.asarray(latent_space) plt.figure(figsize=(8, 6)) fig = plt.scatter(latent_space[:, 0], latent_space[:, 1]) plt.title('latent distribution') plt.xlabel('dim_1') plt.ylabel('dim_2') plt.show()