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How to use test_volume_list method in tempest

Best Python code snippet using tempest_python

unet3d_train.py

Source:unet3d_train.py

1import numpy as np2import scipy.io as sio3import os, re, random, time4import tensorflow as tf5from unet3d import unet3d6def tversky_loss(labels, logits, output_channels, alpha=0.5, beta=0.5):7    prob = tf.nn.softmax(logits=logits)8    p0 = prob9    p1 = 1 - prob10    g0 = labels11    g1 = 1 - labels12    num = tf.reduce_sum(p0 * g0, axis=(0, 1, 2, 3))13    den = num + alpha * tf.reduce_sum(p0 * g1, axis=(0, 1, 2, 3)) + beta * tf.reduce_sum(p1 * g0, axis=(0, 1, 2, 3))14    t = tf.reduce_sum(num / den)15    return output_channels - t16if __name__ == '__main__':17    dataset = 'VISCERAL_multiple'  # 'VISCERAL_multiple', 'SLIVER07_liver'18    os.environ['CUDA_VISIBLE_DEVICES'] = '0'19    cross_validation = 120    restore = False21    vol_size = [8, 512, 512]22    if dataset == 'VISCERAL_multiple':23        data_train_path = '/data2/PEICHAO_LI/data/VISCERAL/multiple_2fold_%d_%d_%d/train_%d/' % (24            vol_size[2], vol_size[1], vol_size[0], cross_validation)25        data_test_path = '/data2/PEICHAO_LI/data/VISCERAL/multiple_2fold_%d_%d_%d/test_%d/' % (26            vol_size[2], vol_size[1], vol_size[0], cross_validation)27    elif dataset == 'SLIVER07_liver':  # TODO28        data_train_path = '/data2/XIAOYUN_ZHOU/SLiver07/liver_2fold/liver_train_%d/' % (cross_validation)29        data_test_path = '/data2/XIAOYUN_ZHOU/SLiver07/liver_2fold/liver_test_%d/' % (cross_validation)30    train_volume_list = sorted(31        [vol_name for vol_name in os.listdir(data_train_path) if vol_name.endswith('_volume.mat')])32    train_label_list = sorted(33        [label_name for label_name in os.listdir(data_train_path) if label_name.endswith('_label.mat')])34    test_volume_list = sorted([vol_name for vol_name in os.listdir(data_test_path) if vol_name.endswith('_volume.mat')])35    test_label_list = sorted(36        [label_name for label_name in os.listdir(data_test_path) if label_name.endswith('_label.mat')])37    train_num = len(train_volume_list)38    test_num = len(test_volume_list)39    batch_size = 140    epoch = 641    feature_channels = 1642    output_channels = 843    downsampling = 344    loss_type = 'cross_entropy'  # 'cross_entropy' or 'dice'45    step_show = 10046    config = tf.ConfigProto()47    config.gpu_options.allow_growth = True48    vol_in = tf.placeholder(dtype='float32', shape=[None, vol_size[0], vol_size[1], vol_size[2], 1])49    labels = tf.placeholder(dtype='float32', shape=[None, vol_size[0], vol_size[1], vol_size[2], output_channels])50    prediction, logits = unet3d(vol_in, labels, feature_channels=feature_channels, output_channels=output_channels,51                                downsampling=downsampling)52    if loss_type.upper() in ['DICE']:53        loss = tversky_loss(labels=tf.stop_gradient(labels), logits=logits, output_channels=output_channels)54    else:55        loss = tf.reduce_mean(56            tf.nn.softmax_cross_entropy_with_logits_v2(labels=tf.stop_gradient(labels), logits=logits))57    tf.summary.scalar('Loss', loss)58    # tf.summary.scalar('IoU', prediction['IoU'])59    for lr_train in [0.1, 0.05, 0.01, 0.005]:60        boundary = [train_num // batch_size, train_num * 2 // batch_size]61        lr_values = [lr_train, lr_train / 2, lr_train / 10]62        save_path = '/media/xz6214/4276F10376F0F90D/trained_model/%s/%d_%d_%d/unet3d/model_lr_%f_crossval_%s/' % (63            dataset, vol_size[2], vol_size[1], vol_size[0], lr_train, cross_validation)64        if not restore:65            os.system('rm -rf %s' % save_path)66        if not os.path.exists(save_path):67            os.makedirs(save_path)68        logfile = open(save_path + 'training_log.txt', 'w+')69        logfile.write('********************* LR = %f *********************\n' % lr_train)70        global_step = tf.Variable(0, trainable=False)71        lr = tf.train.piecewise_constant(x=global_step, boundaries=boundary, values=lr_values)72        optimizer = tf.train.MomentumOptimizer(learning_rate=lr, momentum=0.9)73        train = optimizer.minimize(loss, global_step=global_step)74        tf.summary.scalar('Learning Rate', lr)75        merged_summary_op = tf.summary.merge_all()76        init = tf.global_variables_initializer()77        saver = tf.train.Saver(max_to_keep=epoch)78        with tf.Session(config=config) as sess:79            tf.train.write_graph(graph_or_graph_def=sess.graph_def, logdir=save_path, name='Model')80            writer = tf.summary.FileWriter(save_path, sess.graph)81            sess.run(init)82            total_loss = 083            total_parameters = 084            for variable in tf.trainable_variables():85                shape = variable.get_shape()86                variable_parameters = 187                for dim in shape:88                    variable_parameters *= dim.value89                total_parameters += variable_parameters90            print(total_parameters)91            logfile.write('total parameters: %d\n' % total_parameters)92            if restore:93                ckpt = tf.train.get_checkpoint_state(save_path)94                if ckpt and ckpt.model_checkpoint_path:95                    saver.restore(sess, ckpt.model_checkpoint_path)96            start_time = time.time()97            for step in range((train_num // batch_size) * epoch):98                image_id_list = random.sample(range(train_num), batch_size)99                image_batch = []100                label_batch = []101                for image_id in image_id_list:102                    image_id = np.random.choice(train_num) + 1103                    image_np = sio.loadmat(data_train_path + "%s_volume.mat" % (image_id))['volume']104                    image_np = np.reshape(image_np, (1, image_np.shape[0], image_np.shape[1], image_np.shape[2], 1))105                    image_batch.append(image_np)106                    label_np = sio.loadmat(data_train_path + "%s_label.mat" % (image_id))['label']107                    label_np = np.reshape(label_np,108                                          (1, label_np.shape[0], label_np.shape[1], label_np.shape[2],109                                           label_np.shape[3]))110                    label_batch.append(label_np)111                image_batch = np.concatenate(image_batch, axis=0)112                label_batch = np.concatenate(label_batch, axis=0)113                # _, loss_value, summary, global_step_show, pred, lr_show = sess.run(114                #     [train, loss, merged_summary_op, global_step, prediction, lr],115                #     feed_dict={vol_in: image_batch, labels: label_batch})116                _, loss_value, summary, global_step_show, lr_show = sess.run(117                    [train, loss, merged_summary_op, global_step, lr],118                    feed_dict={vol_in: image_batch, labels: label_batch})119                if (step + 1) % step_show == 0:120                    total_loss += loss_value121                    total_loss = total_loss / step_show122                    # iou = pred['IoU']123                    print('Step: %d, Learning rate: %f, Loss: %f, Running time: %f' %124                          (global_step_show, lr_show, total_loss, time.time() - start_time))125                    total_loss = 0126                    writer.add_summary(summary, global_step=global_step_show)127                    writer.flush()128                    start_time = time.time()129                else:130                    total_loss += loss_value131                # Testing after each epoch132                if (step + 1) % (train_num // batch_size) == 0:133                    saved_path = saver.save(sess, save_path + 'Model', global_step=global_step)134                    print('-------------------------------------------------')135                    logfile.write('-------------------------------------------------\n')136                    i_patient = np.zeros([10, output_channels - 1])137                    u_patient = np.zeros([10, output_channels - 1])138                    for i in range(test_num):139                        volume_path = data_test_path + test_volume_list[i]140                        label_path = data_test_path + test_label_list[i]141                        assert volume_path[:volume_path.find('_volume.mat')] == label_path[142                                                                                :label_path.find('_label.mat')]143                        volume_name = volume_path[:volume_path.find('_volume.mat')]144                        patient_id = volume_name[volume_name.find('test_patient') + 12:]145                        patient_id = int(patient_id[:patient_id.find('_')])146                        volume_np = sio.loadmat(volume_path)['volume']147                        volume_np = np.reshape(volume_np,148                                               (1, volume_np.shape[0], volume_np.shape[1], volume_np.shape[2], 1))149                        label_np = sio.loadmat(label_path)['label']150                        label_np = np.reshape(label_np,151                                              (1, label_np.shape[0], label_np.shape[1], label_np.shape[2],152                                               label_np.shape[3]))153                        [pred, ] = sess.run([prediction], feed_dict={vol_in: volume_np, labels: label_np})154                        i_value = pred['And']155                        u_value = pred['Or']156                        # Calculate IoU for each patient157                        i_patient[patient_id - 1, :] += i_value158                        u_patient[patient_id - 1, :] += u_value159                    iou_all_patients = np.divide(i_patient, u_patient)160                    for patient_id in range(10):161                        msg = 'epoch %d, Testing IoU of each organ for patient %d: %s\n' % (162                            (step + 1) // (train_num // batch_size), patient_id,163                            ','.join(['%.3f' % n for n in iou_all_patients[patient_id, :]]))164                        print(msg)165                        logfile.write(msg)166                    msg = 'epoch %d, Current loss: %f, Average testing IoU of each organ for all %d patients: %s\n' % (167                        (step + 1) // (train_num // batch_size), loss_value, len(iou_all_patients),168                        ','.join(['%.3f' % n for n in np.mean(iou_all_patients, axis=0)]))169                    print(msg)170                    logfile.write(msg)171                    print('-------------------------------------------------')172                    logfile.write('-------------------------------------------------\n\n')173                    logfile.flush()174                    start_time = time.time()175                # Save prediction after each epoch176                if (step + 1) % (train_num // batch_size) == 0:177                    for i in range(len(test_volume_list)):178                        volume_path = data_test_path + test_volume_list[i]179                        label_path = data_test_path + test_label_list[i]180                        assert volume_path[:volume_path.find('_volume.mat')] == label_path[181                                                                                :label_path.find('_label.mat')]182                        volume_name = volume_path[:volume_path.find('_volume.mat')]183                        volume_name = volume_name[volume_name.rfind('/') + 1:]184                        indices = re.search('test_patient[0-9]+', volume_name).span()185                        patient_name = volume_name[indices[0]:indices[1]]186                        volume_np = sio.loadmat(volume_path)['volume']187                        volume_np = np.reshape(volume_np,188                                               (1, volume_np.shape[0], volume_np.shape[1], volume_np.shape[2], 1))189                        label_np = sio.loadmat(label_path)['label']190                        label_np = np.reshape(label_np,191                                              (1, label_np.shape[0], label_np.shape[1], label_np.shape[2],192                                               label_np.shape[3]))193                        [pred_dict, ] = sess.run([prediction], feed_dict={vol_in: volume_np, labels: label_np})194                        prediction_path = save_path + 'prediction_' + str(195                            (step + 1) // (train_num // batch_size)) + '/' + patient_name + '/'196                        if not os.path.exists(prediction_path):197                            os.makedirs(prediction_path)198                        # sio.savemat(prediction_path + volume_name + '_prediction.mat',199                        #             {'probabilities': pred_dict['probabilities']})200                        # sio.savemat(prediction_path + volume_name + '_label.mat', {'label': label_np})201                    print('prediction complete.')...

vnet_with_2dunet_train.py

Source:vnet_with_2dunet_train.py

1import numpy as np2import scipy.io as sio3import os, re, random, time4import tensorflow as tf5from vnet_with_2dunet import vnet6dataset = 'VISCERAL_multiple'  # 'VISCERAL_multiple', 'SLIVER07_liver'7os.environ['CUDA_VISIBLE_DEVICES'] = '2'8cross_validation = 19restore = False10vol_size = [8, 512, 512]11if dataset == 'VISCERAL_multiple':12    data_train_path = '/media/xz6214/4276F10376F0F90D/datasets/VISCERAL/multiple_2fold_%d_%d_%d/train_%d/' % (13        vol_size[2], vol_size[1], vol_size[0], cross_validation)14    data_test_path = '/media/xz6214/4276F10376F0F90D/datasets/VISCERAL/multiple_2fold_%d_%d_%d/test_%d/' % (15        vol_size[2], vol_size[1], vol_size[0], cross_validation)16elif dataset == 'SLIVER07_liver':  # TODO17    data_train_path = '/data2/XIAOYUN_ZHOU/SLiver07/liver_2fold/liver_train_%d/' % (cross_validation)18    data_test_path = '/data2/XIAOYUN_ZHOU/SLiver07/liver_2fold/liver_test_%d/' % (cross_validation)19train_volume_list = sorted([vol_name for vol_name in os.listdir(data_train_path) if vol_name.endswith('_volume.mat')])20train_label_list = sorted(21    [label_name for label_name in os.listdir(data_train_path) if label_name.endswith('_label.mat')])22test_volume_list = sorted([vol_name for vol_name in os.listdir(data_test_path) if vol_name.endswith('_volume.mat')])23test_label_list = sorted([label_name for label_name in os.listdir(data_test_path) if label_name.endswith('_label.mat')])24train_num = len(train_volume_list)25test_num = len(test_volume_list)26batch_size = 127epoch = 428feature_channels = 1629output_channels = 830step_show = 10031config = tf.ConfigProto()32config.gpu_options.allow_growth = True33vol_in = tf.placeholder(dtype='float32', shape=[None, vol_size[0], vol_size[1], vol_size[2], 1])34labels = tf.placeholder(dtype='float32', shape=[None, vol_size[0], vol_size[1], vol_size[2], output_channels])35prediction, logits = vnet(vol_in, labels, feature_channels=feature_channels, output_channels=output_channels)36loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=tf.stop_gradient(labels), logits=logits))37tf.summary.scalar('Loss', loss)38# tf.summary.scalar('IoU', prediction['IoU'])39for lr_train in [0.1, 0.05, 0.01, 0.005]:40    boundary = [train_num // batch_size, train_num * 2 // batch_size]41    lr_values = [lr_train, lr_train / 2, lr_train / 10]42    save_path = '/media/xz6214/4276F10376F0F90D/trained_model/%s/%d_%d_%d/vnet_with_2dunet/model_lr_%f_crossval_%s/' % (43        dataset, vol_size[2], vol_size[1], vol_size[0], lr_train, cross_validation)44    if not restore:45        os.system('rm -rf %s' % save_path)46    if not os.path.exists(save_path):47        os.makedirs(save_path)48    logfile = open(save_path + 'training_log.txt', 'w+')49    logfile.write('********************* LR = %f *********************\n' % lr_train)50    global_step = tf.Variable(0, trainable=False)51    lr = tf.train.piecewise_constant(x=global_step, boundaries=boundary, values=lr_values)52    optimizer = tf.train.MomentumOptimizer(learning_rate=lr, momentum=0.9)53    train = optimizer.minimize(loss, global_step=global_step)54    tf.summary.scalar('Learning Rate', lr)55    merged_summary_op = tf.summary.merge_all()56    init = tf.global_variables_initializer()57    saver = tf.train.Saver(max_to_keep=epoch)58    with tf.Session(config=config) as sess:59        tf.train.write_graph(graph_or_graph_def=sess.graph_def, logdir=save_path, name='Model')60        writer = tf.summary.FileWriter(save_path, sess.graph)61        sess.run(init)62        total_loss = 063        total_parameters = 064        for variable in tf.trainable_variables():65            shape = variable.get_shape()66            variable_parameters = 167            for dim in shape:68                variable_parameters *= dim.value69            total_parameters += variable_parameters70        print(total_parameters)71        logfile.write('total parameters: %d\n' % total_parameters)72        if restore:73            ckpt = tf.train.get_checkpoint_state(save_path)74            if ckpt and ckpt.model_checkpoint_path:75                saver.restore(sess, ckpt.model_checkpoint_path)76        start_time = time.time()77        for step in range((train_num // batch_size) * epoch):78            image_id_list = random.sample(range(train_num), batch_size)79            image_batch = []80            label_batch = []81            for image_id in image_id_list:82                image_id = np.random.choice(train_num) + 183                image_np = sio.loadmat(data_train_path + "%s_volume.mat" % (image_id))['volume']84                image_np = np.reshape(image_np, (1, image_np.shape[0], image_np.shape[1], image_np.shape[2], 1))85                image_batch.append(image_np)86                lable_np = sio.loadmat(data_train_path + "%s_label.mat" % (image_id))['label']87                lable_np = np.reshape(lable_np,88                                      (1, lable_np.shape[0], lable_np.shape[1], lable_np.shape[2], lable_np.shape[3]))89                label_batch.append(lable_np)90            image_batch = np.concatenate(image_batch, axis=0)91            label_batch = np.concatenate(label_batch, axis=0)92            # _, loss_value, summary, global_step_show, pred, lr_show = sess.run(93            #     [train, loss, merged_summary_op, global_step, prediction, lr],94            #     feed_dict={vol_in: image_batch, labels: label_batch})95            _, loss_value, summary, global_step_show, lr_show = sess.run(96                [train, loss, merged_summary_op, global_step, lr],97                feed_dict={vol_in: image_batch, labels: label_batch})98            if (step + 1) % step_show == 0:99                total_loss += loss_value100                total_loss = total_loss / step_show101                # iou = pred['IoU']102                print('Step: %d, Learning rate: %f, Loss: %f, Running time: %f' %103                      (global_step_show, lr_show, total_loss, time.time() - start_time))104                total_loss = 0105                writer.add_summary(summary, global_step=global_step_show)106                writer.flush()107                start_time = time.time()108            else:109                total_loss += loss_value110            # Testing after each epoch111            if (step + 1) % (train_num // batch_size) == 0:112                saved_path = saver.save(sess, save_path + 'Model', global_step=global_step)113                print('-------------------------------------------------')114                logfile.write('-------------------------------------------------\n')115                i_patient = np.zeros([10, output_channels - 1])116                u_patient = np.zeros([10, output_channels - 1])117                for i in range(test_num):118                    volume_path = data_test_path + test_volume_list[i]119                    label_path = data_test_path + test_label_list[i]120                    assert volume_path[:volume_path.find('_volume.mat')] == label_path[121                                                                            :label_path.find('_label.mat')]122                    volume_name = volume_path[:volume_path.find('_volume.mat')]123                    patient_id = volume_name[volume_name.find('test_patient') + 12:]124                    patient_id = int(patient_id[:patient_id.find('_')])125                    volume_np = sio.loadmat(volume_path)['volume']126                    volume_np = np.reshape(volume_np,127                                           (1, volume_np.shape[0], volume_np.shape[1], volume_np.shape[2], 1))128                    label_np = sio.loadmat(label_path)['label']129                    label_np = np.reshape(label_np,130                                          (1, label_np.shape[0], label_np.shape[1], label_np.shape[2],131                                           label_np.shape[3]))132                    [pred, ] = sess.run([prediction], feed_dict={vol_in: volume_np, labels: label_np})133                    i_value = pred['And']134                    u_value = pred['Or']135                    # Calculate IoU for each patient136                    i_patient[patient_id - 1, :] += i_value137                    u_patient[patient_id - 1, :] += u_value138                iou_all_patients = np.divide(i_patient, u_patient)139                print(iou_all_patients)140                for patient_id in range(10):141                    msg = 'epoch %d, Testing IoU of each organ for patient %d: %s\n' % (142                        (step + 1) // (train_num // batch_size), patient_id,143                        ','.join(['%.3f' % n for n in iou_all_patients[patient_id, :]]))144                    print(msg)145                    logfile.write(msg)146                msg = 'epoch %d, Current loss: %f, Average testing IoU of each organ for all %d patients: %s\n' % (147                    (step + 1) // (train_num // batch_size), loss_value, len(iou_all_patients),148                    ','.join(['%.3f' % n for n in np.mean(iou_all_patients, axis=0)]))149                print(msg)150                logfile.write(msg)151                print('-------------------------------------------------')152                logfile.write('-------------------------------------------------\n\n')153                logfile.flush()154                start_time = time.time()155            # Save prediction after each epoch156            if (step + 1) % (train_num // batch_size) == 0:157                for i in range(len(test_volume_list)):158                    volume_path = data_test_path + test_volume_list[i]159                    label_path = data_test_path + test_label_list[i]160                    assert volume_path[:volume_path.find('_volume.mat')] == label_path[161                                                                            :label_path.find('_label.mat')]162                    volume_name = volume_path[:volume_path.find('_volume.mat')]163                    volume_name = volume_name[volume_name.rfind('/') + 1:]164                    indices = re.search('test_patient[0-9]+', volume_name).span()165                    patient_name = volume_name[indices[0]:indices[1]]166                    volume_np = sio.loadmat(volume_path)['volume']167                    volume_np = np.reshape(volume_np,168                                           (1, volume_np.shape[0], volume_np.shape[1], volume_np.shape[2], 1))169                    label_np = sio.loadmat(label_path)['label']170                    label_np = np.reshape(label_np,171                                          (1, label_np.shape[0], label_np.shape[1], label_np.shape[2],172                                           label_np.shape[3]))173                    [pred_dict, ] = sess.run([prediction], feed_dict={vol_in: volume_np, labels: label_np})174                    prediction_path = save_path + 'prediction_' + str(175                        (step + 1) // (train_num // batch_size)) + '/' + patient_name + '/'176                    if not os.path.exists(prediction_path):177                        os.makedirs(prediction_path)178                    sio.savemat(prediction_path + volume_name + '_prediction.mat',179                                {'probabilities': pred_dict['probabilities']})180                    sio.savemat(prediction_path + volume_name + '_label.mat', {'label': label_np})181                print('prediction complete.')182    logfile.close()183def tversky_loss(gt, prob, alpha=0.5, beta=0.5):184    p0 = prob185    p1 = tf.ones(gt.shape) - prob186    g0 = gt187    g1 = tf.ones(gt.shape) - gt188    num = tf.reduce_sum(p0 * g0, axis=(0, 1, 2, 3))189    den = num + alpha * tf.reduce_sum(p0 * g1, axis=(0, 1, 2, 3)) + beta * tf.reduce_sum(p1 * g0, axis=(0, 1, 2, 3))190    t = tf.reduce_sum(num / den)...

depthwise_dilated_unet_train.py

Source:depthwise_dilated_unet_train.py

1import numpy as np2import scipy.io as sio3import os, re, random, time4import tensorflow as tf5from depthwise_dilated_unet import DDUNet6dataset = 'VISCERAL_aorta'  # 'VISCERAL_aorta', 'SLIVER07_liver'7os.environ['CUDA_VISIBLE_DEVICES'] = '2'8cross_validation = 19restore = False10vol_size = [8, 512, 512]11if dataset == 'VISCERAL_aorta':12    data_train_path = '/data2/PEICHAO_LI/data/VISCERAL/aorta_2fold_augmented_%d_%d_%d/aorta_train_%d/' % (13        vol_size[2], vol_size[1], vol_size[0], cross_validation)14    data_test_path = '/data2/PEICHAO_LI/data/VISCERAL/aorta_2fold_augmented_%d_%d_%d/aorta_test_%d/' % (15        vol_size[2], vol_size[1], vol_size[0], cross_validation)16elif dataset == 'SLIVER07_liver':    # TODO17    data_train_path = '/data2/XIAOYUN_ZHOU/SLiver07/liver_2fold/liver_train_%d/' % (cross_validation)18    data_test_path = '/data2/XIAOYUN_ZHOU/SLiver07/liver_2fold/liver_test_%d/' % (cross_validation)19train_volume_list = sorted([vol_name for vol_name in os.listdir(data_train_path) if vol_name.endswith('_volume.mat')])20train_label_list = sorted([label_name for label_name in os.listdir(data_train_path) if label_name.endswith('_label.mat')])21test_volume_list = sorted([vol_name for vol_name in os.listdir(data_test_path) if vol_name.endswith('_volume.mat')])22test_label_list = sorted([label_name for label_name in os.listdir(data_test_path) if label_name.endswith('_label.mat')])23train_num = len(train_volume_list)24test_num = len(test_volume_list)25batch_size = 126epoch = 1227feature_channels = 1628output_channels = 229downsampling = 630downsampling_type = 'conv'  # 'conv', 'max_pooling'31upsampling_type = 'bilinear'  # 'deconv', 'nearest_neighbour', 'bilinear'32norm_type = 'IN'            # 'IN', 'LN', 'BN'33step_show = 10034config = tf.ConfigProto()35config.gpu_options.allow_growth = True36vol_in = tf.placeholder(dtype='float32', shape=[batch_size, vol_size[0], vol_size[1], vol_size[2], 1])37labels = tf.placeholder(dtype='float32', shape=[batch_size, vol_size[0], vol_size[1], vol_size[2], 2])38model = DDUNet(vol_in,39               labels,40               feature_channels,41               output_channels,42               downsampling,43               downsampling_type,44               upsampling_type,45               norm_type)46prediction = model.prediction47logits = model.logits48loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=tf.stop_gradient(labels), logits=logits))49tf.summary.scalar('Loss', loss)50tf.summary.scalar('IoU', prediction['IoU'])51for lr_train in [0.1, 0.05, 0.01, 0.005]:52    boundary = [train_num//batch_size, train_num*4//batch_size, train_num*7//batch_size]53    lr_values = [lr_train, lr_train / 2, lr_train / 10, lr_train / 50]54    save_path = '/media/xz6214/4276F10376F0F90D/trained_model/%s/%d_%d_%d/ddunet/model_lr_%f_crossval_%s/' % (55        dataset, vol_size[2], vol_size[1], vol_size[0], lr_train, cross_validation)56    if not restore:57        os.system('rm -rf %s' % save_path)58    if not os.path.exists(save_path):59        os.makedirs(save_path)60    logfile = open(save_path + 'training_log.txt', 'w+')61    logfile.write('********************* LR = %f *********************\n' % lr_train)62    global_step = tf.Variable(0, trainable=False)63    lr = tf.train.piecewise_constant(x=global_step, boundaries=boundary, values=lr_values)64    optimizer = tf.train.MomentumOptimizer(learning_rate=lr, momentum=0.9)65    train = optimizer.minimize(loss, global_step=global_step)66    tf.summary.scalar('Learning Rate', lr)67    merged_summary_op = tf.summary.merge_all()68    init = tf.global_variables_initializer()69    saver = tf.train.Saver(max_to_keep=3)70    with tf.Session(config=config) as sess:71        tf.train.write_graph(graph_or_graph_def=sess.graph_def, logdir=save_path, name='Model')72        writer = tf.summary.FileWriter(save_path, sess.graph)73        sess.run(init)74        total_loss = 075        total_parameters = 076        for variable in tf.trainable_variables():77            shape = variable.get_shape()78            variable_parameters = 179            for dim in shape:80                variable_parameters *= dim.value81            total_parameters += variable_parameters82        print(total_parameters)83        logfile.write('total parameters: %d\n' % total_parameters)84        if restore:85            ckpt = tf.train.get_checkpoint_state(save_path)86            if ckpt and ckpt.model_checkpoint_path:87                saver.restore(sess, ckpt.model_checkpoint_path)88        start_time = time.time()89        for step in range((train_num//batch_size)*epoch):90            image_id_list = random.sample(range(train_num), batch_size)91            image_batch = []92            label_batch = []93            for image_id in image_id_list:94                image_id = np.random.choice(train_num) + 195                image_np = sio.loadmat(data_train_path + "%s_volume.mat" % (image_id))['volume']96                image_np = np.reshape(image_np, (1, image_np.shape[0], image_np.shape[1], image_np.shape[2], 1))97                image_batch.append(image_np)98                lable_np = sio.loadmat(data_train_path + "%s_label.mat" % (image_id))['label']99                lable_np = np.reshape(lable_np, (1, lable_np.shape[0], lable_np.shape[1], lable_np.shape[2], lable_np.shape[3]))100                label_batch.append(lable_np)101            image_batch = np.concatenate(image_batch, axis=0)102            label_batch = np.concatenate(label_batch, axis=0)103            _, loss_value, summary, global_step_show, pred, lr_show = sess.run([train, loss, merged_summary_op, global_step, prediction, lr], feed_dict={vol_in: image_batch, labels: label_batch})104            if (step+1) % step_show == 0:105                total_loss += loss_value106                total_loss = total_loss/step_show107                iou = pred['IoU']108                print('Step: %d, Learning rate: %f, Loss: %f, Running time: %f' %109                      (global_step_show, lr_show, total_loss, time.time() - start_time))110                total_loss = 0111                writer.add_summary(summary, global_step=global_step_show)112                writer.flush()113                start_time = time.time()114            else:115                total_loss += loss_value116            # Testing after each epoch117            if (step+1) % (train_num//batch_size) == 0:118                saved_path = saver.save(sess, save_path + 'Model', global_step=global_step)119                print('-------------------------------------------------')120                logfile.write('-------------------------------------------------\n')121                i_patient = np.zeros(10)122                u_patient = np.zeros(10)123                for i in range(len(test_volume_list)):124                    volume_path = data_test_path + test_volume_list[i]125                    label_path = data_test_path + test_label_list[i]126                    assert volume_path[:volume_path.find('_volume.mat')] == label_path[:label_path.find('_label.mat')]127                    volume_name = volume_path[:volume_path.find('_volume.mat')]128                    patient_id = volume_name[volume_name.find('test_patient') + 12:]129                    patient_id = int(patient_id[:patient_id.find('_')])130                    volume_np = sio.loadmat(volume_path)['volume']131                    volume_np = np.reshape(volume_np,132                                           (1, volume_np.shape[0], volume_np.shape[1], volume_np.shape[2], 1))133                    label_np = sio.loadmat(label_path)['label']134                    label_np = np.reshape(label_np,135                                          (1, label_np.shape[0], label_np.shape[1], label_np.shape[2],136                                           label_np.shape[3]))137                    [pred, ] = sess.run([prediction], feed_dict={vol_in: volume_np, labels: label_np})138                    i_value = pred['And']139                    u_value = pred['Or']140                    # Calculate IoU for each patient141                    i_patient[patient_id-1] += np.sum(i_value)142                    u_patient[patient_id-1] += np.sum(u_value)143                iou_list = np.divide(i_patient, u_patient)144                assert len(iou_list) == 10145                for patient_id in range(10):146                    msg = 'epoch %d, Testing IoU for patient %d: %f\n' % ((step+1) // (train_num//batch_size), patient_id, iou_list[patient_id])147                    print(msg)148                    logfile.write(msg)149                msg = 'epoch %d, Current loss: %f, Average testing IoU for all %d patients: %f\n' % ((step+1) // (train_num//batch_size), loss_value, len(iou_list), np.mean(iou_list))150                print(msg)151                logfile.write(msg)152                print('-------------------------------------------------')153                logfile.write('-------------------------------------------------\n\n')154                logfile.flush()155                start_time = time.time()156            # Save prediction after each epoch157            if (step + 1) % (train_num // batch_size) == 0:158                for i in range(len(test_volume_list)):159                    volume_path = data_test_path + test_volume_list[i]160                    label_path = data_test_path + test_label_list[i]161                    assert volume_path[:volume_path.find('_volume.mat')] == label_path[162                                                                            :label_path.find('_label.mat')]163                    volume_name = volume_path[:volume_path.find('_volume.mat')]164                    volume_name = volume_name[volume_name.rfind('/') + 1:]165                    indices = re.search('test_patient[0-9]+', volume_name).span()166                    patient_name = volume_name[indices[0]:indices[1]]167                    volume_np = sio.loadmat(volume_path)['volume']168                    volume_np = np.reshape(volume_np,169                                           (1, volume_np.shape[0], volume_np.shape[1], volume_np.shape[2], 1))170                    label_np = sio.loadmat(label_path)['label']171                    label_np = np.reshape(label_np,172                                          (1, label_np.shape[0], label_np.shape[1], label_np.shape[2],173                                           label_np.shape[3]))174                    [pred_dict, ] = sess.run([prediction], feed_dict={vol_in: volume_np, labels: label_np})175                    prediction_path = save_path + 'prediction_' + str(176                        (step + 1) // (train_num // batch_size)) + '/' + patient_name + '/'177                    if not os.path.exists(prediction_path):178                        os.makedirs(prediction_path)179                    sio.savemat(prediction_path + volume_name + '_prediction.mat',180                                {'probabilities': pred_dict['probabilities']})181                    sio.savemat(prediction_path + volume_name + '_label.mat', {'label': label_np})182                print('prediction complete.')...

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