COMET/COMET_train_seggguided.py

import os, sys

import keras

currentdir = os.path.dirname(os.path.realpath(__file__))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir)  # PYTHON > 3.3 does not allow relative referencing

from datetime import datetime

from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping, ReduceLROnPlateau
from tensorflow.python.keras.utils import Progbar
from tensorflow.keras import Input
from tensorflow.keras.models import Model
from tensorflow.python.framework.errors import InvalidArgumentError

import DeepDeformationMapRegistration.utils.constants as C
from DeepDeformationMapRegistration.losses import StructuralSimilarity_simplified, NCC, GeneralizedDICEScore, HausdorffDistanceErosion
from DeepDeformationMapRegistration.ms_ssim_tf import MultiScaleStructuralSimilarity
from DeepDeformationMapRegistration.ms_ssim_tf import _MSSSIM_WEIGHTS
from DeepDeformationMapRegistration.utils.acummulated_optimizer import AdamAccumulated
from DeepDeformationMapRegistration.utils.misc import function_decorator
from DeepDeformationMapRegistration.layers import AugmentationLayer
from DeepDeformationMapRegistration.utils.nifti_utils import save_nifti

from Brain_study.data_generator import BatchGenerator
from Brain_study.utils import SummaryDictionary, named_logs

import COMET.augmentation_constants as COMET_C
from COMET.utils import freeze_layers_by_group

import numpy as np
import tensorflow as tf
import voxelmorph as vxm
import h5py
import re
import itertools
import warnings


def launch_train(dataset_folder, validation_folder, output_folder, model_file, gpu_num=0, lr=1e-4, rw=5e-3, simil='ssim',
                 segm='dice', max_epochs=C.EPOCHS, early_stop_patience=1000, freeze_layers=None,
                 acc_gradients=1, batch_size=16, image_size=64,
                 unet=[16, 32, 64, 128, 256], head=[16, 16], resume=None):
    # 0. Input checks
    assert dataset_folder is not None and output_folder is not None
    if model_file != '':
        assert '.h5' in model_file, 'The model must be an H5 file'

    # 1. Load variables
    os.environ['CUDA_DEVICE_ORDER'] = C.DEV_ORDER
    os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_num) # Check availability before running using 'nvidia-smi'
    C.GPU_NUM = str(gpu_num)

    if batch_size != 1 and acc_gradients != 1:
        warnings.warn('WARNING: Batch size and Accumulative gradient step are set!')

    if resume is not None:
        try:
            assert os.path.exists(resume) and len(os.listdir(os.path.join(resume, 'checkpoints'))), 'Invalid directory: ' + resume
            output_folder = resume
            resume = True
        except AssertionError:
            output_folder = os.path.join(output_folder + '_' + datetime.now().strftime("%H%M%S-%d%m%Y"))
            resume = False
    else:
        resume = False

    os.makedirs(output_folder, exist_ok=True)
    # dataset_copy = DatasetCopy(dataset_folder, os.path.join(output_folder, 'temp'))
    log_file = open(os.path.join(output_folder, 'log.txt'), 'w')
    C.TRAINING_DATASET = dataset_folder #dataset_copy.copy_dataset()
    C.VALIDATION_DATASET = validation_folder
    C.ACCUM_GRADIENT_STEP = acc_gradients
    C.BATCH_SIZE = batch_size if C.ACCUM_GRADIENT_STEP == 1 else 1
    C.EARLY_STOP_PATIENCE = early_stop_patience
    C.LEARNING_RATE = lr
    C.LIMIT_NUM_SAMPLES = None
    C.EPOCHS = max_epochs

    aux = "[{}]\tINFO:\nTRAIN DATASET: {}\nVALIDATION DATASET: {}\n" \
          "GPU: {}\n" \
          "BATCH SIZE: {}\n" \
          "LR: {}\n" \
          "SIMILARITY: {}\n" \
          "SEGMENTATION: {}\n"\
          "REG. WEIGHT: {}\n" \
          "EPOCHS: {:d}\n" \
          "ACCUM. GRAD: {}\n" \
          "EARLY STOP PATIENCE: {}\n" \
          "FROZEN LAYERS: {}".format(datetime.now().strftime('%H:%M:%S\t%d/%m/%Y'),
                                           C.TRAINING_DATASET,
                                           C.VALIDATION_DATASET,
                                           C.GPU_NUM,
                                           C.BATCH_SIZE,
                                           C.LEARNING_RATE,
                                           simil,
                                           segm,
                                           rw,
                                           C.EPOCHS,
                                           C.ACCUM_GRADIENT_STEP,
                                           C.EARLY_STOP_PATIENCE,
                                     freeze_layers)

    log_file.write(aux)
    print(aux)

    # 2. Data generator
    used_labels = [0, 1, 2]
    data_generator = BatchGenerator(C.TRAINING_DATASET, C.BATCH_SIZE if C.ACCUM_GRADIENT_STEP == 1 else 1, True,
                                    C.TRAINING_PERC, labels=used_labels, combine_segmentations=False,
                                    directory_val=C.VALIDATION_DATASET)

    train_generator = data_generator.get_train_generator()
    validation_generator = data_generator.get_validation_generator()

    image_input_shape = train_generator.get_data_shape()[-1][:-1]
    image_output_shape = [image_size] * 3
    nb_labels = len(train_generator.get_segmentation_labels())

    # 3. Load model
    # IMPORTANT: the mode MUST be loaded AFTER setting up the session configuration
    config = tf.compat.v1.ConfigProto()  # device_count={'GPU':0})
    config.gpu_options.allow_growth = True
    config.log_device_placement = False  ## to log device placement (on which device the operation ran)
    sess = tf.Session(config=config)
    tf.keras.backend.set_session(sess)

    loss_fncs = [StructuralSimilarity_simplified(patch_size=2, dim=3, dynamic_range=1.).loss,
                 NCC(image_input_shape).loss,
                 vxm.losses.MSE().loss,
                 MultiScaleStructuralSimilarity(max_val=1., filter_size=3).loss,
                 HausdorffDistanceErosion(3, 10, im_shape=image_output_shape + [nb_labels]).loss,
                 GeneralizedDICEScore(image_output_shape + [nb_labels], num_labels=nb_labels).loss,
                 GeneralizedDICEScore(image_output_shape + [nb_labels], num_labels=nb_labels).loss_macro
                 ]

    metric_fncs = [StructuralSimilarity_simplified(patch_size=2, dim=3, dynamic_range=1.).metric,
                   NCC(image_input_shape).metric,
                   vxm.losses.MSE().loss,
                   MultiScaleStructuralSimilarity(max_val=1., filter_size=3).metric,
                   GeneralizedDICEScore(image_output_shape + [nb_labels], num_labels=nb_labels).metric,
                   HausdorffDistanceErosion(3, 10, im_shape=image_output_shape + [nb_labels]).metric,
                   GeneralizedDICEScore(image_output_shape + [nb_labels], num_labels=nb_labels).metric_macro,]


    try:
        network = tf.keras.models.load_model(model_file, {#'VxmDenseSemiSupervisedSeg': vxm.networks.VxmDenseSemiSupervisedSeg,
                                                          'VxmDense': vxm.networks.VxmDense,
                                                          'AdamAccumulated': AdamAccumulated,
                                                          'loss': loss_fncs,
                                                          'metric': metric_fncs},
                                             compile=False)
    except ValueError as e:
        # enc_features = [16, 32, 32, 32]     # const.ENCODER_FILTERS
        # dec_features = [32, 32, 32, 32, 32, 16, 16]     # const.ENCODER_FILTERS[::-1]
        enc_features = unet  # const.ENCODER_FILTERS
        dec_features = enc_features[::-1] + head  # const.ENCODER_FILTERS[::-1]
        nb_features = [enc_features, dec_features]

        network = vxm.networks.VxmDenseSemiSupervisedSeg(inshape=image_output_shape,
                                                         nb_labels=nb_labels,
                                                         nb_unet_features=nb_features,
                                                         int_steps=0,
                                                         int_downsize=1,
                                                         seg_downsize=1)

        if model_file != '':
            network.load_weights(model_file, by_name=True)
            print('MODEL LOCATION: ', model_file)

    resume_epoch = 0
    if resume:
        cp_dir = os.path.join(output_folder, 'checkpoints')
        cp_file_list = [os.path.join(cp_dir, f) for f in os.listdir(cp_dir) if (f.startswith('checkpoint') and f.endswith('.h5'))]
        if len(cp_file_list):
            cp_file_list.sort()
            checkpoint_file = cp_file_list[-1]
            if os.path.exists(checkpoint_file):
                network.load_weights(checkpoint_file, by_name=True)
                print('Loaded checkpoint file: ' + checkpoint_file)
                try:
                    resume_epoch = int(re.match('checkpoint\.(\d+)-*.h5', os.path.split(checkpoint_file)[-1])[1])
                except TypeError:
                    # Checkpoint file has no epoch number in the name
                    resume_epoch = 0
                print('Resuming from epoch: {:d}'.format(resume_epoch))
            else:
                warnings.warn('Checkpoint file NOT found. Training from scratch')

    # 4. Freeze/unfreeze model layers
    _, frozen_layers = freeze_layers_by_group(network, freeze_layers)
    if frozen_layers is not None:
        msg = "[INF]: Frozen layers {}".format(', '.join([str(a) for a in frozen_layers]))
    else:
        msg = "[INF] None frozen layers"
    print(msg)
    log_file.write(msg)

    network.summary(line_length=C.SUMMARY_LINE_LENGTH)
    network.summary(line_length=C.SUMMARY_LINE_LENGTH, print_fn=log_file.writelines)
    #   Complete the model with the augmentation layer
    augm_train_input_shape = train_generator.get_data_shape()[0]
    input_layer_train = Input(shape=augm_train_input_shape, name='input_train')
    augm_layer_train = AugmentationLayer(max_displacement=COMET_C.MAX_AUG_DISP,   # Max 30 mm in isotropic space
                                         max_deformation=COMET_C.MAX_AUG_DEF,  # Max 6 mm in isotropic space
                                         max_rotation=COMET_C.MAX_AUG_ANGLE,   # Max 10 deg in isotropic space
                                         num_control_points=COMET_C.NUM_CONTROL_PTS_AUG,
                                         num_augmentations=COMET_C.NUM_AUGMENTATIONS,
                                         gamma_augmentation=COMET_C.GAMMA_AUGMENTATION,
                                         brightness_augmentation=COMET_C.BRIGHTNESS_AUGMENTATION,
                                         in_img_shape=image_input_shape,
                                         out_img_shape=image_output_shape,
                                         only_image=False,  # If baseline then True
                                         only_resize=False,
                                         trainable=False)
    augm_model_train = Model(inputs=input_layer_train, outputs=augm_layer_train(input_layer_train))

    input_layer_valid = Input(shape=validation_generator.get_data_shape()[0], name='input_valid')
    augm_layer_valid = AugmentationLayer(max_displacement=COMET_C.MAX_AUG_DISP,   # Max 30 mm in isotropic space
                                         max_deformation=COMET_C.MAX_AUG_DEF,  # Max 6 mm in isotropic space
                                         max_rotation=COMET_C.MAX_AUG_ANGLE,   # Max 10 deg in isotropic space
                                         num_control_points=COMET_C.NUM_CONTROL_PTS_AUG,
                                         num_augmentations=COMET_C.NUM_AUGMENTATIONS,
                                         gamma_augmentation=COMET_C.GAMMA_AUGMENTATION,
                                         brightness_augmentation=COMET_C.BRIGHTNESS_AUGMENTATION,
                                         in_img_shape=image_input_shape,
                                         out_img_shape=image_output_shape,
                                         only_image=False,
                                         only_resize=False,
                                         trainable=False)
    augm_model_valid = Model(inputs=input_layer_valid, outputs=augm_layer_valid(input_layer_valid))

    # 5. Setup training environment: loss, optimizer, callbacks, evaluation

    # Losses and loss weights
    SSIM_KER_SIZE = 5
    MS_SSIM_WEIGHTS = _MSSSIM_WEIGHTS[:3]
    MS_SSIM_WEIGHTS /= np.sum(MS_SSIM_WEIGHTS)
    if simil.lower() == 'mse':
        loss_fnc = vxm.losses.MSE().loss
    elif simil.lower() == 'ncc':
        loss_fnc = NCC(image_input_shape).loss
    elif simil.lower() == 'ssim':
        loss_fnc = StructuralSimilarity_simplified(patch_size=SSIM_KER_SIZE, dim=3, dynamic_range=1.).loss
    elif simil.lower() == 'ms_ssim':
        loss_fnc = MultiScaleStructuralSimilarity(max_val=1., filter_size=SSIM_KER_SIZE, power_factors=MS_SSIM_WEIGHTS).loss
    elif simil.lower() == 'mse__ms_ssim' or simil.lower() == 'ms_ssim__mse':
        @function_decorator('MSSSIM_MSE__loss')
        def loss_fnc(y_true, y_pred):
            return vxm.losses.MSE().loss(y_true, y_pred) + \
                   MultiScaleStructuralSimilarity(max_val=1., filter_size=SSIM_KER_SIZE, power_factors=MS_SSIM_WEIGHTS).loss(y_true, y_pred)
    elif simil.lower() == 'ncc__ms_ssim' or simil.lower() == 'ms_ssim__ncc':
        @function_decorator('MSSSIM_NCC__loss')
        def loss_fnc(y_true, y_pred):
            return NCC(image_input_shape).loss(y_true, y_pred) + \
                   MultiScaleStructuralSimilarity(max_val=1., filter_size=SSIM_KER_SIZE, power_factors=MS_SSIM_WEIGHTS).loss(y_true, y_pred)
    elif simil.lower() == 'mse__ssim' or simil.lower() == 'ssim__mse':
        @function_decorator('SSIM_MSE__loss')
        def loss_fnc(y_true, y_pred):
            return vxm.losses.MSE().loss(y_true, y_pred) + \
                   StructuralSimilarity_simplified(patch_size=SSIM_KER_SIZE, dim=3, dynamic_range=1.).loss(y_true, y_pred)
    elif simil.lower() == 'ncc__ssim' or simil.lower() == 'ssim__ncc':
        @function_decorator('SSIM_NCC__loss')
        def loss_fnc(y_true, y_pred):
            return NCC(image_input_shape).loss(y_true, y_pred) + \
                   StructuralSimilarity_simplified(patch_size=SSIM_KER_SIZE, dim=3, dynamic_range=1.).loss(y_true, y_pred)
    else:
        raise ValueError('Unknown similarity metric: ' + simil)

    if segm == 'hd':
        loss_segm = HausdorffDistanceErosion(3, 10, im_shape=image_output_shape + [nb_labels]).loss
    elif segm == 'dice':
        loss_segm = GeneralizedDICEScore(image_output_shape + [nb_labels], num_labels=nb_labels).loss
    elif segm == 'dice_macro':
        loss_segm = GeneralizedDICEScore(image_output_shape + [nb_labels], num_labels=nb_labels).loss_macro
    else:
        raise ValueError('No valid value for segm')

    os.makedirs(os.path.join(output_folder, 'checkpoints'), exist_ok=True)
    os.makedirs(os.path.join(output_folder, 'tensorboard'), exist_ok=True)
    callback_tensorboard = TensorBoard(log_dir=os.path.join(output_folder, 'tensorboard'),
                                       batch_size=C.BATCH_SIZE, write_images=False, histogram_freq=0,
                                       update_freq='epoch',     # or 'batch' or integer
                                       write_graph=True, write_grads=True
                                       )
    callback_early_stop = EarlyStopping(monitor='val_loss', verbose=1, patience=C.EARLY_STOP_PATIENCE, min_delta=0.00001)

    callback_best_model = ModelCheckpoint(os.path.join(output_folder, 'checkpoints', 'best_model.h5'),
                                          save_best_only=True, monitor='val_loss', verbose=1, mode='min')
    callback_save_checkpoint = ModelCheckpoint(os.path.join(output_folder, 'checkpoints', 'checkpoint.h5'),
                                               save_weights_only=True, monitor='val_loss', verbose=0, mode='min')
    callback_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10)

    losses = {'transformer': loss_fnc,
              'seg_transformer': loss_segm,
              'flow': vxm.losses.Grad('l2').loss}
    metrics = {'transformer': [StructuralSimilarity_simplified(patch_size=SSIM_KER_SIZE, dim=3, dynamic_range=1.).metric,
                               MultiScaleStructuralSimilarity(max_val=1., filter_size=SSIM_KER_SIZE, power_factors=MS_SSIM_WEIGHTS).metric,
                               tf.keras.losses.MSE,
                               NCC(image_input_shape).metric],
               'seg_transformer': [GeneralizedDICEScore(image_output_shape + [train_generator.get_data_shape()[2][-1]], num_labels=nb_labels).metric,
                                   HausdorffDistanceErosion(3, 10, im_shape=image_output_shape + [train_generator.get_data_shape()[2][-1]]).metric,
                                   GeneralizedDICEScore(image_output_shape + [train_generator.get_data_shape()[2][-1]], num_labels=nb_labels).metric_macro,
                                   ],
               #'flow': vxm.losses.Grad('l2').loss
               }
    loss_weights = {'transformer': 1.,
                    'seg_transformer': 1.,
                    'flow': rw}

    optimizer = AdamAccumulated(accumulation_steps=C.ACCUM_GRADIENT_STEP, learning_rate=C.LEARNING_RATE)
    network.compile(optimizer=optimizer,
                    loss=losses,
                    loss_weights=loss_weights,
                    metrics=metrics)

    # 6. Training loop
    callback_tensorboard.set_model(network)
    callback_early_stop.set_model(network)
    callback_best_model.set_model(network)
    callback_save_checkpoint.set_model(network)
    callback_lr.set_model(network)

    summary = SummaryDictionary(network, C.BATCH_SIZE)
    names = network.metrics_names
    log_file.write('\n\n[{}]\tINFO:\tStart training\n\n'.format(datetime.now().strftime('%H:%M:%S\t%d/%m/%Y')))

    with sess.as_default():
        # tf.global_variables_initializer()
        callback_tensorboard.on_train_begin()
        callback_early_stop.on_train_begin()
        callback_best_model.on_train_begin()
        callback_save_checkpoint.on_train_begin()
        callback_lr.on_train_begin()

        for epoch in range(resume_epoch, C.EPOCHS):
            callback_tensorboard.on_epoch_begin(epoch)
            callback_early_stop.on_epoch_begin(epoch)
            callback_best_model.on_epoch_begin(epoch)
            callback_save_checkpoint.on_epoch_begin(epoch)
            callback_lr.on_epoch_begin(epoch)

            print("\nEpoch {}/{}".format(epoch, C.EPOCHS))
            print("TRAIN")

            log_file.write('\n\n[{}]\tINFO:\tTraining epoch {}\n\n'.format(datetime.now().strftime('%H:%M:%S\t%d/%m/%Y'), epoch))
            progress_bar = Progbar(len(train_generator), width=30, verbose=1)
            for step, (in_batch, _) in enumerate(train_generator, 1):
                callback_best_model.on_train_batch_begin(step)
                callback_save_checkpoint.on_train_batch_begin(step)
                callback_early_stop.on_train_batch_begin(step)
                callback_lr.on_train_batch_begin(step)

                try:
                    fix_img, mov_img, fix_seg, mov_seg = augm_model_train.predict(in_batch)
                    np.nan_to_num(fix_img, copy=False)
                    np.nan_to_num(mov_img, copy=False)
                    if np.isnan(np.sum(mov_img)) or np.isnan(np.sum(fix_img)) or np.isinf(np.sum(mov_img)) or np.isinf(np.sum(fix_img)):
                        msg = 'CORRUPTED DATA!! Unique: Fix: {}\tMoving: {}'.format(np.unique(fix_img),
                                                                                    np.unique(mov_img))
                        print(msg)
                        log_file.write('\n\n[{}]\tWAR: {}'.format(datetime.now().strftime('%H:%M:%S\t%d/%m/%Y'), msg))

                except InvalidArgumentError as err:
                    print('TF Error : {}'.format(str(err)))
                    continue

                in_data = (mov_img, fix_img, mov_seg)
                out_data = (fix_img, fix_img, fix_seg)

                ret = network.train_on_batch(x=in_data, y=out_data)  # The second element doesn't matter
                if np.isnan(ret).any():
                    os.makedirs(os.path.join(output_folder, 'corrupted'), exist_ok=True)
                    save_nifti(mov_img, os.path.join(output_folder, 'corrupted', 'mov_img_nan.nii.gz'))
                    save_nifti(fix_img, os.path.join(output_folder, 'corrupted', 'fix_img_nan.nii.gz'))
                    pred_img, dm = network((mov_img, fix_img))
                    save_nifti(pred_img, os.path.join(output_folder, 'corrupted', 'pred_img_nan.nii.gz'))
                    save_nifti(dm, os.path.join(output_folder, 'corrupted', 'dm_nan.nii.gz'))
                    log_file.write('\n\n[{}]\tERR: Corruption error'.format(datetime.now().strftime('%H:%M:%S\t%d/%m/%Y')))
                    raise ValueError('CORRUPTION ERROR: Halting training')

                summary.on_train_batch_end(ret)
                callback_best_model.on_train_batch_end(step, named_logs(network, ret))
                callback_save_checkpoint.on_train_batch_end(step, named_logs(network, ret))
                callback_early_stop.on_train_batch_end(step, named_logs(network, ret))
                callback_lr.on_predict_batch_end(step, named_logs(network, ret))

                progress_bar.update(step, zip(names, ret))
                log_file.write('\t\tStep {:03d}: {}'.format(step, ret))
            val_values = progress_bar._values.copy()
            ret = [val_values[x][0]/val_values[x][1] for x in names]

            print('\nVALIDATION')
            log_file.write('\n\n[{}]\tINFO:\tValidation epoch {}\n\n'.format(datetime.now().strftime('%H:%M:%S\t%d/%m/%Y'), epoch))
            progress_bar = Progbar(len(validation_generator), width=30, verbose=1)
            for step, (in_batch, _) in enumerate(validation_generator, 1):
                try:
                    fix_img, mov_img, fix_seg, mov_seg = augm_model_valid.predict(in_batch)
                except InvalidArgumentError as err:
                    print('TF Error : {}'.format(str(err)))
                    continue

                in_data = (mov_img, fix_img, mov_seg)
                out_data = (fix_img, fix_img, fix_seg)

                ret = network.test_on_batch(x=in_data,
                                            y=out_data)

                summary.on_validation_batch_end(ret)
                progress_bar.update(step, zip(names, ret))
                log_file.write('\t\tStep {:03d}: {}'.format(step, ret))
            val_values = progress_bar._values.copy()
            ret = [val_values[x][0]/val_values[x][1] for x in names]

            train_generator.on_epoch_end()
            validation_generator.on_epoch_end()
            epoch_summary = summary.on_epoch_end()  # summary resets after on_epoch_end() call
            callback_tensorboard.on_epoch_end(epoch, epoch_summary)
            callback_best_model.on_epoch_end(epoch, epoch_summary)
            callback_save_checkpoint.on_epoch_end(epoch, epoch_summary)
            callback_early_stop.on_epoch_end(epoch, epoch_summary)
            callback_lr.on_epoch_end(epoch,epoch_summary)

            print('End of epoch {}: '.format(epoch), ret, '\n')

        callback_tensorboard.on_train_end()
        callback_best_model.on_train_end()
        callback_save_checkpoint.on_train_end()
        callback_early_stop.on_train_end()
        callback_lr.on_train_end()
# 7. Wrap up