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