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import os, sys
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
import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping, ReduceLROnPlateau
from tensorflow.keras import Input
from tensorflow.keras.models import Model
from tensorflow.python.keras.utils import Progbar
from tensorflow.python.framework.errors import InvalidArgumentError
import voxelmorph as vxm
import neurite as ne
import h5py
from datetime import datetime
import pickle
import ddmr.utils.constants as C
from ddmr.utils.misc import try_mkdir, DatasetCopy, function_decorator
from ddmr.networks import WeaklySupervised
from ddmr.losses import HausdorffDistanceErosion, NCC, StructuralSimilarity_simplified, GeneralizedDICEScore
from ddmr.ms_ssim_tf import MultiScaleStructuralSimilarity, _MSSSIM_WEIGHTS
from ddmr.layers import UncertaintyWeighting, AugmentationLayer
from ddmr.utils.acummulated_optimizer import AdamAccumulated
from Brain_study.data_generator import BatchGenerator
from Brain_study.utils import SummaryDictionary, named_logs
import warnings
import re
def launch_train(dataset_folder, validation_folder, output_folder, prior_reg_w=5e-3, lr=1e-4, rw=5e-3,
gpu_num=0, simil=['mse'], segm=['dice'], acc_gradients=16, batch_size=1, max_epochs=10000,
early_stop_patience=1000, image_size=64, unet=[16, 32, 64, 128, 256], head=[16, 16], resume=None):
assert dataset_folder is not None and output_folder is not None
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)
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 {:d}: {}\n" \
"SEGMENTATION {:d}: {}\n" \
"EPOCHS: {:d}" \
"ACCUM. GRAD: {}" \
"EARLY STOP PATIENCE: {}".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,
len(simil), ', '.join(simil),
len(segm), ', '.join(segm),
C.EPOCHS,
C.ACCUM_GRADIENT_STEP,
C.EARLY_STOP_PATIENCE)
log_file.write(aux)
print(aux)
# Load data
# Build data generator
data_generator = BatchGenerator(C.TRAINING_DATASET, C.BATCH_SIZE if C.ACCUM_GRADIENT_STEP == 1 else 1, True,
C.TRAINING_PERC, labels=['all'], 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())
# Config the training sessions
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)
# config.allow_soft_placement = True
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
# Losses and loss weights
SSIM_KER_SIZE = 5
MS_SSIM_WEIGHTS = _MSSSIM_WEIGHTS[:3]
MS_SSIM_WEIGHTS /= np.sum(MS_SSIM_WEIGHTS)
loss_simil = []
prior_loss_w = []
for s in simil:
if s=='ssim':
loss_simil.append(StructuralSimilarity_simplified(patch_size=SSIM_KER_SIZE, dim=3, dynamic_range=1.).loss)
prior_loss_w.append(1.)
elif s=='ms_ssim':
loss_simil.append(MultiScaleStructuralSimilarity(max_val=1., filter_size=SSIM_KER_SIZE, power_factors=MS_SSIM_WEIGHTS).loss)
prior_loss_w.append(1.)
elif s=='ncc':
loss_simil.append(NCC(image_input_shape).loss)
prior_loss_w.append(1.)
elif s=='mse':
loss_simil.append(vxm.losses.MSE().loss)
prior_loss_w.append(1.)
else:
raise ValueError('Unknown similarity function: ', s)
loss_segm = []
for s in segm:
if s=='dice':
loss_segm.append(GeneralizedDICEScore(image_output_shape + [nb_labels], num_labels=nb_labels).loss)
prior_loss_w.append(1.)
elif s=='hd':
loss_segm.append(HausdorffDistanceErosion(3, 10, im_shape=image_output_shape + [nb_labels]).loss)
prior_loss_w.append(1.)
elif s == 'dice_macro':
loss_segm.append(GeneralizedDICEScore(image_output_shape + [nb_labels], num_labels=nb_labels).loss_macro)
prior_loss_w.append(1.)
else:
raise ValueError('Unknown similarity function: ' + s)
# Build augmentation layer model
input_layer_augm = Input(shape=train_generator.get_data_shape()[0], name='input_augmentation')
augm_layer = AugmentationLayer(max_displacement=C.MAX_AUG_DISP, # Max 30 mm in isotropic space
max_deformation=C.MAX_AUG_DEF, # Max 6 mm in isotropic space
max_rotation=C.MAX_AUG_ANGLE, # Max 10 deg in isotropic space
num_control_points=C.NUM_CONTROL_PTS_AUG,
num_augmentations=C.NUM_AUGMENTATIONS,
gamma_augmentation=C.GAMMA_AUGMENTATION,
in_img_shape=image_input_shape,
out_img_shape=image_output_shape,
only_image=False,
only_resize=False,
trainable=False)
augmentation_model = Model(inputs=input_layer_augm, outputs=augm_layer(input_layer_augm))
# 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)
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')
# Network inputs: mov_img, fix_img, mov_seg
# Network outputs: pred_img, disp_map, pred_seg
grad = tf.keras.Input(shape=(*image_output_shape, 3), name='multiLoss_grad_input', dtype=tf.float32)
fix_seg = tf.keras.Input(shape=(*image_output_shape, len(train_generator.get_segmentation_labels())),
name='multiLoss_fix_seg_input', dtype=tf.float32)
multiLoss = UncertaintyWeighting(num_loss_fns=len(loss_simil) + len(loss_segm),
num_reg_fns=1,
loss_fns=[*loss_simil,
*loss_segm],
reg_fns=[vxm.losses.Grad('l2').loss],
prior_loss_w=prior_loss_w,
# prior_loss_w=[1., 0.1, 1., 1.],
prior_reg_w=[prior_reg_w],
name='MultiLossLayer')
loss = multiLoss([*[network.inputs[1]]*len(loss_simil), *[fix_seg]*len(loss_segm),
*[network.outputs[0]]*len(loss_simil), *[network.outputs[2]]*len(loss_simil),
grad,
network.outputs[1]])
# inputs = [mov_img, fix_img, mov_segm, fix_segm, zero_grads]
# outputs = [pred_img, flow, pred_segm, loss]
full_model = tf.keras.Model(inputs=network.inputs + [fix_seg, grad],
outputs=network.outputs + [loss])
# Train
os.makedirs(output_folder, exist_ok=True)
os.makedirs(os.path.join(output_folder, 'checkpoints'), exist_ok=True)
os.makedirs(os.path.join(output_folder, 'tensorboard'), exist_ok=True)
os.makedirs(os.path.join(output_folder, 'history'), exist_ok=True)
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_model = ModelCheckpoint(os.path.join(output_folder, 'checkpoints', 'checkpoint.{epoch:05d}-{val_loss:.2f}.h5'),
save_weights_only=True, monitor='val_loss', verbose=0, mode='min')
# CSVLogger(train_log_name, ';'),
# UpdateLossweights([haus_weight, dice_weight], [const.MODEL+'_resampler_seg', const.MODEL+'_resampler_seg'])
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_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10)
# Compile the model
optimizer = AdamAccumulated(C.ACCUM_GRADIENT_STEP, lr=C.LEARNING_RATE)
full_model.compile(optimizer=optimizer, loss=None)
callback_tensorboard.set_model(full_model)
callback_best_model.set_model(network) # ONLY SAVE THE NETWORK!!!
callback_save_model.set_model(network)
callback_early_stop.set_model(full_model)
callback_lr.set_model(full_model)
# TODO: https://towardsdatascience.com/writing-tensorflow-2-custom-loops-438b1ab6eb6c
summary = SummaryDictionary(full_model, C.BATCH_SIZE)
names = full_model.metrics_names # It give both the loss and metric names
zero_grads = tf.zeros_like(network.references.pos_flow, name='dummy_zero_grads') # Dummy zeros-tensor
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():
callback_tensorboard.on_train_begin()
callback_early_stop.on_train_begin()
callback_best_model.on_train_begin()
callback_save_model.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_model.on_epoch_begin(epoch)
callback_lr.on_epoch_begin(epoch)
print("\nEpoch {}/{}".format(epoch, C.EPOCHS))
print('TRAINING')
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_tensorboard.on_train_batch_begin(step)
callback_best_model.on_train_batch_begin(step)
callback_save_model.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 = augmentation_model.predict(in_batch)
np.nan_to_num(fix_img, copy=False)
np.nan_to_num(mov_img, copy=False)
except InvalidArgumentError as err:
print('TF Error : {}'.format(str(err)))
continue
# inputs = [mov_img, fix_img, mov_segm, fix_segm, zero_grads]
# outputs = [pred_img, flow, pred_segm, loss]
ret = full_model.train_on_batch(x=(mov_img, fix_img, mov_seg, fix_seg, zero_grads))
summary.on_train_batch_end(ret)
# callback_tensorboard.on_train_batch_end(step, named_logs(full_model, ret))
callback_best_model.on_train_batch_end(step, named_logs(full_model, ret))
callback_save_model.on_train_batch_end(step, named_logs(full_model, ret))
callback_early_stop.on_train_batch_end(step, named_logs(full_model, ret))
callback_lr.on_train_batch_end(step, named_logs(full_model, ret))
log_file.write('\t\tStep {:03d}: {}'.format(step, ret))
# print(ret, '\n')
progress_bar.update(step, zip(names, ret))
print('End of epoch{}: '.format(step), ret, '\n')
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):
# callback_tensorboard.on_test_batch_begin(step) # This is cursed, don't do it again
# callback_early_stop.on_test_batch_begin(step)
try:
fix_img, mov_img, fix_seg, mov_seg = augmentation_model.predict(in_batch)
except InvalidArgumentError as err:
print('TF Error : {}'.format(str(err)))
continue
ret = full_model.test_on_batch(x=(mov_img, fix_img, mov_seg, fix_seg, zero_grads))
# pred_segm = network.register(mov_segm, fix_segm)
summary.on_validation_batch_end(ret)
# callback_early_stop.on_test_batch_end(step, named_logs(full_model, ret))
# callback_tensorboard.on_test_batch_end(step, named_logs(network, ret)) # This is cursed, don't do it again
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_early_stop.on_epoch_end(epoch, epoch_summary)
callback_save_model.on_epoch_end(epoch, epoch_summary)
callback_lr.on_epoch_end(epoch, epoch_summary)
callback_tensorboard.on_train_end()
callback_save_model.on_train_end()
callback_best_model.on_train_end()
callback_early_stop.on_train_end()
callback_lr.on_train_end()
if __name__ == '__main__':
os.environ['CUDA_DEVICE_ORDER'] = C.DEV_ORDER
os.environ['CUDA_VISIBLE_DEVICES'] = '0' # Check availability before running using 'nvidia-smi'
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)
tf.keras.backend.set_session(tf.Session(config=config))
launch_train('/mnt/EncryptedData1/Users/javier/Brain_study/ERASE',
'TrainOutput/THESIS/UW_None_mse_ssim_haus',
0)
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