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train_deepvel.py
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train_deepvel.py
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import h5py
import os
import json
import argparse
from contextlib import redirect_stdout
os.environ["KERAS_BACKEND"] = "tensorflow"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from keras.layers import Input, Convolution2D, merge, Activation, BatchNormalization
from keras.callbacks import ModelCheckpoint, Callback
from keras.models import Model, model_from_json
from keras.optimizers import Adam
from keras.utils.visualize_util import plot as kerasPlot
import tensorflow as tf
import keras.backend.tensorflow_backend as ktf
class LossHistory(Callback):
def __init__(self, root, losses):
self.root = root
self.losses = losses
def on_epoch_end(self, batch, logs={}):
self.losses.append(logs)
with open("{0}_loss.json".format(self.root), 'w') as f:
json.dump(self.losses, f)
def finalize(self):
pass
class train_deepvel(object):
def __init__(self, root, noise, option):
"""
Class used to train DeepVel
Parameters
----------
root : string
Name of the output files. Some extensions will be added for different files (weights, configuration, etc.)
noise : float
Noise standard deviation to be added during training. This helps avoid overfitting and
makes the training more robust
option : string
Indicates what needs to be done
"""
# Only allocate needed memory
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
session = tf.Session(config=config)
ktf.set_session(session)
self.root = root
self.option = option
self.n_filters = 64
self.kernel_size = 3
self.batch_size = 32
self.n_conv_layers = 20
self.input_file_images_training = "/scratch1/aasensio/deepLearning/opticalFlow/database/database_images.h5"
self.input_file_velocity_training = "/scratch1/aasensio/deepLearning/opticalFlow/database/database_velocity.h5"
self.input_file_images_validation = "/scratch1/aasensio/deepLearning/opticalFlow/database/database_images_validation.h5"
self.input_file_velocity_validation = "/scratch1/aasensio/deepLearning/opticalFlow/database/database_velocity_validation.h5"
f = h5py.File(self.input_file_images_training, 'r')
self.n_training_orig, self.nx, self.ny, self.n_times = f.get("intensity").shape
f.close()
f = h5py.File(self.input_file_images_validation, 'r')
self.n_validation_orig, _, _, _ = f.get("intensity").shape
f.close()
self.batchs_per_epoch_training = int(self.n_training_orig / self.batch_size)
self.batchs_per_epoch_validation = int(self.n_validation_orig / self.batch_size)
self.n_training = self.batchs_per_epoch_training * self.batch_size
self.n_validation = self.batchs_per_epoch_validation * self.batch_size
print("Original training set size: {0}".format(self.n_training_orig))
print(" - Final training set size: {0}".format(self.n_training))
print(" - Batch size: {0}".format(self.batch_size))
print(" - Batches per epoch: {0}".format(self.batchs_per_epoch_training))
print("Original validation set size: {0}".format(self.n_validation_orig))
print(" - Final validation set size: {0}".format(self.n_validation))
print(" - Batch size: {0}".format(self.batch_size))
print(" - Batches per epoch: {0}".format(self.batchs_per_epoch_validation))
def training_generator(self):
f_images = h5py.File(self.input_file_images_training, 'r')
images = f_images.get("intensity")
f_velocity = h5py.File(self.input_file_velocity_training, 'r')
velocity = f_velocity.get("velocity")
while 1:
for i in range(self.batchs_per_epoch_training):
input_train = images[i*self.batch_size:(i+1)*self.batch_size,:,:,:].astype('float32')
output_train = velocity[i*self.batch_size:(i+1)*self.batch_size,:,:,:].astype('float32')
yield input_train, output_train
f_images.close()
f_velocity.close()
def validation_generator(self):
f_images = h5py.File(self.input_file_images_validation, 'r')
images = f_images.get("intensity")
f_velocity = h5py.File(self.input_file_velocity_validation, 'r')
velocity = f_velocity.get("velocity")
while 1:
for i in range(self.batchs_per_epoch_validation):
input_validation = images[i*self.batch_size:(i+1)*self.batch_size,:,:,:].astype('float32')
output_validation = velocity[i*self.batch_size:(i+1)*self.batch_size,:,:,:].astype('float32')
yield input_validation, output_validation
f_images.close()
f_velocity.close()
def residual(self, inputs):
x = Convolution2D(self.n_filters, 3, 3, border_mode='same', init='he_normal')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Convolution2D(self.n_filters, 3, 3, border_mode='same', init='he_normal')(x)
x = BatchNormalization()(x)
x = merge([x, inputs], 'sum')
return x
def define_network(self):
print("Setting up network...")
inputs = Input(shape=(self.nx, self.ny, self.n_times))
conv = Convolution2D(self.n_filters, 3, 3, activation='relu', border_mode='same', init='he_normal')(inputs)
x = self.residual(conv)
for i in range(self.n_conv_layers):
x = self.residual(x)
x = Convolution2D(self.n_filters, 3, 3, border_mode='same', init='he_normal')(x)
x = BatchNormalization()(x)
x = merge([x, conv], 'sum')
final = Convolution2D(6, 1, 1, activation='linear', border_mode='same', init='he_normal')(x)
self.model = Model(input=inputs, output=final)
json_string = self.model.to_json()
f = open('{0}_model.json'.format(self.root), 'w')
f.write(json_string)
f.close()
with open('{0}_summary.txt'.format(self.root), 'w') as f:
with redirect_stdout(f):
self.model.summary()
kerasPlot(self.model, to_file='{0}_model.png'.format(self.root), show_shapes=True)
def compile_network(self):
self.model.compile(loss='mse', optimizer=Adam(lr=1e-4))
def read_network(self):
print("Reading previous network...")
f = open('{0}_model.json'.format(self.root), 'r')
json_string = f.read()
f.close()
self.model = model_from_json(json_string)
self.model.load_weights("{0}_weights.hdf5".format(self.root))
def train(self, n_iterations):
print("Training network...")
# Recover losses from previous run
if (self.option == 'continue'):
with open("{0}_loss.json".format(self.root), 'r') as f:
losses = json.load(f)
else:
losses = []
self.checkpointer = ModelCheckpoint(filepath="{0}_weights.hdf5".format(self.root), verbose=1, save_best_only=True)
self.history = LossHistory(self.root, losses)
self.metrics = self.model.fit_generator(self.training_generator(), self.n_training, nb_epoch=n_iterations,
callbacks=[self.checkpointer, self.history], validation_data=self.validation_generator(), nb_val_samples=self.n_validation)
self.history.finalize()
if (__name__ == '__main__'):
parser = argparse.ArgumentParser(description='Train DeepVel')
parser.add_argument('-o','--out', help='Output files')
parser.add_argument('-e','--epochs', help='Number of epochs', default=10)
parser.add_argument('-n','--noise', help='Noise to add during training', default=0.0)
parser.add_argument('-a','--action', help='Action', choices=['start', 'continue'], required=True)
parsed = vars(parser.parse_args())
root = parsed['out']
nEpochs = int(parsed['epochs'])
option = parsed['action']
noise = parsed['noise']
out = train_deepvel(root, noise, option)
if (option == 'start'):
out.define_network()
if (option == 'continue'):
out.read_network()
if (option == 'start' or option == 'continue'):
out.compile_network()
out.train(nEpochs)