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run.py
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from deepsleep import DataLoader, Trainer, BuildGPUModel, PrepareDataset, PreparePretrainDataset
import tensorflow as tf
import os, glob
import argparse
def run():
parser = argparse.ArgumentParser()
parser.add_argument("-rt", "--reptrain", help="Compile and run REPR Model", action="store_true")
parser.add_argument("-sq", "--seqtrain", help="Compile and run SEQ Model", action="store_true")
parser.add_argument("-ft", "--finetune", help="Compile and run Finetune network", action="store_true")
parser.add_argument("-t", "--test", help="Run tests", action="store_true")
parser.add_argument("-tr", "--train", help="Train models", action="store_true")
parser.add_argument("-p", "--prepare", help="Prepare and extract dataset from HDF", action="store_true")
parser.add_argument("-pp", "--preparePretrain", help="Prepare and extract dataset from HDF", action="store_true")
parser.add_argument("-r", "--report", help="Get dataset report", action="store_true")
args = parser.parse_args()
if args.preparePretrain:
HDF_DIR = os.path.join(os.getcwd(), "deepsleep/data")
# hdf_file = HDF_DIR + '/Subject8.hdf'
prep_data = PreparePretrainDataset(sampling_mode='random_over_sample', seq_len=7500, batch_size=64, n_classes=4)
prep_data.store_pretrain_files()
# HDF_DIR = os.path.join(os.getcwd(), "deepsleep/data")
# for hdf_file in glob.glob(HDF_DIR + '/' + '*.hdf'):
# print hdf_file
# prep_data = PrepareNonEpochDataset(hdf_file)
# prep_data.store_dataset_to_npy(sampling_mode='over_sample')
if args.prepare:
HDF_DIR = os.path.join(os.getcwd(), "deepsleep/data")
# hdf_file = HDF_DIR + '/Subject41.hdf'
# prep_data = PrepareDataset(hdf_file, seq_len=7500, batch_size=64, n_classes=4)
# prep_data.prep_dataset()
HDF_DIR = os.path.join(os.getcwd(), "deepsleep/data")
for hdf_file in glob.glob(HDF_DIR + '/' + '*.hdf'):
print hdf_file
# hdf_file = HDF_DIR + '/subject36.hdf'
prep_data = PrepareDataset(hdf_file, seq_len=7500, batch_size=64, n_classes=4)
prep_data.prep_dataset()
if args.report:
NPZ_DIR = os.path.join(os.getcwd(), 'dataset')
npz_file = NPZ_DIR + '/' + 'Subject30_16042018.npz'
prep_nonepoch = PrepareNonEpochDataset(npz_file)
prep_nonepoch.get_dataset_report(data=npz_file)
if args.train:
if args.seqtrain:
load_data = DataLoader(split_criterion=0.1, is_pretrain=False)
training_files, validation_files = load_data.split_dataset(val_count=1)
print "Running GPU-optimized models"
build_gpu_model = BuildGPUModel(BATCH_SIZE=64, SAMPLE_RATE=250)
model = build_gpu_model.deepsleep_network(cnn_residual_connection=False,
finetune=False,
pretrain_model_name='pretrain_weights.h5',
blocks=4,
num_layers=2,
filter_block=2,
maxpool_block=2,
learning_rate=1e-4,
num_filters=64,
kernel_size=16,
kernel_regularizer=None,
dropout_rate=None,
plot_arch=False)
trainer = Trainer(gpu_model=None,
model=model,
training_files=training_files,
validation_files=validation_files,
n_epochs=200,
batch_size=64,
nb_workers=1,
shuffle_file_order=True,
model_name='seqtrain_2LS_tr',
class_weight=None,
is_pretrain=False,
val_pretrain=False,
is_stateful_train=True)
trainer.train_model()
if args.reptrain:
load_data = DataLoader(split_criterion=0.1, is_pretrain=False)
training_files, validation_files = load_data.split_dataset(val_count=5)
print "Running GPU-optimized models"
build_gpu_model = BuildGPUModel(BATCH_SIZE=128, SAMPLE_RATE=250, n_gpus=2)
pretrain_model = build_gpu_model.representation_layer(blocks=10,
num_layers=2,
filter_block=4,
maxpool_block=2,
learning_rate=1e-3,
num_filters=64,
kernel_size=16,
dropout_rate=0.3,
kernel_regularizer=None,
plot_arch=False,
residual_connection=True,
include_top=True)
trainer = Trainer(gpu_model=pretrain_model[0],
model=pretrain_model[1],
training_files=training_files,
validation_files=validation_files,
n_epochs=100,
batch_size=128,
nb_workers=1,
shuffle_file_order=True,
model_name='10b2lRes5Val',
class_weight=None,
is_pretrain=False,
val_pretrain=True)
trainer.train_model()
if args.finetune:
load_data = DataLoader(split_criterion=0.1, is_pretrain=False)
training_files, validation_files = load_data.split_dataset(val_count=1)
print "Running GPU-optimized models"
build_gpu_model = BuildGPUModel(BATCH_SIZE=128, SAMPLE_RATE=250)
model = build_gpu_model.deepsleep_network(cnn_residual_connection=True,
finetune=True,
pretrain_model_name='8b2lRes_PTr_128',
blocks=8,
num_layers=2,
filter_block=2,
maxpool_block=2,
learning_rate=1e-5,
num_filters=64,
kernel_size=16,
kernel_regularizer=None,
dropout_rate=0.3,
is_stateful=False,
plot_arch=False)
trainer = Trainer(gpu_model=model[0],
model=model[1],
training_files=training_files,
validation_files=validation_files,
n_epochs=100,
batch_size=128,
nb_workers=1,
shuffle_file_order=True,
model_name='finetune_model',
class_weight=None,
is_pretrain=False,
is_stateful_train=False)
trainer.train_model()
if args.test:
load_data = DataLoader(split_criterion=0.1, is_pretrain=False)
training_files, validation_files = load_data.split_dataset(val_count=1)
print "Running GPU-optimized models"
build_gpu_model = BuildGPUModel(BATCH_SIZE=64, SAMPLE_RATE=250)
model = build_gpu_model.deepsleep_network()
trainer = Trainer(gpu_model=None,
model=model,
training_files=training_files,
validation_files=validation_files,
n_epochs=1,
batch_size=64,
nb_workers=1,
shuffle_file_order=True,
model_name='seqtrain_test',
class_weight=None,
is_pretrain=False)
trainer.train_model()
def run_test():
# load_data = DataLoader(split_criterion=0.2)
# training_files, validation_files = load_data.split_dataset()
# load_data.get_dataset_report(training_data=training_files, validation_data=validation_files)
# # load_data.balance_class(training_data=training_files)
parser = argparse.ArgumentParser()
parser.add_argument("-tr", "--train", help="Train models", action="store_true")
parser.add_argument("-p", "--prepare", help="Prepare and extract dataset from HDF", action="store_true")
args = parser.parse_args()
if args.prepare:
HDF_DIR = os.path.join(os.getcwd(), "tests/test_dataset")
hdf_file = HDF_DIR + '/subject17.hdf'
print hdf_file
if args.train:
from tests import train
#train.test_training()
train.test_training_2()
if __name__ == '__main__':
#sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
#print sess
run()