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R9868/src_pyeddl/train_full_model_onnx.py
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train_full_model_onnx.py
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#Imports
import numpy as np
import pandas as pd
# Utility file
import ModelHandler as mh
# sklearn
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
if __name__ == "__main__":
# Msc
temp_data_folder = "../temp_data/"
model_file = "../model/model.bin"
data_folder = "/shares/eslfiler1/scratch/teijeiro/chbmit_deephealth/"
signal_length = "1min"
# processing
validation_size=0.15
# Model
epochs = 50
batch_size = 32
learning_rate = 0.0075 # 0.00075
handler = mh.ModelHandler()
with open(data_folder+"signal_mit_"+signal_length+".csv", "rb") as file:
x_data_ = file.read().splitlines()
y_data = np.loadtxt(data_folder+"labels_mit_"+signal_length+".txt")
info_data = np.loadtxt(data_folder+"infos_mit_"+signal_length+".txt", dtype="str")
# Convert from string
x_data = []
for sig in x_data_:
x_data.append(np.fromstring(sig, sep=','))
# Reshape in 2D as data are 1D in csv file
x_data = [np.reshape(np.array(x_data_i), (handler.NB_CHNS, int(len(x_data_i)/handler.NB_CHNS))) for x_data_i in x_data]
# Create the pandas df
data = pd.concat([pd.Series(y_data), pd.Series([info[0] for info in info_data]),pd.Series([info[1] for info in info_data])], axis=1)
data.columns = ["label", "patient", "file"]
data["signal"] = ""
data["signal"].astype(object)
for i, sig in enumerate(x_data):
data.at[i, "signal"] = sig
patients = np.unique(data.patient)
data.sort_values(["patient", "file", "label"], inplace=True)
data = data.reset_index(drop=True)
# Cutting the signals in 5-second segments for training
x_train, y_train, _, _ = handler.cut_signal_data(data, data[data.patient==None])
# Train/validation splitting
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=validation_size, stratify=y_train)
x_train = np.array(x_train)
x_val = np.array(x_val)
x_train = x_train.reshape((len(x_train), handler.NB_CHNS, 1280, 1))
x_val = x_val.reshape((len(x_val), handler.NB_CHNS, 1280, 1))
y_train = np.eye(2)[np.array(y_train).astype(int)]
y_val = np.eye(2)[np.array(y_val).astype(int)]
#Model training
handler.train(model_file, epochs, batch_size, learning_rate, x_train, y_train, x_val, y_val)
#Plain evaluation with the training set
handler.evaluate(model_file, x_train, y_train)
#Model storage in ONNX
handler.save_to_onnx('uc13_epilepsy_net.onnx')
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