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inference_cnn.py
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'''
DL models (FNN, 1D CNN and CNN-LSTM) evaluation on N-CMAPSS
12.07.2021
Hyunho Mo
hyunho.mo@unitn.it
'''
## Import libraries in python
import gc
import argparse
import os
import json
import logging
import sys
import h5py
import time
import matplotlib
import numpy as np
import pandas as pd
import seaborn as sns
from pandas import DataFrame
import matplotlib.pyplot as plt
from matplotlib import gridspec
import math
import random
from random import shuffle
import importlib
from scipy.stats import randint, expon, uniform
import sklearn as sk
from sklearn import svm
from sklearn.utils import shuffle
from sklearn import metrics
from sklearn import preprocessing
from sklearn import pipeline
from sklearn.metrics import mean_squared_error
from math import sqrt
from tqdm import tqdm
import scipy.stats as stats
# from sklearn.utils.testing import ignore_warnings
# from sklearn.exceptions import ConvergenceWarning
# import keras
import tensorflow as tf
print(tf.__version__)
# import keras.backend as K
import tensorflow.keras.backend as K
from tensorflow.keras import backend
from tensorflow.keras import optimizers
from tensorflow.keras.models import Sequential, load_model, Model
from tensorflow.keras.layers import Input, Dense, Flatten, Dropout, Embedding
from tensorflow.keras.layers import BatchNormalization, Activation, LSTM, TimeDistributed, Bidirectional
from tensorflow.keras.layers import Conv1D
from tensorflow.keras.layers import MaxPooling1D
from tensorflow.keras.layers import concatenate
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from utils.data_preparation_unit import df_all_creator, df_train_creator, df_test_creator, Input_Gen
from utils.dnn import one_dcnn
# import tensorflow.compat.v1 as tf
# tf.disable_v2_behavior()
seed = 0
random.seed(0)
np.random.seed(seed)
# Ignore tf err log
pd.options.mode.chained_assignment = None # default='warn'
# from tensorflow.compat.v1 import ConfigProto
# from tensorflow.compat.v1 import InteractiveSession
# config = ConfigProto()
# config.gpu_options.allow_growth = True
# session = InteractiveSession(config=config)
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
# tf.get_logger().setLevel(logging.ERROR)
# tf.config.set_visible_devices([], 'GPU')
current_dir = os.path.dirname(os.path.abspath(__file__))
data_filedir = os.path.join(current_dir, 'N-CMAPSS')
data_filepath = os.path.join(current_dir, 'N-CMAPSS', 'N-CMAPSS_DS02-006.h5')
sample_dir_path = os.path.join(data_filedir, 'Samples_whole')
model_temp_path = os.path.join(current_dir, 'Models', 'oned_cnn_rep.h5')
tf_temp_path = os.path.join(current_dir, 'TF_Model_tf')
pic_dir = os.path.join(current_dir, 'Figures')
'''
load array from npz files
'''
def load_part_array (sample_dir_path, unit_num, win_len, stride, part_num):
filename = 'Unit%s_win%s_str%s_part%s.npz' %(str(int(unit_num)), win_len, stride, part_num)
filepath = os.path.join(sample_dir_path, filename)
loaded = np.load(filepath)
return loaded['sample'], loaded['label']
def load_part_array_merge (sample_dir_path, unit_num, win_len, win_stride, partition):
sample_array_lst = []
label_array_lst = []
print ("Unit: ", unit_num)
for part in range(partition):
print ("Part.", part+1)
sample_array, label_array = load_part_array (sample_dir_path, unit_num, win_len, win_stride, part+1)
sample_array_lst.append(sample_array)
label_array_lst.append(label_array)
sample_array = np.dstack(sample_array_lst)
label_array = np.concatenate(label_array_lst)
sample_array = sample_array.transpose(2, 0, 1)
print ("sample_array.shape", sample_array.shape)
print ("label_array.shape", label_array.shape)
return sample_array, label_array
def load_array (sample_dir_path, unit_num, win_len, stride):
filename = 'Unit%s_win%s_str%s.npz' %(str(int(unit_num)), win_len, stride)
filepath = os.path.join(sample_dir_path, filename)
loaded = np.load(filepath)
return loaded['sample'].transpose(2, 0, 1), loaded['label']
def rmse(y_true, y_pred):
return backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis=-1))
def shuffle_array(sample_array, label_array):
ind_list = list(range(len(sample_array)))
shuffle(ind_list)
shuffle_sample = sample_array[ind_list, :, :]
shuffle_label = label_array[ind_list,]
return shuffle_sample, shuffle_label
def figsave(history,index, win_len, win_stride, bs):
fig_acc = plt.figure(figsize=(15, 8))
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Training #%s' %int(index), fontsize=24)
plt.ylabel('loss', fontdict={'fontsize': 18})
plt.xlabel('epoch', fontdict={'fontsize': 18})
plt.legend(['Training loss', 'Validation loss'], loc='upper left', fontsize=18)
plt.show()
print ("saving file:training loss figure")
fig_acc.savefig(pic_dir + "/unit%s_training_w%s_s%s_bs%s.png" %(int(index), int(win_len), int(win_stride), int(bs)))
return
units_index_train = [2.0, 5.0, 10.0, 16.0, 18.0, 20.0]
units_index_test = [11.0, 14.0, 15.0]
def main():
# current_dir = os.path.dirname(os.path.abspath(__file__))
parser = argparse.ArgumentParser(description='sample creator')
parser.add_argument('-w', type=int, default=50, help='sequence length', required=True)
parser.add_argument('-s', type=int, default=1, help='stride of filter')
parser.add_argument('-f', type=int, default=10, help='number of filter')
parser.add_argument('-k', type=int, default=10, help='size of kernel')
parser.add_argument('-bs', type=int, default=256, help='batch size')
parser.add_argument('-ep', type=int, default=30, help='max epoch')
parser.add_argument('-pt', type=int, default=20, help='patience')
parser.add_argument('-vs', type=float, default=0.1, help='validation split')
args = parser.parse_args()
win_len = args.w
win_stride = args.s
partition = 3
n_filters = args.f
kernel_size = args.k
lr = 0.001
bs = args.bs
ep = args.ep
pt = args.pt
vs = args.vs
amsgrad = optimizers.Adam(learning_rate=lr, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True, name='Adam')
for index in units_index_train:
sample_array, label_array = load_array (sample_dir_path, index, win_len, win_stride)
sample_array, label_array = shuffle_array(sample_array, label_array)
print("Training for trajectory of engine %s" %int(index))
print("sample_array.shape", sample_array.shape)
print("label_array.shape", label_array.shape)
if int(index) == int(units_index_train[0]):
input_temp = Input(shape=(sample_array.shape[1], sample_array.shape[2]),name='unit%s' %str(int(index)))
one_d_cnn = one_dcnn(n_filters, kernel_size, sample_array)
cnn_out = one_d_cnn(input_temp)
x = cnn_out
# x = Dropout(0.5)(x)
main_output = Dense(1, activation='linear', name='main_output')(x)
one_d_cnn_model = Model(inputs=input_temp, outputs=main_output)
# model = Model(inputs=[input_1, input_2], outputs=main_output)
print(one_d_cnn_model.summary())
# one_d_cnn_model.compile(loss='mean_squared_error', optimizer=amsgrad, metrics=[rmse, 'mae'])
one_d_cnn_model.compile(loss='mean_squared_error', optimizer=amsgrad, metrics='mae')
history = one_d_cnn_model.fit(sample_array, label_array, epochs=ep, batch_size=bs, validation_split=vs, verbose=2,
callbacks = [EarlyStopping(monitor='val_loss', min_delta=0, patience=pt, verbose=1, mode='min'),
ModelCheckpoint(model_temp_path, monitor='val_loss', save_best_only=True, mode='min', verbose=1)]
)
# one_d_cnn_model.save(tf_temp_path,save_format='tf')
figsave(history, index, win_len, win_stride, bs)
else:
loaded_model = load_model(model_temp_path)
history = loaded_model.fit(sample_array, label_array, epochs=ep, batch_size=bs, validation_split=vs, verbose=2,
callbacks = [EarlyStopping(monitor='val_loss', min_delta=0, patience=pt, verbose=1, mode='min'),
ModelCheckpoint(model_temp_path, monitor='val_loss', save_best_only=True, mode='min', verbose=1)]
)
# loaded_model.save(tf_temp_path,save_format='tf')
figsave(history, index, win_len, win_stride, bs)
# Evaluate (test) the trained network after training each engine
output_lst = []
truth_lst = []
for index in units_index_test:
print("Load data of: ", index)
sample_array, label_array = load_array(sample_dir_path, index, win_len, win_stride)
# estimator = load_model(tf_temp_path, custom_objects={'rmse':rmse})
estimator = load_model(model_temp_path)
y_pred_test = estimator.predict(sample_array)
output_lst.append(y_pred_test)
truth_lst.append(label_array)
print(output_lst[0].shape)
print(truth_lst[0].shape)
print(np.concatenate(output_lst).shape)
print(np.concatenate(truth_lst).shape)
output_array = np.concatenate(output_lst)[:, 0]
trytg_array = np.concatenate(truth_lst)
print(output_array.shape)
print(trytg_array.shape)
rms = sqrt(mean_squared_error(output_array, trytg_array))
print(rms)
### Test (inference after training)
output_lst = []
truth_lst = []
for index in units_index_test:
sample_array, label_array = load_array(sample_dir_path, index, win_len, win_stride)
# estimator = load_model(tf_temp_path, custom_objects={'rmse':rmse})
estimator = load_model(model_temp_path)
y_pred_test = estimator.predict(sample_array)
output_lst.append(y_pred_test)
truth_lst.append(label_array)
print(output_lst[0].shape)
print(truth_lst[0].shape)
print(np.concatenate(output_lst).shape)
print(np.concatenate(truth_lst).shape)
output_array = np.concatenate(output_lst)[:, 0]
trytg_array = np.concatenate(truth_lst)
print(output_array.shape)
print(trytg_array.shape)
rms = sqrt(mean_squared_error(output_array, trytg_array))
print(rms)
for idx in range(len(units_index_test)):
print(output_lst[idx])
print(truth_lst[idx])
fig_verify = plt.figure(figsize=(24, 10))
plt.plot(output_lst[idx], color="green")
plt.plot(truth_lst[idx], color="red", linewidth=2.0)
plt.title('Unit11 inference', fontsize=30)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.ylabel('RUL', fontdict={'fontsize': 24})
plt.xlabel('Timestamps', fontdict={'fontsize': 24})
plt.legend(['Predicted', 'Truth'], loc='upper right', fontsize=28)
plt.show()
fig_verify.savefig(pic_dir + "/unit%s_test_w%s_s%s_bs%s.png" %(str(int(units_index_test[idx])), int(win_len), int(win_stride), int(bs)))
if __name__ == '__main__':
main()