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PLATAE.py
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PLATAE.py
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import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from collections import OrderedDict
import tensorflow as tf
import cv2 as cv
import pickle
from shutil import rmtree, copytree
from random import randint
import models
import dataset_processing
import dataset_augmentation
import reader
import postprocessing
class PLATAE:
def __init__(self, launch_file, check_model=False):
class parameter_container:
pass
class dataset_container:
pass
class model_container:
pass
class predictions_container:
pass
self.parameters = parameter_container()
self.datasets = dataset_container()
self.model = model_container()
self.predictions = predictions_container()
# Setup general parameters
casedata = reader.read_case_setup(launch_file)
self.parameters.analysis = casedata.analysis
self.parameters.training_parameters = casedata.training_parameters
self.parameters.img_processing = casedata.img_processing
self.parameters.img_size = casedata.img_resize
self.parameters.data_augmentation = casedata.data_augmentation
self.parameters.activation_plotting = casedata.activation_plotting
self.parameters.prediction = casedata.prediction
self.case_dir = casedata.case_dir
# Sensitivity analysis variable identification
sens_vars = [parameter for parameter in self.parameters.training_parameters.items()
if type(parameter[1]) == list
if parameter[0] != 'addaugdata']
if len(sens_vars) != 0:
self.parameters.sens_variable = sens_vars[0]
else:
self.parameters.sens_variable = None
# Check for model reconstruction
if self.parameters.analysis['import'] == True:
self.model.imported = True
self.model.Model, self.model.History = self.reconstruct_model()
else:
self.model.imported = False
def __str__(self):
class_name = type(self).__name__
return '{}, a class to detect shockwaves on images based on Deep learning algorithms'.format(class_name)
def launch_analysis(self):
analysis_ID = self.parameters.analysis['type']
analysis_list = {
'singletraining': self.singletraining,
'sensanalysis': self.sensitivity_analysis_on_training,
'trainpredict': self.trainpredict,
'datagen': self.data_generation,
'plotactivations': self.plot_activations,
'predict': self.predict_on_test_set,
}
arguments_list = {
'singletraining': [],
'sensanalysis': [],
'trainpredict': [],
'datagen': [],
'plotactivations': [],
'predict': [],
}
F = analysis_list[analysis_ID]
fun_args = arguments_list[analysis_ID]
F(*fun_args)
def sensitivity_analysis_on_training(self):
case_dir = self.case_dir
img_dims = self.parameters.img_size
batch_size = self.parameters.training_parameters['batch_size']
train_size = self.parameters.training_parameters['train_size']
# Retrieve sensitivity variable
sens_variable = self.parameters.sens_variable
# Perform sensitivity analysis
self.datasets.data_train, self.datasets.data_cv, self.datasets.data_test = \
dataset_processing.get_datasets(case_dir,img_dims,train_size)
self.datasets.dataset_train, self.datasets.dataset_cv, self.datasets.dataset_test = \
dataset_processing.get_tensorflow_datasets(self.datasets.data_train,self.datasets.data_cv,self.datasets.data_test,batch_size)
if self.model.imported == False:
self.train_model(sens_variable)
self.export_model_performance(sens_variable)
self.export_model(sens_variable)
self.export_nn_log()
def singletraining(self):
case_dir = self.case_dir
img_dims = self.parameters.img_size
train_size = self.parameters.training_parameters['train_size']
batch_size = self.parameters.training_parameters['batch_size']
'''
fashion_mnist = tf.keras.datasets.fashion_mnist
self.datasets.mnist_data_train, data_val = fashion_mnist.load_data()
X_cv, X_test, y_cv, y_test = train_test_split(data_val[0],data_val[1],train_size=0.75,shuffle=True)
self.datasets.mnist_data_cv = (X_cv, y_cv)
self.datasets.mnist_data_test = (X_test, y_test)
img_dims = (28,28)
'''
self.datasets.data_train, self.datasets.data_cv, self.datasets.data_test = \
dataset_processing.get_datasets(case_dir,img_dims,train_size)
self.datasets.dataset_train, self.datasets.dataset_cv, self.datasets.dataset_test = \
dataset_processing.get_tensorflow_datasets(self.datasets.data_train,self.datasets.data_cv,self.datasets.data_test,batch_size)
if self.model.imported == False:
self.train_model()
self.export_model_performance()
'''
idx = 10
x = self.datasets.data_train[0][idx,:,:,:].astype('float32')
print('y = ',self.datasets.data_train[1][idx])
Model_r, _ = self.reconstruct_model()
print('Output: ',Model_r.predict(np.expand_dims(x,axis=0)))
'''
self.export_model()
self.export_nn_log()
def trainpredict(self):
# Training
case_dir = self.case_dir
img_dims = self.parameters.img_size
train_size = self.parameters.training_parameters['train_size']
batch_size = self.parameters.training_parameters['batch_size']
self.datasets.data_train, self.datasets.data_cv, self.datasets.data_test = \
dataset_processing.get_datasets(case_dir,img_dims,train_size)
self.datasets.dataset_train, self.datasets.dataset_cv, self.datasets.dataset_test = \
dataset_processing.get_tensorflow_datasets(self.datasets.data_train,self.datasets.data_cv,self.datasets.data_test,batch_size)
if self.model.imported == False:
self.train_model()
self.export_model_performance()
self.export_model()
self.export_nn_log()
# Prediction
model_dir = os.path.join(case_dir,'Results',str(self.parameters.analysis['case_ID']),'Model')
generation_dir = os.path.join(case_dir,'Results','pretrained_model')
if os.path.exists(generation_dir):
rmtree(generation_dir)
copytree(model_dir,generation_dir)
self.model.imported = True
self.predict_on_test_set()
def data_generation(self):
transformations = [{k:v[1:] for (k,v) in self.parameters.img_processing.items() if v[0] == 1}][0]
augdata_size = self.parameters.data_augmentation[1]
self.generate_augmented_data(transformations,augdata_size)
def plot_activations(self):
# Parameters
case_dir = self.case_dir
case_ID = self.parameters.analysis['case_ID']
img_dims = self.parameters.img_size
batch_size = self.parameters.training_parameters['batch_size']
train_size = self.parameters.training_parameters['train_size']
n = self.parameters.activation_plotting['n_samples']
figs_per_row = self.parameters.activation_plotting['n_cols']
rows_to_cols_ratio = self.parameters.activation_plotting['rows2cols_ratio']
storage_dir = os.path.join(self.case_dir,'Results','pretrained_model')
# Generate datasets
self.datasets.data_train, self.datasets.data_cv, self.datasets.data_test = \
dataset_processing.get_datasets(case_dir,img_dims,train_size)
self.datasets.dataset_train, self.datasets.dataset_cv, self.datasets.dataset_test = \
dataset_processing.get_tensorflow_datasets(self.datasets.data_train,self.datasets.data_cv,self.datasets.data_test,batch_size)
m_tr = self.datasets.data_train[0].shape[0]
m_cv = self.datasets.data_cv[0].shape[0]
m_ts = self.datasets.data_test[0].shape[0]
m = m_tr + m_cv + m_ts
# Read datasets
dataset = np.zeros((m,img_dims[1],img_dims[0],3),dtype='uint8')
dataset[:m_tr,:] = self.datasets.data_train[0]
dataset[m_tr:m_tr+m_cv,:] = self.datasets.data_cv[0]
dataset[m_tr+m_cv:m,:] = self.datasets.data_test[0]
# Index image sampling
idx = [randint(1,m) for i in range(n)]
idx_set = set(idx)
while len(idx) != len(idx_set):
extra_item = randint(1,m)
idx_set.add(extra_item)
# Reconstruct model
model, _ = self.reconstruct_model()
#model = Model(image_shape,0.001,0.0,0.0,0.0,activation)
# Load weights
weights_filename = [file for file in os.listdir(storage_dir) if file.endswith('.h5')][0]
model.load_weights(os.path.join(storage_dir,weights_filename))
# Plot
for idx in idx_set:
img = dataset[idx,:]
postprocessing.monitor_hidden_layers(img,model,case_dir,figs_per_row,rows_to_cols_ratio,idx)
def generate_augmented_data(self, transformations, augmented_dataset_size=1):
# Set storage folder for augmented dataset
case_dir = self.case_dir
img_dims = self.parameters.img_size
augmented_dataset_dir = os.path.join(case_dir,'Datasets','Dataset_augmented')
# Unpack data
X, y = dataset_processing.set_dataset(case_dir,img_dims,dataset_foldername='Datasets_to_augment')
# Generate new dataset
data_augmenter = dataset_augmentation.datasetAugmentationClass(X,y,transformations,augmented_dataset_size,augmented_dataset_dir)
data_augmenter.transform_images()
data_augmenter.export_augmented_dataset()
def train_model(self, sens_var=None):
# Parameters
alpha = self.parameters.training_parameters['learning_rate']
nepoch = self.parameters.training_parameters['epochs']
batch_size = self.parameters.training_parameters['batch_size']
l2_reg = self.parameters.training_parameters['l2_reg']
l1_reg = self.parameters.training_parameters['l1_reg']
dropout = self.parameters.training_parameters['dropout']
image_shape = self.parameters.img_size
activation = self.parameters.training_parameters['activation']
#Model = models.vehicle_detection_alexnet_model
Model = models.vehicle_detection_cnn_model
self.model.Model = []
self.model.History = []
if sens_var == None: # If it is a one-time training
self.model.Model.append(Model(image_shape,alpha,l2_reg,l1_reg,dropout,activation))
self.model.History.append(self.model.Model[-1].fit(self.datasets.dataset_train,epochs=nepoch,batch_size=batch_size,
steps_per_epoch=500,validation_data=self.datasets.dataset_cv,
validation_steps=None))
else: # If it is a sensitivity analysis
if type(alpha) == list:
for learning_rate in alpha:
model = Model(image_shape,learning_rate,l2_reg,l1_reg,dropout,activation)
self.model.Model.append(model)
self.model.History.append(model.fit(self.datasets.dataset_train,epochs=nepoch,batch_size=batch_size,
steps_per_epoch=500,validation_data=self.datasets.dataset_cv,
validation_steps=None))
elif type(l2_reg) == list:
for regularizer in l2_reg:
model = Model(image_shape,alpha,regularizer,l1_reg,dropout,activation)
self.model.Model.append(model)
self.model.History.append(model.fit(self.datasets.dataset_train,epochs=nepoch,batch_size=batch_size,
steps_per_epoch=500,validation_data=self.datasets.dataset_cv,
validation_steps=None))
elif type(l1_reg) == list:
for regularizer in l1_reg:
model = Model(image_shape,alpha,l2_reg,regularizer,dropout,activation)
self.model.Model.append(model)
self.model.History.append(model.fit(self.datasets.dataset_train,epochs=nepoch,batch_size=batch_size,
steps_per_epoch=500,validation_data=self.datasets.dataset_cv,
validation_steps=None))
elif type(dropout) == list:
for rate in dropout:
model = Model(image_shape,alpha,l2_reg,l1_reg,rate,activation)
self.model.Model.append(model)
self.model.History.append(model.fit(self.datasets.dataset_train,epochs=nepoch,batch_size=batch_size,
steps_per_epoch=500,validation_data=self.datasets.dataset_cv,
validation_steps=None))
elif type(activation) == list:
for act in activation:
model = Model(image_shape,alpha,l2_reg,l1_reg,rate,act)
self.model.Model.append(model)
self.model.History.append(model.fit(self.datasets.dataset_train,epochs=nepoch,batch_size=batch_size,
steps_per_epoch=500,validation_data=self.datasets.dataset_cv,
validation_steps=None))
def predict_on_test_set(self):
tf.random.set_seed(0)
img_dims = self.parameters.img_size
pred_dir = self.parameters.prediction['dir']
# Import model
self.model.imported = True
Model, History = self.reconstruct_model()
results_dir = os.path.join(pred_dir,'Results')
if os.path.exists(results_dir):
rmtree(results_dir)
os.makedirs(results_dir)
metrics_functions = {
'accuracy': tf.keras.metrics.Accuracy(),
}
metrics = dict.fromkeys(metrics_functions)
X_test, y_test, paths_test = dataset_processing.read_test_datasets(os.path.join(pred_dir),img_dims,return_filepaths=True)
X_test, y_test = dataset_processing.preprocess_data(X_test,y_test)
logits = Model.predict(X_test)
y_pred = np.array([np.argmax(logit) for logit in logits],dtype=int)
for key in metrics.keys():
metric_function = metrics_functions[key]
metric_function.update_state(y_test,y_pred)
metrics[key] = metric_function.result().numpy()
metrics_name = list(metrics.keys())
metrics_data = list(metrics.values())
metrics_df = pd.DataFrame(index=metrics_name,columns=['Pred'],data=metrics_data)
metrics_df.to_csv(os.path.join(results_dir,'Model_pred_metrics.csv'),sep=';',decimal='.')
paths_df = pd.DataFrame(index=paths_test,columns=['Ground_truth','Prediction'],data=np.array([y_test+1,y_pred+1]).T)
paths_df.to_csv(os.path.join(results_dir,'Model_predictions.csv'),sep=';',decimal='.')
def export_model_performance(self, sens_var=None):
try:
History = self.model.History
except:
raise Exception('There is no evolution data for this model. Train model first.')
else:
if type(History) == list:
N = len(History)
else:
N = 1
History = [History]
# Loss evolution plots #
Nepochs = self.parameters.training_parameters['epochs']
epochs = np.arange(1,Nepochs+1)
case_ID = self.parameters.analysis['case_ID']
for i,h in enumerate(History):
loss_train = h.history['loss']
loss_cv = h.history['val_loss']
fig, ax = plt.subplots(1)
ax.plot(epochs,loss_train,label='Training',color='r')
ax.plot(epochs,loss_cv,label='Cross-validation',color='b')
ax.grid()
ax.set_xlabel('Epochs',size=12)
ax.set_ylabel('Loss',size=12)
ax.tick_params('both',labelsize=10)
ax.legend()
plt.suptitle('Loss evolution case = {}'.format(str(case_ID)))
if sens_var:
if type(sens_var[1][i]) == str:
storage_dir = os.path.join(self.case_dir, 'Results', str(case_ID), 'Model_performance',
'{}={}'.format(sens_var[0], sens_var[1][i]))
else:
storage_dir = os.path.join(self.case_dir, 'Results', str(case_ID), 'Model_performance',
'{}={:.3f}'.format(sens_var[0], sens_var[1][i]))
loss_plot_filename = 'Loss_evolution_{}_{}={}.png'.format(str(case_ID), sens_var[0],
str(sens_var[1][i]))
loss_filename = 'Model_loss_{}_{}={}.csv'.format(str(case_ID), sens_var[0], str(sens_var[1][i]))
metrics_filename = 'Model_metrics_{}_{}={}.csv'.format(str(case_ID), sens_var[0],
str(sens_var[1][i]))
else:
storage_dir = os.path.join(self.case_dir, 'Results', str(case_ID), 'Model_performance')
loss_plot_filename = 'Loss_evolution_{}.png'.format(str(case_ID))
loss_filename = 'Model_loss_{}.csv'.format(str(case_ID))
metrics_filename = 'Model_metrics_{}.csv'.format(str(case_ID))
if os.path.exists(storage_dir):
rmtree(storage_dir)
os.makedirs(storage_dir)
fig.savefig(os.path.join(storage_dir, loss_plot_filename), dpi=200)
plt.close()
# Metrics #
metrics_name = [item for item in h.history if item not in ('loss', 'val_loss')]
metrics_val = [(metric, h.history[metric][0]) for metric in metrics_name if metric.startswith('val')]
metrics_train = [(metric, h.history[metric][0]) for metric in metrics_name if
not metric.startswith('val')]
rows = [metric[0] for metric in metrics_train]
metric_fun = lambda L: np.array([item[1] for item in L])
metrics_data = np.vstack((metric_fun(metrics_train), metric_fun(metrics_val))).T
metrics = pd.DataFrame(index=rows, columns=['Training', 'CV'], data=metrics_data)
metrics.to_csv(os.path.join(storage_dir, metrics_filename), sep=';', decimal='.')
# Loss
loss_data = np.vstack((list(epochs), loss_train, loss_cv)).T
loss = pd.DataFrame(columns=['Epoch', 'Training', 'CV'], data=loss_data)
loss.to_csv(os.path.join(storage_dir, loss_filename), index=False, sep=';', decimal='.')
def export_model(self, sens_var=None):
if type(self.model.Model) == list:
N = len(self.model.Model)
else:
N = 1
self.model.History = [self.model.History]
self.model.Model = [self.model.Model]
case_ID = self.parameters.analysis['case_ID']
for i in range(N):
if sens_var:
if type(sens_var[1][i]) == str:
storage_dir = os.path.join(self.case_dir,'Results',str(case_ID),'Model','{}={}'
.format(sens_var[0],sens_var[1][i]))
else:
storage_dir = os.path.join(self.case_dir,'Results',str(case_ID),'Model','{}={:.3f}'
.format(sens_var[0],sens_var[1][i]))
model_weights_name = 'PLATAE_model_{}_{}={}_weights.h5'.format(str(case_ID),sens_var[0],str(sens_var[1][i]))
model_folder_name = 'PLATAE_model_{}_{}={}'.format(str(case_ID),sens_var[0],str(sens_var[1][i]))
else:
storage_dir = os.path.join(self.case_dir,'Results',str(case_ID),'Model')
model_weights_name = 'PLATAE_model_{}_weights.h5'.format(str(case_ID))
model_folder_name = 'PLATAE_model_{}'.format(str(case_ID))
if os.path.exists(storage_dir):
rmtree(storage_dir)
os.makedirs(storage_dir)
# Export history training
with open(os.path.join(storage_dir,'History'),'wb') as f:
pickle.dump(self.model.History[i].history,f)
# Save model
self.model.Model[i].save(os.path.join(storage_dir,model_folder_name.format(str(case_ID))))
# Export model weights to HDF5 file
self.model.Model[i].save_weights(os.path.join(storage_dir,model_weights_name))
def reconstruct_model(self, mode='train'):
storage_dir = os.path.join(self.case_dir,'Results','pretrained_model')
casedata = reader.read_case_logfile(os.path.join(storage_dir,'PLATAE.log'))
img_dim = casedata.img_size
alpha = casedata.training_parameters['learning_rate']
activation = casedata.training_parameters['activation']
# Load weights into new model
Model = models.vehicle_detection_cnn_model(img_dim,alpha,0.0,0.0,0.0,activation)
weights_filename = [file for file in os.listdir(storage_dir) if file.endswith('.h5')][0]
Model.load_weights(os.path.join(storage_dir,weights_filename))
# Reconstruct history
class history_container:
pass
History = history_container()
try:
with open(os.path.join(storage_dir,'History'),'rb') as f:
History.history = pickle.load(f)
History.epoch = np.arange(1,len(History.history['loss'])+1)
History.model = Model
except:
History.epoch = None
History.model = None
return Model, History
def export_nn_log(self):
def update_log(parameters, model):
training = OrderedDict()
training['TRAINING SIZE'] = parameters.training_parameters['train_size']
training['LEARNING RATE'] = parameters.training_parameters['learning_rate']
training['L2 REGULARIZER'] = parameters.training_parameters['l2_reg']
training['L1 REGULARIZER'] = parameters.training_parameters['l1_reg']
training['DROPOUT'] = parameters.training_parameters['dropout']
training['ACTIVATION'] = parameters.training_parameters['activation']
training['NUMBER OF EPOCHS'] = parameters.training_parameters['epochs']
training['BATCH SIZE'] = parameters.training_parameters['batch_size']
training['OPTIMIZER'] = [model.optimizer._name for model in model.Model]
training['METRICS'] = [model.metrics_names[-1] if model.metrics_names != None else None for model in model.Model]
analysis = OrderedDict()
analysis['CASE ID'] = parameters.analysis['case_ID']
analysis['ANALYSIS'] = parameters.analysis['type']
analysis['IMPORTED MODEL'] = parameters.analysis['import']
analysis['LAST TRAINING LOSS'] = ['{:.3f}'.format(history.history['loss'][-1]) for history in model.History]
analysis['LAST CV LOSS'] = ['{:.3f}'.format(history.history['val_loss'][-1]) for history in model.History]
architecture = OrderedDict()
architecture['INPUT SHAPE'] = parameters.img_size
return training, analysis, architecture
parameters = self.parameters
if parameters.analysis['type'] == 'sensanalysis':
varname, varvalues = parameters.sens_variable
for value in varvalues:
parameters.training_parameters[varname] = value
training, analysis, architecture = update_log(parameters,self.model)
case_ID = parameters.analysis['case_ID']
if type(value) == str:
storage_folder = os.path.join(self.case_dir,'Results',str(case_ID),'Model','{}={}'.format(varname,value))
else:
storage_folder = os.path.join(self.case_dir,'Results',str(case_ID),'Model','{}={:.3f}'.format(varname,value))
with open(os.path.join(storage_folder,'PLATAE.log'),'w') as f:
f.write('PLATAE log file\n')
f.write('==================================================================================================\n')
f.write('->ANALYSIS\n')
for item in analysis.items():
f.write(item[0] + '=' + str(item[1]) + '\n')
f.write('--------------------------------------------------------------------------------------------------\n')
f.write('->TRAINING\n')
for item in training.items():
f.write(item[0] + '=' + str(item[1]) + '\n')
f.write('--------------------------------------------------------------------------------------------------\n')
f.write('->ARCHITECTURE\n')
for item in architecture.items():
f.write(item[0] + '=' + str(item[1]) + '\n')
f.write('--------------------------------------------------------------------------------------------------\n')
f.write('->MODEL\n')
for model in self.model.Model:
model.summary(print_fn=lambda x: f.write(x + '\n'))
f.write('==================================================================================================\n')
else:
training, analysis, architecture = update_log(self.parameters,self.model)
case_ID = parameters.analysis['case_ID']
storage_folder = os.path.join(self.case_dir,'Results',str(case_ID))
with open(os.path.join(storage_folder,'Model','PLATAE.log'),'w') as f:
f.write('PLATAE log file\n')
f.write('==================================================================================================\n')
f.write('->ANALYSIS\n')
for item in analysis.items():
f.write(item[0] + '=' + str(item[1]) + '\n')
f.write('--------------------------------------------------------------------------------------------------\n')
f.write('->TRAINING\n')
for item in training.items():
f.write(item[0] + '=' + str(item[1]) + '\n')
f.write('--------------------------------------------------------------------------------------------------\n')
f.write('->ARCHITECTURE\n')
for item in architecture.items():
f.write(item[0] + '=' + str(item[1]) + '\n')
f.write('--------------------------------------------------------------------------------------------------\n')
f.write('->MODEL\n')
for model in self.model.Model:
model.summary(print_fn=lambda x: f.write(x + '\n'))
f.write('==================================================================================================\n')
if __name__ == '__main__':
launcher = r'C:\Users\juan.ramos\PLATAE\Scripts\launcher.dat'
sw_scanner = PLATAE(launcher,check_model=False)
sw_scanner.launch_analysis()