import os
import shutil
import numpy as np
from keras.optimizers import Adam, SGD
from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import load_img
from keras.applications.resnet50 import preprocess_input as resnet_preprocess_input
from keras.applications.xception import preprocess_input as xception_preprocess_input
from keras.applications.inception_v3 import preprocess_input as inception_v3_preprocess_input
from keras import layers
import pandas as pd
from tqdm import tqdm
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
from colorama import init
from termcolor import colored
from transfer.resnet50 import get_resnet_pre_post_model, get_resnet_final_model
from transfer.xception import get_xception_pre_post_model, get_xception_final_model
from transfer.inception_v3 import get_inception_v3_pre_post_model, get_inception_v3_final_model
def gen_minibatches(array_dir, array_names, batch_size, architecture, final = False):
array_names = list(array_names)
while True:
# in place shuffle
np.random.shuffle(array_names)
array_names_mb = array_names[:batch_size]
arrays = []
labels = []
for array_name in array_names_mb:
img_path = os.path.join(array_dir, array_name)
array = np.load(img_path)
if final:
if architecture == 'resnet50':
array = np.squeeze(resnet_preprocess_input(array[np.newaxis].astype(np.float32)))
elif architecture == 'xception':
array = np.squeeze(xception_preprocess_input(array[np.newaxis].astype(np.float32)))
else:
array = np.squeeze(inception_v3_preprocess_input(array[np.newaxis].astype(np.float32)))
arrays.append(array)
labels.append(np.load(img_path.replace('-img-','-label-')))
yield np.array(arrays), np.array(labels)
def no_folds_generator(pre_model_files):
yield [i for i in range(len(pre_model_files))], -1
def train_model(project, final = False, last = False):
weight_label = '-' + project['architecture'] + '-weights-'
source_path = project['path']
weights_path = os.path.join(source_path, 'weights')
plot_path = os.path.join(source_path, 'plots')
if last:
weights = 'last_weights'
else:
weights = 'best_weights'
if final:
weight_label += '-final-'
use_path = os.path.join(source_path, 'augmented')
else:
use_path = os.path.join(source_path, 'pre_model')
project['model_round'] += 1
os.makedirs(weights_path, exist_ok=True)
os.makedirs(plot_path, exist_ok=True)
img_dim = project['img_dim'] * project['img_size']
conv_dim = project['conv_dim'] * project['img_size']
lr = project['learning_rate']
decay = project['learning_rate_decay']
all_files = os.listdir(use_path)
pre_model_files = list(filter(lambda x: r'-img-' in x, all_files))
label_names = list(filter(lambda x: r'-label-' in x, all_files))
pre_model_files_df = pd.DataFrame({'files': pre_model_files})
pre_model_files_df['suffix'] = pre_model_files_df.apply(lambda row: row.files.split('.')[-1], axis = 1)
pre_model_files_df = pre_model_files_df[pre_model_files_df.suffix == 'npy']
pre_model_files_df['ind'] = pre_model_files_df.apply(lambda row: row.files.split('-')[0], axis = 1).astype(int)
pre_model_files_df['label'] = pre_model_files_df.apply(lambda row: row.files.split('-')[3], axis = 1)
pre_model_files_df_dedup = pre_model_files_df.drop_duplicates(subset='ind')
pre_model_files_df = pre_model_files_df.set_index(['ind'])
pre_model_files.sort()
label_names.sort()
pre_model_files_arr = np.array(pre_model_files)
label_names_arr = np.array(label_names)
labels = [np.argmax(np.load(os.path.join(use_path, label_name))) for label_name in label_names]
best_weights = []
last_weights = []
if project['kfold'] >= 3:
kfold = StratifiedKFold(n_splits=project['kfold'], shuffle=True, random_state = project['seed'])
kfold_generator = kfold.split(pre_model_files_df_dedup, pre_model_files_df_dedup.label)
validate = True
else:
print('Too few k-folds selected, fitting on all data')
kfold_generator = no_folds_generator(pre_model_files_df_dedup)
validate = False
for i, (train, test) in enumerate(kfold_generator):
if project['kfold_every']:
print('----- Fitting Fold', i, '-----')
elif i > 0:
break
weights_name = project['name'] + weight_label + '-kfold-' + str(i) + '-round-' + str(project['model_round']) +'.hdf5'
plot_name = project['name'] + weight_label + '-kfold-' + str(i) + '-round-' + str(project['model_round']) +'.png'
if project[weights] is None:
fold_weights = None
else:
fold_weights = project[weights][i]
if final:
if project['architecture'] == 'resnet50':
model = get_resnet_final_model(img_dim, conv_dim, project['number_categories'], fold_weights, project['is_final'])
elif project['architecture'] == 'xception':
model = get_xception_final_model(img_dim, conv_dim, project['number_categories'], fold_weights, project['is_final'])
else:
model = get_inception_v3_final_model(img_dim, conv_dim, project['number_categories'], fold_weights, project['is_final'])
for i, layer in enumerate(model.layers[1].layers):
if len(layer.trainable_weights) > 0:
if i < project['final_cutoff']:
mult = 0.01
else:
mult = 0.1
layer.learning_rate_multiplier = [mult for tw in layer.trainable_weights]
else:
if project['architecture'] == 'resnet50':
pre_model, model = get_resnet_pre_post_model(img_dim,
conv_dim,
len(project['categories']),
model_weights = fold_weights)
elif project['architecture'] == 'xception':
pre_model, model = get_xception_pre_post_model(img_dim,
conv_dim,
len(project['categories']),
model_weights = fold_weights)
else:
pre_model, model = get_inception_v3_pre_post_model(img_dim,
conv_dim,
len(project['categories']),
model_weights = fold_weights)
pre_model_files_dedup_train = pre_model_files_df_dedup.iloc[train]
train_ind = list(set(pre_model_files_dedup_train.ind))
pre_model_files_train = pre_model_files_df.loc[train_ind]
gen_train = gen_minibatches(use_path, pre_model_files_train.files, project['batch_size'], project['architecture'], final = final)
number_train_samples = len(pre_model_files_train)
if validate:
pre_model_files_dedup_test = pre_model_files_df_dedup.iloc[test]
test_ind = list(set(pre_model_files_dedup_test.ind))
pre_model_files_test = pre_model_files_df.loc[test_ind]
gen_test = gen_minibatches(use_path, pre_model_files_test.files, project['batch_size'], project['architecture'], final = final)
number_test_samples = len(pre_model_files_test)
validation_steps = (number_test_samples // project['batch_size'])
weights_checkpoint_file = weights_name.split('.')[0] + '-kfold-' + str(i) + "-improvement-{epoch:02d}-{val_categorical_accuracy:.4f}.hdf5"
checkpoint = ModelCheckpoint(os.path.join(weights_path, weights_checkpoint_file),
monitor='val_categorical_accuracy',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
else:
gen_test = None
validation_steps = None
callbacks_list = None
steps_per_epoch = (number_train_samples // project['batch_size'])
for j in range(project['rounds']):
optimizer = Adam(lr = lr, decay = decay)
model.compile(optimizer = optimizer,
loss = 'categorical_crossentropy',
metrics = ['categorical_accuracy'])
model.fit_generator(gen_train,
steps_per_epoch = steps_per_epoch,
epochs = project['cycle'] * (j + 1),
verbose = 1,
validation_data = gen_test,
validation_steps = validation_steps,
initial_epoch = j * project['cycle'],
callbacks = callbacks_list)
model.save_weights(os.path.join(weights_path, weights_name))
last_weights.append(os.path.join(weights_path, weights_name))
weights_names = os.listdir(weights_path)
max_val = -1
max_i = -1
for j, name in enumerate(weights_names):
if name.find(weights_name.split('.')[0]) >= 0:
if (name.find(weight_label) >= 0) and (name.find('improvement') >= 0):
val = int(name.split('.')[1])
if val > max_val:
max_val = val
max_i = j
if project['plot']:
print('Plotting confusion matrix')
if max_i == -1:
print('Loading last weights:', os.path.join(weights_path, weights_name))
model.load_weights(os.path.join(weights_path, weights_name))
else:
print('Loading best weights:', os.path.join(weights_path, weights_names[max_i]))
model.load_weights(os.path.join(weights_path, weights_names[max_i]))
best_predictions = []
true_labels = []
print('Predicting test files')
if validate:
use_files = pre_model_files_test.files
else:
use_files = pre_model_files_train.files
for array_name in tqdm(use_files):
img_path = os.path.join(use_path, array_name)
img = np.load(img_path)
if final:
if project['architecture'] == 'resnet50':
img = np.squeeze(resnet_preprocess_input(img[np.newaxis].astype(np.float32)))
elif project['architecture'] == 'xception':
img = np.squeeze(xception_preprocess_input(img[np.newaxis].astype(np.float32)))
else:
img = np.squeeze(inception_v3_preprocess_input(img[np.newaxis].astype(np.float32)))
prediction = model.predict(img[np.newaxis])
best_predictions.append(project['categories'][np.argmax(prediction)])
true_label = np.load(img_path.replace('-img-','-label-'))
true_labels.append(project['categories'][np.argmax(true_label)])
cm = confusion_matrix(true_labels, best_predictions, project['categories'])
plt.clf()
sns.heatmap(pd.DataFrame(cm, project['categories'], project['categories']), annot = True, fmt = 'g')
plt.xlabel('Actual')
plt.xlabel('Predicted')
plt.xticks(rotation = 45, fontsize = 8)
plt.yticks(rotation = 45, fontsize = 8)
plt.title('Confusion matrix for fold: ' + str(i) + '\nweights' + weights_name)
plt.savefig(os.path.join(plot_path, plot_name))
print('Confusion matrix plot saved:', colored(os.path.join(plot_path, plot_name), 'magenta'))
if max_i == -1:
best_weights.append(os.path.join(weights_path, weights_name))
else:
best_weights.append(os.path.join(weights_path, weights_names[max_i]))
project['number_categories'] = len(project['categories'])
project['best_weights'] = best_weights
project['last_weights'] = last_weights
project['is_final'] = final
return project
