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train_c3d.py
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train_c3d.py
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# -*- coding:utf-8 -*-
from models import c3d_model
from keras.optimizers import SGD,Adam,RMSprop
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint
import numpy as np
import random
import cv2
import os
import random
import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
def plot_history(history, result_dir):
plt.plot(history.history['acc'], marker='.')
plt.plot(history.history['val_acc'], marker='.')
plt.title('model accuracy')
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.grid()
plt.legend(['acc', 'val_acc'], loc='lower right')
plt.savefig(os.path.join(result_dir, 'model_accuracy.png'))
plt.close()
plt.plot(history.history['loss'], marker='.')
plt.plot(history.history['val_loss'], marker='.')
plt.title('model loss')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.grid()
plt.legend(['loss', 'val_loss'], loc='upper right')
plt.savefig(os.path.join(result_dir, 'model_loss.png'))
plt.close()
def save_history(history, result_dir):
loss = history.history['loss']
acc = history.history['acc']
val_loss = history.history['val_loss']
val_acc = history.history['val_acc']
nb_epoch = len(acc)
with open(os.path.join(result_dir, 'result.txt'), 'w') as fp:
fp.write('epoch\tloss\tacc\tval_loss\tval_acc\n')
for i in range(nb_epoch):
fp.write('{}\t{}\t{}\t{}\t{}\n'.format(
i, loss[i], acc[i], val_loss[i], val_acc[i]))
fp.close()
def process_batch(lines,img_path,train=True):
num = len(lines)
batch = np.zeros((num,16,90,120,3),dtype='float32')
labels = np.zeros(num,dtype='int')
for i in range(num):
path = lines[i].split(' ')[0]
label = lines[i].split(' ')[-1]
symbol = lines[i].split(' ')[1]
label = label.strip('\n')
label = int(label)
symbol = int(symbol)-1
imgs = os.listdir(img_path+path)
imgs.sort(key=str.lower)
for j in range(16):
if train:
is_flip = random.randint(0, 1)
for j in range(16):
img = imgs[symbol + j]
image = cv2.imread(img_path + path + '/' + img)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (120, 90))
if is_flip == 1:
image = cv2.flip(image, 1)
batch[i][j][:][:][:] = image
labels[i] = label
else:
for j in range(16):
img = imgs[symbol + j]
image = cv2.imread(img_path + path + '/' + img)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (120, 90))
batch[i][j][:][:][:] = image
labels[i] = label
return batch, labels
def preprocess(inputs):
#inputs[..., 0] -= 99.9
#inputs[..., 1] -= 92.1
#inputs[..., 2] -= 82.6
#inputs[..., 0] /= 65.8
#inputs[..., 1] /= 62.3
#inputs[..., 2] /= 60.3
inputs /=255.
inputs -= 0.5
inputs *=2.
return inputs
def generator_train_batch(train_txt,batch_size,num_classes,img_path):
ff = open(train_txt, 'r')
lines = ff.readlines()
num = len(lines)
while True:
new_line = []
index = [n for n in range(num)]
random.shuffle(index)
for m in range(num):
new_line.append(lines[index[m]])
for i in range(int(num/batch_size)):
a = i*batch_size
b = (i+1)*batch_size
x_train, x_labels = process_batch(new_line[a:b],img_path,train=True)
x = preprocess(x_train)
y = np_utils.to_categorical(np.array(x_labels), num_classes)
x = np.transpose(x, (0,2,3,1,4))
yield x, y
def generator_val_batch(val_txt,batch_size,num_classes,img_path):
f = open(val_txt, 'r')
lines = f.readlines()
num = len(lines)
while True:
new_line = []
index = [n for n in range(num)]
random.shuffle(index)
for m in range(num):
new_line.append(lines[index[m]])
for i in range(int(num / batch_size)):
a = i * batch_size
b = (i + 1) * batch_size
y_test,y_labels = process_batch(new_line[a:b],img_path,train=False)
x = preprocess(y_test)
test_data = np.transpose(x,(0,2,3,1,4))
y = np_utils.to_categorical(np.array(y_labels), num_classes)
yield test_data, y
def main():
img_path = '/home/kk/TAIL_week_1/datasets/imgs/'
train_file = 'train_list.txt'
valid_file = 'valid_list.txt'
f1 = open(train_file, 'r')
f2 = open(valid_file, 'r')
lines = f1.readlines()
f1.close()
train_samples = len(lines)
lines = f2.readlines()
f2.close()
valid_samples = len(lines)
num_classes = 45
batch_size = 4
epochs = 8
model = c3d_model()
#model.load_weights('C3D01--3.766.hdf5')
#lr = 0.005
#sgd = SGD(lr=lr, momentum=0.9, nesterov=True)
op = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=op, metrics=['accuracy'])
model.summary()
checkpoint = ModelCheckpoint(filepath='C3D{epoch:02d}--{val_loss:.3f}.hdf5', monitor='loss', verbose=1, mode='min', period=1)
history = model.fit_generator(generator_train_batch(valid_file, batch_size, num_classes,img_path),
steps_per_epoch=valid_samples // batch_size,
epochs=epochs,
callbacks=[checkpoint],
#validation_data=generator_val_batch(valid_file,
# batch_size,num_classes,img_path),
#validation_steps=valid_samples // batch_size,
verbose=1)
if not os.path.exists('results/'):
os.mkdir('results/')
plot_history(history, 'results/')
save_history(history, 'results/')
model.save_weights('results/weights_c3d.h5')
if __name__ == '__main__':
main()