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train.py
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train.py
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"""
Retrain the YOLO model for your own dataset.
"""
import numpy as np
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
from yolo3.utils import get_random_data
def _main():
annotation_path = 'model_data/label.txt'
log_dir = 'logs/000/'
classes_path = 'model_data/my_classes.txt'
anchors_path = 'model_data/my_anchors.txt'
class_names = get_classes(classes_path)
anchors = get_anchors(anchors_path)
input_shape = (416, 416) # multiple of 32, hw
model = create_model(input_shape, anchors, len(class_names), weights_path='model_data/best_weights.h5')
train(model, annotation_path, input_shape, anchors, len(class_names), log_dir=log_dir)
def train(model, annotation_path, input_shape, anchors, num_classes, log_dir='logs/'):
model.compile(optimizer=Adam(lr=1e-4), loss={
'yolo_loss': lambda y_true, y_pred: y_pred})
# 该回调函数将日志信息写入TensorBorad,使得你可以动态的观察训练和测试指标的图像以及不同层的激活值直方图。
logging = TensorBoard(log_dir=log_dir)
# 该回调函数将在每个epoch后保存模型到filepath
checkpoint = ModelCheckpoint(log_dir + "best_weights.h5",
verbose=1,
save_weights_only=True, save_best_only=True, mode='auto', period=1)
# 当评价指标不在提升时,减少学习率
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=15, verbose=1)
# 当监测值不再改善时,该回调函数将中止训练
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
callbacks = [checkpoint, logging]
batch_size = 1
val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
np.random.shuffle(lines) # 打乱排序
num_val = int(len(lines) * val_split)
num_train = len(lines) - num_val # 拿出0.1做val集
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
model.fit_generator(data_generator_wrap(lines[:num_train], batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train // batch_size),
validation_data=data_generator_wrap(lines[num_train:], batch_size, input_shape, anchors,
num_classes),
validation_steps=max(1, num_val // batch_size),
epochs=500,
initial_epoch=0, callbacks=callbacks)
model.save_weights(log_dir + 'trained_weights.h5')
model.save(log_dir + 'model.h5')
def get_classes(classes_path):
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def get_anchors(anchors_path):
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
return np.array(anchors).reshape(-1, 2)
def create_model(input_shape, anchors, num_classes, load_pretrained=False, freeze_body=False,
weights_path='model_data/yolo_weights.h5'):
K.clear_session() # get a new session
image_input = Input(shape=(None, None, 3))
h, w = input_shape
num_anchors = len(anchors)
y_true = [Input(shape=(h // {0: 32, 1: 16, 2: 8}[l], w // {0: 32, 1: 16, 2: 8}[l], \
num_anchors // 3, num_classes + 5)) for l in range(3)]
model_body = yolo_body(image_input, num_anchors // 3, num_classes)
print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))
if load_pretrained:
model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
print('Load weights {}.'.format(weights_path))
if freeze_body:
# Do not freeze 3 output layers.
num = len(model_body.layers) - 7
for i in range(num): model_body.layers[i].trainable = False
print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))
model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
[*model_body.output, *y_true])
model = Model([model_body.input, *y_true], model_loss)
return model
def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):
n = len(annotation_lines)
np.random.shuffle(annotation_lines)
i = 0
while True:
image_data = []
box_data = []
for b in range(batch_size):
i %= n
image, box = get_random_data(annotation_lines[i], input_shape, random=True)
image_data.append(image)
box_data.append(box)
i += 1
image_data = np.array(image_data)
box_data = np.array(box_data)
y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
yield [image_data, *y_true], np.zeros(batch_size)
def data_generator_wrap(annotation_lines, batch_size, input_shape, anchors, num_classes):
n = len(annotation_lines)
if n == 0 or batch_size <= 0: return None
return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)
if __name__ == '__main__':
_main()