def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
# 计算总的种类
self.num_classes = len(self.class_names)+1
# 载入模型
inputs = Input(self.model_image_size)
self.m2det = M2det.m2det(self.num_classes,inputs)
self.m2det.load_weights(self.model_path,by_name=True)
self.m2det.summary()
print('{} model, anchors, and classes loaded.'.format(model_path))
# 画框设置不同的颜色
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
import numpy as np
import keras
from keras.optimizers import Adam
from nets.M2det_training import Generator
from nets.M2det_training import conf_loss, smooth_l1
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from utils.utils import BBoxUtility
from utils.anchors import get_anchors
if __name__ == "__main__":
NUM_CLASSES = 21
input_shape = (320, 320, 3)
annotation_path = '2007_train.txt'
inputs = keras.layers.Input(shape=input_shape)
model = M2det.m2det(NUM_CLASSES, inputs)
priors = get_anchors((input_shape[0], input_shape[1]))
bbox_util = BBoxUtility(NUM_CLASSES, priors)
model.load_weights("model_data\M2det_weights.h5",
by_name=True,
skip_mismatch=True)
model.summary()
# 0.1用于验证，0.9用于训练
val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
# 训练之前一定要修改NUM_CLASSES
# 修改成所需要区分的类的个数+1。
#----------------------------------------------------#
NUM_CLASSES = 21
#----------------------------------------------------#
# 输入图像大小
#----------------------------------------------------#
input_shape = (320, 320, 3)
#----------------------------------------------------#
# 获得先验框
#----------------------------------------------------#
anchors_size = [0.08, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05]
priors = get_anchors((input_shape[0],input_shape[1]), anchors_size)
bbox_util = BBoxUtility(NUM_CLASSES, priors)
model = M2det.m2det(NUM_CLASSES,input_shape)
#------------------------------------------------------#
# 权值文件请看README，百度网盘下载
# 训练自己的数据集时提示维度不匹配正常
# 预测的东西都不一样了自然维度不匹配
#------------------------------------------------------#
model.load_weights("model_data/M2det_weights.h5", by_name=True, skip_mismatch=True)
#-------------------------------------------------------------------------------#
# 训练参数的设置
# logging表示tensorboard的保存地址
# checkpoint用于设置权值保存的细节，period用于修改多少epoch保存一次
# reduce_lr用于设置学习率下降的方式
# early_stopping用于设定早停，val_loss多次不下降自动结束训练，表示模型基本收敛
#-------------------------------------------------------------------------------#
logging = TensorBoard(log_dir='logs')