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.'
#----------------------------------------#
# 计算种类数量
#----------------------------------------#
self.num_classes = len(self.class_names)
#----------------------------------------#
# 创建centernet模型
#----------------------------------------#
self.centernet = centernet(self.input_shape,
num_classes=self.num_classes,
backbone=self.backbone,
mode='predict')
self.centernet.load_weights(self.model_path, by_name=True)
print('{} model, anchors, and classes loaded.'.format(self.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))
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.'
#----------------------------------------#
# 创建centernet模型
#----------------------------------------#
self.centernet = centernet([self.input_shape[0], self.input_shape[1], 3], num_classes=self.num_classes, backbone=self.backbone, mode='predict' if not self.heatmap else 'heatmap')
self.centernet.load_weights(self.model_path, by_name=True)
print('{} model, anchors, and classes loaded.'.format(self.model_path))
#--------------------------------------------#
# 该部分代码只用于看网络结构，并非测试代码
# map测试请看get_dr_txt.py、get_gt_txt.py
# 和get_map.py
#--------------------------------------------#
from nets.centernet import centernet
if __name__ == "__main__":
model = centernet([512, 512, 3], 20, backbone='resnet50')
model.summary()
for i, layer in enumerate(model.layers):
print(i, layer.name)
# 获取classes和数量
#----------------------------------------------------#
class_names = get_classes(classes_path)
num_classes = len(class_names)
#-----------------------------#
# 主干特征提取网络的选择
# resnet50
# hourglass
#-----------------------------#
backbone = "resnet50"
#----------------------------------------------------#
# 获取centernet模型
#----------------------------------------------------#
model = centernet(input_shape,
num_classes=num_classes,
backbone=backbone,
mode='train')
#------------------------------------------------------#
# 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_path = r"model_data/centernet_resnet50_voc.h5"
model.load_weights(model_path, by_name=True, skip_mismatch=True)
#----------------------------------------------------#
# 获得图片路径和标签
#----------------------------------------------------#
annotation_path = '2007_train.txt'
#----------------------------------------------------------------------#
if ngpus_per_node > 1:
strategy = tf.distribute.MirroredStrategy()
else:
strategy = None
print('Number of devices: {}'.format(ngpus_per_node))
#----------------------------------------------------#
# 获取classes
#----------------------------------------------------#
class_names, num_classes = get_classes(classes_path)
if ngpus_per_node > 1:
with strategy.scope():
model = centernet([input_shape[0], input_shape[1], 3],
num_classes=num_classes,
backbone=backbone,
mode='train')
if model_path != '':
#------------------------------------------------------#
# 载入预训练权重
#------------------------------------------------------#
print('Load weights {}.'.format(model_path))
model.load_weights(model_path,
by_name=True,
skip_mismatch=True)
else:
model = centernet([input_shape[0], input_shape[1], 3],
num_classes=num_classes,
backbone=backbone,
mode='train')
if model_path != '':
#--------------------------------------------#
# 该部分代码用于看网络结构
#--------------------------------------------#
from nets.centernet import centernet
from utils.utils import net_flops
if __name__ == "__main__":
input_shape = [512, 512]
num_classes = 20
model = centernet([input_shape[0], input_shape[1], 3], num_classes, backbone='resnet50')
#--------------------------------------------#
# 查看网络结构网络结构
#--------------------------------------------#
model.summary()
#--------------------------------------------#
# 计算网络的FLOPS
#--------------------------------------------#
net_flops(model, table=False)
#--------------------------------------------#
# 获得网络每个层的名称与序号
#--------------------------------------------#
# for i,layer in enumerate(model.layers):
# print(i,layer.name)
