【tensorflow-2.x-gpu 】 获得tensorflow-Pb模型所有层的名字
1.背景
时间：2021.02.03
目前tensorflow已经更新到2.4.1。
但是之前训练yolo-v3的tensorflow版本还是1.x版本，训练的权重是pb格式。
tensorflow没有外部可视化配置文件,需要使用netron查看模型参数,或者从程序中打印出层的名字。
在当下使用tensorflow-2.x版本，查看tensorflow-Pb模型所有层的名字。
本博客演示使用tensorflow-gpu:2.1.0,
查看手头一个yolo-v3的pb模型的所有层的名字。
2.代码
Python版本： 3.7.9
tensorflow版本： 2.1.0
# -*- coding:UTF-8 -*-
'''prompt:I only publish in csdn:jn10010537! 2021.02.03;'''
import os
import warnings
import logging
# /
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" # 屏蔽通知信息、警告信息，报错信息，只显示FATAL（致命的）信息； #
warnings.filterwarnings("ignore") # 通过警告过滤器进行控制是否发出警告消息。 #
# --- python日志过滤--------------------------------------------------- #
# 继承logging.Filter类,对日志信息进行过滤，提供更细粒度的日志是否输出的判断 #
class IgnoreWarningFilter(logging.Filter): #
'''定义一个警告过滤的class,名称可以自定义''' #
#重写filter方法 #
def filter(self, record): #
"""忽略带from tensorflow.python.framework的日志""" #
return False if ('from tensorflow.python.ops.variable_scop' in record.getMessage()) else True #
# 定义logger对象 #
# 每个程序在输出信息之前都要获得一个Logger。Logger通常对应了程序的模块名; #
logger = logging.getLogger('tensorflow') #
# 添加日志消息过滤器 #
logger.addFilter(IgnoreWarningFilter()) #
# //
#
#--- tensorflow 2.x 兼容1.x的方式-----------------------------/
# import tensorflow as tf #
import tensorflow.compat.v1 as tf #
#--- 使用tf 1.x的静态图模式运行代码 ---------------------------/
tf.disable_v2_behavior() #
#
import sys
print("Python version: ", sys.version)#3.7.9 (default, Aug 31 2020, 17:10:11) [MSC v.1916 64 bit (AMD64)]
print("tensorflow的版本：",tf.__version__)# 2.1.0
def get_all_layernames_1(pb_file_path):
"""get all layers name"""
from tensorflow.python.platform import gfile
sess = tf.Session()
with gfile.FastGFile(pb_file_path, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
tensor_name_list = [tensor.name for tensor in tf.get_default_graph().as_graph_def().node]
for tensor_name in tensor_name_list:
print(tensor_name, '\n')
def get_all_layernames_2(pb_file_path):
"""get all layers name
Use tf.gfile.GFile.接口更新了。
"""
sess = tf.Session()
with tf.gfile.GFile(pb_file_path, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
tensor_name_list = [tensor.name for tensor in tf.get_default_graph().as_graph_def().node]
for index,tensor_name in enumerate(tensor_name_list):
print("序号：%d, 层名称：%s"%(index,tensor_name))
def get_all_layernames_3(pb_file_path):
"""get all layers name
Use tf.gfile.FastGFile.接口。
"""
with tf.gfile.FastGFile(pb_file_path, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
tensor_name_list = [tensor.name for tensor in tf.get_default_graph().as_graph_def().node]
for index, tensor_name in enumerate(tensor_name_list):
print("序号：%d, 层名称：%s"%(index,tensor_name))
if __name__=="__main__":
pb_file_path="./model/yolov3_coco.pb"
# get_all_layernames_3(pb_file_path)
get_all_layernames_2(pb_file_path)
# get_all_layernames_3(pb_file_path)
3. 打印yolo-v3层名称
Python version: 3.7.9 (default, Aug 31 2020, 17:10:11) [MSC v.1916 64 bit (AMD64)]
tensorflow的版本： 2.1.0
序号：0, 层名称：input/input_data
序号：1, 层名称：darknet/conv0/weight
序号：2, 层名称：darknet/conv0/weight/read
序号：3, 层名称：darknet/conv0/Conv2D
序号：4, 层名称：darknet/conv0/batch_normalization/gamma
序号：5, 层名称：darknet/conv0/batch_normalization/gamma/read
序号：6, 层名称：darknet/conv0/batch_normalization/beta
序号：7, 层名称：darknet/conv0/batch_normalization/beta/read
序号：8, 层名称：darknet/conv0/batch_normalization/moving_mean
序号：9, 层名称：darknet/conv0/batch_normalization/moving_mean/read
序号：10, 层名称：darknet/conv0/batch_normalization/moving_variance
序号：11, 层名称：darknet/conv0/batch_normalization/moving_variance/read
序号：12, 层名称：darknet/conv0/batch_normalization/FusedBatchNorm
序号：13, 层名称：darknet/conv0/LeakyRelu
序号：14, 层名称：darknet/conv1/Const
序号：15, 层名称：darknet/conv1/Pad
序号：16, 层名称：darknet/conv1/weight
序号：17, 层名称：darknet/conv1/weight/read
序号：18, 层名称：darknet/conv1/Conv2D
序号：19, 层名称：darknet/conv1/batch_normalization/gamma
序号：20, 层名称：darknet/conv1/batch_normalization/gamma/read
序号：21, 层名称：darknet/conv1/batch_normalization/beta
序号：22, 层名称：darknet/conv1/batch_normalization/beta/read
序号：23, 层名称：darknet/conv1/batch_normalization/moving_mean
序号：24, 层名称：darknet/conv1/batch_normalization/moving_mean/read
序号：25, 层名称：darknet/conv1/batch_normalization/moving_variance
序号：26, 层名称：darknet/conv1/batch_normalization/moving_variance/read
序号：27, 层名称：darknet/conv1/batch_normalization/FusedBatchNorm
序号：28, 层名称：darknet/conv1/LeakyRelu
序号：29, 层名称：darknet/residual0/conv1/weight
序号：30, 层名称：darknet/residual0/conv1/weight/read
序号：31, 层名称：darknet/residual0/conv1/Conv2D
序号：32, 层名称：darknet/residual0/conv1/batch_normalization/gamma
序号：33, 层名称：darknet/residual0/conv1/batch_normalization/gamma/read
序号：34, 层名称：darknet/residual0/conv1/batch_normalization/beta
序号：35, 层名称：darknet/residual0/conv1/batch_normalization/beta/read
序号：36, 层名称：darknet/residual0/conv1/batch_normalization/moving_mean
序号：37, 层名称：darknet/residual0/conv1/batch_normalization/moving_mean/read
序号：38, 层名称：darknet/residual0/conv1/batch_normalization/moving_variance
序号：39, 层名称：darknet/residual0/conv1/batch_normalization/moving_variance/read
序号：40, 层名称：darknet/residual0/conv1/batch_normalization/FusedBatchNorm
序号：41, 层名称：darknet/residual0/conv1/LeakyRelu
序号：42, 层名称：darknet/residual0/conv2/weight
序号：43, 层名称：darknet/residual0/conv2/weight/read
序号：44, 层名称：darknet/residual0/conv2/Conv2D
序号：45, 层名称：darknet/residual0/conv2/batch_normalization/gamma
序号：46, 层名称：darknet/residual0/conv2/batch_normalization/gamma/read
序号：47, 层名称：darknet/residual0/conv2/batch_normalization/beta
序号：48, 层名称：darknet/residual0/conv2/batch_normalization/beta/read
序号：49, 层名称：darknet/residual0/conv2/batch_normalization/moving_mean
序号：50, 层名称：darknet/residual0/conv2/batch_normalization/moving_mean/read
序号：51, 层名称：darknet/residual0/conv2/batch_normalization/moving_variance
序号：52, 层名称：darknet/residual0/conv2/batch_normalization/moving_variance/read
序号：53, 层名称：darknet/residual0/conv2/batch_normalization/FusedBatchNorm
序号：54, 层名称：darknet/residual0/conv2/LeakyRelu
序号：55, 层名称：darknet/residual0/add
序号：56, 层名称：darknet/conv4/Const
序号：57, 层名称：darknet/conv4/Pad
序号：58, 层名称：darknet/conv4/weight
序号：59, 层名称：darknet/conv4/weight/read
序号：60, 层名称：darknet/conv4/Conv2D
序号：61, 层名称：darknet/conv4/batch_normalization/gamma
序号：62, 层名称：darknet/conv4/batch_normalization/gamma/read
序号：63, 层名称：darknet/conv4/batch_normalization/beta
序号：64, 层名称：darknet/conv4/batch_normalization/beta/read
序号：65, 层名称：darknet/conv4/batch_normalization/moving_mean
序号：66, 层名称：darknet/conv4/batch_normalization/moving_mean/read
序号：67, 层名称：darknet/conv4/batch_normalization/moving_variance
序号：68, 层名称：darknet/conv4/batch_normalization/moving_variance/read
序号：69, 层名称：darknet/conv4/batch_normalization/FusedBatchNorm
序号：70, 层名称：darknet/conv4/LeakyRelu
序号：71, 层名称：darknet/residual1/conv1/weight
序号：72, 层名称：darknet/residual1/conv1/weight/read
序号：73, 层名称：darknet/residual1/conv1/Conv2D
序号：74, 层名称：darknet/residual1/conv1/batch_normalization/gamma
序号：75, 层名称：darknet/residual1/conv1/batch_normalization/gamma/read
序号：76, 层名称：darknet/residual1/conv1/batch_normalization/beta
序号：77, 层名称：darknet/residual1/conv1/batch_normalization/beta/read
序号：78, 层名称：darknet/residual1/conv1/batch_normalization/moving_mean
序号：79, 层名称：darknet/residual1/conv1/batch_normalization/moving_mean/read
序号：80, 层名称：darknet/residual1/conv1/batch_normalization/moving_variance
序号：81, 层名称：darknet/residual1/conv1/batch_normalization/moving_variance/read
序号：82, 层名称：darknet/residual1/conv1/batch_normalization/FusedBatchNorm
序号：83, 层名称：darknet/residual1/conv1/LeakyRelu
序号：84, 层名称：darknet/residual1/conv2/weight
序号：85, 层名称：darknet/residual1/conv2/weight/read
序号：86, 层名称：darknet/residual1/conv2/Conv2D
序号：87, 层名称：darknet/residual1/conv2/batch_normalization/gamma
序号：88, 层名称：darknet/residual1/conv2/batch_normalization/gamma/read
序号：89, 层名称：darknet/residual1/conv2/batch_normalization/beta
序号：90, 层名称：darknet/residual1/conv2/batch_normalization/beta/read
序号：91, 层名称：darknet/residual1/conv2/batch_normalization/moving_mean
序号：92, 层名称：darknet/residual1/conv2/batch_normalization/moving_mean/read
序号：93, 层名称：darknet/residual1/conv2/batch_normalization/moving_variance
序号：94, 层名称：darknet/residual1/conv2/batch_normalization/moving_variance/read
序号：95, 层名称：darknet/residual1/conv2/batch_normalization/FusedBatchNorm
序号：96, 层名称：darknet/residual1/conv2/LeakyRelu
序号：97, 层名称：darknet/residual1/add
序号：98, 层名称：darknet/residual2/conv1/weight
序号：99, 层名称：darknet/residual2/conv1/weight/read
序号：100, 层名称：darknet/residual2/conv1/Conv2D
序号：101, 层名称：darknet/residual2/conv1/batch_normalization/gamma
序号：102, 层名称：darknet/residual2/conv1/batch_normalization/gamma/read
序号：103, 层名称：darknet/residual2/conv1/batch_normalization/beta
序号：104, 层名称：darknet/residual2/conv1/batch_normalization/beta/read
序号：105, 层名称：darknet/residual2/conv1/batch_normalization/moving_mean
序号：106, 层名称：darknet/residual2/conv1/batch_normalization/moving_mean/read
序号：107, 层名称：darknet/residual2/conv1/batch_normalization/moving_variance
序号：108, 层名称：darknet/residual2/conv1/batch_normalization/moving_variance/read
序号：109, 层名称：darknet/residual2/conv1/batch_normalization/FusedBatchNorm
序号：110, 层名称：darknet/residual2/conv1/LeakyRelu
序号：111, 层名称：darknet/residual2/conv2/weight
序号：112, 层名称：darknet/residual2/conv2/weight/read
序号：113, 层名称：darknet/residual2/conv2/Conv2D
序号：114, 层名称：darknet/residual2/conv2/batch_normalization/gamma
序号：115, 层名称：darknet/residual2/conv2/batch_normalization/gamma/read
序号：116, 层名称：darknet/residual2/conv2/batch_normalization/beta
序号：117, 层名称：darknet/residual2/conv2/batch_normalization/beta/read
序号：118, 层名称：darknet/residual2/conv2/batch_normalization/moving_mean
序号：119, 层名称：darknet/residual2/conv2/batch_normalization/moving_mean/read
序号：120, 层名称：darknet/residual2/conv2/batch_normalization/moving_variance
序号：121, 层名称：darknet/residual2/conv2/batch_normalization/moving_variance/read
序号：122, 层名称：darknet/residual2/conv2/batch_normalization/FusedBatchNorm
序号：123, 层名称：darknet/residual2/conv2/LeakyRelu
序号：124, 层名称：darknet/residual2/add
序号：125, 层名称：darknet/conv9/Const
序号：126, 层名称：darknet/conv9/Pad
序号：127, 层名称：darknet/conv9/weight
序号：128, 层名称：darknet/conv9/weight/read
序号：129, 层名称：darknet/conv9/Conv2D
序号：130, 层名称：darknet/conv9/batch_normalization/gamma
序号：131, 层名称：darknet/conv9/batch_normalization/gamma/read
序号：132, 层名称：darknet/conv9/batch_normalization/beta
序号：133, 层名称：darknet/conv9/batch_normalization/beta/read
序号：134, 层名称：darknet/conv9/batch_normalization/moving_mean
序号：135, 层名称：darknet/conv9/batch_normalization/moving_mean/read
序号：136, 层名称：darknet/conv9/batch_normalization/moving_variance
序号：137, 层名称：darknet/conv9/batch_normalization/moving_variance/read
序号：138, 层名称：darknet/conv9/batch_normalization/FusedBatchNorm
序号：139, 层名称：darknet/conv9/LeakyRelu
序号：140, 层名称：darknet/residual3/conv1/weight
序号：141, 层名称：darknet/residual3/conv1/weight/read
序号：142, 层名称：darknet/residual3/conv1/Conv2D
序号：143, 层名称：darknet/residual3/conv1/batch_normalization/gamma
序号：144, 层名称：darknet/residual3/conv1/batch_normalization/gamma/read
序号：145, 层名称：darknet/residual3/conv1/batch_normalization/beta
序号：146, 层名称：darknet/residual3/conv1/batch_normalization/beta/read
序号：147, 层名称：darknet/residual3/conv1/batch_normalization/moving_mean
序号：148, 层名称：darknet/residual3/conv1/batch_normalization/moving_mean/read
序号：149, 层名称：darknet/residual3/conv1/batch_normalization/moving_variance
序号：150, 层名称：darknet/residual3/conv1/batch_normalization/moving_variance/read
序号：151, 层名称：darknet/residual3/conv1/batch_normalization/FusedBatchNorm
序号：152, 层名称：darknet/residual3/conv1/LeakyRelu
序号：153, 层名称：darknet/residual3/conv2/weight
序号：154, 层名称：darknet/residual3/conv2/weight/read
序号：155, 层名称：darknet/residual3/conv2/Conv2D
序号：156, 层名称：darknet/residual3/conv2/batch_normalization/gamma
序号：157, 层名称：darknet/residual3/conv2/batch_normalization/gamma/read
序号：158, 层名称：darknet/residual3/conv2/batch_normalization/beta
序号：159, 层名称：darknet/residual3/conv2/batch_normalization/beta/read
序号：160, 层名称：darknet/residual3/conv2/batch_normalization/moving_mean
序号：161, 层名称：darknet/residual3/conv2/batch_normalization/moving_mean/read
序号：162, 层名称：darknet/residual3/conv2/batch_normalization/moving_variance
序号：163, 层名称：darknet/residual3/conv2/batch_normalization/moving_variance/read
序号：164, 层名称：darknet/residual3/conv2/batch_normalization/FusedBatchNorm
序号：165, 层名称：darknet/residual3/conv2/LeakyRelu
序号：166, 层名称：darknet/residual3/add
序号：167, 层名称：darknet/residual4/conv1/weight
序号：168, 层名称：darknet/residual4/conv1/weight/read
序号：169, 层名称：darknet/residual4/conv1/Conv2D
序号：170, 层名称：darknet/residual4/conv1/batch_normalization/gamma
序号：171, 层名称：darknet/residual4/conv1/batch_normalization/gamma/read
序号：172, 层名称：darknet/residual4/conv1/batch_normalization/beta
序号：173, 层名称：darknet/residual4/conv1/batch_normalization/beta/read
序号：174, 层名称：darknet/residual4/conv1/batch_normalization/moving_mean
序号：175, 层名称：darknet/residual4/conv1/batch_normalization/moving_mean/read
序号：176, 层名称：darknet/residual4/conv1/batch_normalization/moving_variance
序号：177, 层名称：darknet/residual4/conv1/batch_normalization/moving_variance/read
序号：178, 层名称：darknet/residual4/conv1/batch_normalization/FusedBatchNorm
序号：179, 层名称：darknet/residual4/conv1/LeakyRelu
序号：180, 层名称：darknet/residual4/conv2/weight
序号：181, 层名称：darknet/residual4/conv2/weight/read
序号：182, 层名称：darknet/residual4/conv2/Conv2D
序号：183, 层名称：darknet/residual4/conv2/batch_normalization/gamma
序号：184, 层名称：darknet/residual4/conv2/batch_normalization/gamma/read
序号：185, 层名称：darknet/residual4/conv2/batch_normalization/beta
序号：186, 层名称：darknet/residual4/conv2/batch_normalization/beta/read
序号：187, 层名称：darknet/residual4/conv2/batch_normalization/moving_mean
序号：188, 层名称：darknet/residual4/conv2/batch_normalization/moving_mean/read
序号：189, 层名称：darknet/residual4/conv2/batch_normalization/moving_variance
序号：190, 层名称：darknet/residual4/conv2/batch_normalization/moving_variance/read
序号：191, 层名称：darknet/residual4/conv2/batch_normalization/FusedBatchNorm
序号：192, 层名称：darknet/residual4/conv2/LeakyRelu
序号：193, 层名称：darknet/residual4/add
序号：194, 层名称：darknet/residual5/conv1/weight
序号：195, 层名称：darknet/residual5/conv1/weight/read
序号：196, 层名称：darknet/residual5/conv1/Conv2D
序号：197, 层名称：darknet/residual5/conv1/batch_normalization/gamma
序号：198, 层名称：darknet/residual5/conv1/batch_normalization/gamma/read
序号：199, 层名称：darknet/residual5/conv1/batch_normalization/beta
序号：200, 层名称：darknet/residual5/conv1/batch_normalization/beta/read
序号：201, 层名称：darknet/residual5/conv1/batch_normalization/moving_mean
序号：202, 层名称：darknet/residual5/conv1/batch_normalization/moving_mean/read
序号：203, 层名称：darknet/residual5/conv1/batch_normalization/moving_variance
序号：204, 层名称：darknet/residual5/conv1/batch_normalization/moving_variance/read
序号：205, 层名称：darknet/residual5/conv1/batch_normalization/FusedBatchNorm
序号：206, 层名称：darknet/residual5/conv1/LeakyRelu
序号：207, 层名称：darknet/residual5/conv2/weight
序号：208, 层名称：darknet/residual5/conv2/weight/read
序号：209, 层名称：darknet/residual5/conv2/Conv2D
序号：210, 层名称：darknet/residual5/conv2/batch_normalization/gamma
序号：211, 层名称：darknet/residual5/conv2/batch_normalization/gamma/read
序号：212, 层名称：darknet/residual5/conv2/batch_normalization/beta
序号：213, 层名称：darknet/residual5/conv2/batch_normalization/beta/read
序号：214, 层名称：darknet/residual5/conv2/batch_normalization/moving_mean
序号：215, 层名称：darknet/residual5/conv2/batch_normalization/moving_mean/read
序号：216, 层名称：darknet/residual5/conv2/batch_normalization/moving_variance
序号：217, 层名称：darknet/residual5/conv2/batch_normalization/moving_variance/read
序号：218, 层名称：darknet/residual5/conv2/batch_normalization/FusedBatchNorm
序号：219, 层名称：darknet/residual5/conv2/LeakyRelu
序号：220, 层名称：darknet/residual5/add
序号：221, 层名称：darknet/residual6/conv1/weight
序号：222, 层名称：darknet/residual6/conv1/weight/read
序号：223, 层名称：darknet/residual6/conv1/Conv2D
序号：224, 层名称：darknet/residual6/conv1/batch_normalization/gamma
序号：225, 层名称：darknet/residual6/conv1/batch_normalization/gamma/read
序号：226, 层名称：darknet/residual6/conv1/batch_normalization/beta
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序号：1108, 层名称：pred_mbbox/strided_slice/stack
序号：1109, 层名称：pred_mbbox/strided_slice/stack_1
序号：1110, 层名称：pred_mbbox/strided_slice/stack_2
序号：1111, 层名称：pred_mbbox/strided_slice
序号：1112, 层名称：pred_mbbox/strided_slice_1/stack
序号：1113, 层名称：pred_mbbox/strided_slice_1/stack_1
序号：1114, 层名称：pred_mbbox/strided_slice_1/stack_2
序号：1115, 层名称：pred_mbbox/strided_slice_1
序号：1116, 层名称：pred_mbbox/Reshape/shape/3
序号：1117, 层名称：pred_mbbox/Reshape/shape/4
序号：1118, 层名称：pred_mbbox/Reshape/shape
序号：1119, 层名称：pred_mbbox/Reshape
序号：1120, 层名称：pred_mbbox/strided_slice_2/stack
序号：1121, 层名称：pred_mbbox/strided_slice_2/stack_1
序号：1122, 层名称：pred_mbbox/strided_slice_2/stack_2
序号：1123, 层名称：pred_mbbox/strided_slice_2
序号：1124, 层名称：pred_mbbox/strided_slice_3/stack
序号：1125, 层名称：pred_mbbox/strided_slice_3/stack_1
序号：1126, 层名称：pred_mbbox/strided_slice_3/stack_2
序号：1127, 层名称：pred_mbbox/strided_slice_3
序号：1128, 层名称：pred_mbbox/strided_slice_4/stack
序号：1129, 层名称：pred_mbbox/strided_slice_4/stack_1
序号：1130, 层名称：pred_mbbox/strided_slice_4/stack_2
序号：1131, 层名称：pred_mbbox/strided_slice_4
序号：1132, 层名称：pred_mbbox/strided_slice_5/stack
序号：1133, 层名称：pred_mbbox/strided_slice_5/stack_1
序号：1134, 层名称：pred_mbbox/strided_slice_5/stack_2
序号：1135, 层名称：pred_mbbox/strided_slice_5
序号：1136, 层名称：pred_mbbox/range/start
序号：1137, 层名称：pred_mbbox/range/delta
序号：1138, 层名称：pred_mbbox/range
序号：1139, 层名称：pred_mbbox/strided_slice_6/stack
序号：1140, 层名称：pred_mbbox/strided_slice_6/stack_1
序号：1141, 层名称：pred_mbbox/strided_slice_6/stack_2
序号：1142, 层名称：pred_mbbox/strided_slice_6
序号：1143, 层名称：pred_mbbox/Tile/multiples/0
序号：1144, 层名称：pred_mbbox/Tile/multiples
序号：1145, 层名称：pred_mbbox/Tile
序号：1146, 层名称：pred_mbbox/range_1/start
序号：1147, 层名称：pred_mbbox/range_1/delta
序号：1148, 层名称：pred_mbbox/range_1
序号：1149, 层名称：pred_mbbox/strided_slice_7/stack
序号：1150, 层名称：pred_mbbox/strided_slice_7/stack_1
序号：1151, 层名称：pred_mbbox/strided_slice_7/stack_2
序号：1152, 层名称：pred_mbbox/strided_slice_7
序号：1153, 层名称：pred_mbbox/Tile_1/multiples/1
序号：1154, 层名称：pred_mbbox/Tile_1/multiples
序号：1155, 层名称：pred_mbbox/Tile_1
序号：1156, 层名称：pred_mbbox/strided_slice_8/stack
序号：1157, 层名称：pred_mbbox/strided_slice_8/stack_1
序号：1158, 层名称：pred_mbbox/strided_slice_8/stack_2
序号：1159, 层名称：pred_mbbox/strided_slice_8
序号：1160, 层名称：pred_mbbox/strided_slice_9/stack
序号：1161, 层名称：pred_mbbox/strided_slice_9/stack_1
序号：1162, 层名称：pred_mbbox/strided_slice_9/stack_2
序号：1163, 层名称：pred_mbbox/strided_slice_9
序号：1164, 层名称：pred_mbbox/concat/axis
序号：1165, 层名称：pred_mbbox/concat
序号：1166, 层名称：pred_mbbox/strided_slice_10/stack
序号：1167, 层名称：pred_mbbox/strided_slice_10/stack_1
序号：1168, 层名称：pred_mbbox/strided_slice_10/stack_2
序号：1169, 层名称：pred_mbbox/strided_slice_10
序号：1170, 层名称：pred_mbbox/Tile_2/multiples/1
序号：1171, 层名称：pred_mbbox/Tile_2/multiples/2
序号：1172, 层名称：pred_mbbox/Tile_2/multiples/3
序号：1173, 层名称：pred_mbbox/Tile_2/multiples/4
序号：1174, 层名称：pred_mbbox/Tile_2/multiples
序号：1175, 层名称：pred_mbbox/Tile_2
序号：1176, 层名称：pred_mbbox/Cast
序号：1177, 层名称：pred_mbbox/Sigmoid
序号：1178, 层名称：pred_mbbox/add
序号：1179, 层名称：pred_mbbox/mul/y
序号：1180, 层名称：pred_mbbox/mul
序号：1181, 层名称：pred_mbbox/Exp
序号：1182, 层名称：pred_mbbox/mul_1/y
序号：1183, 层名称：pred_mbbox/mul_1
序号：1184, 层名称：pred_mbbox/mul_2/y
序号：1185, 层名称：pred_mbbox/mul_2
序号：1186, 层名称：pred_mbbox/concat_1/axis
序号：1187, 层名称：pred_mbbox/concat_1
序号：1188, 层名称：pred_mbbox/Sigmoid_1
序号：1189, 层名称：pred_mbbox/Sigmoid_2
序号：1190, 层名称：pred_mbbox/concat_2/axis
序号：1191, 层名称：pred_mbbox/concat_2
序号：1192, 层名称：pred_lbbox/Shape
序号：1193, 层名称：pred_lbbox/strided_slice/stack
序号：1194, 层名称：pred_lbbox/strided_slice/stack_1
序号：1195, 层名称：pred_lbbox/strided_slice/stack_2
序号：1196, 层名称：pred_lbbox/strided_slice
序号：1197, 层名称：pred_lbbox/strided_slice_1/stack
序号：1198, 层名称：pred_lbbox/strided_slice_1/stack_1
序号：1199, 层名称：pred_lbbox/strided_slice_1/stack_2
序号：1200, 层名称：pred_lbbox/strided_slice_1
序号：1201, 层名称：pred_lbbox/Reshape/shape/3
序号：1202, 层名称：pred_lbbox/Reshape/shape/4
序号：1203, 层名称：pred_lbbox/Reshape/shape
序号：1204, 层名称：pred_lbbox/Reshape
序号：1205, 层名称：pred_lbbox/strided_slice_2/stack
序号：1206, 层名称：pred_lbbox/strided_slice_2/stack_1
序号：1207, 层名称：pred_lbbox/strided_slice_2/stack_2
序号：1208, 层名称：pred_lbbox/strided_slice_2
序号：1209, 层名称：pred_lbbox/strided_slice_3/stack
序号：1210, 层名称：pred_lbbox/strided_slice_3/stack_1
序号：1211, 层名称：pred_lbbox/strided_slice_3/stack_2
序号：1212, 层名称：pred_lbbox/strided_slice_3
序号：1213, 层名称：pred_lbbox/strided_slice_4/stack
序号：1214, 层名称：pred_lbbox/strided_slice_4/stack_1
序号：1215, 层名称：pred_lbbox/strided_slice_4/stack_2
序号：1216, 层名称：pred_lbbox/strided_slice_4
序号：1217, 层名称：pred_lbbox/strided_slice_5/stack
序号：1218, 层名称：pred_lbbox/strided_slice_5/stack_1
序号：1219, 层名称：pred_lbbox/strided_slice_5/stack_2
序号：1220, 层名称：pred_lbbox/strided_slice_5
序号：1221, 层名称：pred_lbbox/range/start
序号：1222, 层名称：pred_lbbox/range/delta
序号：1223, 层名称：pred_lbbox/range
序号：1224, 层名称：pred_lbbox/strided_slice_6/stack
序号：1225, 层名称：pred_lbbox/strided_slice_6/stack_1
序号：1226, 层名称：pred_lbbox/strided_slice_6/stack_2
序号：1227, 层名称：pred_lbbox/strided_slice_6
序号：1228, 层名称：pred_lbbox/Tile/multiples/0
序号：1229, 层名称：pred_lbbox/Tile/multiples
序号：1230, 层名称：pred_lbbox/Tile
序号：1231, 层名称：pred_lbbox/range_1/start
序号：1232, 层名称：pred_lbbox/range_1/delta
序号：1233, 层名称：pred_lbbox/range_1
序号：1234, 层名称：pred_lbbox/strided_slice_7/stack
序号：1235, 层名称：pred_lbbox/strided_slice_7/stack_1
序号：1236, 层名称：pred_lbbox/strided_slice_7/stack_2
序号：1237, 层名称：pred_lbbox/strided_slice_7
序号：1238, 层名称：pred_lbbox/Tile_1/multiples/1
序号：1239, 层名称：pred_lbbox/Tile_1/multiples
序号：1240, 层名称：pred_lbbox/Tile_1
序号：1241, 层名称：pred_lbbox/strided_slice_8/stack
序号：1242, 层名称：pred_lbbox/strided_slice_8/stack_1
序号：1243, 层名称：pred_lbbox/strided_slice_8/stack_2
序号：1244, 层名称：pred_lbbox/strided_slice_8
序号：1245, 层名称：pred_lbbox/strided_slice_9/stack
序号：1246, 层名称：pred_lbbox/strided_slice_9/stack_1
序号：1247, 层名称：pred_lbbox/strided_slice_9/stack_2
序号：1248, 层名称：pred_lbbox/strided_slice_9
序号：1249, 层名称：pred_lbbox/concat/axis
序号：1250, 层名称：pred_lbbox/concat
序号：1251, 层名称：pred_lbbox/strided_slice_10/stack
序号：1252, 层名称：pred_lbbox/strided_slice_10/stack_1
序号：1253, 层名称：pred_lbbox/strided_slice_10/stack_2
序号：1254, 层名称：pred_lbbox/strided_slice_10
序号：1255, 层名称：pred_lbbox/Tile_2/multiples/1
序号：1256, 层名称：pred_lbbox/Tile_2/multiples/2
序号：1257, 层名称：pred_lbbox/Tile_2/multiples/3
序号：1258, 层名称：pred_lbbox/Tile_2/multiples/4
序号：1259, 层名称：pred_lbbox/Tile_2/multiples
序号：1260, 层名称：pred_lbbox/Tile_2
序号：1261, 层名称：pred_lbbox/Cast
序号：1262, 层名称：pred_lbbox/Sigmoid
序号：1263, 层名称：pred_lbbox/add
序号：1264, 层名称：pred_lbbox/mul/y
序号：1265, 层名称：pred_lbbox/mul
序号：1266, 层名称：pred_lbbox/Exp
序号：1267, 层名称：pred_lbbox/mul_1/y
序号：1268, 层名称：pred_lbbox/mul_1
序号：1269, 层名称：pred_lbbox/mul_2/y
序号：1270, 层名称：pred_lbbox/mul_2
序号：1271, 层名称：pred_lbbox/concat_1/axis
序号：1272, 层名称：pred_lbbox/concat_1
序号：1273, 层名称：pred_lbbox/Sigmoid_1
序号：1274, 层名称：pred_lbbox/Sigmoid_2
序号：1275, 层名称：pred_lbbox/concat_2/axis
序号：1276, 层名称：pred_lbbox/concat_2
4. 资源
yolov3_coco.pb
https://download.csdn.net/download/jn10010537/15021363