【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:1106, 层名称:pred_sbbox/concat_2
序号:1107, 层名称:pred_mbbox/Shape
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