Faster-RCNN_TF的loss曲线可视化（tensorflow版本，Faster RCNN的loss曲线可视化）

我用的Faster-RCNN是tensorflow版本，github地址：Faster-RCNN_TF

查到网上的loss曲线都是直接从log的txt里读取，我的代码不生成txt，所以得自己改。我的思路是每cfg.TRAIN.SNAPSHOT_ITERS步生成一个csv文件，最后读取这个文件再画图。

具体操作如下：

1. 修改train.py

在/lib/fast rcnn/train.py中，train_model(self, sess, max_iters)函数里：

在for iter in range(max_iters):这句的上下对应修改，改后代码如下：

####add
all_loss = []
rpn_cls_loss = []
rpn_box_loss = []
cls_loss = []
box_loss = []
for iter in range(max_iters):
# get one batch
blobs = data_layer.forward()

# Make one SGD update
feed_dict={self.net.data: blobs['data'], self.net.im_info: blobs['im_info'], self.net.keep_prob: 0.5, \
self.net.gt_boxes: blobs['gt_boxes']}

run_options = None
run_metadata = None
if cfg.TRAIN.DEBUG_TIMELINE:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()

timer.tic()

rpn_loss_cls_value, rpn_loss_box_value,loss_cls_value, loss_box_value, _ = sess.run([rpn_cross_entropy, rpn_loss_box, cross_entropy, loss_box, train_op],
feed_dict=feed_dict,
options=run_options,
run_metadata=run_metadata)

timer.toc()
####add loss log
all_loss.append(rpn_loss_cls_value + rpn_loss_box_value + loss_cls_value + loss_box_value)
rpn_cls_loss.append(rpn_loss_cls_value)
rpn_box_loss.append(rpn_loss_box_value)
cls_loss.append(loss_cls_value)
box_loss.append(loss_box_value)

if cfg.TRAIN.DEBUG_TIMELINE:
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
trace_file = open(str(long(time.time() * 1000)) + '-train-timeline.ctf.json', 'w')
trace_file.write(trace.generate_chrome_trace_format(show_memory=False))
trace_file.close()

if (iter+1) % (cfg.TRAIN.DISPLAY) == 0:
print 'iter: %d / %d, total loss: %.4f, rpn_loss_cls: %.4f, rpn_loss_box: %.4f, loss_cls: %.4f, loss_box: %.4f, lr: %f'%\
(iter+1, max_iters, rpn_loss_cls_value + rpn_loss_box_value + loss_cls_value + loss_box_value ,rpn_loss_cls_value, rpn_loss_box_value,loss_cls_value, loss_box_value, lr.eval())
print 'speed: {:.3f}s / iter'.format(timer.average_time)

if (iter+1) % cfg.TRAIN.SNAPSHOT_ITERS == 0:
last_snapshot_iter = iter
self.snapshot(sess, iter)
###add
dataframe = pd.DataFrame({'all_loss': all_loss,'rpn_cls_loss':rpn_cls_loss, 'rpn_box_loss':rpn_box_loss,'cls_loss':cls_loss,'box_loss':box_loss})
dataframe.to_csv("xxxxxxxx/loss%d.csv" %(iter+1))

2. 画图

代码如下：

#!/usr/bin/env python
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import math
import re
import pylab
from pylab import figure, show, legend
from mpl_toolkits.axes_grid1 import host_subplot
import pandas as pd

train_iterations = []
train_loss = []
df = pd.read_csv('xxxxxxxxx/loss2500.csv')
train_iterations = df['index']
train_loss = df['all_loss']

host = host_subplot(111)
plt.subplots_adjust(right=0.8)

# set labels
host.set_xlabel("iterations")
host.set_ylabel("RPN loss")

# plot curves
p1, = host.plot(train_iterations, train_loss, label="train RPN loss")
host.legend(loc=1)

# set label color
host.axis["left"].label.set_color(p1.get_color())
host.set_xlim([-150,2500])
host.set_ylim([0., 4])

plt.draw()
4000

plt.show()

最后画出的结果如下：



额 是不是lr有点大了。。

我只画了all_loss的曲线，其他loss可以相应画出。

有什么问题欢迎交流，谢谢～