caffe 输出信息分析+debug_info
1 caffe输出基本信息
caffe在训练的时候屏幕会输出程序运行的状态信息，通过查看状态信息方便查看程序运行是否正常，且方便查找bug.
caffe debug信息默认是不开启的，此时的输出信息的总体结构如下所示：
1.1 solver信息加载并显示
1.2 train网络结构输出
1.3 Train 各层创建状态信息
I0821 09:53:35.572999 10308 layer_factory.hpp:77] Creating layer mnist ####创建第一层
I0821 09:53:35.572999 10308 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
I0821 09:53:35.572999 10308 net.cpp:100] Creating Layer mnist
I0821 09:53:35.572999 10308 net.cpp:418] mnist -> data
I0821 09:53:35.572999 10308 net.cpp:418] mnist -> label
I0821 09:53:35.572999 10308 data_transformer.cpp:25] Loading mean file from: ....../image_mean.binaryproto
I0821 09:53:35.579999 11064 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
I0821 09:53:35.580999 11064 db_lmdb.cpp:40] Opened lmdb ......./train/trainlmdb
I0821 09:53:35.623999 10308 data_layer.cpp:41] output data size: 100,3,32,32 ###输出blob尺寸
I0821 09:53:35.628999 10308 net.cpp:150] Setting up mnist
I0821 09:53:35.628999 10308 net.cpp:157] Top shape: 100 3 32 32 (307200)
I0821 09:53:35.628999 10308 net.cpp:157] Top shape: 100 (100)
I0821 09:53:35.628999 10308 net.cpp:165] Memory required for data: 1229200
I0821 09:53:35.628999 10308 layer_factory.hpp:77] Creating layer conv1 ##### 创建第二层
I0821 09:53:35.628999 10308 net.cpp:100] Creating Layer conv1
I0821 09:53:35.628999 10308 net.cpp:444] conv1 <- data
I0821 09:53:35.628999 10308 net.cpp:418] conv1 -> conv1
I0821 09:53:35.629999 7532 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead.
I0821 09:53:35.909999 10308 net.cpp:150] Setting up conv1
I0821 09:53:35.909999 10308 net.cpp:157] Top shape: 100 64 28 28 (5017600) #### 输出blob尺寸
I0821 09:53:35.909999 10308 net.cpp:165] Memory required for data: 21299600
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I0821 09:53:35.914000 10308 layer_factory.hpp:77] Creating layer loss
I0821 09:53:35.914000 10308 net.cpp:150] Setting up loss
I0821 09:53:35.914000 10308 net.cpp:157] Top shape: (1)
I0821 09:53:35.914000 10308 net.cpp:160] with loss weight 1
I0821 09:53:35.914000 10308 net.cpp:165] Memory required for data: 49322804
I0821 09:53:35.914000 10308 net.cpp:226] loss needs backward computation. ######## 各层反向传播信息
I0821 09:53:35.914000 10308 net.cpp:226] ip2 needs backward computation.
I0821 09:53:35.914000 10308 net.cpp:226] relu3 needs backward computation.
I0821 09:53:35.914000 10308 net.cpp:226] ip1 needs backward computation.
I0821 09:53:35.914000 10308 net.cpp:226] pool2 needs backward computation.
I0821 09:53:35.914000 10308 net.cpp:226] relu2 needs backward computation.
I0821 09:53:35.914000 10308 net.cpp:226] conv2 needs backward computation.
I0821 09:53:35.914000 10308 net.cpp:226] pool1 needs backward computation.
I0821 09:53:35.914000 10308 net.cpp:226] relu1 needs backward computation.
I0821 09:53:35.914000 10308 net.cpp:226] conv1 needs backward computation.
I0821 09:53:35.914000 10308 net.cpp:228] mnist does not need backward computation.
I0821 09:53:35.914000 10308 net.cpp:270] This network produces output loss ######## 网络输出节点个数及名称（重要），后续参数输出均是此节点的信息 ###############
I0821 09:53:35.914000 10308 net.cpp:283] Network initialization done. ###网络创建完成1、通过查看网络创建信息科了解网络节点blob大小
2、可知道网络后续最终输出信息
3、test的创建过程与train类似，此处不再重复说明
1.4 Train 和Test网络的迭代信息输出
I0821 09:53:35.929999 10308 solver.cpp:60] Solver scaffolding done.
I0821 09:53:35.929999 10308 caffe.cpp:252] Starting Optimization ####### 开始网络训练
I0821 09:53:35.929999 10308 solver.cpp:279] Solving LeNet
I0821 09:53:35.929999 10308 solver.cpp:280] Learning Rate Policy: multistep
I0821 09:53:35.930999 10308 solver.cpp:337] Iteration 0, Testing net (#0) #### Test（Iteration 0）
I0821 09:53:35.993999 10308 blocking_queue.cpp:50] Data layer prefetch queue empty
I0821 09:53:36.180999 10308 solver.cpp:404] Test net output #0: accuracy = 0.1121 #### Test（Iteration 0）网络输出节点0，accuracy信息。（由网络定义决定）
I0821 09:53:36.180999 10308 solver.cpp:404] Test net output #1: loss = 2.30972 (* 1 = 2.30972 loss) #### Test（Iteration 0）网络输出节点1，loss信息。 （由网络定义决定）
I0821 09:53:36.190999 10308 solver.cpp:228] Iteration 0, loss = 2.2891 #### Tain（Iteration 0） 网络loss值
I0821 09:53:36.190999 10308 solver.cpp:244] Train net output #0: loss = 2.2891 (* 1 = 2.2891 loss) #### Tain（Iteration 0） 只有一个输出值
I0821 09:53:36.190999 10308 sgd_solver.cpp:106] Iteration 0, lr = 0.001 #### Tain（Iteration 0）
I0821 09:53:36.700999 10308 solver.cpp:228] Iteration 100, loss = 2.24716 #### Tain（Iteration 100）
I0821 09:53:36.700999 10308 solver.cpp:244] Train net output #0: loss = 2.24716 (* 1 = 2.24716 loss) #### Tain（Iteration 100）
I0821 09:53:36.700999 10308 sgd_solver.cpp:106] Iteration 100, lr = 0.001 #### Tain（Iteration 100）
I0821 09:53:37.225999 10308 solver.cpp:228] Iteration 200, loss = 2.08563
I0821 09:53:37.225999 10308 solver.cpp:244] Train net output #0: loss = 2.08563 (* 1 = 2.08563 loss)
I0821 09:53:37.225999 10308 sgd_solver.cpp:106] Iteration 200, lr = 0.001
I0821 09:53:37.756000 10308 solver.cpp:228] Iteration 300, loss = 2.11631
I0821 09:53:37.756000 10308 solver.cpp:244] Train net output #0: loss = 2.11631 (* 1 = 2.11631 loss)
I0821 09:53:37.756000 10308 sgd_solver.cpp:106] Iteration 300, lr = 0.001
I0821 09:53:38.286999 10308 solver.cpp:228] Iteration 400, loss = 1.89424
I0821 09:53:38.286999 10308 solver.cpp:244] Train net output #0: loss = 1.89424 (* 1 = 1.89424 loss)
I0821 09:53:38.286999 10308 sgd_solver.cpp:106] Iteration 400, lr = 0.001
I0821 09:53:38.819999 10308 solver.cpp:337] Iteration 500, Testing net (#0) #### Test（Iteration 500）
I0821 09:53:39.069999 10308 solver.cpp:404] Test net output #0: accuracy = 0.3232 #### Test（Iteration 500）
I0821 09:53:39.069999 10308 solver.cpp:404] Test net output #1: loss = 1.87822 (* 1 = 1.87822 loss) #### Test（Iteration 500）
I0821 09:53:39.072999 10308 solver.cpp:228] Iteration 500, loss = 1.94478
I0821 09:53:39.072999 10308 solver.cpp:244] Train net output #0: loss = 1.94478 (* 1 = 1.94478 loss)
I0821 09:53:39.072999 10308 sgd_solver.cpp:106] Iteration 500, lr = 0.001从输出可以看出，Train和Test一次输出周期如下：
- Test 一次训练周期
–Iteration 0, Testing net (#0)
–Test net output #0: accuracy = 0.1121
–Test net output #1: loss = 2.30972 (* 1 = 2.30972 loss) - Train 一次训练周期
–Iteration 0, loss = 2.2891
–Train net output #0: loss = 2.2891 (* 1 = 2.2891 loss)
–Iteration 0, lr = 0.001
2 log信息解析
2 debug info信息
- 在solver 中添加 debug_info:true
- 开启caffe的debug信息输出
import os
import re
import extract_seconds
import argparse
import csv
from collections import OrderedDict
def get_datadiff_paradiff(line,data_row,para_row,data_list,para_list,L_list,L_row,top_list,top_row,iteration):
regex_data=re.compile('\[Backward\] Layer (\S+), bottom blob (\S+) diff: ([\.\deE+-]+)')
regex_para=re.compile('\[Backward\] Layer (\S+), param blob (\d+) diff: ([\.\deE+-]+)')
regex_L1L2=re.compile('All net params \(data, diff\): L1 norm = \(([\.\deE+-]+), ([\.\deE+-]+)\); L2 norm = \(([\.\deE+-]+), ([\.\deE+-]+)\)')
regex_topdata=re.compile('\[Forward\] Layer (\S+), (\S+) blob (\S+) data: ([\.\deE+-]+)')
#regex_toppara=re.compile('')
out_match_data=regex_data.search(line)
if out_match_data or iteration>-1:
if not data_row or iteration>-1 :
if data_row:
data_row['NumIters']=iteration
data_list.append(data_row)
data_row = OrderedDict()
if out_match_data :
layer_name=out_match_data.group(1)
blob_name=out_match_data.group(2)
data_diff_value=out_match_data.group(3)
key=layer_name+'-'+blob_name
data_row[key]=float(data_diff_value)
out_match_para=regex_para.search(line)
if out_match_para or iteration>-1:
if not para_row or iteration>-1:
if para_row:
para_row['NumIters']=iteration
para_list.append(para_row)
para_row=OrderedDict()
if out_match_para:
layer_name=out_match_para.group(1)
param_d=out_match_para.group(2)
para_diff_value=out_match_para.group(3)
layer_name=layer_name+'-blob'+'-'+param_d
para_row[layer_name]=para_diff_value
out_match_norm=regex_L1L2.search(line)
if out_match_norm or iteration>-1:
if not L_row or iteration>-1:
if L_row:
L_row['NumIters']=iteration
L_list.append(L_row)
L_row=OrderedDict()
if out_match_norm:
L_row['data-L1']=out_match_norm.group(1)
L_row['diff-L1']=out_match_norm.group(2)
L_row['data-L2']=out_match_norm.group(3)
L_row['diff-L2']=out_match_norm.group(4)
out_match_top=regex_topdata.search(line)
if out_match_top or iteration>-1:
if not top_row or iteration>-1:
if top_row:
top_row['NumIters']=iteration
top_list.append(top_row)
top_row=OrderedDict()
if out_match_top:
layer_name=out_match_top.group(1)
top_para=out_match_top.group(2)
blob_or_num=out_match_top.group(3)
key=layer_name+'-'+top_para+'-'+blob_or_num
data_value=out_match_top.group(4)
top_row[key]=float(data_value)
return data_list,data_row,para_list,para_row,L_list,L_row,top_list,top_row
def parse_log(path_to_log):
"""Parse log file
Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
train_dict_list and test_dict_list are lists of dicts that define the table
rows
train_dict_names and test_dict_names are ordered tuples of the column names
for the two dict_lists
"""
regex_iteration = re.compile('Iteration (\d+)')
regex_train_iteration=re.compile('Iteration (\d+), loss')
regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)')
regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)')
regex_learning_rate = re.compile('lr = ([-+]?[0-9]*\.?[0-9]+([eE]?[-+]?[0-9]+)?)')
regex_backward = re.compile('\[Backward\] Layer ')
# Pick out lines of interest
iteration = -1
train_iter=-1
learning_rate = float('NaN')
train_dict_list = []
test_dict_list = []
train_row = None
test_row = None
data_diff_list=[]
para_diff_list=[]
L1L2_list=[]
top_list=[]
data_diff_row=None
para_diff_row=None
L1L2_row = None
top_row=None
logfile_year = extract_seconds.get_log_created_year(path_to_log)
with open(path_to_log) as f:
start_time = extract_seconds.get_start_time(f, logfile_year)
for line in f:
iteration_match = regex_iteration.search(line)
train_iter_match=regex_train_iteration.search(line)
if iteration_match:
iteration = float(iteration_match.group(1))
if train_iter_match:
train_iter=float(train_iter_match.group(1))
if iteration == -1:
# Only start parsing for other stuff if we've found the first
# iteration
continue
time = extract_seconds.extract_datetime_from_line(line,
logfile_year)
seconds = (time - start_time).total_seconds()
learning_rate_match = regex_learning_rate.search(line)
if learning_rate_match:
learning_rate = float(learning_rate_match.group(1))
back_match=regex_backward.search(line)
# if back_match:
data_diff_list,data_diff_row,para_diff_list,para_diff_row,L1L2_list,L1L2_row,top_list,top_row=get_datadiff_paradiff(
line,data_diff_row,para_diff_row,
data_diff_list,para_diff_list,
L1L2_list,L1L2_row,
top_list,top_row,
train_iter
)
train_iter=-1
train_dict_list, train_row = parse_line_for_net_output(
regex_train_output, train_row, train_dict_list,
line, iteration, seconds, learning_rate
)
test_dict_list, test_row = parse_line_for_net_output(
regex_test_output, test_row, test_dict_list,
line, iteration, seconds, learning_rate
)
fix_initial_nan_learning_rate(train_dict_list)
fix_initial_nan_learning_rate(test_dict_list)
return train_dict_list, test_dict_list,data_diff_list,para_diff_list,L1L2_list,top_list
def parse_line_for_net_output(regex_obj, row, row_dict_list,
line, iteration, seconds, learning_rate):
"""Parse a single line for training or test output
Returns a a tuple with (row_dict_list, row)
row: may be either a new row or an augmented version of the current row
row_dict_list: may be either the current row_dict_list or an augmented
version of the current row_dict_list
"""
output_match = regex_obj.search(line)
if output_match:
if not row or row['NumIters'] != iteration:
# Push the last row and start a new one
if row:
# If we're on a new iteration, push the last row
# This will probably only happen for the first row; otherwise
# the full row checking logic below will push and clear full
# rows
row_dict_list.append(row)
row = OrderedDict([
('NumIters', iteration),
('Seconds', seconds),
('LearningRate', learning_rate)
])
# output_num is not used; may be used in the future
# output_num = output_match.group(1)
output_name = output_match.group(2)
output_val = output_match.group(3)
row[output_name] = float(output_val)
if row and len(row_dict_list) >= 1 and len(row) == len(row_dict_list[0]):
# The row is full, based on the fact that it has the same number of
# columns as the first row; append it to the list
row_dict_list.append(row)
row = None
return row_dict_list, row
def fix_initial_nan_learning_rate(dict_list):
"""Correct initial value of learning rate
Learning rate is normally not printed until after the initial test and
training step, which means the initial testing and training rows have
LearningRate = NaN. Fix this by copying over the LearningRate from the
second row, if it exists.
"""
if len(dict_list) > 1:
dict_list[0]['LearningRate'] = dict_list[1]['LearningRate']
def save_csv_files(logfile_path, output_dir, train_dict_list, test_dict_list,data_diff_list, para_diff_list,L1L2_list,top_list,
delimiter=',', verbose=False):
"""Save CSV files to output_dir
If the input log file is, e.g., caffe.INFO, the names will be
caffe.INFO.train and caffe.INFO.test
"""
log_basename = os.path.basename(logfile_path)
train_filename = os.path.join(output_dir, log_basename + '.train')
write_csv(train_filename, train_dict_list, delimiter, verbose)
test_filename = os.path.join(output_dir, log_basename + '.test')
write_csv(test_filename, test_dict_list, delimiter, verbose)
data_diff_filename=os.path.join(output_dir, log_basename + '.datadiff')
write_csv(data_diff_filename, data_diff_list, delimiter, verbose)
para_diff_filename=os.path.join(output_dir, log_basename + '.paradiff')
write_csv(para_diff_filename, para_diff_list, delimiter, verbose)
L1L2_filename=os.path.join(output_dir, log_basename + '.L1L2')
write_csv(L1L2_filename, L1L2_list, delimiter, verbose)
topdata_filename=os.path.join(output_dir, log_basename + '.topdata')
write_csv(topdata_filename, top_list, delimiter, verbose)
def write_csv(output_filename, dict_list, delimiter, verbose=False):
"""Write a CSV file
"""
if not dict_list:
if verbose:
print('Not writing %s; no lines to write' % output_filename)
return
dialect = csv.excel
dialect.delimiter = delimiter
with open(output_filename, 'w') as f:
dict_writer = csv.DictWriter(f, fieldnames=dict_list[0].keys(),
dialect=dialect)
dict_writer.writeheader()
dict_writer.writerows(dict_list)
if verbose:
print 'Wrote %s' % output_filename
def parse_args():
description = ('Parse a Caffe training log into two CSV files '
'containing training and testing information')
parser = argparse.ArgumentParser(description=description)
parser.add_argument('logfile_path',
help='Path to log file')
parser.add_argument('output_dir',
help='Directory in which to place output CSV files')
parser.add_argument('--verbose',
action='store_true',
help='Print some extra info (e.g., output filenames)')
parser.add_argument('--delimiter',
default=',',
help=('Column delimiter in output files '
'(default: \'%(default)s\')'))
args = parser.parse_args()
return args
def main():
args = parse_args()
train_dict_list, test_dict_list,data_diff_list,para_diff_list,L1L2_list,top_list = parse_log(args.logfile_path)
save_csv_files(args.logfile_path, args.output_dir, train_dict_list,
test_dict_list, data_diff_list, para_diff_list, L1L2_list,top_list,delimiter=args.delimiter)
if __name__ == '__main__':
main()
参数变化趋势图。