Edge-AI-Platform-Tutorials/docs/CATSvsDOGs/caffe/caffe_flow_AlexNet.sh at master · andyluo7/Edge-AI-Platform-Tutorials · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
#!/bin/bash

ML_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null && cd .. && pwd )"
export ML_DIR
echo ML_DIR is $ML_DIR

# adjust this $CAFFE_ROOT to point your Caffe install dir. It must match the CAFFE_ROOT of "cats_vs_dogs_config.py"
[ -z "$CAFFE_ROOT" ] && export CAFFE_ROOT=$HOME/caffe_tools/BVLC1v0-Caffe
CAFFE_TOOLS_DIR=$CAFFE_ROOT/distribute
#working dir
WORK_DIR=$ML_DIR/caffe

NUMIT=12000  # number of iterations
NET=alexnetBNnoLRN
MOD_NUM=2   # model number

#modify the prototxt files to have the correct path (need an absolute path)
for file in $(find $ML_DIR -name *.prototxt.relative); do
	sed -e "s^INSERT_ABSOLUTE_PATH_HERE^$ML_DIR^" ${file} > ${file%.relative}
done


# ################################################################################################################
# SCRIPTS 1 2 3 (DATABASE AND MEAN VALUES)
echo "DATABASE: training and validation in LMDB, test in JPG and MEAN values"

if [ ! -d $ML_DIR/input/lmdb/ ]; then

    # prepare the databases
    python $WORK_DIR/code/1_write_cats-vs-dogs_images.py -p $WORK_DIR/../input/jpg

    #create LMDB databases -training (20K), validation (4K), test (1K) images - and compute mean values
    python $WORK_DIR/code/2a_create_lmdb.py -i  $WORK_DIR/../input/jpg/ -o  $WORK_DIR/../input/lmdb
    python $WORK_DIR/code/2b_compute_mean.py

		# make all *.sh scripts to be executable
		##https://askubuntu.com/questions/484718/how-to-make-a-file-executable
		for file in $(find $HOME/ML/ -name *.sh); do
			chmod +x ${file}
		done

    #remove redundant images
    #cd $WORK_DIR/input/jpg
    #rm -r cats dogs train
fi


# ################################################################################################################
# SCRIPT 4  (SOLVER AND TRAINING AND LEARNING CURVE)
echo "TRAINING. Remember that: <Epoch_index = floor((iteration_index * batch_size) / (# data_samples))>"

python $WORK_DIR/code/4_training.py -s $WORK_DIR/models/$NET/m$MOD_NUM/solver_$MOD_NUM\_$NET.prototxt -l $WORK_DIR/models/$NET/m$MOD_NUM/logfile_$MOD_NUM\_$NET.log

# print image of CNN architecture
echo "PRINT CNN BLOCK DIAGRAM"
python $CAFFE_TOOLS_DIR/python/draw_net.py $WORK_DIR/models/$NET/m$MOD_NUM/train_val_$MOD_NUM\_$NET.prototxt $WORK_DIR/models/$NET/m$MOD_NUM/bd_$MOD_NUM\_$NET.png


# ################################################################################################################
# SCRIPT 5: plot the learning curve
echo "PLOT LEARNING CURVERS"
python $WORK_DIR/code/5_plot_learning_curve.py $WORK_DIR/models/$NET/m$MOD_NUM/logfile_$MOD_NUM\_$NET.log $WORK_DIR/models/$NET/m$MOD_NUM/plt_train_val_$MOD_NUM\_$NET.png


# ################################################################################################################
# SCRIPT 6 (PREDICTION)
echo "COMPUTE PREDICTIONS"
python $WORK_DIR/code/6_make_predictions.py -d $WORK_DIR/models/$NET/m$MOD_NUM/deploy_$MOD_NUM\_$NET.prototxt -w $WORK_DIR/models/$NET/m$MOD_NUM/snapshot_$MOD_NUM\_$NET\__iter_$NUMIT.caffemodel 2>&1 | tee $WORK_DIR/models/$NET/m$MOD_NUM/predictions_$MOD_NUM\_$NET.txt


# ################################################################################################################
# The below code is commented, as not needed to run this tutorial. But I think it can be useful for reference
# ################################################################################################################
: '
#training by direct command
$CAFFE_TOOLS_DIR/bin/caffe.bin train --solver $WORK_DIR/models/$NET/m$MOD_NUM/solver_$MOD_NUM\_$NET.prototxt 2>&1 | tee $WORK_DIR/models/$NET/m$MOD_NUM/logfile_$MOD_NUM\_$NET.log
'

: '
# example of trainining the CNN from a certain snapshot
echo "RETRAINING from previous snapshot"
$CAFFE_TOOLS_DIR/bin/caffe.bin train --solver $WORK_DIR/models/$NET/m$MOD_NUM/solver_$MOD_NUM\_$NET.prototxt --snapshot $WORK_DIR/models/$NET/m3/snapshot_3\$NET__iter_20000.solverstate 2>&1 | tee $WORK_DIR/models/$NET/m$MOD_NUM/retrain_logfile_$MOD_NUM\_$NET.log
cp -f $WORK_DIR/models/$NET/m$MOD_NUM/logfile_$MOD_NUM\_$NET.log $WORK_DIR/models/$NET/m$MOD_NUM/orig_logfile_$MOD_NUM\_$NET.log
cp -f $WORK_DIR/models/$NET/m$MOD_NUM/retrain_logfile_$MOD_NUM\_$NET.log $WORK_DIR/models/$NET/m$MOD_NUM/logfile_$MOD_NUM\_$NET.log
'

: '
# alternative example to plot learing curves
## 0 Test Accuracy vs Iters
## 1 Test Accuracy vs Seconds
## 2 Test Loss     vs Iters
## 3 Test Loss     vs Seconds
## 4 Train lr      vs. Iters
## 5 Train lr      vs. Seconds
## 6 Train Loss     vs Iters
## 7 Train Loss     vs Seconds
python $WORK_DIR/code/plot_training_log.py 6 $WORK_DIR/models/$NET/m$MOD_NUM/plt_trainLoss_$MOD_NUM\_$NET.png     $WORK_DIR/models/$NET/m$MOD_NUM/logfile_$MOD_NUM\_$NET.log
python $WORK_DIR/code/plot_training_log.py 2 $WORK_DIR/models/$NET/m$MOD_NUM/plt_testLoss_$MOD_NUM\_$NET.png      $WORK_DIR/models/$NET/m$MOD_NUM/logfile_$MOD_NUM\_$NET.log
python $WORK_DIR/code/plot_training_log.py 0 $WORK_DIR/models/$NET/m$MOD_NUM/plt_testAccuracy_$MOD_NUM\_$NET.png  $WORK_DIR/models/$NET/m$MOD_NUM/logfile_$MOD_NUM\_$NET.log
'