Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

update

This commit is contained in:
tomasz 2020-07-26 00:43:07 +02:00
parent eff752bba2
commit 43a5d4568a
24 changed files with 12336 additions and 152 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
cfg/yolov3-spp-21cls.cfg
View File

@ -633,14 +633,14 @@ activation=leaky
size=1
stride=1
pad=1
filters=72
filters=78
activation=linear
[yolo]
mask = 6,7,8
anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
classes=19
classes=21
num=9
jitter=.3
ignore_thresh = .7
@ -719,14 +719,14 @@ activation=leaky
size=1
stride=1
pad=1
filters=72
filters=78
activation=linear
[yolo]
mask = 3,4,5
anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
classes=19
classes=21
num=9
jitter=.3
ignore_thresh = .7
@ -806,14 +806,14 @@ activation=leaky
size=1
stride=1
pad=1
filters=72
filters=78
activation=linear
[yolo]
mask = 0,1,2
anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
classes=19
classes=21
num=9
jitter=.3
ignore_thresh = .7

6
cfg/yolov3-spp.cfg
View File

@ -640,7 +640,7 @@ activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
classes=19
num=9
jitter=.3
ignore_thresh = .7
@ -726,7 +726,7 @@ activation=linear
[yolo]
mask = 3,4,5
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
classes=19
num=9
jitter=.3
ignore_thresh = .7
@ -813,7 +813,7 @@ activation=linear
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
classes=19
num=9
jitter=.3
ignore_thresh = .7

6
data/widok01-11.data
View File

@ -1,6 +0,0 @@
classes=18
train=/home/michall/yolov3/data/widok01-11_train_labels.txt
valid=/home/michall/yolov3/data/widok01-11_test_labels.txt
names=/home/michall/yolov3/data/widok01-11.names
backup=backup/
eval=coco

BIN
data/widok01-11_labels.npy
View File

Binary file not shown.

6
data/widok_01_19.data
View File

@ -1,6 +0,0 @@
classes=19
train=/home/tomekb/yolov3/data/widok_01_19/widok_01_19_train_labels.txt
valid=/home/tomekb/yolov3/data/widok_01_19/widok_01_19_test_labels.txt
names=/home/tomekb/yolov3/data/widok_01_19/widok_01_19.names
backup=backup/
eval=coco

6
data/widok_01_21/widok_01_21.data Normal file
View File

@ -0,0 +1,6 @@
classes=21
train=./data/widok_01_21/widok_01_21_train_labels.txt
valid=./data/widok_01_21/widok_01_21_test_labels.txt
names=./data/widok_01_21/widok_01_21.names
backup=backup/
eval=coco

21
data/widok_01_21/widok_01_21.names Normal file
View File

@ -0,0 +1,21 @@
1. rower
2. motocykl
3. osobowy
4. osobowy pickup
5. osobowy dostawczy
6. osobowy van 7-9
7. dostawczy blaszak / BUS sredni dostawczy
8. dostawczy zabudowany
9. dostawczy pickup (w tym pomoc drog.)
10. dostawczy VAN (osobowy) / autobus maly 10-24 / BUS sredni osobowy
11. autobus miejski / autobus turystyczny i inny
12. ciezarowy pow. 3,5t zabudowany / ciezarowy z widoczna przyczepa
13. ciezarowy pow. 3,5t otwarty (w tym duzy holownik)
14. ciezarowy pow. 3,5t inny (wanna, gruszka, dzwig itp.)
15. ciagnik siodlowy z widoczna naczepa / ciagnik siodlowy bez naczepy
16. inne pojazdy silnikowe / camper / woz strazacki / ciagnik roliczy, koparka, spychacz
17. przyczepa
18. BUS-karetka/policja
19. BUS brygadowka
20. BUS sredni dostawczy
21. BUS sredni osobowy

16
data/widok_01_21/widok_01_21.names.save Normal file
View File

@ -0,0 +1,16 @@
1. rower
2. motocykl
3. osobowy
4. osobowy pickup
5. osobowy dostawczy
6. osobowy van 7-9
7. dostawczy blaszak / BUS sredni dostawczy
8. dostawczy zabudowany
9. dostawczy pickup (w tym pomoc drog.)
10. dostawczy VAN (osobowy) / autobus maly 10-24 / BUS sredni osobowy
11. autobus miejski / autobus turystyczny i inny
12. ciezarowy pow. 3,5t zabudowany / ciezarowy z widoczna przyczepa
13. ciezarowy pow. 3,5t otwarty / (w tym duzy holownik)
14. ciezarowy pow. 3,5t inny (wanna, gruszka, dzwig itp.)
15. ciagnik siodlowy z widoczna naczepa /
ciagnik siodlowy bez naczepy

BIN
data/widok_01_21/widok_01_21_labels.npy Normal file
View File

Binary file not shown.

1194
data/widok_01_21/widok_01_21_test_labels.shapes Normal file
View File

File diff suppressed because it is too large Load Diff

1194
data/widok_01_21/widok_01_21_test_labels.txt Normal file
View File

File diff suppressed because it is too large Load Diff

4788
data/widok_01_21/widok_01_21_train_labels.shapes Normal file
View File

File diff suppressed because it is too large Load Diff

4788
data/widok_01_21/widok_01_21_train_labels.txt Normal file
View File

File diff suppressed because it is too large Load Diff

6
detect.py
View File

@ -133,7 +133,7 @@ def detect(save_img=False):
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
#print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
@ -162,7 +162,7 @@ def detect(save_img=False):
if platform == 'darwin': # MacOS
os.system('open ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
#print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
@ -186,7 +186,7 @@ if __name__ == '__main__':
opt = parser.parse_args()
opt.cfg = check_file(opt.cfg) # check file
opt.names = check_file(opt.names) # check file
print(opt)
#print(opt)
with torch.no_grad():
detect(True)

15
our_scripts/config.yml
View File

@ -1,11 +1,11 @@
train:
epochs: 1200
batch-size: 3
epochs: 200
batch-size: 14 #
cfg: ./cfg/yolov3-spp-19cls.cfg
data: ./data/widok_01_19.data
multi-scale: false
img-size: '512 1920'
rect: true
img-size: '768 1280'
rect: false
resume: false
nosave: false
notest: false
@ -16,7 +16,8 @@ train:
device: 1
adam: true
single-cls: false
save-every-nth-epoch: 50
snapshot-every: 50
freeze-layers: true
# inne hiperparametry
other-hyps:
@ -36,14 +37,14 @@ train:
hsv_v: 0.36 # image HSV-Value augmentation (fraction)
degrees: 0 # 1.98 * 0 # image rotation (+/- deg)
translate: 0 # 0.05 * 0 # image translation (+/- fraction)
scale: 0 #0 .05 * 0 # image scale (+/- gain)
scale: 0 #0 .05 * 0 # image scale (+/- gain)
shear: 0 # 0.641 * 0 # image shear (+/- deg)
experiments:
dir: ./experiments
detect:
source: /home/tomekb/yolov3/data/widok_01_19/widok_01_19_test_labels.txt
test-img-size: 1920
test-img-size: 1024
conf-thres: 0.3
iou-thres: 0.6
classes:

59
our_scripts/config_bayes.py
View File

@ -1,29 +1,7 @@
import yaml
class Args:
def get_args_string(self) -> str:
string = ''
for key, value in self.__dict__.items():
if not isinstance(value, Configuration.Train.OtherHyps) and value is not None:
if key == 'img-size':
string += f' --{key} {value.split(" ")[0]} {value.split(" ")[1]}'
elif type(value) == bool:
if value:
string += f" --{key}"
else:
continue
elif type(value) in [int, str] and value != '':
string += f' --{key} {value}'
else:
raise Exception(f"Cannot parse argument {key} {value}")
return string
class Configuration:
class Train(Args):
class Train:
class OtherHyps:
def __init__(self, config_file) -> None:
@ -36,28 +14,55 @@ class Configuration:
if key != 'other-hyps':
self.__dict__[key] = value
class Experiments(Args):
class Experiments:
def __init__(self, config_file) -> None:
for key, value in config_file['experiments'].items():
self.__dict__[key] = value
class Detect(Args):
class Detect:
def __init__(self, config_file) -> None:
for key, value in config_file['detect'].items():
self.__dict__[key] = value
class ConfussionMatrix(Args):
class ConfussionMatrix:
def __init__(self, config_file) -> None:
for key, value in config_file['confussion-matrix'].items():
self.__dict__[key] = value
def __init__(self, config_path='/home/tomekb/yolov3/our_scripts/config_bayes.yml') -> None:
class Bayes:
def __init__(self, config_file) -> None:
for key, value in config_file['bayes'].items():
self.__dict__[key] = value
def __init__(self, config_path='./config_bayes.yml') -> None:
self.config_path = config_path
file = yaml.load(open(config_path, 'r'), Loader=yaml.Loader)
self.train = self.Train(file)
self.experiments = self.Experiments(file)
self.detect = self.Detect(file)
self.confussion_matrix = self.ConfussionMatrix(file)
self.bayes = self.Bayes(file)
def get_bayes_bounds(self) -> list:
result = []
dicts = {**self.train.__dict__, **self.train.other_hyps.__dict__, **self.detect.__dict__}
for key, value in dicts.items():
if type(value) not in [None, Configuration.Train.OtherHyps] and type(value) == dict:
if value['type'] == 'continuous': # continous value
val = (value['min'], value['max'])
item = {'name': key, 'type': value['type'], 'domain': val}
elif value['type'] == 'discrete' and 'step' in value:
val = tuple(n for n in range(value['min'], value['max'], value['step']))
item = {'name': key, 'type': value['type'], 'domain': val}
elif value['type'] == 'discrete': # discrete values without step
val = tuple(n for n in value['values'])
item = {'name': key, 'type': value['type'], 'domain': val}
else: # unknown type
raise Exception("Invalid type", value['type'])
result.append(item)
return result
if __name__ == '__main__':
config = Configuration()

61
our_scripts/config_bayes.yml
View File

@ -1,32 +1,47 @@
bayes:
iterations: 2
train:
epochs: 100
epochs:
type: discrete
values: [10]
batch-size:
type: discrete
values: 1,2,3,4
cfg: ./cfg/yolov3-spp-19cls.cfg
data: ./data/widok_01_19.data
#values: [128]
min: 1
max: 5
step: 1
cfg: ./cfg/yolov3-spp-21cls.cfg
data: ./data/widok_01_21.data
multi-scale:
type: discrete
values: true, false
img-size:
values: [true, false]
img-size-start:
type: discrete
values: (512, 1280),(576, 1280),(640, 1280),(704, 1280),(768, 1280),(832,1280),(960, 1280),(1024, 1280) # trzeba wziąć pod uwagę wszystkie kombinacje i warunek img-size-min < img-size_max których jest cała masa
rect: false
min: 512
max: 1088
step: 64
img-size-end:
type: discrete
values: true,false
min: 512
max: 1088
step: 64
rect:
type: discrete
values: [false]
resume: false
nosave: false
notest: false
evolve: false
bucket:
cache-images: false
weights: /home/tomekb/yolov3/weights/yolov3-spp-ultralytics.pt
weights: ./weights/yolov3-spp-ultralytics.pt
device: 1
adam: true
adam:
type: discrete
values: [true]
single-cls: false
snapshot-every: 50
snapshot-every:
freeze-layers: true
other-hyps:
giou:
type: continuous
@ -52,8 +67,14 @@ train:
type: continuous
min: 0.0
max: 1.0
lr0: 0.01 # initial learning rate (SGD=5E-3 Adam=5E-4) # trzeba wziąć pod uwage zależność lr0 < lrf dlatego nie zmieniam
lrf: 0.0005 # final learning rate (with cos scheduler)
lr0:
type: continuous
min: 0.000001
max: 0.1
lrf:
type: continuous
min: 0.000001
max: 0.1
momentum:
type: continuous
min: 0.0
@ -70,7 +91,7 @@ train:
type: continuous
min: 0.0
max: 1.0
hsv_s: 0.678
hsv_s:
type: continuous
min: 0.0
max: 1.0
@ -97,10 +118,12 @@ train:
experiments:
dir: ./experiments
detect:
source: /home/tomekb/yolov3/data/widok_01_19/widok_01_19_test_labels.txt
source: ./data/widok_01_21/widok_01_21_test_labels.txt
test-img-size:
type: discrete
values: 512,576,640,704,768,832,896,960,1024,1088,1152,1216,1280,
min: 512
max: 1088
step: 64
conf-thres:
type: continuous
min: 0.0
@ -113,4 +136,4 @@ detect:
agnostic-nms:
augment:
confussion-matrix:
labels-dir: /home/tomekb/yolov3/data/widok_01_19/widok_01_19_labels
labels-dir: ./data/widok_01_21/widok_01_21_labels

8
our_scripts/generate-confusion-matrix.js
View File

@ -158,10 +158,10 @@ async function main() {
tsvRows.push([ names[row], ...(summaryRows.map(r => summaryPart[r] && (summaryPart[r] / sum).toFixed(2)))].join('\t'))
}
const allLabeled = rows.slice(0, -1).map(r => summary.sum[r]).reduce((a, b) => a + b, 0)
const allDetected = rows.slice(0, -1).map(r => summary[r].sum).reduce((a, b) => a + b, 0)
const falseNegatives = rows.slice(0, -1).map(r => summary.n[r] || 0).reduce((a, b) => a + b, 0)
const falsePositives = rows.slice(0, -1).map(r => summary[r].n || 0).reduce((a, b) => a + b, 0)
const allDetected = rows.slice(0, -1).map(r => summary.sum[r]).reduce((a, b) => a + b, 0)
const allLabeled = rows.slice(0, -1).map(r => summary[r].sum).reduce((a, b) => a + b, 0)
const falsePositives = rows.slice(0, -1).map(r => summary.n[r] || 0).reduce((a, b) => a + b, 0)
const falseNegatives = rows.slice(0, -1).map(r => summary[r].n || 0).reduce((a, b) => a + b, 0)
const right = rows.slice(0, -1).map(r => summary[r][r] || 0).reduce((a, b) => a + b, 0)
const mistakes = rows.slice(0, -1).map(a => rows.slice(0, -1).map(b => (a!=b && summary[a][b]) || 0).reduce((a, b) => a + b, 0)).reduce((a, b) => a + b, 0)

12
our_scripts/generate_txt_from_xml.py
View File

@ -46,7 +46,7 @@ def map_class_name_to_id(class_name, xml_document, class_distribution):
elif class_name in ['6. osobowy van 7-9']:
class_distribution[5] += 1
return 5
elif class_name in ['7. dostawczy blaszak', '27. BUS sredni dostawczy']:
elif class_name in ['7. dostawczy blaszak']:
class_distribution[6] += 1
return 6
elif class_name in ['8. dostawczy zabudowany']:
@ -55,7 +55,7 @@ def map_class_name_to_id(class_name, xml_document, class_distribution):
elif class_name in ['9. dostawczy pickup (w tym pomoc drog.)']:
class_distribution[8] += 1
return 8
elif class_name in ['10. dostawczy VAN (osobowy)', '11. autobus maly 10-24', '28. BUS sredni osobowy']:
elif class_name in ['10. dostawczy VAN (osobowy)', '11. autobus maly 10-24']:
class_distribution[9] += 1
return 9
elif class_name in ['12. autobus miejski', '13. autobus turystyczny i inny']:
@ -85,13 +85,19 @@ def map_class_name_to_id(class_name, xml_document, class_distribution):
elif class_name in ['26. BUS brygadowka']:
class_distribution[18] += 1
return 18
elif class_name in ['27. BUS sredni dostawczy']:
class_distribution[19] += 1
return 19
elif class_name in ['28. BUS sredni osobowy']:
class_distribution[20] += 1
return 20
else:
raise Exception('Unknown Class ', xml_document, class_name)
#print(f'{xml_document.split("/")[-1]} {class_name}')
def generate_txt_from_xml():
class_distribution = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
class_distribution = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0]
filepaths = glob(join(annotations , '*.xml'))

39
our_scripts/run_yolov3_process.py
View File

@ -1,4 +1,3 @@
import argparse
import datetime
import glob
import io
@ -12,41 +11,44 @@ from config import Configuration
def call_training_script(config):
cmd = '/home/tomekb/miniconda3/envs/conda3.7/bin/python -u /home/tomekb/yolov3/train.py '
cmd += config.train.get_args_string()
cmd += f"--experiment-dir {config.experiments.dir}"
cmd += config.train.get_args_string() # getting rest of train arguments
print("_______ CALLING TRAINING SCRIPT _______")
print(cmd)
os.chdir('..')
process = subprocess.Popen(cmd, stdout=subprocess.PIPE,shell=True)
os.chdir('..') # change to project root directory
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
for line in io.TextIOWrapper(process.stdout, encoding="utf-8"): # print output of training process to console
print(line)
return cmd
def move_training_results_to_experiments_dir(config):
training_results_dir_path = os.path.join(config.experiments.dir, datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
training_results_dir_path = os.path.join(config.experiments.dir, datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')) #creating directory accordint to pattern eg: 2020-06-30_17-52-19
print("_______ CALLING MOVING RESULTS _______")
print(f"MOVING RESUTLS TO {training_results_dir_path}")
os.mkdir(training_results_dir_path)
weights_path = os.path.join(training_results_dir_path, 'best.pt')
shutil.move('/home/tomekb/yolov3/weights/best.pt', weights_path) #move best weights
shutil.move('/home/tomekb/yolov3/weights/best.pt', weights_path) # move best weights
names_path = open(config.train.data).readlines()[3].split('=')[-1].rstrip() # read names path from file
names_file_name = ntpath.basename(names_path)
experiment_names_path = os.path.join(training_results_dir_path, names_file_name)
shutil.copy(names_path, experiment_names_path) # copy names to created experiment dir with training results
shutil.copy(names_path, experiment_names_path) # copy names file from *.data file to created experiment dir with training results
tensorboard_dir = './runs'
last_modified_tensorboard_dir = max(glob.glob(os.path.join(tensorboard_dir, '*/')), key=os.path.getmtime)
shutil.move(last_modified_tensorboard_dir, os.path.join(training_results_dir_path)) #saving related tensorboard dir
shutil.copy2(config.config_path, training_results_dir_path) #copying configuration yaml
shutil.move(last_modified_tensorboard_dir, os.path.join(training_results_dir_path)) # saving related tensorboard dir
#for test purposes only
#shutil.copy2('/home/tomekb/yolov3/experiments/1/best.pt', training_results_dir_path)
shutil.copy2(config.config_path, training_results_dir_path) # copying configuration yaml
# for test purposes only
# shutil.copy2('/home/tomekb/yolov3/experiments/1/best.pt', training_results_dir_path)
return weights_path, experiment_names_path, training_results_dir_path
@ -69,11 +71,10 @@ def call_detection_script(config, weights_path, names_path, dir):
cmd = " ".join(cmd.split())
print("_______ CALLING DETECTION SCRIPT _______")
print(cmd)
process = subprocess.Popen(cmd, stdout=subprocess.PIPE,shell=True)
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
for line in io.TextIOWrapper(process.stdout, encoding="utf-8"): # print output of process to console
print(line)
@ -84,7 +85,7 @@ def call_generate_confussion_matrix(detect_output_dir, config, names_path, train
labels_dir = getattr(config.confussion_matrix, 'labels-dir')
cmd = f"node ./our_scripts/generate-confusion-matrix.js {detect_output_dir} {labels_dir} {names_path} > {train_results_dir}/confussion-matrix.tsv"
process = subprocess.Popen(cmd, stdout=subprocess.PIPE,shell=True)
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
print("_______ CALLING CONFUSSION MATRIX SCRIPT _______")
print(cmd)
for line in io.TextIOWrapper(process.stdout, encoding="utf-8"): # print output of process to console
@ -95,6 +96,6 @@ if __name__ == '__main__':
config = Configuration()
train_cmd = call_training_script(config)
weights_path, names_path,train_results_dir = move_training_results_to_experiments_dir(config)
detect_output_dir = call_detection_script(config, weights_path, names_path,train_results_dir)
call_generate_confussion_matrix(detect_output_dir, config, names_path,train_results_dir)
weights_path, names_path, train_results_dir = move_training_results_to_experiments_dir(config)
detect_output_dir = call_detection_script(config, weights_path, names_path, train_results_dir)
call_generate_confussion_matrix(detect_output_dir, config, names_path, train_results_dir)

171
our_scripts/run_yolov3_process_bayes.py
View File

@ -1,31 +1,56 @@
import datetime
import glob
import io
import ntpath
import os
import shutil
import subprocess
import traceback
from .config import Configuration
import GPyOpt
import numpy as np
from config_bayes import Configuration
from utils import call_subprocess, get_values_from_conff_matrix
config = Configuration()
date_string = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
bayes_params_file = open(os.path.join(config.experiments.dir, f"{date_string}_bayes_params.txt"), 'a+')
def call_training_script(config):
cmd = '/home/tomekb/miniconda3/envs/conda3.7/bin/python -u /home/tomekb/yolov3/train.py '
cmd += f"--experiment-dir {config.experiments.dir}"
cmd += config.train.get_args_string() # getting rest of train arguments
def call_training_script(gaussian_hyps):
cmd = 'python-u /home/tomekb/yolov3/train.py'
cmd += ' --epochs ' + gaussian_hyps['epochs'].__str__()
cmd += ' --batch-size ' + gaussian_hyps['batch-size'].__str__()
cmd += ' --cfg ' + config.train.cfg.__str__()
cmd += ' --data ' + config.train.data.__str__()
cmd += ' --multi-scale ' if gaussian_hyps['multi-scale'] else ""
cmd += ' --img-size ' + gaussian_hyps['img-size']
cmd += ' --rect ' if gaussian_hyps['rect'] else ""
cmd += ' --weights ' + config.train.weights.__str__()
cmd += ' --device ' + config.train.device.__str__()
cmd += ' --adam ' if gaussian_hyps['adam'] else ""
cmd += ' --freeze-layers ' if getattr(config.train, "freeze-layers") else ""
# cmd += ' --snapshot-every ' if getattr(config.train, "snapshot-every") else ""
cmd += ' --experiment-dir ' + config.experiments.dir.__str__()
train_hyps = dict(
(key, gaussian_hyps[key]) for idx, (key, _) in enumerate(gaussian_hyps.items()) if idx in range(6, 24))
cmd += f' --hyp \"{train_hyps}\"'
print("_______ CALLING TRAINING SCRIPT _______")
print(cmd)
os.chdir('..') # change to project root directory
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
for line in io.TextIOWrapper(process.stdout, encoding="utf-8"): # print output of training process to console
print(line)
dir_path = os.path.dirname(os.path.realpath(__file__))
os.chdir(os.path.join(dir_path, '..')) # change to project root directory
call_subprocess(cmd)
return cmd
def move_training_results_to_experiments_dir(config):
training_results_dir_path = os.path.join(config.experiments.dir, datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')) #creating directory accordint to pattern eg: 2020-06-30_17-52-19
def move_training_results_to_experiments_dir():
training_results_dir_path = os.path.join(config.experiments.dir, datetime.datetime.now().strftime(
'%Y-%m-%d_%H-%M-%S')) # creating directory accordint to pattern eg: 2020-06-30_17-52-19
print("_______ CALLING MOVING RESULTS _______")
print(f"MOVING RESUTLS TO {training_results_dir_path}")
@ -38,22 +63,26 @@ def move_training_results_to_experiments_dir(config):
names_path = open(config.train.data).readlines()[3].split('=')[-1].rstrip() # read names path from file
names_file_name = ntpath.basename(names_path)
experiment_names_path = os.path.join(training_results_dir_path, names_file_name)
shutil.copy(names_path, experiment_names_path) # copy names file from *.data file to created experiment dir with training results
tensorboard_dir = './runs'
last_modified_tensorboard_dir = max(glob.glob(os.path.join(tensorboard_dir, '*/')), key=os.path.getmtime)
shutil.move(last_modified_tensorboard_dir, os.path.join(training_results_dir_path)) # saving related tensorboard dir
shutil.copy(names_path,
experiment_names_path) # copy names file from *.data file to created experiment dir with training results
tensorboard_dir = '/home/tomekb/yolov3/runs'
tensorboard_events_files = glob.glob(os.path.join(tensorboard_dir, '*'))
last_modified_events_file = max(tensorboard_events_files, key=os.path.getmtime)
shutil.move(last_modified_events_file,
os.path.join(training_results_dir_path)) # saving related tensorboard dir
shutil.copy2(config.config_path, training_results_dir_path) # copying configuration yaml
# for test purposes only
# shutil.copy2('/home/tomekb/yolov3/experiments/1/best.pt', training_results_dir_path)
# TODO CHANGE ME AFTER TESTS
shutil.copy2('/home/tomekb/yolov3/experiments/yolov3-spp-100-epochs-freeze-layers/best.pt',
training_results_dir_path)
return weights_path, experiment_names_path, training_results_dir_path
def call_detection_script(config, weights_path, names_path, dir):
def call_detection_script(gaussian_hyps, weights_path, names_path, dir):
detect_output_dir = os.path.join(dir, 'output')
cmd = f"""/home/tomekb/miniconda3/envs/conda3.7/bin/python -u /home/tomekb/yolov3/detect.py
--cfg {config.train.cfg}
@ -61,41 +90,101 @@ def call_detection_script(config, weights_path, names_path, dir):
--output {detect_output_dir}
--names {names_path}
--weights {weights_path}
--test-img-size {getattr(config.detect, 'test-img-size')}
--conf-thres {getattr(config.detect, 'conf-thres')}
--iou-thres {getattr(config.detect, 'iou-thres')}
--test-img-size {gaussian_hyps['test-img-size']}
--conf-thres {gaussian_hyps['conf-thres']}
--iou-thres {gaussian_hyps['iou-thres']}
--save-txt"""
cmd += " --agnostic-nms" if getattr(config.detect, 'agnostic-nms') else ""
cmd += " --agument" if getattr(config.detect, 'augment') else ""
cmd += f" --device {config.train.device}" if config.train.device else ""
cmd = " ".join(cmd.split())
print("_______ CALLING DETECTION SCRIPT _______")
print(cmd)
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
for line in io.TextIOWrapper(process.stdout, encoding="utf-8"): # print output of process to console
print(line)
call_subprocess(cmd)
return detect_output_dir
def call_generate_confussion_matrix(detect_output_dir, config, names_path, train_results_dir):
def call_generate_confussion_matrix(detect_output_dir, names_path, train_results_dir):
labels_dir = getattr(config.confussion_matrix, 'labels-dir')
cmd = f"node ./our_scripts/generate-confusion-matrix.js {detect_output_dir} {labels_dir} {names_path} > {train_results_dir}/confussion-matrix.tsv"
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
conff_matrix_path = os.path.join(train_results_dir, 'confussion-matrix.tsv')
cmd = f"node /home/tomekb/yolov3/our_scripts/generate-confusion-matrix.js {detect_output_dir} {labels_dir} {names_path} > {conff_matrix_path}"
print("_______ CALLING CONFUSSION MATRIX SCRIPT _______")
print(cmd)
for line in io.TextIOWrapper(process.stdout, encoding="utf-8"): # print output of process to console
print(line)
call_subprocess(cmd)
return conff_matrix_path
def yolov3(x):
bayes_hyps = {
'epochs': int(x[:, 0]),
'batch-size': int(x[:, 1]),
'multi-scale': bool(x[:, 2]),
'img-size': f"{int(x[:, 3])} {int(x[:, 4])}",
'rect': bool(x[:, 5]),
'adam': bool(x[:, 6]),
'giou': float(x[:, 7]), # train hyps start index
'cls': float(x[:, 8]),
'cls_pw': float(x[:, 9]),
'obj': float(x[:, 10]),
'obj_pw': float(x[:, 11]),
'iou_t': float(x[:, 12]),
'lr0': float(x[:, 13]),
'lrf': float(x[:, 14]),
'momentum': float(x[:, 15]),
'weight_decay': float(x[:, 16]),
'fl_gamma': float(x[:, 17]),
'hsv_h': float(x[:, 18]),
'hsv_s': float(x[:, 19]),
'hsv_v': float(x[:, 20]),
'degrees': float(x[:, 21]),
'translate': float(x[:, 22]),
'scale': float(x[:, 23]),
'shear': float(x[:, 24]), # train hyps end index
'test-img-size': int(x[:, 25]),
'conf-thres': float(x[:, 26]),
'iou-thres': float(x[:, 27])
}
line = ""
try:
call_training_script(bayes_hyps)
weights_path, names_path, train_results_dir = move_training_results_to_experiments_dir()
detect_output_dir = call_detection_script(bayes_hyps, weights_path, names_path, train_results_dir)
conf_matrix_path = call_generate_confussion_matrix(detect_output_dir, names_path, train_results_dir)
y_dict = get_values_from_conff_matrix(conf_matrix_path)
# tutaj wzór na wyliczanie funkcji
y_val = 1 - ((y_dict['match'] * 10 - y_dict['false positives'] * 3) / y_dict['mistakes'])
# zapisywanie do pliku zadeklarowanego globalnie
line = "\t".join([bayes_hyps.__str__(), str(y_val)])
bayes_params_file.writelines([line, '\n'])
return y_val
except:
tb = traceback.format_exc()
print("An error occured during running training-detect-confussion process \n", tb)
print("Returning 1 from current bayessian iteration")
line = "\t".join([bayes_hyps.__str__(), str(1)])
return 1
finally:
bayes_params_file.writelines([line, '\n'])
# uruchamiać z
if __name__ == '__main__':
config = Configuration()
train_cmd = call_training_script(config)
weights_path, names_path, train_results_dir = move_training_results_to_experiments_dir(config)
detect_output_dir = call_detection_script(config, weights_path, names_path, train_results_dir)
call_generate_confussion_matrix(detect_output_dir, config, names_path, train_results_dir)
bounds = config.get_bayes_bounds()
# for b in bounds:
# print(b)
# tutaj będzie wczytywanie z poprzednich eksperymentów plik bayes_params
X = None
Y = None
bayes_optimizer = GPyOpt.methods.BayesianOptimization(f=yolov3, domain=bounds, X=X, Y=Y, verbosity=True,
initial_design_numdata=2)
bayes_optimizer.run_optimization(config.bayes.iterations, verbosity=True)
bayes_params_file.close()

55
our_scripts/utils.py
View File

@ -0,0 +1,55 @@
import io
import subprocess
def call_subprocess(cmd):
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)
error = False
for process_line_output in io.TextIOWrapper(process.stdout,
encoding="utf-8"): # print output of training process to console
if 'Traceback' in process_line_output:
error = True
print(process_line_output)
if error:
raise RuntimeError("An error occured during calling subprocess")
def get_values_from_conff_matrix(path):
lines = open(path, 'r').readlines()[:7]
d = {}
for l in lines:
key, value, *_ = l.split("\t")
d.update({key.replace(":", ""): int(value)})
return d
def get_bayes_params_as_dict(x):
return {
'epochs': int(x[:, 0]),
'batch-size': int(x[:, 1]),
'multi-scale': bool(x[:, 2]),
'img-size': f"{int(x[:, 3])} {int(x[:, 4])}",
'rect': bool(x[:, 5]),
'adam': bool(x[:, 6]),
'giou': float(x[:, 7]), # train hyps start index
'cls': float(x[:, 8]),
'cls_pw': float(x[:, 9]),
'obj': float(x[:, 10]),
'obj_pw': float(x[:, 11]),
'iou_t': float(x[:, 12]),
'lr0': float(x[:, 13]),
'lrf': float(x[:, 14]),
'momentum': float(x[:, 15]),
'weight_decay': float(x[:, 16]),
'fl_gamma': float(x[:, 17]),
'hsv_h': float(x[:, 18]),
'hsv_s': float(x[:, 19]),
'hsv_v': float(x[:, 20]),
'degrees': float(x[:, 21]),
'translate': float(x[:, 22]),
'scale': float(x[:, 23]),
'shear': float(x[:, 24]), # train hyps end index
'test-img-size': int(x[:, 25]),
'conf-thres': float(x[:, 26]),
'iou-thres': float(x[:, 27])
}

22
train.py
View File

@ -10,6 +10,7 @@ from models import *
from utils.datasets import *
from utils.utils import *
from our_scripts.config import Configuration
import ast
mixed_precision = True
@ -117,7 +118,6 @@ def train(hyp):
best_fitness = 0.0
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
print("LOADIN MODEL")
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
ckpt = torch.load(weights, map_location=device)
@ -364,8 +364,9 @@ def train(hyp):
'model': ema.ema.module.state_dict() if hasattr(model, 'module') else ema.ema.state_dict(),
'optimizer': None if final_epoch else optimizer.state_dict()}
if epoch % opt.save_every_nth_epoch == 0:
torch.save(chkpt, f'yolo_{epoch}.pt')
if opt.snapshot_every and epoch % opt.snapshot_every == 0 :
saving_path = os.path.join(opt.experiment_dir, f'weights_{epoch}.pt')
torch.save(ckpt, saving_path)
# Save last, best and delete
torch.save(ckpt, last)
if (best_fitness == fi) and not final_epoch:
@ -416,8 +417,14 @@ if __name__ == '__main__':
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
parser.add_argument('--freeze-layers', action='store_true', help='Freeze non-output layers')
parser.add_argument('--save-every-nth-epoch', type=int, help='Saving every n-th epoth')
# parametry dodane na cele projektu wykrywania aut
parser.add_argument('--snapshot-every', type=int, help='Saving every n-th state of model weights')
parser.add_argument('--experiment-dir', type=str, help='Directory for experiments')
parser.add_argument('--hyp', type=str, help='String that represents dictionary with hyperparameters ')
opt = parser.parse_args()
#print(opt)
#opt.weights = last if opt.resume and not opt.weights else opt.weights
#check_git_status()
opt.cfg = check_file(opt.cfg) # check file
@ -431,9 +438,10 @@ if __name__ == '__main__':
# scale hyp['obj'] by img_size (evolved at 320)
# hyp['obj'] *= opt.img_size[0] / 320.
hyp = Configuration().train.other_hyps.__dict__
#overriding global hyp variable with our bayessian hyps
hyp = ast.literal_eval(opt.hyp)
#print('### TRAIN HYPERPARAMETERS ###')
#print(hyp)
tb_writer = None
if not opt.evolve: # Train normally

1
utils/datasets.py
View File

@ -553,6 +553,7 @@ def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
# Histogram equalization
# if random.random() < 0.2:
# for i in range(3):