def train_yolo_detector(task_id):
"""
:param task_id:
:return:
"""
start = TEvent.objects.get(pk=task_id)
if celery_40_bug_hack(start):
return 0
start.task_id = train_yolo_detector.request.id
start.started = True
start.operation = train_yolo_detector.name
start.save()
start_time = time.time()
args = json.loads(start.arguments_json)
labels = set(args['labels']) if 'labels' in args else set()
object_names = set(args['object_names']) if 'object_names' in args else set()
detector = CustomDetector.objects.get(pk=args['detector_pk'])
create_detector_folders(detector)
args['root_dir'] = "{}/models/{}/".format(settings.MEDIA_ROOT,detector.pk)
class_distribution, class_names, rboxes, rboxes_set, frames, i_class_names = create_detector_dataset(object_names,labels)
images, boxes = [], []
path_to_f = {}
for k,f in frames.iteritems():
path = "{}/{}/frames/{}.jpg".format(settings.MEDIA_ROOT,f.video_id,f.frame_index)
path_to_f[path] = f
images.append(path)
boxes.append(rboxes[k])
# print k,rboxes[k]
with open("{}/input.json".format(args['root_dir']),'w') as input_data:
json.dump({'boxes':boxes,'images':images,'args':args,'class_names':class_names.items()},input_data)
detector.boxes_count = sum([len(k) for k in boxes])
detector.frames_count = len(images)
detector.classes_count = len(class_names)
detector.save()
train_task = trainer.YOLOTrainer(boxes=boxes,images=images,class_names=i_class_names,args=args)
train_task.train()
detector.phase_1_log = file("{}/phase_1.log".format(args['root_dir']))
detector.phase_2_log = file("{}/phase_2.log".format(args['root_dir']))
detector.class_distribution = json.dumps(class_distribution.items())
detector.class_names = json.dumps(class_names.items())
detector.save()
results = train_task.predict()
for path, box_class, score, top, left, bottom, right in results:
r = Region()
r.region_type = r.DETECTION
r.confidence = int(100.0 * score)
r.object_name = "YOLO_{}_{}".format(detector.pk,box_class)
r.y = top
r.x = left
r.w = right - left
r.h = bottom - top
r.frame_id = path_to_f[path].pk
r.video_id = path_to_f[path].video_id
r.save()
start.completed = True
start.seconds = time.time() - start_time
start.save()
return 0
def test_train_yolo():
"""
:return:
"""
setup_django()
from dvaapp.models import TEvent, CustomDetector
from dvaapp.tasks import train_yolo_detector
from django.conf import settings
args = {}
detector = CustomDetector()
detector.save()
args['detector_pk'] = detector.pk
args['object_names'] = [
"red_buoy", "green_buoy", "yellow_buoy", "path_marker", "start_gate",
"channel"
]
args['root_dir'] = "{}/models/{}/".format(settings.MEDIA_ROOT, detector.pk)
train_yolo_detector(
TEvent.objects.create(arguments_json=json.dumps(args)).pk)
def train_yolo(start_pk):
"""
Train a yolo model specified in a TaskEvent.
This is necessary to ensure that the Tensorflow process exits and releases the allocated GPU memory.
:param start_pk: TEvent PK with information about lauching the training task
:return:
"""
setup_django()
from django.conf import settings
from dvaapp.models import Region, Frame, CustomDetector, TEvent
from dvaapp.shared import create_detector_folders, create_detector_dataset
from dvalib.yolo import trainer
start = TEvent.objects.get(pk=start_pk)
args = json.loads(start.arguments_json)
labels = set(args['labels']) if 'labels' in args else set()
object_names = set(args['object_names']) if 'object_names' in args else set()
detector = CustomDetector.objects.get(pk=args['detector_pk'])
create_detector_folders(detector)
args['root_dir'] = "{}/models/{}/".format(settings.MEDIA_ROOT,detector.pk)
class_distribution, class_names, rboxes, rboxes_set, frames, i_class_names = create_detector_dataset(object_names,labels)
images, boxes = [], []
path_to_f = {}
for k,f in frames.iteritems():
path = "{}/{}/frames/{}.jpg".format(settings.MEDIA_ROOT,f.video_id,f.frame_index)
path_to_f[path] = f
images.append(path)
boxes.append(rboxes[k])
# print k,rboxes[k]
with open("{}/input.json".format(args['root_dir']),'w') as input_data:
json.dump({'boxes':boxes,
'images':images,
'args':args,
'class_names':class_names.items(),
'class_distribution':class_distribution.items()},
input_data)
detector.boxes_count = sum([len(k) for k in boxes])
detector.frames_count = len(images)
detector.classes_count = len(class_names)
detector.save()
train_task = trainer.YOLOTrainer(boxes=boxes,images=images,class_names=i_class_names,args=args)
train_task.train()
detector.phase_1_log = file("{}/phase_1.log".format(args['root_dir'])).read()
detector.phase_2_log = file("{}/phase_2.log".format(args['root_dir'])).read()
detector.class_distribution = json.dumps(class_distribution.items())
detector.class_names = json.dumps(class_names.items())
detector.trained = True
detector.save()
results = train_task.predict()
bulk_regions = []
for path, box_class, score, top, left, bottom, right in results:
r = Region()
r.region_type = r.ANNOTATION
r.confidence = int(100.0 * score)
r.object_name = "YOLO_{}_{}".format(detector.pk,box_class)
r.y = top
r.x = left
r.w = right - left
r.h = bottom - top
r.frame_id = path_to_f[path].pk
r.video_id = path_to_f[path].video_id
bulk_regions.append(r)
Region.objects.bulk_create(bulk_regions,batch_size=1000)
folder_name = "{}/models/{}".format(settings.MEDIA_ROOT,detector.pk)
file_name = '{}/exports/{}.dva_detector.zip'.format(settings.MEDIA_ROOT,detector.pk)
zipper = subprocess.Popen(['zip', file_name, '-r', '.'],cwd=folder_name)
zipper.wait()
return 0
