def test_callback(model, experiment):
#create the NEON generator
NEON_generator = create_NEON_generator(DeepForest_config["batch_size"], DeepForest_config)
average_precisions = evalmAP.evaluate(
NEON_generator,
model,
iou_threshold=0.5,
score_threshold=0.15,
max_detections=300,
save_path="../snapshots/",
experiment=experiment
)
def main(DeepForest_config, args=None, experiment=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# make sure keras is the minimum required version
check_keras_version()
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
keras.backend.tensorflow_backend.set_session(get_session())
#Add seperate dir
#save time for logging
dirname = datetime.now().strftime("%Y%m%d_%H%M%S")
# make save path if it doesn't exist
if args.save_path is not None and not os.path.exists(args.save_path +
dirname):
os.makedirs(args.save_path + dirname)
#Evaluation metrics
site = DeepForest_config["evaluation_site"]
#create the NEON mAP generator
NEON_generator = create_NEON_generator(args.batch_size, DeepForest_config)
# load the model
print('Loading model, this may take a second...')
model = models.load_model(args.model,
backbone_name=args.backbone,
convert=args.convert_model,
nms_threshold=DeepForest_config["nms_threshold"])
#print(model.summary())
#NEON plot mAP
average_precisions = evaluate(NEON_generator,
model,
iou_threshold=args.iou_threshold,
score_threshold=args.score_threshold,
max_detections=args.max_detections,
save_path=args.save_path + dirname,
experiment=experiment)
return average_precisions
def main(data, DeepForest_config, experiment, args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# make sure keras is the minimum required version
check_keras_version()
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
keras.backend.tensorflow_backend.set_session(get_session())
#Add seperate dir
#save time for logging
dirname=datetime.now().strftime("%Y%m%d_%H%M%S")
experiment.log_parameter("Start Time", dirname)
# make save path if it doesn't exist
if args.save_path is not None and not os.path.exists(args.save_path + dirname):
os.makedirs(args.save_path + dirname)
# load the model
print('Loading model, this may take a second...')
model = models.load_model(args.model, backbone_name=args.backbone, convert=args.convert_model, input_channels=DeepForest_config["input_channels"], nms_threshold=DeepForest_config["nms_threshold"])
#print(model.summary())
## create the testing generators
#if DeepForest_config["evaluation_images"] > 0:
#generator = create_generator(args, data, DeepForest_config)
#average_precisions = evaluate(
#generator,
#model,
#iou_threshold=args.iou_threshold,
#score_threshold=args.score_threshold,
#max_detections=args.max_detections,
#save_path=args.save_path + dirname
#)
### print evaluation
#present_classes = 0
#precision = 0
#for label, (average_precision, num_annotations) in average_precisions.items():
#print('{:.0f} instances of class'.format(num_annotations),
#generator.label_to_name(label), 'with average precision: {:.3f}'.format(average_precision))
#if num_annotations > 0:
#present_classes += 1
#precision += average_precision
#print('mAP: {:.3f}'.format(precision / present_classes))
#experiment.log_metric("mAP", precision / present_classes)
##Evaluation metrics
#sites = DeepForest_config["evaluation_site"]
##create the NEON mAP generator
NEON_generator = create_NEON_generator(args.batch_size, DeepForest_config)
#NEON_recall_generator = create_NEON_generator(args.batch_size, DeepForest_config)
#recall = neonRecall(
#sites,
#NEON_recall_generator,
#model,
#score_threshold=args.score_threshold,
#save_path=args.save_path + dirname,
#max_detections=args.max_detections
#)
#print("Recall is {:0.3f}".format(recall))
#experiment.log_metric("Recall", recall)
#NEON plot mAP
average_precisions = evaluate(
NEON_generator,
model,
iou_threshold=args.iou_threshold,
score_threshold=args.score_threshold,
max_detections=args.max_detections,
save_path=args.save_path + dirname,
experiment=experiment
)
# print evaluation
## print evaluation
present_classes = 0
precision = 0
for label, (average_precision, num_annotations) in average_precisions.items():
print('{:.0f} instances of class'.format(num_annotations),
generator.label_to_name(label), 'with average precision: {:.3f}'.format(average_precision))
if num_annotations > 0:
present_classes += 1
precision += average_precision
NEON_map = round(precision / present_classes,3)
print('Neon mAP: {:.3f}'.format(NEON_map))
experiment.log_metric("Neon mAP", NEON_map)
return [recall, NEON_map]