def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
test_imgs, test_labels = parse_annotation(
config['test']['test_annot_folder'],
config['test']['test_image_folder'], config['model']['labels'])
###############################
# Construct the model
###############################
yolo = YOLO(feature_extractor=config['model']['backend'],
input_size=config['model']['input_size'],
labels=config['model']['labels'],
max_box_per_image=config['model']['max_box_per_image'],
anchors=config['model']['anchors'])
yolo.load_weights(config['train']['saved_weights_name'])
print('weights loaded from {}'.format(
config['train']['saved_weights_name']))
yolo.evaluate(test_imgs[:500],
iou_threshold=0.3,
obj_threshold=0.2,
nms_threshold=0.2)
def _main_(args):
config_path = args.conf
weights_path = args.weights
with open(config_path) as config_buffer:
config = json.load(config_buffer)
yolo = YOLO(backend=config['model']['backend'],
input_size=config['model']['input_size'],
labels=config['model']['labels'],
max_box_per_image=config['model']['max_box_per_image'],
anchors=config['model']['anchors'])
yolo.load_weights(weights_path)
valid_imgs, valid_labels = parse_annotation(
config['valid']['valid_annot_folder'],
config['valid']['valid_image_folder'], config['model']['labels'])
yolo.batch_size = config['train']['batch_size']
yolo.sequence_length = 1
generator_config = {
'IMAGE_H': yolo.input_size,
'IMAGE_W': yolo.input_size,
'GRID_H': yolo.grid_h,
'GRID_W': yolo.grid_w,
'BOX': yolo.nb_box,
'LABELS': yolo.labels,
'CLASS': len(yolo.labels),
'ANCHORS': yolo.anchors,
'BATCH_SIZE': yolo.batch_size,
'TRUE_BOX_BUFFER': yolo.max_box_per_image,
'SEQUENCE_LENGTH': yolo.sequence_length
}
valid_generator = BatchGenerator(valid_imgs,
generator_config,
norm=yolo.feature_extractor.normalize,
jitter=False)
ave_precisions = yolo.evaluate(valid_generator,
iou_threshold=0.3,
score_threshold=0.2)
print("ave precisions: ", ave_precisions)
print('mAP: {:.4f}'.format(
sum(ave_precisions.values()) / len(ave_precisions)))
yolo = YOLO(backend = None,
input_size = config['model']['input_size'],
labels = config['model']['labels'],
max_box_per_image = config['model']['max_box_per_image'],
anchors = config['model']['anchors'],
load_from_json = model_path,
trained_weights = weights_path)
yolo.set_batch(config['train']['batch_size'])
#TODO insert bactch generator here
if os.path.exists(config['valid']['valid_annot_folder']):
valid_imgs, valid_labels = parse_annotation(config['valid']['valid_annot_folder'],
config['valid']['valid_image_folder'],
config['model']['labels'])
else:
valid_imgs, valid_labels = parse_annotation(config['train']['train_annot_folder'],
config['train']['train_image_folder'],
config['model']['labels'])
valid_generator = yolo.get_generator_from_data(valid_imgs)
average_precisions, average_speed = yolo.evaluate(valid_generator)
# print evaluation
for label, average_precision in average_precisions.items():
print(yolo.get_label(label), '{:.4f}'.format(average_precision))
print('mAP: {:.4f}'.format(sum(average_precisions.values()) / len(average_precisions)))
print('speed: {:.4f}'.format(average_speed))
yolo = YOLO(backend=config['model']['backend'],
input_size=config['model']['input_size'],
labels=config['model']['labels'],
max_box_per_image=config['model']['max_box_per_image'],
anchors=config['model']['anchors'])
yolo.load_weights(config['train']['pretrained_weights'])
generator_config = {
'IMAGE_H': config['model']['input_size'],
'IMAGE_W': config['model']['input_size'],
'GRID_H': config['model']['input_size'] // 32,
'GRID_W': config['model']['input_size'] // 32,
'BOX': len(config['model']['anchors']),
'LABELS': config['model']['labels'],
'CLASS': len(config['model']['labels']),
'ANCHORS': config['model']['anchors'],
'BATCH_SIZE': 1,
'TRUE_BOX_BUFFER': config['model']['max_box_per_image'],
}
val_generator = BatchGenerator(
valid_imgs,
generator_config,
norm=normalize,
flipflop=False,
shoechanger=False,
zeropad=False,
)
yolo.evaluate(val_generator)
def _main_(args):
config_path = args.conf
weights_path = args.weights
with open(config_path) as config_buffer:
config = json.load(config_buffer)
# parse annotations of the validation set, if any, otherwise raise an error
if os.path.exists(config['valid']['valid_annot_folder']):
valid_imgs, valid_labels = parse_annotation(
config['valid']['valid_annot_folder'],
config['valid']['valid_image_folder'], config['model']['labels'])
else:
print('Folder ' + config['valid']['valid_annot_folder'] +
'does not exist')
###############################
# Make the model
###############################
yolo = YOLO(backend=config['model']['backend'],
input_size=config['model']['input_size'],
labels=config['model']['labels'],
max_box_per_image=config['model']['max_box_per_image'],
anchors=config['model']['anchors'])
###############################
# Load trained weights
###############################
yolo.load_weights(weights_path)
############################################
# Make validation generators
############################################
generator_config = {
'IMAGE_H': yolo.input_size,
'IMAGE_W': yolo.input_size,
'GRID_H': yolo.grid_h,
'GRID_W': yolo.grid_w,
'BOX': yolo.nb_box,
'LABELS': yolo.labels,
'CLASS': len(yolo.labels),
'ANCHORS': yolo.anchors,
'BATCH_SIZE': config['train']['batch_size'],
'TRUE_BOX_BUFFER': yolo.max_box_per_image,
}
valid_generator = BatchGenerator(valid_imgs,
generator_config,
norm=yolo.feature_extractor.normalize,
jitter=False)
############################################
# Compute mAP on the validation set
############################################
average_precisions = yolo.evaluate(valid_generator)
# print evaluation
for label, average_precision in average_precisions.items():
print(yolo.labels[label], '{:.4f}'.format(average_precision))
print('mAP: {:.4f}'.format(
sum(average_precisions.values()) / len(average_precisions)))
