def _main_h5(args):
print("\nTraining YOLO on H5!\n")
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
pp.pprint(config)
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("/home/cheng/Desktop/repos/keras-yolo2/snapshots/latest_all_shovel_full_yolo/full_yolo_34.h5")
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
train_times=config['train']['train_times'],
valid_times=config['valid']['valid_times'],
nb_epochs=config['train']['nb_epochs'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
warmup_epochs=config['train']['warmup_epochs'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
saved_weights_name=config['train']['saved_weights_name'],
debug=config['train']['debug'],
full_log_dir=config['train']['full_log_dir'],
early_stop_patience=config['train']['early_stop_patience'],
early_stop_min_delta=config['train']['early_stop_min_delta'],
learning_rate_decay_factor=config['train']
['learning_rate_decay_factor'],
learning_rate_decay_patience=config['train']
['learning_rate_decay_patience'],
learning_rate_decay_min_lr=config['train']
['learning_rate_decay_min_lr'])
return
def main(argstate):
###############################
# Parse the annotations
###############################
# parse annotations of the training set
train_imgs, train_labels = parse_annotation(argstate.train.annot_folder,
argstate.train.image_folder,
argstate.labels)
# parse annotations of the validation set, if any, otherwise split the training set
if os.path.exists(argstate.valid.annot_folder):
valid_imgs, valid_labels = parse_annotation(
argstate.valid.annot_folder, argstate.valid.image_folder,
argstate.labels)
else:
train_valid_split = int(0.8 * len(train_imgs))
np.random.seed(42)
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
print(train_labels)
if len(set(argstate.labels).intersection(set(train_labels.keys()))) == 0:
print(
"Labels to be detected are not present in the dataset! Please revise the list of labels in the config.json file!"
)
return
###############################
# Construct the model
###############################
global yolo
yolo = YOLO(architecture=argstate.architecture,
input_size=argstate.input_size,
labels=argstate.labels,
max_box_per_image=argstate.mbpi,
anchors=argstate.anchors)
###############################
# Load the pretrained weights (if any)
###############################
if os.path.exists(argstate.pretrained_weights):
print("Loading pre-trained weights in", argstate.pretrained_weights)
yolo.load_weights(argstate.pretrained_weights)
# Load pretrained weights into feature extractor and then freeze them
yolo.feature_extractor.feature_extractor.load_weights('yolov2_weights.h5')
yolo.feature_extractor.feature_extractor.trainable = True
# Spawn train pauser
pause_thread = Thread(target=spawn_pause_ui)
pause_thread.isDaemon = True
pause_thread.start()
###############################
# Start the training process
###############################
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
train_times=argstate.train.times,
valid_times=argstate.valid.times,
nb_epoch=argstate.nb_epoch,
learning_rate=argstate.learning_rate,
batch_size=argstate.batch_size,
warmup_bs=argstate.warmup_bs,
object_scale=argstate.object_scale,
no_object_scale=argstate.no_object_scale,
coord_scale=argstate.coord_scale,
class_scale=argstate.class_scale,
saved_weights_name=argstate.saved_weights_name,
debug=argstate.debug)
def _main_(args):
config_path = args.conf
keras.backend.tensorflow_backend.set_session(get_session())
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
if config['backup']['create_backup']:
config = create_backup(config)
###############################
# Parse the annotations
###############################
if config['parser_annotation_type'] == 'xml':
# parse annotations of the training set
train_imgs, train_labels = parse_annotation(
config['train']['train_annot_folder'],
config['train']['train_image_folder'], config['model']['labels'])
# parse annotations of the validation set, if any, otherwise split the training set
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'])
split = False
else:
split = True
elif config['parser_annotation_type'] == 'csv':
# parse annotations of the training set
train_imgs, train_labels = parse_annotation_csv(
config['train']['train_csv_file'], config['model']['labels'],
config['train']['train_csv_base_path'])
# parse annotations of the validation set, if any, otherwise split the training set
if os.path.exists(config['valid']['valid_csv_file']):
valid_imgs, valid_labels = parse_annotation_csv(
config['valid']['valid_csv_file'], config['model']['labels'],
config['valid']['valid_csv_base_path'])
split = False
else:
print("Validation file not found commensing split")
split = True
else:
raise ValueError(
"'parser_annotations_type' must be 'xml' or 'csv' not {}.".format(
config['parser_annotations_type']))
if split:
train_valid_split = int(0.8 * len(train_imgs))
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
if len(config['model']['labels']) > 0:
overlap_labels = set(config['model']['labels']).intersection(
set(train_labels.keys()))
print('Seen labels:\t', train_labels)
print('Given labels:\t', config['model']['labels'])
print('Overlap labels:\t', overlap_labels)
if len(overlap_labels) < len(config['model']['labels']):
print(
'Some labels have no annotations! Please revise the list of labels in the config.json file!'
)
return
else:
print('No labels are provided. Train on all seen labels.')
config['model']['labels'] = train_labels.keys()
with open("labels.json", 'w') as outfile:
json.dump({"labels": list(train_labels.keys())}, outfile)
###############################
# Construct the model
###############################
yolo = YOLO(backend=config['model']['backend'],
input_size=(config['model']['input_size_h'],
config['model']['input_size_w']),
labels=config['model']['labels'],
max_box_per_image=config['model']['max_box_per_image'],
anchors=config['model']['anchors'],
gray_mode=config['model']['gray_mode'])
###############################
# Load the pretrained weights (if any)
###############################
if os.path.exists(config['train']['pretrained_weights']):
print("Loading pre-trained weights in",
config['train']['pretrained_weights'])
yolo.load_weights(config['train']['pretrained_weights'])
###############################
# Start the training process
###############################
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
train_times=config['train']['train_times'],
valid_times=config['valid']['valid_times'],
nb_epochs=config['train']['nb_epochs'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
warmup_epochs=config['train']['warmup_epochs'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
saved_weights_name=config['train']['saved_weights_name'],
debug=config['train']['debug'],
early_stop=config['train']['early_stop'],
workers=config['train']['workers'],
max_queue_size=config['train']['max_queue_size'],
tb_logdir=config['train']['tensorboard_log_dir'])
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
###############################
# Parse the annotations
###############################
# parse annotations of the training set
train_imgs, train_labels = aerial_parse_annotation(
config['train']['train_image_name_list'], config['model']['labels'])
# parse annotations of the validation set.
valid_imgs, valid_labels = aerial_parse_annotation(
config['valid']['valid_image_name_list'], config['model']['labels'])
if len(config['model']['labels']) > 0:
overlap_labels = set(config['model']['labels']).intersection(
set(train_labels.keys()))
print 'Seen labels:\t', train_labels
print 'Given labels:\t', config['model']['labels']
print 'Overlap labels:\t', overlap_labels
if len(overlap_labels) < len(config['model']['labels']):
print 'Some labels have no annotations! Please revise the list of labels in the config.json file!'
return
else:
print 'No labels are provided. Train on all seen labels.'
config['model']['labels'] = train_labels.keys()
###############################
# Construct the model
###############################
yolo = YOLO(architecture=config['model']['architecture'],
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 the pretrained weights (if any)
###############################
if os.path.exists(config['train']['pretrained_weights']):
print "Loading pre-trained weights in", config['train'][
'pretrained_weights']
yolo.load_weights(config['train']['pretrained_weights'])
###############################
# Start the training process
###############################
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
train_times=config['train']['train_times'],
valid_times=config['valid']['valid_times'],
nb_epoch=config['train']['nb_epoch'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
warmup_epochs=config['train']['warmup_epochs'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
saved_weights_name=config['train']['saved_weights_name'],
debug=config['train']['debug'])
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
glob_str = config['train']['train_image_folder'] + '/*.npz'
filelist = glob.glob(glob_str)
logger.info('train_image_folder = %s', glob_str)
logger.info('filelist = %s', filelist)
###############################
# Parse the annotations
###############################
'''
# parse annotations of the training set
train_imgs, train_labels = parse_annotation(config['train']['train_annot_folder'],
config['train']['train_image_folder'],
config['model']['labels'])
# parse annotations of the validation set, if any, otherwise split the training set
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:'''
train_valid_split = int(0.8 * len(filelist))
np.random.shuffle(filelist)
valid_imgs = filelist[train_valid_split:]
train_imgs = filelist[:train_valid_split]
logger.info('Length of filelist: %s', len(filelist))
logger.info('Length of train_images: %s', len(train_imgs))
logger.info('Length of valid_imgs: %s', len(valid_imgs))
###############################
# Construct the model
###############################
yolo = YOLO(backend=config['model']['backend'],
input_shape=config['model']['input_shape'],
labels=config['model']['labels'],
max_box_per_image=config['model']['max_box_per_image'],
anchors=config['model']['anchors'])
###############################
# Start the training process
###############################
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
evts_per_file=config['train']['evts_per_file'],
train_times=config['train']['train_times'],
valid_times=config['valid']['valid_times'],
nb_epochs=config['train']['nb_epochs'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
warmup_epochs=config['train']['warmup_epochs'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
use_caching=config['train']['use_caching'],
saved_weights_name=config['train']['saved_weights_name'],
debug=config['train']['debug'])
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
###############################
# Parse the annotations
###############################
# parse annotations of the training set
train_imgs, train_labels = parse_annotation(config['train']['train_annot_folder'],
config['train']['train_image_folder'],
config['model']['labels'])
# parse annotations of the validation set, if any, otherwise split the training set
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:
train_valid_split = int(0.8*len(train_imgs))
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
print train_labels
if len(set(config['model']['labels']).intersection(set(train_labels.keys()))) == 0:
print "Labels to be detected are not present in the dataset! Please revise the list of labels in the config.json file!"
return
###############################
# Construct the model
###############################
yolo = YOLO(architecture = config['model']['architecture'],
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 the pretrained weights (if any)
###############################
if os.path.exists(config['train']['pretrained_weights']):
print "Loading pre-trained weights in", config['train']['pretrained_weights']
yolo.load_weights(config['train']['pretrained_weights'])
###############################
# Start the training process
###############################
yolo.train(train_imgs = train_imgs,
valid_imgs = valid_imgs,
train_times = config['train']['train_times'],
valid_times = config['valid']['valid_times'],
nb_epoch = config['train']['nb_epoch'],
learning_rate = config['train']['learning_rate'],
batch_size = config['train']['batch_size'],
warmup_bs = config['train']['warmup_batches'],
object_scale = config['train']['object_scale'],
no_object_scale = config['train']['no_object_scale'],
coord_scale = config['train']['coord_scale'],
class_scale = config['train']['class_scale'],
saved_weights_name = config['train']['saved_weights_name'],
debug = config['train']['debug'])
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
if config['train']['gpu_count'] > 1:
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
[str(i) for i in range(config['train']['gpu_count'])])
###############################
# Parse the annotations
###############################
# parse annotations of the training set
train_images, validation_images = load_images(config)
# parse annotations of the validation set, if any, otherwise split the training set
if len(config['model']['labels']) > 0:
print('Given labels:\t', config['model']['labels'])
###############################
# Construct 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 the pretrained weights (if any)
###############################
if os.path.exists(config['train']['pretrained_weights']):
print("Loading pre-trained weights in",
config['train']['pretrained_weights'])
yolo.load_weights(config['train']['pretrained_weights'])
###############################
# Start the training process
###############################
yolo.train(train_imgs=train_images,
valid_imgs=validation_images,
train_times=config['train']['train_times'],
valid_times=1,
nb_epochs=config['train']['nb_epochs'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
warmup_epochs=config['train']['warmup_epochs'],
saved_weights_dir=config['train']['saved_weights_dir'],
save_every_n_epoch=config['train']['save_every_n_epoch'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
multi_gpu=config['train']['gpu_count'] > 1,
debug=config['train']['debug'])
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
###############################
# Parse the annotations
###############################
# parse annotations of the training set
train_imgs, train_labels = parse_annotation(config['train']['train_annot_folder'],
config['train']['train_image_folder'],
config['model']['labels'])
# parse annotations of the validation set, if any, otherwise split the training set
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:
train_valid_split = int(0.8*len(train_imgs))
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
if len(config['model']['labels']) > 0:
overlap_labels = set(config['model']['labels']).intersection(set(train_labels.keys()))
print 'Seen labels:\t', train_labels
print 'Given labels:\t', config['model']['labels']
print 'Overlap labels:\t', overlap_labels
if len(overlap_labels) < len(config['model']['labels']):
print 'Some labels have no annotations! Please revise the list of labels in the config.json file!'
return
else:
print 'No labels are provided. Train on all seen labels.'
config['model']['labels'] = train_labels.keys()
###############################
# Construct the model
###############################
yolo = YOLO(architecture = config['model']['architecture'],
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 the pretrained weights (if any)
###############################
if os.path.exists(config['train']['pretrained_weights']):
print "Loading pre-trained weights in", config['train']['pretrained_weights']
yolo.load_weights(config['train']['pretrained_weights'])
for layer in galaxyModel.layers:
print(layer)
layer.trainable = True
###############################
# Start the training process
###############################
if args.training:
yolo.train(train_imgs = train_imgs,
valid_imgs = valid_imgs,
train_times = config['train']['train_times'],
valid_times = config['valid']['valid_times'],
nb_epoch = config['train']['nb_epoch'],
learning_rate = config['train']['learning_rate'],
batch_size = config['train']['batch_size'],
warmup_epochs = config['train']['warmup_epochs'],
object_scale = config['train']['object_scale'],
no_object_scale = config['train']['no_object_scale'],
coord_scale = config['train']['coord_scale'],
class_scale = config['train']['class_scale'],
saved_weights_name = config['train']['saved_weights_name'],
debug = config['train']['debug'])
image = cv2.imread(config['valid']['valid_image_folder'] + '/10.png')
plt.figure(figsize=(10,10))
boxes = yolo.predict(image)
image = draw_boxes(image, boxes, labels=config['model']['labels'])
plt.imshow(image[:,:,::-1]); plt.show()
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
os.environ["CUDA_VISIBLE_DEVICES"]=config['env']['gpu']
gpus = max(1, len(config['env']['gpu'].split(",")))
#print("{}").format(config)
###############################
# Parse the annotations
###############################
# parse annotations of the training set
train_imgs, train_labels = parse_annotation(config['train']['train_annot_folder'],
config['train']['train_image_folder'],
config['model']['labels'], config['train']['image_ext_name'], config['train']['image_prefix'])
# parse annotations of the validation set, if any, otherwise split the training set
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'], config['train']['image_ext_name'], config['train']['image_prefix'])
else:
train_valid_split = int(0.8*len(train_imgs))
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
overlap_labels = set(config['model']['labels']).intersection(set(train_labels.keys()))
print ('Seen labels:{}'.format(train_labels))
print ('Given labels:{}'.format(config['model']['labels']))
print ('Overlap labels:{}'.format(overlap_labels))
if len(overlap_labels) < len(config['model']['labels']):
print ('Some labels have no images! Please revise the list of labels in the config.json file!')
return
###############################
# Construct the model
###############################
yolo = YOLO(architecture = config['model']['architecture'],
input_size = config['model']['input_size'],
labels = config['model']['labels'],
max_box_per_image = config['model']['max_box_per_image'],
anchors = config['model']['anchors']
gpus = gpus)
###############################
# Load the pretrained weights (if any)
###############################
if os.path.exists(config['train']['pretrained_weights']):
print ("Loading pre-trained weights in {}".format(config['train']['pretrained_weights']))
yolo.load_weights(config['train']['pretrained_weights'])
###############################
# Start the training process
###############################
yolo.train(train_imgs = train_imgs,
valid_imgs = valid_imgs,
train_times = config['train']['train_times'],
valid_times = config['valid']['valid_times'],
nb_epoch = config['train']['nb_epoch'],
learning_rate = config['train']['learning_rate'],
batch_size = config['train']['batch_size'],
warmup_bs = config['train']['warmup_batches'],
object_scale = config['train']['object_scale'],
no_object_scale = config['train']['no_object_scale'],
coord_scale = config['train']['coord_scale'],
class_scale = config['train']['class_scale'],
saved_weights_name = config['train']['saved_weights_name'],
debug = config['train']['debug'])
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
###############################
# Parse the annotations
###############################
# parse annotations of the training set
train_imgs, train_labels = parse_annotation(config['train']['train_annot_folder'],
config['train']['train_image_folder'],
config['model']['labels'])
# parse annotations of the validation set, if any, otherwise split the training set
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:
train_valid_split = int(0.8*len(train_imgs))
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
if len(config['model']['labels']) > 0:
overlap_labels = set(config['model']['labels']).intersection(set(train_labels.keys()))
print 'Seen labels:\t', train_labels
print 'Given labels:\t', config['model']['labels']
print 'Overlap labels:\t', overlap_labels
if len(overlap_labels) < len(config['model']['labels']):
print 'Some labels have no annotations! Please revise the list of labels in the config.json file!'
return
else:
print 'No labels are provided. Train on all seen labels.'
config['model']['labels'] = train_labels.keys()
###############################
# Construct the model
###############################
yolo = YOLO(architecture = config['model']['architecture'],
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 the pretrained weights (if any)
###############################
if os.path.exists(config['train']['pretrained_weights']):
print "Loading pre-trained weights in", config['train']['pretrained_weights']
yolo.load_weights(config['train']['pretrained_weights'])
###############################
# Start the training process
###############################
yolo.train(train_imgs = train_imgs,
valid_imgs = valid_imgs,
train_times = config['train']['train_times'],
valid_times = config['valid']['valid_times'],
nb_epoch = config['train']['nb_epoch'],
learning_rate = config['train']['learning_rate'],
batch_size = config['train']['batch_size'],
warmup_epochs = config['train']['warmup_epochs'],
object_scale = config['train']['object_scale'],
no_object_scale = config['train']['no_object_scale'],
coord_scale = config['train']['coord_scale'],
class_scale = config['train']['class_scale'],
saved_weights_name = config['train']['saved_weights_name'],
debug = config['train']['debug'])
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
pp.pprint(config)
train_imgs, train_labels = parse_annotation(
config['train']['train_annot_folder'],
config['train']['train_image_folder'], config['model']['labels'])
# parse annotations of the validation set, if any, otherwise split the training set
if os.path.exists(str(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("Splitting into train and validation")
train_valid_split = int(0.99 * len(train_imgs))
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
if len(config['model']['labels']) > 0:
overlap_labels = set(config['model']['labels']).intersection(
set(train_labels.keys()))
print(('Seen labels:\t', train_labels))
print(('Given labels:\t', config['model']['labels']))
print(('Overlap labels:\t', overlap_labels))
if len(overlap_labels) < len(config['model']['labels']):
print(
'Some labels have no annotations! Please revise the list of labels in the config.json file!'
)
return
else:
print('No labels are provided. Train on all seen labels.')
config['model']['labels'] = list(train_labels.keys())
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'])
if os.path.exists(str(config['train']['pretrained_weights'])):
print(("Loading pre-trained weights in",
config['train']['pretrained_weights']))
if config['model']['backend'] == "MobileNet":
is_mobilenet = True
else:
print((config['model']['backend']))
is_mobilenet = False
yolo.load_weights(config['train']['pretrained_weights'])
print("\nStarting training...")
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
train_times=config['train']['train_times'],
valid_times=config['valid']['valid_times'],
nb_epochs=config['train']['nb_epochs'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
warmup_epochs=config['train']['warmup_epochs'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
saved_weights_name=config['train']['saved_weights_name'],
debug=config['train']['debug'],
full_log_dir=config['train']['full_log_dir'],
early_stop_patience=config['train']['early_stop_patience'],
early_stop_min_delta=config['train']['early_stop_min_delta'],
learning_rate_decay_factor=config['train']
['learning_rate_decay_factor'],
learning_rate_decay_patience=config['train']
['learning_rate_decay_patience'],
learning_rate_decay_min_lr=config['train']
['learning_rate_decay_min_lr'])
#train_imgs = parse_annotation(config['train']['train_annot_folder'],
# config['train']['train_image_folder'])
train_imgs = pickle.load(open('dataset/annotation.pkl', 'rb'))
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'])
random.shuffle(train_imgs)
N = len(train_imgs)
train_size = int(N * config['train']['validation_split'])
print(train_size)
valid_imgs = train_imgs[train_size:]
train_imgs = train_imgs[:train_size]
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
nb_epochs=config['train']['nb_epochs'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
saved_weights_name=config['train']['saved_weights_name'],
debug=config['train']['debug'])
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
###############################
# Parse the annotations
###############################
"""
parse annotations of the training set
train_imgs is a list of dictionary with the following keys and contents:
object : a list of objects in the image, each object is a dictionary with object name, box coordinates (xmin, xmax, ymin, ymax)
filename : complete path of the image
width : original pixel width
height : original pixel height
train_labels contains the statistics of the count of each type of object
"""
train_imgs, train_labels = parse_annotation(
config['train']['train_annot_folder'],
config['train']['train_image_folder'], config['model']['labels'])
# split the training set into 80% training and 20% validation
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:
train_valid_split = int(0.8 * len(train_imgs))
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
print('{} train images and {} validation images'.format(
len(train_imgs), len(valid_imgs)))
# parse annotations of the testing set
test_imgs, test_labels = parse_annotation(
config['test']['test_annot_folder'],
config['test']['test_image_folder'], config['model']['labels'])
if len(config['model']['labels']) > 0:
overlap_labels = set(config['model']['labels']).intersection(
set(train_labels.keys()))
print('Seen labels:\t', len(train_labels))
print('Given labels:\t', len(config['model']['labels']))
print('Overlap labels:\t', len(overlap_labels))
print(overlap_labels)
if len(overlap_labels) < len(config['model']['labels']):
print(
'Some labels have no annotations! Please revise the list of labels in the config.json file!'
)
return
else:
print('No labels are provided. Train on all seen labels.')
print(train_labels.keys())
config['model']['labels'] = train_labels.keys()
###############################
# 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'])
##############################################################
# Start training the last layer from scratch with warm up
##############################################################
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
test_imgs=test_imgs,
pretrained_weights='',
nb_epochs=config['train']['nb_epochs'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
saved_weights_name=config['train']['saved_weights_name'],
train_last_epoch=3,
freeze_BN=True,
train_mode=False,
debug=True)
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
# parse annotations of the training set
train_imgs, train_labels = parse_annotation(
config['train']['train_annot_folder'],
config['train']['train_image_folder'], config['model']['labels'])
# parse annotations of the validation set, if any, otherwise split the training set
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:
train_valid_split = int(0.8 * len(train_imgs))
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
# detected labels
overlap_labels = set(config['model']['labels']).intersection(
set(train_labels.keys()))
print('Seen labels:\t', train_labels)
print('Given labels:\t', config['model']['labels'])
print('Overlap labels:\t', overlap_labels)
if len(overlap_labels) < len(config['model']['labels']):
print(
'Some labels have no images! Please revise the list of labels in the config.json file!'
)
return
# construct models
yolo = YOLO(architecture=config['model']['architecture'],
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 pretrained models
if os.path.exists(config['train']['pretrained_weights']):
print("Loading pre-trained weights in",
config['train']['pretrained_weights'])
yolo.load_weights(config['train']['pretrained_weights'])
# start trianing
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
train_times=config['train']['train_times'],
valid_times=config['valid']['valid_times'],
nb_epoch=config['train']['nb_epoch'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
warmup_bs=config['train']['warmup_batches'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
saved_weights_name=config['train']['saved_weights_name'],
debug=config['train']['debug'])
train_imgs = imgs[:train_valid_split]
overlap_labels = set(labels).intersection(set(labels.keys()))
# print("Seen labels: "+str(labels))
# print("Given labels: "+str(labels)
print("Overelap labels: " + str(overlap_labels))
if len(overlap_labels) < len(labels):
print("Some labels have no image! Please check it.")
yolo = YOLO(architecture=architecture,
input_size=input_size,
labels=labels,
max_box_per_img=max_box_per_img,
anchors=anchors)
yolo.train(train_imgs,
valid_imgs,
train_times,
valid_times,
nb_epoch,
learning_rate,
batch_size,
warmup_batches,
object_scale,
no_object_scale,
coord_scale,
class_scale,
saved_weights_name=saved_weights_name)
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
###############################
# Parse the annotations
# 预处理部分
###############################
# parse annotations of the training set
"""
train_imgs是一个list，每个元素是一个dict，里面包括了每个图片的信息：
img['filename']: str, 图片完整路径
img['width']: int, 图片宽度
img['height']: int, 图片长度
img['object']: list, 所有标注object的list，每个object是一个dict
obj['name']: str object的label名字
obj['xmin']: int
obj['xmax']: int
obj['ymin']: int
obj['ymax']: int
train label是一个dictionary，key为label，value为出现的次数
"""
train_imgs, train_labels = parse_annotation(
config['train']['train_annot_folder'],
config['train']['train_image_folder'], config['model']['labels'])
# parse annotations of the validation set, if any, otherwise split the training set
np.random.shuffle(train_imgs)
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:
train_valid_split = int(0.8 * len(train_imgs))
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
if len(config['model']['labels']) > 0:
overlap_labels = set(config['model']['labels']).intersection(
set(train_labels.keys()))
print('Seen labels:\t', train_labels)
print('Given labels:\t', config['model']['labels'])
print('Overlap labels:\t', overlap_labels)
if len(overlap_labels) < len(config['model']['labels']):
print(
'Some labels have no annotations! Please revise the list of labels in the config.json file!'
)
return
else:
print('No labels are provided. Train on all seen labels.')
print(f"labels:{train_labels}")
config['model']['labels'] = train_labels.keys()
###############################
# Construct the model
###############################
"""
这里创建了一个yolo类型的对象
模型特征提取部分使用config里backend的设置
注意这里的模型建立只对模型的特征提取部分，即卷积部分做了构建，没有对后面的分类部分，即全连接层做构建
labels是config中定义的labels，当空时为全部找到的label（看preprocessing的代码）
max_box_per_image顾名思义，但是注意在config中的anchors的设置数量一定要一样，不然没有意义
anchors到底是什么可以看论文中对anchor权重的说明。
"""
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 the pretrained weights (if any)
"""
注意这里load的weight是load之前训练好的
也就是说，之前跑过一遍这个代码训练出来的weight才能fit上去。
因为其实这里结构比较特殊，特征提取部分被独立成一个layer了
"""
###############################
if os.path.exists(config['train']['pretrained_weights']):
print("Loading pre-trained weights in",
config['train']['pretrained_weights'])
yolo.load_weights(config['train']['pretrained_weights'])
###############################
# Start the training process
###############################
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
train_times=config['train']['train_times'],
valid_times=config['valid']['valid_times'],
nb_epochs=config['train']['nb_epochs'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
warmup_epochs=config['train']['warmup_epochs'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
saved_weights_name=config['train']['saved_weights_name'],
debug=config['train']['debug'])
def _main_(args):
config_path = args.conf
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
###############################
# Parse the annotations
###############################
# parse annotations of the training set
if 'csv' in config['train']['train_annot_folder']:
train_imgs, train_labels = parse_csv_annotations(
config['train']['train_annot_folder'],
config['train']['train_image_folder'], config['model']['labels'])
else:
train_imgs, train_labels = parse_annotation(
config['train']['train_annot_folder'],
config['train']['train_image_folder'], config['model']['labels'])
# parse annotations of the validation set, if any, otherwise split the training set
if os.path.exists(config['valid']['valid_annot_folder']):
if 'csv' in config['valid']['valid_annot_folder']:
valid_imgs, valid_labels = parse_csv_annotations(
config['valid']['valid_annot_folder'],
config['valid']['valid_image_folder'],
config['model']['labels'])
else:
valid_imgs, valid_labels = parse_annotation(
config['valid']['valid_annot_folder'],
config['valid']['valid_image_folder'],
config['model']['labels'])
else:
train_valid_split = int(0.8 * len(train_imgs))
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
if len(config['model']['labels']) > 0:
overlap_labels = set(config['model']['labels']).intersection(
set(train_labels.keys()))
print('Seen labels:\t', train_labels)
print('Given labels:\t', config['model']['labels'])
print('Overlap labels:\t', overlap_labels)
if len(overlap_labels) < len(config['model']['labels']):
print(
'Some labels have no annotations! Please revise the list of labels in the config.json file!'
)
return
else:
print('No labels are provided. Train on all seen labels.')
config['model']['labels'] = train_labels.keys()
###############################
# Construct 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 the pretrained weights (if any)
###############################
if os.path.exists(config['train']['pretrained_weights']):
print("Loading pre-trained weights in",
config['train']['pretrained_weights'])
yolo.load_weights(config['train']['pretrained_weights'])
###############################
# Start the training process
###############################
yolo.train(train_imgs=train_imgs,
valid_imgs=valid_imgs,
train_times=config['train']['train_times'],
valid_times=config['valid']['valid_times'],
nb_epochs=config['train']['nb_epochs'],
learning_rate=config['train']['learning_rate'],
batch_size=config['train']['batch_size'],
warmup_epochs=config['train']['warmup_epochs'],
object_scale=config['train']['object_scale'],
no_object_scale=config['train']['no_object_scale'],
coord_scale=config['train']['coord_scale'],
class_scale=config['train']['class_scale'],
saved_weights_name=config['train']['saved_weights_name'],
debug=config['train']['debug'])
def trainer(config_path):
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
if 'logdir' in config['train']:
logdir = config['train']['logdir']
else:
Exception("В конфиге должна быть указана папка для логов!")
logdir = os.path.dirname(config_path) + '/' + logdir
os.makedirs(logdir, exist_ok=True)
shutil.copy(config_path, logdir + 'config.json')
###############################
# Parse the annotations
###############################
# parse annotations of the training set
train_imgs, train_labels = parse_annotation(config['train']['train_annot_folder'],
config['train']['train_image_folder'],
config['model']['labels'])
# parse annotations of the validation set, if any, otherwise split the training set
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:
train_valid_split = int(0.8 * len(train_imgs))
np.random.shuffle(train_imgs)
valid_imgs = train_imgs[train_valid_split:]
train_imgs = train_imgs[:train_valid_split]
if len(config['model']['labels']) > 0:
overlap_labels = set(config['model']['labels']).intersection(set(train_labels.keys()))
print('Seen labels:\t', train_labels)
print('Given labels:\t', config['model']['labels'])
print('Overlap labels:\t', overlap_labels)
if len(overlap_labels) < len(config['model']['labels']):
print('Some labels have no annotations! Please revise the list of labels in the config.json file!')
return
else:
print('No labels are provided. Train on all seen labels.')
config['model']['labels'] = train_labels.keys()
###############################
# Construct the model
###############################
yolo = YOLO(architecture = config['model']['architecture'],
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 the pretrained weights (if any)
###############################
if os.path.exists(config['train']['pretrained_weights']):
print("Loading pre-trained weights in {}".format(config['train']['pretrained_weights']))
yolo.load_weights(config['train']['pretrained_weights'])
###############################
# Start the training process
###############################
freq = config['train']['weights_saving_freq'] if 'weights_saving_freq' in config['train'] else 0
yolo.train(train_imgs = train_imgs,
valid_imgs = valid_imgs,
train_times = config['train']['train_times'],
valid_times = config['valid']['valid_times'],
nb_epoch = config['train']['nb_epoch'],
learning_rate = config['train']['learning_rate'],
batch_size = config['train']['batch_size'],
warmup_epochs = config['train']['warmup_epochs'],
object_scale = config['train']['object_scale'],
no_object_scale = config['train']['no_object_scale'],
coord_scale = config['train']['coord_scale'],
class_scale = config['train']['class_scale'],
saved_weights_name = config['train']['saved_weights_name'],
saving_freq = freq,
debug = config['train']['debug'],
logdir = logdir)
