from tensorflow.keras.callbacks import ModelCheckpoint
root_path = os.path.abspath(os.path.join('..'))
if root_path not in sys.path:
sys.path.append(root_path)
import config
from qr_codes_loader import load_qr_codes, build_dataset
from ssd_utils.ssd_loss import SSDLoss
from utils import import_by_name, train_test_split_tensors, MeanAveragePrecisionCallback
# Load train and validation data
trainval_image_paths, trainval_bnd_boxes = load_qr_codes(split='train')
(train_image_paths, valid_image_paths, train_bnd_boxes,
valid_bnd_boxes) = train_test_split_tensors(trainval_image_paths,
trainval_bnd_boxes,
test_size=config.VALID_SAMPLES,
random_state=config.RANDOM_SEED)
valid_data = build_dataset(valid_image_paths,
valid_bnd_boxes,
image_size=config.IMAGE_SIZE,
batch_size=config.BATCH_SIZE)
for run in range(1, config.NUM_RUNS + 1):
weights_dir = 'weights_{}'.format(run)
history_dir = 'history_{}'.format(run)
os.makedirs(weights_dir, exist_ok=True)
os.makedirs(history_dir, exist_ok=True)
for architecture in config.ARCHITECTURES:
history_file = model_name + '_history.pickle'
history_path = os.path.join(history_dir, history_file)
with open(history_path, 'wb') as f:
pickle.dump(history.history, f)
model.load_weights(model_file)
model.save_weights(model_path)
os.remove(model_file)
del model, fake_data
for train_samples in config.TRAIN_SAMPLES:
(_, small_train_image_paths, _,
small_train_bnd_boxes) = train_test_split_tensors(
train_image_paths,
train_bnd_boxes,
test_size=train_samples,
random_state=config.RANDOM_SEED)
assert small_train_image_paths.shape[
0] == small_train_bnd_boxes.shape[0]
assert small_train_image_paths.shape[0] == train_samples
for train_type in ['from_scratch', 'finetuned']:
model_name = architecture.lower() + '_{}_samples_{}'.format(
train_samples, train_type)
model_file = model_name + '.h5'
model_path = os.path.join(weights_dir, model_file)
if not os.path.exists(model_path):
print('\n\nINFO: Training {} {} on {} samples'.format(