def __init__(self, config, COCO):
self.config = config
self.COCO = COCO
self.image_dir = config['image_dir']
self.id_to_label = set_id_to_label(config['label_set'])
self.input_shape = tuple(config['input_shape'])
self.default_boxes = anchor.generate_default_boxes(config["anchor_param"])
self.annotation_path = os.path.join(config['annotation_dir'], 'GDUT_HWD.json')
classes = np.array(out_labels)
scores = out_scores.numpy()
return boxes, classes, scores
if __name__ == '__main__':
with open('./config.yml') as f:
cfg = yaml.load(f)
try:
config = cfg[args.arch.upper()]
except AttributeError:
raise ValueError('Unknown architecture: {}'.format(args.arch))
default_boxes = generate_default_boxes(config)
batch_generator, info = create_batch_generator(args.data_dir,
args.data_year,
default_boxes,
config['image_size'],
BATCH_SIZE,
args.num_examples,
mode='test')
try:
ssd = create_ssd(NUM_CLASSES, args.arch, args.pretrained_type,
args.checkpoint_dir, args.checkpoint_path)
except Exception as e:
print(e)
print('The program is exiting...')
from ssd.ssd import init_ssd
import tensorflow as tf
from tensorflow.keras.optimizers.schedules import PiecewiseConstantDecay
from voc_data import create_batch_generator
from anchor import generate_default_boxes
from losses import create_losses
from settings import *
ARCH = 'pre_ssd300-mobilenetv1'
CHECKPOINT_DIR = 'checkpoint/pre_mobilenetv1_lite'
CHECKPOINT_PATH = 'checkpoint/pre_mobilenetv1_lite/ssd_epoch_200.h5'
os.makedirs(CHECKPOINT_DIR, exist_ok=True)
default_boxes = generate_default_boxes(INFO[ARCH])
with tf.device('/device:GPU:0'):
batch_generator, val_generator, info = create_batch_generator(
DATA_DIR, DATA_YEAR, default_boxes,
SIZE, BATCH_SIZE, NUM_BATCHES,
mode='train', augmentation=['flip']) # the patching algorithm is currently causing bottleneck sometimes
dummy = tf.random.normal((1, 300, 300, 3))
ssd = create_pre_ssd_mobilenetv1_lite(weights=None)
pretrained_type = 'specified'
checkpoint_path = CHECKPOINT_PATH
