def __init__(self, efficient_det):
super(EfficientDet, self).__init__()
self.heads = Config.heads
self.head_conv = Config.head_conv[efficient_det]
self.efficient_net = get_efficient_net(
width_coefficient=Config.get_width_coefficient(efficient_det),
depth_coefficient=Config.get_depth_coefficient(efficient_det),
dropout_rate=Config.get_dropout_rate(efficient_det))
self.bifpn = BiFPN(output_channels=Config.get_w_bifpn(efficient_det),
layers=Config.get_d_bifpn(efficient_det))
self.transpose = TransposeLayer(
out_channels=Config.get_w_bifpn(efficient_det))
for head in self.heads:
classes = self.heads[head]
if self.head_conv > 0:
fc = tf.keras.Sequential([
tf.keras.layers.Conv2D(filters=self.head_conv,
kernel_size=(3, 3),
strides=1,
padding="same",
use_bias=True),
tf.keras.layers.ReLU(),
tf.keras.layers.Conv2D(filters=classes,
kernel_size=(1, 1),
strides=1,
padding="same",
use_bias=True)
])
else:
fc = tf.keras.layers.Conv2D(filters=classes,
kernel_size=(1, 1),
strides=1,
padding="same",
use_bias=True)
self.__setattr__(head, fc)
def __init__(self):
super(EfficientDet, self).__init__()
self.backbone = get_efficient_net(
width_coefficient=Config.get_width_coefficient(),
depth_coefficient=Config.get_depth_coefficient(),
dropout_rate=Config.get_dropout_rate())
self.bifpn = BiFPN(output_channels=Config.get_w_bifpn(),
layers=Config.get_d_bifpn())
self.prediction_net = BoxClassPredict(
filters=Config.get_w_bifpn(),
depth=Config.get_d_class(),
num_classes=Config.num_classes,
num_anchors=Config.num_anchor_per_pixel)
