def YOLOv4(input_layer, NUM_CLASS):
route_1, route_2, conv = backbone.cspdarknet53(input_layer)
route = conv
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.upsample(conv)
route_2 = common.convolutional(route_2, (1, 1, 512, 256))
conv = tf.concat([route_2, conv], axis=-1)
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
route_2 = conv
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.upsample(conv)
route_1 = common.convolutional(route_1, (1, 1, 256, 128))
conv = tf.concat([route_1, conv], axis=-1)
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.convolutional(conv, (3, 3, 128, 256))
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.convolutional(conv, (3, 3, 128, 256))
conv = common.convolutional(conv, (1, 1, 256, 128))
route_1 = conv
conv = common.convolutional(conv, (3, 3, 128, 256))
conv_sbbox = common.convolutional(conv, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
conv = common.convolutional(route_1, (3, 3, 128, 256), downsample=True)
conv = tf.concat([conv, route_2], axis=-1)
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
route_2 = conv
conv = common.convolutional(conv, (3, 3, 256, 512))
conv_mbbox = common.convolutional(conv, (1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
conv = common.convolutional(route_2, (3, 3, 256, 512), downsample=True)
conv = tf.concat([conv, route], axis=-1)
conv = common.convolutional(conv, (1, 1, 1024, 512))
conv = common.convolutional(conv, (3, 3, 512, 1024))
conv = common.convolutional(conv, (1, 1, 1024, 512))
conv = common.convolutional(conv, (3, 3, 512, 1024))
conv = common.convolutional(conv, (1, 1, 1024, 512))
conv = common.convolutional(conv, (3, 3, 512, 1024))
conv_lbbox = common.convolutional(conv, (1, 1, 1024, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
return [conv_sbbox, conv_mbbox, conv_lbbox]
def YOLOv4(input_layer, NUM_CLASS):
print('[INFO][core.yolov4.YOLOv4] Initializing YOLOv4 architecture')
route_1, route_2, conv = backbone.cspdarknet53(input_layer)
print('[INFO][core.yolov4.YOLOv4] cspdarknet53 backbone initialized')
route = conv
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.upsample(conv)
route_2 = common.convolutional(route_2, (1, 1, 512, 256))
conv = tf.concat([route_2, conv], axis=-1)
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
route_2 = conv
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.upsample(conv)
route_1 = common.convolutional(route_1, (1, 1, 256, 128))
conv = tf.concat([route_1, conv], axis=-1)
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.convolutional(conv, (3, 3, 128, 256))
conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.convolutional(conv, (3, 3, 128, 256))
conv = common.convolutional(conv, (1, 1, 256, 128))
route_1 = conv
conv = common.convolutional(conv, (3, 3, 128, 256))
conv_sbbox = common.convolutional(conv, (1, 1, 256, 3 * (NUM_CLASS + 5)),
activate=False,
bn=False)
print(f'[DEBUG][core.yolov4.YOLOv4] conv_ssbox : {conv_sbbox}')
conv = common.convolutional(route_1, (3, 3, 128, 256), downsample=True)
conv = tf.concat([conv, route_2], axis=-1)
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
conv = common.convolutional(conv, (3, 3, 256, 512))
conv = common.convolutional(conv, (1, 1, 512, 256))
route_2 = conv
conv = common.convolutional(conv, (3, 3, 256, 512))
conv_mbbox = common.convolutional(conv, (1, 1, 512, 3 * (NUM_CLASS + 5)),
activate=False,
bn=False)
print(f'[DEBUG][core.yolov4.YOLOv4] conv_mbbox : {conv_mbbox}')
conv = common.convolutional(route_2, (3, 3, 256, 512), downsample=True)
conv = tf.concat([conv, route], axis=-1)
conv = common.convolutional(conv, (1, 1, 1024, 512))
conv = common.convolutional(conv, (3, 3, 512, 1024))
conv = common.convolutional(conv, (1, 1, 1024, 512))
conv = common.convolutional(conv, (3, 3, 512, 1024))
conv = common.convolutional(conv, (1, 1, 1024, 512))
conv = common.convolutional(conv, (3, 3, 512, 1024))
conv_lbbox = common.convolutional(conv, (1, 1, 1024, 3 * (NUM_CLASS + 5)),
activate=False,
bn=False)
print(f'[DEBUG][core.yolov4.YOLOv4] conv_lbbox : {conv_lbbox}')
print(f'[INFO][core.yolov4.YOLOv4] YOLOv4 architecture initialized')
return [conv_sbbox, conv_mbbox, conv_lbbox]
