def yolo3lite_spp_mobilenetv2_body(inputs, num_anchors, num_classes, alpha=1.0):
'''Create YOLO_v3 Lite SPP MobileNetV2 model CNN body in keras.'''
mobilenetv2 = MobileNetV2(input_tensor=inputs, weights='imagenet', include_top=False, alpha=alpha)
print('backbone layers number: {}'.format(len(mobilenetv2.layers)))
# input: 416 x 416 x 3
# out_relu: 13 x 13 x 1280
# block_13_expand_relu: 26 x 26 x (576*alpha)
# block_6_expand_relu: 52 x 52 x (192*alpha)
# f1 :13 x 13 x 1280
f1 = mobilenetv2.get_layer('out_relu').output
# f2: 26 x 26 x (576*alpha)
f2 = mobilenetv2.get_layer('block_13_expand_relu').output
# f3 : 52 x 52 x (192*alpha)
f3 = mobilenetv2.get_layer('block_6_expand_relu').output
f1_channel_num = int(1280*alpha)
f2_channel_num = int(576*alpha)
f3_channel_num = int(192*alpha)
#f1_channel_num = 1024
#f2_channel_num = 512
#f3_channel_num = 256
y1, y2, y3 = yolo3lite_predictions((f1, f2, f3), (f1_channel_num, f2_channel_num, f3_channel_num), num_anchors, num_classes, use_spp=True)
return Model(inputs = inputs, outputs=[y1,y2,y3])
def tiny_yolo3_ultralite_mobilenetv2_body(inputs,
num_anchors,
num_classes,
alpha=1.0):
'''Create Tiny YOLO_v3 Ultra-Lite MobileNetV2 model CNN body in keras.'''
mobilenetv2 = MobileNetV2(input_tensor=inputs,
weights='imagenet',
include_top=False,
alpha=alpha)
print('backbone layers number: {}'.format(len(mobilenetv2.layers)))
# input: 416 x 416 x 3
# out_relu: 13 x 13 x 1280
# block_13_expand_relu: 26 x 26 x (576*alpha)
# block_6_expand_relu: 52 x 52 x (192*alpha)
# f1 :13 x 13 x 1280
f1 = mobilenetv2.get_layer('out_relu').output
# f2: 26 x 26 x (576*alpha)
f2 = mobilenetv2.get_layer('block_13_expand_relu').output
f1_channel_num = int(1280 * alpha)
f2_channel_num = int(576 * alpha)
#f1_channel_num = 1024
#f2_channel_num = 512
y1, y2 = tiny_yolo3_ultralite_predictions(
(f1, f2), (f1_channel_num, f2_channel_num), num_anchors, num_classes)
return Model(inputs, [y1, y2])
