def yolo4lite_resnet50v2_body(inputs, num_anchors, num_classes):
'''Create YOLO_v4 Lite ResNet50V2 model CNN body in keras.'''
resnet50v2 = ResNet50V2(input_tensor=inputs,
weights='imagenet',
include_top=False)
print('backbone layers number: {}'.format(len(resnet50v2.layers)))
# input: 416 x 416 x 3
# post_relu: 13 x 13 x 2048
# conv4_block5_out: 26 x 26 x 1024
# conv3_block3_out: 52 x 52 x 512
# f1 :13 x 13 x 2048
f1 = resnet50v2.get_layer('post_relu').output
# f2: 26 x 26 x 1024
f2 = resnet50v2.get_layer('conv4_block5_out').output
# f3 : 52 x 52 x 512
f3 = resnet50v2.get_layer('conv3_block3_out').output
f1_channel_num = 1024
f2_channel_num = 512
f3_channel_num = 256
y1, y2, y3 = yolo4lite_predictions(
(f1, f2, f3), (f1_channel_num, f2_channel_num, f3_channel_num),
num_anchors, num_classes)
return Model(inputs=inputs, outputs=[y1, y2, y3])
def yolo4lite_mobilenet_body(inputs, num_anchors, num_classes, alpha=1.0):
'''Create YOLO_v4 Lite MobileNet model CNN body in keras.'''
mobilenet = MobileNet(input_tensor=inputs,
weights='imagenet',
include_top=False,
alpha=alpha)
print('backbone layers number: {}'.format(len(mobilenet.layers)))
# input: 416 x 416 x 3
# conv_pw_13_relu :13 x 13 x (1024*alpha)
# conv_pw_11_relu :26 x 26 x (512*alpha)
# conv_pw_5_relu : 52 x 52 x (256*alpha)
# f1: 13 x 13 x (1024*alpha) for 416 input
f1 = mobilenet.get_layer('conv_pw_13_relu').output
# f2: 26 x 26 x (512*alpha) for 416 input
f2 = mobilenet.get_layer('conv_pw_11_relu').output
# f3: 52 x 52 x (256*alpha) for 416 input
f3 = mobilenet.get_layer('conv_pw_5_relu').output
f1_channel_num = int(1024 * alpha)
f2_channel_num = int(512 * alpha)
f3_channel_num = int(256 * alpha)
y1, y2, y3 = yolo4lite_predictions(
(f1, f2, f3), (f1_channel_num, f2_channel_num, f3_channel_num),
num_anchors, num_classes)
return Model(inputs, [y1, y2, y3])
def yolo4lite_mobilenetv2_body(inputs, num_anchors, num_classes, alpha=1.0):
'''Create YOLO_v4 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
# 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 = yolo4lite_predictions(
(f1, f2, f3), (f1_channel_num, f2_channel_num, f3_channel_num),
num_anchors, num_classes)
return Model(inputs=inputs, outputs=[y1, y2, y3])
def yolo4lite_efficientnet_body(inputs, num_anchors, num_classes, level=1):
'''
Create YOLO_v4 Lite EfficientNet model CNN body in keras.
# Arguments
level: EfficientNet level number.
by default we use EfficientNetB1 as backbone
'''
efficientnet, feature_map_info = get_efficientnet_backbone_info(
inputs, level=level)
print('backbone layers number: {}'.format(len(efficientnet.layers)))
f1 = efficientnet.get_layer('top_activation').output
f1_channel_num = feature_map_info['f1_channel_num']
f2 = efficientnet.get_layer('block6a_expand_activation').output
f2_channel_num = feature_map_info['f2_channel_num']
f3 = efficientnet.get_layer('block4a_expand_activation').output
f3_channel_num = feature_map_info['f3_channel_num']
y1, y2, y3 = yolo4lite_predictions(
(f1, f2, f3), (f1_channel_num, f2_channel_num, f3_channel_num),
num_anchors, num_classes)
return Model(inputs, [y1, y2, y3])
def yolo4lite_mobilenetv3large_body(inputs,
num_anchors,
num_classes,
alpha=1.0):
'''Create YOLO_v4 Lite MobileNetV3Large model CNN body in keras.'''
mobilenetv3large = MobileNetV3Large(input_tensor=inputs,
weights='imagenet',
include_top=False,
alpha=alpha)
print('backbone layers number: {}'.format(len(mobilenetv3large.layers)))
# input: 416 x 416 x 3
# activation_38(layer 194, final feature map): 13 x 13 x (960*alpha)
# expanded_conv_14/Add(layer 191, end of block14): 13 x 13 x (160*alpha)
# activation_29(layer 146, middle in block12) : 26 x 26 x (672*alpha)
# expanded_conv_11/Add(layer 143, end of block11) : 26 x 26 x (112*alpha)
# activation_15(layer 79, middle in block6) : 52 x 52 x (240*alpha)
# expanded_conv_5/Add(layer 76, end of block5): 52 x 52 x (40*alpha)
# NOTE: activation layer name may different for TF1.x/2.x, so we
# use index to fetch layer
# f1: 13 x 13 x (960*alpha)
f1 = mobilenetv3large.layers[194].output
# f2: 26 x 26 x (672*alpha) for 416 input
f2 = mobilenetv3large.layers[146].output
# f3: 52 x 52 x (240*alpha) for 416 input
f3 = mobilenetv3large.layers[79].output
f1_channel_num = int(960 * alpha)
f2_channel_num = int(672 * alpha)
f3_channel_num = int(240 * alpha)
#f1_channel_num = 1024
#f2_channel_num = 512
#f3_channel_num = 256
y1, y2, y3 = yolo4lite_predictions(
(f1, f2, f3), (f1_channel_num, f2_channel_num, f3_channel_num),
num_anchors, num_classes)
return Model(inputs, [y1, y2, y3])
def yolo4lite_mobilenetv3small_body(inputs,
num_anchors,
num_classes,
alpha=1.0):
'''Create YOLO_v4 Lite MobileNetV3Small model CNN body in keras.'''
mobilenetv3small = MobileNetV3Small(input_tensor=inputs,
weights='imagenet',
include_top=False,
alpha=alpha)
print('backbone layers number: {}'.format(len(mobilenetv3small.layers)))
# input: 416 x 416 x 3
# activation_31(layer 165, final feature map): 13 x 13 x (576*alpha)
# expanded_conv_10/Add(layer 162, end of block10): 13 x 13 x (96*alpha)
# activation_22(layer 117, middle in block8) : 26 x 26 x (288*alpha)
# expanded_conv_7/Add(layer 114, end of block7) : 26 x 26 x (48*alpha)
# activation_7(layer 38, middle in block3) : 52 x 52 x (96*alpha)
# expanded_conv_2/Add(layer 35, end of block2): 52 x 52 x (24*alpha)
# NOTE: activation layer name may different for TF1.x/2.x, so we
# use index to fetch layer
# f1: 13 x 13 x (576*alpha)
f1 = mobilenetv3small.layers[165].output
# f2: 26 x 26 x (288*alpha) for 416 input
f2 = mobilenetv3small.layers[117].output
# f3: 52 x 52 x (96*alpha)
f3 = mobilenetv3small.layers[38].output
f1_channel_num = int(576 * alpha)
f2_channel_num = int(288 * alpha)
f3_channel_num = int(96 * alpha)
#f1_channel_num = 1024
#f2_channel_num = 512
#f3_channel_num = 256
y1, y2, y3 = yolo4lite_predictions(
(f1, f2, f3), (f1_channel_num, f2_channel_num, f3_channel_num),
num_anchors, num_classes)
return Model(inputs, [y1, y2, y3])