def tiny_yolo4lite_mobilenet_body(inputs,
num_anchors,
num_classes,
alpha=1.0,
use_spp=True):
'''Create Tiny YOLO_v3 Lite MobileNet model CNN body in keras.'''
mobilenet = MobileNet(input_tensor=inputs,
weights='imagenet',
include_top=False,
alpha=alpha)
# 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
#feature map 1 head (13 x 13 x (512*alpha) for 416 input)
x1 = DarknetConv2D_BN_Leaky(int(512 * alpha), (1, 1))(f1)
if use_spp:
x1 = Spp_Conv2D_BN_Leaky(x1, int(512 * alpha))
#upsample fpn merge for feature map 1 & 2
x1_upsample = compose(DarknetConv2D_BN_Leaky(int(256 * alpha), (1, 1)),
UpSampling2D(2))(x1)
x2 = compose(
Concatenate(),
#DarknetConv2D_BN_Leaky(int(512*alpha), (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=int(512 * alpha),
kernel_size=(3, 3),
block_id_str='15'))(
[x1_upsample, f2])
#feature map 2 output (26 x 26 x (512*alpha) for 416 input)
y2 = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x2)
#downsample fpn merge for feature map 2 & 1
x2_downsample = compose(
ZeroPadding2D(((1, 0), (1, 0))),
#DarknetConv2D_BN_Leaky(int(512*alpha), (3,3), strides=(2,2)),
Darknet_Depthwise_Separable_Conv2D_BN_Leaky(int(512 * alpha), (3, 3),
strides=(2, 2),
block_id_str='16'))(x2)
x1 = compose(
Concatenate(),
#DarknetConv2D_BN_Leaky(int(1024*alpha), (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=int(1024 * alpha),
kernel_size=(3, 3),
block_id_str='17'))(
[x2_downsample, x1])
#feature map 1 output (13 x 13 x (1024*alpha) for 416 input)
y1 = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x1)
return Model(inputs, [y1, y2])
def tiny_yolo4_mobilenetv3large_body(inputs,
num_anchors,
num_classes,
alpha=1.0,
spp=True):
'''Create Tiny YOLO_v4 MobileNetV3Large model CNN body in keras.'''
mobilenetv3large = MobileNetV3Large(input_tensor=inputs,
weights='imagenet',
include_top=False,
alpha=alpha)
# 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)
# f1 :13 x 13 x (960*alpha)
# NOTE: activation layer name may different for TF1.x/2.x, so we
# use index to fetch layer
f1 = mobilenetv3large.layers[194].output
# f2: 26 x 26 x (672*alpha) for 416 input
f2 = mobilenetv3large.layers[146].output
#feature map 1 head (13 x 13 x (480*alpha) for 416 input)
x1 = DarknetConv2D_BN_Leaky(int(480 * alpha), (1, 1))(f1)
if spp:
x1 = Spp_Conv2D_BN_Leaky(x1, int(480 * alpha))
#upsample fpn merge for feature map 1 & 2
x1_upsample = compose(DarknetConv2D_BN_Leaky(int(336 * alpha), (1, 1)),
UpSampling2D(2))(x1)
x2 = compose(
Concatenate(),
#Depthwise_Separable_Conv2D_BN_Leaky(filters=int(672*alpha), kernel_size=(3, 3), block_id_str='15'),
DarknetConv2D_BN_Leaky(int(672 * alpha), (3, 3)))([x1_upsample, f2])
#feature map 2 output (26 x 26 x (672*alpha) for 416 input)
y2 = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x2)
#downsample fpn merge for feature map 2 & 1
x2_downsample = compose(
ZeroPadding2D(((1, 0), (1, 0))),
#Darknet_Depthwise_Separable_Conv2D_BN_Leaky(int(480*alpha), (3,3), strides=(2,2), block_id_str='16'),
DarknetConv2D_BN_Leaky(int(480 * alpha), (3, 3), strides=(2, 2)))(x2)
x1 = compose(
Concatenate(),
#Depthwise_Separable_Conv2D_BN_Leaky(filters=int(960*alpha), kernel_size=(3, 3), block_id_str='17'),
DarknetConv2D_BN_Leaky(int(960 * alpha), (3, 3)))([x2_downsample, x1])
#feature map 1 output (13 x 13 x (960*alpha) for 416 input)
y1 = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x1)
return Model(inputs, [y1, y2])
def tiny_yolo4lite_efficientnet_body(inputs,
num_anchors,
num_classes,
level=0,
use_spp=True):
'''
Create Tiny YOLO_v4 Lite EfficientNet model CNN body in keras.
# Arguments
level: EfficientNet level number.
by default we use basic EfficientNetB0 as backbone
'''
efficientnet, feature_map_info = get_efficientnet_backbone_info(
inputs, level=level)
f1 = efficientnet.get_layer('top_activation').output
f2 = efficientnet.get_layer('block6a_expand_activation').output
f1_channel_num = feature_map_info['f1_channel_num']
f2_channel_num = feature_map_info['f2_channel_num']
#feature map 1 head (13 x 13 x (f1_channel_num//2) for 416 input)
x1 = DarknetConv2D_BN_Leaky(f1_channel_num // 2, (1, 1))(f1)
if use_spp:
x1 = Spp_Conv2D_BN_Leaky(x1, f1_channel_num // 2)
#upsample fpn merge for feature map 1 & 2
x1_upsample = compose(DarknetConv2D_BN_Leaky(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x1)
x2 = compose(
Concatenate(),
#DarknetConv2D_BN_Leaky(f2_channel_num, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=f2_channel_num,
kernel_size=(3, 3),
block_id_str='8'))(
[x1_upsample, f2])
#feature map 2 output (26 x 26 x f2_channel_num for 416 input)
y2 = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x2)
#downsample fpn merge for feature map 2 & 1
x2_downsample = compose(
ZeroPadding2D(((1, 0), (1, 0))),
#DarknetConv2D_BN_Leaky(f1_channel_num//2, (3,3), strides=(2,2)),
Darknet_Depthwise_Separable_Conv2D_BN_Leaky(f1_channel_num // 2,
(3, 3),
strides=(2, 2),
block_id_str='9'))(x2)
x1 = compose(
Concatenate(),
#DarknetConv2D_BN_Leaky(f1_channel_num, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=f1_channel_num,
kernel_size=(3, 3),
block_id_str='10'))(
[x2_downsample, x1])
#feature map 1 output (13 x 13 x f1_channel_num for 416 input)
y1 = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x1)
return Model(inputs, [y1, y2])
def tiny_yolo4_mobilenetv3small_body(inputs,
num_anchors,
num_classes,
alpha=1.0,
use_spp=True):
'''Create Tiny YOLO_v4 MobileNetV3Small model CNN body in keras.'''
mobilenetv3small = MobileNetV3Small(input_tensor=inputs,
weights='imagenet',
include_top=False,
alpha=alpha)
# 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)
# f1 :13 x 13 x (576*alpha)
# NOTE: activation layer name may different for TF1.x/2.x, so we
# use index to fetch layer
f1 = mobilenetv3small.layers[165].output
# f2: 26 x 26 x (288*alpha) for 416 input
f2 = mobilenetv3small.layers[117].output
#feature map 1 head (13 x 13 x (288*alpha) for 416 input)
x1 = DarknetConv2D_BN_Leaky(int(288 * alpha), (1, 1))(f1)
if use_spp:
x1 = Spp_Conv2D_BN_Leaky(x1, int(288 * alpha))
#upsample fpn merge for feature map 1 & 2
x1_upsample = compose(DarknetConv2D_BN_Leaky(int(144 * alpha), (1, 1)),
UpSampling2D(2))(x1)
x2 = compose(
Concatenate(),
#Depthwise_Separable_Conv2D_BN_Leaky(filters=int(288*alpha), kernel_size=(3, 3), block_id_str='11'),
DarknetConv2D_BN_Leaky(int(288 * alpha), (3, 3)))([x1_upsample, f2])
#feature map 2 output (26 x 26 x (288*alpha) for 416 input)
y2 = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x2)
#downsample fpn merge for feature map 2 & 1
x2_downsample = compose(
ZeroPadding2D(((1, 0), (1, 0))),
#Darknet_Depthwise_Separable_Conv2D_BN_Leaky(int(288*alpha), (3,3), strides=(2,2), block_id_str='12'),
DarknetConv2D_BN_Leaky(int(288 * alpha), (3, 3), strides=(2, 2)))(x2)
x1 = compose(
Concatenate(),
#Depthwise_Separable_Conv2D_BN_Leaky(filters=int(576*alpha), kernel_size=(3, 3), block_id_str='13'),
DarknetConv2D_BN_Leaky(int(576 * alpha), (3, 3)))([x2_downsample, x1])
#feature map 1 output (13 x 13 x (576*alpha) for 416 input)
y1 = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x1)
return Model(inputs, [y1, y2])