def tiny_yolo3lite_mobilenet_body(inputs, num_anchors, num_classes, alpha=1.0):
'''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)
x1 = mobilenet.get_layer('conv_pw_11_relu').output
x2 = mobilenet.get_layer('conv_pw_13_relu').output
x2 = DarknetConv2D_BN_Leaky(int(512 * alpha), (1, 1))(x2)
y1 = compose(
#DarknetConv2D_BN_Leaky(int(1024*alpha), (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=int(1024 * alpha),
kernel_size=(3, 3),
block_id_str='14'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x2)
x2 = compose(DarknetConv2D_BN_Leaky(int(256 * alpha), (1, 1)),
UpSampling2D(2))(x2)
y2 = 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'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))([x2, x1])
return Model(inputs, [y1, y2])
def tiny_yolo3lite_shufflenetv2_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 Lite ShuffleNetV2 model CNN body in keras.'''
shufflenetv2 = ShuffleNetV2(input_tensor=inputs,
weights=None,
include_top=False)
# input: 416 x 416 x 3
# 1x1conv5_out: 13 x 13 x 1024
# stage4/block1/relu_1x1conv_1: 26 x 26 x 464
# stage3/block1/relu_1x1conv_1: 52 x 52 x 232
x1 = shufflenetv2.get_layer('stage4/block1/relu_1x1conv_1').output
x2 = shufflenetv2.get_layer('1x1conv5_out').output
x2 = DarknetConv2D_BN_Leaky(464, (1, 1))(x2)
y1 = compose(
#DarknetConv2D_BN_Leaky(1024, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=1024,
kernel_size=(3, 3),
block_id_str='17'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x2)
x2 = compose(DarknetConv2D_BN_Leaky(232, (1, 1)), UpSampling2D(2))(x2)
y2 = compose(
Concatenate(),
#DarknetConv2D_BN_Leaky(464, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=464,
kernel_size=(3, 3),
block_id_str='18'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))([x2, x1])
return Model(inputs, [y1, y2])
def tiny_yolo3lite_mobilenetv3large_body(inputs,
num_anchors,
num_classes,
alpha=1.0):
'''Create Tiny YOLO_v3 Lite 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)
# 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)
f2 = mobilenetv3large.layers[146].output
f1_channel_num = int(960 * alpha)
f2_channel_num = int(672 * alpha)
#f1_channel_num = 1024
#f2_channel_num = 512
#feature map 1 transform
x1 = DarknetConv2D_BN_Leaky(f1_channel_num // 2, (1, 1))(f1)
#feature map 1 output (13x13 for 416 input)
y1 = compose(
#DarknetConv2D_BN_Leaky(f1_channel_num, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=f1_channel_num,
kernel_size=(3, 3),
block_id_str='15'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x1)
#upsample fpn merge for feature map 1 & 2
x2 = compose(DarknetConv2D_BN_Leaky(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x1)
#feature map 2 output (26x26 for 416 input)
y2 = 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='16'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))([x2, f2])
return Model(inputs, [y1, y2])
def tiny_yolo3lite_mobilenetv3small_body(inputs,
num_anchors,
num_classes,
alpha=1.0):
'''Create Tiny YOLO_v3 Lite 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)
# 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)
f2 = mobilenetv3small.layers[117].output
f1_channel_num = int(576 * alpha)
f2_channel_num = int(288 * alpha)
#f1_channel_num = 1024
#f2_channel_num = 512
#feature map 1 transform
x1 = DarknetConv2D_BN_Leaky(f1_channel_num // 2, (1, 1))(f1)
#feature map 1 output (13x13 for 416 input)
y1 = compose(
#DarknetConv2D_BN_Leaky(f1_channel_num, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=f1_channel_num,
kernel_size=(3, 3),
block_id_str='15'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x1)
#upsample fpn merge for feature map 1 & 2
x2 = compose(DarknetConv2D_BN_Leaky(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x1)
#feature map 2 output (26x26 for 416 input)
y2 = 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='16'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))([x2, f2])
return Model(inputs, [y1, y2])
def tiny_yolo3lite_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 Lite model CNN body in keras.'''
x1 = compose(
Depthwise_Separable_Conv2D_BN_Leaky(16, (3, 3)),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(32, (3, 3)),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(64, (3, 3)),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(128, (3, 3)),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(256, (3, 3)))(inputs)
x2 = compose(
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(512, (3, 3)),
MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(1024, (3, 3)),
DarknetConv2D_BN_Leaky(256, (1, 1)))(x1)
y1 = compose(Depthwise_Separable_Conv2D_BN_Leaky(512, (3, 3)),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x2)
x2 = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x2)
y2 = compose(Concatenate(),
Depthwise_Separable_Conv2D_BN_Leaky(256, (3, 3)),
DarknetConv2D(num_anchors * (num_classes + 5),
(1, 1)))([x2, x1])
return Model(inputs, [y1, y2])
def tiny_yolo3lite_mobilenetv2_body(inputs,
num_anchors,
num_classes,
alpha=1.0):
'''Create Tiny YOLO_v3 Lite MobileNetV2 model CNN body in keras.'''
mobilenetv2 = MobileNetV2(input_tensor=inputs,
weights='imagenet',
include_top=False,
alpha=alpha)
# 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
#feature map 1 transform
x1 = DarknetConv2D_BN_Leaky(f1_channel_num // 2, (1, 1))(f1)
#feature map 1 output (13x13 for 416 input)
y1 = compose(
#DarknetConv2D_BN_Leaky(f1_channel_num, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=f1_channel_num,
kernel_size=(3, 3),
block_id_str='17'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x1)
#upsample fpn merge for feature map 1 & 2
x2 = compose(DarknetConv2D_BN_Leaky(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x1)
#feature map 2 output (26x26 for 416 input)
y2 = 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='18'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))([x2, f2])
return Model(inputs, [y1, y2])
def tiny_yolo3lite_xception_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 Lite Xception model CNN body in keras.'''
xception = Xception(input_tensor=inputs,
weights='imagenet',
include_top=False)
# input: 416 x 416 x 3
# block14_sepconv2_act: 13 x 13 x 2048
# block13_sepconv2_bn(middle in block13): 26 x 26 x 1024
# add_46(end of block12): 26 x 26 x 728
# block4_sepconv2_bn(middle in block4) : 52 x 52 x 728
# add_37(end of block3) : 52 x 52 x 256
# f1 :13 x 13 x 2048
f1 = xception.get_layer('block14_sepconv2_act').output
# f2 :26 x 26 x 1024
f2 = xception.get_layer('block13_sepconv2_bn').output
f1_channel_num = 2048
f2_channel_num = 1024
#f1_channel_num = 1024
#f2_channel_num = 512
#feature map 1 transform
x1 = DarknetConv2D_BN_Leaky(f1_channel_num // 2, (1, 1))(f1)
#feature map 1 output (13x13 for 416 input)
y1 = compose(
#DarknetConv2D_BN_Leaky(f1_channel_num, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=f1_channel_num,
kernel_size=(3, 3),
block_id_str='14'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x1)
#upsample fpn merge for feature map 1 & 2
x2 = compose(DarknetConv2D_BN_Leaky(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x1)
#feature map 2 output (26x26 for 416 input)
y2 = 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='15'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))([x2, f2])
return Model(inputs, [y1, y2])
def tiny_yolo3lite_shufflenetv2_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 Lite ShuffleNetV2 model CNN body in keras.'''
shufflenetv2 = ShuffleNetV2(input_tensor=inputs,
weights=None,
include_top=False)
# input: 416 x 416 x 3
# 1x1conv5_out: 13 x 13 x 1024
# stage4/block1/relu_1x1conv_1: 26 x 26 x 464
# stage3/block1/relu_1x1conv_1: 52 x 52 x 232
# f1: 13 x 13 x 1024
f1 = shufflenetv2.get_layer('1x1conv5_out').output
# f2: 26 x 26 x 464
f2 = shufflenetv2.get_layer('stage4/block1/relu_1x1conv_1').output
f1_channel_num = 1024
f2_channel_num = 464
#f1_channel_num = 1024
#f2_channel_num = 512
#feature map 1 transform
x1 = DarknetConv2D_BN_Leaky(f1_channel_num // 2, (1, 1))(f1)
#feature map 1 head & output (13x13 for 416 input)
y1 = compose(
#DarknetConv2D_BN_Leaky(f1_channel_num, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=f1_channel_num,
kernel_size=(3, 3),
block_id_str='17'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x1)
#upsample fpn merge for feature map 1 & 2
x2 = compose(DarknetConv2D_BN_Leaky(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x1)
#feature map 2 head & output (26x26 for 416 input)
y2 = 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='18'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))([x2, f2])
return Model(inputs, [y1, y2])
def tiny_yolo3lite_efficientnet_body(inputs,
num_anchors,
num_classes,
level=0):
'''
Create Tiny YOLO_v3 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 transform
x1 = DarknetConv2D_BN_Leaky(f1_channel_num // 2, (1, 1))(f1)
#feature map 1 output (13x13 for 416 input)
y1 = compose(
#DarknetConv2D_BN_Leaky(f1_channel_num, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=f1_channel_num,
kernel_size=(3, 3),
block_id_str='8'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x1)
#upsample fpn merge for feature map 1 & 2
x2 = compose(DarknetConv2D_BN_Leaky(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x1)
#feature map 2 output (26x26 for 416 input)
y2 = 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='9'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))([x2, f2])
return Model(inputs, [y1, y2])
def tiny_yolo3lite_xception_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 Lite Xception model CNN body in keras.'''
xception = Xception(input_tensor=inputs,
weights='imagenet',
include_top=False)
# input: 416 x 416 x 3
# block14_sepconv2_act: 13 x 13 x 2048
# block13_sepconv2_bn(middle in block13): 26 x 26 x 1024
# add_46(end of block12): 26 x 26 x 728
# block4_sepconv2_bn(middle in block4) : 52 x 52 x 728
# add_37(end of block3) : 52 x 52 x 256
x1 = xception.get_layer('block13_sepconv2_bn').output
# x1 :26 x 26 x 1024
x2 = xception.get_layer('block14_sepconv2_act').output
# x2 :13 x 13 x 2048
x2 = DarknetConv2D_BN_Leaky(1024, (1, 1))(x2)
y1 = compose(
#DarknetConv2D_BN_Leaky(2048, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=2048,
kernel_size=(3, 3),
block_id_str='14'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x2)
x2 = compose(DarknetConv2D_BN_Leaky(512, (1, 1)), UpSampling2D(2))(x2)
y2 = compose(
Concatenate(),
#DarknetConv2D_BN_Leaky(1024, (3,3)),
Depthwise_Separable_Conv2D_BN_Leaky(filters=1024,
kernel_size=(3, 3),
block_id_str='15'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))([x2, x1])
return Model(inputs, [y1, y2])
def tiny_yolo3lite_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 Lite model CNN body in keras.'''
#feature map 2 (26x26x256 for 416 input)
f2 = compose(
Depthwise_Separable_Conv2D_BN_Leaky(16, (3,3)),
MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(32, (3,3)),
MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(64, (3,3)),
MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(128, (3,3)),
MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(256, (3,3)))(inputs)
#feature map 1 (13x13x1024 for 416 input)
f1 = compose(
MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(512, (3,3)),
MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='same'),
Depthwise_Separable_Conv2D_BN_Leaky(1024, (3,3)))(f2)
#feature map 1 transform
x1 = DarknetConv2D_BN_Leaky(256, (1,1))(f1)
#feature map 1 output (13x13 for 416 input)
y1 = compose(
Depthwise_Separable_Conv2D_BN_Leaky(512, (3,3)),
DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2)
#upsample fpn merge for feature map 1 & 2
x2 = compose(
DarknetConv2D_BN_Leaky(128, (1,1)),
UpSampling2D(2))(x1)
#feature map 2 output (26x26 for 416 input)
y2 = compose(
Concatenate(),
Depthwise_Separable_Conv2D_BN_Leaky(256, (3,3)),
DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2, f2])
return Model(inputs, [y1,y2])