def yolo3lite_spp_shufflenetv2_body(inputs, num_anchors, num_classes):
'''Create YOLO_v3 Lite SPP ShuffleNetV2 model CNN body in keras.'''
shufflenetv2 = ShuffleNetV2(input_tensor=inputs,
weights=None,
include_top=False)
print('backbone layers number: {}'.format(len(shufflenetv2.layers)))
# 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
# f3: 52 x 52 x 232
f3 = shufflenetv2.get_layer('stage3/block1/relu_1x1conv_1').output
f1_channel_num = 1024
f2_channel_num = 464
f3_channel_num = 232
#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_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_yolo3_shufflenetv2_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 ShuffleNetV2 model CNN body in keras.'''
shufflenetv2 = ShuffleNetV2(input_tensor=inputs,
weights=None,
include_top=False)
print('backbone layers number: {}'.format(len(shufflenetv2.layers)))
# 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
y1, y2 = tiny_yolo3_predictions((f1, f2), (f1_channel_num, f2_channel_num),
num_anchors, num_classes)
return Model(inputs, [y1, y2])
def yolo3_shufflenetv2_body(inputs, num_anchors, num_classes):
"""Create YOLO_V3 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 = shufflenetv2.get_layer('1x1conv5_out').output
# f1 :13 x 13 x 1024
x, y1 = make_last_layers(f1, 464, num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(232, (1, 1)), UpSampling2D(2))(x)
f2 = shufflenetv2.get_layer('stage4/block1/relu_1x1conv_1').output
# f2: 26 x 26 x 464
x = Concatenate()([x, f2])
x, y2 = make_last_layers(x, 232, num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(116, (1, 1)), UpSampling2D(2))(x)
f3 = shufflenetv2.get_layer('stage3/block1/relu_1x1conv_1').output
# f3 : 52 x 52 x 232
x = Concatenate()([x, f3])
x, y3 = make_last_layers(x, 116, num_anchors * (num_classes + 5))
return Model(inputs=inputs, outputs=[y1, y2, y3])
def yolo3_shufflenetv2_body(inputs, num_anchors, num_classes):
"""Create YOLO_V3 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
# f3: 52 x 52 x 232
f3 = shufflenetv2.get_layer('stage3/block1/relu_1x1conv_1').output
f1_channel_num = 1024
f2_channel_num = 464
f3_channel_num = 232
#f1_channel_num = 1024
#f2_channel_num = 512
#f3_channel_num = 256
y1, y2, y3 = yolo3_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 yolo3lite_spp_shufflenetv2_body(inputs, num_anchors, num_classes):
'''Create YOLO_v3 Lite SPP 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 = shufflenetv2.get_layer('1x1conv5_out').output
# f1 :13 x 13 x 1024
#x, y1 = make_depthwise_separable_last_layers(f1, 464, num_anchors * (num_classes + 5), block_id_str='14')
x, y1 = make_spp_depthwise_separable_last_layers(f1, 464, num_anchors * (num_classes + 5), block_id_str='17')
x = compose(
DarknetConv2D_BN_Leaky(232, (1,1)),
UpSampling2D(2))(x)
f2 = shufflenetv2.get_layer('stage4/block1/relu_1x1conv_1').output
# f2: 26 x 26 x 464
x = Concatenate()([x,f2])
x, y2 = make_depthwise_separable_last_layers(x, 232, num_anchors * (num_classes + 5), block_id_str='18')
x = compose(
DarknetConv2D_BN_Leaky(116, (1,1)),
UpSampling2D(2))(x)
f3 = shufflenetv2.get_layer('stage3/block1/relu_1x1conv_1').output
# f3 : 52 x 52 x 232
x = Concatenate()([x, f3])
x, y3 = make_depthwise_separable_last_layers(x, 116, num_anchors * (num_classes + 5), block_id_str='19')
return Model(inputs = inputs, outputs=[y1,y2,y3])
def yolo3lite_spp_shufflenetv2_body(inputs, num_anchors, num_classes):
'''Create YOLO_v3 Lite SPP 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
# f3: 52 x 52 x 232
f3 = shufflenetv2.get_layer('stage3/block1/relu_1x1conv_1').output
f1_channel_num = 1024
f2_channel_num = 464
f3_channel_num = 232
#f1_channel_num = 1024
#f2_channel_num = 512
#f3_channel_num = 256
#feature map 1 head & output (13x13 for 416 input)
#x, y1 = make_depthwise_separable_last_layers(f1, f1_channel_num//2, num_anchors * (num_classes + 5), block_id_str='14')
x, y1 = make_spp_depthwise_separable_last_layers(f1,
f1_channel_num // 2,
num_anchors *
(num_classes + 5),
block_id_str='17')
#upsample fpn merge for feature map 1 & 2
x = compose(DarknetConv2D_BN_Leaky(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x)
x = Concatenate()([x, f2])
#feature map 2 head & output (26x26 for 416 input)
x, y2 = make_depthwise_separable_last_layers(x,
f2_channel_num // 2,
num_anchors *
(num_classes + 5),
block_id_str='18')
#upsample fpn merge for feature map 2 & 3
x = compose(DarknetConv2D_BN_Leaky(f3_channel_num // 2, (1, 1)),
UpSampling2D(2))(x)
x = Concatenate()([x, f3])
#feature map 3 head & output (52x52 for 416 input)
x, y3 = make_depthwise_separable_last_layers(x,
f3_channel_num // 2,
num_anchors *
(num_classes + 5),
block_id_str='19')
return Model(inputs=inputs, outputs=[y1, y2, y3])
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])
