def resblock_body(x, num_filters, num_blocks):
'''A series of resblocks starting with a downsampling Convolution2D'''
# Darknet uses left and top padding instead of 'same' mode
x = ZeroPadding2D(((1, 0), (1, 0)))(x)
x = DarknetConv2D_BN_Mish(num_filters, (3, 3), strides=(2, 2))(x)
for i in range(num_blocks):
y = compose(DarknetConv2D_BN_Mish(num_filters // 2, (1, 1)),
DarknetConv2D_BN_Mish(num_filters, (3, 3)))(x)
x = Add()([x, y])
return x
def Spp_Conv2D_BN_Mish(x, num_filters):
y1 = MaxPooling2D(pool_size=(5, 5), strides=(1, 1), padding='same')(x)
y2 = MaxPooling2D(pool_size=(9, 9), strides=(1, 1), padding='same')(x)
y3 = MaxPooling2D(pool_size=(13, 13), strides=(1, 1), padding='same')(x)
y = compose(Concatenate(), DarknetConv2D_BN_Mish(num_filters,
(1, 1)))([y3, y2, y1, x])
return y
def tiny_yolo5lite_predictions(feature_maps, feature_channel_nums, num_anchors,
num_classes, use_spp):
f1, f2 = feature_maps
f1_channel_num, f2_channel_num = feature_channel_nums
#feature map 1 head (13 x 13 x f1_channel_num//2 for 416 input)
x1 = DarknetConv2D_BN_Mish(f1_channel_num // 2, (1, 1))(f1)
if use_spp:
x1 = Spp_Conv2D_BN_Mish(x1, f1_channel_num // 2)
#upsample fpn merge for feature map 1 & 2
x1_upsample = compose(DarknetConv2D_BN_Mish(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x1)
x2 = compose(
Concatenate(),
#DarknetConv2D_BN_Mish(f2_channel_num, (3,3)),
Depthwise_Separable_Conv2D_BN_Mish(filters=f2_channel_num,
kernel_size=(3, 3),
block_id_str='pred_1'))(
[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),
name='predict_conv_2')(x2)
#downsample fpn merge for feature map 2 & 1
x2_downsample = compose(
ZeroPadding2D(((1, 0), (1, 0))),
#DarknetConv2D_BN_Mish(f1_channel_num//2, (3,3), strides=(2,2)),
Darknet_Depthwise_Separable_Conv2D_BN_Mish(f1_channel_num // 2, (3, 3),
strides=(2, 2),
block_id_str='pred_2'))(x2)
x1 = compose(
Concatenate(),
#DarknetConv2D_BN_Mish(f1_channel_num, (3,3)),
Depthwise_Separable_Conv2D_BN_Mish(filters=f1_channel_num,
kernel_size=(3, 3),
block_id_str='pred_3'))(
[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),
name='predict_conv_1')(x1)
return y1, y2
def csp_darknet53_body(x):
'''CSPDarknet53 body having 52 Convolution2D layers'''
x = DarknetConv2D_BN_Mish(32, (3,3))(x)
x = resblock_body(x, 64, 1, False)
x = resblock_body(x, 128, 2)
x = resblock_body(x, 256, 8)
x = resblock_body(x, 512, 8)
x = resblock_body(x, 1024, 4)
return x
def make_csp_yolo_depthwise_separable_head(x, num_filters, block_id_str=None):
'''6 Conv2D_BN_Mish layers followed by a Conv2D_linear layer'''
if not block_id_str:
block_id_str = str(K.get_uid())
x = DarknetConv2D_BN_Mish(num_filters, (1, 1))(x)
res_connection = DarknetConv2D_BN_Mish(num_filters, (1, 1))(x)
x = compose(
DarknetConv2D_BN_Mish(num_filters, (1, 1)),
Depthwise_Separable_Conv2D_BN_Mish(filters=num_filters,
kernel_size=(3, 3),
block_id_str=block_id_str + '_1'),
DarknetConv2D_BN_Mish(num_filters, (1, 1)),
Depthwise_Separable_Conv2D_BN_Mish(filters=num_filters,
kernel_size=(3, 3),
block_id_str=block_id_str +
'_2'))(x)
x = Concatenate()([x, res_connection])
x = DarknetConv2D_BN_Mish(num_filters, (1, 1))(x)
return x
def make_csp_yolo_head(x, num_filters):
'''6 Conv2D_BN_Mish layers followed by a Conv2D_linear layer'''
x = DarknetConv2D_BN_Mish(num_filters, (1, 1))(x)
res_connection = DarknetConv2D_BN_Mish(num_filters, (1, 1))(x)
x = compose(DarknetConv2D_BN_Mish(num_filters, (1, 1)),
DarknetConv2D_BN_Mish(num_filters, (3, 3)),
DarknetConv2D_BN_Mish(num_filters, (1, 1)),
DarknetConv2D_BN_Mish(num_filters, (3, 3)))(x)
x = Concatenate()([x, res_connection])
x = DarknetConv2D_BN_Mish(num_filters, (1, 1))(x)
return x
def csp_resblock_body(x, num_filters, num_blocks, all_narrow=True):
'''A series of resblocks starting with a downsampling Convolution2D'''
# Darknet uses left and top padding instead of 'same' mode
x = ZeroPadding2D(((1, 0), (1, 0)))(x)
x = DarknetConv2D_BN_Mish(num_filters, (3, 3), strides=(2, 2))(x)
res_connection = DarknetConv2D_BN_Mish(
num_filters // 2 if all_narrow else num_filters, (1, 1))(x)
x = DarknetConv2D_BN_Mish(num_filters // 2 if all_narrow else num_filters,
(1, 1))(x)
for i in range(num_blocks):
y = compose(
DarknetConv2D_BN_Mish(num_filters // 2, (1, 1)),
DarknetConv2D_BN_Mish(
num_filters // 2 if all_narrow else num_filters, (3, 3)))(x)
x = Add()([x, y])
x = DarknetConv2D_BN_Mish(num_filters // 2 if all_narrow else num_filters,
(1, 1))(x)
x = Concatenate()([x, res_connection])
return DarknetConv2D_BN_Mish(num_filters, (1, 1))(x)
def yolo5lite_predictions(feature_maps, feature_channel_nums, num_anchors,
num_classes):
f1, f2, f3 = feature_maps
f1_channel_num, f2_channel_num, f3_channel_num = feature_channel_nums
#feature map 1 head (13 x 13 x f1_channel_num//2 for 416 input)
x1 = make_csp_yolo_spp_depthwise_separable_head(f1,
f1_channel_num // 2,
block_id_str='pred_1')
#upsample fpn merge for feature map 1 & 2
x1_upsample = compose(DarknetConv2D_BN_Mish(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x1)
x2 = DarknetConv2D_BN_Mish(f2_channel_num // 2, (1, 1))(f2)
x2 = Concatenate()([x2, x1_upsample])
#feature map 2 head (26 x 26 x f2_channel_num//2 for 416 input)
x2 = make_csp_yolo_depthwise_separable_head(x2,
f2_channel_num // 2,
block_id_str='pred_2')
#upsample fpn merge for feature map 2 & 3
x2_upsample = compose(DarknetConv2D_BN_Mish(f3_channel_num // 2, (1, 1)),
UpSampling2D(2))(x2)
x3 = DarknetConv2D_BN_Mish(f3_channel_num // 2, (1, 1))(f3)
x3 = Concatenate()([x3, x2_upsample])
#feature map 3 head & output (52 x 52 x f3_channel_num for 416 input)
x3 = make_csp_yolo_depthwise_separable_head(x3,
f3_channel_num // 2,
block_id_str='pred_3')
y3 = compose(
Depthwise_Separable_Conv2D_BN_Mish(f3_channel_num, (3, 3),
block_id_str='pred_3_3'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1),
name='predict_conv_3'))(x3)
#downsample fpn merge for feature map 3 & 2
x3_downsample = compose(
ZeroPadding2D(((1, 0), (1, 0))),
Darknet_Depthwise_Separable_Conv2D_BN_Mish(
f2_channel_num // 2, (3, 3),
strides=(2, 2),
block_id_str='pred_3_4'))(x3)
x2 = Concatenate()([x3_downsample, x2])
#feature map 2 output (26 x 26 x f2_channel_num for 416 input)
x2 = make_csp_yolo_depthwise_separable_head(x2,
f2_channel_num // 2,
block_id_str='pred_4')
y2 = compose(
Depthwise_Separable_Conv2D_BN_Mish(f2_channel_num, (3, 3),
block_id_str='pred_4_3'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1),
name='predict_conv_2'))(x2)
#downsample fpn merge for feature map 2 & 1
x2_downsample = compose(
ZeroPadding2D(((1, 0), (1, 0))),
Darknet_Depthwise_Separable_Conv2D_BN_Mish(
f1_channel_num // 2, (3, 3),
strides=(2, 2),
block_id_str='pred_4_4'))(x2)
x1 = Concatenate()([x2_downsample, x1])
#feature map 1 output (13 x 13 x f1_channel_num for 416 input)
x1 = make_csp_yolo_depthwise_separable_head(x1,
f1_channel_num // 2,
block_id_str='pred_5')
y1 = compose(
Depthwise_Separable_Conv2D_BN_Mish(f1_channel_num, (3, 3),
block_id_str='pred_5_3'),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1),
name='predict_conv_1'))(x1)
return y1, y2, y3
def yolo5_predictions(feature_maps, feature_channel_nums, num_anchors,
num_classes):
f1, f2, f3 = feature_maps
f1_channel_num, f2_channel_num, f3_channel_num = feature_channel_nums
#feature map 1 head (19x19 for 608 input)
x1 = make_csp_yolo_spp_head(f1, f1_channel_num // 2)
#upsample fpn merge for feature map 1 & 2
x1_upsample = compose(DarknetConv2D_BN_Mish(f2_channel_num // 2, (1, 1)),
UpSampling2D(2))(x1)
x2 = DarknetConv2D_BN_Mish(f2_channel_num // 2, (1, 1))(f2)
x2 = Concatenate()([x2, x1_upsample])
#feature map 2 head (38x38 for 608 input)
x2 = make_csp_yolo_head(x2, f2_channel_num // 2)
#upsample fpn merge for feature map 2 & 3
x2_upsample = compose(DarknetConv2D_BN_Mish(f3_channel_num // 2, (1, 1)),
UpSampling2D(2))(x2)
x3 = DarknetConv2D_BN_Mish(f3_channel_num // 2, (1, 1))(f3)
x3 = Concatenate()([x3, x2_upsample])
#feature map 3 head & output (76x76 for 608 input)
#x3, y3 = make_last_layers(x3, f3_channel_num//2, num_anchors*(num_classes+5))
x3 = make_csp_yolo_head(x3, f3_channel_num // 2)
y3 = compose(
DarknetConv2D_BN_Mish(f3_channel_num, (3, 3)),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1),
name='predict_conv_3'))(x3)
#downsample fpn merge for feature map 3 & 2
x3_downsample = compose(
ZeroPadding2D(((1, 0), (1, 0))),
DarknetConv2D_BN_Mish(f2_channel_num // 2, (3, 3), strides=(2, 2)))(x3)
x2 = Concatenate()([x3_downsample, x2])
#feature map 2 output (38x38 for 608 input)
#x2, y2 = make_last_layers(x2, 256, num_anchors*(num_classes+5))
x2 = make_csp_yolo_head(x2, f2_channel_num // 2)
y2 = compose(
DarknetConv2D_BN_Mish(f2_channel_num, (3, 3)),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1),
name='predict_conv_2'))(x2)
#downsample fpn merge for feature map 2 & 1
x2_downsample = compose(
ZeroPadding2D(((1, 0), (1, 0))),
DarknetConv2D_BN_Mish(f1_channel_num // 2, (3, 3), strides=(2, 2)))(x2)
x1 = Concatenate()([x2_downsample, x1])
#feature map 1 output (19x19 for 608 input)
#x1, y1 = make_last_layers(x1, f1_channel_num//2, num_anchors*(num_classes+5))
x1 = make_csp_yolo_head(x1, f1_channel_num // 2)
y1 = compose(
DarknetConv2D_BN_Mish(f1_channel_num, (3, 3)),
DarknetConv2D(num_anchors * (num_classes + 5), (1, 1),
name='predict_conv_1'))(x1)
return y1, y2, y3