Create Tiny YOLO_v3 model CNN body in keras.
(inputs, num_anchors, num_classes)
| 87 | return Model(inputs, [y1,y2,y3]) |
| 88 | |
| 89 | def tiny_yolo_body(inputs, num_anchors, num_classes): |
| 90 | '''Create Tiny YOLO_v3 model CNN body in keras.''' |
| 91 | x1 = compose( |
| 92 | DarknetConv2D_BN_Leaky(16, (3,3)), |
| 93 | MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'), |
| 94 | DarknetConv2D_BN_Leaky(32, (3,3)), |
| 95 | MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'), |
| 96 | DarknetConv2D_BN_Leaky(64, (3,3)), |
| 97 | MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'), |
| 98 | DarknetConv2D_BN_Leaky(128, (3,3)), |
| 99 | MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'), |
| 100 | DarknetConv2D_BN_Leaky(256, (3,3)))(inputs) |
| 101 | x2 = compose( |
| 102 | MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'), |
| 103 | DarknetConv2D_BN_Leaky(512, (3,3)), |
| 104 | MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='same'), |
| 105 | DarknetConv2D_BN_Leaky(1024, (3,3)), |
| 106 | DarknetConv2D_BN_Leaky(256, (1,1)))(x1) |
| 107 | y1 = compose( |
| 108 | DarknetConv2D_BN_Leaky(512, (3,3)), |
| 109 | DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2) |
| 110 | |
| 111 | x2 = compose( |
| 112 | DarknetConv2D_BN_Leaky(128, (1,1)), |
| 113 | UpSampling2D(2))(x2) |
| 114 | y2 = compose( |
| 115 | Concatenate(), |
| 116 | DarknetConv2D_BN_Leaky(256, (3,3)), |
| 117 | DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2,x1]) |
| 118 | |
| 119 | return Model(inputs, [y1,y2]) |
| 120 | |
| 121 | |
| 122 | def yolo_head(feats, anchors, num_classes, input_shape, calc_loss=False): |
no test coverage detected