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# Layer 20 | |
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_20', use_bias=False)(x) | |
x = BatchNormalization(name='norm_20')(x) | |
x = LeakyReLU(alpha=0.1)(x) | |
# Layer 21 | |
skip_connection = Conv2D(64, (1,1), strides=(1,1), padding='same', name='conv_21', use_bias=False)(skip_connection) | |
skip_connection = BatchNormalization(name='norm_21')(skip_connection) | |
skip_connection = LeakyReLU(alpha=0.1)(skip_connection) | |
skip_connection = Lambda(space_to_depth_x2)(skip_connection) | |
x = concatenate([skip_connection, x]) | |
# Layer 22 | |
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_22', use_bias=False)(x) | |
x = BatchNormalization(name='norm_22')(x) | |
x = LeakyReLU(alpha=0.1)(x) | |
# Layer 23 | |
x = Conv2D(BOX * (4 + 1 + CLASS), (1,1), strides=(1,1), padding='same', name='conv_23')(x) | |
output = Reshape((GRID_H, GRID_W, BOX, 4 + 1 + CLASS))(x) | |
# small hack to allow true_boxes to be registered when Keras build the model | |
# for more information: https://github.com/fchollet/keras/issues/2790 | |
output = Lambda(lambda args: args[0])([output, true_boxes]) | |
model = Model([input_image, true_boxes], output) |
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