squeezenet_model.py 文件源码

def squeezenet_inference(inputs, is_training, keep_prob):
    nets = slim.conv2d(inputs, 64,
                       [3, 3], scope='conv1')
    nets = slim.max_pool2d(nets, [3, 3], padding='SAME', scope='pool1')  # 56*48*64

    nets = fire_module(nets, 16, 64, scope='fire2')

    nets = fire_module(nets, 16, 64, scope='fire3')

    nets = slim.max_pool2d(nets, [3, 3], padding='SAME', scope='pool1')  # 28*24*128

    nets = fire_module(nets, 32, 128, scope='fire4')

    nets = fire_module(nets, 32, 128, scope='fire5')

    nets = slim.max_pool2d(nets, [3, 3], padding='SAME', scope='pool5')  # 14*12*256

    nets = fire_module(nets, 48, 192, scope='fire6')

    nets = fire_module(nets, 48, 192, scope='fire7')

    nets = slim.max_pool2d(nets, [3, 3], padding='SAME', scope='pool6')  # 7*6*384

    nets = fire_module(nets, 64, 256, scope='fire8')

    nets = fire_module(nets, 64, 256, scope='fire9')  # 7*6*512

    nets = slim.dropout(nets, keep_prob, is_training=is_training, scope='dropout9')

    nets = slim.avg_pool2d(nets, [7, 6], scope='pool9')  # 1*1*512

    return nets