def cnn_layers(inputs, scope, end_points_collection, dropout_keep_prob=0.8, is_training=True):
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.conv2d],
normalizer_fn=slim.batch_norm, normalizer_params={'is_training': is_training},
activation_fn=leaky_relu):
net = slim.conv2d(inputs, 32, [3, 3], scope='conv1')
net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
net = slim.conv2d(net, 64, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
net = slim.conv2d(net, 128, [3, 3], scope='conv3')
net = slim.conv2d(net, 64, [1, 1], scope='conv4')
box_net = net = slim.conv2d(net, 128, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [2, 2], 2, scope='pool5')
net = slim.conv2d(net, 256, [3, 3], scope='conv6')
net = slim.conv2d(net, 128, [1, 1], scope='conv7')
net = slim.conv2d(net, 256, [3, 3], scope='conv8')
box_net = _reorg(box_net, 2)
net = tf.concat([box_net, net], 3)
net = slim.conv2d(net, 256, [3, 3], scope='conv9')
net = slim.conv2d(net, 75, [1, 1], activation_fn=None, scope='conv10')
return net, end_points_collection
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